Validation of CanAssist Breast immunohistochemistry biomarkers on an automated platform and its applicability in tissue microarray.
CanAssist Breast (CAB) is a prognostic test for early-stage hormone receptor-positive invasive breast cancer. The test involves performing immunohistochemical (IHC) analysis for five biomarkers, namely CD44, ABCC4, ABCC11, N-cadherin, and pan-cadherin. In addition to IHC grading information, three clinical features, i.e., tumor size, grade, and lymph node status, serve as input into the machine learning-based algorithm to generate the CAB risk score. CAB was developed and initially validated using manual IHC. This study's objectives included: i) automate CAB IHC on an autostainer and establish its performance equivalence with manual IHC ii) validate CAB test using samples in Tissue MicroArray (TMA) format. IHC for CAB biomarkers was standardized on Ventana BenchMark XT autostainer. Two IHC methods were compared for IHC gradings and corresponding CAB risk scores/risk categories. A concordance analysis was done using MedCalcTM software. The manual and automated IHC staining methods exhibited a high level of concordance on IHC gradings for 40 cases with an Intra-class Correlation Coefficient (ICC) of >0.85 for 4 of 5 biomarkers. 100% concordance was achieved in risk categorization (low- or high-risk), with very good agreement between the risk scores demonstrated by a kappa statistic of 0.83. TMA versus whole tissue section concordance was analyzed using 45 samples on an autostainer, and the data showed 92% concordance in terms of risk category. The results confirm the equivalence between manual and automated staining methods and demonstrate the utility of TMA as an acceptable format for CanAssist Breast testing.
['Serkad|Chandra Prakash V|CPV|', 'Attuluri|Arun Kumar|AK|', 'Basavaraj|Chetana|C|', 'Adinarayan|Manjula|M|', 'Krishnamoorthy|Naveen|N|', 'Ananthamurthy|Savitha B|SB|', 'Mallikarjuna|Siraganahalli E|SE|', 'Bakre|Manjiri M|MM|']
Breast cancer machine learning method comparison prognosis risk classifier