Prostate cancer (PCa) is the most common non-skin cancer type among men and one of the leading causes of cancer deaths worldwide [1]. The current diagnostic options of biopsy in combination with PSA-based tests are not conclusive to the aggressiveness of the disease. Novel diagnostic tests for a reliable non-invasive identification of aggressive PCa with high sensitivity and specificity are urgently needed and would be a great advance in clinical routine. At the moment, no single biomarker could be identified to be specific to prostate cancer. In this project, we attempt the identification and validation of urinary metabolite biomarkers and biomarker networks, i.e. high-dimensional classifiers, associated with aggressive prostate cancer.

1. ASSESSMENT OF PROSTATE CANCER AGGRESSIVENESS:
A METABOLOMICS EVALUATION OF URINE BY NMR SPECTROSCOPY
Kdadra Marouane1
, Rudolf Jagdhuber1
, Kremer Werner2
, Eiglsperger Johannes1
& Schiffer Eric1
1
numares AG, Am BioPark 9, 93053 Regensburg, Germany
2
Institute of Biophysics and Physical Biochemistry, University of Regensburg, Regensburg, Germany
BACKGROUND & GOAL
Prostate cancer (PCa) is the most common non-skin cancer type among men and one of the leading
causes of cancer deaths worldwide [1]. The current diagnostic options of biopsy in combination with
PSA-based tests are not conclusive to the aggressiveness of the disease. Novel diagnostic tests for a
reliable non-invasive identification of aggressive PCa with high sensitivity and specificity are urgently
needed and would be a great advance in clinical routine. At the moment, no single biomarker could be
identified to be specific to prostate cancer. In this project, we attempt the identification and validation
of urinary metabolite biomarkers and biomarker networks, i.e. high-dimensional classifiers, associated
with aggressive prostate cancer.
CASE CONTROL DEFINITION
Post surgery Gleason score is used as a response
definition
Indolent Aggressive
1 2 3 4 5 6 7 8 9 10
Training n = 64 n = 238
Test n = 39 n = 183
MATERIALS AND METHODS
urine sample 1H-NMR (600MHz) Raw data
Data processing
• Phasing and baseline correction
• Background treatment
• Normalization by creatinine
Binning
Statistical modeling
• Feature selection
• Cross validation
• Model selection
List of models
RESULTS
Model performance
Test on Evaluation Set
Specificity
Sensitivity
1.0 0.8 0.6 0.4 0.2 0.0
0.00.20.40.60.81.0
AUC
0.660
Literature search
In a recent systematic review article [2], we have summarized a number
of PCa biomarkers which were repeatedly reported and/or independently
validated.
citrate choline 2-aminoadipic acid
glycine spermine glycerol-3-phosphate
proline alanine glycerophosphocholine
histidine uracil phosphocholine
REFERENCES
[1] Gabriel P Haas, Nicolas Delongchamps, Otis W Brawley, Ching Y Wang, and Gustavo de la
Roza. The worldwide epidemiology of prostate cancer: perspectives from autopsy studies.
The Canadian journal of urology, 15(1):3866, 2008.
[2] Marouane Kdadra, Sebastian Höckner, Hing Leung, Werner Kremer, and Eric Schiffer.
Metabolomics biomarkers of prostate cancer: a systematic review. Diagnostics, 9(1):21, 2019.
FUTURE WORK
The next part of the project is to unveil the biology behind this specific step
of the pathological progression by performing a metabolomics, proteomics
and genomics analysis in a systems biology using a holistic and integrative
approach. Clarification of the molecular pathways that may lead to CRPC is
important for discovering novel therapeutic strategies to delay or reverse the
progression of disease.
Figure 1: Prostate cancer plasticity/evolution results in treatment resistance
ACKNOWLEDGEMENT
This project has received funding from the European
Union’s Horizon 2020 research and innovation program
under the Marie Sklodowska-Curie grant agreement No
721746
Contact: marouane.kdadra@numares.com @TransPotITN @numares AG
@TransPot_ITN
Learn more about TransPot