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.T o identify genes potentially important in cancer, scientists have compared the global gene expression profiles of cancer tissue and corresponding normal tissue (1-11). Such analyses usually generate hundreds of genes differentially expressed in cancer relative to normal tissue, making it difficult to distinguish the genes that play a critical role in the neoplastic phenotype from those that represent epiphenomena or are spuriously differentially expressed. Another common experimental design is to compare cancer samples based on their degree of progression, as determined by histological grade, invasiveness, or metastatic potential (2,(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22). For example, it is known that high-grade undifferentiatedappearing cancers tend to behave more aggressively than their low-grade counterparts, often leading to poorer patient outcomes. To understand the mechanisms by which this progression occurs, many studies have compared the global gene expression profiles of undifferentiated and well differentiated cancers of the same origin. But again, like the ''cancer vs. normal'' studies, these analyses can also yield hundreds of differentially expressed genes. Thus, it remains a critical problem to elucidate the essential transcriptional features of neoplastic transformation and progression both to direct future research and to define candidate therapeutic targets.A logical approach for identifying the essential features of a process, given a large set of possibilities observed in a variety of independent systems, is to search for the intersection of observed possibilities across the set of systems, because it is expected that the essential features will be overrepresented and the system-specific, epiphenomenal, and spurious features will be underrepresented. Given the multitude of studies that have attempted to capture the cancer type-specific gene expression programs of neoplastic transformation and progressi...

The cell cycle and cancer

“…Moreover, Mcm2-7 expression levels are powerful prognostic indicators in diverse tumour types, including cancers of the lung, breast, kidney, bladder, prostate and ovary [71][72][73][74][75][76][77]. This finding is consistent with large-scale meta-analysis of cancer microarray data, which identified up-regulation of the MCM2-6 genes as a component of poor prognostic signatures [78]. In most tumour types, the up-regulation of MCM and other licensing proteins is likely to reflect oncogene-driven engagement of the cell division cycle.…”

“…In a study of 182 breast cancers, the accelerated cell cycle phenotype had a much higher risk of relapse when compared with the out-of-cycle and G 1 -delayed/arrested phenotypes (HR = 3.90, p < 0.001) [110,111]. These early proofof-concept studies, applying the cell cycle phenotype test, are consistent with published gene expression profiling studies showing that conserved tumour expression patterns include many proliferation-associated genes and that increased expression of these so-called 'proliferation signatures' is associated with enhanced malignancy [78,112,113]. It will be of major interest to determine how this simple cell cycle biomarker test, which is highly suited to routine surgical biopsy material, compares with expensive multigene tests such as Oncotype DX [114].…”

A Lin-9 complex is recruited by B-Myb to activate transcription of G2/M genes in undifferentiated embryonal carcinoma cells

“…Elevated expression levels of B-Myb and G 2 /M genes such as Cyclin B1 and Survivin correlate with poor prognosis in a range of human cancers and often lead to evasion of chemotherapies such as tamoxifen for breast cancer (Paik et al, 2004;Rhodes et al, 2004). It is now axiomatic that E2F-mediated repression of cell-cycle gene transcription is defective in cancers through inactivation of pocket proteins, most often through failure to regulate Cdk activity.…”

Probabilistic model of the human protein-protein interaction network

“…We identified coexpressed genes from 65 genome-wide gene expression data sets present in the Oncomine Cancer Microarray Database 3,21 . The 65 data sets consisted of more than 5,000 diverse microarray profiles representing many tissue types, differentiation states and cellular compositions, thus covering a broad spectrum of gene expression (Supplementary Table 2).…”