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

ABSTRACT: Metastasis occurs frequently after resection of pancreatic cancer (PC). 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 PC and 18 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 (LM), extrahepatic (EH) metastasis, and disease-free survivors (NED). Overall, PC 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 LM were characterized by scant T cell lobular infiltration, less steatosis, and higher levels of citrullinated H3, compared to patients who developed EH 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 LM. 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 PC diagnosis may determine metastatic risk and organotropism, and guide clinical stratification for optimal treatment selection.

ORGANISM(S): Homo sapiens

PROVIDER: GSE245535 | GEO | 2024/06/28

REPOSITORIES: GEO

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Project description:Introduction: The majority of pulmonary carcinoid (PC) tumors can be cured by surgical resection alone, but a significant proportion of patients experience recurrences. PC is insensitive to conventional chemotherapy, and it would be necessary to reveal the molecular mechanisms of metastasis and develop targeted therapeutics. Methods: We performed the comprehensive whole-exome sequencing (WES) of primary tumors and corresponding normal lung tissues from 14 PC patients (including 4 patients who developed postsurgical distant metastasis) and the RNA sequencing of primary tumors from 6 PC patients (including 4 patients who developed postsurgical distant metastasis). Exon array-based gene expression analysis was performed in 25 PC. Results: We identified a total of 139 alterations in 136 genes. MUC6 and SPTA1 were recurrently mutated with the frequency of 21 % (3/14) and 14% (2/14), respectively. The mucin protein family genes including MUC2, MUC4 and MUC6 were mutated in 36% (5/14) in a mutually exclusive manner. The pathway analysis of mutated genes revealed the enrichment of genes involved in the mitogen-activated protein kinase (MAPK) signaling, regulation of actin cytoskeleton and focal adhesion, and transforming growth factor (TGF)-β signaling pathways. RNA sequencing reveals a total of 8 novel fusion transcripts including one derived from chromosomal translocation between the TRIB2 and PRKCE genes. All the 8 fusion genes were detected in primary PC that had developed metastasis after surgical resection. We identified 14 genes (DENND1B, GRID1, CLMN, DENND1B, NRP1, SEL1L3, C5orf13, TNFRSF21, TES, STK39, MTHFD2, OPN3, MET, and HIST1H3C) up-regulated in 5 PC that had relapsed after surgical resection. Conclusions: In this study we identified novel somatic mutations and chromosomal rearrangements by examining clinically aggressive cases that had developed postsurgical metastasis. It is essential to validate their clinical significance in an independent larger patient cohort.

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

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