Sushant Patkar

AI-Driven Spatial Transcriptomics Unlocks Large-Scale Breast Cancer Biomarker Discovery from Histopathology

Path2Space predicts spatial gene expression and tumor microenvironment from histopathology slides, identifying new breast cancer subgroups and improving biomarker prediction.

OncoBERT: Context-Aware Modeling of Somatic Mutations for Precision Oncology

OncoBERT models somatic mutations in cancer to identify patient-specific subtypes and improve prediction of treatment response by integrating mutational context with clinical biomarkers.

Towards interpretable molecular and spatial analysis of the tumor microenvironment from digital histopathology images with HistoTME-v2

HistoTME-v2 predicts cell type-specific transcriptomic signatures and spatial distributions from H&E images, outperforming baselines across 25 tumor types and multiple validation datasets.

Deactivation of ligand-receptor interactions enhancing lymphocyte infiltration drives melanoma resistance to Immune Checkpoint Blockade

The paper identifies resistance-deactivated interactions (RDIs) as predictive biomarkers for ICB response in melanoma by analyzing transcriptomics data from multiple cohorts.

Deconvolving clinically relevant cellular immune crosstalk from bulk gene expression using CODEFACS and LIRICS

The paper develops CODEFACS and LIRICS to deconvolve bulk gene expression and identify clinically relevant ligand-receptor interactions in tumors, finding specific T-cell interactions linked to anti-PD1 response.