Positive allosteric modulation of CD11b as a novel therapeutic strategy against lung cancer

Lung cancer is one of the leading causes of cancer-related deaths in the United States. A major hurdle for improved therapies is immune suppression mediated by the tumor and its microenvironment. The lung tumor microenvironment (TME) contains large numbers of tumor-associated macrophages (TAMs), which suppress the adaptive immune response, increase neo-vascularization of the tumor, and provide pro-tumor factors to promote tumor growth. CD11b is highly expressed on myeloid cells, including TAMs, where it forms a heterodimeric integrin receptor with CD18 (known as CD11b/CD18, Mac-1, CR3, and αMβ2), and plays an important role in recruitment and biological functions of these cells, and is a validated therapeutic target. Here, we describe our pre-clinical studies targeting CD11b in the context of lung cancer, using pharmacologic and genetic approaches that work via positive allosteric modulation of CD11b function. GB1275 is a novel small molecule modulator of CD11b that is currently in Phase 1/2 clinical development. We assess GB1275 treatment effects on tumor growth and immune infiltrates in the murine Lewis Lung Carcinoma (LLC) syngeneic tumor model. Additionally, as an orthogonal approach to determine mechanisms of action, we utilize our recently developed novel CD11b knock-in (KI) mouse that constitutively expresses CD11b containing an activating isoleucine to glycine substitution at residue 332 in the ligand binding CD11b A-domain (I332G) that acts as a positive allosteric modulator of CD11b activity. We report that pharmacologic modulation of CD11b with GB1275 significantly reduces LLC tumor growth. CD11b KI mice similarly show significant reduction in both the size and rate of LLC tumor growth, as compared to WT mice, mimicking our observed treatment effects with GB1275. Tumor profiling revealed a significant reduction in TAM infiltration in GB1275-treated and in CD11b KI mice, increase in the ratio of M1/M2-like TAMs, and concomitant increase in cytotoxic T cells. The profiling also showed a significant decrease in CCL2 levels and a concomitant reduction in Ly6

BONE FORMATION IN RAT’S CALVARIAL DEFECT AFTER APPLICATION OF DEMINERALIZED FREEZE DRIED BOVINE CORTICAL BONE MEMBRANE

Bovine pericardium collagen membrane (BPCM) had been widely used in guided bone regeneration (GBR) for alveolar bone augmentation. However, it is associated with prolonged biodegradation. A newly developed Demineralized Freeze-Dried Bovine Cortical Bone Membrane (DFDBCBM) assumed to be rich in osteogenic growth factors was found to be biocompatible. Its osteogenic capacity yet to be proven. This study evaluated bone defects healing in rats’ calvarial critical sized defectafter the application of DFDBCBM and BPCM. Critical sized defect was made on calvaria bone of 30 Wistar rats. The samples were divided into 3 groups, each contains of 10 rats. The defects were then covered by DFDBCBM and BPCM in the first two groups, the control group was left to heal without membrane application. Samples were collected at 4 and 8 weeks from each group. Degree of bone healing was examined using histology examination with HE staining using bone healing score. Data was analyzed statistically with the Kruskal-Wallis test and Mann Whitney accordingly. The degree of bone healing was significantly higher in DFDBCBM groups compared to BPCM groups both in 4 and 8 weeks observation. DFDBCBM has higher capacity for bone defect healing compared to BPCM in rat’s calvaria defect

Predicting Machine Translation Performance on Low-Resource Languages: The Role of Domain Similarity

Fine-tuning and testing a multilingual large language model is expensive and challenging for low-resource languages (LRLs). While previous studies have predicted the performance of natural language processing (NLP) tasks using machine learning methods, they primarily focus on high-resource languages, overlooking LRLs and shifts across domains. Focusing on LRLs, we investigate three factors: the size of the fine-tuning corpus, the domain similarity between fine-tuning and testing corpora, and the language similarity between source and target languages. We employ classical regression models to assess how these factors impact the model's performance. Our results indicate that domain similarity has the most critical impact on predicting the performance of Machine Translation models.Comment: 13 pages, 5 figures, accepted to EACL 2024, finding

Worldcuisines: a massive-scale benchmark for multilingual and multicultural visual question answering on global cuisines

Vision Language Models (VLMs) often struggle with culture-specific knowledge, particularly in languages other than English and in underrepresented cultural contexts. To evaluate their understanding of such knowledge, we introduce WorldCuisines, a massive-scale benchmark for multilingual and multicultural, visually grounded language understanding. This benchmark includes a visual question answering (VQA) dataset with text-image pairs across 30 languages and dialects, spanning 9 language families and featuring over 1 million data points, making it the largest multicultural VQA benchmark to date. It includes tasks for identifying dish names and their origins. We provide evaluation datasets in two sizes (12k and 60k instances) alongside a training dataset (1 million instances). Our findings show that while VLMs perform better with correct location context, they struggle with adversarial contexts and predicting specific regional cuisines and languages. To support future research, we release a knowledge base with annotated food entries and images along with the VQA data

Integrin CD11b activation drives anti-tumor innate immunity

Myeloid cells are recruited to damaged tissues where they can resolve infections and tumor growth or stimulate wound healing and tumor progression. Recruitment of these cells is regulated by integrins, a family of adhesion receptors that includes integrin CD11b. Here we report that, unexpectedly, integrin CD11b does not regulate myeloid cell recruitment to tumors but instead controls myeloid cell polarization and tumor growth. CD11b activation promotes pro-inflammatory macrophage polarization by stimulating expression of microRNA Let7a. In contrast, inhibition of CD11b prevents Let7a expression and induces cMyc expression, leading to immune suppressive macrophage polarization, vascular maturation, and accelerated tumor growth. Pharmacological activation of CD11b with a small molecule agonist, Leukadherin 1 (LA1), promotes pro-inflammatory macrophage polarization and suppresses tumor growth in animal models of murine and human cancer. These studies identify CD11b as negative regulator of immune suppression and a target for cancer immune therapy

SeqStain using fluorescent-DNA conjugated antibodies allows efficient, multiplexed, spatialomic profiling of human and murine tissues

ABSTRACTSpatial organization of molecules and cells in complex tissue microenvironments provides essential cues during healthy growth and in disease. Novel techniques are needed for elucidation of their spatial relationships and architecture. Although a few multiplex immunofluorescence based techniques have been developed for visualization of the spatial relationships of various molecules in cells and tissues, there remains a significant need for newer methods that are rapid, easy to adapt and are gentle during the cyclic steps of fluorescence staining and de-staining. Here, we describe a novel, multiplex immunofluorescence imaging method, termed SeqStain, that uses fluorescent-DNA labelled antibodies for immunofluorescence staining of cells and tissues, and nuclease treatment for de-staining that allows selective enzymatic removal of the fluorescent signal. SeqStain can be used with primary antibodies, secondary antibodies and antibody fragments, such as Fabs, to efficiently analyse complex cells and tissues in multiple rounds of staining and de-staining. Additionally, incorporation of specific endonuclease restriction sites in antibody labels allows for selective removal of fluorescent signals, while retaining other signals that can serve as marks for subsequent analyses. The application of SeqStain on human kidney tissue provided spatialomic profile of the organization of &gt;25 markers in the kidney, highlighting it as a versatile, easy to use and gentle new technique for spatialomic analyses of complex microenvironments.</jats:p

Predicting machine translation performance on low-resource languages : the role of domain similarity

Fine-tuning and testing a multilingual large language model is a challenge for low-resource languages (LRLs) since it is an expensive process. While previous studies have predicted the performance of natural language processing (NLP) tasks using machine learning methods, they primarily focus on high-resource languages, overlooking LRLs and shifts across domains. Focusing on LRLs, we investigate three factors (the size of the fine-tuning corpus, domain similarity between fine-tuning and testing corpora, and language similarity between source and target languages), which can potentially impact the model performance by using classical regression models. Our results indicate that domain similarity has the most important impact on predicting the performance of Machine Translation models