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Poster session 01

93P - An AI-driven computational biomarker from H&E slides recovers cases with low levels of HER2 from immunohistochemically HER2-negative breast cancers

Date

10 Sep 2022

Session

Poster session 01

Topics

Molecular Oncology;  Cancer Diagnostics

Tumour Site

Breast Cancer

Presenters

Antonio Marra

Citation

Annals of Oncology (2022) 33 (suppl_7): S27-S54. 10.1016/annonc/annonc1037

Authors

A. Marra1, M. Goldfinger2, E. Millar3, M. Hanna1, B. Rothrock4, M. Lee4, Y. Wang4, A. van Eck5, L. Trlifaj Tydlitatova6, J. Sue7, H. Wen8, J. Stefanelli9, J. Retamero10, P. Hamilton9, T. Fuchs4, D. Klimstra11, S. Chandarlapaty12, J.S. Reis-Filho13

Author affiliations

  • 1 Department Of Pathology, Memorial Sloan Kettering Cancer Center, 10065 - New York/US
  • 2 Science, Paige, 10036 - New York/US
  • 3 Department Of Anatomical Pathology, Garvan Institute of Medical Research, 2010 - Darlinghurst/AU
  • 4 Ai Science, Paige, 10036 - New York/US
  • 5 Ai, Paige, 10036 - New York/US
  • 6 Engineering, Paige, 10036 - New York/US
  • 7 Product, Paige, 10036 - New York/US
  • 8 Pathology Department, Memorial Sloan Kettering Cancer Center, 10065 - New York/US
  • 9 Development, Paige, 10036 - New York/US
  • 10 Pathology, Paige, 10036 - New York/US
  • 11 Medicine, Paige, 10036 - New York/US
  • 12 Medicine, MSKCC - Memorial Sloan Kettering Cancer Center, 10065 - New York/US
  • 13 Department Of Pathology, MSKCC - Memorial Sloan Kettering Cancer Center, 10065 - New York/US
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Abstract 93P

Background

Novel anti-HER2 antibody drug conjugates (ADCs) have shown efficacy in breast cancers (BCs) expressing low levels of HER2 (i.e., IHC 1+/2+). A subset of BCs classified as HER2 0 by current IHC methods may express HER2 protein. We sought to define whether BCs expressing HER2 could be accurately detected by deep learning (DL) methods applied to H&E whole slide images (WSIs), using a combination of IHC and HER2 mRNA expression as ‘gold standard’.

Methods

1479 H&E-stained WSIs from 417 primary BCs were categorized according to HER2 IHC, FISH and HER2 copy number amplification from a cohort of 2188 H&E-stained WSI. All HER2 0 and HER2-low (i.e., 1+ and 2+) samples were also tested for HER2 mRNA expression. A SE-ResNet-50 CNN and aggregator were trained from WSIs of H&E sections at 20x. Slide-level predictions were evaluated with 8-fold cross-validation.

Results

A total of 1098, 820, 122, and 148 WSIs were categorized as HER2 0, 1+, 2+ and 3+ by IHC staining, respectively. mRNA expression data revealed a range of <7.6 to 11.6 (SD=0.63) for HER2 mRNA expression level. When stratified by HER2 mRNA expression, a cut-off of <7.6 mRNA was selected to represent HER2-null, which included IHC 0 (n=32), IHC 1+ (n=3) and IHC 2+ (n=1). HER2 IHC 0 to 2+ with HER2 mRNA expression >9 and HER2 FISH not amplified were considered as being HER2-low (IHC 0, n=494; IHC 1+, n=562; IHC 2+, n=103). Cases with HER2 IHC 3+ and/or FISH amplification were considered HER2-positive. Model development was based on 417 cases (1479 WSIs) including 32 HER2-null, 292 HER2-low and 93 HER2-positive cases. When distinguishing HER2-low and amplified cases from HER2-null, the model’s performance had an AUC 0.78, a sensitivity of 76%, a specificity of 73%, PPV of 97%, NPV of 21%, and F1=0.85. The model identified 21/25 (84%) HER2 IHC 0 and mRNA<7.6, classified as HER2-null, and 136/167 (81%) HER2 IHC 1+/2+ and mRNA>9, classified as HER2-low.

Conclusions

Our AI system applied to H&E-stained WSIs can distinguish between BCs lacking any HER2 protein and mRNA (HER2-null) and HER2-low tumors, warranting further validation in cohorts of patients treated by new HER2 ADCs to support its use in trials and future clinical decision-making.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

Paige.AI, Inc.

Funding

Paige.AI, Inc.

Disclosure

M. Goldfinger: Financial Interests, Institutional, Full or part-time Employment: Paige. E. Millar: Financial Interests, Institutional, Advisory Role: Paige. M. Hanna: Financial Interests, Personal, Advisory Role: Paige. B. Rothrock, M. Lee, Y. Wang, A. van Eck, L. Trlifaj Tydlitatova, J. Sue, J. Stefanelli, J. Retamero, P. Hamilton, T. Fuchs, D. Klimstra: Financial Interests, Personal, Full or part-time Employment: Paige. Financial Interests, Personal, Full or part-time Employment: Paige. J.S. Reis-Filho: Financial Interests, Personal, Advisory Board: Paige. All other authors have declared no conflicts of interest.

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