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Harnan S, Tappenden P, Cooper K, Stevens J, Bessey A, Rafia R, Ward S, Wong R, Stein RC, Brown J. Tumour profiling tests to guide adjuvant chemotherapy decisions in early breast cancer: a systematic review and economic analysis. Health Technol Assess 2020; 23:1-328. [PMID: 31264581 DOI: 10.3310/hta23300] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Breast cancer and its treatment can have an impact on health-related quality of life and survival. Tumour profiling tests aim to identify whether or not women need chemotherapy owing to their risk of relapse. OBJECTIVES To conduct a systematic review of the effectiveness and cost-effectiveness of the tumour profiling tests oncotype DX® (Genomic Health, Inc., Redwood City, CA, USA), MammaPrint® (Agendia, Inc., Amsterdam, the Netherlands), Prosigna® (NanoString Technologies, Inc., Seattle, WA, USA), EndoPredict® (Myriad Genetics Ltd, London, UK) and immunohistochemistry 4 (IHC4). To develop a health economic model to assess the cost-effectiveness of these tests compared with clinical tools to guide the use of adjuvant chemotherapy in early-stage breast cancer from the perspective of the NHS and Personal Social Services. DESIGN A systematic review and health economic analysis were conducted. REVIEW METHODS The systematic review was partially an update of a 2013 review. Nine databases were searched in February 2017. The review included studies assessing clinical effectiveness in people with oestrogen receptor-positive, human epidermal growth factor receptor 2-negative, stage I or II cancer with zero to three positive lymph nodes. The economic analysis included a review of existing analyses and the development of a de novo model. RESULTS A total of 153 studies were identified. Only one completed randomised controlled trial (RCT) using a tumour profiling test in clinical practice was identified: Microarray In Node-negative Disease may Avoid ChemoTherapy (MINDACT) for MammaPrint. Other studies suggest that all the tests can provide information on the risk of relapse; however, results were more varied in lymph node-positive (LN+) patients than in lymph node-negative (LN0) patients. There is limited and varying evidence that oncotype DX and MammaPrint can predict benefit from chemotherapy. The net change in the percentage of patients with a chemotherapy recommendation or decision pre/post test ranged from an increase of 1% to a decrease of 23% among UK studies and a decrease of 0% to 64% across European studies. The health economic analysis suggests that the incremental cost-effectiveness ratios for the tests versus current practice are broadly favourable for the following scenarios: (1) oncotype DX, for the LN0 subgroup with a Nottingham Prognostic Index (NPI) of > 3.4 and the one to three positive lymph nodes (LN1-3) subgroup (if a predictive benefit is assumed); (2) IHC4 plus clinical factors (IHC4+C), for all patient subgroups; (3) Prosigna, for the LN0 subgroup with a NPI of > 3.4 and the LN1-3 subgroup; (4) EndoPredict Clinical, for the LN1-3 subgroup only; and (5) MammaPrint, for no subgroups. LIMITATIONS There was only one completed RCT using a tumour profiling test in clinical practice. Except for oncotype DX in the LN0 group with a NPI score of > 3.4 (clinical intermediate risk), evidence surrounding pre- and post-test chemotherapy probabilities is subject to considerable uncertainty. There is uncertainty regarding whether or not oncotype DX and MammaPrint are predictive of chemotherapy benefit. The MammaPrint analysis uses a different data source to the other four tests. The Translational substudy of the Arimidex, Tamoxifen, Alone or in Combination (TransATAC) study (used in the economic modelling) has a number of limitations. CONCLUSIONS The review suggests that all the tests can provide prognostic information on the risk of relapse; results were more varied in LN+ patients than in LN0 patients. There is limited and varying evidence that oncotype DX and MammaPrint are predictive of chemotherapy benefit. Health economic analyses indicate that some tests may have a favourable cost-effectiveness profile for certain patient subgroups; all estimates are subject to uncertainty. More evidence is needed on the prediction of chemotherapy benefit, long-term impacts and changes in UK pre-/post-chemotherapy decisions. STUDY REGISTRATION This study is registered as PROSPERO CRD42017059561. FUNDING The National Institute for Health Research Health Technology Assessment programme.
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Affiliation(s)
- Sue Harnan
- Health Economics and Decision Science, School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Paul Tappenden
- Health Economics and Decision Science, School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Katy Cooper
- Health Economics and Decision Science, School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - John Stevens
- Health Economics and Decision Science, School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Alice Bessey
- Health Economics and Decision Science, School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Rachid Rafia
- Health Economics and Decision Science, School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Sue Ward
- Health Economics and Decision Science, School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Ruth Wong
- Health Economics and Decision Science, School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Robert C Stein
- University College London Hospitals Biomedical Research Centre, London, UK.,Research Department of Oncology, University College London, London, UK
| | - Janet Brown
- Department of Oncology and Metabolism, University of Sheffield, Sheffield, UK
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Adabor ES, Acquaah-Mensah GK, Mazandu GK. MSclassifier: median-supplement model-based classification tool for automated knowledge discovery. F1000Res 2020; 9:1114. [PMID: 33456763 PMCID: PMC7788522 DOI: 10.12688/f1000research.25501.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/03/2020] [Indexed: 11/20/2022] Open
Abstract
High-throughput technologies have resulted in an exponential growth of publicly available and accessible datasets for biomedical research. Efficient computational models, algorithms and tools are required to exploit the datasets for knowledge discovery to aid medical decisions. Here, we introduce a new tool, MSclassifier, based on median-supplement approaches to machine learning to enable an automated and effective binary classification for optimal decision making. The MSclassifier package estimates medians of features (attributes) to deduce supplementary data, which is subsequently introduced into the training set for balancing and building superior models for classification. To test our approach, it is used to determine HER2 receptor expression status phenotypes in breast cancer and also predict protein subcellular localization (plasma membrane and nucleus). Using independent sample and cross-validation tests, the performance of MSclassifier is evaluated and compared with well established tools that could perform such tasks. In the HER2 receptor expression status phenotype identification tasks, MSclassifier achieved statistically significant higher classification rates than the best performing existing tool (90.30% versus 89.83%, p=8.62e-3). In the subcellular localization prediction tasks, MSclassifier and one other existing tool achieved equally high performances (93.42% versus 93.19%, p=0.06) although they both outperformed tools based on Naive Bayes classifiers. Overall, the application and evaluation of MSclassifier reveal its potential to be applied to varieties of binary classification problems. The MSclassifier package provides an R-portable and user-friendly application to a broad audience, enabling experienced end-users as well as non-programmers to perform an effective classification in biomedical and other fields of study.
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Affiliation(s)
- Emmanuel S. Adabor
- School of Technology, Ghana Institute of Management and Public Administration, Accra, Ghana
| | - George K. Acquaah-Mensah
- Pharmaceutical Sciences Department, Massachusetts College of Pharmacy and Health Sciences, Worcester, MA, USA
| | - Gaston K. Mazandu
- African Institute for Mathematical Sciences and Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
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Sharma A, Rani R. C-HMOSHSSA: Gene selection for cancer classification using multi-objective meta-heuristic and machine learning methods. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 178:219-235. [PMID: 31416551 DOI: 10.1016/j.cmpb.2019.06.029] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2019] [Revised: 06/24/2019] [Accepted: 06/27/2019] [Indexed: 05/21/2023]
Abstract
BACKGROUND AND OBJECTIVE Over the last two decades, DNA microarray technology has emerged as a powerful tool for early cancer detection and prevention. It helps to provide a detailed overview of disease complex microenvironment. Moreover, online availability of thousands of gene expression assays made microarray data classification an active research area. A common goal is to find a minimum subset of genes and maximizing the classification accuracy. METHODS In pursuit of a similar objective, we have proposed framework (C-HMOSHSSA) for gene selection using multi-objective spotted hyena optimizer (MOSHO) and salp swarm algorithm (SSA). The real-life optimization problems with more than one objective usually face the challenge to maintain convergence and diversity. Salp Swarm Algorithm (SSA) maintains diversity but, suffers from the overhead of maintaining the necessary information. On the other hand, the calculation of MOSHO requires low computational efforts hence is used for maintaining the necessary information. Therefore, the proposed algorithm is a hybrid algorithm that utilizes the features of both SSA and MOSHO to facilitate its exploration and exploitation capability. RESULTS Four different classifiers are trained on seven high-dimensional datasets using a subset of features (genes), which are obtained after applying the proposed hybrid gene selection algorithm. The results show that the proposed technique significantly outperforms existing state-of-the-art techniques. CONCLUSION It is also shown that the new sets of informative and biologically relevant genes are successfully identified by the proposed technique. The proposed approach can also be applied to other problem domains of interest which involve feature selection.
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Affiliation(s)
- Aman Sharma
- Computer Science and Engineering Department, Thapar Institute of Engineering & Technology, Patiala, Punjab, India.
| | - Rinkle Rani
- Computer Science and Engineering Department, Thapar Institute of Engineering & Technology, Patiala, Punjab, India.
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Nagarajan R, Miller CS, Dawson D, Ebersole JL. Biologic modelling of periodontal disease progression. J Clin Periodontol 2019; 46:160-169. [DOI: 10.1111/jcpe.13064] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Revised: 12/03/2018] [Accepted: 01/05/2019] [Indexed: 12/17/2022]
Affiliation(s)
- Radhakrishnan Nagarajan
- Division of Biomedical Informatics College of Medicine University of Kentucky Lexington Kentucky
| | - Craig S. Miller
- Division of Oral Diagnosis, Oral Medicine and Oral Radiology University of Kentucky Lexington Kentucky
- Center for Oral Health Research College of Dentistry University of Kentucky Lexington Kentucky
| | - Dolph Dawson
- Center for Oral Health Research College of Dentistry University of Kentucky Lexington Kentucky
- Division of Periodontics University of Kentucky Lexington Kentucky
| | - Jeffrey L. Ebersole
- Center for Oral Health Research College of Dentistry University of Kentucky Lexington Kentucky
- Department of Biomedical Sciences School of Dental Medicine University of Nevada Las Vegas Las Vegas Nevada
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An ensemble predictive modeling framework for breast cancer classification. Methods 2017; 131:128-134. [DOI: 10.1016/j.ymeth.2017.07.011] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2017] [Revised: 07/11/2017] [Accepted: 07/12/2017] [Indexed: 12/22/2022] Open
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Adabor ES, Acquaah-Mensah GK. Machine learning approaches to decipher hormone and HER2 receptor status phenotypes in breast cancer. Brief Bioinform 2017; 20:504-514. [DOI: 10.1093/bib/bbx138] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2017] [Revised: 09/27/2017] [Indexed: 11/14/2022] Open
Affiliation(s)
- Emmanuel S Adabor
- African Institute for Mathematical Sciences, Muizenberg, South Africa
| | - George K Acquaah-Mensah
- Massachusetts College of Pharmacy and Health Sciences, Pharmaceutical Sciences, Worcester, Massachusetts, United States
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Ebersole JL, Dawson D, Emecen-Huja P, Nagarajan R, Howard K, Grady ME, Thompson K, Peyyala R, Al-Attar A, Lethbridge K, Kirakodu S, Gonzalez OA. The periodontal war: microbes and immunity. Periodontol 2000 2017; 75:52-115. [DOI: 10.1111/prd.12222] [Citation(s) in RCA: 95] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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Nagarajan R, Al-Sabbagh M, Dawson D, Ebersole JL. Integrated biomarker profiling of smokers with periodontitis. J Clin Periodontol 2017; 44:238-246. [PMID: 27925695 DOI: 10.1111/jcpe.12659] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/26/2016] [Indexed: 02/06/2023]
Abstract
BACKGROUND In the context of precision medicine, understanding patient-specific variation is an important step in developing targeted and patient-tailored treatment regimens for periodontitis. While several studies have successfully demonstrated the usefulness of molecular expression profiling in conjunction with single classifier systems in discerning distinct disease groups, the majority of these studies do not provide sufficient insights into potential variations within the disease groups. AIM The goal of this study was to discern biological response profiles of periodontitis and non-periodontitis smoking subjects using an informed panel of biomarkers across multiple scales (salivary, oral microbiome, pathogens and other markers). MATERIAL & METHODS The investigation uses a novel ensemble classification approach (SVA-SVM) to differentiate disease groups and patient-specific biological variation of systemic inflammatory mediators and IgG antibody to oral commensal and pathogenic bacteria within the groups. RESULTS Sensitivity of SVA-SVM is shown to be considerably higher than several traditional independent classifier systems. Patient-specific networks generated from SVA-SVM are also shown to reveal crosstalk between biomarkers in discerning the disease groups. High-confidence classifiers in these network abstractions comprised of host responses to microbial infection elucidated their critical role in discerning the disease groups. CONCLUSIONS Host adaptive immune responses to the oral colonization/infection contribute significantly to creating the profiles specific for periodontitis patients with potential to assist in defining patient-specific risk profiles and tailored interventions.
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Affiliation(s)
- Radhakrishnan Nagarajan
- Division of Biomedical Informatics, College of Medicine, University of Kentucky, Lexington, KY, USA
| | - Mohanad Al-Sabbagh
- Division of Periodontics, College of Dentistry, University of Kentucky, Lexington, KY, USA
| | - Dolph Dawson
- Division of Periodontics, College of Dentistry, University of Kentucky, Lexington, KY, USA.,Center for Oral Health Research, College of Dentistry, University of Kentucky, Lexington, KY, USA
| | - Jeffrey L Ebersole
- Division of Periodontics, College of Dentistry, University of Kentucky, Lexington, KY, USA.,Center for Oral Health Research, College of Dentistry, University of Kentucky, Lexington, KY, USA
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Beumer IJ, Persoon M, Witteveen A, Dreezen C, Chin SF, Sammut SJ, Snel M, Caldas C, Linn S, van ’t Veer LJ, Bernards R, Glas AM. Prognostic Value of MammaPrint ® in Invasive Lobular Breast Cancer. Biomark Insights 2016; 11:139-146. [PMID: 27980389 PMCID: PMC5153320 DOI: 10.4137/bmi.s38435] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2016] [Revised: 10/16/2016] [Accepted: 10/22/2016] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND MammaPrint® is a microarray-based gene expression test cleared by the US Food and Drug Administration to assess recurrence risk in early-stage breast cancer, aimed to guide physicians in making neoadjuvant and adjuvant treatment decisions. The increase in the incidence of invasive lobular carcinomas (ILCs) over the past decades and the modest representation of ILC in the MammaPrint development data set calls for a stratified survival analysis dedicated to this specific subgroup. STUDY AIM The current study aimed to validate the prognostic value of the MammaPrint test for breast cancer patients with early-stage ILCs. MATERIALS AND METHODS Univariate and multivariate survival associations for overall survival (OS), distant metastasis-free interval (DMFI), and distant metastasis-free survival (DMFS) were studied in a study population of 217 early-stage ILC breast cancer patients from five different clinical studies. RESULTS AND DISCUSSION A significant association between MammaPrint High Risk and poor clinical outcome was shown for OS, DMFI, and DMFS. A subanalysis was performed on the lymph node-negative study population. In the lymph node-negative study population, we report an up to 11 times higher change in the diagnosis of an event in the MammaPrint High Risk group. For DMFI, the reported hazard ratio is 11.1 (95% confidence interval = 2.3-53.0). CONCLUSION Study results validate MammaPrint as an independent factor for breast cancer patients with early-stage invasive lobular breast cancer. Hazard ratios up to 11 in multivariate analyses emphasize the independent value of MammaPrint, specifically in lymph node-negative ILC breast cancers.
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Affiliation(s)
| | | | | | | | - Suet-Feung Chin
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK
| | - Stephen-John Sammut
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK
| | - Mireille Snel
- Agendia NV, Science Park, Amsterdam, the Netherlands
| | - Carlos Caldas
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK
| | - Sabine Linn
- Division of Molecular Pathology, Netherlands Cancer Institute, Plesmanlaan, Amsterdam, the Netherlands
- Division of Medical Oncology, Netherlands Cancer Institute, Plesmanlaan, Amsterdam, the Netherlands
- Department of Pathology, University Medical Center Utrecht, Heidelberglaan, Utrecht, the Netherlands
| | - Laura J. van ’t Veer
- Agendia NV, Science Park, Amsterdam, the Netherlands
- Helen Diller Family Comprehensive Cancer Center, University of California at San Francisco, San Francisco, CA, USA
| | - Rene Bernards
- Agendia NV, Science Park, Amsterdam, the Netherlands
- Division of Molecular Carcinogenesis, Cancer Genomics Centre, Utrecht, the Netherlands
- Division of Molecular Carcinogenesis, Cancer Genomics Centre Netherlands. Netherlands Cancer Institute, Amsterdam, the Netherlands
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