Dataset Information

Analysing Outcomes After Prostate Cancer Diagnosis and Treatment in Carriers of Rare Germline Mutations

ABSTRACT: GENPROS aims to analyse the outcomes of patients with rare gene mutations in the cancer predisposition genes, BRCA1, BRCA2, HOXB13, and Lynch Syndrome, after a diagnosis of and treatment for prostate cancer (PCa). The study includes a cohort of gene mutation carriers with PCa matched with a control group of men with PCa who are known not to carry a mutation in the same gene. Clinical data regarding treatment and patient outcome will be collected retrospectively and prospectively. Archived tumour samples will also be collected for tumour profiling. A blood or saliva sample will be taken, if the participant consents to this part of the study, for genetic profiling to investigate any association of other inherited factors with PCa outcomes. Information obtained from this study will be of critical importance to support clinical trials investigating the most appropriate management of PCa in this group of patients at increased risk of prostate cancer.

DISEASE(S): Prostatic Neoplasms,Prostate Cancer

PROVIDER: 2210056 | ecrin-mdr-crc |

REPOSITORIES: ECRIN MDR

ACCESS DATA

Similar Datasets

Project description:Predicting Outcomes of Prostate Cancer Immunotherapyby Personalized Mathematical Models Natalie Kronik1¤, Yuri Kogan1, Moran Elishmereni1, Karin Halevi-Tobias1, Stanimir Vuk-Pavlovic ́2.,Zvia Agur1*.1Institute for Medical BioMathematics, Bene Ataroth, Israel,2College of Medicine, Mayo Clinic, Rochester, Minnesota, United States of America Abstract Background:Therapeutic vaccination against disseminated prostate cancer (PCa) is partially effective in some PCa patients.We hypothesized that the efficacy of treatment will be enhanced by individualized vaccination regimens tailored by simplemathematical models.Methodology/Principal Findings:We developed a general mathematical model encompassing the basic interactions of avaccine, immune system and PCa cells, and validated it by the results of a clinical trial testing an allogeneic PCa whole-cellvaccine. For model validation in the absence of any other pertinent marker, we used the clinically measured changes inprostate-specific antigen (PSA) levels as a correlate of tumor burden. Up to 26 PSA levels measured per patient were dividedinto each patient’s training set and his validation set. The training set, used for model personalization, contained thepatient’s initial sequence of PSA levels; the validation set contained his subsequent PSA data points. Personalized modelswere simulated to predict changes in tumor burden and PSA levels and predictions were compared to the validation set.The model accurately predicted PSA levels over the entire measured period in 12 of the 15 vaccination-responsive patients(the coefficient of determination between the predicted and observed PSA values wasR2= 0.972). The model could notaccount for the inconsistent changes in PSA levels in 3 of the 15 responsive patients at the end of treatment. Each validatedpersonalized model was simulated under many hypothetical immunotherapy protocols to suggest alternative vaccinationregimens. Personalized regimens predicted to enhance the effects of therapy differed among the patients.Conclusions/Significance:Using a few initial measurements, we constructed robust patient-specific models of PCaimmunotherapy, which were retrospectively validated by clinical trial results. Our results emphasize the potential value andfeasibility of individualized model-suggested immunotherapy protocols.

Dataset Information

Analysing Outcomes After Prostate Cancer Diagnosis and Treatment in Carriers of Rare Germline Mutations

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure