Andrea Sottoriva

The paper models subclonal interactions in diffuse midline gliomas using a modified Lotka-Volterra model to show how Allee effects influence therapeutic outcomes and treatment scheduling.

SPECTRA predicts cellular perturbation responses using graph learning on gene regulatory networks, improving recovery of sparse transcriptional effects compared to latent-shift models.

CRISPR-VUS identifies rare variants linked to gene dependency in cancer cell lines, prioritizing potential drivers and therapeutic targets through functional validation.

This study examines the epigenetic evolution of colorectal cancer and its microenvironment, identifying new therapeutic targets through single-cell analysis and functional validation.

This study uses whole genome sequencing of endometrial cancers to identify novel subgroups, drivers, and actionable alterations, improving precision medicine approaches.

This paper introduces SCOUT, a synthetic whole-genome sequencing resource for benchmarking cancer genomic analysis, revealing performance issues in complex tumours and highlighting the importance of evolutionary ground truth.

This paper identifies cross resistance between cancer drugs using single-drug exposure data, showing that high cross resistance reduces the effectiveness of switching between CDK4/6 inhibitors.

This paper develops a mathematical framework to infer cancer drug resistance dynamics from genetic lineage tracing and population size data, validated using experimental evolution and single-cell sequencing.

The paper shows how epigenetic heritability of cell plasticity contributes to cancer drug resistance through a one-to-many genotype-to-phenotype mapping.

The paper measures mutation rates in microsatellite stable colorectal cancers and finds they vary between tumors and increase during metastasis.

This paper introduces dATAC, a single-cell multi-omic method for simultaneous whole genome sequencing and chromatin accessibility profiling, revealing genome-epigenome coevolution in colorectal cancer.

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.

This study shows that immune evasion in colorectal cancer follows a 'Big Bang' pattern, where early genetic and epigenetic changes drive subsequent tumor-immune evolution.

This paper introduces MIDAA, a framework for multi-omic data integration using deep learning and biological principles, showing it identifies interpretable and biologically relevant patterns better than existing methods.

NeuroVelo reconstructs cellular dynamics and identifies gene regulatory networks from single-cell transcriptomic data using dynamical systems theory.

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

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

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

This study links dN/dS ratios to selective coefficients in somatic evolution, revealing the distribution of fitness effects for mutations in human tissues.

This paper shows that spatial tumour structure affects mutation patterns in sequencing data and introduces a framework to account for these effects in cancer genomic analysis.

The paper models tumor-immune dynamics in colorectal cancer progression, showing immune evasion enables transition from immune-hot adenomas to immune-cold carcinomas.

This paper demonstrates evolutionary herding in non-small cell lung cancer to control drug resistance by shifting clonal composition and inducing collateral sensitivity.

This study measures single-cell division rates and survival in human cancers using multi-region sequencing data, revealing per-cell mutation and survival rates across different cancer types and healthy tissues.

This paper introduces a machine learning method to detect repeated cancer evolution patterns across patients using multi-region sequencing data, identifying prognostic patient subgroups.

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.

The paper develops a mathematical model to estimate cancer stem cell fractions in tumors using tumor size measurements and linear regression, without requiring detailed microscopic data.