Abstract A048: Robust Pancreatic Cancer Liver Metastatic Model System Reveals Cancer Cell Dependent Organotropism and Site-specific Tumor Microenvironment Regulation
Christina R. Larson, Jace Baines, Ayushi Mandloi, Meet Patel, Tuan Tran, Nailah Jones, Ateeq M. Khaliq, Christopher A. Risley, Robert S. Welner, Satwik Acharyya, Julienne L. Carstens
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引用次数: 0
Abstract
Metastatic pancreatic adenocarcinoma is the dominant clinical presentation with a grim 3% 5-year survival rate. Over 80% of metastatic disease occurs in the liver and has the poorest outcomes. Overall, there is little mechanistic understanding of what promotes liver metastatic outgrowth and organotropism. Our recent work using spatial transcriptomics on unique matched primary tumors and metastases revealed distinct cellular ecosystems, noting reduced desmoplasia, high proliferation, spatially constrained metabolism, and heightened pro-tumorigenic myeloid infiltration and T-cell dysfunction at the invasive border of liver metastases. However, moving beyond these observations requires establishing causal links between molecular drivers and metastatic competence. Causal studies are limited by current model systems, the spontaneous genetically engineered mouse models (GEMM) that form the basis of pre-clinical studies do not sufficiently model the clinical metastatic reality with robust matched pancreas and liver tumors, and are further hampered with inconsistent metastatic rates and unpredictable progression for timed analysis. Syngeneic transplants of GEMM-derived cancer lines into wild-type mice provide a rapid pre-clinical model system of liver disease. However, the liver-metastatic rates varies between cell lines, even with identical driving mutations. We suspected that the unexplored mechanisms driving these differences in metastatic outgrowth present an opportunity to understand critical biology. Here we report the development a consistent model system of matched pancreatic and liver tumors using syngeneic cell lines with high and low tropism for liver metastatic outgrowth in C57Bl/6 mice. These lines are transplanted at low cell numbers to better allow the evolution of the site-specific tumor microenvironment and provide a reliable model system to examine both cancer-cell intrinsic and site-specific microenvironmental factors dictating liver outgrowth. Our observations of comparable pancreatic growth, successful metastatic growth in other organs (peritoneum or lung), and micro-metastatic lesions in the liver at early time points, suggests the liver-tropic differences fall within the ability of these cells to successfully outgrow in the liver microenvironment, rather than in vivo proliferative or extravasation differences. Comparison of gene expression between high and low liver-tropic cell lines identified several immune regulatory genes and a general increase in lipid metabolism, consistent with our published patient data. Finally, spatial quantifications of these lesions using a novel 46-plex murine immunotyping panel on FFPE tissues show similar suppressive immune cells at the interface of the tumor and normal liver as observed in our patient data, but evolves across differing lesion size, suggestive of a spatiotemporal progression. Altogether, this model system provides a robust, efficient pre-clinical platform to dissect spatiotemporal drivers of liver metastatic disease. Citation Format: Christina R. Larson, Jace Baines, Ayushi Mandloi, Meet Patel, Tuan Tran, Nailah Jones, Ateeq M. Khaliq, Christopher A. Risley, Robert S. Welner, Satwik Acharyya, Julienne L. Carstens. Robust Pancreatic Cancer Liver Metastatic Model System Reveals Cancer Cell Dependent Organotropism and Site-specific Tumor Microenvironment Regulation [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research—Emerging Science Driving Transformative Solutions; Boston, MA; 2025 Sep 28-Oct 1; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl_3): nr A048.
转移性胰腺腺癌是主要的临床表现,5年生存率只有3%。超过80%的转移性疾病发生在肝脏,预后最差。总的来说,对于促进肝转移生长和器官亲和性的机制了解甚少。我们最近利用空间转录组学对独特匹配的原发肿瘤和转移瘤进行了研究,揭示了不同的细胞生态系统,注意到在肝转移瘤浸润边界处,结缔组织增生减少,增殖高,空间代谢受限,促瘤性骨髓浸润和t细胞功能障碍增加。然而,超越这些观察需要建立分子驱动和转移能力之间的因果关系。因果研究受到当前模型系统的限制,自发基因工程小鼠模型(GEMM)构成临床前研究的基础,不能充分模拟胰腺和肝脏肿瘤的临床转移现实,并且进一步受到不一致的转移率和不可预测的时间分析进展的阻碍。将gemm衍生的癌细胞系同基因移植到野生型小鼠体内,提供了一种快速的肝脏疾病临床前模型系统。然而,即使具有相同的驱动突变,肝转移率在细胞系之间也是不同的。我们怀疑,未探索的机制驱动这些转移性产物的差异提供了一个了解关键生物学的机会。在这里,我们报告了在C57Bl/6小鼠中使用高和低趋向性的同基因细胞系建立了一个一致的胰腺和肝脏肿瘤匹配模型系统。这些细胞系在低细胞数量下移植,以更好地允许位点特异性肿瘤微环境的进化,并提供可靠的模型系统来检查癌细胞固有的和位点特异性的微环境因素决定肝脏的生长。我们对早期胰腺生长、其他器官(腹膜或肺)成功转移生长和肝脏微转移病变的观察表明,嗜肝性差异在于这些细胞在肝脏微环境中成功生长的能力,而不是体内增殖或外渗的差异。高亲肝细胞系和低亲肝细胞系之间的基因表达比较发现了几个免疫调节基因和脂质代谢的普遍增加,这与我们发表的患者数据一致。最后,在FFPE组织上使用一种新型46-plex小鼠免疫分型面板对这些病变进行空间量化,结果显示,在我们的患者数据中观察到的肿瘤和正常肝脏界面处存在相似的抑制性免疫细胞,但随着病变大小的不同而发展,这表明了时空进展。总之,该模型系统为剖析肝转移性疾病的时空驱动因素提供了一个强大、高效的临床前平台。引用格式:Christina R. Larson, Jace Baines, Ayushi Mandloi, Meet Patel, Tuan Tran, Nailah Jones, Ateeq M. Khaliq, Christopher A. Risley, Robert S. Welner, Satwik Acharyya, Julienne L. Carstens。强大的胰腺癌肝转移模型系统揭示癌细胞依赖的器官亲和性和位点特异性肿瘤微环境调节[摘要]。摘自:AACR癌症研究特别会议论文集:胰腺癌研究进展-新兴科学驱动变革解决方案;波士顿;2025年9月28日至10月1日;波士顿,MA。费城(PA): AACR;癌症研究2025;85(18_Suppl_3): nr A048。
期刊介绍:
Cancer Research, published by the American Association for Cancer Research (AACR), is a journal that focuses on impactful original studies, reviews, and opinion pieces relevant to the broad cancer research community. Manuscripts that present conceptual or technological advances leading to insights into cancer biology are particularly sought after. The journal also places emphasis on convergence science, which involves bridging multiple distinct areas of cancer research.
With primary subsections including Cancer Biology, Cancer Immunology, Cancer Metabolism and Molecular Mechanisms, Translational Cancer Biology, Cancer Landscapes, and Convergence Science, Cancer Research has a comprehensive scope. It is published twice a month and has one volume per year, with a print ISSN of 0008-5472 and an online ISSN of 1538-7445.
Cancer Research is abstracted and/or indexed in various databases and platforms, including BIOSIS Previews (R) Database, MEDLINE, Current Contents/Life Sciences, Current Contents/Clinical Medicine, Science Citation Index, Scopus, and Web of Science.