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Development of a Novel Proteomic Risk-Classifier for Prognostication of Patients With Early-Stage Hormone Receptor–Positive Breast Cancer

Use of proteomic strategies to identify a risk classifier that estimates probability of distant recurrence in early-stage hormone receptor (HR)-positive breast cancer is relevant to physiological cellular function and therefore to intrinsic tumor biology. We used a 298-sample retrospective training...

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Autores principales: Ramkumar, Charusheila, Buturovic, Ljubomir, Malpani, Sukriti, Kumar Attuluri, Arun, Basavaraj, Chetana, Prakash, Chandra, Madhav, Lekshmi, Doval, Dinesh Chandra, Mehta, Anurag, Bakre, Manjiri M
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6066801/
https://www.ncbi.nlm.nih.gov/pubmed/30083053
http://dx.doi.org/10.1177/1177271918789100
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author Ramkumar, Charusheila
Buturovic, Ljubomir
Malpani, Sukriti
Kumar Attuluri, Arun
Basavaraj, Chetana
Prakash, Chandra
Madhav, Lekshmi
Doval, Dinesh Chandra
Mehta, Anurag
Bakre, Manjiri M
author_facet Ramkumar, Charusheila
Buturovic, Ljubomir
Malpani, Sukriti
Kumar Attuluri, Arun
Basavaraj, Chetana
Prakash, Chandra
Madhav, Lekshmi
Doval, Dinesh Chandra
Mehta, Anurag
Bakre, Manjiri M
author_sort Ramkumar, Charusheila
collection PubMed
description Use of proteomic strategies to identify a risk classifier that estimates probability of distant recurrence in early-stage hormone receptor (HR)-positive breast cancer is relevant to physiological cellular function and therefore to intrinsic tumor biology. We used a 298-sample retrospective training set to develop an immunohistochemistry-based novel risk classifier called CanAssist-Breast (CAB) which combines 5 prognostically relevant biomarkers and 3 clinico-pathological parameters to arrive at probability of distant recurrence within 5 years from diagnosis. Five selected biomarkers, namely, CD44, ABCC4, ABCC11, N-cadherin, and pan-cadherin, were chosen based on their role in tumor metastasis. The chosen biomarkers represent the hallmarks of cancer and are distinct from other proliferation and gene expression–based prognostic signatures. The 3 clinico-pathological parameters integrated into the machine learning–based CAB algorithm are tumor size, tumor grade, and node status. These features are used to calculate a “CAB risk score” that classifies patients into low- or high-risk groups and predicts probability of distant recurrence in 5 years. Independent clinical validation of CAB in a retrospective study comprising 196 patients indicated that distant metastasis-free survival (DMFS) was significantly different in the 2 risk groups. The difference in DMFS between the low- and high-risk categories was 19% in the validation cohort (P = .0002). In multivariate analysis, CAB risk score was the most significant independent predictor of distant recurrence with a hazard ratio of 4.3 (P = .0003). CanAssist-Breast is a precise and unique machine learning–based proteomic risk-classifier that can assist in risk stratification of patients with early-stage HR+ breast cancer.
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spelling pubmed-60668012018-08-06 Development of a Novel Proteomic Risk-Classifier for Prognostication of Patients With Early-Stage Hormone Receptor–Positive Breast Cancer Ramkumar, Charusheila Buturovic, Ljubomir Malpani, Sukriti Kumar Attuluri, Arun Basavaraj, Chetana Prakash, Chandra Madhav, Lekshmi Doval, Dinesh Chandra Mehta, Anurag Bakre, Manjiri M Biomark Insights Original Research Use of proteomic strategies to identify a risk classifier that estimates probability of distant recurrence in early-stage hormone receptor (HR)-positive breast cancer is relevant to physiological cellular function and therefore to intrinsic tumor biology. We used a 298-sample retrospective training set to develop an immunohistochemistry-based novel risk classifier called CanAssist-Breast (CAB) which combines 5 prognostically relevant biomarkers and 3 clinico-pathological parameters to arrive at probability of distant recurrence within 5 years from diagnosis. Five selected biomarkers, namely, CD44, ABCC4, ABCC11, N-cadherin, and pan-cadherin, were chosen based on their role in tumor metastasis. The chosen biomarkers represent the hallmarks of cancer and are distinct from other proliferation and gene expression–based prognostic signatures. The 3 clinico-pathological parameters integrated into the machine learning–based CAB algorithm are tumor size, tumor grade, and node status. These features are used to calculate a “CAB risk score” that classifies patients into low- or high-risk groups and predicts probability of distant recurrence in 5 years. Independent clinical validation of CAB in a retrospective study comprising 196 patients indicated that distant metastasis-free survival (DMFS) was significantly different in the 2 risk groups. The difference in DMFS between the low- and high-risk categories was 19% in the validation cohort (P = .0002). In multivariate analysis, CAB risk score was the most significant independent predictor of distant recurrence with a hazard ratio of 4.3 (P = .0003). CanAssist-Breast is a precise and unique machine learning–based proteomic risk-classifier that can assist in risk stratification of patients with early-stage HR+ breast cancer. SAGE Publications 2018-07-30 /pmc/articles/PMC6066801/ /pubmed/30083053 http://dx.doi.org/10.1177/1177271918789100 Text en © The Author(s) 2018 http://www.creativecommons.org/licenses/by-nc/4.0/ This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Original Research
Ramkumar, Charusheila
Buturovic, Ljubomir
Malpani, Sukriti
Kumar Attuluri, Arun
Basavaraj, Chetana
Prakash, Chandra
Madhav, Lekshmi
Doval, Dinesh Chandra
Mehta, Anurag
Bakre, Manjiri M
Development of a Novel Proteomic Risk-Classifier for Prognostication of Patients With Early-Stage Hormone Receptor–Positive Breast Cancer
title Development of a Novel Proteomic Risk-Classifier for Prognostication of Patients With Early-Stage Hormone Receptor–Positive Breast Cancer
title_full Development of a Novel Proteomic Risk-Classifier for Prognostication of Patients With Early-Stage Hormone Receptor–Positive Breast Cancer
title_fullStr Development of a Novel Proteomic Risk-Classifier for Prognostication of Patients With Early-Stage Hormone Receptor–Positive Breast Cancer
title_full_unstemmed Development of a Novel Proteomic Risk-Classifier for Prognostication of Patients With Early-Stage Hormone Receptor–Positive Breast Cancer
title_short Development of a Novel Proteomic Risk-Classifier for Prognostication of Patients With Early-Stage Hormone Receptor–Positive Breast Cancer
title_sort development of a novel proteomic risk-classifier for prognostication of patients with early-stage hormone receptor–positive breast cancer
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6066801/
https://www.ncbi.nlm.nih.gov/pubmed/30083053
http://dx.doi.org/10.1177/1177271918789100
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