Gene expression inference from cell-free DNA using uncertainty-aware deep learning

Tumor gene expression profiling provides crucial diagnostic information for guiding therapy, but standard tissue biopsies are invasive, spatially biased, and may inadequately sample metastatic disease. Cell-free DNA (cfDNA) provides a minimally invasive alternative for tumor genotyping, yet reconstructing robust, transcriptome-wide expression from standard-depth cfDNA whole-genome sequencing (WGS) remains a major challenge. We developed a deep learning framework comprising Triton, for comprehensive cfDNA feature extraction, and Proteus, a probabilistic model that infers single-gene expression from standard-depth cfDNA WGS. Proteus outperformed prior cfDNA approaches in reconstructing molecular phenotypes from matched tumor transcriptomes across multiple cancer types, including prostate, lung, and bladder cancer cohorts, with uncertainty-guided withholding improving model reliability. Proteus further enabled assessment of therapeutic target activity, prognostic transcriptional programs, and candidate treatment-emergent resistance states, establishing a generalizable framework for minimally invasive functional genomics in precision oncology.