HER2 in situ hybridization testing in breast cancer: Applying algorithm-assisted assessment to reduce interobserver variability in difficult cases.

Authors

null

Madeline Gough

Mater Health Services-Mater Research Institute, South Brisbane, Australia

Madeline Gough , Cheng Liu , Bhuvana Srinivasan , Lisa Wilkinson , Louisa Dunk , Yuanhao Yang , Haitham Tuffaha , Thomas Kryza , John D. Hooper , Sunil R. Lakhani , Cameron Edward Snell

Organizations

Mater Health Services-Mater Research Institute, South Brisbane, Australia, Mater Health Services, Brisbane, Australia, Mater Health Services, Queensland, Australia, Mater Research Institute, Brisbane, Australia, Centre for the Business and Economics of Health, University of Queensland, Brisbane, Australia, University of Queensland, Brisbane, Queensland, Australia, Mater Research Institute-University of Queensland, Woolloongabba, QLD, Australia, The University of Queensland Centre for Clinical Research & Pathology Queensland, Brisbane, Australia, Peter MacCallum Cancer Centre, Melbourne, Australia

Research Funding

Other
Royal College of Pathologists of Australasia, Mater Foundation (Brisbane, Australia)

Background: Accurate assessment of HER2 expression by HER2 immunohistochemistry and in-situ hybridization (ISH) is critical for the management of patients with breast cancer. The revised 2018 ASCO/CAP guidelines defined 5 subgroups based on HER2 expression and copy number. Few reports have investigated interobserver variability when applying the revised guidelines. Digital algorithms may have a role in assisting pathologists in assessing cases with equivocal and uncommon patterns of HER2 amplification. This study was designed to evaluate interobserver variability in the gold standard manual assessment of HER2 ISH, particularly in less common HER2 ISH groups. Subsequently, we evaluated whether a new digital image analysis algorithm (Roche uPath HER2 Dual ISH image analysis) could reduce interobserver variability and potentially improve patient selection for targeted anti-HER2 therapy. Methods: Of 2982 breast cancer cases, we derived an enriched cohort of 99 patient cases with HER2 IHC 2+ or 3+ and selective ISH results (groups 2-4, evidence of HER2 heterogeneity, borderline negative group 5 and borderline positive group 1). As per guidelines, a minimum of 20 tumour cells per slide were manually and independently counted by 4 anatomical pathologists. After a washout period and second round of de-identification, the slides were scanned (DP200; Roche) and re-scored using the digital algorithm. Results: Pathologist reporting of patient HER2 status (HER2 positive vs. HER2 negative) using manual microscopy demonstrated significant interobserver variability, with a Fleiss’s kappa value of 0.471 (95% CI: 0.378-0.564; P= 1.24E-21) indicating fair-moderate agreement. Interobserver variability in manual scoring of HER2 ISH at the level of HER2 group designation (groups 1-5) showed poor-moderate reliability between pathologists, with an intraclass correlation coefficient (ICC) of 0.526 (95% CI: 0.407-0.641; P= 9.28E-21). Repeat analysis of the cohort using algorithm-assisted quantification resulted in moderate-substantial concordance in HER2 patient status reporting, with a Fleiss’s kappa value of 0.666 (95% CI: 0.572-0.759; P= 1.70E-40). Algorithm-assisted quantification resulted in moderate-good agreement of HER2 group designation, with an ICC of 0.763 (95% CI: 0.683-0.832; P= 8.76E-48). In sub-group analysis, use of the algorithm improved concordance particularly in groups 2,4 and 5. Time to report cases was also significantly reduced with the use of the algorithm. Conclusions: Our study demonstrates the potential to improve the concordance of HER2 amplification status reporting in less common HER2 subgroups using a digital algorithm and potentially improve therapy selection and outcomes for patients with HER2-low and borderline HER2-amplified breast cancers.

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Abstract Details

Meeting

2023 ASCO Annual Meeting

Session Type

Publication Only

Session Title

Publication Only: Care Delivery and Regulatory Policy

Track

Care Delivery and Quality Care

Sub Track

Clinical Informatics/Advanced Algorithms/Machine Learning

Citation

J Clin Oncol 41, 2023 (suppl 16; abstr e13561)

DOI

10.1200/JCO.2023.41.16_suppl.e13561

Abstract #

e13561

Abstract Disclosures