Maxime Tarabichi

The paper quantifies parental mutation burden symmetry in human cancers using PhaSoMix, finding near-equal mutation accumulation between maternal and paternal genomes.

This study uses single-cell and spatial multi-omics to analyze a malignant peripheral nerve sheath tumor, revealing chromosomal instability drives phenotypic diversity and spatial-temporal evolution.

This study uses single nuclei RNA-seq and spatial transcriptomics to analyze transcriptional patterns in papillary and anaplastic thyroid cancers, revealing idiosyncratic and generic transcriptional variations and EMT-related features.

The paper links somatic mutations in cancer to DNA electronic properties by showing anticorrelations between mutation frequencies and vertical ionization potential (vIP) values of nucleobase motifs.

The paper builds a histological atlas using computational methods and the GTEx dataset, revealing how age, sex, genetics, and sub-clinical pathologies influence tissue structures across 40 organs.

This study integrates single-cell and spatial transcriptomics to identify prognostic fibroblast subpopulations in thyroid cancer progression.

This paper develops a framework to predict future oncogene amplification or tumour suppressor deletion in cancer using genomic data, achieving an average AUC of 0.87.

This study shows brush biopsies can accurately reflect the genomic profile of oral premalignant lesions and their progression to OSCC, capturing most SNVs and CNAs comparable to tissue biopsies.

The paper develops CliPP to estimate subclonal mutation load (sML) and shows it predicts survival and immunotherapy response in cancers with low to moderate TMB.

This paper introduces GRITIC, a method to time genomic gains in tumors, finding that copy number gains often accumulate after genome duplication.

MEDICC2 infers cancer phylogenies from copy-number data, accounting for whole-genome doubling and parallel evolution, and performs well on simulated and real datasets.

The paper benchmarks 31 subclonal reconstruction algorithms on simulated tumours, finding that algorithm choice significantly impacts performance and no single algorithm excels across all tasks.

The paper analyzes somatic substitutions in non-unique regions of the human genome across 2,658 cancers, revealing hidden mutations in coding sequences and regulatory elements.

CliPP reconstructs subclonal architecture in cancer DNA sequencing data efficiently and accurately, outperforming existing methods in speed while maintaining high concordance with consensus calls.

This study identifies genomic and methylation patterns associated with benign metastases and malignant transformation in giant cell tumour of bone.

This study develops quantitative metrics and simulated datasets to evaluate subclonal reconstruction methods in tumours.

The paper critiques a study claiming cancer risk is mainly due to random mutations, highlighting overlooked preventable cases in specific risk groups.