Multiomic Data Analytics Integration in Prostate Cancer
Summary
We plan to perform a multi-omics technologies analysis of data generated from genomic, transcriptomic, epigenomic, proteomic and metabolomics studies, in patients with prostate cancer. The aim of the study is to uncover the mechanism of the tumor progression, identifying distinct tumor subtypes and ultimately finding new prostate cancer biomarkers for effective prognosis, prevention, and therapy and monitoring of drug responses.
Introduction
Background
Prostate cancer (PCa), a hormone-dependent oncological disease, is a deadly disease for men associated with heterogeneous clinical outcome. Clinically, it is characterized by indolent phenotypes, rapid progression and aggressive metastatic disease (Sathianathen et al., 2018). Among cancer worldwide, PCa as a common male tumor has an incidence of second lung cancer (Siegel et al., 2019). This neoplastic process according to the Global Cancer Incidence, Mortality and Prevalence (GLOBOCAN) has become a leading cause of cancer worldwide, with over 1.4 million new cases and a total of 375,000 deaths in 2020 (Sung et al., 2020). The heterogeneity of the disease calls for the use of all types of omics data necessary to promote precision medicine. PCa progression is slow compared to other tumors but still harms patient long-term health (Eggener et al., 2015). The multifocal and heterogeneity of primary PCa is associated with poor prognosis (wang et al., 2018), this means that intervention for patients with metastasis are still urgently needed. While patients with slow disease progression show better outcomes, patients with higher aggressiveness show worse treatment prognosis and current clinicopathological indicators do not distinguish well between patients based on their outcomes at the initial stage of the disease (Zhang et al., 2020). Consequently, efficient identification of the risk level of prostate cancer patients and precise therapeutic targets has always been an important equation to solve in PCa. The occurrence and development of PCa is related to multisystem and multilevel pathological changes. Studies at single omics level often have limitations, while combined analysis of multiple omics data can better and more comprehensively develop targeted markers for PCa therapy. A study done by Sinha et al PCa multi omics revealed that proteomics features were significantly more informative for biochemical recurrence prediction than genomic, epigenomics or transcriptomics (Sinha et al., 2019). As much as the single technologies have identified and shed light on the mechanisms of the tumor progression, subtypes and finding new treatment targets, a holistic molecular perspective of the mechanisms, prospective biomarkers and drug targets and responses can only be uncovered when a systems biology approach is adopted. Hence in the current study we shall be using good quality multi-omics data sets from public databases to gain a better understanding of tumor progression, subtyping and finding novel biomarkers that potentially address individual variations in drug responses among prostate cancer patients. In addition, we shall also provide a simplified protocol for routine integration of multi-omics data sets to answer biological questions. Research question: Is there any promising biomarker for better management of Prostate cancer in African men descent using a multi omics approach?
Problem statement
The study of single omics has played an irreplaceable role in the diagnosis and treatment of diseases. However, with the improvement of research means and research demands, single omics may not be comprehensive enough, and changes at a single level may not necessarily represent the overall changes of the organism. For example, only 10%–20% of changes in the transcriptome correlate with proteomic data (Mertins et al., 2016). The heterogeneous clinical outcome status associated with prostate cancer can be uncovered using all single omics approaches at once in order to provide most efficient, specifics and sensitives novel metabolic signatures for a better clinical management of prostate cancer patients. Although there is a lack of data and bioinformatics skills in the country, the prostate cancer multi omics approach should be performed in African men with available data to understand the entire mechanism of the cancer for new biomarker discovery for early, comfortable and efficient screening, diagnostics and treatment.
Justification
In the field of precision oncology, genomics approaches, and, more recently, other omics analyses have helped reveal several key mechanisms in cancer development, treatment resistance, and recurrence risk, and several of these findings have been implemented in clinical oncology to help guide treatment decisions. truly integrated multi-omics analyses have not been applied widely, preventing further advances in precision medicine (Olivier et al., 2019). Despite the efforts to improve the diagnosis, risk stratification, and treatment of PCa patients, a number of challenges still need to be addressed (Gómez-Cebrián et al., 2022). Studies have shown that proteomics data explain better the complexity of prostate cancer. This study is going to apply multi omics approach data analysis using two omics data genomics and proteomics to explain PCa mechanism and provide a simplified and standardized tool for its implementation in patient management.
Objectives:
Main objective: Identify African descent biomarkers associated with Prostate cancer using multi omics available data for better management of patients.
Specifics objectives: 1.Explain prostate cancer biological complexity using multi omics approach 2.Provide a simplified and standardized African specific prostate cancer management Primary Outcome Measures
- Genetic profiling results - Mutation identified via whole-genome sequencing will be recorded.
- Transcriptional profiling results - Determining the transcriptomic information of prostate cancer
- Epigenomic profiling results - Determining the epigenomic status of specific genes
- Proteomic profiling results – determine the proteomic information of the prostate cancer
- Metabolomic profiling results – determine the profiles of the metabolites in prostate cancer patients
References
C. Manzoni, D. A. Kia, J. Vandrovcova et al., “Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences,” Briefings in Bioinformatics, vol. 19, no. 2, pp. 286–302, 2018.
Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 2021, 71, 209–249.
Sathianathen, N.J.; Konety, B.R.; Crook, J.; Saad, F.; Lawrentschuk, N. Landmarks in Prostate Cancer. Nat. Rev. Urol. 2018, 15, 627–642.
Gómez-Cebrián, N.; Poveda, J.L.; Pineda-Lucena, A.; Puchades-Carrasco, L. Metabolic Phenotyping in Prostate Cancer Using Multi-Omics Approaches. Cancers 2022, 14, 596. https://doi.org/10.3390/ cancers14030596 R.L. Siegel, K.D. Miller, A. Jemal, Cancer statistics, 2019, CA Cancer J. Clin. 69 (1) (2019), https://doi.org/10.3322/caac.21551 (PubMed PMID: 30620402).
P. Mertins, D.R. Mani, K.V. Ruggles, M.A. Gillette, K.R. Clauser, P. Wang. Proteogenomics connects somatic mutations to signalling in breast cancer, Nature 534 (7605) (2016) 55–62, https://doi.org/10.1038/nature18003 (PubMed PMID: 27251275).
H. Zhang, T. Liu, Z. Zhang, S.H. Payne, B. Zhang, J.E. McDermott, et al., Integrated proteogenomic characterization of human high-grade serous ovarian cancer, Cell 166 (3) (2016) 755–765, https://doi.org/10.1016/j.cell.2016.05.069 (PubMed PMID: 27372738).
S.E. Eggener, A.S. Cifu, C. Nabhan, Prostate cancer screening, JAMA 314 (8) (2015) 825–826, https://doi.org/10.1001/jama.2015.8033 (PubMed PMID:26305653).
G. Wang, D. Zhao, D.J. Spring, R.A. DePinho, Genetics and biology of prostate cancer, Genes Dev. 32 (17–18) (2018) 1105–1140, https://doi.org/10.1101/gad. 315739.118 (PubMed PMID: 30181359). R.L. Siegel, K.D. Miller, A. Jemal, Cancer statistics, 2019, CA Cancer J. Clin. 69 (1) (2019), https://doi.org/10.3322/caac.21551 (PubMed PMID: 30620402).
A. Sinha, V. Huang, J. Livingstone, J. Wang, N.S. Fox, N. Kurganovs, et al., The Proteogenomic landscape of curable prostate cancer, Cancer Cell 35 (3) (2019), https://doi.org/10.1016/j.ccell.2019.02.005 (PubMed PMID: 30889379).
Team task assignment Dataset Team: Glory, Page, David enoma Literature review Team: Lawrence and Marie Scripting Team: Page, Glory and Marie Writing for progress: Team: Marie Slides Team: Glory and Lawrence Manuscript Team: Lawrence and David Juma Workflow Team: Page, Vincent
Datasets
Whole genome sequencing data from an African men with aggressive prostate cancer, Project ID: PRJN412953 Targeted RNA-Seq from African population affected with prostate cancer: PRJNA531736
Workflow:
Single Omics data analysis approach
Whole Genome sequencing
RNA-Seq
Multiomics analysis approach
Supervised methods
Unsupervised methods