Biology subjects

Ciccarelli, F. D.

Publications and source records attributed to Ciccarelli, F. D..

13 recordsLinked to original sources

bioRxiv2025

Kandinsky: enabling neighbourhood analysis of spatial omics data for functional insights on cell ecosystems

Spatially resolved omics technologies enable investigation of how cells interact within their local environments or neighbourhoods directly in situ. Although a few computational methods have been developed to aid this analysis, significant limitations still exist in the way neighbourhoods are defined and exploited for downstream analyses. Here, we present Kandinsky, a computational tool that implements multiple approaches for neighbourhood identification, enabling high flexibility and versatility to address a variety of biological questions. Once identified, Kandinsky applies neighbourhoods for downstream studies, including proximity-based cell grouping for functional comparisons, spatial co-localisation and dispersion, and identification of hot and cold expression areas within the tissue. We apply Kandinsky to transcriptomic and proteomic data from different spatial technologies to showcase how it can reveal functional interactions between cells across multiple biological contexts. Availability and implementationKandinsky is freely available as an R package at https://github.com/ciccalab/Kandinsky.

bioinformatics↗

bioRxiv2024

Context-dependent effects of CDKN2A and other 9p21 gene losses during the evolution of oesophageal cancer

CDKN2A is a tumour suppressor located in chromosome 9p21 and frequently lost in Barretts oesophagus (BO) and oesophageal adenocarcinoma (OAC). How CDKN2A and other 9p21 gene co-deletions affect OAC evolution remains understudied. We explored the effects of 9p21 loss in OACs and cancer progressor and non-progressor BOs with matched genomic, transcriptomic, and clinical data. Despite its cancer driver role, CDKN2A loss in BO prevents OAC initiation by counter-selecting subsequent TP53 alterations. 9p21 gene co-deletions predict poor patient survival in OAC but not BO through context-dependent effects on cell cycle, oxidative phosphorylation, and interferon response. Immune quantifications using bulk transcriptome, RNAscope and high-dimensional tissue imaging showed that IFNE loss reduces immune infiltration in BO but not OAC. Mechanistically, CDKN2A loss suppresses the maintenance of squamous epithelium, contributing to a more aggressive phenotype. Our study demonstrates context-dependent roles of cancer genes during disease evolution, with consequences for cancer detection and patient management.

cancer biology↗

bioRxiv2023

Unanticipated interacting features of human gut-associated lymphoid tissues link microbiota, intestinal immunity and autoimmunity

Gut-associated lymphoid tissue (GALT) is organised lymphoid tissue that is chronically activated by the intestinal microbiota. It generates the IgA response that is critical for intestinal homeostasis. By iterative application of multiplexed technologies, we identify enrichment of double-negative 2 (DN2:CD27-IgD-CD21loCD11chi) B cells in GALT, where they comprise the majority of intraepithelial and subepithelial B cells. We show that DN2 B cells in GALT interact with DC in the sub-epithelial dome that express DNASE1L3 and microbicides. Unlike in mice, DNASE1L3 in humans does not associate with apoptotic debris, but is located between sampled bacteria and host tissue where it is co-expressed with C1Q, consistent with management of bacterial debris. Thus we demonstrate that DN2 B cells that are otherwise associated with lupus nephritis, and DNASE1L3 and C1q that are lupus autoantigens, are microbiota-associated, interacting components of normal intestinal immunity.

immunology↗

bioRxiv2023

Mechanistic insights into the interactions between cancer drivers and the tumour immune microenvironment

The crosstalk between cancer and the tumour immune microenvironment (TIME) has attracted significant interest because of its impact on cancer evolution and response to treatment. Despite this, cancer-specific tumour-TIME interactions and their mechanisms of action are still poorly understood. Here we identified the interactions between cancer-specific genetic drivers and anti- or pro-tumour TIME features in individual samples of 32 cancer types. The resulting 477 TIME drivers are multifunctional genes whose alterations are selected early in cancer evolution and recur across and within cancer types. Moreover, the anti-tumour TIME driver burden is predictive of overall response to immunotherapy. Focusing on head and neck squamous cancer (HNSC), we rebuilt the functional networks linking specific TIME driver alterations to the TIME state. We showed that TIME driver alterations predict the immune profiles of HNSC molecular subtypes, and that deregulation of keratinization, apoptosis and interferon signalling underpin specific driver-TIME interactions. Overall, our study provides a comprehensive resource of TIME drivers giving mechanistic insights into their immune-regulatory role.

cancer biology↗

bioRxiv2022

Rarity: Discovering rare cell populations from single-cell imaging data

BackgroundCell type identification plays an important role in the analysis and interpretation of single-cell data and can be carried out via supervised or unsupervised clustering approaches. Supervised methods are best suited where we can list all cell types and their respective marker genes a priori. While unsupervised clustering algorithms look for groups of cells with similar expression properties. This property permits the identification of both known and unknown cell populations, making unsupervised methods suitable for discovery.. Success is dependent on the relative strength of the expression signature of each group as well as the number of cells. Rare cell types therefore present a particular challenge that are magnified when they are defined by differentially expressing a small number of genes. Typical unsupervised approaches fail to identify such rare subpopulations, and these cells tend to be absorbed into more prevalent cell types. ResultsIn order to balance these competing demands, we have developed a novel statistical framework for unsupervised clustering, named Rarity, that enables the discovery process for rare cell types to be more robust, consistent and interpretable. We achieve this by devising a novel clustering method based on a Bayesian latent variable model in which we assign cells to inferred latent binary on/off expression profiles. This lets us achieve increased sensitivity to rare cell populations while also allowing us to control and interpret potential false positive discoveries. ConclusionsWe systematically study the challenges associated with rare cell type identification and demonstrate the utility of Rarity on various IMC data sets.

bioinformatics↗

bioRxiv2021

Comparative assessment of genes driving cancer and somatic evolution.

Genetic alterations of somatic cells can drive nonmalignant clone formation and promote cancer initiation. However, the link between these processes remains unclear hampering our understanding of tissue homeostasis and cancer development. Here we collect a literature-based repertoire of 3355 well-known or predicted drivers of cancer and noncancer somatic evolution in 122 cancer types and 12 noncancer tissues. Mapping the alterations of these genes in 7953 pancancer samples reveals that, despite the large size, the known compendium of drivers is still incomplete and biased towards frequently occurring coding mutations. High overlap exists between drivers of cancer and noncancer somatic evolution, although significant differences emerge in their recurrence. We confirm and expand the unique properties of drivers and identify a core of evolutionarily conserved and essential genes whose germline variation is strongly counter-selected. Somatic alteration in even one of these genes is sufficient to drive clonal expansion but not malignant transformation. Our study offers a comprehensive overview of our current understanding of the genetic events initiating clone expansion and cancer revealing significant gaps and biases that still need to be addressed. The compendium of cancer and noncancer somatic drivers, their literature support and properties are accessible at http://www.network-cancer-genes.org/.

cancer biology↗

bioRxiv2021

A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially resolved tissue phenotypingat single-cell resolution.

Multiplexed imaging technologies enable the study of biological tissues at single-cell resolution while preserving spatial information. Currently, high-dimension imaging data analysis is technology-specific and requires multiple tools, restricting analytical scalability and result reproducibility. Here we present SIMPLI (Single-cell Identification from MultiPlexed Images), a novel, flexible and technology-agnostic software that unifies all steps of multiplexed imaging data analysis. After raw image processing, SIMPLI performs a spatially resolved, single-cell analysis of the tissue slide as wells as cell-independent quantifications of marker expression to investigate features undetectable at the cell level. SIMPLI is highly customisable and can run on desktop computers as well as high-performance computing environments, enabling workflow parallelisation for large datasets. SIMPLI produces multiple tabular and graphical outputs at each step of the analysis. Its containerised implementation and minimum configuration requirements make SIMPLI a portable and reproducible solution for multiplexed imaging data analysis. SIMPLI is available at: https://github.com/ciccalab/SIMPLI.

bioinformatics↗

bioRxiv2020

Immunogenomic profile of colorectal cancer response to immune checkpoint blockade

Colorectal cancers (CRCs) show variable response to immune checkpoint blockade, which can only partially be explained by the variability of tumour mutational burden. To dissect the cellular and molecular determinants of response we performed a multi-omic screen of 721 cancer regions from patients treated with Pembrolizumab (KEYNOTE 177 clinical trial) or Nivolumab. Multi-regional whole exome, RNA and T-cell receptor sequencing show that, within hypermutated CRCs, response to both anti-PD1 agents is not positively associated with tumour mutational burden but with high clonality of immunogenic mutations, expanded T cells, low activation of the WNT pathway and active immune escape mechanisms. Coupling high-dimensional imaging mass cytometry with multiplexed immunofluorescence and computational spatial analysis, we observe that responsive hypermutated CRCs are rich in cytotoxic and proliferating PD1-expressing CD8 cells interacting with high-density clusters of PDL1-expressing antigen presenting macrophages. We propose that anti-PD1 agents release the PD1-PDL1 interaction between CD8 T cells and macrophages thus promoting cytotoxic anti-tumour activity.

cancer biology↗

bioRxiv2020

Pan-cancer detection of driver genes at the single-patient resolution

BackgroundIdentifying the complete repertoire of genes that drive cancer in individual patients is crucial for precision oncology. Most established methods identify driver genes that are recurrently altered across patient cohorts. However, mapping these genes back to patients leaves a sizeable fraction with few or no drivers, hindering our understanding of cancer mechanisms and limiting the choice of therapeutic interventions. ResultsWe present sysSVM2, a machine learning software that integrates cancer genetic alterations with gene systems-level properties to predict drivers in individual patients. Using simulated pan-cancer data, we optimise sysSVM2 for application to any cancer type. We benchmark its performance on real cancer data and validate its applicability to a rare cancer type with few known driver genes. We show that drivers predicted by sysSVM2 have a low false-positive rate, are stable and disrupt well-known cancer-related pathways. ConclusionssysSVM2 can be used to identify driver alterations in patients lacking sufficient canonical drivers or belonging to rare cancer types for which assembling a large enough cohort is challenging, furthering the goals of precision oncology. As resources for the community, we provide the code to implement sysSVM2 and the pre-trained models in all TCGA cancer types (https://github.com/ciccalab/sysSVM2).

cancer biology↗

bioRxiv2019

O-linked mucin-type glycosylation regulates the transcriptional programme downstream of EGFR in breast cancer

Aberrant mucin type O-linked glycosylation is a common occurrence in cancer. This type of O-linked glycosylation is not limited to mucins but can occur on many cell surface glycoproteins where only a small number of sites may be present. Upon EGF ligation, EGFR induces a signaling cascade but can also translocate to the nucleus where it can directly regulate gene transcription. Here we show that upon EGF binding, human breast cancer cells carrying different O-linked glycans respond by transcribing different gene expression signatures. This is not a result of changes in signal transduction but due to the differential nuclear translocation of EGFR in the two glyco-phenotypes. This is regulated by the formation of an EGFR/galectin-3/MUC1/{beta}-catenin complex at the cell surface that is present in cells carrying short core-1-based O-glycans characteristic of tumour cells but absent in core-2-carrying cells.

cancer biology↗

bioRxiv2018

Interleukin-36 promotes systemic Type-I IFN responses in severe psoriasis.

Psoriasis is an immune-mediated skin disorder associated with severe systemic co-morbidities. Both chronic and acute forms of the disease are characterised by abnormal interleukin (IL)-36 signalling. While the mechanisms whereby IL-36 promotes cutaneous inflammation are well established, its systemic effects have not been investigated. To address this issue, we initially measured leukocyte gene expression in generalised pustular psoriasis, an acute disease variant caused by mutations of the IL-36 receptor antagonist. By undertaking whole-blood and neutrophil RNA-sequencing in affected individuals, we identified a Type-I IFN signature, which correlated with IL-36 signalling up-regulation. We then validated these observations in patients with chronic plaque psoriasis. Finally, we demonstrated that IL-36 acts directly on plasmacytoid dendritic cells, where it potentiates Toll-like Receptor (TLR)-9 activation and IFN production. This effect was mediated by the induction of PLSCR1, an endosomal TLR-9 transporter. These results define an IL-36/TLR-9/Type-I IFN axis that could be targeted for the treatment of psoriasis co-morbidities.

immunology↗

bioRxiv2018

The Network of Cancer Genes (NCG): a comprehensive catalogue of known and candidate cancer genes from cancer sequencing screens.

The Network of Cancer Genes (NCG) is a manually curated repository of 2,372 genes whose somatic modifications have a known or predicted cancer driver role. These genes were collected from 275 publications, including two sources of known cancer genes and 273 cancer sequencing screens of 119 cancer types in 31 primary sites from 34,905 cancer donors. This represents a more than 1.5-fold increase in content as compared to the previous version. NCG also annotates properties of cancer genes, such as duplicability, evolutionary origin, RNA and protein expression, miRNA and protein interactions, protein function and essentiality. NCG is accessible at http://ncg.kcl.ac.uk/.

cancer biology↗

bioRxiv2018

Patient-specific detection of cancer genes reveals recurrently perturbed processes in esophageal adenocarcinoma

The identification of somatic alterations with a cancer promoting role is challenging in highly unstable and heterogeneous cancers, such as esophageal adenocarcinoma (EAC). Here we developed a machine learning algorithm to identify cancer genes in individual patients considering all types of damaging alterations simultaneously (mutations, copy number alterations and structural rearrangements). Analysing 261 EACs from the OCCAMS Consortium, we discovered a large number of novel cancer genes that, together with well-known drivers, help promote cancer. Validation using 107 additional EACs confirmed the robustness of the approach. Unlike known drivers whose alterations recur across patients, the large majority of the newly discovered cancer genes are rare or patient-specific. Despite this, they converge towards perturbing cancer-related processes, including intracellular signalling, cell cycle regulation, proteasome activity and Toll-like receptor signalling. Recurrence of process perturbation, rather than individual genes, divides EACs into six clusters that differ in their molecular and clinical features and suggest patient stratifications for personalised treatments. By experimentally mimicking or reverting alterations of predicted cancer genes, we validated their contribution to cancer progression and revealed EAC acquired dependencies, thus demonstrating their potential as therapeutic targets.

cancer biology↗