Real-World Impact of Comprehensive Genomic Profiling on Biomarker Detection, Receipt of Therapy, and Clinical Outcomes in Advanced Non-Small Cell Lung Cancer.

PURPOSE: Therapeutic decision making for patients with advanced non-small cell lung cancer (aNSCLC) includes a growing number of options for genomic, biomarker-guided, targeted therapies. We compared actionable biomarker detection, targeted therapy receipt, and real-world overall survival (rwOS) in patients with aNSCLC tested with comprehensive genomic profiling (CGP) versus small panel testing (SP) in real-world community health systems. METHODS: Patients older than 18 years diagnosed with aNSCLC between January 1, 2015, and December 31, 2020, who received biomarker testing were followed until death or study end (September 30, 2021), and categorized by most comprehensive testing during follow-up: SP (≤52 genes) or CGP (>52 genes). RESULTS: Among 3,884 patients (median age, 68 years; 50% female; 73% non-Hispanic White), 20% received CGP and 80% SP. The proportion of patients with ≥one actionable biomarker (actionability) was significantly higher in CGP than in SP (32% v 14%; P < .001). Of patients with actionability, 43% (CGP) and 38% (SP) received matched therapies (P = .20). Among treated patients, CGP before first-line treatment was associated with higher likelihood of matched therapy in any line (odds ratio, 3.2 [95% CI, 1.84 to 5.53]). CGP testing (hazard ratio [HR], 0.80 [95% CI, 0.72 to 0.89]) and actionability (HR, 0.84 [95% CI, 0.77 to 0.91]) were associated with reduced risk of mortality. Among treated patients with actionability, matched therapy receipt showed improved median rwOS in months in CGP (34 [95% CI, 21 to 49] matched v 14 [95% CI, 10 to 18] unmatched) and SP (27 [95% CI, 21 to 43] matched v 10 [95% CI, 8 to 14] unmatched). CONCLUSION: Patients who received CGP had improved detection of actionable biomarkers and greater use of matched therapies, both of which were associated with significant increases in survival.

The Friends of Cancer Research Real-World Data Collaboration Pilot 2.0: Methodological Recommendations from Oncology Case Studies.

The purpose of this study was to evaluate the potential collective opportunities and challenges of transforming real-world data (RWD) to real-world evidence for clinical effectiveness by focusing on aligning analytic definitions of oncology end points. Patients treated with a qualifying therapy for advanced non-small cell lung cancer in the frontline setting meeting broad eligibility criteria were included to reflect the real-world population. Although a trend toward improved outcomes in patients receiving PD-(L)1 therapy over standard chemotherapy was observed in RWD analyses, the magnitude and consistency of treatment effect was more heterogeneous than previously observed in controlled clinical trials. The study design and analysis process highlighted the identification of pertinent methodological issues and potential innovative approaches that could inform the development of high-quality RWD studies.

Real-world Overall Survival Using Oncology Electronic Health Record Data: Friends of Cancer Research Pilot.

In prior work, Friends of Cancer Research convened multiple data partners to establish standardized definitions for oncology real-world end points derived from electronic health records (EHRs) and claims data. Here, we assessed the performance of real-world overall survival (rwOS) from data sets sourced from EHRs by evaluating the ability of the end point to reflect expected differences from a previous randomized controlled trial across five data sources, after applying inclusion/exclusion criteria. The KEYNOTE-189 clinical trial protocol of platinum doublet chemotherapy (chemotherapy) vs. programmed cell death protein 1 (PD-1) in combination with platinum doublet chemotherapy (PD-1 combination) in first-line nonsquamous metastatic non-small cell lung cancer guided retrospective cohort selection. The Kaplan-Meier product limit estimator was used to calculate 12-month rwOS with 95% confidence intervals (CIs) in each data source. Cox proportional hazards models estimated hazard ratios (HRs) and associated 95% CIs, controlled for prognostic factors. Once the inclusion/exclusion criteria were applied, the five resulting data sets included 155 to 1,501 patients in the chemotherapy cohort and 36 to 405 patients in the PD-1 combination cohort. Twelve-month rwOS ranged from 45% to 58% in the chemotherapy cohort and 44% to 68% in the PD-1 combination cohort. The adjusted HR for death ranged from 0.80 (95% CI: 0.69, 0.93) to 1.15 (95% CI: 0.71, 1.85), controlling for age, gender, performance status, and smoking status. This study yielded insights regarding data capture, including ability of real-world data to precisely identify patient populations and the impact of criteria on end points. Sensitivity analyses could elucidate data set-specific factors that drive results.

Prototyping a precision oncology 3.0 rapid learning platform.

BACKGROUND: We describe a prototype implementation of a platform that could underlie a Precision Oncology Rapid Learning system. RESULTS: We describe the prototype platform, and examine some important issues and details. In the Appendix we provide a complete walk-through of the prototype platform. CONCLUSIONS: The design choices made in this implementation rest upon ten constitutive hypotheses, which, taken together, define a particular view of how a rapid learning medical platform might be defined, organized, and implemented.