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BackgroundCanAssist-Breast is an immunohistochemistry based test that predicts risk of distant recurrence in early-stage hormone receptor positive breast cancer patients within first five years of diagnosis. Immunohistochemistry gradings for 5 biomarkers (CD44, ABCC4, ABCC11, N-Cadherin and pan-Cadherins) and 3 clinical parameters (tumor size, tumor grade and node status) of 298 patient cohort were used to develop a machine learning based statistical algorithm. The algorithm generates a risk score based on which patients are stratified into two groups, low- or high-risk for recurrence. The aim of the current study is to demonstrate the analytical performance with respect to repeatability and reproducibility of CanAssist-Breast.MethodsAll potential sources of variation in CanAssist-Breast testing involving operator, run and observer that could affect the immunohistochemistry performance were tested using appropriate statistical analysis methods for each of the CanAssist-Breast biomarkers using a total 309 samples. The cumulative effect of these variations in the immunohistochemistry gradings on the generation of CanAssist-Breast risk score and risk category were also evaluated. Intra-class Correlation Coefficient, Bland Altman plots and pair-wise agreement were performed to establish concordance on IHC gradings, risk score and risk categorization respectively.ResultsCanAssist-Breast test exhibited high levels of concordance on immunohistochemistry gradings for all biomarkers with Intra-class Correlation Coefficient of ≥0.75 across all reproducibility and repeatability experiments. Bland-Altman plots demonstrated that agreement on risk scores between the comparators was within acceptable limits. We also observed > 90% agreement on risk categorization (low- or high-risk) across all variables tested.ConclusionsThe extensive analytical validation data for the CanAssist-Breast test, evaluating immunohistochemistry performance, risk score generation and risk categorization showed excellent agreement across variables, demonstrating that the test is robust.Electronic supplementary materialThe online version of this article (10.1186/s12885-019-5443-5) contains supplementary material, which is available to authorized users.

Accurate recurrence risk assessment in hormone receptor positive, HER2/neu negative breast cancer is critical to plan precise therapy. CanAssist Breast (CAB) assesses recurrence risk based on tumor biology using artificial intelligence-based approach. We report CAB risk assessment correlating with disease outcomes in multiple clinically high- and low-risk subgroups. In this retrospective cohort of 925 patients [median age-54 (22–86)] CAB had hazard ratio (HR) of 3 (1.83–5.21) and 2.5 (1.45–4.29), P = 0.0009) in univariate and multivariate analysis. CAB's HR in sub-groups with the other determinants of outcome, T2 (HR: 2.79 (1.49–5.25), P = 0.0001); age [< 50 (HR: 3.14 (1.39–7), P = 0.0008)]. Besides application in node-negative patients, CAB's HR was 2.45 (1.34–4.47), P = 0.0023) in node-positive patients. In clinically low-risk patients (N0 tumors up to 5 cms) (HR: 2.48 (0.79–7.8), P = 0.03) and with luminal-A characteristics (HR: 4.54 (1–19.75), P = 0.004), CAB identified >16% as high-risk with recurrence rates of up to 12%. In clinically high-risk patients (T2N1 tumors (HR: 2.65 (1.31–5.36), P = 0.003; low-risk DMFS: 92.66 ± 1.88) and in women with luminal-B characteristics (HR: 3.24; (1.69–6.22), P < 0.0001; low-risk DMFS: 93.34 ± 1.34)), CAB identified >64% as low-risk. Thus, CAB prognostication was significant in women with clinically low- and high-risk disease. The data imply the use of CAB for providing helpful information to stratify tumors based on biology incorporated with clinical features for Indian patients, which can be extrapolated to regions with similarly characterized patients, South-East Asia.

Background CanAssist Breast (CAB) is a prognostic test for early stage hormone receptor‐positive (HR+), human epidermal growth factor receptor 2 negative (HER2−) breast cancer patients, validated on Indian and Caucasian patients. The 21‐gene signature Oncotype DX (ODX) is the most widely used commercially available breast cancer prognostic test. In the current study, risk stratification of CAB is compared with that done with ODX along with the respective outcomes of these patients. Methods A cohort of 109 early stage breast cancer patients who had previously taken the ODX test were retested with CAB, and the results respectively compared with old cut‐offs of ODX as well as cut‐offs suggested by TAILORx, a prospective randomized trial of ODX. Distant metastasis‐free survival after 5 years was taken as the end point. Results CanAssist Breast stratified 83.5% of the cohort into low‐risk and 16.5% into high‐risk. With the TAILORx cut‐offs, ODX stratified the cohort into 89.9% low‐risk and 10.1% into high‐risk. The low, intermediate, and high‐risk groups with ODX old cut‐offs were 62.4%, 31.2%, and 6.4%, respectively. The overall concordance of CAB with ODX using both cut‐offs is 75%‐76%, with ~82%‐83% concordance in the low‐risk category of these tests. The NPV of the low‐risk category of CAB was 93.4%, and of ODX with TAILORx cut‐offs was 91.8% and 89.7% with old cut‐offs. Conclusions Compared to the concordance reported for other tests, CAB shows high concordance with ODX, and in addition shows comparable performance in the patient outcomes in this cohort. CAB is thus an excellent and cost‐effective alternative to ODX.

Background Hormone receptor (HR)-positive, HER2/neu-negative breast cancers have a sustained risk of recurrence up to 20 years from diagnosis. TEAM (Tamoxifen, Exemestane Adjuvant Multinational) is a large, multi-country, phase III trial that randomized 9776 women for the use of hormonal therapy. Of these 2754 were Dutch patients. The current study aims for the first time to correlate the ten-year clinical outcomes with predictions by CanAssist Breast (CAB)—a prognostic test developed in South East Asia, on a Dutch sub-cohort that participated in the TEAM. The total Dutch TEAM cohort and the current Dutch sub-cohort were almost similar with respect to patient age and tumor anatomical features. Methods Of the 2754 patients from the Netherlands, which are part of the original TEAM trial, 592 patients’ samples were available with Leiden University Medical Center (LUMC). The risk stratification of CAB was correlated with outcomes of patients using logistic regression approaches entailing Kaplan–Meier survival curves, univariate and multivariate cox-regression hazards model. We used hazard ratios (HRs), the cumulative incidence of distant metastasis/death due to breast cancer (DM), and distant recurrence-free interval (DRFi) for assessment. Results Out of 433 patients finally included, the majority, 68.4% had lymph node-positive disease, while only a minority received chemotherapy (20.8%) in addition to endocrine therapy. CAB stratified 67.5% of the total cohort as low-risk [DM = 11.5% (95% CI, 7.6–15.2)] and 32.5% as high-risk [DM = 30.2% (95% CI, 21.9–37.6)] with an HR of 2.90 (95% CI, 1.75–4.80; P < 0.001) at ten years. CAB risk score was an independent prognostic factor in the consideration of clinical parameters in multivariate analysis. At ten years, CAB high-risk had the worst DRFi of 69.8%, CAB low-risk in the exemestane monotherapy arm had the best DRFi of 92.7% [vs CAB high-risk, HR, 0.21 (95% CI, 0.11–0.43), P < 0.001], and CAB low-risk in the sequential arm had a DRFi of 84.2% [vs CAB high-risk, HR, 0.48 (95% CI, 0.28–0.82), P = 0.009]. Conclusions Cost-effective CAB is a statistically robust prognostic and predictive tool for ten-year DM for postmenopausal women with HR+/HER2−, early breast cancer. CAB low-risk patients who received exemestane monotherapy had an excellent ten-year DRFi.