Emphasizing the Role of Multi-omics Approach to Increase Survival Rate of Breast and Prostate Cancer Patients

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  • Division of Biomedical and Life-Sciences, School of Science, Navrachana University, Vadodara - 391410, Gujarat ,IN
  • Division of Biomedical and Life-Sciences, School of Science, Navrachana University, Vadodara - 391410, Gujarat ,IN
  • Division of Biomedical and Life-Sciences, School of Science, Navrachana University, Vadodara - 391410, Gujarat ,IN
  • Division of Biomedical and Life-Sciences, School of Science, Navrachana University, Vadodara - 391410, Gujarat ,IN
  • Division of Biomedical and Life-Sciences, School of Science, Navrachana University, Vadodara - 391410, Gujarat ,IN




Biomarker Discovery, Cancer, Omics, Personalized Medicine, Risk Assessment


The understanding of cancer biology has greatly advanced since the advent of genomics. A remarkable heterogeneity at the whole-genome (or omics) level exists amongst even histologically comparable cancers, demonstrating the enormous complexity of the cancer genome. A powerful resource that has the potential to translate high-throughput omics to better and quick overall survival is the massive accrual and public accessibility of multi-omics databases with accompanying clinical annotation, including tumor histology, patient response, and outcome. In this new era of high-throughput omics, this paper emphasizes the distinct benefits of a multidimensional approach to genomic analysis. It discusses the implications of translational omics research for the cancer population. Single-level data analysis of high-throughput technologies has constraints because it only displays a small window of cellular processes. Understanding the links across several cellular organization levels made possible by data integration across various platforms, including genomes, epigenomics, transcriptomics, proteomics, and metabolomics, is important. This review examines a few popular frameworks for integrating multi-omics data. It provides a general overview of multi-omics applications in tumor classification, prognosis, diagnostics, and the function of data integration in searching for novel biomarkers and treatment options.


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Upadhyay, K., Patel, F., Patel, Y., Ramachandran, A. V., & Baxi, D. (2023). Emphasizing the Role of Multi-omics Approach to Increase Survival Rate of Breast and Prostate Cancer Patients. Journal of Endocrinology and Reproduction, 27(3), 147–155. https://doi.org/10.18311/jer/2023/34985



Review Article Presented at SRBCE 2023



Jeibouei S, Akbari ME, Kalbasi A, et al. Personalized medicine in breast cancer: pharmacogenomics approaches. Pharmacogenomics and Personalized Medicine. 2019; 12:59-73. https://doi.org/10.2147/PGPM.S167886. DOI: https://doi.org/10.2147/PGPM.S167886

Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians. 2021; 71(3):209-49. https://doi.org/10.3322/caac.21660. DOI: https://doi.org/10.3322/caac.21660

Sahu I, Mohapatro P. Effect Of Structured Teaching Programme On Knowledge Regarding Breast SelfExamination (Bse) Among B. Sc Nursing Students. European Journal of Molecular and Clinical Medicine. 2020; 7(11).

Chen X, Shachter RD, Kurian AW, Rubin DL. Dynamic strategy for personalized medicine: An application to metastatic breast cancer. Journal of Biomedical Informatics. 2017; 68:50-7. https://doi.org/10.1016/j.jbi.2017.02.012. DOI: https://doi.org/10.1016/j.jbi.2017.02.012

Rowan E, Poll A, Narod SA. A prospective study of breast cancer risk in relatives of BRCA1/BRCA2 mutation carriers. Journal of Medical Genetics. 2007; 44(8):e89.

Somasundaram K. BRCA1 and BRCA1 genes and inherited breast and/or ovarian cancer: benefits of genetic testing. Indian Journal of Surgical Oncology. 2010; 1:245-9. https://doi.org/10.1007/s13193-011-0049-7. DOI: https://doi.org/10.1007/s13193-011-0049-7

Chen S, Parmigiani G. Meta-analysis of BRCA1 and BRCA2 penetrance. Journal of Clinical Oncology. 2007; 25(11):1329. https://doi.org/10.1200/JCO.2006.09.1066. DOI: https://doi.org/10.1200/JCO.2006.09.1066

Alenezi WM, Fierheller CT, Recio N, Tonin PN. Literature review of BARD1 as a cancer-predisposing gene with a focus on breast and ovarian cancers.Genes. 2020; 11(8):856. https://doi.org/10.3390/genes11080856. DOI: https://doi.org/10.3390/genes11080856

Irminger-Finger I, Soriano JV, Vaudan G, et al. In vitro, repression of the Brca1-associated RING domain gene, Bard1, induces phenotypic changes in mammary epithelial cells. The Journal of Cell Biology. 1998; 143(5):1329-39. https://doi.org/10.1083/jcb.143.5.1329. DOI: https://doi.org/10.1083/jcb.143.5.1329

Moyer CL, Ivanovich J, Gillespie JL, et al. Rare BRIP1 missense alleles confer risk for ovarian and breast cancer. Cancer Research. 2020; 80(4):857-67. https://doi.org/10.1158/0008-5472.CAN-19-1991. DOI: https://doi.org/10.1158/0008-5472.CAN-19-1991

Wu L, Wu Y, Gathings B, et al. Smad4 as a transcription corepressor for estrogen receptor α. Journal of Biological Chemistry. 2003; 278(17):15192-200. https://doi.org/10.1074/jbc.M212332200. DOI: https://doi.org/10.1074/jbc.M212332200

Zhong D, Morikawa A, Guo L, et al. Homozygous deletion of SMAD4 in breast cancer cell lines and invasive ductal carcinomas. Cancer Biology and Therapy. 2006; 5(6):601-7. https://doi.org/10.4161/cbt.5.6.2660.

Deckers M, van Dinther M, Buijs J, et al. The tumor suppressor Smad4 is required for transforming growth factor β–induced epithelial to mesenchymal transition and bone metastasis of breast cancer cells. Cancer Research. 2006; 66(4):2202-9. https://doi.org/10.1158/0008-5472.CAN-05-3560. DOI: https://doi.org/10.1158/0008-5472.CAN-05-3560

Stuelten CH, Buck MB, Dippon J, et al. Smad4expression is decreased in breast cancer tissues: a retrospective study. BMC cancer. 2006; 6(1):1-0. https://doi.org/10.1186/1471-2407-6-25. DOI: https://doi.org/10.1186/1471-2407-6-25

Rahman N, Seal S, Thompson D, et al. PALB2, which encodes a BRCA2-interacting protein, is a breast cancer susceptibility gene. Nature Genetics. 2007; 39(2):165-7. https://doi.org/10.1038/ng1959. DOI: https://doi.org/10.1038/ng1959

Zhang F, Fan Q, Ren K, et al. PALB2 Functionally Connects the Breast Cancer Susceptibility Proteins BRCA1 and BRCA2PALB2 Connects BRCA1 and BRCA2. Molecular Cancer Research. 2009; 7(7):1110-8.

https://doi.org/10.1158/1541-7786.MCR-09-0123. DOI: https://doi.org/10.1158/1541-7786.MCR-09-0123

Foo TK, Tischkowitz M, Simhadri S, et al. Compromised BRCA1–PALB2 interaction is associated with breast cancer risk. Oncogene. 2017; 36(29):4161-70. https:// doi.org/10.1038/onc.2017.46. DOI: https://doi.org/10.1038/onc.2017.46

Bagherzadeh M, Szymiczek A, Donenberg T, et al. Association of RAD51C germline mutations with breast cancer among Bahamians. Breast cancer research and treatment. 2020; 184:649-51. https://doi.org/10.1007/s10549-020-05872-3. DOI: https://doi.org/10.1007/s10549-020-05872-3

Konstanta I, Fostira F, Apostolou P, et al. Contribution of RAD51D germline mutations in breast and ovarian cancer in Greece. Journal of Human Genetics. 2018; 63(11):1149-58. https://doi.org/10.1038/s10038-0180498-8. DOI: https://doi.org/10.1038/s10038-018-0498-8

Yang X, Song H, Leslie G, et al. Ovarian and breast cancer risks associated with pathogenic variants in RAD51C and RAD51D.Journal of the National Cancer Institute. 2020; 112(12):1242-50. https://doi.org/10.1093/jnci/djaa030. DOI: https://doi.org/10.1093/jnci/djaa030

Tien JF, Mazloomian A, Cheng SW, et al. CDK12 regulates alternative last exon mRNA splicing and promotes breast cancer cell invasion. Nucleic Acids Research. 2017; 45(11):6698-716. https://doi.org/10.1093/nar/ gkx187. DOI: https://doi.org/10.1093/nar/gkx187

Naidoo K, Wai PT, Maguire SL, et al. Evaluation of CDK12 protein expression as a potential novel bio-marker for DNA damage response–targeted therapies in breast cancer. Molecular Cancer Therapeutics. 2018; 17(1):306-15. https://doi.org/10.1158/1535-7163.MCT17-0760. DOI: https://doi.org/10.1158/1535-7163.MCT-17-0760

Pharoah PD, Guilford P, Caldas C. International Gastric Cancer Linkage Consortium. Incidence of gastric cancer and breast cancer in CDH1 (E-cadherin) mutation carriers from hereditary diffuse gastric cancer families. Gastroenterology. 2001; 121(6):1348-53. https://doi.org/10.1053/gast.2001.29611. DOI: https://doi.org/10.1053/gast.2001.29611

Mukherjee N, Bhattacharya N, Sinha S, et al. Association of APC and MCC polymorphisms with increased breast cancer risk in an Indian population. The International Journal of Biological Markers. 2011; 26(1):43-9. https://doi.org/10.5301/JBM.2011.6266. DOI: https://doi.org/10.5301/JBM.2011.6266

Makridakis M, Vlahou A. Secretome proteomics for discovery of cancer biomarkers. Journal of Proteomics. 2010; 73(12):2291-305. https://doi.org/10.1016/j.jprot.2010.07.001. DOI: https://doi.org/10.1016/j.jprot.2010.07.001

Rodini CO, Lopes NM, Lara VS, Mackenzie IC. Oral cancer stem cells-properties and consequences. Journal of Applied Oral Science. 2017; 25:708-15. https://doi.org/10.1590/1678-7757-2016-0665. DOI: https://doi.org/10.1590/1678-7757-2016-0665

Song M, Giovannucci EL. Cancer risk: many factors contribute. Science. 2015; 347(6223):728-9. https://doi.org/10.1126/science.aaa6094. DOI: https://doi.org/10.1126/science.aaa6094

Zhong D, Morikawa A, Guo L, et al. Homozygous deletion of SMAD4 in breast cancer cell lines and invasive ductal carcinomas. Cancer Biology and Therapy. 2006; 5(6):601-7. https://doi.org/10.4161/cbt.5.6.2660. DOI: https://doi.org/10.4161/cbt.5.6.2660

Scumaci D, Tamme L, Fiumara CV, et al. Plasma proteomic profiling in hereditary breast cancer reveals a BRCA1-specific signature: diagnostic and functional implications. PloS One. 2015; 10(6):e0129762. https://doi.org/10.1371/journal.pone.0129762. DOI: https://doi.org/10.1371/journal.pone.0129762

Asiago VM, Alvarado LZ, Shanaiah N, et al. Early detection of recurrent breast cancer using metabolite profiling. Cancer Research. 2010; 70(21):8309-18. https://doi.org/10.1158/0008-5472.CAN-10-1319. DOI: https://doi.org/10.1158/0008-5472.CAN-10-1319

Oakman C, Tenori L, Claudino WM, et al. Identification of a serum-detectable metabolomic fingerprint potentially correlated with the presence of micrometastatic disease in early breast cancer patients at varying risks of disease relapse by traditional prognostic methods. Annals of Oncology. 2011; 22(6):1295-301. https://doi.org/10.1093/annonc/mdq606. DOI: https://doi.org/10.1093/annonc/mdq606

Slupsky CM, Steed H, Wells TH, et al. Urine Metabolite Analysis Offers Potential Early Diagnosis of Ovarian and Breast Cancers-Early Diagnosis of Breast and Ovarian Cancers. Clinical Cancer Research. 2010; 16(23):583541. https://doi.org/10.1158/1078-0432.CCR-10-1434. DOI: https://doi.org/10.1158/1078-0432.CCR-10-1434

Budczies J, Denkert C, Müller BM, et al. Remodeling of central metabolism in invasive breast cancer compared to normal breast tissue–a GC-TOFMS based metabolomics study. BMC genomics. 2012; 13(1):1-1. https://doi.org/10.1186/1471-2164-13-334. DOI: https://doi.org/10.1186/1471-2164-13-334

Jové M, Collado R, Quiles JL, et al. A plasma metabolomic signature discloses human breast cancer. Oncotarget. 2017; 8(12):19522. https://doi.org/10.18632/oncotarget.14521. DOI: https://doi.org/10.18632/oncotarget.14521

Wang Q, Sun T, Cao Y, et al. A dried blood spot mass spectrometry metabolomic approach for rapid breast cancer detection. OncoTargets and Therapy. 2016; 11:1389-98. https://doi.org/10.2147/OTT.S95862. DOI: https://doi.org/10.2147/OTT.S95862

Cavaco C, Pereira JA, Taunk K, et al. Screening of salivary volatiles for putative breast cancer discrimination: An exploratory study involving geographically distant populations. Analytical and Bioanalytical Chemistry. 2018; 410:4459-68. https://doi.org/10.1007/s00216-0181103-x. DOI: https://doi.org/10.1007/s00216-018-1103-x

Takayama T, Tsutsui H, Shimizu I, et al. Diagnostic approach to breast cancer patients based on target metabolomics in saliva by liquid chromatography with tandem mass spectrometry. Clinica Chimica Acta. 2016; 452:18-26. https://doi.org/10.1016/j.cca.2015.10.032. DOI: https://doi.org/10.1016/j.cca.2015.10.032

Abida W, Cheng ML, Armenia J, et al. Analysis of the prevalence of microsatellite instability in prostate cancer and response to immune checkpoint blockade. JAMA Oncology. 2019; 5(4):471-8. https://doi.org/10.1001/jamaoncol.2018.5801. DOI: https://doi.org/10.1001/jamaoncol.2018.5801

Xie T, Song XL, Wang C, et al. The role of androgen therapy in prostate cancer: from testosterone replacement therapy to bipolar androgen therapy. Drug Discovery Today. 2021; 26(5):1293-301. https://doi.org/10.1016/j.drudis.2021.01.034. DOI: https://doi.org/10.1016/j.drudis.2021.01.034

Bastos DC, Ribeiro CF, Ahearn T, et al. Genetic ablation of FASN attenuates the invasive potential of prostate cancer driven by Pten loss. The Journal of Pathology. 2021; 253(3):292-303. https://doi.org/10.1002/path.5587. DOI: https://doi.org/10.1002/path.5587

Chen M, Zhang J, Sampieri K, et al. An aberrant SREBPdependent lipogenic program promotes metastatic prostate cancer. Nature Genetics. 2018; 50(2):206-18. https://doi.org/10.1038/s41588-017-0027-2. DOI: https://doi.org/10.1038/s41588-017-0027-2

Maughan BL, Guedes LB, Boucher K, et al. p53 status in the primary tumor predicts efficacy of subsequent abiraterone and enzalutamide in castration-resistant prostate cancer. Prostate Cancer and Prostatic Diseases. 2018; 21(2):260-8. https://doi.org/10.1038/s41391-0170027-4. DOI: https://doi.org/10.1038/s41391-017-0027-4

Chappell WH, Candido S, Abrams SL, et al. Roles of p53, NF-κB and the androgen receptor in controlling NGAL expression in prostate cancer cell lines. Advances in Biological Regulation. 2018; 1;69:43-62. https://doi.org/10.1016/j.jbior.2018.05.002. DOI: https://doi.org/10.1016/j.jbior.2018.05.002

Barfeld SJ, Urbanucci A, Itkonen HM, et al. c-Myc antagonizes the transcriptional activity of the androgen receptor in prostate cancer affecting key gene networks. EBioMedicine. 2017; 18:83-93. https://doi.org/10.1016/j.ebiom.2017.04.006. DOI: https://doi.org/10.1016/j.ebiom.2017.04.006

Long T, Hicks M, Yu HC, et al. Whole-genome sequencing identifies common-to-rare variants associated with human blood metabolites. Nature Genetics. 2017; 49(4):568-78. https://doi.org/10.1038/ng.3809. DOI: https://doi.org/10.1038/ng.3809

Pértega-Gomes N, Baltazar F. Lactate transporters in the context of prostate cancer metabolism: what do we know? International Journal of Molecular Sciences. 2014; 15(10):18333-48. https://doi.org/10.3390/ ijms151018333. DOI: https://doi.org/10.3390/ijms151018333

Tsouko E, Khan AS, White MA, et al. Regulation of the pentose phosphate pathway by an androgen receptor–mTOR-mediated mechanism and its role in prostate cancer cell growth. Oncogenesis. 2014; 3(5):e103-. https://doi.org/10.1038/oncsis.2014.18. DOI: https://doi.org/10.1038/oncsis.2014.18

Huang WC, Zhau HE, Chung LW. Androgen receptor survival signaling is blocked by anti-β2-microglobulin monoclonal antibody via a MAPK/lipogenic pathway in human prostate cancer cells. Journal of Biological Chemistry. 2010; 285(11):7947-56. https://doi.org/10.1074/jbc.M109.092759. DOI: https://doi.org/10.1074/jbc.M109.092759

Fiaschi T, Marini A, Giannoni E, et al. Reciprocal Metabolic Reprogramming through Lactate Shuttle Coordinately Influences Tumor-Stroma InterplayTumorStroma Metabolic Reprogramming. Cancer Research. 2012; 72(19):5130-40. https://doi.org/10.1158/00085472.CAN-12-1949. DOI: https://doi.org/10.1158/0008-5472.CAN-12-1949

Cernei N, Heger Z, Gumulec J, et al. Sarcosine as a potential prostate cancer biomarker—A review. International Journal of Molecular Sciences. 2013; 14(7):13893-908. https://doi.org/10.3390/ijms140713893. DOI: https://doi.org/10.3390/ijms140713893

Sreekumar A, Poisson LM, Rajendiran TM, et al. Metabolomic profiles delineate the potential role for sarcosine in prostate cancer progression. Nature. 2009; 457(7231):910-4. https://doi.org/10.1038/nature07762. DOI: https://doi.org/10.1038/nature07762

Wu H, Liu T, Ma C, et al. GC/MS-based metabolomic approach to validate the role of urinary sarcosine and target biomarkers for human prostate cancer by microwave-assisted derivatization. Analytical and Bioanalytical Chemistry. 2011; 401:635-46. https://doi.org/10.1007/s00216-011-5098-9. DOI: https://doi.org/10.1007/s00216-011-5098-9

Jentzmik F, Stephan C, Miller K, et al. Sarcosine in urine after digital rectal examination fails as a marker in prostate cancer detection and identification of aggressive tumors. European Urology. 2010; 58(1):12-8. https://doi.org/10.1016/j.eururo.2010.01.035. DOI: https://doi.org/10.1016/j.eururo.2010.01.035

Wright JL, Plymate SR, Porter MP, et al. Hyperglycemia and prostate cancer recurrence in men treated for localized prostate cancer. Prostate Cancer and Prostatic Diseases. 2013; 16(2):204-8. https://doi.org/10.1038/pcan.2013.5. DOI: https://doi.org/10.1038/pcan.2013.5

Saylor PJ, Karoly ED, Smith MR. Prospective Study of Changes in the Metabolomic Profiles of Men during Their First Three Months of Androgen Deprivation Therapy for Prostate Cancer Metabolomic Changes during ADT. Clinical Cancer Research. 2012; 18(13):3677-85. https://doi.org/10.1158/10780432.CCR-11-3209. DOI: https://doi.org/10.1158/1078-0432.CCR-11-3209

Kline EE, Treat EG, Averna TA, et al. Citrate concentrations in human seminal fluid and expressed

prostatic fluid determined via 1H nuclear magnetic resonance spectroscopy outperform prostate-specific

antigen in prostate cancer detection. The Journal of Urology. 2006; 176(5):2274-9. https://doi.org/10.1016/j.juro.2006.07.054. DOI: https://doi.org/10.1016/j.juro.2006.07.054

Gregório EP, Alexandrino AP, Schuquel IT, et al. Seminal citrate is superior to PSA for detecting clinically significant prostate cancer. International Brazilian Journal of Urology. 2019; 45:1113-21. https://doi.org/10.1016/j.juro.2006.07.054. DOI: https://doi.org/10.1590/s1677-5538.ibju.2018.0730

Roberts MJ, Richards RS, Gardiner RA, Selth LA. Seminal fluid: a useful source of prostate cancer biomarkers? Biomarkers in Medicine. 2015; 9(2):77-80. https://doi.org/10.2217/bmm.14.110. DOI: https://doi.org/10.2217/bmm.14.110

Lodi A, Ronen SM. Magnetic resonance spectroscopy detectable metabolomic fingerprint of response to antineoplastic treatment. PloS One. 2011; 6(10):e26155. https://doi.org/10.1371/journal.pone.0026155. DOI: https://doi.org/10.1371/journal.pone.0026155

Kailavasan M, Rehman I, Reynolds S, et al. NMR‐based evaluation of the metabolic profile and response to dichloroacetate of human prostate cancer cells. NMR in Biomedicine. 2014; 27(5):610-6. https://doi.org/10.1002/nbm.3101. DOI: https://doi.org/10.1002/nbm.3101

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