Biology subjects

Bertotti, A.

Publications and source records attributed to Bertotti, A..

12 recordsLinked to original sources

bioRxiv2026

TGF-β1-induced differentiation enhances chemotherapy response in metastatic colorectal cancer organoids

BackgroundIn metastatic colorectal cancer, systemic therapies frequently fail, partly due to underlying phenotypic plasticity rooted in pre-existing multi-type cell populations. Intratumoral lineage hierarchies within colorectal tumors require renovated efforts to decode growth principles, design rational therapeutic approaches, and accurately interpret drug response. Understanding the cell-state dynamics of untreated tumors and the degree of cell responsiveness to exogenous stimuli is therefore crucial to improving currently underwhelming therapeutic outcomes. MethodsHere, we leveraged patient-derived organoids established from hepatic metastases of colorectal cancer patients to deconstruct population hierarchies by combining single-cell transcriptomics with single-molecule RNA fluorescent in situ hybridization. Computational frameworks were used to identify independent gene modules. We then employed flow cytometry analysis to track cytokine-induced population shifts using a cell surface marker, validating our findings through bulk RNA analysis, functional assays, and viability assays in response to oxaliplatin. ResultsOur data substantiate the existence of a dual population configuration within untreated metastatic colorectal cancer organoids with diverse genetic backgrounds. Gene modules detected via single-cell transcriptomics delineate a stem-like (LGR5+) and a differentiated-like (KRT20+) population that fluctuate dynamically over time. Spatially and temporally resolved, single-cell level analysis through single molecule FISH captures the inherent stochasticity in cell fate decisions revealing surprising phenotypic variability even across different organoids derived from the same patient. By using GABRA2 as a surface marker we track the emergence of differentiated cells over the course of time and investigate the respective roles of TGF-{beta}1 and IL-6 in the differentiation of organoids. Our findings indicate that IL-6 exerts no major effect within our cell autonomous setting. In stark contrast, TGF-{beta}1 triggered cell cycle arrest and differentiation, while simultaneously reducing clonogenic capacity and significantly amplifying the cytotoxic potency of oxaliplatin. ConclusionsOur findings provide evidence of the dual Stem and Differentiated population hierarchy in metastatic colorectal cancer organoids, and demonstrate how this axis can be effectively hijacked by TGF-{beta}1 to suppress tumor growth. Our results suggest that further mechanistic exploitation of this cell-autonomous, tumor-suppressive arm of TGF-{beta}1 signalling could open unappreciated therapeutic windows in advanced colorectal cancer.

cancer biology↗

bioRxiv2025

On-target mutations confer resistance to WRN helicase inhibitors in Microsatellite Unstable Cancer Cells.

Werner helicase inhibitors (WRNi) are in clinical development for microsatellite-unstable (MSI) tumors with defective DNA mismatch repair. Here, we investigate how cancer cell evolution shapes response to WRN inhibition and informs potential resistance mechanisms. Genome-wide CRISPR screens combined with WRN knockout did not identify bypass mechanisms, underscoring WRNs essential, non-redundant function in MSI cells. Pharmacogenomic screens identified modulators of WRNi sensitivity, including SMARCAL1, which links it to WRN-MSI synthetic lethality. Semi-saturation mutagenesis of WRN and prolonged drug treatment identified on-target WRN mutations driving acquired resistance to multiple WRNi in vitro and in vivo, which was mitigated by combination with standard chemotherapies. Some resistance mutations conferred broad cross-resistance, whereas others preserved sensitivity to alternative clinical-grade WRNi with distinct mechanism of action. Our findings could inform clinical trial design by suggesting the feasibility of real-time tracking of emerging resistance and enabling early therapeutic adaptations. SignificanceWe present the first exploration of how MSI cancer cells evolve under the selective pressure of WRN helicase inhibition, providing a framework for understanding adaptive responses to this newly identified synthetic-lethal dependency. This study identifies on-target WRN mutations as key drivers of resistance in MSI cancers, supporting the use of combination strategies with other standard-of-care treatments to prevent resistance. It highlights how mutation tracking can guide therapeutic switching to clinically available WRN inhibitors with distinct mechanisms of action, thereby refining clinical development and potentially improving biomarker-informed patient outcomes.

cancer biology↗

bioRxiv2025

Inference of lineage hierarchies, growth and drug response mechanisms in cancer cell populations without tracking

Lineage hierarchies and plasticity regulate development and tissue homeostasis, while diverted lineage dynamics and aberrant phenotypic plasticity are among the causes of incomplete drug response and secondary resistance in cancer. Knowing the dynamics of phenotypically plastic populations is therefore central to understand growth regulation principles and to rationally design therapeutic approaches that might anticipate drug-tolerant states. Lineage inference however largely relies on single-cell tracking techniques, which are notoriously difficult in complex biological models. To overcome these limitations, we developed a method to infer active phenotypic transitions in a multi-lineage tumor or clone and to quantify them, solely relying on counting lineage abundances with no pedigree. We demonstrate the effectiveness of our approach to cancer cell plasticity and drug treatment in silico. We then perform experiments on cancer cell populations and show that our method correctly predicts growth mechanisms and transition probabilities.

cancer biology↗

bioRxiv2025

μSeq: Universal mutation rate quantification via deep sequencing of a single clonal expansion.

Understanding and quantifying mutational processes is fundamental for studying evolution in both clinical and experimental contexts. However, current methods are labor intensive and often lack robustness, particularly in mammalian cells. For example, current approaches that rely on subclonal mutations derived directly from patient samples often result in inconsistent or biased outcomes, leading to potentially unreliable conclusions on adaptive dynamics. We used patient-derived colorectal cancer organoids to introduce {micro}Seq, a universal framework for inferring mutation rates in diverse biological systems. Our approach extracts mutation rates from deep sequencing of single clonal expansions, with a time gain of ten-fold or more compared to a mutation accumulation line, at the cost of three billion read whole genome sequencing. {micro}Seq relies on four critical components: (i) a controlled experimental setup enabling validation (ii) a quantitative estimate inspired by the classic Luria-Delbruck spectrum for subclonal mutations derived from population dynamics, (iii) robust statistical models accounting for sampling noise and sequencing errors, and (iv) a data analysis pipeline for subclonal mutation detection that compares endpoint populations to a closely related ancestor. Building on the legacy of Luria and Delbruck, our model adopts core concepts from statistical physics--stochasticity, fluctuation spectra, and inference under noise--to construct a rigorous and scalable inference framework. We demonstrate that the Luria-Delbruck distribution extends to subclonal mutations in expanding populations, and show how this generalization enables robust estimation of the underlying mutation rate. Our models establish precise requirements for sequencing depth, genome size, and mutation frequency detection necessary for accurate mutation rate estimates. Crucially, we show that failing to meet these criteria can lead to errors spanning several orders of magnitude suggesting that biases in patients data arise from analyzing incorrect frequency intervals and from lack of a closely related reference. We validate our approach using parallel mutation accumulation experiments in colorectal cancer organoids, finding mutation rate estimates consistent with previous studies. However, we find that mutation accumulation lines operate under purifying selection in both yeast and human organoids, contradicting the long standing assumption of neutrality in such experiments. This insight has important implications for both evolutionary biology and cancer evolution. Finally, to demonstrate the adaptability of {micro}Seq, we apply it to yeast, leveraging multiple independent replicates to compensate for its much smaller genome, as well as in mouse xenografts, which feature much more complex in vivo population dynamics. The robustness and broad applicability of {micro}Seq establish it as a powerful and universal tool for mutation rate quantification, and imply that existing claims based on subclonal mutations from patient samples must be revisited. Short AbstractUnderstanding and quantifying mutational processes is fundamental to studying evolution in clinical and experimental contexts. However, current methods are labor intensive and often lack robustness, particularly in mammalian cells. For example, quantification of subclonal mutations directly from patient samples produces inconsistent estimates, leading to unreliable conclusions about adaptive dynamics. Using patient derived colorectal cancer organoids as a model system, we developed {micro}Seq, a novel framework to estimate mutation rates from deep sequencing of a single clonal expansion with a close reference. {micro}Seq builds on the Luria Delbruck model, combining statistical physics concepts with modern sequencing and inference techniques. It integrates (i) a controlled experimental setup, (ii) robust statistical and population dynamics models, and (iii) a data analysis pipeline for subclonal mutation detection. We establish precise requirements for sequencing depth, genome size, and mutation frequency to ensure accuracy despite sequencing errors. {micro}Seq enables accurate estimation of mutation rates as low as 10-9 mutations per base pair per generation, and we validate it across species using yeast data. Critically, {micro}Seq reveals that mutation accumulation lines undergo purifying selection in both human organoids and yeast, It underscores the need to reconsider claims based on subclonal mutations in patient samples.

evolutionary biology↗

bioRxiv2024

Genotyping Short Tandem Repeats Across Copy Number Alterations, Aneuploidies, and Polyploid Organisms

Short tandem repeats (STRs) are a rich source of genetic variation, but are difficult to genotype. While specialized repeat variant callers exist, they typically assume a euploid human genome. This means recent findings regarding phenotypic effects of STR variants in human health and disease cannot be readily extended to polyploid organisms or cancer, which is characterised by copy number alterations (CNAs). Here we present ConSTRain, a novel STR variant caller that explicitly accounts for the copy number of loci in its genotyping approach. We benchmark ConSTRain using a euploid human 100X whole genome sequencing sample where it calls STR allele lengths for over 1.7 x 106 loci in under 20 minutes with an accuracy of 98.28%. Subsequently, we show that ConSTRain resolves complex STR genotypes in an artificial trisomy 21 sample and a polyploid Dwarf Cavendish banana harbouring a large duplication. Finally, we analyse a microsatellite instable colorectal cancer tumoroid, where ConSTRain tackles CNAs and whole-genome duplications. ConSTRain is the first STR variant caller that allows for the investigation of repeats affected by CNAs, aneuploidies, and polyploid genomes. This unlocks the investigation of STRs across a wide range of contexts and organisms where they previously could not be easily studied.

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↗

bioRxiv2023

XENTURION, a multidimensional resource of xenografts and tumoroids from metastatic colorectal cancer patients for population-level translational oncology

The breadth and depth at which cancer models are interrogated contribute to successful translation of drug discovery efforts to the clinic. In colorectal cancer (CRC), model availability is limited by a dearth of large-scale collections of patient-derived xenografts (PDXs) and paired tumoroids from metastatic disease, the setting where experimental therapies are typically tested. XENTURION is a unique open-science resource that combines a platform of 129 PDX models and a sister platform of 129 matched PDX-derived tumoroids (PDXTs) from patients with metastatic CRC, with accompanying multidimensional molecular and therapeutic characterization. A PDXT-based population trial with the anti-EGFR antibody cetuximab revealed variable sensitivities that were consistent with clinical response biomarkers, mirrored tumor growth changes in matched PDXs, and recapitulated the outcome of EGFR genetic deletion. Adaptive signals upregulated by EGFR blockade were computationally and functionally prioritized, and inhibition of top candidates increased the magnitude of response to cetuximab. These findings illustrate the probative value and accuracy of large ex vivo and in vivo living biobanks, highlight the importance of cross-platform and cross-methodology systematic validation, and offer avenues for molecularly informed preclinical research.

cancer biology↗

bioRxiv2023

Integrative ensemble modelling of cetuximab sensitivity in colorectal cancer PDXs

AO_SCPLOWBSTRACTC_SCPLOWPatient-derived xenografts (PDXs) are tumour fragments engrafted into mice for preclinical studies. PDXs offer clear advantages over simpler in vitro cancer models - such as cancer cell lines (CCLs) and organoids - in terms of structural complexity, heterogeneity, and stromal interactions. We characterised 231 colorectal cancer PDXs at the genomic, transcriptomic, and epigenetic level and measured their response to cetuximab, an EGFR inhibitor in clinical use for metastatic colorectal cancer. After assessing PDXs quality, stability, and molecular concordance with publicly available patient cohorts, we trained, interpreted, and validated an integrated ensemble classifier (CeSta) which takes in input the PDXs multi-omic characterisation and predicts their sensitivity to cetuximab treatment (AUROC > 0.9). Our study shows that large PDX collections can be used to train accurate, interpretable models of drug sensitivity, which 1) better recapitulate patient-derived therapeutic biomarkers than other models trained on CCL data, 2) can be robustly validated across independent PDX cohorts, and 3) can be used for the development of novel therapeutic biomarkers.

bioinformatics↗

bioRxiv2022

CONNECTOR, fitting and clustering of longitudinal data to reveal a new risk stratification system.

The transition from the evaluation of a single time point to the examination of the entire dynamic evolution of a system is possible only in the presence of the proper framework. The strong variability of dynamic evolution makes the definition of an explanatory procedure for data fitting and data clustering challenging. Here we present CONNECTOR, a data-driven framework able to analyze and inspect longitudinal data in a straightforward and revealing way. When used to analyze tumor growth kinetics over time in 1599 patient-derived xenograft (PDX) growth curves from ovarian and colorectal cancers, CONNECTOR allowed the aggregation of time-series data through an unsupervised approach in informative clusters. Through the lens of a new perspective of mechanism interpretation, CONNECTOR shed light onto novel model aggregations and identified unanticipated molecular associations with response to clinically approved therapies.

bioinformatics↗

bioRxiv2021

Drug-induced colorectal cancer persister cells show increased mutation rate

Compelling evidence shows that cancer persister cells limit the efficacy of targeted therapies. However, it is unclear whether persister cells are induced by anticancer drugs, and if their mutation rate quantitatively increases during treatment. Here, combining experimental characterization and mathematical modeling, we show that, in colorectal cancer, persisters are induced by drug treatment and show a 7- to 50-fold increase of mutation rate when exposed to clinically approved targeted therapies. These findings reveal that treatment may influence persistence and mutability in cancer cells and pinpoints new strategies to restrict tumor recurrence.

cancer biology↗

bioRxiv2019

Conservation of copy number profiles during engraftment and passaging of patient-derived cancer xenografts

Patient-derived xenografts (PDXs) are resected human tumors engrafted into mice for preclinical studies and therapeutic testing. It has been proposed that the mouse host affects tumor evolution during PDX engraftment and propagation, impacting the accuracy of PDX modeling of human cancer. Here we exhaustively analyze copy number alterations (CNAs) in 1451 PDX and matched patient tumor (PT) samples from 509 PDX models. CNA inferences based on DNA sequencing and microarray data displayed substantially higher resolution and dynamic range than gene expression-based inferences, and they also showed strong CNA conservation from PTs through late-passage PDXs. CNA recurrence analysis of 130 colorectal and breast PT/PDX-early/PDX-late trios confirmed high-resolution CNA retention. We observed no significant enrichment of cancer-related genes in PDX-specific CNAs across models. Moreover, CNA differences between patient and PDX tumors were comparable to variations in multi-region samples within patients. Our study demonstrates the lack of systematic copy number evolution driven by the PDX mouse host.

cancer biology↗

bioRxiv2019

Evolving neoantigen profiles in colorectal cancers with DNA repair defects

BackgroundNeoantigens that arise as a consequence of tumour-specific mutations can be recognized by T lymphocytes leading to effective immune surveillance. In colorectal cancer (CRC) and other tumour types, a high number of neoantigens is associated with patient response to immune therapies. The molecular processes governing the generation of neoantigens and their turnover in cancer cells are poorly understood. We exploited CRC as a model system to understand how alterations in DNA repair pathways modulate neoantigen profiles over time.\n\nMethodsWe performed Whole Exome Sequencing (WES) and RNA sequencing (RNAseq) in CRC cell lines, in vitro and vivo, and in CRC patient-derived xenografts (PDXs) to track longitudinally genomic profiles, clonal evolution, mutational signatures and predicted neoantigens.\n\nResultsThe majority of CRC models showed remarkably stable mutational and neoantigen profiles, however those carrying defects in DNA repair genes continuously diversified. Rapidly evolving and evolutionary stable CRCs displayed characteristic genomic signatures, and transcriptional profiles. Downregulation of molecules implicated in antigen presentation occurred selectively in highly mutated and rapidly-evolving CRC.\n\nConclusionsThese results indicate that CRC carrying alterations in DNA repair pathways display dynamic neoantigen patterns that fluctuate over time. We define CRC subsets characterized by slow and fast evolvability and link this phenotype to downregulation of antigen-presenting cellular mechanisms. Longitudinal monitoring of the neoantigen landscape could be relevant in the context of precision medicine.

cancer biology↗