Key Points
Questions
How does the use of targeted therapy during the initial line of therapy compare for patients with no biomarker testing, non–comprehensive genomic profiling (CGP) testing, and CGP testing?
Findings
In this cohort study using claims data from 26 311 adults with 1 of 6 types of advanced cancer, biomarker testing rates were suboptimal although increased over time. There were no differences in costs during first-line therapy between patients who received CGP testing vs patients who received non-CGP testing across evaluated cancer types.
Meaning
These findings highlight the need for improved access to biomarker testing.
This cohort study of adults with advanced cancer compares the use of targeted therapy during first-line therapy for patients with no biomarker testing, comprehensive genomic profiling testing, and non–comprehensive genomic profiling testing.
Abstract
Importance
Clinical guidelines recommend biomarker testing to identify patients eligible for targeted therapy. However, evidence suggests that biomarker testing rates are below guideline recommendations, which has been associated with worsened clinical outcomes, including overall survival.
Objectives
To identify patients with newly diagnosed advanced cancer receiving comprehensive genomic profiling (CGP), non-CGP, or no biomarker testing and to explore the change in rates of testing over time and compare targeted therapy rates and health care costs during first-line therapy.
Design, Setting, and Participants
This retrospective cohort study used the deidentified Optum Labs Data Warehouse, a claims database of longitudinal health information on commercial health plan and Medicare Advantage enrollees, to identify patients diagnosed with advanced cancer between January 1, 2018, and January 1, 2022. The study included 26 311 adults with newly diagnosed advanced cancer (breast, colorectal, gastric, non–small cell lung, ovarian, and pancreatic) and continuous enrollment in a commercial or Medicare Advantage health plan for 12 months before and 6 months after their first advanced cancer diagnosis. Data were analyzed between February 1, 2023, and March 31, 2024.
Exposure
Biomarker testing.
Main Outcomes and Measures
Evidence of biomarker testing, the receipt of targeted therapy during first-line therapy, and per-patient, per-month (PPPM) costs during first-line therapy.
Results
Among 26 311 patients (mean [SD] age, 68 [11] years; 62% female; 70% Medicare Advantage enrollees), molecular testing rates were suboptimal (35% had evidence of molecular testing), but testing rates increased across time for most cancer types (from 32% in 2018 to 39% in 2021-2022). Patients with non–small cell lung cancer and colorectal cancer with CGP testing were more likely to receive targeted therapy (odds ratio [OR], 1.57; 95% CI, 1.31-1.90; P < .001) compared with patients who received non-CGP testing (OR, 2.34; 95% CI, 1.58-3.47; P < .001). Costs among patients with CGP testing were not statistically different from those with non-CGP testing (cost ratios of 1.03; 95% CI, 0.91-1.17 [P = .63] for breast cancer, 0.98; 95% CI, 0.89-1.09 [P = .71] for colorectal cancer, 1.10; 95% CI, 0.87-1.40 [P = .42] for gastric cancer, 1.06; 95% CI, 1.00-1.13 [P = .054] for non–small cell lung, 0.94; 95% CI, 0.76-1.15 [P = .55] for ovarian cancer, and 1.00; 95% CI, 0.83-1.21 [P = .98] for pancreatic cancer).
Conclusions and Relevance
In this cohort study, although increasing over time, biomarker testing rates were suboptimal despite guideline recommendations and increasing insurance coverage for testing. Given the potential benefits of CGP testing, such as increasing rates of targeted therapy without increased treatment-related costs, increasing CGP testing may improve outcomes.
Introduction
During the past 20 years, treatment options for patients with advanced cancers have expanded substantially. The emergence of biomarker-targeted treatments and immune-oncology therapies have driven the development and implementation of biomarker tests that predict response or nonresponse to targeted treatment. The number of biomarker-targeted therapies approved by the US Food and Drug Administration for one or more tumor types has increased over time,1 with multiple therapies approved for pan-cancer indications based solely on the presence of a biomarker.2,3 This growth is reflected in the inclusion of biomarker testing as part of standard treatment pathway recommendations for clinical practice guidelines for many different cancer types.4 Even in cancers in which targeted treatment is limited or not part of first-line treatment regimens, biomarker testing is still recommended at the initial workup of metastatic disease.5 Adherence to these clinical guidelines can result in improved clinical outcomes when leading to concordant guideline-directed care.6
As the number of biomarker-targeted therapies has increased and clinical guidelines recommend biomarker testing to identify matched therapy or the availability of clinical trials for eligible patients, there has been an ongoing evolution in testing options—from single-gene testing to small multigene panels (≤50 genes) to larger panels with next-generation sequencing–based comprehensive genomic profiling (CGP) approaches. CGP approaches, if optimized, can allow for the identification of different types of genetic alterations, including single-nucleotide polymorphisms and substitutions, insertions and deletions, copy number alternations, and rearrangements or fusions.7,8,9 Although other testing modalities can also identify these alterations, unlike single-gene or smaller panel tests, larger panels used for CGP can also include assessment of signatures of genomic instability, such as tumor mutational burden,10 an established biomarker for eligibility of immune-oncology therapy, in a single assay. CGP approaches may also improve the likelihood of identifying actionable alterations that are not assessed by single-gene tests11 and increase the opportunity for receipt of appropriate matched targeted therapy and improved overall survival.8,12,13 Published studies10,14,15,16,17,18,19,20 of patients who received cancer care in a large health system demonstrates that patients who received CGP are more likely to receive targeted therapy. Additionally, Yorio and colleagues21 found that CGP testing in a patient sample resulted in more timely targeted therapy as well as a reduction in less effective alternative therapies. Scott and colleagues22 similarly found that receiving treatment before testing results in inferior outcomes compared with receiving testing first. However, there is evidence to suggest that CGP-guided targeted therapy may not meaningfully improve patient outcomes and may even cause delays in care.23,24
Currently, there is a practice gap between clinical guidelines and implementation of biomarker testing.25,26,27 Despite the increase in test development, evidence of the benefits of testing is limited to nonsquamous non–small cell lung cancer (NSCLC).28 In addition, there is mixed evidence assessing the clinical and economic impact of CGP approaches compared with non-CGP biomarker testing. The primary objective of this study was to compare the use of targeted therapy during the initial line of therapy for patients with no biomarker testing, non-CGP testing, and CGP testing. Secondary study objectives were to compare rates of testing by cancer type and health care costs during the initial line of therapy between patients by testing category.
Methods
Study Design and Data Source
This retrospective cohort study used deidentified administrative claims data from the Optum Labs Data Warehouse and included medical and pharmacy claims, laboratory results, and enrollment records for commercial health plan and Medicare Advantage (MA) enrollees. This database contains longitudinal health information on enrollees, representing a mixture of ages and geographic regions across the US. The data are deidentified in compliance with the Health Insurance Portability and Accountability Act Privacy Rule. Because this study did not constitute human participants research as defined by the Common Rule, institutional review board approval and informed consent were not required. This study adhered to Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines for observational studies.29
Study Population
The criteria for the study population were intended to capture patients diagnosed with advanced cancer between January 1, 2018, and January 1, 2022, who were likely eligible for biomarker testing (ie, patients with advanced solid cancer whose management included anticancer systemic therapy). The cancer cohorts, including breast cancer (BC) colorectal cancer (CRC), gastric cancer (GC), NSCLC, ovarian cancer (OC), and pancreatic cancer (PC), (eTable 1 in Supplement 1) were selected because they are among the most common cancer types, clinical guidelines currently recommend CGP testing, and/or there is potential to benefit from CGP testing as targeted therapies are developed.30 Participants' self-reported race and ethnicity data are not captured in claims data and therefore are not presented in this study. Figure 1 shows the inclusion and exclusion criteria. A more in-depth description is included in eTable 4 in Supplement 1.
Figure 1. Sample Attrition.
CGP indicates comprehensive genomic profiling; MA, Medicare Advantage; NSCLC, non–small cell lung cancer; SCLC, small cell lung cancer.
Test Type Cohorts
Patients were assigned to 1 of 3 biomarker testing cohorts based on claims indicating service dates for testing from 90 days before the index date through the day before the start of first-line therapy: (1) no evidence of biomarker testing, (2) non-CGP biomarker testing, and (3) CGP biomarker testing (eTable 2 in Supplement 1). Patients with inconclusive evidence of CGP or other test types were removed from the subsequent analyses. The algorithm for creating the biomarker testing cohorts was based on molecular pathology procedure codes and laboratory provider and is further described in the eMethods of Supplement 1. Fluorescence in situ hybridization was included in the non-CGP testing category, but not immunohistochemistry (IHC) tests because the procedure codes available do not reliably identify whether IHC tests were used for diagnosis and prognosis or for biomarker testing.
Outcome measures were determined during a line of therapy. The first-line therapy period began on the date of the first claim for systemic therapy (oral or intravenously administered drugs) during follow-up. All agents filled or administered within the 30 days after the first observed drug comprised the first regimen. The line of therapy ended at the earliest of (1) a treatment gap of 60 days or more after the run-out date of all the drugs of the regimen, (2) addition of a new drug not observed during the first 30 days, or (3) death or (4) disenrollment from the health plan or study end date on July 31, 2022. A second-line therapy was captured if indicated by the initiation of systemic therapy after the end of first-line therapy. See eMethods in Supplement 1 for further description of the creation of lines of therapy. Patients with evidence of biomarker-targeted therapy based on OncoKB level 1 and level 2 evidence13,31 were flagged (eTable 3 in Supplement1). However, immune checkpoint inhibitors, including the anti–programmed cell death 1 (PD-1) and programmed cell death ligand 1 (PD-L1) inhibitors, and ERBB2 (formerly HER2 or HER2/neu)–directed therapies were categorized separately because PD-L1 and ERBB2 overexpression is often identified by IHC (which as noted above was not explicitly accounted for in testing categories). The timing of biomarker testing and targeted therapy was determined by the date of service on the administrative claim.
Study Measures
Patient characteristics at index date (Table) and baseline Quan-Charlson Comorbidity Index32 scores were examined. During first-line therapy and a fixed 6-month follow-up period (or less if the patient died before the end of the 6-month follow-up period) after the index date, all-cause health care costs were captured and presented as per-patient, per-month (PPPM) costs. Costs were adjusted using the annual medical care component of the Consumer Price Index to reflect inflation between the date of the claim and 2021, the most recent complete year of the study period.33
Table. Patient Demographics by Testing Cohort and Cancer Type.
| Demographic | No. (%) of patients | |||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| No testing | CGP | Non-CGP | ||||||||||||||||
| BC (n = 3353) | CRC (n = 3472) | GC (n = 544) | NSCLC (n = 5484) | OC (n = 1819) | PC (n = 2330) | BC (n = 258) | CRC (n = 646) | GC (n = 101) | NSCLC (n = 2215) | OC (n = 174) | PC (n = 349) | BC (n = 1644) | CRC (n = 1644) | GC (n = 214) | NSCLC (n = 2246) | OC (n = 208) | PC (n = 190) | |
| Mean age (SD), y | 66 (12) | 66 (12) | 68 (11) | 71 (8) | 68 (11) | 70 (9) | 66 (11) | 67 (12) | 67 (11) | 71 (9) | 66 (12) | 69 (10) | 64 (13) | 65 (13) | 65 (13) | 70 (9) | 67 (13) | 69 (9) |
| Sex | ||||||||||||||||||
| Female | 3311 (99) | 1557 (45) | 175 (32) | 2662 (49) | 1819 (100) | 1142 (49) | >244 (>90) | 291 (45) | 33 (33) | 1146 (52) | 174 (100) | <181 (>50) | 1620 (99) | 464 (44) | 67 (31) | 1083 (48) | 208 (100) | 92 (48) |
| Male | 52 (1) | 1915 (55) | 369 (68) | 2822 (51) | 0 | 1188 (51) | <11 | 355 (55) | 68 (67) | 1069 (48) | 0 | >168 | 24 (1) | 600 (56) | 147 (69) | 1163 (52) | 0 | 98 (52) |
| US Census region | ||||||||||||||||||
| Midwest | 974 (29) | 940 (27) | 150 (28) | 1722 (31) | 511 (28) | 657 (28) | 68 (26) | 174 (27) | 24 (24) | 578 (26) | 44 (25) | 77 (22) | 446 (27) | 305 (29) | 54 (25) | 679 (30) | 60 (29) | 61 (32) |
| Northeast | 456 (14) | 476 (14) | 89 (16) | 822 (15) | 246 (14) | 390 (17) | 29 (11) | 87 (13) | 18 (18) | 336 (15) | 22 (13) | 51 (15) | 180 (11) | 158 (15) | 31 (15) | 356 (16) | 37 (18) | 31 (16) |
| South | 1521 (45) | 1604 (46) | 236 (43) | 2513 (46) | 795 (44) | 1027 (44) | 118 (46) | 310 (48) | 47 (47) | 1107 (50) | 83 (48) | 183 (52) | 771 (47) | 511 (48) | 104 (49) | 1025 (46) | 90 (43) | 81 (43) |
| West | 402 (12) | 452 (13) | 69 (13) | 427 (8) | 267 (15) | 256 (11) | 43 (17) | 75 (12) | 12 (12) | 194 (9) | 25 (14) | 38 (11) | 247 (15) | 90 (8) | 25 (12) | 186 (8) | 21 (10) | 17 (9) |
| Commercial health plans | 1291 (39) | 1365 (39) | 170 (31) | 1001 (18) | 595 (33) | 594 (26) | 97 (38) | 223 (35) | 32 (32) | 438 (20) | 60 (35) | 84 (24) | 770 (47) | 435 (41) | 86 (40) | 531 (24) | 75 (36) | 61 (32) |
| Quan-Charlson Comorbidity Index, mean (SD) | 3.7 (2.6) | 3.6 (2.8) | 3.7 (2.7) | 4.3 (3.0) | 3.1 (2.7) | 4.2 (3.0) | 4.1 (2.5) | 3.9 (2.9) | 4.3 (2.6) | 4.0 (3.0) | 3.3 (2.7) | 4.8 (2.9) | 3.6 (2.6) | 3.7 (2.8) | 4.1 (2.7) | 3.9 (2.9) | 2.8 (2.7) | 4.1 (3.1) |
Abbreviations: BC, breast cancer; CGP, comprehensive genomic profiling; CRC, colorectal cancer; GC, gastric cancer; NSCLC, non–small cell lung cancer; OC, ovarian cancer; PC, pancreatic cancer.
Statistical Analysis
To identify differences in the reported outcomes between the biomarker testing cohorts, generalized linear models were conducted with the appropriate link function: γ for health care costs or cost ratios (CRs), logistic for binary outcomes, or odds ratios (ORs). However, given the small number of cases for certain cancer types, some models were only presented for the NSCLC and CRC cohorts. Analyses were conducted using SAS software, version 9.4 (SAS Institute Inc). Data were analyzed between February 1, 2023, and March 31, 2024. Two-sided tests for significance were conducted and α was set to .05 a priori.
Results
Patient Characteristics
Among the 26 311 individuals (mean [SD] age, 68 [11] years) who met all the study criteria (Figure 1, Table; eTables 4 and 5 in Supplement 1), 70% were MA enrollees and 62% were female. The mean (SD) Quan-Charlson score was 3.87 (2.86). Patients with NSCLC comprised the highest percentage (38%) of the study population and patients with GC the lowest (3%).
Across tumor types, the proportion of patients receiving any biomarker testing before first-line therapy ranged from 17% (OC) to 45% (NSCLC) (Figure 2). Across all tumor types, less than 50% of patients had evidence of biomarker testing before beginning first-line therapy. Patients with NSCLC had the highest testing rate, with 45% of patients having evidence of biomarker testing before first-line therapy (22% CGP and 23% non-CGP), whereas patients with OC had the lowest testing rate (8% CGP and 9% non-CGP).
Figure 2. Biomarker Testing Rates Before First- and Second-Line Therapy.
BC indicates breast cancer; CGP, comprehensive genomic profiling; CRC, colorectal cancer; GC, gastric cancer; NSCLC, non–small cell lung cancer; OC, ovarian cancer; PC, pancreatic cancer.
Across all cancer types, cumulative testing increased when assessed before second-line therapy (Figure 2). The proportion of patients receiving biomarker testing before second-line therapy ranged from 49% (BC and PC) to 64% (GC). Descriptive results of biomarker testing patterns indicate lower rates of testing in MA patients compared with commercial health plan patients across several cancer types.
Biomarker testing rates (particularly by CGP) before first-line therapy increased across time (Figure 3). For example, the percentage of patients with NSCLC who received CGP testing increased from 12% in 2018 to 33% in 2021 to 2022 (percentages were aggregated for 2021 to 2022 because patients were identified only through January 2022). Time trends in biomarker testing were similar between commercial health plan and MA patients across all cancer types.
Figure 3. Biomarker Testing Across Time.
The years 2021 and 2022 were combined into a single category because the dataset did include the full calendar year. BC indicates breast cancer; CGP, comprehensive genomic profiling; CRC, colorectal cancer; GC, gastric cancer; NSCLC, non–small cell lung cancer; OC, ovarian cancer; PC, pancreatic cancer.
Next, we investigated receipt of targeted therapy during first-line therapy. Higher percentages of patients in the CGP and non-CGP testing groups received targeted therapy compared with the no testing groups (12%, 6%, and 3%, respectively). Due to limited sample sizes, we only report results separately for patients with NSCLC and CRC (eFigure 2 and eTables 6-8 in Supplement 1). After controlling for patient characteristics, patients with CGP remained at significantly higher odds for receiving a targeted therapy. Patients with NSCLC with either CGP (OR, 3.41; 95% CI, 2.87-4.05; P < .001) or non-CGP testing (OR, 2.16; 95% CI, 1.80-2.60; P < .001) had significantly higher odds of receiving targeted therapy during first-line therapy compared with the no testing cohort (eTable 8 in Supplement 1). Additionally, the odds of patients with CGP testing receiving targeted therapy were significantly higher (OR, 1.57; 95% CI, 1.31-1.90, P < .001) than the odds of patients with non-CGP testing receiving targeted therapy. Similarly, patients with CRC and CGP (OR, 3.46; 95% CI, 2.50-4.80; P < .001) or non-CGP (OR, 1.48; 95% CI, 1.06-2.07; P = .02) testing were more likely to receive targeted therapy compared with the no testing cohort (eTable 7 in Supplement 1). Patients with CGP testing were more likely to receive targeted therapy compared with patients with non-CGP testing (OR, 2.34; 95% CI, 1.58-3.47; P < .001). We report the use of immune checkpoint inhibitors and anti-ERBB2 therapies separately in eTable 6 in Supplement 1.
Finally, first-line therapy PPPM costs were compared among the cohorts (eTables 9 and 10 in Supplement 1). Overall, PPPM costs were higher for both first-line treatment costs and first-line therapy all-cause health care costs in commercial health plan patients compared with MA patients (Figure 4) for all cancers. The CGP testing groups had no greater PPPM all-cause total health care costs during first-line therapy compared with the non-CGP testing group, and costs for both groups were higher than the costs for the no testing group (eTables 10-15 in Supplement 1). Costs among patients with CGP testing were not statistically different from those with non-CGP testing (cost ratios of 1.03 [P = .63] for breast cancer, 0.98; 95% CI, 0.89-1.09 [P = .71] for colorectal cancer, 1.10; 95% CI, 0.87-1.40 [P = .42] for gastric cancer, 1.06; 95% CI, 1.00-1.13 [P = .054] for non–small cell lung, 0.94; 95% CI, 0.76-1.15 [P = .55] for ovarian cancer, and 1.00; 95% CI, 0.83-1.21 [P = .98] for pancreatic cancer) (eTables 11-16 in Supplement 1). Considering only patients with NSCLC, all-cause PPPM costs were also higher during first-line therapy for patients with CGP (CR, 1.17; 95% CI, 1.11-1.24; P < .001) and non-CGP testing (CR, 1.10; 95% CI, 1.05-1.16; P < .001) compared with the no testing cohort (eTable 14 in Supplement 1). Similar patterns were observed for patients with CRC receiving CGP (cost ratio, 1.14; 95% CI, 1.04-1.24; P = .004) or non-CGP testing (cost ratio, 1.16; 95% CI, 1.08-1.24; P < .001) where all-cause PPPM costs were higher compared with no testing (eTable 12 in Supplement 1). Additionally, we did not observe differences in all-cause PPPM costs by biomarker testing categories for the other cancers examined (eTables 11, 13, 15, and 16 in Supplement 1).
Figure 4. Mean Per-Patient Per-Month Costs During First-Line Therapy.
See eTables 9 and 10 in Supplement 1 for full table of cost comparisons. Regimen costs are defined as costs attributable to first-line treatments. Nonregimen costs are defined as costs not attributable to first-line treatments. BC indicates breast cancer; CGP, comprehensive genomic profiling; CRC, colorectal cancer; GC, gastric cancer; NSCLC, non–small cell lung cancer; OC, ovarian cancer; PC, pancreatic cancer.
Discussion
In the current study, we compared the use of targeted therapy during the initial line of therapy for patients with no biomarker testing, non-CGP testing, and CGP testing. We also compared the rates of testing by cancer type and health care costs during first-line therapy by testing category.
Overall, rates of CGP and non-CGP biomarker testing before the initiation of systemic therapy for metastatic disease were low, although they increased over time. Given the well-established survival advantage for patients treated with biomarker-matched targeted therapies,34,35,36,37 this finding represents potential suboptimal care of patients with metastatic cancer, especially in cancer types in which targeted therapy is most appropriate. All patients in our cohort received first-line therapy, so the gap in testing is likely not related to patients who were not willing or able to receive systemic treatment. Patients with NSCLC and CRC with CGP testing had the highest levels of biomarker-targeted therapy compared with the no testing and non-CGP testing cohorts in part because biomarker-targeted therapy in these cancers is most established. Although our results suggest that a suboptimal percentage of tested patients received targeted therapy, a higher percentage of the CGP-tested groups had immune checkpoint inhibitor therapy, which could suggest high tumor mutational burden status derived from CGP testing.
A large body of research has demonstrated the value of biomarker testing, especially for NSCLC.18,19,38 Biomarker testing, when completed before first-line therapy, can meaningfully improve outcomes of patients with NSCLC.6,22,39 National clinical guidelines recommend completing broad molecular profiling during the diagnostic evaluation and before initiation of first-line treatment. Despite this evidence, only 45% of patients with NSCLC had evidence of biomarker testing by the start of first-line therapy. Although the number of patients receiving biomarker testing before first-line therapy increased over time, failure to perform adequate testing is inconsistent with clinical guidelines recommendations4 and the evidence of the clinical benefit of identifying patients who are eligible for biomarker-matched therapy.
Additionally, in NSCLC, in which biomarker testing to inform LOT1 regimens is most relevant due to the number of actionable biomarkers known with matched targeted therapy, outcomes for patients with CGP before first-line therapy had improved intermediate outcomes (receipt of targeted therapy) and no impact on costs. This finding aligns with previous work40 that found that CGP testing in patients with NSCLC can result in similar or even improved outcomes compared with non-CGP testing. We found a similar pattern of increases in targeted therapy for patients with CRC. We did not find evidence of increased all-cause health care costs among patients with GCP testing in cancer groups with fewer options for targeted therapy during first-line therapy.
Limitations
Limitations of this study include those typically associated with administrative claims data used for secondary research purposes. Administrative claims can be subject to selection bias. For example, these data are limited in the ability to provide cancer diagnoses details beyond International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes and cannot precisely distinguish between squamous and nonsquamous NSCLC, which has implications for the appropriateness of CGP during the study period. In addition, administrative claims do not include biomarker test results. Biomarker testing may not have identified an actionable mutation for some test recipients. However, the population estimate of patients with NSCLC with a targeted therapy–related mutation is higher than the observed receipt of targeted therapy in this study.41,42
Some care received (such as during a clinical trial) may not have generated a claim; for example, patients may have been tested without a claim submitted and miscategorized in the no testing category. However, given the incentive of the laboratory provider to receive reimbursement for services provided and for the patient to reduce their payment, it is likely that most biomarker tests and treatments are associated with an administrative claim. Additionally, prior studies43,44 suggest that secondary neoplasm ICD-10 codes have only moderate sensitivity but high specificity. Therefore, it is likely that when a secondary neoplasm code is present, it indicates the presence of advanced or metastatic disease.
Another limitation is that our algorithm for the testing categories (based on procedure codes and laboratory providers) may have miscategorized testing types. Patients who receive only IHC testing and no other biomarker testing may be miscategorized in the no biomarker testing group because, based on Current Procedural Terminology codes alone, it is not possible to reliably determine whether IHC tests are being used for diagnostic purposes or for treatment selection. This caveat is important to consider because during the study period of this analysis, other than for NSCLC, CGP testing may have provided only minimal additional actionable information above IHC testing because targeted therapy options were limited. However, these cancer types were included in the current study because more targeted therapy options are becoming available over time, which necessitates testing for multiple various biomarkers.30 Also of note, PD-L1 and ERBB2 protein expression is determined by IHC testing; therefore, we did not aggregate immune checkpoint inhibitors and anti-ERBB2 therapy in the group list of targeted agents. Although IHC-based testing is used to capture important biomarkers (such as PD-L1 and ERBB2 overexpression), other molecular testing techniques (eg, fluorescence in situ hybridization and next-generation sequencing) are required to identify whether there are targetable mutations or rearrangements (eg, EGFR, KRAS, NRAF, BRAF) that influence the decision for other initial targeted therapy instead of anti–PD-1 and PD-L1–based or anti-ERBB2–based therapy.
Additionally, we were not able to assess whether patients in the tested cohorts (non-CGP and CGP) were appropriately evaluated for the full breath of biomarkers recommended in clinical guidelines or whether they received appropriate treatment concordant with testing results due to a lack of test result data. Claims data are limited in their ability to account for other potential confounders or bias that could affect the testing rates observed, including differences in socioeconomic factors and variability in care across sites of service. Randomized clinical trials could further establish the efficacy of CGP testing. Furthermore, our conclusions are limited to commercial health plan and MA enrollees and may not be generalizable to other patient populations, including those outside the US.
Conclusions
In this cohort study, we found evidence that rates of biomarker testing among patients with advanced cancer are potentially lower than optimal despite well-established guideline recommendations. Although inadequate commercial insurance coverage is often cited as a barrier to comprehensive genomic profiling,45 testing rates in MA beneficiaries were generally lower or comparable to commercial populations despite CGP coverage.
Interventions to improve biomarker testing are needed, especially as more targeted therapies are developed for all cancer types.27 Given our findings that CGP testing does not significantly increase total all-cause costs and the potential benefits of CGP over non-CGP approaches, including optimization of tissue stewardship, detection of genomic signatures (eg, tumor mutational burden), and potential identification for trial eligibility, the increased adoption of CGP that includes assessment of all guideline-recommended biomarkers may offer opportunities for improved outcomes.
eMethods. Supplementary Methods
eFigure 1. Study Period
eTable 1. Cancer Diagnosis Codes
eTable 2. Biomarker Testing Codes
eTable 3. Systemic Anticancer Drugs
eTable 4. Study Sample Attrition
eTable 5. Testing Cohorts by Cancer Type and Business Line
eTable 6. Targeted Therapy Received During LOT1 by Cancer Type and Testing Categories
eFigure 2. Percent of Patients that Received Targeted Therapy in LOT1 by Cohort and Cancer Type (CRC and NSCLC)
eTable 7. Multivariate Analysis of Receipt of OncoKB Targeted Therapy in LOT1 Among CRC Patients
eTable 8. Multivariable Analysis of Receipt of OncoKB Targeted Therapy in LOT1 Among Non-Small-cell Lung Cancer Patients
eTable 9. Regimen Costs During LOT1 Total Healthcare and Costs During LOT1 and 6-month Follow-up – Commercial Patients
eTable 10. Regimen Costs During LOT1 Total Healthcare and Costs During LOT1 and 6-month Follow-up – Medicare Advantage Patients
eTable 11. Multivariable Analysis of Breast Cancer LOT1 Costs
eTable 12. Multivariable Analysis of Colorectal Cancer LOT1 Costs
eTable 13. Multivariable Analysis of Gastric Cancer LOT1 Costs
eTable 14. Multivariable Analysis of Non-Small-Cell Lung Cancer LOT1 Costs
eTable 15. Multivariable Analysis of Ovarian Cancer LOT1 Costs
eTable 16. Multivariable Analysis of Pancreatic Cancer LOT1 Costs
Data Sharing Statement
References
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
eMethods. Supplementary Methods
eFigure 1. Study Period
eTable 1. Cancer Diagnosis Codes
eTable 2. Biomarker Testing Codes
eTable 3. Systemic Anticancer Drugs
eTable 4. Study Sample Attrition
eTable 5. Testing Cohorts by Cancer Type and Business Line
eTable 6. Targeted Therapy Received During LOT1 by Cancer Type and Testing Categories
eFigure 2. Percent of Patients that Received Targeted Therapy in LOT1 by Cohort and Cancer Type (CRC and NSCLC)
eTable 7. Multivariate Analysis of Receipt of OncoKB Targeted Therapy in LOT1 Among CRC Patients
eTable 8. Multivariable Analysis of Receipt of OncoKB Targeted Therapy in LOT1 Among Non-Small-cell Lung Cancer Patients
eTable 9. Regimen Costs During LOT1 Total Healthcare and Costs During LOT1 and 6-month Follow-up – Commercial Patients
eTable 10. Regimen Costs During LOT1 Total Healthcare and Costs During LOT1 and 6-month Follow-up – Medicare Advantage Patients
eTable 11. Multivariable Analysis of Breast Cancer LOT1 Costs
eTable 12. Multivariable Analysis of Colorectal Cancer LOT1 Costs
eTable 13. Multivariable Analysis of Gastric Cancer LOT1 Costs
eTable 14. Multivariable Analysis of Non-Small-Cell Lung Cancer LOT1 Costs
eTable 15. Multivariable Analysis of Ovarian Cancer LOT1 Costs
eTable 16. Multivariable Analysis of Pancreatic Cancer LOT1 Costs
Data Sharing Statement