Peripheral Immune Cell Gene Expression Predicts Survival of Patients with Non-Small Cell Lung Cancer

Prediction of cancer recurrence in patients with non-small cell lung cancer (NSCLC) currently relies on the assessment of clinical characteristics including age, tumor stage, and smoking history. A better prediction of early stage cancer patients with poorer survival and late stage patients with better survival is needed to design patient-tailored treatment protocols. We analyzed gene expression in RNA from peripheral blood mononuclear cells (PBMC) of NSCLC patients to identify signatures predictive of overall patient survival. We find that PBMC gene expression patterns from NSCLC patients, like patterns from tumors, have information predictive of patient outcomes. We identify and validate a 26 gene prognostic panel that is independent of clinical stage. Many additional prognostic genes are specific to myeloid cells and are more highly expressed in patients with shorter survival. We also observe that significant numbers of prognostic genes change expression levels in PBMC collected after tumor resection. These post-surgery gene expression profiles may provide a means to re-evaluate prognosis over time. These studies further suggest that patient outcomes are not solely determined by tumor gene expression profiles but can also be influenced by the immune response as reflected in peripheral immune cells

Demographic and clinico-pathologic characteristics of the study population.

a<p>Fisher’s exact test for categorical variables; two-tailed t tests assuming equal group variances for continuous variables.</p>b<p>Standard deviation.</p

MicroRNA Expression Profiles of Whole Blood in Lung Adenocarcinoma

<div><p>The association of lung cancer with changes in microRNAs in plasma shown in multiple studies suggests a utility for circulating microRNA biomarkers in non-invasive detection of the disease. We examined if presence of lung cancer is reflected in whole blood microRNA expression as well, possibly because of a systemic response. Locked nucleic acid microarrays were used to quantify the global expression of microRNAs in whole blood of 22 patients with lung adenocarcinoma and 23 controls, ten of whom had a radiographically detected non-cancerous lung nodule and the other 13 were at high risk for developing lung cancer because of a smoking history of >20 pack-years. Cases and controls differed significantly for age with a mean difference of 10.7 years, but not for gender, race, smoking history, blood hemoglobin, platelet count, or white blood cell count. Of 1282 quantified human microRNAs, 395 (31%) were identified as expressed in the study’s subjects, with 96 (24%) differentially expressed between cases and controls. Classification analyses of microRNA expression data were performed using linear kernel support vector machines (SVM) and top-scoring pairs (TSP) methods, and classifiers to identify presence of lung adenocarcinoma were internally cross-validated. In leave-one-out cross-validation, the TSP classifiers had sensitivity and specificity of 91% and 100%, respectively. The values with SVM were both 91%. In a Monte Carlo cross-validation, average sensitivity and specificity values were 86% and 97%, respectively, with TSP, and 88% and 89%, respectively, with SVM. MicroRNAs <em>miR-190b</em>, <em>miR-630, miR-942</em>, and <em>miR-1284</em> were the most frequent constituents of the classifiers generated during the analyses. These results suggest that whole blood microRNA expression profiles can be used to distinguish lung cancer cases from clinically relevant controls. Further studies are needed to validate this observation, including in non-adenocarcinomatous lung cancers, and to clarify upon the confounding effect of age.</p> </div

Correlation between microRNA quantification by reverse transcription-PCR (RT-PCR) and microarray.

<p>The scatter-plots show RT-PCR quantification cycle (C_q) values and log₂-transformed microarray signal values for microRNAs <i>let-7e</i>, <i>miR-22</i>, <i>miR-30a-5p</i>, <i>miR-185</i>, <i>miR-210</i>, and <i>miR-423-5p</i> (n = 11). Pearson correlation coefficients (r) and their 95% confidence intervals and associated P values, and best fitting (least squares) lines are also shown.</p

Whole blood microRNA expression in lung adenocarcinoma cases and controls.

<p><i>A</i>. Unsupervised clustering of the 45 samples of this study by log₂-transformed microarray signal values of all 395 expressed microRNAs. The numbers indicate identities of the 45 subjects, with cases (n = 22) and controls (n = 23) shown in black and grey, respectively. The sample tree with optimized leaf-ordering is drawn using Pearson correlation for distance metric and average linkage for cluster-to-cluster distance, and the scale for it represents node-heights. <i>B</i>. Supervised clustering of microRNAs by their log₂-transformed microarray signal values. The heat-map, with the pseudo-color scale underneath, shows log₂-transformed microarray signal values of the 43 microRNAs whose expression is altered >25% in either direction in the cases compared to the controls. The gene tree is drawn as in <i>A</i>.</p

Expression of miR-1284, miR-942, miR-630, and miR-190b.

<p>Dot-plots with medians and inter-quartile ranges of log₂-transformed microarray signal values for the 22 cases (<i>black</i>) and 23 controls (<i>grey</i>) are shown for the four microRNAs that are present in a majority of the classifiers generated in internal cross-validation analyses using the linear support vector machines and top-scoring pairs classification methods.</p

Twelve differentially expressed small RNAs each that are most over- or under-expressed in cases compared to controls.

a<p>Microarray signal values are shown.</p>b<p>Both <i>RNU6-1</i> and <i>RNU6-2</i> RNAs are detected by the same microarray probe.</p

Association with lung adenocarcinoma of age, and blood hemoglobin level, and white blood cell (WBC) and platelet counts.

<p><i>A</i>. Receiver operating characteristic curves, the areas under curve (<i>AUC</i>) for age, and the line of identity, <i>x</i> = <i>y</i>, with an AUC of 0.5, are shown. <i>B</i>. Correlation with microRNA expression. Values for the clinical variables were correlated with microarray signal values for the 395 expressed microRNAs (n = 45 for age; n = 39 for others). The curves depict frequency histograms of Pearson correlation coefficients (<i>r</i>) with a bin of 0.025. Curves were smoothened using four neighbors for averaging and a zero order polynomial. Correlations are also shown for the random variable <i>resampled WBC count</i> for which values were generated by resampling the WBC count data.</p

Differentially expressed apoptotic genes in early stage lung adenocarcinoma predicted by expression profiling.

OBJECTIVE: In undiseased lung epithelial cells, apoptosis is an evolutionarily conserved and genetically regulated form of cell suicide which plays an important role in development and in the maintenance of tissue homeostasis. Neoplastic lung cells develop the ability to deregulate growth by alterations in these genes which control apoptosis. Genomic profiling was used to compare gene expression levels in early stage lung adenocarcinomas and nonneoplastic pulmonary tissue in order to comprehensively identify alterations in the process of apoptosis. METHODS: RNA extracted from node negative, poorly differentiated lung adenocarcinomas (15 patients) and nonneoplastic pulmonary tissue (5 patients) was hybridized to oligonucleotide microarray filters containing 44,363 genes. Ontological classification was used to extract genes involved with apoptosis. Further analysis discovered a subset of differentially expressed genes for further study. RESULTS: Of the 308 apoptotic genes on the microarray filters, 24 genes were predicted to be differentially expressed in lung adenocarcinomas. Alterations in several genes (i.e., Akt, BcL-xL, PTEN, FAS) are consistent with the literature. We also identified 10 novel genes that have not been described in nonsmall cell lung cancer (i.e., RIP, Caspase 1, PDK-1). CONCLUSIONS: These results identified several potential apoptotic genes altered in lung cancer