Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression

Abstract

Many studies have used DNA microarrays to identify the gene expression signatures of human cancer, yet the critical features of these often unmanageably large signatures remain elusive. To address this, we developed a statistical method, comparative metaprofiling, which identifies and assesses the intersection of multiple gene expression signatures from a diverse collection of microarray data sets. We collected and analyzed 40 published cancer microarray data sets, comprising 38 million gene expression measurements from >3,700 cancer samples. From this, we characterized a common transcriptional profile that is universally activated in most cancer types relative to the normal tissues from which they arose, likely reflecting essential transcriptional features of neoplastic transformation. In addition, we characterized a transcriptional profile that is commonly activated in various types of undifferentiated cancer, suggesting common molecular mechanisms by which cancer cells progress and avoid differentiation. Finally, we validated these transcriptional profiles on independent data sets.

Fig. 1.

Comparative meta-profiling flow diagram (see Methods for details).

Fig. 2.

Meta-signature of neoplastic transformation. (A) Sixty-seven genes overexpressed in cancer relative to normal tissue counterpart in at least 12 of 39 “cancer vs. normal” signatures. Twelve distinct cancer types were selected for the figure. White boxes signify either not present or not significant. Red boxes signify significant overexpression in cancer relative to normal tissue (Q < 0.10), the shade of red indicating the percentage of cancer samples that had an expression value greater than the 90th percentile of normal samples. (B) The signature significantly predicts “cancer vs. normal” status in 32 of 39 analyses. The two bars above each heat map represent the predicted class (P) and the true class (T): red signifies cancer and blue signifies normal tissue. Fisher's exact test was used to assess the significance of classification. In the heat maps, black signifies data not available, white signifies less than or equal to the normal class mean expression level, and red signifies the degree of overexpression relative to the mean normal class expression level. FL, follicular lymphoma; DLBCL, diffuse large B cell lymphoma; SCLC, small cell lung cancer; SqCLC, squamous cell lung cancer; adeno., adenocarcinoma; Prost., prostate; Glio., glioblastoma; Rh, rhabdomyosarcoma; PNET, primitive neuroectodermal tumor.

Fig. 3.

Meta-signature of undifferentiated cancer. Sixty-nine genes that are overexpressed in undifferentiated cancer relative to well differentiated cancer (Q < 0.10) in at least four of seven signatures representing six types of cancer. See Fig. 2 legend for description.