Work

Inference of upstream-mutation and metabolomic-signature causality identifies prognostic biomarkers and therapeutic targets in pancreatic cancer

Year: 2026

Type: article

Abstract: Genomic mutations in pancreatic ductal adenocarcinoma (PDAC) are hypothesized to drive poor prognosis and low response rates to targeted therapy through crosstalk among downstream regulatory networks. Here, we apply a causal inference-based approach, Mutation-Upstream-of-Metabolomic-Signature (MUMS), to show that prognostic serum metabolomic signatures can capture such crosstalk and reflect the collective impact of mutation-driven networks on tumor progression. We identify a panel of nine serum metabolites that predicts survival outcomes across multiple independent PDAC cohorts. MUMS analysis further identifies and functionally validates GRPEL1 as a tumor-promoting gene whose downstream metabolic signature converges with that of the mTOR/PI3K/Akt signaling pathway. Consistently, GRPEL1 sensitizes PDAC cells to proliferation arrest induced by mTOR inhibition. Together, our findings provide proof-of-concept evidence that serum metabolic signatures can reflect crosstalk within the tumor mutational landscape. These co-regulatory patterns offer a framework for uncovering new therapeutic targets and guiding the design of rational combination therapies. Crosstalk among mutation-driven regulatory networks is linked to poor prognosis in pancreatic cancer. Here, the authors show that using a causal inference-based mutation-upstream-of metabolomic-signature approach, they establish a nine-metabolite panel that predicts survival outcomes and identify GRPEL1 as a tumour-promoting gene that sensitizes cells to mTOR inhibition.

Source: Nature Communications

DOI: 10.1038/s41467-026-73871-x