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Multiple Biomarker Testing Tissue Consumption and Completion Rates With Single-gene Tests and Investigational Use of Oncomine Dx Target Test for Advanced Non–Small-cell Lung Cancer: A Single-center Analysis

Open AccessPublished:August 22, 2018DOI:https://doi.org/10.1016/j.cllc.2018.08.010

      Abstract

      Introduction

      First-line targeted therapies have been developed for advanced non–small-cell lung cancer (NSCLC). However, small biopsy samples pose a challenge to testing all relevant biomarkers. The present study characterized clinician-ordered single-gene lung cancer testing and evaluated tissue stewardship and the ability to successfully determine mutation status with single-gene testing or investigational use of the Oncomine Dx Target Test.

      Materials and Methods

      Clinician-submitted orders for 3659 single-gene tests (EGFR, ALK, ROS1, BRAF, KRAS, ERBB2, MET, RET, FGFR1) across 1402 samples at a large US-based commercial reference laboratory and 169 investigational Oncomine Dx Target Tests were retrospectively evaluated. The testing success rates and tissue consumption were evaluated by sample type, test type, and number of single-gene tests per sample.

      Results

      The large majority of lung tissue samples submitted for clinical testing were small (70.5% core needle biopsies; 10.0% fine needle aspirations). With single-gene testing, mutation status was successfully reported for ≥ 1 biomarker for 88.4% of the clinical samples. The success rates decreased and tissue consumption increased with testing of additional biomarkers. Investigational Oncomine Dx Target Tests were permitted 1 tissue slide each and demonstrated success rates similar to single-gene testing for ≥ 5 biomarkers on core needle biopsies, ≥ 4 biomarkers on fine needle aspirations, and ≥ 2 biomarkers on surgical resection specimens.

      Conclusion

      Tissue stewardship is important to enable successful completion of genetic testing and informed NSCLC treatment decisions. Preliminary assessment of the investigational Oncomine Dx Target Test suggests it could facilitate access to multiple biomarker testing using small tissue samples to support therapy decisions for patients with advanced NSCLC.

      Keywords

      Introduction

      In the United States, lung cancer was expected to be newly diagnosed in 222,500 people and lead to 155,870 deaths in 2017. Most lung cancer cases have been non–small-cell lung cancers (NSCLC), and > 60% of cases were regional or metastatic disease at diagnosis.
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      For patients with stage IIIB or IIIC NSCLC at diagnosis, the 5-year cancer-specific survival has ranged from 26% or 24% (for clinically staged) to 46% or 52% (for pathologically staged).
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      The 5-year overall survival (OS) rates for patients with advanced NSCLC (aNSCLC) patients have been reported at 26% for stage IIIB, 13% for stage IIIC, 10% for stage IVA, and 1% for stage IVB.
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      Non-Small Cell Lung Cancer Survival Rates, by Stage 2017.
      The clinical practice guidelines have recommended genetic testing to guide first-line treatment for aNSCLC.
      National Comprehensive Cancer Network®
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      For patients with activating genetic mutations, targeted therapies have been shown to increase progression-free survival (PFS), and, more recently, OS compared with nontargeted chemotherapy.
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      The small lung cancer tissue samples available for testing can make it difficult for patients and physicians to access the mutation status across multiple relevant genes to guide treatment-decisions using traditional, sequential, single-gene testing. A study at the University of Pittsburgh Medical Center found that only 67% of computed tomography-guided core needle biopsies (CNBs) and 46% of fine needle aspirations (FNAs) had sufficient tumor to successfully determine the EGFR, ALK, and KRAS mutation status.
      • Schneider F.
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      Adequacy of core needle biopsy specimens and fine-needle aspirates for molecular testing of lung adenocarcinomas.
      With the continuing development and approval of targeted therapies for NSCLC (eg, crizotinib for ROS1+ patients, dabrafenib plus trametinib for BRAF+ patients), physicians and patients selecting appropriate treatments face the challenge of the increasing number of genes that must be tested using small amounts of available tumor tissue.
      The feasibility of next generation sequencing (NGS) for multiple biomarker testing and targeted treatment guidance for lung cancer and other solid tumors is being investigated in clinics and cancer trials. The ongoing NCI-MATCH (National Cancer Institute Molecular Analysis for Therapy Choice) trial has used a customized NGS assay and the Ion Torrent platform (Thermo Fisher Scientific, Waltham, MA) and reported a testing completion rate of 85% at the interim analysis across all tumor biopsy samples. This success rate increased to 94% in later updated reports, when adjustments to the protocol required that supplemental cytology specimens be submitted with the biopsy tissue sample.
      • Doroshow J.H.
      Update: NCI Formulary, NCI-MATCH Trial, NCI Patient-Derived Models Repository. National Cancer Institutes of Health 2017.
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      The Oncomine Dx Target Test (Ion Torrent PGM Dx Sequencer; Thermo Fisher Scientific) is an NGS panel for NSCLC testing approved by the US Food and Drug Administration (FDA) in June 2017. It is a qualitative, in vitro diagnostic test that uses high-throughput parallel sequencing technology to detect sequence variations in 23 genes on DNA and RNA isolated from formalin-fixed paraffin-embedded (FFPE) specimens. The diagnostic is validated against standard references, including clinical concordance, limit of blank, limit of detection, and variant-level detection accuracy and reproducibility.
      • Food and Drug Administration
      Summary of Safety and Effectiveness Data 2017.
      The results from completed clinical studies have supported the concordance and clinical efficacy of the Oncomine Dx Target Test for 3 companion diagnostic genetic targets: BRAF, EGFR, and ROS1 (overall clinical concordance, 100%, 99%, and 96.5%, respectively), with corresponding accuracy and reproducibility across standard measures. Likewise, analytical performance has been established for 4 additional variants, and the Oncomine Dx Target Test’s performance for outstanding gene targets has been validated using a representative method.
      As more gene targets are identified and recognized in clinical practice guidelines for aNSCLC, physicians and patients need to be able to test more biomarkers on small amounts of available tissue to make fully informed treatment decisions. As such, tissue stewardship is critical to enable testing; however, little evidence has been reported on the rates of successful determination of mutation status across multiple biomarkers for a single patient comparing single-gene testing or NGS methods or on the amounts of tissue needed to support these methods. The present study characterized the current single-gene testing paradigm in the molecular assessment for lung cancer patients and evaluated the success rates and tissue consumption with clinician-ordered single-gene testing and investigational use of the Oncomine Dx Target Test across different types of lung tumor tissue samples.

      Materials and Methods

      The present retrospective study analyzed the records from a large, US-based, Clinical Laboratory Improvement Amendments–certified, commercial reference laboratory. This laboratory conducted clinical molecular assessment of physician-submitted FFPE lung tissue samples and performed investigational testing using the Oncomine Dx Target Test before FDA approval on archival FFPE lung tissue samples. Investigational use of the Oncomine Dx Target Test was conducted in accordance with the FDA-approved protocol, except that only 1 tissue slide was permitted per test. The laboratory data contained the status (successful completion or reason for failure) of a test procedure but not the patient’s mutation status. Because the laboratory data were accessed retrospectively and provided to the researchers such that the subjects could not be identified, directly or through identifiers linked to the subject, no patient consent forms or institutional review board approval was necessary.
      US Department of Health and Human Services
      Protection of Human Subjects. US Department of Health and Human Services. Vol 45, part 46. HHS.gov 2009.
      The present study evaluated the proportion of tests that were successful (able to report results) and the tissue stewardship (number of slides cut for testing). Both outcomes were assessed by test type, sample type, and, for samples evaluated using single-gene testing, the number of tests per sample.

      Clinical Single-gene Testing

      The clinical single-gene testing paradigm was characterized using data from physician-ordered genetic tests on lung tumor samples submitted to the laboratory from September 2015 through October 2016. Single-gene tests ordered for activating mutations relevant to first-line treatment for aNSCLC were included: therascreen EGFR RGQ PCR Kit (QIAGEN Manchester Ltd, Manchester, UK); Vysis IntelliFISH for ALK (Abbott Laboratories, Abbott Park, IL); cobas 4800 BRAF V600 Mutation Test (Roche Molecular Systems, Pleasanton, CA); and laboratory-developed tests (LDTs) for BRAF, KRAS, MET amplification, RET, ERBB2, FGFR1, and ROS1. The BRAF and KRAS LDTs used real-time, or quantitative, polymerase chain reaction. All other LDTs used fluorescent in situ hybridization.
      Testing for CD274 gene expression (ie, programmed cell death ligand 1 expression), was not offered at this laboratory until nearly halfway through the study period (earliest test was in March 2016). Therefore, these tests were not included in the present study. Clinical samples were excluded if any type of NGS panel test was initiated or if a CD274 gene expression test was initiated, because the use of sample tissue to attempt these tests this could have affected the ability to complete other single-gene tests using the sample.
      Data on each clinical test order included the type of sample submitted for testing: CNB, FNA, surgical resection, or cell block. The single-gene tests ordered per sample, the status of each ordered test showing whether test results were successfully reported, and the reason for not reporting results, if applicable, were also available from the data.
      Lung tissue samples with ≥ 1 study-eligible single-gene test were included in the present analysis. Cancelled tests were excluded from the present study. A test was considered cancelled if the test status showed that the reason for not reporting results was cancellation by the client, duplication of the order, or conversion of the order to NGS. A test was considered unsuccessful if the test status was any of the following: PCR amplification failure, insufficient DNA quantity, poor DNA quality, high background, no hybridization, decalcification failure, wrong tumor type, insufficient tumor quantity, or poor tumor quality. This included tests that were ordered by physicians but were not initiated, which could have occurred if the sample’s tumor content did not meet the minimum testing requirements as defined by the laboratory’s protocol for single-gene tests. Although numerical data on the tumor content percentage were missing for some clinical samples, all samples included in the present analysis had > 0% tumor content. A clinical single-gene test was considered successfully completed if the test status indicated that results were reported.
      The number of slides cut from the sample for each single-gene test was used to inform the analyses of tissue consumption. In some cases, these data fields were blank, which could indicate that no slides had been cut (ie, sample rejection) or that the data were missing. The tests for which these data was not reported—whether that was because of sample rejection or missing data—were excluded from the tissue consumption analyses.

      Investigational Use of the Oncomine Dx Target Test

      The investigational use of the Oncomine Dx Target Test was conducted on archival FFPE lung cancer samples as part of a previous study conducted by the laboratory from April 2016 to July 2016. The tests were conducted using the Ion Torrent PGM Dx platform. The panel included 23 genes: AKT1, ALK, BRAF, CDK4, DDR2, EGFR, ERBB2, ERBB3, FGFR2, FGFR3, HRAS, KIT, KRAS, MAP2K1, MAP2K2, MET, MTOR, NRAS, PDGFRA, PIK3CA, RAF1, RET, and ROS1 (the list of specific gene variants tested is provided in Supplemental Table 1; available in the online version). The investigational protocol was identical to the protocol for the FDA-approved Oncomine Dx Target Test, except for the number of tissue slides used per test.
      • Food and Drug Administration
      Oncomine™ Dx Target Test Part I: Sample Preparation and Quantification User Guide. Revision C.0 2017.
      The pre-established protocol for the investigational study allowed only 1 slide per test, and the protocol for the FDA-approved test recommended ≥ 2 slide-mounted 5-μm sections of surgical resection samples or 9 slide-mounted 5-μm sections of CNB samples.
      Investigational Oncomine Dx Target Tests were included in the present analysis if the archival sample was a CNB, FNA, or surgical resection sample. The laboratory data included the type of sample tested and the date stamps for completion of the procedural steps, if successful. Investigational Oncomine Dx Target Tests were considered successfully completed if a date stamp for completion of sequencing was available. The lack of a date stamp for sequencing completion indicated an unsuccessful Oncomine Dx Target Test. A successfully completed Oncomine Dx Target Test sequenced and reported the genetic results for all the target genes; however, an unsuccessful test would not be able to report the genetic results for any target genes.

      Statistical Analysis

      The success rates and tissue consumption in clinical single-gene testing were evaluated by type of single-gene test and by the total number of single-gene tests ordered per sample. The success rate was defined as the percentage of physician-ordered tests or physician-submitted samples for which the biomarker status could be reported, regardless of test initiation or sample rejection. Each success rate was evaluated only if ≥ 10 applicable tests or samples were available in the data set.
      Analyses stratified by the number of tests per clinical sample were conducted to evaluate both the resources required to test increasing numbers of single-gene tests and the ability of the current single-gene testing paradigm to provide information to support first-line treatment decisions for aNSCLC. The total tissue consumption per sample was evaluated by the number of tests on that sample with data available on the number of slides cut. The average number of slides required to attempt a certain number of single-gene tests was assessed across samples that had slides cut for that exact number of tests. The probability of successfully completing a certain number of single-gene tests was evaluated among samples on which at least that many included tests had been ordered. For example, the probability that 5 single-gene tests could be successfully completed on a single sample was evaluated among the clinical samples for which ≥ 5 single-gene tests had been ordered.
      Overall analyses were conducted across all clinical samples to describe the success rates and tissue consumption of the single-gene testing paradigm in general. Differences between the success rates for increasing numbers of single-gene tests were evaluated using the χ2 test if the number of samples was > 5 and the Fisher exact test otherwise. The success rates for > 1 single-gene test were compared to the success rate of completing 1 single-gene test. The differences in sequential success rates were also evaluated (eg, 2 vs. 3 tests, 3 vs. 4 tests, etc.). Statistical significance was considered present at P < .05. Statistical analyses were conducted using SAS, version 9.4 (SAS Institute, Cary, NC).
      The success rates and tissue consumption with the investigational Oncomine Dx Target Test were not assessed overall across all the archived samples but were assessed for different lung tumor sample subcategories. Subanalyses by sample type and tumor content were conducted for both the clinical single-gene testing and the investigational use of the Oncomine Dx Target Test.
      Subanalyses were conducted for the sample types present among both the clinical and the archival samples: CNB, FNA, or surgical resection. Because most tissue samples submitted for aNSCLC testing were CNBs, further subanalyses were conducted to evaluate both the clinical and the archival CNB samples stratified by tumor content. CNB samples for which tumor content data were available were evaluated in 2 subgroups: < 25% and ≥ 25% tumor content. CNB samples for which data on tumor content were missing were evaluated as a separate “unknown” category. Just as with the overall analysis of clinical single-gene testing, the success rates in the subanalyses were only evaluated if ≥ 10 tests or samples were available.

      Results

      Data from the reference laboratory on 3659 physician-ordered clinical single-gene tests were included in the present analysis. The clinical single-gene tests were ordered on 1402 samples from 1368 patients. The clinical samples for testing were predominantly from biopsies, with CNBs comprising 70.5% of the submitted samples and FNAs comprising an additional 10.0% (Table 1). The range in tumor content was as high as 100% for all sample types and as low as 2% among CNBs, 5% among FNAs, 5% among surgical resection samples, and 1% among cell blocks. Of the CNB samples, 21.2% had < 25% tumor content recorded.
      Table 1Tissue Sample Characteristics
      CharacteristicSample TypeAll Sample Types
      CNBFNAResectionCell Block
      AnyTumor Content
      < 25%≥ 25%NA
      Clinical samples submitted for single-gene testing
       Samples, n (% of total)988 (70.5)209 (14.9)685 (48.9)94 (6.7)140 (10.0)167 (11.9)107 (7.6)1.402 (100.0)
       Patients, n971209677921401651041,368
       Tumor content of samples
      Samples with tumor content data, n8942096850112166821,254
      Tumor proportion, %
      Mean45.218.353.4NA53.256.242.347.2
      Range2-1002-2225-100NA5-1005-1001-1001-100
      Tests ordered, n (% of samples)
      EGFR therascreen947 (95.9)201 (96.2)663 (96.8)83 (88.3)131 (93.6)153 (91.6)102 (95.3)1,333 (95.1)
      ALK Vysis745 (75.4)160 (76.6)542 (79.1)43 (45.7)113 (80.7)128 (76.6)71 (66.4)1,057 (75.4)
      ROS1 LDT480 (48.6)100 (47.8)345 (50.4)35 (37.2)61 (43.6)73 (43.7)49 (45.8)663 (47.3)
      BRAF cobas66 (6.7)13 (6.2)47 (6.9)6 (6.4)3 (2.1)5 (3.6)5 (4.7)79 (5.6)
      BRAF LDT16 (1.6)2 (1.0)14 (2.0)0 (0.0)2 (1.4)2 (1.2)1 (0.9)21 (1.5)
      KRAS LDT146 (14.8)33 (15.8)105 (15.3)8 (8.5)12 (8.6)41 (24.6)10 (9.3)209 (14.9)
      MET LDT101 (10.2)18 (8.6)79 (11.5)4 (4.3)5 (3.6)16 (9.6)7 (6.5)129 (9.2)
      RET LDT99 (10.0)17 (8.1)78 (11.4)4 (4.3)5 (3.6)13 (7.8)8 (7.5)125 (8.9)
      FGFR1 LDT29 (2.9)5 (2.4)22 (3.2)2 (2.1)2 (1.4)4 (2.4)3 (2.8)38 (2.7)
      ERBB2 LDT4 (0.4)0 (0.0)3 (0.4)1 (1.1)1 (0.7)0 (0.0)0 (0.0)5 (0.4)
      Archival samples for investigational use of Oncomine Dx Target Test
       Samples, n69 (40.8)41 (24.3)28 (16.6)0 (0.0)13 (7.7)87 (51.5)0 (0.0)169 (100.0)
      Tumor, %NANA
       Mean29.015.049.528.942.435.9
       Range1-901-2025-901-905-901-90
      Abbreviations: CNB = core needle biopsy; FNA = fine needle aspiration; LDT = laboratory developed test.
      The laboratory data also included 169 investigational use cases of the Oncomine Dx Target Test. Compared with the clinical samples, the archival samples used for the investigational Oncomine Dx Target Tests included a greater proportion of surgical resection samples (11.9% of clinical samples vs. 51.5% of archival samples) and a lower proportion of CNBs (70.5% of clinical samples vs. 40.8% of archival samples). Regardless of sample type, the archival samples tended to have lower tumor content than the clinical samples. The range in tumor content among the archival samples was ≤ 90% across all sample types and as low as 1% among the CNBs or FNAs and 5% among the surgical resection samples.
      Across all clinical samples submitted for testing, the single-gene tests most commonly ordered by physicians were EGFR therascreen (95.1%), ALK Vysis (75.4%), and ROS1 LDT (47.3%; Figure 1). Among the clinical samples on which single-gene tests for ≥ 3 biomarkers were ordered, a large majority included tests for EGFR, ALK, and ROS1. When single-gene tests for ≥ 4 biomarkers were ordered on a single sample, the tests for MET, RET, KRAS, or BRAF were commonly included.
      Figure thumbnail gr1
      Figure 1Probability of Test Order Stratified by Number of Genes
      Abbreviation: LDT = laboratory developed test.
      The proportion of physician-ordered single-gene tests for which mutation status was successfully reported varied by test type from 62.4% for RET LDT to 89.1% for ALK Vysis (Figure 2A). Evaluations by sample type found greater success rates on surgical resection samples and lower success rates on FNA samples across nearly all the single-gene tests (Figure 2B). The exception was RET LDT, for which the success rates were lower from the surgical resection samples than from the CNB samples.
      Figure thumbnail gr2
      Figure 2Clinical Testing Success Rates Across Single-Gene Tests: (A) Overall and (B) Stratified by Sample Type. *ERBB2 Was Not Evaluated Owing to the Small Number of Samples. Success Rates Were Evaluated Only if ≥ 10 Tests Were Available
      Abbreviation: LDT = laboratory developed test
      Tissue consumption analyses included 3314 single-gene tests on 1258 samples. The number of slides needed to run a single-gene test was greatest for the BRAF LDT, with a mean tissue consumption of 7.1 slides per test (n = 20 tests). The KRAS LDT was the second-most tissue-intensive test, consuming 6.8 slides per test on average (n = 180). The EGFR therascreen consumed 2.7 slides per test on average (n = 1107). The MET LDT, RET LDT, ROS1 LDT, FGFR1 LDT, and ALK Vysis tests consumed an average of 2.0 (n = 117), 1.6 (n = 112), 1.3 (n = 614), 1.3 (n = 32), and 1.1 (n = 979) slides per test, respectively. BRAF cobas (n = 67) and ERBB2 LDT (n = 2) each consumed 1.0 slide per test.
      Some clinical samples did not have sufficient tissue to allow slides to be cut for all the single-gene tests requested by the ordering physician. For those samples and tests for which slides could be cut, the average number of slides consumed per sample increased steadily with the number of biomarker tests attempted (Figure 3A). This likely reflects histotechnologist standard operating procedures for uniformity of sectioning and not pathologic evaluation of the amount of tumor present in the different types of samples. When slides could be cut to run tests, tissue consumption increased from 2.4 slides to run 1 single-gene test, to 8.7 to run 4 single-gene tests, to 17.0 slides to run 8 single-gene tests on a single sample.
      Figure thumbnail gr3
      Figure 3Multiple Biomarker Testing on Clinical Samples Stratified by Number of Single-Gene Tests. (A) Tissue Consumption Stratified by Number of Tests Run. (B) Testing Success Rates Stratified by Number of Single-Gene Tests Ordered. Lines Show (A) the Number of Tissue Slides Cut to Attempt Exactly Each Number of Single-Gene Tests or (B) the Testing Success Rates for at Least Each Number of Single-Gene Tests. Columns Show the Number of Samples Available for Each Evaluation. *Success Rates Were Evaluated Only if ≥ 10 Samples Were Available
      When multiple single-gene tests were ordered for the same clinical sample, the probabilities of successfully reporting the molecular status for multiple biomarkers decreased as the number of biomarkers increased (Figure 3B). Across all submitted clinical samples, 88.4% had successfully reported mutation status results from ≥ 1 single-gene test. Among the samples on which ≥ 4 tests were ordered, the probability of having successfully reported the mutation status for ≥ 4 biomarkers was 76.6%. The success rates for ≥ 2 tests through ≥ 7 tests were all significantly lower than the success rate for ≥ 1 test (P < .05 for ≥ 2 tests; P < .0001 for ≥ 3-7 tests). Furthermore, the probability of successfully completing ≥ 3 tests was significantly lower than the probability of successfully completing ≥ 2 tests (P < .0001). Subsequent sequential differences in the success rates (eg, ≥ 3 vs. ≥ 4 tests) did not reach statistical significance, although the difference in the success rates for ≥ 4 versus ≥ 5 tests did show marginal statistical significance (P = .052). Even so, the observed success rates continued to show a trend downward for greater numbers of single-gene tests.
      The evaluations stratified by sample type found that the numbers of slides cut to run multiple single-gene tests on clinical samples were similar across CNBs, FNAs, and surgical resection samples, again likely reflecting histotechnologist standard operating procedures for uniformity of sectioning and not pathologic evaluation of the amount of tumor present in the different sample types (Figure 4A). Investigational use of the Oncomine Dx Target Test was artificially restricted to 1 slide per test, regardless of the sample type, in accordance with the pre-established protocol specific to the investigational use testing study for which they were conducted. Similar to clinical single-gene testing, the success rates with the Oncomine Dx Target Test were lowest for the FNA samples and greatest for the surgical resection samples (Figure 4B). The success rates with the investigational use Oncomine Dx Target Test was 69.2% for the FNA samples, 75.4% for the CNB samples, and 98.9% for the surgical resection samples. These success rates decreased between the clinical single-gene testing success rates for 3 and 4 biomarkers on FNA samples (75.0% and 40.0%, respectively), 4 and 5 biomarkers on CNB samples (77.9% and 70.8%, respectively), and 1 and 2 biomarkers on surgical resection samples (100.0% and 98.5%, respectively).
      Figure thumbnail gr4
      Figure 4Multiple Biomarker Testing Stratified by Sample Type for Clinical Single-Gene Testing and Investigational Use of the Oncomine Dx Target Test. (A) Tissue Consumption and (B) Testing Success Rates. Lines Show (A) the Number of Tissue Slides Cut to Attempt Exactly Each Number of Single-Gene Tests and (B) the Testing Success Rates for At Least Each Number of Single-Gene Tests. The Numbers of Samples Available for Each Evaluation Are Graphed as Columns and Shown in Parentheses in the Table. *Success Rates Were Evaluated Only if ≥ 10 Samples Were Available
      Abbreviations: CNB = core needle biopsy; FNA = fine needle aspiration.
      Further subgroup analyses of the CNB samples stratified by tumor content found similar numbers of slides consumed to run multiple single-gene tests on samples with < 25% tumor content compared with samples with ≥ 25% tumor content (Figure 5A). When multiple single-gene tests were ordered, the rates of successfully reporting the mutation status for multiple biomarkers were consistently greater among the samples with ≥ 25% tumor content than among the samples with < 25% tumor content (Figure 5B). The tumor content was not reported for 9.5% of the clinical CNB samples (n = 94 of 988). The success rates were very low among these CNB samples with unknown tumor content. The success rates with the investigational use Oncomine Dx Target Test were 70.7% for CNB samples with < 25% tumor content and 82.1% for CNB samples with ≥ 25% tumor content. Just as with the overall analysis of the success rates on CNB samples, these success rates were between the clinical single-gene testing success rates for 4 and 5 biomarkers.
      Figure thumbnail gr5
      Figure 5Multiple Biomarker Testing on Core Needle Biopsy (CNB) Samples Stratified by Tumor Content: Clinical Single-Gene Testing and Investigational Use of the Oncomine Dx Target Test: (A) Tissue Consumption and (B) Testing Success Rates. Lines Show (A) the Number of Tissue Slides Cut to Attempt Exactly Each Number of Single-Gene Tests, and (B) the Testing Success Rates for at Least Each Number of Single-Gene Tests. The Numbers of Samples Available for Each Evaluation Are Graphed as Columns and Are In Parentheses in the Table. *Success Rates Were Evaluated Only if ≥ 10 Samples Were Available

      Discussion

      Most of the 1402 lung cancer patients’ tumor tissue samples submitted by physicians for clinical testing at this large US-based reference laboratory were small samples. More than three fourths were CNB samples and another one tenth were FNA samples. Among the CNB samples, more than one fifth had < 25% tumor content. These findings demonstrate the small amounts of tissue available for most aNSCLC patients who attempt to use genetic testing to inform treatment decisions.
      The amount of tissue required to attempt a single-gene test for these patients varied widely across test types. Although the BRAF LDT had the greatest average tissue consumption among the single-gene tests (7.1 slides), the laboratory switched to the FDA-approved BRAF cobas test (1.0 slide) during the study period, reducing the tissue consumption necessary to test for BRAF-activating mutations. KRAS has been acknowledged by the recommended guidelines as having prognostic value in lung cancer. However, running the KRAS LDT required an average of 6.8 slides per test. The EGFR therascreen required 2.7 slides per test on average to run, and the other single-gene tests required < 2 slides per test on average. For some samples in this real-world analysis, some of the physician-ordered single-gene tests were not attempted and no slides were cut, likely owing to insufficient or depleted tumor tissue.
      Across all physician-ordered tests in the data set, the success rates for different types of single-gene tests were all < 90%, including those for activating mutations addressed by FDA-approved targeted therapies. The ALK Vysis testing success rate was greatest at 89.1%. The success rates for EGFR therascreen or BRAF cobas were both ∼83%, and the success rate for the ROS1 LDT was only 76.6%. The subanalyses showed that the success rates were greatest with the surgical resection samples but lowest for the FNA samples, showing that lung cancer patients with smaller samples have greater challenges in accessing the genetic information needed to guide targeted treatment decisions.
      Physicians ordered > 1 single-gene test for nearly 80% of the submitted clinical samples. The number of slides needed to attempt multiple single-gene tests increased steadily with the number of biomarkers to be tested. At the same time, the probability of successfully reporting the mutation status results from the additional single-gene tests decreased. Only 88.4% of the clinical samples submitted for single-gene testing had ≥ 1 biomarker successfully reported, suggesting that > 1 in 10 of these patients (11.6%) had no genetic information available to inform their treatment decisions. The success rates for determining the mutation status decreased significantly for 2 biomarkers and 3 biomarkers and continued to show a trend downward after that. Currently, FDA-approved targeted therapies are available for activating mutations in 4 genes in aNSCLC. The success rate for reporting the mutation status for 4 biomarkers in the present study was 76.6%, suggesting that the necessary genetic information to guide appropriate selection for current targeted therapies might be unavailable for 23.4% of aNSCLC patients in the current single-gene testing paradigm.
      The subanalyses stratified by sample type and tumor content included both clinical single-gene testing and investigational use of the Oncomine Dx Target Test. Although tissue consumption for multiple single-gene tests did not vary widely by sample type or tumor content, the testing success rates showed more variation. The success rates were consistently lower among CNB than among surgical resection samples and were lower still among FNA samples. Among the CNB samples, the success rates were consistently lower among those samples with < 25% tumor content compared with those with ≥ 25% tumor content. The lower success rates underscore the difficulties in molecular assessment for patients with aNSCLC whose samples are mostly small and for whom the number of therapeutically relevant biomarkers is only increasing.
      Investigational use of the Oncomine Dx Target Test was artificially restricted to a single tissue slide based on a previous study’s protocol but still showed testing success rates that were at least comparable to the clinical success rates for ≥ 2 single-gene tests on surgical resection samples, ≥ 4 single-gene tests on FNA samples, and ≥ 5 single-gene tests on CNB samples. Running that number of single-gene tests required a mean of 4.5 slides, 9.8 slides, and 8.5 slides per surgical resection, FNA, and CNB sample, respectively. Although only 1 slide was used for each investigational Oncomine Dx Target Test, the FDA-approved protocol has recommended 2 slides when testing a surgical resection sample and 9 when testing a CNB sample. Therefore, in the real-world clinical testing setting, in which the number of tissue slides is limited only by the amount of sample available, the success rates and tissue consumption with the Oncomine Dx Target Test might be greater than that reported in the present retrospective study.
      A previously reported prospective study conducted from July 2011 to June 2013 at the University of Pittsburgh Medical Center (UPMC) conducted multiple biomarker testing on CNB and FNA lung tumor tissue samples for EGFR, ALK, and KRAS, specifically. The mutation status for all three genes could be successfully reported for 67% of the CNB samples and 46% of FNA samples.
      • Schneider F.
      • Smith M.A.
      • Lane M.C.
      • Pantanowitz L.
      • Dacic S.
      • Ohori N.P.
      Adequacy of core needle biopsy specimens and fine-needle aspirates for molecular testing of lung adenocarcinomas.
      The present retrospective study found testing success rates for 3 biomarkers of 76.2% among CNB samples and 75.0% among FNA samples. The differences in testing success rates for the 3 biomarkers between these 2 studies might be attributable to the specific genes tested. The study at UPMC specifically aimed to test EGFR, ALK, and KRAS on all their samples. In contrast, although the orders in the present study for ≥ 3 single-gene tests on a single sample overwhelmingly included EGFR and/or ALK testing (98.7% and 97.9%, respectively), only 23.8% included KRAS testing. The present study showed that evaluating KRAS mutation status required the largest number of tissue slides of all the included biomarkers. The rate of success for reporting the mutation status of 3 biomarkers might have been greater in the present study than in the UPMC study owing to the inclusion of less tissue-intensive single-gene tests. This suggests that the success rates for single-gene testing for multiple biomarkers might be even lower than reported in the present study for patients and physicians interested in the prognostic information provided by KRAS.

      Study Limitations

      The retrospective clinical testing data contained some clinical samples for which data on the number of slides cut for single-gene tests or the tumor content of the sample were missing. Analyses of tissue consumption excluded the single-gene tests in which the number of slides cut was missing. However, these analyses included 90% of all single-gene tests and samples in the data set, suggesting that the results are representative of single-gene testing at this large, US-based, Clinical Laboratory Improvement Amendments–certified, commercial, reference laboratory.
      Samples for which tumor content was unknown were included and evaluated separately in the subanalyses of CNB samples stratified by tumor content. The subanalyses found that the tissue consumption and success rates among these samples were very low compared with that from the samples for which the tumor content was known. This suggests that these samples might have been smaller or might have had lower tumor content. Therefore, the true success rates for CNB samples with low tumor content (< 25%) might be lower than reported in the present study.
      The clinical samples included in the present analysis were submitted from September 2015 to October 2016. At that time, the only FDA-approved, National Comprehensive Cancer Network guideline-recommended, first-line targeted therapies for aNSCLC patients were for EGFR or ALK activating mutations.
      National Comprehensive Cancer Network®
      NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) Non-Small Cell Lung Cancer. Version 4.2016.
      As expected, the EGFR therascreen and ALK Vysis were consistently the most commonly ordered single-gene tests in the present study, and ≥ 3 single-gene tests were ordered for only 14.6% (n = 205 of 1402) of the clinical samples. Since then, targeted therapies for aNSCLC patients with ROS1 or BRAF activating mutations have been approved by the FDA and recommended by the National Comprehensive Cancer Network guidelines.
      National Comprehensive Cancer Network®
      NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) Non-Small Cell Lung Cancer. Version 8.2017.
      With these developments, the genetic testing ordering patterns in clinical practice are likely to include single-gene tests for ROS1 and BRAF more frequently than was described in the present study. Furthermore, with more genes identified as therapeutically relevant to aNSCLC, it is more likely that additional biomarkers will be ordered on a single sample. Therefore, the distribution of genetic testing order patterns described in the present study might no longer be representative of the current single-gene testing paradigm, and the results among samples with larger numbers of single-gene tests ordered might be more applicable to the molecular assessment needs for targeted treatment selection for patients with aNSCLC in current clinical practice.
      The sample characteristics available for both sets of samples were limited to sample type and tumor content. Other potentially relevant characteristics were unknown and could limit the comparability of these findings on clinical single-gene testing and the investigational Oncomine Dx Target Test. The clinical single-gene testing was run on a large number of real patient samples submitted for testing and is therefore likely to be reflective of clinical practice and real-world genetic testing. In comparison, relatively fewer Oncomine Dx Target Tests were included in the present study, and the archival tissue samples were purchased commercially, were more likely from surgical resections, and had lower tumor content regardless of sample type. Therefore, the results with the Oncomine Dx Target Test were presented by sample type and, for CNBs, by tumor content group. However, other unobserved characteristics could have also influenced the success rates. Future prospective studies might benefit from evaluating more runs of the FDA-approved Oncomine Dx Target Test ordered by physicians as part of real-world clinical testing and collecting and controlling for more sample characteristics.
      Finally, the retrospective data on the investigational use of the Oncomine Dx Target Test were from tests run using a previous study’s protocol which limited each test to using only a single slide. This differs from the FDA-approved protocol, which has recommended 2 tissue slides to test surgical resection samples and 9 tissue slides to test CNB samples. Therefore, the potential mean tissue consumption of the newly FDA-approved Oncomine Dx Target Test for clinical use cannot be estimated from our results. Both tissue consumption and success rates might be greater in clinical settings in which additional slides can be used.

      Conclusion

      The large majority of lung cancer samples submitted for genetic testing were small tissue samples. Sequential single-gene testing can require large numbers of tissue slides and might not be able to determine the mutation status for all relevant biomarkers for aNSCLC, especially for patients with smaller sample types or lower tumor content samples. This highlights the need for tissue stewardship and efficient molecular assessment methods for lung cancer, especially as new targeted therapies are developed for activating mutations in additional genes. The present preliminary assessment of the investigational use of the Oncomine Dx Target Test suggests that it could provide a viable option for testing multiple biomarkers with fewer slides required for patients with aNSCLC. Additional studies of this test in the clinical setting would be useful to inform aNSCLC genetic testing decisions and strategies to inform treatment selection.

      Clinical Practice Points

      • Several targeted aNSCLC treatments have shown the potential to improve outcomes by delaying disease progression for patients with activating mutations.
      • Directing patients to appropriate treatment rests on the ability to complete multiple biomarker genetic testing using the small tissue samples available for lung cancer patients.
      • Tissue stewardship is essential as more treatments are developed and more genes need to be tested.
      • The present retrospective analysis of clinical testing for lung cancer patients showed that sequential single-gene testing required increasing tissue consumption and had decreasing rates of successful tests completion as the number of genes tested increased.
      • Investigational use of the Oncomine Dx Target Test on 1 tissue slide per test demonstrated success rates comparable to, or better than, those with single-gene testing for 2 genes on a surgical resection sample, 4 genes on an FNA sample, and 5 genes on a CNB sample, suggesting that it might facilitate multiple biomarker assessments on small tissue samples to inform treatment decisions for aNSCLC patients.

      Disclosure

      T.M.Y., A.T., and A.J.L. are employees of Navigant Consulting, Inc. C.M. is an OmniSeq employee and stockholder and an employee of Roswell Park Cancer Institute. The remaining author declares that he has no competing interests.

      Acknowledgments

      The authors acknowledge and thank Dr Raj Stewart for his insights and assistance in the writing of this paper. The authors further thank Thermo Fisher Scientific for providing funding for this research to Quorum Consulting, Inc, now Navigant Consulting, Inc.

      Supplemental Data

      Supplemental Table 1Gene Variants Included in the Oncomine™ Dx Target Test
      GeneDisplay NameAmino Acid ChangeNucleotide ChangeHotspot ID
      AKT1p.Glu17Lysc.49G>ACOSM33765
      ALKp.Arg1275Glnc.3824G>ACOSM28056
      ALKp.Arg1275Leuc.3824G>TCOSM28060
      ALKp.Cys1156Tyrc.3467G>ACOSM99136
      ALKp.Gly1128Alac.3383G>CCOSM98475
      ALKp.Gly1202Argc.3604G>ACOSM144250
      ALKp.Ile1171Asnc.3512T>ACOSM28498
      ALKp.Ile1171Thrc.3512T>CCOSM4381100
      ALKp.Leu1152Argc.3455T>GCOSM97185
      ALKp.Leu1152Proc.3455T>CCOSM1407659
      ALKp.Leu1196Glnc.3587T>ACOSM1169447
      ALKp.Leu1196Metc.3586C>ACOSM99137
      ALKp.Phe1174Cysc.3521T>GCOSM28059
      ALKp.Phe1174Ilec.3520T>ACOSM28491
      ALKp.Phe1174Leuc.3522C>GCOSM28061
      ALKp.Phe1174Leuc.3522C>ACOSM28055
      ALKp.Phe1174Leuc.3520T>CCOSM28057
      ALKp.Phe1174Serc.3521T>CCOSM53063
      ALKp.Phe1174Valc.3520T>GCOSM28054
      ALKp.Phe1245Cysc.3734T>GCOSM28500
      ALKp.Phe1245Ilec.3733T>ACOSM28492
      ALKp.Phe1245Leuc.3735C>GCOSM28062
      ALKp.Phe1245Leuc.3735C>ACOSM28493
      ALKp.Phe1245Valc.3733T>GCOSM28499
      ALKp.Ser1206Tyrc.3617C>ACOSM144251
      ALKp.Val1180Leuc.3538G>CCOSM4381101
      BRAFBRAF V600Ep.Val600Gluc.1799T>ACOSM476
      BRAFBRAF V600Ep.Val600Gluc.1799_1800delTGin sAACOSM475
      BRAFp.Asp594Asnc.1780G>ACOSM27639
      BRAFp.Asp594Glyc.1781A>GCOSM467
      BRAFp.Gly466Gluc.1397G>ACOSM453
      BRAFp.Gly466Valc.1397G>TCOSM451
      BRAFp.Gly469Alac.1406G>CCOSM460
      BRAFp.Gly469Argc.1405G>ACOSM457
      BRAFp.Gly469Valc.1406G>TCOSM459
      BRAFp.Lys601Gluc.1801A>GCOSM478
      BRAFp.Val600Argc.1798_1799delGTinsAGCOSM474
      BRAFp.Val600Lysc.1798_1799delGTinsAACOSM473
      BRAFp.Val600_Lys601delinsGluc.1799_1801delTGACOSM1133
      CDK4p.Arg24Cysc.70C>TCOSM1677139
      CDK4p.Arg24Hisc.71G>ACOSM1989836
      CDK4p.Arg24Leuc.71G>TCOSM363684
      CDK4p.Arg24Serc.70C>ACOSM3463914
      CDK4p.Lys22Argc.65A>GCOSM232013
      CDK4p.Lys22Glnc.64A>COM3153
      CDK4p.Lys22Metc.65A>TCOSM3463915
      DDR2p.Arg124Leuc.371G>TCOSM400880
      DDR2p.Arg124Trpc.370C>TCOSM4024594
      EGFREGFR Exon 19 deletionp.Glu746_Ala750delc.2235_2249delGGAA TTAAGAGAAGCCOSM6223
      EGFREGFR Exon 19 deletionp.Glu746_Ala750delc.2236_2250delGAAT TAAGAGAAGCACOSM6225
      EGFREGFR Exon 19 deletionp.Glu746_Arg748delc.2239_2247delTTAA GAGAACOSM6218
      EGFREGFR Exon 19 deletionp.Glu746_Glu749delc.2235_2246delGGAA TTAAGAGACOSM28517
      EGFREGFR Exon 19 deletionp.Glu746_Ser752delinsAspc.2238_2255delATTA AGAGAAGCAACATCCOSM6220
      EGFREGFR Exon 19 deletionp.Glu746_Ser752delinsValc.2237_2255delAATT AAGAGAAGCAACATCi TCOSM12384
      EGFREGFR Exon 19 deletionp.Glu746_Thr751delc.2236_2253delGAAT TAAGAGAAGCAACACOSM12728
      EGFREGFR Exon 19 deletionp.Glu746_Thr751delinsAlac.2237_2251delAATT AAGAGAAGCAACOSM12678
      EGFREGFR Exon 19 deletionp.Glu746_Thr751delinsIlec.2235_2252delGGAA TTAAGAGAAGCAACins ATCOSM13551
      EGFREGFR Exon 19 deletionp.Glu746_Thr751delinsValA lac.2237_2253delAATT AAGAGAAGCAACAinsT GCTCOSM12416
      EGFREGFR Exon 19 deletionp.Leu747_Ala750delinsProc.2239_2248delTTAA GAGAAGinsCCOSM12382
      EGFREGFR Exon 19 deletionp.Leu747_Ala750delinsProc.2238_2248delATTA AGAGAAGinsGCCOSM12422
      EGFREGFR Exon 19 deletionp.Leu747_Pro753delinsGlnc.2239_2258delTTAA GAGAAGCAACATCTCC sCACOSM12387
      EGFREGFR Exon 19 deletionp.Leu747_Pro753delinsSerc.2240_2257delTAAG AGAAGCAACATCTCCOSM12370
      EGFREGFR Exon 19 deletionp.Leu747_Ser752delc.2239_2256delTTAA GAGAAGCAACATCTCOSM6255
      EGFREGFR Exon 19 deletionp.Leu747_Thr751delc.2240_2254delTAAG AGAAGCAACATCOSM12369
      EGFREGFR Exon 19 deletionp.Leu747_Thr751delinsGlnc.2238_2252delATTA AGAGAAGCAACinsGCACOSM12419
      EGFREGFR Exon 19 deletionp.Leu747_Thr751delinsProc.2239_2251delTTAA GAGAAGCAAinsCCOSM12383
      EGFREGFR Exon 19 deletionp.Leu747_Thr751delinsSerc.2240_2251delTAAG AGAAGCAACOSM6210
      EGFREGFR L858Rp.Leu858Argc.2573T>GCOSM6224
      EGFREGFR Exon 19 deletionp.Lys745_Ala750delinsThrc.2234_2248delAGGA ATTAAGAGAAGCOSM1190791
      EGFREGFR Exon 19 deletionp.Lys745_Glu749delc.2233_2247delAAGG AATTAAGAGAACOSM26038
      EGFRp.Arg108Glyc.322A>GCOSM1451536
      EGFRp.Leu861Argc.2582T>GCOSM12374
      EGFRp.Ala289Aspc.866C>ACOSM21685
      EGFRp.Ala289Thrc.865G>ACOSM21686
      EGFRp.Ala289Valc.866C>TCOSM21687
      EGFRp.Arg108Lysc.323G>ACOSM21683
      EGFRp.Glu709Alac.2126A>CCOSM13427
      EGFRp.Glu709Glyc.2126A>GCOSM13009
      EGFRp.Glu709Lysc.2125G>ACOSM12988
      EGFRp.Glu709Valc.2126A>TCOSM12371
      EGFRp.Gly598Alac.1793G>CCOSM3412196
      EGFRp.Gly598Valc.1793G>TCOSM21690
      EGFRp.Gly719Alac.2156G>CCOSM6239
      EGFRp.Gly719Aspc.2156G>ACOSM18425
      EGFRp.Gly719Cysc.2155G>TCOSM6253
      EGFRp.Gly719Serc.2155G>ACOSM6252
      EGFRp.Leu858Metc.2572C>ACOSM12366
      EGFRp.Leu861Glnc.2582T>ACOSM6213
      EGFRp.Ser492Argc.1474A>CCOSM236671
      EGFRp.Ser492Argc.1476C>ACOSM236670
      EGFRp.Ser768Ilec.2303G>TCOSM6241
      ERBB2p.Arg678Glnc.2033G>ACOSM436498
      ERBB2p.Arg896Cysc.2686C>TCOSM14066
      ERBB2p.Arg896Hisc.2687G>ACOSM119971
      ERBB2p.Asp769Hisc.2305G>CCOSM13170
      ERBB2p.Asp769Tyrc.2305G>TCOSM1251412
      ERBB2p.Gly776Valc.2327G>TCOSM18609
      ERBB2p.Leu755Metc.2263T>ACOSM1205571
      ERBB2p.Leu755Proc.2263_2264delTTinsCCCOSM683
      ERBB2p.Ser310Phec.929C>TCOSM48358
      ERBB2p.Ser310Tyrc.929C>ACOSM94225
      ERBB2p.Thr733Ilec.2198C>TCOSM14059
      ERBB2p.Val777Leuc.2329G>TCOSM14062
      ERBB2p.Val842Ilec.2524G>ACOSM14065
      ERBB3p.Ala232Thrc.694G>ACOSM4043440
      ERBB3p.Ala232Valc.695C>TCOSM1242239
      ERBB3p.Asp297Tyrc.889G>TCOSM160822
      ERBB3p.Asp297Valc.890A>TCOSM941490
      ERBB3p.Glu332Lysc.994G>ACOSM254677
      ERBB3p.Met60Argc.179T>GCOSM941484
      ERBB3p.Met60Leuc.178A>TCOSM1606366
      ERBB3p.Met60Lysc.179T>ACOSM254678
      ERBB3p.Met91Ilec.273G>ACOSM122890
      ERBB3p.Met91Ilec.273G>CCOSM1299636
      ERBB3p.Val104Leuc.310G>CCOSM160824
      ERBB3p.Val104Leuc.310G>TCOSM191840
      ERBB3p.Val104Metc.310G>ACOSM172423
      FGFR2p.Ala314Aspc.941C>ACOSM49171
      FGFR2p.Asn549Hisc.1645A>CCOSM250083
      FGFR2p.Asn549Lysc.1647T>GCOSM36902
      FGFR2p.Asn549Lysc.1647T>ACOSM36912
      FGFR2p.Asn549Serc.1646A>GCOSM3665553
      FGFR2p.Cys382Argc.1144T>CCOSM36906
      FGFR2p.Cys382Tyrc.1145G>ACOSM915493
      FGFR2p.Lys659Asnc.1977G>TCOSM49173
      FGFR2p.Lys659Asnc.1977G>CCOSM683054
      FGFR2p.Lys659Gluc.1975A>GCOSM36909
      FGFR2p.Lys659Metc.1976A>TCOSM49175
      FGFR2p.Pro253Argc.758C>GCOSM49170
      FGFR2p.Pro253Leuc.758C>TCOSM537801
      FGFR2p.Ser252Trpc.755C>GCOSM36903
      FGFR2p.Tyr375Cysc.1124A>GCOSM36904
      FGFR2p.Tyr375Hisc.1123T>CCOSM1560916
      FGFR3p.Arg248Cysc.742C>TCOSM714
      FGFR3p.Gly697Cysc.2089G>TCOSM24802
      FGFR3p.Lys650Asnc.1950G>TCOSM1428730
      FGFR3p.Lys650Glnc.1948A>CCOSM726
      FGFR3p.Lys650Gluc.1948A>GCOSM719
      FGFR3p.Ser249Cysc.746C>GCOSM715
      HRASp.Gln61Argc.182A>GCOSM499
      HRASp.Gln61Hisc.183G>TCOSM502
      HRASp.Gln61Hisc.183G>CCOSM503
      HRASp.Gln61Leuc.182A>TCOSM498
      HRASp.Gln61Lysc.181C>ACOSM496
      HRASp.Gln61Proc.182A>CCOSM500
      HRASp.Gly12Alac.35G>CCOSM485
      HRASp.Gly12Argc.34G>CCOSM482
      HRASp.Gly12Aspc.35G>ACOSM484
      HRASp.Gly12Cysc.34G>TCOSM481
      HRASp.Gly12Serc.34G>ACOSM480
      HRASp.Gly12Valc.35G>TCOSM483
      HRASp.Gly13Argc.37G>CCOSM486
      HRASp.Gly13Aspc.38G>ACOSM490
      HRASp.Gly13Cysc.37G>TCOSM488
      HRASp.Gly13Serc.37G>ACOSM487
      HRASp.Gly13Valc.38G>TCOSM489
      KITp.Asn822Lysc.2466T>ACOSM1321
      KITp.Asn822Lysc.2466T>GCOSM1322
      KITp.Asp419_Arg420delc.1255_1260delGACAGGCOSM1578132
      KITp.Asp419delc.1255_1257delGACCOSM29014
      KITp.Asp579delc.1735_1737delGATCOSM1294
      KITp.Asp816Hisc.2446G>CCOSM1311
      KITp.Asp816Tyrc.2446G>TCOSM1310
      KITp.Asp816Valc.2447A>TCOSM1314
      KITp.Leu576Proc.1727T>CCOSM1290
      KITp.Lys642Gluc.1924A>GCOSM1304
      KITp.Trp557Argc.1669T>ACOSM1216
      KITp.Trp557Argc.1669T>CCOSM1219
      KITp.Trp557Glyc.1669T>GCOSM1221
      KITp.Trp557_Lys558delc.1669_1674delTGGAAGCOSM1217
      KITp.Trp557_Val559delinsPhec.1670_1675delGGAAGGCOSM1226
      KITp.Val559Alac.1676T>CCOSM1255
      KITp.Val559Aspc.1676T>ACOSM1252
      KITp.Val559Glyc.1676T>GCOSM1253
      KITp.Val559delc.1679_1681delTTGCOSM1247
      KITp.Val560Aspc.1679T>ACOSM1257
      KITp.Val654Alac.1961T>CCOSM12706
      KITp.Val825Alac.2474T>CCOSM1323
      KITp.Arg796Lysc.2387G>ACOSM1600411
      KRASp.Ala146Proc.436G>CCOSM19905
      KRASp.Ala146Thrc.436G>ACOSM19404
      KRASp.Ala146Valc.437C>TCOSM19900
      KRASp.Ala59Gluc.176C>ACOSM547
      KRASp.Ala59Glyc.176C>GCOSM28518
      KRASp.Ala59Thrc.175G>ACOSM546
      KRASp.Gln61Argc.182A>GCOSM552
      KRASp.Gln61Gluc.181C>GCOSM550
      KRASp.Gln61Hisc.183A>TCOSM555
      KRASp.Gln61Hisc.183A>CCOSM554
      KRASp.Gln61Leuc.182A>TCOSM553
      KRASp.Gln61Lysc.181C>ACOSM549
      KRASp.Gln61Lysc.180_181delTCinsAACOSM87298
      KRASp.Gln61Proc.182A>CCOSM551
      KRASp.Gly12Alac.35G>CCOSM522
      KRASp.Gly12Argc.34G>CCOSM518
      KRASp.Gly12Aspc.35G>ACOSM521
      KRASp.Gly12Cysc.34G>TCOSM516
      KRASp.Gly12Phec.34_35delGGinsTTCOSM512
      KRASp.Gly12Serc.34G>ACOSM517
      KRASp.Gly12Valc.35G>TCOSM520
      KRASp.Gly13Alac.38G>CCOSM533
      KRASp.Gly13Argc.37G>CCOSM529
      KRASp.Gly13Aspc.38_39delGCinsATCOSM531
      KRASp.Gly13Aspc.38G>ACOSM532
      KRASp.Gly13Cysc.37G>TCOSM527
      KRASp.Gly13Serc.37G>ACOSM528
      KRASp.Gly13Valc.38G>TCOSM534
      KRASp.Lys117Asnc.351A>TCOSM28519
      KRASp.Lys117Asnc.351A>CCOSM19940
      MAP2K1p.Glu203Lysc.607G>ACOSM232755
      MAP2K1p.Glu203Valc.608A>TCOSM3386991
      MAP2K1p.Lys57Asnc.171G>COM3156
      MAP2K1p.Lys57Asnc.171G>TOM3157
      MAP2K1p.Lys57Metc.170A>TCOSM1235478
      MAP2K1p.Lys57Thrc.170A>COM3155
      MAP2K1p.Phe53Ilec.157T>ACOSM3503329
      MAP2K1p.Phe53Leuc.157T>CCOSM555604
      MAP2K1p.Phe53Leuc.159T>ACOSM1725008
      MAP2K1p.Phe53Leuc.159T>GOM3154
      MAP2K1p.Phe53Valc.157T>GCOSM1562837
      MAP2K1p.Pro124Glnc.371C>ACOSM1167912
      MAP2K1p.Pro124Leuc.371C>TCOSM1315861
      MAP2K1p.Pro124Serc.370C>TCOSM235614
      MAP2K2p.Gln60Proc.179A>CCOSM145610
      MAP2K2p.Phe57Leuc.171T>GOM3158
      MAP2K2p.Phe57Leuc.171T>ACOSM3389034
      MAP2K2p.Phe57Leuc.169T>CCOSM1235618
      MAP2K2p.Phe57Valc.169T>GCOSM3534171
      METNANACOSM29633
      METNANACOSM24687
      METNANACOSM35468
      METp.His1112Argc.3335A>GCOSM703
      METp.His1112Leuc.3335A>TCOSM698
      METp.His1112Tyrc.3334C>TCOSM696
      METp.Met1268Ilec.3804G>ACOSM694
      METp.Met1268Thrc.3803T>CCOSM691
      METp.Thr1010Ilec.3029C>TCOSM707
      METp.Tyr1021Asnc.3061T>ACOSM48564
      METp.Tyr1021Phec.3062A>TCOSM339515
      METp.Tyr1248Cysc.3743A>GCOSM699
      METp.Tyr1248Hisc.3742T>CCOSM690
      METp.Tyr1253Aspc.3757T>GCOSM700
      MTORp.Cys1483Argc.4447T>CCOSM3747775
      MTORp.Cys1483Phec.4448G>TCOSM462616
      MTORp.Cys1483Trpc.4449C>GOM3149
      MTORp.Cys1483Tyrc.4448G>ACOSM462615
      MTORp.Glu1799Lysc.5395G>ACOSM180789
      MTORp.Leu2427Argc.7280T>GOM3148
      MTORp.Leu2427Glnc.7280T>ACOSM1185313
      MTORp.Phe1888Ilec.5662T>ACOSM3358968
      MTORp.Phe1888Leuc.5664C>GCOSM462604
      MTORp.Phe1888Leuc.5664C>ACOSM893813
      MTORp.Phe1888Leuc.5662T>CCOSM3358967
      MTORp.Phe1888Valc.5662T>GCOSM893814
      MTORp.Ser2215Phec.6644C>TCOSM1686998
      MTORp.Ser2215Proc.6643T>CCOSM1560108
      MTORp.Ser2215Tyrc.6644C>ACOSM20417
      MTORp.Thr1977Argc.5930C>GCOSM462602
      MTORp.Thr1977Lysc.5930C>ACOSM462601
      MTORp.Thr1977Serc.5929A>TCOSM1289945
      MTORp.Val2006Ilec.6016G>ACOSM893804
      MTORp.Val2006Leuc.6016G>CCOSM1134662
      MTORp.Val2006Phec.6016G>TCOSM249481
      NRASp.Ala146Thrc.436G>ACOSM27174
      NRASp.Ala146Valc.437C>TCOSM4170228
      NRASp.Ala59Thrc.175G>ACOSM578
      NRASp.Gln61Argc.182A>GCOSM584
      NRASp.Gln61Gluc.181C>GCOSM581
      NRASp.Gln61Hisc.183A>TCOSM585
      NRASp.Gln61Hisc.183A>CCOSM586
      NRASp.Gln61Leuc.182A>TCOSM583
      NRASp.Gln61Lysc.181C>ACOSM580
      NRASp.Gln61Proc.182A>CCOSM582
      NRASp.Gly12Alac.35G>CCOSM565
      NRASp.Gly12Argc.34G>CCOSM561
      NRASp.Gly12Aspc.35G>ACOSM564
      NRASp.Gly12Cysc.34G>TCOSM562
      NRASp.Gly12Serc.34G>ACOSM563
      NRASp.Gly12Valc.35G>TCOSM566
      NRASp.Gly13Alac.38G>CCOSM575
      NRASp.Gly13Argc.37G>CCOSM569
      NRASp.Gly13Aspc.38G>ACOSM573
      NRASp.Gly13Cysc.37G>TCOSM570
      NRASp.Gly13Serc.37G>ACOSM571
      NRASp.Gly13Valc.38G>TCOSM574
      NRASp.Lys117Asnc.351G>TMAN13
      PDGFRAp.Asn659Lysc.1977C>ACOSM22415
      PDGFRAp.Asn659Lysc.1977C>GCOSM22414
      PDGFRAp.Asn659Tyrc.1975A>TCOSM22416
      PDGFRAp.Asp842Tyrc.2524G>TCOSM12396
      PDGFRAp.Asp842Valc.2525A>TCOSM736
      PDGFRAp.Asp842_His845delc.2526_2537delCATCATGCATGACOSM737
      PDGFRAp.Asp842_Met844delc.2524_2532delGACATCATGCOSM12401
      PDGFRAp.Ile843_Asp846delc.2527_2538delATCATGCATGATCOSM12400
      PDGFRAp.Ile843_Ser847delinsThrc.2528_2539delTCATGCATGATTCOSM12407
      PDGFRAp.Val561Aspc.1682T>ACOSM739
      PIK3CAp.Arg108Hisc.323G>ACOSM27497
      PIK3CAp.Arg38Cysc.112C>TCOSM744
      PIK3CAp.Arg38Glyc.112C>GCOSM40945
      PIK3CAp.Arg38Hisc.113G>ACOSM745
      PIK3CAp.Arg38Serc.112C>ACOSM87310
      PIK3CAp.Arg88Glnc.263G>ACOSM746
      PIK3CAp.Arg93Glnc.278G>ACOSM86041
      PIK3CAp.Arg93Trpc.277C>TCOSM27493
      PIK3CAp.Asn1044Lysc.3132T>ACOSM12592
      PIK3CAp.Asn345Ilec.1034A>TCOSM94978
      PIK3CAp.Asn345Lysc.1035T>ACOSM754
      PIK3CAp.Cys378Argc.1132T>CCOSM756
      PIK3CAp.Cys378Phec.1133G>TCOSM21450
      PIK3CAp.Cys378Tyrc.1133G>ACOSM1041478
      PIK3CAp.Cys420Argc.1258T>CCOSM757
      PIK3CAp.Cys901Argc.2701T>CCOSM1420899
      PIK3CAp.Cys901Phec.2702G>TCOSM769
      PIK3CAp.Cys901Tyrc.2702G>ACOSM1420901
      PIK3CAp.Gln546Argc.1637A>GCOSM12459
      PIK3CAp.Gln546Gluc.1636C>GCOSM6147
      PIK3CAp.Gln546Lysc.1636C>ACOSM766
      PIK3CAp.Gln546Proc.1637A>CCOSM767
      PIK3CAp.Glu365Glyc.1094A>GCOSM1420797
      PIK3CAp.Glu365Valc.1094A>TCOSM1484860
      PIK3CAp.Glu39Lysc.115G>ACOSM30625
      PIK3CAp.Glu542Lysc.1624G>ACOSM760
      PIK3CAp.Glu542Valc.1625A>TCOSM762
      PIK3CAp.Glu545Alac.1634A>CCOSM12458
      PIK3CAp.Glu545Aspc.1635G>CCOSM27374
      PIK3CAp.Glu545Aspc.1635G>TCOSM765
      PIK3CAp.Glu545Glnc.1633G>CCOSM27133
      PIK3CAp.Glu545Glyc.1634A>GCOSM764
      PIK3CAp.Glu545Lysc.1633G>ACOSM763
      PIK3CAp.Glu547Lysc.1639G>ACOSM29315
      PIK3CAp.Glu726Glyc.2177A>GCOSM1420887
      PIK3CAp.Glu726Lysc.2176G>ACOSM87306
      PIK3CAp.Glu81Lysc.241G>ACOSM27502
      PIK3CAp.Gly1049Argc.3145G>CCOSM12597
      PIK3CAp.Gly1049Serc.3145G>ACOSM777
      PIK3CAp.Gly106Valc.317G>TCOSM748
      PIK3CAp.His1047Argc.3140A>GCOSM775
      PIK3CAp.His1047Leuc.3140A>TCOSM776
      PIK3CAp.His1047Tyrc.3139C>TCOSM774
      PIK3CAp.His701Argc.2102A>GCOSM1420881
      PIK3CAp.His701Proc.2102A>CCOSM778
      PIK3CAp.Lys111Gluc.331A>GCOSM13570
      PIK3CAp.Met1043Ilec.3129G>ACOSM29313
      PIK3CAp.Met1043Ilec.3129G>TCOSM773
      PIK3CAp.Met1043Valc.3127A>GCOSM12591
      PIK3CAp.Thr1025Alac.3073A>GCOSM771
      PIK3CAp.Tyr1021Cysc.3062A>GCOSM12461
      PIK3CAp.Val344Alac.1031T>CCOSM86951
      PIK3CAp.Val344Glyc.1031T>GCOSM22540
      PIK3CAp.Glu365Lysc.1093G>ACOSM86044
      PIK3CAp.Pro539Argc.1616C>GCOSM759
      RAF1p.Ser257Leuc.770C>TCOSM181063
      RAF1p.Ser257Trpc.770C>GCOSM581519
      RAF1p.Thr421Metc.1262_1263delCCinsTGMAN9
      RETp.Ala883Phec.2646_2648delAGCinsTTTCOSM981
      RETp.Ala883Serc.2647G>TCOSM133167
      RETp.Asp898_Glu901delc.2694_2705delTGTTTATGAAGACOSM962
      RETp.Cys618Argc.1852T>CCOSM29803
      RETp.Cys618Tyrc.1853G>ACOSM980
      RETp.Cys620Argc.1858T>CCOSM29804
      RETp.Cys634Argc.1900T>CCOSM966
      RETp.Glu768Aspc.2304G>CCOSM21338
      RETp.Glu768Glyc.2303A>GCOSM1347811
      RETp.Met918Thrc.2753T>CCOSM965
      ROS1ROS1 Fusion
      ROS1p.Gly2032Argc.6094G>CMAN11
      ROS1p.Gly2032Argc.6094G>AMAN10
      ROS1p.Leu1951Metc.5851C>ACOSM1072521

      References

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        Cancer Facts & Figures 2017.
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        Trends in stage distribution for patients with non-small cell lung cancer: a National Cancer Database survey.
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        NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) Non-Small Cell Lung Cancer. Version 8.2017.
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        Summary of Safety and Effectiveness Data 2017.
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        Oncomine™ Dx Target Test Part I: Sample Preparation and Quantification User Guide. Revision C.0 2017.
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        NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) Non-Small Cell Lung Cancer. Version 4.2016.
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