Daniel R. Rhodes

ONCOMINE: A Cancer Microarray Database and Integrated Data-Mining Platform

DNA microarray technology has led to an explosion of oncogenomic analyses, generating a wealth of data and uncovering the complex gene expression patterns of cancer. Unfortunately, due to the lack of a unifying bioinformatic resource, the majority of these data sit stagnant and disjointed following publication, massively underutilized by the cancer research community. Here, we present ONCOMINE, a cancer microarray database and web-based data-mining platform aimed at facilitating discovery from genome-wide expression analyses. To date, ONCOMINE contains 65 gene expression datasets comprising nearly 48 million gene expression measurements form over 4700 microarray experiments. Differential expression analyses comparing most major types of cancer with respective normal tissues as well as a variety of cancer subtypes and clinical-based and pathology-based analyses are available for exploration. Data can be queried and visualized for a selected gene across all analyses or for multiple genes in a selected analysis. Furthermore, gene sets can be limited to clinically important annotations including secreted, kinase, membrane, and known gene-drug target pairs to facilitate the discovery of novel biomarkers and therapeutic targets.

The mutational landscape of lethal castration-resistant prostate cancer

Characterization of the prostate cancer transcriptome and genome has identified chromosomal rearrangements and copy number gains and losses, including ETS gene family fusions, PTEN loss and androgen receptor (AR) amplification, which drive prostate cancer development and progression to lethal, metastatic castration-resistant prostate cancer (CRPC). However, less is known about the role of mutations. Here we sequenced the exomes of 50 lethal, heavily pre-treated metastatic CRPCs obtained at rapid autopsy (including three different foci from the same patient) and 11 treatment-naive, high-grade localized prostate cancers. We identified low overall mutation rates even in heavily treated CRPCs (2.00 per megabase) and confirmed the monoclonal origin of lethal CRPC. Integrating exome copy number analysis identified disruptions of CHD1 that define a subtype of ETS gene family fusion-negative prostate cancer. Similarly, we demonstrate that ETS2, which is deleted in approximately one-third of CRPCs (commonly through TMPRSS2:ERG fusions), is also deregulated through mutation. Furthermore, we identified recurrent mutations in multiple chromatin- and histone-modifying genes, including MLL2 (mutated in 8.6% of prostate cancers), and demonstrate interaction of the MLL complex with the AR, which is required for AR-mediated signalling. We also identified novel recurrent mutations in the AR collaborating factor FOXA1, which is mutated in 5 of 147 (3.4%) prostate cancers (both untreated localized prostate cancer and CRPC), and showed that mutated FOXA1 represses androgen signalling and increases tumour growth. Proteins that physically interact with the AR, such as the ERG gene fusion product, FOXA1, MLL2, UTX (also known as KDM6A) and ASXL1 were found to be mutated in CRPC. In summary, we describe the mutational landscape of a heavily treated metastatic cancer, identify novel mechanisms of AR signalling deregulated in prostate cancer, and prioritize candidates for future study.

Recurrent gene fusions in prostate cancer

The discovery of recurrent gene fusions in a majority of prostate cancers has important clinical and biological implications in the study of common epithelial tumours. Gene fusion and chromosomal rearrangements were previously thought to be primarily the oncogenic mechanism of haematological malignancies and sarcomas. The prostate cancer gene fusions that have been identified thus far are characterized by 5′ genomic regulatory elements, most commonly controlled by androgen, fused to members of the Ets family of transcription factors, leading to the overexpression of oncogenic transcription factors. Ets gene fusions probably define a distinct class of prostate cancer, and this might have a bearing on diagnosis, prognosis and rational therapeutic targeting.

Integrative analysis of the cancer transcriptome

DNA microarrays have been widely applied to the study of human cancer, delineating myriad molecular subtypes of cancer, many of which are associated with distinct biological underpinnings, disease progression and treatment response. These primary analyses have begun to decipher the molecular heterogeneity of cancer, but integrative analyses that evaluate cancer transcriptome data in the context of other data sources are often capable of extracting deeper biological insight from the data. Here we discuss several such integrative computational and analytical approaches, including meta-analysis, functional enrichment analysis, interactome analysis, transcriptional network analysis and integrative model system analysis.

TMPRSS2:ETV4 gene fusions define a third molecular subtype of prostate cancer.

Although common in hematologic and mesenchymal malignancies, recurrent gene fusions have not been well characterized in epithelial carcinomas. Recently, using a novel bioinformatic approach, we identified recurrent gene fusions between TMPRSS2 and the ETS family members ERG or ETV1 in the majority of prostate cancers. Here, we interrogated the expression of all ETS family members in prostate cancer profiling studies and identified marked overexpression of ETV4 in 2 of 98 cases. In one such case, we confirmed the overexpression of ETV4 using quantitative PCR, and by rapid amplification of cDNA ends, quantitative PCR, and fluorescence in situ hybridization, we show that the TMPRSS2 (21q22) and ETV4 (17q21) loci are fused in this case. This result defines a third molecular subtype of prostate cancer and supports the hypothesis that dysregulation of ETS family members through fusions with TMRPSS2 may be an initiating event in prostate cancer development.

Gene expression profiling of clear cell renal cell carcinoma: Gene identification and prognostic classification

To better understand the molecular mechanisms that underlie the tumorigenesis and progression of clear cell renal cell carcinoma (ccRCC), we studied the gene expression profiles of 29 ccRCC tumors obtained from patients with diverse clinical outcomes by using 21,632 cDNA microarrays. We identified gene expression alterations that were both common to most of the ccRCC studied and unique to clinical subsets. There was a significant distinction in gene expression profile between patients with a relatively nonaggressive form of the disease [100% survival after 5 years with the majority (15/17 or 88%) having no clinical evidence of metastasis] versus patients with a relatively aggressive form of the disease (average survival time 25.4 months with a 0% 5-year survival rate). Approximately 40 genes most accurately make this distinction, some of which have previously been implicated in tumorigenesis and metastasis. To test the robustness and potential clinical usefulness of this molecular distinction, we simulated its use as a prognostic tool in the clinical setting. In 96% of the ccRCC cases tested, the prediction was compatible with the clinical outcome, exceeding the accuracy of prediction by staging. These results suggest that two molecularly distinct forms of ccRCC exist and that the integration of expression profile data with clinical parameters could serve to enhance the diagnosis and prognosis of ccRCC. Moreover, the identified genes provide insight into the molecular mechanisms of aggressive ccRCC and suggest intervention strategies.

Probabilistic model of the human protein-protein interaction network

A catalog of all human protein-protein interactions would provide scientists with a framework to study protein deregulation in complex diseases such as cancer. Here we demonstrate that a probabilistic analysis integrating model organism interactome data, protein domain data, genome-wide gene expression data and functional annotation data predicts nearly 40,000 protein-protein interactions in humans—a result comparable to those obtained with experimental and computational approaches in model organisms. We validated the accuracy of the predictive model on an independent test set of known interactions and also experimentally confirmed two predicted interactions relevant to human cancer, implicating uncharacterized proteins into definitive pathways. We also applied the human interactome network to cancer genomics data and identified several interaction subnetworks activated in cancer. This integrative analysis provides a comprehensive framework for exploring the human protein interaction network.

Identification of targetable FGFR gene fusions in diverse cancers.

Through a prospective clinical sequencing program for advanced cancers, four index cases were identified which harbor gene rearrangements of FGFR2, including patients with cholangiocarcinoma, breast cancer, and prostate cancer. After extending our assessment of FGFR rearrangements across multiple tumor cohorts, we identified additional FGFR fusions with intact kinase domains in lung squamous cell cancer, bladder cancer, thyroid cancer, oral cancer, glioblastoma, and head and neck squamous cell cancer. All FGFR fusion partners tested exhibit oligomerization capability, suggesting a shared mode of kinase activation. Overexpression of FGFR fusion proteins induced cell proliferation. Two bladder cancer cell lines that harbor FGFR3 fusion proteins exhibited enhanced susceptibility to pharmacologic inhibition in vitro and in vivo. Because of the combinatorial possibilities of FGFR family fusion to a variety of oligomerization partners, clinical sequencing efforts, which incorporate transcriptome analysis for gene fusions, are poised to identify rare, targetable FGFR fusions across diverse cancer types.

Urine TMPRSS2:ERG Fusion Transcript Stratifies Prostate Cancer Risk in Men with Elevated Serum PSA

Urine TMPRSS2:ERG gene fusion could be used for stratification of patients at higher risk for prostate cancer. Old Gene Fusion, New Diagnostic Tricks The “PSA test” is a routine test for men over the age of 50 or for those at risk for prostate cancer. It measures the level of prostate-specific antigen (PSA) in the blood, and if that level is above a predefined cutoff, a biopsy is recommended for definitive diagnosis. This test is not perfect; benign conditions, such as an enlarged prostate, can contribute to high levels of PSA, resulting in a “false-positive” and subsequent overdiagnosis and overtreatment. Because of the high prevalence of prostate cancer (it is estimated that nearly 250,000 men will be diagnosed with the disease in 2011), it is clear that a more accurate test for prostate cancer is needed. Here, Tomlins et al. improve on the PSA test by taking a new twist on a known gene fusion, using it to stratify more than 1000 men in two multicenter cohorts based on risk for developing the disease. Recently, it was discovered that the fusion of two genes, the transmembrane protease, serine 2 (TMPRSS2) gene and the v-ets erythroblastosis virus E26 oncogene homolog (avian) (ERG) gene, known as TMPRSS2:ERG, is overexpressed in more than 50% of PSA-screened prostate cancers. The protein product of this fusion cannot be detected in serum, so the authors decided to test for the presence of TMPRSS2:ERG mRNA in urine. First, they developed a clinical-grade, transcription-mediated amplification assay for quantifying fusion mRNA—this generated a TMPRSS2:ERG “score.” Urine TMPRSS2:ERG score was linked to the presence of cancer, tumor volume, and clinically significant cancer in patients. Then, the authors combined the TMPRSS2:ERG score with the level of prostate cancer antigen 3 (PCA3) in urine. TMPRSS2:ERG+PCA3 improved the performance of the multivariate Prostate Cancer Prevention Trial risk calculator, thus demonstrating clinical utility. Who said you can’t teach an old gene fusion new tricks? By combining the cancer-specific fusion TMPRSS2:ERG score with levels of PSA (in serum) and PCA3 (in urine), Tomlins and colleagues demonstrated more accurate, individualized stratification of men at high risk for developing clinically significant prostate cancer—an important step in streamlining diagnosis and treatment. Moreover, men with extremes of TMPRSS2:ERG+PCA3 had different risks of cancer on biopsy; in combination with other clinicopathological features, urine TMPRSS2:ERG+PCA3 might also inform the urgency of biopsy after PSA screening. More than 1,000,000 men undergo prostate biopsy each year in the United States, most for “elevated” serum prostate-specific antigen (PSA). Given the lack of specificity and unclear mortality benefit of PSA testing, methods to individualize management of elevated PSA are needed. Greater than 50% of PSA-screened prostate cancers harbor fusions between the transmembrane protease, serine 2 (TMPRSS2) and v-ets erythroblastosis virus E26 oncogene homolog (avian) (ERG) genes. Here, we report a clinical-grade, transcription-mediated amplification assay to risk stratify and detect prostate cancer noninvasively in urine. The TMPRSS2:ERG fusion transcript was quantitatively measured in prospectively collected whole urine from 1312 men at multiple centers. Urine TMPRSS2:ERG was associated with indicators of clinically significant cancer at biopsy and prostatectomy, including tumor size, high Gleason score at prostatectomy, and upgrading of Gleason grade at prostatectomy. TMPRSS2:ERG, in combination with urine prostate cancer antigen 3 (PCA3), improved the performance of the multivariate Prostate Cancer Prevention Trial risk calculator in predicting cancer on biopsy. In the biopsy cohorts, men in the highest and lowest of three TMPRSS2:ERG+PCA3 score groups had markedly different rates of cancer, clinically significant cancer by Epstein criteria, and high-grade cancer on biopsy. Our results demonstrate that urine TMPRSS2:ERG, in combination with urine PCA3, enhances the utility of serum PSA for predicting prostate cancer risk and clinically relevant cancer on biopsy.

The role of SPINK1 in ETS rearrangement-negative prostate cancers

ETS gene fusions have been characterized in a majority of prostate cancers; however, the key molecular alterations in ETS-negative cancers are unclear. Here we used an outlier meta-analysis (meta-COPA) to identify SPINK1 outlier expression exclusively in a subset of ETS rearrangement-negative cancers ( approximately 10% of total cases). We validated the mutual exclusivity of SPINK1 expression and ETS fusion status, demonstrated that SPINK1 outlier expression can be detected noninvasively in urine, and observed that SPINK1 outlier expression is an independent predictor of biochemical recurrence after resection. We identified the aggressive 22RV1 cell line as a SPINK1 outlier expression model and demonstrate that SPINK1 knockdown in 22RV1 attenuates invasion, suggesting a functional role in ETS rearrangement-negative prostate cancers.