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Research Papers:

Prevalence of actionable mutations and copy number alterations and the price of a genomic testing panel

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Oncotarget. 2016; 7:71686-71695. https://doi.org/10.18632/oncotarget.11994

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Chan Shen _, Funda Meric-Bernstam, Xiaoping Su, John Mendelsohn and Sharon Giordano

Abstract

Chan Shen1,2, Funda Meric-Bernstam3, Xiaoping Su4, John Mendelsohn5,6, Sharon Giordano1

1Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

2Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

3Departments of Investigational Cancer Therapeutics and Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

4Departments of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

5Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

6Center for Health and Biosciences, Baker Institute, Rice University, Houston, TX, USA

Correspondence to:

Chan Shen, email: cshen@mdanderson.org

Keywords: genomic testing panel, cancer, costs, mutations, copy number alterations

Received: March 28, 2016    Accepted: August 24, 2016    Published: September 13, 2016

ABSTRACT

Interest in genomic testing for the selection of cancer therapy is growing. However, the cost of genomic testing has not been well studied. We sought to determine the price of identifying mutations and copy number alterations (CNAs) in theoretically actionable genes across multiple tumor types. We reviewed data from The Cancer Genome Atlas to determine the frequency of alterations in nine tumor types. We used price information from a commonly used commercial genomic testing platform (FoundationOne) to determine the price of detecting mutations and CNAs in different types of tumors. Although there are large variations in the prevalence by tumor type, when the detection of both mutations and CNAs was considered overall, most patients had at least one alteration in a potentially actionable gene (84% overall, range 51%- 98% among tumor types assessed). The corresponding average price of identifying at least one alteration per patient ranges from $5,897 to $11,572. Although the frequency of mutations and CNAs in actionable genes differs by tumor type, most patients have an actionable genomic alteration detectable by a commercially available panel. Determining CNAs as well as mutations improves actionability and reduces the price of detecting an alteration.


Prevalence of actionable mutations and copy number alterations and the price of a genomic testing panel | Shen | Oncotarget

INTRODUCTION

Genomic medicine is a rapidly growing field in oncology. In the past decade, we have seen growth in the number of new genomic tests available, and genomic testing is now often used to match patients to approved or investigational agents. However, genomic testing is expensive and may not be covered by insurance providers; thus, it can pose a significant financial burden on cancer patients. Currently, the literature on the cost of genomic testing in patients with cancer is limited. Although there are a few studies on the cost-effectiveness of genomic testing panel for specific cancer and subpopulation [1, 2], the comparison of prices of detection between different cancer types is largely unknown. Herein, we evaluate the prevalence of genomic alterations, the likelihood of detecting mutations and copy number alterations (CNAs) in actionable genes, and the relevant prices for detecting these alterations in several cancer types. This study aims to help researchers and practitioners understand the costs of identifying theoretically actionable alterations in multiple tumor types.

RESULTS

Table 1 shows the prevalence of testable mutations that were theoretically or pharmaceutically actionable and the average price for identifying one patient with mutations in actionable genes by cancer type. Among 986 breast cancer patients in TCGA data, 586 (59%) had mutations in genes that were theoretically actionable and tested in the FoundationOne test. The frequency of mutations in theoretically actionable genes ranged from 25% in ovarian cancer to 93% in endometrial cancer. The price ranged from $22,907 in ovarian cancer to $6,254 in endometrial cancer. The prevalence of testable mutations in pharmaceutically actionable genes is relatively lower. The frequency of mutations in pharmaceutically actionable genes ranged from 10% in ovarian cancer to 72% in endometrial cancer, with corresponding price ranging from $55,556 in ovarian cancer to $8,035 in endometrial cancer.

Table 1: Prevalence of Actionable Mutations by Cancer Type

cancer type

Total number of patients

Theoretically Actionable

Pharmaceutically Actionable

Frequency

Percentage

Cost/case*

Frequency

Percentage

Cost/case*

breast

986

586

59.4

$9,759

388

39.4

$14,740

colon adenocarcinoma

154

129

83.8

$6,924

105

68.2

$8,507

lung adenocarcinoma

248

205

82.7

$7,017

163

65.7

$8,824

lung squamous cell carcinoma

178

150

84.3

$6,883

95

53.4

$10,868

ovarian

316

80

25.3

$22,907

33

10.4

$55,556

glioblastoma multiforme

283

225

79.5

$7,295

129

45.6

$12,725

endometrial cancer

248

230

92.7

$6,254

179

72.2

$8,035

kidney clear cell carcinoma

491

234

47.7

$12,170

98

20.0

$29,058

head and neck cancer

306

252

82.4

$7,043

132

43.1

$13,445

Table 2 shows the prevalence of testable CNAs that were theoretically or pharmaceutically actionable and the price for identifying at least one actionable CNA. Notably, the rate of CNAs in theoretically actionable genes varied significantly by disease, from 475 (83%) of 571 glioblastoma multiforme patients to 15 (3%) of 504 clear cell renal cell carcinoma patients. Similarly, the prevalence of mutations in pharmaceutically actionable genes also varied substantially from 55% in glioblastoma multiforme patients to 1% in kidney clear cell carcinoma. Table 3 shows the prevalence of testable mutations and CNAs combined. In this table, we considered any patient who had at least one testable mutation or CNA as one actionable case. The table shows a higher prevalence and lower price than the first two tables. The prevalence of theoretically actionable mutations or CNAs was above 80% for all cancer types we studied except clear cell renal cell carcinoma where the prevalence was 50%. Endometrial cancer patients had the highest prevalence of 98%. Accordingly, the price ranged from $5,897 to $11,572 to identify one patient with theoretically actionable alterations. The prevalence of mutations or CNAs pharmaceutically actionable showed a similar pattern.

Table 2: Prevalence of Actionable Copy Number Alterations by Cancer Type

cancer type

Total number of patients

Theoretically Actionable

Pharmaceutically Actionable

Frequency

Percentage

Cost/case*

Frequency

Percentage

Cost/case*

Breast

1033

535

51.8

$11,199

281

27.2

$21,324

colon adenocarcinoma

427

144

33.7

$17,200

66

15.5

$37,516

lung adenocarcinoma

493

175

35.5

$16,338

67

13.6

$42,678

lung squamous cell carcinoma

489

326

66.7

$8,700

228

46.6

$12,438

Ovarian

569

410

72.1

$8,049

224

39.4

$14,732

glioblastoma multiforme

571

475

83.2

$6,972

316

55.3

$10,481

endometrial cancer

504

132

26.2

$22,146

54

10.7

$54,155

kidney clear cell carcinoma

504

15

3.0

$194,631

5

1.0

$585,859

head and neck cancer

388

204

52.6

$11,031

90

23.2

$25,000

Table 3: Prevalence of Either Actionable Mutations or Actionable CNAs by Cancer Type

cancer type

Total number of patients

Theoretically Actionable

Pharmaceutically Actionable

Frequency

Percentage

Cost/case*

Frequency

Percentage

Cost/case*

breast

962

791

82.2

$7,054

568

59.0

$9,824

colon adenocarcinoma

152

142

93.4

$6,209

118

77.6

$7,471

lung adenocarcinoma

172

161

93.6

$6,197

132

76.7

$7,558

lung squamous cell carcinoma

178

170

95.5

$6,073

141

79.2

$7,322

ovarian

311

254

81.7

$7,102

140

45.0

$12,883

glioblastoma multiforme

273

266

97.4

$5,952

211

77.3

$7,504

endometrial cancer

242

238

98.4

$5,897

188

77.7

$7,466

kidney clear cell carcinoma

415

208

50.1

$11,572

86

20.7

$27,992

head and neck cancer

302

278

92.1

$6,301

171

56.6

$10,244

Cost/case* indicates the average cost for identifying one patient that has testable and actionable gene(s) based on FoundationOne test list price.

DISCUSSION

In this study we found significant variations in the prevalence of actionable gene mutations and CNAs among different types of tumors. This finding is in line with previous studies using hot-spot mutation testing platforms [7, 8]. However, for all the cancer types that we considered, the majority of patients had theoretically actionable gene mutations or CNAs that can be detected in one commercially available genomic test panel. In this paper, we focused on the next generation sequencing gene panels and did not consider routine tumor molecular profiling that may involve multiple assessments, each of which targets a single gene or type of mutation (e.g. HER2, BRCA1, BRCA2 in breast cancer, and EGFR, HER2, KRAS, and ALK in lung cancer). Although the price of a single gene test may be lower, it is likely that when traditional methods are used for multiple assessments, a larger quantity of DNA is needed and it leads to longer turnaround time. Given the rapidly growing number of genes tested in genomic test panels, we expect that the proportion of patients with testable and actionable gene mutations or CNAs will continue to grow. The number of targeted therapies has been growing rapidly in recent years. The targeted therapies in use today may cost 10,000 to 25,000 dollars for each treatment given. The genomic testing results can steer physicians and patients towards the experimental treatments that may be effective and away from the treatments that are unlikely to be effective for that patient. Combining the growing number of genes tested in panels with the growing number of expensive targeted drug therapies and the trend of falling prices for genomic tests, genomic testing is poised to become more cost-effective when the entire course of treatment is taken into account.

This study has several limitations. First, the prevalence of gene mutations and CNAs was based on TCGA data, which may not reflect advanced/metastatic disease. Second, mutations may differ in their functional impact, and thus not all mutations in actionable genes are actionable. Third, not all theoretically actionable alterations are actionable in the context of the specific disease or genomic co-alterations. Fourth, KRAS was considered actionable in our analyses, which may inflate the prevalence of actionable genes. Fifth, we focused on mutations and CNAs only, without taking fusions into account; use of assays such as FoundationOne which provide not only mutation and CNA but also fusion information and common fusions, would increase the prevalence of actionable genomes. Finally it is important to recognize that the actual actionability for patients depends heavily on the trial availability [9]. Nevertheless, this is the first study that aims at understanding the costs of identifying actionable alterations using a genomic testing panel.

MATERIALS AND METHODS

We downloaded the most recent data from The Cancer Genome Atlas (TCGA) via the TCGA Data Portal [3]. TCGA provides data on clinical information, genomic characterization, and high-level sequence analysis of tumor genomes. In this study, we examined both somatic mutations and CNAs for nine cancer types: breast cancer, colon adenocarcinoma, lung adenocarcinoma, lung squamous cell carcinoma, ovarian cancer, glioblastoma multiforme, endometrial cancer, clear cell renal cell carcinoma, and head and neck cancer. For each cancer type, we determined the prevalence of specific somatic mutations and CNAs using TCGA data. Of note the TCGA data included a sample of patients with somatic mutation information, a sample of patients with CNA information and another subsample of patients with both somatic mutation and CNA information. We were not able to identify copy-neutral loss of heterozygosity (LOH) since this type of data was not provided by TCGA analysis group. Notice that we used curated TCGA somatic SNV nutation data instead of pipeline-generated SNV in this study. We only used focal copy number alterations, which were generated by GISTIC analysis. For the prevalence of CNAs, we used conservative thresholds to define copy number amplification and deletion. More specifically, if the copy number was above 6, the patient was considered to have copy number amplification, and if the copy number was below 1, the patient was considered to have copy number deletion; otherwise, the patient was considered to have non-significant CNAs. Such cutoffs are in line with reporting thresholds for next generation sequencing gene panels such as FoundationOne testing, on which we focus in our price calculation.

After establishing the prevalence of mutations and CNAs for the different cancer types that we studied, we matched it with the list of mutations that are testable in FoundationOne to obtain the prevalence of “testable” mutations and CNAs. We chose FoundationOne because it is a commonly used commercial genomic testing panel and because it is the only genomic testing panel currently available on the market with clear information on price and the list of genes that are covered in the test panel [4]. Starting from this testable list, we established the prevalence of “actionable” mutations and CNAs so as to arrive at a list that was both testable and actionable. Here, we distinguished between amplification and deletion for CNAs. Genes were determined as theoretically actionable if a FDA-approved or clinically available investigational drug either directly or indirectly targets the gene, as previously described [5]. For each gene under consideration, public Web sites (NCI Drug Dictionary, NCI Thesaurus, Selleckchem, Medkoo, DGIdb, PubMed, and ClinicalTrials.gov) were consulted to identify drugs that target the encoded protein at clinically relevant IC50 values, as determined experimentally. PubMed was used to search for relevant literature that demonstrated either preclinical or clinical sensitivity of the drug to genetic alterations in the gene of interest. Drugs targeting proteins downstream of the gene of interest (indirect targets) were also identified in this manner with corroborating published literature indicating their sensitivity to genetic alterations in the gene of interest. Potentially actionable genes are listed in Table 4. As the impact of genomic analysis on therapeutic decisions may differ depending on specific genes, we have also included a table (Table 5) that shows the five most frequently observed mutations for each tumor type to allow researchers to best assess the prevalence of actionable genes.

Table 4: Therapeutic implications of potentially actionable genes

Gene

Potential therapeutic implications

Actionability

Mutations

CNAs

Amplification

Deletion

ABL1

Treatment with ABL or BCR-ABL inhibitors

Yes

Yes

No

ABL2

Treatment with ABL inhibitors

Yes

Yes

No

AKT1

Treatment with AKT or mTOR inhibitors

Yes

Yes

No

AKT2

Treatment with AKT or mTOR inhibitors

Yes

Yes

No

AKT3

Treatment with AKT or mTOR inhibitors

Yes

Yes

No

ALK

Treatment with ALK inhibitors

Yes

Yes

No

AR*

Resistance to anti-hormone therapy

Yes

Yes

No

ARAF

Treatment with RAF inhibitor

Yes

No

No

ATM

Treatment with PARP inhibitors

Yes

No

Yes

ATR

Treatment with PARP inhibitors

Yes

No

Yes

AURKA

Treatment with AURKA inhibitors

Yes

Yes

No

AURKB

Treatment with AURKB inhibitors

Yes

Yes

No

BAP1

Treatment with HDAC inhibitors

Yes

No

Yes

BCL2

Treatment with BCL2 inhibitor and potential resistance to mTOR inhibitors
Resistance to BCL2 inhibitor

Yes

Yes

No

BRAF

Treatment with BRAF inhibitors

Yes

Yes

No

BRCA1

Treatment with PARP inhibitors

Yes

No

Yes

BRCA2

Treatment with PARP inhibitors

Yes

No

Yes

CCND1

Treatment with CDK 4/6 inhibitors

Yes

Yes

No

CCND2

Treatment with CDK 4/6 inhibitors

Yes

Yes

No

CCND3

Treatment with CDK 4/6 inhibitors

Yes

Yes

No

CCNE1

Treatment with CDK 2 Inhibitors

Yes

Yes

No

CDK4

Treatment with CDK 4/6 inhibitors

Yes

Yes

No

CDK6

Treatment with CDK 4/6 inhibitors

Yes

Yes

No

CDKN1B

Treatment with CDK 2 Inhibitors

Yes

No

Yes

CDKN2A

Treatment with CDK 4/6 inhibitors

Yes

No

Yes

CDKN2B

Treatment with CDK 4/6 inhibitors

Yes

No

Yes

CDKN2C

Treatment with CDK 4/6 inhibitors

Yes

No

Yes

CHEK2

Treatment with Chk2 inhibitor

Yes

Yes

No

CSF1R

Treatment with CSF1R monoclonal antibody and inhibitors

Yes

Yes

No

DDR2

Treatment with DDR2 inhibitor

Yes

Yes

No

DNMT3A

High risk” factor of myelodysplastic or myeloproliferative disorders required for trial enrollment.

Yes

No

No

DOT1L

Treatment with DOT1L inhibitor

Yes

Yes

No

EGFR

Treatment with EGFR inhibitors

Yes

Yes

No

EPHA3*

Treatment with Dasatinib

Yes

Yes

No

ERBB2 (HER2)

Treatment with HER2 inhibitors, monoclonal antibodies, and targeted vaccines

Yes

Yes

No

ERBB3 (HER3)

Treatment with HER3 inhibitors

Yes

Yes

No

ERBB4 (HER4)

Treatment with HER4 inhibitors

Yes

Yes

No

ESR1

Anti-hormone resistance

Yes

No

No

FGF10

Trial enrollment

Yes

Yes

No

FGF14

FGF19

FGF23

FGF3

FGF4

FGF6

FGFR1

Treatment with FGFR1 inhibitors

Yes

Yes

No

FGFR2

Treatment with FGFR2 inhibitors

Yes

Yes

No

FGFR3

Treatment with FGFR3 inhibitors

Yes

Yes

No

FGFR4

Treatment with FGFR4 inhibitors

Yes

Yes

No

FLT1

Treatment with FLT1 inhibitors

Yes

Yes

No

FLT4

Treatment with FLT4 inhibitors

Yes

Yes

No

GNA11

Treatment with PKC and MEK inhibitors

Yes

Yes

No

GNAQ

Treatment with PKC and MEK inhibitors

Yes

Yes

No

HGF

Treatment HGF monoclonal antibody

Yes

Yes

No

HRAS

Treatment with MEK Inhibitors

Yes

Yes

No

IGF1R

Treatment with IGF1R monoclonal antibodies or inhibitors

Yes

Yes

No

IGF2

Treatment with IGF1R monoclonal antibodies or inhibitors

Yes

Yes

No

JAK1

Treatment with JAK inhibitors

Yes

Yes

No

JAK2

Treatment with JAK inhibitors

Yes

Yes

No

JAK3

Treatment with JAK inhibitors

Yes

Yes

No

KDR

Treatment with KDR inhibitors

Yes

Yes

No

KIT

Treatment with KIT inhibitors

Yes

Yes

No

KRAS

Treatment with MEK Inhibitors

Yes

Yes

No

MAP2K1

Treatment with MEK Inhibitors

Yes

Yes

No

MAP2K2

Treatment with MEK Inhibitors

Yes

Yes

No

MAP2K4

Treatment with JNK1 inhibitor

Yes

Yes

No

MAP3K1

Treatment with JNK1 inhibitor

Yes

Yes

No

MDM2

Treatment with MDM2 inhibitor or Nutlins that inhibit MDM2-p53 interaction.

Yes

Yes

No

MET

Treatment with MET inhibitors (Crizotinib, Cabozantinib)

Yes

Yes

No

MPL*

Treatment with JAK2 inhibitors.

Yes

No

No

MTOR

Treatment with mTOR inhibitors

Yes

Yes

No

MYCN

Treatment with BET inhibitors

Yes

Yes

No

NF1

Treatment with PI3K pathway inhibitors (PI3K/AKT/MTOR), MAPK pathway inhibitors (RAF/MEK/ERK), or HSP90 inhibitors

Yes

No

Yes

NF2

Treatment with PI3K pathway inhibitors (PI3K/AKT/MTOR), MAPK pathway inhibitors (RAF/MEK/ERK), or HSP90 inhibitors

Yes

No

Yes

NOTCH1

Treatment with Gamma Secretase inhibitors (GSIs)

Yes

Yes

No

NOTCH2

Treatment with GSIs
Resistance to GSIs

Yes

Yes

No

NOTCH3

Treatment with GSIs

Yes

Yes

No

NPM1

Correlate with positive response to all-trans retinoic acid therapy and chemotherapy in AML.

Yes

No

No

NRAS

Treatment with MEK inhibitors

Yes

Yes

No

NTRK1

Treatment with NTRK1 (TrkA) inhibitor

Yes

Yes

No

NTRK2

Treatment with NTRK2 (TrkB) inhibitor

Yes

Yes

No

NTRK3

Treatment with NTRK3 (TrkC) inhibitor

Yes

Yes

No

PDGFRA

Treatment with PDGFRA inhibitors

Yes

Yes

No

PDGFRB

Treatment with PDGFRB inhibitors

Yes

Yes

No

PIK3CA

Treatment with PI3K, AKT, or mTOR inhibitors

Yes

Yes

No

PIK3CB

Treatment with PIK3CB inhibitors

Yes

Yes

No

PIK3R1

Treatment with PI3K, AKT or mTOR inhibitors

Yes

No

No

PIK3R2

Trial selecting for mutations

Yes

No

No

PTCH1

Treatment with SMO inhibitors

Yes

No

Yes

PTEN

Treatment with p110beta, AKT, or mTOR inhibitors

Yes

No

Yes

PTPN11

Treatment with MEK Inhibitors

Yes

Yes

No

RAD50

Treatment with PARP inhibitors

Yes

No

Yes

RAF1

Potential resistance to RAF inhibitors
Treatment with MEK inhibitors
Resistance to Dasatinib

Yes

Yes

Yes

RET

Treatment with Ret inhibitors

Yes

Yes

No

SMO

Treatment with SMO inhibitors

Yes

Yes

No

SRC

Treatment with SRC inhibitors

Yes

Yes

No

STK11

Treatment with mTOR or AMPK inhibitors

Yes

No

Yes

SYK

Treatment with Syk inhibitors

Yes

Yes

No

TOP2A*

Treatment with topoisomerase 2A inhibitors

Yes

Yes

Yes

TSC1

Treatment with mTOR inhibitors

Yes

No

Yes

TSC2

Treatment with mTOR inhibitors

Yes

No

Yes

Note. Genes were determined as theoretically actionable if there is an FDA-approved or clinically available investigational drug that either directly or indirectly targets the gene as previously described.

*Borderline classification as actionable.

Table 5: Top five most common mutations by cancer type

Cancer type

Gene

Percentage

breast

PIK3CA

32.05%

MAP3K1

7.10%

PTEN

3.55%

MAP2K4

3.25%

NF1

2.74%

colon adenocarcinoma

KRAS

37.66%

PIK3CA

16.88%

ATM

13.64%

BRAF

12.99%

NRAS

9.74%

Lung adenocarcinoma

KRAS

24.19%

NF1

11.29%

EGFR

10.89%

KDR

10.48%

HGF

10.08%

Lung squamous cell carcinoma

PIK3CA

15.17%

CDKN2A

14.61%

NF1

11.80%

NOTCH1

7.87%

PTEN

7.87%

Ovarian

NF1

2.53%

BRCA1

2.22%

BRCA2

2.22%

EGFR

1.90%

KIT

1.58%

Glioblastoma multiforme

PTEN

30.74%

EGFR

26.15%

PIK3R1

11.31%

PIK3CA

10.60%

NF1

10.25%

Endometrial cancer

PTEN

64.92%

PIK3CA

53.23%

PIK3R1

33.47%

KRAS

21.37%

FGFR2

12.50%

Kidney clear cell carcinoma

BAP1

8.55%

MTOR

5.09%

PTEN

3.67%

ATM

2.44%

PIK3CA

2.44%

Head and neck cancer

CDKN2A

21.57%

PIK3CA

20.92%

NOTCH1

19.28%

ATR

5.88%

NOTCH2

5.23%

Further, we examined a smaller list of “pharmaceutically actionable” genes as this is important for the clinical implementation of biomarker-based therapy [5]. These included genes that have already been linked to FDA-approval of a drug (e.g. BRAF inhibitors), and gene variants known to affect drug effectiveness or toxicity, and that affect dosing guidelines and/or drug label information. This list is derived from genes that have well-known pharmacogenomics associations with drugs available on the market based on the Pharmacogenomics Knowledgebase [6]. We provided the list of pharmaceutically actionable genes with the corresponding drugs in the Supplementary Table S1.

Finally, we calculated the average price of identifying one patient with actionable alterations, using the list price of FoundationOne ($5,800) divided by the proportion of patients with at least one actionable alteration. Of note, we focused on the price of detecting “actionable patients”. If the patient had more than one mutation, he/she would still be counted as one. By doing this, we avoided the problem of overestimating the number of patients detected for actionable genes.

The Institutional Review Board at The University of Texas MD Anderson Cancer Center approved this study and waived the requirement for patient consent.

ACKNOWLEDGMENTS

This study is funded in part by L.E. and Virginia Simmons Fellow fund at Rice University’s Baker Institute Center for Health and Biosciences, Duncan Family Institute, Sheikh Khalifa Al Nahyan Ben Zayed Institute for Personalized Cancer Therapy, NCI U01 CA180964, NCATS grant UL1 TR000371 (Center for Clinical and Translational Sciences), CPRIT RP110584, and the MD Anderson Cancer Center Support grant (P30 CA016672).

CONFLICTS OF INTEREST

The authors declare no conflict of interest.

REFERENCES

1. Gallego CJ, Shirts BH, Bennette CS, Guzauskas G, Amendola LM, Horike-Pyne M, Hisama FM, Pritchard CC, Grady WM, Burke W, Jarvik GP, Veenstra DL. Next-Generation Sequencing Panels for the Diagnosis of Colorectal Cancer and Polyposis Syndromes: A Cost-Effectiveness Analysis. J Clin Oncol. 2015; 33: 2084-91. doi: 10.1200/jco.2014.59.3665.

2. Li Y, Bare LA, Bender RA, Sninsky JJ, Wilson LS, Devlin JJ, Waldman FM. Cost Effectiveness of Sequencing 34 Cancer-Associated Genes as an Aid for Treatment Selection in Patients with Metastatic Melanoma. Mol Diagn Ther. 2015; 19: 169-77. doi: 10.1007/s40291-015-0140-9.

3. Boland GM, Piha-Paul SA, Subbiah V, Routbort M, Herbrich SM, Baggerly K, Patel KP, Brusco L, Horombe C, Naing A, Fu S, Hong DS, Janku F, et al. Clinical next generation sequencing to identify actionable aberrations in a phase I program. Oncotarget. 2015. doi: 10.18632/oncotarget.4040.

4. Meric-Bernstam F, Brusco L, Shaw K, Horombe C, Kopetz S, Davies MA, Routbort M, Piha-Paul SA, Janku F, Ueno N, Hong D, De Groot J, Ravi V, et al. Feasibility of Large-Scale Genomic Testing to Facilitate Enrollment Onto Genomically Matched Clinical Trials. J Clin Oncol. 2015. doi: 10.1200/jco.2014.60.4165.

5. Meric-Bernstam F, Brusco L, Shaw K, Horombe C, Kopetz S, Davies MA, Routbort M, Piha-Paul SA, Janku F, Ueno N, Hong D, De Groot J, Ravi V, et al. Feasibility of Large-Scale Genomic Testing to Facilitate Enrollment Onto Genomically Matched Clinical Trials. J Clin Oncol. 2015; 33: 2753-62. doi: 10.1200/jco.2014.60.4165.

6. Broad Institute. http://gdac.broadinstitute.org/runs/.

7. FoundationOne. http://www.foundationone.com/docs/ONE-F-004-20131115%20Billing%20Guide%20Physicians.pdf.

8. Meric-Bernstam F, Johnson A, Holla V, Bailey AM, Brusco L, Chen K, Routbort M, Patel KP, Zeng J, Kopetz S, Davies MA, Piha-Paul SA, Hong DS, et al. A decision support framework for genomically informed investigational cancer therapy. J Natl Cancer Inst. 2015; 107. doi: 10.1093/jnci/djv098.

9. The Pharmacogenomics Knowledgebase. https://www.pharmgkb.org/.


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