Nishanth Ulhas Nair

This study identifies hepatobiliary progenitor-like reprogramming in liver metastases, driven by TGF-β and hypoxia, and shows its association with poor survival.

This study identifies TAZ deficiency as synthetically lethal with BRAF inhibitors in anaplastic thyroid cancer, revealing a mechanism of resistance and a potential therapeutic target.

Path2Omics predicts gene expression and methylation from histopathology slides across 23 cancer types, outperforming previous models and showing effectiveness in predicting patient outcomes.

This paper develops a machine learning framework, EXPRESSO, to predict treatment response from tumor transcriptomics across multiple cancer types and therapies, showing improved performance over existing methods.

The paper shows that inducing epithelial traits in breast cancer cells increases autophagy and DNA damage, potentially contributing to tumor dormancy and reactivation.

ENLIGHT predicts patient response to targeted and immunotherapies using tumor transcriptomes without prior treatment response data, showing better accuracy than existing transcriptomics-based signatures.

The paper predicts anti-SARS-CoV-2 targets by identifying synthetic lethal partners of host genes altered during infection, validated through CRISPR-Cas9 screens and siRNA experiments.

The paper uses genome-scale metabolic modeling to identify SARS-CoV-2-induced metabolic changes and predict antiviral targets validated through drug and genetic screens.

This study compares proliferation and migration transcriptomic signatures in breast cancer, finding migration is more strongly linked to patient survival.