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Patients with early-stage hormone receptor-positive, human epidermal growth factor receptor 2-negative (HER2−) breast cancer (BC) are typically treated with surgery, followed by adjuvant systemic endocrine therapy with or without adjuvant chemotherapy and radiation therapy. Current guidelines regarding the use of adjuvant systemic therapy depend on clinical and pathological factors, such as the morphological assessment of tumor subtype; histological grade; tumor size; lymphovascular invasion; and lymph node status combined with estrogen receptor, progesterone receptor, and HER2 biomarker profiles assessed using immunohistochemistry and in situ hybridization. Additionally, the prognostic and predictive value of tumor-infiltrating lymphocytes and their composition is emerging as a key marker in triple negative (TNBC) and HER2-enriched molecular breast tumor subtypes. However, all these factors do not necessarily reflect the molecular heterogeneity and complexity of breast cancer. In the last two decades, gene expression signatures or profiling (GEP) tests have been developed to predict the risk of disease recurrence and estimate the potential benefit of receiving adjuvant systemic chemotherapy in patients with luminal breast cancer. GEPs have been utilized to help physicians to refine decision-making process, complementing clinicopathological parameters, and can now be used to classify the risk of recurrence and tailoring personalized treatments. Several clinical trials using GEPs validate the increasing value of such assays in different clinical settings, addressing relevant clinical endpoints. Finally, the recent approval of immune checkpoint inhibitors in TNBC and the increasing use of immunotherapy in different molecular BC populations highlight the opportunity to refine current GEPs by including a variety of immune-related genes that may help to improve predicting drug response and finetune prognosis.

Cancer hallmark genes are responsible for the most essential phenotypic characteristics of malignant transformation and progression. In this study, our aim was to estimate the prognostic effect of the established cancer hallmark genes in multiple distinct cancer types.RNA-seq HTSeq counts and survival data from 26 different tumor types were acquired from the TCGA repository. DESeq was used for normalization. Correlations between gene expression and survival were computed using the Cox proportional hazards regression and by plotting Kaplan-Meier survival plots. The false discovery rate was calculated to correct for multiple hypothesis testing.Signatures based on genes involved in genome instability and invasion reached significance in most individual cancer types. Thyroid and glioblastoma were independent of hallmark genes (61 and 54 genes significant, respectively), while renal clear cell cancer and low grade gliomas harbored the most prognostic changes (403 and 419 genes significant, respectively). The eight genes with the highest significance included BRCA1 (genome instability, HR=4.26, pWe observed unexpected heterogeneity and tissue specificity when correlating cancer hallmark genes and survival. These results will help to prioritize future targeted therapy development in different types of solid tumors.

Tumor heterogeneity is a consequence of clonal evolution, resulting in a fractal-like architecture with spatially separated main clones, sub-clones and single-cells. As sequencing an entire tumor is not feasible, we ask the question whether there is an optimal clinical sampling strategy that can handle heterogeneity and hypermutations? Here, we tested the effect of sample size, pooling strategy as well as sequencing depth using whole-exome sequencing of ovarian tumor specimens paired with normal blood samples. Our study has an emphasis on clinical application—hence we compared single biopsy, combined local biopsies and combined multi-regional biopsies. Our results show that sequencing from spatially neighboring regions show similar genetic compositions, with few private mutations. Pooling samples from multiple distinct regions of the primary tumor did not increase the overall number of identified mutations but may increase the robustness of detecting clonal mutations. Hypermutating tumors are a special case, since increasing sample size can easily dilute sub-clonal private mutations below detection thresholds. In summary, we compared the effects of sampling strategies (single biopsy, multiple local samples, pooled global sample) on mutation detection by next generation sequencing. In view of the limitations of present tools and technologies, only one sequencing run per sample combined with high coverage (100–300 ×) sequencing is affordable and practical, regardless of the number of samples taken from the same patient.

// Otilia Menyhart 1 , Jan Budczies 2 , Gyongyi Munkacsy 1 , Francisco J. Esteva 3 , Andras Szabo 1 , Teresa Puig Miquel 4 and Balazs Győrffy 1, 5 1 Semmelweis University 2nd Department of Pediatrics, Budapest, Hungary 2 Institute of Pathology, Charite University Hospital, Berlin, Germany 3 Clinical Cancer Center, NYU Langone Medical Center, New York, NY, USA 4 New Terapeutics Targets Laboratory (TargetsLab), Department of Medical Sciences, University of Girona, Girona, Spain 5 MTA TTK Lendulet Cancer Biomarker Research Group, Institute of Enzymology, Budapest, Hungary Correspondence to: Balazs Győrffy, email: gyorffy.balazs@ttk.mta.hu Keywords: DUSP4, targeted therapy, trastuzumab, biomarker, breast cancer Received: March 09, 2017 Accepted: June 27, 2017 Published: August 24, 2017 ABSTRACT The majority of patients develop resistance against suppression of HER2-signaling mediated by trastuzumab in HER2 positive breast cancer (BC). HER2 overexpression activates multiple signaling pathways, including the mitogen-activated protein kinase (MAPK) cascade. MAPK phosphatases (MKPs) are essential regulators of MAPKs and participate in many facets of cellular regulation, including proliferation and apoptosis. We aimed to identify whether differential MKPs are associated with resistance to targeted therapy in patients previously treated with trastuzumab. Using gene chip data of 88 HER2-positive, trastuzumab treated BC patients, candidate MKPs were identified by Receiver Operator Characteristics analysis performed in R. Genes were ranked using their achieved area under the curve (AUC) values and were further restricted to markers significantly associated with worse survival. Functional significance of the two strongest predictive markers was evaluated in vitro by gene silencing in HER2 overexpressing, trastuzumab resistant BC cell lines SKTR and JIMT-1. The strongest predictive MKPs were DUSP4/MKP-2 (AUC=0.75, p =0.0096) and DUSP6/MKP-3 (AUC=0.77, p =5.29E-05). Higher expression for these correlated to worse survival (DUSP4: HR=2.05, p =0.009 and DUSP6: HR=2, p =0.0015). Silencing of DUSP4 had significant sensitization effects – viability of DUSP4 siRNA transfected, trastuzumab treated cells decreased significantly compared to scramble-siRNA transfected controls (SKTR: p =0.016; JIMT-1: p =0.016). In contrast, simultaneous treatment with DUSP6 siRNA and trastuzumab did not alter cell proliferation. Our findings suggest that DUSP4 may represent a new potential target to overcome trastuzumab resistance.

The proper validation of prognostic biomarkers is an important clinical issue in breast cancer research. MicroRNAs (miRNAs) have emerged as a new class of promising breast cancer biomarkers. In the present work, we developed an integrated online bioinformatic tool to validate the prognostic relevance of miRNAs in breast cancer. A database was set up by searching the GEO, EGA, TCGA, and PubMed repositories to identify datasets with published miRNA expression and clinical data. Kaplan–Meier survival analysis was performed to validate the prognostic value of a set of 41 previously published survival-associated miRNAs. All together 2178 samples from four independent datasets were integrated into the system including the expression of 1052 distinct human miRNAs. In addition, the web-tool allows for the selection of patients, which can be filtered by receptors status, lymph node involvement, histological grade, and treatments. The complete analysis tool can be accessed online at: www.kmplot.com/mirpower . We used this tool to analyze a large number of deregulated miRNAs associated with breast cancer features and outcome, and confirmed the prognostic value of 26 miRNAs. A significant correlation in three out of four datasets was validated only for miR-29c and miR-101. In summary, we established an integrated platform capable to mine all available miRNA data to perform a survival analysis for the identification and validation of prognostic miRNA markers in breast cancer.

// Hajnalka Harami-Papp 1, * , Lőrinc S. Pongor 1, 3, * , Gyongyi Munkacsy 1 , Gergő Horvath 2 , Adam M. Nagy 2 , Attila Ambrus 2 , Peter Hauser 3 , Andras Szabo 3 , Laszlo Tretter 2 , Balazs Győrffy 1 1 MTA TTK Lendulet Cancer Biomarker Research Group, H-1117, Budapest, Hungary 2 Semmelweis University, Department of Medical Biochemistry, H-1094, Budapest, Hungary 3 Semmelweis University, Second Department of Pediatrics, H-1094, Budapest, Hungary * These authors have contributed equally to this work Correspondence to: Balazs Győrffy, email: gyorffy.balazs@ttk.mta.hu Laszlo Tretter, email: tretter.laszlo@med.semmelweis-univ.hu Keywords: glycolytic efficiency, breast cancer, Warburg effect, next generation sequencing Received: May 24, 2016 Accepted: August 13, 2016 Published: August 25, 2016 ABSTRACT Promising new hallmarks of cancer is alteration of energy metabolism that involves molecular mechanisms shifting cancer cells to aerobe glycolysis. Our goal was to evaluate the correlation between mutation in the commonly mutated tumor suppressor gene TP53 and metabolism. We established a database comprising mutation and RNA-seq expression data of the TCGA repository and performed receiver operating characteristics (ROC) analysis to compare expression of each gene between TP53 mutated and wild type samples. All together 762 breast cancer samples were evaluated of which 215 had TP53 mutation. Top up-regulated metabolic genes include glycolytic enzymes ( e.g. HK3, GPI, GAPDH, PGK1 , ENO1 ), glycolysis regulator ( PDK1 ) and pentose phosphate pathway enzymes ( PGD, TKT , RPIA ). Gluconeogenesis enzymes ( G6PC3, FBP1 ) were down-regulated. Oxygen consumption and extracellular acidification rates were measured in TP53 wild type and mutant breast cell lines with a microfluorimetric analyzer. Applying metabolic inhibitors in the presence and absence of D-glucose and L-glutamine in cell culture experiments resulted in higher glycolytic and mitochondrial activity in TP53 mutant breast cancer cell lines. In summary, TP53 mutation influences energy metabolism at multiple levels. Our results provide evidence for the synergistic activation of multiple hallmarks linking to these the mutation status of a key driver gene.

Although numerous studies containing induced gene expression have already been published, independent authentication of their results has not yet been performed. Here, we utilized available transcriptomic data to validate the achieved efficiency in overexpression studies. Microarray data of experiments containing cell lines with induced overexpression in one or more genes were analyzed. All together 342 studies were processed, these include 242 different genes overexpressed in 184 cell lines. The final database includes 4,755 treatment-control sample pairs. Successful gene induction (fold change induction over 1.44) was validated in 39.3% of all genes at p

Background. The majority of patients develop resistance against suppression of HER2-mediated signaling by trastuzumab in HER2 positive breast cancer (BC). HER2 overexpression activates multiple signaling pathways, including the mitogen-activated protein kinase (MAPK) cascade. MAPK phosphatases (MKPs) are essential regulators of MAPKs and participate in many facets of cellular regulation, including proliferation and apoptosis. We aimed to identify whether differential MKPs are associated with resistance to targeted therapy in patients previously treated with trastuzumab. Methods. Using Affymetrix HGU133plus2 gene chip data of 88 HER2-positive, trastuzumab treated BC patients, candidate MKPs were identified by Receiver Operator Characteristics analysis performed in R. Genes were ranked using their achieved area under the curve (AUC) values and were further constricted to those markers significantly associated to worse survival. Functional significance of the two strongest predictive biomarkers was evaluated in vitro experiments after gene silencing in the HER2 overexpressing, trastuzumab resistant breast cancer cell lines SKBR-3-TR and JIMT-1. Results. Out of 10 investigated MKPs, the strongest predictive genes were DUSP4/MKP2 (AUC=0.75, p=0.0096) and DUSP6/MKP3 (AUC=0.77, p=5.29E-05). Furthermore, higher expression for these correlated to worse survival in 221 HER2 positive BC patients (DUSP4: HR=1.6, p=0.04 and DUSP6: HR=1.8, p=0.0053). Silencing of DUSP4 had significant sensitization effects - viability of DUSP4 siRNA transfected, trastuzumab treated cells decreased significantly compared to scramble-siRNA transfected, trastuzumab treated controls (SKBR-3-TR: p=0.016; JIMT-1: p=0.016). In contrast, simultaneous treatment with DUSP6 siRNA and trastuzumab did not alter cell proliferation. Conclusions. Our findings suggest that DUSP4 is involved in the development of trastuzumab resistance in HER2 positive BC. Citation Format: Györffy B, Munkacsy G, Esteva FJ, Miquel TP, Menyhart O. DUSP4 is associated with increased resistance against anti-HER2 therapy in breast cancer. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P5-08-22.

DNA methylation has a substantial impact on gene expression, affecting the prognosis of breast cancer (BC) patients dependent on molecular subtypes. In this study, we investigated the prognostic relevance of the expression of genes reported as aberrantly methylated, and the link between gene expression and DNA methylation in BC subtypes. The prognostic value of the expression of 144 aberrantly methylated genes was evaluated in ER+/HER2−, HER2+, and ER−/HER2− molecular BC subtypes, in a meta-analysis of two large transcriptomic cohorts of BC patients (n = 1,938 and n = 1,640). The correlation between gene expression and DNA methylation in distinct gene regions was also investigated in an independent dataset of 104 BCs. Survival and Pearson correlation analyses were computed for each gene separately. The expression of 48 genes was significantly associated with BC prognosis (p

Introduction. Genetic heterogeneity of ovarian tumors is caused by the accumulation of novel mutations through time. A result of this process is a spatially heterogeneous tumor with a fractal-like architecture, comprising of a mixture of spatially separated clones, sub-clones, and single cells. Spatial heterogeneity renders clinical sequencing a challenging task because clinically relevant mutations can go undetected. Here, we compare the genetic composition of single tumors with multiple DNA samples extracted from different regions of the tumor in diverse combinations to examine the effects of spatial separation on tumor heterogeneity. Methods. We derived tumor samples from five patients with ovarian cancer. Three tumor tissue samples were collected from each patient, in each of these, DNA was extracted from a total of eight segments of different sizes (5x3x8 total segments). Exome sequencing was performed and we examined DNA mutations identified from a biopsy sample, from three merged tissue segments with immediate vicinity to the biopsy, and from segments from spatially distant tissue samples merged into one. We compared identified mutations and copy-number variations called from the exome-seq data. Results. Three patients had heterozygous germline alterations affecting the BRCA pathway, paired with somatic TP53 mutations, one patient had multiple somatic mutations affecting the PI3K pathway, the last patient had the most common activating mutations in the PIK3CA and KRAS genes. When comparing the different sequencing runs, increasing the size of the tumor sample did not affect the overall quantity of mutations identified. Clonal mutations were identifiable in all samples, while sub-clonal mutations shifted. In case of a hypermutating phenotype, increasing the sequenced sample size strongly decreased the number of identified somatic mutations, most probably due to dilution of sub-clones. These observations are confirmed by an in silico model, in which we shuffled regions from publicly available multi-region sequencing data into known compositions. Conclusion. In hypermutating tumors the number of detected mutations increases as the size of the sequenced sample decreases. Generally, increasing the tumor sample size did not affect the number of identifiable mutations. Our findings suggest that clinical sequencing using small biopsy samples can generate adequate mutation calling results. Citation Format: Lorinc Pongor, Gyongyi Munkacsy, Ildiko Vereczkey, Imre Pete, Balazs Gyorffy. Multiregion joint somatic genotyping in ovarian cancer proves optimal tumor sampling for clinical sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 201.

The major cause of cancer therapy failure is either primary drug resistance or the development of secondary resistance against currently available antineoplastic agents. Many different mechanisms of drug resistance have been identified, including the overexpression of members of the family of adenosine triphosphate-binding cassette (ABC)-transporters such as P-glycoprotein (ABCB1), the multidrug resistance-associated proteins MRP1 and MRP2 and the mitoxantrone resistance protein/breast cancer resistance protein (Gottesman et al, 2002). Drug resistance is also mediated by defects in intrinsic cellular safeguard mechanisms capable of activating apoptosis. The loss of programmed cell death induced by antitumour drugs can be achieved through disruption of regulators of DNA damage signalling, such as p53 and bcl-2. Moreover, alterations in cell cycle checkpoints, repair of damaged cellular targets and various additional more or less well-characterised mechanisms can contribute to the resistance. The anthracycline antibiotic doxorubicin (adriamycin) is widely used for treatment of breast, ovarian, bronchogenic and gastric solid tumours, lymphomas and leukaemias (Martin et al, 2003). Triple negative (ER-, PR-, HER2-) breast patients receive anthracycline-based chemotherapeutic agents in monotherapy or in combination protocols. Although a number of different mechanisms have been proposed for the cytotoxic effect of anthracyclines, the primary mechanism of drug action is likely to be the inhibition of DNA biosynthesis through topoisomerase II binding and consequently conferring an S/G2 cell cycle arrest (Gewirtz, 1999). As a topoisomerase inhibitor, it potentially induces apoptosis in normal cells as well (Sabourin and Osheroff, 2000). In several reports, authors applied DNA array technologies to find genes correlated with doxorubicin resistance (Ayers et al, 2004; Kang et al, 2004; Folgueira et al, 2005; Gianni et al, 2005; Cleator et al, 2006; Gyorffy et al, 2006; Hess et al, 2006). In our earlier study, we used gene expression patterns of closely related chemotherapy-resistant and -sensitive parental cancer cell lines to identify discriminatory genes associated with drug resistance. The use of a set of different cell lines for the identification of discriminating genes allows a tissue-independent application for predicting therapy response. We applied our model to predict sensitivity in a set of 44 breast cancer samples. The patient group characterised by the gene expression profile similar to that of drug-sensitive cell lines exhibited over 50% longer survival than the group exhibiting the profile characteristic of resistant cells (Gyorffy et al, 2005). Overall, in these gene expression signatures, a considerable number of genes were associated with doxorubicin resistance. However, it is desirable to discriminate markers associated with treatment response and markers responsible for resistance. The proteasome is a multicatalytic proteinase complex with a highly ordered ring-shaped 20S core structure. The core structure is composed of 4 rings of 28 non-identical subunits; 2 rings are composed of 7 α subunits and 2 rings are composed of 7 β subunits. Proteasomes are distributed throughout eukaryotic cells at a high concentration and cleave damaged and needless peptides in an ATP/ubiquitin-dependent process in a non-lysosomal pathway. Proteasome inhibitors are drugs blocking the action of cellular complexes that break down proteins. They have an antitumour activity in cell culture, and induce apoptosis by disturbing regulation of cell cycle proteins (Adams et al, 1999). Bortezomib was the first proteasome inhibitor, which is already used in clinics as a chemotherapy agent (Kane et al, 2006). Bortezomib effectively eradicates myelome multiplex by activating the stress apoptosis signal of endoplasmic reticulum (Schewe and guirre-Ghiso, 2009). So far, several studies examined its effect on proteasomes in different circumstances (Ludwig et al, 2005; Montagut et al, 2005; Montagut et al, 2006; Sterz et al, 2008). Proteasome inhibitors might also increase chemosensitivity. Cotreatment of doxorubicin and bortezomib can conversely increase the efficiency of each other (Ciolli et al, 2008). However, to date the direct association with doxorubicin resistance as well as the potential prognostic or predictive value of selected proteasome subunits has not been investigated. In this study, our intention was to identify gene expression signatures obtained from resistant cells in long-term culture, untreated sensitive parental cells and parental cells after short-term drug administration. On the basis of relevance in clinical samples, we selected one proteasome subunit, the proteasome unit β-type 7 (PSMB7) for further examination using RNA interference (RNAi) to validate its function in doxorubicin resistance. Our aim was also to identify effects of paclitaxel cross-resistance on the resistant cell line. Finally, microarray data from 1592 patients were used to examine the clinical relevance of PSMB7.

Az RNS-interferencia a poszttranszkripciós génelcsendesítés olyan formája, amelynek során rövid, specifikusan RNS-molekulák elnyomják a gének kifejeződésében kulcsszerepet játszó hírvivő RNS-ek működését. A sejtbe juttatott dupla szálú vagy rövid interferáló RNS-molekulák aktiválják az RNS-indukált elcsendesítő komplexet, amely a célgén hírvivő RNS-ét lebontja. A sejtek saját szabályozó mikro-RNS-molekulákkal is rendelkeznek, amelyeknek hírvivő RNS-e képes önmagával hajtűt képezni, amit a sejt dupla szálú RNS-ként értelmez. Az RNS-interferencia élettani működései közé tartozik a vírusok és a transzpozonok elleni védekezés, valamint a génkifejeződés szabályozása. Az RNS-interferencia nemcsakin vitroalkalmazható az egyes gének működésének vizsgálatára, hanem klinikai alkalmazásainak lehetőségei is megjelentek. Eddig vírusfertőzésekben, az időskori makuladegeneráció gátlására, a vér koleszterinszint-csökkentésére, daganatellenes és neurodegeneratív betegségek kezelésében alkalmazták. Az RNS-interferencia alkalmazását azonban nehezíti, hogy a megfelelő rövid interferáló RNS-molekulák tervezéséhez szükséges bioinformatikai algoritmusok nem tökéletesek; a szervezet szöveteibe való bejuttatásuk nehéz; illetve csak olyan esetekben alkalmazható, amelyekben átmeneti antagonista génelcsendesítő hatás és nem hosszú távú kezelés szükséges. Az alkalmazás legnagyobb előnye a jelentős specificitás, ami miatt mellékhatása is kevés. Az RNS-interferencia alapú kezelések megjelenése már a közeli jövőben várható.

Az öregedő lakosság ellátása egyre nagyobb kihívást jelent Európában. Az eredményesebb gyógyítás és megelőzés érdekében javítani kell a rendelkezésre álló technológiai eszközök felhasználását, hogy felgyorsuljon a betegségasszociált rizikófaktorok és gének azonosítása. E feladat megvalósításának azonban előfeltétele egy ellenőrzött minőségű, jól osztályozott és elegendően nagy betegminta-gyűjtemény/bank létrehozása, amelyben a DNS/szérum/plazma/sejtek/szövetek/RNS/fehérje fizikai tárolása egy információ alapú infrastruktúrával kombinálva garantálja a kutatók számára a minták és adatok széles körű hozzáférését. A Danubian Biobank egy olyan kezdeményezés, mely egyetemeket, oktató kórházakat és rehabilitációs klinikákat kapcsol össze a Duna mentén lévő, illetve az ezzel szomszédos államokban. A tudományos program fő célja a vascularis betegségek (pl. myocardialis infarktus, stroke, szívelégtelenség, artériás trombózis, veseelégtelenség), a metabolikus betegségek (pl. obesitas, diabétesz, metabolikus szindróma) és a neurodegeneratív betegségek (pl. demenciaszindrómák, Parkinson-kór) vizsgálata. A biobankprojekt egyes részfeladatainak megoldására munkacsoportok jöttek létre, hogy segítsék a betegek bevonásának, minták és adatok kezelésének, népegészségügyi, epidemiológiai, genetikai technológiai alkalmazásoknak és a kutatási stratégiáknak az összehangolását. A folyamatosság, illetve belső fejlesztés, optimalizálása érdekében a konzorcium, illetve az egyes munkacsoportok önmagukban is rendszeresen konferenciákat szerveznek, valamint az együttműködést finanszírozó pályázatokat készítenek.

DNA methylation has a substantial impact on gene expression, affecting the prognosis of breast cancer (BC) patients dependent on molecular subtypes. In this study, we investigated the prognostic relevance of the expression of genes reported as aberrantly methylated, and the link between gene expression and DNA methylation in BC subtypes. The prognostic value of the expression of 144 aberrantly methylated genes was evaluated in ER+/HER2-, HER2+, and ER-/HER2- molecular BC subtypes, in a meta-analysis of two large transcriptomic cohorts of BC patients (n = 1,938 and n = 1,640). The correlation between gene expression and DNA methylation in distinct gene regions was also investigated in an independent dataset of 104 BCs. Survival and Pearson correlation analyses were computed for each gene separately. The expression of 48 genes was significantly associated with BC prognosis (p 0.05), and 32 of these prognostic genes exhibited a direct expression-methylation correlation. The expression of several immune-related genes, including CD3D and HLA-A, was associated with both relapse-free survival (HR = 0.42, p = 3.5E-06; HR = 0.35, p = 1.7E-08) and overall survival (HR = 0.50, p = 5.5E-04; HR = 0.68, p = 4.5E-02) in ER-/HER2- BCs. On the overall, the distribution of both positive and negative expression-methylation correlation in distinct gene regions have different effects on gene expression and prognosis in BC subtypes. This large-scale meta-analysis allowed the identification of several genes consistently associated with prognosis, whose DNA methylation could represent a promising biomarker for prognostication and clinical stratification of patients with distinct BC subtypes.

Genomic assays measuring the expression of multiple genes have made their way into clinical practice and their utilization is now recommended by major international guidelines. A basic property of these tests is their capability to sub-divide patients into high- and low-risk cohorts thereby providing prognostic, and in certain settings, predictive decision support. Here, we summarize commercially available assays for breast cancer including RT-PCR and gene chip-based tests. Given the relative uncertainty in cancer treatment, multigene tests have the potential for a significant cost reduction as they can pinpoint those patients for whom chemotherapy proves to be unnecessary. However, concordance of risk assessment for an individual patient is still far from optimal. Additionally, emerging multigene approaches focus on predicting therapy response, which is a black spot of current tests. Promising techniques include the homologous recombination deficiency score, utilization of massive parallel sequencing to identify driver genes, employment of internet-based meta-analysis tools and investigation of miRNA expression signatures. Combination of multiple simultaneous analyses at diagnosis, including classical histopathological diagnostics, monogenic markers, genomic signatures and clinical parameters will most likely bring maximal benefit for patients. As the main driving force behind such genomic tests is the power to achieve cost reduction due to avoiding unnecessary systemic treatment, the future is most likely to hold a further proliferation of such assays.

Transcriptomic analysis of global gene expression in ovarian carcinoma can identify dysregulated genes capable to serve as molecular markers for histology subtypes and survival. The aim of our study was to validate previous candidate signatures in an independent setting and to identify single genes capable to serve as biomarkers for ovarian cancer progression. As several datasets are available in the GEO today, we were able to perform a true meta-analysis. First, 829 samples (11 datasets) were downloaded, and the predictive power of 16 previously published gene sets was assessed. Of these, eight were capable to discriminate histology subtypes, and none was capable to predict survival. To overcome the differences in previous studies, we used the 829 samples to identify new predictors. Then, we collected 64 ovarian cancer samples (median relapse-free survival 24.5 months) and performed TaqMan Real Time Polimerase Chain Reaction (RT-PCR) analysis for the best 40 genes associated with histology subtypes and survival. Over 90% of subtype-associated genes were confirmed. Overall survival was effectively predicted by hormone receptors (PGR and ESR2) and by TSPAN8. Relapse-free survival was predicted by MAPT and SNCG. In summary, we successfully validated several gene sets in a meta-analysis in large datasets of ovarian samples. Additionally, several individual genes identified were validated in a clinical cohort.

Microarray studies used to describe altered gene expression patterns in tissues are a promising new way to provide insight into the pathomechanism of biliary atresia (BA). The hypothesis in this study was that altered gene expression in BA may be linked to regulatory transcription factors (TF). The overrepresentation of transcription factor binding sites (TFBS) in the promoter regions of genes with altered expression would support this hypothesis.Using previously published data, the prevalence of TFBSs in the promoter regions of genes with altered expression in BA was analyzed and compared with the overall prevalence in known promoter sequences.In the pooled BA gene list 195 different TFBS were identified. The prevalence of TFBSs of the members of NFkappaB/c-Rel family was higher compared with the background model.NFkappaB/c-Rel play a role in the development and function of the immune system. Thus, the results of this study support current theories and experiments which link the immune system to perinatal BA. The information obtained in this analysis will help to understand the pathogenesis of BA, but further experimental and human data are required to validate the significance of these TFs in disease pathogenesis.

Clear cell renal carcinoma is the most frequent type of kidney cancer, with an increasing incidence rate worldwide. In this research, we used a proteotranscriptomic approach to differentiate normal and tumor tissues in clear cell renal cell carcinoma (ccRCC). Using transcriptomic data of patients with malignant and paired normal tissue samples from gene array cohorts, we identified the top genes over-expressed in ccRCC. We collected surgically resected ccRCC specimens to further investigate the transcriptomic results on the proteome level. The differential protein abundance was evaluated using targeted mass spectrometry (MS). We assembled a database of 558 renal tissue samples from NCBI GEO and used these to uncover the top genes with higher expression in ccRCC. For protein level analysis 162 malignant and normal kidney tissue samples were acquired. The most consistently upregulated genes were IGFBP3, PLIN2, PLOD2, PFKP, VEGFA, and CCND1 (p < 10−5 for each gene). Mass spectrometry further validated the differential protein abundance of these genes (IGFBP3, p = 7.53 × 10−18; PLIN2, p = 3.9 × 10−39; PLOD2, p = 6.51 × 10−36; PFKP, p = 1.01 × 10−47; VEGFA, p = 1.40 × 10−22; CCND1, p = 1.04 × 10−24). We also identified those proteins which correlate with overall survival. Finally, a support vector machine-based classification algorithm using the protein-level data was set up. We used transcriptomic and proteomic data to identify a minimal panel of proteins highly specific for clear cell renal carcinoma tissues. The introduced gene panel could be used as a promising tool in the clinical setting.