Stefano Mangiola

Dr Stefano Mangiola

GroupLeader,ComputationalCancerImmunogenomics

South Australian Immunogenomics Cancer Institute

Faculty of Health and Medical Sciences

Eligible to supervise Masters and PhD (as Co-Supervisor) - email supervisor to discuss availability.


Stefano Mangiola graduated in Biotechnology and Bioinformatics at the University Milano Bicocca (2010). He moved to Melbourne and completed an MPhil in molecular parasitology under the supervision of Robin Gasser (Melbourne University, 2013). He designed and applied computational methods to DNA and RNA sequencing data to investigate the host-parasite interaction. After that, he shifted his focus to cancer research, and in 2019, he obtained a PhD in bioinformatics and applied biostatistics (Melbourne University and WEHI) with the thesis “Investigation of the prostate tumour microenvironment” under the supervision of Chris Hovens and Tony Papenfuss. There, he focused on the immune-cell-cancer interaction and Bayesian statistics applied to transcriptional data. He continued his work in Papenfuss’ lab, where he specialised in data-driven cancer immunology. There, he developed a Bayesian model for single-cell analyses and large-scale single-cell data platforms that allowed him to model a comprehensive map of the immune system across organs and demographic groups. For his work, Stefano was awarded the Victorian Cancer Agency Early Career Research Fellowship to focus on the immunodiagnosis of metastatic breast cancer.

In 2024, he established his independent research group at SAiGENCI to continue his work in computational biology, artificial intelligence (AI) and data-driven cancer immunology. His work on statistical methods for single-cell compositional data and transcriptomics has been published in journals such as PNAS and Genome Biology. His recent work on tidyomics, a language to improve data manipulation and analyses across omic types, was published in Nature Methods. He has been awarded CZI and CSL grants to continue this work. His present and future work is focused on studying the patient’s immune system with analytical and AI tools to inform on therapy resistance in breast and other cancers.

The Computational Cancer Immunogenomics group, led by Dr Mangiola, is interested in applying cutting-edge computational methods for the study of the immune system's role in cancer progression and treatment response. Dr Mangiola's hybrid laboratory is at the edge of artificial intelligence and multiomic data production.

By profiling a patient’s immune system through modern spatial and single-cell technologies, we model the propensity to enter metastatic progression and be resilient to metastatic spread (e.g., in breast cancer). Similarly, we intend to identify systemic immune features that explain local immunity (within the tumour microenvironment) and predict resistance to neoadjuvant therapy in breast and other cancer types.

The immune system is diverse across the human population. We pioneered population-scale immune system modelling using large-scale single-cell data (Human Cell Atlas) and quantified its heterogeneity across tissues. This heterogeneity includes tissue-specific ageing programs, sexual dimorphism, and ethnical diversity in immunotherapy targets. Now, we aim to use artificial intelligence (AI) models (i.e. LLM) to extend our immune map to cancer. Specifically, we are interested in building foundation models that can identify stable immunotherapy targets across ethnic groups.

Our work includes the construction of scalable infrastructure and interfaces that allow multi-atlas-level analyses and annotation. This includes tidyomics, CuratedAtlasQuery and HPCell.

 We are particularly interested in the following areas:

1) Integration of spatial and single-cell transcriptomics and proteomics.

2) Machine learning and classification.

3) Large-language AI models applied to cellular biology.

4) Cancer immunodiagnosis.

5) R tidy programming applied to multiomics.

6) Large-scale inference from single-cell multi-atlases.

 

Higher Degree Research Projects

1) Use large-language AI models to study the immune system. 

Demographic factors like age and sex critically influence disease outcomes and treatment responses, including cancer treatments targeting the immune system (immunotherapy). Australia has an ethnically diverse and ageing population. Despite its importance, a unified resource to test the population-level diversity of therapy and diagnosis targets is missing, with the current paradigm of population-level investigation of complex cancer traits still relying on homogenised tissue data such as 15-year-old TCGA. With a recent breakthrough3, we pioneered the population-level immune system investigation from massive-scale single-cell resources. Across 12,981 healthy individuals and 30 organs, we mapped profound immunological changes across ageing, sexes and ethnicity, uncovering diversity in key pathways used in immunotherapy (e.g. LAG3, SLAMF7 and CD83). This project aims to quantify the population-level diversity of immunotherapy targets, providing the scientific and clinical community with an unprecedented encyclopaedic resource. We will translate our infrastructure to generative single-cell large-language models (ChatGPT-like) to identify novel immune targets conserved in ageing and across sexes. These artificial intelligence (AI) models are revolutionising data-driven cell biology research. However, they were not designed for clinically related questions. We will pivot this technology to our massive clinically annotated cancer cell compendium. We will then improve the current static-cell paradigm by extending AI models to a dynamic representation of cells. 

 

2) Integrate spatial and single-cell multiomics to predict neoadjuvant response in cancer with locally-assisted systemic immunodiagnosis.

The effective prediction of neoadjuvant therapy outcomes in cancer treatment remains a pivotal challenge. This project proposes a novel approach by integrating spatial and single-cell multi-omics analyses to enhance the precision of neoadjuvant response predictions. Our methodology leverages cutting-edge techniques in both spatial omics and single-cell sequencing to capture a comprehensive molecular landscape at the tumour site. By delineating the intricate interplay between tumour cells and the immune environment, this approach aims to uncover specific biomarkers and signalling pathways indicative of therapy responses.

Further innovation lies in implementing locally-assisted systemic immunodiagnosis, which utilises local tumour data to inform systemic immune profiling. This dual approach promises to improve the accuracy of predicting patient responses to neoadjuvant therapies and aims to personalise treatment plans, thereby potentially enhancing clinical outcomes.

The project's multi-disciplinary framework combines advanced bioinformatics tools with clinical oncology insights, enabling a more targeted and efficient diagnostic process. By predicting therapeutic efficacy before treatment initiation, this strategy seeks to spare patients from the adverse effects of ineffective therapies and streamline clinical decision-making. 

 

3) Build large-scale and scalable computational infrastructure for analysis, deployment and exploration of the single-cell universe.

Single-cell and spatial omic technologies have fundamentally transformed biological research by generating vast quantities of data. This influx challenges existing bioinformatics pipelines and the capacity of individual users to keep up with the rapidly evolving demands of impactful data-driven research. In collaboration with CSL, this project enhances the capabilities of CuratedAtlasQuery and HPCell to establish a privately deployable intelligence hub for single-cell and spatial data. CuratedAtlasQuery has already facilitated extensive profiling of the immune system at the human population level. We aim to expand this database by integrating biological annotations and data summarisation techniques to democratise access to large-scale single-cell analyses. HPCell is being developed as an analytical language that allows the execution of massively parallel single-cell analysis workflows in a tidy R style, and enables their deployment on high-performance computing platforms.

 

4) Developing the tidyomics ecosystem

Tidyomics (Nat Methods., 2024) is an R software ecosystem that enhances the analysis and visualisation of high-dimensional omics data, applying the principles of tidy data analysis, a de facto standard in data science. Given its extensive adoption, we propose to improve the documentation, robustness, and interoperability of the Tidyomics ecosystem and extend it to spatial profiling technologies. Tidyomics packages enable computational biologists to employ a user-friendly grammar to manipulate popular data containers across omics (genomics, transcriptomics, cytometry) and platforms (Bioconductor, Seurat). Tidyomics aggregates a growing user base and community of developers, forming an international network that spans five continents. In a manuscript (Hutchison and Keyes 2023), we formalised the Tidyomics ecosystem and established a roadmap with our community GitHub Project space. Single-cell data repositories like The Human Cell Atlas and Curated Cancer Atlas drive next-generation research. Examining tissue biology at the single-cell level is refining our choice of cell and gene markers for specific groups, organs, and cells. We are focusing on several enhancements to the Tidyomics ecosystem for better interfacing with large-scale single-cell atlas collections.

 

  • Journals

    Year Citation
    2024 Mangiola, S., Thomas, E. A., Modrak, M., Vehtari, A., & Papenfuss, A. T. (2024). Correction to 'Probabilistic outlier identification for RNA sequencing generalized linear models' (vol 6, lqae024,2024). NAR GENOMICS AND BIOINFORMATICS, 6(1), 2 pages.
    DOI
    2024 Hutchison, W. J., Keyes, T. J., Crowell, H. L., Serizay, J., Soneson, C., Davis, E. S., . . . Mangiola, S. (2024). The tidyomics ecosystem: enhancing omic data analyses. Nature Methods, 21(7), 11 pages.
    DOI
    2023 Mangiolaa, S., Schulzea, A. J. R., Trussarta, M., Valdesa, E. Z., Maa, M., Gaoc, Z., . . . Papenfussa, A. T. (2023). Sccomp: Robust differential composition and variability analysis for single-cell data. Proceedings of the National Academy of Sciences of the United States of America, 120(33), 12 pages.
    DOI Scopus2 Europe PMC1
    2023 Whitfield, H. J., Berthelet, J., Mangiola, S., Bell, C., Anderson, R. L., Pal, B., . . . Davis, M. J. (2023). Single-cell RNA sequencing captures patient-level heterogeneity and associated molecular phenotypes in breast cancer pleural effusions. CLINICAL AND TRANSLATIONAL MEDICINE, 13(9), 18 pages.
    DOI Europe PMC1
    2023 Khan, M. A. A. K., Wu, J., Sun, Y., Barrow, A. D., Papenfuss, A. T., & Mangiola, S. (2023). cellsig plug-in enhances CIBERSORTx signature selection for multidataset transcriptomes with sparse multilevel modelling. Bioinformatics, 39(12), 10 pages.
    DOI
    2023 Singh, P., Gollapalli, K., Mangiola, S., Schranner, D., Yusuf, M. A., Chamoli, M., . . . Yadav, V. K. (2023). Taurine deficiency as a driver of aging. Science, 380(6649), 12 pages.
    DOI Scopus68 Europe PMC41
    2023 Sun, Y., Khan, M. A. A. K., Mangiola, S., & Barrow, A. D. (2023). IL17RB and IL17REL Expression Are Associated with Improved Prognosis in HPV-Infected Head and Neck Squamous Cell Carcinomas. Pathogens, 12(4), 18 pages.
    DOI Scopus1
    2023 Cain, S. A., Pope, B., Mangiola, S., Mantamadiotis, T., & Drummond, K. J. (2023). Somatic mutation landscape in a cohort of meningiomas that have undergone grade progression. BMC Cancer, 23(1), 11 pages.
    DOI
    2023 Dinevska, M., Widodo, S. S., Furst, L., Cuzcano, L., Fang, Y., Mangiola, S., . . . Mantamadiotis, T. (2023). Cell signaling activation and extracellular matrix remodeling underpin glioma tumor microenvironment heterogeneity and organization. Cellular Oncology, 46(3), 589-602.
    DOI Scopus6 Europe PMC4
    2021 McCoy, P., Mangiola, S., Macintyre, G., Hutchinson, R., Tran, B., Pope, B., . . . Hovens, C. M. (2021). MSH2-deficient prostate tumours have a distinct immune response and clinical outcome compared to MSH2-deficient colorectal or endometrial cancer. Prostate Cancer and Prostatic Diseases, 24(4), 1167-1180.
    DOI Scopus6 Europe PMC4
    2021 Mangiola, S., Doyle, M. A., & Papenfuss, A. T. (2021). Interfacing Seurat with the R tidy universe. Bioinformatics, 37(22), 4100-4107.
    DOI Scopus48 Europe PMC43
    2021 Sun, Y., Sedgwick, A. J., Khan, M. A. A. K., Palarasah, Y., Mangiola, S., & Barrow, A. D. (2021). A Transcriptional Signature of IL-2 Expanded Natural Killer Cells Predicts More Favorable Prognosis in Bladder Cancer. Frontiers in Immunology, 12, 13 pages.
    DOI Scopus18 Europe PMC11
    2021 Sun, Y., Sedgwick, A. J., Palarasah, Y., Mangiola, S., & Barrow, A. D. (2021). A Transcriptional Signature of PDGF-DD Activated Natural Killer Cells Predicts More Favorable Prognosis in Low-Grade Glioma. Frontiers in Immunology, 12, 13 pages.
    DOI Scopus22 Europe PMC23
    2021 Cmero, M., Kurganovs, N. J., Stuchbery, R., McCoy, P., Grima, C., Ngyuen, A., . . . Corcoran, N. M. (2021). Loss of SNAI2 in prostate cancer correlates with clinical response to androgen deprivation therapy. JCO Precision Oncology, 5(5), 1048-1059.
    DOI Scopus10 Europe PMC10
    2021 Mangiola, S., McCoy, P., Modrak, M., Souza-Fonseca-Guimaraes, F., Blashki, D., Stuchbery, R., . . . Hovens, C. M. (2021). Transcriptome sequencing and multi-plex imaging of prostate cancer microenvironment reveals a dominant role for monocytic cells in progression. BMC Cancer, 21(1), 18 pages.
    DOI Scopus2 Europe PMC2
    2021 Mangiola, S., Thomas, E. A., Modrák, M., Vehtari, A., & Papenfuss, A. T. (2021). Probabilistic outlier identification for RNA sequencing generalized linear models. NAR Genomics and Bioinformatics, 3(1), 9 pages.
    DOI Scopus8 Europe PMC5
    2021 Widodo, S. S., Hutchinson, R. A., Fang, Y., Mangiola, S., Neeson, P. J., Darcy, P. K., . . . Mantamadiotis, T. (2021). Toward precision immunotherapy using multiplex immunohistochemistry and in silico methods to define the tumor immune microenvironment. Cancer Immunology, Immunotherapy, 70(7), 1811-1820.
    DOI Scopus10 Europe PMC10
    2021 Kwan, E. M., Fettke, H., Docanto, M. M., To, S. Q., Bukczynska, P., Mant, A., . . . Azad, A. A. (2021). Prognostic Utility of a Whole-blood Androgen Receptor-based Gene Signature in Metastatic Castration-resistant Prostate Cancer. European Urology Focus, 7(1), 63-70.
    DOI Scopus11 Europe PMC7
    2021 Mangiola, S., Molania, R., Dong, R., Doyle, M. A., & Papenfuss, A. T. (2021). tidybulk: an R tidy framework for modular transcriptomic data analysis. Genome Biology, 22(1), 15 pages.
    DOI Scopus17 Europe PMC14
    2021 Lelliott, E. J., Mangiola, S., Ramsbottom, K. M., Zethoven, M., Lim, L., Lau, P. K. H., . . . Sheppard, K. E. (2021). Combined BRAF, MEK, and CDK4/6 inhibition depletes intratumoral immune-potentiating myeloid populations in melanoma. Cancer Immunology Research, 9(2), 136-146.
    DOI Scopus12 Europe PMC10
    2021 Berthelet, J., Wimmer, V. C., Whitfield, H. J., Serrano, A., Boudier, T., Mangiola, S., . . . Merino, D. (2021). The site of breast cancer metastases dictates their clonal composition and reversible transcriptomic profile. Science Advances, 7(28), 1-17.
    DOI Scopus19 WoS15 Europe PMC12
    2020 Patchett, A. L., Coorens, T. H. H., Darby, J., Wilson, R., McKay, M. J., Kamath, K. S., . . . Tovar, C. (2020). Two of a kind: transmissible Schwann cell cancers in the endangered Tasmanian devil (Sarcophilus harrisii). Cellular and Molecular Life Sciences, 77(9), 1847-1858.
    DOI Scopus21 Europe PMC17
    2020 Owen, K. L., Gearing, L. J., Zanker, D. J., Brockwell, N. K., Khoo, W. H., Roden, D. L., . . . Parker, B. S. (2020). Prostate cancer cell-intrinsic interferon signaling regulates dormancy and metastatic outgrowth in bone. EMBO Reports, 21(6), e50162-1-e50162-24.
    DOI Scopus56 WoS49 Europe PMC49
    2020 Mangiola, S., & Papenfuss, A. (2020). tidyHeatmap: an R package for modular heatmap production based on tidy principles. Journal of Open Source Software, 5(52), 2472.
    DOI
    2020 Lau, E., McCoy, P., Reeves, F., Chow, K., Clarkson, M., Kwan, E. M., . . . Corcoran, N. M. (2020). Detection of ctDNA in plasma of patients with clinically localised prostate cancer is associated with rapid disease progression. Genome Medicine, 12(1), 11 pages.
    DOI Scopus32 Europe PMC21
    2019 Atkins, R. J., Stylli, S. S., Kurganovs, N., Mangiola, S., Nowell, C. J., Ware, T. M., . . . Mantamadiotis, T. (2019). Cell quiescence correlates with enhanced glioblastoma cell invasion and cytotoxic resistance. Experimental Cell Research, 374(2), 353-364.
    DOI Scopus26 Europe PMC17
    2019 Mangiola, S., Stuchbery, R., McCoy, P., Chow, K., Kurganovs, N., Kerger, M., . . . Corcoran, N. M. (2019). Androgen deprivation therapy promotes an obesity-like microenvironment in periprostatic fat. Endocrine Connections, 8(5), 518-527.
    DOI Scopus19
    2018 Chow, K., Mangiola, S., Vazirani, J., Peters, J. S., Costello, A. J., Hovens, C. M., & Corcoran, N. M. (2018). Obesity suppresses tumor attributable PSA, affecting risk categorization. Endocrine-Related Cancer, 25(5), 561-568.
    DOI Scopus4 Europe PMC2
    2018 Mangiola, S., Stuchbery, R., Macintyre, G., Clarkson, M. J., Peters, J. S., Costello, A. J., . . . Corcoran, N. M. (2018). Periprostatic fat tissue transcriptome reveals a signature diagnostic for high-risk prostate cancer. Endocrine-Related Cancer, 25(5), 569-581.
    DOI Scopus19 Europe PMC16
    2016 Mangiola, S., Hong, M. K. H., Cmero, M., Kurganovs, N., Ryan, A., Costello, A. J., . . . Hovens, C. M. (2016). Comparing nodal versus bony metastatic spread using tumour phylogenies. Scientific Reports, 6(1), 10 pages.
    DOI Scopus18 Europe PMC14
    2015 Hong, M. K. H., Macintyre, G., Wedge, D. C., Van Loo, P., Patel, K., Lunke, S., . . . Hovens, C. M. (2015). Tracking the origins and drivers of subclonal metastatic expansion in prostate cancer. Nature Communications, 6(1), 12 pages.
    DOI Scopus292 Europe PMC201
    2014 Breugelmans, B., Jex, A. R., Korhonen, P. K., Mangiola, S., Young, N. D., Sternberg, P. W., . . . Gasser, R. B. (2014). Bioinformatic exploration of RIO protein kinases of parasitic and free-living nematodes. International Journal for Parasitology, 44(11), 827-836.
    DOI Scopus11 Europe PMC10
    2014 Campos, T. D. L., Young, N. D., Korhonen, P. K., Hall, R. S., Mangiola, S., Lonie, A., & Gasser, R. B. (2014). Identification of G protein-coupled receptors in Schistosoma haematobium and S. mansoni by comparative genomics. Parasites and Vectors, 7(1), 11 pages.
    DOI Scopus35 Europe PMC23
    2014 Mangiola, S., Young, N. D., Sternberg, P. W., Strube, C., Korhonen, P. K., Mitreva, M., . . . Gasser, R. B. (2014). Analysis of the transcriptome of adult Dictyocaulus filaria and comparison with Dictyocaulus viviparus, with a focus on molecules involved in host-parasite interactions. International Journal for Parasitology, 44(3-4), 251-261.
    DOI Scopus6 Europe PMC2
    2013 Mangiola, S., Young, N. D., Korhonen, P., Mondal, A., Scheerlinck, J. P., Sternberg, P. W., . . . Gasser, R. B. (2013). Getting the most out of parasitic helminth transcriptomes using HelmDB: Implications for biology and biotechnology. Biotechnology Advances, 31(8), 1109-1119.
    DOI Scopus22 Europe PMC13
    2013 Ansell, B. R. E., Schnyder, M., Deplazes, P., Korhonen, P. K., Young, N. D., Hall, R. S., . . . Gasser, R. B. (2013). Insights into the immuno-molecular biology of Angiostrongylus vasorum through transcriptomics-Prospects for new interventions. Biotechnology Advances, 31(8), 1486-1500.
    DOI Scopus15 Europe PMC9
    - McCoy, P., Mangiola, S., Macintyre, G., Hutchinson, R., Tran, B., Hong, M. K. H., . . . Hovens, C. M. (n.d.). MSH2 is Inactivated by Multiple Mechanisms in Prostate Tumors, Leading to a Distinct Immune Response and Clinical Outcome Compared to MSH2 Deficient Colorectal Cancer. SSRN Electronic Journal.
    DOI
  • Conference Papers

    Year Citation
    2022 Whitfield, H. J., Berthelet, J., Mangiola, S., Bell, C., Anderson, R. L., Pal, B., . . . Davis, M. J. (2022). Defining the cellular composition and associated molecular phenotypes of malignant and nonmalignant cells in breast cancer pleural effusions.. In CANCER RESEARCH Vol. 82 (pp. 2 pages). LA, New Orleans: AMER ASSOC CANCER RESEARCH.
    2022 Whitfield, H. J., Berthelet, J., Mangiola, S., Bell, C., Anderson, R. L., Pal, B., . . . Davis, M. J. (2022). Defining the cellular composition and associated molecular phenotypes of malignant and non-malignant cells in breast cancer pleural effusions. In CANCER RESEARCH Vol. 82 (pp. 2 pages). LA, New Orleans: AMER ASSOC CANCER RESEARCH.
    2019 Kwan, E. M., Fettke, H., Docanto, M. M., To, S. Q., Bukczynska, P., Mant, A., . . . Azad, A. A. (2019). International Speakers. In ASIA-PACIFIC JOURNAL OF CLINICAL ONCOLOGY Vol. 15 (pp. 28-29). WILEY.
    DOI
    2018 Mangiola, S., Papenfuss, T., Chow, K., Corcoran, N., & Hovens, C. (2018). Differential tissue composition analysis from whole tissue gene expression. In BJU INTERNATIONAL Vol. 122 (pp. 29). AUSTRALIA, Brisbane: WILEY.
    2018 Mangiola, S., Papenfuss, T., Chow, K., Corcoran, N., & Hovens, C. (2018). Interplay among cell types in the tumour microenvironment reveals the activation of key hallmarks in prostate cancer. In BJU INTERNATIONAL Vol. 122 (pp. 35). AUSTRALIA, Brisbane: WILEY.
    2018 Mangiola, S., Chow, K., Stuchbery, R., Macintyre, G., Clarkson, M. J., Peters, J. S., . . . Corcoran, N. M. (2018). Transcriptional profiling of periprostatic fat tissue reveals a signature diagnostic for high-risk prostate cancer. In BJU INTERNATIONAL Vol. 122 (pp. 25). AUSTRALIA, Brisbane: WILEY.
    2018 Mccoy, P., Chow, K., Macintyre, G., Clarkson, M., Kurganovs, N., Ryan, A., . . . Hovens, C. (2018). The potential for androgen influenced disruption of the mismatch repair gene MSH2 in prostate cancer. In BJU INTERNATIONAL Vol. 122 (pp. 29-30). AUSTRALIA, Brisbane: WILEY.
    2017 Mangiola, S. (2017). A computational tool for deconvolving cell type specific transcriptomes from tumour microenvironment mixtures. In BJU INTERNATIONAL Vol. 120 (pp. 23). AUSTRALIA: WILEY.
    2017 Stuchbery, R., Mangiola, S., Macintyre, G., Clarkson, M. J., Kowalczyk, A., Peters, J. S., . . . Corcoran, N. M. (2017). Periprostatic fat holds a field-effect transcriptional signature of cancer grade. In BJU INTERNATIONAL Vol. 120 (pp. 22). AUSTRALIA: WILEY.
    2017 Mccoy, P., Macintyre, G., Clarkson, M., Kurganovs, N., Ryan, A., Lunke, S., . . . Hovens, C. (2017). The potential for androgen influenced disruption of the mismatch repair gene Msh2 in Prostate Cancer. In BJU INTERNATIONAL Vol. 120 (pp. 20-21). AUSTRALIA: WILEY.
    2016 Cmero, M., Kurganovs, N., Mangiola, S., Macintyre, G., Hovens, C. M., & Corcoran, N. M. (2016). Neo-adjuvant treatment resistance in aggressive prostate cancer is not driven by clonal selection. In BJU INTERNATIONAL Vol. 118 (pp. 32-33). AUSTRALIA, Melbourne: WILEY.
    2016 Mangiola, S. (2016). Are we missing the obvious? A tool to identify the contribution of benign cells to tumour progression. In BJU INTERNATIONAL Vol. 118 (pp. 32). AUSTRALIA, Melbourne: WILEY-BLACKWELL.
    2016 Cmero, M., Mangiola, S., Mo, K., Yuan, K., Corcoran, N. M., Markowetz, F., . . . Macintyre, G. (2016). Investigating intra-tumour heterogeneity in prostate cancer using structural variation. In BJU INTERNATIONAL Vol. 118 (pp. 32). AUSTRALIA, Melbourne: WILEY-BLACKWELL.
    2016 Mangiola, S., Hong, M. K. H., Cmero, M., Kurganovs, N., Ryan, A., Costello, A. J., . . . Hovens, C. M. (2016). Lymph node and bone metastases have distinct sites of origin in primary prostate cancer. In BJU INTERNATIONAL Vol. 118 (pp. 31-32). AUSTRALIA, Melbourne: WILEY-BLACKWELL.
    2016 Mccoy, P., Macintyre, G., Clarkson, M., Kurganovs, N., Ryan, A., Lunke, S., . . . Hovens, C. (2016). Hormonally regulated mechanisms of MSH2 disruption in prostate cancer. In BJU INTERNATIONAL Vol. 118 (pp. 23). AUSTRALIA, Melbourne: WILEY-BLACKWELL.
    2015 Mangiola, S., Stuchbery, R., Macintyre, G., Hovens, C., & Corcoran, N. (2015). A specific expression signature accurately predicts high grade prostate tumours in fat but not from adjacent benign tissues. In BJU INTERNATIONAL Vol. 116 (pp. 47). AUSTRALIA, Cairns: WILEY-BLACKWELL.
    2015 Stuchbery, R., Mangiola, S., Tan, Y., Rupasinghe, T., Dayalan, S., Tull, D., . . . Corcoran, N. (2015). High risk prostate cancer is associated with distinct transcriptional and lipid profiles in adipose tissue. In BJU INTERNATIONAL Vol. 116 (pp. 39-40). AUSTRALIA, Cairns: WILEY-BLACKWELL.
    2015 Mccoy, P., Macintyre, G., Clarkson, M., Kurganovs, N., Lunke, S., Ryan, A., . . . Hovens, C. (2015). MSH2 translocations are associated with clinically aggressive prostate cancer. In BJU INTERNATIONAL Vol. 116 (pp. 38-39). AUSTRALIA, Cairns: WILEY-BLACKWELL.
    2015 Hong, M. K. H., Macintyre, G., Wedge, D. C., Van Loo, P., Lunke, S., Alexandrov, L. B., . . . Hovens, C. M. (2015). Unexpected complexity in the origins of prostate cancer metastases. In BJU INTERNATIONAL Vol. 115 (pp. 92). AUSTRALIA, Adelaide: WILEY-BLACKWELL.
  • Preprint

    Year Citation
    2023 Mangiola, S., Brown, R., Berthelet, J., Guleria, S., Liyanage, C., Ostrouska, S., . . . Pal, B. (2023). The circulating immune cell landscape stratifies metastatic burden in breast cancer patients.
    DOI
    2023 Hutchison, W., Keyes, T., Crowell, H., Serizay, J., Soneson, C., Davis, E., . . . The tidyomics Consortium. (2023). The<i>tidyomics</i>ecosystem: Enhancing omic data analyses.
    DOI
    2023 Mangiola, S., Milton, M., Ranathunga, N., Li-Wai-Suen, C. S. N., Odainic, A., Yang, E., . . . Papenfuss, A. T. (2023). A multi-organ map of the human immune system across age, sex and ethnicity.
    DOI
    2022 Mangiola, S., Schulze, A., Trussart, M., Zozaya, E., Ma, M., Gao, Z., . . . Papenfuss, A. T. (2022). Robust differential composition and variability analysis for multisample cell omics.
    DOI
    2021 Dinevska, M., Widodo, S., Furst, L., Cuzcano, L., Fang, Y., Mangiola, S., . . . Mantamadiotis, T. (2021). Tissue remodeling and cell signaling underpin changes in tumor microenvironment heterogeneity in glioma oncogenesis.
    DOI
    2021 Mangiola, S., Doyle, M., & Papenfuss, A. (2021). Interfacing Seurat with the R tidy universe.
    DOI
    2020 Mangiola, S., McCoy, P., Modrak, M., Souza-Fonseca-Guimaraes, F., Blashki, D., Stuchbery, R., . . . Hovens, C. (2020). Dissection of prostate tumour, stroma and immune transcriptional components reveals a key contribution of the microenvironment for disease progression.
    DOI
    2020 Mangiola, S., Thomas, E., Modrák, M., & Papenfuss, A. (2020). A Bayesian inference tool for identifying artifactual calls from differential transcript abundance analyses.
    DOI

2024-2026 CZI (Co-I; $565K, of which 282 as beneficiary); Project: Opening Data Science to all with tidyomics

2023-2024 CSL-WEHI Alliance, asset positioning (PI; $75K): Development of a body map of the immune system in health and disease through AI 

2022 - 2025 VCA Early Career Research Fellowship (PI; $330K) Project: Immunodiagnosis of metastatic breast cancer

2022            CZI (Co-I; $220K, of which $59K as beneficiary) Project: A single-cell body reference map of immune cell composition and communication through ageing.

2022 - 2026 NBCF IIRS, (Co-I; $719K) Project: Targeting dual receptors on Tregs to design novel breast cancer immunotherapy

2022            ONJCRI CSPP Program, (Co-I; $96,480) Project: Immunotherapy resistance in MSS colorectal cancer

 

  • Position: GroupLeader,ComputationalCancerImmunogenomics
  • Phone: 83134024
  • Email: stefano.mangiola@adelaide.edu.au
  • Campus: West End Health Precinct
  • Building: AHMS - Adelaide Health and Medical Sciences, floor Ninth Floor
  • Room: 9056
  • Org Unit: South Australian Immunogenomics Cancer Institute

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