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Olivier M, Asmis R, Hawkins GA, Howard TD, Cox LA. The Need for Multi-Omics Biomarker Signatures in Precision Medicine. Int J Mol Sci 2019; 20:ijms20194781. [PMID: 31561483 PMCID: PMC6801754 DOI: 10.3390/ijms20194781] [Citation(s) in RCA: 242] [Impact Index Per Article: 48.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 09/11/2019] [Accepted: 09/25/2019] [Indexed: 12/12/2022] Open
Abstract
Recent advances in omics technologies have led to unprecedented efforts characterizing the molecular changes that underlie the development and progression of a wide array of complex human diseases, including cancer. As a result, multi-omics analyses—which take advantage of these technologies in genomics, transcriptomics, epigenomics, proteomics, metabolomics, and other omics areas—have been proposed and heralded as the key to advancing precision medicine in the clinic. 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. However, truly integrated multi-omics analyses have not been applied widely, preventing further advances in precision medicine. Additional efforts are needed to develop the analytical infrastructure necessary to generate, analyze, and annotate multi-omics data effectively to inform precision medicine-based decision-making.
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Affiliation(s)
- Michael Olivier
- Center for Precision Medicine, Department of Internal Medicine, Wake Forest Baptist Health Comprehensive Cancer Center, Wake Forest University Health Sciences, Winston-Salem, NC 27157, USA.
| | - Reto Asmis
- Center for Precision Medicine, Department of Internal Medicine, Wake Forest Baptist Health Comprehensive Cancer Center, Wake Forest University Health Sciences, Winston-Salem, NC 27157, USA.
| | - Gregory A Hawkins
- Center for Precision Medicine, Department of Biochemistry, Wake Forest Baptist Health Comprehensive Cancer Center, Wake Forest University Health Sciences, Winston-Salem, NC 27157, USA.
| | - Timothy D Howard
- Center for Precision Medicine, Department of Biochemistry, Wake Forest University Health Sciences, Winston-Salem, NC 27157, USA.
| | - Laura A Cox
- Center for Precision Medicine, Department of Internal Medicine, Wake Forest Baptist Health Comprehensive Cancer Center, Wake Forest University Health Sciences, Winston-Salem, NC 27157, USA.
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Briffa R, Um I, Faratian D, Zhou Y, Turnbull AK, Langdon SP, Harrison DJ. Multi-Scale Genomic, Transcriptomic and Proteomic Analysis of Colorectal Cancer Cell Lines to Identify Novel Biomarkers. PLoS One 2015; 10:e0144708. [PMID: 26678268 PMCID: PMC4692059 DOI: 10.1371/journal.pone.0144708] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2015] [Accepted: 11/23/2015] [Indexed: 12/18/2022] Open
Abstract
Selecting colorectal cancer (CRC) patients likely to respond to therapy remains a clinical challenge. The objectives of this study were to establish which genes were differentially expressed with respect to treatment sensitivity and relate this to copy number in a panel of 15 CRC cell lines. Copy number variations of the identified genes were assessed in a cohort of CRCs. IC50's were measured for 5-fluorouracil, oxaliplatin, and BEZ-235, a PI3K/mTOR inhibitor. Cell lines were profiled using array comparative genomic hybridisation, Illumina gene expression analysis, reverse phase protein arrays, and targeted sequencing of KRAS hotspot mutations. Frequent gains were observed at 2p, 3q, 5p, 7p, 7q, 8q, 12p, 13q, 14q, and 17q and losses at 2q, 3p, 5q, 8p, 9p, 9q, 14q, 18q, and 20p. Frequently gained regions contained EGFR, PIK3CA, MYC, SMO, TRIB1, FZD1, and BRCA2, while frequently lost regions contained FHIT and MACROD2. TRIB1 was selected for further study. Gene enrichment analysis showed that differentially expressed genes with respect to treatment response were involved in Wnt signalling, EGF receptor signalling, apoptosis, cell cycle, and angiogenesis. Stepwise integration of copy number and gene expression data yielded 47 candidate genes that were significantly correlated. PDCD6 was differentially expressed in all three treatment responses. Tissue microarrays were constructed for a cohort of 118 CRC patients and TRIB1 and MYC amplifications were measured using fluorescence in situ hybridisation. TRIB1 and MYC were amplified in 14.5% and 7.4% of the cohort, respectively, and these amplifications were significantly correlated (p≤0.0001). TRIB1 protein expression in the patient cohort was significantly correlated with pERK, Akt, and Caspase 3 expression. In conclusion, a set of candidate predictive biomarkers for 5-fluorouracil, oxaliplatin, and BEZ235 are described that warrant further study. Amplification of the putative oncogene TRIB1 has been described for the first time in a cohort of CRC patients.
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Affiliation(s)
- Romina Briffa
- Division of Pathology, Institute of Genetics and Molecular Medicine,
University of Edinburgh, Crewe Road South, Edinburgh, EH4 2XU, United
Kingdom
| | - Inhwa Um
- School of Medicine, University of St Andrews, St Andrews, KY16 9TF, United
Kingdom
| | - Dana Faratian
- Division of Pathology, Institute of Genetics and Molecular Medicine,
University of Edinburgh, Crewe Road South, Edinburgh, EH4 2XU, United
Kingdom
| | - Ying Zhou
- Division of Pathology, Institute of Genetics and Molecular Medicine,
University of Edinburgh, Crewe Road South, Edinburgh, EH4 2XU, United
Kingdom
| | - Arran K. Turnbull
- Division of Pathology, Institute of Genetics and Molecular Medicine,
University of Edinburgh, Crewe Road South, Edinburgh, EH4 2XU, United
Kingdom
| | - Simon P. Langdon
- Division of Pathology, Institute of Genetics and Molecular Medicine,
University of Edinburgh, Crewe Road South, Edinburgh, EH4 2XU, United
Kingdom
| | - David J. Harrison
- School of Medicine, University of St Andrews, St Andrews, KY16 9TF, United
Kingdom
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Dmitriev AA, Rosenberg EE, Krasnov GS, Gerashchenko GV, Gordiyuk VV, Pavlova TV, Kudryavtseva AV, Beniaminov AD, Belova AA, Bondarenko YN, Danilets RO, Glukhov AI, Kondratov AG, Alexeyenko A, Alekseev BY, Klein G, Senchenko VN, Kashuba VI. Identification of Novel Epigenetic Markers of Prostate Cancer by NotI-Microarray Analysis. DISEASE MARKERS 2015; 2015:241301. [PMID: 26491211 PMCID: PMC4602334 DOI: 10.1155/2015/241301] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2015] [Revised: 07/11/2015] [Accepted: 07/14/2015] [Indexed: 12/30/2022]
Abstract
A significant need for reliable and accurate cancer diagnostics and prognosis compels the search for novel biomarkers that would be able to discriminate between indolent and aggressive tumors at the early stages of disease. The aim of this work was identification of potential diagnostic biomarkers for characterization of different types of prostate tumors. NotI-microarrays with 180 clones associated with chromosome 3 genes/loci were applied to determine genetic and epigenetic alterations in 33 prostate tumors. For 88 clones, aberrations were detected in more than 10% of tumors. The major types of alterations were DNA methylation and/or deletions. Frequent methylation of the discovered loci was confirmed by bisulfite sequencing on selective sampling of genes: FGF12, GATA2, and LMCD1. Three genes (BHLHE40, BCL6, and ITGA9) were tested for expression level alterations using qPCR, and downregulation associated with hypermethylation was shown in the majority of tumors. Based on these data, we proposed the set of potential biomarkers for detection of prostate cancer and discrimination between prostate tumors with different malignancy and aggressiveness: BHLHE40, FOXP1, LOC285205, ITGA9, CTDSPL, FGF12, LOC440944/SETD5, VHL, CLCN2, OSBPL10/ZNF860, LMCD1, FAM19A4, CAND2, MAP4, KY, and LRRC58. Moreover, we probabilistically estimated putative functional relations between the genes within each set using the network enrichment analysis.
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Affiliation(s)
- Alexey A. Dmitriev
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow 119991, Russia
- P.A. Herzen Moscow Cancer Research Institute, Ministry of Healthcare of the Russian Federation, Moscow 125284, Russia
| | - Eugenia E. Rosenberg
- Institute of Molecular Biology and Genetics, National Academy of Sciences of Ukraine, Kiev 03680, Ukraine
| | - George S. Krasnov
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow 119991, Russia
| | - Ganna V. Gerashchenko
- Institute of Molecular Biology and Genetics, National Academy of Sciences of Ukraine, Kiev 03680, Ukraine
| | - Vasily V. Gordiyuk
- Institute of Molecular Biology and Genetics, National Academy of Sciences of Ukraine, Kiev 03680, Ukraine
| | - Tatiana V. Pavlova
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institute, 17177 Stockholm, Sweden
| | - Anna V. Kudryavtseva
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow 119991, Russia
| | - Artemy D. Beniaminov
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow 119991, Russia
| | - Anastasia A. Belova
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow 119991, Russia
| | - Yuriy N. Bondarenko
- Institute of Urology, National Academy of Medical Sciences of Ukraine, Kiev 04053, Ukraine
| | - Rostislav O. Danilets
- Institute of Urology, National Academy of Medical Sciences of Ukraine, Kiev 04053, Ukraine
| | - Alexander I. Glukhov
- Department of Molecular Biology, Kurchatov NBIC Centre NRC “Kurchatov Institute”, Moscow 123182, Russia
| | - Aleksandr G. Kondratov
- Institute of Molecular Biology and Genetics, National Academy of Sciences of Ukraine, Kiev 03680, Ukraine
| | - Andrey Alexeyenko
- Bioinformatics Infrastructure for Life Sciences, Science for Life Laboratory, Karolinska Institute, 17177 Stockholm, Sweden
| | - Boris Y. Alekseev
- P.A. Herzen Moscow Cancer Research Institute, Ministry of Healthcare of the Russian Federation, Moscow 125284, Russia
| | - George Klein
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institute, 17177 Stockholm, Sweden
| | - Vera N. Senchenko
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow 119991, Russia
| | - Vladimir I. Kashuba
- Institute of Molecular Biology and Genetics, National Academy of Sciences of Ukraine, Kiev 03680, Ukraine
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institute, 17177 Stockholm, Sweden
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Wik E, Trovik J, Kusonmano K, Birkeland E, Raeder MB, Pashtan I, Hoivik EA, Krakstad C, Werner HMJ, Holst F, Mjøs S, Halle MK, Mannelqvist M, Mauland KK, Oyan AM, Stefansson IM, Petersen K, Simon R, Cherniack AD, Meyerson M, Kalland KH, Akslen LA, Salvesen HB. Endometrial Carcinoma Recurrence Score (ECARS) validates to identify aggressive disease and associates with markers of epithelial-mesenchymal transition and PI3K alterations. Gynecol Oncol 2014; 134:599-606. [PMID: 24995579 DOI: 10.1016/j.ygyno.2014.06.026] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2014] [Revised: 06/21/2014] [Accepted: 06/25/2014] [Indexed: 10/25/2022]
Abstract
PURPOSE Our previously reported 29-gene expression signature identified an aggressive subgroup of endometrial cancer patients with PI3K activation. We here wanted to validate these findings by independent patient series. PATIENTS AND METHODS The 29-gene expression signature was assessed in fresh frozen tumor tissue from 280 primary endometrial carcinomas (three independent cohorts), 19 metastatic lesions and in 333 primary endometrial carcinomas using TCGA data, and expression was related to clinico-pathologic features and survival. The 29-gene signature was assessed by real-time quantitative PCR, DNA oligonucleotide microarrays, or RNA sequencing. PI3K alterations were assessed by immunohistochemistry, DNA microarrays, DNA sequencing, SNP arrays or fluorescence in situ hybridization. A panel of markers of epithelial-mesenchymal transition (EMT) was also correlated to the 29-gene signature score. RESULTS High 29-gene Endometrial Carcinoma Recurrence Score (ECARS) values consistently validated to identify patients with aggressive clinico-pathologic phenotype and reduced survival. Within the presumed favorable subgroups of low grade, endometrioid tumors confined to the uterus, high ECARS still predicted a poor prognosis. The score was higher in metastatic compared to primary lesions (P<0.001) and was significantly associated with potential measures of PI3K activation, markers of EMT and vascular invasion as an indicator of metastatic spread (all P<0.001). CONCLUSIONS ECARS validates to identify aggressive endometrial carcinomas in multiple, independent patients cohorts. The higher signature score in metastatic compared to primary lesions, and the potential link to PI3K activation and EMT, support further studies of ECARS in relation to response to PI3K and EMT inhibitors in clinical trials of metastatic endometrial carcinoma.
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Affiliation(s)
- E Wik
- Centre for Cancer Biomarkers CCBIO, Department of Clinical Medicine, University of Bergen, Norway; Department of Pathology, The Gade Institute, Haukeland University Hospital, Bergen, Norway.
| | - J Trovik
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway; Centre for Cancer Biomarkers CCBIO, Department of Clinical Science, University of Bergen, Norway
| | - K Kusonmano
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway; Computational Biology Unit, University of Bergen, Bergen, Norway
| | - E Birkeland
- Centre for Cancer Biomarkers CCBIO, Department of Clinical Medicine, University of Bergen, Norway; Department of Pathology, The Gade Institute, Haukeland University Hospital, Bergen, Norway
| | - M B Raeder
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway; Centre for Cancer Biomarkers CCBIO, Department of Clinical Science, University of Bergen, Norway
| | - I Pashtan
- Department of Radiation Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, MA, USA
| | - E A Hoivik
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway; Centre for Cancer Biomarkers CCBIO, Department of Clinical Science, University of Bergen, Norway
| | - C Krakstad
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway; Centre for Cancer Biomarkers CCBIO, Department of Clinical Science, University of Bergen, Norway
| | - H M J Werner
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway; Centre for Cancer Biomarkers CCBIO, Department of Clinical Science, University of Bergen, Norway
| | - F Holst
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway; Centre for Cancer Biomarkers CCBIO, Department of Clinical Science, University of Bergen, Norway
| | - S Mjøs
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway; Centre for Cancer Biomarkers CCBIO, Department of Clinical Science, University of Bergen, Norway
| | - M K Halle
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway; Centre for Cancer Biomarkers CCBIO, Department of Clinical Science, University of Bergen, Norway
| | - M Mannelqvist
- Centre for Cancer Biomarkers CCBIO, Department of Clinical Medicine, University of Bergen, Norway; Department of Pathology, The Gade Institute, Haukeland University Hospital, Bergen, Norway
| | - K K Mauland
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway; Centre for Cancer Biomarkers CCBIO, Department of Clinical Science, University of Bergen, Norway
| | - A M Oyan
- Department of Microbiology, Haukeland University Hospital, Bergen, Norway
| | - I M Stefansson
- Centre for Cancer Biomarkers CCBIO, Department of Clinical Medicine, University of Bergen, Norway; Department of Pathology, The Gade Institute, Haukeland University Hospital, Bergen, Norway
| | - K Petersen
- Computational Biology Unit, University of Bergen, Bergen, Norway
| | - R Simon
- Department of Pathology, University Medical Center Hamburg Eppendorf, Hamburg, Germany
| | - A D Cherniack
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - M Meyerson
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02115, USA; Department of Pathology, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - K H Kalland
- Centre for Cancer Biomarkers CCBIO, Department of Clinical Science, University of Bergen, Norway; Department of Microbiology, Haukeland University Hospital, Bergen, Norway
| | - L A Akslen
- Centre for Cancer Biomarkers CCBIO, Department of Clinical Medicine, University of Bergen, Norway; Department of Pathology, The Gade Institute, Haukeland University Hospital, Bergen, Norway
| | - H B Salvesen
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway; Centre for Cancer Biomarkers CCBIO, Department of Clinical Science, University of Bergen, Norway
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Mäbert K, Cojoc M, Peitzsch C, Kurth I, Souchelnytskyi S, Dubrovska A. Cancer biomarker discovery: current status and future perspectives. Int J Radiat Biol 2014; 90:659-77. [PMID: 24524284 DOI: 10.3109/09553002.2014.892229] [Citation(s) in RCA: 79] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
PURPOSE Cancer is a multigene disease which arises as a result of mutational and epigenetic changes coupled with activation of complex signaling networks. The use of biomarkers for early cancer detection, staging and individualization of therapy might improve patient care. A few fundamental issues such as tumor heterogeneity, a highly dynamic nature of the intrinsic and extrinsic determinants of radio- and chemoresistance, along with the plasticity and diversity of cancer stem cells (CSC) make biomarker development a challenging task. In this review we outline the preclinical strategies of cancer biomarker discovery including genomic, proteomic, metabolomic and microRNomic profiling, comparative genome hybridization (CGH), single nucleotide polymorphism (SNP) analysis, high throughput screening (HTS) and next generation sequencing (NGS). Other promising approaches such as assessment of circulating tumor cells (CTC), analysis of CSC-specific markers and cell-free circulating tumor DNA (ctDNA) are also discussed. CONCLUSIONS The emergence of powerful proteomic and genomic technologies in conjunction with advanced bioinformatic tools allows the simultaneous analysis of thousands of biological molecules. These techniques yield the discovery of new tumor signatures, which are sensitive and specific enough for early cancer detection, for monitoring disease progression and for proper treatment selection, paving the way to individualized cancer treatment.
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Affiliation(s)
- Katrin Mäbert
- OncoRay-National Center for Radiation Research in Oncology, Medical Faculty Dresden Carl Gustav Carus , TU Dresden , Germany
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Highly accurate two-gene signature for gastric cancer. Med Oncol 2013; 30:584. [DOI: 10.1007/s12032-013-0584-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2013] [Accepted: 04/11/2013] [Indexed: 12/12/2022]
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Qian L, Zheng H, Zhou H, Qin R, Li J. Classification of time series gene expression in clinical studies via integration of biological network. PLoS One 2013; 8:e58383. [PMID: 23516469 PMCID: PMC3596388 DOI: 10.1371/journal.pone.0058383] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2012] [Accepted: 02/04/2013] [Indexed: 12/24/2022] Open
Abstract
The increasing availability of time series expression datasets, although promising, raises a number of new computational challenges. Accordingly, the development of suitable classification methods to make reliable and sound predictions is becoming a pressing issue. We propose, here, a new method to classify time series gene expression via integration of biological networks. We evaluated our approach on 2 different datasets and showed that the use of a hidden Markov model/Gaussian mixture models hybrid explores the time-dependence of the expression data, thereby leading to better prediction results. We demonstrated that the biclustering procedure identifies function-related genes as a whole, giving rise to high accordance in prognosis prediction across independent time series datasets. In addition, we showed that integration of biological networks into our method significantly improves prediction performance. Moreover, we compared our approach with several state-of-the-art algorithms and found that our method outperformed previous approaches with regard to various criteria. Finally, our approach achieved better prediction results on early-stage data, implying the potential of our method for practical prediction.
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Affiliation(s)
- Liwei Qian
- School of Computer Science and Technology, University of Science and Technology of China, Hefei, People's Republic of China
| | - Haoran Zheng
- School of Computer Science and Technology, University of Science and Technology of China, Hefei, People's Republic of China
- Anhui Key Laboratory of Software Engineering in Computing and Communication, University of Science and Technology of China, Hefei, People's Republic of China
- Department of Systems Biology, University of Science and Technology of China, Hefei, People's Republic of China
- * E-mail:
| | - Hong Zhou
- School of Computer Science and Technology, University of Science and Technology of China, Hefei, People's Republic of China
| | - Ruibin Qin
- School of Computer Science and Technology, University of Science and Technology of China, Hefei, People's Republic of China
| | - Jinlong Li
- School of Computer Science and Technology, University of Science and Technology of China, Hefei, People's Republic of China
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