The University of North Carolina at Chapel Hill

Naim Ur Rashid

Jen Jen Yeh

Richard Moffitt

Provided are methods for identifying pancreatic cancer subtypes in a subject and treating the same. In some embodiments, the method comprise obtaining gene expression levels for each of the following genes in the biological sample: GPR87, KRT6A, BCAR3, PTGES, 1TGA3, C16orf74, S100A2, KRT5, REG4, ANXA10, GATA6, CLDN18, LGALS4, DDC, SLC40A1, CLRN3; performing pair-wise comparisons of gene expression levels for combinations of these genes, and calculating a Raw Score for the biological sample, wherein the Raw Score is indicative of the pancreatic cancer subtype in the subject. Also provided are methods for identifying differential treatment strategies for subjects diagnosed with PDAC, methods for treating PDAC patients based on the subtype of PD AC the patients have; and methods for classifying subjects diagnosed with PDAC as having a basal-like subtype or a classical subtype of PDAC.

Platform and sample type independent single sample classifier for treatment decision making in pancreatic ductal adenocarcinoma cancer

Methods for generating a prognostic and/or subtype signature for a subject with pancreatic ductal adenocarcinoma (PDAC) are provided. In some embodiments, the methods include determining expression levels for one or more genes listed in Tables 2-5, 9, 10, or 11, and/or the DE-S and/or DE-T subset of genes in PDAC cells obtained from the subject, wherein the determining provides a prognostic and/or subtype signature for the subject. Also provided are methods for classifying a subject diagnosed with pancreatic ductal adenocarcinoma (PDAC) as having an activated stroma subtype or a normal stroma subtype of PDAC and/or a basal subtype or a classical subtype of PDAC; and methods for identifying a differential treatment strategy for a subject diagnosed with pancreatic ductal adenocarcinoma (PDAC).

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Raleigh, NC, United States of America

Naim Ur Rashid