TEAD transcription factors enable the oncogenic activity of deregulated Hippo signaling and are a promising therapeutic target in oncology. Targeting the TEAD lipid pocket is an established path to inhibit the oncogenic activities of cofactors YAP and TAZ. Here we present two pan-TEAD inhibitors, GNE-8025 and its in vivo brain-penetrant derivative GNE-2181, that covalently bind the lipid pocket at a conserved cysteine. Both small molecules show growth inhibition of YAP-driven tumor cells in vitro and in vivo. Moreover, we show that GNE-8025 increases the activity of a broad range of MAPK pathway inhibitors in vitro as well as the KRASG12C inhibitor Divarasib both in vitro and in vivo. In addition, GNE-2181 inhibits growth of an intracranial tumor model in vivo. Altogether we present a next-generation class of TEAD inhibitors representing a significant advancement towards potent, specific, and effective Hippo-targeting cancer therapies.

The analysis of hematoxylin and eosin (HE)-stained bone marrow (BM) tissue sections in drug development toxicologic pathology studies is a key step in the in vivo safety assessment of new drug candidates. Routine histologic analysis provides critical information about the cellularity and tissue architecture of the BM but only limited insights into the cell lineages that comprise the hematopoietic tissue. The evaluation of BM cell types can be augmented by examining BM smear preparations or using immunohistochemical (IHC) labeling of histologic sections to identify lineages of interest; however, neither of these approaches is included in the standard assessment. In addition, manual evaluation is time-consuming, subject to inter-observer variability, and challenging due to the complexity of BM morphology and architecture. In this project, we developed a deep learning model to predict IHC labeling of BM cell lineages on HE-stained BM tissue sections. The model is trained on an immunohistochemistry-informed, HE-based ground truth for the sequential labeling of CD11b and myeloperoxidase markers and can predict cell segmentation mappings with over 69% agreement with the ground truth, using only HE slides as input. Furthermore, our method can discern cell population changes that reflect qualitative diagnoses identified by pathologists during routine slide interpretation. Our automated method holds promise for enhancing routine pathologist assessments of BM HE slides by extending the evaluation of hematopoietic cell lineages without the need to generate additional samples.

The immunohistochemistry (IHC) methods widely used in diagnostic medicine and biomedical research are kinetically complex reaction-diffusion processes that, ideally, produce stain intensities correlated with the local antigen concentration. Yet after 75 years of use, practical theoretical tools to rigorously plan and interpret IHC experiments are still lacking. Because modeling the reactions requires time-consuming computer simulation, impractical for regular use, most protocols are optimized empirically, without detailed knowledge of the reaction rates and antigen-antibody equilibria. The resulting stain intensities can be calibrated against standards with known antigen abundance, but they are typically not interpretable in terms of chemical antigen concentrations. To address these limitations, we developed a fast interpolation method to model reaction-diffusion behavior, and experimental methods to characterize IHC kinetic parameters in formalin-fixed paraffin-embedded (FFPE) samples. Used together, these allow experimental measurement of both the chemical concentration of antigen in the sample and the reaction-diffusion parameters consistent with the assay results. Results show 1) direct immunofluorescent detection has low nanomolar sensitivity with >1000-fold dynamic range, and 2) antibody diffusion rates in FFPE samples can be >1000-fold slower than in aqueous solutions, producing diffusion-limited conditions in which the IHC reaction time course may depend on the sample antigen concentration. Awareness of these details is necessary to avoid potential underestimation of both the absolute and relative antigen concentrations in different samples that may occur if staining is stopped before reaching equilibrium. Software tools are provided to allow users to rapidly model IHC reaction time courses and to fit experimental time course data with candidate reaction parameters. The principles described here apply equally to other tissue-based "spatial omics" analyses and should be considered when designing and interpreting experiments requiring any macromolecule to diffuse into and react in a tissue section.

Background Cancer immunotherapy has transformed the clinical approach to patients with malignancies as profound benefits can be seen in a subset of patients. To identify this subset, biomarker analyses increasingly focus on phenotypic and functional evaluation of the tumor microenvironment (TME) to determine if density, spatial distribution, and cellular composition of immune cell infiltrates can provide prognostic and/or predictive information. Attempts have been made to develop standardized methods to evaluate immune infiltrates in the routine assessment of certain tumor types; however, broad adoption of this approach in clinical decision-making is still missing. Methods We developed approaches to categorize solid tumors into “Desert”, “Excluded” and “Inflamed” types according to the spatial distribution of CD8+ immune effector cells to determine the prognostic and/or predictive implications of such labels. To overcome the limitations of this subjective approach we incrementally developed four automated analysis pipelines of increasing granularity and complexity for density and pattern assessment of immune effector cells. Results We show that categorization based on “manual” observation is predictive for clinical benefit from anti-programmed cell death ligand-1 (PD-L1) therapy in two large cohorts of patients with non-small cell lung cancer (NSCLC) or triple-negative breast cancer (TNBC). For the automated analysis we demonstrate that a combined approach outperforms individual pipelines and successfully relates spatial features to pathologist-based read-outs and patient response to therapy. Conclusions Our findings suggest tumor immunophenotype (IP) generated by automated analysis pipelines should be evaluated further as potential predictive biomarkers for cancer immunotherapy. What is already known on this topic Clinical benefit from checkpoint inhibitor-targeted therapies is realized only in a subset of patients. Robust biomarkers to identify patients who may respond to such therapies are needed. What this study adds We have developed manual and automated approaches to categorize tumors into immunophenotypes based on the spatial distribution of CD8+ T effector cells that predict clinical benefit from anti-PD-L1 immunotherapy for patients with advanced non-small cell lung cancer or triple-negative breast cancer. How this study might affect research, practice or policy Tumor immunophenotypes should be further validated as predictive biomarker for checkpoint inhibitor-targeted therapies in prospective clinical studies.

The utility of deep neural nets has been demonstrated for mapping hematoxylin-and-eosin (H&E) stained image features to expression of individual genes. However, these models have not been employed to discover clinically relevant spatial biomarkers. Here we develop MOSBY ( M ulti- Omic translation of whole slide images for S patial B iomarker discover Y ) that leverages contrastive self-supervised pretraining to extract improved H&E whole slide images features, learns a mapping between image and bulk omic profiles (RNA, DNA, and protein), and utilizes tile-level information to discover spatial biomarkers. We validate MOSBY gene and gene set predictions with spatial transcriptomic and serially-sectioned CD8 IHC image data. We demonstrate that MOSBY-inferred colocalization features have survival-predictive power orthogonal to gene expression, and enable concordance indices highly competitive with survival-trained multimodal networks. We identify and validate 1) an ER stress-associated colocalization feature as a chemotherapy-specific risk factor in lung adenocarcinoma, and 2) the colocalization of T effector cell vs cysteine signatures as a negative prognostic factor in multiple cancer indications. The discovery of clinically relevant biologically interpretable spatial biomarkers showcases the utility of the model in unraveling novel insights in cancer biology as well as informing clinical decision-making.

Supplementary figure 2 shows supportive naïve T cell transcriptional profiling data and additional supportive TF analyses.

Supplemental Methods, followed by Supplemental Figures S1, S2, S3, S4 and S5. Supplementary Fig. S1 In vitro binding of TDBs to HER2 and CD3 expressing cell lines. Supplementary Fig. S2 HER2 expression in model cell lines in vitro and tumors in vivo. Supplementary Fig. S3 HER2, CD3Æ and MECA-32 staining and vascular characterization of HER2 positive and negative tumors. Supplementary Fig. S4 T cell infiltration and CD3 expression in murine CT26 and CT26-HER2 tumors. Supplementary Fig. S5 Pharmacokinetic data from whole blood and cell pellet fractions

Supplementary figure 4 shows additional mass spectrometry analyses of CBP/p300 histone substrates along with prostacyclin measurement across cell types (naïve T cell and Treg) following treatment with CBP/p300 inhibitor GNE-781.

Supplementary figure 3 shows H3K27 acetylation ChIPseq QC analyses.

Digital pathology workflows in toxicologic pathology rely on whole slide images (WSIs) from histopathology slides. Inconsistent color reproduction by WSI scanners of different models and from different manufacturers can result in different color representations and inter-scanner color variation in the WSIs. Although pathologists can accommodate a range of color variation during their evaluation of WSIs, color variability can degrade the performance of computational applications in digital pathology. In particular, color variability can compromise the generalization of artificial intelligence applications to large volumes of data from diverse sources. To address these challenges, we developed a process that includes two modules: (1) assessing the color reproducibility of our scanners and the color variation among them and (2) applying color correction to WSIs to minimize the color deviation and variation. Our process ensures consistent color reproduction across WSI scanners and enhances color homogeneity in WSIs, and its flexibility enables easy integration as a post-processing step following scanning by WSI scanners of different models and from different manufacturers.

Supplementary Figures S1-S2 from Blocking Vascular Endothelial Growth Factor-A Inhibits the Growth of Pituitary Adenomas and Lowers Serum Prolactin Level in a Mouse Model of Multiple Endocrine Neoplasia Type 1

Supplementary figure 6 shows additional assessments of B cells in FL patient tissue samples and their activation status and supportive in vitro B cells findings.

Preclinical and clinical studies demonstrate that T cell-dependent bispecific antibodies (TDBs) induce systemic changes in addition to tumor killing, leading to adverse events. Here, we report an in-depth characterization of acute responses to TDBs in tumor-bearing mice. Contrary to modest changes in tumors, rapid and substantial lymphocyte accumulation and endothelial cell (EC) activation occur around large blood vessels in normal organs including the liver. We hypothesize that organ-specific ECs may account for the differential responses in normal tissues and tumors, and we identify a list of genes selectively upregulated by TDB in large liver vessels. Using one of the genes as an example, we demonstrate that CD9 facilitates ICAM-1 to support T cell-EC interaction in response to soluble factors released from a TDB-mediated cytotoxic reaction. Our results suggest that multiple factors may cooperatively promote T cell infiltration into normal organs as a secondary response to TDB-mediated tumor killing. These data shed light on how different vascular beds respond to cancer immunotherapy and may help improve their safety and efficacy.

Supplementary Data from Suppression of HER2/HER3-Mediated Growth of Breast Cancer Cells with Combinations of GDC-0941 PI3K Inhibitor, Trastuzumab, and Pertuzumab

Supplementary figure 1 shows supportive RT-qPCR data on the transcriptional modulation of Treg differentiation by CBP/p300 along with the proliferation effects of CBP/p300 inhibitor GNE-781 on T cells and Tregs.

Background. The THEORY study evaluated the effects of single and multiple doses of obinutuzumab, a type 2 anti-CD20 antibody that induces antibody-dependent cell-mediated cytotoxicity and direct cell death, in combination with standard of care in patients with end-stage renal disease. Methods. We measured B-cell subsets and protein biomarkers of B-cell activity in peripheral blood before and after obinutuzumab administration in THEORY patients, and B-cell subsets in lymph nodes in THEORY patients and an untreated comparator cohort. Results. Obinutuzumab treatment resulted in a rapid loss of B-cell subsets (including naive B, memory B, double-negative, immunoglobulin D + transitional cells, and plasmablasts/plasma cells) in peripheral blood and tissue. This loss of B cells was associated with increased B cell–activating factor and decreased CXCL13 levels in circulation. Conclusions. Our data further characterize the mechanistic profile of obinutuzumab and suggest that it may elicit greater efficacy in indications such as lupus where B-cell targeting therapeutics are limited by the resistance of pathogenic tissue B cells to depletion.

Supplementary figure 5 shows additional analyses of the CBP/p300 LOF mutations identified in the patient tissue samples along with predictions of functional effects of these mutations; additional immunofluorescence scores of immune cells and their activation status.

Supplementary table 1 shows the list of 2153 genes identified as the human Treg differentiation geneset. Supplementary table 2 shows the list of 2602 genes regulated by CBP/p300 bromodomain during Treg differentiation. Supplementary table 3 shows the list of 824 genes identified as Treg-specific CBP/p300 bromodomain-dependent geneset. Supplementary table 4 shows the list of 959 genes identified by H3K27Ac ChIPseq that are directly acetylated by CBP/p300 during Treg differentiation. Supplementary table 5 shows the list of 57 genes in the Treg-specific gene signature identified by combined analysis of H3K27Ac ChIPseq and RNAseq dataset analyses.