SHERLOC: New Deep Learning Model Improves Cancer Prognosis Using Blood-Based Tumor DNA Monitoring
Researchers have developed SHERLOC, a deep learning framework that analyzes longitudinal circulating tumor DNA (ctDNA) measurements from blood samples to predict survival outcomes in cancer patients. The model was benchmarked against a non-small-cell lung cancer cohort from the phase III IMpower150 clinical trial, outperforming existing statistical and machine learning approaches in survival discrimination and calibration. The tool could serve as an early, non-invasive complement to standard imaging-based assessments for guiding cancer treatment decisions.
SHERLOC (a deep learning framework for survival analysis) integrates multiple layers of genomic information — including gene-level variant allele frequencies, panel-level ctDNA biomarkers, and survival-aware representations pre-trained on the large pan-cancer MSK-CHORD tissue-biopsy dataset — within an interpretable Cox proportional hazards model. The system is designed to handle the methodological challenges common in clinical trial data, such as high-dimensional but short longitudinal sequences and limited patient sample sizes. Tested on non-small-cell lung cancer patients from the phase III IMpower150 trial, SHERLOC consistently outperformed a range of competing statistical, ensemble, and deep learning methods. Notably, the ctDNA-based risk score it generates provided prognostic value independent of and complementary to standard radiographic response assessments (RECIST), and could stratify patients even within groups that appeared homogeneous under imaging criteria. The model also remained robust when fewer longitudinal blood draw time points were available per patient, suggesting practical utility in real-world clinical settings where data collection may be incomplete.
What's missing
The study is a preprint posted on bioRxiv and has not yet undergone formal peer review, so findings should be interpreted with caution. The model was validated on a single disease type (non-small-cell lung cancer) from one clinical trial (IMpower150), leaving its generalizability to other cancer types or treatment regimens undemonstrated. The authors do not report external prospective validation, and the relatively limited sample size inherent to clinical trial cohorts remains a noted constraint. It is also unclear how SHERLOC would perform in community oncology settings outside of controlled trial conditions.
What different sources said
- bioRxivCenter
SHERLOC: An interpretable deep learning model for longitudinal circulating tumor DNA data in survival analysis
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