Biology subjects

Mangiola, S.

Publications and source records attributed to Mangiola, S..

12 recordsLinked to original sources

bioRxiv2026

ICOS blockade alleviates immune suppression in breast tumor microenvironment

Immune checkpoint blockade has become a standard of care for triple-negative breast cancer, yet only around half of patients benefit. Compensatory immunoregulatory pathways may limit the efficacy of PD-1/PD-L1 blockade. ICOS, an inducible co-stimulatory receptor enriched on activated regulatory T cells (Tregs), represents a potential route of such immune regulation, but its role in breast cancer immunotherapy remains unclear. Here, we identify ICOS as selectively upregulated on tumor-infiltrating Tregs and show that ICOS Tregs localize within a tumor niche enriched for exhausted CD8 T cells. We confirmed this cellular and spatial association across 1,359 tumor regions from 279 triple-negative breast cancer patients profiled by imaging mass cytometry, and single-cell transcriptomic data from 129 breast cancer donors. Genetic ablation or pharmacological blockade of ICOS disrupted this intratumoral Treg niche, reducing Treg activation and tissue-residency programs while expanding cytotoxic CD8 T cells with a tissue-resident memory phenotype. In syngeneic orthotopic breast cancer models, ICOS blockade restricted primary tumor growth, reduced spontaneous metastasis and prolonged survival without disrupting systemic immune homeostasis. Therapeutic activity was observed with both neoadjuvant and adjuvant treatment, while combined ICOS and PD-1 blockade further enhanced tumor control. These findings support ICOS blockade as a therapeutic strategy to improve checkpoint immunotherapy.

cancer biology↗

bioRxiv2026

Finetuning masking challenges narrow-task evaluation of cell foundation models

Single-cell foundation models are large, self-supervised deep learning networks pretrained on millions of cellular transcriptomes. These models promise to deliver cell representations that are transferable across diverse biological domains and, when used in specific tasks, would outperform narrowly scoped models. A central assumption is that more pretraining data translates to better downstream performance. However, despite its centrality, this assumption remains largely untested. Here, we tested downstream performance on gold-standard benchmarking tasks across massive dataset reductions, showing that performance was largely insensitive to pretraining data size once finetuning was allowed. This trend reveals a finetuning masking effect that offsets differences in representation quality induced by pretraining, making the benefit of additional pretraining scale largely invisible under current benchmark settings. These findings challenge current benchmarking standards, which rely on closed-ended finetuning tasks that are too narrow to expose the full representational value of pretraining. They also challenge the main driving force in single-cell foundation-model development when evaluated through common narrow tasks. We propose that the next generation of foundation models should be assessed less by performance on highly optimised finetuning tasks and more by their ability to support open-ended biological inference, frozen-representation evaluation and zero-shot capability.

bioinformatics↗

bioRxiv2026

cellNexus: Quality control, annotation, aggregation and analytical layers for the Human Cell Atlas data

Large-scale single-cell atlases such as the Human Cell Atlas have transformed our understanding of human biology. Yet, the lack of a robust framework that standardises quality control, expands cellular annotation, and adds normalisation and analytical layers, limits multi-study analyses and the usefulness of this resource. Here we present cellNexus, a comprehensive resource that enhances the Human Cell Atlas collection into analysis-ready data by linking quality control layers, metadata enrichment, expression normalisation, analysis and data aggregation. These enhancements enable robust large-scale statistical modelling across studies, exemplified here by a multi-tissue map of immune cell communication during ageing. All harmonised layers are accessible via a public web interface and with R and Python APIs. By providing continuous integration with CELLxGENE releases, cellNexus transforms large cell atlas corpora into an accessible, reproducible, interoperable foundation for large-scale biological discovery and the next generation of single-cell foundation models.

bioinformatics↗

bioRxiv2026

Multi-omic and spatial analysis reveals tumour-derived paracrine signals drive suppressive macrophage polarisation via activation of the cAMP-CREB axis in glioblastoma

Tumor-associated macrophages (TAMs) are key mediators of tumor immunosuppression, yet the factors governing their polarization remain poorly understood, especially in highly immunosuppressive cancers, including cancers affecting the central nervous system. This study investigates the molecular pathways underlying TAM polarization in glioblastoma, one of the most immunosuppressive cancer types. Using a multi-omics approach integrating spatial proteomics, RNA-sequencing, and proteomic profiling of tumor cells and macrophages, we demonstrate that circulating monocytes polarize toward an immunosuppressive state when they exit tumor blood vessels, in response to glioblastoma cell-secreted cytokines. In situ and in vitro data shows that macrophage polarization is regulated via the cAMP-CREB signaling-transcription axis.

cancer biology↗

bioRxiv2023

The circulating immune cell landscape stratifies metastatic burden in breast cancer patients

Advanced breast cancers show varying degrees of metastasis; however, reliable biomarkers of metastatic disease progression remain unknown. In circulation, immune cells are the first line of defence against tumour cells. Herein, using >109,591 peripheral blood mononuclear cells from healthy individuals and breast cancer patients, we tested whether molecular traits of the circulating immune cells, probed with single-cell transcriptomics, can be used to segregate metastatic profiles. Our analyses revealed significant compositional and transcriptional differences in PBMCs of patients with restricted or high metastatic burden versus healthy subjects. The abundance of T cell and monocyte subtypes segregated cancer patients from healthy individuals, while memory and unconventional T cells were enriched in low metastatic burden disease. The cell communication axes were also found to be tightly associated with the extent of metastatic burden. Additionally, we identified a PBMC-derived metastatic gene signature capable of discerning metastatic condition from a healthy state. Our study provides unique molecular insights into the peripheral immune system operating in metastatic breast cancer, revealing potential new biomarkers of the extent of the metastatic state. Tracking such immune traits associated with metastatic spread could complement existing diagnostic tools.

cancer biology↗

bioRxiv2023

The tidyomics ecosystem: Enhancing omic data analyses

The growth of omic data presents evolving challenges in data manipulation, analysis, and integration. Addressing these challenges, Bioconductor1 provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming2 offers a revolutionary standard for data organisation and manipulation. Here, we present the tidyomics software ecosystem, bridging Bioconductor to the tidy R paradigm. This ecosystem aims to streamline omic analysis, ease learning, and encourage cross-disciplinary collaborations. We demonstrate the effectiveness of tidyomics by analysing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas3, spanning six data frameworks and ten analysis tools.

bioinformatics↗

bioRxiv2023

Whole body cell map tracks tissue-specific immune cell accumulation and plasticity loss through ageing

Understanding tissue biologys heterogeneity is crucial for advancing precision medicine. Despite the centrality of the immune system in tissue homeostasis, a detailed and comprehensive map of immune cell distribution and interactions across human tissues and demographics remains elusive. To fill this gap, we harmonised data from 12,981 single-cell RNA sequencing samples and curated 29 million cells from 45 anatomical sites to create a comprehensive compositional and transcriptional healthy map of the healthy immune system. We used this resource and a novel multilevel modelling approach to track immune ageing and test differences across sex and ethnicity. We uncovered conserved and tissue-specific immune-ageing programs, resolved sex-dependent differential ageing and identified ethnic diversity in clinically critical immune checkpoints. This study provides a quantitative baseline of the immune system, facilitating advances in precision medicine. By sharing our immune map, we hope to catalyse further breakthroughs in cancer, infectious disease, immunology and precision medicine.

immunology↗

bioRxiv2022

Robust differential composition and variability analysis for multisample cell omics

Cell omics such as single-cell genomics, proteomics and microbiomics allow the characterisation of tissue and microbial community composition, which can be compared between conditions to identify biological drivers. This strategy has been critical to unveiling markers of disease progression such as cancer and pathogen infection. For cell omic data, no method for differential variability analysis exists, and methods for differential composition analysis only take a few fundamental data properties into account. Here we introduce sccomp, a generalised method for differential composition and variability analyses able to jointly model data count distribution, compositionality, group-specific variability and proportion mean-variability association, with awareness against outliers. Sccomp is an extensive analysis framework that allows realistic data simulation and cross-study knowledge transfer. Here, we demonstrate that mean-variability association is ubiquitous across technologies showing the inadequacy of the very popular Dirichlet-multinomial modelling and provide mandatory principles for differential variability analysis. We show that sccomp accurately fits experimental data, with a 50% incremental improvement over state-of-the-art algorithms. Using sccomp, we identified novel differential constraints and composition in the microenvironment of primary breast cancer. Significance statementDetermining the composition of cell populations is made possible by technologies like single-cell transcriptomics, CyTOF and microbiome sequencing. Such analyses are now widespread across fields (~800 publications/month, Scopus). However, existing methods for differential abundance do not model all data features, and cell-type/taxa specific differential variability is not yet possible. Increase in the variability of tissue composition and microbial communities is a well-known indicator of loss of homeostasis and disease. A suitable statistical method would enable new types of analyses to identify component-specific loss of homeostasis for the first time. This and other innovations are now possible through our discovery of the mean-variability association for compositional data. Based on this fundamental observation, we have developed a new statistical model, sccomp, that enables differential variability analysis for composition data, improved differential abundance analyses, with cross-sample information borrowing, outlier identification and exclusion, realistic data simulation, based on experimental datasets, cross-study knowledge transfer.

bioinformatics↗

bioRxiv2021

Tissue remodeling and cell signaling underpin changes in tumor microenvironment heterogeneity in glioma oncogenesis

Brain tumor cells thrive by adapting to the signals in their microenvironment. Understanding how the tumor microenvironment evolves during disease progression is crucial to deciphering the mechanisms underlying the functional behavior of cancer cells. To adapt, cancer cells activate signaling and transcriptional programs and migrate to establish micro-niches, in response to signals from neighboring cells and non-cellular stromal factors. Using multiple tissue analysis approaches to identify and measure immune cell infiltration and extracellular matrix deposition in brain tumors, we show that low-grade glioma is largely devoid of infiltrating immune cells and extracellular matrix proteins, while high-grade glioma exhibits abundant immune cell infiltration and activation, as well as extensive collagen deposition. Spatial analysis shows that most T-cells are sequestered in perivascular nests, but macrophages penetrate deep into tumor cell rich regions. High-grade gliomas exhibit heterogeneous PI3K and MAPK signaling, which correlates with distinct pathological hallmarks, including tumor angiogenesis, tumor cell density and extracellular matrix deposition. Our results also provide compelling evidence that tissue remodeling is an important element in glioma progression, and that targeting the extracellular matrix will be critical to improving GBM therapy.

cancer biology↗

bioRxiv2021

Interfacing Seurat with the R tidy universe

MotivationSeurat is one of the most popular software suites for the analysis of single-cell RNA sequencing data. Considering the popularity of the tidyverse ecosystem, which offers a large set of data display, query, manipulation, integration and visualisation utilities, a great opportunity exists to interface the Seurat object with the tidyverse. This gives the large data science community of tidyverse users the possibility to operate with a familiar grammar. ResultsIn order to provide Seurat with a tidyverse-oriented interface without compromising on efficiency, we developed tidyseurat, a light-weight adapter to the tidyverse. Cell information is automatically displayed as a tibble abstraction, which interfaces Seurat with dplyr, tidyr, ggplot2 and plotly packages powering efficient data manipulation, integration and visualisation. Iterative analyses on data subsets is enabled by interfacing with the popular nest-map framework. Availability and implementationThe software is freely available at cran.r-project.org/web/packages/tidyseurat/ and github.com/stemangiola/tidyseurat ContactStefano Mangiola (mangiola.s@wehi.edu.au) and Anthony T Papenfuss (papenfuss@wehi.edu.au).

bioinformatics↗

bioRxiv2020

Dissection of prostate tumour, stroma and immune transcription reveals a key contribution by the microenvironment to disease progression

Prostate cancer is caused by genomic aberrations in normal epithelial cells, however clinical translation of findings from analyses of cancer cells alone has been very limited. A deeper understanding of the tumour microenvironment is needed to identify the key drivers of disease progression and reveal novel therapeutic opportunities. In this study, the experimental enrichment of selected cell-types and the development of a Bayesian inference model for continuous differential transcript abundance permitted us to define the transcriptional landscape of the prostate cancer microenvironment along the disease progression axis. An important role of monocytes and macrophages in prostate cancer progression and disease recurrence was uncovered, supported by both transcriptional landscape findings and by differential tissue composition analyses. These findings were corroborated and validated by spatial analyses at the single-cell level using multiplex immunohistochemistry. This study advances our knowledge concerning the role of monocyte-derived recruitment in primary prostate cancer, and supports their key role in disease progression, patient survival and prostate microenvironment immune modulation.

cancer biology↗

bioRxiv2020

A Bayesian inference tool for identifying artifactual calls from differential transcript abundance analyses

Relative transcript abundance has proven to be a valuable tool for inferring the phenotype of biological systems from genetic material. Several methods for the analysis of differential transcript abundance have been developed, and some of the most popular are based on negative binomial models. Although most genes are fitted reasonably well by the negative binomial distribution, the presence of outlier observations that do not fit such models can lead to artifactual identification of significant changes in transcription. Identifying those transcripts for the correct interpretation of results is extremely important. A robust and automated tool for detecting sample/transcript pairs that do not fit a negative binomial regression model is currently lacking. Here we propose ppcseq, a robust statistical framework that models hierarchically sample- and gene-wise features such as sequencing depth bias, the association between mean transcript abundance and its over-dispersion, and provides a theoretical transcript abundance distribution, on which the observed transcript abundance can be tested for outliers. We show using a publicly available data set where nearly 10% of differentially abundant transcripts had fold change inflated by the presence of outliers. This method has broad utility in filtering artifactual results of differential transcript abundance analyses based on a negative binomial framework.

bioinformatics↗