Abstract
Objective:
A cost-effectiveness analysis comparing comprehensive genomic profiling (CGP) of 10 oncogenes, targeted gene panel testing (TGPT) of 4 oncogenes, and no tumor profiling over the lifetime for patients with metastatic lung adenocarcinoma from the Centers for Medicare and Medicaid Services’ perspective was conducted.
Methods:
A decision analytic model used 10,000 hypothetical Medicare beneficiaries with metastatic lung adenocarcinoma to simulate outcomes associated with CGP (ALK, BRAF, EGFR, ERBB2, MET, NTRK1, NTRK2, NTRK3, RET, ROS1), TGPT (ALK, BRAF, EGFR, ROS1), and no tumor profiling (no genes tested). First-line targeted cancer-directed therapies were assigned if actionable gene variants were detected; otherwise, non-targeted cancer-directed therapies were assigned. Model inputs were derived from randomized trials (progression-free survival, adverse events), the Veterans Health Administration (VHA) and Medicare (drug costs), published studies (non-drug cancer-related management costs, health state utilities), published databases (actionable variant prevalences). Costs (2019 US$) and quality-adjusted life years (QALYs) were discounted at 3% per year. Probabilistic sensitivity analyses (PSA) used 1,000 Monte Carlo simulations.
Results:
No tumor profiling was the least costly/person ($122,613 vs $184,063 for TGPT and $188,425 for CGP), and yielded the least QALYs/person (0.53 vs 0.73 for TGPT and 0.74 for CGP). The costs/QALY gained and corresponding 95% CI were $310,735 ($278,323-$347,952) for TGPT vs no tumor profiling and $445,545 ($322,297-$572,084) for CGP vs TGPT. All PSA simulations for both comparisons surpassed the willingness-to-pay threshold ($150,000/QALY gained).
Conclusion:
Compared to no tumor profiling in patients with metastatic lung adenocarcinoma, tumor profiling (TGPT, CGP) improves quality-adjusted survival but is not cost-effective.
Précis:
Compared to no tumor genomic profiling, tumor genomic profiling directing selection of first-line anticancer targeted therapies for patients with metastatic lung adenocarcinoma is not cost-effective.
INTRODUCTION
Lung cancer remains the greatest contributor to cancer-related mortality in the US. Non-small cell lung cancer (NSCLC) comprises the majority of lung cancer diagnoses, with adenocarcinoma being the most common histologic subtype.(1) Unfortunately, the majority of lung cancers are metastatic when diagnosed, and carry a low 5-year survival rate even in the era of immunotherapy.(2) While histology has long played an important role in tumor classification, identification of driver oncogenic alterations provides additional predictive and prognostic information for clinicians managing patients with metastatic disease.
The growing field of precision oncology, where systemic treatments are targeted to patients with specific genetic tumor profiles, has evolved dramatically since FDA approval of the first epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) for advanced NSCLC in 2003.(3) Prior to this, overall survival of patients with advanced NSCLC was measured in months(4), with only modest improvement in survival with chemotherapy.(5) The discovery of new driver and resistance gene alterations, which are typically mutually exclusive(6), through tumor profiling and improvements in the development of targeted TKIs has expanded the number of patients eligible for targeted therapies and their overall survival. The overall median survival for patients with advanced NSCLC who received first-line targeted therapies has been shown to vary by the specific targeted therapy that was prescribed (e.g., 38.6 months for osimertinib, 17.3 months for trametinib + dabrafenib).(6) Recent improvements in survival and decreases in population-level mortality with NSCLC among patients with NSCLC are likely driven in part by the use of targeted therapies.(7) It is estimated that 25.9%-46.7% of individuals with NSCLC will carry one of the most frequently detected driver oncogenic alterations in EGFR, ALK, BRAF, HER2, ROS1, RET, MET, or NTRK that are associated with a first-line targeted therapy option.(6)
Common tumor profiling approaches include targeted gene panel testing (TGPT), which tests for common alterations in select cancer-associated genes (often via targeted sequencing and/or fluorescence in situ hybridization (FISH)) and comprehensive genomic profiling (CGP), which utilizes next-generation sequencing (NGS) to test for a broad range of alterations in a more comprehensive set of cancer-associated genes. TGPT, an older tumor profiling approach, assesses the most commonly altered genomic regions of select cancer-associated genes – typically ALK, BRAF, EGFR, ROS1 in NSCLC. TGPT has advantages of affordability and quicker turnaround time than more extensive genomic profiling approaches.(8, 9) However, the growing number of targetable driver alterations with available targeted therapies, and “basket trials” based on specific molecular alterations have required more detailed and broader genomic mapping through NGS-based approaches to identify more patients eligible for targeted therapies.(10, 11) Despite their higher cost and longer turnaround times, benefits of CGP approaches have led major oncologic guidelines to advocate for broad, panel-based tumor profiling in patients with metastatic NSCLC to guide therapeutic decision-making.(12)
The optimal approach to tumor profiling in advanced NSCLC remains uncertain, and both strategies remain in clinical use. Several cost-effectiveness analyses have shown molecular profiling to be cost-effective when compared to no testing.(13-15) One study showed multi-gene NGS testing to be cost-effective compared to single-gene testing.(15) However, to keep up with newly identified driver oncogenic alterations and technological advancements that allow for more comprehensive tumor profiling, updated cost-effectiveness analyses that reflect these advancements are warranted.
To address these knowledge gaps and compare the health and cost outcomes associated with common tumor profiling approaches, we conducted a cost-effectiveness analysis to compare CGP, TGPT, and no tumor profiling with the objective of determining the lifetime cost-effectiveness for Medicare beneficiaries with metastatic lung adenocarcinoma from the Centers for Medicare and Medicaid Services’ (CMS) perspective.
METHODS
Model Structure and Tumor Profiling Strategies
A closed cohort of 10,000 Medicare beneficiaries with metastatic lung adenocarcinoma considering frontline cancer-directed therapy was simulated and assigned to each of the three tumor profiling strategies. This simulation-based study did not require institutional review board approval.
A decision analytic model using a decision tree (Figure 1) was developed in Microsoft Excel 2019 (Redmond, WA), to determine the lifetime cost-effectiveness of three tumor profiling strategies: CGP, TGPT, and no tumor profiling. Preference was given to studies with strong study designs when selecting data sources. When studies were unavailable to inform model inputs, expert opinion based on consensus among study clinicians (P.P., M.K., J.B., and V.V.) were used (i.e., breakdown of some treatment selections, and time of adverse event occurrence). Genes in CGP and TGPT were based on OncoKB gene alterations with high evidence levels (1 and 2) that guide first-line targeted therapy selection per January 2020 (Supplemental Table 1).(16, 17) All genes with OncoKB evidence levels 1 and 2 were included in CGP: ALK, BRAF, EGFR, ERBB2, MET, NTRK1, NTRK2, NTRK3, RET, ROS1. TGPT included genes most insurance companies require prior to authorizing targeted therapy: ALK, BRAF, EGFR, ROS1.
Figure 1. Model Structure.
10,000 hypothetical patients with metastatic adenocarcinoma entered the decision analytic model and were assigned to each intervention strategy (i.e., CGP, TGPT, no tumor profiling). First-line targeted therapy options were available to individuals who received CGP and TGPT and had an identified actionable gene variant associated with a targeted therapy. First-line non-targeted therapy options were available to individuals who received CGP and TGPT and no actionable gene variants were identified and to all in the no tumor profiling strategy. Once disease progression occurred, patients were moved on to the next therapy line with a maximum of 4 therapy lines. Key outcomes for all cancer-directed therapies included adverse events, therapy discontinuation, and death.
Actionable Gene Variant Prevalence
Eligibility for first-line targeted therapies in the CGP and TGPT strategies was based on prevalence data for actionable gene variants.(18-21) Data from the VA Oncology Program and the cBioPortal for Cancer Genomics Lung Adenocarcinoma datasets (Broad, Cell 2012; MSKCC, Science 2015; TCGA, Provisional, TSP, Nature 2008; MSKCC, Cancer Discov 2017) were used to determine the lower and upper bounds of genetic alteration prevalence, respectively, for ALK, BRAF, EGFR, ERBB2, MET, and ROS1.(18, 19) Prevalences of NTRK fusions and RET rearrangements were from published studies.(20, 21) The base-case value represents the average value of the minimum and maximum values.
Treatment Options
Treatment algorithms were constructed per February 2020 National Comprehensive Cancer Network (NCCN) guidelines(12), and included targeted therapies, non-targeted therapies, and non-cancer-directed therapy (e.g., supportive, hospice, palliative care). When disease progression or therapy discontinuation occurred on a therapy, subsequent therapy lines were initiated.
If tumor profiling was performed, tumor profiling results were available at the time of cancer-directed therapy decision-making. The likelihood of harboring an actionable gene variant associated with a first-line targeted therapy was based on actionable gene variant prevalence data.(18-21) For first-line therapies, 95% of patients with actionable gene variants associated with first-line targeted therapies received the targeted therapy and 5% received non-cancer-directed therapy. For second-line therapies, 29% remaining alive received no anti-cancer therapy, 69% received chemotherapy, and 2% received immunotherapy.(22, 23) For third-line therapies, 66% remaining alive received no anti-cancer therapy and 34% received a salvage treatment approach (50% of those previously on chemotherapy were switched to another set of chemotherapy and the other 50% were switched to immunotherapy; 100% of those previously on immunotherapy were switched to chemotherapy). For fourth-line therapy, 100% of patients remaining alive received no anti-cancer therapy (Table 1).
Table 1.
Model Inputs: Events, Costs, and Health State Utilities
| Parameter | Base-case values |
One-way sensitivity analysis values (min, max) |
Probabilistic sensitivity analysis distribution values |
Reference |
|---|---|---|---|---|
| Actionable gene variants associated with 1st line targeted therapies, prevalence (%) | ||||
| ALK (+) | 7.1 | 1.9, 12.1 | Triangular (1.9, 7.1, 12.1) | (18, 19) |
| BRAF (+) | 2.2 | 1.6, 2.9 | Triangular (1.6, 2.2, 2.9) | (18, 19) |
| EGFR (+) | 11.2 | 3.7, 18.6 | Triangular (3.7, 11.2, 18.6) | (18, 19) |
| NTRK1/2/3 (+) | 0.2 | NA | NA | (20) |
| ROS1 (+) | 1.5 | 0.2, 2.7 | Triangular (0.2, 1.5, 2.7) | (18, 19) |
| ERBB2 (+) | 2.7 | 1.2, 4.3 | Triangular (2.3, 4.3, 6.2) | (18, 19) |
| MET (+) | 3.1 | 1.8, 4.4 | Triangular (2.3, 4.3, 6.2) | (18, 19) |
| RET (+) | 1.65 | 1.5, 1.8 | Triangular (1.5, 1.65, 1.8) | (21) |
| PD-L1 results breakdown to inform 1st line non-targeted therapies, proportions | ||||
| <1% PD-L1, chemotherapy | 0.387 | NA | NA | (24) |
| 1-49% PD-L1, chemotherapy/immunotherapy combination | 0.286 | NA | NA | (24) |
| ≥50% PD-L1, immunotherapy | 0.327 | NA | NA | (24) |
| Treatment breakdown, proportions | ||||
| Non-cancer-directed therapy | ||||
| 1st line therapy | 0.33 | NA | NA | Expert opinion |
| No actionable gene variants present | 0.05 | NA | NA | Expert opinion |
| Actionable gene variants present | ||||
| 2nd line therapy | 0.33 | NA | NA | Expert opinion |
| No actionable gene variants present | 0.29 | NA | NA | (22, 23) |
| Actionable gene variants present | ||||
| 3rd line therapy | 1 | NA | NA | Expert opinion |
| No actionable gene variants present | 0.66 | NA | NA | Expert opinion |
| Actionable gene variants present | ||||
| 4th line therapy | 0 | NA | NA | Expert opinion |
| No actionable gene variants present | 1 | NA | NA | Expert opinion |
| Actionable gene variants present | ||||
| Targeted therapy 1st line, subsequent therapy treatment breakdown, proportions | ||||
| 0.97 | NA | NA | Expert opinion | |
| Chemotherapy | 0.03 | NA | NA | Expert opinion |
| Immunotherapy | ||||
| Treatment breakdown, proportions | ||||
| Salvage therapy treatment breakdown, proportions | ||||
| Chemotherapy to chemotherapy | 0.5 | NA | NA | Expert opinion |
| Chemotherapy to immunotherapy | 0.5 | NA | NA | Expert opinion |
| Immunotherapy to chemotherapy | 1 | NA | NA | Expert opinion |
| Chemotherapy/Immunotherapy combination to chemotherapy | 0.5 | NA | NA | Expert opinion |
| Chemotherapy/Immunotherapy combination to chemotherapy | 0.5 | NA | NA | Expert opinion |
| Median progression free survival, months | ||||
| Targeted therapies | ||||
| ALK (+), Alectinib | 25.7 | 17.7, 25.7 | Triangular (17.7, 25.7, 25.7) | (25) |
| BRAF (+), Dabrafenib/trametinib | 10.9 | 7, 16.6 | Triangular (7, 10.9, 16.6) | (27) |
| EGFR (+), Osimertinib | 18.9 | 15.2, 21.4 | Triangular (15.2, 18.9, 21.4) | (22) |
| NTRK1/2/3 (+), Entrectinib | 11.2 | 8, 14.9 | Triangular (8, 11.2, 14.9) | (31) |
| ROS1 (+), Crizotinib | 19.2 | 14.4, 19.2 | Triangular (14.4, 19.2, 19.2) | (26) |
| ERBB2 (+), Ado-trastuzumab | 5 | 3, 9 | Triangular (3, 5, 9) | (28) |
| MET (+), Crizotinib | 4.1 | 3.1, 5.1 | Triangular (2.1, 4.1, 5.1) | (32) |
| RET (+), Cabozantinib | 5.5 | 3.8, 8.4 | Triangular (3.8, 5.5, 8.4) | (29) |
| RET (+), Vandetanib | 4.5 | NA | NA | (30) |
| Non-targeted therapies | ||||
| Chemotherapy | 3.6 | 3.3, 3.9 | Triangular (3.3, 3.6, 3.9) | (33) |
| Chemotherapy/Immunotherapy combination | 8.8 | 7.6, 9.2 | Triangular (7.6, 8.8, 9.2) | (34) |
| Immunotherapy | 7.1 | 5.9, 9 | Triangular (5.9, 7.1, 9) | (35) |
| Life expectancy, months | ||||
| Non-cancer-directed therapy | 5.03 | 4.17, 5.89 | Triangular (4.17, 5.03, 5.89) | (4) |
| Death at median progression free survival, proportion | ||||
| Targeted therapies | ||||
| ALK (+), Alectinib | 0.26 | 0.20, 0.26 | Triangular (0.20, 0.26, 0.26) | (25) |
| BRAF (+), Dabrafenib/trametinib | 0.23 | 0.15, 0.45 | Triangular (0.15, 0.23, 0.45) | (27) |
| EGFR (+), Osimertinib | 0.19 | 0.15, 0.24 | Triangular (0.15, 0.19, 0.24) | (22) |
| NTRK1/2/3 (+), Entrectinib | 0.11 | 0, 0.25 | Triangular (0, 0.11, 0.25) | (31) |
| ROS1 (+), Crizotinib | 0.15 | 0.07, 0.28 | Triangular (0.07, 0.15, 0.28) | (26) |
| ERBB2 (+), Ado-trastuzumab | 0 | NA | NA | (28) |
| MET (+), Crizotinib | 0.15 | NA | NA | (32) |
| RET (+), Cabozantinib | 0.23 | 0.09, 0.28 | Triangular (0.09, 0.23, 0.28) | (29) |
| RET (+), Vandetanib | 0.35 | NA | NA | (30) |
| Death at median progression free survival, proportion | ||||
| Non-targeted therapies | ||||
| Chemotherapy | 0.24 | 0.22, 0.26 | Triangular (0.22, 0.24, 0.26) | (33) |
| Chemotherapy/Immunotherapy combination | 0.21 | 0.17, 0.23 | Triangular (0.17, 0.21, 0.23) | (34) |
| Immunotherapy | 0.27 | 0.26, 0.31 | Triangular (0.26, 0.27, 0.31) | (35) |
| Non-cancer-directed therapy | 1 | NA | NA | (4) |
| Permanent therapy discontinuation, proportion | ||||
| Targeted therapies | ||||
| ALK (+), Alectinib | 0.11 | NA | NA | (25) |
| BRAF (+), Dabrafenib/trametinib | 0.22 | NA | NA | (27) |
| EGFR (+), Osimertinib | 0.13 | NA | NA | (22) |
| NTRK1/2/3 (+), Entrectinib | 0 | NA | NA | (31) |
| ROS1 (+), Crizotinib | 0.13 | NA | NA | (26) |
| ERBB2 (+), Ado-trastuzumab | 0 | NA | NA | (28) |
| MET (+), Crizotinib | 0 | NA | NA | (32) |
| RET (+), Cabozantinib | 0.08 | NA | NA | (29) |
| RET (+), Vandetanib | 0 | NA | NA | (30) |
| Non-targeted therapies | ||||
| Chemotherapy | 0.21 | NA | NA | (33) |
| Chemotherapy/Immunotherapy combination | 0.138 | NA | NA | (34) |
| Immunotherapy | 0.253 | NA | NA | (35) |
| Adverse events, proportion | ||||
| Targeted therapies | ||||
| ALK (+), Alectinib | 0.18 | NA | NA | (25) |
| BRAF (+), Dabrafenib/trametinib | 1.00 | NA | NA | (27) |
| EGFR (+), Osimertinib | 0.13 | NA | NA | (22) |
| NTRK1/2/3 (+), Entrectinib | 0.35 | NA | NA | (31) |
| ROS1 (+), Crizotinib | 0.56 | NA | NA | (26) |
| ERBB2 (+), Ado-trastuzumab | 0.06 | NA | NA | (28) |
| MET (+), Crizotinib | 0.56 | NA | NA | (32) |
| RET (+), Cabozantinib | 0.69 | NA | NA | (29) |
| RET (+), Vandetanib | 0.33 | NA | NA | (30) |
| Adverse events, proportion | ||||
| Non-targeted therapies | ||||
| Chemotherapy | 0.44 | NA | NA | (33) |
| Chemotherapy/Immunotherapy combination | 0.85 | NA | NA | (34) |
| Immunotherapy | 0.03 | NA | NA | (35) |
| Adverse events, time of occurrence, days | ||||
| Targeted therapies | 45 | NA | NA | Expert opinion |
| Chemotherapy | 21 | NA | NA | Expert opinion |
| Chemotherapy/Immunotherapy combination | 21 | NA | NA | Expert opinion |
| Immunotherapy | 120 | NA | NA | Expert opinion |
| Testing cost, 2019 USD | ||||
| Multi-gene panel | $3,105 | $2,710, $3,500 | Triangular ($2,710, $3,105, $3,500) | (41) |
| Targeted gene panel | $679 | $598, $760 | Triangular ($598, $679, $760) | (41) |
| Drug price per day, 2019 USD | ||||
| Targeted therapies | ||||
| ALK (+), Alectinib | $445 | $364, $527 | Triangular ($364, $445, $527) | (43) |
| BRAF (+), Dabrafenib/trametinib | $689 | $651, $727 | Triangular ($651, $689, $727) | (43) |
| EGFR (+), Osimertinib | $457 | $398, $517 | Triangular ($398, $457, $517) | (43) |
| NTRK1/2/3 (+), Entrectinib | $466 | NA | NA | (43) |
| ROS1 (+), Crizotinib | $498 | $417, $580 | Triangular ($417, $498, $580) | (43) |
| ERBB2 (+), Ado-trastuzumab | $345 | $296, $395 | Triangular ($296, $345, $395) | (43) |
| MET (+), Crizotinib | $498 | $417, $580 | Triangular ($417, $498, $580) | (43) |
| RET (+), Cabozantinib | $384 | $310, $459 | Triangular ($310, $384, $459) | (43) |
| RET (+), Vandetanib | $555 | $347, $762 | Triangular ($347, $555, $762) | (43) |
| Non-targeted therapies | ||||
| Chemotherapy | $85 | $2, $168 | Triangular ($2, $85, $168) | (42) |
| Chemotherapy/Immunotherapy combination | $638 | $637, $638 | Triangular ($637, $638, $638) | (42) |
| Immunotherapy | $471 | NA | NA | (42) |
| Non-drug cancer-related management cost, 2019 USD | ||||
| Targeted therapies, per day | $216 | $173, $259 | Triangular ($173, $216, $259) | (44) |
| Ado-trastuzumab administration cost | $36 | $9.22, $63.52 | Triangular ($9.22, $36, $63.52) | (1) |
| Chemotherapy, per day | $177 | $142, $212 | Triangular ($142, $177, $212) | (1) |
| Chemotherapy/Immunotherapy combination, per day | $301 | $241, $361 | Triangular ($241, $301, $361) | (1, 59) |
| Immunotherapy, per day | $204 | $163, $245 | Triangular ($163, $204, $245) | (1, 59) |
| Other costs, 2019 USD | ||||
| Non-cancer-directed therapy, per day | $43 | $36, $50 | Triangular ($36, $43, $50) | (45) |
| Death | $23,502 | $23,240, $24,552 | Triangular ($23,240, $23,502, $24,552) | (1, 45) |
| Health state utilities | ||||
| Targeted therapy | 0.81 | 0.76, 0.85 | Beta (α=310.84, β=72.91) | (39) |
| Chemotherapy | 0.78 | 0.76, 0.80 | Beta (α=1337.70, β=377.30) | (39) |
| Chemotherapy/Immunotherapy combination | 0.78 | 0.76, 0.80 | Beta (α=1337.70, β=377.30) | (39) |
| Immunotherapy | 0.80 | 0.73, 0.86 | Beta (α=141.42, β=35.36) | (39) |
| Adverse event, applied for 30 days | 0.45 | 0.33, 0.57 | Beta (α=40.74, β=49.79) | (38) |
| Cancer-directed therapy | 0.59 | 0.52, 0.66 | Beta (α=147.92, β=102.79) | (38) |
| Death | 0 | NA | NA | (37) |
If tumor profiling did not identify actionable gene variants or if tumor profiling was not performed, cancer-directed therapy options included non-targeted treatment options. For first-line therapy, 33% received non-cancer-directed therapy and 67% received non-targeted treatment options based on expected PD-L1 results and its corresponding therapy (i.e., 38.7% have <1% PD-L1 tumor expression and prescribed chemotherapy, 28.6% have 1-49% PD-L1 tumor expression and prescribed a combination of chemotherapy and immunotherapy, and 32.7% have ≥50% PD-L1 tumor expression and prescribed immunotherapy).(24) For second-line therapies for those who remained alive, 33% received non-cancer-directed therapy, and 67% received a salvage treatment approach (previously described). For third-line therapy, 100% of patients remaining alive chose no anti-cancer therapy (Table 1).
Model Inputs
Clinical Outcomes
Clinical outcomes were derived from primary trials conducted in patients with metastatic NSCLC (Supplemental Table 2)(4, 22, 25-35) and included progression free survival (PFS), proportion of patients experiencing adverse events (AEs), permanent drug discontinuation, and death (Figure 1). Median PFS and 95% CI, proportions of patients with permanent drug discontinuation, AE (grade 3+ and reported in ≥5% of patients(36)), and death were derived at the time of disease progression from cancer-directed therapy trials. Overall life expectancy was PFS time on cancer-directed therapies and time alive while on non-cancer-directed therapy.
QALYs
The impact a disease or age has on quality of life can take on health state utility values (HSUVs) from 1 (perfect health) to 0 (death).(37, 38) HSUVs are multiplied by the time spent in the health state and reported as QALYs over a specific time horizon. In this analysis, the reported QALYs represent the summation of cancer-related health states over the lifetime time horizon. HSUVs were assigned based on the cancer-directed therapy received (i.e., targeted therapy, chemotherapy, immunotherapy, and combination of chemotherapy and immunotherapy) and were based on a study conducted in metastatic NSCLC patients.(39) In this study, patients were prescribed cancer-directed therapy based on the presence of driver alterations per clinical guidelines and HSUVs were determined separately for patients on targeted therapy, immunotherapy, and chemotherapy.(39) The HSUV for patients on a combination of chemotherapy and immunotherapy was assumed to be similar to patients on chemotherapy. An AE HSUV was applied for 30 days across all therapies when an AE occurred before returning back to therapy-specific HSUVs, regardless of the number of AEs patients experienced. The AE HSUV was derived from a study conducted in metastatic NSCLC patients.(38) AEs associated with anti-cancer therapies occurred once after the first exposure to therapies using the following specified amount of time: 45 days for targeted therapies, 21 days for chemotherapy and combination of chemotherapy and immunotherapy, and 120 days for immunotherapy. For patients on non-cancer-directed therapy, it was assumed the HSUV would be similar to palliative care, and this HSUV was derived from a study conducted in cancer patients.(38) QALYs were discounted at 3% per year.(40)
Costs
Direct medical payments from CMS’ perspective relevant to cancer care included tumor profiling, cancer-directed drugs, non-drug cancer-related management on targeted and non-targeted therapy (e.g., AEs, procedures), non-cancer-directed therapy, and death.(41-45)
Tumor profiling costs were approximated with data from the 2019 Medicare Clinical Diagnostics Laboratory Fee Schedule.(41) For each tumor profiling strategy, two healthcare common procedure coding system (HCPCS) codes were used to determine the lower and upper cost bounds. The average of the minimum and maximum values was used as the base-case value. Supplemental Table 3 outlines the HCPCS codes used in the analysis.
Cancer-directed therapy costs reflect regimens recommended in clinical guidelines and are summarized in Supplemental Table 4.(46) Targeted therapies are considered specialty drugs covered under Medicare Part D. Prices for these drugs are not publicly available, so the VA drug pricing was used. The Veterans Affairs Federal Supply Schedule (VAFSS) has been noted as the lower bound of drug pricing when estimating drug prices for the US payers of health.(47) To convert VA prices to Medicare prices, 121% of drug prices reported in the FSS was used per VA recommendations.(48) Given the variability in patient out-of-pocket costs, it is assumed that Medicare would cover 90% of these costs. The VAFSS had multiple price points listed for all targeted therapies except for trametinib and entrectinib (i.e., each price point is a contracted price for the drug with a vendor), making a range of price points possible for these drugs.(47) The base-case value of these drugs was the average price of all entries; drug price ranges reflected the lowest and highest prices of these entries. Only base-case values were used for trametinib and entrectinib since only one price was listed.(47) For non-targeted therapy prices, base-case values were the prices reported in the 2019 Medicare Part B Drug Average Sales Price.(42) Chemotherapy regimens were the only non-targeted therapy with a price range, which represented a feasible dosing range for carboplatin. The base-case value for carboplatin represented the average of the minimum and maximum dosing levels.
A claims-based approach was taken to estimate the cost of non-drug cancer-related management expenses. Two recent retrospective analyses assessed healthcare utilization costs among Medicare patients with advanced NSCLC during cancer-directed therapy treatment and provides a detailed breakdown of non-drug cancer-related management costs.(1, 44) Descriptions of these studies and its application in this cost-effectiveness analysis are provided in Supplemental Methods 1. Base-case values were derived from these studies and a ±20% range was applied to the base-case value to derive the minimum and maximum range values for the sensitivity analyses.
Published studies using Medicare data linked with the Surveillance, Epidemiology, and End Results (SEER) program, the authoritative source of cancer incidence and mortality information in the U.S., was used for the cost of best supportive care and death.(49) Medicare’s average monthly cancer-attributable cost for patients with NSCLC stage IV receiving best supportive care was $1,296 (95% CI $1,048-$1,490) (2017 USD).(45) The point cost estimate was used as the base-case value in the analysis and the 95% CI formed the minimum and maximum values. For patients with advanced NSCLC, 81.5% of deaths were related to lung cancer and 18.5% of deaths were related to other causes. Lung cancer death cost was estimated to be $21,603 (2017 USD) and non-lung cancer death was estimated to be $23,179 (2017 USD).(45) The base-case value in analysis was $23,502, which was determined by weighing the cost of death based on the cause of death. The minimum value represented the cost of lung cancer deaths and the maximum represented the cost of non-lung cancer deaths.
Costs were discounted at 3% per year(40), and adjusted to 2019 USD using the U.S. Bureau of Labor Statistics Medicare-specific Producer Price Index components.(50, 51)
Table 1 summarizes all input parameters used in the model.
Analysis
Base-case Scenario
Point estimates for each input parameter were used in the base-case scenario and unless specified, represented the midpoint value if a range was reported in the literature (Table 1). Base-case scenario outcomes for each tumor profiling strategy included drug discontinuations, AEs, life years, death after each therapy line, QALYs, and cost. The cost/QALY gained, or the incremental cost-effectiveness ratio (ICER), was the primary outcome and was calculated by rank ordering tumor profiling strategies by increasing costs and comparing sequentially.(40)
One-way and Probabilistic Sensitivity Analysis
One-way sensitivity analysis and probabilistic sensitivity analysis (PSA) used reported ranges from the literature for input parameters to investigate the influence on the ICER, and were completed using Oracle® Crystal Ball v11.1 (Redwood City, CA). The minimum and maximum values were individually tested in the one-way sensitivity analysis for 54 of the 108 model inputs (i.e., gene variant prevalence, PFS, life expectancy, HSUVs, and costs related to tumor profiling, drugs, non-drug cancer-related management, non-cancer-directed therapy options (e.g., hospice) (Table 1)).
In the PSA, 1,000 Monte Carlo simulations were completed using predefined distributions for input parameters. Triangular distributions were used to parameterize gene variant prevalence, PFS, death, and costs; beta distributions were used for HSUVs.(40)
Threshold Analyses and Alternate Scenarios
Threshold analyses and clinically relevant alternate scenarios varied select parameters identified in the one-way sensitivity analysis using published data or hypothetical ranges to further investigate the impact on the ICER.
A willingness-to-pay threshold (WTP) of $150,000/QALY gained was used to confer cost-effectiveness.(52)
RESULTS
Base-case Scenario
No tumor profiling detected 0% of the cohort with actionable gene variants as expected, compared to 22.0% for TGPT and 29.6% for CGP (Table 2). 95% of cases where actionable gene variants were detected resulted in assignment to a first-line targeted therapy with remaining cases assigned to non-cancer-directed therapy. No tumor profiling resulted in the lowest average length of survival (9.2 months/person) when compared to TGPT (12.2 months/person) and CGP (12.3 months/person) (Table 2). No tumor profiling yielded the highest number of deaths at the end of the first-line therapy (45.7% vs 40.6% for TGPT or 37.9% for CGP) and had the fewest remaining alive to receive third-line therapy (29.4% vs 32.6% for TGPT or 34.3% for CGP) (Table 2).
Table 2.
Base-case Event Outcome Results for a Cohort of 10,000 Hypothetical Patients with Metastatic Lung Adenocarcinoma
| Starting proportion of initial cohort alive in each therapy line (%) |
Proportion of initial cohort with drug discontinuations (%) |
Proportion of cohort with adverse events (%) |
Average cost per person (2019 USD) |
Total life years |
Life years per person |
Total QALYs |
QALY per person |
Proportion of initial cohort dead at end of n-line therapy (%) |
|
|---|---|---|---|---|---|---|---|---|---|
| No tumor profiling: 0 (0%) actionable gene variants present and none eligible for or received targeted therapies | |||||||||
| 1st line therapy | 100% | 13.6% | 28.4% | $86,458 | 4,461 | 0.45 | 3,153 | 0.32 | 45.7% |
| 2nd line therapy | 54.3% | 8.2% | 11.0% | $50,736 | 1,999 | 0.37 | 1,430 | 0.26 | 25.0% |
| 3rd line therapy | 29.4% | NA | NA | $29,294 | 1,214 | 0.41 | 705 | 0.24 | 29.4% |
| 4th line therapy | 0 | NA | NA | NA | NA | NA | NA | NA | NA |
| Targeted gene panel testing a : 2,198 (22%) actionable gene variants present and eligible for targeted therapies; 2,089 (21%) received targeted therapies | |||||||||
| 1st line therapy b | 100% | 13.2% | 27.6% | $145,121 | 6,627 | 0.66 | 4,951 | 0.50 | 40.6% |
| 2nd line therapy | 59.4% | 8.8% | 9.7% | $44,369 | 2,072 | 0.35 | 1,468 | 0.25 | 26.8% |
| 3rd line therapy | 32.6% | 1.5% | 3.3% | $40,357 | 1,338 | 0.41 | 785 | 0.24 | 29.9% |
| 4th line therapy | 2.7% | NA | NA | $28,235 | 110 | 0.41 | 61 | 0.23 | 2.7% |
| Comprehensive genomic profiling c : 2,960 (30%) actionable gene variants present and eligible for targeted therapies; 2813 (28%) received targeted therapies | |||||||||
| 1st line therapy a | 100% | 12.5% | 28.3% | $147,785 | 6,589 | 0.66 | 4,944 | 0.49 | 37.9% |
| 2nd line therapy | 62% | 9.3% | 11.0% | $48,347 | 2,125 | 0.34 | 1,504 | 0.24 | 27.8% |
| 3rd line therapy | 34.3% | 2.3% | 4.2% | $38,828 | 1,404 | 0.41 | 829 | 0.24 | 30.6% |
| 4th line therapy | 3.7% | NA | NA | $28,476 | 155 | 0.41 | 87 | 0.23 | 3.7% |
Abbreviations: QALYs: quality-adjusted life years
Targeted gene panel testing: ALK, BRAF, EGFR, ROS1
Tumor profiling test costs are applied to 1st line therapy
Comprehensive genomic profiling: ALK, BRAF, EGFR, ERBB2, MET, NTRK1, NTRK2, NTRK3, RET, ROS1
Notes: 10,000 hypothetical individuals were assigned to each strategy and key outcomes for up to four therapy lines were tracked. These outcomes reflect the lifetime time horizon of 10,000 hypothetical patients with metastatic lung adenocarcinoma. Cost and QALYs were discounted at 3% per year.
No tumor profiling was the least costly strategy ($122,613/person) and resulted in the lowest number of QALYs (0.53/person), TGPT was more costly ($184,063/person) with higher number of QALYs (0.72/person), while CGP was the costliest strategy ($188,425/person) and with the highest number of QALYs (0.74/person) (Table 3). Details of the base-case scenario are further summarized in Supplemental Results 1, Supplemental Figure 1 (Breakdown of cost and QALY sources by tumor profiling strategy), and Supplemental Figure 2 (Breakdown of cost and QALY sources by treatment type). The ICER for TGPT vs no tumor profiling was $310,735 (95% CI: $278,323-$347,952) and CGP vs TGPT was $445,545 (95% CI $322,297-$572,084), indicating neither comparisons were cost-effective at the $150,000/QALY gained WTP threshold (Table 3).
Table 3.
Incremental cost-effectiveness ratios of tumor profiling strategies for base-case scenarioa
| Remaining lifetime outcome totals for tumor profiling strategies |
Incremental comparisons for tumor profiling strategies | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Cost per person (2019 US$) |
LY per person |
QALY per person |
Comparison for incremental analyses |
Incremental total cost per person (2019 US$) |
Incremental LY gained per person |
Incremental QALY gained per person |
Incremental cost per LY saved (95% credible intervals) |
Incremental cost per QALY gained (95% credible intervals) |
ICER interpretation at the $150k/QALY WTP threshold |
|
| No tumor profiling | $122,613 | 0.77 | 0.53 | NA | NA | NA | NA | NA | NA | NA |
| Targeted gene panel testing (TGPT) b | $184,063 | 1.01 | 0.73 | TGPT vs. No tumor profiling | $62,449 | 0.24 | 0.20 | $248,426 ($228,094-$273,769) | $310,735 ($278,323-$347,952) | Not cost-effective |
| Comprehensive Genomic Profiling (CGP) c | $188,425 | 1.03 | 0.74 | CGP vs. TGPT | $4,362 | 0.02 | 0.01 | $347,706 ($256,778-$431,341) | $445,545 ($322,297-$572,084) | Not cost-effective |
Abbreviations: ICER: incremental cost-effectiveness ratio; LY: life year; QALY: quality-adjusted life year; WTP: willingness-to-pay threshold
Cost and QALY discounted at 3% per year
Targeted gene panel testing: ALK, BRAF, EGFR, ROS1
Comprehensive Genomic Profiling: ALK, BRAF, EGFR, ERBB2, MET, NTRK1, NTRK2, NTRK3, RET, ROS1
Notes: Interventions were rank-ordered by total cost and sequentially compared. Targeted gene panel testing was compared to no tumor profiling; comprehensive genomic profiling was compared to targeted gene panel testing. Comparisons reflect the remaining lifetime time horizon of 10,000 hypothetical patients with metastatic lung adenocarcinoma.
One-way Sensitivity Analysis
Input parameters explaining at least 90% of the cumulative variation on the ICER in the one-way sensitivity analysis were identified. When comparing TGPT to no tumor profiling, the minimum and maximum values for four of the 54 model inputs explained 94% of the cumulative variation in the ICER and were unique to targeted therapy: alectinib and osimertinib prices, non-drug cancer-related management costs, and the HSUV (Supplemental Figure 3). The remaining inputs (e.g., genetic alteration prevalence, tumor profiling cost, PFS, life expectancy) together explained 6% of the ICER variation.
When comparing CGP to TGPT, nine of the 54 model inputs explained 91% of the cumulative variation in the ICER and included HSUVs for immunotherapy and targeted therapies, PFS for ado-trastuzumab, cabozantinib, crizotinib (MET), actionable ERBB2 gene variant prevalence, and costs of non-cancer-directed therapy disease management on targeted therapies, crizotinib, and CGP (Supplemental Figure 3). Varying the remaining parameters (e.g., TGPT cost, drug prices, life expectancy) together accounted for 9% of the ICER variation.
Probabilistic Sensitivity Analysis
100% of simulations indicated TGPT (vs no tumor profiling) and CGP (vs TGPT) resulted in higher QALYs at increased costs and fell above the $150,000/QALY WTP threshold, indicating all simulations were not cost-effective (Supplemental Figure 4).
WTP thresholds ($0-$700,000) across which each intervention was most likely to convey the greatest QALYs at an acceptable incremental cost were summarized in Figure 2. No tumor profiling was most likely cost-effective across $0-$313,000 WTP thresholds. TGPT was most likely to be cost-effective across $313,000-$420,000 WTP thresholds. CGP was most likely to be cost-effective across WTP thresholds beyond $420,000.
Figure 2. Likelihood of Cost-Effectiveness for Each Tumor Profiling Strategy Across Various Willingness-to-pay Thresholds.
Abbreviations: QALY: quality-adjusted life year; WTP: willingness to pay
The percentage of 1,000 simulations each strategy was most likely cost-effective across the WTP thresholds of $0-$700,000 are depicted. The $150,000 per QALY gained WTP threshold is indicated by the dotted line on the graphs. Comparisons reflect the lifetime time horizon of 10,000 hypothetical patients with metastatic lung adenocarcinoma. Cost and QALYs were discounted at 3% per year. See Supplemental Figure 6 for all threshold analyses and alternate scenario results.
Threshold Analyses and Alternate Scenarios
Price reductions in osimertinib and alectinib required to make CGP and TGPT (vs no tumor profiling) cost-effective at the $150,000 WTP threshold were at least 80% for CGP and 72% for TGPT (Threshold analyses 1, 2, Table 4).
Table 4.
Description of Threshold Analyses and Alternate Scenarios
| Reference | Description | Targeted therapy prices (cost per day) | Non-drug cancer-related management (cost per day) |
PFS (months) |
|---|---|---|---|---|
| Base-case | Base-case assumptions | Alectinib: $445 ($364-$527) Dabrafenib/trametinib: $689 ($651-$727) Osimertinib: $457 ($398-$517) Entrectinib: $466 (no range) Crizotinib: $498 ($417-$580) Ado-trastuzumab per 21 days: $7,246 ($6,199-$8,293) Cabozantinib: $384 ($310-$459) Vandetanib: $555 ($347-$762) |
Targeted therapy: $216 ($173-$259) Chemotherapy: $177 ($142-$212) Immunotherapy: $204 ($163-$245) Chemotherapy +Immunotherapy: $301 ($241-$361) |
Ado-trastuzumab: 5 (3–9) Cabozantinib: 5.5 (3.8–8.4) Entrectinib: 11.2 (8–14.9) Crizotinib (MET): 4.1 (3.1–5.1) |
| Threshold Analysis 1 | 80% price reductions in Osimertinib and Alectinib (CGP vs no tumor profiling becomes cost-effective) | Osimertinib: $91 ($80-$103) Alectinib: $89 ($73-$105) All other therapies are the same as base-case |
No change from base-case | No change from base-case |
| Threshold Analysis 2 | 72% price reductions in Osimertinib and Alectinib (TGPT vs no tumor profiling becomes cost-effective) | Osimertinib: $128 ($111 -$145) Alectinib: $125 ($102-$147) All other therapies are the same as base-case |
No change from base-case | No change from base-case |
| Scenario 1 | All targeted therapies price reduced by 25% | 75% of base-case values and ranges | No change from base-case | No change from base-case |
| Scenario 2 | All targeted therapies price reduced by 50% | 50% of base-case values and ranges | No change from base-case | No change from base-case |
| Scenario 3 | All targeted therapies price reduced by 75% | 25% of base-case values and ranges | No change from base-case | No change from base-case |
| Scenario 4 | Non-drug cancer-related management cost the same across all therapies as chemotherapy | No change from base-case | Targeted therapy: $177 ($142-$212) Chemotherapy: $177 ($142-$212) Immunotherapy: $177 ($142-$212) Chemotherapy +Immunotherapy: $177 ($142-$212) |
No change from base-case |
| Scenario 5 | Maximum PFS value used as base-case values for Ado-trastuzumab, Cabozantinib, Entrectinib, Crizotinib (MET) | No change from base-case | No change from base-case | Ado-trastuzumab: 9 Cabozantinib: 8.4 Entrectinib: 14.9 Crizotinib (MET): 5.1 |
| Scenario 6 | Combination of Threshold Analysis 1, Scenarios 4 and 5 | Osimertinib: $63 ($50-$76) Alectinib: $63 ($50-$76) All other therapies are the same as base-case |
Targeted therapy: $177 ($142-$212) Chemotherapy: $177 ($142-$212) Immunotherapy: $177 ($142-$212) Chemotherapy +Immunotherapy: $177 ($142-$212) |
Ado-trastuzumab: 9 Cabozantinib: 8.4 Entrectinib: 14.9 Crizotinib (MET): 5.1 |
Abbreviations: CGP: comprehensive genomic profiling, PFS: progression free survival, TGPT: Targeted gene panel testing
To determine the influence of key input parameters on results, six alternate scenarios varied several input parameters (Table 4). At the $150,000/QALY gained WTP threshold, no tumor profiling was most likely cost-effective in all scenarios except when targeted therapy prices were reduced by 75% (scenario 3, Table 4) where CGP was most likely cost-effective (Supplemental Figure 6E). When targeted therapy prices were reduced to their threshold values, non-drug therapy costs were fixed across all therapies, and maximum PFS used for selected therapies (scenario 6, Table 4) TGPT was most likely cost-effective (Supplemental Figure 6H). Results are summarized in Supplemental Results 2, Supplemental Table 5 (ICER point estimates), Supplemental Figures 5-6 (PSA results).
DISCUSSION
Although tumor profiling to identify patients eligible for targeted cancer-directed therapies is considered standard of care in advanced NSCLC, clinical uptake of tumor profiling is variable with no tumor profiling a common strategy that patients will encounter.(53) Professional society guidelines recommend “broad” molecular profiling(12, 54), however the optimal tumor profiling strategy remains uncertain, and tumor profiling uptake is not uniform. To address this gap, we completed a cost-effectiveness analysis comparing two widely utilized tumor profiling strategies (CGP and TGPT), as well as comparing TGPT to no tumor profiling. In contrast to similar prior analyses(13-15), we found that among patients with metastatic lung adenocarcinoma, neither TGPT (vs no tumor profiling) nor CGP (vs TGPT) were cost-effective ($150,000 WTP threshold(55)), however both resulted in QALY gains. The higher costs of both tumor profiling strategies were driven mainly by the high price of targeted therapies (Supplemental Figure 2A).
When comparing to prior cost-effectiveness analyses, we attribute the discrepancy in our findings - in part - to a more accurate accounting of targeted therapy treatment costs. Based on recent drug(42, 47, 48) prices and non-drug cancer-related management costs(1, 44), we approximated costs associated with targeted therapies 3-4x the cost of non-targeted therapies. In contrast, a previous analysis focused on limited AEs and approximated targeted treatments to be approximately a third of the cost of non-targeted treatments.(15) Our targeted drug prices were similar to another prior analysis, although we included monthly costs, rather than a one-time cost, of non-drug cancer-related management.(14) A cohort-based analysis conducted from France’s payer perspective found that targeted therapies cost patients an average of $2,785, a much lower cost than our estimate, which is due to differences in the patients studied and healthcare systems.(13) Additionally, we thoroughly evaluated costs, including those from initial and subsequent lines of therapy while testing for a more comprehensive set of genes, which had not been previously undertaken. Taken together, our analysis provides an updated estimate of the comprehensive downstream costs and benefits of tumor profiling in NSCLC than in prior work.
Our study has several limitations to highlight. First, we extrapolated front-line trial PFS outcomes to subsequent lines where data are unavailable. Therefore, the benefits of subsequent lines of therapy are likely smaller, which may inflate our QALY estimates and thus reduce our ICER values. Second, we assumed test results were available at the time of clinical decision-making and all clinical recommendations were followed. Deviations from these assumptions may affect the number of patients receiving targeted therapies. Third, we used median PFS data from landmark clinical trials, which does not account for longer survival a minority of patients with durable responses experience. While methods, (e.g., mixture cure models (MCMs)), have previously accounted for such heterogeneity in clinical outcomes, recent work comparing MCMs to traditional parametric survival models showed little difference in cost-effectiveness.(56) Finally, our study is limited by the rapid evolution of NSCLC management and subsequent updates to clinical guidelines. Our model reflects February 2020 NCCN guideline recommendations, which have since been updated with additional level 1 actionable MET and RET variants. This limitation provides support for CGP, where comprehensive information provides additional clinical utility as oncologic care advances, though would be expected to increase the costs/QALYs of CGP.
Our findings not only provide evidence of incremental gains in QALYs, on average, with tumor profiling, but also motivate a discussion regarding value and cost in oncologic care. Specifically, our scenario analyses highlight important policy implications for prices of targeted therapies for patients with advanced NSCLC. We found in threshold analyses that tumor profiling could be cost-effective if there were a 72%-80% reduction in osimertinib and alectinib price. Beyond these two medications, reductions in the price of all targeted drugs by 75% (scenario 3) were required to make CGP cost-effective (Supplemental Figure 6E). Adjusting for health benefits and inflation, cancer-directed drug prices have increased by almost 12% per year since the 1990s.(57) As more actionable gene variants are added to panel testing, targeted therapy will be indicated for more patients with advanced NSCLC. Assuming similar drug pricing moving forward, this may lead to higher ICERs when compared to tumor profiling strategies that do not detect these newer actionable gene variants, driven by the cost of placing more patients on targeted drugs for longer time periods. Our analyses show that, with current targeted therapy drug pricing, payers could generate more QALYs for patients with NSCLC if those funds were directed toward more cost-effective interventions.
While tumor profiling in advanced NSCLC improves patient outcomes, it is not cost-effective. Although costs relative to QALY gains is important, it is not the only measure of value to consider when evaluating tumor profiling. Additional value not captured in our analysis include utility gains from patients’ belief that their treatments may lead to significant health gains (“value of hope”), the ability to extend life so patients may benefit from future advances in oncologic care (“real option value”), the benefit of scientific advances which arise from widespread testing for clinical trial enrollment (“scientific spillover value”), the effect of health improvement on labor productivity (“value of productivity”), and QALYs gained at the end of life are more valuable (“value of severity of disease”).(58) Patients, payers, and providers need to aggregate all aspects of value, including cost-effectiveness, in determining the optimal approach for treating lung adenocarcinomas.
CONCLUSION
Tumor profiling directing selection of first-line cancer-directed targeted therapies is not cost-effective (i.e., does not generate sufficient QALY gains to offset associated costs).
Supplementary Material
Highlights.
Common tumor profiling approaches in clinical use include comprehensive genomic profiling (CGP), which tests for a broad range of alterations among comprehensive sets of oncogenes while targeted gene panel testing (TGPT) tests for common alterations in selected oncogenes, however the optimal approach to tumor profiling in advanced non-small cell lung cancer remains uncertain.
From the Centers for Medicare and Medicaid’ perspective, the lifetime cost per quality-adjusted life years (QALY) gained is $310,735 (TGPT vs no tumor profiling) and $445,545 (CGP vs TGPT) for patients with metastatic lung adenocarcinoma. None of the simulated results generated estimates below the $150,000 per QALY gained willingness-to-pay threshold.
Tumor genomic profiling directing selection of first-line cancer-directed targeted therapies does not generate sufficient gains in QALYs to offset the high price of these therapies.
Acknowledgment:
A preliminary version of this analysis was presented at the 2020 American Society of Clinical Oncology (ASCO) Annual Meeting.
Funding/Support:
This work was supported by grant 5T32HG008955-03 from the National Human Genome Research Institute of the National Institutes of Health.
Role of the Funder/Sponsor:
The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Conflict of Interest Disclosures:
Dr Dong reported receiving grants from National Human Genome Research Institute of the National Institutes of Health (award number 5T32HG008955-03) during the conduct of the study. Dr Kelley reported receiving grants from AstraZenec, Bristol-Myers Squibb, Genentech, Novartis, and Regeneron; personal fees and donation of use of Watson for Genomics from IBM; and personal fees from PRIME Inc outside the submitted work. Dr Voora reported receiving grants from National Institutes of Health during the conduct of the study. No other disclosures were reported.
Footnotes
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