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Anghel C, Deng D, Golesorkhi S, Shreeves P, Bingham D, Trottier A. Emulating loss of coolant simulations in a pressurized heavy water reactor. ANN NUCL ENERGY 2022. [DOI: 10.1016/j.anucene.2022.109311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
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Milligan K, Van Nest SJ, Deng X, Ali-Adeeb R, Shreeves P, Punch S, Costie N, Pavey N, Crook JM, Berman DM, Brolo AG, Lum JJ, Andrews JL, Jirasek A. Raman spectroscopy and supervised learning as a potential tool to identify high-dose-rate-brachytherapy induced biochemical profiles of prostate cancer. J Biophotonics 2022; 15:e202200121. [PMID: 35908273 DOI: 10.1002/jbio.202200121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 06/14/2022] [Accepted: 07/08/2022] [Indexed: 06/15/2023]
Abstract
High-dose-rate-brachytherapy (HDR-BT) is an increasingly attractive alternative to external beam radiation-therapy for patients with intermediate risk prostate cancer. Despite this, no bio-marker based method currently exists to monitor treatment response, and the changes which take place at the biochemical level in hypo-fractionated HDR-BT remain poorly understood. The aim of this pilot study is to assess the capability of Raman spectroscopy (RS) combined with principal component analysis (PCA) and random-forest classification (RF) to identify radiation response profiles after a single dose of 13.5 Gy in a cohort of nine patients. We here demonstrate, as a proof-of-concept, how RS-PCA-RF could be utilised as an effective tool in radiation response monitoring, specifically assessing the importance of low variance PCs in complex sample sets. As RS provides information on the biochemical composition of tissue samples, this technique could provide insight into the changes which take place on the biochemical level, as result of HDR-BT treatment.
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Affiliation(s)
- Kirsty Milligan
- Department of Physics, University of British Columbia, Kelowna, Canada
| | - Samantha J Van Nest
- Trev and Joyce Deeley Research Centre, BC Cancer-Victoria, Victoria, Canada
- Department of Radiation Oncology, Weill Cornell Medicine, New York, New York, USA
| | - Xinchen Deng
- Department of Physics, University of British Columbia, Kelowna, Canada
| | - Ramie Ali-Adeeb
- Department of Physics, University of British Columbia, Kelowna, Canada
| | - Phillip Shreeves
- Department of Mathematics and Statistics, University of British Columbia, Kelowna, Canada
| | - Samantha Punch
- Trev and Joyce Deeley Research Centre, BC Cancer-Victoria, Victoria, Canada
| | - Nathalie Costie
- Trev and Joyce Deeley Research Centre, BC Cancer-Victoria, Victoria, Canada
| | - Nils Pavey
- Trev and Joyce Deeley Research Centre, BC Cancer-Victoria, Victoria, Canada
| | - Juanita M Crook
- Sindi Ahluwalia Hawkins Centre for the Southern Interior, BC Cancer, Kelowna, Canada
- Department of Radiation Oncology, University of British Columbia, Kelowna, Canada
| | - David M Berman
- Department of Pathology and Molecular Medicine, Queens University, Kingston, Canada
| | | | - Julian J Lum
- Trev and Joyce Deeley Research Centre, BC Cancer-Victoria, Victoria, Canada
- Department of Biochemistry and Microbiology, University of Victoria, Victoria, Canada
| | - Jeffrey L Andrews
- Department of Mathematics and Statistics, University of British Columbia, Kelowna, Canada
| | - Andrew Jirasek
- Department of Physics, University of British Columbia, Kelowna, Canada
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Milligan K, Deng X, Ali-Adeeb R, Shreeves P, Punch S, Costie N, Crook JM, Brolo AG, Lum JJ, Andrews JL, Jirasek A. Prediction of disease progression indicators in prostate cancer patients receiving HDR-brachytherapy using Raman spectroscopy and semi-supervised learning: a pilot study. Sci Rep 2022; 12:15104. [PMID: 36068275 PMCID: PMC9448740 DOI: 10.1038/s41598-022-19446-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 08/29/2022] [Indexed: 11/09/2022] Open
Abstract
This work combines Raman spectroscopy (RS) with supervised learning methods-group and basis restricted non-negative matrix factorisation (GBR-NMF) and linear discriminant analysis (LDA)-to aid in the prediction of clinical indicators of disease progression in a cohort of 9 patients receiving high dose rate brachytherapy (HDR-BT) as the primary treatment for intermediate risk (D'Amico) prostate adenocarcinoma. The combination of Raman spectroscopy and GBR-NMF-sparseLDA modelling allowed for the prediction of the following clinical information; Gleason score, cancer of the prostate risk assessment (CAPRA) score of pre-treatment biopsies and a Ki67 score of < 3.5% or > 3.5% in post treatment biopsies. The three clinical indicators of disease progression investigated in this study were predicted using a single set of Raman spectral data acquired from each individual biopsy, obtained pre HDR-BT treatment. This work highlights the potential of RS, combined with supervised learning, as a tool for the prediction of multiple types of clinically relevant information to be acquired simultaneously using pre-treatment biopsies, therefore opening up the potential for avoiding the need for multiple immunohistochemistry (IHC) staining procedures (H&E, Ki67) and blood sample analysis (PSA) to aid in CAPRA scoring.
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Affiliation(s)
- Kirsty Milligan
- Department of Physics, University of British Columbia, Kelowna, BC, Canada
| | - Xinchen Deng
- Department of Physics, University of British Columbia, Kelowna, BC, Canada
| | - Ramie Ali-Adeeb
- Department of Physics, University of British Columbia, Kelowna, BC, Canada
| | - Phillip Shreeves
- Department of Statistics, University of British Columbia, Kelowna, Canada
| | - Samantha Punch
- Trev and Joyce Deeley Research Centre, BC Cancer, Victoria, BC, Canada
| | - Nathalie Costie
- Trev and Joyce Deeley Research Centre, BC Cancer, Victoria, BC, Canada
| | - Juanita M Crook
- Department of Radiation Oncology, University of British Columbia, Kelowna, BC, Canada
| | - Alexandre G Brolo
- Department of Chemistry, University of Victoria, British Columbia, Canada
| | - Julian J Lum
- Trev and Joyce Deeley Research Centre, BC Cancer, Victoria, BC, Canada.,Department of Biochemistry and Microbiology, University of Victoria, Victoria, Canada
| | - Jeffrey L Andrews
- Department of Statistics, University of British Columbia, Kelowna, Canada
| | - Andrew Jirasek
- Department of Physics, University of British Columbia, Kelowna, BC, Canada.
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Deng X, Milligan K, Ali-Adeeb R, Shreeves P, Brolo A, Lum JJ, Andrews JL, Jirasek A. Group and Basis Restricted Non-Negative Matrix Factorization and Random Forest for Molecular Histotype Classification and Raman Biomarker Monitoring in Breast Cancer. Appl Spectrosc 2022; 76:462-474. [PMID: 34355582 PMCID: PMC9003771 DOI: 10.1177/00037028211035398] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Raman spectroscopy is a non-invasive optical technique that can be used to investigate biochemical information embedded in cells and tissues exposed to ionizing radiation used in cancer therapy. Raman spectroscopy could potentially be incorporated in personalized radiation treatment design as a tool to monitor radiation response in at the metabolic level. However, tracking biochemical dynamics remains challenging for Raman spectroscopy. Here we developed a novel analytical framework by combining group and basis restricted non-negative matrix factorization and random forest (GBR-NMF-RF). This framework can monitor radiation response profiles in different molecular histotypes and biochemical dynamics in irradiated breast cancer cells. Five subtypes of; human breast cancer (MCF-7, BT-474, MDA-MB-230, and SK-BR-3) and normal cells derived from human breast tissue (MCF10A) which had been exposed to ionizing radiation were tested in this framework. Reference Raman spectra of 20 biochemicals were collected and used as the constrained Raman biomarkers in the GBR-NMF-RF framework. We obtained scores for individual biochemicals corresponding to the contribution of each Raman reference spectrum to each spectrum obtained from the five cell types. A random forest classifier was then fitted to the chemical scores for performing molecular histotype classifications (HER2, PR, ER, Ki67, and cancer versus non-cancer) and assessing the importance of the Raman biochemical basis spectra for each classification test. Overall, the GBR-NMF-RF framework yields classification results with high accuracy (>97%), high sensitivity (>97%), and high specificity (>97%). Variable importance calculated in the random forest model indicated high contributions from glycogen and lipids (cholesterol, phosphatidylserine, and stearic acid) in molecular histotype classifications.
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Affiliation(s)
- Xinchen Deng
- Department of Physics, The University of British Columbia Kelowna, Canada
| | - Kirsty Milligan
- Department of Physics, The University of British Columbia Kelowna, Canada
| | - Ramie Ali-Adeeb
- Department of Physics, The University of British Columbia Kelowna, Canada
| | - Phillip Shreeves
- Department of Statistics, The University of British Columbia, Kelowna, Canada
| | - Alexandre Brolo
- Department of Chemistry, University of Victoria, Victoria, Canada
| | - Julian J. Lum
- Department of Biochemistry and Microbiology, University of Victoria, Victoria, Canada
- Trev and Joyce Deeley Research Centre, BC Cancer, Victoria, Canada
| | - Jeffrey L. Andrews
- Department of Statistics, The University of British Columbia, Kelowna, Canada
| | - Andrew Jirasek
- Department of Physics, The University of British Columbia Kelowna, Canada
- Andrew Jirasek, Department of Physics, The University of British Columbia–Okanagan Campus, Kelowna V1V 1V7, Canada.
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Milligan K, Deng X, Shreeves P, Ali-Adeeb R, Matthews Q, Brolo A, Lum JJ, Andrews JL, Jirasek A. Raman spectroscopy and group and basis-restricted non negative matrix factorisation identifies radiation induced metabolic changes in human cancer cells. Sci Rep 2021; 11:3853. [PMID: 33594122 PMCID: PMC7886912 DOI: 10.1038/s41598-021-83343-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 01/11/2021] [Indexed: 12/12/2022] Open
Abstract
This work combines single cell Raman spectroscopy (RS) with group and basis restricted non-negative matrix factorisation (GBR-NMF) to identify individual biochemical changes associated with radiation exposure in three human cancer cell lines. The cell lines analysed were derived from lung (H460), breast (MCF7) and prostate (LNCaP) tissue and are known to display varying degrees of radio sensitivity due to the inherent properties of each cell type. The GBR-NMF approach involves the deconstruction of Raman spectra into component biochemical bases using a library of Raman spectra of known biochemicals present in the cells. Subsequently, scores are obtained on each of these bases which can be directly correlated with the contribution of each chemical to the overall Raman spectrum. We validated GBR-NMF through the correlation of GBR-NMF-derived glycogen scores with scores that were previously observed using principal component analysis (PCA). Phosphatidylcholine, glucose, arginine and asparagine showed a distinct differential score pattern between radio-resistant and radio-sensitive cell types. In summary, the GBR-NMF approach allows for the monitoring of individual biochemical radiation-response dynamics previously unattainable with more traditional PCA-based approaches.
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Affiliation(s)
- Kirsty Milligan
- Department of Physics, The University of British Columbia, Kelowna, Canada
| | - Xinchen Deng
- Department of Physics, The University of British Columbia, Kelowna, Canada
| | - Phillip Shreeves
- Department of Statistics, The University of British Columbia, Kelowna, Canada
| | - Ramie Ali-Adeeb
- Department of Physics, The University of British Columbia, Kelowna, Canada
| | | | - Alexandre Brolo
- Department of Chemistry, University of Victoria, Victoria, Canada
| | - Julian J Lum
- Department of Biochemistry and Microbiology, University of Victoria, Victoria, Canada
- Trev and Joyce Deeley Research Centre, BC Cancer, Victoria, Canada
| | - Jeffrey L Andrews
- Department of Statistics, The University of British Columbia, Kelowna, Canada
| | - Andrew Jirasek
- Department of Physics, The University of British Columbia, Kelowna, Canada.
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Deng X, Ali-Adeeb R, Andrews JL, Shreeves P, Lum JJ, Brolo A, Jirasek A. Monitor Ionizing Radiation-Induced Cellular Responses with Raman Spectroscopy, Non-Negative Matrix Factorization, and Non-Negative Least Squares. Appl Spectrosc 2020; 74:701-711. [PMID: 32098482 DOI: 10.1177/0003702820906221] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Radiation therapy (RT) is one of the most commonly prescribed cancer treatments. New tools that can accurately monitor and evaluate individual patient responses would be a major advantage and lend to the implementation of personalized treatment plans. In this study, Raman spectroscopy (RS) was applied to examine radiation-induced cellular responses in H460, MCF7, and LNCaP cancer cell lines across different dose levels and times post-irradiation. Previous Raman data analysis was conducted using principal component analysis (PCA), which showed the ability to extract biological information of glycogen. In the current studies, the use of non-negative matrix factorization (NMF) allowed for the discovery of multiplexed biological information, specifically uncovering glycogen-like and lipid-like component bases. The corresponding scores of glycogen and previously unidentified lipids revealed the content variations of these two chemicals in the cellular data. The NMF decomposed glycogen and lipid-like bases were able to separate the cancer cell lines into radiosensitive and radioresistant groups. A further lipid phenotype investigation was also attempted by applying non-negative least squares (NNLS) to the lipid-like bases decomposed individually from three cell lines. Qualitative differences found in lipid weights for each lipid-like basis suggest the lipid phenotype differences in the three tested cancer cell lines. Collectively, this study demonstrates that the application of NMF and NNLS on RS data analysis to monitor ionizing radiation-induced cellular responses can yield multiplexed biological information on bio-response to RT not revealed by conventional chemometric approaches.
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Affiliation(s)
- Xinchen Deng
- Department of Physics, I.K. Barber School of Arts and Sciences, The University of British Columbia, Kelowna, Canada
| | - Ramie Ali-Adeeb
- Department of Physics, I.K. Barber School of Arts and Sciences, The University of British Columbia, Kelowna, Canada
| | - Jeffrey L Andrews
- Department of Statistics, I.K. Barber School of Arts and Sciences, The University of British Columbia, Kelowna, Canada
| | - Phillip Shreeves
- Department of Statistics, I.K. Barber School of Arts and Sciences, The University of British Columbia, Kelowna, Canada
| | - Julian J Lum
- Department of Biochemistry and Microbiology, University of Victoria, Victoria, Canada
- Trev and Joyce Deeley Research Centre, BC Cancer, Victoria, Canada
| | - Alexandre Brolo
- Department of Chemistry, University of Victoria, Victoria, Canada
| | - Andrew Jirasek
- Department of Physics, I.K. Barber School of Arts and Sciences, The University of British Columbia, Kelowna, Canada
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Shreeves P, Andrews JL. A bootstrap‐augmented alternating expectation‐conditional maximization algorithm for mixtures of factor analyzers. Stat (Int Stat Inst) 2019. [DOI: 10.1002/sta4.243] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Phillip Shreeves
- Department of StatisticsThe University of British Columbia, Okanagan Campus Kelowna British Columbia V1V 1V7 Canada
| | - Jeffrey L. Andrews
- Department of StatisticsThe University of British Columbia, Okanagan Campus Kelowna British Columbia V1V 1V7 Canada
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