Precision health based management of prostate cancer patients

Aim/goals:

To help better understand factors responsible for response/resistance to therapy in prostate cancer patients and develop precise and personalized targeted therapy (e.g. with PARP Inhibitors, AKT inhibitors), APCaRI investigators shall undertake:

1. Molecular characterization of mCRPC & nmCRPC patients in Alberta. This will include Whole Genome Sequencing (WGS), Transcriptomics, and Methylation analyses.
2. Biomarker profiles will be developed for DDR alterations (germline and somatic), genomic alterations for PI3K/AKT/mTOR and Mismatch repair (MMR) pathways and through identification of other genes and pathways of potential clinical significance.
3. The biomarker profiles will also be investigated for association with PSMA PET/CT expression.
4. Investigate prostate cancer progression in indigenous men.
5. Using AI and ML, the biomarker profile will be integrated with matched histopathology imaging and clinical data (e.g., PSA, treatment, metastasis, imaging, family history etc.).

Summary:

Prostate cancer is one of the most common cancers and the third leading cause of death in North American men. It is one of the most heritable and heterogenous cancers and accounts for over 21% of all newly diagnosed cancers in men and 10% of all cancer deaths. Using Whole Genome Sequencing (WGS), Transcriptomics, and Methylation analyses, biomarker profiles for DDR alterations, and alterations in PI3K/AKT/mTOR and Mismatch repair (MMR) pathways will be developed to help identify genes and pathways of potential clinical significance. Using AI and ML tools, genomic data will be integrated with matching histopathology, imaging, and clinical data for these patients. Similar investigations will be carried out in indigenous men to understand differences in progression of prostate cancer and response to therapy. The data generated by this study is expected to help predict prostate cancer aggressivity, course of cancer progression (i.e., metastasis and relapse), identify patients to be prioritized for genetic testing, and identify the specific molecular biomarkers that predict therapeutic outcomes.  Integration of OMIC, imaging and clinical data, and using AI/ML will allow the development of predictive and decision-making tools to provide the right management to the right patient at the right time.