Ben S. Sidders

This study identifies immunogenomic drivers of acquired resistance to immunotherapy across multiple cancer types using a large-scale clinicogenomic dataset.

PAIRWISE predicts effective personalized drug combinations in cancer using deep learning and transcriptome data, achieving high AUROC scores in validation tests.

The paper evaluates the feasibility of using TMT-iBAQ to identify tumor-enriched and highly expressed cell surface antigens for cancer therapeutics.

The paper introduces OncMTR, a metric identifying genic regions intolerant to germline variation but enriched for cancer-driving mutations, and shows it improves genetic signal detection for hematologic malignancies.

This study integrates drug sensitivity data with CRISPR screens to identify drug mechanisms and biomarkers for cancer therapy.