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

• Published in: BMC cancer Vol. 19; no. 1; p. 249
• Main Authors: Attuluri, Arun Kumar, Serkad, Chandra Prakash V, Gunda, Aparna, Ramkumar, Charusheila, Basavaraj, Chetana, Buturovic, Ljubomir, Madhav, Lekshmi, Naidu, Nirupama, Krishnamurthy, Naveen, Prathima, R, Kanaldekar, Suchita, Bakre, Manjiri M
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 20.03.2019; BioMed Central; BMC
• Subjects: Algorithms; Analytical validation; Biological markers; Biomarkers; Biomarkers, Tumor - analysis; Breast - pathology; Breast - surgery; Breast cancer; Breast Neoplasms - diagnosis; Breast Neoplasms - pathology; Breast Neoplasms - therapy; Cadherins; CanAssist-breast; Cancer patients; CD44 antigen; Chemotherapy; Chemotherapy, Adjuvant - methods; Data processing; Diagnosis; Female; Gene expression; Genetic aspects; Hormones; Humans; Immunohistochemistry; Immunohistochemistry - methods; Laboratories; Learning algorithms; Lymphatic Metastasis - pathology; Machine learning; Medical prognosis; N-Cadherin; Neoplasm Grading; Neoplasm Recurrence, Local - diagnosis; Neoplasm Recurrence, Local - pathology; Neoplasm Recurrence, Local - prevention & control; Patient Selection; Prognosis; Receptors, Estrogen - metabolism; Receptors, Progesterone - metabolism; Repeatability; Reproducibility; Reproducibility of Results; Risk Assessment - methods; Statistical analysis; Statistics; Treatment Outcome; Tumor Burden; Tumors; Working groups; United States; Immunohistochemistry; CanAssist-breast; Repeatability; Analytical validation; Reproducibility
• ISSN: 1471-2407; 1471-2407
• DOI: 10.1186/s12885-019-5443-5

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. 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. 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. 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.