https://researchers.adelaide.edu.au/profile/stefano.mangiola
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.
