Abstract PD6-01: Novel approach to HER2 quantification: Digital pathology coupled with AI-based image and data analysis delivers objective and quantitative HER2 expression analysis for enrichment of responders to trastuzumab deruxtecan (T-DXd; DS-8201), specifically in HER2-low patients

Abstract

Background T-DXd (Enhertu®) is an FDA-approved antibody-drug conjugate (ADC) targeting HER2. T-DXd has shown anti-tumor activity, not only in patients with HER2-overexpressing (IHC3+/2+ ISH+) breast cancer (BC) but also in patients with BC with low HER2 expression (IHC1+/2+ ISH−). Current HER2 protein expression assessment is based on manual pathologist scoring that classifies tumors by the percentage of tumor cells with highest intensity and completeness of staining. A critical need exists for more objective and quantitative methods to assess HER2 expression, specifically to better identify patients with low-level expression if T-DXd proves to be efficacious in this patient population. Methods We used deep learning (DL)-based image analysis (IA) to generate a novel HER2 Quantitative Continuous Score (QCS). Data analytic techniques determined optimal HER2 QCS for the J101 trial (NCT02564900) of 151 patients with varying HER2 expression levels (1+, 2+, 3+). HER2 QCS consists of DL models to detect membrane, cytoplasm, and nuclei of all tumor cells. QCS was extensively trained using pathologists’ annotations, and the performance was validated on unseen data to ensure its generalization and robustness. QCS was blindly applied to J101 data. The optical density (OD; level of brown stain intensity) was computed on detected membrane to derive features that could be linked to survival prediction. QCS features were selected to maximize ORR in positive group, minimize ORR in negative group maintaining while high prevalence in the positive group. Results Analytical validation showed high correlation between QCS from automatically detected membranes and QCS from those annotated by pathologists (R=0.993). This is in the same range as correlation between three pathologists (R=0.995). HER2 QCS was largely consistent with pathologist HER2 scoring as well but showed broad quantitative overlap between IHC and ISH categories. HER2 QCS showed a direct linear relationship between ORR and increased HER2 expression across the entire assay range. In the HER2-low population (n = 65), for whom HER2-targeting therapies are not currently approved, 42% of patients responded to T-DXd, with a median PFS (mPFS) of 11 mo. Using HER2 QCS, we were able to further stratify this population into a subgroup of QCS-high patients (above a staining intensity cut-off determined by IA), with response and mPFS increased to 53% (95% CI: 36%-68%) and 14.5 mo (95% CI: 10.9 mo-NR) respectively, while the QCS-low group only showed ORR of 24% (95% CI: 9%-45%) and mPFS of 8.6 mo (95% CI: 4.2 mo-NR). Generally, best-performing QCS cutoffs were driven by most tumor cells expressing a minimal amount of HER2, in contrast to current clinical guidelines that are driven by a minority of cells expressing higher levels of HER2. We also examined spatial heterogeneity by characterizing cells as either bearing membrane stain above a determined OD threshold (positive cell) or lying within certain distances from a positive cell. We observed similar efficacy with best performing-cutoffs, again, being found when a minimal level of HER2 expression (OD) was examined. Conclusions Taken together, these data establish a first proof-of-concept demonstrating that use of HER2 QCS can potentially enhance prediction of patient outcome with T-DXd by increasing sensitivity and specificity of response, especially in the HER2-low population. The ability to identify patients in the HER2-low group who could benefit from T-DXd is critical for its use in a patient population with a high unmet need that would otherwise not be treated with anti-HER2 therapy. Further clinical verification and validation is ongoing. Citation Format: Mark Gustavson, Susanne Haneder, Andreas Spitzmueller, Ansh Kapil, Katrin Schneider, Fabiola Cecchi, Sriram Sridhar, Guenter Schmidt, Sotirios Lakis, Regina Teichert, Anatoliy Shumilov, Ana Hidalgo-Sastre, Magdalena Wienken, Hadassah Sade, J. Carl Barrett, Danielle Carroll. Novel approach to HER2 quantification: Digital pathology coupled with AI-based image and data analysis delivers objective and quantitative HER2 expression analysis for enrichment of responders to trastuzumab deruxtecan (T-DXd; DS-8201), specifically in HER2-low patients [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PD6-01.

Overcoming Resistance to HER2-Directed Therapies in Breast Cancer

“…IHC is qualitative, and the implementation of quantitative HER2-assessment methods is being studied to determine if it yields more reliable and reproducible results in improving the prognostic and predictive yield of this biomarker [ 104 ]. Some of the studied quantitative methods include a dual-antibody, proximity-based approach (HERmark) [ 105 ] and artificial intelligence-based imaging [ 106 ]. Although these are currently part of clinical practice, it is possible that, in the near future, we will incorporate quantitative HER2 analysis to guide clinical decisions.…”

Complete remission in a patient with widely metastatic HER2-amplified pancreatic adenocarcinoma following multimodal therapy informed by tumor sequencing and organoid profiling

“…As one explanation, this patient had an exceptionally high HER2 copy number state, perhaps suggesting a component of oncogene addiction driven by a quantitative relationship between HER2 amplification and response in PDAC. Accordingly, prior literature in gastric cancer 40 , breast cancer with trastuzumab 46 and trastuzumab deruxtecan 47 support a positive correlation between HER2 gene copy number and response to anti-HER2 therapy. Conversely, loss of HER2 expression has been observed as a mechanism of resistance following anti-HER2 therapy in gastric cancer 48,49 .…”

Pathological identification of HER2-low breast cancer: Tips, tricks, and troubleshooting for the optimal test

“…The results showed a high correlation between QCS obtained from automatically detected membranes and those annotated by pathologists (R = 0.993). Moreover, HER2 QCS results had a broad quantitative overlap between IHC and ISH ( Gustavson et al, 2021 ). The use of the artificial neural network (ANN) models that can learn and shape non-linear and complex relationships has been implemented to assist in refining the HER2‐low definition ( Atallah et al, 2022 ).…”