Complementary Roles for Tissue- and Blood-Based Comprehensive Genomic Profiling for Detection of Actionable Driver Alterations in Advanced NSCLC.

Introduction: Whereas tumor biopsy is the reference standard for genomic profiling of advanced NSCLC, there are now multiple assays approved by the Food and Drug Administration for liquid biopsy testing of circulating tumor DNA. Here, we study the incremental value that liquid biopsy comprehensive genomic profiling (CGP) adds to tissue molecular testing. Methods: Patients with metastatic NSCLC were enrolled in a prospective diagnostic study to receive circulating tumor DNA CGP; tissue CGP was optional in addition to their standard tissue testing. Focusing on nine genes listed per the National Comprehensive Cancer Network (NCCN) guidelines, liquid CGP was compared with available tissue testing results across three subcohorts: tissue CGP, standard-of-care testing of up to five biomarkers, or no tissue testing. Results: A total of 515 patients with advanced nonsquamous NSCLC received liquid CGP. Among 131 with tissue CGP results, NCCN biomarkers were detected in 86 (66%) with tissue CGP and 56 (43%) with liquid CGP (p < 0.001). Adding liquid CGP to tissue CGP detected no additional patients with NCCN biomarkers, whereas tissue CGP detected NCCN biomarkers in 30 patients (23%) missed by liquid CGP. Studying 264 patients receiving tissue testing of up to five genes, 102 (39%) had NCCN biomarkers detected in tissue, with an additional 48 (18%) detected using liquid CGP, including 18 with RET, MET, or ERBB2 drivers not studied in tissue. Conclusions: For the detection of patients with advanced nonsquamous NSCLC harboring 9 NCCN biomarkers, liquid CGP increases detection in patients with limited tissue results, but does not increase detection in patients with tissue CGP results available. In contrast, tissue CGP can add meaningfully to liquid CGP for detection of NCCN biomarkers and should be considered as a follow-up when an oncogenic driver is not identified by liquid biopsy.

Analysis of a Real-World Progression Variable and Related Endpoints for Patients with Five Different Cancer Types.

INTRODUCTION: We previously demonstrated that real-world progression (rwP) can be ascertained from unstructured electronic health record (EHR)-derived documents using a novel abstraction approach for patients with advanced non-small cell lung cancer (base case). The objective of this methodological study was to assess the reliability, clinical relevance, and the need for disease-specific adjustments of this abstraction approach in five additional solid tumor types. METHODS: Patients with metastatic breast cancer (mBC), advanced melanoma (aMel), small cell lung cancer (SCLC), metastatic renal cell carcinoma (mRCC), and advanced gastric/esophageal cancer (aGEC) were selected from a real-world database. Disease-specific additions to the base case were implemented as needed. The resulting abstraction approach was applied to each disease cohort to capture rwP events and dates. To provide comprehensive clinical context, real-world progression-free survival (rwPFS) and time to progression (rwTTP) were compared to real-world overall survival (rwOS), time to next treatment (rwTTNT), and time to treatment discontinuation (rwTTD). Endpoint estimates were assessed using the Kaplan-Meier method. Correlations between real-world endpoints and rwOS were calculated using Spearman's ρ. RESULTS: Additions to the base-case rwP abstraction approach were required for mBC, aMel, and SCLC. Inter-abstractor agreement for rwP occurrence, irrespective of date, ranged from 88% to 97%. Occurrence of clinically relevant downstream events (new antineoplastic systemic therapy start, antineoplastic systemic therapy end, or death relative to the rwP event) ranged from 59% (aMel) to 72% (mBC). Median rwPFS ranged from 3.7 (aMel) to 7.7 (mBC) months, and median rwTTP ranged from 4.6 (aMel) to 8.3 (mRCC) months. Correlations between rwOS and rwPFS ranged from 0.52 (aMel) to 0.82 (SCLC). The correlation between rwOS and rwTTD was often lower relative to other comparisons (range 0.40-0.62). CONCLUSION: Derivation of a rwP variable from EHR documentation is feasible and reliable across the five solid tumors. Endpoint analyses show that rwP produces clinically meaningful information.

Differential frequency in imaging-based outcome measurement: Bias in real-world oncology comparative-effectiveness studies.

BACKGROUND: Comparative-effectiveness studies using real-world data (RWD) can be susceptible to surveillance bias. In solid tumor oncology studies, analyses of endpoints such as progression-free survival (PFS) are based on progression events detected by imaging assessments. This study aimed to evaluate the potential bias introduced by differential imaging assessment frequency when using electronic health record (EHR)-derived data to investigate the comparative effectiveness of cancer therapies. METHODS: Using a nationwide de-identified EHR-derived database, we first analyzed imaging assessment frequency patterns in patients diagnosed with advanced non-small cell lung cancer (aNSCLC). We used those RWD inputs to develop a discrete event simulation model of two treatments where disease progression was the outcome and PFS was the endpoint. Using this model, we induced bias with differential imaging assessment timing and quantified its effect on observed versus true treatment effectiveness. We assessed percent bias in the estimated hazard ratio (HR). RESULTS: The frequency of assessments differed by cancer treatment types. In simulated comparative-effectiveness studies, PFS HRs estimated using real-world imaging assessment frequencies differed from the true HR by less than 10% in all scenarios (range: 0.4% to -9.6%). The greatest risk of biased effect estimates was found comparing treatments with widely different imaging frequencies, most exaggerated in disease settings where time to progression is very short. CONCLUSIONS: This study provided evidence that the frequency of imaging assessments to detect disease progression can differ by treatment type in real-world patients with cancer and may induce some bias in comparative-effectiveness studies in some situations.

Bridging the divide between clinical research and clinical care in oncology: An integrated real-world evidence generation platform.

Real world data (RWD) are data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources; real-world evidence (RWE) generated by RWD analyses can become an important component of drug development programs and, potentially, regulatory decision-making. As a RWD source, electronic health records (EHRs) can now provide patient-level data at unparalleled depth and granularity. We propose a RWE generation framework that could maximize the synergy between RWD and prospective clinical trials by capitalizing on an emerging data curation infrastructure that may be applied to both retrospective and prospective research. In this platform, centralized data collection and monitoring could be enabled via routine EHR use, and seamlessly integrated with select intentional data capture during prospective study periods. By bridging the divide between routine care and clinical research, this integrated platform aggregates retrospective and prospective data, collected both routinely and intentionally. This approach makes clinical trial participation more available to patients, increasing the potential depth of data, representativeness and efficiency of clinical research.

Characterizing the Feasibility and Performance of Real-World Tumor Progression End Points and Their Association With Overall Survival in a Large Advanced Non-Small-Cell Lung Cancer Data Set.

PURPOSE: Large, generalizable real-world data can enhance traditional clinical trial results. The current study evaluates reliability, clinical relevance, and large-scale feasibility for a previously documented method with which to characterize cancer progression outcomes in advanced non-small-cell lung cancer from electronic health record (EHR) data. METHODS: Patients who were diagnosed with advanced non-small-cell lung cancer between January 1, 2011, and February 28, 2018, with two or more EHR-documented visits and one or more systemic therapy line initiated were identified in Flatiron Health's longitudinal EHR-derived database. After institutional review board approval, we retrospectively characterized real-world progression (rwP) dates, with a random duplicate sample to ascertain interabstractor agreement. We calculated real-world progression-free survival, real-world time to progression, real-world time to next treatment, and overall survival (OS) using the Kaplan-Meier method (index date was the date of first-line therapy initiation), and correlations between OS and other end points were assessed at the patient level (Spearman's ρ). RESULTS: Of 30,276 eligible patients,16,606 (55%) had one or more rwP event. Of these patients, 11,366 (68%) had subsequent death, treatment discontinuation, or new treatment initiation. Correlation of real-world progression-free survival with OS was moderate to high (Spearman's ρ, 0.76; 95% CI, 0.75 to 0.77; evaluable patients, n = 20,020), and for real-world time to progression correlation with OS was lower (Spearman's ρ, 0.69; 95% CI, 0.68 to 0.70; evaluable patients, n = 11,902). Interabstractor agreement on rwP occurrence was 0.94 (duplicate sample, n = 1,065) and on rwP date 0.85 (95% CI, 0.81 to 0.89; evaluable patients n = 358 [patients with two independent event captures within 30 days]). Median rwP abstraction time from individual EHRs was 18.0 minutes (interquartile range, 9.7 to 34.4 minutes). CONCLUSION: We demonstrated that rwP-based end points correlate with OS, and that rwP curation from a large, contemporary EHR data set can be reliable, clinically relevant, and feasible on a large scale.