Biology subjects

Tavare, S.

Publications and source records attributed to Tavare, S..

9 recordsLinked to original sources

bioRxiv2026

Accurate detection of tumor clonality and ongoing expansion mode from genomic data

Recent evidence shows that despite considerable effort, currently available algorithms for estimating intratumor heterogeneity (ITH) remain limited. We developed DECODE (Deciphering Cancer Origin from DNA Evolution), a novel mutation clustering method that incorporates the impact of sample-specific sequencing coverage and mutation calling biases. On synthetic data, DECODE outperformed existing methods across multiple clonality metrics and accurately detected and characterized the neutral tail in the site frequency spectrum (SFS), which encodes the tumors ongoing expansion mode. In acute myeloid leukemia, accounting for the neutral tail enabled DECODE to yield more parsimonious clonal decompositions that align more closely with known subclonal dynamics that drive relapse. Applied to data from The Cancer Genome Atlas, DECODE not only detected a neutral SFS tail in most samples across tumor types but also uncovered a clinically meaningful link between ITH and survival in low-grade glioma. By jointly inferring clonality and expansion mode, DECODE provides two complementary and prognostically relevant readouts of tumor evolution from single tumor genomic samples.

bioinformatics↗

bioRxiv2026

Single-nucleus multiome sequencing identifies candidate regulators of mouse gastric epithelial homeostasis

Background & AimsGastric epithelial cells maintain homeostasis through dynamic self-renewal mechanisms involving stem and progenitor cells; however, identifying them has been challenging. This study aims to identify stem cells of healthy gastric epithelium and cell type-specific regulators defining gastric epithelial homeostasis via single-nucleus multiome analysis. MethodsTen unique gastric samples were collected from 8-12 week old wildtype mice. Isolated nuclei were subjected to simultaneous profiling of gene expression and chromatin accessibility. After quality control, 31,598 cells were analyzed with Seurat and Signac using weighted-nearest neighbors analysis for joint RNA and ATAC clustering. Furthermore, SCENIC+, MultiVelo, EpiCHAOS and Cell plasticity score were used to uncover gene regulatory networks, cell state dynamics and lineage trajectories. ResultsOur analyses were validated by the identification of known regulators of stem-cell differentiation into mature cell types. More importantly, it revealed previously uncharacterized regulatory networks comprising novel transcription factor combinations that define cell identities, including Ppara, Pparg, Arid5b and Sox5 as candidate regulators of parietal, foveolar, chief and neck cells, respectively. Further, our data support the identity of isthmus cells as stem-like cells of healthy gastric epithelium, as evidenced by epigenetic plasticity that simultaneously contains open chromatin states of all differentiated cell types in the absence of transcriptional reprogramming. ConclusionConsistent with Waddingtons epigenetic landscape hypothesis, gastric epithelial homeostasis is controlled by orchestrated epigenetic and transcriptional programs. Contrary to the prevailing hypothesis, stem cells can be defined not by a separate epigenetic state but by epigenetic superposition of differentiated cell states. Future work is needed to define the universality of these results.

genomics↗

bioRxiv2025

Wavelet Based Whole Genome Doubling Aware Single Cell Copy Number Calling

Advances in single cell whole genome sequencing enable profiling of the copy number state of thousands of cells with minimal sequencing bias across the genome. The Direct Library Preparation + technique is an whole genome amplification-free single cell whole genome sequencing method that achieves high throughput by fragmenting each cells genome and ligating sequencing adapters using a modified Tn5 transposase, and sequencing to less than 0.1x coverage. Despite recent advances in experimental approaches, data analysis of single cell whole genome sequencing lags behind and the existing methods are not optimized for the analysis of frozen samples with variable DNA preservation. Furthermore, existing tools predominantly rely on read depth ratio in predefined genomic bins to call copy number, making whole genome duplication unidentifiable. To address this, we introduce Songbird, a single cell whole genome sequencing copy number caller that is whole genome duplication sensitive, and outperforms existing tools both in breakpoints identification and true copy number detection. We demonstrate that Songbird is robust down to extremely low coverage, adaptable to a variety of genome versions (hg19, hg38, hs.1), and is extensible to other single cell whole genome sequencing methods that rely on Tn5 tagmentation to fragment the genome.

bioinformatics↗

bioRxiv2024

CINner: modeling and simulation of chromosomal instability in cancer at single-cell resolution

Cancer development is characterized by chromosomal instability, manifesting in frequent occurrences of different genomic alteration mechanisms ranging in extent and impact. Mathematical modeling can help evaluate the role of each mutational process during tumor progression, however existing frameworks can only capture certain aspects of chromosomal instability (CIN). We present CINner, a mathematical framework for modeling genomic diversity and selection during tumor evolution. The main advantage of CINner is its flexibility to incorporate many genomic events that directly impact cellular fitness, from driver gene mutations to copy number alterations (CNAs), including focal amplifications and deletions, missegregations and whole-genome duplication (WGD). We apply CINner to find chromosome-arm selection parameters that drive tumorigenesis in the absence of WGD in chromosomally stable cancer types. We found that the selection parameters predict WGD prevalence among different chromosomally unstable tumors, hinting that the selective advantage of WGD cells hinges on their tolerance for aneuploidy and escape from nullisomy. Direct application of CINner to model the WGD proportion and fraction of genome altered (FGA) further uncovers the increase in CNA probabilities associated with WGD in each cancer type. CINner can also be utilized to study chromosomally stable cancer types, by applying a selection model based on driver gene mutations and focal amplifications or deletions. Finally, we used CINner to analyze the impact of CNA probabilities, chromosome selection parameters, tumor growth dynamics and population size on cancer fitness and heterogeneity. We expect that CINner will provide a powerful modeling tool for the oncology community to quantify the impact of newly uncovered genomic alteration mechanisms on shaping tumor progression and adaptation.

genomics↗

bioRxiv2021

The Imaging and Molecular Annotation of Xenografts and Tumours (IMAXT) High Throughput Data and Analysis Infrastructure

With the aim of producing a 3D representation of tumours, IMAXT uses a large variety of modalities in order to acquire tumour samples and produce a map of every cell in the tumour and its host environment. With the large volume and variety of data produced in the project we develop automatic data workflows and analysis pipelines and introduce a research methodology where scientists connect to a cloud environment to perform analysis close to where data are located instead of bringing data to their local computers. Here we present the data and analysis infrastructure, discuss the unique computational challenges and describe the analysis chains developed and deployed to generate molecularly annotated tumour models. Registration is achieved by use of a novel technique involving spherical fiducial marks that are visible in all imaging modalities used within IMAXT.

cancer biology↗

bioRxiv2020

A single gene expressed in fibroblastic reticular cells predicts response to cancer immunotherapy

While the role of CD8+ T cells in mediating response to cancer immunotherapy is well established, the role of B cells remains more controversial (1-3). By conducting a large gene expression study of response to immune checkpoint inhibitors (ICI), we show that pre-treatment expression of B cell genes is associated with ICI response independently of CD8+ T cells. However, we discovered that such association can be completely explained by a single gene (FDCSP) expressed outside of the B cell compartment, in fibroblastic reticular cells (FRCs), which form the reticular network that facilitates interactions between B cells, T cells and cognate antigens (4-6) and are required to initiate efficient adaptive immune responses in secondary lymphoid organs (SLO) and tertiary lymphoid structures (TLS) (4, 7). We validated this finding in three independent cohorts of patients treated with ICI in melanoma and renal cell carcinoma. Taken together, these results suggest that FDCSP is an independent predictor of ICI response, thus opening new avenues to explain the mechanisms of resistance to cancer immunotherapy.

immunology↗

bioRxiv2019

Statistical inference for the evolutionary history of cancer genomes

Recent years have produced a large amount of work on inference about cancer evolution from mutations identified in cancer samples. Much of the modeling work has been based on classical models of population genetics, generalized to accommodate time-varying cell population size. Reverse-time genealogical views of such models, commonly known as coalescents, have been used to infer aspects of the past of growing populations. Another approach is to use branching processes, the simplest scenario being the linear birth-death process (lbdp), a binary fission Markov age-dependent branching process. A genealogical view of such models is also available. The two approaches lead to similar but not identical results. Inference from evolutionary models of DNA often exploits summary statistics of the sequence data, a common one being the so-called Site Frequency Spectrum (SFS). In a sequencing experiment with a known number of sequences, we can estimate for each site at which a novel somatic mutation has arisen, the number of cells that carry that mutation. These numbers are then grouped into sites which have the same number of copies of the mutant. SFS can be computed from the statistics of mutations in a sample of cells, in which DNA has been sequenced. In this paper, examine how the SFS based on birth-death processes differ from those based on the coalescent model. This may stem from the different sampling mechanisms in the two approaches. However, we also show mathematically and computationally that despite this, they can be made quantitatively comparable at least for the range of parameters typical for tumor cell populations. We also present a model of tumor evolution with selective sweeps, based on coalescence, and demonstrate how it may help in understanding the past history of tumor as well the influence of data pre-processing. We illustrate the theory with applications to several examples of The Cancer Genome Atlas tumors.

cancer biology↗

bioRxiv2018

The landscape of selection in 551 Esophageal Adenocarcinomas defines genomic biomarkers for the clinic

Esophageal Adenocarcinoma (EAC) is a poor prognosis cancer type with rapidly rising incidence. Our understanding of genetic events which drive EAC development is limited and there are few molecular biomarkers for prognostication or therapeutics. We have accumulated a cohort of 551 genomically characterised EACs (73% WGS and 27% WES) with clinical annotation and matched RNA-seq. Using a variety of driver gene detection methods, we discover 77 EAC driver genes (73% novel) and 21 non-coding driver elements (95% novel), and describe mutation and CNV types with specific functional impact. We identify a mean of 4.4 driver events per case derived from both copy number events and mutations. We compare driver mutation rates to the exome-wide mutational excess calculated using Non-synonymous vs Synonymous mutation rates (dNdS). We observe mutual exclusivity or co-occurrence of events within and between a number of EAC pathways (GATA factors, Core Cell cycle genes, TP53 regulators and the SWI/SNF complex) suggestive of important functional relationships. These driver variants correlate with tumour differentiation, sex and prognosis. Poor prognostic indicators (SMAD4, GATA4) are verified in independent cohorts with significant predictive value. Over 50% of EACs contain sensitising events for CDK4/6 inhibitors which are highly correlated with clinically relevant sensitivity in a panel EAC cell lines and organoids.

cancer biology↗

bioRxiv2017

Division-independent differentiation mandates proliferative competition among stem cells

Cancer-initiating gatekeeper mutations that arise in stem cells would be especially potent if they stabilize and expand an affected stem lineage (1, 2). It is therefore important to understand how different stem cell organization strategies promote or prevent variant stem cell amplification in response to different types of mutation, including those that activate stem cell proliferation. Stem cell numbers can be maintained constant while producing differentiated products through individually asymmetric division outcomes or by population asymmetry strategies, in which individual stem cell lineages necessarily compete for niche space. We considered alternative mechanisms underlying population asymmetry and used quantitative modeling to predict starkly different consequences of altering proliferation rate: a variant, faster-proliferating mutant stem cell should compete better only when stem cell division and differentiation are independent processes. For most types of stem cell it has not been possible to ascertain experimentally whether division and differentiation are coupled. However, Drosophila Follicle Stem Cells (FSCs) provided a favorable model system to investigate population asymmetry mechanisms and also for measuring the impact of altered proliferation on competition. We found from detailed cell lineage studies that FSC division and FSC differentiation are not coupled. We also found that FSC representation, reflecting maintenance and amplification, was highly responsive to genetic changes that altered only the rate of FSC proliferation. The FSC paradigm therefore provides definitive experimental evidence for the general principle that relative proliferation rate will always be a major determinant of competition among stem cells specifically when stem cell division and differentiation are independent.\n\nSIGNIFICANCEAdult stem cells support tissue maintenance throughout life but they also can be cells of origin for cancer, allowing clonal expansion and long-term maintenance of the first oncogenic mutations. We considered how a mutation that increases the proliferation rate of a stem cell would affect the probability of its competitive survival and amplification for different potential organizations of stem cells. Quantitative modeling showed that the key characteristic predicting the impact of relative proliferation rate on competition is whether differentiation of a stem cell is coupled to its division. We then used Drosophila Follicle Stem Cells to provide definitive experimental evidence for the general prediction that relative proliferation rates dictate stem cell competition specifically for stem cells that exhibit division-independent differentiation.

developmental biology↗