Breast cancer biomarker discovery in the functional genomic age: a systematic review of 42 gene expression signatures.
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ABSTRACT:
SUBMITTER: Abba MC
PROVIDER: S-EPMC2978930 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Risk stratification by genomic signatures has been shown to improve prognostication and guide treatment decisions among patients with hormone-sensitive breast cancer. However, their role in young women has not been fully elucidated. In this review, a systematic search was conducted for published articles and abstracts from major congresses that evaluated the use of genomic signatures in young breast cancer patients. A total of 71 studies were analyzed, including 561,188 patients of whom 27,748 (4.9%) were young. Women aged ≤40 years were subjected to genomic testing at a similar rate to older women but had a higher proportion of intermediate- to high-risk tumors when classified by EndoPredict (p = 0.04), MammaPrint (p < 0.01), and Oncotype DX (p < 0.01). In young women with low genomic risk, 6-year distant recurrence-free survival was 94%, while 5-year overall survival was nearly 100%. Nonetheless, young patients classified as low-risk had a higher tendency to receive chemotherapy compared to their older counterparts. In conclusion, genomic tests are useful tools for identifying young patients in whom chemotherapy omission is appropriate.
| S-EPMC7519162 | biostudies-literature
Project description:Diagnostic and prognostic biomarkers for cancer based on gene expression profiles are viewed as a major step towards a better personalized medicine. Many studies using various computational approaches have been published in this direction during the last decade. However, when comparing different gene signatures for related clinical questions often only a small overlap is observed. This can have various reasons, such as technical differences of platforms, differences in biological samples or their treatment in lab, or statistical reasons because of the high dimensionality of the data combined with small sample size, leading to unstable selection of genes. In conclusion retrieved gene signatures are often hard to interpret from a biological point of view. We here demonstrate that it is possible to construct a consensus signature from a set of seemingly different gene signatures by mapping them on a protein interaction network. Common upstream proteins of close gene products, which we identified via our developed algorithm, show a very clear and significant functional interpretation in terms of overrepresented KEGG pathways, disease associated genes and known drug targets. Moreover, we show that such a consensus signature can serve as prior knowledge for predictive biomarker discovery in breast cancer. Evaluation on different datasets shows that signatures derived from the consensus signature reveal a much higher stability than signatures learned from all probesets on a microarray, while at the same time being at least as predictive. Furthermore, they are clearly interpretable in terms of enriched pathways, disease associated genes and known drug targets. In summary we thus believe that network based consensus signatures are not only a way to relate seemingly different gene signatures to each other in a functional manner, but also to establish prior knowledge for highly stable and interpretable predictive biomarkers.
| S-EPMC3201953 | biostudies-literature
Project description:BackgroundThe use of gene signatures can potentially be of considerable value in the field of clinical diagnosis. However, gene signatures defined with different methods can be quite various even when applied the same disease and the same endpoint. Previous studies have shown that the correct selection of subsets of genes from microarray data is key for the accurate classification of disease phenotypes, and a number of methods have been proposed for the purpose. However, these methods refine the subsets by only considering each single feature, and they do not confirm the association between the genes identified in each gene signature and the phenotype of the disease. We proposed an innovative new method termed Minimize Feature's Size (MFS) based on multiple level similarity analyses and association between the genes and disease for breast cancer endpoints by comparing classifier models generated from the second phase of MicroArray Quality Control (MAQC-II), trying to develop effective meta-analysis strategies to transform the MAQC-II signatures into a robust and reliable set of biomarker for clinical applications.ResultsWe analyzed the similarity of the multiple gene signatures in an endpoint and between the two endpoints of breast cancer at probe and gene levels, the results indicate that disease-related genes can be preferably selected as the components of gene signature, and that the gene signatures for the two endpoints could be interchangeable. The minimized signatures were built at probe level by using MFS for each endpoint. By applying the approach, we generated a much smaller set of gene signature with the similar predictive power compared with those gene signatures from MAQC-II.ConclusionsOur results indicate that gene signatures of both large and small sizes could perform equally well in clinical applications. Besides, consistency and biological significances can be detected among different gene signatures, reflecting the studying endpoints. New classifiers built with MFS exhibit improved performance with both internal and external validation, suggesting that MFS method generally reduces redundancies for features within gene signatures and improves the performance of the model. Consequently, our strategy will be beneficial for the microarray-based clinical applications.
| S-EPMC3287502 | biostudies-literature
Project description:BackgroundBreast cancer is a very heterogeneous disease and luminal breast carcinomas represent the hormone receptor-positive tumors among all breast cancer subtypes. In this context, multigene signatures were developed to gain further prognostic and predictive information beyond clinical parameters and traditional immunohistochemical markers.SummaryFor early breast cancer patients these molecular tools can guide clinicians to decide on the extension of endocrine therapy to avoid over- and undertreatment by adjuvant chemotherapy. Beside the predictive and prognostic value, a few genomic tests are also able to provide intrinsic subtype classification. In this review, we compare the most frequently used and commercially available molecular tests (OncotypeDX®, MammaPrint®, Prosigna®, EndoPredict®, and Breast Cancer IndexSM). Moreover, we discuss the clinical utility of molecular profiling for advanced breast cancer of the luminal subtype.Key messagesMultigene assays can help to de-escalate systemic therapy in early-stage breast cancer. Only the Oncotype DX® and MammaPrint®test are validated by entirely prospective and randomized phase 3 trials. More clinical evidence is needed to support the use of genomic tests in node-positive disease. Recent developments in high-throughput sequencing technology will provide further insights to understand the heterogeneity of luminal breast cancers in early-stage and metastatic disease.
| S-EPMC7490652 | biostudies-literature
Project description:High throughput technologies in genomics enable the analysis of small alterations in gene expression levels. Patterns of such deviations are an important starting point for the discovery and verification of new biomarker candidates. Identifying such patterns is a challenging task that requires sophisticated machine learning approaches. Currently, there are a variety of classification models, and a common approach is to compare the performance and select the best one for a given classification problem. Since the association between the features of a data set and the performance of a particular classification method is still not fully understood, the main contribution of this work is to provide a new methodology for predicting the prediction results of different classifiers in the field of biomarker discovery. We propose here a three-steps computational workflow that includes an analysis of the data set characteristics, the calculation of the classification accuracy and, finally, the prediction of the resulting classification error. The experiments were carried out on synthetic and microarray datasets. Using this method, we showed that the predictability strongly depends on the discriminatory ability of the features, e.g., sets of genes, in two or multi-class datasets. If a dataset has a certain discriminatory ability, this method enables prediction of the classification performance before applying a learning model. Thus, our results contribute to a better understanding of the relationship between dataset characteristics and the corresponding performance of a machine learning method, and suggest the optimal classification method for a given dataset based on its discriminatory ability.
| S-EPMC9645616 | biostudies-literature
Project description:Metabolomics is the emerging member of "omics" sciences advancing the understanding, diagnosis and treatment of many cancers, including ovarian cancer (OC).To systematically identify the metabolomic abnormalities in OC detection, and the dominant metabolic pathways associated with the observed alterations.An electronic literature search was performed, up to and including January 15th 2016, for studies evaluating the metabolomic profile of patients with OC compared to controls. QUADOMICS tool was used to assess the quality of the twenty-three studies included in this systematic review.Biological samples utilized for metabolomic analysis include: serum/plasma (n = 13), urine (n = 4), cyst fluid (n = 3), tissue (n = 2) and ascitic fluid (n = 1). Metabolites related to cellular respiration, carbohydrate, lipid, protein and nucleotide metabolism were significantly altered in OC. Increased levels of tricarboxylic acid cycle intermediates and altered metabolites of the glycolytic pathway pointed to perturbations in cellular respiration. Alterations in lipid metabolism included enhanced fatty acid oxidation, abnormal levels of glycerolipids, sphingolipids and free fatty acids with common elevations of palmitate, oleate, and myristate. Increased levels of glutamine, glycine, cysteine and threonine were commonly reported while enhanced degradations of tryptophan, histidine and phenylalanine were found. N-acetylaspartate, a brain amino acid, was found elevated in primary and metastatic OC tissue and ovarian cyst fluid. Further, elevated levels of ketone bodies including 3-hydroxybutyrate were commonly reported. Increased levels of nucleotide metabolites and tocopherols were consistent through out the studies.Metabolomics presents significant new opportunities for diagnostic biomarker development, elucidating previously unknown mechanisms of OC pathogenesis.
| S-EPMC5557039 | biostudies-literature
Project description:We present evidence for potential biomarker utility of a mitochondrial complex I subunit, (NDUFS3) in discriminating normal and highly invasive breast carcinoma specimens obtained from clinical patients. Besides being a robust indicator of breast cancer aggressiveness, NDUFS3 also shows clear signatures of a hypoxia/necrosis marker in invasive ductal carcinoma specimens. Statistically significant positive correlation was observed between nuclear grade and NDUFS3 expression level in the tumor specimens analyzed. We support these findings with a plausible mechanism involving mitochondrial complex I assembly defects and/or redox buffering induced mitochondrial dysfunction during the process of cancer cell transformation. From a clinical standpoint, this novel observation adds value in augmenting the current receptor-based biomarkers for better accuracy in diagnosis and predicting survival rate in patients with breast carcinoma.
| S-EPMC3171595 | biostudies-literature
Project description:BACKGROUND:In 2012, Venet et al. proposed that at least in the case of breast cancer, most published signatures are not significantly more associated with outcome than randomly generated signatures. They suggested that nominal p-value is not a good estimator to show the significance of a signature. Therefore, one can reasonably postulate that some information might be present in such significant random signatures. METHODS:In this research, first we show that, using an empirical p-value, these published signatures are more significant than their nominal p-values. In other words, the proposed empirical p-value can be considered as a complimentary criterion for nominal p-value to distinguish random signatures from significant ones. Secondly, we develop a novel computational method to extract information that are embedded within significant random signatures. In our method, a score is assigned to each gene based on the number of times it appears in significant random signatures. Then, these scores are diffused through a protein-protein interaction network and a permutation procedure is used to determine the genes with significant scores. The genes with significant scores are considered as the set of significant genes. RESULTS:First, we applied our method on the breast cancer dataset NKI to achieve a set of significant genes in breast cancer considering significant random signatures. Secondly, prognostic performance of the computed set of significant genes is evaluated using DMFS and RFS datasets. We have observed that the top ranked genes from this set can successfully separate patients with poor prognosis from those with good prognosis. Finally, we investigated the expression pattern of TAT, the first gene reported in our set, in malignant breast cancer vs. adjacent normal tissue and mammospheres. CONCLUSION:Applying the method, we found a set of significant genes in breast cancer, including TAT, a gene that has never been reported as an important gene in breast cancer. Our results show that the expression of TAT is repressed in tumors suggesting that this gene could act as a tumor suppressor in breast cancer and could be used as a new biomarker.
| S-EPMC6842262 | biostudies-literature
Project description:Multiple studies have investigated global DNA methylation profiles and gene-specific DNA methylation in blood-based DNA to develop powerful screening markers for cancer. This systematic review summarizes the current evidence on methylation studies that investigated methylation level of blood-derived DNA of breast cancer (BC) patients in comparison to healthy controls by conducting a systematic literature review in PubMed and Web of Science. Essential results, such as methylation levels of BC cases and healthy controls, p values, and odds ratios, were extracted from these studies by two investigators independently. Overall, 45 publications met the inclusion criteria for this review. DNA from whole blood, as well as cell-free DNA (cfDNA) from serum or plasma, was used in these studies. The most common method used for measuring global DNA methylation was the investigation of repetitive elements as surrogates and the application of array-based genome-wide methylation analysis. For measuring gene-specific methylation level, methylation-specific PCR and pyrosequencing were the most frequently used methods. Epigenome-wide blood DNA hypomethylation in BC patients were reported in several studies; however, the evidence is still not conclusive. The most frequently investigated gene in whole blood was BRCA1, which was found more frequently methylated in patients compared to controls. RASSF1A was the most widely investigated gene in cfDNA of serum or plasma, which was also found more frequently methylated in patients compared to controls. Several of the eligible studies reported the associations of global hypomethylation and increased BC risk. Studies investigated associations between gene-specific methylation and BC risk, while got heterogeneous results. But two studies reported that hypermethylation of ATM gene was associated with increased BC risk, which suggest the potential use of this gene for BC risk stratification. Overall, our review suggests the possibility of using blood-based DNA methylation marker as promising marker for BC risk stratification, as several studies found associations between certain methylation level in blood and BC risk. However, so far, the evidence is still quite limited. Optimal markers are yet to be developed and promising results needed to be validated in prospective study cohorts and tested in large screening populations.
| S-EPMC5109688 | biostudies-literature
Project description:Metabolomics is emerging as a powerful tool for studying metabolic processes, identifying crucial biomarkers responsible for metabolic characteristics and revealing metabolic mechanisms, which construct the content of discovery metabolomics. The crucial biomarkers can be used to reprogram a metabolome, leading to an aimed metabolic strategy to cope with alteration of internal and external environments, naming reprogramming metabolomics here. The striking feature on the similarity of the basic metabolic pathways and components among vastly different species makes the reprogramming metabolomics possible when the engineered metabolites play biological roles in cellular activity as a substrate of enzymes and a regulator to other molecules including proteins. The reprogramming metabolomics approach can be used to clarify metabolic mechanisms of responding to changed internal and external environmental factors and to establish a framework to develop targeted tools for dealing with the changes such as controlling and/or preventing infection with pathogens and enhancing host immunity against pathogens. This review introduces the current state and trends of discovery metabolomics and reprogramming metabolomics and highlights the importance of reprogramming metabolomics.
| S-EPMC4537470 | biostudies-literature