Kite Pharma, Inc.

CD19-targeted chimeric antigen receptor T cells (CAR-T19) have shown remarkable success in B cell malignancies, but most patients relapse within 1-2 years. Here, we identified interleukin-1 (IL-1) receptor antagonist (IL-1ra) as a mediator of M2-like macrophage-derived inhibition of CAR-T19 in mantle cell lymphoma (MCL), as well as a potential target to enhance CAR-T19 efficacy. In preclinical models that recapitulated interactions between tumor, macrophages, and T cells, we demonstrated that M2-derived IL-1ra impairs IL-1 signaling and functions of CAR-T19. These findings were validated using clinical samples from the ZUMA-2 trial that led to the FDA approval of CAR-T19 in MCL. Single-cell RNA sequencing of CAR-T19 products and baseline myeloid cells indicated downregulated IL-1β production, enriched immunosuppressive phenotypes, and IL-1ra upregulation in the non-responder monocytes, as well as impaired IL-1β signaling and T cell functions in the non-responder CAR-T19 products. Furthermore, our preclinical studies of IL-1β showed enhanced CAR-T antitumor activities. Collectively, these data present a potential role for IL-1 signaling and IL-1ra in CAR-T19 failure.

We introduce an end-to-end framework for the automated visual annotation of histopathology whole slide images. Our method integrates deep learning models to achieve precise localization and classification of cell nuclei with spatial data aggregation to extend classes of sparsely distributed nuclei across the entire slide. We introduce a novel and cost-effective approach to localization, leveraging a U-Net architecture and a ResNet-50 backbone. The performance is boosted through color normalization techniques, helping achieve robustness under color variations resulting from diverse scanners and staining reagents. The framework is complemented by a YOLO detection architecture, augmented with generative methods. For classification, we use context patches around each nucleus, fed to various deep architectures. Sparse nuclei-level annotations are then aggregated using kernel density estimation, followed by color-coding and isocontouring. This reduces visual clutter and provides per-pixel probabilities with respect to pathology taxonomies. Finally, we use Morse-Smale theory to generate abstract annotations, highlighting extrema in the density functions and potential spatial interactions in the form of abstract graphs. Thus, our visualization allows for exploration at scales ranging from individual nuclei to the macro-scale. We tested the effectiveness of our framework in an assessment by six pathologists using various neoplastic cases. Our results demonstrate the robustness and usefulness of the proposed framework in aiding histopathologists in their analysis and interpretation of whole slide images.

Cancer immunotherapy—including immune checkpoint inhibition (ICI) and adoptive cell therapy (ACT)—has become a standard, potentially curative treatment for a subset of advanced solid and liquid tumors. However, most patients with cancer do not benefit from the rapidly evolving improvements in the understanding of principal mechanisms determining cancer immune responsiveness (CIR); including patient-specific genetically determined and acquired factors, as well as intrinsic cancer cell biology. Though CIR is multifactorial, fundamental concepts are emerging that should be considered for the design of novel therapeutic strategies and related clinical studies. Recent advancements as well as novel approaches to address the limitations of current treatments are discussed here, with a specific focus on ICI and ACT.