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...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2018-07-01
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| Series: | Biomarker Insights |
| Online Access: | https://doi.org/10.1177/1177271918789100 |
| _version_ | 1818159209534980096 |
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| author | Charusheila Ramkumar Ljubomir Buturovic Sukriti Malpani Arun Kumar Attuluri Chetana Basavaraj Chandra Prakash Lekshmi Madhav Dinesh Chandra Doval Anurag Mehta Manjiri M Bakre |
| author_facet | Charusheila Ramkumar Ljubomir Buturovic Sukriti Malpani Arun Kumar Attuluri Chetana Basavaraj Chandra Prakash Lekshmi Madhav Dinesh Chandra Doval Anurag Mehta Manjiri M Bakre |
| author_sort | Charusheila Ramkumar |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-11T15:42:21Z |
| format | Article |
| id | doaj.art-9e374843199e40d28fcc6ff12281eec0 |
| institution | Directory Open Access Journal |
| issn | 1177-2719 |
| language | English |
| last_indexed | 2024-12-11T15:42:21Z |
| publishDate | 2018-07-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Biomarker Insights |
| spelling | doaj.art-9e374843199e40d28fcc6ff12281eec02022-12-22T00:59:47ZengSAGE PublishingBiomarker Insights1177-27192018-07-011310.1177/1177271918789100Development of a Novel Proteomic Risk-Classifier for Prognostication of Patients With Early-Stage Hormone Receptor–Positive Breast CancerCharusheila Ramkumar0Ljubomir Buturovic1Sukriti Malpani2Arun Kumar Attuluri3Chetana Basavaraj4Chandra Prakash5Lekshmi Madhav6Dinesh Chandra Doval7Anurag Mehta8Manjiri M Bakre9OncoStem Diagnostics, Bangalore, IndiaClinical Persona, Inc., East Palo Alto, CA, USAOncoStem Diagnostics, Bangalore, IndiaOncoStem Diagnostics, Bangalore, IndiaOncoStem Diagnostics, Bangalore, IndiaOncoStem Diagnostics, Bangalore, IndiaOncoStem Diagnostics, Bangalore, IndiaChair Medical Oncology & Chief of Breast & Thoracic Services, Rajiv Gandhi Cancer Institute and Research Centre, New Delhi, IndiaDirector Department of Laboratory & Transfusion Services and Director Research, Rajiv Gandhi Cancer Institute and Research Centre, New Delhi, IndiaOncoStem Diagnostics, Bangalore, IndiaUse 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.https://doi.org/10.1177/1177271918789100 |
| spellingShingle | Charusheila Ramkumar Ljubomir Buturovic Sukriti Malpani Arun Kumar Attuluri Chetana Basavaraj Chandra Prakash Lekshmi Madhav Dinesh Chandra Doval Anurag Mehta Manjiri M Bakre Development of a Novel Proteomic Risk-Classifier for Prognostication of Patients With Early-Stage Hormone Receptor–Positive Breast Cancer Biomarker Insights |
| 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 |
| url | https://doi.org/10.1177/1177271918789100 |
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