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Banjar H, Ranasinghe D, Brown F, Adelson D, Kroger T, Leclercq T, White D, Hughes T, Chaudhri N. Modelling Predictors of Molecular Response to Frontline Imatinib for Patients with Chronic Myeloid Leukaemia. PLoS One 2017; 12:e0168947. [PMID: 28045960 PMCID: PMC5207707 DOI: 10.1371/journal.pone.0168947] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2016] [Accepted: 12/08/2016] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Treatment of patients with chronic myeloid leukaemia (CML) has become increasingly difficult in recent years due to the variety of treatment options available and challenge deciding on the most appropriate treatment strategy for an individual patient. To facilitate the treatment strategy decision, disease assessment should involve molecular response to initial treatment for an individual patient. Patients predicted not to achieve major molecular response (MMR) at 24 months to frontline imatinib may be better treated with alternative frontline therapies, such as nilotinib or dasatinib. The aims of this study were to i) understand the clinical prediction 'rules' for predicting MMR at 24 months for CML patients treated with imatinib using clinical, molecular, and cell count observations (predictive factors collected at diagnosis and categorised based on available knowledge) and ii) develop a predictive model for CML treatment management. This predictive model was developed, based on CML patients undergoing imatinib therapy enrolled in the TIDEL II clinical trial with an experimentally identified achieving MMR group and non-achieving MMR group, by addressing the challenge as a machine learning problem. The recommended model was validated externally using an independent data set from King Faisal Specialist Hospital and Research Centre, Saudi Arabia. PRINCIPLE FINDINGS The common prognostic scores yielded similar sensitivity performance in testing and validation datasets and are therefore good predictors of the positive group. The G-mean and F-score values in our models outperformed the common prognostic scores in testing and validation datasets and are therefore good predictors for both the positive and negative groups. Furthermore, a high PPV above 65% indicated that our models are appropriate for making decisions at diagnosis and pre-therapy. Study limitations include that prior knowledge may change based on varying expert opinions; hence, representing the category boundaries of each predictive factor could dramatically change performance of the models.
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Affiliation(s)
- Haneen Banjar
- School of Computer Science, University of Adelaide, Adelaide, South Australia, Australia
- The Department of Computer Science, King AbdulAziz University, Jeddah, Saudi Arabia
| | - Damith Ranasinghe
- School of Computer Science, University of Adelaide, Adelaide, South Australia, Australia
- Auto-ID Lab, School of Computer Science, University of Adelaide, Adelaide, South Australia, Australia
| | - Fred Brown
- School of Computer Science, University of Adelaide, Adelaide, South Australia, Australia
| | - David Adelson
- School of Molecular and Biomedical Science, The University of Adelaide, Adelaide, Australia
| | - Trent Kroger
- School of Computer Science, University of Adelaide, Adelaide, South Australia, Australia
| | - Tamara Leclercq
- Cancer Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide. South Australia, Australia
- University of Adelaide, Discipline of Medicine, Adelaide, South Australia, Australia
| | - Deborah White
- Cancer Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide. South Australia, Australia
- University of Adelaide, Discipline of Medicine, Adelaide, South Australia, Australia
- University of Adelaide, Discipline of Paediatrics, Adelaide, South Australia, Australia
- Centre for Cancer Biology, University of South Australia, Adelaide, South Australia, Australia
- Centre for Personalised Cancer Medicine, University of Adelaide, Adelaide, South Australia, Australia
| | - Timothy Hughes
- Cancer Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide. South Australia, Australia
- University of Adelaide, Discipline of Medicine, Adelaide, South Australia, Australia
- Centre for Cancer Biology, University of South Australia, Adelaide, South Australia, Australia
- Centre for Personalised Cancer Medicine, University of Adelaide, Adelaide, South Australia, Australia
- Haematology Department, SA Pathology, Adelaide, South Australia, Australia
| | - Naeem Chaudhri
- King Faisal Specialist Hospital and Research Centre, Oncology Center, Riyadh, Saudi Arabia
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Al-Dewik NI, Jewell AP, Yassin MA, El-Ayoubi HR, Morsi HM. Molecular Monitoring of patients with Chronic Myeloid Leukemia (CML) in the state of Qatar: Optimization of Techniques and Response to Imatinib. QSCIENCE CONNECT 2014. [DOI: 10.5339/connect.2014.24] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Affiliation(s)
- Nader I. Al-Dewik
- 1National Center for Cancer Care and Research (NCCCR), Hamad Medical Corporation (HMC), Doha, Qatar
- 2Qatar Medical Genetics Center, Hamad General Hospital (HGH), HMC, Doha, Qatar
- 4Faculty of Health and Social Care Sciences, Kingston University and St George's University of London, London, United Kingdom
| | - Andrew P. Jewell
- 3Medical Research Centre, HMC, Doha, Qatar
- 4Faculty of Health and Social Care Sciences, Kingston University and St George's University of London, London, United Kingdom
| | - Mohammed A. Yassin
- 1National Center for Cancer Care and Research (NCCCR), Hamad Medical Corporation (HMC), Doha, Qatar
| | - Hanadi R. El-Ayoubi
- 1National Center for Cancer Care and Research (NCCCR), Hamad Medical Corporation (HMC), Doha, Qatar
| | - Hisham M. Morsi
- 3Medical Research Centre, HMC, Doha, Qatar
- 4Faculty of Health and Social Care Sciences, Kingston University and St George's University of London, London, United Kingdom
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