Keren Yizhak

Revealing pan-cancer clonal niches of T cells from single-cell RNA sequencing using contrastive learning

CloneTrast uses contrastive learning to infer T-cell clonal niches from single-cell RNA-seq, identifying NK-like niches linked to poor immunotherapy response across cancers.

scXpand: Pan-cancer detection of T-cell clonal expansion from single-cell RNA sequencing without paired single-cell TCR sequencing

scXpand detects T-cell clonal expansion in cancer from scRNA-seq without TCR sequencing, showing accuracy across tissues and subtypes.

FFixR: A Machine Learning Framework for Accurate Somatic Mutation Calling from FFPE RNA-Seq Data in Cancer

FFixR filters artefactual mutations in FFPE RNA-seq data, improving accuracy for somatic variant calling in cancer.

Systematic inference of mutation rates and spectra across the tree of life via a scalable read-based framework

The paper introduces CORAL, a scalable framework to infer mutation rates and spectra across species, revealing variation linked to life-history traits and identifying evolutionary mutational signatures.

A Single-Cell Atlas of the Breast Cancer Microenvironment Identifies Subtype-Specific Tumor-Immune Landscapes and Vulnerabilities

This study creates a single-cell atlas of breast cancer microenvironments, identifying subtype-specific tumor-immune interactions and therapeutic vulnerabilities.

Single-cell meta-analysis of T cells reveals clonal dynamics of response to checkpoint immunotherapy

This study analyzes T cell clones in cancer patients to identify transcriptional differences linked to response to checkpoint immunotherapy.

Uncovering Predictive Gene and Cellular Signatures for Checkpoint Immunotherapy Response through Machine Learning Analysis of Immune Single-Cell RNA-seq Data

The paper develops a machine learning pipeline to predict immune checkpoint inhibitor response using single-cell RNA-seq data, identifying an 11-gene signature and cell-based predictors with high accuracy across multiple cancer types.

Metabolic predictors of response to immune checkpoint blockade therapy

The paper identifies metabolic gene signatures associated with response to immune checkpoint blockade therapy using single-cell RNA-seq data.

Estimating tumor mutational burden from RNA-sequencing without a matched-normal sample

This paper develops a method to estimate tumor mutational burden from RNA-sequencing data without matched-normal samples, achieving high precision and recall in melanoma samples and showing association with patient survival.

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.

A comprehensive analysis of RNA sequences reveals macroscopic somatic clonal expansion across normal tissues

This study analyzes RNA-seq data from 6,700 normal tissues to identify somatic mutations and their association with age and tissue proliferation, finding tissue-specific cancer mutation hotspots.