Multiparametric 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). We hypothesized that multiparametric analysis of pre-metastatic liver biopsies will 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 three years) and classified into four recurrence groups; early (<6 months post resection) or late (>6 months post resection) liver metastasis (LiM), extrahepatic (EHM) metastasis, and disease-free survivors (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 metastasis, who had overexpression of interferon target genes (MX1, NR1D1) and an increase of CD11B+ 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 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, multiparametric profiling of liver biopsies at the time of PaC diagnosis may determine metastatic risk and organotropism, and guide clinical stratification for optimal treatment selection.