Deep learning-based non-invasive profiling of tumor transcriptomes from cell-free DNA for precision oncology

Patton, R. D.; Netzley, A.; Persse, T. W.; Nair, A.; Galipeau, P. C.; Coleman, I. M.; Itagi, P.; Chandra, P.; Adil, M.; Vashisth, M.; Sayar, E.; Hiatt, J. B.; Dumpit, R.; Kollath, L.; Demirci, R. A.; Ghodsi, A.; Lam, H.-M.; Morrissey, C.; Iravani, A.; Chen, D. L.; Hsieh, A. C.; MacPherson, D.; Haffner, M. C.; Nelson, P. S.; Ha, G.

doi:10.64898/2026.02.10.705188

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bioRxiv · 10.64898/2026.02.10.705188

Deep learning-based non-invasive profiling of tumor transcriptomes from cell-free DNA for precision oncology

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Abstract

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.

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Patton, R. D., Netzley, A., Persse, T. W., Nair, A., Galipeau, P. C., Coleman, I. M., Itagi, P., Chandra, P., Adil, M., Vashisth, M., Sayar, E., Hiatt, J. B., Dumpit, R., Kollath, L., Demirci, R. A., Ghodsi, A., Lam, H.-M., Morrissey, C., Iravani, A., Chen, D. L., Hsieh, A. C., MacPherson, D., Haffner, M. C., Nelson, P. S., Ha, G.. 2026-02-12. Deep learning-based non-invasive profiling of tumor transcriptomes from cell-free DNA for precision oncology. https://doi.org/10.64898/2026.02.10.705188

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