Preclinical Evaluation of AZD6422, an Armored Chimeric Antigen Receptor T Cell Targeting CLDN18.2 in Gastric, Pancreatic, and Esophageal Cancer.

PURPOSE: CLDN18.2 is a surface membrane protein crucial for maintaining tight junctions in gastric mucosal cells and is highly expressed in gastric, esophageal, and pancreatic cancers. Thus, CLDN18.2 is suited for exploration as a clinical target for chimeric antigen receptor T-cell (CAR-T) therapy in these indications. Although CAR-T therapies show promise, a challenge faced in their development for solid tumors is the immunosuppressive tumor microenvironment, often characterized by the presence of immune and stromal cells secreting high levels of transforming growth factor beta (TGF-ß). Addition of TGF-ß armoring can potentially expand CAR-T activity in solid tumors. We report on the preclinical development of a CLDN18.2-targeting CAR-T showing effectiveness in CLDN18.2-positive gastric, esophageal, and pancreatic tumor models. EXPERIMENTAL DESIGN: The lead lentivirus product contains a unique single-chain variable fragment, CD28 and CD3z costimulatory and signaling domains, and dominant negative TGF-ß receptor armoring, enhancing targeting and safety and counteracting suppression. We developed a shortened cell manufacturing process to enhance the potency of the final product, AZD6422. RESULTS: AZD6422 exhibited significant antitumor activity and tolerability in multiple patient-derived tumor xenograft models with various CLDN18.2 and TGF-ß levels, as determined by immunohistochemistry. Efficacy of armored CAR-Ts in tumor models with elevated TGF-ß was increased in vitro and in vivo. In vitro restimulation assays established greater persistence and cytolytic function of AZD6422 compared with a traditionally manufactured CAR-T. CONCLUSIONS: AZD6422 was safe and efficacious in patient-derived, CLDN18.2-positive murine models of gastrointestinal cancers. Our data support further clinical development of AZD6422 for patients with these cancers.

HER2 quantitative continuous scoring for accurate patient selection in HER2 negative trastuzumab deruxtecan treated breast cancer.

Many targeted cancer therapies rely on biomarkers assessed by scoring of immunohistochemically (IHC)-stained tissue, which is subjective, semiquantitative, and does not account for expression heterogeneity. We describe an image analysis-based method for quantitative continuous scoring (QCS) of digital whole-slide images acquired from baseline human epidermal growth factor receptor 2 (HER2) IHC-stained breast cancer tissue. Candidate signatures for patient stratification using QCS of HER2 expression on subcellular compartments were identified, addressing the spatial distribution of tumor cells and tumor-infiltrating lymphocytes. Using data from trastuzumab deruxtecan-treated patients with HER2-positive and HER2-negative breast cancer from a phase 1 study (NCT02564900; DS8201-A-J101; N = 151), QCS-based patient stratification showed longer progression-free survival (14.8 vs 8.6 months) with higher prevalence of patient selection (76.4 vs 56.9%) and a better cross-validated log-rank p value (0.026 vs 0.26) than manual scoring based on the American Society of Clinical Oncology / College of American Pathologists guidelines. QCS-based features enriched the HER2-negative subgroup by correctly predicting 20 of 26 responders.

Antitumor activity of AZD0754, a dnTGFßRII-armored, STEAP2-targeted CAR-T cell therapy, in prostate cancer.

Prostate cancer is generally considered an immunologically "cold" tumor type that is insensitive to immunotherapy. Targeting surface antigens on tumors through cellular therapy can induce a potent antitumor immune response to "heat up" the tumor microenvironment. However, many antigens expressed on prostate tumor cells are also found on normal tissues, potentially causing on-target, off-tumor toxicities and a suboptimal therapeutic index. Our studies revealed that six-transmembrane epithelial antigen of prostate-2 (STEAP2) was a prevalent prostate cancer antigen that displayed high, homogeneous cell surface expression across all stages of disease with limited distal normal tissue expression, making it ideal for therapeutic targeting. A multifaceted lead generation approach enabled development of an armored STEAP2 chimeric antigen receptor T cell (CAR-T) therapeutic candidate, AZD0754. This CAR-T product was armored with a dominant-negative TGF-ß type II receptor, bolstering its activity in the TGF-ß-rich immunosuppressive environment of prostate cancer. AZD0754 demonstrated potent and specific cytotoxicity against antigen-expressing cells in vitro despite TGF-ß-rich conditions. Further, AZD0754 enforced robust, dose-dependent in vivo efficacy in STEAP2-expressing cancer cell line-derived and patient-derived xenograft mouse models, and exhibited encouraging preclinical safety. Together, these data underscore the therapeutic tractability of STEAP2 in prostate cancer as well as build confidence in the specificity, potency, and tolerability of this potentially first-in-class CAR-T therapy.

Spatial heterogeneity of cancer associated protein expression in immunohistochemically stained images as an improved prognostic biomarker.

The identification of new tumor biomarkers for patient stratification before therapy, for monitoring of disease progression, and for characterization of tumor biology plays a crucial role in cancer research. The status of these biomarkers is mostly scored manually by a pathologist and such scores typically, do not consider the spatial heterogeneity of the protein's expression in the tissue. Using advanced image analysis methods, marker expression can be determined quantitatively with high accuracy and reproducibility on a per-cell level. To aggregate such per-cell marker expressions on a patient level, the expression values for single cells are usually averaged for the whole tissue. However, averaging neglects the spatial heterogeneity of the marker expression in the tissue. We present two novel approaches for quantitative scoring of spatial marker expression heterogeneity. The first approach is based on a co-occurrence analysis of the marker expression in neighboring cells. The second approach accounts for the local variability of the protein's expression by tiling the tissue with a regular grid and assigning local spatial heterogeneity phenotypes per tile. We apply our novel scores to quantify the spatial expression of four different membrane markers, i.e., HER2, CMET, CD44, and EGFR in immunohistochemically (IHC) stained tissue sections of colorectal cancer patients. We evaluate the prognostic relevance of our spatial scores in this cohort and show that the spatial heterogeneity scores clearly outperform the marker expression average as a prognostic factor (CMET: p-value=0.01 vs. p-value=0.3).

Automatic discovery of image-based signatures for ipilimumab response prediction in malignant melanoma.

In the context of precision medicine with immunotherapies there is an increasing need for companion diagnostic tests to identify potential therapy responders and avoid treatment coming along with severe adverse events for non-responders. Here, we present a retrospective case study to discover image-based signatures for developing a potential companion diagnostic test for ipilimumab (IPI) in malignant melanoma. Signature discovery is based on digital pathology and fully automatic quantitative image analysis using virtual multiplexing as well as machine learning and deep learning on whole-slide images. We systematically correlated the patient outcome data with potentially relevant local image features using a Tissue Phenomics approach with a sound cross validation procedure for reliable performance evaluation. Besides uni-variate models we also studied combinations of signatures in several multi-variate models. The most robust and best performing model was a decision tree model based on relative densities of CD8+ tumor infiltrating lymphocytes in the intra-tumoral infiltration region. Our results are well in agreement with observations described in previously published studies regarding the predictive value of the immune contexture, and thus, provide predictive potential for future development of a companion diagnostic test.

Tissue Phenomics for prognostic biomarker discovery in low- and intermediate-risk prostate cancer.

Tissue Phenomics is the discipline of mining tissue images to identify patterns that are related to clinical outcome providing potential prognostic and predictive value. This involves the discovery process from assay development, image analysis, and data mining to the final interpretation and validation of the findings. Importantly, this process is not linear but allows backward steps and optimization loops over multiple sub-processes. We provide a detailed description of the Tissue Phenomics methodology while exemplifying each step on the application of prostate cancer recurrence prediction. In particular, we automatically identified tissue-based biomarkers having significant prognostic value for low- and intermediate-risk prostate cancer patients (Gleason scores 6-7b) after radical prostatectomy. We found that promising phenes were related to CD8(+) and CD68(+) cells in the microenvironment of cancerous glands in combination with the local micro-vascularization. Recurrence prediction based on the selected phenes yielded accuracies up to 83% thereby clearly outperforming prediction based on the Gleason score. Moreover, we compared different machine learning algorithms to combine the most relevant phenes resulting in increased accuracies of 88% for tumor progression prediction. These findings will be of potential use for future prognostic tests for prostate cancer patients and provide a proof-of-principle of the Tissue Phenomics approach.

An objective comparison of cell-tracking algorithms.

We present a combined report on the results of three editions of the Cell Tracking Challenge, an ongoing initiative aimed at promoting the development and objective evaluation of cell segmentation and tracking algorithms. With 21 participating algorithms and a data repository consisting of 13 data sets from various microscopy modalities, the challenge displays today's state-of-the-art methodology in the field. We analyzed the challenge results using performance measures for segmentation and tracking that rank all participating methods. We also analyzed the performance of all of the algorithms in terms of biological measures and practical usability. Although some methods scored high in all technical aspects, none obtained fully correct solutions. We found that methods that either take prior information into account using learning strategies or analyze cells in a global spatiotemporal video context performed better than other methods under the segmentation and tracking scenarios included in the challenge.

The long non-coding RNA LINC00152 is essential for cell cycle progression through mitosis in HeLa cells.

In recent years, long non-coding RNA (lncRNA) research has identified essential roles of these transcripts in virtually all physiological cellular processes including tumorigenesis, but their functions and molecular mechanisms are poorly understood. In this study, we performed a high-throughput siRNA screen targeting 638 lncRNAs deregulated in cancer entities to analyse their impact on cell division by using time-lapse microscopy. We identified 26 lncRNAs affecting cell morphology and cell cycle including LINC00152. This transcript was ubiquitously expressed in many human cell lines and its RNA levels were significantly upregulated in lung, liver and breast cancer tissues. A comprehensive sequence analysis of LINC00152 revealed a highly similar paralog annotated as MIR4435-2HG and several splice variants of both transcripts. The shortest and most abundant isoform preferentially localized to the cytoplasm. Cells depleted of LINC00152 arrested in prometaphase of mitosis and showed reduced cell viability. In RNA affinity purification (RAP) studies, LINC00152 interacted with a network of proteins that were associated with M phase of the cell cycle. In summary, we provide new insights into the properties and biological function of LINC00152 suggesting that this transcript is crucial for cell cycle progression through mitosis and thus, could act as a non-coding oncogene.

High-Throughput RNAi Screening Identifies a Role for the Osteopontin Pathway in Proliferation and Migration of Human Aortic Smooth Muscle Cells.

PURPOSE: Understanding of the mechanisms of vascular smooth muscle cells (VSMCs) phenotypic regulation is critically important to identify novel candidates for future therapeutic intervention. While HTS approaches have recently been used to identify novel regulators in many cell lines, such as cancer cells and hematopoietic stem cells, no studies have so far systematically investigated the effect of gene inactivation on VSMCs with respect to cell survival and growth response. METHODS AND RESULTS: 257 out of 2000 genes tested resulted in an inhibition of cell proliferation in HaoSMCs. After pathway analysis, 38 significant genes were selected for further study. 23 genes were confirmed to inhibit proliferation, and 13 genes found to induce apoptosis in the synthetic phenotype. 11 genes led to an aberrant nuclear phenotype indicating a central role in cell mitosis. 4 genes affected the cell migration in synthetic HaoSMCs. Using computational biological network analysis, 11 genes were identified to have an indirect or direct interaction with the Osteopontin pathway. For 10 of those genes, levels of proteins downstream of the Osteopontin pathway were found to be down-regulated, using RNAi methodology. CONCLUSIONS: A phenotypic high-throughput siRNA screen could be applied to identify genes relevant for the cell biology of HaoSMCs. Novel genes were identified which play a role in proliferation, apoptosis, mitosis and migration of HaoSMCs. These may represent potential drug candidates in the future.

Quantitative analysis of chromatin interaction changes upon a 4.3 Mb deletion at mouse 4E2.

BACKGROUND: Circular chromosome conformation capture (4C) has provided important insights into three dimensional (3D) genome organization and its critical impact on the regulation of gene expression. We developed a new quantitative framework based on polymer physics for the analysis of paired-end sequencing 4C (PE-4Cseq) data. We applied this strategy to the study of chromatin interaction changes upon a 4.3 Mb DNA deletion in mouse region 4E2. RESULTS: A significant number of differentially interacting regions (DIRs) and chromatin compaction changes were detected in the deletion chromosome compared to a wild-type (WT) control. Selected DIRs were validated by 3D DNA FISH experiments, demonstrating the robustness of our pipeline. Interestingly, significant overlaps of DIRs with CTCF/Smc1 binding sites and differentially expressed genes were observed. CONCLUSIONS: Altogether, our PE-4Cseq analysis pipeline provides a comprehensive characterization of DNA deletion effects on chromatin structure and function.