Robust prognostic gene expression signatures in bladder cancer and lung adenocarcinoma depend on cell cycle related genes.

Few prognostic biomarkers are approved for clinical use primarily because their initial performance cannot be repeated in independent datasets. We posited that robust biomarkers could be obtained by identifying deregulated biological processes shared among tumor types having a common etiology. We performed a gene set enrichment analysis in 20 publicly available gene expression datasets comprising 1968 patients having one of the three most common tobacco-related cancers (lung, bladder, head and neck) and identified cell cycle related genes as the most consistently prognostic class of biomarkers in bladder (BL) and lung adenocarcinoma (LUAD). We also found the prognostic value of 13 of 14 published BL and LUAD signatures were dependent on cell cycle related genes, supporting the importance of cell cycle related biomarkers for prognosis. Interestingly, no prognostic gene classes were identified in squamous cell lung carcinoma or head and neck squamous cell carcinoma. Next, a specific 31 gene cell cycle proliferation (CCP) signature, previously derived in prostate tumors was evaluated and found predictive of outcome in BL and LUAD cohorts in univariate and multivariate analyses. Specifically, CCP score significantly enhanced the predictive ability of multivariate models based on standard clinical variables for progression in BL patients and survival in LUAD patients in multiple cohorts. We then generated random CCP signatures of various sizes and found sets of 10-15 genes had robust performance in these BL and LUAD cohorts, a finding that was confirmed in an independent cohort. Our work characterizes the importance of cell cycle related genes in prognostic signatures for BL and LUAD patients and identifies a specific signature likely to survive additional validation

Analysis of refined CCP signatures in prostate cancer.

<p>Analysis of refined CCP signatures in prostate cancer.</p

Multivariate survival analysis in patients with lung adenocarcinoma.

*<p>Variables include the following (see <b>Table S8 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085249#pone.0085249.s004" target="_blank">File S4</a></b> for complete multivariate analysis):</p><p>Stage: I vs. II (CANDF), I vs. II vs. III (MKS, Takeuchi, Tomida).</p><p>Grade: Well vs. moderately vs. poorly differentiated.</p><p>Smoking history: current/former vs. never-smoker.</p><p>EGFR, KRAS, and p53 status: mutant vs. wild-type.</p>†<p>Final model is constructed from forward step-wise regression of significant variables (P<0.05).</p

Prognostic value of a refined 12-gene cell cycle proliferation (CCP-12) score.

<p>Prognostic value of a refined 12-gene cell cycle proliferation (CCP-12) score in lung adenocarcinoma patients with gene expression profiling by RNASeq is shown. Kaplan-Meier (KM) curves for overall survival (OS) were generated for patients (N = 88) with CCP-12 scores at the lower (green), middle (blue), and upper (red) 33% and the log rank P-value of the continuous CCP-12 score is reported. Abbreviations: HR, hazard ratio, corresponding to 1-unit increase in CCP-12 score.</p

Prognostic modules associated with survival in tobacco-related cancers.

<p>In each cohort, over-represented Gene Ontology (GO) terms and KEGG pathways were identified from lists of genes significantly predictive of disease outcome (P<0.01) using the DAVID gene annotation enrichment analysis toolkit. Consistently prognostic modules were identified by ranking all modules first by the number of cohorts with significant results (FDR<20%) and then by average p-value. Each subfigure includes ten modules: the most consistently prognostic modules and the ‘top hit’ for each cohort, marked by an asterisk (*), which is defined as the module with the lowest FDR in that cohort that has an FDR<20% in multiple cohorts. <b>A,</b> over-represented GO terms associated with survival in bladder cancer. <b>B,</b> over-represented GO terms associated with survival in lung adenocarcinoma. <b>C,</b> over-represented GO terms associated with survival in squamous cell lung carcinoma. <b>D–F,</b> same as A–C except over-represented KEGG pathways are identified. There were no significantly over-represented prognostic modules in the head and neck squamous cell carcinoma cohorts at FDR<20%. LUSC: Squamous cell lung carcinoma, FDR: false discovery rate.</p

Study overview and summary of major findings.

<p><b>A,</b> Biological processes associated with survival in tobacco-related tumors were identified through a gene set enrichment analysis. This analysis identified cell cycle as the only biological process consistently associated with outcome, in bladder and lung adenocarcinoma while no processes were identified that were predictive of outcome in lung adenocarcinoma, squamous cell lung carcinoma, or head and neck squamous cell carcinoma. <b>B,</b> Given the findings in <b>A,</b> the clinical relevance of cell cycle related genes was assessed in two ways. First, we evaluated the prognostic value of a specific 31 gene cell cycle proliferation (CCP) signature in bladder and lung adenocarcinoma in univariate and multivariate analysis. Second, we found that the prognostic value of previously published gene signatures predicting survival in bladder progression and lung adenocarcinoma was dependent on cell-cycle correlated genes. <b>C,</b> Because additional analysis revealed that the prognostic value of the CCP score was dependent on signature size, we optimized the CCP signature and found that a smaller 12 gene signature (CCP-12) was prognostic in an external dataset.</p

The prognostic value of published prognostic signatures in bladder cancer and lung adenocarcinoma.

<p>The expression values of prognostic signature genes were adjusted for CCP score or by a constant (negative control) as described in <b>Supporting Materials and Methods in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085249#pone.0085249.s001" target="_blank">File S1</a></b>. <b>A,</b> heatmap showing the impact of CCP score adjustment on the predictive ability of each signature (rows) on each cohort (columns). Signatures either lose their predictive ability following adjustment (P<0.05 in the control but P>0.05 in the CCP-adjusted cohort ; blue box); remain prognostic following the adjustment (P<0.05 in both the control and CCP-adjusted cohorts; green box); or were not prognostic in either case (P>0.05 in both groups, white box). <b>B,</b> stacked bar chart summarizing the prognostic value of each signature by categories described in (A). Loss of predictive ability is calculated as the percentage of signatures that lose their predictive ability following adjustment (blue boxes) with respect to the total number of control cohorts a signature is prognostic in (blue + green boxes). <b>C–D,</b> adjustment results in lung adenocarcinoma cohorts, in the same format as A–B.</p