Multi-parametric atlas of the pre-metastatic liver for prediction of metastatic outcome in early-stage pancreatic cancer.

Metastasis occurs frequently after resection of pancreatic cancer (PaC). In this study, we hypothesized that multi-parametric analysis of pre-metastatic liver biopsies would classify patients according to their metastatic risk, timing and organ site. Liver biopsies obtained during pancreatectomy from 49 patients with localized PaC and 19 control patients with non-cancerous pancreatic lesions were analyzed, combining metabolomic, tissue and single-cell transcriptomics and multiplex imaging approaches. Patients were followed prospectively (median 3 years) and classified into four recurrence groups; early (<6 months after resection) or late (>6 months after resection) liver metastasis (LiM); extrahepatic metastasis (EHM); and disease-free survivors (no evidence of disease (NED)). Overall, PaC livers exhibited signs of augmented inflammation compared to controls. Enrichment of neutrophil extracellular traps (NETs), Ki-67 upregulation and decreased liver creatine significantly distinguished those with future metastasis from NED. Patients with future LiM were characterized by scant T cell lobular infiltration, less steatosis and higher levels of citrullinated H3 compared to patients who developed EHM, who had overexpression of interferon target genes (MX1 and NR1D1) and an increase of CD11B+ natural killer (NK) cells. Upregulation of sortilin-1 and prominent NETs, together with the lack of T cells and a reduction in CD11B+ NK cells, differentiated patients with early-onset LiM from those with late-onset LiM. Liver profiles of NED closely resembled those of controls. Using the above parameters, a machine-learning-based model was developed that successfully predicted the metastatic outcome at the time of surgery with 78% accuracy. Therefore, multi-parametric profiling of liver biopsies at the time of PaC diagnosis may determine metastatic risk and organotropism and guide clinical stratification for optimal treatment selection.

Tumour extracellular vesicles and particles induce liver metabolic dysfunction.

Cancer alters the function of multiple organs beyond those targeted by metastasis1,2. Here we show that inflammation, fatty liver and dysregulated metabolism are hallmarks of systemically affected livers in mouse models and in patients with extrahepatic metastasis. We identified tumour-derived extracellular vesicles and particles (EVPs) as crucial mediators of cancer-induced hepatic reprogramming, which could be reversed by reducing tumour EVP secretion via depletion of Rab27a. All EVP subpopulations, exosomes and principally exomeres, could dysregulate hepatic function. The fatty acid cargo of tumour EVPs-particularly palmitic acid-induced secretion of tumour necrosis factor (TNF) by Kupffer cells, generating a pro-inflammatory microenvironment, suppressing fatty acid metabolism and oxidative phosphorylation, and promoting fatty liver formation. Notably, Kupffer cell ablation or TNF blockade markedly decreased tumour-induced fatty liver generation. Tumour implantation or pre-treatment with tumour EVPs diminished cytochrome P450 gene expression and attenuated drug metabolism in a TNF-dependent manner. We also observed fatty liver and decreased cytochrome P450 expression at diagnosis in tumour-free livers of patients with pancreatic cancer who later developed extrahepatic metastasis, highlighting the clinical relevance of our findings. Notably, tumour EVP education enhanced side effects of chemotherapy, including bone marrow suppression and cardiotoxicity, suggesting that metabolic reprogramming of the liver by tumour-derived EVPs may limit chemotherapy tolerance in patients with cancer. Our results reveal how tumour-derived EVPs dysregulate hepatic function and their targetable potential, alongside TNF inhibition, for preventing fatty liver formation and enhancing the efficacy of chemotherapy.

Adipose MDM2 regulates systemic insulin sensitivity.

The intimate association between obesity and type II diabetes urges for a deeper understanding of adipocyte function. We and others have previously delineated a role for the tumor suppressor p53 in adipocyte biology. Here, we show that mice haploinsufficient for MDM2, a key regulator of p53, in their adipose stores suffer from overt obesity, glucose intolerance, and hepatic steatosis. These mice had decreased levels of circulating palmitoleic acid [non-esterified fatty acid (NEFA) 16:1] concomitant with impaired visceral adipose tissue expression of Scd1 and Ffar4. A similar decrease in Scd and Ffar4 expression was found in in vitro differentiated adipocytes with perturbed MDM2 expression. Lowered MDM2 levels led to nuclear exclusion of the transcriptional cofactors, MORC2 and LIPIN1, and thereby possibly hampered adipocyte function by antagonizing LIPIN1-mediated PPARγ coactivation. Collectively, these data argue for a hitherto unknown interplay between MDM2 and MORC2/LIPIN1 involved in balancing adipocyte function.

Extracellular Vesicle and Particle Biomarkers Define Multiple Human Cancers.

There is an unmet clinical need for improved tissue and liquid biopsy tools for cancer detection. We investigated the proteomic profile of extracellular vesicles and particles (EVPs) in 426 human samples from tissue explants (TEs), plasma, and other bodily fluids. Among traditional exosome markers, CD9, HSPA8, ALIX, and HSP90AB1 represent pan-EVP markers, while ACTB, MSN, and RAP1B are novel pan-EVP markers. To confirm that EVPs are ideal diagnostic tools, we analyzed proteomes of TE- (n = 151) and plasma-derived (n = 120) EVPs. Comparison of TE EVPs identified proteins (e.g., VCAN, TNC, and THBS2) that distinguish tumors from normal tissues with 90% sensitivity/94% specificity. Machine-learning classification of plasma-derived EVP cargo, including immunoglobulins, revealed 95% sensitivity/90% specificity in detecting cancer. Finally, we defined a panel of tumor-type-specific EVP proteins in TEs and plasma, which can classify tumors of unknown primary origin. Thus, EVP proteins can serve as reliable biomarkers for cancer detection and determining cancer type.