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Network-based analysis to identify drivers of metastatic prostate cancer using GoNetic

(2021) CANCERS. 13(21).
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Abstract
Simple Summary:& nbsp;The identification of cancer driver genes is, for statistical reasons, often biased toward genes that are altered frequently in a cohort. However, genes that are less frequently mutated can also alter cancer hallmarks. To detect such rarely mutated genes involved in driving metastatic prostate cancer, we analyzed the Hartwig Medical Foundation metastatic prostate cancer cohort. Hereto, we developed GoNetic, a novel network-based method that can detect genes with a lower mutational rate as members of recurrently mutated sets of genes connected on a prior interaction network. In contrast to state-of-the-art network-based driver identification methods, GoNetic retains information on sample-specific mutations and uses more properties of the prior interaction network. When applied to the Hartwig Medical Foundation cohort, GoNetic successfully prioritized both known drivers and rarely mutated driver candidates of metastatic prostate cancer. Comprehensive validation with other public data sets further supported the driver potential of these novel candidates.& nbsp;Most known driver genes of metastatic prostate cancer are frequently mutated. To dig into the long tail of rarely mutated drivers, we performed network-based driver identification on the Hartwig Medical Foundation metastatic prostate cancer data set (HMF cohort). Hereto, we developed GoNetic, a method based on probabilistic pathfinding, to identify recurrently mutated subnetworks. In contrast to most state-of-the-art network-based methods, GoNetic can leverage sample-specific mutational information and the weights of the underlying prior network. When applied to the HMF cohort, GoNetic successfully recovered known primary and metastatic drivers of prostate cancer that are frequently mutated in the HMF cohort (TP53, RB1, and CTNNB1). In addition, the identified subnetworks contain frequently mutated genes, reflect processes related to metastatic prostate cancer, and contain rarely mutated driver candidates. To further validate these rarely mutated genes, we assessed whether the identified genes were more mutated in metastatic than in primary samples using an independent cohort. Then we evaluated their association with tumor evolution and with the lymph node status of the patients. This resulted in forwarding several novel putative driver genes for metastatic prostate cancer, some of which might be prognostic for disease evolution.
Keywords
Cancer Research, Oncology, network-based cancer data analysis, driver identification, metastatic prostate cancer, somatic mutations, SOMATIC MUTATIONS, GENES

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MLA
de Schaetzen van Brienen, Louise, et al. “Network-Based Analysis to Identify Drivers of Metastatic Prostate Cancer Using GoNetic.” CANCERS, vol. 13, no. 21, 2021, doi:10.3390/cancers13215291.
APA
de Schaetzen van Brienen, L., Miclotte, G., Larmuseau, M., Van den Eynden, J., & Marchal, K. (2021). Network-based analysis to identify drivers of metastatic prostate cancer using GoNetic. CANCERS, 13(21). https://doi.org/10.3390/cancers13215291
Chicago author-date
Schaetzen van Brienen, Louise de, Giles Miclotte, Maarten Larmuseau, Jimmy Van den Eynden, and Kathleen Marchal. 2021. “Network-Based Analysis to Identify Drivers of Metastatic Prostate Cancer Using GoNetic.” CANCERS 13 (21). https://doi.org/10.3390/cancers13215291.
Chicago author-date (all authors)
de Schaetzen van Brienen, Louise, Giles Miclotte, Maarten Larmuseau, Jimmy Van den Eynden, and Kathleen Marchal. 2021. “Network-Based Analysis to Identify Drivers of Metastatic Prostate Cancer Using GoNetic.” CANCERS 13 (21). doi:10.3390/cancers13215291.
Vancouver
1.
de Schaetzen van Brienen L, Miclotte G, Larmuseau M, Van den Eynden J, Marchal K. Network-based analysis to identify drivers of metastatic prostate cancer using GoNetic. CANCERS. 2021;13(21).
IEEE
[1]
L. de Schaetzen van Brienen, G. Miclotte, M. Larmuseau, J. Van den Eynden, and K. Marchal, “Network-based analysis to identify drivers of metastatic prostate cancer using GoNetic,” CANCERS, vol. 13, no. 21, 2021.
@article{8727700,
  abstract     = {{Simple Summary:& nbsp;The identification of cancer driver genes is, for statistical reasons, often biased toward genes that are altered frequently in a cohort. However, genes that are less frequently mutated can also alter cancer hallmarks. To detect such rarely mutated genes involved in driving metastatic prostate cancer, we analyzed the Hartwig Medical Foundation metastatic prostate cancer cohort. Hereto, we developed GoNetic, a novel network-based method that can detect genes with a lower mutational rate as members of recurrently mutated sets of genes connected on a prior interaction network. In contrast to state-of-the-art network-based driver identification methods, GoNetic retains information on sample-specific mutations and uses more properties of the prior interaction network. When applied to the Hartwig Medical Foundation cohort, GoNetic successfully prioritized both known drivers and rarely mutated driver candidates of metastatic prostate cancer. Comprehensive validation with other public data sets further supported the driver potential of these novel candidates.& nbsp;Most known driver genes of metastatic prostate cancer are frequently mutated. To dig into the long tail of rarely mutated drivers, we performed network-based driver identification on the Hartwig Medical Foundation metastatic prostate cancer data set (HMF cohort). Hereto, we developed GoNetic, a method based on probabilistic pathfinding, to identify recurrently mutated subnetworks. In contrast to most state-of-the-art network-based methods, GoNetic can leverage sample-specific mutational information and the weights of the underlying prior network. When applied to the HMF cohort, GoNetic successfully recovered known primary and metastatic drivers of prostate cancer that are frequently mutated in the HMF cohort (TP53, RB1, and CTNNB1). In addition, the identified subnetworks contain frequently mutated genes, reflect processes related to metastatic prostate cancer, and contain rarely mutated driver candidates. To further validate these rarely mutated genes, we assessed whether the identified genes were more mutated in metastatic than in primary samples using an independent cohort. Then we evaluated their association with tumor evolution and with the lymph node status of the patients. This resulted in forwarding several novel putative driver genes for metastatic prostate cancer, some of which might be prognostic for disease evolution.}},
  articleno    = {{5291}},
  author       = {{de Schaetzen van Brienen, Louise and Miclotte, Giles and Larmuseau, Maarten and Van den Eynden, Jimmy and Marchal, Kathleen}},
  issn         = {{2072-6694}},
  journal      = {{CANCERS}},
  keywords     = {{Cancer Research,Oncology,network-based cancer data analysis,driver identification,metastatic prostate cancer,somatic mutations,SOMATIC MUTATIONS,GENES}},
  language     = {{eng}},
  number       = {{21}},
  pages        = {{25}},
  title        = {{Network-based analysis to identify drivers of metastatic prostate cancer using GoNetic}},
  url          = {{http://dx.doi.org/10.3390/cancers13215291}},
  volume       = {{13}},
  year         = {{2021}},
}

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