bioRxivCancer BiologyPreprint
Detection of Chemotherapy-Resistant Pancreatic Cancer Using a Glycan Biomarker
C. Gao, L. Wisniewski, Y. Liu, B. Staal, I. Beddows, D. Plenker, M. Aldakkak, J. Hall, D. Barnett, M. Kheir Gouda, P. Allen, R. Drake, A. Zureikat, Y. Huang, D. Evans, A. Singhi, R. E. Brand, D. A. Tuveson, S. Tsai, B. Haab
Abstract
Background and AimsA subset of pancreatic ductal adenocarcinomas (PDACs) is highly resistant to systemic chemotherapy, but no markers are available in clinical settings to identify this subset. We hypothesized that chemotherapy-resistant PDACs express a glycan biomarker called sTRA. Methods. We tested this marker to identify treatment-resistant PDAC in multiple systems: sets of cell lines, organoids, and isogenic cell lines; primary tumors; and blood plasma from cohorts of human subjects. Results. Among a panel of 27 cell lines, high levels of cell-surface sTRA identified higher resistance to seven chemotherapeutics used against PDAC. Using primary tumors from two different cohorts, patients who were positive for a gene-expression classifier for sTRA received no statistically significant benefit from adjuvant chemotherapy, in contrast to those negative for the signature. In another cohort, using direct measurements of sTRA in tissue microarrays by quantitative immunofluorescence, patients who were high in sTRA again had no statistically significant benefit from adjuvant chemotherapy. Further, a blood-plasma test for the sTRA glycan identified the PDACs that showed rapid relapse following neoadjuvant chemotherapy. This blood test performed with 96% specificity and 56% sensitivity in a blinded cohort using samples collected before the start of treatment. Conclusion. These findings establish that tissue or plasma sTRA can identify PDACs that are resistant to neoadjuvant or adjuvant chemotherapy. This capability could help apply systemic treatments more precisely and facilitate biomarker-guided trials targeting resistant PDAC.
- organism: human
- assay: proteomics
- research focus: cell type annotation
- research focus: differential expression
- approach: statistical model
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