A signature inferred from Drosophila mitotic genes predicts survival of breast cancer patients

Prognostic genes of triple-negative breast cancer identified by weighted gene co-expression network analysis

Triple-negative breast cancer (TNBC) is characterized by a deficiency in the estrogen receptor (ER), progesterone receptor (PR) and HER2/neu genes. Patients with TNBC have an increased likelihood of distant recurrence and mortality, compared with patients with other subtypes of breast cancer. The current study aimed to identify novel biomarkers for TNBC. Weighted gene co-expression network analysis (WGCNA) was applied to construct gene co-expression networks; these were used to explore the correlation between mRNA profiles and clinical data, thus identifying the most significant co-expression network associated with the American Joint Committee on Cancer-TNM stage of TNBC. Using RNAseq datasets from The Cancer Genome Atlas, downloaded from the University of California, Santa Cruz, WGCNA identified 23 modules via K-means clustering. The most significant module consisted of 248 genes, on which gene ontology analysis was subsequently performed. Differently Expressed Gene (DEG) analysis was then applied to determine the DEGs between normal and tumor tissues. A total of 42 genes were positioned in the overlap between DEGs and the most significant module. Following survival analysis, 5 genes [GIPC PDZ domain containing family member 1 (GIPC1), hes family bHLH transcription factor 6 (HES6), calmodulin-regulated spectrin-associated protein family member 3 (KIAA1543), myosin light chain kinase 2 (MYLK2) and peter pan homolog (PPAN)] were selected and their association with the American Joint Committee on Cancer-TNM diagnostic stage was investigated. The expression level of these genes in different pathological stages varied, but tended to increase in more advanced pathological stages. The expression of these 5 genes exhibited accurate capacity for the identification of tumor and normal tissues via receiver operating characteristic curve analysis. High expression of GIPC1, HES6, KIAA1543, MYLK2 and PPAN resulted in poor overall survival (OS) in patients with TNBC. In conclusion, via unsupervised clustering methods, a co-expressed gene network with high inter-connectivity was constructed, and 5 genes were identified as biomarkers for TNBC.

Poly-omic prediction of complex traits: OmicKriging

High-confidence prediction of complex traits such as disease risk or drug response is an ultimate goal of personalized medicine. Although genome-wide association studies have discovered thousands of well-replicated polymorphisms associated with a broad spectrum of complex traits, the combined predictive power of these associations for any given trait is generally too low to be of clinical relevance. We propose a novel systems approach to complex trait prediction, which leverages and integrates similarity in genetic, transcriptomic, or other omics-level data. We translate the omic similarity into phenotypic similarity using a method called Kriging, commonly used in geostatistics and machine learning. Our method called OmicKriging emphasizes the use of a wide variety of systems-level data, such as those increasingly made available by comprehensive surveys of the genome, transcriptome, and epigenome, for complex trait prediction. Furthermore, our OmicKriging framework allows easy integration of prior information on the function of subsets of omics-level data from heterogeneous sources without the sometimes heavy computational burden of Bayesian approaches. Using seven disease datasets from the Wellcome Trust Case Control Consortium (WTCCC), we show that OmicKriging allows simple integration of sparse and highly polygenic components yielding comparable performance at a fraction of the computing time of a recently published Bayesian sparse linear mixed model method. Using a cellular growth phenotype, we show that integrating mRNA and microRNA expression data substantially increases performance over either dataset alone. Using clinical statin response, we show improved prediction over existing methods. We provide an R package to implement OmicKriging (http://www.scandb.org/newinterface/tools/OmicKriging.html).

Computer-based image studies on tumor nests mathematical features of breast cancer and their clinical prognostic value

Background

The expending and invasive features of tumor nests could reflect the malignant biological behaviors of breast invasive ductal carcinoma. Useful information on cancer invasiveness hidden within tumor nests could be extracted and analyzed by computer image processing and big data analysis.

Methods

Tissue microarrays from invasive ductal carcinoma (n = 202) were first stained with cytokeratin by immunohistochemical method to clearly demarcate the tumor nests. Then an expert-aided computer analysis system was developed to study the mathematical and geometrical features of the tumor nests. Computer recognition system and imaging analysis software extracted tumor nests information, and mathematical features of tumor nests were calculated. The relationship between tumor nests mathematical parameters and patients' 5-year disease free survival was studied.

Results

There were 8 mathematical parameters extracted by expert-aided computer analysis system. Three mathematical parameters (number, circularity and total perimeter) with area under curve >0.5 and 4 mathematical parameters (average area, average perimeter, total area/total perimeter, average (area/perimeter)) with area under curve <0.5 in ROC analysis were combined into integrated parameter 1 and integrated parameter 2, respectively. Multivariate analysis showed that integrated parameter 1 (P = 0.040) was independent prognostic factor of patients' 5-year disease free survival. The hazard risk ratio of integrated parameter 1 was 1.454 (HR 95% CI [1.017–2.078]), higher than that of N stage (HR 1.396, 95% CI [1.125–1.733]) and hormone receptor status (HR 0.575, 95% CI [0.353–0.936]), but lower than that of histological grading (HR 3.370, 95% CI [1.125–5.364]) and T stage (HR 1.610, 95% CI [1.026 –2.527]).

Conclusions

This study indicated integrated parameter 1 of mathematical features (number, circularity and total perimeter) of tumor nests could be a useful parameter to predict the prognosis of early stage breast invasive ductal carcinoma.

Prediction of breast cancer metastasis by gene expression profiles: a comparison of metagenes and single genes

Background

The popularity of a large number of microarray applications has in cancer research led to the development of predictive or prognostic gene expression profiles. However, the diversity of microarray platforms has made the full validation of such profiles and their related gene lists across studies difficult and, at the level of classification accuracies, rarely validated in multiple independent datasets. Frequently, while the individual genes between such lists may not match, genes with same function are included across such gene lists. Development of such lists does not take into account the fact that genes can be grouped together as metagenes (MGs) based on common characteristics such as pathways, regulation, or genomic location. Such MGs might be used as features in building a predictive model applicable for classifying independent data. It is, therefore, demanding to systematically compare independent validation of gene lists or classifiers based on metagene or individual gene (SG) features.

Methods

In this study we compared the performance of either metagene-or single gene-based feature sets and classifiers using random forest and two support vector machines for classifier building. The performance within the same dataset, feature set validation performance, and validation performance of entire classifiers in strictly independent datasets were assessed by 10 times repeated 10-fold cross validation, leave-one-out cross validation, and one-fold validation, respectively. To test the significance of the performance difference between MG- and SG-features/classifiers, we used a repeated down-sampled binomial test approach.

Results

MG- and SG-feature sets are transferable and perform well for training and testing prediction of metastasis outcome in strictly independent data sets, both between different and within similar microarray platforms, while classifiers had a poorer performance when validated in strictly independent datasets. The study showed that MG- and SG-feature sets perform equally well in classifying independent data. Furthermore, SG-classifiers significantly outperformed MG-classifier when validation is conducted between datasets using similar platforms, while no significant performance difference was found when validation was performed between different platforms.

Conclusion

Prediction of metastasis outcome in lymph node–negative patients by MG- and SG-classifiers showed that SG-classifiers performed significantly better than MG-classifiers when validated in independent data based on the same microarray platform as used for developing the classifier. However, the MG- and SG-classifiers had similar performance when conducting classifier validation in independent data based on a different microarray platform. The latter was also true when only validating sets of MG- and SG-features in independent datasets, both between and within similar and different platforms.

Weighted frequent gene co-expression network mining to identify genes involved in genome stability

Gene co-expression network analysis is an effective method for predicting gene functions and disease biomarkers. However, few studies have systematically identified co-expressed genes involved in the molecular origin and development of various types of tumors. In this study, we used a network mining algorithm to identify tightly connected gene co-expression networks that are frequently present in microarray datasets from 33 types of cancer which were derived from 16 organs/tissues. We compared the results with networks found in multiple normal tissue types and discovered 18 tightly connected frequent networks in cancers, with highly enriched functions on cancer-related activities. Most networks identified also formed physically interacting networks. In contrast, only 6 networks were found in normal tissues, which were highly enriched for housekeeping functions. The largest cancer network contained many genes with genome stability maintenance functions. We tested 13 selected genes from this network for their involvement in genome maintenance using two cell-based assays. Among them, 10 were shown to be involved in either homology-directed DNA repair or centrosome duplication control including the well-known cancer marker MKI67. Our results suggest that the commonly recognized characteristics of cancers are supported by highly coordinated transcriptomic activities. This study also demonstrated that the co-expression network directed approach provides a powerful tool for understanding cancer physiology, predicting new gene functions, as well as providing new target candidates for cancer therapeutics.

Mixed effects modeling of proliferation rates in cell-based models: consequence for pharmacogenomics and cancer

The International HapMap project has made publicly available extensive genotypic data on a number of lymphoblastoid cell lines (LCLs). Building on this resource, many research groups have generated a large amount of phenotypic data on these cell lines to facilitate genetic studies of disease risk or drug response. However, one problem that may reduce the usefulness of these resources is the biological noise inherent to cellular phenotypes. We developed a novel method, termed Mixed Effects Model Averaging (MEM), which pools data from multiple sources and generates an intrinsic cellular growth rate phenotype. This intrinsic growth rate was estimated for each of over 500 HapMap cell lines. We then examined the association of this intrinsic growth rate with gene expression levels and found that almost 30% (2,967 out of 10,748) of the genes tested were significant with FDR less than 10%. We probed further to demonstrate evidence of a genetic effect on intrinsic growth rate by determining a significant enrichment in growth-associated genes among genes targeted by top growth-associated SNPs (as eQTLs). The estimated intrinsic growth rate as well as the strength of the association with genetic variants and gene expression traits are made publicly available through a cell-based pharmacogenomics database, PACdb. This resource should enable researchers to explore the mediating effects of proliferation rate on other phenotypes.

Cell-based models provide a convenient system to conduct studies that would be impossible to apply to human subjects, but the phenotypes measured on these models can be marred with biological noise. We propose a method (MEM) to address this issue by statistically combining data from various sources, and we apply it to the proliferation rates of cell lines collected as part of the International HapMap project. We show that the proliferation rate computed using our method is a better measure of the true proliferation rate of the cells and produces a much stronger association with gene expression phenotypes on the same cell lines: more than 30% of the genes tested were significantly associated with proliferation rate. We also demonstrate that genetic variants have an effect on growth rate. Finally, we make these intrinsic proliferation rates and the strength of the association with gene expression phenotypes public, which should allow other researchers to explore the mediating effects of proliferation on other phenotypes.