YouTube video by BlueBolt VFX

Eight integrated mutational footprints collectively explain the mutational processes in 85% of primary prostate cancer genomes.

Eight integrated mutational footprints collectively explain the mutational processes in 85% of primary prostate cancer genomes.

YouTube video by RamZaes

PanProstate Cancer Group website.

Saturday November 29, 2025 | LANGFORD, BC Some of you may have heard that the Urban Food Resilience Initiatives Society (UFRIS) is stepping back from hands-on operation of Porcher Park Community Garden (PPCG) in Langford. Our last day there as the Operator will be December 24, 2025. UFRIS is truly pleased with PPCG — a […]

Via the Centers for Medicare & Medicaid Services (CMS): February 16: CMS approved coverage expansions in Arizona through an amendmentof two policies, “Parents as Paid Caregivers” (PPCG) and “KidsCare

PanProstate Cancer Group website.

African ancestry is a known risk factor for prostate cancer development, but the genetic determinants of this are incompletely understood. Here, the authors identify 172 potentially pathogenic variants which are enriched in an African population.

Epigenetic biallelic inactivation in prostate cancer remains to be explored. Here, the authors develop a computational method Epi2Hit that integrates the hemizygous genomic disruptions with patterns of hypermethylation at regulatory CpG sites to identify biallelic inactivation in tumour suppressor genes.

PPCG mutational processes working group. Contribute to panprostate/mutational-signatures development by creating an account on GitHub.

We used data from the PPCG consortium of primary prostate cancer samples from a total of 1,001 prostate cancer donors with 1,172 tumour samples. Informed ethical consent was obtained at clinical follow-up, and was consistent with local research ethics and International Cancer Genome Consortium (ICGC) guidelines (https://icgc.org/). Ethical approval was obtained from local research ethical committees (details are provided in the Supplementary Methods). Analysis of SVs from genomic data Simple and complex SV classification method We conducted SV classification on the PPCG cohort using the cSVc tool18, including also SVs near TARBS and chromothripsis, both abundant in prostate cancer. First, exact breakpoint estimation from our WGS short-read sequencing data was conducted by using split-read information. Specifically, for each sample, we used the median of soft-clipped reads from the corresponding tumour BAM file to obtain corrected breakpoint positions. Next, we used ClusterSV18 to obtain clusters of SVs from the corrected SV data and we also used Battenberg68 to obtain segmented CN files from tumour and normal read coverage files. For each sample, we generated CN files segmented by corrected SV breakpoints by using the corrected SV data, the CN segmentation file, CN coverage and the tumour BAM file. Finally, we conducted...