BackgroundIt has been suggested that bladder cancer can be divided into two molecular subtypes referred to as luminal and basal with distinct clinical behaviors and sensitivities to chemotherapy. We aimed to validate these subtypes in several clinical cohorts and identify signature immunohistochemical markers that would permit simple and cost-effective classification of the disease in primary care centers.MethodsWe analyzed genomic expression profiles of bladder cancer in three cohorts of fresh frozen tumor samples: MD Anderson (n = 132), Lund (n = 308), and The Cancer Genome Atlas (TCGA) (n = 408) to validate the expression signatures of luminal and basal subtypes and relate them to clinical follow-up data. We also used an MD Anderson cohort of archival bladder tumor samples (n = 89) and a parallel tissue microarray to identify immunohistochemical markers that permitted the molecular classification of bladder cancer.FindingsBladder cancers could be assigned to two candidate intrinsic molecular subtypes referred to here as luminal and basal in all of the datasets analyzed. Luminal tumors were characterized by the expression signature similar to the intermediate/superficial layers of normal urothelium. They showed the upregulation of PPARγ target genes and the enrichment for FGFR3, ELF3, CDKN1A, and TSC1 mutations. In addition, luminal tumors were characterized by the overexpression of E-Cadherin, HER2/3, Rab-25, and Src. Basal tumors showed the expression signature similar to the basal layer of normal urothelium. They showed the upregulation of p63 target genes, the enrichment for TP53 and RB1 mutations, and overexpression of CD49, Cyclin B1, and EGFR. Survival analyses showed that the muscle-invasive basal bladder cancers were more aggressive when compared to luminal cancers. The immunohistochemical expressions of only two markers, luminal (GATA3) and basal (KRT5/6), were sufficient to identify the molecular subtypes of bladder cancer with over 90% accuracy.InterpretationThe molecular subtypes of bladder cancer have distinct clinical behaviors and sensitivities to chemotherapy, and a simple two-marker immunohistochemical classifier can be used for prognostic and therapeutic stratification.FundingU.S. National Cancer Institute and National Institute of Health.

BackgroundColorectal cancer (CRC) remains one of the major cancer types and cancer related death worldwide. Sensitive, non-invasive biomarkers that can facilitate disease detection, staging and prediction of therapeutic outcome are highly desirable to improve survival rate and help to determine optimized treatment for CRC. The small non-coding RNAs, microRNAs (miRNAs), have recently been identified as critical regulators for various diseases including cancer and may represent a novel class of cancer biomarkers. The purpose of this study was to identify and validate circulating microRNAs in human plasma for use as such biomarkers in colon cancer.Methodology/Principal FindingsBy using quantitative reverse transcription-polymerase chain reaction, we found that circulating miR-141 was significantly associated with stage IV colon cancer in a cohort of 102 plasma samples. Receiver operating characteristic (ROC) analysis was used to evaluate the sensitivity and specificity of candidate plasma microRNA markers. We observed that combination of miR-141 and carcinoembryonic antigen (CEA), a widely used marker for CRC, further improved the accuracy of detection. These findings were validated in an independent cohort of 156 plasma samples collected at Tianjin, China. Furthermore, our analysis showed that high levels of plasma miR-141 predicted poor survival in both cohorts and that miR-141 was an independent prognostic factor for advanced colon cancer.Conclusions/SignificanceWe propose that plasma miR-141 may represent a novel biomarker that complements CEA in detecting colon cancer with distant metastasis and that high levels of miR-141 in plasma were associated with poor prognosis.

Summary Integrated genomic analyses revealed a miRNA-regulatory network, which further defined a robust integrated mesenchymal subtype associated with poor overall survival in 459 cases of serous ovarian cancer (OvCa) from The Cancer Genome Atlas and 560 cases from independent cohorts. Eight key miRNAs, including miR-506, miR-141 and miR-200a, were predicted to regulate 89% of the targets in this network. Follow-up functional experiments illustrate that miR-506 augmented E-cadherin expression, inhibited cell migration and invasion, and prevented TGFβ-induced epithelial-mesenchymal transition (EMT) by targeting SNAI2, a transcriptional repressor of E-cadherin. In human OvCa, miR-506 expression was correlated with decreased SNAI2 and VIM, elevated E-cadherin, and beneficial prognosis. Nanoparticle delivery of miR-506 in orthotopic OvCa mouse models led to E-cadherin induction and reduced tumor growth.

Fusion genes are chromosomal aberrations that are found in many cancers and can be used as prognostic markers and drug targets in clinical practice. Fusions can lead to production of oncogenic fusion proteins or to enhanced expression of oncogenes. Several recent studies have reported that some fusion genes can escape microRNA regulation via 3′-untranslated region (3′-UTR) deletion. We performed whole transcriptome sequencing to identify fusion genes in glioma and discovered

We regret that we failed to acknowledge the unpublished data provided by Bagnoli et al. in our paper. The case material first reported by Bagnoli et al. (Oncotarget, 2012) was part of Table 1. The unpublished clinical follow-up data was generously provided by the authors and used for the curve shown in Figure 6E in our paper. The correct text on page 193 of the original article should be as follows: ''To further confirm miR-506's association with good prognosis in ovarian cancer, we obtained two miRNAs microarray data sets from GEO and ArrayExpress (Bentink Data set and Bagnoli Data set, Table 1) with 129 and 55 OvCa cases, respectively. In both data sets, miR-506 expression is significantly associated with longer progression-free survival (Bagnoli et al., personal communication) (log-rank p = 0.02 and 0.0006 for Bentink and Bagnoli Data sets, respectively, Figures 6D and 6E).'' We would like to apologize for this oversight and sincerely thank the authors for providing this unpublished data to our study.

Gastrointestinal stromal tumor (GIST) has emerged as a clinically distinct type of sarcoma with frequent overexpression and mutation of the c-Kit oncogene and a favorable response to imatinib mesylate [also known as STI571 (Gleevec)] therapy. However, a significant diagnostic challenge remains in the differentiation of GIST from leiomyosarcomas (LMSs). To improve on the diagnostic evaluation and to complement the immunohistochemical evaluation of these tumors, we performed a whole-genome gene expression study on 68 well characterized tumor samples. Using bioinformatic approaches, we devised a two-gene relative expression classifier that distinguishes between GIST and LMS with an accuracy of 99.3% on the microarray samples and an estimated accuracy of 97.8% on future cases. We validated this classifier by using RT-PCR on 20 samples in the microarray study and on an additional 19 independent samples, with 100% accuracy. Thus, our two-gene relative expression classifier is a highly accurate diagnostic method to distinguish between GIST and LMS and has the potential to be rapidly implemented in a clinical setting. The success of this classifier is likely due to two general traits, namely that the classifier is independent of data normalization and that it uses as simple an approach as possible to achieve this independence to avoid overfitting. We expect that the use of simple marker pairs that exhibit these traits will be of significant clinical use in a variety of contexts.cancer ͉ classification ͉ diagnostic ͉ machine learning ͉ molecular signature G astrointestinal stromal tumors (GISTs) and leiomyosarcomas (LMSs) are common mesenchymal tumors with remarkably similar phenotypic features (1, 2). Until recently, the differentiation between these two entities had not been thought to be clinically relevant. Chemotherapeutic agents, such as doxorubicin and ifosfamide used in the treatment of soft-tissue sarcomas have resulted in response rates of 0-10% in patients with advanced GIST (3-5). However, the use of the selective tyrosine kinase inhibitor imatinib mesylate [also known as STI571 (Gleevec; Novartis Pharmaceuticals Corp., East Hanover, NJ)] has resulted in response rates of Ͼ50% for patients with GIST (6, 7, **). Conversely, patients with advanced LMS expect response rates of 27-53% when treated with doxorubicin or newer regimens combining gemcitabine with docetaxel (8, 9) but do not benefit from imatinib therapy (10, 11, † †). Thus, there is clear clinical importance in distinguishing between these two entities to guide the most effective therapy. Currently, the best marker to differentiate GIST from LMS is Kit immunostaining, which is subjective and variable due to cellular heterogeneity that may result in false-negative diagnoses. Kitnegative GISTs and Kit-expressing LMS have been reported on the basis of tumor cell morphology and other markers such as CD34, desmin, and smooth muscle actin ( ‡ ‡). The occurrence of Kit-negative GIST in the literature is Ϸ4-10% (2, 12). We used whole human genome microarray d...

Genomic profiling studies have demonstrated that bladder cancer can be divided into two molecular subtypes referred to as luminal and basal with distinct clinical behaviors and sensitivities to frontline chemotherapy. We analyzed the mRNA expressions of signature luminal and basal genes in bladder cancer tumor samples from publicly available and MD Anderson Cancer Center cohorts. We developed a quantitative classifier referred to as basal to luminal transition (BLT) score which identified the molecular subtypes of bladder cancer with 80–94% sensitivity and 83–93% specificity. In order to facilitate molecular subtyping of bladder cancer in primary care centers, we analyzed the protein expressions of signature luminal (GATA3) and basal (KRT5/6) markers by immunohistochemistry, which identified molecular subtypes in over 80% of the cases. In conclusion, we provide a tool for assessment of molecular subtypes of bladder cancer in routine clinical practice.

Akt is a robust oncogene that plays key roles in the development and progression of many cancers, including glioma. We evaluated the differential propensities of the Akt isoforms toward progression in the well-characterized RCAS/Ntv-a mouse model of PDGFB-driven low grade glioma. A constitutively active myristoylated form of Akt1 did not induce high-grade glioma (HGG). In stark contrast, Akt2 and Akt3 showed strong progression potential with 78% and 97% of tumors diagnosed as HGG, respectively. We further revealed that significant variations in polarity and hydropathy values among the Akt isoforms in both the pleckstrin homology domain (P domain) and regulatory domain (R domain) were critical in mediating glioma progression. Gene expression profiles from representative Aktderived tumors indicated dominant and distinct roles for Akt3, consisting primarily of DNA repair pathways. TCGA data from human GBM closely reflected the DNA repair function, as Akt3 was significantly correlated with a 76-gene signature DNA repair panel. Consistently, compared with Akt1 and Akt2 overexpression models, Akt3-expressing human GBM cells had enhanced activation of DNA repair proteins, leading to increased DNA repair and subsequent resistance to radiation and temozolomide. Given the wide range of Akt3-amplified cancers, Akt3 may represent a key resistance factor.Akt | glioma | DNA repair | RCAS/tv-a mouse model