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Purpose: Studies based on data from routine clinical practice (real-world data, RWD) benefit from larger patient numbers and are more representative of patient diversity than clinical trial studies. When combined with comprehensive genomic profiling (CGP), RWD may uncover the impact of therapies and patient characteristics on tumor genomic landscape. Here we aimed at assessing the feasibility of using RWD to identify changes in the prevalence of genomic alterations upon treatment and proposed a methodology to address RWD inherent caveats. Experimental Design: To explore associations between tumor genomics and treatment chosen by physicians, we evaluated data from 5323 patients with metastatic hormone receptor-positive HER2-negative (HR+/HER2-) breast cancers from a nationwide real-world clinico-genomic database, originating from approximately 280 US cancer clinics (~800 sites of care). To perform our comparisons, we defined groups based on the therapy administered in the metastatic setting and the timing of the CGP relative to treatment. We used bootstrapping to estimate the significance of the effect and stratified analyses to assess the impact of potential confounders such as the site of the collected samples or disease history. Results: ESR1 alteration prevalence increased from 5.6% (CI: 2.8-8.9) pre-treatment to 21.4% (CI: 13.3-29.6) following aromatase inhibitor. Yet, it was significantly less than the prevalence following treatments including CDK4/6 inhibitors (CDK4/6i; 37.1% [CI: 27.8-46.4]; P=0.006). Further, exposure to CDK4/6i led to an increase in FGFR1 and TP53 alterations as well as genes of the cell cycle (FDR< 0.2). Overall, we found that more pathways were likely to be altered in a given tumor following AI+CDK4/6i than after AI alone (P=0.02). In particular, alterations of the MAPK pathway were not exclusive to ESR1 alterations in the post-AI+CDK4/6i group compared to AI only, suggesting that MAPK pathway alterations alone may not overcome CDK4/6i-based treatments. Differences following exposure to CDK4/6i were retained in samples taken after the second-line treatment. Stratified analyses confirmed that these results are independent of exposure to adjuvant therapy or treatment duration and showed that ESR1 mutations occurred in both primary and metastatic samples. Conclusions: Analysis of EHR-derived clinical data linked to CGP results from routine care can replicate associations previously observed in clinical trials and uncover unknown changes in tumor genomic landscape. Bootstrapping and stratified analysis reinforced our confidence in the results and thus allowed us to identify that CDK4/6i exposure led to a different – more altered – genomic landscape in HR+/HER2- breast cancer patients. This finding can inform design of clinical trials post-CDK4/6i and may help guide treatment choice for late stage patients. Thus, our work demonstrates the feasibility of leveraging real-world clinico-genomic data for translational research in oncology and leads the way for analyses including a more diverse patient population. Citation Format: Nayan Chaudhary, Ciara Mecalfe, Marc Hafner. Real-world clinico-genomic data reveal differences in genomic landscape associated with CDK4/6 inhibitors in HR+/HER2- breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P5-13-03.

Purpose: The utility of real-world data (RWD) for use as external controls in drug development is informed by studies that replicate trial control arms for different endpoints. The purpose of this study was to replicate control arms from four non–small cell lung cancer (NSCLC) randomized controlled trials (RCT) to analyze overall survival (OS), progression-free survival (PFS), and overall response rate (ORR) using RWD. Patients and Methods: This study used RWD from a nationwide de-identified database and a clinico-genomic database to replicate OS, PFS, and ORR endpoints in the chemotherapy control arms of four first-line NSCLC RCTs evaluating atezolizumab [IMpower150–wild-type (WT), IMpower130-WT, IMpower131, and IMpower132]. Additional objectives were to develop a definition of real-world PFS (rwPFS) and to evaluate the real-world response rate (rwRR) endpoint. Results: Baseline demographic and clinical characteristics were balanced after application of propensity score weighting methods. For rwPFS and OS, RWD external controls were generally similar to their RCT control counterparts. Across all four trials, the hazard ratio (HR) point estimates comparing trial controls with external controls were closer to 1.0 for the PFS endpoint than for the OS endpoint. An exploratory assessment of rwRR in RWD revealed a slight but nonsignificant overestimation of RCT ORR, which was unconfounded by baseline characteristics. Conclusions: RWD can be used to reasonably replicate the OS and PFS of chemotherapy control arms of first-line NSCLC RCTs. Additional studies can provide greater insight into the utility of RWD in drug development.

Supplementary Figure from Replication of Overall Survival, Progression-Free Survival, and Overall Response in Chemotherapy Arms of Non–Small Cell Lung Cancer Trials Using Real-World Data

Purpose:The utility of real-world data (RWD) for use as external controls in drug development is informed by studies that replicate trial control arms for different endpoints. The purpose of this study was to replicate control arms from four non–small cell lung cancer (NSCLC) randomized controlled trials (RCT) to analyze overall survival (OS), progression-free survival (PFS), and overall response rate (ORR) using RWD.Patients and Methods:This study used RWD from a nationwide de-identified database and a clinico-genomic database to replicate OS, PFS, and ORR endpoints in the chemotherapy control arms of four first-line NSCLC RCTs evaluating atezolizumab [IMpower150–wild-type (WT), IMpower130-WT, IMpower131, and IMpower132]. Additional objectives were to develop a definition of real-world PFS (rwPFS) and to evaluate the real-world response rate (rwRR) endpoint.Results:Baseline demographic and clinical characteristics were balanced after application of propensity score weighting methods. For rwPFS and OS, RWD external controls were generally similar to their RCT control counterparts. Across all four trials, the hazard ratio (HR) point estimates comparing trial controls with external controls were closer to 1.0 for the PFS endpoint than for the OS endpoint. An exploratory assessment of rwRR in RWD revealed a slight but nonsignificant overestimation of RCT ORR, which was unconfounded by baseline characteristics.Conclusions:RWD can be used to reasonably replicate the OS and PFS of chemotherapy control arms of first-line NSCLC RCTs. Additional studies can provide greater insight into the utility of RWD in drug development.

Supplementary Table from Replication of Overall Survival, Progression-Free Survival, and Overall Response in Chemotherapy Arms of Non–Small Cell Lung Cancer Trials Using Real-World Data

Genomic testing is an important part of personalized oncology. This study explored testing patterns and results, as well as patient (pt) characteristics, in the BC setting. Data from the nationwide, de-identified Flatiron Health (FH)-Foundation Medicine, Inc. (FMI) BC clinico-genomic database were included; pts had to have been treated in a FH network oncology practice and tested with a FMI CGP test. Pts were >18 years old and had a primary or metastatic BC (mBC) diagnosis between 1/1/2011 and 3/30/2020. Only pts with solid-tumor tissue of origin and solid-tumor assay tests were considered; pts whose tests occurred >120 days after their last visit (based on report date) were excluded. Pt cohorts were classified by timing of test result receipt: cohort 1, between primary diagnosis and 120 days post-diagnosis if no surgery recorded; cohort 3a, within 60 days prior to/after mBC diagnosis; and cohort 3b, >60 days after mBC diagnosis. Pt characteristics and actionable mutations according to the FMI report only (associated with sensitivity or resistance to ≥1 therapy) were described by cohort and BC subtype, defined at primary diagnosis for cohorts 1/2 and at mBC diagnosis for cohorts 3a/3b. Pt characteristics were compared with those of the FH cohort (not tested with next-generation sequencing or received other next-generation sequencing tests). Overall, 4183 pts were included: 285 in cohort 1, 412 in cohort 2, and 3486 in cohorts 3a/3b. There was an overrepresentation of triple-negative BC (TNBC) among pts tested in the early setting (cohorts 1/2) vs the underlying population (33%/32% vs 10%), and an underrepresentation of hormone receptor (HR)-positive/HER2-negative BC (45%/48% vs 71%). TNBC remained overrepresented among pts tested at mBC diagnosis (cohort 3a; 30% vs 11%), but not during mBC follow-up (cohort 3b; 14% vs 11%), which followed the underlying FH distribution. The majority of patients were tested using samples collected within their respective test windows: 100% in cohort 1, 63% in cohort 2, and 78% and 46% in cohorts 3a/3b. For pts tested in the mBC setting, 22% and 15% in cohorts 3a and 3b, respectively, were tested using tissue collected >60 days prior to mBC diagnosis. Percentages of pts with at least one FMI-report-actionable alteration ranged from 94% (n = 601) in HER2-positive samples to 75% (n = 668) in pts with TNBC. Among this BC cohort, FMI-CGP predominantly occurred in the mBC setting, and use of archival tissue for CGP occurred in later lines of treatment. Testing, in particular prior to mBC diagnosis, reflected a disproportionately higher prevalence of pts with TNBC, potentially due to there being fewer satisfactory treatment options in this setting. The distribution of actionable alterations varied by HER2 and HR status. Citation Format: Achim Rody, Nayan Chaudhary, Christopher Craggs, Marcio Debiasi, Gilles Erb, Virginia Fisher, Margaret McCusker, Tamara Snow, Maria Vidal Losada, Patricia Luhn. Comprehensive genomic profiling (CGP) in breast cancer (BC): Patterns and results from a clinico-genomic database [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 437.

Real-world data (RWD) are potentially a highly valuable resource to accelerate drug discovery and improve clinical practice. In particular, datasets combining clinical outcomes and comprehensive genomic profiling (CGP) could uncover new mechanisms of resistance and identify new patient populations for targeted therapies. Here, we used a de-identified clinico-genomic database (CGDB) from routine clinical care based on a clinical EHR-derived data from Flatiron Health linked to tumor sequencing results from Foundation Medicine to assess how prevalence of genomic alterations changes following progression on targeted therapies in the metastatic setting. One of the main challenges we faced is the lack of systematic and longitudinal genomic testing: among the patients receiving a specific treatment, some had a CGP performed prior to treatment, others only around the end of the line of therapy, but only a handful had multiple tests. We therefore established a methodology to assess changes in alteration prevalence and tested how our results are sensitive to cohort building parameters such as timing of testing, duration of therapy, or prior lines of treatment. Our approach successfully recovered three established cases of therapeutic resistance: (1) EGFR T790M in non-small cell lung cancer patients treated with erlotinib or gefitinib; (2) AR alterations following hormonal therapies in prostate cancers; and (3) ESR1 mutations in hormone-receptor positive breast cancer patients progressing on endocrine therapies other than fulvestrant. We delved into the latter case and identified differences in the type of ESR1 mutation that occurred following endocrine therapies if CDK4/6 inhibitors were co-administered or not. On one hand, RWD lack randomization and can thus be subject to confounding variables but on the other hand, RWD have the advantage of including a larger number of patients in a real-world setting compared to clinical trials. Our analysis shows that real-world CGDB can recapitulate the expected emergence of mutations following specific lines of treatment. Our study is a proof of concept of usage of RWD to assess how prevalence of genomic alterations changes following treatment. Our method allowed us to assess differences between patient cohorts and generate novel hypotheses on treatment-related changes in genomic profiles, which can ultimately lead to new drug indications. Citation Format: Nayan Chaudhary, Patricia Luhn, Gunther Jansen, Ciara Metcalfe, Marc Hafner. Identification of genomic alterations related to treatment progression in RWD [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6323.