SHERLOC: An interpretable deep learning model for longitudinal circulating tumor DNA data in survival analysis

Longitudinal circulating tumor DNA (ctDNA) measurements offer a noninvasive means to monitor treatment response, but clinical trial data present substantial methodological challenges due to high-dimensional short longitudinal ctDNA sequences and limited sample sizes. We introduce SHERLOC, a deep learning framework specifically designed for survival analysis using longitudinal on-treatment ctDNA data, which integrates shared temporal representations of gene-level variant allele frequencies, feature-specific temporal trajectories of panel-level ctDNA biomarkers, and survival-aware genomic representations pre-trained on a large pan-cancer tissue-biopsy dataset (MSK-CHORD), within an interpretable Cox proportional hazards framework. Benchmarked against diverse statistical, ensemble, and deep learning approaches in a non-small-cell lung cancer cohort from the phase III IMpower150 trial, SHERLOC consistently achieved superior survival discrimination and calibration, while remaining interpretable and robust to reductions in the number of available longitudinal liquid biopsy time points per patient. The resulting ctDNA-based risk score provided prognostic information both independent of and complementary to standard radiographic response assessments, and enabled patient stratification within homogeneous RECIST response groups--highlighting its potential as an early, non-invasive decision-support tool to guide treatment adaptation and patient management.