Keith T. Flaherty

Deep learning localizes focal biology implicated in therapeutic resistance within routine histology

Weakly supervised deep learning localizes focal tissue regions enriched for therapeutically relevant biology in routine histology, offering a scalable strategy to study therapeutic resistance across large patient populations.

Genomic, Clinical, and Spatial Predictors of Durable Response to BRAF/MEK Inhibition in BRAF-Mutant Melanoma

Lymphotoxin-driven cancer cell eradication by tumoricidal CD8+ tumor-infiltrating lymphocytes

This study identifies a novel CD8+ TIL subset that uses lymphotoxin and IFN pathways to kill cancer cells, linking LTB expression to clinical response in melanoma.

DeepVul: A Multi-Task Transformer Model for Joint Prediction of Gene Essentiality and Drug Response

DeepVul predicts gene essentiality and drug response using a multi-task transformer model trained on cancer transcriptome data, improving precision oncology by identifying treatment vulnerabilities.

A Comprehensive Proteogenomic and Spatial Analysis of Innate and Acquired Resistance of Metastatic Melanoma to Immune Checkpoint Blockade Therapies

This study identifies molecular mechanisms of resistance to immune checkpoint blockade in metastatic melanoma using proteogenomic and spatial analyses of tumor and immune microenvironment data.

Proteasomal downregulation of the pro-apoptotic MST2 pathway contributes to BRAF inhibitor resistance in melanoma

Combined tumor and immune signals from genomes or transcriptomes predict outcomes of checkpoint inhibition in melanoma

The paper develops DNA- and RNA-based models combining immune and tumor features to predict outcomes of checkpoint inhibition in melanoma.