Fraunhofer Institute for Cell Therapy and Immunology IZIBioinformatics
Prostate cancer is the most prevalent cancer disease and the third most common cancer-related cause of death in European men [1]. Clinical behavior of localized prostate cancer is highly variable. Some men have aggressive cancer leading to death but many others have indolent cancers that are cured with initial therapy or may be safely observed. Patients often face unnecessary surgery because clinical and histopathological risk factors, as well as biomarkers and their according classification models, lack discrimination accuracy. Hence, there is a high clinical demand of biomarkers for the early prognosis of prostate cancer.
To address this, we strive for a better understanding of the molecular dysregulation in PCa. We conducted whole-transcriptome variation studies to detect gene signatures of prognostic value comprising protein and non-protein coding genes in fresh frozen radical prostatectomy samples and confirmed them in routine clinical materials of formalin-fixed and paraffin-embedded (FFPE) radical prostatectomy or biopsy samples.
We assessed the transcriptional landscape of more than two hundred tissue specimens of prostate cancer patients with long-term clinical follow up. For an unbiased assessment of transcriptional changes, we used analytical methods like custom expression microarrays and transcriptome-wide next-generation sequencing. We applied survival models to the expression values of each gene and combined evidence from different types of samples via a statistical meta-analysis. We combined all selected genes in a gene expression prognostic score per patient. The combined score showed a strong prognostic effect and correlates with time to death because of the disease. We could confirm the prognostic score in an independent testing cohort of a representative sample size and showed that the score also correlates with time to biochemical recurrence.
We developed a transcriptome-based score that predicts aggressive types of prostate cancer in cohorts of prostate cancer patients treated by radical prostatectomy. We further confirmed the score in an independent cohort of tissue specimens. The score is suitable to support treatment stratification and clinical decision-making for patients diagnosed with prostate cancer. We are currently confirming the score in tissue specimens that represent hands-on clinical material.
References
[1] https://ecis.jrc.ec.europa.eu
Development of a novel classifier for the prognosis of aggressive disease progression in prostate carcinoma in needle biopsies
Despite a growing number of treatment options, the therapeutic situation for oncological diseases is often unsatisfactory. Personalized medicine can bring fundamental progress through a therapy selection individually adapted to the patient. The most common malignant tumor indication in men in Europe is prostate cancer. Screening by PSA, the prostate specific antigen, leads to a high rate of detected tumors even with low aggressiveness. Clinical and histopathological risk factors as well as previous biomarkers and their respective classification models inadequately divide the tumors into risk classes. On the one hand, this results in patients being overtreated, which may be associated with a severe impairment of quality of life. On the other hand, patients with aggressive prostate cancer who are not operated have an increased tumor-related mortality. Accordingly, there is a need to improve the risk categorization of prostate cancer. Using next-generation sequencing (NGS), detailed knowledge of the relationship between the activity of molecular signaling pathways and the risk of aggressive disease progression has been obtained at the Fraunhofer Institute for Cell Therapy and Immunology1. Within the framework of "RiboTrend", prototypical software for a classifier for the prediction of aggressive disease progression of prostate carcinoma is being developed, which should enable improved tumor categorization.
The project "RiboTrend - Development of a novel classifier for the prediction of aggressive disease progression in prostate cancer in needle biopsies" is funded by the European Regional Development Fund (ERDF) through the state of Saxony.
1 Cross et al. ProstaTrend - A Multivariable Prognostic RNA Expression Score for Aggressive Prostate Cancer. Eur. Urology. 2020 Sep. doi: 10.1016/j.eururo.2020.06.001
PIONEER - Prostate Cancer DIagnOsis and TreatmeNt Enhancement through the Power of Big Data in EuRope
Since 2018 we are an active member of the PIONEER consortium, a European Network of Excellence for Big Data in Prostate Cancer, consisting of 32 partners across 9 countries. PIONEER’s goal is to ensure the optimal care for all European men living with prostate cancer by unlocking the potential of Big Data and Big Data analytics. A key objective of PIONEER is to standardise and integrate existing ‘big data’ from quality multidisciplinary data sources into a single innovative open access data platform, to accelerate prostate cancer research. Within PIONEER, we contribute our strengths and expertise in data harmonisation of transcriptome-wide expression studies as well as statistical data analyses to identify and confirm biomarkers.
PIONEER is funded through the IMI2 Joint Undertaking and is listed under grant agreement No. 777492 and is part of the Big Data for Better Outcomes Programme (BD4BO). IMI2 receives support from the European Union’s Horizon 2020 research and innovation programme and the European Federation of Pharmaceutical Industries and Associations (EFPIA). The above text represents only the views of Fraunhofer IZI.
