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Li M, Cai Y, Zhang M, Deng S, Wang L. NNBGWO-BRCA marker: Neural Network and binary grey wolf optimization based Breast cancer biomarker discovery framework using multi-omics dataset. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 254:108291. [PMID: 38909399 DOI: 10.1016/j.cmpb.2024.108291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 05/09/2024] [Accepted: 06/16/2024] [Indexed: 06/25/2024]
Abstract
BACKGROUND AND OBJECTIVE Breast cancer is a multifaceted condition characterized by diverse features and a substantial mortality rate, underscoring the imperative for timely detection and intervention. The utilization of multi-omics data has gained significant traction in recent years to identify biomarkers and classify subtypes in breast cancer. This kind of research idea from part to whole will also be an inevitable trend in future life science research. Deep learning can integrate and analyze multi-omics data to predict cancer subtypes, which can further drive targeted therapies. However, there are few articles leveraging the nature of deep learning for feature selection. Therefore, this paper proposes a Neural Network and Binary grey Wolf Optimization based BReast CAncer bioMarker (NNBGWO-BRCAMarker) discovery framework using multi-omics data to obtain a series of biomarkers for precise classification of breast cancer subtypes. METHODS NNBGWO-BRCAMarker consists of two phases: in the first phase, relevant genes are selected using the weights obtained from a trained feedforward neural network; in the second phase, the binary grey wolf optimization algorithm is leveraged to further screen the selected genes, resulting in a set of potential breast cancer biomarkers. RESULTS The SVM classifier with RBF kernel achieved a classification accuracy of 0.9242 ± 0.03 when trained using the 80 biomarkers identified by NNBGWO-BRCAMarker, as evidenced by the experimental results. We conducted a comprehensive gene set analysis, prognostic analysis, and druggability analysis, unveiling 25 druggable genes, 16 enriched pathways strongly linked to specific subtypes of breast cancer, and 8 genes linked to prognostic outcomes. CONCLUSIONS The proposed framework successfully identified 80 biomarkers from the multi-omics data, enabling accurate classification of breast cancer subtypes. This discovery may offer novel insights for clinicians to pursue in further studies.
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Affiliation(s)
- Min Li
- School of Information Engineering, Nanchang Institute of Technology, No. 289 Tianxiang Road, Nanchang Jiangxi, PR China.
| | - Yuheng Cai
- School of Information Engineering, Nanchang Institute of Technology, No. 289 Tianxiang Road, Nanchang Jiangxi, PR China
| | - Mingzhuang Zhang
- School of Information Engineering, Nanchang Institute of Technology, No. 289 Tianxiang Road, Nanchang Jiangxi, PR China
| | - Shaobo Deng
- School of Information Engineering, Nanchang Institute of Technology, No. 289 Tianxiang Road, Nanchang Jiangxi, PR China
| | - Lei Wang
- School of Information Engineering, Nanchang Institute of Technology, No. 289 Tianxiang Road, Nanchang Jiangxi, PR China
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Snijesh VP, Nimbalkar VP, Patil S, Rajarajan S, Anupama CE, Mahalakshmi S, Alexander A, Soundharya R, Ramesh R, Srinath BS, Jolly MK, Prabhu JS. Differential role of glucocorticoid receptor based on its cell type specific expression on tumor cells and infiltrating lymphocytes. Transl Oncol 2024; 45:101957. [PMID: 38643748 PMCID: PMC11039344 DOI: 10.1016/j.tranon.2024.101957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 03/12/2024] [Accepted: 04/03/2024] [Indexed: 04/23/2024] Open
Abstract
BACKGROUND The glucocorticoid receptor (GR) is frequently expressed in breast cancer (BC), and its prognostic implications are contingent on estrogen receptor (ER) status. To address conflicting reports and explore therapeutic potential, a GR signature (GRsig) independent of ER status was developed. We also investigated cell type-specific GR protein expression in BC tumor epithelial cells and infiltrating lymphocytes. METHODS GRsig was derived from Dexamethasone treated cell lines through a bioinformatic pipeline. Immunohistochemistry assessed GR protein expression. Associations between GRsig and tumor phenotypes (proliferation, cytolytic activity (CYT), immune cell distribution, and epithelial-to-mesenchymal transition (EMT) were explored in public datasets. Single-cell RNA sequencing data evaluated context-dependent GR roles, and a cell type-specific prognostic role was assessed in an independent BC cohort. RESULTS High GRsig levels were associated with a favorable prognosis across BC subtypes. Tumor-specific high GRsig correlated with lower proliferation, increased CYT, and anti-tumorigenic immune cells. Single-cell data analysis revealed higher GRsig expression in immune cells, negatively correlating with EMT while a positive correlation was observed with EMT primarily in tumor and stromal cells. Univariate and multivariate analyses demonstrated the robust and independent predictive capability of GRsig for favorable prognosis. GR protein expression on immune cells in triple-negative tumors indicated a favorable prognosis. CONCLUSION This study underscores the cell type-specific role of GR, where its expression on tumor cells is associated with aggressive features like EMT, while in infiltrating lymphocytes, it predicts a better prognosis, particularly within TNBC tumors. The GRsig emerges as a promising independent prognostic indicator across diverse BC subtypes.
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Affiliation(s)
- V P Snijesh
- Division of Molecular Medicine, St. John's Research Institute, St. John's Medical College, Bangalore, Karnataka, India; Centre for Doctoral Studies, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India
| | - Vidya P Nimbalkar
- Division of Molecular Medicine, St. John's Research Institute, St. John's Medical College, Bangalore, Karnataka, India
| | - Sharada Patil
- Division of Molecular Medicine, St. John's Research Institute, St. John's Medical College, Bangalore, Karnataka, India
| | - Savitha Rajarajan
- Division of Molecular Medicine, St. John's Research Institute, St. John's Medical College, Bangalore, Karnataka, India; Centre for Doctoral Studies, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India
| | - C E Anupama
- Division of Molecular Medicine, St. John's Research Institute, St. John's Medical College, Bangalore, Karnataka, India
| | - S Mahalakshmi
- Division of Molecular Medicine, St. John's Research Institute, St. John's Medical College, Bangalore, Karnataka, India
| | - Annie Alexander
- Division of Molecular Medicine, St. John's Research Institute, St. John's Medical College, Bangalore, Karnataka, India
| | - Ramu Soundharya
- IISc Mathematics Initiative, Indian Institute of Science, Bangalore, Karnataka-560012, India
| | - Rakesh Ramesh
- Department of Surgical Oncology, St. John's Medical College and Hospital, Bangalore, Karnataka, India
| | - B S Srinath
- Department of Surgery, Sri Shankara Cancer Hospital and Research Centre, Bangalore, Karnataka, India
| | - Mohit Kumar Jolly
- Department of Bioengineering, Indian Institute of Science, Bangalore, Karnataka-560012, India
| | - Jyothi S Prabhu
- Division of Molecular Medicine, St. John's Research Institute, St. John's Medical College, Bangalore, Karnataka, India.
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Wang L, Han C, Cai C, Wu J, Chen J, Su C. Identification of immune-related gene signature for non-small cell lung cancer patients with immune checkpoint inhibitors. Heliyon 2024; 10:e26974. [PMID: 38463866 PMCID: PMC10923664 DOI: 10.1016/j.heliyon.2024.e26974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 01/31/2024] [Accepted: 02/22/2024] [Indexed: 03/12/2024] Open
Abstract
Background The utilization of immune checkpoint inhibitors (ICIs) has become the established protocol for treating advanced non-small cell lung cancer (NSCLC). This work aimed to identify the immune-related gene signature that can predict the prognosis of NSCLC patients receiving ICI treatment. Methods The ImmPort database was queried to obtain a list of immune-related genes (IRGs). Differentially expressed IRGs in NSCLC patients were identified using the TCGA database. RNA-seq data and clinical information from NSCLC patients receiving immunotherapy were obtained from the GEO database (GSE93157 and ////). A gene signature was generated through multivariate Cox and LASSO regression analyses. The prognostic value and function of this gene signature were thoroughly investigated using comprehensive bioinformatics analyses. Results A total of 6 prognostic-related genes were identified from 617 differentially expressed genes, and two prognostic-related differentially expressed genes (CAMP and IL17A) were determined to construct gene signature. Our gene signature demonstrated superior performance compared to other clinicopathological parameters in predicting the prognosis of NSCLC patients receiving immunotherapy, with an area under the ROC curve (AUC) of 0.812. Furthermore, immune infiltration analysis indicated that the high-risk group was enriched with resting CD4 T cell memory, while the low-risk group showed a "hot" tumor microenvironment that promotes anti-tumor immunity in NSCLC patients. Conclusion Gene signatures based on immune-related genes exhibited excellent indicator performance of prognosis and immune infiltration, which has the potential to be an effective biomarker for NSCLC with ICI treatment.
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Affiliation(s)
- Li Wang
- Department of Medical Oncology, Shanghai Pulmonary Hospital & Thoracic Cancer Institute, Tongji University School of Medicine, Shanghai, 200433, PR China
| | - Chaonan Han
- Department of Medical Oncology, Shanghai Pulmonary Hospital & Thoracic Cancer Institute, Tongji University School of Medicine, Shanghai, 200433, PR China
| | - Chenlei Cai
- Department of Medical Oncology, Shanghai Pulmonary Hospital & Thoracic Cancer Institute, Tongji University School of Medicine, Shanghai, 200433, PR China
| | - Jing Wu
- Department of Medical Oncology, Shanghai Pulmonary Hospital & Thoracic Cancer Institute, Tongji University School of Medicine, Shanghai, 200433, PR China
| | - Jianing Chen
- Department of Medical Oncology, Shanghai Pulmonary Hospital & Thoracic Cancer Institute, Tongji University School of Medicine, Shanghai, 200433, PR China
| | - Chunxia Su
- Department of Medical Oncology, Shanghai Pulmonary Hospital & Thoracic Cancer Institute, Tongji University School of Medicine, Shanghai, 200433, PR China
- Department of Clinical Research Center, Shanghai Pulmonary Hospital & Thoracic Cancer Institute, Tongji University School of Medicine, Shanghai, 200433, PR China
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Campone M, Bidard FC, Neven P, Wang L, Ling B, Dong Y, Paux G, Herold C, De Giorgi U. AMEERA-4: a randomized, preoperative window-of-opportunity study of amcenestrant versus letrozole in early breast cancer. Breast Cancer Res 2023; 25:141. [PMID: 37950338 PMCID: PMC10638815 DOI: 10.1186/s13058-023-01740-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 11/06/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND Window-of-opportunity (WOO) studies provide insights into the clinical activity of new drugs in breast cancer. METHODS AMEERA-4 (NCT04191382) was a WOO study undertaken to compare the pharmacodynamic effects of amcenestrant, a selective estrogen receptor degrader, with those of letrozole in postmenopausal women with newly diagnosed, operable estrogen receptor-positive, human epidermal growth factor receptor 2-negative (ER+/HER2-) breast cancer. Women were randomized (1:1:1) to receive amcenestrant 400 mg, amcenestrant 200 mg, or letrozole 2.5 mg once daily for 14 days before breast surgery. The primary endpoint was change in Ki67 between baseline and Day 15 (i.e., day of surgery). RESULTS Enrollment was stopped early because of slow recruitment, in the context of the COVID-19 pandemic. The modified intent-to-treat population consisted of 95 study participants with baseline and post-treatment Ki67 values, whereas the safety population included 104 participants who had received at least one dose of study medication. Relative change from baseline in Ki67 was - 75.9% (95% confidence interval [CI] - 81.9 to - 67.9) for amcenestrant 400 mg, - 68.2% (- 75.7 to - 58.4) for amcenestrant 200 mg, and - 77.7% (- 83.4 to - 70.0) for letrozole (geometric least-squares mean [LSM] estimates). Absolute change in ER H-score from baseline (LSM estimate) was - 176.7 in the amcenestrant 400 mg arm, - 202.9 in the amcenestrant 200 mg arm, and - 32.5 in the letrozole arm. There were no Grade ≥ 3 treatment-related adverse events. CONCLUSIONS Both amcenestrant and letrozole demonstrated antiproliferative activity in postmenopausal women with previously untreated, operable ER+/HER2- breast cancer and had good overall tolerability. TRIAL REGISTRATION ClinicalTrials.gov, NCT04191382 https://clinicaltrials.gov/ct2/show/NCT04191382 . Registered 9 December 2019.
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Affiliation(s)
- Mario Campone
- Institut de Cancérologie de l'Ouest, René Gauducheau, Boulevard Jacques Monod, 44805, Saint-Herblain, France.
| | - François-Clément Bidard
- Institut Curie, Paris and Saint-Cloud, France
- Versailles Saint Quentin, Saint-Cloud, France
- Paris-Saclay University, Saint-Cloud, France
| | - Patrick Neven
- Department of Gynaecological Oncology, Multidisciplinary Breast Center, University Hospitals Louvain, Campus Gasthuisberg, Leuven, Belgium
| | | | | | | | | | | | - Ugo De Giorgi
- Department of Medical Oncology, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola, Italy
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Mirsadeghi L, Haji Hosseini R, Banaei-Moghaddam AM, Kavousi K. EARN: an ensemble machine learning algorithm to predict driver genes in metastatic breast cancer. BMC Med Genomics 2021; 14:122. [PMID: 33962648 PMCID: PMC8105935 DOI: 10.1186/s12920-021-00974-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 04/27/2021] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Today, there are a lot of markers on the prognosis and diagnosis of complex diseases such as primary breast cancer. However, our understanding of the drivers that influence cancer aggression is limited. METHODS In this work, we study somatic mutation data consists of 450 metastatic breast tumor samples from cBio Cancer Genomics Portal. We use four software tools to extract features from this data. Then, an ensemble classifier (EC) learning algorithm called EARN (Ensemble of Artificial Neural Network, Random Forest, and non-linear Support Vector Machine) is proposed to evaluate plausible driver genes for metastatic breast cancer (MBCA). The decision-making strategy for the proposed ensemble machine is based on the aggregation of the predicted scores obtained from individual learning classifiers to be prioritized homo sapiens genes annotated as protein-coding from NCBI. RESULTS This study is an attempt to focus on the findings in several aspects of MBCA prognosis and diagnosis. First, drivers and passengers predicted by SVM, ANN, RF, and EARN are introduced. Second, biological inferences of predictions are discussed based on gene set enrichment analysis. Third, statistical validation and comparison of all learning methods are performed by some evaluation metrics. Finally, the pathway enrichment analysis (PEA) using ReactomeFIVIz tool (FDR < 0.03) for the top 100 genes predicted by EARN leads us to propose a new gene set panel for MBCA. It includes HDAC3, ABAT, GRIN1, PLCB1, and KPNA2 as well as NCOR1, TBL1XR1, SIRT4, KRAS, CACNA1E, PRKCG, GPS2, SIN3A, ACTB, KDM6B, and PRMT1. Furthermore, we compare results for MBCA to other outputs regarding 983 primary tumor samples of breast invasive carcinoma (BRCA) obtained from the Cancer Genome Atlas (TCGA). The comparison between outputs shows that ROC-AUC reaches 99.24% using EARN for MBCA and 99.79% for BRCA. This statistical result is better than three individual classifiers in each case. CONCLUSIONS This research using an integrative approach assists precision oncologists to design compact targeted panels that eliminate the need for whole-genome/exome sequencing. The schematic representation of the proposed model is presented as the Graphic abstract.
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Affiliation(s)
- Leila Mirsadeghi
- Department of Biology, Faculty of Science, Payame Noor University, Tehran, Iran
| | - Reza Haji Hosseini
- Department of Biology, Faculty of Science, Payame Noor University, Tehran, Iran.
| | - Ali Mohammad Banaei-Moghaddam
- Laboratory of Genomics and Epigenomics (LGE), Department of Biochemistry, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran.
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Chen TH, Wei JR, Lei J, Chiu JY, Shih KH. A Clinicogenetic Prognostic Classifier for Prediction of Recurrence and Survival in Asian Breast Cancer Patients. Front Oncol 2021; 11:645853. [PMID: 33816299 PMCID: PMC8010242 DOI: 10.3389/fonc.2021.645853] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 02/25/2021] [Indexed: 11/13/2022] Open
Abstract
Background Several prognostic factors affect the recurrence of breast cancer in patients who undergo mastectomy. Assays of the expression profiles of multiple genes increase the probability of overexpression of certain genes and thus can potentially characterize the risk of metastasis. Methods We propose a 20-gene classifier for predicting patients with high/low risk of recurrence within 5 years. Gene expression levels from a quantitative PCR assay were used to screen 473 luminal breast cancer patients treated at Taiwan Hospital (positive for estrogen and progesterone receptors, negative for human epidermal growth factor receptor 2). Gene expression scores, along with clinical information (age, tumor stage, and nodal stage), were evaluated for risk prediction. The classifier could correctly predict patients with and without relapse (logistic regression, P<0.05). Results A Cox proportional hazards regression analysis showed that the 20-gene panel was prognostic with hazard ratios of 5.63 (95% confidence interval 2.77-11.5, univariate) and 5.56 (2.62-11.8, multivariate) for the “genetic” model, and of 8.02 (3.52-18.3, univariate) and 19.8 (5.96-65.87, multivariate) for the “clinicogenetic” model during a 5-year follow-up. Conclusions The proposed 20-gene classifier can successfully separate the patients into two risk groups, and the two risk group had significantly different relapse rate and prognosis. This 20-gene classifier can provide better estimation of prognosis, which can help physicians to make better personalized treatment plans.
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Affiliation(s)
- Ting-Hao Chen
- Department of Medical Operation, Amwise Diagnostics Pte. Ltd., Singapore, Singapore
| | - Jun-Ru Wei
- Department of Medical Operation, Amwise Diagnostics Pte. Ltd., Singapore, Singapore
| | - Jason Lei
- Department of Product Development, Amwise Diagnostics Pte. Ltd., Singapore, Singapore
| | - Jian-Ying Chiu
- Department of Medical Operation, Amwise Diagnostics Pte. Ltd., Singapore, Singapore
| | - Kuan-Hui Shih
- Department of Medical Operation, Amwise Diagnostics Pte. Ltd., Singapore, Singapore
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Li Y, Duche A, Sayer MR, Roosan D, Khalafalla FG, Ostrom RS, Totonchy J, Roosan MR. SARS-CoV-2 early infection signature identified potential key infection mechanisms and drug targets. BMC Genomics 2021; 22:125. [PMID: 33602138 PMCID: PMC7889713 DOI: 10.1186/s12864-021-07433-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 02/05/2021] [Indexed: 12/15/2022] Open
Abstract
Background The ongoing COVID-19 outbreak has caused devastating mortality and posed a significant threat to public health worldwide. Despite the severity of this illness and 2.3 million worldwide deaths, the disease mechanism is mostly unknown. Previous studies that characterized differential gene expression due to SARS-CoV-2 infection lacked robust validation. Although vaccines are now available, effective treatment options are still out of reach. Results To characterize the transcriptional activity of SARS-CoV-2 infection, a gene signature consisting of 25 genes was generated using a publicly available RNA-Sequencing (RNA-Seq) dataset of cultured cells infected with SARS-CoV-2. The signature estimated infection level accurately in bronchoalveolar lavage fluid (BALF) cells and peripheral blood mononuclear cells (PBMCs) from healthy and infected patients (mean 0.001 vs. 0.958; P < 0.0001). These signature genes were investigated in their ability to distinguish the severity of SARS-CoV-2 infection in a single-cell RNA-Sequencing dataset. TNFAIP3, PPP1R15A, NFKBIA, and IFIT2 had shown bimodal gene expression in various immune cells from severely infected patients compared to healthy or moderate infection cases. Finally, this signature was assessed using the publicly available ConnectivityMap database to identify potential disease mechanisms and drug repurposing candidates. Pharmacological classes of tricyclic antidepressants, SRC-inhibitors, HDAC inhibitors, MEK inhibitors, and drugs such as atorvastatin, ibuprofen, and ketoconazole showed strong negative associations (connectivity score < − 90), highlighting the need for further evaluation of these candidates for their efficacy in treating SARS-CoV-2 infection. Conclusions Thus, using the 25-gene SARS-CoV-2 infection signature, the SARS-CoV-2 infection status was captured in BALF cells, PBMCs and postmortem lung biopsies. In addition, candidate SARS-CoV-2 therapies with known safety profiles were identified. The signature genes could potentially also be used to characterize the COVID-19 disease severity in patients’ expression profiles of BALF cells. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-021-07433-4.
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Affiliation(s)
- Yue Li
- School of Pharmacy, Chapman University, Irvine, CA, 92618, USA
| | - Ashley Duche
- School of Pharmacy, Chapman University, Irvine, CA, 92618, USA
| | - Michael R Sayer
- School of Pharmacy, Chapman University, Irvine, CA, 92618, USA
| | - Don Roosan
- College of Pharmacy, Western University of Health Sciences, Pomona, CA, 91766, USA
| | - Farid G Khalafalla
- College of Pharmacy, California Health Sciences University, Clovis, CA, 93612, USA
| | | | | | - Moom R Roosan
- School of Pharmacy, Chapman University, Irvine, CA, 92618, USA.
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Liu J, Zhang J. Elevated EXO1 expression is associated with breast carcinogenesis and poor prognosis. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:135. [PMID: 33569437 PMCID: PMC7867906 DOI: 10.21037/atm-20-7922] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Background Breast cancer is the most common cancer and leading cause of cancer mortality in women worldwide. Exonuclease 1 (EXO1), a protein with 5' to 3' exonuclease and RNase H activity, could be involved in mismatch repair and recombination. This study aims to investigate the prognostic value of EXO1 in breast cancer and explore the association between EXO1 expression and breast carcinogenesis. Methods The data of 1,215 breast cancer susceptibility gene (BRCA) samples were obtained from The Cancer Genome Atlas (TCGA). Real-time quantitative polymerase chain reaction (RT-qPCR) further verified the elevated mRNA expression level of EXO1 in human BRCA cells MDA-MB231 compared with that in human breast epithelial cells MCF-10A. EXO1 copy number was proved to be correlated with its expression level. Besides, Kaplan-Meier analysis, differentially expressed genes and function enrichment analysis were performed. Results Analysis of data from The Cancer Genome Atlas (TCGA) revealed that the EXO1 expression level in breast cancer tissues was significantly increased. Real-time quantitative polymerase chain reaction (RT-qPCR) supported the elevated mRNA expression level of EXO1 in human breast cancer cells MDA-MB231 compared with that in human breast epithelial cells MCF-10A. EXO1 copy number was shown to be correlated with its expression level. Kaplan-Meier analysis showed that elevated EXO1 was an indicator of poor breast cancer prognosis. Furthermore, differentially expressed genes and function enrichment analysis indicated that the cell cycle pathway and cardiac muscle contraction pathway were activated and inhibited respectively in breast cancer samples with high EXO1 expression. Conclusions Therefore, this study shows that elevated EXO1 expression is associated with carcinogenesis and poor prognosis in breast cancer, and might be a biomarker for breast cancer treatment.
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Affiliation(s)
- Jingjing Liu
- 3rd Department of Breast Cancer, China Tianjin Breast Cancer Prevention, Treatment and Research Center, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,Key Laboratory of Breast Cancer Prevention and Therapy of Ministry of Education, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China.,Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
| | - Jin Zhang
- 3rd Department of Breast Cancer, China Tianjin Breast Cancer Prevention, Treatment and Research Center, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,Key Laboratory of Breast Cancer Prevention and Therapy of Ministry of Education, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China.,Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
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Zafari P, Golpour M, Hafezi N, Bashash D, Esmaeili SA, Tavakolinia N, Rafiei A. Tuberculosis comorbidity with rheumatoid arthritis: Gene signatures, associated biomarkers, and screening. IUBMB Life 2020; 73:26-39. [PMID: 33217772 DOI: 10.1002/iub.2413] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 11/01/2020] [Accepted: 11/04/2020] [Indexed: 12/19/2022]
Abstract
Rheumatoid arthritis (RA) is known to be related to an elevated risk of infections because of its pathobiology and the use of immunosuppressive therapies. Reactivation of latent tuberculosis (TB) infection is a serious issue in patients with RA, especially after receiving anti-TNFs therapy. TNF blocking reinforces the TB granuloma formation and maintenance and the growth of Mycobacterium tuberculosis (Mtb). After intercurrent of TB infection, the standard recommendation is that the treatment with TNF inhibitors to be withheld despite its impressive effect on suppression of inflammation until the infection has resolved. Knowing pathways and mechanisms that are common between two diseases might help to find the mechanistic basis of this comorbidity, as well as provide us a new approach to apply them as therapeutic targets or diagnostic biomarkers. Also, screening for latent TB before initiation of an anti-TNF therapy can minimize complications. This review summarizes the shared gene signature between TB and RA and discusses the biomarkers for early detection of this infection, and screening procedures as well.
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Affiliation(s)
- Parisa Zafari
- Department of Immunology, School of Medicine, Mazandaran University of Medical Sciences, Sari, Iran.,Student Research Committee, Mazandaran University of Medical Sciences, Sari, Iran
| | - Monireh Golpour
- Molecular and Cellular Biology Research Center, Student Research Committee, Faculty of Medicine, Mazandaran University of Medical Sciences, Sari, Iran
| | - Nasim Hafezi
- Department of Immunology, School of Medicine, Mazandaran University of Medical Sciences, Sari, Iran
| | - Davood Bashash
- Department of Hematology and Blood Banking, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Seyed-Alireza Esmaeili
- Immunology Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.,Department of Immunology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Naeimeh Tavakolinia
- Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Alireza Rafiei
- Department of Immunology, School of Medicine, Mazandaran University of Medical Sciences, Sari, Iran
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Chen YX, Rong Y, Jiang F, Chen JB, Duan YY, Dong SS, Zhu DL, Chen H, Yang TL, Dai Z, Guo Y. An integrative multi-omics network-based approach identifies key regulators for breast cancer. Comput Struct Biotechnol J 2020; 18:2826-2835. [PMID: 33133424 PMCID: PMC7585874 DOI: 10.1016/j.csbj.2020.10.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 09/13/2020] [Accepted: 10/01/2020] [Indexed: 02/06/2023] Open
Abstract
Although genome-wide association studies (GWASs) have successfully identified thousands of risk variants for human complex diseases, understanding the biological function and molecular mechanisms of the associated SNPs involved in complex diseases is challenging. Here we developed a framework named integrative multi-omics network-based approach (IMNA), aiming to identify potential key genes in regulatory networks by integrating molecular interactions across multiple biological scales, including GWAS signals, gene expression-based signatures, chromatin interactions and protein interactions from the network topology. We applied this approach to breast cancer, and prioritized key genes involved in regulatory networks. We also developed an abnormal gene expression score (AGES) signature based on the gene expression deviation of the top 20 rank-ordered genes in breast cancer. The AGES values are associated with genetic variants, tumor properties and patient survival outcomes. Among the top 20 genes, RNASEH2A was identified as a new candidate gene for breast cancer. Thus, our integrative network-based approach provides a genetic-driven framework to unveil tissue-specific interactions from multiple biological scales and reveal potential key regulatory genes for breast cancer. This approach can also be applied in other complex diseases such as ovarian cancer to unravel underlying mechanisms and help for developing therapeutic targets.
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Affiliation(s)
- Yi-Xiao Chen
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, PR China
| | - Yu Rong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, PR China
| | - Feng Jiang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, PR China
| | - Jia-Bin Chen
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, PR China
| | - Yuan-Yuan Duan
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, PR China
| | - Shan-Shan Dong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, PR China
| | - Dong-Li Zhu
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, PR China.,Research Institute of Xi'an Jiaotong University, Zhejiang Province 311215, PR China
| | - Hao Chen
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, PR China
| | - Tie-Lin Yang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, PR China.,Research Institute of Xi'an Jiaotong University, Zhejiang Province 311215, PR China
| | - Zhijun Dai
- Department of Breast Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province 310003, PR China
| | - Yan Guo
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, PR China
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11
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Chang YS, Chang CM, Lin CY, Chao DS, Huang HY, Chang JG. Pathway Mutations in Breast Cancer Using Whole-Exome Sequencing. Oncol Res 2020; 28:107-116. [PMID: 31575382 PMCID: PMC7851574 DOI: 10.3727/096504019x15698362825407] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
The genomic landscape of breast cancer (BC) is complex. The purpose of this study was to decipher the mutational profiles of Taiwanese patients with BC using next-generation sequencing. We performed whole-exome sequencing on DNA from 24 tumor tissue specimens from BC patients. Sanger sequencing was used to validate the identified variants. Sanger sequencing was also performed on paired adjacent nontumor tissues. After genotype calling and algorithmic annotations, we identified 49 deleterious variants in canonical cancer-related genes in our BC cohort. The most frequently mutated genes were PIK3CA (16.67%), FKBP9 (12.5%), TP53 (12.5%), ATM (8.33%), CHEK2 (8.33%), FOXO3 (8.33%), NTRK1 (8.33%), and NUTM2B (8.33%). Seven mutated variants (ATR p.V1581fs, CSF1R p.R579Q, GATA3 p.T356delinsTMKS, LRP5 p.W389*, MAP3K1 p.T918fs, MET p.K1161fs, and MTR p.P1178S) were novel variants that are not present in any gene mutation database. After grouping the samples according to molecular subtype, we found that the cell cycle, MAPK, and chemokine signaling pathways in the luminal A subtype of BC; the focal adhesion, axon guidance, and endocytosis pathways in the luminal B subtype; and amyotrophic lateral sclerosis in the basal-like subtype were exclusively altered. Survival curve analysis showed that the presence of the MAPK signaling pathway and endocytosis mutations were correlated with a poor prognosis. These survival data were consistent with cBioPortal analyses of 2,051 BC cases. We discovered novel mutations in patients with BC. These results have implications for developing strategic, adjuvant, and gene-targeted therapies.
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Affiliation(s)
- Ya-Sian Chang
- Epigenome Research Center, China Medical University HospitalTaichungTaiwan
| | - Chieh-Min Chang
- Department of Laboratory Medicine, China Medical University HospitalTaichungTaiwan
| | - Chien-Yu Lin
- Graduate Institute of Clinical Medical Science and School of Medicine, China Medical UniversityTaichungTaiwan
| | - Dy-San Chao
- Department of Laboratory Medicine, China Medical University HospitalTaichungTaiwan
| | - Hsi-Yuan Huang
- Department of Laboratory Medicine, China Medical University HospitalTaichungTaiwan
| | - Jan-Gowth Chang
- Epigenome Research Center, China Medical University HospitalTaichungTaiwan
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12
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Wu X, Wang L, Feng F, Tian S. Weighted gene expression profiles identify diagnostic and prognostic genes for lung adenocarcinoma and squamous cell carcinoma. J Int Med Res 2019; 48:300060519893837. [PMID: 31854219 PMCID: PMC7607763 DOI: 10.1177/0300060519893837] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
OBJECTIVE To construct a diagnostic signature to distinguish lung adenocarcinoma from lung squamous cell carcinoma and a prognostic signature to predict the risk of death for patients with nonsmall-cell lung cancer, with satisfactory predictive performances, good stabilities, small sizes and meaningful biological implications. METHODS Pathway-based feature selection methods utilize pathway information as a priori to provide insightful clues on potential biomarkers from the biological perspective, and such incorporation may be realized by adding weights to test statistics or gene expression values. In this study, weighted gene expression profiles were generated using the GeneRank method and then the LASSO method was used to identify discriminative and prognostic genes. RESULTS The five-gene diagnostic signature including keratin 5 (KRT5), mucin 1 (MUC1), triggering receptor expressed on myeloid cells 1 (TREM1), complement C3 (C3) and transmembrane serine protease 2 (TMPRSS2) achieved a predictive error of 12.8% and a Generalized Brier Score of 0.108, while the five-gene prognostic signature including alcohol dehydrogenase 1C (class I), gamma polypeptide (ADH1C), alpha-2-glycoprotein 1, zinc-binding (AZGP1), clusterin (CLU), cyclin dependent kinase 1 (CDK1) and paternally expressed 10 (PEG10) obtained a log-rank P-value of 0.03 and a C-index of 0.622 on the test set. CONCLUSIONS Besides good predictive capacity, model parsimony and stability, the identified diagnostic and prognostic genes were highly relevant to lung cancer. A large-sized prospective study to explore the utilization of these genes in a clinical setting is warranted.
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Affiliation(s)
- Xing Wu
- Department of Teaching, The First Hospital of Jilin University, Changchun, Jilin Province, China
| | - Linlin Wang
- Department of Ultrasound, China-Japan Union Hospital of Jilin University, Changchun, Jilin Province, China
| | - Fan Feng
- School of Mathematics, Jilin University, Changchun, Jilin Province, China
| | - Suyan Tian
- Division of Clinical Research, The First Hospital of Jilin University, Changchun, Jilin Province, China
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13
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Di Nanni N, Gnocchi M, Moscatelli M, Milanesi L, Mosca E. Gene relevance based on multiple evidences in complex networks. Bioinformatics 2019; 36:865-871. [PMID: 31504182 PMCID: PMC9883679 DOI: 10.1093/bioinformatics/btz652] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 05/17/2019] [Accepted: 08/19/2019] [Indexed: 02/02/2023] Open
Abstract
MOTIVATION Multi-omics approaches offer the opportunity to reconstruct a more complete picture of the molecular events associated with human diseases, but pose challenges in data analysis. Network-based methods for the analysis of multi-omics leverage the complex web of macromolecular interactions occurring within cells to extract significant patterns of molecular alterations. Existing network-based approaches typically address specific combinations of omics and are limited in terms of the number of layers that can be jointly analysed. In this study, we investigate the application of network diffusion to quantify gene relevance on the basis of multiple evidences (layers). RESULTS We introduce a gene score (mND) that quantifies the relevance of a gene in a biological process taking into account the network proximity of the gene and its first neighbours to other altered genes. We show that mND has a better performance over existing methods in finding altered genes in network proximity in one or more layers. We also report good performances in recovering known cancer genes. The pipeline described in this article is broadly applicable, because it can handle different types of inputs: in addition to multi-omics datasets, datasets that are stratified in many classes (e.g., cell clusters emerging from single cell analyses) or a combination of the two scenarios. AVAILABILITY AND IMPLEMENTATION The R package 'mND' is available at URL: https://www.itb.cnr.it/mnd. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Noemi Di Nanni
- Department of Biomedical Sciences, Institute of Biomedical Technologies, National Research Council, 20090 Segrate (MI), Italy,Department of Industrial and Information Engineering, University of Pavia, Italy
| | - Matteo Gnocchi
- Department of Biomedical Sciences, Institute of Biomedical Technologies, National Research Council, 20090 Segrate (MI), Italy
| | - Marco Moscatelli
- Department of Biomedical Sciences, Institute of Biomedical Technologies, National Research Council, 20090 Segrate (MI), Italy
| | - Luciano Milanesi
- Department of Biomedical Sciences, Institute of Biomedical Technologies, National Research Council, 20090 Segrate (MI), Italy
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14
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Huang S, Murphy L, Xu W. Genes and functions from breast cancer signatures. BMC Cancer 2018; 18:473. [PMID: 29699511 PMCID: PMC5921990 DOI: 10.1186/s12885-018-4388-4] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2016] [Accepted: 04/17/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Breast cancer is a heterogeneous disease and personalized medicine is the hope for the improvement of the clinical outcome. Multi-gene signatures for breast cancer stratification have been extensively studied in the past decades and more than 30 different signatures have been reported. A major concern is the minimal overlap of genes among the reported signatures. We investigated the breast cancer signature genes to address our hypothesis that the genes of different signature may share common functions, as well as to use these previously reported signature genes to build better prognostic models. METHODS A total of 33 signatures and the corresponding gene lists were investigated. We first examined the gene frequency and the gene overlap in these signatures. Then the gene functions of each signature gene list were analysed and compared by the KEGG pathways and gene ontology (GO) terms. A classifier built using the common genes was tested using the METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) data. The common genes were also tested for building the Yin Yang gene mean expression ratio (YMR) signature using public datasets (GSE1456 and GSE2034). RESULTS Among a total of 2239 genes collected from the 33 breast cancer signatures, only 238 genes overlapped in at least two signatures; while from a total of 1979 function terms enriched in the 33 signature gene lists, 429 terms were common in at least two signatures. Most of the common function terms were involved in cell cycle processes. While there is almost no common overlapping genes between signatures developed for ER-positive (e.g. 21-gene signature) and those developed for ER-negative (e.g. basal signatures) tumours, they have common function terms such as cell death, regulation of cell proliferation. We used the 62 genes that were common in at least three signatures as a classifier and subtyped 1141 METABRIC cases including 144 normal samples into nine subgroups. These subgroups showed different clinical outcome. Among the 238 common genes, we selected those genes that are more highly expressed in normal breast tissue than in tumours as Yang genes and those more highly expressed in tumours than in normal as Yin genes and built a YMR model signature. This YMR showed significance in risk stratification in two datasets (GSE1456 and GSE2034). CONCLUSIONS The lack of significant numbers of overlapping genes among most breast cancer signatures can be partially explained by our discovery that these signature genes represent groups with similar functions. The genes collected from these previously reported signatures are valuable resources for new model development. The subtype classifier and YMR signature built from the common genes showed promising results.
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Affiliation(s)
- Shujun Huang
- Research Institute of Oncology and Hematology, CancerCare Manitoba, 675 McDermot Ave, Winnipeg, Manitoba, R3E 0V9, Canada.,College of Pharmacy, Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, R3E 0J9, Canada
| | - Leigh Murphy
- Research Institute of Oncology and Hematology, CancerCare Manitoba, 675 McDermot Ave, Winnipeg, Manitoba, R3E 0V9, Canada.,Department of Biochemistry and Medical Genetics, Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, R3E 0J9, Canada
| | - Wayne Xu
- Research Institute of Oncology and Hematology, CancerCare Manitoba, 675 McDermot Ave, Winnipeg, Manitoba, R3E 0V9, Canada. .,Department of Biochemistry and Medical Genetics, Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, R3E 0J9, Canada. .,College of Pharmacy, Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, R3E 0J9, Canada.
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15
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Tian F, Wang Y, Seiler M, Hu Z. Functional characterization of breast cancer using pathway profiles. BMC Med Genomics 2014; 7:45. [PMID: 25041817 PMCID: PMC4113668 DOI: 10.1186/1755-8794-7-45] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2013] [Accepted: 07/09/2014] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND The molecular characteristics of human diseases are often represented by a list of genes termed "signature genes". A significant challenge facing this approach is that of reproducibility: signatures developed on a set of patients may fail to perform well on different sets of patients. As diseases are resulted from perturbed cellular functions, irrespective of the particular genes that contribute to the function, it may be more appropriate to characterize diseases based on these perturbed cellular functions. METHODS We proposed a profile-based approach to characterize a disease using a binary vector whose elements indicate whether a given function is perturbed based on the enrichment analysis of expression data between normal and tumor tissues. Using breast cancer and its four primary clinically relevant subtypes as examples, this approach is evaluated based on the reproducibility, accuracy and resolution of the resulting pathway profiles. RESULTS Pathway profiles for breast cancer and its subtypes are constructed based on data obtained from microarray and RNA-Seq data sets provided by The Cancer Genome Atlas (TCGA), and an additional microarray data set provided by The European Genome-phenome Archive (EGA). An average reproducibility of 68% is achieved between different data sets (TCGA microarray vs. EGA microarray data) and 67% average reproducibility is achieved between different technologies (TCGA microarray vs. TCGA RNA-Seq data). Among the enriched pathways, 74% of them are known to be associated with breast cancer or other cancers. About 40% of the identified pathways are enriched in all four subtypes, with 4, 2, 4, and 7 pathways enriched only in luminal A, luminal B, triple-negative, and HER2+ subtypes, respectively. Comparison of profiles between subtypes, as well as other diseases, shows that luminal A and luminal B subtypes are more similar to the HER2+ subtype than to the triple-negative subtype, and subtypes of breast cancer are more likely to be closer to each other than to other diseases. CONCLUSIONS Our results demonstrate that pathway profiles can successfully characterize both common and distinct functional characteristics of four subtypes of breast cancer and other related diseases, with acceptable reproducibility, high accuracy and reasonable resolution.
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Affiliation(s)
- Feng Tian
- Center for Advanced Genomic Technology, Boston University, Boston, MA 02215, USA
| | - Yajie Wang
- Core Laboratory for Clinical Medical Research, Beijing Tiantan Hospital, Capital Medical University, Beijing, P. R. China
- Department of Clinical Laboratory Diagnosis, Beijing Tiantan Hospital, Capital Medical University, Beijing, P. R. China
| | - Michael Seiler
- Center for Advanced Genomic Technology, Boston University, Boston, MA 02215, USA
| | - Zhenjun Hu
- Center for Advanced Genomic Technology, Boston University, Boston, MA 02215, USA
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16
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Koussounadis A, Langdon SP, Harrison DJ, Smith VA. Chemotherapy-induced dynamic gene expression changes in vivo are prognostic in ovarian cancer. Br J Cancer 2014; 110:2975-84. [PMID: 24867692 PMCID: PMC4056064 DOI: 10.1038/bjc.2014.258] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2013] [Revised: 03/13/2014] [Accepted: 04/17/2014] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND The response of ovarian cancer patients to carboplatin and paclitaxel is variable, necessitating identification of biomarkers that can reliably predict drug sensitivity and resistance. In this study, we sought to identify dynamically controlled genes and pathways associated with drug response and its time dependence. METHODS Gene expression was assessed for 14 days post-treatment with carboplatin or carboplatin-paclitaxel in xenografts from two ovarian cancer models: platinum-sensitive serous adenocarcinoma-derived OV1002 and a mixed clear cell/endometrioid carcinoma-derived HOX424 with reduced sensitivity to platinum. RESULTS Tumour volume reduction was observed in both xenografts, but more dominantly in OV1002. Upregulated genes in OV1002 were involved in DNA repair, cell cycle and apoptosis, whereas downregulated genes were involved in oxygen-consuming metabolic processes and apoptosis control. Carboplatin-paclitaxel triggered a more comprehensive response than carboplatin only in both xenografts. In HOX424, apoptosis and cell cycle were upregulated, whereas Wnt signalling was inhibited. Genes downregulated after day 7 from both xenografts were predictive of overall survival. Overrepresented pathways were also predictive of outcome. CONCLUSIONS Late expressed genes are prognostic in ovarian tumours in a dynamic manner. This longitudinal gene expression study further elucidates chemotherapy response in two models, stressing the importance of delayed biomarker detection and guiding optimal timing of biopsies.
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Affiliation(s)
- A Koussounadis
- School of Biology, Sir Harold Mitchell Building, University of St Andrews, St Andrews, Fife KY16 9TH, UK
| | - S P Langdon
- Division of Pathology, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - D J Harrison
- School of Medicine, University of St Andrews, St Andrews, Fife KY16 9TF, UK
| | - V A Smith
- School of Biology, Sir Harold Mitchell Building, University of St Andrews, St Andrews, Fife KY16 9TH, UK
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17
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Improving the prediction of chemotherapeutic sensitivity of tumors in breast cancer via optimizing the selection of candidate genes. Comput Biol Chem 2014; 49:71-8. [DOI: 10.1016/j.compbiolchem.2013.12.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2013] [Revised: 12/14/2013] [Accepted: 12/17/2013] [Indexed: 01/21/2023]
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18
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HIF-1 is involved in the negative regulation of AURKA expression in breast cancer cell lines under hypoxic conditions. Breast Cancer Res Treat 2013; 140:505-17. [PMID: 23925655 DOI: 10.1007/s10549-013-2649-0] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2013] [Accepted: 07/20/2013] [Indexed: 12/27/2022]
Abstract
Numerous microarray-based gene expression studies performed on several types of solid tumors revealed significant changes in key genes involved in progression and regulation of the cell cycle, including AURKA that is known to be overexpressed in many types of human malignancies. Tumor hypoxia is associated with poor prognosis in several cancer types, including breast cancer (BC). Since hypoxia is a condition that influences the expression of many genes involved in tumorigenesis, proliferation, and cell cycle regulation, we performed a microarray-based gene expression analysis in order to identify differentially expressed genes in BC cell lines exposed to hypoxia. This analysis showed that hypoxia induces a down-regulation of AURKA expression. Although hypoxia is a tumor feature, the molecular mechanisms that regulate AURKA expression in response to hypoxia in BC are still unknown. For the first time, we demonstrated that HIF-1 activation downstream of hypoxia could drive AURKA down-regulation in BC cells. In fact, we found that siRNA-mediated knockdown of HIF-1α significantly reduces the AURKA down-regulation in BC cells under hypoxia. The aim of our study was to obtain new insights into AURKA transcriptional regulation in hypoxic conditions. Luciferase reporter assays showed a reduction of AURKA promoter activity in hypoxia. Unlike the previous findings, we hypothesize a new possible mechanism where HIF-1, rather than inducing transcriptional activation, could promote the AURKA down-regulation via its binding to hypoxia-responsive elements into the proximal region of the AURKA promoter. The present study shows that hypoxia directly links HIF-1 with AURKA expression, suggesting a possible pathophysiological role of this new pathway in BC and confirming HIF-1 as an important player linking an environmental signal to the AURKA promoter. Since AURKA down-regulation overrides the estrogen-mediated growth and chemoresistance in BC cells, these findings could be important for the development of new possible therapies against BC.
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19
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Ahmad FK, Deris S, Othman NH. The inference of breast cancer metastasis through gene regulatory networks. J Biomed Inform 2011; 45:350-62. [PMID: 22179053 DOI: 10.1016/j.jbi.2011.11.015] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2010] [Revised: 11/26/2011] [Accepted: 11/28/2011] [Indexed: 11/30/2022]
Abstract
Understanding the mechanisms of gene regulation during breast cancer is one of the most difficult problems among oncologists because this regulation is likely comprised of complex genetic interactions. Given this complexity, a computational study using the Bayesian network technique has been employed to construct a gene regulatory network from microarray data. Although the Bayesian network has been notified as a prominent method to infer gene regulatory processes, learning the Bayesian network structure is NP hard and computationally intricate. Therefore, we propose a novel inference method based on low-order conditional independence that extends to the case of the Bayesian network to deal with a large number of genes and an insufficient sample size. This method has been evaluated and compared with full-order conditional independence and different prognostic indices on a publicly available breast cancer data set. Our results suggest that the low-order conditional independence method will be able to handle a large number of genes in a small sample size with the least mean square error. In addition, this proposed method performs significantly better than other methods, including the full-order conditional independence and the St. Gallen consensus criteria. The proposed method achieved an area under the ROC curve of 0.79203, whereas the full-order conditional independence and the St. Gallen consensus criteria obtained 0.76438 and 0.73810, respectively. Furthermore, our empirical evaluation using the low-order conditional independence method has demonstrated a promising relationship between six gene regulators and two regulated genes and will be further investigated as potential breast cancer metastasis prognostic markers.
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Affiliation(s)
- F K Ahmad
- Graduate Department of Computer Science, Universiti Utara Malaysia, 06010 Sintok, Kedah, Malaysia.
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20
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Liu J, Jolly RA, Smith AT, Searfoss GH, Goldstein KM, Uversky VN, Dunker K, Li S, Thomas CE, Wei T. Predictive Power Estimation Algorithm (PPEA)--a new algorithm to reduce overfitting for genomic biomarker discovery. PLoS One 2011; 6:e24233. [PMID: 21935387 PMCID: PMC3174148 DOI: 10.1371/journal.pone.0024233] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2011] [Accepted: 08/03/2011] [Indexed: 01/24/2023] Open
Abstract
Toxicogenomics promises to aid in predicting adverse effects, understanding the mechanisms of drug action or toxicity, and uncovering unexpected or secondary pharmacology. However, modeling adverse effects using high dimensional and high noise genomic data is prone to over-fitting. Models constructed from such data sets often consist of a large number of genes with no obvious functional relevance to the biological effect the model intends to predict that can make it challenging to interpret the modeling results. To address these issues, we developed a novel algorithm, Predictive Power Estimation Algorithm (PPEA), which estimates the predictive power of each individual transcript through an iterative two-way bootstrapping procedure. By repeatedly enforcing that the sample number is larger than the transcript number, in each iteration of modeling and testing, PPEA reduces the potential risk of overfitting. We show with three different cases studies that: (1) PPEA can quickly derive a reliable rank order of predictive power of individual transcripts in a relatively small number of iterations, (2) the top ranked transcripts tend to be functionally related to the phenotype they are intended to predict, (3) using only the most predictive top ranked transcripts greatly facilitates development of multiplex assay such as qRT-PCR as a biomarker, and (4) more importantly, we were able to demonstrate that a small number of genes identified from the top-ranked transcripts are highly predictive of phenotype as their expression changes distinguished adverse from nonadverse effects of compounds in completely independent tests. Thus, we believe that the PPEA model effectively addresses the over-fitting problem and can be used to facilitate genomic biomarker discovery for predictive toxicology and drug responses.
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Affiliation(s)
- Jiangang Liu
- Translational Science, Lilly Research Laboratories, a Division of Eli Lilly & Co., Indianapolis, Indiana, United States of America
- School of Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, Indiana, United States of America
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, United States of America
| | - Robert A. Jolly
- Toxicology, Lilly Research Laboratories, a Division of Eli Lilly & Co., Indianapolis, Indiana, United States of America
| | - Aaron T. Smith
- Toxicology, Lilly Research Laboratories, a Division of Eli Lilly & Co., Indianapolis, Indiana, United States of America
| | - George H. Searfoss
- Toxicology, Lilly Research Laboratories, a Division of Eli Lilly & Co., Indianapolis, Indiana, United States of America
| | - Keith M. Goldstein
- Toxicology, Lilly Research Laboratories, a Division of Eli Lilly & Co., Indianapolis, Indiana, United States of America
| | - Vladimir N. Uversky
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, United States of America
- Department of Molecular Medicine, University of South Florida, Tampa, Florida, United States of America
- Institute for Biological Instrumentation, Russian Academy of Sciences, Pushchino, Moscow Region, Russia
| | - Keith Dunker
- School of Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, Indiana, United States of America
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, United States of America
| | - Shuyu Li
- Translational Science, Lilly Research Laboratories, a Division of Eli Lilly & Co., Indianapolis, Indiana, United States of America
| | - Craig E. Thomas
- Toxicology, Lilly Research Laboratories, a Division of Eli Lilly & Co., Indianapolis, Indiana, United States of America
- * E-mail: (TW); (CET)
| | - Tao Wei
- Translational Science, Lilly Research Laboratories, a Division of Eli Lilly & Co., Indianapolis, Indiana, United States of America
- * E-mail: (TW); (CET)
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Abstract
Primary brain tumors are a leading cause of cancer-related mortality among young adults and children. The most common primary malignant brain tumor, glioma, carries a median survival of only 14 months. Two major multi-institutional programs, the Glioma Molecular Diagnostic Initiative and The Cancer Genome Atlas, have pursued a comprehensive genomic characterization of a large number of clinical glioma samples using a variety of technologies to measure gene expression, chromosomal copy number alterations, somatic and germline mutations, DNA methylation, microRNA, and proteomic changes. Classification of gliomas on the basis of gene expression has revealed six major subtypes and provided insights into the underlying biology of each subtype. Integration of genome-wide data from different technologies has been used to identify many potential protein targets in this disease, while increasing the reliability and biological interpretability of results. Mapping genomic changes onto both known and inferred cellular networks represents the next level of analysis, and has yielded proteins with key roles in tumorigenesis. Ultimately, the information gained from these approaches will be used to create customized therapeutic regimens for each patient based on the unique genomic signature of the individual tumor. In this Review, we describe efforts to characterize gliomas using genomic data, and consider how insights gained from these analyses promise to increase understanding of the biological underpinnings of the disease and lead the way to new therapeutic strategies.
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Abstract
OBJECTIVES To discuss how understanding and manipulation of tumor genetics information and technology shapes cancer care today and what changes might be expected in the near future. DATA SOURCES Published articles, web resources, clinical practice. CONCLUSIONS Advances in our understanding of genes and their regulation provide a promise of more personalized cancer care, allowing selection of the most safe and effective therapy in an individual situation. IMPLICATIONS FOR NURSING PRACTICE Rapid progress in the technology of tumor profiling and targeted cancer therapies challenges nurses to keep up-to-date to provide quality patient education and care.
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Affiliation(s)
- Cathleen M Goetsch
- Virginia Mason Medical Center Cancer Institute, 1100 Ninth Ave., Seattle, WA 98101, USA.
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23
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Taylor KJ, Sims AH, Liang L, Faratian D, Muir M, Walker G, Kuske B, Dixon JM, Cameron DA, Harrison DJ, Langdon SP. Dynamic changes in gene expression in vivo predict prognosis of tamoxifen-treated patients with breast cancer. Breast Cancer Res 2010; 12:R39. [PMID: 20569502 PMCID: PMC2917034 DOI: 10.1186/bcr2593] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2009] [Revised: 01/02/2010] [Accepted: 06/22/2010] [Indexed: 11/10/2022] Open
Abstract
INTRODUCTION Tamoxifen is the most widely prescribed anti-estrogen treatment for patients with estrogen receptor (ER)-positive breast cancer. However, there is still a need for biomarkers that reliably predict endocrine sensitivity in breast cancers and these may well be expressed in a dynamic manner. METHODS In this study we assessed gene expression changes at multiple time points (days 1, 2, 4, 7, 14) after tamoxifen treatment in the ER-positive ZR-75-1 xenograft model that displays significant changes in apoptosis, proliferation and angiogenesis within 2 days of therapy. RESULTS Hierarchical clustering identified six time-related gene expression patterns, which separated into three groups: two with early/transient responses, two with continuous/late responses and two with variable response patterns. The early/transient response represented reductions in many genes that are involved in cell cycle and proliferation (e.g. BUB1B, CCNA2, CDKN3, MKI67, UBE2C), whereas the continuous/late changed genes represented the more classical estrogen response genes (e.g. TFF1, TFF3, IGFBP5). Genes and the proteins they encode were confirmed to have similar temporal patterns of expression in vitro and in vivo and correlated with reduction in tumour volume in primary breast cancer. The profiles of genes that were most differentially expressed on days 2, 4 and 7 following treatment were able to predict prognosis, whereas those most changed on days 1 and 14 were not, in four tamoxifen treated datasets representing a total of 404 patients. CONCLUSIONS Both early/transient/proliferation response genes and continuous/late/estrogen-response genes are able to predict prognosis of primary breast tumours in a dynamic manner. Temporal expression of therapy-response genes is clearly an important factor in characterising the response to endocrine therapy in breast tumours which has significant implications for the timing of biopsies in neoadjuvant biomarker studies.
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Affiliation(s)
- Karen J Taylor
- CRUK Cancer Research Centre and Academic Breast Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Crewe Road South, Edinburgh, UK.
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Thurlow JK, Peña Murillo CL, Hunter KD, Buffa FM, Patiar S, Betts G, West CM, Harris AL, Parkinson EK, Harrison PR, Ozanne BW, Partridge M, Kalna G. Spectral Clustering of Microarray Data Elucidates the Roles of Microenvironment Remodeling and Immune Responses in Survival of Head and Neck Squamous Cell Carcinoma. J Clin Oncol 2010; 28:2881-8. [DOI: 10.1200/jco.2009.24.8724] [Citation(s) in RCA: 64] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Purpose To identify functionally related prognostic gene sets for head and neck squamous cell carcinoma (HNSCC) by unsupervised statistical analysis of microarray data. Patients and Methods Microarray analysis was performed on 14 normal oral epithelium and 71 HNSCCs from patients with outcome data. Spectral clustering (SC) analysis of the data set identified multiple vectors representing distinct aspects of gene expression heterogeneity between samples. Gene ontology (GO) analysis of vector gene lists identified gene sets significantly enriched within defined biologic pathways. The prognostic significance of these was established by Cox survival analysis. Results The most influential SC vectors were V2 and V3. V2 separated normal from tumor samples. GO analysis of V2 gene lists identified pathways with heterogeneous expression between HNSCCs, notably focal adhesion (FA)/extracellular matrix remodeling and cytokine-cytokine receptor (CR) interactions. Similar analysis of V3 gene lists identified further heterogeneity in CR pathways. V2CR genes represent an innate immune response, whereas high expression of V3CR genes represented an adaptive immune response that was not dependent on human papillomavirus status. Survival analysis demonstrated that the FA gene set was prognostic of poor outcome, whereas classification for adaptive immune response by the CR gene set was prognostic of good outcome. A combined FA&CR model dramatically exceeded the performance of current clinical classifiers (P < .001 in our cohort and, importantly, P = .007 in an independent cohort of 60 HNSCCs). Conclusion The application of SC and GO algorithms to HNSCC microarray data identified gene sets highly significant for predicting patient outcome. Further large-scale studies will establish the usefulness of these gene sets in the clinical management of HNSCC.
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Affiliation(s)
- Johanna K. Thurlow
- From The Beatson Institute for Cancer Research; Glasgow Dental School, Faculty of Medicine, University of Glasgow, Glasgow, Scotland; Oral and Maxillofacial Surgery, King's College London; Centre for Clinical and Diagnostic Oral Sciences, Institute of Dentistry, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, London; Cancer Research UK Molecular Oncology Laboratories, Weatherall Institute, University of Oxford, Oxford; and School of Cancer and Enabling Sciences,
| | - Claudia L. Peña Murillo
- From The Beatson Institute for Cancer Research; Glasgow Dental School, Faculty of Medicine, University of Glasgow, Glasgow, Scotland; Oral and Maxillofacial Surgery, King's College London; Centre for Clinical and Diagnostic Oral Sciences, Institute of Dentistry, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, London; Cancer Research UK Molecular Oncology Laboratories, Weatherall Institute, University of Oxford, Oxford; and School of Cancer and Enabling Sciences,
| | - Keith D. Hunter
- From The Beatson Institute for Cancer Research; Glasgow Dental School, Faculty of Medicine, University of Glasgow, Glasgow, Scotland; Oral and Maxillofacial Surgery, King's College London; Centre for Clinical and Diagnostic Oral Sciences, Institute of Dentistry, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, London; Cancer Research UK Molecular Oncology Laboratories, Weatherall Institute, University of Oxford, Oxford; and School of Cancer and Enabling Sciences,
| | - Francesca M. Buffa
- From The Beatson Institute for Cancer Research; Glasgow Dental School, Faculty of Medicine, University of Glasgow, Glasgow, Scotland; Oral and Maxillofacial Surgery, King's College London; Centre for Clinical and Diagnostic Oral Sciences, Institute of Dentistry, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, London; Cancer Research UK Molecular Oncology Laboratories, Weatherall Institute, University of Oxford, Oxford; and School of Cancer and Enabling Sciences,
| | - Shalini Patiar
- From The Beatson Institute for Cancer Research; Glasgow Dental School, Faculty of Medicine, University of Glasgow, Glasgow, Scotland; Oral and Maxillofacial Surgery, King's College London; Centre for Clinical and Diagnostic Oral Sciences, Institute of Dentistry, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, London; Cancer Research UK Molecular Oncology Laboratories, Weatherall Institute, University of Oxford, Oxford; and School of Cancer and Enabling Sciences,
| | - Guy Betts
- From The Beatson Institute for Cancer Research; Glasgow Dental School, Faculty of Medicine, University of Glasgow, Glasgow, Scotland; Oral and Maxillofacial Surgery, King's College London; Centre for Clinical and Diagnostic Oral Sciences, Institute of Dentistry, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, London; Cancer Research UK Molecular Oncology Laboratories, Weatherall Institute, University of Oxford, Oxford; and School of Cancer and Enabling Sciences,
| | - Catharine M.L. West
- From The Beatson Institute for Cancer Research; Glasgow Dental School, Faculty of Medicine, University of Glasgow, Glasgow, Scotland; Oral and Maxillofacial Surgery, King's College London; Centre for Clinical and Diagnostic Oral Sciences, Institute of Dentistry, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, London; Cancer Research UK Molecular Oncology Laboratories, Weatherall Institute, University of Oxford, Oxford; and School of Cancer and Enabling Sciences,
| | - Adrian L. Harris
- From The Beatson Institute for Cancer Research; Glasgow Dental School, Faculty of Medicine, University of Glasgow, Glasgow, Scotland; Oral and Maxillofacial Surgery, King's College London; Centre for Clinical and Diagnostic Oral Sciences, Institute of Dentistry, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, London; Cancer Research UK Molecular Oncology Laboratories, Weatherall Institute, University of Oxford, Oxford; and School of Cancer and Enabling Sciences,
| | - Eric K. Parkinson
- From The Beatson Institute for Cancer Research; Glasgow Dental School, Faculty of Medicine, University of Glasgow, Glasgow, Scotland; Oral and Maxillofacial Surgery, King's College London; Centre for Clinical and Diagnostic Oral Sciences, Institute of Dentistry, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, London; Cancer Research UK Molecular Oncology Laboratories, Weatherall Institute, University of Oxford, Oxford; and School of Cancer and Enabling Sciences,
| | - Paul R. Harrison
- From The Beatson Institute for Cancer Research; Glasgow Dental School, Faculty of Medicine, University of Glasgow, Glasgow, Scotland; Oral and Maxillofacial Surgery, King's College London; Centre for Clinical and Diagnostic Oral Sciences, Institute of Dentistry, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, London; Cancer Research UK Molecular Oncology Laboratories, Weatherall Institute, University of Oxford, Oxford; and School of Cancer and Enabling Sciences,
| | - Bradford W. Ozanne
- From The Beatson Institute for Cancer Research; Glasgow Dental School, Faculty of Medicine, University of Glasgow, Glasgow, Scotland; Oral and Maxillofacial Surgery, King's College London; Centre for Clinical and Diagnostic Oral Sciences, Institute of Dentistry, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, London; Cancer Research UK Molecular Oncology Laboratories, Weatherall Institute, University of Oxford, Oxford; and School of Cancer and Enabling Sciences,
| | - Max Partridge
- From The Beatson Institute for Cancer Research; Glasgow Dental School, Faculty of Medicine, University of Glasgow, Glasgow, Scotland; Oral and Maxillofacial Surgery, King's College London; Centre for Clinical and Diagnostic Oral Sciences, Institute of Dentistry, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, London; Cancer Research UK Molecular Oncology Laboratories, Weatherall Institute, University of Oxford, Oxford; and School of Cancer and Enabling Sciences,
| | - Gabriela Kalna
- From The Beatson Institute for Cancer Research; Glasgow Dental School, Faculty of Medicine, University of Glasgow, Glasgow, Scotland; Oral and Maxillofacial Surgery, King's College London; Centre for Clinical and Diagnostic Oral Sciences, Institute of Dentistry, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, London; Cancer Research UK Molecular Oncology Laboratories, Weatherall Institute, University of Oxford, Oxford; and School of Cancer and Enabling Sciences,
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[Genome-wide expression profiling as a clinical tool: are we there yet?]. DER PATHOLOGE 2009; 30:141-6. [PMID: 19219435 DOI: 10.1007/s00292-008-1104-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Breast cancer is a heterogeneous disease, encompassing a plethora of histological types and clinical courses. Current histopathological classification systems for breast cancer are based on descriptive entities that are of prognostic significance. Few prognostic markers beyond those offered by histopathological analysis are available. Furthermore, a very limited armamentarium of predictive biomarkers has been introduced in clinical practice. High throughput molecular technologies are reshaping our understanding of breast cancer, of which microarray-based gene expression has received the most attention. This method has been successfully used to derive a molecular taxonomy for breast cancer, which has provided interesting insights into the biology of the disease. Microarray-based class prediction studies have generated a multitude of prognostic/predictive signatures. Although these signatures have not been fully translated to clinical practice as yet, they herald the promise of an improvement in breast cancer treatment decision-making. It should be noted, however, that most of the signatures developed to date seem to have discriminatory power almost restricted to oestrogen receptor-positive disease. This review addresses the contribution of gene expression profiling to our understanding of breast cancer and its clinical management and what has yet to be done for these classifiers to be incorporated in clinical practice.
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Correa Geyer F, Reis-Filho JS. Microarray-based Gene Expression Profiling as a Clinical Tool for Breast Cancer Management: Are We There Yet? Int J Surg Pathol 2008; 17:285-302. [DOI: 10.1177/1066896908328577] [Citation(s) in RCA: 56] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Breast cancer is a heterogeneous disease, encompassing several histological types and clinical behaviors. Current histopathological classification systems are based on descriptive entities with prognostic significance. Few prognostic and predictive markers beyond those offered by histopathological analysis are available. High-throughput molecular technologies are reshaping our understanding of breast cancer, of which microarray-based gene expression has received most attention. This method has been used to derive a molecular taxonomy for breast cancer, which has provided interesting insights into the biology of the disease. Class prediction studies have generated a multitude of prognostic/predictive signatures, which herald the promise for an improvement in treatment decision making. However, most of the signatures developed to date seem to have discriminatory power almost restricted to estrogen receptor—positive disease. This review addresses the contribution of gene expression profiling to our understanding of breast cancer and its clinical management.
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Affiliation(s)
- Felipe Correa Geyer
- Molecular Pathology Laboratory, Breakthrough Breast Cancer Research Centre, Institute of Cancer Research, London, UK,
| | - Jorge Sergio Reis-Filho
- Molecular Pathology Laboratory, Breakthrough Breast Cancer Research Centre, Institute of Cancer Research, London, UK,
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