Brain metastases are the most common brain tumours in adults and are associated with poor outcomes. We are performing whole genome sequencing and assaying methylation genome-wide in matched primary and brain metastases across different tumour types to identify molecular predictors of brain metastasis.

Prof Tony Papenfuss, Deputy Director | WEHI Researcher Profile

Q: Tumour evolution, heterogeneity and the micro-environment

Evolution underlies tumour initiation and progression, including metastasis and the development of resistance to treatment. We are investigating the evolution of a variety of cancers using different technologies. This requires developing new statistical tools to identify evolutionary changes between tumour genomes, including changes in subclonal populations.One aspect of this work is the role of the tumour micro-environment in progression. Deconvolution of bulk RNAseq and direct use of single-cell and spatial transcriptomics make insights into the cellular composition of tumours and its relationship to tumour evolution and patient outcome.

Q: Pan-Prostate Cancer genome (PPCG) project

We are involved in an international consortium analysing the genome, transcriptome and methylome of >1000 prostate cancers with high-quality clinical outcome information. This big data project is providing new insights into how prostate cancer evolves and identifying potential prognostic biomarkers.We co-lead the PPCG Transcriptome and the PPCG Prostate Cancer Subtyping Working Groups.

Q: Stafford Fox Rare Cancer Program

This project is aimed at developing new strategies for diagnosing and treating patients with rare cancers. It is supported by two Centenary Fellowships:Stafford Fox Centenary Fellowship in rare cancer researchStafford Fox Centenary Fellowship in bioinformaticsThis project aims to develop new strategies for diagnosing and treating patients with rare cancers and patients with multiple primary tumours.

Q: Mechanistic and functional drivers of neochromosome evolution

Neochromosomes are massive, extra chromosomes found in 3 per cent of cancers but are common in some cancer types, such as liposarcomas.Neochromosomes harbour the oncogenic changes that drive these cancers. We recently mapped the structure of neochromosomes at high resolution. This revealed that punctuated chromothriptic events and hundreds of breakage-fusion-bridge (BFB) cycles underlie their formation (Garsed et al, Cancer Cell 2014).We are now pursuing the molecular machinery that contributes to this process.

Q: Multi-omics data integration

We are testing and developing statistical and machine learning methods for multi-omics data integration. The purpose of this research is to improve the identification of biomarkers that identify the state of a tumour or the cancer subtype and predict the patient outcome.

Q: Good to bad melanomas

Thin primary melanomas (T1a) typically have good outcomes following surgery alone, but sometimes recur and can progress rapidly. We are applying novel computational approaches to multi-omics data (WES, single cell, spatial transcriptomics) to identify the molecular and cellular determinants of low risk primary melanomas that go bad. This will lead to new biomarkers or therapeutic targets and help to refine patient risk stratification, identifying patients who might benefit from a higher level of surveillance.

Q: PPCG CombiMets - Pan-Prostate Cancer Metastasis Working Group

International collaboration researching the genomic changes associated with prostate cancer metastasis.

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About

My research spans bioinformatics methods development and computational cancer biology. My team and I have developed advanced methods for detection of genomic rearrangements using short read sequencing technology and methods for multiomics data analysis. We’ve applied these in a wide range of projects that have been either collaborative or that we have driven ourselves. Major areas of research include performing the first genome project for an Australian marsupial, mapping the genome of the scabies mite, uncovering the tissue of origin of the contagious Tasmanian Facial Devil Tumour, identifying the catastrophic process underlying cancer neo chromosome formation, and evolution of advanced melanoma.

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Selected publications from Prof Tony Papenfuss

Al Kamran Khan A, Wu J, Sun Y, Barrow AD, Papenfuss AT, Mangiola S. cellsig plug-in enhances CIBERSORTx signature selection for multidataset transcriptomes with sparse multilevel modelling. Bioinformatics. 2023;39(12):10.1093/bioinformatics/btad685

Mangiola S, Roth-Schulze AJ, Trussart M, Zozaya-Valdés E, Ma M, Gao Z, Rubin AF, Speed TP, Shim H, Papenfuss AT. sccomp: Robust differential composition and variability analysis for single-cell data. Proceedings of the National Academy of Sciences of the United States of America. 2023;120(33):10.1073/pnas.2203828120

Giner G, Ikram S, Herold MJ, Papenfuss AT. A systematic review of computational methods for designing efficient guides for CRISPR DNA base editor systems. Briefings in Bioinformatics. 2023;24(4):10.1093/bib/bbad205

Molania R, Foroutan M, Gagnon-Bartsch JA, Gandolfo LC, Jain A, Sinha A, Olshansky G, Dobrovic A, Papenfuss AT, Speed TP. Removing unwanted variation from large-scale RNA sequencing data with PRPS. Nature Biotechnology. 2023;41(1):10.1038/s41587-022-01440-w

Cameron DL, Dong R, Papenfuss AT. StructuralVariantAnnotation: a R/Bioconductor foundation for a caller-agnostic structural variant software ecosystem. Bioinformatics. 2022;38(7):10.1093/bioinformatics/btac042

Dong R, Cameron D, Bedo J, Papenfuss AT. svaRetro and svaNUMT: modular packages for annotating retrotransposed transcripts and nuclear integration of mitochondrial DNA in genome sequencing data. Gigabyte. 2022;2022:10.46471/gigabyte.70

Mangiola S, Doyle MA, Papenfuss AT. Interfacing Seurat with the R tidy universe. Bioinformatics. 2021;37(22):10.1093/bioinformatics/btab404

Cameron DL, Jacobs N, Roepman P, Priestley P, Cuppen E, Papenfuss AT. VIRUSBreakend: Viral Integration Recognition Using Single Breakends. Bioinformatics. 2021;37(19):10.1093/bioinformatics/btab343

Cameron DL, Baber J, Shale C, Valle-Inclan JE, Besselink N, van Hoeck A, Janssen R, Cuppen E, Priestley P, Papenfuss AT. GRIDSS2: comprehensive characterisation of somatic structural variation using single breakend variants and structural variant phasing. Genome Biology. 2021;22(1):10.1186/s13059-021-02423-x

Vergara IA, Mintoff CP, Sandhu S, McIntosh L, Young RJ, Wong SQ, Colebatch A, Cameron DL, Kwon JL, Wolfe R, Peng A, Ellul J, Dou X, Fedele C, Boyle S, Arnau GM, Raleigh J, Hatzimihalis A, Szeto P, Mooi J, Widmer DS, Cheng PF, Amann V, Dummer R, Hayward N, Wilmott J, Scolyer RA, Cho RJ, Bowtell D, Thorne H, Alsop K, Cordner S, Woodford N, Leditschke J, O’Brien P, Dawson S-J, McArthur GA, Mann GJ, Levesque MP, Papenfuss AT, Shackleton M. Evolution of late-stage metastatic melanoma is dominated by aneuploidy and whole genome doubling. Nature Communications. 2021;12(1):10.1038/s41467-021-21576-8

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Lab research projects

Tumour evolution, heterogeneity and the micro-environment

Pan-Prostate Cancer genome (PPCG) project

Stafford Fox Rare Cancer Program

Mechanistic and functional drivers of neochromosome evolution

Algorithms for structural variant detection

Brain metastasis

Multi-omics data integration

Good to bad melanomas

PPCG CombiMets – Pan-Prostate Cancer Metastasis Working Group

Student research projects

Integration of multi-resolution and multi-omics data

PhD

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Integration of multi-resolution and multi-omics data

PhD

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Prof Tony Papenfuss

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