Benjamin Besse

SHERLOC is a deep learning model for survival analysis using longitudinal ctDNA data, showing improved discrimination and calibration over existing methods in non-small-cell lung cancer.

This study uses single-cell RNA sequencing to analyze metastatic urothelial carcinoma and identifies macrophage-driven resistance to PD-(L)1 blockade as a key mechanism.

This paper develops a method using circulating DNA fragment end motifs and machine learning to detect cancer with high accuracy across multiple cancer types.

This study compares KRASG12C and KRASG12D mutant NSCLC models, finding that KRASG12D relies more on the PI3K-AKT-mTOR pathway, suggesting tailored combination therapies.