Patient-matched analysis identifies deregulated networks in prostate cancer to guide personalized therapeutic intervention

Abstract

Prostate cancer (PrCa) is the second most common malignancy in men 1 . More than 50% of advanced prostate cancers display the TMPRSS2-ERG fusion 2 . Despite extensive cancer genome/transcriptome 2-4 and phosphoproteome 5 data, little is known about the impact of mutations and altered transcription on regulatory networks in the PrCa of individual patients.Using patient-matched normal and tumor samples, we established somatic variations and differential transcriptome profiles of primary ERG-positive prostate cancers. Integration of protein-protein interaction and gene-regulatory network databases 6,7 defined highly diverse patient-specific network alterations. We found that different components of a given regulatory pathway were altered by novel and known mutations and/or aberrant gene expression, including deregulated ERG targets, such that different sets of pathways were altered in each individual PrCa. In a given PrCa, several deregulated pathways share common factors, predicting synergistic effects on cancer progression. Our integrated analysis provides a paradigm to identify key deregulated factors within regulatory networks to guide personalized therapies.The mutational landscapes of primary and advanced/metastatic PrCa have been extensively analyzed 2,3,8,9 , as has been the prevalence of the androgen-sensitive TMPRSS2 promoter fusion with ETS transcription factors 10 , which endows ETS with responsiveness to the androgen receptor (AR) that is frequently overexpressed in antiandrogen-resistant PrCa 11 . Recurrent mutations have been found in genes coding for factors regulating a plethora of pathways and key cellular functions, such as the androgen receptor signaling, PI3K/RAS/RAF/WNT pathways, Extended Data Fig. 5: Patient-specific deregulations of the PDGF and EGFR pathway components for the indicated patients (P). a, Merged network of 10 patients used a background in (b) to (l) for PDGF pathway. j, Merged network of 7 patients used as a background in (k) to (o) for EGFR pathway. Frame and color codes are shown below P12. b to i, deregulated and/or mutated components of the PDGF pathway in each patient. k to o deregulated and/or mutated components of EGFR pathway in each patient. Extended Data Fig. 6: Patient-specific deregulations of the PDGF and EGFR pathway components for the indicated patients (P). a, Network display of deregulated and mutated factors of patient P1 in the WNT, Angiogenesis and Cytokine Pathways to reveal connectivities between the different pathways. b, Similar representation of the de-regulated and mutated factors in P2 for the WNT, PDGF and EGFR pathways.

SPLICE-q: a Python tool for genome-wide quantification of splicing efficiency

“…For this purpose, we used data coming from a prostate cancer sample along with its matched normal tissue (patient 15 of ref. [27]). Since for each of the tissues two replicates were available, we computed splicing efficiencies for each replicate and then averaged the results for the tumor tissue and the normal tissue.…”

Complexity against current cancer research: Are we on the wrong track?

“…These systemic spreading effects have been inferred recently in a patient-matched analysis of cancer and normal prostate epithelial tissues. 8 This analysis has two important messages. First, the deregulated and mutated genes are not isolated but form a subnetwork within larger communities, exemplified here by the Wnt-network 9 (Figure 1A).…”

“…The second key message from the patient-matched analysis was that even for the very similar group of ERG (member of the erythroblast transformation-specific (ETS) family of transcriptions factors)-positive prostate cancers, subnetworks of differentially expressed genes (DEGs) within the Wnt network varied from one patient to the other. 8 Thus, the Wnt network was commonly affected in the large majority of patients, but the deregulation affected very different subsets of interconnected actors.…”

“…8 This premise was based on the observation of large intertumoral/interindividual variation of signaling pathways in a very well-defined subset of prostate patients, including functionally divergent paralogues like the many Wnt genes and their receptors, and leading to different cross-talks between pathways, not even considering the potential impact of tumor-specifically altered expression of noncoding regulatory RNAs. 8 Patient-specific networks have also been derived from an integrative genomic, transcriptomic and phosphoproteomic study which compared treatment-naïve and metastatic prostate cancer. 49 There is no doubt that cancer at the time of diagnosis, is an advanced disease with complex temporally deteriorating genetic lesions.…”

“…Indeed, we have observed that even for the same signaling pathway, the affected genes (mutated, deregulated) are often distinct, and that common deregulation is rather an exception. 8 As a consequence, also the drugs that have been developed-albeit not necessarily for prostate cancer-and are available, vary for the two cancers (Table 1).…”