RecurrenceOnline: an online analysis tool to determine breast cancer recurrence and hormone receptor status using microarray data

Prognostic values of aldehyde dehydrogenase 1 isoenzymes in ovarian cancer

Aldehyde dehydrogenase 1 (ALDH1) activity has been used as a functional stem cell marker to isolate cancer stem cells in different cancer types, including ovarian cancer. However, which ALDH1’s isoenzymes are contributing to ALDH1 activity in ovarian cancer remains elusive. In addition, the prognostic value of an individual ALDH1 isoenzyme in ovarian cancer is not clear. Thus, we accessed the prognostic value of ALDH1 isoenzymes in ovarian cancer patients through the “Kaplan–Meier plotter” online database, which can be used to determine the effect of the genes on ovarian cancer prognosis. We found that high mRNA expression of five ALDH1 isoenzymes, such as ALDH1A1, ALDH1A2, ALDH1A3, ALDH1B1, and ALDH1L1, was not correlated with overall survival (OS) for all 1,306 ovarian cancer patients. In addition, all five of the ALDH1 isoenzymes’ high mRNA expression was found to be uncorrelated with OS in serous cancer or endometrioid cancer patients. However, ALDH1A3’s high mRNA expression is associated with worse OS in grade II ovarian cancer patients, hazard ratio (HR) 1.53 (1.14–2.07), P=0.005. ALDH1A2’s high mRNA expression is significantly associated with worse OS in TP53 wild-type ovarian cancer patients, HR 2.86 (1.56–5.08), P=0.00036. In addition, ALDH1A3’s high mRNA expression is significantly associated with better OS in TP53 wild-type ovarian cancer patients, HR 0.56 (0.32–1.00), P=0.04. Our results indicate that although ALDH1 isoenzyme mRNA might not be a prognostic marker for overall ovarian cancer patients, some isoenzymes, such as ALDH1A2 and ALDH1A3, might be a good prognostic marker for some types of ovarian cancer patients.

Fibulin-5 downregulates Ki-67 and inhibits proliferation and invasion of breast cancer cells

Fibulins not only function as molecular bridges within the cellular microenvironment but also influence cell behavior. Thus, fibulins may contribute to create a permissive microenvironment for tumor growth but can also stimulate different mechanisms that may impede tumor progression. This is the case with Fibulin-5, which has been shown to display both tumor-promoting and tumor-protective functions by mechanisms that are not totally defined. We show new evidence on the tumor-protective functions displayed by Fibulin-5 in MCF-7, T47D and MDA-MB-231 breast cancer cells including the inhibition of invasion and proliferation capacity and hampering the ability to form mammospheres. Reduction in the level of phosphorylation of Ser residues involved in the nuclear translocation of β-catenin may underlie these antitumor effects. We also found that Fibulin-5 reduces the level of expression of Ki-67, a nuclear protein associated with cell proliferation. Moreover, reduction in Fibulin-5 expression corresponds to an increase of Ki-67 detection in breast tissue samples. Overall, our data provide new insights into the influence of Fibulin-5 to modify breast cancer cell behavior and contribute to better understand the connections between fibulins and cancer.

Relative Prognostic and Predictive Value of Gene Signature and Histologic Grade in Estrogen Receptor-Positive, HER2-Negative Breast Cancer

BACKGROUND

In estrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative breast cancer, first-generation genomic signatures serve predominately as prognostic biomarkers and secondarily as predictors of response to chemotherapy. We compared both the prognostic and predictive value of histologic grades and genomic markers.

METHODS

We retrieved publicly available cDNA microarray data from 1373 primary ER(+)/HER2(-) breast cancers and developed a genomic signature simulated from Recurrence Online (http://www.recurrenceonline.com/) to calculate the recurrence score and risk using predefined sets of genes in the cDNA microarray. We then compared the prognostic and predictive information provided by histologic grade and genomic signature.

RESULTS

Based on genomic signatures, 55%, 28%, and 17% of breast cancers were classified as low, intermediate, and high risk, respectively, whereas the histologic grades were I, II, and III in 22%, 59%, and 19% of breast cancers, respectively. Univariate analysis in the untreated cohort revealed that both histologic grade (overall P = .007) and genomic signature (P < .001) could predict prognosis. Results were similar using the genomic signature, with pathologic complete response rates of 4.6%, 5.7%, and 16.5% for low-, intermediate-, and high-risk cancers, respectively. Neither biomarker was statistically significant in multivariate analysis for predictive response to neoadjuvant chemotherapy (NAC).

CONCLUSION

Genomic signature was better at identifying low-risk cases compared to histologic grade alone, but both markers had similar predictive values for NAC response. Better predictive biomarkers for NAC response are still needed.

A genome-wide approach to link genotype to clinical outcome by utilizing next generation sequencing and gene chip data of 6,697 breast cancer patients

Background

The use of somatic mutations for predicting clinical outcome is difficult because a mutation can indirectly influence the function of many genes, and also because clinical follow-up is sparse in the relatively young next generation sequencing (NGS) databanks. Here we approach this problem by linking sequence databanks to well annotated gene-chip datasets, using a multigene transcriptomic fingerprint as a link between gene mutations and gene expression in breast cancer patients.

Methods

The database consists of 763 NGS samples containing mutational status for 22,938 genes and RNA-seq data for 10,987 genes. The gene chip database contains 5,934 patients with 10,987 genes plus clinical characteristics. For the prediction, mutations present in a sample are first translated into a ‘transcriptomic fingerprint’ by running ROC analysis on mutation and RNA-seq data. Then correlation to survival is assessed by computing Cox regression for both up- and downregulated signatures.

Results

According to this approach, the top driver oncogenes having a mutation prevalence over 5 % included AKT1, TRANK1, TRAPPC10, RPGR, COL6A2, RAPGEF4, ATG2B, CNTRL, NAA38, OSBPL10, POTEF, SCLT1, SUN1, VWDE, MTUS2, and PIK3CA, and the top tumor suppressor genes included PHEX, TP53, GGA3, RGS22, PXDNL, ARFGEF1, BRCA2, CHD8, GCC2, and ARMC4. The system was validated by computing correlation between RNA-seq and microarray data (r² = 0.73, P < 1E-16). Cross-validation using 20 genes with a prevalence of approximately 5 % confirmed analysis reproducibility.

Conclusions

We established a pipeline enabling rapid clinical validation of a discovered mutation in a large breast cancer cohort. An online interface is available for evaluating any human gene mutation or combinations of maximum three such genes (http://www.g-2-o.com).

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-015-0228-1) contains supplementary material, which is available to authorized users.

Global phosphotyrosine survey in triple-negative breast cancer reveals activation of multiple tyrosine kinase signaling pathways

Breast cancer is the most prevalent cancer in women worldwide. About 15-20% of all breast cancers are triple negative breast cancer (TNBC) and are often highly aggressive when compared to other subtypes of breast cancers. To better characterize the biology that underlies the TNBC phenotype, we profiled the phosphotyrosine proteome of a panel of twenty-six TNBC cell lines using quantitative high resolution Fourier transform mass spectrometry. A heterogeneous pattern of tyrosine kinase activation was observed based on 1,789 tyrosine-phosphorylated peptides identified from 969 proteins. One of the tyrosine kinases, AXL, was found to be activated in a majority of aggressive TNBC cell lines and was accompanied by a higher level of AXL expression. High levels of AXL expression are correlated with a significant decrease in patient survival. Treatment of cells bearing activated AXL with a humanized AXL antibody inhibited cell proliferation and migration in vitro, and tumor growth in mice. Overall, our global phosphoproteomic analysis provided new insights into the heterogeneity in the activation status of tyrosine kinase pathways in TNBCs. Our approach presents an effective means of identifying important novel biomarkers and targets for therapy such as AXL in TNBC.

Identification of Circulating Biomarker Candidates for Hepatocellular Carcinoma (HCC): An Integrated Prioritization Approach

Hepatocellular carcinoma (HCC) is the world's third most widespread cancer. Currently available circulating biomarkers for this silently progressing malignancy are not sufficiently specific and sensitive to meet all clinical needs. There is an imminent and pressing need for the identification of novel circulating biomarkers to increase disease-free survival rate. In order to facilitate the selection of the most promising circulating protein biomarkers, we attempted to define an objective method likely to have a significant impact on the analysis of vast data generated from cutting-edge technologies. Current study exploits data available in seven publicly accessible gene and protein databases, unveiling 731 liver-specific proteins through initial enrichment analysis. Verification of expression profiles followed by integration of proteomic datasets, enriched for the cancer secretome, filtered out 20 proteins including 6 previously characterized circulating HCC biomarkers. Finally, interactome analysis of these proteins with midkine (MDK), dickkopf-1 (DKK-1), current standard HCC biomarker alpha-fetoprotein (AFP), its interacting partners in conjunction with HCC-specific circulating and liver deregulated miRNAs target filtration highlighted seven novel statistically significant putative biomarkers including complement component 8, alpha (C8A), mannose binding lectin (MBL2), antithrombin III (SERPINC1), 11β-hydroxysteroid dehydrogenase type 1 (HSD11B1), alcohol dehydrogenase 6 (ADH6), beta-ureidopropionase (UPB1) and cytochrome P450, family 2, subfamily A, polypeptide 6 (CYP2A6). Our proposed methodology provides a swift assortment process for biomarker prioritization that eventually reduces the economic burden of experimental evaluation. Further dedicated validation studies of potential putative biomarkers on HCC patient blood samples are warranted. We hope that the use of such integrative secretome, interactome and miRNAs target filtration approach will accelerate the selection of high-priority biomarkers for other diseases as well, that are more amenable to downstream clinical validation experiments.

Distinct prognostic values and potential drug targets of ALDH1 isoenzymes in non-small-cell lung cancer

Increased aldehyde dehydrogenase 1 (ALDH1) activity has been found in the stem cell populations of leukemia and some solid tumors including non-small-cell lung cancer (NSCLC). However, which ALDH1’s isoenzymes are contributing to ALDH1 activity remains elusive. In addition, the prognostic value of individual ALDH1 isoenzyme is not clear. In the current study, we investigated the prognostic value of ALDH1 isoenzymes in NSCLC patients through the Kaplan–Meier plotter database, which contains updated gene expression data and survival information from a total of 1,926 NSCLC patients. High expression of ALDH1A1 mRNA was found to be correlated to a better overall survival (OS) in all NSCLC patients followed for 20 years (hazard ratio [HR] 0.88 [0.77–0.99], P=0.039). In addition, high expression of ALDH1A1 mRNA was also found to be correlated to better OS in adenocarcinoma (Ade) patients (HR 0.71 [0.57–0.9], P=0.0044) but not in squamous cell carcinoma (SCC) patients (HR 0.92 [0.72–1.16], P=0.48). High expression of ALDH1A2 and ALDH1B1 mRNA was found to be correlated to worser OS in all NSCLC patients, as well as in Ade, but not in SCC patients. High expression of both ALDH1A3 and ALDH1L1 mRNA was not found to be correlated to OS in all NSCLC patients. These results strongly support that ALDH1A1 mRNA in NSCLC is associated with better prognosis. In addition, our current study also supports that ALDH1A2 and ALDH1B1 might be major contributors to the ALDH1 activity in NSCLC, since high expression of ALDH1A2 and ALDH1B1 mRNA was found to be significantly correlated to worser OS in all NSCLC patients. Based on our study, ALDH1A2 and ALDH1B1 might be excellent potential drug targets for NSCLC patients.

Prognostic values of Notch receptors in breast cancer

Notch receptors are frequently deregulated in several human malignancies including human breast cancer. Activation of Notch has been reported to cause mammary carcinomas in mice. However, the prognostic value of individual Notch receptors in breast cancer (BC) patients remains elusive. In the current study, we investigated the prognostic value of Notch receptors in human BC patients. More specifically, we investigated the prognostic value of four Notch receptors in breast cancer patients through "the Kaplan-Meier plotter" (KM plotter) database, in which updated gene expression data and survival information are from a total of 3554 breast cancer patients. Our results showed that Notch1 messenger RNA (mRNA) high expression was correlated to worsen overall survival (OS) in PgR-negative BC patients. Notch2, Notch3, and Notch4 mRNA high expressions were found to be correlated to better OS for all breast cancer patients. Notch2 was also found to be correlated to better OS in lymph node-negative breast cancer patients and HER2-positive breast cancer patients. These results will be useful for better understanding of the heterogeneity and complexity in the molecular biology of breast cancer and for developing tools to more accurately predict their prognosis and design their customized treatment strategies.

Aberrant DNA methylation impacts gene expression and prognosis in breast cancer subtypes

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.

Meta-Analysis of Public Microarray Datasets Reveals Voltage-Gated Calcium Gene Signatures in Clinical Cancer Patients

Voltage-gated calcium channels (VGCCs) are well documented to play roles in cell proliferation, migration, and apoptosis; however, whether VGCCs regulate the onset and progression of cancer is still under investigation. The VGCC family consists of five members, which are L-type, N-type, T-type, R-type and P/Q type. To date, no holistic approach has been used to screen VGCC family genes in different types of cancer. We analyzed the transcript expression of VGCCs in clinical cancer tissue samples by accessing ONCOMINE (www.oncomine.org), a web-based microarray database, to perform a systematic analysis. Every member of the VGCCs was examined across 21 different types of cancer by comparing mRNA expression in cancer to that in normal tissue. A previous study showed that altered expression of mRNA in cancer tissue may play an oncogenic role and promote tumor development; therefore, in the present findings, we focus only on the overexpression of VGCCs in different types of cancer. This bioinformatics analysis revealed that different subtypes of VGCCs (CACNA1C, CACNA1D, CACNA1B, CACNA1G, and CACNA1I) are implicated in the development and progression of diverse types of cancer and show dramatic up-regulation in breast cancer. CACNA1F only showed high expression in testis cancer, whereas CACNA1A, CACNA1C, and CACNA1D were highly expressed in most types of cancer. The current analysis revealed that specific VGCCs likely play essential roles in specific types of cancer. Collectively, we identified several VGCC targets and classified them according to different cancer subtypes for prospective studies on the underlying carcinogenic mechanisms. The present findings suggest that VGCCs are possible targets for prospective investigation in cancer treatment.

Construction of multi-gene classifier for prediction of response to and prognosis after neoadjuvant chemotherapy for estrogen receptor positive breast cancers

The aims of this study were to develop a multi-gene expression-based prediction model for pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) and to evaluate its prognosis prediction for estrogen receptor (ER) positive breast cancers. The training set included the NAC-treated patients (n = 104) with ER+ breast tumors in our hospital and the validation set included the NAC-treated patients (n = 259) with ER+/HER2- breast tumors in the public database (GSE25066). Gene expression in the tumor biopsy specimens obtained before NAC was analyzed with DNA microarray, and the prediction model (MPCP155) for pCR was constructed for the training set by using the genes (155 probes) involved in the metabolic pathways which the pathway analysis identified as being significantly associated with pathological response. With MPCP155, the tumors in the validation set could be classified into low chemo-sensitive (low-CS) (pCR rate = 2.6%) and high-CS (pCR rate = 15.3%; P = 0.0006) groups. Furthermore, the low-CS group showed a significantly better prognosis than the high-CS group (P = 2.0E-6). Moreover, prognosis prediction by MPCP155 was independent of the residual cancer burden score. MPCP155 may be helpful for decision making regarding the indication for neoadjuvant chemotherapy. In addition, MPCP155 was found to be useful for prognosis prediction for NAC-treated patients with ER+/HER2- tumors.

Gene expression-based prognostic and predictive tools in breast cancer

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.

Interaction between the ADAMTS-12 metalloprotease and fibulin-2 induces tumor-suppressive effects in breast cancer cells

Balance between pro-tumor and anti-tumor effects may be affected by molecular interactions within tumor microenvironment. On this basis we searched for molecular partners of ADAMTS-12, a secreted metalloprotease that shows both oncogenic and tumor-suppressive effects. Using its spacer region as a bait in a yeast two-hybrid screen, we identified fibulin-2 as a potential ADAMTS-12-interacting protein. Fibulins are components of basement membranes and elastic matrix fibers in connective tissue. Besides this structural function, fibulins also play crucial roles in different biological events, including tumorigenesis. To examine the functional consequences of the ADAMTS-12/fibulin-2 interaction, we performed different in vitro assays using two breast cancer cell lines: the poorly invasive MCF-7 and the highly invasive MDA-MB-231. Overall our data indicate that this interaction promotes anti-tumor effects in breast cancer cells. To assess the in vivo relevance of this interaction, we induced tumors in nude mice using MCF-7 cells expressing both ADAMTS-12 and fibulin-2 that showed a remarkable growth deficiency. Additionally, we also found that ADAMTS-12 may elicit pro-tumor effects in the absence of fibulin-2. Immunohistochemical staining of breast cancer samples allowed the detection of both ADAMTS-12 and fibulin-2 in the connective tissue surrounding tumor area in less aggressive carcinomas. However, both proteins are hardly detected in more aggressive tumors. These data and survival analysis plots of breast cancer patients suggest that concomitant detection of ADAMTS-12 and fibulin-2 could be a good prognosis marker in breast cancer diagnosis.

Distinct prognostic values of ALDH1 isoenzymes in breast cancer

Aldehyde dehydrogenase 1 (ALDH1), also known as aldehyde dehydrogenase 1 family, is composed of six enzymes that are expressed at high levels in stem cells and are involved in the regulation of stem cell function. Increased ALDH1 activity has been found in the stem cell populations of leukemia and some solid tumors including breast cancer (BC). However, which ALDH1's isoenzymes are contributing to ALDH1 activity has not been determined. In addition, the prognostic value of individual ALDH1 isoenzyme is not clear. In the current study, we investigated the prognostic value of ALDH1 isoenzymes in BC patients through "the Kaplan-Meier plotter" (KM plotter) database, in which updated gene expression data and survival information are from a total of 3455 BC patients. ALDH1A1 messenger RNA (mRNA) high expression was found to be correlated to worsen overall survival (OS) for all BC patients. ALDH1A2 and ALDH1L1 mRNA high expressions were found to be correlated to better OS for all BC patients. Both of ALDH1A3 and ALDH1B1 mRNA high expressions were not found to be correlated to OS for all BC patients. These results strongly support that ALDH1A1 was only a biomarker for predicting poor survival of BC patients among ALDH1 isoenzymes. ALDH1A1 might be a major contributor of ALDH1 activity in BC, since only ALDH1A1 mRNA high expression was found to be significantly correlated to worsen OS for all BC patients.

Survival analysis tools in genomics research

There is an increasing demand to determine the clinical implication of experimental findings in molecular biomedical research. Survival (or failure time) analysis methodologies have been adapted to the analysis of genomics data to link molecular information with clinical outcomes of interest. Genome-wide molecular profiles have served as sources for discovery of predictive/prognostic biomarkers as well as therapeutic targets in the past decade. In this review, we overview currently available software, web applications, and databases specifically developed for survival analysis in genomics research and discuss issues in assessing clinical utility of molecular features derived from genomic profiling.

Dynamic classification using case-specific training cohorts outperforms static gene expression signatures in breast cancer

The molecular diversity of breast cancer makes it impossible to identify prognostic markers that are applicable to all breast cancers. To overcome limitations of previous multigene prognostic classifiers, we propose a new dynamic predictor: instead of using a single universal training cohort and an identical list of informative genes to predict the prognosis of new cases, a case-specific predictor is developed for each test case. Gene expression data from 3,534 breast cancers with clinical annotation including relapse-free survival is analyzed. For each test case, we select a case-specific training subset including only molecularly similar cases and a case-specific predictor is generated. This method yields different training sets and different predictors for each new patient. The model performance was assessed in leave-one-out validation and also in 325 independent cases. Prognostic discrimination was high for all cases (n = 3,534, HR = 3.68, p = 1.67 E-56). The dynamic predictor showed higher overall accuracy (0.68) than genomic surrogates for Oncotype DX (0.64), Genomic Grade Index (0.61) or MammaPrint (0.47). The dynamic predictor was also effective in triple-negative cancers (n = 427, HR = 3.08, p = 0.0093) where the above classifiers all failed. Validation in independent patients yielded similar classification power (HR = 3.57). The dynamic classifier is available online at http://www.recurrenceonline.com/?q=Re_training. In summary, we developed a new method to make personalized prognostic prediction using case-specific training cohorts. The dynamic predictors outperform static models developed from single historical training cohorts and they also predict well in triple-negative cancers.

OPG and PgR show similar cohort specific effects as prognostic factors in ER positive breast cancer

The RANK/RANKL/OPG pathway is well known for bone destruction in skeletal metastases but has also been implicated in osteoclast-independent roles in tumorigenesis and de novo metastasis. Experimental data suggest contribution of progesterone to tumorigenesis may be mediated by RANKL. Importantly, modulation of this pathway became possible through the availability of denosumab, an artificial counterpart of OPG, but significant gaps remain in the translation of preclinical findings on the pathway. We analyzed gene expression of RANK, RANKL and OPG from 40 Affymetrix datasets encompassing 4467 primary breast cancers and focused on ER positive disease. We did not observe a significant prognostic value of RANK and RANKL mRNA expression. In contrast, OPG was associated with a better prognosis among 1941 ER positive cancers (HR 0.64, 95% CI 0.53-0.77; P < 0.0001) using a cutoff from its highly bimodal expression. We detected considerable heterogeneity regarding the prognostic value of OPG between different datasets. This heterogeneity could neither be attributed to technical reasons nor to differences in standard clinical parameters or treatments of the cohorts. Interestingly, the prognostic value of the progesterone receptor and of OPG showed similar cohort specific effects. Still both factors were no surrogates for each other but contributed independent prognostic value in multivariate analyses. Thus, both OPG and PgR are independently associated with good prognosis in ER positive breast cancer. However both markers share common cohort specific differences in contrast to proliferation markers as Ki67 which may be based on the underlying biology.

A functional interplay between ZNF217 and estrogen receptor alpha exists in luminal breast cancers

We aimed at highlighting the role of ZNF217, a Krüppel-like finger protein, in Estrogen Receptor-α (ERα)-positive (ER+) and luminal breast cancers. Here we report for the first time that ZNF217 and ERα proteins bind to each other in both breast cancer cells and breast tumour samples, via the ERα hinge domain and the ZNF217 C-terminal domain. ZNF217 enhances the recruitment of ERα to its estrogen response elements (ERE) and the ERα-dependent transcription of the GREB1 estrogen-regulated gene. The prognostic power of ZNF217 mRNA expression levels is most discriminatory in breast cancers classified with a "good prognosis", particularly the Luminal-A subclass. A new immunohistochemistry ZNF217 index, based on nuclear and cytoplasmic ZNF217 staining, also allowed the identification of intermediate/poor relapse-free survivors in the Luminal-A subgroup. ZNF217 confers tamoxifen resistance in ER+ breast cancer cells and is a predictor of relapse under endocrine therapy in patients with ER+ breast cancer. ZNF217 thus allows the re-stratification of patients with ER+ breast cancers considered as cancers with good prognosis where no other biomarkers are currently available and widely used. Here we propose a model in ER+ breast cancer where ZNF217-driven aggressiveness incorporates ZNF217 as a positive enhancer of ERα direct genomic activity and where ZNF217 possesses its highest discriminatory prognostic value.

Novel combination markers for predicting survival in patients with muscle invasive bladder cancer: USP18 and DGCR2

We performed gene expression profiling in bladder cancer patients to identify cancer-specific survival-related genes in muscle invasive bladder cancer (MIBC) patients. Sixty-two patients with MIBC were selected as the original cohort and another 118 MIBC patients were chosen as a validation cohort. The expression of USP18, DGCR2, and ZNF699 genes were measured and we analyzed the association between gene signatures and survival. USP18 and DGCR2, were significantly correlated to cancer-specific death (P=0.020, P=0.007, respectively). Cancer-specific survival in the low USP18 or DGCR2 expression group was significantly longer than the high expression group (P=0.018, P=0.006, respectively). In multivariate Cox regression analysis, a combination of USP18 and DGCR2 mRNA expression levels were significant risk factors for cancer-specific death (HR, 2.106; CI, 1.043-4.254, P=0.038). Overall survival and cancer-specific survival rates in the low-combination group were significantly longer than those in the high-expression group (P=0.001, both). In conclusion, decreased expressions of USP18 and DGCR2 were significantly associated with longer cancer-specific survival, and also the combination of two genes was correlated to a longer survival for MIBC patients. Thus, the combination of USP18 and DGCR2 expression was shown to be a reliable prognostic marker for cancer-specific survival in MIBC.

TP53 mutation-correlated genes predict the risk of tumor relapse and identify MPS1 as a potential therapeutic kinase in TP53-mutated breast cancers

Breast cancers (BC) carry a complex set of gene mutations that can influence their gene expression and clinical behavior. We aimed to identify genes driven by the TP53 mutation status and assess their clinical relevance in estrogen receptor (ER)-positive and ER-negative BC, and their potential as targets for patients with TP53 mutated tumors. Separate ROC analyses of each gene expression according to TP53 mutation status were performed. The prognostic value of genes with the highest AUC were assessed in a large dataset of untreated, and neoadjuvant chemotherapy treated patients. The mitotic checkpoint gene MPS1 was the most significant gene correlated with TP53 status, and the most significant prognostic marker in all ER-positive BC datasets. MPS1 retained its prognostic value independently from the type of treatment administered. The biological functions of MPS1 were investigated in different BC cell lines. We also assessed the effects of a potent small molecule inhibitor of MPS1, SP600125, alone and in combination with chemotherapy. Consistent with the gene expression profiling and siRNA assays, the inhibition of MPS1 by SP600125 led to a reduction in cell viability and a significant increase in cell death, selectively in TP53-mutated BC cells. Furthermore, the chemical inhibition of MPS1 sensitized BC cells to conventional chemotherapy, particularly taxanes. Our results collectively demonstrate that TP53-correlated kinase MPS1, is a potential therapeutic target in BC patients with TP53 mutated tumors, and that SP600125 warrant further development in future clinical trials.

Reduced annexin A6 expression promotes the degradation of activated epidermal growth factor receptor and sensitizes invasive breast cancer cells to EGFR-targeted tyrosine kinase inhibitors

Background

The expression of annexin A6 (AnxA6) in AnxA6-deficient non-invasive tumor cells has been shown to terminate epidermal growth factor receptor (EGFR) activation and downstream signaling. However, as a scaffolding protein, AnxA6 may stabilize activated cell-surface receptors to promote cellular processes such as tumor cell motility and invasiveness. In this study, we investigated the contribution of AnxA6 in the activity of EGFR in invasive breast cancer cells and examined whether the expression status of AnxA6 influences the response of these cells to EGFR-targeted tyrosine kinase inhibitors (TKIs) and/or patient survival.

Results

We demonstrate that in invasive BT-549 breast cancer cells AnxA6 expression is required for sustained membrane localization of activated (phosho-Y1068) EGFR and consequently, persistent activation of MAP kinase ERK1/2 and phosphoinositide 3-kinase/Akt pathways. Depletion of AnxA6 in these cells was accompanied by rapid degradation of activated EGFR, attenuated downstream signaling and as expected enhanced anchorage-independent growth. Besides inhibition of cell motility and invasiveness, AnxA6-depleted cells were also more sensitive to the EGFR-targeted TKIs lapatinib and PD153035. We also provide evidence suggesting that reduced AnxA6 expression is associated with a better relapse-free survival but poorer distant metastasis-free and overall survival of basal-like breast cancer patients.

Conclusions

Together this demonstrates that the rapid degradation of activated EGFR in AnxA6-depleted invasive tumor cells underlies their sensitivity to EGFR-targeted TKIs and reduced motility. These data also suggest that AnxA6 expression status may be useful for the prediction of the survival and likelihood of basal-like breast cancer patients to respond to EGFR-targeted therapies.

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.

SurvExpress: an online biomarker validation tool and database for cancer gene expression data using survival analysis

Validation of multi-gene biomarkers for clinical outcomes is one of the most important issues for cancer prognosis. An important source of information for virtual validation is the high number of available cancer datasets. Nevertheless, assessing the prognostic performance of a gene expression signature along datasets is a difficult task for Biologists and Physicians and also time-consuming for Statisticians and Bioinformaticians. Therefore, to facilitate performance comparisons and validations of survival biomarkers for cancer outcomes, we developed SurvExpress, a cancer-wide gene expression database with clinical outcomes and a web-based tool that provides survival analysis and risk assessment of cancer datasets. The main input of SurvExpress is only the biomarker gene list. We generated a cancer database collecting more than 20,000 samples and 130 datasets with censored clinical information covering tumors over 20 tissues. We implemented a web interface to perform biomarker validation and comparisons in this database, where a multivariate survival analysis can be accomplished in about one minute. We show the utility and simplicity of SurvExpress in two biomarker applications for breast and lung cancer. Compared to other tools, SurvExpress is the largest, most versatile, and quickest free tool available. SurvExpress web can be accessed in http://bioinformatica.mty.itesm.mx/SurvExpress (a tutorial is included). The website was implemented in JSP, JavaScript, MySQL, and R.

Improving Pathological Assessment of Breast Cancer by Employing Array-Based Transcriptome Analysis

Breast cancer research has paved the way of personalized oncology with the introduction of hormonal therapy and the measurement of estrogen receptor as the first widely accepted clinical biomarker. The expression of another receptor—HER2/ERBB2/neu—was initially a sign of worse prognosis, but targeted therapy has granted improved outcome for these patients so that today HER2 positive patients have better prognosis than HER2 negative patients. Later, the introduction of multigene assays provided the pathologists with an unbiased assessment of the tumors’ molecular fingerprint. The recent FDA approval of complete microarray pipelines has opened new possibilities for the objective classification of breast cancer samples. Here we review the applications of microarrays for determining ER and HER2 status, molecular subtypes as well as predicting prognosis and grade for breast cancer patients. An open question remains the role of single genes within such signatures. Openly available microarray datasets enable the execution of an independent cross-validation of new marker and signature candidates. In summary, we review the current state regarding clinical applications of microarrays in breast cancer molecular pathology.

A meta-analysis of gene expression-based biomarkers predicting outcome after tamoxifen treatment in breast cancer

To date, three molecular markers (ER, PR, and CYP2D6) have been used in clinical setting to predict the benefit of the anti-estrogen tamoxifen therapy. Our aim was to validate new biomarker candidates predicting response to tamoxifen treatment in breast cancer by evaluating these in a meta-analysis of available transcriptomic datasets with known treatment and follow-up. Biomarker candidates were identified in Pubmed and in the 2007-2012 ASCO and 2011-2012 SABCS abstracts. Breast cancer microarray datasets of endocrine therapy-treated patients were downloaded from GEO and EGA and RNAseq datasets from TCGA. Of the biomarker candidates, only those identified or already validated in a clinical cohort were included. Relapse-free survival (RFS) up to 5 years was used as endpoint in a ROC analysis in the GEO and RNAseq datasets. In the EGA dataset, Kaplan-Meier analysis was performed for overall survival. Statistical significance was set at p < 0.005. The transcriptomic datasets included 665 GEO-based and 1,208 EGA-based patient samples. All together 68 biomarker candidates were identified. Of these, the best performing genes were PGR (AUC = 0.64, p = 2.3E-07), MAPT (AUC = 0.62, p = 7.8E-05), and SLC7A5 (AUC = 0.62, p = 9.2E-05). Further genes significantly correlated to RFS include FOS, TP53, BTG2, HOXB7, DRG1, CXCL10, and TPM4. In the RNAseq dataset, only ERBB2, EDF1, and MAPK1 reached statistical significance. We evaluated tamoxifen-resistance genes in three independent platforms and identified PGR, MAPT, and SLC7A5 as the most promising prognostic biomarkers in tamoxifen treated patients.

Application of microarray in breast cancer: An overview

There are more than 1.15 million cases of breast cancer diagnosed worldwide annually. At present, only small numbers of accurate prognostic and predictive factors are used clinically for managing the patients with breast cancer. DNA microarrays have the potential to assess the expression of thousands of genes simultaneously. Recent preliminary researches indicate that gene expression profiling based on DNA microarray can offer potential and independent prognostic information in patients with newly diagnosed breast cancer. In this paper, an overview upon the applications of microarray techniques in breast cancer is presented.