Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/140112
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Type: | Journal article |
Title: | A Boolean-based machine learning framework identifies predictive biomarkers of HSP90-targeted therapy response in prostate cancer |
Author: | Shin, S.Y. Centenera, M.M. Hodgson, J.T. Nguyen, E.V. Butler, L.M. Daly, R.J. Nguyen, L.K. |
Citation: | Frontiers in Molecular Biosciences, 2023; 10:1094321-1-1094321-16 |
Publisher: | Frontiers Media S.A. |
Issue Date: | 2023 |
ISSN: | 2296-889X 2296-889X |
Statement of Responsibility: | Sung-Young Shin, Margaret M. Centenera, Joshua T. Hodgson, Elizabeth V. Nguyen, Lisa M. Butler, Roger J. Daly and Lan K. Nguyen |
Abstract: | Precision medicine has emerged as an important paradigm in oncology, driven by the significant heterogeneity of individual patients' tumour. A key prerequisite for effective implementation of precision oncology is the development of companion biomarkers that can predict response to anti-cancer therapies and guide patient selection for clinical trials and/or treatment. However, reliable predictive biomarkers are currently lacking for many anti-cancer therapies, hampering their clinical application. Here, we developed a novel machine learning-based framework to derive predictive multi-gene biomarker panels and associated expression signatures that accurately predict cancer drug sensitivity. We demonstrated the power of the approach by applying it to identify response biomarker panels for an Hsp90-based therapy in prostate cancer, using proteomic data profiled from prostate cancer patient-derived explants. Our approach employs a rational feature section strategy to maximise model performance, and innovatively utilizes Boolean algebra methods to derive specific expression signatures of the marker proteins. Given suitable data for model training, the approach is also applicable to other cancer drug agents in different tumour settings. |
Keywords: | 17-AAG Boolean function minimization feature selection Hsp90 inhibitor machine learning precision oncology predictive biomarker prostate cancer |
Rights: | © 2023 Shin, Centenera, Hodgson, Nguyen, Butler, Daly and Nguyen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
DOI: | 10.3389/fmolb.2023.1094321 |
Published version: | http://dx.doi.org/10.3389/fmolb.2023.1094321 |
Appears in Collections: | Medicine publications |
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hdl_140112.pdf | Published version | 3.35 MB | Adobe PDF | View/Open |
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