Post on 17-Jul-2020
transcript
PROJECT OUTLINE
PROSTATE CANCER
M. Koletou1, 2, 3, M. Gabrani2, T. Guo3, Q. Zhong1, U. Wagner1, R. Aebersold3, P. Wild1, M. Rodríguez Martínez2
1 Institute of Pathology and Molecular Pathology, University Hospital Zurich, Switzerland 2 IBM Research Laboratory Zurich, Switzerland 3 Institute of Molecular Systems Biology, ETH Zurich, Switzerland
First and foremost we would like to acknowledge SystemsX.ch whose funding made this interdisciplinary
PhD project possible. We thank The Cancer Genome Atlas (TCGA) Network for granting us access to the
Prostate Adenocarcinoma datasets. We also want to thank the SystemsX.ch project PhosphoNet
Personalized-Precision Medicine (SystemsX.ch project no. 2012/191), for the genomic and proteomic data
that are made available to us. Finally, we would also like to show our gratitude towards the University
Hospital of Zurich, IBM Research Laboratory Zurich and ETH Zurich for their amicable collaboration and their
expertise that greatly assist this project.
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Acknowledgments
A COMPUTATIONAL FRAMEWORK FOR SYSTEMS PATHOLOGY OF PROSTATE CANCER
[1] de Morsier, F. et al., 2014. In K. Lai & A. Erdmann, eds. SPIE Advanced Lithography. International Society for Optics and Photonics, p. 905211.
The 2016 WHO Classification of Tumors of the
Urinary System and Male Genital Organs-Part B:
Prostate and Bladder Tumors.European urology.
• Grade group 1: Gleason score ≤ 6
• Grade group 2: Gleason score 3 + 4 = 7
• Grade group 3: Gleason score 4 + 3 = 7
• Grade group 4: Gleason score 8
• Grade group 5: Gleason scores 9–10
*Age-adjusted to the 2000 US standard population and adjusted for relays in reporting.†Includes the intrahepatic bile duct
Sourec: Surveilance, Epidemiology, and End Results (SEER) Program, National Cancer
Institute, 2014.
image source: American Cancer Society, Cancer Facts & Figures 2015
Incidence and Significance
• very high incidence
• but most cases are insignificant
• diagnostic screening is controversial
• overdiagnosis and overtreatment
Need for biomarker candidates
• stratify aggressive from insignificant PCa
• more accurate prognosis
− better than Gleason score (a histopathological
grading of prostate tissue obtained by biopsy)
NOVEL COMPUTATIONAL
FRAMEWORK
stratify prostate cancer into
insignificant and aggressive
identify genomic alteration
profiles that enable the
stratification
integrate multi-omics datasets
to strengthen the analysis
D G
D
P
G
(i) sparse
dictionary learning
(ii) network
visualization
(iii) mapping dictionary to
phenotypic traits and
genomic alterations
Molecular Dataset Dictionary Dictionary NetworkPhenotype - Genotype
association network
raw genomic
datasets
molecular fingerprints
of cancer
PD
DICTIONARY LEARNING WITH SPARSE CODING[1]
Pathway Example: HALLMARK PI3K-AKT-MTOR SIGNALING
pathway network from Data correlations
DATA: Copy Number Alteration (CNA) profilesfrom TCGA prostate tumor samples
Dictionary (D)
Sparse Coefficients (X)
pathway network from Dictionary sparse correlations
Data
Dictionary
Comparison of
Betweenness Centrality
of the pathway networks
the number of the shortest paths
from node to node
that pass through node
Betweenness Centrality Betweenness Centrality
Co
rre
lati
on
Co
rre
lati
on
Dictionary Mapping
Phenotype – Genotype association with Dictionary Learning
patient to gene
specific mapping
allows us to explore a
more personalized
molecular fingerprint of
prostate cancer
all pathway genes in the CNA dataset
selected genes for all patients with dictionary mapping
selected genes for each patient with dictionary mapping