Luis Zapata

Expanding the definition of MHC Class I peptide binding promiscuity to support vaccine discovery across cancers with CARMEN

The paper expands the concept of MHC Class I peptide binding promiscuity to support vaccine discovery across cancers by analyzing data from 2,323 samples.

Long deletion signatures in repetitive genomic regions track somatic evolution and enable sensitive detection of microsatellite instability

This paper identifies long deletion signatures in repetitive regions as markers of microsatellite instability and develops a classifier to detect MSI in low-quality sequencing data.

Immune evasion impacts the selective landscape of driver genes during tumorigenesis

The paper identifies differences in driver gene selection and mutational landscapes between immune-escaped and non-escaped tumors using TCGA data.

Nuclear gene proximity and protein interactions shape transcript covariances in mammalian single cells

The paper identifies >80,000 gene pairs with intrinsic covariance in mouse embryonic stem cells, showing nuclear organization and protein interactions drive transcript covariances.

Model-based tumor subclonal reconstruction

This paper introduces a model-based subclonal reconstruction method that integrates evolutionary theory with machine learning to improve accuracy in tumor subclonal analysis.

dN/dS dynamics quantify tumour immunogenicity and predict response to immunotherapy

The paper quantifies tumor immunogenicity using dN/dS ratios in the immunopeptidome and shows it predicts immunotherapy response by distinguishing adaptation from escape mechanisms.

The rate and spectrum of mosaic mutations during embryogenesis revealed by RNA sequencing of 49 tissues

The paper uses RNA sequencing from 49 tissues to estimate the rate and spectrum of mosaic mutations during embryogenesis, finding a similar frequency to germline de novo mutations and identifying a new mutational signature in the gastrointestinal tract.

Reply: Neutral tumor evolution?

The paper responds to criticism of a method used to infer neutral cancer evolution from variant allele frequency distributions, arguing that neutral evolution explains intra-tumour heterogeneity in some cancers.

Bayesian Inference Of Cancer Driver Genes Using Signatures Of Positive Selection

This study introduces a Bayesian model to identify cancer driver genes by integrating evolutionary signatures, revealing novel gene-tumor type associations and highlighting chromatin modifiers as key targets.