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

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.