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Kaplan, D. E.

Publications and source records attributed to Kaplan, D. E..

2 recordsLinked to original sources

GM-CSF and IL-3 expression increases immune engraftment and tumor infiltration in a humanized patient-derived xenograft model of hepatocellular carcinoma

Background & AimsResponses to immunotherapies in hepatocellular carcinoma (HCC) are suboptimal with no biomarkers to guide patient selection. "Humanized" mice represent promising models to address this deficiency but are limited by variable chimerism and underdeveloped myeloid compartments. We hypothesized that expression of human GM-CSF and IL-3 increases tumor immune cell infiltration, especially myeloid-derived cells, in humanized HCC patient-derived xenografts (PDXs). Material and MethodsNOG (NOD/Shi-scid/IL-2R null) and NOG-EXL (huGM-CSF/huIL-3 NOG) mice conditioned with Busulfan underwent i.v. injection of human CD34+ cells. HCC PDX tumors were then implanted subcutaneously (SQ) or orthotopically (OT). Following serial blood sampling, mice were euthanized at defined tumor sizes. Tumor, blood, liver, and spleen were analyzed by flow cytometry and immunohistochemistry. ResultsHumanized NOG-EXL mice demonstrated earlier and increased human chimerism compared to humanized NOG mice (82.1% vs 43.8%, p<0.0001) with increased proportion of human monocytes (3.2% vs 1.1%, p=0.001) and neutrophils (0.8% vs 0.3%, p=0.02) in circulation. HCC tumors in humanized NOG-EXL mice had increased human immune cell infiltration (57.6% vs 30.2%, p=0.04), noting increased regulatory T cells (14.6% vs 6.8%, p=0.04), CD4+ PD-1 expression (84.7% vs 32.0%, p<0.01), macrophages (1.2% vs 0.6%, p=0.02), and neutrophils (0.5% vs 0.1%, p<0.0001). No differences were observed in tumor engraftment or growth latency in SQ tumors, but OT tumors required implantation at two rather than four weeks post-humanization for successful engraftment. Finally, utilizing adult bone marrow instead of fetal livers enabled partial HLA-matching to HCC tumors but required more CD34+ cells. ConclusionsHuman GM-CSF and IL-3 expression in humanized mice resulted in features more closely approximating the immune microenvironment of human disease, providing a promising model for investigating critical questions in immunotherapy for HCC. Impact and ImplicationsThis study introduces a unique mouse model at a critical point in the evolution of treatment paradigms for patients with hepatocellular carcinoma (HCC). Immunotherapies have become first line treatment for advanced HCC; however, response rates remain low with no clear predictors of response to guide patient selection. In this context, animal models that recapitulate human disease are greatly needed. Leveraging xenograft tumors derived from patients with advanced HCCs and a commercially available immunodeficient mouse strain that expresses human GM-CSF and IL-3, we demonstrate a novel but accessible approach for modeling the HCC tumor microenvironment.

cancer biology↗

Intrinsic instability of the dysbiotic microbiome revealed through dynamical systems inference at scale

Dynamical systems models are a powerful tool for analyzing interactions, stability, resilience, and other key properties in biomedically important microbial ecosystems, such as the gut microbiome. Challenges to modeling and inference in this setting include the large number of species present, and data sparsity/noise characteristics. Here, we introduce a Bayesian statistical method, the Microbial Dynamical Systems Inference Engine 2 (MDSINE2), which infers compact and interpretable ecosystems-scale dynamical systems models from microbiome time-series data. We model microbial dynamics as stochastic processes driven by inferred interaction modules, or groups of microbes with similar interaction structure and responses to perturbations. Additionally, we model the noise characteristics of sequencing and qPCR measurements to provide uncertainty quantification for all outputs. To evaluate MDSINE2, and provide a benchmarking resource for the community, we generated the most densely sampled microbiome time-series to date, which consists of a cohort of mice that received fecal transplants from a human donor and were then subjected to dietary and antibiotic perturbations. Benchmarking on simulated and real data demonstrate that MDSINE2 significantly outperforms state-of-the-art methods, and moreover identifies interaction modules that shed new light on ecosystems-scale interactions in the gut microbiome. We provide MDSINE2 as an open-source Python package at: https://github.com/gerberlab/MDSINE2.

systems biology↗