Biology subjects

Grassi, E.

Publications and source records attributed to Grassi, E..

11 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

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

Prediction of gene expression from regulatory sequence composition enhances transcriptome-wide association studies

Transcriptome-wide association studies (TWAS) can prioritize trait-associated genes by finding correlations between a trait and the genetically regulated component of gene expression. A basic ingredient of a TWAS is a regression model, typically trained in an external reference data set, used to impute the genetically-regulated expression. We devised a model that improves the accuracy of the imputation by using, as predictors, not the genotypes directly but rather the sequence composition of the proximal gene regulatory region, expressed as its profile of affinities for a set of position weight matrices. When trained on 48 tissues from GTEx, the regression model showed improved performance compared with models regressing expression directly on the genotype. We imputed the expression levels in genotyped individuals from the ADNI data set, and used the imputed expression to perform a TWAS. We also developed a method to perform the TWAS based on summary statistics from genome-wide association studies, and applied it to 11 complex traits from the UK Biobank. The greater accuracy in the prediction of gene expression allowed us to report hundreds of new gene-phenotype association candidates.

genetics↗

bioRxiv2019

The genetic determinants of the relative expression of alternative 3'UTR isoforms in human

In the last decades, genome wide association studies (GWAS) have uncovered tens of thousands of associations between common genetic variants and complex diseases. However, these statistical associations can rarely be interpreted functionally and mechanistically. As the majority of the disease-associated variants are located far from coding sequences, even the relevant gene is often unclear. A way to gain insight into the relevant mechanisms is to study the genetic determinants of intermediate molecular phenotypes, such as gene expression and transcript structure. We propose a computational strategy to discover genetic variants affecting the relative expression of alternative 3 untranslated region (UTR) isoforms, generated through alternative polyadenylation, a widespread post-transcriptional regulatory mechanism known to have relevant functional consequences. When applied to a large dataset in which whole genome and RNA sequencing data are available for 373 European individuals, 2,530 genes with alternative polyadenylation quantitative trait loci (apaQTL) were identified. We analyze and discuss possible mechanisms of action of these variants, and we show that they are significantly enriched in GWAS hits, in particular those concerning immune-related and neurological disorders. Our results point to an important role for genetically determined alternative polyadenylation in affecting predisposition to complex diseases, and suggest new ways to extract functional information from GWAS data.

genetics↗

bioRxiv2017

Evolutionary rewiring of the human regulatory network by waves of genome expansion

Genome expansion is believed to be an important driver of the evolution of gene regulation. To investigate the role of newly arising sequence in rewiring the regulatory network we estimated the age of each region of the human genome by applying maximum parsimony to genome-wide alignments with 100 vertebrates. We then studied the age distribution of several types of functional regions, with a focus on regulatory elements. The age distribution of regulatory elements reveals the extensive use of newly formed genomic sequence in the evolution of regulatory interactions. Many transcription factors have expanded their repertoire of targets through waves of genomic expansions that can be traced to specific evolutionary times. Repeated elements contributed a major part of such expansion: many classes of such elements are enriched in binding sites of one or a few specific transcription factors, whose binding sites are localized in specific portions of the element and characterized by distinctive motif words. These features suggest that the binding sites were available as soon as the new sequence entered the genome, rather than being created later by accumulation of point mutations. By comparing the age of regulatory regions to the evolutionary shift in expression of nearby genes we show that rewiring through genome expansion played an important role in shaping the human regulatory network.

evolutionary biology↗