Matching methods in precision oncology: An introduction and illustrative example

• Published in: Molecular genetics & genomic medicine Vol. 9; no. 1; pp. e1554 - n/a
• Main Authors: Weymann, Deirdre, Laskin, Janessa, Jones, Steven J.M., Lim, Howard, Renouf, Daniel J., Roscoe, Robyn, Schrader, Kasmintan A., Sun, Sophie, Yip, Stephen, Marra, Marco A., Regier, Dean A.
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.01.2021; John Wiley and Sons Inc; Wiley
• Subjects: administrative data; Algorithms; Bias; Clinical trials; Estimates; Gene expression; genomic sequencing; Health hazards; Hypotheses; Matching; Methods; Oncology; Original; Patients; Precision medicine; Quasi-experimental methods; Researchers; Survival; Transcriptomes; genomic sequencing; precision medicine; administrative data; quasi-experimental methods; matching
• ISSN: 2324-9269; 2324-9269
• DOI: 10.1002/mgg3.1554

ABSTRACT Background Randomized controlled trials (RCTs) are uncommon in precision oncology. We provide an introduction and illustrative example of matching methods for evaluating precision oncology in the absence of RCTs. We focus on British Columbia's Personalized OncoGenomics (POG) program, which applies whole‐genome and transcriptome analysis (WGTA) to inform advanced cancer care. Methods Our cohort comprises 230 POG patients enrolled between 2014 and 2015 and matched POG‐naive controls. We generated our matched cohort using 1:1 propensity score matching (PSM) and genetic matching prior to exploring survival differences. Results We find that genetic matching outperformed PSM when balancing covariates. In all cohorts, overall survival did not significantly differ across POG and POG‐naive patients (p > 0.05). Stratification by WGTA‐informed treatment indicated unmatched survival differences. Patients whose WGTA information led to treatment change were at a reduced hazard of death compared to POG‐naive controls in all cohorts, with estimated hazard ratios ranging from 0.33 (95% CI: 0.13, 0.81) to 0.41 (95% CI: 0.17, 0.98). Conclusion These results signal that clinical effectiveness of precision oncology approaches will depend on rates of genomics‐informed treatment change. Our study will guide future evaluations of precision oncology and support reliable effect estimation when RCT data are unavailable.