Biology subjects

Caravagna, G.

Publications and source records attributed to Caravagna, G..

28 records · Page 2Linked to original sources

bioRxiv2021

Pan-cancer landscape of AID-related mutations, composite mutations and its potential role in the ICI response

Activation-induced cytidine deaminase, AICDA or AID, is a driver of somatic hypermutation and class-switch recombination in immunoglobulins. In addition, this deaminase belonging to the APOBEC family, may have off-target effects genome-wide, but its effects at pan-cancer level are not well elucidated. Here, we used different pan-cancer datasets, totaling more than 50,000 samples analyzed by whole-genome, whole-exome or targeted sequencing. AID synergizes initial hotspot mutations by a second composite mutation. Analysis of 2.5 million cells, normal and oncogenic, revealed AICDA expression activation after oncogenic transformation and cell cycle regulation loss. AID mutational load was found to be independently associated with favorable outcome in immune-checkpoint inhibitors (ICI) treated patients across cancers after analyzing 2,000 samples. Finally, we found that AID related neoepitopes, resulting from mutations at more frequent hotspots if compared to other mutational signatures, enhance CXCL13/CCR5 expression, immunogenicity and T-cell exhaustion, which may increase ICI sensitivity. In BriefA combined bulk and single cell multi-omic analysis of over 50,000 patients and 2.5 million cells across 80 tumor types reveals oncogenic acquired AICDA expression inducing composite mutations and clonal immunogenic neoepitopes that are associated with favorable outcome in patients treated by immune-checkpoint inhibitors. Highlights* Pan-cancer analysis of AID mutations using > 50,000 samples, 2,000 ICI treated cases and 2.5 million cells with genome, exome and transcriptome data * Oncogenic transient AICDA expression induces mutations mainly during transcription of its off-target genes in virtually all cancers * AID is implicated in composite mutations on weakly functional alleles and immunogenic clonal neoepitopes at hotspots with greater positive selection * AID mutational load predicts response and is associated with favorable outcome in ICI treated patients

bioinformatics↗

bioRxiv2021

Germline loss-of-function variants in the base-excision repair gene MBD4 cause a Mendelian recessive syndrome of adenomatous colorectal polyposis and acute myeloid leukaemia

Inherited defects in base-excision repair (BER) predispose to adenomatous polyposis and colorectal cancer (CRC), yet our understanding of this important DNA repair pathway remains incomplete. By combining detailed clinical, histological and molecular profiling, we reveal biallelic germline loss-of-function (LOF) variants in the BER gene MBD4 to predispose to adenomatous polyposis and -uniquely amongst CRC predisposition syndromes- to myeloid neoplasms. Neoplasms from MBD4-deficient patients almost exclusively accumulate somatic CpG>TpG mutations, resembling mutational signature SBS1. MBD4-deficient adenomas harbour mutations in known CRC driver genes, although AMER1 mutations were more common and KRAS mutations less frequent. We did not find an increased risk for colorectal tumours in individuals with a monoallelic MBD4 LOF variant. We suggest that this condition should be termed MBD4-associated neoplasia syndrome (MANS) and that MBD4 is included in testing for the genetic diagnosis of polyposis and/or early-onset AML.

genetics↗

bioRxiv2021

A fully automated approach for quality control of cancer mutations in the era of high-resolution whole genome sequencing

AbstractThe identification of chromosome number alterations is now widespread in cancer research, but three features of genomic data hinder copy number calling and downstream analyses: the purity of the tumour sample, intra-tumour heterogeneity, and the ploidy of the tumour. To assess these features, consensus methods are often utilised, though these become onerous in projects that involve thousands of genomes. To facilitate the validation of clonal and subclonal copy number variants we present CNAqc, an evolution-inspired toolset that leverages the known quantitative relationships of purity, ploidy and heterogeneity. We validate the algorithms in CNAqc using low-pass single-cell data, as well as extensive simulations. Its application is demonstrated using over 4000 whole genomes and exomes from TCGA, and PCAWG. A real world application of CNAqc in the analysis of clinical tumour samples, has been demonstrated by its incorporation into the validation of clinically accredited bioinformatics pipeline at Genomics England. Our approach is compatible with most bioinformatic pipelines and designed to augment algorithms with automated quality control procedures for data validation.

bioinformatics↗

bioRxiv2021

Genotyping Copy Number Alterations from single-cell RNA sequencing

MotivationCancers are composed by several heterogeneous subpopulations, each one harbouring different genetic and epigenetic somatic alterations that contribute to disease onset and therapy response. In recent years, copy number alterations leading to tumour aneuploidy have been identified as potential key drivers of such populations, but the definition of the precise makeup of cancer subclones from sequencing assays remains challenging. In the end, little is known about the mapping between complex copy number alterations and their effect on cancer phenotypes. ResultsWe introduce CONGAS, a Bayesian probabilistic method to phase bulk DNA and single-cell RNA measurements from independent assays. CONGAS jointly identifies clusters of single cells with subclonal copy number alterations, and differences in RNA expression. The model builds statistical priors leveraging bulk DNA sequencing data, does not require a normal reference and scales fast thanks to a GPU backend and variational inference. We test CONGAS on both simulated and real data, and find that it can determine the tumour subclonal composition at the single-cell level together with clone-specific RNA phenotypes in tumour data generated from both 10x and Smart-Seq assays. AvailabilityCONGAS is available as 2 packages: CONGAS (https://github.com/caravagnalab/congas), which implements the model in Python, and RCONGAS (https://caravagnalab.github.io/rcongas/), which provides R functions to process inputs, outputs, and run CONGAS fits. The analysis of real data and scripts to generate figures of this paper are available via RCONGAS; code associated to simulations is available at https://github.com/caravagnalab/rcongas_test. Contactgcaravagna@units.it Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2020

dN/dS dynamics quantify tumour immunogenicity and predict response to immunotherapy

Immunoediting is a major force during cancer evolution that selects for clones with low immunogenicity (adaptation), or clones with mechanisms of immune evasion (escape). However, quantifying immunogenicity in the cancer genome and how the tumour-immune coevolutionary dynamics impact patient outcomes remain unexplored. Here we show that the ratio of nonsynonymous to synonymous mutations (dN/dS) in the immunopeptidome quantifies tumor immunogenicity and differentiates between adaptation and escape. We analysed 8,543 primary tumors from TCGA and validated immune dN/dS as a measure of selection associated with immune infiltration in immune-adapted tumours. In a cohort of 308 metastatic patients that received immunotherapy, pre-treatment lesions in non-responders showed increased immune selection (dN/dS<1), whereas responders did not and instead harboured a higher proportion of genetic escape mechanisms. Ultimately, these findings highlight the potential of evolutionary genomic measures to predict clinical response to immunotherapy.

genomics↗

bioRxiv2020

Stabilising selection causes grossly altered but stable karyotypes in metastatic colorectal cancer

Aneuploidy, the loss and gain of whole and part chromosomes, is near-ubiquitous in cancer genomes and likely defines cancer cell biology. However, the temporal evolutionary dynamics that select for aneuploidy remain uncharacterised. Here we perform longitudinal genomic analysis of 755 samples from a total of 167 patients with colorectal-derived neoplastic lesions that represent distinct stages of tumour evolution through metastasis and treatment. Adenomas typically had few copy number alterations (CNAs) and most were subclonal, whereas cancers had many clonal CNAs, suggesting that progression goes through a CNA bottleneck. Individual CRC glands from the same tumour typically had very similar karyotypes, despite evidence of ongoing instability at the cell level in patient tumours, cell lines and organoids. CNAs in metastatic lesions sampled from liver and other organs, after chemotherapy or targeted therapies, and in late recurrences were typically similar to the primary tumour. Mathematical modelling and statistical inference indicated that these data are consistent with the action of negative selection on CNAs that traps cancer cell genomes on a fitness peak defined by the specific pattern of chromosomal aberrations. These data suggest that the initial progression of colorectal cancer requires the traversal of a rugged fitness landscape and subsequent CNA evolution, including metastatic dissemination and therapeutic resistance, is constrained by negative selection.

cancer biology↗

bioRxiv2019

Model-based tumor subclonal reconstruction

The vast majority of cancer next-generation sequencing data consist of bulk samples composed of mixtures of cancer and normal cells. To study tumor evolution, subclonal reconstruction approaches based on machine learning are used to separate subpopulation of cancer cells and reconstruct their ancestral relationships. However, current approaches are entirely data-driven and agnostic to evolutionary theory. We demonstrate that systematic errors occur in subclonal reconstruction if tumor evolution is not accounted for, and that those errors increase when multiple samples are taken from the same tumor. To address this issue, we present a novel approach for model-based subclonal reconstruction that combines data-driven machine learning with evolutionary theory. Using public, synthetic and newly generated data, we show the method is more robust and accurate than current techniques in both single-sample and multi-region sequencing data. With careful data curation and interpretation, we show how the method allows minimizing the confounding factors that affect non-evolutionary methods, leading to a more accurate recovery of the evolutionary history of human tumors.

bioinformatics↗

bioRxiv2019

Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data

Quantification of the effect of spatial tumour sampling on the patterns of mutations detected in next-generation sequencing data is largely lacking. Here we use a spatial stochastic cellular automaton model of tumour growth that accounts for somatic mutations, selection, drift and spatial constrains, to simulate multi-region sequencing data derived from spatial sampling of a neoplasm. We show that the spatial structure of a solid cancer has a major impact on the detection of clonal selection and genetic drift from bulk sequencing data and single-cell sequencing data. Our results indicate that spatial constrains can introduce significant sampling biases when performing multi-region bulk sampling and that such bias becomes a major confounding factor for the measurement of the evolutionary dynamics of human tumours. We present a statistical inference framework that takes into account the spatial effects of a growing tumour and allows inferring the evolutionary dynamics from patient genomic data. Our analysis shows that measuring cancer evolution using next-generation sequencing while accounting for the numerous confounding factors requires a mechanistic model-based approach that captures the sources of noise in the data. SummarySequencing the DNA of cancer cells from human tumours has become one of the main tools to study cancer biology. However, sequencing data are complex and often difficult to interpret. In particular, the way in which the tissue is sampled and the data are collected, impact the interpretation of the results significantly. We argue that understanding cancer genomic data requires mathematical models and computer simulations that tell us what we expect the data to look like, with the aim of understanding the impact of confounding factors and biases in the data generation step. In this study, we develop a spatial simulation of tumour growth that also simulates the data generation process, and demonstrate that biases in the sampling step and current technological limitations severely impact the interpretation of the results. We then provide a statistical framework that can be used to overcome these biases and more robustly measure aspects of the biology of tumours from the data.

bioinformatics↗

bioRxiv2017

Detecting repeated cancer evolution in human tumours from multi-region sequencing data

Carcinogenesis is an evolutionary process driven by the accumulation of genomic aberrations. Recurrent sequences of genomic changes, both between and within patients, reflect repeated evolution that is valuable for anticipating cancer progression. Multi-region sequencing and phylogenetic analysis allow inference of the partial temporal order of genomic changes within a patients tumour. However, the inherent stochasticity of the evolutionary process makes phylogenetic trees from different patients appear very distinct, preventing the robust identification of recurrent evolutionary trajectories. Here we present a novel quantitative method based on a machine learning approach called Transfer Learning (TL) that allows overcoming the stochastic effects of cancer evolution and highlighting hidden recurrences in cancer patient cohorts. When applied to multi-region sequencing datasets from lung, breast and renal cancer (708 samples from 160 patients), our method detected repeated evolutionary trajectories that determine novel patient subgroups, which reproduce in large singlesample cohorts (n=2,641) and have prognostic value. Our method provides a novel patient classification measure that is grounded in the cancer evolution paradigm, and which reveals repeated evolution during tumorigenesis, with implications for our ability to anticipate malignant evolution.

bioinformatics↗

bioRxiv2017

A Computational Framework To Infer The Order Of Accumulating Mutations In Individual Tumors

BackgroundA large number of algorithms is being developed to reconstruct evolutionary models of individual tumours from genome sequencing data. Most methods can analyze multiple samples collected either through bulk multi-region sequencing experiments or the sequencing of individual cancer cells. However, rarely the same method can support both data types. ResultsWe introduce TRaIT, a computational framework to infer mutational graphs that model the accumulation of multiple types of somatic alterations driving tumour evolution. Compared to other tools, TRaIT supports multi-region and single-cell sequencing data within the same statistical framework, and delivers expressive models that capture many complex evolutionary phenomena. TRaIT improves accuracy, robustness to data-specific errors and computational complexity compared to competing methods. ConclusionsWe show that the application of TRaIT to single-cell and multi-region cancer datasets can produce accurate and reliable models of single-tumour evolution, quantify the extent of intra-tumour heterogeneity and generate new testable experimental hypotheses.

bioinformatics↗