Biology subjects

Sottoriva, A.

Publications and source records attributed to Sottoriva, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Cooperativity through Allee Effects drives growth of diffuse midline gliomas impacting the optimal scheduling of therapeutic interventions

Diffuse midline gliomas are a group of tumours for which no effective therapies exist. These tumours have substantial intratumoural heterogeneity and harbour beneficial subclonal interactions. Therapeutic failure due to resistance is a major concern in these tumours. We demonstrate how distinct subpopulations derived from the same tumour can interact with one another to alleviate the impacts of therapeutic intervention. This is achieved through the establishment of a mathematical extension to the Lotka-Volterra competition model, where interactions perturbing the Allee effect were demonstrated to exist. These interactions had substantial downstream consequences as they were determined to be perturbed by therapeutic interventions and strikingly, when simulating a two-drug paradigm, the interactions were demonstrated to substantially later the outcomes of treatment scheduling.

cancer biology↗

bioRxiv2026

SPECTRA: predicting cellular perturbation responses with Graph Learning over Gene Regulatory Networks

Predicting the transcriptomic consequences of cellular perturbations is challenging due to the sparsity of single-cell transcriptional responses, heterogeneous perturbation efficiency, and the difficulty of identifying the small subset of genes that are truly differentially expressed after perturbation. Current approaches typically encode interventions as latent shifts from unperturbed to perturbed cellular state, making the models hard to interpret. Here we introduce SPECTRA (SPEctral CRISPR Transcriptome Regulatory Autoencoder) a graph-based model that treats CRISPR perturbations as interpretable, localized signals injected into a Gene Regulatory Network and propagated through directed graph neural networks. SPECTRA combines a variational control-cell encoder and a directed heterophilic graph decoder, integrating single-cell expression, pretrained transcriptomic context, and prior regulatory topology into a single node-level representation. We evaluate SPECTRA on a large-scale single-cell CRISPRi benchmark, measuring differentially expressed gene recovery through precision, F1 score, and AUPRC-based DEG classification. Since perturbations in SPECTRA are interpretable, we tested the predictions of the model against known biology, in cases where the effects of a gene knock out have been repeatedly demonstrated with orthogonal experiments. Our results show that graph-based signal propagation is a biologically grounded alternative to latent-shift perturbation modeling and can improve the recovery of sparse perturbation-induced transcriptional effects.

systems biology↗

bioRxiv2026

Epigenetic evolution of colorectal cancer and its microenvironment reveals new vulnerabilities

Epigenetics is central to tumorigenesis, but the co-evolution of the cancer epigenome and its microenvironment is severely understudied. Here, we measure chromatin accessibility and transcriptome, at single cell resolution, of a set of normal colon, primary colorectal cancers and metastases, and identify recurrent epigenetic alterations in tumour cells. We also found that the normal epithelium adjacent to the cancer had recurrent epigenetic alterations associated with inflammatory programs that were partially shared with tumour cells. Distinct tumour-intrinsic transcription factor binding programs were associated with differential abundance of malignant stroma cell identities. We then leveraged matched patient-derived organoids, to assess the functional impact of the most recurrent epigenetic alterations on cancer cell viability, using CRISPR interference. We found a set of epigenetic-driven cancer dependencies, related to developmental reprogramming and cellular stress resilience, representing new potential therapeutic targets.

cancer biology↗

bioRxiv2026

Whole genome sequencing of endometrial cancer identifies novel subgroups, drivers, and actionable alterations

Endometrial cancer (EC) is the most common gynaecological malignancy in high income countries, and is increasing in incidence. While molecular stratification has improved its management, precision care is hampered by incomplete characterization of the EC genome. We address this by analysis of whole genome sequencing (WGS) of 665 ECs generated by the UK Genomics England 100,000 Genome Project (100kGP). 5% of cases were associated with germline pathogenic variants in cancer genes, including BRCA1 which we confirmed predisposes to EC. We identified 107 putative coding driver genes, 35% of which had no prior established role in EC. Novel structural variants included gains of MYCN and loss of its negative regulator NEDD4.1 which were significantly mutually exclusive in copy number (CN) high tumours. Immunogenomic analysis confirmed selection for driver alterations of low immunogenicity based on patient HLA haplotype, and pervasive immune escape through multiple mechanisms. Unsupervised clustering of mutational signatures and genomic alterations identified known and novel molecular subgroups, including a CN-high subset with mutational signatures of homologous recombination deficiency (HRD) and favourable outcome. Independent prognostic value of single nucleotide variant (SNV) burden, CN burden and multiple coding drivers, along with the identification of targetable molecular alterations in over one-third of cases, underscores the promise of WGS for precision medicine in EC.

genomics↗

bioRxiv2026

Tumour evolution as ground truth for cancer whole-genome sequencing

Cancer genomes are shaped by evolutionary processes that couple mutagenesis, clonal selection, chromosomal instability, spatial growth and treatment response into structured genomic patterns, yet current benchmarking strategies largely ignore this evolutionary dependency. Here, we present SCOUT, a large-scale synthetic whole-genome sequencing resource of over 200 samples, designed for systematic benchmarking of tumour genomic analysis and evolutionary inference under controlled evolutionary ground truth. Unlike conventional task-specific simulations, SCOUT models tumour evolution as a latent generative process that simultaneously shapes mutations, copy-number alterations, variant allele frequencies, mutational signatures and clonal architectures. SCOUT recapitulates key features of solid and haematological malignancies, including driver mutations, chromosomal instability, intratumour heterogeneity, spatial sampling and treatment-associated evolutionary dynamics in tumour and matched-normal longitudinal and multi-region sequencing designs. Using SCOUT, we benchmarked widely used methods for somatic variant detection, copy-number analysis, mutational signature inference and tumour evolutionary reconstruction. Across analytical tasks, performance deteriorated in low-purity, highly subclonal and structurally complex tumours, while spatial sampling bias and hypermutation generated spurious evolutionary signals that confounded tumour interpretation across multiple inference layers. Evolutionary simulations further distinguished lineage-restricted genetic bottlenecks from multi-lineage resistance dynamics associated with tumour plasticity. Tumour purity consistently exerted a stronger effect on inference accuracy than sequencing depth. Together, our results establish evolutionary ground truth as a prerequisite for reproducible benchmarking and biologically interpretable analysis of cancer whole-genome sequencing data.

bioinformatics↗

bioRxiv2026

CRISPR-enhanced assessment of variants of unknown significance nominates oncology therapeutic targets and drug repositioning opportunities

Interpreting infrequent somatic variants remains a challenge in cancer genomics. We developed CRISPR-VUS, a framework that uses public Cancer Dependency Map data to identify Dependency-Associated Mutations (DAMs) - variants linked to increased host-gene dependency - with resolution extending to singleton events. Analysis of 977 cell lines across 36 cancer types identified 2,376 DAMs in 1,383 genes, including 1,260 not established as cancer drivers. DAM-bearing genes converge on oncogenic networks, while recurrence in histology-matched tumours, functional-impact predictions, tractability and pharmacological associations enable systematic prioritisation. Prime editing showed that the prioritised NSCLC-specific RTN4IP1-A80T DAM conferred a significant competitive growth advantage in a lung epithelial model, nominating a candidate driver allele. Exploratory pharmacological testing showed a greater maximal response to istaroxime in ATP1B3-I189M-bearing than in ATP1B3-wild-type cells. CRISPR-VUS combines dependency-based rare-variant discovery with evidence-guided prioritisation to nominate candidate drivers, therapeutic targets and drug-repositioning hypotheses. Interactive results are available at https://vus-portal.fht.org/.

bioinformatics↗

bioRxiv2025

Single cell multi-omic whole genome sequencing and chromatin accessibility profiling reveals genome-epigenome coevolution in colorectal cancer

Epigenetic alterations co-evolve with genetic mutations to drive carcinogenesis and treatment response. Resolving genome-epigenome coevolution requires accurate multi-omic single cell measurement. Here we develop a new technology called "double ATAC" (dATAC) for high-throughput, high-quality, concurrent whole genome sequencing and chromatin accessibility profiling of individual somatic cells. dATAC is a "one pot" method that uses two rounds of tagmentation to sequentially label open chromatin regions and then the whole genome, and produces data of the same quality as current leading single-omic methods. Using colorectal cancer as a model system, we apply dATAC to reveal convergent reorganisation of the epigenome across expanding drug-resistant clones during 5-FU chemotherapy exposure, remarkable stoichiometry of chromatin accessibility at somatic copy number alterations, and the clonal expansion of copy-number altered T cells in the stroma of metastatic disease. dATAC is a robust single cell technology to accurately profile genome-epigenome coevolution across tissues, diseases and species.

molecular biology↗

bioRxiv2025

Quantitative measurement of phenotype dynamics during cancer drug resistance evolution using genetic barcoding

Cancer treatment frequently fails due to the evolution of drug-resistant cell phenotypes driven by genetic or non-genetic changes. The origin, timing, and rate of spread of these adaptations are critical for understanding drug resistance mechanisms but remain challenging to observe directly. We present a mathematical framework to infer drug resistance dynamics from genetic lineage tracing and population size data without direct measurement of resistance phenotypes. Simulation experiments demonstrate that the framework accurately recovers ground-truth evolutionary dynamics. Experimental evolution to 5-Fu chemotherapy in colorectal cancer cell lines SW620 and HCT116 validates the framework. In SW620 cells, a stable pre-existing resistant subpopulation was inferred, whereas in HCT116 cells, resistance emerged through phenotypic switching into a slow-growing resistant state with stochastic progression to full resistance. Functional assays, including scRNA-seq and scDNA-seq, validate these distinct evolutionary routes. This framework facilitates rapid characterisation of resistance mechanisms across diverse experimental settings.

cancer biology↗

bioRxiv2024

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

Deficiency in the mismatch repair system (MMRd) causes microsatellite instability (MSI) in cancers and determines eligibility for immunotherapy. Here, we show that MMRd tumours harbour long-deletion signatures ([≥]2-5+ base pairs deleted in repetitive regions), which provide new insights into MSI evolution and enable sensitive MSI detection particularly in challenging clinical samples. Long deletions, accumulated through stepwise DNA slippage errors, are significantly more prevalent in metastatic MMRd tumours compared to primary tumours. Importantly, we show that long-deletion signatures harbour features that are distinct from background noise, making them robustly detectable even in shallow whole genome sequencing (sWGS, [~]0.1X coverage) of formalin-fixed samples. We constructed a machine learning classifier that uses these distinct features to detect Microsatellite Instability in LOw-quality (MILO) samples. MILO achieved 100% accuracy in detecting MSI in sWGS data with only 2%-15% tumour purity and demonstrated promise in identifying MMRd clones in precancerous intestinal lesions. We propose that MILO could be clinically used for the sensitive monitoring of MMRd cancer evolution from early to late stages, using minimal sequencing data from both archival and fresh-frozen samples with low tumour content. SignificanceMutational signatures characterised by long deletions in repetitive genomic regions provide a sensitive route to detect and track MMRd clone evolution, even with low purity shallow whole genome sequencing data.

bioinformatics↗

bioRxiv2024

Interpretable Multi-Omics Data Integration with Deep Archetypal Analysis

High-throughput multi-omic molecular profiling allows probing biological systems at unprecedented resolution. However, the integration and interpretation of high-dimensional, sparse, and noisy multimodal datasets remains challenging. Deriving new biology using current methods is particularly difficult because they are not based on biological principles, but instead focus exclusively on a dimensionality reduction task. Here we introduce MIDAA (Multiomic Integration with Deep Archetypal Analysis), a framework that combines archetypal analysis, an approach grounded in biological principles, with deep learning. Using the concept of archetypes that are based on evolutionary trade-offs and Pareto optimality - MIDAA finds extreme data points that define the geometry of the latent space, preserving the complexity of biological interactions while retaining an interpretable output. We demonstrate that indeed these extreme points represent cellular programmes reflecting the underlying biology. We show on real and simulated multi-omics data how MIDAA outperforms state-of-the-art methods in identifying parsimonious, interpretable, and biologically relevant patterns.

bioinformatics↗

bioRxiv2024

Heterogeneity and evolution of DNA mutation rates in microsatellite stable colorectal cancer

DNA sequence mutability in tumors with chromosomal instability is conventionally believed to remain uniform, constant, and low, based on the assumption that further mutational accrual in a context of marked aneuploidy is evolutionarily disadvantageous. However, this concept lacks robust experimental verification. We adapted the principles of mutation accumulation experiments, traditionally performed in lower organisms, to clonal populations of patient-derived tumoroids and empirically measured the spontaneous rates of accumulation of new DNA sequence variations in seven chromosomally unstable, microsatellite stable colorectal cancers (CRCs) and one microsatellite unstable CRC. Our findings revealed heterogeneous mutation rates (MRs) across different tumors, with variations in magnitude within microsatellite stable tumors as prominent as those distinguishing them from microsatellite unstable tumors. Moreover, comparative assessment of microsatellite stable primary tumors and matched synchronous metastases consistently documented a pattern of MR intensification during tumor progression. Therefore, wide-range diversity and progression-associated evolvability of DNA sequence mutational instability emerge as unforeseen hallmarks of microsatellite stable CRC, complementing karyotype alterations as selectable traits to increase genetic variation. One sentence summaryTumors with chromosomal instability accrue DNA sequence mutations at highly variable rates, which increase during metastatic progression.

cancer biology↗

bioRxiv2024

Epigenome and early selection determine the tumour-immune evolutionary trajectory of colorectal cancer

Immune system control is a major hurdle that cancer evolution must circumvent. The relative timing and evolutionary dynamics of subclones that have escaped immune control remain incompletely characterized, and how immune-mediated selection shapes the epigenome has received little attention. Here, we infer the genome- and epigenome-driven evolutionary dynamics of tumour-immune coevolution within primary colorectal cancers (CRCs). We utilise our existing CRC multi-region multi-omic dataset that we supplement with high-resolution spatially-resolved neoantigen sequencing data and highly multiplexed imaging of the tumour microenvironment (TME). Analysis of somatic chromatin accessibility alterations (SCAAs) reveals frequent somatic loss of accessibility at antigen presenting genes, and that SCAAs contribute to silencing of neoantigens. We observe that strong immune escape and exclusion occur at the outset of CRC formation, and that within tumours, including at the microscopic level of individual tumour glands, additional immune escape alterations have negligible consequences for the immunophenotype of cancer cells. Further minor immuno-editing occurs during local invasion and is associated with TME reorganisation, but that evolutionary bottleneck is relatively weak. Collectively, we show that immune evasion in CRC follows a "Big Bang" evolutionary pattern, whereby genetic, epigenetic and TME-driven immune evasion acquired by the time of transformation defines subsequent cancer-immune evolution.

cancer biology↗

bioRxiv2023

NeuroVelo: interpretable learning of cellular dynamics from single-cell transcriptomic data

Reconstructing temporal cellular dynamics from static single-cell transcriptomics remains a major challenge. Methods based on RNA velocity are useful, but interpreting their results to learn new biology remains difficult, and their predictive power is limited. Here we propose NeuroVelo, a method that couples learning of an optimal linear projection with non-linear Neural Ordinary Differential Equations. Unlike current methods, it uses dynamical systems theory to model biological processes over time, hence NeuroVelo can identify gene interactions that drive the observed temporal dynamics of gene expression. We benchmark NeuroVelo against several state-of-the-art methods using single-cell datasets, demonstrating that NeuroVelo simultaneously reconstructs correct cell-type transitions and identifies gene regulatory networks that drive cell fate directly from the data.

bioinformatics↗

bioRxiv2023

Epigenetic heritability of cell plasticity drives cancer drug resistance through one-to-many genotype to phenotype mapping

Cancer drug resistance is multi-factorial, driven by heritable (epi)genetic changes but also phenotypic plasticity. Here we dissect it by perturbing colorectal cancer patient-derived organoids longitudinally with drugs in sequence. Combining longitudinal tracking, single cell omics, evolutionary modelling, and machine leaning, we found that different targeted drugs select for distinct subclones, supporting rationally designed drug sequences. The cellular memory was encoded as a heritable epigenetic configuration, from which multiple transcriptional programmes could run, supporting a one-to-many (epi)genotype-to- phenotype map that explains how clonal expansions and plasticity manifest together. This may ensure drug resistance subclones can exhibit distinct phenotypes in changing environments while still preserving the cellular memory encoding for their selective advantage. Chemotherapies resistance was instead entirely driven by plasticity. Inducing further chromosomal instability before drug application changed clonal evolution but not convergent transcriptional programmes. Collectively, our data show how genetic and epigenetic alterations are selected to "permissive epigenome" enabling phenotypic plasticity.

cancer biology↗

bioRxiv2022

Whole genome sequencing of 2,023 colorectal cancers reveals mutational landscapes, new driver genes and immune interactions

To characterise the somatic alterations in colorectal cancer (CRC), we conducted whole-genome sequencing analysis of 2,023 tumours. We provide the most detailed high-resolution map to date of somatic mutations in CRC, and demonstrate associations with clinicopathological features, in particular location in the large bowel. We refined the mutational processes and signatures acting in colorectal tumorigenesis. In analyses across the sample set or restricted to molecular subtypes, we identified 185 CRC driver genes, of which 117 were previously unreported. New drivers acted in various molecular pathways, including Wnt (CTNND1, AXIN1, TCF3), TGF-{beta}/BMP (TGFBR1) and MAP kinase (RASGRF1, RASA1, RAF1, and several MAP2K and MAP3K loci). Non-coding drivers included intronic neo-splice site alterations in APC and SMAD4. Whilst there was evidence of an excess of mutations in functionally active regions of the non-coding genome, no specific drivers were called with high confidence. Novel recurrent copy number changes included deletions of PIK3R1 and PWRN1, as well as amplification of CCND3 and NEDD9. Putative driver structural variants included BRD4 and SOX9 regulatory elements, and ACVR2A and ANKRD11 hotspot deletions. The frequencies of many driver mutations, including somatic Wnt and Ras pathway variants, showed a gradient along the colorectum. The Pks-pathogenic E. coli signature and TP53 mutations were primarily associated with rectal cancer. A set of unreported immune escape driver genes was found, primarily in hypermutated CRCs, most of which showed evidence of genetic evasion of the anti-cancer immune response. About 25% of cancers had a potentially actionable mutation for a known therapy. Thirty-three of the new driver genes were predicted to be essential, 17 possessed a druggable structure, and nine had a bioactive compound available. Our findings provide further insight into the genetics and biology of CRC, especially tumour subtypes defined by genomic instability or clinicopathological features.

genomics↗

bioRxiv2022

Immune evasion impacts the selective landscape of driver genes during tumorigenesis

Carcinogenesis is an evolutionary process fueled by the interplay of somatic mutations and the local microenvironment. In recent years, hundreds of cancer related genes have been discovered using cancer cohorts. However, these cohorts are heterogenous mixtures of different molecular phenotypes, which hampers the identification of driver genes associated to a specific cancer hallmark or microenvironment. Here, we compared the landscape of positively selected somatic mutations in immune-escaped (escape+) versus non-escaped (escape-) tumors. We applied the ratio of non-synonymous to synonymous mutations (dN/dS) to 9896 individuals from 31 primary tumor tissues from the Cancer Genome Atlas (TCGA) separated by escape status. Altogether, we found 85 driver genes, including 27 and 16 novel driver genes in escape- and escape+ tumors, respectively. Overall, driver dN/dS of escape+ tumors (dN/dS=1.23) was significantly lower and closer to neutrality than driver dN/dS of escape-tumors (dN/dS=1.62), suggesting a relaxation of positive selection in driver genes, a relaxation of negative selection on immunogenic driver sites, or a combination of both fueled by immune escape. We also found that the proportion of unique sites mutated in escape+ tumors is almost double than in escape-tumors, and that immune evasion allows for a more diverse repertoire of mutational signatures. We also identified that strong immunoediting in the absence of escape leads to a better overall survival in tumors enriched by an inflamed phenotype. Ultimately, our findings reveal differences in the evolutionary strategies used by cancer cells to establish tumorigenesis and highlight the need for better patient stratification to develop tailored treatments based on molecular targets.

genomics↗

bioRxiv2022

Mutation Rate Evolution Drives Immune Escape In Mismatch Repair-Deficient Cancer

Mutation rate optimisation drives evolution and immune evasion of bacteria and lentiviral strains, including HIV. Whether evolving cancer lineages similarly adapt mutation rates to increase tumour cell fitness is unknown. Here, by mapping the clonal topography of mismatch repair-deficient (MMRd) colorectal cancer, we show that genomic MMRd mutability co-evolves with neoantigen selection to drive intratumour diversification and immune escape. Mechanistically, we find that microsatellite instability modulates subclonal DNA repair by toggling two hypermutable mononucleotide homopolymer runs in the mismatch repair genes MSH6 and MSH3 (C8 and A8, respectively) through stochastic frameshift switching. Spontaneous mutation and reversion at these evolvability switches modulates subclonal mutation rate, mutation bias, and clonal HLA diversity during MMRd cancer evolution. Combined experimental and simulation studies demonstrate that subclonal immune selection favours incremental MMR mutations. MMRd cancers thus fuel intratumour heterogeneity by adapting subclonal mutation rate and mutation bias to immune selection, revealing a conserved co-evolutionary arms race between neoantigen selection and adaptive genomic mutability. Our work reveals layers of mutational complexity and microsatellite biology in MMRd cancer evolution previously hidden in bulk analyses.

cancer biology↗

bioRxiv2021

Phenotypic plasticity and genetic control in colorectal cancer evolution

Cancer evolution is driven by natural selection acting upon phenotypic trait variation. However, the extent to which phenotypic variation within a tumour is a consequence of intra-tumour genetic heterogeneity remains undetermined. Here we show that colorectal cancer cells frequently have highly plastic phenotypic traits in vivo in patient tumours. We measured the degree to which trait variation reflects genetic ancestry by quantifying the phylogenetic signal of gene expression across 297 samples with multi-region paired whole genome and transcriptome sequencing collected from 27 primary colorectal cancers. Within-tumour phylogenetic signal for genes and pathways was detected only infrequently, suggesting that the majority of intra-tumour variation in gene expression programmes was not strongly heritable. Expression quantitative trait loci analyses (eQTL) identified a small number of putative mechanisms of genetic control of gene expression due to the cis-acting coding, non-coding and structural genetic alteration, but most gene expression variation was not explained by our genetic analysis. Leveraging matched chromatin-accessibility sequencing data, enhancer mutations with cis regulatory effects on gene expression were associated with a change in chromatin accessibility, indicating that non-coding variation can have phenotypic consequence through modulation of the 3D architecture of the genome. This study maps the evolution of transcriptional variation during cancer evolution, highlighting that intra-tumour phenotypic plasticity is pervasive in colorectal malignancies, and may play key roles in further tumour evolution, from metastasis to therapy resistance.

cancer biology↗