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.
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.
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