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Analytical validation of CanAssist-Breast: an immunohistochemistry based prognostic test for hormone receptor positive breast cancer patients.

ABSTRACT: BACKGROUND:CanAssist-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. METHODS:All 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. RESULTS:CanAssist-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. CONCLUSIONS:The 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.

SUBMITTER: Attuluri AK

PROVIDER: S-EPMC6425559 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Analytical validation of CanAssist-Breast: an immunohistochemistry based prognostic test for hormone receptor positive breast cancer patients.

Attuluri Arun Kumar AK Serkad Chandra Prakash V CPV Gunda Aparna A Ramkumar Charusheila C Basavaraj Chetana C Buturovic Ljubomir L Madhav Lekshmi L Naidu Nirupama N Krishnamurthy Naveen N Prathima R R Kanaldekar Suchita S Bakre Manjiri M MM

BMC cancer 20190320 1

<h4>Background</h4>CanAssist-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 ...[more]

PMID: 30894144

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Project description: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.

Project description:PurposeMore accurate prediction of patient outcome based on molecular subtype is required to identify patients who will benefit from specific treatments.MethodsWe selected novel 16 candidate prognostic genes, including 10 proliferation-related genes (p-genes) and 6 immune response-related genes (i-genes), from the gene list identified in our previous study. We then analyzed the association between their expression, measured by quantitative real-time reverse transcription-PCR in formalin-fixed, paraffin-embedded tissues, and clinical outcome in 819 breast cancer patients according to molecular subtype.ResultsThe prognostic significance of clinical and gene variables varied according to the molecular subtype. Univariate analysis showed that positive lymph node status was significantly correlated with the increased risk of distant metastasis in all subtypes except the hormone receptor-negative, HER2-positive (HR-/HER2+) subtype. Most p-genes were significantly associated with poor prognosis in patients with the HR+/HER2- subtype, whereas i-genes correlated with a favorable outcome in patients with HR-/HER2+ breast cancer. In HR-/HER2+ breast cancer, four genes (three i-genes BTN3A2, CD2, and TRBC1 and the p-gene MMP11) were significantly associated with distant metastasis-free survival (DMFS). A new prognostic model for HR-/HER2+ breast cancer based on the expression of MMP11 and CD2 was developed and the DMFS for patients in the high-risk group according to our model was significantly lower than that for those in the low-risk group. Multivariate analyses revealed that our risk score is an independent prognostic factor for DMFS. Moreover, C-index showed that our risk score has a superior prognostic performance to traditional clinicopathological factors.ConclusionsOur new prognostic model for HR-/HER2+ breast cancer provides more accurate information on the risk of distant metastasis than traditional clinical prognostic factors and may be used to identify patients with a good prognosis in this aggressive subtype of breast cancer.

Dataset Information

Analytical validation of CanAssist-Breast: an immunohistochemistry based prognostic test for hormone receptor positive breast cancer patients.

Publications

Analytical validation of CanAssist-Breast: an immunohistochemistry based prognostic test for hormone receptor positive breast cancer patients.

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