An 8-gene qRT-PCR-based gene expression score that has prognostic value in early breast cancer

Downregulated mRNA Expression of ZNF385B Is an Independent Predictor of Breast Cancer

Background

ZNF385B, a zinc finger protein, has been known as a potential biomarker in some neurological and hematological studies recently. Although numerous studies have demonstrated the potential function of zinc finger proteins in tumor progression, the effects of ZNF385B in breast cancer (BC) are less studied.

Methods

The Oncomine database and “ESurv” tool were used to explore the differential expression of ZNF385B in pan-cancer. Furthermore, data of patients with BC were downloaded from The Cancer Genome Atlas (TCGA). The receiver operating characteristic (ROC) curve of ZNF385B expression was established to explore the diagnostic value of ZNF385B and to obtain the cut-off value of high or low ZNF385B expression in BC. The chi-square test as well as Fisher exact test was used for identification of the relationships between clinical features and ZNF385B expression. Furthermore, the effects of ZNF385B on BC patients' survival were evaluated by the Kaplan-Meier and Cox regression. Data from the Gene Expression Omnibus (GEO) database were employed to validate the results of TCGA. Protein expression of ZNF385B in BC patient specimens was detected by immunohistochemistry (IHC) staining.

Results

ZNF385B expression was downregulated in most types of cancer including BC. Low ZNF385B expression was related with survival status, overall survival (OS), and recurrence of BC. ZNF385B had modest diagnostic value, which is indicated by the area under the ROC curve (AUC = 0.671). Patients with lower ZNF385B expression had shorter OS and RFS (relapse-free survival). It had been demonstrated that low ZNF385B expression represented independent prognostic value for OS and RFS by multivariate survival analysis. The similar results were verified by datasets from the GEO database as well. The protein expression of ZNF385B was decreased in patients' samples compared with adjacent tissues by IHC.

Conclusions

Low ZNF385B expression was an independent predictor for worse prognosis of BC patients.

Computational models applied to metabolomics data hints at the relevance of glutamine metabolism in breast cancer

Background

Metabolomics has a great potential in the development of new biomarkers in cancer and it has experiment recent technical advances.

Methods

In this study, metabolomics and gene expression data from 67 localized (stage I to IIIB) breast cancer tumor samples were analyzed, using (1) probabilistic graphical models to define associations using quantitative data without other a priori information; and (2) Flux Balance Analysis and flux activities to characterize differences in metabolic pathways.

Results

On the one hand, both analyses highlighted the importance of glutamine in breast cancer. Moreover, cell experiments showed that treating breast cancer cells with drugs targeting glutamine metabolism significantly affects cell viability. On the other hand, these computational methods suggested some hypotheses and have demonstrated their utility in the analysis of metabolomics data and in associating metabolomics with patient’s clinical outcome.

Conclusions

Computational analyses applied to metabolomics data suggested that glutamine metabolism is a relevant process in breast cancer. Cell experiments confirmed this hypothesis. In addition, these computational analyses allow associating metabolomics data with patient prognosis.

Clinical and molecular aspects of breast cancer: Targets and therapies

Breast Cancer is a complex disease characterized by the occurrence of multiple molecular alterations. Currently, some molecular markers are in use for breast cancer diagnostic, prognostic, and predictive purposes. Thus, genetic signatures are available for improving the decision-making. The biomarkers are also essential as therapeutic approaches, but many questions remain due to the lack of efficacy on breast cancer treatment, mainly for triple-negative breast cancer subtype. Since the genetic profile of breast cancer can also be related to different ethnic groups and geographic areas, the reference populations of the genetic assays and clinical trials need to include a broader population beyond the European and North American patients. In this review, we analyzed the current and potential molecular markers that could help to improve the strategies for breast cancer therapy.

Genes and functions from breast cancer signatures

BACKGROUND

Breast cancer is a heterogeneous disease and personalized medicine is the hope for the improvement of the clinical outcome. Multi-gene signatures for breast cancer stratification have been extensively studied in the past decades and more than 30 different signatures have been reported. A major concern is the minimal overlap of genes among the reported signatures. We investigated the breast cancer signature genes to address our hypothesis that the genes of different signature may share common functions, as well as to use these previously reported signature genes to build better prognostic models.

METHODS

A total of 33 signatures and the corresponding gene lists were investigated. We first examined the gene frequency and the gene overlap in these signatures. Then the gene functions of each signature gene list were analysed and compared by the KEGG pathways and gene ontology (GO) terms. A classifier built using the common genes was tested using the METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) data. The common genes were also tested for building the Yin Yang gene mean expression ratio (YMR) signature using public datasets (GSE1456 and GSE2034).

RESULTS

Among a total of 2239 genes collected from the 33 breast cancer signatures, only 238 genes overlapped in at least two signatures; while from a total of 1979 function terms enriched in the 33 signature gene lists, 429 terms were common in at least two signatures. Most of the common function terms were involved in cell cycle processes. While there is almost no common overlapping genes between signatures developed for ER-positive (e.g. 21-gene signature) and those developed for ER-negative (e.g. basal signatures) tumours, they have common function terms such as cell death, regulation of cell proliferation. We used the 62 genes that were common in at least three signatures as a classifier and subtyped 1141 METABRIC cases including 144 normal samples into nine subgroups. These subgroups showed different clinical outcome. Among the 238 common genes, we selected those genes that are more highly expressed in normal breast tissue than in tumours as Yang genes and those more highly expressed in tumours than in normal as Yin genes and built a YMR model signature. This YMR showed significance in risk stratification in two datasets (GSE1456 and GSE2034).

CONCLUSIONS

The lack of significant numbers of overlapping genes among most breast cancer signatures can be partially explained by our discovery that these signature genes represent groups with similar functions. The genes collected from these previously reported signatures are valuable resources for new model development. The subtype classifier and YMR signature built from the common genes showed promising results.

Reduced expression of SCUBE2 predicts poor prognosis in gastric cancer patients

Signal peptide-CUB-EGF (epidermal growth factor) domain-containing protein 2 (SCUBE2) is a secreted cell-surface glycoprotein. Decreased SCUBE2 expression has been reported in a variety of human cancers, including breast cancer, but its role in gastric cancer (GC) is still unknown. The present study was designed to evaluate the role of SCUBE2 expression in the prognosis of GC patients. SCUBE2 expression in GC tissues was detected by quantitative real-time reverse transcription polymerase chain reaction, Western blotting, and immunohistochemistry. The association between SCUBE2 expression and clinicopathological characteristics was evaluated using the Chi-square test. The Kaplan-Meier method and Cox proportional hazards models were applied to estimate the effect of SCUBE2 expression on survival. Our results show that expression of SCUBE2 in GC tissues is significantly lower than that in adjacent normal gastric mucosa tissues. Loss of SCUBE2 expression was associated with larger tumors (P = 0.001), advanced clinical stage (P = 0.001), T3 or T4 lesion (P = 0.017), lymph node metastasis (P = 0.033), higher histological grade (P = 0.041), and vascular invasion (P = 0.002). Patients with decreased SCUBE2 expression showed poorer recurrence free survival (RFS) and overall survival (OS) than those with higher SCUBE2 expression levels. Furthermore, multivariate analysis indicated that reduced expression of SCUBE2 was an independent prognostic factor predicting poor RFS (HR = 1.764, P = 0.029) and OS (HR = 1.811, P = 0.026). Therefore, expression of SCUBE2 in GC tends to be downregulated, and may serve an important role in predicting the prognosis of GC patients.

Urothelial cancer proteomics provides both prognostic and functional information

Traditionally, bladder cancer has been classified based on histology features. Recently, some works have proposed a molecular classification of invasive bladder tumors. To determine whether proteomics can define molecular subtypes of  muscle invasive urothelial cancer (MIUC) and allow evaluating the status of biological processes and its clinical value. 58 MIUC patients who underwent curative surgical resection at our institution between 2006 and 2012 were included. Proteome was evaluated by high-throughput proteomics in routinely archive FFPE tumor tissue. New molecular subgroups were defined. Functional structure and individual proteins prognostic value were evaluated and correlated with clinicopathologic parameters. 1,453 proteins were quantified, leading to two MIUC molecular subgroups. A protein-based functional structure was defined, including several nodes with specific biological activity. The functional structure showed differences between subtypes in metabolism, focal adhesion, RNA and splicing nodes. Focal adhesion node has prognostic value in the whole population. A 6-protein prognostic signature, associated with higher risk of relapse (5 year DFS 70% versus 20%) was defined. Additionally, we identified two MIUC subtypes groups. Prognostic information provided by pathologic characteristics is not enough to understand MIUC behavior. Proteomics analysis may enhance our understanding of prognostic and classification. These findings can lead to improving diagnosis and treatment selection in these patients.

Functional proteomics outlines the complexity of breast cancer molecular subtypes

Breast cancer is a heterogeneous disease comprising a variety of entities with various genetic backgrounds. Estrogen receptor-positive, human epidermal growth factor receptor 2-negative tumors typically have a favorable outcome; however, some patients eventually relapse, which suggests some heterogeneity within this category. In the present study, we used proteomics and miRNA profiling techniques to characterize a set of 102 either estrogen receptor-positive (ER+)/progesterone receptor-positive (PR+) or triple-negative formalin-fixed, paraffin-embedded breast tumors. Protein expression-based probabilistic graphical models and flux balance analyses revealed that some ER+/PR+ samples had a protein expression profile similar to that of triple-negative samples and had a clinical outcome similar to those with triple-negative disease. This probabilistic graphical model-based classification had prognostic value in patients with luminal A breast cancer. This prognostic information was independent of that provided by standard genomic tests for breast cancer, such as MammaPrint, OncoType Dx and the 8-gene Score.

Genetic Variation and Neuroplasticity: Role in Rehabilitation After Stroke

BACKGROUND AND PURPOSE

In many neurologic diagnoses, significant interindividual variability exists in the outcomes of rehabilitation. One factor that may impact response to rehabilitation interventions is genetic variation. Genetic variation refers to the presence of differences in the DNA sequence among individuals in a population. Genetic polymorphisms are variations that occur relatively commonly and, while not disease-causing, can impact the function of biological systems. The purpose of this article is to describe genetic polymorphisms that may impact neuroplasticity, motor learning, and recovery after stroke.

SUMMARY OF KEY POINTS

Genetic polymorphisms for brain-derived neurotrophic factor (BDNF), dopamine, and apolipoprotein E have been shown to impact neuroplasticity and motor learning. Rehabilitation interventions that rely on the molecular and cellular pathways of these factors may be impacted by the presence of the polymorphism. For example, it has been hypothesized that individuals with the BDNF polymorphism may show a decreased response to neuroplasticity-based interventions, decreased rate of learning, and overall less recovery after stroke. However, research to date has been limited and additional work is needed to fully understand the role of genetic variation in learning and recovery.

RECOMMENDATIONS FOR CLINICAL PRACTICE

Genetic polymorphisms should be considered as possible predictors or covariates in studies that investigate neuroplasticity, motor learning, or motor recovery after stroke. Future predictive models of stroke recovery will likely include a combination of genetic factors and other traditional factors (eg, age, lesion type, corticospinal tract integrity) to determine an individual's expected response to a specific rehabilitation intervention.

Prediction of adjuvant chemotherapy response in triple negative breast cancer with discovery and targeted proteomics

BACKGROUND

Triple-negative breast cancer (TNBC) accounts for 15-20% of all breast cancers and usually requires the administration of adjuvant chemotherapy after surgery but even with this treatment many patients still suffer from a relapse. The main objective of this study was to identify proteomics-based biomarkers that predict the response to standard adjuvant chemotherapy, so that patients at are not going to benefit from it can be offered therapeutic alternatives.

METHODS

We analyzed the proteome of a retrospective series of formalin-fixed, paraffin-embedded TNBC tissue applying high-throughput label-free quantitative proteomics. We identified several protein signatures with predictive value, which were validated with quantitative targeted proteomics in an independent cohort of patients and further evaluated in publicly available transcriptomics data.

RESULTS

Using univariate Cox analysis, a panel of 18 proteins was significantly associated with distant metastasis-free survival of patients (p<0.01). A reduced 5-protein profile with prognostic value was identified and its prediction performance was assessed in an independent targeted proteomics experiment and a publicly available transcriptomics dataset. Predictor P5 including peptides from proteins RAC2, RAB6A, BIEA and IPYR was the best performance protein combination in predicting relapse after adjuvant chemotherapy in TNBC patients.

CONCLUSIONS

This study identified a protein combination signature that complements histopathological prognostic factors in TNBC treated with adjuvant chemotherapy. The protein signature can be used in paraffin-embedded samples, and after a prospective validation in independent series, it could be used as predictive clinical test in order to recommend participation in clinical trials or a more exhaustive follow-up.

Decreased expression of SCUBE2 is associated with progression and prognosis in colorectal cancer

Signal peptide-CUB-epidermal growth factor-like domain-containing protein 2 (SCUBE2), a member of the SCUBE protein family, is a secreted and membrane-associated multi-domain glycoprotein. SCUBE2 is known as a novel tumor suppressor and a useful prognostic marker in breast cancer. In the present study, we investigated the expression (including mRNA and protein levels) of SCUBE2 in colorectal cancer (CRC) and adjacent normal tissues, using quantitative real-time PCR, western blot analysis and immunohistochemistry on a tissue microarray. Upregulation of SCUBE2 was achieved by transient transfection in RKO cell lines, and the effects of SCUBE2 on tumor proliferation, invasion, migration and apoptosis were evaluated by a series of functional experiments. The results indicated that SCUBE2 expression was decreased at the transcriptional and translational levels in CRC tissues and significantly associated with clinical stage, the depth of tumor invasion, lymph‑node metastasis, distant metastasis and histological grade. Patients with SCUBE2‑positive tumors had a lower recurrence rate and better survival than patients with SCUBE2-negative tumors. Moreover, upregulation of SCUBE2 had a limited effect on cell apoptosis but significantly inhibited tumor cell proliferation, migration and invasion in vitro. In conclusion, SCUBE2 plays an important role in suppressing CRC progression and prognosis. Our findings suggested that SCUBE2 may serve as a novel tumor suppressor and a potential therapeutic target for CRC patients.

Expression of SCUBE2 gene declines in high grade endometrial cancer and associates with expression of steroid hormone receptors and tumor suppressor PTEN

SCUBE2 (Signal peptide-CUB-epidermal growth factor-like domain-containing 2) gene codes for a cell-surface glycoprotein. In breast cancer, SCUBE2 transcript levels are part of important prognostic and predictive gene signatures and are linked to expression of estrogen receptor α (ERα). To elucidate the role of this gene in endometrial cancer, we compared SCUBE2 expression in malignant and normal endometrial tissue specimens. We then examined its correlation with steroid hormone receptors and PTEN and compared it to SCUBE2 expression in breast cancer samples. Expression of SCUBE2 was found to be decreased in G3 endometrial cancer when compared to postmenopausal endometrium or to G1 tumors (p < 0.05). In postmenopausal endometrium, SCUBE2 transcript levels were more than twice as high as in premenopausal women. In breast cancer, SCUBE2 expression was found to be notably reduced particularly in ERα-negative G3 tumors. Both in endometrial and breast cancer we observed a significant positive correlation of SCUBE2 transcript levels with expression of ERα, PR and PTEN. Our data suggest that SCUBE2, like in breast cancer, associates with ERα and might have a potential as prognostic or predictive marker in endometrial cancer.

MicroRNA expression profiling of peripheral blood samples predicts resistance to first-line sunitinib in advanced renal cell carcinoma patients

Anti-angiogenic therapy benefits many patients with advanced renal cell carcinoma (RCC), but there is still a need for predictive markers that help in selecting the best therapy for individual patients. MicroRNAs (miRNAs) regulate cancer cell behavior and may be attractive biomarkers for prognosis and prediction of response. Forty-four patients with RCC were recruited into this observational prospective study conducted in nine Spanish institutions. Peripheral blood samples were taken before initiation of therapy and 14 days later in patients receiving first-line therapy with sunitinib for advanced RCC. miRNA expression in peripheral blood was assessed using microarrays and L2 boosting was applied to filtered miRNA expression data. Several models predicting poor and prolonged response to sunitinib were constructed and evaluated by binary logistic regression. Blood samples from 38 patients and 287 miRNAs were evaluated. Twenty-eight miRNAs of the 287 were related to poor response and 23 of the 287 were related to prolonged response to sunitinib treatment. Predictive models identified populations with differences in the established end points. In the poor response group, median time to progression was 3.5 months and the overall survival was 8.5, whereas in the prolonged response group these values were 24 and 29.5 months, respectively. Ontology analyses pointed out to cancer-related pathways, such angiogenesis and apoptosis. miRNA expression signatures, measured in peripheral blood, may stratify patients with advanced RCC according to their response to first-line therapy with sunitinib, improving diagnostic accuracy. After proper validation, these signatures could be used to tailor therapy in this setting.

Gene signatures in breast cancer: current and future uses

Gene signatures have been developed for estrogen receptor-positive breast cancer to complement pathological factors in providing prognostic information. The 70-gene and the 21-gene signatures identify patients who may not require adjuvant chemotherapy. Gene signatures in triple-negative disease and HER2-positive disease have not been fully developed yet, although studies demonstrate heterogeneity within these subgroups. Further research is needed before genotyping will help in making clinical decisions in triple-negative and HER2-positive disease. Molecular subtyping of breast cancer led to define luminal, basal, and HER2-enriched subtypes, which have specific clinical behavior. This approach may lead to identify new subgroups requiring specific therapies. Standardization of techniques will be required to translate investigations to the clinic.

The present and future of gene profiling in breast cancer

Gene signatures can provide prognostic and predictive information to help in the treatment of early-stage breast cancer. Although many of these signatures have been described, only a few have been properly validated. MammaPrint and OncoType offer prognostic information and identify low-risk patients who do not benefit from adjuvant chemotherapy. With regard to prediction of response, molecular subtypes of breast cancer differ in their sensitivity to chemotherapy, although further studies are needed in this field. Cost, small sample size, and the need to use central laboratories are common limitations to the widespread use of these tools.

Testing an aflatoxin B1 gene signature in rat archival tissues

Archival tissues from laboratory studies represent a unique opportunity to explore the relationship between genomic changes and agent-induced disease. In this study, we evaluated the applicability of qPCR for detecting genomic changes in formalin-fixed, paraffin-embedded (FFPE) tissues by determining if a subset of 14 genes from a 90-gene signature derived from microarray data and associated with eventual tumor development could be detected in archival liver, kidney, and lung of rats exposed to aflatoxin B1 (AFB1) for 90 days in feed at 1 ppm. These tissues originated from the same rats used in the microarray study. The 14 genes evaluated were Adam8, Cdh13, Ddit4l, Mybl2, Akr7a3, Akr7a2, Fhit, Wwox, Abcb1b, Abcc3, Cxcl1, Gsta5, Grin2c, and the C8orf46 homologue. The qPCR FFPE liver results were compared to the original liver microarray data and to qPCR results using RNA from fresh frozen liver. Archival liver paraffin blocks yielded 30 to 50 μg of degraded RNA that ranged in size from 0.1 to 4 kB. qPCR results from FFPE and fresh frozen liver samples were positively correlated (p ≤ 0.05) by regression analysis and showed good agreement in direction and proportion of change with microarray data for 11 of 14 genes. All 14 transcripts could be amplified from FFPE kidney RNA except the glutamate receptor gene Grin2c; however, only Abcb1b was significantly upregulated from control. Abundant constitutive transcripts, S18 and β-actin, could be amplified from lung FFPE samples, but the narrow RNA size range (25-500 bp length) prevented consistent detection of target transcripts. Overall, a discrete gene signature derived from prior transcript profiling and representing cell cycle progression, DNA damage response, and xenosensor and detoxication pathways was successfully applied to archival liver and kidney by qPCR and indicated that gene expression changes in response to subchronic AFB1 exposure occurred predominantly in the liver, the primary target for AFB1-induced tumors. We conclude that an evaluation of gene signatures in archival tissues can be an important toxicological tool for evaluating critical molecular events associated with chemical exposures.

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

In the last decades, several gene expression-based predictors of clinical behavior were developed for breast cancer. A common feature of these is the use of multiple genes to predict hormone receptor status and the probability of tumor recurrence, survival or response to chemotherapy. We developed an online analysis tool to compute ER and HER2 status, Oncotype DX 21-gene recurrence score and an independent recurrence risk classification using gene expression data obtained by interrogation of Affymetrix microarray profiles. We implemented rigorous quality control algorithms to promptly exclude any biases related to sample processing, hybridization and scanning. After uploading the raw microarray data, the system performs the complete evaluation automatically and provides a report summarizing the results. The system is accessible online at http://www.recurrenceonline.com . We validated the system using data from 2,472 publicly available microarrays. The validation of the prediction of the 21-gene recurrence score was significant in lymph node negative patients (Cox-Mantel, P = 5.6E-16, HR = 0.4, CI = 0.32-0.5). A correct classification was obtained for 88.5% of ER- and 90.5% of ER + tumors (n = 1,894). The prediction of recurrence risk in all patients by using the mean of the independent six strongest genes (P < 1E-16, HR = 2.9, CI = 2.5-3.3), of the four strongest genes in lymph node negative ER positive patients (P < 1E-16, HR = 2.8, CI = 2.2-3.5) and of the three genes in lymph node positive patients (P = 3.2E-9, HR = 2.5, CI = 1.8-3.4) was highly significant. In summary, we integrated available knowledge in one platform to validate currently used predictors and to provide a global tool for the online determination of different prognostic parameters simultaneously using genome-wide microarrays.

NCBI GEO: archive for functional genomics data sets--10 years on

A decade ago, the Gene Expression Omnibus (GEO) database was established at the National Center for Biotechnology Information (NCBI). The original objective of GEO was to serve as a public repository for high-throughput gene expression data generated mostly by microarray technology. However, the research community quickly applied microarrays to non-gene-expression studies, including examination of genome copy number variation and genome-wide profiling of DNA-binding proteins. Because the GEO database was designed with a flexible structure, it was possible to quickly adapt the repository to store these data types. More recently, as the microarray community switches to next-generation sequencing technologies, GEO has again adapted to host these data sets. Today, GEO stores over 20,000 microarray- and sequence-based functional genomics studies, and continues to handle the majority of direct high-throughput data submissions from the research community. Multiple mechanisms are provided to help users effectively search, browse, download and visualize the data at the level of individual genes or entire studies. This paper describes recent database enhancements, including new search and data representation tools, as well as a brief review of how the community uses GEO data. GEO is freely accessible at http://www.ncbi.nlm.nih.gov/geo/.