Determining the Survival Impact and Cost-Effectiveness of Multi-Gene Panel Sequencing in Metastatic Colorectal Cancer With Super Learning Approaches.

Objective: To determine the effectiveness and cost-effectiveness of multi-gene panel sequencing compared to single-gene KRAS testing for metastatic colorectal cancer (mCRC).

Study setting and design: British Columbia, Canada (BC) is a provincial single-payer public healthcare system, and it was the first province to publicly reimburse multi-gene sequencing for mCRC. Panels expand treatment de-escalation by expanding RAS testing for more precise targeting of anti-EGFR therapies. Reimbursement of panels remains unequal across healthcare systems given uncertain clinical and economic impacts. Our quasi-experimental study design followed the target trial emulation approach, emulating random treatment assignment with two different methods to examine the sensitivity of estimates: inverse probability of treatment weighting estimated with super learning (SL-IPTW) and 1:1 genetic algorithm-based matching, a machine learning approach. We then estimated mean three-year survival time and costs (public healthcare payer perspective; 2021CAD) and calculated the incremental net monetary benefit (INMB) for life-years gained (LYG) at $50,000/LYG using weighted linear regression and nonparametric bootstrapping, also accounting for inverse probability of censoring weights. Our sensitivity analysis estimated LYG using targeted minimum-based loss estimation (TMLE), a doubly robust approach that also uses super learning.

Data sources and analytical sample: Patient-level linked administrative health databases capturing cancer and non-cancer care for all BC adults with a metastatic colorectal cancer between 2016 and 2019.

Principal findings: Our study included 892 patients (84.3%) receiving multi-gene panels and 166 (15.7%) receiving single-gene testing. INMB estimates were similar for SL-IPTW ($20,397; 95% CI: $9317, $34,862) and matching ($19,569; 95% CI: $8509, $31,447), with 99.3% and 98.8% probabilities, respectively, of panels being cost-effective. We found statistically significant survival benefits with LYG of 0.31 (SL-IPTW; 95% CI: 0.04, 0.54), 0.25 (matching; 95% CI: 0.03, 0.47) and 0.19 (TMLE; 95% CI: 0.02, 0.37).

Conclusions: Survival impacts were robust to super learning approaches. Real-world evidence demonstrates that reimbursing multi-gene sequencing for more precise targeting of mCRC treatments provides value for healthcare systems and clinically important benefits to patients.