Concordance of Clinician-Documented and Imaging Response in Patients With Stage IV Non-Small Cell Lung Cancer Treated With First-Line Therapy

Centralized Investigator Review of Radiological and Functional Imaging Reports in Real-World Oncology Studies: The SACHA-France Experience

SACHA-France (NCT04477681) is a prospective real-world study that collects clinical safety and efficacy data of novel anticancer therapies prescribed off-label or on compassionate use to patients <25 years. From March 2020 until February 2024, 640 patients with solid tumors or lymphomas were included, with 176 (28%) reported objective tumor responses. Centralized medical monitoring of local radiological/functional imaging reports by the SACHA coordinating investigator led to response modification in 45 out of 176 cases (26%), highlighting the relevance of the medical review of study data. We suggest this pragmatic approach for improving clinical trial data when centralized radiological review is not performed.

Dissection of Progressive Disease Patterns for a Modified Classification for Immunotherapy

Importance

Progressive disease (PD) in patients treated with immune checkpoint inhibitors (ICIs) varies widely in outcomes according to the Response Evaluation Criteria in Solid Tumors (RECIST), version 1.1. Efforts to modify RECIST for ICI treatment have not resolved the heterogeneity in PD patterns, posing a clinical challenge.

Objective

To develop and validate a modified PD classification based on PD patterns and evaluate its association with postprogression survival (PPOS) in patients treated with the programmed cell death protein ligand 1 antibody atezolizumab across various solid tumors.

Design, Setting, and Participants

This study analyzed data from 5 phase 3 trials (IMmotion151, IMvigor211, OAK, Impower133, and IMspire150) involving patients treated with atezolizumab for renal cell carcinoma (RCC), urothelial carcinoma, small cell lung cancer, non-small cell lung cancer, and melanoma. This post hoc analysis was conducted from March to September 2024.

Exposure

Treatment with atezolizumab.

Main Outcomes and Measures

The primary outcome was the association of PD patterns with PPOS. Seven PD patterns were identified based on the enlargement of target and nontarget lesions or new lesions and their combinations.

Results

A total of 1377 patients were analyzed across the 5 trials. In RCC, 7 PD patterns significantly affected prognosis. The 6-month PPOS probability ranged from 26% for progression in target and nontarget lesions plus new lesions to 90% for progression in either target or nontarget lesions alone. A modified PD classification was developed that categorized PD into 3 risk levels: low risk (progression of existing lesions), intermediate risk (new lesions without progression of existing lesions), and high risk (progression of existing lesions plus new lesions). This score was associated with PPOS in ICI-treated RCC, with hazard ratios of 0.23 (95% CI, 0.13-0.41; P < .001) and 0.39 (95% CI, 0.23-0.66; P < .001) for low-risk and intermediate-risk PD compared with high-risk PD, respectively. Validation in additional trials confirmed the score's applicability across various tumors.

Conclusions and Relevance

In this study, a survival score was developed based on PD patterns. The risk classification was associated with PPOS across various solid tumors treated with immunotherapy and may therefore enhance prognostication and clinical decision-making, potentially providing a valuable tool for treating patients with PD who are receiving immunotherapy.

Framework for the Use of External Controls to Evaluate Treatment Outcomes in Precision Oncology Trials

Advances in genomics have enabled anticancer therapies to be tailored to target specific genomic alterations. Single-arm trials (SATs), including those incorporated within umbrella, basket, and platform trials, are widely adopted when it is not feasible to conduct randomized controlled trials in rare biomarker-defined subpopulations. External controls (ECs), defined as control arm data derived outside the clinical trial, have gained renewed interest as a strategy to supplement evidence generated from SATs to allow comparative analysis. There are increasing examples demonstrating the application of EC in precision oncology trials. The prospective application of EC in conducting comparative studies is associated with distinct methodological challenges, the specific considerations for EC use in biomarker-defined subpopulations have not been adequately discussed, and a formal framework is yet to be established. In this review, we present a framework for conducting a prospective comparative analysis using EC. Key steps are (1) defining the purpose of using EC to address the study question, (2) determining if the external data are fit for purpose, (3) developing a transparent study protocol and a statistical analysis plan, and (iv) interpreting results and drawing conclusions on the basis of a prespecified hypothesis. We specify the considerations required for the biomarker-defined subpopulations, which include (1) specifying the comparator and biomarker status of the comparator group, (2) defining lines of treatment, (3) assessment of the biomarker testing panels used, and (4) assessment of cohort stratification in tumor-agnostic studies. We further discuss novel clinical trial designs and statistical techniques leveraging EC to propose future directions to advance evidence generation and facilitate drug development in precision oncology.

Raising the Bar for Real-World Data in Oncology: Approaches to Quality Across Multiple Dimensions

PURPOSE

Electronic health record (EHR)-based real-world data (RWD) are integral to oncology research, and understanding fitness for use is critical for data users. Complexity of data sources and curation methods necessitate transparency into how quality is approached. We describe the application of data quality dimensions in curating EHR-derived oncology RWD.

METHODS

A targeted review was conducted to summarize data quality dimensions in frameworks published by the European Medicines Agency, The National Institute for Healthcare and Excellence, US Food and Drug Administration, Duke-Margolis Center for Health Policy, and Patient-Centered Outcomes Research Institute. We then characterized quality processes applied to curation of Flatiron Health RWD, which originate from EHRs of a nationwide network of academic and community cancer clinics, across the summarized quality dimensions.

RESULTS

The primary quality dimensions across frameworks were relevance (including subdimensions of availability, sufficiency, and representativeness) and reliability (including subdimensions of accuracy, completeness, provenance, and timeliness). Flatiron Health RWD quality processes were aligned to each dimension. Relevancy to broad or specific use cases is optimized through data set size and variable breadth and depth. Accuracy is addressed using validation approaches, such as comparison with external or internal reference standards or indirect benchmarking, and verification checks for conformance, consistency, and plausibility, selected on the basis of feasibility and criticality of the variable to the intended use case. Completeness is assessed against expected source documentation; provenance by recording data transformation, management procedures, and auditable metadata; and timeliness by setting refresh frequency to minimize data lags.

CONCLUSION

Development of high-quality, scaled, EHR-based RWD requires integration of systematic processes across the data lifecycle. Approaches to quality are optimized through knowledge of data sources, curation processes, and use case needs. By addressing quality dimensions from published frameworks, Flatiron Health RWD enable transparency in determining fitness for real-world evidence generation.

Overview of approaches to estimate real-world disease progression in lung cancer

BACKGROUND

Randomized clinical trials of novel treatments for solid tumors normally measure disease progression using the Response Evaluation Criteria in Solid Tumors. However, novel, scalable approaches to estimate disease progression using real-world data are needed to advance cancer outcomes research. The purpose of this narrative review is to summarize examples from the existing literature on approaches to estimate real-world disease progression and their relative strengths and limitations, using lung cancer as a case study.

METHODS

A narrative literature review was conducted in PubMed to identify articles that used approaches to estimate real-world disease progression in lung cancer patients. Data abstracted included data source, approach used to estimate real-world progression, and comparison to a selected gold standard (if applicable).

RESULTS

A total of 40 articles were identified from 2008 to 2022. Five approaches to estimate real-world disease progression were identified including manual abstraction of medical records, natural language processing of clinical notes and/or radiology reports, treatment-based algorithms, changes in tumor volume, and delta radiomics-based approaches. The accuracy of these progression approaches were assessed using different methods, including correlations between real-world endpoints and overall survival for manual abstraction (Spearman rank ρ = 0.61-0.84) and area under the curve for natural language processing approaches (area under the curve = 0.86-0.96).

CONCLUSIONS

Real-world disease progression has been measured in several observational studies of lung cancer. However, comparing the accuracy of methods across studies is challenging, in part, because of the lack of a gold standard and the different methods used to evaluate accuracy. Concerted efforts are needed to define a gold standard and quality metrics for real-world data.