Characteristic patterns of gene expression measured by DNA microarrays have been used to classify tumors into clinically relevant subgroups. In this study, we have refined the previously defined subtypes of breast tumors that could be distinguished by their distinct patterns of gene expression. A total of 115 malignant breast tumors were analyzed by hierarchical clustering based on patterns of expression of 534 "intrinsic" genes and shown to subdivide into one basal-like, one ERBB2-overexpressing, two luminal-like, and one normal breast tissue-like subgroup. The genes used for classification were selected based on their similar expression levels between pairs of consecutive samples taken from the same tumor separated by 15 weeks of neoadjuvant treatment. Similar cluster analyses of two published, independent data sets representing different patient cohorts from different laboratories, uncovered some of the same breast cancer subtypes. In the one data set that included information on time to development of distant metastasis, subtypes were associated with significant differences in this clinical feature. By including a group of tumors from BRCA1 carriers in the analysis, we found that this genotype predisposes to the basal tumor subtype. Our results strongly support the idea that many of these breast tumor subtypes represent biologically distinct disease entities.

https://www.pnas.org/content/pnas/100/14/8418.full.pdf

Repeated observation of breast tumor subtypes in independent gene expression data sets

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Pathology and genetics of tumours of the lung , pleura, thymus and heart

Although prognostic gene expression signatures for survival in early-stage lung cancer have been proposed, for clinical application, it is critical to establish their performance across different subject populations and in different laboratories. Here we report a large, training-testing, multi-site, blinded validation study to characterize the performance of several prognostic models based on gene expression for 442 lung adenocarcinomas. The hypotheses proposed examined whether microarray measurements of gene expression either alone or combined with basic clinical covariates (stage, age, sex) could be used to predict overall survival in lung cancer subjects. Several models examined produced risk scores that substantially correlated with actual subject outcome. Most methods performed better with clinical data, supporting the combined use of clinical and molecular information when building prognostic models for early-stage lung cancer. This study also provides the largest available set of microarray data with extensive pathological and clinical annotation for lung adenocarcinomas.

/pdf/gene-expression-based-survival-prediction-in-lung-3dt5hnq0dx.pdf

Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study.

BACKGROUND Current staging methods are inadequate for predicting the outcome of treatment of non-small-cell lung cancer (NSCLC). We developed a five-gene signature that is closely associated with survival of patients with NSCLC. METHODS We used computer-generated random numbers to assign 185 frozen specimens for microarray analysis, real-time reverse-transcriptase polymerase chain reaction (RT-PCR) analysis, or both. We studied gene expression in frozen specimens of lung-cancer tissue from 125 randomly selected patients who had undergone surgical resection of NSCLC and evaluated the association between the level of expression and survival. We used risk scores and decision-tree analysis to develop a gene-expression model for the prediction of the outcome of treatment of NSCLC. For validation, we used randomly assigned specimens from 60 other patients. RESULTS Sixteen genes that correlated with survival among patients with NSCLC were identified by analyzing microarray data and risk scores. We selected five genes (DUSP6, MMD, STAT1, ERBB3, and LCK) for RT-PCR and decision-tree analysis. The five-gene signature was an independent predictor of relapse-free and overall survival. We validated the model with data from an independent cohort of 60 patients with NSCLC and with a set of published microarray data from 86 patients with NSCLC. CONCLUSIONS Our five-gene signature is closely associated with relapse-free and overall survival among patients with NSCLC.

https://www.nejm.org/doi/pdf/10.1056/NEJMoa060096

A five-gene signature and clinical outcome in non-small-cell lung cancer.

Purpose Recent findings that human serum contains stably expressed microRNA (miRNA) have revealed a great potential of serum miRNA signature as disease fingerprints to predict survival. We used genome-wide serum miRNA expression analysis to investigate the role of serum miRNA in predicting prognosis of non‐small-cell lung cancer (NSCLC). Patients and Methods To control disease heterogeneity, we used patients with stages I to IIIa lung adenocarcinoma and squamous cell carcinoma, who were treated with both operation and adjuvant chemotherapies. In the discovery stage, Solexa sequencing followed by individual quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) assays was used to test the difference in levels of serum miRNAs between 30 patients with longer survival (alive and mean survival time, 49.54 months) and 30 patients with shorter survival matched by age, sex, and stage (dead and mean survival time, 9.54 months). The detected serum miRNAs then were validated in 243 patients (randomly classified into two subgroups: n 120 for the training set, and n 123 for the testing set). Results Eleven serum miRNAs were found to be altered more than five-fold by Solexa sequencing between longer-survival and shorter-survival groups, and levels of four miRNAs (ie, miR-486, miR-30d, miR-1 and miR-499) were significantly associated with overall survival. The four-miRNA signature also was consistently an independent predictor of overall survival for both training and testing samples.

/pdf/serum-microrna-signatures-identified-in-a-genome-wide-serum-qe2yskhrq0.pdf

Serum MicroRNA Signatures Identified in a Genome-Wide Serum MicroRNA Expression Profiling Predict Survival of Non–Small-Cell Lung Cancer

High expression of Met/hepatocyte growth factor receptor suppresses tumorigenicity in NCI-H1264 lung carcinoma cells.

Recent studies have suggested that information from gene expression profiles could be used to develop molecular classifications of cancer. We hypothesized that expression levels of specific genes in operative specimens could be correlated to recurrence risk in non-small cell lung cancer (NSCLC). We performed expression profiling using 19.2 K cDNA microarrays on tumor specimens from a total of 39 NSCLC patients with known clinical follow-up information. Statistical analysis and clustering approaches were used to determine patterns of gene expression segregating with clinical outcome. The results provide evidence that molecular subtyping of NSCLC can identify distinct profiles of gene expression correlating with disease-free survival.

/pdf/molecular-profiling-of-non-small-cell-lung-cancer-and-9fozlrxlgp.pdf

Molecular profiling of non-small cell lung cancer and correlation with disease-free survival.

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High expression of Met/hepatocyte growth factor receptor suppresses tumorigenicity in NCI-H1264 lung carcinoma cells.

Molecular profiling of non-small cell lung cancer and correlation with disease-free survival.