CAM 204115

Expanded Molecular Panel Testing of Cancers to Identify Targeted Therapies

Category:Laboratory   Last Reviewed:July 2018
Department(s):Medical Affairs   Next Review:July 2019
Original Date:December 2014    

Description
There is interest in treating cancers by targeting biologic pathways characterized by specific genetic markers. Genetic panel testing offers the potential to evaluate a large number of genetic markers at a single time to identify treatments that target specific pathways. Some individual markers have established benefit in certain types of cancers; they are not addressed in this evidence review. Rather, this review focuses on "expanded" panels, which are defined as panels that test a wide variety of genetic markers in cancers without regard for whether specific targeted treatment has demonstrated benefit. This approach may result in a treatment different from that usually selected for a patient based on the type of cancer and stage.

For individuals who have cancers that have not responded to standard therapy who receive testing of tumor tissue with an expanded cancer mutation panel, the evidence includes 1 randomized controlled trial (RCT), nonrandomized trials and numerous case series. Relevant outcomes are overall survival, disease-specific survival, test accuracy and validity and other test performance measures. The analytic validity of these panels is likely to be high when next-generation sequencing is used. The clinical validity of the individual mutations for particular types of cancer is not easily determined from the published literature. The large number of mutations and many types of cancer preclude determination of the clinical validity of the panels as a whole. Some evidence has reported that many of the identified mutations are false positives (i.e., not biologically active), after filtering by comparison with matched normal tissue and cancer mutation databases. To demonstrate clinical utility, direct evidence from interventional trials, ideally RCTs, are needed that compare the strategy of targeted treatment based on panel results with standard care. The first such published RCT (the SHIVA trial) reported that there was no difference in progression-free survival when panels were used in this way. Some nonrandomized comparative studies, comparing matched treatment with nonmatched treatment, have reported that outcomes are superior for patients receiving matched treatment. However, these studies are inadequate to determine treatment efficacy because the populations with matched and unmatched cancers may differ on several important clinical and prognostic variables. In addition, there is potential for harm if ineffective therapy is given based on test results, because there may be adverse effects of therapy in absence of a benefit. The evidence is insufficient to determine the effects of the technology on health outcomes.  

Background  
Tumor location, grade, stage and the patient’s underlying physical condition have traditionally been used in clinical oncology to determine the therapeutic approach to a specific cancer, which could include surgical resection, ionizing radiation, systemic chemotherapy or combinations thereof. Currently, some 100 different types are broadly categorized according to the tissue, organ or body compartment in which it arises. Most treatment approaches in clinical care were developed and evaluated in studies that recruited subjects and categorized results based on this traditional classification scheme.

This traditional approach to cancer treatment does not reflect the wide diversity of cancer at the molecular level. While treatment by organ type, stage and grade may demonstrate statistically significant therapeutic efficacy overall, only a subgroup of patients may actually derive clinically significant benefit. It is unusual for a cancer treatment to be effective for all patients treated in a traditional clinical trial. Spear et al. analyzed the efficacy of major drugs used to treat several important diseases.1 They reported heterogeneity of therapeutic responses ranging from a low of 25% for cancer chemotherapeutics, with response rates for most drugs falling in the range of 50% to 75%. The low rate for cancer treatments is indicative of the need for better identification of characteristics associated with treatment response and better targeting of treatment in order to have higher rates of therapeutic responses. 

Much of the variability in clinical response may result from genetic variations. Within each broad type of cancer, there may be a large amount of variability in the genetic underpinnings of the cancer. Targeted cancer treatment refers to the identification of genetic abnormalities present in the cancer of a particular patient, and the use of drugs that target the specific genetic abnormality. Using genetic markers, cancers can be further classified by "pathways" defined at the molecular level. An expanding number of genetic markers have been identified. Dienstmann et al. categorized these findings into 3 classes.2 These are: (1) genetic markers that have a direct impact on care for the specific cancer of interest, (2) genetic markers that may be biologically important but are not currently actionable and (3) genetic markers of uncertain importance. 

A smaller number of individual genetic markers fall into the first category (i.e., have established utility for a particular cancer type). Utility of these markers has generally been demonstrated by randomized controlled trials that select patients with the marker and report significant improvements in outcomes with targeted therapy compared with standard therapy. This evidence review does not apply to the individual markers that have demonstrated efficacy. According to recent National Comprehensive Cancer Network guidelines,3 the following markers have demonstrated utility for predicting treatment response to targeted therapies for the specific cancers listed: 

  • Breast cancer 
    • HER2 (ERBB2) 
  • Colon cancer 
    • RAS mutations (KRAS, NRAS) 
    • BRAF c1799T>A
  • Non-small-cell lung cancer 
    • EGFR 
    • ALK/ROS1 
    • KRAS
    • RET
    • MET
  • Metastatic melanoma 
    • BRAF v600 
    • KIT
  • Ovarian cancer
    • BRCA (germline)  
  • Chronic myeloid leukemia 
    • BRC-ABL 
  • Gastrointestinal stromal tumors 
    • KIT 

Testing for these individual mutations with established utility is not covered herein. In some cases, limited panels may be offered that are specific to 1 type of cancer (e.g., a panel of several markers for NSCLC). This review is also not intended to address the use of cancer-specific panels that include a few mutations. Rather, the intent is to address expanded panels that test for many potential mutations that do not have established efficacy for the specific cancer in question. 

When advanced cancers are tested with expanded mutation panels, most patients are found to have at least 1 potentially pathogenic mutation.4-6 The number of mutations varies widely by types of cancers, different mutations included in testing and different testing methods among the available studies. In a 2015 study, 439 patients with diverse cancers were tested with a 236-gene panel.6 A total of 1,813 molecular alterations were identified, and almost all patients (420/439 [96%]) had at least 1 molecular alteration. Median number of alterations per patient was 3, and 85% of patients (372/439) had 2 or more alterations. The most common alterations were in the genes TP53 (44%), KRAS (16%) and PIK3CA (12%). 

Some evidence is available on the generalizability of targeted treatment based on a specific mutation among cancers that originate from different organs.2,3,7 There are several examples of mutation-directed treatment that was effective in 1 type of cancer but ineffective in another. For example, targeted therapy for epidermal growth factor receptor (EGFR) mutations has been successful in NSCLC but not in trials of other cancer types. Treatment with tyrosine kinase inhibitors based on mutation testing has been effective for renal cell carcinoma, but has not demonstrated effectiveness for other cancer types tested. "Basket" studies, in which tumors of various histologic types that share a common genetic mutation are treated with a targeted agent, also have been performed. One such study was published in 2015 by Hyman et al.8 In this study, 122 patients with BRAF V600 mutations in nonmelanoma cancers were treated with vemurafenib. The authors reported that there appeared to be antitumor activity for some but not all cancers, with the most promising results seen for NSCLC, Erdheim-Chester disease and Langerhans cell histiocytosis. 

EXPANDED CANCER MUTATION PANELS
Table 1 provides a select list of commercially available expanded cancer mutation panels.  

Table 1. Commercially Available Solid and Hematologic Tumor Expanded Cancer Mutation Panels 

Test(Manufacturer) Tumor Type No. of Genes Tested Technology
FoundationOne test (Foundation Medicine)9 Solid 236 cancer-related genes and 47 introns from another 19 genes NGS
FoundationOne Heme test (Foundation Medicine)9 Hematologic 405 cancer-related genes and introns from another 31 genes RNA sequencing
OnkoMatch (GenPath Diagnostics)10 Solid 68 mutations in 14 oncogenes and tumor suppressor genes Multiplex PCR
GeneTrails Solid Tumor Panel (Knight Diagnostic Labs)11 Solid 37 genes  
Tumor profiling service (Caris Molecular Intelligence through Caris Life Sciences)12 Solid Up to 56 tumor-associated genes NGS, IHC, FISH, Sanger sequencing, pyrosequencing, quantitative PCR, fragmentation analysis
SmartGenomics (PathGroup)13 Solid  and hematologic Up to 62 cancer-associated genes NGS, cytogenomic array, other technologies
Guardant360 panel (GuardantHealth)14 Solid Digital sequencing  
Paradigm Cancer Diagnostic (PcDx) Panel (Paradigm)15 Solid >500 genetic targets NGS
Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT)16 Solid 341 cancer-associated genes NGS
TruSeq® Amplicon Panel (Illumina)17 Solid 48 cancer-related genes NGS
Illumina TruSight Tumor (Illumina)18 Solid 26 cancer-related genes  
Ion AmpliSeq Comprehensive Cancer Panel (Life Technologies)19 Solid >400 cancer-related genes and tumor suppressor genes  
Ion AmpliSeq Cancer Hotspot Panel v2 (Life Technologies)19 Solid “Hotspot” regions of 50 cancer-related and tumor suppressor genes  

FISH: fluorescence in situ hybridization; IHC: immunohistochemistry; NGS: next-generation sequencing; PCR: polymerase chain reaction.

Regulatory Status
There are no U.S. Food and Drug Administration (FDA)-approved genetic panels for targeted cancer treatment. Commercially available panels are laboratory-developed tests that are not subject to FDA approval. Clinical laboratories may develop and validate tests in-house ("home-brew") and market them as a laboratory service; such tests must meet the general regulatory standards of the Clinical Laboratory Improvement Act.

Related Policies
20493 Genetic Cancer Susceptibility Panels using Next Generation Sequencing

Policy 
Multiplex panels of up to 50 genes that analyze for a subset of 5 or more genes considered to be standard-of-care for use with a given diagnosis, as defined in nationally recognized clinical guidelines such as those of the National Cancer Comprehensive Network (NCCN) or the American Society of Clinical Oncology (ASCO), are considered MEDICALLY NECESSARY.  Current genes scientifically shown to be impactful in the care of solid organ and hematolymphoid tumors are indicated in the coding table below. If less than 5 genes panel testing is needed, please consult individual policies.

Specific genes for solid organ tumors and hematolymphoid neoplasms based on current NCCN guidelines are shown in table below. See individual policies for staging of cancers in which testing is appropriate. 

Tumor Type Disease State Genes
Solid Tumor Bone CA (Ewing Sarcoma) EWSR1-ERG, EWSR1-ETV1, EWSR1-ETV4, EWSR1-FEV, EWSR1-FL1, FUS-ERG, FUS-FEV, 
Breast CA

BRCA1, BRCA2, ERBB2, PTEN, TP53, CDH1, STK11, 21 gene expression pattern, recurrence score

Solid Tumor       Non small cell lung CA (nonsquamous) ALK, EGFR, ERBB2, KRAS, ROS1 
Colon CA BRAF, CEACAM5, KRAS, MLH1, MSH2, MSH6, NRAS, PMS2, APC, EPCAM, MLH1, MSH2, MSH6, PMS2, MUTYH 
Melanoma BRAF, KIT 
Myelodysplastic Syndromes

ASXL1, EZH2, ETV6, RUNX1, SF3B1, TP53, GATA2, JAK2, MPL, CALR, PDGFRB, RUNX1, TRG, TRA, TRB, TRD

Neuroendocrine CA MEN1, RET
Ovarian CA ATM, BRCA1, BRCA2, BRIP1, CHEK2, PALB2, RAD51C, RAD51D, MLH1, MLH2, MSH6, PMS2 
Pancreatic CA MLH1, MSH2, MSH6, PMS2 
Penile CA MLH1, MSH2, MSH6, PMS2 
Prostate CA

Men with clinically localized disease may consider the use of tumor-based molecular assays 

Rectal CA

BRAF, MLH1, MSH2, MSH6, PMS2, NRAS, KRAS 

Soft Tissue Sarcoma

APC, ATIC-ALK, CARS-ALK, CLTC-ALK, RANBP2-ALK, TPM3-ALK, TPM4-ALK, BRAF, COL1A1-PDGFB, CSF1-COL6A3, ETV6-NTRK3, EWSR1-ATF1, EWSR1-CREB1, FUS-ATF1, EWSR1-DDIT3, FUS-DDIT3, EWSR1-ERG, EWSR1-ETV1, EWSR1-ETV4, EWSR1-FEV, EWSR1-FLI1, EWSR1-PATZ1, FUS-ERG, EWSR1-NR4A3, TAF15-NR4A3, TCF12-NR4A3, TFG-NR4A3, EWSR1-WT1, FUS-CREB3L1, FUS-CREB3L2, GLI1, TSPAN31, CDK4, HMGA2, MDM2, HEY1-NCOA2, KIT, MYOD1, NAB-STAT6, NF1, CDKN2A, EED, SUZ12, PAX3-FOXO1, PAX3-FOXO4, PAX7-FOXO1, PDGFRA, SDHB, SDHC, SDHD, SMARCB1, SS18-SSX1, SS18-SSX2, SS18-SSX4, WWTR1-CAMTA1, YAP1-TFE3

Thyroid CA

Abnormal gene/gene expression profiles, CALCA, CEACAM5, CGA, PTH, RET, TG, TSHB

Testicular CA

MLH1, MSH2, MSH6, PMS2 

Uterine CA

MLH1, MSH2, MSH6, PMS2 

Hematolymphoid    ALL

ABL1, BCR-ABL1, ETV-RUNX1, IL3-IGH, KMT2A, TCF3-PBX1

AML

ASXL1, BCR-ABL1, CBFB-MYH11, CEBPA, DEK-NUP214, DNMT3A, FLT3, IDH1, IDH2 KIT, MLL, MLLT3-MLL, NPM1, PML-RARA, RPN1-EV11, RUNX1, RUNX1-RUNX1T1, TET2, TP53, WT1

CML

ABL1, BCR-ABL1, DNTT, MPO

CLL

BTK, TP53, IGHV

Primary Cutaneous B-Cell Lymphoma

BCL2

Extranodal NK/T-Cell Lymphoma, nasal type

TRG, TRA, TRB, TRD

Peripheral T-Cell Lymphomas ALK

Primary Cutaneous CD30+ T-Cell Lymphoproliferative Disorders

DUSP22, TRG, TRB

T-Cell Large Granular Lymphocytic Leukemia

STAT3, STAT5B, TRG, TRA, TRB, TRD

T-Cell Prolymphocytic Leukemia

TRA-TRD/MTCP1, TRA-TRD/TCL1A, TRG, TRA, TRB, TRD

Waldenström's Macroglobulinemia/Lymphoplasmacytic Lymphoma

MyD88 L265, CXCR4

The covered panels must fit the American Medical Association’s Current Procedural Terminology (CPT®) codes for panels comprising 5 to 50 genes for solid organ neoplasms (CPT® 81445) or hematolymphoid neoplasms or disorders (CPT® 81450).

Panels containing more than 50 genes (CPT® code 81455) are INVESTIGATIONAL.  

Specific genes for solid organ tumors and hematolymphoid neoplasms based on current NCCN guidelines are shown in table below. See individual policies for staging of cancers in which testing is appropriate.

Please review attached Table

81455  Targeted genomic sequence analysis panel, solid organ or hematolymphoid neoplasm, DNA analysis, and RNA analysis when performed, 51 or greater genes (e.g., ALK, BRAF, CDKN2A, CEBPA, DNMT3A, EGFR, ERBB2, EZH2, FLT3, IDH1, IDH2, JAK2, KIT, KRAS, MLL, NPM1, NRAS, MET, NOTCH1, PDGFRA, PDGFRB, PGR, PIK3CA, PTEN, RET), interrogation for sequence variants and copy number variants or rearrangements, if performed
84999 Unlisted chemistry procedure

Policy Guidelines 
If a panel meets the requirements for one of the specific CPT codes for targeted genomic sequence analysis panel (81445-81455), the code may be reported for the test.

If the panel does not meet the requirements for a CPT panel code, any specific mutation listed in the codes 81200-81409 would be reported using those codes and the other mutations in the panel that are not specifically listed would be reported with 1 unit of the unlisted molecular pathology code 81479.

As an example of the coding that might be used, GenPath recommends the following CPT codes in its test catalog for OnkoMatchTumor Genotyping (with the number of units indicated in parentheses): 81210 (1), 81235 (1), 81275 (1), 81323 (1). For OnkoMatch Tumor Genotyping + for Lung, it recommends the following CPT codes: 81210 (1), 81235 (1), 81275 (1), 81323 (1), 88368 (2), 88381 (1).

Benefit Application
Blue Card®/National Account Issues
None identified

Rationale 
The evaluation of a genetic test focuses on 3 main principles: (1) analytic validity (technical accuracy of the test in detecting a mutation that is present or in excluding a mutation that is absent); (2) clinical validity (diagnostic performance of the test [sensitivity, specificity, positive and negative predictive values] in detecting clinical disease); and (3) clinical utility (how the results of the diagnostic test will be used to change management of the patient and whether these changes in management lead to clinically important improvements in health outcomes). 

ANALYTIC VALIDITY 
No published studies were identified that evaluated the analytic validity of these panels. The panels are performed primarily by next-generation sequencing (NGS), which has a high analytic validity. Some panels supplement NGS with additional testing methods, such as polymerase chain reaction (PCR), for intronic regions included as components of the panel. PCR is generally considered to have an analytic validity of more than 95%.

Information on analytic validity of the FoundationOne test was reported on the Foundation website.20 This site states that the analytic sensitivity is greater than 99% for base substitutions at a mutant allele frequency of 5% or more, 98% for indels at a mutant allele frequency of 10% or more, less than 95% for copy number alterations. It also reports an analytic specificity of more than 99%.   

CLINICAL VALIDITY 
The clinical validity of the panels as a whole cannot be determined because of the different mutations and large number of potential cancers for which they can be used. Clinical validity would need to be reported for each mutation for a particular type of cancer. Because there are hundreds of mutations included in the panels and dozens of cancer types, evaluation of the individual clinical validity for each pairing is beyond the scope of this review.

A major concern with clinical validity is differentiating mutations that drive cancer growth from genetic variants that are not clinically important. It is expected that variants of uncertain significance will be very frequent with panels that include several hundred markers.

Comparison of cancer mutations with matched normal tissue can provide evidence about whether mutations are truly somatic cancer mutations or whether they are incidental variants that do not have meaningful biologic activity. Jones et al. performed comprehensive mutation testing on 815 pairs of tumor tissue and matched normal tissue from patients with 15 different tumor types.21 Each sample was analyzed by both targeted sequencing and whole exome sequencing. A total of 105,672 somatic alterations were identified. After filtering for mutations present in normal tissue, there was an average of 4.34 mutations per patient on targeted analysis and 135 mutations per patient on whole exome sequencing. After additional filtering using the COSMIC (Catalog of Somatic Mutations in Cancer) database, the authors estimated that 38% of the mutations identified by targeted analysis were true positives and 62% were false positives; on whole exome analysis, 10% of mutations were true positives and 90% were false positives.   

Section Summary: Clinical Validity 
The evidence on clinical validity of expanded panels is incomplete. Because of the large number of mutations contained in expanded panels, it is not possible to determine clinical validity for the panels as a whole. While some mutations have a strong association with 1 or a small number of specific malignancies, none has demonstrated high clinical validity across a wide variety of cancers. Some have reported that, after filtering mutations by comparison with matched normal tissue and cancer mutation databases, most identified mutations are found to be false positives. Thus, it is likely that clinical validity will need to be determined for each mutation and for each type of cancer individually.

CLINICAL UTILITY 
The most direct way to demonstrate clinical utility is through controlled trials that compare a strategy of cancer mutation testing followed by targeted treatment with a standard treatment strategy without mutation testing. Randomized trials are necessary to control for selection bias in treatment decisions, because clinicians may select candidates for mutation testing based on clinical, demographic and other factors. Outcomes of these trials would be the morbidity and mortality associated with cancer and cancer treatment. Overall survival (OS) is most important; cancer-related survival and/or progression-free survival (PFS) may be acceptable surrogates. Quality-of-life measurement may also be important if study designs allow for treatments with different toxicities in the experimental and control groups. 

Systematic Reviews 
Schwaederle et al. published a meta-analysis of studies comparing personalized treatment with nonpersonalized treatment in 2015.22 Their definition of personalized treatment was driven by a biomarker, which could be genetic or nongenetic. Therefore, this analysis not only included studies of matched versus unmatched treatment based on genetic markers, but also included studies that personalized treatment based on nongenetic markers. A total of 111 arms of identified trials received personalized treatment, and they were compared with 529 arms that received nonpersonalized treatment. On random-effects meta-analysis, the personalized treatment group had a higher response rate (31% vs. 10.5%, p<0.001), and a longer PFS (5.9 months vs. 2.7 months, p<0.001) compared with the nonpersonalized treatment group. Another meta-analysis by this group compared outcomes from 44 Food and Drug Administration-regulated drug trials that used a personalized treatment approach to 68 trials that used a nonpersonalized approach to cancer treatment.23 Response rates were significantly higher in the personalized treatment trials (48%) than in the nonpersonalized approach (23%; p<0.001). PFS was 8.3 months in the personalized treatment trials compared to 5.5 months in the nonpersonalized approach (p<0.001). For trials that used a personalized treatment strategy, OS was significantly longer (19.3 months) than in trials that did not (13.5 months, p=0.01). Personalized treatment in these studies was based on various biomarkers, both genetic and nongenetic.  

Randomized Controlled Trials 
The SHIVA trial was a randomized controlled trial of treatment directed by cancer mutation testing versus standard care, with the first results published in 2015.24,25 In this study, 195 patients with a variety of advanced cancers refractory to standard treatment were enrolled from 8 academic centers in France. Mutation testing included comprehensive analysis for 3 molecular pathways (hormone receptor pathway, PI3K/AKT/mTOR pathway, RAF/MEK pathway) performed by targeted NGS, analysis of copy number variations and hormone expression by immunohistochemistry. Based on the pattern of abnormalities found, 9 different regimens of established cancer treatments were assigned to the experimental treatment arm (see Table 2). The primary outcome was PFS analyzed by intention to treat.  

Table 2. Treatment Algorithm for Experimental Arm, From the SHIVA Trial24  

Molecular Abnormalities Molecularly  Targeted Agent
KIT, ABL, RET Imatinib
AKT, mTORC1/2, PTEN, PI3K Everolimus
BRAF V600E Vemurafenib
PDGFRA/B, FLT-3 Sorafenib
EGFR Erlotinib
HER2 Lapatinib and trastuzumab
SRC, EPHA2, LCK, YES Dasatinib
Estrogen receptor, progesterone receptor Tamoxifen (or letrozole if contraindications)
Androgen receptor Abiraterone

Ninety-nine patients were randomized to the targeted treatment group and 96 to standard care. Baseline clinical characteristics and tumor types were similar between groups. Molecular alterations affecting the hormonal pathway were found in 82 (42%) of 195 patients, alterations affecting the PI3K/AKT/mTOR pathway were found in 89 (46%) of 195 patients and alterations affecting the RAF/MED pathway were found in 24 (12%) of 195 patients. After a median follow-up pf 11.3 months, the median PFS was 2.3 months (95% confidence interval [CI], 1.7 of 3.8 months) in the targeted treatment group versus 2.0 months (95% CI, 1.7 of 2.7 months) in the standard care group (hazard ratio, 0.88; 95% CI, 0.65 of 1.19, p=0.41). Objective responses were reported for 4 (4.1%) of 98 assessable patients in the targeted treatment group versus 3 (3.4%) of 89 assessable patients in the standard care group. On subgroup analysis by molecular pathway, there were no significant differences in PFS between groups.

Nonrandomized Controlled Trials
Numerous nonrandomized studies have been published that use some type of control. Some of these studies had a prospective, interventional design. In 2016, Wheler et al. reported a prospective comparative trial of patients who had failed standard treatment and had been referred to their tertiary center for admission into phase 1 trials.26 Comprehensive molecular profiling (Foundation One tumor panel) was performed on 339 patients, of whom 122 went onto a phase 1 therapy that was matched to their genetic profile; based on physician evaluation of additional information, 66 patients went onto a phase 1 trial not matched to their genetic profile. Table 3 summarizes study results; there was a significant benefit on time to treatment failure and a trend for an increased percentage of patients with stable disease and median OS in patients matched to their genetic profile. When exploratory analysis divided patients into groups that had high matching results or low matching results (number of molecular matches per patient divided by the number of molecular alterations per patient), the percentage of patients with stable disease and the median time to failure were significantly better in the high-match group. Median OS did not differ significantly between groups. Notably, those patients had failed multiple prior therapies (median, 4) and had a number (median, 5; range, 1-14) of gene alterations in the tumors. For comparison, response rates in phase 1 trials with treatment-resistant tumors are typically 5% to 10%.   

Table 3. Survival Outcomes After Genetic Profile-Based Therapy (Adapted from Wheler et al, 2016)

Group N % SD (95% CI) Median TTF (95% CI), mo Median OS (95%), mo
Matched 122 19% 2.8 (2.1 to 3.5) 9.3 (7.3 to 11.3)
Unmatched 66 8% 1.9 (1.5 TO 2.3) 7.2 (4.9 TO 9.5)
p   0.061 0.001 0.087
High match 92 22% 3.4 (2.6 TO 4.2) 9.3 (7.3 TO 11.3)
Low match 90 9% 1.9 (1.6 TO 2.2) 7.5 (5.0 TO 10.0)
p   0.028 <0.001 0.121

CI: confidence interval; OS: overall survival; SD: stable disease ≥6 mo; TTF: time to failure. 

Another type of study compares patients matched to targeted treatment with patients not matched. In this type of study, all patients undergo comprehensive genetic testing, but only a subset is matched to targeted therapy. Patients who are not matched continue to get standard care.

An individual study of this type is Tsimberidou et al.27 In it, patients with advanced or metastatic cancer refractory to standard therapy underwent molecular profiling. PCR-based targeted sequencing was used to assess mutations in 10 cancer genes. Loss of PTEN was determined using immunohistochemistry, and anaplastic lymphoma kinase (ALK) translocation was assessed using FISH. Of 1,144 patients, 460 had a molecular aberration based on this panel of tests. From this group of 460 patients, 211 were given "matched" treatment and 141 were given nonmatched treatment. The principal analysis presented was of a subgroup of the 460 patients who had only 1 molecular aberration (n=379). Patients were enrolled in 1 of 51 phase 1 clinical trials of experimental agents. It was not stated how patients were assigned to matched or unmatched therapy, or how a particular therapy was considered a match or not. In the list of trials in which patients were enrolled, it appears that many of the investigational agents were inhibitors of specific kinases, and thus a patient with a particular aberration of that kinase would probably be considered a match for that agent.27

Among the 175 patients treated with matched therapy, the overall response rate was 27%. Among the 116 patients treated with nonmatched therapy, the response rate was 5% (p<0.001 for the difference in response rates). The median time-to-failure was 5.2 months for patients on matched therapy and 2.2 months for those on nonmatched therapy (p<0.001). At a median 15-month follow-up, survival was 13.4 months versus 9.0 months (p=0.017) in favor of matched therapy. Due to small numbers, individual molecular aberrations could not be analyzed, but some sensitivity analyses, excluding certain aberrations, demonstrated that the results were robust to exclusion of certain groups.

Section Summary: Clinical Utility
Clinical utility has not been demonstrated for the use of expanded mutation panels to direct targeted cancer treatment. One published RCT (SHIVA trial) used an expanded panel in this way, and reported no difference in PFS compared with standard treatment. Nonrandomized studies have compared patients who received matched treatment with patients who did not , and have reported that outcomes are superior in patients receiving matched treatment. However, there are potential issues with this design that could compromise the validity of comparing these 2 populations. They include: (1) differences in clinical and demographic factors, (2) differences in the severity of disease or prognosis of disease (i.e., patients with more undifferentiated anaplastic cancers might be less likely to express genetic markers) and (3) differences in the treatments received. It is possible that one of the "targeted" drugs could be more effective than standard treatment in general whether or not patients were matched. As a result, these types of nonrandomized studies do not provide definitive evidence on treatment efficacy. Further RCTs are needed that randomize patients to a treatment strategy of mutation testing followed by targeted treatment versus standard care.

SUMMARY OF EVIDENCE 
For individuals who have cancers that have not responded to standard therapy who receive testing of tumor tissue with an expanded cancer mutation panel, the evidence includes 1 randomized controlled trial (RCT), nonrandomized trials and numerous case series. Relevant outcomes are overall survival, disease-specific survival, test accuracy and validity and other test performance measures. The analytic validity of these panels is likely to be high when next-generation sequencing is used. The clinical validity of the individual mutations for particular types of cancer is not easily determined from the published literature. The large number of mutations and many types of cancer preclude determination of the clinical validity of the panels as a whole. Some evidence has reported that many of the identified mutations are false positives (i.e., not biologically active), after filtering by comparison with matched normal tissue and cancer mutation databases. To demonstrate clinical utility, direct evidence from interventional trials, ideally RCTs, are needed that compare the strategy of targeted treatment based on panel results with standard care. The first such published RCT (the SHIVA trial) reported that there was no difference in progression-free survival when panels were used in this way. Some nonrandomized comparative studies, comparing matched treatment with nonmatched treatment, have reported that outcomes are superior for patients receiving matched treatment. However, these studies are inadequate to determine treatment efficacy because the populations with matched and unmatched cancers may differ on several important clinical and prognostic variables. In addition, there is potential for harm if ineffective therapy is given based on test results, because there may be adverse effects of therapy in absence of a benefit. The evidence is insufficient to determine the effects of the technology on health outcomes.

PRACTICE GUIDELINES AND POSITION STATEMENTS
The National Comprehensive Cancer Network guidelines do not contain recommendations for the general strategy of testing a tumor for a wide range of mutations. The guidelines do contain recommendations for specific genetic testing for individual cancers, based on situations where there is a known mutation-drug combination that has demonstrated benefits for that specific tumor type. Some examples of recommendations for testing of common solid tumors are listed below:

PRACTICE GUIDELINES AND POSITION STATEMENTS
The National Comprehensive Cancer Network guidelines do not contain recommendations for the general strategy of testing a tumor for a wide range of mutations. The guidelines do contain recommendations for specific genetic testing for individual cancers, based on situations where there is a known mutation-drug combination that has demonstrated benefits for that specific tumor type. Some examples of recommendations for testing of common solid tumors are listed below:

  • Breast cancer28
    • HER2 testing, when specific criteria are met. 
  • Colon cancer29
    • KRAS and NRAS testing for patients with metastatic colon cancer.  
    • Consider BRAF V600E testing for patients with metastatic colon cancer 
  • Non-small-cell lung cancer30
    • KRAS, EGFR [epidermal growth factor receptor], and ALK [anaplastic lymphoma kinase] testing for patients with metastatic adenocarcinoma  
    • Consider EGFR and ALK testing especially in never smokers, mixed histology, or small biopsy specimen  
    • Strongly endorses broader molecular profiling to identify rare driver mutations (HER2, BRAF V600E, ROS1, and RET gene rearrangements, and MET amplification or MET exon skipping) 
  • Melanoma31
    • BRAF V600 testing for patients with metastatic disease 
    • Activating KIT mutations for patients with metastatic disease 
  • Ovarian cancer32
    • BRCA 
  • Chronic myelogenous leukemia33  
    • BCR-ACL
  • Gastrointestinal stromal tumors34
    • KIT
  • Bladder cancer35,36
    • Comprehensive molecular profiling for advanced disease.  

U.S. Preventive Services Task Force Recommendations
Not applicable. 

ONGOING AND UNPUBLISHED CLINICAL TRIALS
Some currently unpublished trials that might influence this review are listed in Table 4.

NCT No. Trial Name Planned Enrollment Completion Date
Ongoing
NCT02299999 Evaluation of the Efficacy of High Throughput Genome Analysis as a Therapeutic Decision Tool for Patients with Metastatic Breast Cancer (SAFIR02_Breast) 460 Oct 2018
NCT02152254 Randomized Study Evaluating Molecular Profiling and Targeted Agents in Metastatic Cancer: Initiative for Molecular Profiling and Advanced Cancer Therapy (IMPACT 2) 1,362 May 2019
NCT02029001 Adapting Treatment to the Tumor Molecular Alterations for Patients with Advanced Solid Tumors: My Own Specific Treatment 560 Feb 2020
NCT02645149 Molecular Profiling and Matched Targeted Therapy for Patients With Metastatic Melanoma 1,000 Jun 2021
NCT02154490 A Biomarker-Driven Master Protocol for Previously Treated Squamous Cell Lung Cancer (Lung-MAP) 10,000 Apr 2022
NCT02465060 Molecular Analysis for Therapy Choice (MATCH) 3,000 Jun 2022

NCT: national clinical trial.

There are also large-scale nonrandomized studies evaluating the efficacy of targeted treatment directed by genetic testing. The TAPUR study, sponsored by the American Society for Clinical Oncology, seeks to evaluate antitumor activity of targeted treatment based on genomic analysis.37 The following is a description from the study website:

"The Targeted Agent and Profiling Utilization Registry (TAPUR) Study is a prospective, non-randomized clinical trial that aims to describe the performance (both safety and efficacy) of commercially available, targeted anticancer drugs prescribed for treatment of patients with advanced cancer that has a potentially actionable genomic variant. The study also aims to simplify patient access to approved targeted therapies that are contributed to the program by collaborating pharmaceutical companies, catalogue the choice of genomic profiling test by clinical oncologists and learn about the utility of registry data to develop hypotheses for additional clinical trials."

The trial plans to enroll patients with advanced solid tumors, multiple myeloma and B-cell non-Hodgkin lymphoma that are refractory to standard care. The primary outcome is tumor response, as measured by RECIST criteria. A response rate of less than 10% will signify lack of efficacy, while a response rate of greater than 30% will signify potential efficacy, which will need to be corroborated in confirmatory trials. 

References

  1. Spear BB, Heath-Chiozzi M, Huff J. Clinical application of pharmacogenetics. Trends Mol Med. May 2001;7(5):201-204. PMID 11325631
  2. Dienstmann R, Rodon J, Barretina J, et al. Genomic medicine frontier in human solid tumors: prospects and challenges. J Clin Oncol. May 20 2013;31(15):1874-1884. PMID 23589551 
  3. National Comprehensive Cancer Network (NCCN). NCCN Biomarkers Compendium. https://www.nccn.org/professionals/biomarkers/default.aspx. Accessed September 26, 2016.
  4. Drilon A, Wang L, Arcila ME, et al. Broad, Hybrid Capture-Based Next-Generation Sequencing Identifies Actionable Genomic Alterations in Lung Adenocarcinomas Otherwise Negative for Such Alterations by Other Genomic Testing Approaches. Clin Cancer Res. Jan 7 2015. PMID 25567908
  5. Johnson DB, Dahlman KH, Knol J, et al. Enabling a genetically informed approach to cancer medicine: a retrospective evaluation of the impact of comprehensive tumor profiling using a targeted next-generation sequencing panel. Oncologist. Jun 2014;19(6):616-622. PMID 24797823
  6. Schwaederle M, Daniels GA, Piccioni DE, et al. On the road to precision cancer medicine: analysis of genomic biomarker actionability in 439 patients. Mol Cancer Ther. Jun 2015;14(6):1488-1494. PMID 25852059
  7. O'Brien CP, Taylor SE, O'Leary JJ, et al. Molecular testing in oncology: Problems, pitfalls and progress. Lung Cancer. Mar 2014;83(3):309-315. PMID 24472389
  8. Hyman DM, Puzanov I, Subbiah V, et al. Vemurafenib in multiple nonmelanoma cancers with BRAF V600 mutations. N Engl J Med. Aug 20 2015;373(8):726-736. PMID 26287849
  9. FoundationOne Web Site. FoundationOne for Solid Tumors. http://foundationone.com/order.php#2. Accessed October 3, 2016.
  10. GenPath®. OnkoMatch: Rapid Tumor Genotyping in Solid Tumors. https://www.genpathdiagnostics.com/wp-content/uploads/2012/01/OnkoMatch.pdf. Accessed October 3, 2016.
  11. Laboratories KD. GeneTrails Solid Tumor Genotyping Panel. 2015; http://www.knightdxlabs.com/home/test-details?id=GeneTrails+Solid+Tumor+Genotyping+Panel. Accessed October 3, 2016.
  12. Caris Life Sciences. Caris Molecular Intelligence: Evidence-Based Tumor Profiling Service. http://www.carismolecularintelligence.com/targeting_cancer. Accessed October 3, 2016.
  13. PathGroup SmartGenomics. SmartGenomics: 35 Gene Solid Tumor NGS and aCGH. http://www.pathgroup.com/smartgenomics-35-gene-solid-tumor-ngs-and-acgh/. Accessed October 3, 2016.
  14. GuardantHealth I. Guardant360 Know Cancer. http://www.guardanthealth.com/guardant360/. Accessed October 3, 2016.
  15. Paradigm Web Site. Next Generation Cancer Diagnostics: About PcDx. http://www.paradigmdx.org/pcdx/about/. Accessed October 3, 2016.
  16. Cheng DT, Mitchell TN, Zehir A, et al. Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT): a hybridization capture-based next-generation sequencing clinical assay for solid tumor molecular oncology. J Mol Diagn. May 2015;17(3):251-264. PMID 25801821
  17. Illumina Inc. TruSeq Amplican - Cancer Panel. http://www.illumina.com/products/truseq_amplicon_cancer_panel.ilmn. Accessed October 3, 2016.
  18. Illumina Inc. TruSight Tumor 26. http://www.illumina.com/content/dam/illumina-marketing/documents/products/datasheets/datasheet_trusight_tumor.pdf. Accessed October 3, 2016.
  19. Life Technologies. Cancer Genomics Data Analysis - Compendia Bioscience Products. https://www.lifetechnologies.com/us/en/home/life-science/cancer-research/cancer-genomics/cancer-genomics-data-analysis-compendia-bioscience.html. Accessed October 3, 2016.
  20. FoundationOne. Technical Information and Test Overview. http://www.foundationone.com/docs/FoundationOne_tech-info-and-overview.pdf. Accessed October 3, 2016.
  21. Jones S, Anagnostou V, Lytle K, et al. Personalized genomic analyses for cancer mutation discovery and interpretation. Sci Transl Med. Apr 15 2015;7(283):283ra253. PMID 25877891
  22. Schwaederle M, Zhao M, Lee JJ, et al. Impact of precision medicine in diverse cancers: a meta-analysis of phase II clinical trials. J Clin Oncol. Nov 10 2015;33(32):3817-3825. PMID 26304871
  23. Jardim DL, Schwaederle M, Wei C, et al. Impact of a biomarker-based strategy on oncology drug development: a meta-analysis of clinical trials leading to FDA approval. J Natl Cancer Inst. Nov 2015;107(11). PMID 26378224
  24. Le Tourneau C, Kamal M, Tredan O, et al. Designs and challenges for personalized medicine studies in oncology: focus on the SHIVA trial. Target Oncol. Dec 2012;7(4):253-265. PMID 23161020
  25. Le Tourneau C, Delord JP, Goncalves A, et al. Molecularly targeted therapy based on tumour molecular profiling versus conventional therapy for advanced cancer (SHIVA): a multicentre, open-label, proof-of-concept, randomised, controlled phase 2 trial. Lancet Oncol. Oct 2015;16(13):1324-1334. PMID 26342236
  26. Wheler JJ, Janku F, Naing A, et al. Cancer therapy directed by comprehensive genomic profiling: a single center study. Cancer Res. Jul 1 2016;76(13):3690-3701. PMID 27197177
  27. Tsimberidou AM, Iskander NG, Hong DS, et al. Personalized medicine in a phase I clinical trials program: the MD Anderson Cancer Center initiative. Clin Cancer Res. Nov 15 2012;18(22):6373-6383. PMID 22966018
  28. National Comprehensive Cancer Network (NCCN). NCCN Clinical Practice Guidelines in Oncology: Breast Cancer. Version 2.2016 https://www.nccn.org/professionals/physician_gls/pdf/breast.pdf. Accessed September 26, 2016.
  29. National Comprehensive Cancer Network (NCCN). NCCN Clinical Practice Guidelines in Oncology: Colon Cancer. Version 2.2016. https://www.nccn.org/professionals/physician_gls/pdf/colon.pdf. Accessed September 26, 2016. 
  30. National Comprehensive Cancer Network (NCCN). NCCN Clinical Practice Guidelines in Oncology: Non-Small Cell Lung Cancer. Version 4.2016. https://www.nccn.org/professionals/physician_gls/pdf/nscl.pdf. Accessed September 26, 2016.
  31. National Comprehensive Cancer Network (NCCN). NCCN Clinical Practice Guidelines in Oncology: Melanoma. Version 3.2016. https://www.nccn.org/professionals/physician_gls/pdf/melanoma.pdf. Accessed September 26, 2016.
  32. National Comprehensive Cancer Network (NCCN). NCCN Clinical Practice Guidelines in Oncology: Ovarian Cancer. Version 1.2016. https://www.nccn.org/professionals/physician_gls/pdf/ovarian.pdf. Accessed September 26, 2016.
  33. National Comprehensive Cancer Network (NCCN). NCCN Clinical Practice Guidelines in Oncology: Chronic Myelogenous Leukemia. Version 1.2016. https://www.nccn.org/professionals/physician_gls/pdf/cml.pdf. Accessed September 26, 2016.
  34. Demetri GD, Benjamin RS, Blanke CD, et al. NCCN Task Force report: management of patients with gastrointestinal stromal tumor (GIST)--update of the NCCN clinical practice guidelines. J Natl Compr Canc Netw. Jul 2007;5 Suppl 2:S1-29; quiz S30. PMID 17624289
  35. National Comprehensive Cancer Network (NCCN). NCCN Clinical Practice Guidelines in Oncology: Bladder Cancer Version 2.2016. https://www.nccn.org/professionals/physician_gls/pdf/bladder.pdf. Accessed October 3, 2016.
  36. Pal SK, Agarwal N, Boorjian SA, et al. National Comprehensive Cancer Network recommendations on molecular profiling of advanced bladder cancer. J Clin Oncol. Sep 20 2016;34(27):3346-3348. PMID 27458279
  37. American Society for Clinical Oncology. Targeted Agent and Profiling Utilization Registry Study. 2015; http://www.asco.org/practice-research/targeted-agent-and-profiling-utilization-registry-study. Accessed October 1, 2015.

Coding Section

Codes Number Description
CPT   See Policy Guidelines
  81455  Targeted genomic sequence analysis panel, solid organ or hematolymphoid neoplasm, DNA analysis, and RNA analysis when performed, 51 or greater genes (eg, ALK, BRAF, CDKN2A, CEBPA, DNMT3A, EGFR, ERBB2, EZH2, FLT3, IDH1, IDH2, JAK2, KIT, KRAS, MLL, NPM1, NRAS, MET, NOTCH1, PDGFRA, PDGFRB, PGR, PIK3CA, PTEN, RET), interrogation for sequence variants and copy number variants or rearrangements, if performed 
  84999  Unlisted chemistry procedure 
ICD-9-CM Diagnosis   Investigational for all diagnoses.
ICD-10-CM (effective 10/01/15)    Investigational for all diagnoses.
  C00-D49  Neoplasms code range
ICD-10-PCS (effective 10/01/15)     Not applicable. ICD-10-PCS codes are only used for inpatient services. There are no ICD procedure codes for laboratory tests.
Type of Service     
Place of Service     

Procedure and diagnosis codes on Medical Policy documents are included only as a general reference tool for each policy. They may not be all-inclusive.

Index 
Targeted cancer therapy, genetic testing

This medical policy was developed through consideration of peer-reviewed medical literature generally recognized by the relevant medical community, U.S. FDA approval status, nationally accepted standards of medical practice and accepted standards of medical practice in this community, Blue Cross and Blue Shield Association technology assessment program (TEC) and other non-affiliated technology evaluation centers, reference to federal regulations, other plan medical policies and accredited national guidelines.

"Current Procedural Terminology© American Medical Association.  All Rights Reserved" 

History From 2014 Forward     

11/15/2018 

Updating policy section, adding table. No other changes. 

07/23/2018 

Annual review, previously considered investigational for all uses. Now adding some medical necessity criteria. 

07/19/2017 

Annual review, no change to policy intent. 

04/25/2017 

Updated category to Laboratory. No other changes 

12/07/2016 

Annual review, no change to policy intent. Updating background, description, rationale and references.

12/01/2016 

Annual review, no change to policy intent. Updating title. 

11/9/2015 

Annual review, no change to policy intent. Updated background, description, guidelines, rationale and references. Added regulatory status.

12/08/2014

New Policy.


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