CI lead: Professor Melissa Southey, Precision Medicine, School of Clinical Sciences, Monash Health

Team members: A/Professor Pierre-Antoine Dugué, A/Professor Robert J. MacInnis, Dr Robert O’Reilly, Jared Burke, Dr Fiona Chen

Awarded: $100,000 in GERA’s Grant Round 1 (2025)

About the project: Whilst many prostate tumours progress slowly and are treatable using procedures that are usually minimally invasive, identifying highly aggressive forms of prostate cancers that proliferates rapidly, metastasises and lead to death remains challenging. It is critical to identify the aggressive cases associated with high rates of biochemical recurrence and mortality at the time of diagnosis in order to provide timely and targeted treatment. The Lifestyle and Genetic Risk Factors for Prostate Cancer Study (LGRFPCS) was established to address this need. We have done extensive work to molecularly characterise the germline DNA and the tumours from men participating in this study.

The Lifestyle and Genetic Risk Factor Prostate Cancer Study
The LGRFPCS includes 1,461 incident cases recruited through the Victorian Cancer Registry and urology clinics in Victoria. 974, eligible matched controls were selected from men that underwent a prostate biopsy at the same practice and had a negative biopsy result. Of the 1,461 cases, 360 men in the LGRFPCS were linked to the Victorian Prostate Cancer Outcomes Registry to obtain treatment response and clinical outcome data. These 360 men became the focus for collection of additional data for multi-omic analysis.

Germline molecular analyses
Men in these studies have been included in genome-wide studies of genomic variation using the OncoArray¹, in panel testing studies of putative cancer susceptibility genes^2,3, and many were included in the 1R01CA196931 exome sequencing initiative led by Chris Haiman⁴.

Molecular studies of the prostate cancers
The tumours arising in the 360 men have been systematically reviewed by a pathologist which included marking up of the relevant tumours areas for macrodissection. DNA extracted from tissue macrodissected from these marked up tumour areas has been measured for genome-wide methylation using the Infinium methylationEPICv1.0 array^5,6. Via a collaboration with Prof Renea Taylor (BDI, Monash University), these tumours have undergone staining for several proteins important to prostate cancer characterisation via immunohistochemistry (IHC). We have conducted whole exome sequencing (WES) for a proportion of these tumours with funding from the NHMRC (Southey) and VCA (Dugué). Of the 360 tumours, 160 still require WES data generation.

Epigenetic scores
We have already made great strides in terms of the epigenetic characterisation of prostate cancer aggressiveness using the LGRFPCS resource by assessing a range of epigenetic scores including mitotic clocks and machine-learning based scores⁶. In addition, by pooling data from ~2,000 tumours and applying penalised regression to genome-wide DNA methylation, we have generated scores predictive of Gleason score (mGS) and recurrence (mBCR) that far surpass those found in the literature in their prognostic value in validation sets, both at the time of radical prostatectomy (mGS: RR per SD: 1.7, 95%CI: 1.4-2.1), and especially at diagnosis (mGS: RR per SD: 2.6, 95%CI: 1.93.4) corresponding to substantial AUC increases (beyond clinicopathological variables); this strongly suggests that such scores may be valuable to prioritise or minimise treatment.

Aim 1: Conduct whole-exome sequencing for 160 tumours arising in men participating in the LGRFPCS.

Tumour enriched DNA will be prepared from macrodissected areas of tumour tissue as guided by the reviewing pathologist. Germline DNA (extracted from blood), corresponding to each man, will be retrieved from the biorepository in Precision Medicine. Tumour and blood DNA pairs will undergo quality and quantity assessment before being shipped to Azenta for whole exome sequencing (Agilent v5 library and NovaSeq PE150 12G). We will use standard bioinformatic pipelines and tools to align reads and perform quality control. Somatic variant calling, using multiple callers, will be performed by comparing tumour-enriched
DNA with the paired germline DNA that was sequenced in parallel. Sequence artifacts will be identified and removed using standard thresholds in conjunction with concordance between variant callers before annotating variants. Tumour molecular signatures will be extracted from existing tools that decompose the mutation spectrum into known mutation signatures. Single
variants classified as either tier 1 or tier 2 and gene signatures will be used in the analyses described in aim 2.

Aim 2: Develop integrative models of prostate cancer outcomes using multi-omic data.

Additional somatic WES data will allow the development of genetic x epigenetic models of progression using relevant machine learning methods such as DIABLO implemented in the MixOmics R package or ensemble learning, e.g. SuperLearner⁷. Given the very highdimensionality of these data, we will consider dimension reduction techniques, including via consideration of epigenetic signatures such as mGS and mBCR, and somatic genetic factors such as tumour mutational signatures⁸ and already identified germline or somatic genomic variation associated with prostate cancer aggressiveness. Internal cross-validation will be applied where appropriate to minimise overfitting; gains in prediction will be evaluated using relevant metrics implemented in the R package timeROC. External validation will be sought using cohorts from the PRACTICAL consortium and data that is publicly available (e.g.UKB).

Aim 3: Analyse the somatic genetic and epigenetic features underlying prostate cancer risk factors and its subtypes.

3.1 Unravelling the somatic features of aggressive prostate cancer
This aim will investigate the tumour characteristics associated with (aggressive) prostate cancer risk factors, considering both i) germline genetics: polygenic risk score¹, family history, and carriers of high-risk pathogenic variants in BRCA1, BRCA2 and the Lynch syndrome genes, and more moderate risk-associated pathogenic variants in ATM, CHEK2 and HOXB13^2,3 and ii) non-genetic factors such as obesity, hormonal factors, diet, physical inactivity, smoking and alcohol consumption. A combination of GWAS/EWAS, multi-omic machine learning, and pathway analysis methods will be applied to unravel the somatic genetic and epigenetic landscapes associated with these factors; aiming to a better understanding of prostate cancer aetiology and potential targets for treatment and outcome prediction.
3.2 Rare subpathologies and neuroendocrine differentiation
Emerging, largely unexplored features of prostate cancer were assessed by an experienced pathologist in the LGRPFCS, including the presence of neuroendocrine cells in the tumours via IHC markers: chromogranin A (CgA), synaptophysin (Syn), CD56 protein, histological assessment of cribriform pattern, intraductal carcinoma, ductal carcinoma, and IHC markers of androgen receptor
and TMPRSS2:ERG fusion. We have already profiled these factors epigenetically using EWAS/machine learning and will aim to further characterise them using germline and somatic genetics.

References

1. Schumacher FR, Association analyses of more than 140,000 men identify 63 new prostate cancer
   susceptibility loci. Nat Genet. 2018 Jul;50(7):928-936.
2. Nguyen-Dumont T, et al., Rare Germline Genetic Variants and Risk of Aggressive Prostate Cancer Int J
   Cancer. 2020 Apr 27.
3. Nguyen-Dumont T, et al., Rare Germline Pathogenic Variants Identified by Multigene Panel Testing and
   the Risk of Aggressive Prostate Cancer. Cancers (Basel). 2021 Mar 24;13(7):1495
4. Darst BF, et al., Germline sequencing DNA repair genes in 5,545 men with aggressive and non-aggressive
   prostate cancer. J Natl Cancer Inst. 2020.
5. O’Reilly RL, et al., Quality control checkpoints for high throughput DNA methylation measurement using
   the human MethylationEPICv1 array: application to formalin-fixed paraffin embedded prostate tissue.
   BMC Res Notes. 2025 18(1):280.
6. Zhu Y, et al., Tumour-based Epigenetic Signatures as Markers of Prostate Cancer Aggressiveness after
   Radical Prostatectomy, Br J Cancer, in press
7. Phillips RV, et al. Practical considerations for specifying a super learner. Int J Epidemiol. 2023
8. Alexandrov LB, et al., The repertoire of mutational signatures in human cancer, Nature, 2020

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