Transcriptome Analysis of Primary Prostate Tumor Foci and Corresponding Lymph Node Metastases Identifies Pathways Associated with Metastatic Disease Restricted; Files Only

Abstract

Prostate cancer (PCa) is a highly heterogeneous disease, and mortality is mainly due to metastases. However, the molecular underpinnings that lead to the initial steps of metastasis have not been well characterized. We performed genomic analysis of primary prostate tumor foci (PTF) and corresponding lymph node metastases (LNM). PTF and LNM from patients with high-risk PCa were analyzed by RNAseq and Whole-Exome Sequencing (WES). Computational pipelines were developed with Linux, R, and Python scripting. Through the RNA-seq pipeline, differentially expressed genes between PTF, LNM, benign LNs, and normal prostate were identified. A median of 57 million paired-end reads were obtained per sample. Comparing PTF to LNM, 8110 transcripts were differentially expressed (p-adj < 0.01). PTF were enriched relative to LNM in gene sets associated with Notch signaling, TGFb signaling, hypoxia, and the epithelial to mesenchymal transition. Comparing PTF from metastatic patients to non-metastatic patients, 581 transcripts were differentially expressed (p-adj < 0.01). PTF from metastatic patients were enriched in cell cycle progression, MYC targets, ER stress, androgen response, and DNA repair. LNM gene sets were enriched in endoplasmic reticulum (ER) stress and oxidative phosphorylation. We also identified a set of 193 genes with significantly increased expression in primary tumors over benign LNs and in LNM over primary tumors. This gene set was significantly enriched in genes related to oxidative phosphorylation and included oncogenes such as PIK3CB, NCOA2, and SCHLAP1. The WES pipeline revealed genomic variant and tumoral heterogeneity information. The top mutated genes include SPOP, EYA1, and NCOR2. These somatic mutations may drive cancer proliferation via dysregulation of the AR signaling pathway. Through WES analysis, we identified mutations associated with PCa metastasis. The top mutated genes associated with metastasis are SOGA1, LRRC4C, TP53, COL5A1, PCDHA13, and SLC16A14. Our results are vital to the investigation of prostate cancer metastasis, as genomic changes drive oncogenic progression. By understanding the mechanism of metastasis, we may be able to improve clinical strategies to target PCa.

Table of Contents

Introduction ........................................................................................................................ 1 

Prostate Cancer Biology.......................................................................................... 1

Symptoms & Treatments........................................................................................ 3

Genomic Sequencing & Informatics....................................................................... 5

Methodology ...................................................................................................................... 6 

Main Objectives...................................................................................................... 6

Experimental Design............................................................................................... 8

RNA-seq Pipeline.................................................................................................. 12

WES Pipeline........................................................................................................ 16

Results

RNA-seq Analysis................................................................................................. 17

WES Analysis....................................................................................................... 27

Integrative DNA/RNA Analysis............................................................................33

Discussion

RNA-seq Pipeline.................................................................................................. 35

WES Pipeline........................................................................................................ 36

Conclusions....................................................................................................................... 38

Acronym Appendix............................................................................................................ 39

References......................................................................................................................... 41

About this Honors Thesis

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School	Emory College
Department	Biology
Degree	B.S.
Submission	Honors Thesis
Language	English
Research Field	Computer Science Health Sciences, Oncology Biology, Bioinformatics
Palavra-chave	Sequencing Oncology Genomics
Committee Chair / Thesis Advisor	Dr. Carlos Moreno, Emory University
Committee Members	Dr. Ymir Vigfusson, Emory University Dr. David Gorkin, Emory University

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