The identification of clinically relevant driver alterations in prostate cancer remains elusive. Here, integrative analysis of cancer driver genes using the Pan Prostate Cancer Group whole genome sequ...

YouTube video by 日本映画ファン

Excited to share that our latest study is now published in Nature Communications! 🍾 🎉 Using data from the largest prostate cohort cohort (PPCG), we systematically characterised the genomic driver la...

Excited to share that our latest study is now published in Nature Communications! 🍾 🎉 Using data from the largest prostate cohort cohort (PPCG), we systematically characterised the genomic driver la...

Excited to share that our latest study is now published in Nature Communications! 🍾 🎉 Using data from the largest prostate cohort cohort (PPCG), we systematically characterised the genomic driver la...

The identification of clinically relevant driver alterations in prostate cancer remains elusive. Here, integrative analysis of cancer driver genes using the Pan Prostate Cancer Group whole genome sequ...

We analysed 961 matched tumour-normal whole genomes from primary, localized, and predominantly untreated prostate cancers in the Pan Prostate Cancer Group (PPCG) cohort processed using standardized consensus pipelines14. The analysed subset comprised fresh-frozen samples from six contributing countries (Canada, Germany, UK, Australia, Denmark, France), with median tumour and normal sequencing depths of 69 and 41, respectively; matched RNA-seq data were available for 494 (51%) tumours, and one representative tumour sample per donor was selected (Supplementary Data 1). Key clinical and demographic features of the cohort, including donor age, PSA at tumour collection, Gleason grade group, pathological T-stage, metastasis during follow-up, and predicted ancestry, are summarised in Supplementary Fig. 1. Most of the cohort (709 or 74%) had the annotations required for metastasis-free survival analysis. The cohort was enriched for European ancestry (90%), with smaller groups for African (3.5%), East Asian (1.7%), Admixed American (0.6%), or unassigned (3.3%). Pathogenic germline risk variants in established prostate cancer predisposition genes were identified in 4.6% of donors, including germline variants in BRCA1 or BRCA2 in 2.2% of donors. A cancer driver is a genomic alteration that provides a selective growth advantage to a cancer cell, and can be identified in large datasets of tumour...

PanProstate Cancer Group website.

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.

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.

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...

PanProstate Cancer Group website.

YouTube video by Edições Valentim de Carvalho

State auditors say PPCG cost controls were not fully implemented, citing delays that continued to cost the state hundreds of millions. GOP leaders now talk cuts or closure while Hobbs and Mayes defend emergency rulemaking that paused a key tool.