Inhibition of the HER2/ERBB2 receptor is a keystone to treating HER2-positive malignancies, particularly breast cancer, but a significant fraction of HER2-positive (HER2+) breast cancers recur or fail to respond. Anti-HER2 monoclonal antibodies, like trastuzumab or pertuzumab, and ATP active site inhibitors like lapatinib, commonly lack durability because of adaptive changes in the tumor leading to resistance. HER2+ cell line responses to inhibition with lapatinib were analyzed by RNAseq and ChIPseq to characterize transcriptional and epigenetic changes. Motif analysis of lapatinib-responsive genomic regions implicated the pioneer transcription factor FOXA1 as a mediator of adaptive responses. Lapatinib in combination with FOXA1 depletion led to dysregulation of enhancers, impaired adaptive upregulation of HER3, and decreased proliferation. HER2-directed therapy using clinically relevant drugs (trastuzumab with or without lapatinib or pertuzumab) in a 7-day clinical trial designed to examine early pharmacodynamic response to antibody-based anti-HER2 therapy showed reduced FOXA1 expression was coincident with decreased HER2 and HER3 levels, decreased proliferation gene signatures, and increased immune gene signatures. This highlights the importance of the immune response to anti-HER2 antibodies and suggests that inhibiting FOXA1-mediated adaptive responses in combination with HER2 targeting is a potential therapeutic strategy.

Intra-operative specimen mammography is a valuable tool in breast cancer surgery, providing immediate assessment of margins for a resected tumor. However, the accuracy of specimen mammography in detecting microscopic margin positivity is low. We sought to develop a deep learning-based model to predict the pathologic margin status of resected breast tumors using specimen mammography. A dataset of specimen mammography images matched with pathology reports describing margin status was collected. Models pre-trained on radiologic images were developed and compared with models pre-trained on non-medical images. Model performance was assessed using sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). The dataset included 821 images and 53% had positive margins. For three out of four model architectures tested, models pre-trained on radiologic images outperformed domain-agnostic models. The highest performing model, InceptionV3, showed a sensitivity of 84%, a specificity of 42%, and AUROC of 0.71. These results compare favorably with the published literature on surgeon and radiologist interpretation of specimen mammography. With further development, these models could assist clinicians with identifying positive margins intra-operatively and decrease the rate of positive margins and re-operation in breast-conserving surgery.

Proliferative and invasive breast tumors evolve heterogeneously in individual patients, posing significant challenges in identifying new druggable targets for precision, effective therapy. Here we present a functional multi-omics method, interaction-Correlated Multi-omic Aberration Patterning (iC-MAP), which dissects intra-tumor heterogeneity and identifies in situ the oncogenic consequences of multi-omics aberrations that drive proliferative and invasive tumors. First, we perform chromatin activity-based chemoproteomics (ChaC) experiments on breast cancer (BC) patient tissues to identify genetic/transcriptomic alterations that manifest as oncogenically active proteins. ChaC employs a biotinylated small molecule probe that specifically binds to the oncogenically active histone methyltransferase G9a, enabling sorting/enrichment of a G9a-interacting protein complex that represents the predominant BC subtype in a tissue. Second, using patient transcriptomic/genomic data, we retrospectively identified some G9a interactor-encoding genes that showed individualized iC-MAP. Our iC-MAP findings represent both new diagnostic/prognostic markers to identify patient subsets with incurable metastatic disease and targets to create individualized therapeutic strategies.