Distinct malignant cell states and myeloid glutamate signaling associated with aggressive pancreatic neuroendocrine tumors
Arbesfeld-Qiu, J. M.; Cho, J.-W.; Ngyuen, P. T. T.; Lester, N. A.; Su, J.; Guo, J. A.; Hoffman, H.; Shiau, C.; Caldwell, N.; Muratani, S.; Galvan, M.; Proctor, J. E.; Ely, Z.; Wang, S.; Ganci, M.; Dries, R.; Hong, T.; Wo, J.; Boland, G.; Fernandez-del Castillo, C.; Ferrone, C.; Heaphy, C. M.; Zhang, M. L.; Mino-Kenudson, M.; Hemberg, M.; Hwang, W. L.
Show abstract
Pancreatic neuroendocrine tumors (PNET) are rare malignancies of the endocrine pancreas with diverse clinical outcomes. While some PNETs are indolent, others are aggressive and metastasize quickly. However, clinically-relevant molecular stratification for PNET to predict outcomes and guide therapeutic decision-making is limited. Thus, there is an urgent need to understand the molecular heterogeneity of PNETs to refine prognostication and discover novel therapeutic vulnerabilities. We performed single-nucleus RNA sequencing on resected primary and metastatic PNETs (n = 20), including two PNETs with neoadjuvant treatment. We inferred gene expression programs (GEPs) of malignant and non-malignant cells and investigated associations with clinical outcomes. Next, we inferred interactions in the tumor microenvironment (TME) and performed transwell assays for functional validation. Finally, we explored genomic and transcriptomic evolution in a unique case study of an untreated primary PNET with two asynchronous hepatic metastases. A malignant GEP enriched for neural/synaptic signaling genes was associated with worse overall survival, broad chromosomal loss of heterozygosity, and alternative lengthening of telomeres. Another malignant GEP enriched for VEGF signaling increased throughout metastatic progression in our case study. We found that macrophage-derived glutamate drives polarization towards an immunosuppressive phenotype and activates the MAPK/ERK pathway in malignant cells to increase migratory capacity. This study provides a detailed single-nucleus transcriptomic classification of malignant, stromal, and immune cell types and states in PNETs, their interactions in the TME, and associations with clinical outcomes. The refined molecular taxonomy of PNET may guide the development of more efficacious biomarkers and therapeutic strategies.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Single-Cell RNA Sequencing Reveals the Effects of Chemotherapy on Human Pancreatic Adenocarcinoma and its Tumor Microenvironment 97%
- Mapping and modeling human colorectal carcinoma interactions with the tumor microenvironment 96%
- XENTURION, a multidimensional resource of xenografts and tumoroids from metastatic colorectal cancer patients for population-level translational oncology 96%
Similar papers in this journal
- Inflammatory reprogramming of the tumor microenvironment by infiltrating clonal hematopoiesis is associated with adverse outcomes in solid cancer 97%
- Determination of permissive and restraining cancer-associated fibroblast (DeCAF) subtypes 97%
- Cell states and neighborhoods in distinct clinical stages of primary and metastatic esophageal adenocarcinoma 96%
Similar papers in this journal
- Cancer-associated fibroblast compositions change with breast cancer progression linking S100A4 and PDPN ratios with clinical outcome 96%
- Glutamine mimicry suppresses tumor progression through asparagine metabolism in pancreatic ductal adenocarcinoma 95%
- Cell lineage as a predictor of immune response in neuroblastoma 94%
Similar papers in this journal
- Predicting the Tumor Microenvironment Composition and Immunotherapy Response in Non-Small Cell Lung Cancer from Digital Histopathology Images 95%
- Single-Cell Spatial Proteomics Analyses of Head and Neck Squamous Cell Carcinoma Reveal Tumor Heterogeneity and Immune Architectures Associated with Clinical Outcome 95%
- A single-cell based precision medicine approach using glioblastoma patient-specific models 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.