From aetiology to improved risk prediction for clinically significant disease
Postdoc Recipient: Dr Hamzeh M Tanha, The Daffodil Centre, The University of Sydney, and Cancer Council NSW, Sydney, Australia
Supervisors: Associate Professor Julia Steinberg (Daffodil Centre), Professor Loic Yengo (University of Queensland)
Awarded: $40,000 in GERA’s Grant Round 1 (2025)
About the project: Prostate cancer has a high disease burden, and early detection is essential to improve survival. Current early detection relies on PSA testing, which leads to substantial over diagnosis. Risk based screening has the potential to improve the balance of benefits and harms at the population level. PGSs capture a substantial proportion of prostate cancer risk but are based on data influenced by PSA-driven diagnosis. As a result, current PGSs do not specifically identify individuals at high risk of aggressive disease, and improved approaches are needed.
This project aims to develop an improved PGS for prostate cancer that is independent of PSA levels, addressing a key limitation in current genetic risk prediction. It is hoped the new PGS may better capture true cancer susceptibility, improving specificity in risk prediction. This PGS could help identify men at increased risk of clinically significant prostate cancer, potentially reducing overdiagnosis and increasing screening efficiency.
Research aims:
Aim 1: Identify prostate cancer loci independent of PSA levels (1a) post GWAS analysis
This study will leverage summary statistics from two major GWASs: (1) a 2023 multi-ancestry prostate cancer GWAS (944,762 men; 156,319 cases)4, and (2) a 2025 PSA levels GWAS (392,522 men without prostate cancer)3. I will adapt the case-case GWAS (CC-GWAS)5 method to compare genetic effects between these GWAS using summary statistics. This approach will contrast prostate cancer cases with individuals genetically predisposed to high PSA levels, enabling a genome-wide analysis of variants more strongly associated with cancer than with PSA levels. These variants may represent key aspects of cancer risk that are overlooked in standard GWAS due to PSA-driven detection bias. To enable the CC-GWAS, I will convert PSA GWAS results from beta coefficients to odds-ratios using an existing tool6.
Aim 2: Construct and evaluate a PSA-independent PGS
Using CC-GWAS results, I will construct a novel PSA-independent prostate cancer PGS using SBayesR12, a Bayesian method that jointly models SNP effects from summary statistics. For comparison, I will also generate an SBayesR-based PGS from the original 2023 prostate cancer GWAS. The new score will be evaluated in an independent cohort—the 45 and Up Study (NSW) with genetic data for ~3,300 men, including >1,800 prostate cancer cases. The genetic data were recently generated through the Australian Cancer Risk Study, with exclusive access for our team. I will assess PGS performance for prostate cancer risk prediction, overall and for aggressive disease (using Gleason scores from linked pathology data—a key strength of this analysis). Performance of the PSA-independent PGS will be compared with three existing scores: PGS4514, PGS4004 (excluding 51 PSA-associated variants), and the 2023 SBayesR PGS. Evaluation metrics include AUC, risk stratification (e.g. top vs bottom PGS quintiles), and calibration. I hypothesise the PSA-independent PGS may improve specificity, particularly for aggressive disease.
Data Sharing:
To support transparency and future research, CC-GWAS results and new PGS will be deposited
in the GWAS Catalog and PGS Catalog, respectively.
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