ZBTB46 coordinates angiogenesis and immunity to control tumor outcome.

Tumor angiogenesis and immunity show an inverse correlation in cancer progression and outcome1. Here, we report that ZBTB46, a repressive transcription factor and a widely accepted marker for classical dendritic cells (DCs)2,3, controls both tumor angiogenesis and immunity. Zbtb46 was downregulated in both DCs and endothelial cells by tumor-derived factors to facilitate robust tumor growth. Zbtb46 downregulation led to a hallmark pro-tumor microenvironment (TME), including dysfunctional vasculature and immunosuppressive conditions. Analysis of human cancer data revealed a similar association of low ZBTB46 expression with an immunosuppressive TME and a worse prognosis. In contrast, enforced Zbtb46 expression led to TME changes to restrict tumor growth. Mechanistically, Zbtb46-deficient endothelial cells were highly angiogenic, and Zbtb46-deficient bone marrow progenitors upregulated Cebpb and diverted the DC program to immunosuppressive myeloid lineage output, potentially explaining the myeloid lineage skewing phenomenon in cancer4. Conversely, enforced Zbtb46 expression normalized tumor vessels and, by suppressing Cebpb, skewed bone marrow precursors toward immunostimulatory myeloid lineage output, leading to an immune-hot TME. Remarkably, Zbtb46 mRNA treatment synergized with anti-PD1 immunotherapy to improve tumor management in preclinical models. These findings identify ZBTB46 as a critical factor for angiogenesis and for myeloid lineage skewing in cancer and suggest that maintaining its expression could have therapeutic benefits.

Systemic delivery of murine SOD2 mRNA to experimental abdominal aortic aneurysm mitigates expansion and rupture.

Background: Oxidative stress is implicated in the pathogenesis and progression of abdominal aortic aneurysm (AAA). Antioxidant delivery as a therapeutic for AAA is of substantial interest although clinical translation of antioxidant therapy has met with significant challenges due to limitations in achieving sufficient antioxidant levels at the site of AAA. We posit that nanoparticle-based approaches hold promise to overcome challenges associated with systemic administration of antioxidants. Methods: We employed a peptide-based nanoplatform to overexpress a key modulator of oxidative stress, superoxide dismutase 2 (SOD2). The efficacy of systemic delivery of SOD2 mRNA as a nanotherapeutic agent was studied in two different murine AAA models. Unbiased mass spectrometry-enabled proteomics and high-dimensional bioinformatics were used to examine pathways modulated by SOD2 overexpression. Results: The murine SOD2 mRNA sequence was mixed with p5RHH, an amphipathic peptide capable of delivering nucleic acids in vivo to form self-assembled nanoparticles of ∼55 nm in diameter. We further demonstrated that the nanoparticle was stable and functional up to four weeks following self-assembly when coated with hyaluronic acid. Delivery of SOD2 mRNA mitigated the expansion of small AAA and largely prevented rupture. Mitigation of AAA was accompanied by enhanced SOD2 protein expression in aortic wall tissue. Concomitant suppression of nitric oxide, inducible nitric oxide synthase expression, and cell death was observed. Proteomic profiling of AAA tissues suggests that SOD2 overexpression augments levels of microRNAs that regulate vascular inflammation and cell apoptosis, inhibits platelet activation/aggregation, and downregulates mitogen-activated protein kinase signaling. Gene set enrichment analysis shows that SOD2 mRNA delivery is associated with activation of oxidative phosphorylation, lipid metabolism, respiratory electron transportation, and tricarboxylic acid cycle pathways. Conclusions: These results confirm that SOD2 is key modulator of oxidative stress in AAA. This nanotherapeutic mRNA delivery approach may find translational application in the medical management of small AAA and the prevention of AAA rupture.

Synergizing Deep Learning-Enabled Preprocessing and Human-AI Integration for Efficient Automatic Ground Truth Generation.

The progress of incorporating deep learning in the field of medical image interpretation has been greatly hindered due to the tremendous cost and time associated with generating ground truth for supervised machine learning, alongside concerns about the inconsistent quality of images acquired. Active learning offers a potential solution to these problems of expanding dataset ground truth by algorithmically choosing the most informative samples for ground truth labeling. Still, this effort incurs the costs of human labeling, which needs minimization. Furthermore, automatic labeling approaches employing active learning often exhibit overfitting tendencies while selecting samples closely aligned with the training set distribution and excluding out-of-distribution samples, which could potentially improve the model's effectiveness. We propose that the majority of out-of-distribution instances can be attributed to inconsistent cross images. Since the FDA approved the first whole-slide image system for medical diagnosis in 2017, whole-slide images have provided enriched critical information to advance the field of automated histopathology. Here, we exemplify the benefits of a novel deep learning strategy that utilizes high-resolution whole-slide microscopic images. We quantitatively assess and visually highlight the inconsistencies within the whole-slide image dataset employed in this study. Accordingly, we introduce a deep learning-based preprocessing algorithm designed to normalize unknown samples to the training set distribution, effectively mitigating the overfitting issue. Consequently, our approach significantly increases the amount of automatic region-of-interest ground truth labeling on high-resolution whole-slide images using active deep learning. We accept 92% of the automatic labels generated for our unlabeled data cohort, expanding the labeled dataset by 845%. Additionally, we demonstrate expert time savings of 96% relative to manual expert ground-truth labeling.