Evidence on Genomic Tests – PLOS Currents Evidence on Genomic Tests http://currents.plos.org/genomictests Tue, 21 Aug 2018 20:46:31 +0000 en-US hourly 1 https://wordpress.org/?v=4.5.3 Analytical and Clinical Validity Study of FirstStepDx PLUS: A Chromosomal Microarray Optimized for Patients with Neurodevelopmental Conditions http://currents.plos.org/genomictests/article/analytical-and-clinical-validity-study-of-firststepdx-plus-a-chromosomal-microarray-optimized-for-patients-with-neurodevelopmental-conditions/ http://currents.plos.org/genomictests/article/analytical-and-clinical-validity-study-of-firststepdx-plus-a-chromosomal-microarray-optimized-for-patients-with-neurodevelopmental-conditions/#respond Mon, 27 Feb 2017 15:00:20 +0000 http://currents.plos.org/genomictests/?post_type=article&p=23164 Introduction: Chromosomal microarray analysis (CMA) is recognized as the first-tier test in the genetic evaluation of children with developmental delays, intellectual disabilities, congenital anomalies and autism spectrum disorders of unknown etiology.

Array Design: To optimize detection of clinically relevant copy number variants associated with these conditions, we designed a whole-genome microarray, FirstStepDx PLUS (FSDX). A set of 88,435 custom probes was added to the Affymetrix CytoScanHD platform targeting genomic regions strongly associated with these conditions. This combination of 2,784,985 total probes results in the highest probe coverage and clinical yield for these disorders.

Results and Discussion: Clinical testing of this patient population is validated on DNA from either non-invasive buccal swabs or traditional blood samples. In this report we provide data demonstrating the analytic and clinical validity of FSDX and provide an overview of results from the first 7,570 consecutive patients tested clinically. We further demonstrate that buccal sampling is an effective method of obtaining DNA samples, which may provide improved results compared to traditional blood sampling for patients with neurodevelopmental disorders who exhibit somatic mosaicism.

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Clinical Scenario

Neurodevelopmental disabilities, including developmental delays (DD), intellectual disabilities (ID), and autism spectrum disorders (ASD), affect up to 15% of children1. In the majority of cases, a child’s clinical presentation does not allow for a definitive etiological diagnosis. In such cases, CMA is recommended as the first-tier test that should be used to evaluate for a potential genetic etiology2,3,4,5,6,7. A definitive genetic diagnosis allows patients to more often receive appropriate medical care tailored to their condition, as reflected by medical management changes and improved access to necessary support and educational services8,9,10,11,12,13.

Test Description

FirstStepDx PLUS (FSDX) is an optimized clinical microarray test provided in the context of a comprehensive clinical service. Testing starts with either a non-invasive buccal swab sample or traditional blood sample from which DNA extraction using a Gentra Puregene® kit specific to the sample type (Qiagen, Inc., Valencia, CA) is performed in one of several contracted CLIA/CAP credentialed laboratories according to manufacturers’ protocols. High quality genomic DNA is fragmented, labeled and hybridized to FSDX arrays using reagents, equipment and methodology as specified by the manufacturer (Affymetrix, Inc., Santa Clara, CA)14. Washed arrays are scanned and raw data files are processed to CYCHP files using a reference file comprising at least 100 samples with normal array findings. Data analysis is performed using Chromosome Analysis Suite software version 2.0.1 (Affymetrix). Hybridization of patient DNA to oligonucleotide and SNP probes is independently compared against a previously analyzed cohort of normal samples to call CNVs and allele genotypes. The percentage mosaicism of whole-chromosome aneuploidies is determined using the average log2 ratio of the entire chromosome14.

Microarray Design

FSDX was optimized by the addition of 88,435 custom probes targeting genomic regions strongly associated with ID/DD/ASD15,16,17,18,19,20,21,22,23,24. This was effected, under GMP by Affymetrix, to the CytoScanHD platform using their custom microarray design process. This is consistent with the ACMG recommendation of “enrichment of probes targeting dosage-sensitive genes known to result in phenotypes consistent with common indications for a genomic screen”25. Critical regions that did not meet a desired probe density >1 probe/1000 bp on the CytoScanHD were supplemented with additional probe content to allow for improved detection of smaller deletions and duplications in these critical regions. Finally, additional probes were added to improve detection of CNVs encompassing genes involved in other well-characterized neurodevelopmental disorders, for example GAMT26 and GATM27. All incremental probes were added in substitution for probes deemed sub-optimal by Affymetrix and previously masked, bringing FSDX to a grand total of 2,784,985 probes. Custom SNP probes (n =416) on FSDX are targeted by 12 oligonucleotides, three for each strand of each allele, which is approximately double the typical probe coverage for SNPs.

Test Interpretation

CYCHP files are evaluated by ABMG certified cytogeneticists. Determination of CNVs is consistent with established cytogenetic standards. A minimum of 25-consecutive impacted probes is the baseline determinant for deletions and 50 probes for duplications independent of variant size. Rare CNVs are determined to be “pathogenic” if there is sufficient evidence published (at least two independent publications) to indicate that haploinsufficiency or triplosensitivity of the region or gene(s) involved is causative of clinical features or of sufficient overall size28. If, however, there is insufficient but at least preliminary evidence for a causative role for the region or gene(s) therein they are classified as variants of unknown significance (VOUS) independent of CNV size. Detection of these VOUS is important, as they serve as source for further delineation of causal variants as well as benign variants. (For a more complete discussion of this topic see the following link: http://blog.goldenhelix.com/clambert/sustaining-competitive-advantage-in-array-based-cytogenetics/.) Further, surveys of families who received reports containing VOUS findings indicate that these findings, when communicated appropriately, unquestionably contribute to families’ understanding of the disorder as well as their ability to explain it to others29,30,31.

Areas of absence of heterozygosity (AOH) are also classed as VOUS if of sufficient size and location to increase the risk for conditions with autosomal recessive inheritance or conditions with parent-of-origin/imprinting effects. Other CNVs are typically not reported after determination that they most likely represent normal common population variants and are contained in databases documenting presumptively benign CNVs, e.g., the Database of Genomic Variants (DGV)32. These parameters were standard independent of the microarray used for analysis in comparative studies.

Public Health Importance

A definitive genetic diagnosis facilitates patient access to appropriate and necessary medical and support services. Defining the underlying genetic cause of DD/ID/ASD and/or multiple congenital anomalies (MCA) in each unique patient is vital to understanding etiology, prognosis, and course. It informs physicians of potential comorbid conditions for which a patient should be evaluated and treated proactively and optimally. Improved understandings of the appropriate therapeutic and behavioral approaches to that patient are also enabled. Genetic testing is best provided in the context of an integrated service33, so FSDX aims to provide comprehensive, clear, readable, and personalized reports for the healthcare provider and a family-friendly section to facilitate understanding of the often-complex results. The report is complemented by availability of pre- and post-test genetic counseling and technical support to providers. Moreover, FSDX includes personalized insurance pre-authorization and appeals assistance intended to help overcome barriers encountered by both providers and families that, in many circumstances, prevent access to crucial genetic testing services10,11.

Published Reviews, Recommendations and Guidelines

The American College of Medical Genetics (ACMG)2,3, the American Academy of Child and Adolescent Psychiatry4,7, the American Academy of Pediatrics5, and the American Academy of Neurology/Child Neurology Society6 recommend CMA as the first-tier test in the genetic evaluation of children with unexplained DD, ID, or ASD. Considerable data supporting these guidelines are documented in numerous reviews and publications34,35,36,37,38,39. The ACMG has also published guidelines on both array design25 and the validation of arrays, including validation of a new version of a platform in use by the laboratory from the same manufacturer, and of additional sample types28.

EVIDENCE OVERVIEW

Validation of Novel (Blood vs. Buccal) Sample Type on the Established and Optimized Platforms

It is highly desirable to avail clinicians and families a less invasive sampling method for individuals with ID/DD/ASD due to the potential implications of venipuncture in many such individuals. We validated a buccal sampling methodology in conjunction with two independent CLIA-certified laboratories, ARUP (Salt Lake City, UT) and Fullerton Genetics Center (Asheville, NC) first on the CytoScanHD array and then the FSDX array. After preliminary consideration of multiple saliva and buccal DNA collection kits, we selected the ORAcollect-100® (DNA Genotek, Ottawa) (now cleared as a class II IVD medical device: ORAcollect-DX®) based upon ease of use for the intended patient population, ease of shipping processes, DNA stability, and post-extraction quality studies for further validation. Buccal swabs and blood samples from twenty-two individuals underwent parallel microarray analysis for concordance by each protocol. Twenty-two individuals’ buccal and blood samples were analyzed in terms of array quality control metrics and no significant difference between the two sample sources was observed. In addition, there was 100% concordance of CNV calls between the two sample types. Finally, twenty-three individuals’ blood and buccal samples underwent microarray analysis on the FSDX platform. Again no significant difference between the two sample sources in terms of quality metrics and 100% clinical concordance of CNV calls was observed.

Clinical Validation of a New Version of a Previously Established Platform

FSDX was validated independently in four CLIA-certified laboratories (Asuragen – Asuragen, Inc., Austin, TX; AGI – Affiliated Genetics, Inc., Salt Lake City, UT; Fullerton – Mission Hospital/Fullerton Genetics Center, Asheville, NC; CUMC – Columbia University Medical Center, Dept. of Pathology and Cell Biology, New York, NY) all previously familiar with performing CMA on CytoScanHD (or its predecessor the Affymetrix 2.7M Cytogenetics array) and cross-validated between these laboratories as well. Data demonstrating the concordance of FSDX with these alternative arrays and across laboratories are shown in Tables I-II.

A total of six samples from patients having clinically significant CNVs findings on prior clinical analysis were re-analyzed using FSDX. This study mirrors ACMG guidance on clinical validation of a new version of a previously established platform when the total probe content change is less than 5% (here 3.3% increase)28. There was complete clinical concordance between the initial clinical results and the results generated with FSDX in two independent laboratories (AGI and CUMC). Although the cross-platform and cross-laboratory results are unequivocally clinically concordant, minor differences in breakpoint determinations were observed in the majority of analyses (8 of 11), which is expectable and reasonable within the limits of the technology and interpretation overall. The differences in breakpoints between FSDX and CytoScanHD resulted in CNVs that were smaller, but only on average by <0.25% of the total CNV size. It was previously observed that arrays with increasing increased probe density result in smaller estimates of total CNV size presumably through the higher resolving power with increased probe density14. In contrast, only a single inter-laboratory analysis of FSDX differed in breakpoint calls by the different cytogeneticists, and in this case the change was only 0.08% of the overall CNV size.

Table II

Table I: Clinical validation of the FirstStepDx PLUS array Samples from patients analyzed clinically on commercially available Samples from patients analyzed clinically on commercially available Affymetrix microarrays were run independently on FSDX. Laboratory designations are as follows: Asuragen – Asuragen, Inc., Austin, TX; AGI – Affiliated Genetics, Inc., Salt Lake City, UT; Fullerton – Mission Hospital/Fullerton Genetics Center, Asheville, NC; CUMC – Columbia University Medical Center, Dept. of Pathology and Cell Biology, New York, NY. All arrays used clinically were purchased from Affymetrix, Inc., Santa Clara, CA.

As further demonstration of the clinical validity of this array, 16 samples from an earlier research study using an alternative technology platform (Illumina, San Diego, CA)15 were analyzed on FSDX in two participating CLIA-laboratories: ARUP & Asuragen (Table II). Samples all had significant CNVs present, which had been validated by quantitative PCR in the research study, and spanned both custom and standard probes. All results were concordant both across platforms and between laboratories.

Table IV

Table II: Research findings validated on FirstStepDx PLUS Samples derived from research studies with significant CNVs present15 which spanned both custom and standard probes were evaluated independently on FSDX in two laboratories and evaluated for agreement with the research findings as well their inter-laboratory concordance.

Inter-laboratory Clinical Performance Validation

Further evidence of the inter-laboratory performance (Table III) is shown on twelve additional patients with clinically significant CNVs detected by clinical testing with FSDX at Asuragen, and then re-analyzed by both AGI and CUMC, again with completely concordant results. In addition, two patients run clinically at CUMC were concordant with results subsequently generated by AGI, and six patients run clinically at AGI were concordant with results generated by Fullerton Genetics Laboratory Center.

Table III

Table III: Inter-laboratory validation of the FirstStepDx PLUS array Independent samples from patients analyzed clinically at our participating laboratories were re-analyzed at other participating laboratories. Laboratory designations are as described in Table II.

These data clearly demonstrate the ability of FSDX to detect copy number variants on a consistent basis in independent laboratories, across a range of genomic locations with all samples yielding concordant results to those performed on three separate array platforms. The excellent performance in cases with CNVs previously detected in regions with custom probes supports the overall clinical consistency and appropriate performance of the custom probe content on the array.

Extended Comparative Clinical Sensitivity Studies in a Real-World Clinical Population

Data from 7570 consecutive patient samples tested clinically with FSDX from July 2012 (when the optimized FirstStepDx PLUS (FSDX) microarray was implemented into routine use) through May 2016 are shown in Figure 1. Overall there were 717 (9.5%) pathogenic abnormalities and 1534 (20.2%) VOUS observed, or a 29.7% overall CNV diagnostic yield for potentially abnormal findings. We also compared these results to patients who were tested by Lineagen through the same referral channels and in comparable patient cohorts and general time windows using the 2.7M and CytoScanHD arrays. The 2.7M arrays (n=378) detected 7.4% pathogenic and 8.5% VOUS for an overall yield of 15.9%. The higher density CytoScanHD arrays (n=1194) detected 9.0% pathogenic and 14.2% VOUS for an overall yield of 23.2%. It is clear that these incremental probe additions translate into potentially clinically significant yields.

Figure 1 visually summarizes the genomic range of CNVs detected to date using FSDX. These include known microdeletion and microduplication syndrome regions as well as variants of unknown significance.

combined

Fig. 1: Summary of clinical CNV findings using FirstStepDx PLUS

Reported clinical findings are displayed next to chromosome Reported clinical findings are displayed next to chromosome ideograms. Deletions are shown on the left in red, and duplications are shown on the right in blue. Numbers in parentheses after chromosome band labels indicate the number of custom-designed probes in those bands. An excess of abnormalities is observed in the 4p region due to a research cohort; however this data is not reflected in the overall detection rates cited in the text.

Quality Control

Similar to the CytoScanHD, the independent analysis of CNVs with oligonucleotide and SNP probes provides both detection and confirmation simultaneously14. Three empirically determined quality control metrics are used that reflect overall data quality on an Affymetrix array: 1) waviness-SD, 2) median of the absolute values of all pairwise differences (MAPD), and 3) SNPQC (measure of how well genotype alleles are resolved). Details regarding these quality control features can be found in the Affymetrix ChAS User Guide (http://www.affymetrix.com/support/downloads/manuals/chas3_1_userguide.pdf). The criteria determined empirically for the CytoScanHD array extend to the FSDX array by virtue of design, with all three needed to meet minimum requirements for an array to be analyzed. FSDX arrays meeting these requirements can be interpreted over 99% of the time.

Analytic Validity

The results in this section are intended to demonstrate the functionality of custom probes that have been added to a microarray that is already in broad clinical use. Our goal was to show that these probes function within the parameters of standard probes on the array and respond in the expected manner to increases in input DNA.

To rigorously demonstrate the comparable analytic validity of FSDX, we employed an independent tool, the Golden Helix, Inc. Copy Number Analysis Method (CNAM) (Golden Helix, Inc. Bozeman, MT)36. First, Affymetrix Chromosome Analysis Suite (ChAS) version 2.1 (Affymetrix, Santa Clara, CA) was used to create an evaluation set of 205 samples with no reportable clinical finding from a single laboratory. To characterize the analytical function of custom probes added to the FSDX microarray, we used the sum of raw probe signal data, from these 205 samples, for all probes on across the array as a proxy for input DNA concentration. Across the 205 samples, there was roughly a 4.6-fold difference in total array signal, which we interpreted as a 4.6-fold range of input probe DNA amount. We then plotted individual probe signal intensity vs. total array signal for all probes on the array. Finally, we used regression analysis to calculate slopes and y-intercepts for each probe. To simplify the X axis, we divided total intensity by 933,696,453 (the lowest total intensity sum in the 205 samples) to generate an X-axis that ranged from 1 to 4.6. Finally, we plotted y-intercept vs. slope for each probe and compared custom probes to CytoScanHD probes. Figure 2 shows a scatter-plot based on data from this set of 205 samples. Data in red reflect custom probes only found on FSDX, while data in black designate standard CytoScanHD probes. The plot indicates that there is significant overlap between the intensity signals of the custom probes and standard probes. However, at the extreme left of the plot, some custom probes with slope near zero or negative do not appear to respond well to increasing DNA input. One can also observe in this figure that the average signal for custom probes is lower than the average signal for standard probes. These data indicate that some of the custom probes are less likely to be strongly copy number responsive, but are not necessarily non-informative. We estimate this “sub-par” population to represent approximately 20% of the custom FSDX probes. This shift in performance characteristics of the single-pass design custom probes is still within the overall desired operating parameters (note the overlap with standard probe signals in Fig. 2). Assuming that 20% of the custom probes are non-functional, custom probe content, the data suggest at most a 0.64% (% poorly functioning probes over total probe content) deviation in overall analytical sensitivity. However, given that the total probe content on FSDX is 3.27% greater, these data suggest a net gain in sensitivity of 2.63% (net increase in total optimally performing probes compared to CytoScanHD).

FullertonSlopeInterceptFull_Red_on_Black_512

Fig. 2: Functional Overlap of Custom Probes with CytoScanHD Probes

Histogram of slope and Y-intercept for each probe on the FirstStep Dx PLUS array. Probes shown in red are custom probes found only on FSDX, which overlie probes shown in black common to both FSDX and CytoScanHD. The slope for each probe indicates the change in signal relative to increases in DNA input. Probes functioning appropriately will have a positive slope. Y intercept values serve as an approximation to the background probe intensity when no sample is added to the array.

We next analyzed the relative impact of the custom probes compared to the standard array probes on overall detection rates of CNVs. The weighted log2 ratios from 184 arrays that each had at least one clinically reported finding previously analyzed using ChAS in a single laboratory were evaluated again with CNAM36. This utilized the univariate option with no moving window; a maximum of 10 segments per 10,000 probes; a minimum segment size of 1 marker; and a stringent permutation p-value threshold of 0.001. After segmentation, we classified segments as losses if their mean was <0.20 with 25 or more probes and classified segments as gains if their mean was >0.20 with 50 or more probes (consistent with our clinical workflows). These calls were compared to the clinically reported findings generated with ChAS. The analysis with CNAM was performed in two ways. First, only probes present on the CytoScanHD standard array were considered. Second, FSDX custom probes in addition to the standard probes were considered. All clinically relevant findings were observed using both analysis methods (Table IV). Each reported CNV was detectable independent of the presence of custom probes in the CNV and independent of the use of the custom probes in the analysis. These data show no evidence that the previously described sub-optimally responding subset of custom probes interfere with the function of responsive probes, whether standard or custom, or with the overall sensitivity for CNV detection.

The data in Table IV also demonstrate the analytic validity of custom probes that are included in some of the clinically reported findings. Increased sensitivity and resolution has been demonstrated with increasing probe density14, and since the custom probes were added specifically to regions important for ASD and other neurodevelopmental disorders, the resolution of FSDX for these conditions is predicted to be enhanced, since approximately 80% of the custom probes are fully analytically responsive to DNA input. Preliminary analysis (data not shown) suggests that the overall detection of CNVs may be increased by inclusion of the custom probes in this analysis, and further evaluation of this is under investigation.

Table I

Table IV: Analytic validity of FirstStepDxPLUS Data from clinical samples were evaluated using CNAM on weighted log2 ratios from 184 arrays as described above. The data from CNVs with and without custom FSDX probe content were evaluated to determine any discrepancy in detection based on inclusion of the custom probes and no evidence of non-concordance was observed.

Clinical Utility

The goal of any diagnostic test is improvement in medical management of patients and overall improvement in patient outcomes. This is achieved first by reaching the correct diagnosis and second by following the appropriate management and surveillance procedures for that diagnosis. Although there are no published reviews that analyze outcome data for patients who received a genetic diagnosis through CMA, a clear and positive impact on medical management has been documented in several studies8,9,10,11,12,13. Further, CMA testing often results in a correct, additional, or modified diagnosis for those who are difficult to diagnose with clinical observation alone41,42.

More importantly, buccal sampling in some of these patients has revealed mosaicism that would not have been detected using blood samples as a DNA source38. These findings are consistent with previous publications on mosaic FMR1 repeat expansions43, mutations in Cornelia de Lange syndrome44 and chromosomal abnormalities in Pallister-Killian syndrome45.

Even in patients with well-defined conditions, better determining CNV breakpoints with a higher resolution methodology can provide information beyond what is known or assumed from other tests46. A test which allows us to identify multiple individually rare diagnoses – such as CMA – is difficult to assess by traditional measures of clinical utility. This is due, in part, to the fact that each potential diagnosis has specific benefits in terms health and cost of care for a given patient. However, the literature has documented numerous improvements in care8,9,10,11,12,13 as a result of reaching a genetic diagnosis for individuals with DD, ID, and/or ASD. Further, this represents a continuum as clinical understanding and care evolves stepwise as a direct result of improved diagnostic clarity. The ability of increased probe density in genomic regions of interest to improve diagnosis is becoming apparent, and each additional correct diagnosis allows incremental opportunities for increased composite clinical utility of such tools.

Limitations

Given the rarity of individual CNVs and current limits of understanding, we report a relatively small clinical validation cohort. This could limit the certainty and range of conclusions from this evaluation; however, it exceeds established guidelines for such validations28. Analytical validity across millions of data points, as in a CMA, can only be assessed by in silico methods such as applied here. While this may be less than ideal, it is superior to mere presumptions of performance typical in the genomic literature.

CMA has proven an important clinical diagnostic advance but is limited in its ability to detect a diagnosis in a majority of affected individuals even with the increased performance and added probe content on FSDX. However, emerging evidence suggests that another significant portion of these cases (those without a diagnosis from CMA) will have mutations detectable by massively parallel sequencing (NGS) in the future47. In addition, roughly two-thirds of reported CNVs are classified as a VOUS, largely due to the limitations of our clinical experience with these rare conditions. The use of new informatics approaches and databases is beginning to better define the potential relevance and pathogenesis of such currently uncertain findings48.

Conclusions

Analytic validation of FirstStepDx PLUS coupled with over three years of clinical use have demonstrated the utility of FirstStepDx PLUS in identifying genetic causes for neurodevelopmental conditions. The simple non-invasive sampling procedure, high clinical sensitivity and extensive support services make FirstStepDx PLUS an ideal choice for the clinical genetic evaluation of patients with these disorders.

Samples and Methods

All samples were referred to Lineagen for routine clinical microarray testing. All participants, or their representatives, provided written informed consent to use the samples for research purposes (i.e., test development and validation) at the time of testing. The data collected here are covered by a protocol reviewed by the Western Institutional Review Board (WIRB protocol #20162032). No identifiers were used for any of the data evaluated in this work.

Clinical analyses were performed using Chromosome Analysis Suite v2.0.1 (Affymetrix, Inc., Santa Clara, CA). Analysis of probe function utilized Copy Number Analysis Module (Golden Helix, Inc., Bozeman, MT).

Competing Interests

The authors received funding from Lineagen, Inc. and 23&Me (commercial companies), and from Affiliated Genetics, Inc. and Columbia University Medical Center (contract service laboratories performing FSDX tests) for this study. There are no patents, products in development or marketed products to declare. This does not alter our adherence to all the PLOS ONE policies on sharing data and materials.

Corresponding Author

Charles Hensel: chensel@lineagen.com

Data Availability

Data used in this analysis are available from the NCBI dbGaP repository (https://www.ncbi.nlm.nih.gov/gap) under accession number phs001308.v1.p1.

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A 22 Gene-expression Assay, Decipher® (GenomeDx Biosciences) to Predict Five-year Risk of Metastatic Prostate Cancer in Men Treated with Radical Prostatectomy http://currents.plos.org/genomictests/article/a-22-gene-expression-assay-decipher-genomedx-biosciences-to-predict-five-year-risk-of-metastatic-prostate-cancer-in-men-treated-with-radical-prostatectomy/ http://currents.plos.org/genomictests/article/a-22-gene-expression-assay-decipher-genomedx-biosciences-to-predict-five-year-risk-of-metastatic-prostate-cancer-in-men-treated-with-radical-prostatectomy/#respond Tue, 17 Nov 2015 11:30:43 +0000 http://currents.plos.org/genomictests/?post_type=article&p=23022 Among the estimated 230,000 men diagnosed with prostate cancer in the US each year there has been a rise in the number of radical prostatectomies (RP). There is some debate over the value of immediate adjuvant therapy following RP in men with high-risk pathological features versus delayed salvage radiation therapy when signs of disease progression are observed. Thus, it would be potentially useful to inform post-RP management strategies by more clearly identifying those patients at higher risk of progression and death from prostate cancer. A 22 gene-expression assay, Decipher® (GenomeDx Biosciences), has been developed in men treated with radical prostatectomy to predict the five-year risk of metastatic prostate cancer. Published and unpublished literature was evaluated to determine the analytic validity, clinical validity and clinical utility of Decipher. Limited information is available on the analytic validity of Decipher. In both discovery and validation studies, Decipher was shown to have good performance in discriminating men with metastasis from men without metastasis five years after surgery (AUC 0.75 to 0.90). In terms of clinical utility, no evidence was found reporting improved outcomes (lower prostate cancer specific mortality and treatment related adverse effects) from using this test to guide post-operative treatment. Four studies provided weak indirect evidence of clinical utility in which 31% to 43% of post-operative treatment recommendations were changed in men with high-risk prostate cancer based on test results, with 27% to 52% of treatment recommendations changing from any treatment to no treatment.

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Clinical scenario

In men diagnosed with prostate cancer in the US, prognosis is generally excellent following radical prostatectomy (RP) with 15-year disease free survival of 93%.1 However, RP may be associated with significant complications and adverse-effects, primarily sexual dysfunction and urinary incontinence. Furthermore, between 19% and 30% of men experience biochemical recurrence (rise in PSA over 0.2 ng/ml) 5 to 10 years after surgery, and these men have a 37% risk of developing distant metastasis within five years if not treated and have a 17% risk of dying from prostate cancer within six years of biochemical recurrence.2,3 The most appropriate course of treatment is based on standard risk assessment that incorporates clinical characteristics such as cancer stage, grade, and size as well as histopathological features including PSA and Gleason score to estimate risk of recurrence from nomograms.4 Current post-RP practice includes observation for low risk disease, adjuvant therapy for adverse pathology and salvage therapy after biochemical recurrence.4 However, not all men with high-risk prostate cancer with adverse pathological features will require or will benefit from adjuvant therapy. Furthermore, the adverse events and complications associated with postoperative adjuvant therapy are not insignificant and range from incontinence, bowel complications as well as urogenital toxicities resulting from radiotherapy.5 Thus, there remains a need for increased accuracy in estimating a man’s prognosis (risk of metastasis) to ensure appropriate follow up care is given in a timely manner.

Test Description

Decipher® (GenomeDx Biosciences) is a genomic test that was developed to predict the risk of metastatic prostate cancer within five years of RP in men at high risk of recurrence (extraprostatic extension, seminal vesicle invasion, positive margins, or biochemical recurrence).6 A genomic classifier score calculated from a gene-expression microarray analysis of 22 genes on formalin-fixed, paraffin embedded (FFPE) prostate tumor tissue ranges between 0 and 1 and classifies patients as high risk (1 in 5 risk of metastasis), average risk, or low risk (1 in 42 risk of metastasis).6No information was available on the specific genes included in the 22-geen analysis. Decipher is available as a laboratory developed test through physicians or as part of clinical investigations sponsored by GenomDx, which is licensed under the Clinical Laboratory Improvement Act (CLIA).

Public Health Importance

In the US, prostate cancer is the most common cancer in men with estimates showing over 230,000 cases will be diagnosed in 2014 and just under 30,000 men will die from the disease.7 For men with high-risk prostate cancer based on post-RP pathological findings the strongest predictors of prostate cancer metastasis and death are baseline Gleason score, time of biochemical recurrence after RP, and PSA doubling-time.2,3 Within this high-risk group of men with prostate cancer the benefit of adjuvant or salvage therapy varies, and the high costs and complications associated with adjuvant radiotherapy underscore the need for improved prognostic markers.5

Methods

PubMed was searched (14 July 2015) to identify published reports of studies investigating the analytic validity, clinical validity and clinical utility of Decipher. Supplemental searches for grey literature, guidelines, and coverage decisions included Google, Genetic Testing Registry (GTR), guidelines.gov, and the Centers for Medicare and Medicaid using the terms “Decipher genomic classifier” and “Decipher”. Websites for Decipher6 and GenomeDx8 were also reviewed to identified additional published and unpublished information. No trial registries were searched, which is a possible limitation.

PubMed search strategy:

((classifier[tiab]) AND ((genomic*[tiab]) OR (genome*[tiab]))) AND ((prostate[tiab] OR prostatic[tiab] OR (prostatic neoplasm[mesh]) OR (biochemical recurrence[tiab]))

Published Reviews, Recommendations, and Guidelines

No published reviews, recommendations or guidelines describing the Decipher genomic classifier were identified from the methods described above. One local coverage decision from Palmetto GBA was identified providing limited coverage for Decipher in order to determine which patients at high-risk of recurrence following RP should receive radiation therapy vs. continued observation.9

Analytic Validity

Data reported in a conference abstract10 showed RNA was successfully extracted from 91% (63/69) FFPE biopsies and 100% (69/69) of RP tissue samples, and 98% (62/63) and 99% (68/69) of the FFPE and RP samples passed gene-expression data quality control, respectively. In this study, the correlation between matched biopsy FFPE tissue and RP tissue samples in the genomic classifier scores predicting metastasis was 0.74 (p=0.0003).

Two other studies are listed on the Decipher website under ‘Analytic Validity’,11,12 but it is unclear whether the methods and procedures described in these reports are integrated into the Decipher analytic pipeline.

Clinical Validity

Eight published studies were identified evaluating the clinical validity of the genomic classifier, based on its ability to predict clinical metastasis13,14,15,16,17,18,19,20. The first study used data from a Mayo Clinic patient registry to compare men with evidence of clinical metastasis following RP (cases) to men with no evidence of metastasis after RP (controls) with median follow-up greater than five years. One publication reported results from the initial training and validation datasets in which participants with no evidence of disease recurrence and those with biochemical recurrence (PSA > 0.20 ng/ml within 30 days of RP) served as controls due to “very limited differential expression” between these two groups.16 Therefore, the genomic classifier was developed to discriminate men who develop clinical metastasis from men who have no evidence of disease recurrence (clinical or biochemical) within five years of RP. Two additional studies using data from the Mayo Clinic registry described subsequent validation of the genomic classifier in men with biochemical recurrence after RP19 and in men with high-risk prostate cancer (pre-RP PSA > 20ng/mL, Gleason score > 8, pT3b, or Mayo Clinic nomogram score > 10).17

Overall, the genomic classifier had good discriminatory ability (e.g. ability to predict clinical metastasis) in both the discovery and validation studies (Table 1). Across the nine analyses, the area under the receiver operating curve (AUC) ranged between 0.75 and 0.90. None of the studies examining the predictive accuracy of the genomic classifier explicitly reported whether the gene-expression analysis was performed on tissue collected at the time of surgery or at the time of clinical end point (e.g. metastasis). There was potential overlap in participants used to develop and validate the genomic classifier across four studies interrogating a patient registry13,16,17,19. In three full-text validation studies16,17,19 there were noticeable differences between cases and controls in the frequency of high-risk pathological features, risk factors for metastasis, and adjuvant and salvage therapy (Table 2). The possibility of confounding due to these differences may have yielded an overestimate of the prognostic ability of the genomic classifier. Another limitation is the change in risk of metastasis with more follow up time. The 5-year risk of metastasis among men with biochemical recurrence is only 4%, whereas the 10- and 15-year risk increases dramatically (11% and 19%)21, suggesting the genomic classifier may lead to false-negative results for those men who experience metastasis beyond five years. In terms of prostate cancer-specific mortality, one study reported an AUC of 0.78 (95% CI: 0.68 to 0.87) among high-risk patients13. In a group of men treated with adjuvant radiation therapy, the AUC for metastasis was 0.83 (95% CI: 0.27 to 0.89).15

A conference abstract22 reported that the genomic classifier could predict metastasis in a subset of prostate cancer tumors with ETS gene overexpression, PTEN-loss, or both very well. Among the tumors with ETS gene overexpression and PTEN loss the genomic classifier had an AUC of 0.82 and 0.85, respectively. In tumors with both ETS overexpression and PTEN loss, the AUC was 0.85.

The cumulative incidence of metastasis five years after RP was 2.4%, 6.0%, and 22.5% for the low, intermediate, and high risk groups defined by the genomic classifier respectively17 and 0%, 12%, 17% eight years after RP14 and 12%, 31% and 47% 10 years after RP for the same three risk groups respectively20.

Table1

Table 1: Performance characteristics of the genomic classifier

Table2

Table 2: Participant characteristics in clinical validation studies

Clinical Utility

No direct evidence for clinical utility for the genomic classifier is currently available showing changes in selection of primary or adjuvant treatment based on the genomic classifier or showing that the genomic classifier can successfully predict treatment outcomes. There is only limited evidence of clinical utility from four publications23,24,25,26 and one conference abstract27 comparing physician’s treatment recommendations before and after knowledge of the results from the genomic classifier. Study investigators recruited physicians to review the medical records of selected patients from an actual practice with high-risk prostate cancer in the adjuvant setting (no evidence of biochemical recurrence after RP) and salvage setting (evidence of biochemical recurrence after RP). Physicians were asked to make treatment recommendations based on their reviews. The overall change in treatment recommendations, after asking physicians to consider the scores from the genomic classifier, ranged between 31% and 43% in the adjuvant setting and 53% in the salvage setting (Table 3). The change from no treatment (observation) to any therapy ranged from 8% to 38%, and from any treatment to no treatment ranged from 27% to 52%. All study reports included case histories of high-risk prostate cancer in which the most significant change in treatment recommendations was from any adjuvant therapy to no treatment. These studies provide indirect evidence that only suggests some potential for the genomic classifier to achieve clinical utility.

Table3

Table 3: Change in treatment recommendation in high-risk prostate cancer based on knowledge of the genomic classifier

Conclusions

The recently introduced genomic classifier was developed to identify men with increased risk for metastatic prostate cancer following radical prostatectomy. The genomic classifier was shown to have good discrimination in detecting men at risk for metastatic prostate cancer five years after surgery (AUC 0.75 to 0.90). However, these estimates may be biased given differences in important prognostic characteristics between cases and controls. Independent validation in patients with high-risk prostate cancer may provide more reliable estimates of the prognostic ability of the genomic classifier. At this time it is unclear what the most appropriate clinical actions are based on test results. No studies yet published have reported the ability of the genomic classifier to predict significant clinical outcomes from any of the available adjuvant therapies. In order to demonstrate clinical utility additional evidence is needed showing improved outcomes (clinical metastasis or prostate cancer-specific mortality) in men whose post-operative treatment was guided by the genomic classifier compared to standard practice.

Competing Interests

The authors have declared that no competing interests exist. The findings and conclusions are those of authors, including NIH co-authors, and do not necessarily represent the views of the National Institutes of Health (NIH). The information provided in this manuscript does not constitute an endorsement of any of the commercially available tests described in this report by NIH nor the Department of Health and Human Services of the U.S. government.

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http://currents.plos.org/genomictests/article/a-22-gene-expression-assay-decipher-genomedx-biosciences-to-predict-five-year-risk-of-metastatic-prostate-cancer-in-men-treated-with-radical-prostatectomy/feed/ 0
Predicting Prognosis of Early-Stage Non-Small Cell Lung Cancer Using the GeneFx® Lung Signature http://currents.plos.org/genomictests/article/predicting-prognosis-of-early-stage-non-small-cell-lung-cancer-using-the-genefx-lung-signature/ http://currents.plos.org/genomictests/article/predicting-prognosis-of-early-stage-non-small-cell-lung-cancer-using-the-genefx-lung-signature/#comments Mon, 26 Oct 2015 15:15:06 +0000 http://currents.plos.org/genomictests/?post_type=article&p=23006 Use of adjuvant chemotherapy remains a complex decision in the treatment of early stage non-small cell lung cancer (NSCLC), with risk of recurrence being the primary indicator (i.e. adjuvant chemotherapy is considered for patients at high risk of recurrence but may not be beneficial for patients at low risk). However, although several clinical and pathological factors are typically considered when assessing the risk of recurrence, none are significantly associated with clinical outcome with the exception of tumor size. GeneFx® Lung (Helomics™ Corporation, Pittsburgh, PA) is a multi-gene RNA expression signature that classifies early stage NSCLC patients as high-risk or low-risk for disease recurrence. GeneFx Lung risk category has been shown to be significantly associated with overall survival in several independent clinical studies. The published literature regarding the analytical validity, clinical validity and clinical utility of GeneFx Lung is summarized herein.

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Clinical Scenarios

Despite successful surgery, 50-70% of early stage (stages I and II) non-small cell lung cancer (NSCLC) patients die within 5 years.1 Although the standard of care treatment for stage II patients, as well as high risk stage IB patients, is surgery followed by adjuvant chemotherapy,2 some stage II patients with a better prognosis may be spared the costs and adverse effects associated with chemotherapy. However, there are currently no reliable methods to identify these patients. There are 30-40% of stage I patients with a worse prognosis who may benefit from adjuvant treatment but methods to identify these patients are similarly lacking.3 Several clinicopathologic factors are currently used to estimate high risk of recurrence in NSCLC – poorly differentiated tumors, vascular invasion, wedge resection, tumors >4 cm, visceral pleural involvement, and incomplete lymph node samples. However, none of these factors (with the exception of tumor size) have been shown to be significantly associated with clinical outcome. Furthermore, as the current approach to oncology treatment moves toward stratified medicine, there is more focus on using the genetic composition of the tumor in individualizing patient treatment.4 As such, a prognostic marker for early stage NSCLC could have high clinical utility.

Test Description

GeneFx® Lung is a 15 gene signature that can predict risk of recurrence in early stage NSCLC, independent of other, relevant clinicopathologic factors. RNA is isolated from NSCLC tissue, containing a minimum of 20% tumor which has been preserved in RNAlater® (an RNA stabilization reagent) or by being ‘fresh frozen’, using a phenol-chloroform method. Extracted RNA is reverse transcribed into cDNA and amplified using single primer isothermal amplification (SPIA) chemistry. The amplified cDNA is chemically and enzymatically fragmented and labelled with biotin. The cDNA targets are hybridized to an Affymetrix Human Genome U133 Plus 2.0 microarray, followed by a wash and stain procedure that binds streptavidin-phycoerythrin (SAPE) stain to the biotinylated cDNA molecules. Scanning identifies the number of cDNA transcripts that are present. The resulting data are processed using a proprietary algorithm to generate a risk score which is dichotomized into “high-risk” (risk score ≥ -0.10) or “low-risk” (risk score ˂ -0.10) categories. Patients whose tumors are categorized as high-risk should be considered, in concert with other clinical factors, for treatment with adjuvant chemotherapy.

GeneFx Lung distinguishes itself from other prognostic gene expression signatures for early stage NSCLC in a number of ways:

  • GeneFx Lung is applicable to the two major histological subtypes of NSCLC, adenocarcinoma and squamous cell carcinoma.
  • GeneFx Lung results are dichotomized into 2 categories (high-risk, low-risk), making results interpretation intuitive.
  • GeneFx Lung is clinically validated across several independent patient cohorts.
  • GeneFx Lung is clinically validated on multiple technology platforms (Affymetrix microarrays, Illumina and Agilent microarrays, RT-qPCR).
  • GeneFx Lung shows promise as a predictive signature for response to adjuvant chemotherapy.5,6

Public Health Importance

Lung cancer is the leading cause of cancer death. In 2014, it is estimated that there will be more than 225,000 new lung cancer cases in North America, 85% of which are NSCLC.7,8,9 Early stage NSCLC may be curable with surgical resection,10 and survival may be improved with adjuvant chemotherapy, especially in stage II patients.11,12,13 Thus, guidelines recommend that stage II NSCLC patients are treated with adjuvant chemotherapy2 although it is suspected that patients with a better prognosis (lower risk of recurrence) may be unnecessarily subjected to the costs and morbidity associated with chemotherapy with no clinical benefit. Likewise, guidelines recommend that stage I NSCLC patients are not to be treated with adjuvant chemotherapy,2 but a recurrence rate of 35-50% in this group9 suggests that adjuvant chemotherapy may be beneficial to the portion of stage I patients with a poorer prognosis (higher risk of recurrence). Currently, aside from tumor size, there are no clinically validated markers to discern prognosis and potential chemotherapy benefit in early stage NSCLC that may assist in developing a personalized treatment approach for these patients.

Published Reviews, Recommendations and Guidelines

Recommendations by independent groups

The Wadsworth Center of the State of New York performed an independent review of the technology, standard operating procedures, quality measures, and analytical and clinical validation results of GeneFx Lung, resulting in full approval and licensure in the state of New York. GeneFx Lung testing is performed exclusively in the Helomics™ Corporation laboratory in Pittsburgh, Pennsylvania, which is certified to comply with the Centers for Medicare & Medicaid Services Clinical Laboratory Improvement Amendments (CLIA) program. The Helomics laboratory is licensed by CLIA, with independent licenses in the states of New York, California, Florida, Maryland, Pennsylvania and Rhode Island.

Guidelines by professional groups

The American Society of Clinical Oncology (ASCO) and The Cancer Care Ontario Program in Evidence-Based Care (CCO) jointly reviewed and provided recommendations regarding the role of adjuvant chemotherapy and radiation therapy in the treatment of patients with early stage NSCLC.14 Due to the lack of clinical evidence regarding the role of adjuvant chemotherapy, specifically in stage I disease, the recommendations do not support use of chemotherapy in this setting while still recognizing that high-risk patients could benefit from its use. These recommendations were generated prior to the advent of genomic signatures for prognosis of early stage NSCLC and, thus, do not mention use of this technology to discern high-risk patients.

Independent review articles

In 2009, Zhu et al. reviewed the concepts and methodologies involved in identifying, developing and validating multi-gene signatures in lung cancer.15 In addition to describing the variety of approaches to these processes, this review summarized a number of prognostic signatures independently validated in NSCLC, underscoring the strong clinical need for better prediction of patient prognosis in this disease. Similar ‘guidelines’ for developing and validating prognostic signatures in NSCLC were described by Subramanian and Simon.16 More recently, a variety of NSCLC prognostic markers (including single gene, immunohistochemical and multi-gene makers) were reviewed, suggesting the potential value of such markers in predicting benefit from adjuvant chemotherapy in early stage NSCLC.17

Evidence Overview

Analytical Validity

  • The precision, sensitivity and specificity of GeneFx Lung were recently evaluated, confirming the robust and reliable nature of the test.18 Specifically, agreement of 97% or greater amongst numerous replicates of the assay both between runs and within the same run reveal the highly repeatable and reproducible nature of GeneFx Lung. In addition, the lower limit of quantitation was established, and genomic DNA was found to not interfere with assay results.
  • The accuracy of GeneFx Lung was evaluated in a study of 34 NSCLC samples in which biological replicates were assayed in the commercial laboratory (Helomics™ Corporation, Pittsburgh, PA, USA) as well as an accredited reference laboratory (Almac Diagnostics Ltd., Craigavon, Northern Ireland, UK).18 The concordance in risk categorization between the two laboratories was 94% (95% CI 86%-100%), with a Pearson correlation of 0.88 (95% CI 0.77-0.94). This level of concordance is deemed to be acceptable as it exceeds the level of concordance observed within the commercial laboratory (see next bullet point). Furthermore, there was no evidence of bias between the laboratories.
  • Although originally developed and validated using fresh frozen tissue, GeneFx Lung has been shown to perform equivalently in tissue preserved in RNAlater.18 In a study of matched fresh frozen and RNAlater-preserved NSCLC tissue from 43 patients, the percent concordance in risk category between the tissue formats was 84% (95% CI 73%-95%), with a Pearson correlation of 0.74 (95% CI 0.63-0.85) of the risk scores. The level of agreement observed between matched fresh frozen and RNAlater-preserved tissues is comparable with the inherent reproducibility observed within biological replicates of fresh frozen tissue (79% concordance, 0.83 Pearson correlation).
  • GeneFx Lung maintains performance across various gene expression technology platforms. Although developed and validated on the Affymetrix U133 microarray platform, GeneFx Lung maintained statistical significance when using the Agilent 44K platform [hazard ratio (HR) 2.27, 95% CI 1.18-4.35, p=0.014] as well as RT-qPCR (HR 2.29, 95% CI 1.06-4.94, p=0.034).5

Clinical Validity

  • In a prospective evaluation of 181 untreated early stage NSCLC patients, GeneFx Lung-designated risk of recurrence (high-risk vs. low-risk) was significantly correlated with overall survival, with a multivariate HR of 1.95 (95% CI 1.15-3.30, p=0.013). The prognostic ability of GeneFx Lung was corroborated in subgroup analysis for stage I (HR 2.17, 95% CI 1.12-4.20, p=0.018) and stage IA (HR 5.61, 95% CI 1.19-26.45, p=0.014) patients, with 92% of low-risk, but only 61% of high-risk, stage IA patients achieving survival at 5 years. Furthermore, the signature’s prognostic ability is independent of histology, with utility in both adenocarcinoma and squamous cell carcinoma cases.6
  • Independent, published microarray datasets were used to validate the prognostic property of GeneFx Lung, which had been derived from 62 untreated early stage NSCLC patients in the JBR.10 clinical trial. GeneFx Lung was shown to be a significant prognostic factor (independent of other clinicopathological factors) in three datasets totaling 308 early stage NSCLC patients, with HRs of 2.26, 2.27 and 3.57, spanning both adenocarcinoma and squamous cell carcinoma, as well as other less common histological subtypes.5

Clinical Utility and Other Supportive Studies

  • Initial studies have suggested that the GeneFx Lung signature may also be predictive of improved overall survival in early stage NSCLC patients treated with adjuvant chemotherapy. In two separate datasets, patients designated as high-risk by GeneFx Lung and treated with adjuvant chemotherapy experienced significantly improved overall survival compared to those left untreated.5,6 Alternatively, adjuvant chemotherapy in low-risk patients may not be beneficial.5

Limitations

GeneFx Lung has been shown to perform equivalently in both fresh frozen and RNAlater-preserved tissue formats, however, performance in formalin-fixed paraffin embedded (FFPE) tissue has yet to be explored. FFPE represents a more stable and clinically accessible tissue format for clinical studies as well as commercial use.

Clinical validations performed to date clearly indicate the prognostic significance of GeneFx Lung in designating the risk of recurrence in early stage NSCLC tumors. Because risk of recurrence may play a large role in making treatment decisions, the predictive significance of the signature should also be considered. Exploratory analyses have been promising, and additional, independent validations of GeneFx Lung as a predictive marker are warranted and should consider use of tumor recurrence-based endpoints (e.g. disease free survival).

Conclusions

Although numerous factors are considered when staging NSCLC, there remains a clinical unmet need to delineate risk of recurrence in early stage patients as stage alone does not fully elucidate which patients may benefit from adjuvant chemotherapy. Clinicopathological factors currently employed are not significantly associated with treatment effectiveness, with the exception of tumor size. GeneFx Lung is a robust gene signature that has been validated in several independent cohorts to estimate risk of recurrence in early stage NSCLC, with overall survival being significantly associated with GeneFx Lung risk category. The studies reviewed herein support use of GeneFx Lung to assess risk of recurrence in both adenocarcinoma and squamous cell carcinoma cases of early stage NSCLC, thereby facilitating adjuvant chemotherapy treatment decisions in these patients.

Competing Interests

SB and AU are paid employees of Helomics Corporation and hold stock options. These competing interests do not affect our adherence to the PLOS Currents policies on sharing data and materials.

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Use of ChemoFx® for Identification of Effective Treatments in Epithelial Ovarian Cancer http://currents.plos.org/genomictests/article/use-of-chemofx-for-identification-of-effective-treatments-in-epithelial-ovarian-cancer/ http://currents.plos.org/genomictests/article/use-of-chemofx-for-identification-of-effective-treatments-in-epithelial-ovarian-cancer/#respond Mon, 13 Jul 2015 10:45:52 +0000 http://currents.plos.org/genomictests/?post_type=article&p=22780 Selection of appropriate chemotherapy, including identification of platinum resistance, is critical to effective management of advanced epithelial ovarian cancer (EOC). ChemoFx®, a multiple treatment marker (chemoresponse assay), has been developed to address this challenge and to improve outcomes in patients with advanced EOC. While much work has been done that has demonstrated the analytical validity of this assay, more recent studies have highlighted the unique clinical benefits offered by the assay. A prospective, multicenter trial has shown an increase in overall survival (OS) of 14 months and an increase in progression-free survival (PFS) by 3 months in patients with recurrent EOS treated by a “sensitive” therapy based on ChemoFx results. Along with other studies showing similar gains in OS and PFS, ChemoFx has been shown to be both a prognostic and predictive marker in patients with recurrent EOC where current treatment options are sorely lacking. In addition to these clinical benefits, economic analyses have shown that ChemoFx is a cost-effective intervention. Current guidelines and technology assessments relating to ChemoFx are largely outdated and refer primarily to metrics of analytical validity. Thus, in addition to analytical validity, the clinical validity, clinical utility and economic impact of ChemoFx are reviewed herein, including published literature, technology assessments by independent parties, and regulatory approvals of this marker.

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Clinical Scenarios

Aggressive cytoreductive surgery followed by platinum-based chemotherapy is the standard first-line approach for the management of EOC. Currently, 6 different first-line chemotherapy regimens are recommended options in the National Comprehensive Cancer Network (NCCN) guidelines for Ovarian Cancer.1 Patient outcomes, however, are similar among the recommended options. Intravenous (IV) carboplatin/paclitaxel is the most commonly used regimen. While 60-80% of patients initially respond to this standard approach, the median PFS remains just 17 months and the median OS is 44 months.2 Therefore, in practically all women, the disease returns despite initial response to chemotherapy.

Selecting optimally effective treatment(s) for recurrent EOC is challenging. Considerations include: site(s) of recurrence, patient comorbidities, and cumulative toxicity from first-line chemotherapy. Repeat cytoreductive surgery and second-line chemotherapy are the most common treatments for recurrent or persistent disease. While most patients eventually succumb to EOC, many will experience prolonged remissions and symptom-free survival.3,4 Relapses in patients occurring more than 6 months from the completion of first-line therapy are considered to be ‘platinum-sensitive’. A portion of these patients will respond to re-exposure to platinum-based therapy. However, treatment guidelines outline numerous platinum-based regimens,1 with little or no difference in clinical performance for the population across the various options. Patients who progress during treatment or within 6 months of completion of first-line therapy are considered to be ‘platinum-resistant’, and decisions regarding second-line treatment are especially difficult. Platinum-resistant EOC is typically treated with sequential non-platinum single agent therapies. In general, the PFS and OS are poor for this group of patients compared to those who manifest platinum-sensitive disease. Currently, the treatment of EOC remains largely empiric. As with most clinical factors, the accuracy of platinum sensitivity and resistance is not absolute, and additional measures of responsiveness may be beneficial in personalizing treatment strategies.

Test Description

As defined by the National Institutes of Health (NIH), a marker is “a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention”.5 Therefore, ChemoFx is considered a marker of multiple treatments and provides tumor-specific information to assist physicians in the selection of effective chemotherapy for individual patients with gynecologic cancers. ChemoFx is intended to be utilized in conjunction with treatment guidelines, heuristics and pathways. Physicians indicate which of the multiple, guideline-recommended, FDA-approved chemotherapy options are under consideration for each patient when ordering the assay, and ChemoFx reports a result for each of the single agent or combination treatments selected for testing.

ChemoFx is a cell culture-based chemoresponse assay that measures the sensitivity of tumor-derived malignant epithelial cells to chemotherapeutic agents in vitro, using quantification of cellular DNA as the assay endpoint.6 The assay employs tumor tissue samples available at the time of clinically indicated surgery, biopsy or paracentesis; so, no additional procedures are required to fulfill tissue collection requirements for the assay. Tissue is shipped to the ChemoFx laboratory (Helomics™ Corporation, Pittsburgh, PA), and primary cultures are initiated by mincing each tissue sample into 1 mm3explants, which are then seeded into culture flasks. Upon near confluency, primary cultures are trypsinized and seeded into 384-well microtiter plates and used immediately for in vitro testing. Multiple concentrations of each treatment are prepared by serial dilution. Each concentration is added to three replicate wells on the microtiter plate; three replicates of control (no treatment) wells are also associated with each treatment. Culture seeding into microtiter plates, as well as serial treatment dilution and application, are completed using highly automated liquid handling robotics ensuring a consistent and repeatable process.7 After 72 hours of incubation with treatment, DNA in the nucleus of surviving adherent cells is stained with DAPI and counted using proprietary, automated, computer-assisted microscopy.7 The inhibition of tumor growth is measured for each concentration (average of cell counts in three replicates) of a given treatment. The survival fraction (SF) of tumor cells at each concentration is calculated as compared to control.8 The summation of SF values is computed as the drug response score, which represents the area under the dose response curve (AUC). A smaller AUC score indicates that a tumor is more sensitive to a treatment in vitro; a larger score indicates greater resistance to a treatment. For each treatment, in vitro tumor response is classified into one of three categories according to the AUC score: sensitive (S), intermediate sensitive (IS), or resistant (R). The cut-point threshold for the classifications were previously and independently established based on the 25th and 75th AUC percentiles in referent specimens, with an AUC score less than 25th rank classified as S, between 25th and 75th rank as IS, and greater than 75th rank as R.

Unique, Distinguishing Features of ChemoFx

ChemoFx is distinguished from prior chemoresponse assays in a number of ways:

  • ChemoFx requires a small amount of cells or tissue (as little as 35 mm3, or smaller than the size of a pencil eraser), an amount that is typically easily accessible by surgery, biopsy or paracentesis.
  • ChemoFx laboratory processes are highly automated. Robotic and computerized microscopy technologies allow for high throughput and exceptional reproducibility that is superior to other cell-based assays.
  • The ChemoFx culture process is robust and preferentially supports outgrowth and proliferation of epithelial cells, using immunoctyochemisty to verify that the majority of the culture is made up of epithelial cells. Nearly 90% of specimens submitted for ChemoFx testing yield cultures that proceed through to the assay process.
  • Cells cultured during the ChemoFx assay are actively cycling prior to chemotherapy exposure, ensuring that the efficacies of cell cycle-specific, cytostatic and cytotoxic agents are appropriately assessed.

Genomic Relevance

While ChemoFx is more a phenotypic, than a typical genomics assay, it represents the phenotypic response represents an integrated manifestation of genetic, genomic, proteomic and functional characteristics of a tumor. Published studies suggest that ChemoFx represents a novel platform for multigene (microarray) signature development, by associating in vitro assay response data with gene expression profiling data.9,10,11 Specifically, a multigene signature developed utilizing the ChemoFx platform has been independently validated, differentiating pathologic complete response from residual disease after neoadjuvant chemotherapy.9 The ability of ChemoFx to simultaneously assess response to multiple therapies makes this assay especially useful for developing genomic signatures that may predict differential response to therapies.

Public Health Importance

It is estimated that, in 2015, there will be 21,290 new cases of ovarian cancer and 14,180 deaths due to this disease in the United States; EOC represents the leading cause of death from gynecologic cancer.12 The poor prognosis observed with EOC is largely attributed to detection of the disease at an advanced stage, as well as drug resistance at initial presentation or developed later. Although standard first-line treatment is initially effective for the majority of EOC patients, most of these patients will relapse within 1-2 years, and only 30% will live beyond 5 years.13Given this unfortunate prognosis, there is a strong desire, as well as clinical opportunity, to extend the overall survival of EOC patients. More than 30 different acceptable treatment choices are identified in current treatment guidelines for recurrent EOC.1 Yet, evidence on a population-wide level is insufficient to show that any one of the recommended regimens is superior to any others. ChemoFx can help guide personalized therapy decisions through the phenotypic approach of a chemoresponse assay. Further, in EOC, unlike some other solid tumors, biomarkers have not been well validated to document their ability to stratify patients for individualized treatment choices that improve outcomes. Recently published clinical trials provide evidence in support of the analytical validity, clinical validity and clinical utility of ChemoFx and are described in detail below. These new studies, including the first prospective clinical trials examining a chemoresponse assay, are published in the context of a significant of negative reviews of chemoresponse assays due to lack of prospectively designed clinical trials. These new studies address a need for validation proven through prospective clinical trials which has been requested for a number of years.

Published Reviews, Recommendations and Guidelines

Systemic evidence reviews/technology assessments

The Blue Cross Blue Shield Association Technology Evaluation Center (BCBS TEC) evaluated chemoresponse assays in 1995 and subsequently issued minor updates in 2000 and 2002. BCBS TEC found that chemoresponse assays do not meet all of the TEC criteria, primarily due to a lack of prospective, randomized clinical trials to compare the outcomes of assay-guided treatment and empiric treatment.14,15,16 More than a decade has passed since the most recent BCBS TEC evaluation of 2002. Since then, multiple clinical trials, both retrospective and prospective, have reported on the clinical validity and clinical utility of ChemoFx,17,18,19,20,21,22,23 and, as such, these data were not included in any of the BCBS TEC assessments.

The American Society of Clinical Oncology (ASCO) also published technology assessments of chemoresponse assays in 2004 and 2011, with consideration of literature published through May 2010 for the 2011 assessment.24,25 Both ASCO assessments concluded that the then-current clinical literature did not support use of chemoresponse assays in routine oncology practice, citing low success rates, lack of appropriate prospective evaluation in clinical trials and a tendency to simply recommend treatments that would have been given empirically (i.e. low utility).24 However, the assessments noted the potential importance and impact of these assays and encouraged participation in clinical trials evaluating chemoresponse assays. It is, once again, noteworthy that the ASCO technology assessments were conducted prior to the publication of key clinical validations of ChemoFx which demonstrate the high technical success of ChemoFx compared to other CSRAs, prospective evaluation of the assay, and clinical utility by comparing clinical outcomes of patients with and without access to the assay.18,19,20,21,22,23Specific details of these studies are noted below.

Recommendations by independent groups

The Helomics laboratory, where ChemoFx is performed, has undergone a number of evaluations of its analytical validity. The Wadsworth Center of the State of New York performed an independent review of the technology, standard operating procedures, quality measures, and analytical and clinical validation results of ChemoFx, resulting in approval and licensure in the state of New York. Furthermore, ChemoFx testing is performed in the Helomics Corporation laboratory in Pittsburgh, Pennsylvania, which is certified to comply with the Centers for Medicare & Medicaid Services Clinical Laboratory Improvement Amendments (CLIA) program. The ChemoFx laboratory is licensed by CLIA nationwide and also has specified licensure by the states of New York, California, Florida, Maryland, Pennsylvania and Rhode Island.

Guidelines by professional groups

In 2010, the NCCN Clinical Practice Guidelines in Oncology for Ovarian Cancer: Including Fallopian Tube Cancer and Primary Peritoneal Cancer were amended to state that “Chemosensitivity/resistance and/or other biomarker assays are being used in some NCCN Member Institutions for decisions related to future chemotherapy in situations where there are multiple equivalent chemotherapy options available.” Use of chemoresponse assays is classified as a category 3 level of evidence and has not been re-evaluated by the NCCN since 2010. ChemoFx has been used in 20 of 21 NCCN institutions. As with the above two situations the, this was completed before the most recent round of publications.

Evidence Overview

Analytical Validity

  • The reproducibility of ChemoFx has been evaluated in several studies and under numerous conditions.7,8,26 The coefficient of variance (CoV) in an ovarian cancer cell line, SK-OV-3, treated with doxorubicin has been measured to be 3.6-4.6% across three operators and over nine days.7 The CoV has been measured to be as low as 2-3% under conditions that mimic the current commercial process, which includes the use of liquid handling and process automation for plating cell cultures, preparing/diluting chemotherapy agents, treating cultures with chemotherapy agents, and fixing/staining cultures post-treatment, as well as counting live cells. Process variability due to the stability of chemotherapy agents (both within a given day and across multiple days) has also been measured and reported.26
  • The primary cell culture process in ChemoFx enriches for malignant cells. In a study of 50 ChemoFx primary cultures, the percentage of malignant cells increased throughout the culture period for 86% (43/50) of them with an average magnitude of increase of 37%. Notably, the minimum proportion of malignant cells at the conclusion of the culture period across all 50 cultures was 60%.6

Clinical Validity

  • A prospective, multicenter study demonstrated improved clinical outcomes (both PFS and OS) in patients with recurrent ovarian cancer who were treated with a therapy that was ‘sensitive’ according to the ChemoFx assay (PFS: HR=0.67, 95% CI=0.50-0.91, p=0.009; OS: HR=0.61, 95% CI=0.41-0.89, p=0.010).19 These improvements amounted to median increases in PFS of 3 months (9 vs. 6 months) and OS of 14 months (38 vs. 24 months). In multivariate analysis, ChemoFx result was shown to be independently associated with PFS (HR=0.66, 95% CI=0.47-0.94, p=0.020) and OS (HR=0.59, 95% CI=0.38-0.93, p=0.023). Improved clinical outcomes in patients treated with ChemoFx ‘sensitive’ therapies (as compared to patients treated with ‘non-sensitive’ therapies) were evident in both platinum-sensitive and platinum-resistant sub-populations. Furthermore, 52% of tumors demonstrated in vitro sensitivity to at least one agent, suggesting that, although generalized resistance is common in recurrent EOC, a majority of patients benefit from assay-informed treatment selections.
  • Using the Rutherford, et al. cohort,19 four independent statistical methods were employed to assess the predictive properties of ChemoFx. All four analyses yielded the same result – ChemoFx has the ability to identify specific therapies that are likely to be more effective, predicting both response and prognosis. The association between ChemoFx result and clinical outcome was enhanced for the therapy used in clinical treatment (“match”), as compared to those that were randomly selected from all assayed therapies (“mismatch”) (PFS HR=0.67 vs. 0.81). Furthermore, improved outcome was associated with treatment with an assay-sensitive therapy, regardless of homogeneous (all sensitive or all resistant) or heterogeneous (mixed sensitive and resistant) responses among the assayed therapies.21
  • Patients prospectively enrolled in an observational study and displaying in vitro resistance to carboplatin (via ChemoFx) had significantly shorter PFS after standard first-line therapy (carboplatin/paclitaxel) (HR=1.87, 95% CI=1.29-2.70, p<0.001), progressing 4.8 months sooner than patients displaying in vitro sensitivity to carboplatin (median PFS: 11.8 vs. 16.6 months). This association was confirmed in multivariate analysis to be independent of other relevant covariates (HR=1.71, 95% CI=1.12-2.62, p=0.013). Furthermore, for tumors that showed in vitro resistance to carboplatin, 59% displayed in vitro sensitivity to at least one non-platinum agent, suggesting that ChemoFx has the ability to narrow treatment choices in platinum-resistant disease. ChemoFx was shown to be independently associated with PFS in primary ovarian cancer patients. Patients predicted for poorer outcome (i.e. platinum resistance) by ChemoFx may be considered for alternate treatment options.20
  • Primary ovarian cancer patients (n=192) treated with therapy categorized as sensitive via ChemoFx experienced a median OS more than twice as long (72.5 vs. 28.2 months) as patients treated with an assay-resistant treatment (HR= 0.70, 95% CI=0.504-0.968, p=0.031). ChemoFx prediction of response to platinum agents is an independent predictor of OS (HR=0.68, 95% CI=0.490-0.948, p=0.023).18
  • In patients with evaluable disease, there was a statistically significant correlation between ChemoFx result and progression-free interval (PFI) in a retrospectively accrued, prospectively analyzed study of 135 EOC patients whose tumors were submitted for ChemoFx testing (HR=2.9, 95% CI=1.4-6.3, p<0.01). Patients treated with an assay-sensitive therapy experienced three-times longer PFIs compared to those treated with an assay-resistant therapy.17

Clinical Utility and Other Supportive Studies

  • Clinical utility of ChemoFx was recently evaluated in an analysis of OS between advanced stage EOC patients (n=192) whose treatment was assay-informed and a combined cohort of >7000 primary EOC patients whose treatments were non-assay-informed. Despite a worse prognosis at baseline based on clinical covariates, assay-informed patients experienced a 10% improvement (48 vs. 44 months) in OS compared to non-assay-informed patients when not stratifying by an assay selected-treatment. When assay-informed patients were treated with an assay-sensitive therapy, they experienced a 28.5 month (65%) increase in OS (72.5 vs. 44 months), while those treated with an assay-resistant therapy demonstrated a 15.8 month (36%) decrease in OS (28.2 vs. 44 months).22
  • In a survey of 23 gynecologic oncologists who have ordered a chemoresponse assay [conducted by an independent physician polling organization (Leerink Swann) in 2012], nearly all (95.7%) of the physicians indicated that assay results have helped them to decide between equivalent treatments. In addition, just over half (52.2%) of them maintained that assay results were used to select a less toxic and/or less expensive treatment.
  • Metachronous paired tumors from 242 EOC patients exhibited in vitro chemoresponse profiles that, as clinically and biologically expected, displayed a general shift toward resistance over time. The shift towards increased resistance was more pronounced for therapies that are typically used in first-line treatment (carboplatin, cisplatin, paclitaxel, docetaxel) and, thus, are most likely to be previously administered. Collectively, the results of this study indicate that ChemoFx is most useful when a tumor sample is available immediately preceding a treatment decision; however, if a tumor sample is not obtained at recurrence, assay results obtained within the prior 17 months (median PFS for EOC after first-line treatment) may help select effective therapy, especially for therapies not previously administered.27

Economic Impact

  • ChemoFx is considered to be a cost-effective health care intervention, using a Markov state transition model based on patient characteristics and survival data from a recent clinical study of ChemoFx in recurrent EOC [19]. ChemoFx presented an incremental cost effectiveness ratio (ICER) of $6,206 per life-year saved (LYS) for patients with recurrent EOC who are treated according to ChemoFx results (compared to similar patients whose treatment decisions were made without ChemoFx). Cost-effectiveness was further demonstrated in both platinum-sensitive and platinum-resistant populations treated with assay-sensitive therapies with ICERs of $2,773 per LYS and $2,736 per LYS, respectively. Furthermore, if the least expensive, sensitive therapy is chosen for treatment, use of ChemoFx has the potential to be cost saving.23
  • The mean costs of chemotherapy treatment for recurrent ovarian cancer were estimated to be $48,758 for empirically treated patients, $33,187 for assay-assisted patients (oncologist’s choice of chemotherapy following chemoresponse testing, with 65% adherence to assay results) and $23,986 for assay-adherent patients (modeled group of patients assuming 100% adherence to assay results).28 According to this study, assay-assisted treatment decisions in recurrent ovarian cancer may result in reduced costs compared to empiric treatment selections.

Limitations

Although a prospective, randomized controlled trial design has been recommended for use in the validation of markers due to its successful use in the validation of drugs, it is suggested that alternate study designs may be more appropriate for evaluating markers (especially tests that have the ability to report multiple markers simultaneously) which interface with and impact clinical scenarios differently than drugs.29,30 As such, clinical validations of ChemoFx employ a study design based on the marker-stratified (non-randomized, blinded) approach that has been successfully used in the validations of several markers that are considered to be standards of care in oncology (e.g. KRAS, Oncotype DX®, VeriStrat®).31,32,33,34,35,36,37 This approach overcomes the pragmatic obstacles of the design recommended in technology assessments (e.g. large sample size, heterogeneity of possible therapies) while allowing evaluation of multiple markers simultaneously and evaluating a marker’s predictive properties.

Although ChemoFx provides relative effectiveness within a given therapy (as compared to like patients), the comparative effectiveness between multiple therapies is not considered. Furthermore, other factors which influence treatment selection (e.g. toxicity) are not considered in the ChemoFx results, but remain as independent components of the patient’s profile that the physician considers when selecting the appropriate course of therapy.

Conclusions

Outcomes for women with gynecologic cancers who have been empirically treated are stagnant and disappointing. ChemoFx helps clinicians individualize treatment selections and improve patient outcomes in gynecologic cancer. Numerous publications have outlined the strength of the analytical validity behind the ChemoFx assay in this setting. Several more recent studies, including those with a prospective design, have clinically validated that ChemoFx is an assay capable of predicting the effectiveness of specific therapies and that patients treated with ChemoFx sensitive therapies experience improved outcomes. Patients experienced improvements in OS of up to 14 months when treatments sensitive in ChemoFx were used clinically. Benefits of using ChemoFx, however, are not only limited to increases in OS and PFS. By limiting treatment with ineffective therapies, patients experience fewer unnecessary adverse effects and drug-related toxicities. These factors contribute to the evidence that has shown use of ChemoFx results in reduced overall treatment costs when compared to the current empiric based standard of care. The current evidence strongly suggests that ChemoFx provides a compelling and exciting option for improving the treatment paradigm and patient outcomes in EOC.

Competing Interests

SR is a paid member of Helomics Corporation’s speakers bureau. AW is a paid consultant for Helomics Corporation and holds stock options with the company.

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Multi-marker Solid Tumor Panels Using Next-generation Sequencing to Direct Molecularly Targeted Therapies http://currents.plos.org/genomictests/article/multi-marker-solid-tumor-panels-using-next-generation-sequencing-to-direct-molecularly-targeted-therapies/ http://currents.plos.org/genomictests/article/multi-marker-solid-tumor-panels-using-next-generation-sequencing-to-direct-molecularly-targeted-therapies/#respond Tue, 27 May 2014 12:02:16 +0000 http://currents.plos.org/genomictests/?post_type=article&p=22452

Clinical scenario

Traditional pharmacogenomic applications used to direct molecularly targeted therapy rely on testing tumor tissue for a single genomic marker followed by using tumor-marker specific therapy. There are several established pharmacogenomic applications that are used clinically to aid in treatment decisions for breast, colon, lung and other solid-tumor cancers (Table 1). With advances in high-throughput –omic technologies and plummeting costs of next-generation sequencing (NGS), researchers have begun to move beyond testing single genes, to multi-gene panels, to sequencing the entire human cancer genome in order to better understand the underlying molecular pathways driving tumorigenesis1. Cumulative efforts drawing on resources such as The Cancer Genome Atlas (TCGA) have allowed researchers to develop molecular blueprints common across a wide number of cancer types2 and have identified multiple genomic alterations or ‘driver-mutations’ linked to biological pathways in cell proliferation, apoptosis, tumor metabolism, and chromatin biology. Current clinical oncology practice, which has emphasized tumor site and histology, is undergoing a paradigm shift towards what some have referred to as “genomics-driven oncology” focusing on these mechanistic pathways3.

Table 1. Examples of single-marker single-drug pharmacogenetic associations used in solid-tumor oncology.

Clinically available NGS tests are used to characterize an individual’s cancer genome through targeted sequencing of pre-specified candidate genes believed to provide clinically actionable molecular targets. Using a single test to detect a broad spectrum of genomic alterations in the biological pathways from a single biopsy is thought to be a more efficient treatment decision process than the single-marker single-treatment approach4. The genomics-driven oncology approach using multi-marker panels is intended to expand clinician’s armamentarium to treat patients who may have exhausted standard therapies, especially those with metastatic disease. One key assumption underlying this approach is that a molecular target predictive of treatment response with a currently available therapy in a specific tumor type will have the same clinical effect (predictive of treatment response) in an entirely different tumor type harboring such molecular profile. Complicating this assumption is the reality that additional mutations downstream from the primary molecular target have unknown clinical significance, which may influence treatment response differentially across cancer types. Additional complications arise from molecular heterogeneity within primary tumors as well as secondary tumors5, which could lead to limited effectiveness when matching therapies to specific genomic alterations based on a single tumor biopsy.

Test description

An established clinical test integrating NGS technology for tumor DNA sequencing requires a standardized protocol with details describing pre-analytic, analytic and post-analytic processes. The pre-analytic variables include the patient’s clinical characteristics as well as details describing the collection and preparation of tumor samples. The analytic variables that may affect the precision and accuracy of the targeted sequencing of pre-specified molecular targets (whole-genome, exome, SNPs, etc.) refer to the actual sequencing process itself and are related to the specific NGS platform used to conduct the massively parallel sequencing as well as individual laboratory procedures. The post-analytic variables (i.e. data entry, result validation, interpretation of results, transfer of data and reporting of test results) relevant to NGS include variant calling, functional and clinical interpretation, reporting results to clinicians, and data storage1,6,7. The post-analytic process at the foundation of successfully integrating NGS into clinical practice is variant calling which includes the alignment of the raw tumor sequence data using a reference human genome followed by variant identification from the aligned tumor sequence and molecular annotation of the identified genomic variants with corresponding clinical interpretations. Each of these processes have specialized computational algorithms designed to handle large amounts of sequencing data and to date were primarily developed for research purposes. For clinical applications, such algorithms will need to be adapted to better integrate with the downstream interpretive processes, including clinical decision making7.

The expanded reach of NGS platforms provides researchers with a much more comprehensive picture of a tumors genomic architecture. In an effort to leverage the technical advances in sequencing technology and expanded vision of a tumors molecular signature several NGS platforms have been integrated into commercially available solid-tumor sequencing panels to identify clinically actionable variants (Table 2). Despite the lack of consensus as to what constitutes a clinically actionable variant, three general categories may be used to classify variants: 1) variants linked to an FDA-approved drug within a specific tumor type; 2) variants linked to an FDA-approved drug outside of the patients specific tumor type; 3) variants linked to non-FDA approved drugs in preclinical testing or early phase clinical trials8. It should be noted variants of unknown significance may also be identified which may not be considered clinically actionable owing to diverse interpretations of what these variants may represent. Similar tests employing NGS technology for directing treatment decisions are available at both academic and community-based cancer treatment centers. The limited commercial availability of the academic and community-based tests fall outside the scope of this review and are not listed in Table 2. As companies refine their workflows and emerging evidence identifies variants common in non-solid tumor, additional liquid-tumor sequencing panels have recently become available, but are not reviewed here.

Table 2. Commercially available multi-marker solid tumor panels using next-generation sequencing (NGS).

Public Health importance

To date, the application of multi-marker tumor panels using NGS has primarily focused on individuals with advanced metastatic disease who have exhausted standard therapies for their particular condition. In such cases, these tests may provide unconventional therapeutic options against an otherwise refractory disease. It is unclear at what point this testing and treatment strategy will become a part of the standard molecular profiling of newly diagnosed malignancies, expanding the potential population impact. As evidence emerges on driver-mutations common across a number of cancer types, newly developed molecularly targeted therapies will provide a means to improve cancer treatment outcomes across a number of cancers with common biological/molecular mechanistic pathways. A more complete picture of an individual’s tumor genome may also be integrated within existing frameworks including age, disease burden, and histologic/molecular features for developing more effective cancer treatment and management strategies4.

Published reviews, recommendations and guidelines

This approach to cancer therapy has received widespread interest from patients, healthcare providers and payers9. Despite the interest from a wide range of perspectives, there is limited guidance in the form of evidence-based guidelines and recommendations. In order to provide guidance on the best-available evidence of the clinical utility, the Blue Cross and Blue Shield (BCBS) Technology Evaluation Center released in 2013 a review of the implementation of multiple molecular testing in clinical decision making10. The available evidence summarized is based on three observational studies reporting outcomes in study participants who received molecularly targeted treatment based on the results of multi-marker tumor panels1113. The BCBS review also discussed an ongoing randomized controlled trial designed to evaluate the clinical utility of treatment decisions directed by multi-marker tumor panels14. None of the commercially available tests (Table 2) were evaluated in any of the studies reviewed in the BCBS report.

In a working paper, UnitedHealth spoke to the larger contextual issues of using molecular testing in clinical oncology. A broad emphasis was placed on the expanding opportunities for molecularly targeted therapy using tumor genome sequencing and for improving the process of determining the effectiveness of molecular testing15.

Analytic validity

The analytic workflow of FoundationOne™, hybrid-capture libraries sequenced with Illumina HiSeq2000, was shown to have an analytic sensitivity between 95% and 99% (depending on the allele frequency) and a positive predictive value greater than 99% using pooled cell lines as the reference standard 16. Using Sanger sequencing as a reference standard, the AsuraGen® SuraSeq™ analytic workflow incorporating PCR-based enrichment followed by NGS with Illumina and Ion Torrent massively parallel sequencing platforms had an analytic sensitivity between 93.8% and 100% and specificity between 95.3% and 100% in 38 FFPE colorectal tumor resections17. No information on the validation of post-analytic factors (variant calling) was described on the websites of any tests1820.

There appears to be consistency in the pre-analytic factors across the commercially available tests related to tissue sample requirements, collection and preservation (Table 2). All the organizations listed in Table 2 report the sequencing is done in Clinical Laboratory Improvement Amendments (CLIA) certified facilities. While this does provide a limited amount of standardization, the fundamental requirements to establish analytic validity requires a reliable reference standard to align and annotate massively parallel sequence data6,7. Proper alignment against the reference genome is critical for integrating NGS in the genomics-driven medicine paradigm. Currently, there are no universally accepted reference standards for genome alignment, and future analyses comparing different alignment approaches are needed to inform the analytic sensitivity and specificity of existing computational approaches that characterize the full spectrum of genomic alterations detected using NGS (e.g. mutations, insertion/deletions, and copy number variants). Analytic validation of the computational procedures applied to additional post-analytic processes (e.g. clinical annotation and interpretation) require the same level of quality-assurance and evidence-based evaluation as the alignment algorithms.

Clinical validity

Table 3 shows the vast array of molecular targets included in the commercially available tumor panels, with only 34 genes included in all three tests (Figure 1). FoundationOne was reported to detect at least one clinically actionable variant in 76% of samples (N=2200) with an average of 1.57 clinically actionable variants detected per sample (range: 0 to 16)16. The frequency at which clinically actionable variants are detected bridges aspects of clinical validity and clinical utility. However, direct evidence of the association of each marker and treatment response is currently lacking for the many possible on- and off-label therapeutic options across all types of cancers. The absence of well-defined clinic effects (treatment response) leaves room for a wide-spectrum of interpretations of the variants identified with potential targeted therapies as well as variants of unknown significance and how gene-gene interaction in both up-stream and down-stream variants may affect response to targeted therapies. Critical to establishing clinical validity is knowledge of the specific clinical effect. Standardized criteria that can be systematically applied during the clinical annotation is necessary for determining if the variant is clinically actionable (i.e. can be matched to an available targeted therapy).

Fig. 1: Common molecular markers included in commercially available test.

There is a growing body of evidence identifying genomic alterations common across multiple cancer types2. Such variants may be associated with various clinical effects (e.g. diagnostic, prognostic, or predictive of treatment response/toxicity) or may have more than one effect. Therefore, evidence is needed to not only determine the presence of potentially actionable variants in multiple cancer types, but more importantly, to determine whether an actionable variant matched with a targeted therapy is associated with the same treatment response regardless of cancer type. This will help standardize the interpretation of test results; ultimately influencing clinical utility. The iterative process of cultivating new markers into established panels as emerging evidence fills in the gaps in the current understanding of the underlying molecular pathways adds additional challenges to systematically evaluating the clinical validity of these tests.

Table 3. Multi-marker tumor panel composition

Clinical utility

Benefits

At this time there is no evidence of improved treatment outcomes from using the tests described in Table 2 to direct molecularly targeted therapy in solid-tumors. In addition to the absence of evidence of clinical utility for the commercially available tests, there is also a need to further explore the validity and utility of the underlying hypothesis driving the development and implementation of these tests. One challenge in developing studies investigating the general hypothesis as well as establishing clinical utility is the limited generalizability due to diversity in the evolving panel composition and variation in the clinical interpretation and treatment recommendations (i.e., rule-based or tumor-board based). There is a need to standardize the composition of panels and develop standard criteria for classifying variants as clinically actionable. It may also be necessary to prioritize specific variant/therapy combinations when multiple variants with associated therapies are identified, as there may be overlapping toxicities and drug-drug interactions. Characteristics of studies investigating clinical utility, including molecular heterogeneity of study participants as well as specific study designs will also impact generalizability.

Harms

None of the test descriptions included details of patient consent or how incidental findings would be communicated to clinicians or patients. As these tests are intended to be implemented at the point of care, such protocols may be left to the ordering physician and their home institution to determine processes for informed consent, delivery of appropriate pre-test genetic counseling, and disclosure of incidental findings. Given the analytic workflow of the available tests (Table 2) and diverse panel composition (Table 3) it is uncertain how the ACMG recommendations21 for reporting incidental findings from germline sequencing apply to tumor sequencing. This is not to say tumor DNA sequencing is immune to potential incidental findings. There remains the possibility of detecting significant germline variants through subtractive analyses comparing sequenced normal tissue with tumor sequences22. This would require both tumor tissue and normal tissue to be sequenced which is not currently described by the companies offering the commercial tests.

Another potential harm from implementing this testing and treatment strategy arises from uncertainty in the cost-benefit ratio of the off-label use of expensive chemotherapeutic drugs. As mentioned previously, these tests are currently being offered to patients with advanced disease in whom time spent delivering ineffective therapy may pose a significant risk as a wasted opportunity. Many of the available drugs are associated with well characterized adverse effects (e.g. cardiotoxicity from the kinase inhibitors)23,24. When the possibility of these adverse effects are considered alongside the unknown effectiveness of the off-label use of these drugs in various tumor types, there is the real potential of clinical and economic harms.

Ongoing studies

Several ongoing clinical trials have incorporated the commercially available tests (Table 4). Designed as feasibility assessments, two non-randomized trials are using FoundationOne (Foundation Medicine) to identify participants with metastatic breast cancer (IMAGE)25 and participants with various solid tumor types26 that could benefit from molecularly targeted therapy. The objectives for the IMAGE trial is to determine the time to report molecular profiling results, the ability to make treatment suggestions based on the molecular profile and why clinicians/patients followed treatment recommendations from a molecular profiling tumor board. The primary objectives of the second study26 is to assess the number of participants screened, number of tests attempted and number of successful tests, and the ability or inability to implement NGS results-based non-FDA-approved treatment plan. Foundation Medicine is also participating in another non-randomized trial evaluating the performance of molecularly targeted therapy based on NGS results compared to prior therapy (WINTHER)27. A unique feature of the WINTHER trial is the “N of 1” design in which progression-free survival in participants is compared between periods when they received targeted therapy versus periods prior to their entry in the study.

Table 4. Clinical trials incorporating commercially available multi-marker tumor panels for making treatment decisions.

Conclusions

Recent advances in sequencing technology provide new opportunities for genomic medicine. With these opportunities come the promises of personalized medicine that are changing oncology practice. However, aspects of analytic and clinical validity and clinical utility of the current paradigm shift has yet to be clearly established. Developing the necessary evidence to establish clinical validity and utility may require novel thinking to adapt to the dynamic challenges associated with implementing NGS tumor sequencing into clinical practice. Researchers must also address several limitations in the underlying concepts of this approach including patient selection, analytic workflows, characteristics of the tumor panels (performance characteristics and panel composition), study design, and outcome selection. Several ongoing clinical trials are investing both the feasibility and utility of incorporating NGS technology into clinical practice and will help define the evidentiary standards for evaluating such tests.

Methods

The GAPP Knowledge Base (GAPP KB) was searched using the term “next-generation sequencing” to identify commercially available multi-marker solid tumor tests using NGS technology. A Google search supplemented the GAPP KB search for eligible tests as well. The following search string was used to search PubMed to identify relevant systematic reviews and guidelines:

(((neoplasms/therapy[mesh]) OR (neoplasms/genetics[mesh]) OR (neoplasms/diagnosis[mesh])) AND ((tumor markers, biological/genetics[mesh]) OR (molecular targeted therapy/methods[mesh]) OR (genetic testing[mesh]) OR (DNA mutational analysis[mesh]))) AND (humans[mesh])

In addition to searching PubMed, the commercially available tests websites were searched to identify relevant literature pertaining to the analytic validity, clinical validity, and clinical utility of the respected test. Ongoing studies were identified in clinicaltrials.gov by searching on the trade name of each commercially available test and company offering the tests described in Table 2. This was supplemented by using the term “next-generation sequencing” to identify additional studies not using any of the tests listed in Table 2.

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Comprehensive Carrier Screening and Molecular Diagnostic Testing for Recessive Childhood Diseases http://currents.plos.org/genomictests/article/comprehensive-carrier-screening-and-molecular-diagnostic-testing-for-recessive-childhood-diseases/ http://currents.plos.org/genomictests/article/comprehensive-carrier-screening-and-molecular-diagnostic-testing-for-recessive-childhood-diseases/#respond Wed, 02 May 2012 23:41:46 +0000 http://currents.plos.org/genomictests/?post_type=article&p=20987

Clinical Scenarios

The test is designed both for preconception carrier testing of couples wishing to start a family and for molecular diagnosis in children suspected of being affected by a recessive childhood disease. The published (research) version of the test included 448 childhood recessive illnesses with severe clinical manifestations1. A revised panel is undergoing clinical validation for use as a laboratory developed test (LDT) with an intention of being offered via a laboratory regulated by the Clinical Laboratory Improvement Amendments (CLIA). The clinical panel contains 595 childhood recessive diseases that are deemed to meet American College of Medical Genetics (ACMG) criteria for implementation of genetic testing for ultra-rare disorders2. Validation of analytic utility is being performed for the clinical scenarios detailed below prior to test offering. Initial validation of clinical utility and cost effectiveness will occur over the next year.

1. Preconception carrier testing for recessively inherited diseases of childhood.

Prepregnancy carrier testing is currently offered to couples desiring to start a family in order to provide individualized genetic counseling about risk of conceiving a child affected by a specific recessively inherited diseas34. The test performs preconception carrier testing for 595 recessive diseases simultaneously and three target populations are envisaged:

i. Couples undergoing in vitro fertilization (IVF) procedures. Testing of couples, pretesting of sperm and egg donors and genetic counseling is of utility for reduction in risk of having an affected child. Given the economics of IVF, the incremental cost of carrier testing is unlikely to be a barrier to adoption5. Screening of sperm and oocyte donors has lower counseling burden than other clinical scenarios6. Further, the motivation of couples undergoing IVF procedures is anticipated to facilitate adoption. Since testing is performed before conception, some of the ethical concerns of carrier testing in other clinical scenarios are not relevant36. We are not aware of published studies of efficacy in this target population. It should be noted that knowledge of mutations in many of the 595 diseases is incomplete and testing is anticipated to reduce but not eliminate the risk of an affected child.

ii. Individuals and populations at high risk of recessive disorders. Examples include populations with genetic bottlenecks and / or higher rates of consanguinity. Ashkenazi Jewish populations, Arab populations, Amish populations and individuals with a family history of recessive diseases are examples. Preconception testing of motivated populations for recessive disease mutations, together with education and genetic counseling of carriers, can dramatically reduce disease incidence in a generation. The broad rationale is the success of testing north American Ashkenazi Jewish populations for carrier status of Tay-Sachs disease (TSD; Mendelian Inheritance in Man accession number OMIM# 272800)78910111213.

iii. General population testing. Given a recent report that we each harbor an average of 2.8 known recessive severe childhood disease mutations1, there is theoretical utility of voluntary carrier testing in general populations14. The broad rationale is the success of general population testing for carrier status of cystic fibrosis [CF, OMIM#219700]12131516171819. Practical clinical utility requires a). the cost to be low, b). provision of pre- and post-test genetic counseling (including delineation of the potential benefits and harms of carrier test results) and, c). protections for confidentiality, privacy and against stigmatization or discrimination. The ideal age for recessive disease screening is in early adulthood and before pregnancy. In the US, preconception carrier testing is hospital-based, whereas community-based testing has had success in Canada and Australia919202122. Community-based population testing has advantages over testing in a hospital setting, where information about carrier testing often is communicated during pregnancy or after the birth of an affected child919202122. Community-based carrier testing has had high uptake, without apparent stigma or discrimination and with substantial reductions in the frequencies of tested disorders919202122. Of note, the United Kingdom’s Human Genetics Commission recently reported that it found no specific social, ethical or legal principles that would make preconception genetic testing within the framework of a population screening program unacceptable14.

Preconception carrier testing for 595 diseases is anticipated to be offered initially as an LDT in late 2011 in the first two clinical scenarios. Expansion to general population testing is anticipated subsequently upon demonstration of cost effectiveness and validation of clinical utility in targeted populations. Revision of national policies for carrier testing is anticipated to be needed in response to next-generation-sequencing based multiplexed tests such as this.

2. Diagnostic testing in potentially affected children.

Diagnostic carrier testing is offered to affected children (via parents) suspected of having a recessively inherited disease in order to determine a definitive diagnosis and, thereby, individualize treatment and genetic counseling2. The broad rationale is that the test is an extension of conventional, univariate, serial molecular genetic testing. However, conventional approaches have severe limitations: Hundreds of recessive illnesses exist for which conventional molecular diagnosis is technically feasible but not available. They are too uncommon for commercially-viable conventional genetic testing or blocking patents exist. As a result, knowledge of mutation spectrum, genotype-phenotype relationships and allele frequencies in diseases without molecular diagnosis are rudimentary, inhibiting development of investigational new drugs. Of those for which molecular tests are available, many present as progressive multisystem disorders, requiring lengthy and costly differential diagnosis in a conventional genetic testing scenario, exhausting resources of patients, families and physicians. Thus, typically, <50% of patients undergoing conventional genetic testing receive a molecular diagnosis despite average testing cost per patient of >$10,000. Furthermore, serial univariate testing can take over a year, delaying timely intervention or counseling. It should be noted that our knowledge of mutations in many of the 595 diseases is incomplete and thus testing will not provide definitive diagnosis in all affected children. The scope of diagnostic use of the test is in differential diagnosis of affected children suspected of having one of the 595 diseases. The intended test use is in molecular diagnosis.

Test Description

The test is as described1, but has been modified for clinical testing as follows: Genomic DNA is prepared from patient EDTA-blood samples. 2.6 million nucleotides of target genomic regions, representing exons, intron boundaries and non-exonic mutation containing regions in 527 genes are enriched ~500-fold from the 3.16 billion nucleotide (nt) genome of each sample. Enrichment uses hybrid capture, in which tens of thousands of oligonucleotide probes capture 8,614 genomic DNA fragments, collectively comprising 592 disease genes. Patient DNA is fragmented, denatured and incubated with the oligonucleotides. The target-oligonucleotide hybrids are isolated by magnetic capture23. Next generation sequencing of the enriched targets is performed with Illumina HiSeq and TruSeq sequencing-by-synthesis, yielding ~3 billion nucleotides of sequence per sample, each ~125 nucleotides long. Sequences are aligned to the reference human genome uniquely, covering each target nucleotide ~150 times. Alignment uses the algorithm GSNAP2425, with parameters that have been optimized for clinical diagnostic use. Enrichment and sequencing are performed on multiplexed samples, which are disambiguated by molecular barcodes. ~1% of target nucleotides are not covered, while ~95% of target nucleotides have at least 16-fold sequence coverage. The majority of missed nucleotides are in high GC-content targets, are missed reproducibly, and are labelled as such. An automated bioinformatic decision tree is used to identify and genotype variations in the aligned sequences242627282930. Variants are retained if present in at least 8 sequences of quality score >25 and in exons with at least 16-fold sequence coverage24. Variants detected in >86% of reads are considered homozygous, while those present in 14-86% of reads are heterozygous. Variants are classified according to ACMG and other guidelines2183132333435, using literature knowledge as well as in silicotools, such as comparison with a variety of mutation and human variation databases, PolyPhen-2 and SIFT, to determine the pathogenicity of each variant. Pathogenic variants are assembled into genotypes and reported. For diagnostic testing, where variants are of uncertain significance, further evidence is sought, using additional in silico tools, literature evidence, clinico-pathologic correlation, confirmatory family studies or functional assays, as appropriate. In general, variant interpretation is identical to that performed using conventional molecular diagnostic assays with the exceptions that clinico-pathologic interpretation and masking of non-relevant genes are routine in diagnostic use of the assay and that ~90% of variant annotation and reporting is automated, facilitating interpretation and standardization of reporting. Reporting of variants differs in carrier testing of adults and diagnostic testing of children31. Carrier testing reports carrier status in all genes. Diagnostic testing reports positive and negative results in genes relevant to the clinical presentation. Diagnostic testing in children does not report carrier status in genes that are not relevant to presentation31. In a subset of cases, further communication between the laboratory director and ordering physician is necessary to guide additional studies and assist in interpretation.

Public Health Importance

Mendelian diseases collectively affect 13 million people in the US, accounting for ~20% of infant mortality and ~18% of pediatric hospitalizations3637383940.

Diagnostic testing in potentially affected children

Simultaneous diagnostic testing for 595 recessive childhood diseases is anticipated to have several public health impacts: 1). Extension of the prevention, diagnosis, and treatment benefits demonstrated for conventional genetic testing to hundreds of recessive diseases for which testing is not available today; 2). Reduction in time-to-diagnosis, particularly in illnesses where the differential diagnosis is broad and the conventional approach is serial univariate testing. Serial univariate testing can take over a year, delaying timely intervention or counseling. The initial turnaround time of the test will be 4 weeks. 3). Reduction in cost of diagnosis. The average cost per patient of serial univariate molecular diagnostic testing is ~$10,000 at our institution. The test is anticipated to cost ~$600. 4). Increased rate of definitive molecular diagnosis. Less than 50% of patients undergoing serial univariate molecular diagnostic testing receive a molecular diagnosis. This is anticipated to increase with test use, particularly in illnesses where the differential diagnosis is broad, such as mitochondrial myopathies or intellectual disability. Timely diagnosis of affected individuals has several potential benefits:

1. Prevention of death or markedly diminished disease severity where curative treatments are available. Quite a large number of recessive diseases have specific therapies. Neonatal diagnosis and treatment of phenylketonuria (PKU) and congenital hypothyroidism prevent severe intellectual disability. Likewise, death is prevented in certain forms of congenital adrenal hyperplasia (CAH), medium chain acyl-coA dehydrogenase deficiency (MCAD), and galactosemia (OMIM #230400).

2. Genetic counseling of patients and families about risks for relatives and in additional offspring.

3. Improvement in quality of life in disorders where treatments are ameliorative. While many recessive diseases lack curative treatments, timely diagnosis nevertheless allows specific interventions that can substantially improve quality of life. Such interventions may slow disease progression, lessen symptoms, prevent complications or improve function in affected organ systems.

4. Substantial psychosocial benefits with respect to anxiety, self-image, uncertainty and lifestyle decisions.

5. Multiplexed testing allows rule-out of differential diagnoses, decreasing unnecessary treatments.

Use of the research version of the test revealed that 27% of literature mutations are common polymorphisms or misannotated1. Thus, it is critical to establish a clinical grade mutation database for recessive illnesses. Implementation of the test for diagnosis in affected children will, with time, improve the quality and quantity of annotated mutations, particularly for diseases that for which no molecular test is available currently.

In addition, test results have a cumulative potential to inform an understanding of disease mechanisms. In each individual with a Mendelian disorder, the specific mutations impact the age of onset, disease severity, rates of progression, distribution of affected organs, complications, pleiotropy and outcomes. Only in diseases for which molecular diagnosis is undertaken can such knowledge be accumulated. A broad understanding of genotype-phenotype relationships can enable individualized care of patients with recessive diseases. This can potentially include individualized treatment intensity and prediction of disease progression, severity and likely complications. Thus, in the long term, the test, when performed in a research setting, can allow identification of genotype-phenotype relationships that allow conveyance of individualized diagnostic information.

Initial experience with the test has revealed the existence of novel modifier mutations and pleiotropy in patients with recessive illnesses (Kingsmore et al., submitted). Only through multiplexed molecular testing can such knowledge be accumulated. A broad understanding of modifier genes can further enable individualized care of patients with recessive diseases. Thus, in the long term, the test, when performed in a research setting, can allow identification of modifier genes that allow conveyance of individualized diagnostic information.

Finally, timely molecular diagnosis can allow intervention before organ decompensation, when treatment is likely to alter outcomes. Currently, study of new therapies for rare disorders are hampered by diagnosis after organ damage and low rates of ascertainment. Timely diagnosis can permit regional referral of affected individuals for specialized treatment.

It should be noted that substantiation of the potential public health impacts in prevention, diagnosis, or treatment of recessive childhood illnesses is needed. Such assessments should include measurement of cost effectiveness including costs of follow up of ambiguous test results and counseling.

Published Reviews, Recommendations and Guidelines

Systematic evidence reviews

The emerging use of targeted sequencing of panels of genes, whole exome sequencing and whole genome sequencing for molecular diagnosis of Mendelian diseases was recently reviewed45.

Recommendations by independent group

Currently none.

Guidelines by professional groups

The United Kingdom’s Human Genetics Commission recently reported guidance on preconception genetic testing within the framework of a population screening program14.

Evidence Overview

Analytic Validity:

The 437 genes responsible for 448 childhood recessive diseases are listed in Table 1. Using genotyping cut-offs of 14% and 86% to differentiate homozygotes and heterozygotes and >20X nucleotide coverage and >10 reads of quality >20 to call a variant, the accuracy of the test for SNP genotyping was 98.8%, analytic sensitivity was 94.9% and analytic specificity was 99.99% for 92,106 SNPs in 26 samples genotyped both by high density arrays and the test1. The positive predictive value (PPV) of the test for SNP genotyping was 99.96% and negative predictive value was 98.5%, as ascertained by array hybridization1. As sequence depth increased from 0.7 to 2.7GB, test sensitivity increased from 93.9% to 95.6%, whereas PPV remained ~100%. Area under the curve (AUC) of the receiver operating characteristic (ROC) of the test for 92,106 SNP genotypes in 26 samples, when compared with array hybridization, was 0.99 when the number and % reads calling a SNP was varied.

For known substitution, indel, splicing, gross deletion and regulatory alleles in 76 samples, analytic sensitivity was 100% (113 of 113 known alleles). The higher sensitivity for detection of known mutations reflected manual curation. The twenty known indels were confirmed by PCR and Sanger sequencing. Of note, substitutions, indels, splicing mutations and gross deletions account for the vast majority (96%) of annotated mutations27.

Unexpectedly, 14 of 113 literature-annotated disease mutations were either incorrect or incomplete. PCR and Sanger sequencing confirmed that the 14 variants and genotypes called by the test were correct1.

Gross deletions were detected both by perfect alignment to mutant junction reference sequences and by local decreases in normalized coverage (normalized to total sequence generated). Eleven of eleven gross deletion mutations for which boundaries had been defined were identified1. Further analytic validation of ability to detect and genotype gross deletions, gross insertions and complex rearrangements is required.

It should be noted that the clinical version of the test will feature several improvements that are anticipated to improve analytic sensitivity and specificity. These are: 1). Increased depth of sequencing to 3 GB per sample; 2). Automation of the sequencing library preparation and target enrichment; 3). Re-design of the target enrichment oligonucleotides; 4). Change in the variant detection parameters to >16X nucleotide coverage and >6 reads of quality >25 to call a variant; 5). Further refinement of alignment parameters to prevent variant detection solely at the ends of reads; 6). Increased library size to reduce overlap redundancy; 7). Improved sequencing-by-synthesis chemistry (TruSeq); 8). Improved HiSeq instrument specification. Repetition of analytic validation is ongoing in a CLIA-compliant laboratory setting.

Clinical Validity

There are no published systematic evidence reviews of test accuracy, reliability or predictive value in a clinical setting. Experience is being garnered with the use of whole exome or whole genome sequencing for molecular diagnosis of Mendelian diseases and was recently reviewed.

Clinical Utility

There are no published systematic evidence reviews or published clinical trials. Published experience was in a research setting and was not blinded to sample diagnosis1. Test development and assessment of analytic and clinical validity and utility are ongoing.

Links

http://www.beyondbatten.org/

http://www.ncgr.org/preventing-rare-genetic-diseases

http://hematite.ncgr.org/

www.sciencemag.org/content/331/6014/130.full

http://www.npr.org/2011/01/13/132908098/new-gene-test-screens-nearly-500-childhood-diseases

Last updated: March 18, 2011

Table 1

OMIM# NAME GENE
102700 SEVERE COMBINED IMMUNODEFICIENCY, AR, T CELL-NEGATIVE, ADA
102770 MYOADENYLATE DEAMINASE DEFICIENCY, MYOPATHY DUE TO AMPD1
105830 ANGELMAN SYNDROME AS MECP2
107400 PROTEASE INHIBITOR 1; PI SERPINA1
124000 MITOCHONDRIAL COMPLEX III DEFICIENCY BCS1L
124000 MITOCHONDRIAL COMPLEX III DEFICIENCY UQCRB
124000 MITOCHONDRIAL COMPLEX III DEFICIENCY UQCRQ
133540 COCKAYNE SYNDROME, B; CSB ERCC6
141800 HEMOGLOBIN–ALPHA LOCUS 1; HBA1 HBA1
141900 HEMOGLOBIN–BETA LOCUS; HBB HBB
145900 HYPERTROPHIC NEUROPATHY OF DEJERINE-SOTTAS. CMT3, CMT4F EGR2
145900 HYPERTROPHIC NEUROPATHY OF DEJERINE-SOTTAS. CMT3, CMT4F MPZ
145900 HYPERTROPHIC NEUROPATHY OF DEJERINE-SOTTAS. CMT3, CMT4F PMP22
145900 HYPERTROPHIC NEUROPATHY OF DEJERINE-SOTTAS. CMT3, CMT4F PRX
188055 THROMBOPHILIA DUE TO ACTIVATED PROTEIN C RESISTANCE F5
190685 DOWN SYNDROME GATA1
200100 ABETALIPOPROTEINEMIA; ABL MTTP
200990 ACROCALLOSAL SYNDROME; ACLS GLI3
201000 CARPENTER SYNDROME RAB23
201450 ACYL-CoA DEHYDROGENASE, MEDIUM-CHAIN, DEFICIENCY OF ACADM
201460 ACYL-CoA DEHYDROGENASE, LONG-CHAIN, DEFICIENCY OF ACADL
201470 ACYL-CoA DEHYDROGENASE, SHORT-CHAIN, DEFICIENCY OF ACADS
201475 ACYL-CoA DEHYDROGENASE, VERY LONG-CHAIN, DEFICIENCY OF ACADVL
201710 LIPOID CONGENITAL ADRENAL HYPERPLASIA CYP11A1
201710 LIPOID CONGENITAL ADRENAL HYPERPLASIA STAR
201910 CONGENITAL ADRENAL HYPERPLASIA, 21-HYDROXYLASE DEFICIENCY CYP21A2
202400 AFIBRINOGENEMIA, CONGENITAL FGA
202400 AFIBRINOGENEMIA, CONGENITAL FGB
202400 AFIBRINOGENEMIA, CONGENITAL FGG
203500 ALKAPTONURIA HGD
203700 ALPERS DIFFUSE CEREBRAL DEGENERATION WITH HEPATIC CIRRHOSIS POLG
203780 ALPORT SYNDROME, AR COL4A3
203780 ALPORT SYNDROME, AR COL4A4
203800 ALSTROM SYNDROME; ALMS ALMS1
204200 CEROID LIPOFUSCINOSIS, NEURONAL, 3; CLN3 CLN3
204500 CEROID LIPOFUSCINOSIS, NEURONAL, 2; CLN2 TPP1
205100 AMYOTROPHIC LATERAL SCLEROSIS 2, JUVENILE; ALS2 ALS2
206700 ANIRIDIA, CEREBELLAR ATAXIA, AND MENTAL DEFICIENCY PAX6
207410 ANTLEY-BIXLER SYNDROME; ABS FGFR2
207900 ARGININOSUCCINIC ACIDURIA ASL
208000 ARTERIAL CALCIFICATION, GENERALIZED, OF INFANCY; GACI ENPP1
208085 ARTHROGRYPOSIS, RENAL DYSFUNCTION, AND CHOLESTASIS VPS33B
208150 FETAL AKINESIA DEATION SEQUENCE; FADS RAPSN
208400 ASPARTYLGLUCOSAMINURIA AGA
208540 RENAL-HEPATIC-PANCREATIC DYSPLASIA; RHPD NPHP3
208900 ATAXIA-TELANGIECTASIA; AT ATM
208920 EARLY-ONSET ATAXIA WITH OCULOMOTOR APRAXIA AND HYPOALBUMINEMIA APTX
210210 3-METHYLCROTONYL-CoA CARBOXYLASE 2 DEFICIENCY MCCC2
210600 SECKEL SYNDROME 1 ATR
210900 BLOOM SYNDROME; BLM BLM
211600 CHOLESTASIS, PROGRESSIVE FAMILIAL INTRAHEPATIC 1; PFIC1 ATP8B1
211750 C SYNDROME CD96
212065 CONGENITAL DISORDER OF GLYCOSYLATION, Ia; CDG1A PMM2
212066 CONGENITAL DISORDER OF GLYCOSYLATION, IIa; CDG2A MGAT2
212720 MARTSOLF SYNDROME RAB3GAP2
213700 CEREBROTENDINOUS XANTHOMATOSIS CYP27A1
214150 CEREBROOCULOFACIOSKELETAL SYNDROME 1; COFS1 ERCC6
214450 GRISCELLI SYNDROME, 1; GS1 MYO5A
214500 CHEDIAK-HIGASHI SYNDROME; CHS LYST
214950 BILE ACID SYNTHESIS DEFECT, CONGENITAL, 4 AMACR
215045 CHONDRODYSPLASIA, BLOMSTRAND ; BOCD PTH1R
215100 RHIZOMELIC CHONDRODYSPLASIA PUNCTATA, 1; RCDP1 PEX7
215140 HYDROPS-ECTOPIC CALCIFICATION-MOTH-EATEN SKELETAL DYSPLASIA LBR
215150 OTOSPONDYLOMEGAEPIPHYSEAL DYSPLASIA; OSMED COL11A2
215150 OTOSPONDYLOMEGAEPIPHYSEAL DYSPLASIA; OSMED COL2A1
215600 CIRRHOSIS, FAMILIAL KRT18
215600 CIRRHOSIS, FAMILIAL KRT8
215700 CITRULLINEMIA, CLASSIC ASS1
216400 COCKAYNE SYNDROME, A; CSA ERCC8
216550 COHEN SYNDROME; COH1 VPS13B
217090 PLASMINOGEN DEFICIENCY, I PLG
217400 CORNEAL DYSTROPHY AND PERCEPTIVE DEAFNESS SLC4A11
218000 AGENESIS OF THE CORPUS CALLOSUM WITH PERIPHERAL NEUROPATHY; ACCPN SLC12A6
219000 FRASER SYNDROME FRAS1
219000 FRASER SYNDROME FREM2
219100 CUTIS LAXA, AR, I EFEMP2
219100 CUTIS LAXA, AR, I FBLN5
219200 CUTIS LAXA, AR, II ATP6V0A2
219700 CYSTIC FIBROSIS; CF CFTR
219750 CYSTINOSIS, ADULT NONNEPHROPATHIC CTNS
219800 CYSTINOSIS, NEPHROPATHIC; CTNS CTNS
219900 CYSTINOSIS, LATE-ONSET JUVENILE OR ADOLESCENT NEPHROPATHIC CTNS
220111 LEIGH SYNDROME, FRENCH-CANADIAN ; LSFC LRPPRC
220290 DEAFNESS, AR 1A GJB2
220400 JERVELL AND LANGE-NIELSEN SYNDROME 1; JLNS1 KCNQ1
222448 DONNAI-BARROW SYNDROME LRP2
222600 DIASTROPHIC DYSPLASIA SLC26A2
223900 NEUROPATHY, HEREDITARY SENSORY AND AUTONOMIC, III; HSAN3 IKBKAP
224050 CEREBELLAR HYPOPLASIA AND MENTAL RETARDATION VLDLR
224410 DYSSEGMENTAL DYSPLASIA, SILVERMAN-HANDMAKER ; DDSH HSPG2
225320 EHLERS-DANLOS SYNDROME, AR, CARDIAC VALVULAR COL1A2
225410 EHLERS-DANLOS SYNDROME, VII, AR ADAMTS2
225750 AICARDI-GOUTIERES SYNDROME 1; AGS1 TREX1
225753 PONTOCEREBELLAR HYPOPLASIA 4; PCH4 TSEN54
226600 EPIDERMOLYSIS BULLOSA DYSTROPHICA, AR; RDEB COL7A1
226650 EPIDERMOLYSIS BULLOSA, JUNCTIONAL, NON-HERLITZ COL17A1
226650 EPIDERMOLYSIS BULLOSA, JUNCTIONAL, NON-HERLITZ ITGB4
226650 EPIDERMOLYSIS BULLOSA, JUNCTIONAL, NON-HERLITZ LAMA3
226650 EPIDERMOLYSIS BULLOSA, JUNCTIONAL, NON-HERLITZ LAMB3
226650 EPIDERMOLYSIS BULLOSA, JUNCTIONAL, NON-HERLITZ LAMC2
226670 EPIDERMOLYSIS BULLOSA SIMPLEX WITH MUSCULAR DYSTROPHY PLEC1
226700 EPIDERMOLYSIS BULLOSA, JUNCTIONAL, HERLITZ LAMA3
226700 EPIDERMOLYSIS BULLOSA, JUNCTIONAL, HERLITZ LAMB3
226700 EPIDERMOLYSIS BULLOSA, JUNCTIONAL, HERLITZ LAMC2
226730 EPIDERMOLYSIS BULLOSA JUNCTIONALIS WITH PYLORIC ATRESIA ITGA6
226730 EPIDERMOLYSIS BULLOSA JUNCTIONALIS WITH PYLORIC ATRESIA ITGB4
226980 EPIPHYSEAL DYSPLASIA, MULTIPLE, WITH EARLY-ONSET DIABETES MELLITUS EIF2AK3
228600 FIBROMATOSIS, JUVENILE HYALINE ANTXR2
228930 FIBULAR APLASIA OR HYPOPLASIA WNT7A
229200 BRITTLE CORNEA SYNDROME; BCS ZNF469
229600 FRUCTOSE INTOLERANCE, HEREDITARY ALDOB
230000 FUCOSIDOSIS FUCA1
230400 GALACTOSEMIA GALT
230500 GM1-GANGLIOSIDOSIS, I GLB1
230600 GM1-GANGLIOSIDOSIS, II GLB1
230800 GAUCHER DISEASE, I GBA
230900 GAUCHER DISEASE, II GBA
231000 GAUCHER DISEASE, III GBA
231050 GELEOPHYSIC DYSPLASIA ADAMTSL2
231530 3-HYDROXYACYL-CoA DEHYDROGENASE DEFICIENCY HADH
231550 ACHALASIA-ADDISONIANISM-ALACRIMA SYNDROME; AAA AAAS
231670 GLUTARIC ACIDEMIA I GCDH
231680 MULTIPLE ACYL-CoA DEHYDROGENASE DEFICIENCY; MADD ETFA
231680 MULTIPLE ACYL-CoA DEHYDROGENASE DEFICIENCY; MADD ETFB
231680 MULTIPLE ACYL-CoA DEHYDROGENASE DEFICIENCY; MADD ETFDH
232200 GLYCOGEN STORAGE DISEASE I G6PC3
232220 GLYCOGEN STORAGE DISEASE Ib SLC37A4
232240 GLYCOGEN STORAGE DISEASE Ic SLC37A4
232300 GLYCOGEN STORAGE DISEASE II GAA
232400 GLYCOGEN STORAGE DISEASE III AGL
232500 GLYCOGEN STORAGE DISEASE IV GBE1
235200 HEMOCHROMATOSIS; HFE HFE
235200 HEMOCHROMATOSIS; HFE HFE2
235550 HEPATIC VENOOCCLUSIVE DISEASE WITH IMMUNODEFICIENCY; VODI SP110
236200 HOMOCYSTINURIA CBS
236250 HOMOCYSTINURIA DUE TO DEFICIENCY OF METHYLENETETRAHYDROFOLATE MTHFR
236490 HYALINOSIS, INFANTILE SYSTEMIC ANTXR2
236670 WALKER-WARBURG SYNDROME; WWS POMT1
236670 WALKER-WARBURG SYNDROME; WWS POMT2
236680 HYDROLETHALUS SYNDROME 1 HYLS1
237300 CARBAMOYL PHOSPHATE SYNTHETASE I DEFICIENCY, HYPERAMMONEMIA CPS1
237310 N-ACETYLGLUTAMATE SYNTHASE DEFICIENCY NAGS
238970 HYPERORNITHINEMIA-HYPERAMMONEMIA-HOMOCITRULLINURIA SYNDROME SLC25A15
239000 PAGET DISEASE, JUVENILE TNFRSF11B
240300 AUTOIMMUNE POLYENDOCRINE SYNDROME, I; APS1 AIRE
241200 BARTTER SYNDROME, ANTENATAL, 2 KCNJ1
241410 HYPOPARATHYROIDISM-RETARDATION-DYSMORPHISM SYNDROME; HRD TBCE
241510 HYPOPHOSPHATASIA, CHILDHOOD ALPL
241520 HYPOPHOSPHATEMIC RICKETS, AR DMP1
241550 HYPOPLASTIC LEFT HEART SYNDROME GJA1
242300 ICHTHYOSIS, LAMELLAR, 1; LI1 TGM1
242500 ICHTHYOSIS CONGENITA, HARLEQUIN FETUS ABCA12
242860 IMMUNODEFICIENCY-CENTROMERIC INSTABILITY-FACIAL ANOMALIES SYNDROME DNMT3B
243500 ISOVALERIC ACIDEMIA; IVA IVD
243800 JOHANSON-BLIZZARD SYNDROME; JBS UBR1
244460 KENNY-CAFFEY SYNDROME, 1; KCS TBCE
245200 KRABBE DISEASE GALC
245349 PYRUVATE DEHYDROGENASE E3-BINDING PROTEIN DEFICIENCY PDHX
245400 LACTIC ACIDOSIS, FATAL INFANTILE SUCLG1
245660 LARYNGOONYCHOCUTANEOUS SYNDROME; LOCS LAMA3
246200 DONOHUE SYNDROME INSR
246450 3-HYDROXY-3-METHYLGLUTARYL-CoA LYASE DEFICIENCY HMGCL
248190 HYPOMAGNESEMIA, RENAL, WITH OCULAR INVOLVEMENT CLDN19
248500 MANNOSIDOSIS, ALPHA B, LYSOSOMAL MAN2B1
248600 MAPLE SYRUP URINE DISEASE Ia BCKDHA
248600 MAPLE SYRUP URINE DISEASE, CLASSIC, IB BCKDHB
248600 MAPLE SYRUP URINE DISEASE III DLD
248800 Marinesco-Sjogren Syndrome SIL1
249000 MECKEL SYNDROME, 1; MKS1 MKS1
249100 FAMILIAL MEDITERRANEAN FEVER; FMF MEFV
249900 METACHROMATIC LEUKODYSTROPHY DUE TO SAPOSIN B DEFICIENCY PSAP
250100 METACHROMATIC LEUKODYSTROPHY ARSA
250250 CARTILAGE-HAIR HYPOPLASIA; CHH RMRP
250620 BETA-HYDROXYISOBUTYRYL CoA DEACYLASE, DEFICIENCY OF HIBCH
250950 3-METHYLGLUTACONIC ACIDURIA, I AUH
251000 METHYLMALONIC ACIDURIA DUE TO METHYLMALONYL-CoA MUTASE DEFICIENCY MUT
251110 METHYLMALONIC ACIDURIA, cblB MMAB
251260 NIJMEGEN BREAKAGE SYNDROME NBN
251880 MITOCHONDRIAL DNA DEPLETION SYNDROME, HEPATOCEREBRAL C10ORF2
251880 MITOCHONDRIAL DNA DEPLETION SYNDROME, HEPATOCEREBRAL DGUOK
251880 MITOCHONDRIAL DNA DEPLETION SYNDROME, HEPATOCEREBRAL MPV17
252150 MOLYBDENUM COFACTOR DEFICIENCY MOCS1
252150 MOLYBDENUM COFACTOR DEFICIENCY MOCS2
252500 MUCOLIPIDOSIS II ALPHA/BETA GNPTAB
252600 MUCOLIPIDOSIS III ALPHA/BETA GNPTAB
252650 MUCOLIPIDOSIS IV MCOLN1
252900 MUCOPOLYSACCHARIDOSIS IIIA SGSH
252930 MUCOPOLYSACCHARIDOSIS IIIC HGSNAT
253200 MUCOPOLYSACCHARIDOSIS VI ARSB
253220 MUCOPOLYSACCHARIDOSIS VII GUSB
253230 MUCOPOLYSACCHARIDOSIS VIII GNS
253250 MULIBREY NANISM TRIM37
253260 BIOTINIDASE DEFICIENCY BTD
253280 MUSCLE-EYE-BRAIN DISEASE; MEB FKRP
253280 MUSCLE-EYE-BRAIN DISEASE; MEB POMGNT1
253290 MULTIPLE PTERYGIUM SYNDROME, LETHAL CHRNA1
253290 MULTIPLE PTERYGIUM SYNDROME, LETHAL CHRND
253290 MULTIPLE PTERYGIUM SYNDROME, LETHAL CHRNG
253300 SPINAL MUSCULAR ATROPHY, I; SMA1 SMN1
253310 LETHAL CONGENITAL CONTRACTURE SYNDROME 1; LCCS1 GLE1
253400 SPINAL MUSCULAR ATROPHY, III; SMA3 SMN1
253550 SPINAL MUSCULAR ATROPHY, II; SMA2 SMN1
253800 FUKUYAMA CONGENITAL MUSCULAR DYSTROPHY; FCMD FKTN
254780 MYOCLONIC EPILEPSY OF LAFORA EPM2A
254780 MYOCLONIC EPILEPSY OF LAFORA NHLRC1
254800 MYOCLONIC EPILEPSY OF UNVERRICHT AND LUNDBORG CSTB
255110 CARNITINE PALMITOYLTRANSFERASE II DEFICIENCY, LATE-ONSET CPT2
255120 CARNITINE PALMITOYLTRANSFERASE I DEFICIENCY CPT1A
255960 MYXOMA, INTRACARDIAC PRKAR1A
256030 NEMALINE MYOPATHY 2; NEM2 NEB
256050 ATELOSTEOGENESIS, II; AOII SLC26A2
256100 NEPHRONOPHTHISIS 1; NPHP1 NPHP1
256300 NEPHROSIS 1, CONGENITAL, FINNISH ; NPHS1 NPHS1
256370 NEPHROTIC SYNDROME, EARLY-ONSET, WITH DIFFUSE MESANGIAL SCLEROSIS WT1
256550 NEURAMINIDASE DEFICIENCY NEU1
256600 NEUROAXONAL DYSTROPHY, INFANTILE; INAD1 PLA2G6
256710 ELEJALDE DISEASE MYO5A
256730 CEROID LIPOFUSCINOSIS, NEURONAL, 1; CLN1 PPT1
256731 CEROID LIPOFUSCINOSIS, NEURONAL, 5; CLN5 CLN5
256800 INSENSITIVITY TO PAIN, CONGENITAL, WITH ANHIDROSIS; CIPA NTRK1
256810 NAVAJO NEUROHEPATOPATHY; NN MPV17
257200 NIEMANN-PICK DISEASE, A SMPD1
257220 NIEMANN-PICK DISEASE, C1; NPC1 NPC1
257320 LISSENCEPHALY 2; LIS2 RELN
257980 ODONTOONYCHODERMAL DYSPLASIA; OODD WNT10A
258501 3-METHYLGLUTACONIC ACIDURIA, III OPA3
259700 OSTEOPETROSIS, AR 1; OPTB1 TCIRG1
259720 OSTEOPETROSIS, AR 5; OPTB5 OSTM1
259730 OSTEOPETROSIS, AR 3; OPTB3 CA2
259770 OSTEOPOROSIS-PSEUDOGLIOMA SYNDROME; OPPG LRP5
259775 RAINE SYNDROME; RNS FAM20C
259900 HYPEROXALURIA, PRIMARY, I AGXT
260000 HYPEROXALURIA, PRIMARY, II GRHPR
260400 SHWACHMAN-DIAMOND SYNDROME; SDS SBDS
261515 D-BIFUNCTIONAL PROTEIN DEFICIENCY HSD17B4
261600 PHENYLKETONURIA; PKU PAH
261740 GLYCOGEN STORAGE DISEASE OF HEART, LETHAL CONGENITAL PRKAG2
262300 ACHROMATOPSIA 3; ACHM3 CNGB3
262600 PITUITARY DWARFISM III HESX1
262600 PITUITARY DWARFISM III LHX3
262600 PITUITARY DWARFISM III POU1F1
262600 PITUITARY DWARFISM III PROP1
263200 POLYCYSTIC KIDNEY DISEASE, AR; ARPKD PKHD1
263700 PORPHYRIA, CONGENITAL ERYTHROPOIETIC UROS
264350 PSEUDOHYPOALDOSTERONISM, I, AR; PHA1 SCNN1A
264350 PSEUDOHYPOALDOSTERONISM, I, AR; PHA1 SCNN1B
264350 PSEUDOHYPOALDOSTERONISM, I, AR; PHA1 SCNN1G
264470 PEROXISOMAL ACYL-CoA OXIDASE DEFICIENCY ACOX1
264700 VITAMIN D-DEPENDENT RICKETS, I CYP27B1
265000 MULTIPLE PTERYGIUM SYNDROME, ESCOBAR CHRNG
265100 PULMONARY ALVEOLAR MICROLITHIASIS SLC34A2
265120 SURFACTANT METABOLISM DYSFUNCTION, PULMONARY, 1; SMDP1 SFTPB
265380 NEWBORN PULMONARY HYPERTENSION, FAMILIAL PERSISTENT CPS1
265450 PULMONARY VENOOCCLUSIVE DISEASE; PVOD BMPR2
265800 PYCNODYSOSTOSIS CTSK
266130 GLUTATHIONE SYNTHETASE DEFICIENCY GSS
266150 PYRUVATE CARBOXYLASE DEFICIENCY PC
266200 PYRUVATE KINASE DEFICIENCY OF RED CELLS PKLR
266265 CONGENITAL DISORDER OF GLYCOSYLATION, IIc; CDG2C SLC35C1
266900 SENIOR-LOKEN SYNDROME 1; SLSN1 NPHP1
267430 RENAL TUBULAR DYSGENESIS; RTD ACE
267430 RENAL TUBULAR DYSGENESIS; RTD AGT
267430 RENAL TUBULAR DYSGENESIS; RTD AGTR1
267430 RENAL TUBULAR DYSGENESIS; RTD REN
267450 RESPIRATORY DISTRESS SYNDROME IN PREMATURE INFANTS SFTPA1
267450 RESPIRATORY DISTRESS SYNDROME IN PREMATURE INFANTS SFTPB
267450 RESPIRATORY DISTRESS SYNDROME IN PREMATURE INFANTS SFTPC
268300 ROBERTS SYNDROME; RBS ESCO2
268800 SANDHOFF DISEASE HEXB
269250 SCHNECKENBECKEN DYSPLASIA SLC35D1
269920 INFANTILE SIALIC ACID STORAGE DISORDER SLC17A5
270200 SJOGREN-LARSSON SYNDROME; SLS ALDH3A2
270400 SMITH-LEMLI-OPITZ SYNDROME; SLOS DHCR7
270450 INSULIN-LIKE GROWTH FACTOR I, RESISTANCE TO IGF1
270550 SPASTIC ATAXIA, CHARLEVOIX-SAGUENAY ; SACS SACS
271245 INFANTILE-ONSET SPINOCEREBELLAR ATAXIA; IOSCA C10ORF2
271900 CANAVAN DISEASE ASPA
271930 STRIATONIGRAL DEGENERATION, INFANTILE; SNDI NUP62
271980 SUCCINIC SEMIALDEHYDE DEHYDROGENASE DEFICIENCY ALDH5A1
272300 SULFOCYSTEINURIA SUOX
272800 TAY-SACHS DISEASE; TSD HEXA
273395 TETRA-AMELIA, AR WNT3
274150 THROMBOTIC THROMBOCYTOPENIC PURPURA, CONGENITAL; TTP ADAMTS13
274270 DIHYDROPYRIMIDINE DEHYDROGENASE; DPYD DPYD
274600 PENDRED SYNDROME; PDS SLC26A4
275100 HYPOTHYROIDISM, CONGENITAL, NONGOITROUS, 4; CHNG4 TSHB
275210 TIGHT SKIN CONTRACTURE SYNDROME, LETHAL LMNA
275210 TIGHT SKIN CONTRACTURE SYNDROME, LETHAL ZMPSTE24
276700 TYROSINEMIA, I FAH
276820 ULNA AND FIBULA, ABSENCE OF WNT7A
276900 USHER SYNDROME, I MYO7A
276901 USHER SYNDROME, IIA; USH2A USH2A
276902 USHER SYNDROME, III; USH3 CLRN1
276904 USHER SYNDROME, IC; USH1C USH1C
277300 SPONDYLOCOSTAL DYSOSTOSIS, AR 1; SCDO1 DLL3
277400 METHYLMALONIC ACIDURIA AND HOMOCYSTINURIA, cblC MMACHC
277440 VITAMIN D-DEPENDENT RICKETS, II VDR
277460 VITAMIN E, FAMILIAL ISOLATED DEFICIENCY OF; VED TTPA
277470 PONTOCEREBELLAR HYPOPLASIA 2A; PCH2A TSEN54
277580 WAARDENBURG-SHAH SYNDROME EDN3
277580 WAARDENBURG-SHAH SYNDROME EDNRB
277580 WAARDENBURG-SHAH SYNDROME SOX10
277900 WILSON DISEASE ATP7B
278700 XERODERMA PIGMENTOSUM, COMPLEMENTATION GROUP A; XPA XPA
278730 XERODERMA PIGMENTOSUM, COMPLEMENTATION GROUP D; XPD ERCC2
278740 XERODERMA PIGMENTOSUM, COMPLEMENTATION GROUP E DDB2
278760 XERODERMA PIGMENTOSUM, COMPLEMENTATION GROUP F; XPF ERCC4
278780 XERODERMA PIGMENTOSUM, COMPLEMENTATION GROUP G; XPG ERCC5
278800 DE SANCTIS-CACCHIONE SYNDROME ERCC6
278800 DE SANCTIS-CACCHIONE SYNDROME XPA
300004 CORPUS CALLOSUM, AGENESIS OF, WITH ABNORMAL GENITALIA ARX
300018 DOSAGE-SENSITIVE SEX REVERSAL; DSS NR0B1
300048 INTESTINAL PSEUDOOBSTRUCTION, NEURONAL, CHRONIC IDIOPATHIC, XLR FLNA
300067 LISSENCEPHALY, XLR, 1; LISX1 DCX
300069 CARDIOMYOPATHY, DILATED, 3A; CMD3A TAZ
300100 ADRENOLEUKODYSTROPHY; ALD ABCD1
300209 SIMPSON-GOLABI-BEHMEL SYNDROME, 2 OFD1
300215 LISSENCEPHALY, XLR, 2 LISX2 ARX
300220 MENTAL RETARDATION, XLR, SYNDROMIC 10; MRXS10 HSD17B10
300240 HOYERAAL-HREIDARSSON SYNDROME; HHS DKC1
300243 MENTAL RETARDATION, XLR, SYNDROMIC, CHRISTIANSON SLC9A6
300291 ECTODERMAL DYSPLASIA, HYPOHIDROTIC, WITH IMMUNE DEFICIENCY IKBKG
300301 OSTEOPETROSIS, LYMPHEDEMA, ECTODERMAL DYSPLASIA, ANHIDROSIS, IMMUNODEFICIENCY IKBKG
300322 LESCH-NYHAN SYNDROME; LNS HPRT1
300352 CREATINE DEFICIENCY SYNDROME, XLR SLC6A8
300400 SEVERE COMBINED IMMUNODEFICIENCY, XLR; SCIDX1 IL2RG
300472 AGENESIS OF CORPUS CALLOSUM WITH MENTAL RETARDATION, OCULAR COLOBOMA IGBP1
300523 ALLAN-HERNDON-DUDLEY SYNDROME AHDS SLC16A2
300672 EPILEPTIC ENCEPHALOPATHY, EARLY INFANTILE, 2 CDKL5
300673 ENCEPHALOPATHY, NEONATAL SEVERE, DUE TO MECP2 MUTATIONS MECP2
300755 AGAMMAGLOBULINEMIA, XLR XLA BTK
301000 WISKOTT-ALDRICH SYNDROME; WAS WAS
301040 α-THALASSEMIA/MENTAL RETARDATION SYNDROME,NONDELETION , XLR ATRX ATRX
301500 FABRY DISEASE GLA
301830 SPINAL MUSCULAR ATROPHY, XLR 2; SMAX2 UBA1
301835 ARTS SYNDROME; ARTS PRPS1
302045 CARDIOMYOPATHY, DILATED, 3B; CMD3B DMD
302060 BARTH SYNDROME; BTHS TAZ
302950 CHONDRODYSPLASIA PUNCTATA 1, XLR RECESSIVE; CDPX1 ARSE
303100 CHOROIDEREMIA; CHM CHM
303350 MASA SYNDROME L1CAM
304100 CORPUS CALLOSUM, PARTIAL AGENESIS OF, XLR L1CAM
304790 IMMUNODYSREGULATION, POLYENDOCRINOPATHY, AND ENTEROPATHY, XLR FOXP3
305100 ECTODERMAL DYSPLASIA, HYPOHIDROTIC, XLR; XHED EDA
305900 GLUCOSE-6-PHOSPHATE DEHYDROGENASE; G6PD G6PD
306955 HETEROTAXY, VISCERAL, 1, XLR; HTX1 ZIC3
307000 HYDROCEPHALUS DUE TO CONGENITAL STENOSIS OF AQUEDUCT OF SYLVIUS; HSAS L1CAM
308230 IMMUNODEFICIENCY WITH HYPER-IgM, 1; HIGM1 CD40LG
308240 LYMPHOPROLIFERATIVE SYNDROME, XLR, 1; XLP1 SH2D1A
308350 EPILEPTIC ENCEPHALOPATHY, EARLY INFANTILE, 1 ARX
308370 INFERTILE MALE SYNDROME AR
308930 LEIGH SYNDROME, XLR PDHA1
309000 LOWE OCULOCEREBRORENAL SYNDROME; OCRL OCRL
309400 MENKES DISEASE ATP7A
309500 RENPENNING SYNDROME 1; RENS1 PQBP1
309520 LUJAN-FRYNS SYNDROME MED12
310200 MUSCULAR DYSTROPHY, DUCHENNE ; DMD DMD
310400 MYOTUBULAR MYOPATHY 1; MTM1 MTM1
310600 NORRIE DISEASE; ND NDP
311150 OPTICOACOUSTIC NERVE ATROPHY WITH DEMENTIA TIMM8A
311250 ORNITHINE TRANSCARBAMYLASE DEFICIENCY, HYPERAMMONEMIA DUE TO OTC
312060 PROPERDIN DEFICIENCY, XLR CFP
312080 PELIZAEUS-MERZBACHER DISEASE; PMD PLP1
312700 RETINOSCHISIS 1, XLR, JUVENILE; RS1 RS1
312750 RETT SYNDROME; RTT MECP2
312863 COMBINED IMMUNODEFICIENCY, XLR; CIDX IL2RG
312920 SPASTIC PARAPLEGIA 2, XLR; SPG2 PLP1
314390 VACTERL ASSOCIATION WITH HYDROCEPHALUS, XLR FANCB
600060 DEAFNESS, NEUROSENSORY, AR 2; DFNB2 MYO7A
600118 WARBURG MICRO SYNDROME; WARBM RAB3GAP1
600121 RHIZOMELIC CHONDRODYSPLASIA PUNCTATA, 3; RCDP3 AGPS
600143 CEROID LIPOFUSCINOSIS, NEURONAL, 8; CLN8 CLN8
600501 ABCD SYNDROME EDNRB
600649 CARNITINE PALMITOYLTRANSFERASE II DEFICIENCY, INFANTILE CPT2
600721 D-2-HYDROXYGLUTARIC ACIDURIA D2HGDH
600737 INCLUSION BODY MYOPATHY 2, AR; IBM2 GNE
600802 SEVERE COMBINED IMMUNODEFICIENCY, AR, T CELL- B CELL+, NK CELL- JAK3
600972 ACHONDROGENESIS, IB; ACG1B SLC26A2
601067 USHER SYNDROME, ID; USH1D CDH23
601186 MICROPHTHALMIA, SYNDROMIC 9; MCOPS9 STRA6
601378 CRISPONI SYNDROME CRLF1
601451 NEVO SYNDROME PLOD1
601457 SEVERE COMBINED IMMUNODEFICIENCY, AR, T CELL-NEGATIVE, RAG1
601457 SEVERE COMBINED IMMUNODEFICIENCY, AR, T CELL-NEGATIVE, RAG2
601559 STUVE-WIEDEMANN SYNDROME LIFR
601675 TRICHOTHIODYSTROPHY, PHOTOSENSITIVE; TTDP ERCC2
601675 TRICHOTHIODYSTROPHY, PHOTOSENSITIVE; TTDP ERCC3
601675 TRICHOTHIODYSTROPHY, PHOTOSENSITIVE; TTDP GTF2H5
601678 BARTTER SYNDROME, ANTENATAL, 1 SLC12A1
601705 T-CELL IMMUNODEFICIENCY, CONGENITAL ALOPECIA, AND NAIL DYSTROPHY FOXN1
601706 YEMENITE DEAF-BLIND HYPOPIGMENTATION SYNDROME SOX10
601780 CEROID LIPOFUSCINOSIS, NEURONAL, 6; CLN6 CLN6
601847 CHOLESTASIS, PROGRESSIVE FAMILIAL INTRAHEPATIC 2; PFIC2 ABCB11
602083 USHER SYNDROME, IF; USH1F PCDH15
602088 NEPHRONOPHTHISIS 2; NPHP2 INVS
602390 HEMOCHROMATOSIS, JUVENILE; JH HAMP
602390 HEMOCHROMATOSIS, JUVENILE; JH HFE2
602398 DESMOSTEROLOSIS DHCR24
602473 ENCEPHALOPATHY, ETHYLMALONIC ETHE1
602579 CONGENITAL DISORDER OF GLYCOSYLATION, Ib; CDG1B MPI
602771 RIGID SPINE MUSCULAR DYSTROPHY 1; RSMD1 SEPN1
603147 CONGENITAL DISORDER OF GLYCOSYLATION, Ic; CDG1C ALG6
603358 GRACILE SYNDROME BCS1L
603554 OMENN SYNDROME DCLRE1C
603554 OMENN SYNDROME RAG1
603554 OMENN SYNDROME RAG2
603585 CONGENITAL DISORDER OF GLYCOSYLATION, IIf; CDG2F SLC35A1
603903 SICKLE CELL ANEMIA HBB
604004 MEGALENCEPHALIC LEUKOENCEPHALOPATHY WITH SUBCORTICAL CYSTS; MLC MLC1
604250 HEMOCHROMATOSIS, 3 TFR2
604320 SPINAL MUSCULAR ATROPHY, DISTAL, AR, 1; DSMA1 IGHMBP2
604369 SIALURIA, FINNISH SLC17A5
604377 CARDIOENCEPHALOMYOPATHY, FATAL INFANTILE, DUE TO CYTOCHROME c OXIDASE SCO2
604498 AMEGAKARYOCYTIC THROMBOCYTOPENIA, CONGENITAL; CAMT MPL
605039 C-LIKE SYNDROME CD96
605253 NEUROPATHY, HYPOMYELINATING/CHARCOT-MARIE-TOOTH DISEASE, 4E EGR2
605253 NEUROPATHY, HYPOMYELINATING/CHARCOT-MARIE-TOOTH DISEASE, 4E MPZ
605355 NEMALINE MYOPATHY 5; NEM5 TNNT1
605407 SEGAWA SYNDROME, AR TH
605472 USHER SYNDROME, IIC; USH2C GPR98
605899 GLYCINE ENCEPHALOPATHY; GCE AMT
605899 GLYCINE ENCEPHALOPATHY; GCE GCSH
605899 GLYCINE ENCEPHALOPATHY; GCE GLDC
606056 CONGENITAL DISORDER OF GLYCOSYLATION, IIb; CDG2B MOGS
606353 PRIMARY LATERAL SCLEROSIS, JUVENILE; PLSJ ALS2
606369 EPILEPTIC ENCEPHALOPATHY, LENNOX-GASTAUT MAPK10
606407 HYPOTONIA-CYSTINURIA SYNDROME PREPL
606407 HYPOTONIA-CYSTINURIA SYNDROME SLC3A1
606612 MUSCULAR DYSTROPHY, CONGENITAL, 1C; MDC1C FKRP
606812 FUMARASE DEFICIENCY FH
606943 USHER SYNDROME, IG; USH1G USH1G
606966 NEPHRONOPHTHISIS 4; NPHP4 NPHP4
607014 HURLER SYNDROME IDUA
607091 CONGENITAL DISORDER OF GLYCOSYLATION, IId; CDG2D B4GALT1
607095 ANAUXETIC DYSPLASIA RMRP
607330 LATHOSTEROLOSIS SC5DL
607426 COENZYME Q10 DEFICIENCY APTX
607426 COENZYME Q10 DEFICIENCY CABC1
607426 COENZYME Q10 DEFICIENCY COQ2
607426 COENZYME Q10 DEFICIENCY PDSS1
607426 COENZYME Q10 DEFICIENCY PDSS2
607598 ICOS DEFICIENCY; LCCS2 ERBB3
607616 NIEMANN-PICK DISEASE, B SMPD1
607624 GRISCELLI SYNDROME, 2; GS2 RAB27A
607625 NIEMANN-PICK DISEASE, C2 NPC2
607626 ICHTHYOSIS, LEUKOCYTE VACUOLES, ALOPECIA, AND SCLEROSING CHOLANGITIS CLDN1
607655 SKIN FRAGILITY-WOOLLY HAIR SYNDROME DSP
607855 MUSCULAR DYSTROPHY, CONGENITAL MEROSIN-DEFICIENT, 1A; MDC1A LAMA2
608013 GAUCHER DISEASE, PERINATAL LETHAL GBA
608093 CONGENITAL DISORDER OF GLYCOSYLATION, Ij; CDG1J DPAGT1
608099 MUSCULAR DYSTROPHY, LIMB-GIRDLE, 2D; LGMD2D SGCA
608456 COLORECTAL ADENOMATOUS POLYPOSIS, AR MUTYH
608540 CONGENITAL DISORDER OF GLYCOSYLATION, Ik; CDG1K ALG1
608612 MANDIBULOACRAL DYSPLASIA WITH B LIPODYSTROPHY; MADB ZMPSTE24
608629 JOUBERT SYNDROME 3; JBTS3 AHI1
608643 AROMATIC L-AMINO ACID DECARBOXYLASE DEFICIENCY DDC
608688 AICAR TRANSYLASE/IMP CYCLOHYDROLASE, DEFICIENCY OF ATIC
608782 PYRUVATE DEHYDROGENASE PHOSPHATASE DEFICIENCY PDP1
608799 CONGENITAL DISORDER OF GLYCOSYLATION, Ie; CDG1E DPM1
608800 SUDDEN INFANT DEATH WITH DYSGENESIS OF THE TESTES SYNDROME; SIDDT TSPYL1
608804 LEUKODYSTROPHY, HYPOMYELINATING, 2 GJC2
608836 CARNITINE PALMITOYLTRANSFERASE II DEFICIENCY, LETHAL NEONATAL CPT2
608840 MUSCULAR DYSTROPHY, CONGENITAL, 1D LARGE
609015 TRIFUNCTIONAL PROTEIN DEFICIENCY HADHA
609015 TRIFUNCTIONAL PROTEIN DEFICIENCY HADHB
609016 LONG-CHAIN 3-HYDROXYACYL-CoA DEHYDROGENASE DEFICIENCY HADHA
609049 PIERSON SYNDROME LAMB2
609056 AMISH INFANTILE EPILEPSY SYNDROME ST3GAL5
609060 COMBINED OXIDATIVE PHOSPHORYLATION DEFICIENCY 1; COXPD1 GFM1
609241 SCHINDLER DISEASE, I NAGA
609254 SENIOR-LOKEN SYNDROME 5; SLSN5 IQCB1
609304 EPILEPTIC ENCEPHALOPATHY, EARLY INFANTILE, 3 SLC25A22
609311 CHARCOT-MARIE-TOOTH DISEASE, 4H; CMT4H FGD4
609528 CEREBRAL DYSGENESIS, NEUROPATHY, ICHTHYOSIS, PALMOPLANTAR KERATODERMA SNAP29
609560 MITOCHONDRIAL DNA DEPLETION SYNDROME, MYOPATHIC TK2
609583 JOUBERT SYNDROME 4; JBTS4 NPHP1
609638 EPIDERMOLYSIS BULLOSA, LETHAL ACANTHOLYTIC DSP
610003 CEROID LIPOFUSCINOSIS, NEURONAL, 8, NORTHERN EPILEPSY CLN8
610006 2-METHYLBUTYRYL-CoA DEHYDROGENASE DEFICIENCY ACADSB
610090 PYRIDOXAMINE 5-PRIME-PHOSPHATE OXIDASE DEFICIENCY PNPO
610127 CEROID LIPOFUSCINOSIS, NEURONAL, 10; CLN10 CTSD
610188 JOUBERT SYNDROME 5; JBTS5 CEP290
610198 3-METHYLGLUTACONIC ACIDURIA, V DNAJC19
610370 DIARRHEA 4, MALABSORPTIVE, CONGENITAL NEUROG3
610377 MEVALONIC ACIDURIA MVK
610498 COMBINED OXIDATIVE PHOSPHORYLATION DEFICIENCY 2; COXPD2 MRPS16
610505 COMBINED OXIDATIVE PHOSPHORYLATION DEFICIENCY 3; COXPD3 TSFM
610532 LEUKODYSTROPHY, HYPOMYELINATING, 5 FAM126A
610651 XERODERMA PIGMENTOSUM, COMPLEMENTATION GROUP B; XPB ERCC3
610688 JOUBERT SYNDROME 6; JBTS6 TMEM67
610725 NEPHROTIC SYNDROME, 3; NPHS3 PLCE1
610768 CONGENITAL DISORDER OF GLYCOSYLATION, Im; CDG1M DOLK
610854 OSTEOGENESIS IMPERFECTA, IIB CRTAP
610915 OSTEOGENESIS IMPERFECTA, VIII LEPRE1
610951 CEROID LIPOFUSCINOSIS, NEURONAL, 7; CLN7 MFSD8
610992 PHOSPHOSERINE AMINOTRANSFERASE DEFICIENCY PSAT1
611067 SPINAL MUSCULAR ATROPHY, DISTAL, AR, 4; DSMA4 PLEKHG5
611126 ACYL-CoA DEHYDROGENASE FAMILY, MEMBER 9, DEFICIENCY OF ACAD9
611561 MECKEL SYNDROME, 5; MKS5 RPGRIP1L
611705 MYOPATHY, EARLY-ONSET, WITH FATAL CARDIOMYOPATHY TTN
611719 COMBINED OXIDATIVE PHOSPHORYLATION DEFICIENCY 5; COXPD5 MRPS22
611721 COMBINED SAPOSIN DEFICIENCY PSAP
611722 KRABBE DISEASE, ATYPICAL, DUE TO SAPOSIN A DEFICIENCY PSAP
611726 EPILEPSY, PROGRESSIVE MYOCLONIC 3; EPM3 KCTD7
612164 EPILEPTIC ENCEPHALOPATHY, EARLY INFANTILE, 4 STXBP1
612304 THROMBOPHILIA, HEREDITARY, DUE TO PROTEIN C DEFICIENCY, AUTOSOMAL PROC
612416 FACTOR XI DEFICIENCY F11

Competing Interests

The author has received in-kind funding from private companies (Illumina Inc., Life Technologies Inc., Roche-Nimblegen and British Airways PLC).

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http://currents.plos.org/genomictests/article/comprehensive-carrier-screening-and-molecular-diagnostic-testing-for-recessive-childhood-diseases/feed/ 0
Fecal DNA testing for Colorectal Cancer Screening: the ColoSure™ test http://currents.plos.org/genomictests/article/fecal-dna-testing-for-colorectal-cancer-od1hzthyodr3-1/ http://currents.plos.org/genomictests/article/fecal-dna-testing-for-colorectal-cancer-od1hzthyodr3-1/#comments Tue, 22 Mar 2011 13:58:17 +0000 http://currents.plos.org/genomictests/article/fecal-dna-testing-for-colorectal-cancer-od1hzthyodr3-1/

Application: Screening

Background

Colorectal Cancer (CRC) screening

Screening by colonoscopy, sigmoidoscopy and fecal occult blood testing has been shown to prevent colorectal cancer (CRC) and to reduce mortality through the detection and removal of pre-cancerous lesions and through the detection of CRC in its early stages [1] [2]. Indeed, CRC incidence and mortality have been decreasing since 1985 [2] [3]. Research suggests that CRC screening may be responsible for approximately half of the declines [2]. However, uptake of CRC screening recommendations in the U.S. is not optimal. In 2008, only about 62% of men and women aged 50-75 years reported getting the most commonly recommended CRC screening tests, a percentage that varied from 49-75% among states [4].

Several tests are available to identify colorectal cancer and pre-cancerous polyps in asymptomatic individuals. Colonoscopy visually inspects the interior walls of the entire rectum and colon. Performance characteristics (such as sensitivity and specificity) of new tests are commonly evaluated in comparison with colonoscopy [1] [5]. Flexible sigmoidoscopy involves a more limited visual inspection of the distal colon and rectum. Fecal occult blood tests (FOBTs), which include conventional guaiac FOBT, high-sensitivity guaiac FOBT, and fecal immunochemical tests (FITs), chemically detect small amounts of fecal blood (which can originate from pre-cancerous and cancerous colorectal lesions). CT colonography (i.e., virtual colonoscopy) and double-contrast barium enema (DCBE) are additional tests, offering enhanced x-ray images of the interior rectum and colon to aid in detecting abnormalities.

Fecal (stool) DNA tests have been under continuous development over the past several years. These tests are designed to detect in stool samples any number of DNA markers shown to be associated with CRC. ColoSure™ is the latest example of a clinically available stool DNA test.

Clinical Scenario

The clinical scenario for fecal DNA testing in general is most often presented as colorectal cancer screening in average-risk individuals.

A technical brochure for ColoSure [6] states that:

“ColoSure is not intended to replace a colonoscopy in those patients who are willing and able to undergo the procedure. Additionally, while it may be used adjunctively or in patients noncompliant with screening recommendations, it is not a screening tool for individuals at increased risk for developing disease.”

Test Description

ColoSure™ (Laboratory Corporation of America, http://www.labcorp.com ) is currently the only commercially or clinically available fecal DNA test marketed for CRC screening in the U.S. The at-home test requires that patients collect and mail one whole stool sample. The test was developed by the Laboratory Corporation of America (LabCorp), which required licensing intellectual property from Exact Sciences Corporation ( www.exactsciences.com ). As a laboratory-developed (“home-brewed”) test, ColoSure is not subject to regulation by the U.S. Food and Drug Administration (FDA) and has not obtained FDA clearance or approval.

What is the theory behind stool DNA testing? Colorectal cancer cells, which are shed into the feces, are known to have several genetic alterations which offer an array of molecular targets for DNA-based stool testing for both pre-cancerous and cancerous lesions [7] [8]. Consequently, fecal DNA has been explored for its potential as a non-invasive CRC screening methodology.

ColoSure is a single-marker test that detects methylation of the vimentin gene. Increased DNA methylation in the promoter region of genes is an epigenetic change that is common in human cancers, including colorectal cancer [9] [10]. Vimentin is a protein characteristically expressed in cells of mesenchymal origin, such as fibroblasts, macrophages, smooth muscle cells, and endothelial cells. Studies have demonstrated that the vimentin gene is not (or rarely) methylated in normal colonic epithelial cells, but is methylated in colorectal cancer and adenomas [11] [12] [13]. Aberrant methylation of vimentin has been detected in 53-83% of colorectal cancer tissue, 50-84% of adenoma samples, and 0-11% of normal colon tissue samples [11] [12] [13] [14] [15], though one preliminary study detected methylated vimentin in 29% of normal colon tissue [16].

ColoSure requires a prescription for testing. It is currently available from two sources: LabCorp [17] and from DNA Direct’s Genomic Medicine Institutes (which only offers referrals to physicians who can prescribe the test) [18] [19].

Public Health Importance

Colorectal cancer (CRC) is currently the third leading cancer diagnosed in the United States, where the lifetime risk is approximately 5% in the general population [3] [7]. According to the U.S. Cancer Statistics, ~143,000 cases of CRC occurred in the U.S. in 2007 [20]. CRC is also the second leading cause of cancer-related deaths in the United States, with approximately 53,000 deaths occurring in 2007 [20]. Approximately half of colorectal cancers are diagnosed at a late stage, when survival is poorer [4].

The most effective way of reducing the risk of developing CRC and of reducing CRC mortality is early detection and removal of pre-cancerous or cancerous lesions. It is thought that the natural history of CRC development takes between 10 and 20 years, offering an excellent opportunity for early intervention [1] [21].

Three types of tests (colonoscopy, flexible sigmoidoscopy, and fecal occult blood tests) are currently recommended as evidence-based CRC screening options by the U.S. Preventive Services Task Force [1]. However, only a modest percentage of adults meet the recommended CRC screening guidelines [4].

Stool-based DNA tests are suggested by some experts as another option for CRC screening. However, these tests are under rapid development and research to establish analytic validity, clinical validity, and clinical utility within the general (average-risk) population is needed before any fecal DNA test can be integrated into current CRC screening strategies. We now examine these factors for the ColoSure test based on the current literature.

Published Reviews, Recommendations and Guidelines (see Table 1 below)

Important Note: The following groups considered fecal DNA testing in general, but largely based their recommendations and guidelines on published research relevant to stool DNA tests that are no longer commercially available.

Systematic evidence reviews

The Agency for Healthcare Research and Quality (AHRQ) commissioned an evidence report/technology assessment on enhancing the use and quality of CRC screening [22] [23]. They found no reliable data among the included studies concerning the trends in use or quality (evidence of misuse, overuse, or underuse) of fecal DNA testing.

A systematic evidence review was performed that guided the current recommendations on CRC screening by the U.S. Preventive Services Task Force (USPSTF) [5] [24] (see subsection below).

Recommendations by independent group

Fecal DNA testing was considered by the USPSTF in its most recent recommendation statement on CRC screening (see Table 1 below). The USPSTF found insufficient evidence to evaluate the benefits and harms of this kind of testing as a screening modality for CRC (I statement) [1].

Guidelines by professionalgroups (in order by year of publication)

A Joint Guideline was published in 2008 by the American Cancer Society, the U.S. Multi-Society Task Force on Colorectal Cancer, and the American College of Radiology (ACS-USMSTF-ACR) [25] . Stool DNA testing in general was recommended for those aged 50 years or older, but the testing interval could not be determined. In 2009, the American College of Gastroenterology (ACG) published CRC screening guidelines, in which a weak recommendation (Grade 2B) was made for stool DNA testing every 3 years for persons 50 years of age or older [26]. The American Academy of Family Physicians (AAFP) [27] published recommendations in 2010, which deferred to the analysis and findings of the USPSTF. Also in 2010, the guidelines of the National Comprehensive Cancer Network (NCCN) [28] stated that: 1) stool DNA testing is not currently considered a first-line screening test except in specific circumstances; and, that 2) the testing interval is uncertain.

Health Plan/Payer policies

CRC screening guidelines have been issued by Kaiser Permanente [29], Aetna, Inc. [30], and United Healthcare Group [31][32], all of which describe fecal DNA screening as experimental and not recommended for use.

A summary of all mentioned recommendations and guidelines appear in Table 1 below.

Table 1. Routine Colorectal Cancer Screening Guidelines and Recommendations for average-risk adults.

CTC = CT colonoscopy; DCBE = double-contrast barium enema; FIT = fecal immunochemical test; FOBT = fecal occult blood test; FSIG = flexible sigmoidoscopy; sDNA = stool DNA.

NM = not mentioned; NR = Not Recommended. 1 In combination with high-sensitivity FOBT every 3 years.

Evidence Overview

We performed literature searches (through PubMed and Ovid MEDLINE) that included search terms such as “vimentin”, “fecal DNA”, and “colorectal cancer”.

Analytic Validity: Test accuracy and reliability in measuring methylated vimentin (analytic sensitivity and specificity).

  • We found no published data on the analytic sensitivity or specificity of the ColoSure test for methylated vimentin.
  • The amount (total and relative) of methylated vimentin in stool samples can vary widely in patients with adenoma or colorectal cancer [33].
  • The methylation-specific PCR (MSP) assay used in the ColoSure test [6] may not have adequate sensitivity for detecting methylated vimentin. Researchers have shown an inability to detect methylated vimentin using MSP in CRC patient stool samples (n = 8) that contained low concentrations of human DNA. However, these researchers demonstrated that methyl-binding domain protein enrichment prior to MSP increased assay sensitivity [34]. In addition, a new technology— methyl-BEAMing— has been developed that enhances the overall technical sensitivity for detecting methylated vimentin by at least 62-fold relative to MSP [33].

Summary: the analytic validity of the ColoSure test could not be determined from the identified research.

Clinical Validity: Test accuracy and reliability in detecting colorectal cancer or adenomas (clinical sensitivity and specificity; predictive value).

  • In general, one potential advantage of DNA-based stool tests over FOBTs is the continuous exfoliation of colorectal cells into the feces (as opposed to occult bleeding, which is intermittent). This finding possibly increases the sensitivity of stool DNA tests [8][35].
  • Six studies relevant to the clinical validity of the ColoSure test were identified [11][14][33][36][37][38]. These studies are summarized below in Table 2, in which research findings on methylated vimentin as a stand-alone marker are highlighted.
  • All 6 studies were case-control in design, having selected patients known to have CRC confirmed by colonoscopy compared with controls who were negative for CRC after colonoscopy. None of these analyses were conducted prospectively or in a general screening population (ages 50-75 yrs, average CRC risk). It is, therefore, important to interpret these observational data with caution, as some methodologists report that case-control studies tend to overestimate screening or diagnostic accuracy due to design-related bias [39][40][41].

Table 2. Summary of the published case-control studies relevant to ColoSure that reported measures of clinical validity using fecal DNA testing in selected populations

Sensitivity Specificity
Study Marker(s) CRC Adenoma1
Chen 2005 [11] Methylated vimentin 46% (43/94) 90% (178/198)
Itzkowitz 2007 [38] Methylated vimentin 73% (29/40) 2 87% (106/122) 2
(Phase 1a) DY 65% (26/40) 2 93% (113/122) 2
Methylated vimentin or DY 88% (35/40) 2 82% (100/122) 2
Itzkowitz 2008 [37]
(Phase 1b) Methylated vimentin 81% (34/42) 2 82% (198/241) 2
DY 60% (25/42) 2 85% (205/241) 2
Methylated vimentin or DY 86% (36/42) 2 73% (176/241) 2
(Combined Data) Methylated vimentin 77% (63/82) 2,3 83% ( 301/363) 2,3
DY 48% (39/82) 2,3 96% (348/363) 2,3
Methylated vimentin or DY 83% (68/82) 2,3 82% (298/363) 2,3
Ahlquist 2008 [14] Test SDT-2 (point mutations on K-ras , scanned mutator cluster region of APC , methylated vimentin ) 58% (7/12) 2 46% (47/103) 2 Not calculated
Baek 2009 [36] mMLH1 30% (18/60) 12% (6/52) 100% (37/37) 4
Methylated vimentin 38% (23/60) 15% (8/52) 100% (37/37) 4
MGMT 52% (31/60) 37% (19/52) 86% (32/37)
All three markers (combined) 75% (45/60) 60% (31/52) 86% (32/37)
Li 2009 [33] Methylated vimentin 41% (9/22) 45% (9/20) 95% (36/38)

DY = refers to a specific test for DNA integrity.

— Not measured.

1 Refers to adenomas ≥ 1 cm.

2 We calculated the numerator using data presented in the article.

3 In the study, sensitivity and specificity were calculated using optimal cutpoints based on the combined dataset (Phases 1a + 1b).

4 We calculated specificity using data presented in the article.

  • Due to the processes for sample collection, sample preparation, and laboratory analysis, the most relevant findings on ColoSure appear to be contained in the two Itzkowitz, et al. reports [37][38]. In these studies, a second assay (for DNA integrity) was also examined alone and in combination with methylated vimentin. The combined findings from both phases of the study [37] suggest a sensitivity for CRC of 77% and a specificity of 83% for methylated vimentin. However, informational materials for ColoSure also reference internal LabCorp data, which, combined with the Itzkowitz, et al. studies, suggest that ColoSure has 72-77% sensitivity and 83-94% specificity for CRC [6][17].
  • Itzkowitz, et al. did not specifically enroll patients with adenomas in their study populations [37][38], so the clinical validity of ColoSure for pre-cancerous lesions is unclear.
  • Using a more advanced technical method for detecting methylated vimentin, Li, et al. reported sensitivities of 45% and 41% for detecting adenomas and colorectal cancer, respectively, with ~95% specificity [33]. This research demonstrates that more sensitive methods of methylated vimentin detection (such as that in [33][34]) would likely affect the clinical validity of ColoSure. Of note, Exact Sciences is in the process of developing more sensitive methods to detect methylated vimentin [42].
  • It is unclear how fecal DNA screening using methylated vimentin compares to other established CRC screening tests. A pre-commercial version of the first-generation PreGen-Plus fecal DNA test was directly compared to a guaiac FOBT in a large multi-center study of asymptomatic persons [43], but no research has been published directly comparing ColoSure to other CRC screening methods. The SDT-2 test (which contains methylated vimentin) has been compared to two guaiac tests [14].

Summary: the clinical validity of methylated vimentin as a biomarker for CRC screening remains to be determined in a general (average-risk) screening population. This is re-iterated in the LabCorp technical review for the ColoSure test [6], which states that: “The detection rates for general population screening have not been determined.”

Clinical Utility : Net benefit of test in improving health outcomes

  • The clinical utility of ColoSure for CRC screening has not been established through randomized controlled trials of CRC incidence or mortality outcomes. One ongoing prospective cohort study [NCT01270360] is examining the performance characteristics of both blood and/or stool based molecular DNA markers in identifying CRC in patients with positive FOBT, though it is unclear exactly which DNA markers are being tested. The study also aims to determine the cost-effectiveness of adding fecal DNA testing to the screening algorithm for patients with positive FOBT prior to colonoscopy.
  • ColoSure specifically has not been recommended by independent groups or professional organizations [1][25][26][27][28] to replace colonoscopy in any patient, regardless of whether they are willing and able to undergo the procedure and regardless of CRC risk level.
  • From the patient’s perspective, stool DNA testing in general may have some advantages over colonoscopy for CRC screening since it: is non-invasive; does not require a formal health care visit; does not require dietary or medication restrictions, bowel preparation, or sedation; and does not require hours of time for testing and recovery, thus alleviating the need to take leave from normal activities (such as a job).
  • From the patient’s perspective, DNA-based stool tests may offer some advantages over FOBT, which requires multiple stool smears as well as some pre-test dietary and medication restrictions (which are necessary for guaiac-based testing). However, ColoSure does require handling a minimum 36 g sample of stool, which may be less acceptable than handling stool smears.
  • Some studies have noted high patient satisfaction with fecal DNA testing or a patient preference for stool DNA testing over colonoscopy, though colonoscopy was perceived as the more accurate test [38][44][45]. However, other populations surveyed had a higher preference for colonoscopy than for fecal DNA testing [46].
  • There is potential for the improvement in health outcomes if more people are willing to undergo fecal DNA testing compared to a screening colonoscopy or other invasive test methods, thereby increasing the percentage of adults who undergo CRC screening. In addition, the USPSTF notes that the chief benefit of less invasive screening tests (assuming they have adequate clinical sensitivity and specificity) is that they may reduce the number of colonoscopies required, since colonoscopies have risks of their own [1]. However, there are a few issues related to these ideas that need to be addressed:
    • There is an uncertain disease detection benefit, unless fecal DNA is at least as sensitive as FOBTs [5][24][47] for pre-cancerous and cancerous lesions;
    • Current research suggests that fecal DNA tests have poorer specificity than FOBT (especially guaiac-based or FIT) [5][24][47], which would lead to unnecessary colonoscopies due to a higher number of false positives;
    • There is no research available to determine re-screening intervals for stool DNA testing;
    • In general, fecal DNA testing may not be cost-effective when compared to other CRC screening tests [48];
    • Patients may not comply with recommendations for frequent (e.g., annual or biennial) screening intervals. Indeed, longitudinal data have shown less than 50% adherence with screening frequency recommendations for stool-based tests such as FOBT [49];
    • There may also be poor follow-up (e.g., colonoscopy) after a positive result on a fecal DNA test, as has been shown for FOBT [22].

Summary: the clinical utility of ColoSure (or methylated vimentin in general) in an average-risk screening population could not be determined from the identified research.

Final important note:

Fecal DNA tests are under rapid development. Exact Sciences Corporation has developed several approaches to fecal DNA testing for colorectal cancer screening over the past few years. Previous tests were replaced sequentially with newer versions, which differed in laboratory methodology or tested for a different panel of DNA markers. The current ColoSure test is a replacement of a version of the PreGen-Plus™ test (Laboratory Corporation of America), which has been discontinued. Exact Sciences recently reported results from a validation study of its newest stool-based DNA test for colorectal cancer screening, named Cologuard™. The panel that was presented included methylated vimentin as one of the tested markers [42] [50] [51]. Exact Science is currently funding a case-only study [ NCT01260168 ] to determine the sensitivity of this new multi-marker DNA panel in CRC cases. The company is planning to pursue FDA approval for Cologuard in 2012 [50]. These developments likely mean that ColoSure will be replaced in the future by this, or other, tests.

Concluding remarks:

In order to consider integrating fecal DNA testing into current CRC screening strategies, additional research is needed to establish analytic validity, clinical validity, and clinical utility within the general (average-risk) population. The estimates of DNA marker sensitivity and specificity found from small case-control studies should not be extrapolated to make any estimates of the performance of methylated vimentin or ColoSure in the general population.

In addition, the ongoing development and refinement of stool DNA tests presents some difficulty for the integration of these tests as a CRC screening approach. Currently, only one fecal DNA test is commercially available in the U.S., a test that will likely be replaced by a newer version for which FDA approval will be sought.

Other critical matters must also be addressed, including the determination of cost-effectiveness, optimal testing intervals, and strategies for the follow-up evaluation of patients who test positive on a fecal DNA test. Moreover, the willingness of individuals from the general population to adopt fecal DNA test protocols and future screening recommendations is a vital consideration. All of these factors will be crucial in affecting the impact of fecal DNA testing on the overall CRC screening paradigm and on colorectal cancer incidence and mortality.

Links (not referenced above)

Last updated: March 14, 2011

Acknowledgments

The authors would like to thank the following individuals for invaluable input and guidance on the content of this manuscript: Dave Dotson, Ralph Coates and Katie Kolor (Office of Public Health Genomics, CDC); Lisa Richardson and Djenaba Joseph (Division of Cancer Prevention and Control, National Center for Chronic Disease Prevention and Health Promotion, CDC); and Evelyn Whitlock, Beth Webber, and their colleagues (Kaiser Permanente Center for Health Research).

Funding information

This work was funded by the Office of Public Health Genomics, Centers for Disease Control and Prevention.

Competing interests

The authors have declared that no competing interests exist.

Disclaimers

The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention (CDC).

The information provided in this manuscript does not constitute an endorsement of ColoSure(TM) or of any fecal DNA test by the CDC nor the Department of Health and Human Services (DHHS) of the U.S. government. No endorsement should be inferred.

The CDC does not offer medical advice to individuals. If you have specific concerns about your health or genetic testing, we suggest that you discuss them with your health care provider.

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