Abstract A82: Virtual microdissection reveals tumor specific heterogeneity in pancreatic cancer

Using matrix factorization, we have removed confounding normal tissue gene expression from profiles of primary and metastatic tumors, facilitating the study of underlying tumor biology without the need for laser capture microdissection. Understanding molecular mechanisms of metastasis in pancreatic cancer has the potential to yield rationally designed therapies. Previous work has used gene expression arrays and next generation sequencing to look for potential targets, focusing on identifying differences between metastatic lesions and primary tumors. However, such analyses are hampered by the low cellularity of malignant epithelium in patient samples. Since profiles of primary and metastatic tumors often contain contributions from adjacent normal tissues from the tumor site, comparison of these samples are confounded by the differences between normal pancreas and normal distant organs. We seek to avoid this pitfall by leveraging the blind source separation technique, nonnegative matrix factorization (NMF)[1], to the analysis of gene expression data. Microarray data were obtained from matched primary (n=8) and metastatic tumors (n=31) as well as adjacent normal tissues (n=85) for both local and distant sites of patients who died of metastatic pancreatic cancer[2]. Pathological analysis showed that tumor cellularity varied with a mean of 48% and a standard deviation of 31% across all sites. We applied NMF to our cohort, thus computationally microdissecting tumors into the source tissues composing our samples. 200 iterations of 5-fold resampling were performed to achieve stable NMF results. Genes with expression ranked in the top 50 of any factor together were recorded in a consensus matrix. This consensus matrix was then used as the basis of a hierarchical clustering as to yield k gene clusters. These k gene-clusters were used to seed a supervised NMF using the MATLAB NMF multiplicative update solver until completion. Using an unbiased approach, we identified distinct molecular signatures associated with adjacent normal and tumor tissue in primary and metastatic pancreatic cancer samples. Specifically, we identified confounding liver, lung, lymph, and muscle tissue gene expression from our metastatic samples as well as both endocrine and exocrine pancreas gene expression from our primary tumors. Furthermore, we estimated the relative composition of each of our samples in terms of a weighted sum of these tissue-specific signatures. Both primary tumors and metastases appeared to be admixtures of pancreatic tumor epithelial expression and adjacent normal tissue. For example, in liver tissues histological assessment of tumor content correlated well with NMF predictions of tumor content, (n=39, R 2 =0.72, p We have demonstrated a method to virtually microdissect tissues, thereby identifying tumor-specific gene expression data in pancreatic cancer. By applying a fresh computational approach to a large cohort of data, we can generate new insight into the complex nature of low-cellularity tumors such as pancreatic cancer, and facilitate the study of inter- and intra-patient heterogeneity. This approach may be further leveraged to study the role of tumor and stroma signatures in pancreatic cancer. 1. Brunet, J.P., et al., Metagenes and molecular pattern discovery using matrix factorization. Proceedings of the National Academy of Sciences, 2004. 101(12): p. 4164-4169. 2. Stratford, J.K., et al., A six-gene signature predicts survival of patients with localized pancreatic ductal adenocarcinoma. PLoS medicine, 2010. 7(7): p. e1000307. Citation Format: Richard A. Moffitt, Keith A. Volmar, Judy M. Anderson, Michael A. Hollingsworth, Jen Jen Yeh. Virtual microdissection reveals tumor specific heterogeneity in pancreatic cancer. [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Innovations in Research and Treatment; May 18-21, 2014; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2015;75(13 Suppl):Abstract nr A82.