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.

Growth factor stimulation induces a distinct ER(alpha) cistrome underlying breast cancer endocrine resistance.

Estrogen receptor α (ERα) expression in breast cancer is predictive of response to endocrine therapy; however, resistance is common in ERα-positive tumors that overexpress the growth factor receptor ERBB2. Even in the absence of estrogen, ERα can be activated by growth factors, including the epidermal growth factor (EGF). EGF induces a transcriptional program distinct from estrogen; however, the mechanism of the stimulus-specific response is unknown. Here we show that the EGF-induced ERα genomic targets, its cistromes, are distinct from those induced by estrogen in a process dependent on the transcription factor AP-1. The EGF-induced ERα cistrome specifically regulates genes found overexpressed in ERBB2-positive human breast cancers. This provides a potential molecular explanation for the endocrine therapy resistance seen in ERα-positive breast cancers that overexpress ERBB2. These results suggest a central role for ERα in hormone-refractory breast tumors dependent on growth factor pathway activation and favors the development of therapeutic strategies completely antagonizing ERα, as opposed to blocking its estrogen responsiveness alone.

Integrative molecular concept modeling of prostate cancer progression.

Despite efforts to profile prostate cancer, the genetic alterations and biological processes that correlate with the observed histological progression are unclear. Using laser-capture microdissection to isolate 101 cell populations, we have profiled prostate cancer progression from benign epithelium to metastatic disease. By analyzing expression signatures in the context of over 14,000 'molecular concepts', or sets of biologically connected genes, we generated an integrative model of progression. Molecular concepts that demarcate critical transitions in progression include protein biosynthesis, E26 transformation-specific (ETS) family transcriptional targets, androgen signaling and cell proliferation. Of note, relative to low-grade prostate cancer (Gleason pattern 3), high-grade cancer (Gleason pattern 4) shows an attenuated androgen signaling signature, similar to metastatic prostate cancer, which may reflect dedifferentiation and explain the clinical association of grade with prognosis. Taken together, these data show that analyzing gene expression signatures in the context of a compendium of molecular concepts is useful in understanding cancer biology.

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.

Mining for regulatory programs in the cancer transcriptome.

DNA microarrays have been widely applied to cancer transcriptome analysis. The Oncomine database contains a large collection of such data, as well as hundreds of derived gene-expression signatures. We studied the regulatory mechanisms responsible for gene deregulation in these cancer signatures by searching for the coordinate regulation of genes with common transcription factor binding sites. We found that genes with binding sites for the archetypal cancer transcription factor, E2F, were disproportionately overexpressed in a wide variety of cancers, whereas genes with binding sites for other transcription factors, such as Myc-Max, c-Rel and ATF, were disproportionately overexpressed in specific cancer types. These results suggest that alterations in pathways activating these transcription factors may be responsible for the observed gene deregulation and cancer pathogenesis.

Integrative genomic and proteomic analysis of prostate cancer reveals signatures of metastatic progression.

Molecular profiling of cancer at the transcript level has become routine. Large-scale analysis of proteomic alterations during cancer progression has been a more daunting task. Here, we employed high-throughput immunoblotting in order to interrogate tissue extracts derived from prostate cancer. We identified 64 proteins that were altered in prostate cancer relative to benign prostate and 156 additional proteins that were altered in metastatic disease. An integrative analysis of this compendium of proteomic alterations and transcriptomic data was performed, revealing only 48%-64% concordance between protein and transcript levels. Importantly, differential proteomic alterations between metastatic and clinically localized prostate cancer that mapped concordantly to gene transcripts served as predictors of clinical outcome in prostate cancer as well as other solid tumors.

Development and validation of an integrative pan-solid tumor predictor of PD-1/PD-L1 blockade benefit.

BACKGROUND: Anti-PD-1 and PD-L1 (collectively PD-[L]1) therapies are approved for many advanced solid tumors. Biomarkers beyond PD-L1 immunohistochemistry, microsatellite instability, and tumor mutation burden (TMB) may improve benefit prediction. METHODS: Using treatment data and genomic and transcriptomic tumor tissue profiling from an observational trial (NCT03061305), we developed Immunotherapy Response Score (IRS), a pan-tumor predictive model of PD-(L)1 benefit. IRS real-world progression free survival (rwPFS) and overall survival (OS) prediction was validated in an independent cohort of trial patients. RESULTS: Here, by Cox modeling, we develop IRS-which combines TMB with CD274, PDCD1, ADAM12 and TOP2A quantitative expression-to predict pembrolizumab rwPFS (648 patients; 26 tumor types; IRS-High or -Low groups). In the 248 patient validation cohort (248 patients; 24 tumor types; non-pembrolizumab PD-[L]1 monotherapy treatment), median rwPFS and OS are significantly longer in IRS-High vs. IRS-Low patients (rwPFS adjusted hazard ratio [aHR] 0.52, p = 0.003; OS aHR 0.49, p = 0.005); TMB alone does not significantly predict PD-(L)1 rwPFS nor OS. In 146 patients treated with systemic therapy prior to pembrolizumab monotherapy, pembrolizumab rwPFS is only significantly longer than immediately preceding therapy rwPFS in IRS-High patients (interaction test p = 0.001). In propensity matched lung cancer patients treated with first-line pembrolizumab monotherapy or pembrolizumab+chemotherapy, monotherapy rwPFS is significantly shorter in IRS-Low patients, but is not significantly different in IRS-High patients. Across 24,463 molecularly-evaluable trial patients, 7.6% of patients outside of monotherapy PD-(L)1 approved tumor types are IRS-High/TMB-Low. CONCLUSIONS: The validated, predictive, pan-tumor IRS model can expand PD-(L)1 monotherapy benefit outside currently approved indications.

Therapies activating the immune system (checkpoint inhibitors) have revolutionized the treatment of patients with advanced cancer, however new molecular tests may better identify patients who could benefit. Using treatment data and clinical molecular test results, we report the development and validation of Immunotherapy Response Score (IRS) to predict checkpoint inhibitor benefit. Across patients with more than 20 advanced cancer types, IRS better predicted checkpoint inhibitor benefit than currently available tests. Data from >20,000 patients showed that IRS identifies ~8% of patients with advanced cancer who may dramatically benefit from checkpoint inhibitors but would not receive them today based on currently available tests. Our approach may help clinicians to decide which patients should receive checkpoint inhibitors to treat their disease.

Validation of Immunotherapy Response Score as Predictive of Pan-solid Tumor Anti-PD-1/PD-L1 Benefit.

Immunotherapy response score (IRS) integrates tumor mutation burden (TMB) and quantitative expression biomarkers to predict anti-PD-1/PD-L1 [PD-(L)1] monotherapy benefit. Here, we evaluated IRS in additional cohorts. Patients from an observational trial (NCT03061305) treated with anti-PD-(L)1 monotherapy were included and assigned to IRS-High (-H) versus -Low (-L) groups. Associations with real-world progression-free survival (rwPFS) and overall survival (OS) were determined by Cox proportional hazards (CPH) modeling. Those with available PD-L1 IHC treated with anti-PD-(L)1 with or without chemotherapy were separately assessed. Patients treated with PD-(L)1 and/or chemotherapy (five relevant tumor types) were assigned to three IRS groups [IRS-L divided into IRS-Ultra-Low (-UL) and Intermediate-Low (-IL), and similarly assessed]. In the 352 patient anti-PD-(L)1 monotherapy validation cohort (31 tumor types), IRS-H versus IRS-L patients had significantly longer rwPFS and OS. IRS significantly improved CPH associations with rwPFS and OS beyond microsatellite instability (MSI)/TMB alone. In a 189 patient (10 tumor types) PD-L1 IHC comparison cohort, IRS, but not PD-L1 IHC nor TMB, was significantly associated with anti-PD-L1 rwPFS. In a 1,103-patient cohort (from five relevant tumor types), rwPFS did not significantly differ in IRS-UL patients treated with chemotherapy versus chemotherapy plus anti-PD-(L)1, nor in IRS-H patients treated with anti-PD-(L)1 versus anti-PD-(L)1 + chemotherapy. IRS associations were consistent across subgroups, including both Europeans and non-Europeans. These results confirm the utility of IRS utility for predicting pan-solid tumor PD-(L)1 monotherapy benefit beyond available biomarkers and demonstrate utility for informing on anti-PD-(L)1 and/or chemotherapy treatment. Significance: This study confirms the utility of the integrative IRS biomarker for predicting anti-PD-L1/PD-1 benefit. IRS significantly improved upon currently available biomarkers, including PD-L1 IHC, TMB, and MSI status. Additional utility for informing on chemotherapy, anti-PD-L1/PD-1, and anti-PD-L1/PD-1 plus chemotherapy treatments decisions is shown.

AGTR1 overexpression defines a subset of breast cancer and confers sensitivity to losartan, an AGTR1 antagonist.

Breast cancer patients have benefited from the use of targeted therapies directed at specific molecular alterations. To identify additional opportunities for targeted therapy, we searched for genes with marked overexpression in subsets of tumors across a panel of breast cancer profiling studies comprising 3,200 microarray experiments. In addition to prioritizing ERBB2, we found AGTR1, the angiotensin II receptor type I, to be markedly overexpressed in 10-20% of breast cancer cases across multiple independent patient cohorts. Validation experiments confirmed that AGTR1 is highly overexpressed, in several cases more than 100-fold. AGTR1 overexpression was restricted to estrogen receptor-positive tumors and was mutually exclusive with ERBB2 overexpression across all samples. Ectopic overexpression of AGTR1 in primary mammary epithelial cells, combined with angiotensin II stimulation, led to a highly invasive phenotype that was attenuated by the AGTR1 antagonist losartan. Similarly, losartan reduced tumor growth by 30% in AGTR1-positive breast cancer xenografts. Taken together, these observations indicate that marked AGTR1 overexpression defines a subpopulation of ER-positive, ERBB2-negative breast cancer that may benefit from targeted therapy with AGTR1 antagonists, such as losartan.

Development and Validation of StrataNGS, a Multiplex PCR, Semiconductor Sequencing-Based Comprehensive Genomic Profiling Test.

Despite widespread use in targeted tumor testing, multiplex PCR/semiconductor (Ion Torrent) sequencing-based assessment of all comprehensive genomic profiling (CGP) variant classes has been limited. Herein, we describe the development and validation of StrataNGS, a 429-gene, multiplex PCR/semiconductor sequencing-based CGP laboratory-developed test performed on co-isolated DNA and RNA from formalin-fixed, paraffin-embedded tumor specimens with ≥2 mm2 tumor surface area. Validation was performed in accordance with MolDX CGP validation guidelines using 1986 clinical formalin-fixed, paraffin-embedded samples and an in-house developed optimized bioinformatics pipeline. Across CGP variant classes, accuracy ranged from 0.945 for tumor mutational burden (TMB) status to >0.999 for mutations and gene fusions, positive predictive value ranged from 0.915 for TMB status to 1.00 for gene fusions, and reproducibility ranged from 0.998 for copy number alterations to 1.00 for splice variants and insertions/deletions. StrataNGS TMB estimates were highly correlated to those from whole exome- or FoundationOne CDx-determined TMB (Pearson r = 0.998 and 0.960, respectively); TMB reproducibility was 0.996 (concordance correlation coefficient). Limit of detection for all variant classes was <20% tumor content. Together, we demonstrate that multiplex PCR/semiconductor sequencing-based tumor tissue CGP is feasible using optimized bioinformatic approaches described herein.

Virus-positive Merkel Cell Carcinoma Is an Independent Prognostic Group with Distinct Predictive Biomarkers.

PURPOSE: Merkel cell carcinoma (MCC) is an aggressive cutaneous neuroendocrine carcinoma that can be divided into two classes: virus-positive (VP) MCC, associated with oncogenic Merkel cell polyomavirus (MCPyV); and virus-negative (VN) MCC, associated with photodamage. EXPERIMENTAL DESIGN: We classified 346 MCC tumors from 300 patients for MCPyV using a combination of IHC, ISH, and qPCR assays. In a subset of tumors, we profiled mutation status and expression of cancer-relevant genes. MCPyV and molecular profiling results were correlated with disease-specific outcomes. Potential prognostic biomarkers were further validated by IHC. RESULTS: A total of 177 tumors were classified as VP-MCC, 151 tumors were VN-MCC, and 17 tumors were indeterminate. MCPyV positivity in primary tumors was associated with longer disease-specific and recurrence-free survival in univariate analysis, and in multivariate analysis incorporating age, sex, immune status, and stage at presentation. Prioritized oncogene or tumor suppressor mutations were frequent in VN-MCC but rare in VP-MCC. TP53 mutation developed with recurrence in one VP-MCC case. Importantly, for the first time we find that VP-MCC and VN-MCC display distinct sets of prognostic molecular biomarkers. For VP-MCC, shorter survival was associated with decreased expression of immune markers including granzyme and IDO1. For VN-MCC, shorter survival correlated with high expression of several genes including UBE2C. CONCLUSIONS: MCPyV status is an independent prognostic factor for MCC. Features of the tumor genome, transcriptome, and microenvironment may modify prognosis in a manner specific to viral status. MCPyV status has clinicopathologic significance and allows for identification of additional prognostic subgroups.

Real-World Performance of a Comprehensive Genomic Profiling Test Optimized for Small Tumor Samples.

PURPOSE: Tissue-based comprehensive genomic profiling (CGP) is increasingly used for treatment selection in patients with advanced cancer; however, tissue availability may limit widespread implementation. Here, we established real-world CGP tissue availability and assessed CGP performance on consecutively received samples. MATERIALS AND METHODS: We conducted a post hoc, nonprespecified analysis of 32,048 consecutive tumor tissue samples received for StrataNGS, a multiplex polymerase chain reaction (PCR)-based comprehensive genomic profiling (PCR-CGP) test, as part of an ongoing observational trial (NCT03061305). Sample characteristics and PCR-CGP performance were assessed across all tested samples, including exception samples not meeting minimum input quality control (QC) requirements (< 20% tumor content [TC], < 2 mm2 tumor surface area [TSA], DNA or RNA yield < 1 ng/µL, or specimen age > 5 years). Tests reporting ≥ 1 prioritized alteration or meeting TC and sequencing QC were considered successful. For prostate carcinoma and lung adenocarcinoma, tests reporting ≥ 1 actionable or informative alteration or meeting TC and sequencing QC were considered actionable. RESULTS: Among 31,165 (97.2%) samples where PCR-CGP was attempted, 10.7% had < 20% TC and 59.2% were small (< 25 mm2 tumor surface area). Of 31,101 samples evaluable for input requirements, 8,089 (26.0%) were exceptions not meeting requirements. However, 94.2% of the 31,101 tested samples were successfully reported, including 80.5% of exception samples. Positive predictive value of PCR-CGP for ERBB2 amplification in exceptions and/or sequencing QC-failure breast cancer samples was 96.7%. Importantly, 84.0% of tested prostate carcinomas and 87.9% of lung adenocarcinomas yielded results informing treatment selection. CONCLUSION: Most real-world tissue samples from patients with advanced cancer desiring CGP are limited, requiring optimized CGP approaches to produce meaningful results. An optimized PCR-CGP test, coupled with an inclusive exception testing policy, delivered reportable results for > 94% of samples, potentially expanding the proportion of CGP-testable patients and impact of biomarker-guided therapies.

A genomic survey of sarcomas on sun-exposed skin reveals distinctive candidate drivers and potentially targetable mutations.

Sarcomas on photodamaged skin vary in prognosis and management, but can display overlapping microscopic and immunophenotypic features. Improved understanding of molecular alterations in these tumors may provide diagnostic and therapeutic insights. We characterized 111 cutaneous sarcomatoid malignancies and their counterparts, including primary cutaneous angiosarcoma (n = 7), atypical fibroxanthoma (AFX) (n = 21), pleomorphic dermal sarcoma (PDS) (n = 17), extracutaneous undifferentiated pleomorphic sarcoma (n = 8), cutaneous leiomyosarcoma (LMS) (n = 5), extracutaneous LMS (n = 9), sarcomatoid squamous cell carcinoma (spindle cell squamous cell carcinoma) (S-SCC) (n = 24), and conventional cutaneous squamous cell carcinoma (SCC) (n = 20), by next-generation sequencing (NGS) using the StrataNGS panel for copy number variations, mutations, and/or fusions in more than 60 cancer-related genes. TP53 mutations were highly recurrent in most groups. Angiosarcoma displayed previously reported MYC amplifications, as well as CCND1 gains. RB1 mutations were relatively restricted to cutaneous LMS. As previously reported, PIK3CA mutations occurred in AFX, whereas RAS activation was more frequent in PDS. CDKN2A mutations were recurrent in AFX and S-SCC, whereas PDS displayed frequent CDKN2A deletion. S-SCC displayed mutational similarity to conventional SCC. BRCA1/2 mutations were specific to tumors with disease progression. In a subset, we detected potential driver events novel to these tumor types: activating mutations in IDH2 (PDS), MAP2K1 (angiosarcoma, PDS), and JAK1 (S-SCC) and copy gains in FGFR1 (angiosarcoma, S-SCC), KIT (AFX), MET (PDS), and PDGFRA (PDS). Our findings confirm and expand the spectrum of known genomic aberrations, including potential targetable drivers, in cutaneous sarcomatoid malignancies. In addition, certain events are relatively specific to particular tumors within this differential diagnosis and hence might be diagnostically informative.

Identification of TP53RK-Binding Protein (TPRKB) Dependency in TP53-Deficient Cancers.

Tumor protein 53 (TP53; p53) is the most frequently altered gene in human cancer. Identification of vulnerabilities imposed by TP53 alterations may enable effective therapeutic approaches. Through analyzing short hairpin RNA (shRNA) screening data, we identified TP53RK-Binding Protein (TPRKB), a poorly characterized member of the tRNA-modifying EKC/KEOPS complex, as the most significant vulnerability in TP53-mutated cancer cell lines. In vitro and in vivo, across multiple benign-immortalized and cancer cell lines, we confirmed that TPRKB knockdown in TP53-deficient cells significantly inhibited proliferation, with minimal effect in TP53 wild-type cells. TP53 reintroduction into TP53-null cells resulted in loss of TPRKB sensitivity, confirming the importance of TP53 status in this context. In addition, cell lines with mutant TP53 or amplified MDM2 (E3-ubiquitin ligase for TP53) also showed high sensitivity to TPRKB knockdown, consistent with TPRKB dependence in a wide array of TP53-altered cancers. Depletion of other EKC/KEOPS complex members exhibited TP53-independent effects, supporting complex-independent functions of TPRKB. Finally, we found that TP53 indirectly mediates TPRKB degradation, which was rescued by coexpression of PRPK, an interacting member of the EKC/KEOPS complex, or proteasome inhibition. Together, these results identify a unique and specific requirement of TPRKB in a variety of TP53-deficient cancers. IMPLICATIONS: Cancer cells with genomic alterations in TP53 are dependent on TPRKB.

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.

Bioinformatics approaches in the study of cancer.

A revolution is underway in the approach to studying the genetic basis of cancer. Massive amounts of data are now being generated via high-throughput techniques such as DNA microarray technology and new computational algorithms have been developed to aid in analysis. At the same time, standards-based repositories, including the Stanford Microarray Database and the Gene Expression Omnibus have been developed to store and disseminate the results of microarray experiments. Bioinformatics, the convergence of biology, information science, and computation, has played a key role in these developments. Recently developed techniques include Module Maps, SLAMS (Stepwise Linkage Analysis of Microarray Signatures), and COPA (Cancer Outlier Profile Analysis). What these techniques have in common is the application of novel algorithms to find high-level gene expression patterns across heterogeneous microarray experiments. Large-scale initiatives are underway as well. The Cancer Genome Atlas (TCGA) project is a logical extension of the Human Genome Project and is meant to produce a comprehensive atlas of genetic changes associated with cancer. The Cancer Biomedical Informatics Grid (caBIG), led by the NCI, also represents a colossal initiative involving virtually all aspects of cancer research and may help to transform the way cancer research is conducted and data are shared.

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.

The CARMA3-Bcl10-MALT1 Signalosome Drives NFκB Activation and Promotes Aggressiveness in Angiotensin II Receptor-Positive Breast Cancer.

The angiotensin II receptor AGTR1, which mediates vasoconstrictive and inflammatory signaling in vascular disease, is overexpressed aberrantly in some breast cancers. In this study, we established the significance of an AGTR1-responsive NFκB signaling pathway in this breast cancer subset. We documented that AGTR1 overexpression occurred in the luminal A and B subtypes of breast cancer, was mutually exclusive of HER2 expression, and correlated with aggressive features that include increased lymph node metastasis, reduced responsiveness to neoadjuvant therapy, and reduced overall survival. Mechanistically, AGTR1 overexpression directed both ligand-independent and ligand-dependent activation of NFκB, mediated by a signaling pathway that requires the triad of CARMA3, Bcl10, and MALT1 (CBM signalosome). Activation of this pathway drove cancer cell-intrinsic responses that include proliferation, migration, and invasion. In addition, CBM-dependent activation of NFκB elicited cancer cell-extrinsic effects, impacting endothelial cells of the tumor microenvironment to promote tumor angiogenesis. CBM/NFκB signaling in AGTR1+ breast cancer therefore conspires to promote aggressive behavior through pleiotropic effects. Overall, our results point to the prognostic and therapeutic value of identifying AGTR1 overexpression in a subset of HER2-negative breast cancers, and they provide a mechanistic rationale to explore the repurposing of drugs that target angiotensin II-dependent NFκB signaling pathways to improve the treatment of this breast cancer subset.Significance: These findings offer a mechanistic rationale to explore the repurposing of drugs that target angiotensin action to improve the treatment of AGTR1-expressing breast cancers. Cancer Res; 78(5); 1225-40. ©2017 AACR.

Delineation, functional validation, and bioinformatic evaluation of gene expression in thyroid follicular carcinomas with the PAX8-PPARG translocation.

A subset of follicular thyroid carcinomas contains a balanced translocation, t(2;3)(q13;p25), that results in fusion of the paired box gene 8 (PAX8) and peroxisome proliferator-activated receptor gamma (PPARG) genes with concomitant expression of a PAX8-PPARgamma fusion protein, PPFP. PPFP is thought to contribute to neoplasia through a mechanism in which it acts as a dominant-negative inhibitor of wild-type PPARgamma. To better understand this type of follicular carcinoma, we generated global gene expression profiles using DNA microarrays of a cohort of follicular carcinomas along with other common thyroid tumors and used the data to derive a gene expression profile characteristic of PPFP-positive tumors. Transient transfection assays using promoters of four genes whose expression was highly associated with the translocation showed that each can be activated by PPFP. PPFP had unique transcriptional activities when compared with PAX8 or PPARgamma, although it had the potential to function in ways qualitatively similar to PAX8 or PPARgamma depending on the promoter and cellular environment. Bioinformatics analyses revealed that genes with increased expression in PPFP-positive follicular carcinomas include known PPAR target genes; genes involved in fatty acid, amino acid, and carbohydrate metabolism; micro-RNA target genes; and genes on chromosome 3p. These results have implications for the neoplastic mechanism of these follicular carcinomas.

Meta-analysis of microarrays: interstudy validation of gene expression profiles reveals pathway dysregulation in prostate cancer.

The increasing availability and maturity of DNA microarray technology has led to an explosion of cancer profiling studies. To extract maximum value from the accumulating mass of publicly available cancer gene expression data, methods are needed to evaluate, integrate, and intervalidate multiple datasets. Here we demonstrate a statistical model for performing meta-analysis of independent microarray datasets. Implementation of this model revealed that four prostate cancer gene expression datasets shared significantly similar results, independent of the method and technology used (i.e., spotted cDNA versus oligonucleotide). This interstudy cross-validation approach generated a cohort of genes that were consistently and significantly dysregulated in prostate cancer. Bioinformatic investigation of these genes revealed a synchronous network of transcriptional regulation in the polyamine and purine biosynthesis pathways. Beyond the specific implications for prostate cancer, this work establishes a much-needed model for the evaluation, cross-validation, and comparison of multiple cancer profiling studies.