James Brugarolas

A Visually Interpretable Histopathology-Based Immune Model Predicts T-effector Biology and Response to Immune checkpoint inhibition in Clear Cell Renal Cell Carcinoma Clinical Trial and Contemporary Real-World Datasets

The paper develops a deep learning model from H&E images to predict T-effector biology and ICI response in ccRCC, showing strong correlation with RNA-based scores and clinical outcomes.

Clear cell renal cell carcinoma consensus transcriptomic programs reveal converging trajectories towards aggressive disease

The paper identifies 17 consensus transcriptomic programs in ccRCC using NMF and links them to cellular sources and clinical outcomes, revealing converging trajectories toward aggressive disease.

A single chromosome 3p break initiates clear cell renal cell carcinoma evolution

This study shows that a single chromosome 3p break initiates ccRCC evolution by creating a fitness bottleneck that selects for recurrent aneuploidies.

Differential Transcript Usage Reveals Isoform-Level Remodeling of Tumor Biology in Clear Cell Renal Cell Carcinoma

The paper identifies isoform-level changes in ccRCC using DTU analysis, revealing subgroups with distinct survival outcomes and potential biomarkers.

Glycosaminoglycan-mediated lipoprotein uptake protects cancer cells from ferroptosis

This study identifies glycosaminoglycan-mediated lipoprotein uptake as a mechanism by which cancer cells resist ferroptosis and lipid oxidative stress.

An ultraconserved snoRNA-like element in long noncoding RNA CRNDE promotes ribosome biogenesis and cell proliferation

The paper identifies an ultraconserved snoRNA-like element in the lncRNA CRNDE that promotes ribosome biogenesis and cell proliferation in renal cell carcinoma.

Tumor Neoantigenicity Assessment with CSiN Score Incorporates Clonality and Immunogenicity to Predict Immunotherapy Outcomes

The paper introduces the CSiN score to predict immunotherapy outcomes by assessing tumor neoantigen quality through clonality and immunogenicity, showing improved performance over existing methods.

SCINA: Semi-Supervised Analysis of Single Cells in silico

SCINA is a semi-supervised model for analyzing scRNA-Seq and flow cytometry data, improving accuracy and efficiency over unsupervised methods by leveraging gene signatures.