Pedram Razavi

This study identifies liver-resident ILC1s as a critical barrier against breast cancer liver metastasis, showing that DR5 expression and host metabolism influence this immunosurveillance.

The paper analyzes the evolutionary trajectories of high-level copy-number amplifications in cancer cell genomes to distinguish between extrachromosomal and intrachromosomal amplification mechanisms.

This paper develops a computational framework using single-cell whole-genome sequencing to identify ongoing chromosomal instability, showing its utility in cancer models and clinical samples.

The paper develops Mosaic, a machine learning system that classifies tumors into detailed subtypes and identifies molecular phenotypes using histopathologic and genomic data from a large patient cohort.

This study identifies APOBEC3 mutagenesis as a common driver of therapy resistance in breast cancer, associated with poorer clinical outcomes.

This paper develops a multimodal transformer model using H&E images and text reports to predict recurrence scores in hormone receptor-positive breast cancer, achieving high accuracy and generalizability.

The paper identifies how germline and somatic mutations interact to cause resistance to CDK4/6 inhibitors in breast cancer and proposes a sequential treatment strategy using PARP inhibitors.

The paper describes the development and clinical application of MSK-ACCESS, a liquid biopsy assay that improves the specificity of somatic variant detection in cell-free DNA by using paired normal sequencing.

The paper develops PSIONIC, a machine learning method that integrates chromatin accessibility and RNA-seq data to infer transcriptional programs in gynecologic and basal breast cancers, revealing regulatory differences and survival associations.