Isidro Cortés‐Ciriano

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

This study shows that a genetically engineered mouse model of osteosarcoma recapitulates the genomic complexity and driver mutations observed in human osteosarcoma.

The paper identifies genomic and transcriptomic features of telomere maintenance mechanisms in complex karyotype sarcomas, linking ALT status to therapeutic vulnerabilities.

This study shows that stem-like CD8+ T cells, when used in adoptive cell therapy followed by vaccination, improve tumor control by altering the tumor microenvironment's transcriptomic profile.

The paper introduces MAGIC, a platform combining imaging and genomics to study de novo chromosome rearrangements, finding that dicentric chromosomes initiate CA events and TP53 deficiency increases CA rates.

The paper identifies African Pan Genome contigs that are missing from current human reference genomes, showing they contain functionally relevant sequences and are enriched in African ancestry.

SAVANA improves detection of somatic structural variants and copy number aberrations in cancer using long-read sequencing with higher sensitivity and specificity than existing tools.

scAI-SNP infers ancestry from single-cell data using ancestry-informative SNPs, showing robustness across diverse tissues and modalities.

The paper identifies second somatic NF1 mutations in normal tissues of neurofibromatosis type 1 patients through whole genome and exome sequencing, showing positive selection of truncating mutations in these tissues.

This study identifies chromothripsis and a novel LTA chromothripsis mechanism driving genome complexity and evolution in osteosarcoma, with prognostic implications for high-grade tumors.

The paper identifies mutational signatures SBS26 and SBS54 in MMRD tumors that correlate with immune checkpoint blockade response and survival, suggesting their use as prognostic biomarkers.

ReConPlot is an R package for visualizing and interpreting complex genome rearrangements by integrating somatic copy number aberrations and structural variants.

DeepMosaic detects mosaic single nucleotide variants without control samples using deep convolutional neural networks, achieving high accuracy and uncovering many variants missed by existing methods.

SComatic detects somatic mutations in single-cell RNA-seq and ATAC-seq data without bulk DNA sequencing, achieving higher precision than existing methods.

The paper evaluates brain somatic mosaicism in the adult human to identify rules governing cellular distribution during embryogenesis.

The paper introduces a machine learning method to detect somatic L1 insertions in human brain tissue, revealing their widespread distribution in neurons and glia and potential role in neuropsychiatric diseases.

The paper provides a user guide to five online tools for exploring and visualizing PCAWG data, demonstrating their integration with external resources to enhance cancer genomics research.

The paper analyzes whole-genome sequencing data from cancer samples to identify genomic footprints of telomere maintenance mechanisms, finding specific telomere variant repeat patterns associated with ATRX/DAXX mutations.

The paper examines how somatic structural variations affect TAD boundaries and gene regulation in human cancers using whole genome sequencing data.

This study analyzes chromothripsis in 2,658 cancers using whole-genome sequencing, finding it prevalent across multiple cancer types with complex structural alterations.

The paper identifies hundreds of genes deregulated by SVs in cancer, showing increased expression linked to SV breakpoints and enhancer elements.

The paper presents a large-scale cancer pharmacogenomic screen with 11,576 experiments on 910 drug combinations across 85 cancer cell lines, enabling crowd-sourced prediction of synergistic drug pairs and biomarkers.

The paper introduces LiRA, a method to distinguish true mutations from artifacts in single-cell DNA sequencing data by phasing somatic variants with germline variants.

CCLP predicts compound activity across the NCI60 panel using a multi-task Random Forest model and provides prediction errors via conformal prediction.