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Clinical validation of an immunohistochemistry‐based CanAssist‐Breast test for distant recurrence prediction in hormone receptor‐positive breast cancer patients
CanAssist‐Breast (CAB) is an immunohistochemistry (IHC)‐based prognostic test for early‐stage Hormone Receptor (HR+)‐positive breast cancer patients. CAB uses a Support Vector Machine (SVM) trained algorithm which utilizes expression levels of five biomarkers (CD44, ABCC4, ABCC11, N‐Cadherin, and Pa...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6488210/ https://www.ncbi.nlm.nih.gov/pubmed/30848103 http://dx.doi.org/10.1002/cam4.2049 |
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author | Bakre, Manjiri M. Ramkumar, Charusheila Attuluri, Arun Kumar Basavaraj, Chetana Prakash, Chandra Buturovic, Ljubomir Madhav, Lekshmi Naidu, Nirupama R, Prathima Somashekhar, S. P. Gupta, Sudeep Doval, Dinesh Chandra Pegram, Mark D. |
author_facet | Bakre, Manjiri M. Ramkumar, Charusheila Attuluri, Arun Kumar Basavaraj, Chetana Prakash, Chandra Buturovic, Ljubomir Madhav, Lekshmi Naidu, Nirupama R, Prathima Somashekhar, S. P. Gupta, Sudeep Doval, Dinesh Chandra Pegram, Mark D. |
author_sort | Bakre, Manjiri M. |
collection | PubMed |
description | CanAssist‐Breast (CAB) is an immunohistochemistry (IHC)‐based prognostic test for early‐stage Hormone Receptor (HR+)‐positive breast cancer patients. CAB uses a Support Vector Machine (SVM) trained algorithm which utilizes expression levels of five biomarkers (CD44, ABCC4, ABCC11, N‐Cadherin, and Pan‐Cadherin) and three clinical parameters such as tumor size, grade, and node status as inputs to generate a risk score and categorizes patients as low‐ or high‐risk for distant recurrence within 5 years of diagnosis. In this study, we present clinical validation of CAB. CAB was validated using a retrospective cohort of 857 patients. All patients were treated either with endocrine therapy or chemoendocrine therapy. Risk categorization by CAB was analyzed by calculating Distant Metastasis‐Free Survival (DMFS) and recurrence rates using Kaplan‐Meier survival curves. Multivariate analysis was performed to calculate Hazard ratios (HR) for CAB high‐risk vs low‐risk patients. The results showed that Distant Metastasis‐Free Survival (DMFS) was significantly different (P‐0.002) between low‐ (DMFS: 95%) and high‐risk (DMFS: 80%) categories in the endocrine therapy treated alone subgroup (n = 195) as well as in the total cohort (n = 857, low‐risk DMFS: 95%, high‐risk DMFS: 84%, P < 0.0001). In addition, the segregation of the risk categories was significant (P = 0.0005) in node‐positive patients, with a difference in DMFS of 12%. In multivariate analysis, CAB risk score was the most significant predictor of distant recurrence with hazard ratio of 3.2048 (P < 0.0001). CAB stratified patients into discrete risk categories with high statistical significance compared to Ki‐67 and IHC4 score‐based stratification. CAB stratified a higher percentage of the cohort (82%) as low‐risk than IHC4 score (41.6%) and could re‐stratify >74% of high Ki‐67 and IHC4 score intermediate‐risk zone patients into low‐risk category. Overall the data suggest that CAB can effectively predict risk of distant recurrence with clear dichotomous high‐ or low‐risk categorization. |
format | Online Article Text |
id | pubmed-6488210 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-64882102019-05-23 Clinical validation of an immunohistochemistry‐based CanAssist‐Breast test for distant recurrence prediction in hormone receptor‐positive breast cancer patients Bakre, Manjiri M. Ramkumar, Charusheila Attuluri, Arun Kumar Basavaraj, Chetana Prakash, Chandra Buturovic, Ljubomir Madhav, Lekshmi Naidu, Nirupama R, Prathima Somashekhar, S. P. Gupta, Sudeep Doval, Dinesh Chandra Pegram, Mark D. Cancer Med Cancer Biology CanAssist‐Breast (CAB) is an immunohistochemistry (IHC)‐based prognostic test for early‐stage Hormone Receptor (HR+)‐positive breast cancer patients. CAB uses a Support Vector Machine (SVM) trained algorithm which utilizes expression levels of five biomarkers (CD44, ABCC4, ABCC11, N‐Cadherin, and Pan‐Cadherin) and three clinical parameters such as tumor size, grade, and node status as inputs to generate a risk score and categorizes patients as low‐ or high‐risk for distant recurrence within 5 years of diagnosis. In this study, we present clinical validation of CAB. CAB was validated using a retrospective cohort of 857 patients. All patients were treated either with endocrine therapy or chemoendocrine therapy. Risk categorization by CAB was analyzed by calculating Distant Metastasis‐Free Survival (DMFS) and recurrence rates using Kaplan‐Meier survival curves. Multivariate analysis was performed to calculate Hazard ratios (HR) for CAB high‐risk vs low‐risk patients. The results showed that Distant Metastasis‐Free Survival (DMFS) was significantly different (P‐0.002) between low‐ (DMFS: 95%) and high‐risk (DMFS: 80%) categories in the endocrine therapy treated alone subgroup (n = 195) as well as in the total cohort (n = 857, low‐risk DMFS: 95%, high‐risk DMFS: 84%, P < 0.0001). In addition, the segregation of the risk categories was significant (P = 0.0005) in node‐positive patients, with a difference in DMFS of 12%. In multivariate analysis, CAB risk score was the most significant predictor of distant recurrence with hazard ratio of 3.2048 (P < 0.0001). CAB stratified patients into discrete risk categories with high statistical significance compared to Ki‐67 and IHC4 score‐based stratification. CAB stratified a higher percentage of the cohort (82%) as low‐risk than IHC4 score (41.6%) and could re‐stratify >74% of high Ki‐67 and IHC4 score intermediate‐risk zone patients into low‐risk category. Overall the data suggest that CAB can effectively predict risk of distant recurrence with clear dichotomous high‐ or low‐risk categorization. John Wiley and Sons Inc. 2019-03-07 /pmc/articles/PMC6488210/ /pubmed/30848103 http://dx.doi.org/10.1002/cam4.2049 Text en © 2019 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Cancer Biology Bakre, Manjiri M. Ramkumar, Charusheila Attuluri, Arun Kumar Basavaraj, Chetana Prakash, Chandra Buturovic, Ljubomir Madhav, Lekshmi Naidu, Nirupama R, Prathima Somashekhar, S. P. Gupta, Sudeep Doval, Dinesh Chandra Pegram, Mark D. Clinical validation of an immunohistochemistry‐based CanAssist‐Breast test for distant recurrence prediction in hormone receptor‐positive breast cancer patients |
title | Clinical validation of an immunohistochemistry‐based CanAssist‐Breast test for distant recurrence prediction in hormone receptor‐positive breast cancer patients |
title_full | Clinical validation of an immunohistochemistry‐based CanAssist‐Breast test for distant recurrence prediction in hormone receptor‐positive breast cancer patients |
title_fullStr | Clinical validation of an immunohistochemistry‐based CanAssist‐Breast test for distant recurrence prediction in hormone receptor‐positive breast cancer patients |
title_full_unstemmed | Clinical validation of an immunohistochemistry‐based CanAssist‐Breast test for distant recurrence prediction in hormone receptor‐positive breast cancer patients |
title_short | Clinical validation of an immunohistochemistry‐based CanAssist‐Breast test for distant recurrence prediction in hormone receptor‐positive breast cancer patients |
title_sort | clinical validation of an immunohistochemistry‐based canassist‐breast test for distant recurrence prediction in hormone receptor‐positive breast cancer patients |
topic | Cancer Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6488210/ https://www.ncbi.nlm.nih.gov/pubmed/30848103 http://dx.doi.org/10.1002/cam4.2049 |
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