Monday, May 6, 2024
5/6/2024
The current assays for assessing HER2 were “fit-for-purpose” (over 25 years ago) to identify HER2 overexpression/gene amplification and are not built to subclassify unamplified HER2 expression in cancer. Applying these legacy assays results in high inter-rater disagreement rates for HER2-negative vs. HER2-low cases (up to 75% discordance for 0 vs. 1+/2+). With the emergence of new therapy options for HER2-low cancer – namely trastuzumab deruxtecan (T-DXd) – a reliable companion diagnostic is needed for patients. The incorrect application of these HER2 legacy assays has led to controversial findings in the field, including the paradoxical 30% objective response rate seen in IHC=0 breast cancer in the DAISY trial. This proposal aims to accurately stratify patients with HER2-low/negative breast cancer using our analytic, high-sensitivity HER2 (HS- HER2) assay to develop algorithms for the selection of the most appropriate therapy for breast cancer patients. Specifically, the proposal has two aims: Aim 1 will offer insight on clinically measurable pathologic & spatial parameters that influence the “bystander effect” associated with these newer, linker cleavable antibody drug conjugates like T-DXd. Specifically, this aim focuses on quantifying the spatial heterogeneity and HER2 expression patterns alongside HER2 tumor concentration to ultimately predict T-DXd response. I hypothesize that subclassifying HER2 expression in breast cancer with quantitative spatial heterogeneity metrics for “cluster”-type and single-cell “mosaic”-type patterns will further explain why only ~30% of IHC=0 benefit from T-DXd in the DAISY trial (although ~70% of IHC=0 cases have detectable attomole/mm2 HER2 concentrations by our HS-HER2 assay). I propose to investigate large retrospective and prospective breast cancer cohorts with our HS-HER2 assay, as well as cohorts of HER2-low breast cancer from ongoing T-DXd clinical trials. Aim 2 seeks to explore if an H&E-based "virtual HER2 stain" can predict low HER2 expression status. Our lab and others have illustrated that HER2 gene amplification is associated with detectable morphological changes from the H&E image alone. I hypothesize that HER2-low breast cancer will also have distinct morphologic associations that can be discovered. The study proposes to build a dataset of HS-HER2 and H&E aligned, adjacent serial section whole slide images. Then, train state-of-the-art computer vision architectures on H&E images with a multi-task, multi-image pre-training and mixed-supervision multiple instance learning strategy to predict low HER2 expression status (defined by the HS-HER2 assay in attomole/mm2). This aim will reveal H&E morphologic features of breast cancer associated with low HER2 expression and T-DXd response. Overall, this proposal addresses the critical unmet clinical need for accurately assessing HER2 expression in breast cancer patients and proposes two innovative aims to develop algorithms for T-DXd patient selection.
HHS’ Tracking Accountability in Government Grants System (TAGGS) website provides access to detailed descriptions of grants, loans, aggregated direct payments and other types of financial assistance awarded by the HHS Operating Divisions (OPDIVs) and the Office of the Secretary Staff Divisions (STAFFDIVs).
