Bulk RNA-seq deconvolution heterogeneity across paired pancreatic cancer human samples

Rick J Jansen^1*Sarah A Munro¹Samuel O. Antwi²Kari G Rabe³Hugues Sicotte³

• ¹University of Minnesota Twin Cities, Minneapolis, United States
• ²Mayo Clinic in Florida, Jacksonville, United States
• ³Mayo Clinic Minnesota, Rochester, United States

Introduction: There is great promise in using genomic data to inform individual cancer treatment plans. Assessing intratumor genetic heterogeneity, studies have shown it may be possible to target biopsies to tumor subclones driving disease progression or treatment resistance. Here, we explore if the interpretation of tumor gene expression analysis varies across two specimens from the same patient. Material and Methods: We performed bulk RNA-seq using FFPE samples from 16 patients who also had a previous separate bulk RNA-seq performed and deposited in TCGA. We used three different deconvolution methods to compare cell type proportions for these paired data. We normalized study-specific gene expression values per gene by calculating transcripts per million and adjusted for batch effect across study to compare median expression values. We also compared the reliability of gene expression measurements. We selected KRAS, TP53, SMAD4, and CDKN2A, as the most mutated genes in pancreatic cancer, and CTNNB1, JUN, SMAD3, SMAD7, and TCF7, as these tend to be enriched in pancreatic cancer compared with adjacent normal tissue. Results: We found that average cell type proportion varied the most between studies (i.e., samples for each patient) for NK and macrophages (using adjusted p-value 0.05/21=0.002). For the differential expression analysis, we did not observe significant differences in average expression of any of the selected genes. We observed substantial concordance (kappa=0.75) only for JUN with low to moderate concordance (i.e., Kappa value 0.25-0.5) for the remaining 8 genes across the two studies. Discussion: Together, the findings suggest that more than one tumor sample may be needed for effective treatment planning. Any potential difference in observed expression values across the paired samples could be related to the different cell type proportions across the samples. The sample size was small, and each study used different sequencing technologies, so any interpretation should be confirmed with additional studies.