A5033643165- Cancer Genomics and Diagnostics
- Gene Regulatory Network Analysis
- Cancer Immunotherapy and Biomarkers
- Genetic factors in colorectal cancer
- Lung Cancer Treatments and Mutations
- Evolution and Genetic Dynamics
- Bioinformatics and Genomic Networks
- Lung Cancer Research Studies
- Single-cell and spatial transcriptomics
- Gene expression and cancer classification
- Epigenetics and DNA Methylation
- Mathematical Biology Tumor Growth
- Bayesian Modeling and Causal Inference
- DNA and Biological Computing
- Formal Methods in Verification
- Genomics and Phylogenetic Studies
- Microbial Metabolic Engineering and Bioproduction
- Statistical Methods and Inference
- Genomics and Rare Diseases
- Acute Myeloid Leukemia Research
- Genomic variations and chromosomal abnormalities
- Microtubule and mitosis dynamics
- stochastic dynamics and bifurcation
- Immune Cell Function and Interaction
- Modular Robots and Swarm Intelligence
Institute of Cancer Research
2017-2025
University of Trieste
2014-2025
Genomics (United Kingdom)
2018-2024
Cancer Research UK
2022
University of Edinburgh
2015-2018
University of Milano-Bicocca
2012-2016
University of Milan
2014-2015
Courant Institute of Mathematical Sciences
2015
University of California, Los Angeles
2015
University of Toronto
2013
Abstract Genetic and epigenetic variation, together with transcriptional plasticity, contribute to intratumour heterogeneity 1 . The interplay of these biological processes their respective contributions tumour evolution remain unknown. Here we show that genetic ancestry only infrequently affects gene expression traits subclonal in colorectal cancer (CRC). Using spatially resolved paired whole-genome transcriptome sequencing, find the majority variation is not strongly heritable but rather...
Abstract Colorectal malignancies are a leading cause of cancer-related death 1 and have undergone extensive genomic study 2,3 . However, DNA mutations alone do not fully explain malignant transformation 4–7 Here we investigate the co-evolution genome epigenome colorectal tumours at single-clone resolution using spatial multi-omic profiling individual glands. We collected 1,370 samples from 30 primary cancers 8 concomitant adenomas generated 1,207 chromatin accessibility profiles, 527 whole...
Abstract In cancer, evolutionary forces select for clones that evade the immune system. Here we analyzed >10,000 primary tumors and 356 immune-checkpoint-treated metastases using dN/dS, ratio of nonsynonymous to synonymous mutations in immunopeptidome, measure selection cohorts individuals. We classified as edited when antigenic were removed by negative escaped antigenicity was covered up aberrant modulation. Only immune-edited predation linked CD8 T cell infiltration. Immune-escaped...
Abstract Colorectal carcinoma (CRC) is a common cause of mortality 1 , but comprehensive description its genomic landscape lacking 2–9 . Here we perform whole-genome sequencing 2,023 CRC samples from participants in the UK 100,000 Genomes Project, thereby providing highly detailed somatic mutational this cancer. Integrated analyses identify more than 250 putative driver genes, many not previously implicated or other cancers, including several recurrent changes outside coding genome. We...
Abstract Drug resistance mediated by clonal evolution is arguably the biggest problem in cancer therapy today. However, evolving to one drug may come at a cost of decreased fecundity or increased sensitivity another drug. These evolutionary trade-offs can be exploited using ‘evolutionary steering’ control tumour population and delay resistance. recapitulating dynamics experimentally remains challenging. Here, we present an approach for steering based on combination single-cell barcoding,...
We devise a novel inference algorithm to effectively solve the cancer progression model reconstruction problem. Our empirical analysis of accuracy and convergence rate our algorithm, CAncer PRogression Inference (CAPRI), shows that it outperforms state-of-the-art algorithms addressing similar problems. Motivation: Several cancer-related genomic data have become available (e.g., The Cancer Genome Atlas, TCGA) typically involving hundreds patients. At present, most these are aggregated in...
Significance A causality-based machine learning Pipeline for Cancer Inference (PiCnIc) is introduced to infer the underlying somatic evolution of ensembles tumors from next-generation sequencing data. PiCnIc combines techniques sample stratification, driver selection, and identification fitness-equivalent exclusive alterations exploit an algorithm based on Suppes’ probabilistic causation. The accuracy translational significance results are studied in detail, with application colorectal...
The mutational landscape is shaped by many processes. Genic regions are vulnerable to mutation but preferentially protected transcription-coupled repair
We report an autosomal recessive, multi-organ tumor predisposition syndrome, caused by bi-allelic loss-of-function germline variants in the base excision repair (BER) gene MBD4. identified five individuals with MBD4 within four families and these had a personal and/or family history of adenomatous colorectal polyposis, acute myeloid leukemia, uveal melanoma. encodes glycosylase involved G:T mismatches resulting from deamination 5'-methylcytosine. The adenomas MBD4-deficient showed mutator...
Abstract Colorectal cancer (CRC) is a histologically heterogeneous disease with variable clinical outcome. The role the tumour microenvironment (TME) plays in determining progression complex and not fully understood. To improve our understanding, it critical that TME studied systematically within clinically annotated patient cohorts long‐term follow‐up. Here we three of metastatic CRC diverse molecular subtype treatment history. MISSONI cohort included cases microsatellite instability...
Existing techniques to reconstruct tree models of progression for accumulative processes, such as cancer, seek estimate causation by combining correlation and a frequentist notion temporal priority. In this paper, we define novel theoretical framework called CAPRESE (CAncer PRogression Extraction with Single Edges) based on the probabilistic defined Suppes. We consider general reconstruction setting complicated presence noise in data due biological variation, well experimental or measurement...
Quantification of the effect spatial tumour sampling on patterns mutations detected in next-generation sequencing data is largely lacking. Here we use a stochastic cellular automaton model growth that accounts for somatic mutations, selection, drift and constraints, to simulate multi-region derived from neoplasm. We show structure solid cancer has major impact detection clonal selection genetic both bulk single-cell data. Our results indicate constrains can introduce significant biases when...
BackgroundGlioblastoma is the most common and aggressive adult brain malignancy against which conventional surgery chemoradiation provide limited benefit. Even when a good treatment response obtained, recurrence inevitably occurs either locally (∼80%) or distally (∼20%), driven by cancer clones that are often genomically distinct from those in primary tumour. Glioblastoma cells display characteristic infiltrative phenotype, invading surrounding tissue spreading across whole brain. Cancer...
Motivation: We introduce TRONCO (TRanslational ONCOlogy), an open-source R package that implements the state-of-the-art algorithms for inference of cancer progression models from (epi)genomic mutational profiles. can be used to extract population-level describing trends accumulation alterations in a cohort cross-sectional samples, e.g., retrieved publicly available databases, and individual-level reveal clonal evolutionary history single patients, when multiple biopsies or single-cell...
After being considered as a nuisance to be filtered out, it became recently clear that biochemical noise plays complex role, often fully functional, for genetic network. The influence of intrinsic and extrinsic noises on networks has intensively been investigated in last ten years, though contributions the co-presence both are sparse. Extrinsic is usually modeled an unbounded white or colored gaussian stochastic process, even realistic perturbations clearly bounded. In this paper we consider...
Abstract Copy number alterations (CNAs) are among the most important genetic events in cancer, but their detection from sequencing data is challenging because of unknown sample purity, tumor ploidy, and general intra-tumor heterogeneity. Here, we present CNAqc, an evolution-inspired method to perform computational validation clonal subclonal CNAs detected bulk DNA sequencing. CNAqc validated using single-cell simulations, applied over 4000 TCGA PCAWG samples, incorporated into process for...
Abstract Mismatch repair (MMR)-deficient cancer evolves through the stepwise erosion of coding homopolymers in target genes. Curiously, MMR genes MutS homolog 6 ( MSH6) and 3 MSH3 ) also contain homopolymers, these are frequent mutational targets MMR-deficient cancers. The impact incremental mutations on evolution is unknown. Here we show that microsatellite instability modulates DNA by toggling hypermutable mononucleotide homopolymer runs MSH6 stochastic frameshift switching. Spontaneous...
A large number of algorithms is being developed to reconstruct evolutionary models individual tumours from genome sequencing data. Most methods can analyze multiple samples collected either through bulk multi-region experiments or the cancer cells. However, rarely same method support both data types. We introduce TRaIT, a computational framework infer mutational graphs that model accumulation types somatic alterations driving tumour evolution. Compared other tools, TRaIT supports and...