András Lánczky

Online survival analysis software to assess the prognostic value of biomarkers using transcriptomic data in non-small-cell lung cancer.

In the last decade, optimized treatment for non-small cell lung cancer had lead to improved prognosis, but the overall survival is still very short. To further understand the molecular basis of the disease we have to identify biomarkers related to survival. Here we present the development of an online tool suitable for the real-time meta-analysis of published lung cancer microarray datasets to identify biomarkers related to survival. We searched the caBIG, GEO and TCGA repositories to identify samples with published gene expression data and survival information. Univariate and multivariate Cox regression analysis, Kaplan-Meier survival plot with hazard ratio and logrank P value are calculated and plotted in R. The complete analysis tool can be accessed online at: www.kmplot.com/lung. All together 1,715 samples of ten independent datasets were integrated into the system. As a demonstration, we used the tool to validate 21 previously published survival associated biomarkers. Of these, survival was best predicted by CDK1 (p<1E-16), CD24 (p<1E-16) and CADM1 (p = 7E-12) in adenocarcinomas and by CCNE1 (p = 2.3E-09) and VEGF (p = 3.3E-10) in all NSCLC patients. Additional genes significantly correlated to survival include RAD51, CDKN2A, OPN, EZH2, ANXA3, ADAM28 and ERCC1. In summary, we established an integrated database and an online tool capable of uni- and multivariate analysis for in silico validation of new biomarker candidates in non-small cell lung cancer.

Cross-validation of survival associated biomarkers in gastric cancer using transcriptomic data of 1,065 patients.

Introduction Multiple gene expression based prognostic biomarkers have been repeatedly identified in gastric carcinoma. However, without confirmation in an independent validation study, their clinical utility is limited. Our goal was to establish a robust database enabling the swift validation of previous and future gastric cancer survival biomarker candidates. Results The entire database incorporates 1,065 gastric carcinoma samples, gene expression data. Out of 29 established markers, higher expression of BECN1 (HR = 0.68, p = 1.5E-05), CASP3 (HR = 0.5, p = 6E-14), COX2 (HR = 0.72, p = 0.0013), CTGF (HR = 0.72, p = 0.00051), CTNNB1 (HR = 0.47, p = 4.3E-15), MET (HR = 0.63, p = 1.3E-05), and SIRT1 (HR = 0.64, p = 2.2E-07) correlated to longer OS. Higher expression of BIRC5 (HR = 1.45, p = 1E-04), CNTN1 (HR = 1.44, p = 3.5E- 05), EGFR (HR = 1.86, p = 8.5E-11), ERCC1 (HR = 1.36, p = 0.0012), HER2 (HR = 1.41, p = 0.00011), MMP2 (HR = 1.78, p = 2.6E-09), PFKB4 (HR = 1.56, p = 3.2E-07), SPHK1 (HR = 1.61, p = 3.1E-06), SP1 (HR = 1.45, p = 1.6E-05), TIMP1 (HR = 1.92, p = 2.2E- 10) and VEGF (HR = 1.53, p = 5.7E-06) were predictive for poor OS. MATERIALS AND METHODS We integrated samples of three major cancer research centers (Berlin, Bethesda and Melbourne datasets) and publicly available datasets with available follow-up data to form a single integrated database. Subsequently, we performed a literature search for prognostic markers in gastric carcinomas (PubMed, 2012–2015) and re-validated their findings predicting first progression (FP) and overall survival (OS) using uni- and multivariate Cox proportional hazards regression analysis. Conclusions The major advantage of our analysis is that we evaluated all genes in the same set of patients thereby making direct comparison of the markers feasible. The best performing genes include BIRC5, CASP3, CTNNB1, TIMP-1, MMP-2, SIRT, and VEGF.

Genome-wide reprogramming of the chromatin landscape underlies endocrine therapy resistance in breast cancer.

Significance Resistance to treatment with endocrine therapy occurs in ∼50% of all breast cancer patients. The pathway(s) leading to drug resistance is ill-defined. We show that accessibility to the genome is altered in drug-resistant compared with responsive breast cancer cells. This coincides with the overactivation of the NOTCH pathway in drug-resistant compared with responsive cancer cells. The transcription factor PBX1, a known NOTCH target gene, is required for the growth of endocrine therapy-resistant breast cancer cells. Accordingly, a gene expression signature based on NOTCH-PBX1 activity can discriminate a priori breast cancer patients that are responsive or not to endocrine therapy. The estrogen receptor (ER)α drives growth in two-thirds of all breast cancers. Several targeted therapies, collectively termed endocrine therapy, impinge on estrogen-induced ERα activation to block tumor growth. However, half of ERα-positive breast cancers are tolerant or acquire resistance to endocrine therapy. We demonstrate that genome-wide reprogramming of the chromatin landscape, defined by epigenomic maps for regulatory elements or transcriptional activation and chromatin openness, underlies resistance to endocrine therapy. This annotation reveals endocrine therapy-response specific regulatory networks where NOTCH pathway is overactivated in resistant breast cancer cells, whereas classical ERα signaling is epigenetically disengaged. Blocking NOTCH signaling abrogates growth of resistant breast cancer cells. Its activation state in primary breast tumors is a prognostic factor of resistance in endocrine treated patients. Overall, our work demonstrates that chromatin landscape reprogramming underlies changes in regulatory networks driving endocrine therapy resistance in breast cancer.

Systematic Analysis of AU-Rich Element Expression in Cancer Reveals Common Functional Clusters Regulated by Key RNA-Binding Proteins

Defects in AU-rich elements (ARE)-mediated posttranscriptional control can lead to several abnormal processes that underlie carcinogenesis. Here, we performed a systematic analysis of ARE-mRNA expression across multiple cancer types. First, the ARE database (ARED) was intersected with The Cancer Genome Atlas databases and others. A large set of ARE-mRNAs was over-represented in cancer and, unlike non-ARE-mRNAs, correlated with the reversed balance in the expression of the RNA-binding proteins tristetraprolin (TTP, ZFP36) and HuR (ELAVL1). Serial statistical and functional enrichment clustering identified a cluster of 11 overexpressed ARE-mRNAs (CDC6, KIF11, PRC1, NEK2, NCAPG, CENPA, NUF2, KIF18A, CENPE, PBK, TOP2A) that negatively correlated with TTP/HuR mRNA ratios and was involved in the mitotic cell cycle. This cluster was upregulated in a number of solid cancers. Experimentally, we demonstrated that the ARE-mRNA cluster is upregulated in a number of tumor breast cell lines when compared with noninvasive and normal-like breast cancer cells. RNA-IP demonstrated the association of the ARE-mRNAs with TTP and HuR. Experimental modulation of TTP or HuR expression led to changes in the mitosis ARE-mRNAs. Posttranscriptional reporter assays confirmed the functionality of AREs. Moreover, TTP augmented mitotic cell-cycle arrest as demonstrated by flow cytometry and histone H3 phosphorylation. We found that poor breast cancer patient survival was significantly associated with low TTP/HuR mRNA ratios and correlated with high levels of the mitotic ARE-mRNA signature. These results significantly broaden the role of AREs and their binding proteins in cancer, and demonstrate that TTP induces an antimitotic pathway that is diminished in cancer. Cancer Res; 76(14); 4068-80. ©2016 AACR.

MEK1 is associated with carboplatin resistance and is a prognostic biomarker in epithelial ovarian cancer

BackgroundPrimary systemic treatment for ovarian cancer is surgery, followed by platinum based chemotherapy. Platinum resistant cancers progress/recur in approximately 25% of cases within six months. We aimed to identify clinically useful biomarkers of platinum resistance.MethodsA database of ovarian cancer transcriptomic datasets including treatment and response information was set up by mining the GEO and TCGA repositories. Receiver operator characteristics (ROC) analysis was performed in R for each gene and these were then ranked using their achieved area under the curve (AUC) values. The most significant candidates were selected and in vitro functionally evaluated in four epithelial ovarian cancer cell lines (SKOV-3-, CAOV-3, ES-2 and OVCAR-3), using gene silencing combined with drug treatment in viability and apoptosis assays. We collected 94 tumor samples and the strongest candidate was validated by IHC and qRT-PCR in these.ResultsAll together 1,452 eligible patients were identified. Based on the ROC analysis the eight most significant genes were JRK, CNOT8, RTF1, CCT3, NFAT2CIP, MEK1, FUBP1 and CSDE1. Silencing of MEK1, CSDE1, CNOT8 and RTF1, and pharmacological inhibition of MEK1 caused significant sensitization in the cell lines. Of the eight genes, JRK (p = 3.2E-05), MEK1 (p = 0.0078), FUBP1 (p = 0.014) and CNOT8 (p = 0.00022) also correlated to progression free survival. The correlation between the best biomarker candidate MEK1 and survival was validated in two independent cohorts by qRT-PCR (n = 34, HR = 5.8, p = 0.003) and IHC (n = 59, HR = 4.3, p = 0.033).ConclusionWe identified MEK1 as a promising prognostic biomarker candidate correlated to response to platinum based chemotherapy in ovarian cancer.

Validation of miRNA prognostic power in hepatocellular carcinoma using expression data of independent datasets

Multiple studies suggested using different miRNAs as biomarkers for prognosis of hepatocellular carcinoma (HCC). We aimed to assemble a miRNA expression database from independent datasets to enable an independent validation of previously published prognostic biomarkers of HCC. A miRNA expression database was established by searching the TCGA (RNA-seq) and GEO (microarray) repositories to identify miRNA datasets with available expression and clinical data. A PubMed search was performed to identify prognostic miRNAs for HCC. We performed a uni- and multivariate Cox regression analysis to validate the prognostic significance of these miRNAs. The Limma R package was applied to compare the expression of miRNAs between tumor and normal tissues. We uncovered 214 publications containing 223 miRNAs identified as potential prognostic biomarkers for HCC. In the survival analysis, the expression levels of 55 and 84 miRNAs were significantly correlated with overall survival in RNA-seq and gene chip datasets, respectively. The most significant miRNAs were hsa-miR-149, hsa-miR-139, and hsa-miR-3677 in the RNA-seq and hsa-miR-146b-3p, hsa-miR-584, and hsa-miR-31 in the microarray dataset. Of the 223 miRNAs studied, the expression was significantly altered in 102 miRNAs in tumors compared to normal liver tissues. In summary, we set up an integrated miRNA expression database and validated prognostic miRNAs in HCC.

Author Correction: Validation of miRNA prognostic power in hepatocellular carcinoma using expression data of independent datasets

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

Abstract 1958: Developing an online tool to validate survival-associated miRNAs utilizing expression data from 2,061 breast cancer patients

Background. MicroRNAs (miRNAs) are small non-coding RNAs capable of simultaneously regulating multiple gene networks, and affecting breast cancer patients’ outcome. Here we present the development of an online tool for the real-time meta-analysis of published miRNA datasets to identify novel prognostic biomarkers in breast cancer. Methods. First, a comprehensive database was set up by searching GEO, TCGA, and PubMed repositories to identify datasets with published miRNA expression and clinical data. Due to platform differences, each dataset was processed separately. Kaplan-Meier and Cox regression analyses were performed to investigate the prognostic value of a set of dysregulated miRNAs in breast cancer. The complete analysis tool can be accessed online at: www.kmplot.com/mirna. Results. Overall, 2,061 samples from four independent datasets were integrated into the tool including the expression signature of 1,302 distinct miRNAs. In addition to overall survival data, ER status (n = 2,039), histological grade (n = 1,396) and lymph node status (n = 1,933) are also available. By using our online tool, we demonstrated the prognostic value of some of the previous reported miRNAs implicated in breast cancer. For instance, miR-29c, miR-34c, miR-101, miR-125, and miR-190b, showed a consistent prognostic value in at least three out of four breast cancer datasets. Discussion. In summary, we established the first online integrated platform for the rapid identification of the prognostic value of miRNAs in breast cancer. Citation Format: Balazs Győrffy, Andras Lanczky, Giulia Bottai, Adam Nagy, Andras Szabo, Libero Santarpia. Developing an online tool to validate survival-associated miRNAs utilizing expression data from 2,061 breast cancer patients. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1958.

Abstract 2368: An online tool for the validation of survival-predicting biomarkers in non small-cell lung cancer using microarray data of 1,329 patients.

Proceedings: AACR 104th Annual Meeting 2013; Apr 6-10, 2013; Washington, DC To decipher the molecular basis of lung cancer and to improve treatment strategies we have to identify genes correlated to therapy response and to survival. Here, we present the development of a new, freely available online tool suitable for the real-time meta-analysis of published lung cancer microarray datasets to validate expression based survival related biomarker candidates. First we have searched the caBIG, GEO and TCGA repositories to identify datasets with published gene expression data and survival information. In this, three GEO platforms ([GPL96][1], [GPL570][2] and [GPL3921][3]) were considered as these possess 22,277 common probes, which were used for constructing the database. All together 1,329 NSCLC samples in 9 independent datasets were identified, 85% have overall survival and 45% progression free survival info, one-third adenocarcinomas and one-third squamous cell carcinomas, 57% male and the average overall survival is 45 months. Kaplan-Meier survival plot, and the hazard ratio with 95% confidence intervals and logrank P value are calculated and plotted in R using Bioconductor packages. To assess the prognostic value of a gene, each percentile (of expression) between the lower and upper quartiles are computed and the best performing threshold is used as the final cutoff in a Cox regression analysis. The complete meta-analysis tool can be accessed online at: www.kmplot.com/lung. We demonstrate the application of this integrative analysis pipeline by validating 22 previously published survival associated biomarkers including VEGF, ADAM28, ANXA3, CADM1, CD24, CD82, CDK1, CEA, cyclin E, ERCC1, EZH2, HER2, IFNAR2, MMP9, OPN, P16, p53R2, RAD51, S100A4, survivin, XAF1 and XIAP. Of these, high significances (p<1E-16) were achieved by ANXA3 (HR=0.63), CADM1 (HR=1.7), IFNAR2 (HR=0.54), RAD51 (HR=1.9) and XAF1 (HR=0.58). Five additional genes were significant: S100A4 (HR=1.46, p=3.6E-4), CD82 (HR=1.34, p=0.018), OPN (HR=1.33, p=0.02), HER2 (HR=1.3, p=0.02) and CD24 (HR=0.75, p=0.3). The remaining genes did not achieved statistical significance. In summary, we developed an online available meta-analysis tool for the validation of genes associated with survival in NSCLC. Citation Format: Balazs Gyorffy, Andras Lanczky. An online tool for the validation of survival-predicting biomarkers in non small-cell lung cancer using microarray data of 1,329 patients. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2368. doi:10.1158/1538-7445.AM2013-2368 [1]: /lookup/external-ref?link_type=NCBIGEO&access_num=GPL96&atom=%2Fcanres%2F73%2F8_Supplement%2F2368.atom [2]: /lookup/external-ref?link_type=NCBIGEO&access_num=GPL570&atom=%2Fcanres%2F73%2F8_Supplement%2F2368.atom [3]: /lookup/external-ref?link_type=NCBIGEO&access_num=GPL3921&atom=%2Fcanres%2F73%2F8_Supplement%2F2368.atom

P1-07-18: Expanding an Online Tool for Genome-Wide Validation of Survival-Associated Biomarkers in Breast and Ovarian Cancer Using Microarray Data of 3,862 Patients.

The pre-clinical validation of prognostic gene candidates in large independent patient cohorts is a pre-requisite for the development of robust biomarkers. We earlier implemented an online tool to assess the prognostic or predictive value of the expression levels of all microarray quantified genes in breast cancer patients. In present study, we further expanded our database, added additional analytical options and implemented the tool for ovarian cancer patients. The database was set up using gene expression data and survival information of breast and ovarian cancer patients downloaded from GEO and TCGA (Affymetrix HGU133A, HGU133A 2.0 and HGU133+2 microarrays). After quality control and normalization only probes present on all three Affymetrix platforms were retained (n=22,277). Patients can be stratified into the various robust subtypes either by histology or by various gene expression profiling based methods. To analyze the prognostic value of the selected gene in the various cohorts the patients are divided into two groups according to the median expression of the gene. A Kaplan-Meier survival plot is generated and significance is computed. All together 2,472 breast cancer patients and 1,390 ovarian cancer patients were entered into the database. These groups can be compared using relapse free survival (n=2,414 in breast cancer and 1,090 in ovarian cancer) or overall survival (n=463 and n=1,290). Follow-up threshold has been implemented to exclude long-term effects. The combination of several probe sets can be employed to assess the mean of their expression as a multigene predictor of survival and therapy efficiency. In summary, we expanded our global online biomarker validation platform to mine all available microarray data to assess the prognostic power of 22,277 genes in 2,472 breast and 1,390 ovarian cancer patients. The tool can be accessed online at: www.kmplot.com/breast and www.kmplot.com/dev/ovar. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P1-07-18.