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Clinical validation of an immunohistochemistry-based CanAssist-Breast test for distant recurrence prediction in hormone receptor-positive breast cancer patients.

ABSTRACT: 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.

SUBMITTER: Bakre MM

PROVIDER: S-EPMC6488210 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Clinical validation of an immunohistochemistry-based CanAssist-Breast test for distant recurrence prediction in hormone receptor-positive breast cancer patients.

Bakre Manjiri M MM Ramkumar Charusheila C Attuluri Arun Kumar AK Basavaraj Chetana C Prakash Chandra C Buturovic Ljubomir L Madhav Lekshmi L Naidu Nirupama N R Prathima P Somashekhar S P SP Gupta Sudeep S Doval Dinesh Chandra DC Pegram Mark D MD

Cancer medicine 20190307 4

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 ye ...[more]

PMID: 30848103

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Project description: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.

Project description:BACKGROUND:Biomarkers to improve the risk-benefit of extended adjuvant endocrine therapy for late recurrence in patients with oestrogen-receptor-positive breast cancer would be clinically valuable. We compared the prognostic ability of the breast-cancer index (BCI) assay, 21-gene recurrence score (Oncotype DX), and an immunohistochemical prognostic model (IHC4) for both early and late recurrence in patients with oestrogen-receptor-positive, node-negative (N0) disease who took part in the Arimidex, Tamoxifen, Alone or in Combination (ATAC) clinical trial. METHODS:In this prospective comparison study, we obtained archival tumour blocks from the TransATAC tissue bank from all postmenopausal patients with oestrogen-receptor-positive breast cancer from whom the 21-gene recurrence score and IHC4 values had already been derived. We did BCI analysis in matched samples with sufficient residual RNA using two BCI models-cubic (BCI-C) and linear (BCI-L)-using previously validated cutoffs. We assessed prognostic ability of BCI for distant recurrence over 10 years (the primary endpoint) and compared it with that of the 21-gene recurrence score and IHC4. We also tested the ability of the assays to predict early (0-5 years) and late (5-10 years) distant recurrence. To assess the ability of the biomarkers to predict recurrence beyond standard clinicopathological variables, we calculated the change in the likelihood-ratio ?(2) (LR-??(2)) from Cox proportional hazards models. FINDINGS:Suitable tissue was available from 665 patients with oestrogen-receptor-positive, N0 breast cancer for BCI analysis. The primary analysis showed significant differences in risk of distant recurrence over 10 years in the categorical BCI-C risk groups (p<0·0001) with 6·8% (95% CI 4·4-10·0) of patients in the low-risk group, 17·3% (12·0-24·7) in the intermediate group, and 22·2% (15·3-31·5) in the high-risk group having distant recurrence. The secondary analysis showed that BCI-L was a much stronger predictor for overall (0-10 year) distant recurrence compared with BCI-C (interquartile HR 2·30 [95% CI 1·62-3·27]; LR-??(2)=22·69; p<0·0001). When compared with BCI-L, the 21-gene recurrence score was less predictive (HR 1·48 [95% CI 1·22-1·78]; LR-??(2)=13·68; p=0·0002) and IHC4 was similar (HR 1·69 [95% CI 1·51-2·56]; LR-??(2)=22·83; p<0·0001). All further analyses were done with the BCI-L model. In a multivariable analysis, all assays had significant prognostic ability for early distant recurrence (BCI-L HR 2·77 [95% CI 1·63-4·70], LR-??(2)=15·42, p<0·0001; 21-gene recurrence score HR 1·80 [1·42-2·29], LR-??(2)=18·48, p<0·0001; IHC4 HR 2·90 [2·01-4·18], LR-??(2)=29·14, p<0·0001); however, only BCI-L was significant for late distant recurrence (BCI-L HR 1·95 [95% CI 1·22-3·14], LR-??(2)=7·97, p=0·0048; 21-gene recurrence score HR 1·13 [0·82-1·56], LR-??(2)=0·48, p=0·47; IHC4 HR 1·30 [0·88-1·94], LR-??(2)=1·59, p=0·20). INTERPRETATION:BCI-L was the only significant prognostic test for risk of both early and late distant recurrence and identified two risk populations for each timeframe. It could help to identify patients at high risk for late distant recurrence who might benefit from extended endocrine or other therapy. FUNDING:Avon Foundation, National Institutes of Health, Breast Cancer Foundation, US Department of Defense Breast Cancer Research Program, Susan G Komen for the Cure, Breakthrough Breast Cancer through the Mary-Jean Mitchell Green Foundation, AstraZeneca, Cancer Research UK, and the National Institute for Health Research Biomedical Research Centre at the Royal Marsden (London, UK).

Dataset Information

Clinical validation of an immunohistochemistry-based CanAssist-Breast test for distant recurrence prediction in hormone receptor-positive breast cancer patients.

Publications

Clinical validation of an immunohistochemistry-based CanAssist-Breast test for distant recurrence prediction in hormone receptor-positive breast cancer patients.

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