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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. More than 50% of advanced prostate cancers display the TMPRSS2-ERG fusion. Despite extensive cancer genome/transcriptome 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 defined highly diverse patient-specific network alterations. Different components of a given regulatory pathway were altered by novel and known mutations and/or aberrant gene expression, including deregulated ERG targets, and were validated by using a novel in silico methodology. Consequently, 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 druggable key deregulated factors within regulatory networks to guide personalized therapies.

SUBMITTER: Kumar A

PROVIDER: S-EPMC8640800 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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

Kumar Akinchan A Kasikci Yasenya Y Badredine Alaa A Azzag Karim K Quintyn Ranty Marie L ML Zaidi Falek F Aragou Nathalie N Mazerolles Catherine C Malavaud Bernard B Mendoza-Parra Marco A MA Vandel Laurence L Gronemeyer Hinrich H

American journal of cancer research 20211115 11

Prostate cancer (PrCa) is the second most common malignancy in men. More than 50% of advanced prostate cancers display the TMPRSS2-ERG fusion. Despite extensive cancer genome/transcriptome 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. Integratio ...[more]

PMID: 34873462

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Project description:BackgroundProstate cancer (PCa) is the most frequently diagnosed non-skin cancer and a leading cause of mortality among males in developed countries. However, our understanding of the global changes of protein complexes within PCa tissue specimens remains very limited, although it has been well recognized that protein complexes carry out essentially all major processes in living organisms and that their deregulation drives the pathogenesis and progression of various diseases.MethodsBy coupling tandem mass tagging-synchronous precursor selection-mass spectrometry/mass spectrometry/mass spectrometry with differential expression and co-regulation analyses, the present study compared the differences between protein complexes in normal prostate, low-grade PCa, and high-grade PCa tissue specimens.ResultsGlobally, a large downregulated putative protein-protein interaction (PPI) network was detected in both low-grade and high-grade PCa, yet a large upregulated putative PPI network was only detected in high-grade but not low-grade PCa, compared with normal controls. To identify specific protein complexes that are deregulated in PCa, quantified proteins were mapped to protein complexes in CORUM (v3.0), a high-quality collection of 4274 experimentally verified mammalian protein complexes. Differential expression and gene ontology (GO) enrichment analyses suggested that 13 integrin complexes involved in cell adhesion were significantly downregulated in both low- and high-grade PCa compared with normal prostate, and that four Prothymosin alpha (ProTα) complexes were significantly upregulated in high-grade PCa compared with normal prostate. Moreover, differential co-regulation and GO enrichment analyses indicated that the assembly levels of six protein complexes involved in RNA splicing were significantly increased in low-grade PCa, and those of four subcomplexes of mitochondrial complex I were significantly increased in high-grade PCa, compared with normal prostate.ConclusionsIn summary, to the best of our knowledge, the study represents the first large-scale and quantitative, albeit indirect, comparison of individual protein complexes in human PCa tissue specimens. It may serve as a useful resource for better understanding the deregulation of protein complexes in primary PCa.

Project description:BackgroundThe emergence of targeted therapies and immunotherapy has broadened treatment options for patients with pancreatic ductal adenocarcinoma (PDAC). Despite this, traditional drug selection, predominantly relies on tumor markers and clinical staging, has underutilized these drugs due to ignoring patient genomic diversity. Patient-derived organoids (PDOs) and corresponding patient-derived organoid xenograft (PDOX) models offer a way to better understand and address this.MethodsIn this study, we established PDOs and PDOX models from PDAC clinical samples. These models were analyzed using immunohistochemistry, H&E staining, and genomic profiling. Drug screening with 111 FDA-approved drugs was performed on PDOs, and drug responses in PDOs and PDOX models were compared to assess consistency with clinical treatment outcomes. Gene analysis was conducted to explore the molecular mechanisms underlying variations in drug responses. Additionally, by analyzing the sequencing results from various drug-sensitive groups, the identified differential gene-drug metabolism gene UGT1A10 were modulated in PDOs to evaluate its impact on drug efficacy. A co-culture system of PDOs with immune cells was developed to study the efficacy of immunotherapies.ResultsPDOs and matched PDOX models retain the morphological, biological, and genomic characteristics of the primary tumor. Exome sequencing and RNA sequencing confirmed both the consistency and heterogeneity among the PDOs. High-throughput drug screening revealed significant variability in drug sensitivity across different organoids, yet PDOs and PDOX derived from the same patient exhibited a high degree of concordance in response to clinical chemotherapy agents. The gene expression analysis of PDOs with significant differences in drug sensitivity revealed UGT1A10 as a crucial regulator. The knockdown of UGT1A10 notably increased drug sensitivity. Furthermore, immune cells demonstrated specific cytotoxicity towards the organoids, underscoring the potential of the co-culture system for application in tumor immunotherapy.ConclusionOur results highlight the necessity for personalized treatment strategies that consider genomic diversity beyond tumor markers, thus validating the utility of PDOs and PDOX models in advancing PDAC research and personalized medicine.

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

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

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