Joseph R. Evans

The traditional view of genome organization has been upended in the last decade with the discovery of vast amounts of non-protein-coding transcription. After initial concerns that this "dark matter" of the genome was transcriptional noise, it is apparent that a subset of these noncoding RNAs are functional. Long noncoding RNA (lncRNA) genes resemble protein-coding genes in several key aspects, and they have myriad molecular functions across many cellular pathways and processes, including oncogenic signaling. The number of lncRNA genes has recently been greatly expanded by our group to triple the number of protein-coding genes; therefore, lncRNAs are likely to play a role in many biological processes. Based on their large number and expression specificity in a variety of cancers, lncRNAs are likely to serve as the basis for many clinical applications in oncology.

Long non-coding RNAs (lncRNAs) represent an emerging layer of cancer biology, contributing to tumor proliferation, invasion, and metastasis. Here, we describe a role for the oncogenic lncRNA PCAT-1 in prostate cancer proliferation through cMyc. We find that PCAT-1-mediated proliferation is dependent on cMyc protein stabilization, and using expression profiling, we observed that cMyc is required for a subset of PCAT-1-induced expression changes. The PCAT-1-cMyc relationship is mediated through the post-transcriptional activity of the MYC 3' untranslated region, and we characterize a role for PCAT-1 in the disruption of MYC-targeting microRNAs. To further elucidate a role for post-transcriptional regulation, we demonstrate that targeting PCAT-1 with miR-3667-3p, which does not target MYC, is able to reverse the stabilization of cMyc by PCAT-1. This work establishes a basis for the oncogenic role of PCAT-1 in cancer cell proliferation and is the first study to implicate lncRNAs in the regulation of cMyc in prostate cancer.

Background: Precision therapy for lung cancer will require comprehensive genomic testing to identify actionable targets as well as ascertain disease prognosis. RNA-seq is a robust platform that meets these requirements, but microarray-derived prognostic signatures are not optimal for RNA-seq data. Thus, we undertook the first prognostic analysis of lung adenocarcinoma RNA-seq data and generated a prognostic signature. Methods: Lung adenocarcinoma RNA-seq and clinical data from The Cancer Genome Atlas (TCGA) were divided chronologically into training (n = 255) and validation (n = 157) cohorts. In the training cohort, prognostic association was assessed by univariate Cox analysis. A prognostic signature was built with stepwise multivariable Cox analysis. Outcomes by risk group, stage, and mutation status were analyzed with Kaplan-Meier and multivariable Cox analyses. All the statistical tests were two-sided. Results: , including five long noncoding RNAs (lncRNAs). Stepwise regression generated a four-gene signature, including one lncRNA. Signature high-risk cases had worse overall survival (OS) in the TCGA validation cohort (hazard ratio [HR] = 3.07, 95% confidence interval [CI] = 2.00 to 14.62) and a University of Michigan institutional cohort (n = 67; HR = 2.05, 95% CI = 1.18 to 4.55), and worse metastasis-free survival in the TCGA validation cohort (HR = 3.05, 95% CI = 2.31 to 13.37). The four-gene prognostic signature also statistically significantly stratified overall survival in important clinical subsets, including stage I (HR = 2.78, 95% CI = 1.91 to 11.13), EGFR wild-type (HR = 3.01, 95% CI = 1.73 to 14.98), and EGFR mutant (HR = 8.99, 95% CI = 62.23 to 141.44). The four-gene prognostic signature also stood out on top when compared with other prognostic signatures. Conclusions: Here, we present the first RNA-seq prognostic signature for lung adenocarcinoma that can provide a powerful prognostic tool for precision oncology as part of an integrated RNA-seq clinical sequencing program.