An integrative genomics approach to biomarker discovery in breast cancer

Role of Genetic Variations in CDK2, CCNE1 and p27KIP1 in Prostate Cancer

BACKGROUND/AIM

Our aim was to investigate possible influences of genetic variants in genes involved in the G₁/S transition [cyclin-dependent kinase-2 (CDK2), cyclin E1 (CCNE1) and cyclin-dependent kinase inhibitor 1B (p27^KIP1)] on the expression/activity of their corresponding proteins and to assess the functional impact of these variants on the risk of prostate cancer.

MATERIALS AND METHODS

We genotyped 530 cases and 562 healthy controls for two relevant single nucleotide polymorphisms (CDK2 rs2069408 and CCNE1 rs997669) by TaqMan genotyping assay. p27^KIP1 rs2066827 polymorphisms were studied by polymerase chain reaction-restriction fragment length polymorphism assay. In addition, the expression of CDK2, CCNE1 and p27^KIP1 was evaluated by quantitative real-time-polymerase chain reaction and western blotting in 44 prostate cancer tissues and 31 benign prostatic hyperplasia tissues.

RESULTS

No association was found between CDK2 rs2069408, CCNE1 rs997669 or p27^KIP1 rs2066827 polymorphisms and an increased risk of prostate cancer development. Higher CDK2 expression was more prevalent in those with rs2069408 GG genotype than in AA carriers (p>0.05). We also noted reduced p27^KIP1 protein expression in those with the p27^KIP1 G109 allele. No difference was observed for CCNE1 expression in relation to the risky genotype (CC). A significant association was detected between CCNE1 mRNA overexpression and development of higher-grade carcinomas (Gleason score >7, p<0.05).

CONCLUSION

Polymorphisms CDK2 rs2069408, CCNE1 rs997669 and p27^KIP1 rs2066827 have no significant impact on prostate cancer risk nor on the gene and protein expression of CDK2, CCNE1 and p27^KIP1, although high CCNE1 expression was significantly associated with a higher tumour grade in patients with prostate cancer.

Systematic review of genome-wide association studies of abdominal aortic aneurysm

BACKGROUND AND AIMS

Abdominal aortic aneurysm (AAA) is an important cause of death worldwide and has an estimated heritability between 70 and 77%. Genome-wide association studies (GWAS) are an established way to discover genetic risk variants. The aim of this study was to systematically review the findings and quality of previous AAA GWAS.

METHODS

The Medline, PubMed, Web of Science and relevant genetic databases were searched to identify previous AAA GWAS. A framework was developed to grade the methodological quality of the GWAS. Data from included studies were extracted to assess methods and findings.

RESULTS

Eight case-control studies were included. Thirty-three of the 38 total single nucleotide polymorphisms (SNPs) previously reported were associated with AAA diagnosis at genome-wide significance (p < 5.0 × 10^-8). The CDKN2B antisense RNA-1 gene had the most significant association with AAA diagnosis (p = 6.94 × 10^-29 and p = 1.54 × 10^-33 for rs4007642 and rs10757274 respectively). Age, sex and smoking history were not reported for the complete cohort in any of the included studies, although five of the eight studies adjusted or matched for at least two confounding variables. All included studies had important design limitations including lack of sample size estimation, inconsistent case and control ascertainment and limited phenotyping of the AAAs. AAA growth was assessed in one GWAS, however, no significant associations with the reported SNPs were found.

CONCLUSIONS

This systematic review identified 33 SNPs associated with AAA diagnosis at genome-wide significance previously validated in multiple cohorts. The association between SNPs and AAA growth was not adequately examined. Previous GWAS have a number of design limitations.

Delineation of the Germline and Somatic Mutation Interaction Landscape in Triple-Negative and Non-Triple-Negative Breast Cancer

Background

Breast cancer development and progression involve both germline and somatic mutations. High-throughput genotyping and next-generation sequencing technologies have enabled discovery of genetic risk variants and acquired somatic mutations driving the disease. However, the possible oncogenic interactions between germline genetic risk variants and somatic mutations in triple-negative breast cancer (TNBC) and non-triple-negative breast cancer (non-TNBC) have not been characterized. Here, we delineated the possible oncogenic interactions between genes containing germline and somatic mutations in TNBC and non-TNBC and investigated whether there are differences in gene expression and mutation burden between the two types of breast cancer.

Methods

We addressed this problem by integrating germline mutation information from genome-wide association studies with somatic mutation information from next-generation sequencing using gene expression data as the intermediated phenotype. We performed network and pathway analyses to discover molecular networks and signalling pathways enriched for germline and somatic mutations.

Results

The investigation revealed signatures of differentially expressed and differentially somatic mutated genes between TNBC and non-TNBC. Network and pathway analyses revealed functionally related genes interacting in gene regulatory networks and multiple signalling pathways enriched for germline and somatic mutations for each type of breast cancer. Among the signalling pathways discovered included the DNA repair and Androgen and ATM signalling pathways for TNBC and the DNA damage response, molecular mechanisms of cancer, and ATM and GP6 signalling pathways for non-TNBC.

Conclusions

The results show that integrative genomics is a powerful approach for delineating oncogenic interactions between genes containing germline and genes containing somatic mutations in TNBC and non-TNBC and establishes putative functional bridges between genetic and somatic alterations and the pathways they control in the two types of breast cancer.

pETM: a penalized Exponential Tilt Model for analysis of correlated high-dimensional DNA methylation data

Motivation

DNA methylation plays an important role in many biological processes and cancer progression. Recent studies have found that there are also differences in methylation variations in different groups other than differences in methylation means. Several methods have been developed that consider both mean and variance signals in order to improve statistical power of detecting differentially methylated loci. Moreover, as methylation levels of neighboring CpG sites are known to be strongly correlated, methods that incorporate correlations have also been developed. We previously developed a network-based penalized logistic regression for correlated methylation data, but only focusing on mean signals. We have also developed a generalized exponential tilt model that captures both mean and variance signals but only examining one CpG site at a time.

Results

In this article, we proposed a penalized Exponential Tilt Model (pETM) using network-based regularization that captures both mean and variance signals in DNA methylation data and takes into account the correlations among nearby CpG sites. By combining the strength of the two models we previously developed, we demonstrated the superior power and better performance of the pETM method through simulations and the applications to the 450K DNA methylation array data of the four breast invasive carcinoma cancer subtypes from The Cancer Genome Atlas (TCGA) project. The developed pETM method identifies many cancer-related methylation loci that were missed by our previously developed method that considers correlations among nearby methylation loci but not variance signals.

Availability and Implementation

The R package 'pETM' is publicly available through CRAN: http://cran.r-project.org .

Contact

sw2206@columbia.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

A Genome-Wide Association Study and Complex Network Identify Four Core Hub Genes in Bipolar Disorder

Bipolar disorder is a common and severe mental illness with unsolved pathophysiology. A genome-wide association study (GWAS) has been used to find a number of risk genes, but it is difficult for a GWAS to find genes indirectly associated with a disease. To find core hub genes, we introduce a network analysis after the GWAS was conducted. Six thousand four hundred fifty eight single nucleotide polymorphisms (SNPs) with p < 0.01 were sifted out from Wellcome Trust Case Control Consortium (WTCCC) dataset and mapped to 2045 genes, which are then compared with the protein–protein network. One hundred twelve genes with a degree >17 were chosen as hub genes from which five significant modules and four core hub genes (FBXL13, WDFY2, bFGF, and MTHFD1L) were found. These core hub genes have not been reported to be directly associated with BD but may function by interacting with genes directly related to BD. Our method engenders new thoughts on finding genes indirectly associated with, but important for, complex diseases.

Integrative genomic analysis for the discovery of biomarkers in prostate cancer

Genome-wide association studies (GWAS) have achieved great success in identifying single nucleotide polymorphisms (SNPs, herein called genetic variants) and genes associated with risk of developing prostate cancer. However, GWAS do not typically link the genetic variants to the disease state or inform the broader context in which the genetic variants operate. Here, we present a novel integrative genomics approach that combines GWAS information with gene expression data to infer the causal association between gene expression and the disease and to identify the network states and biological pathways enriched for genetic variants. We identified gene regulatory networks and biological pathways enriched for genetic variants, including the prostate cancer, IGF-1, JAK2, androgen, and prolactin signaling pathways. The integration of GWAS information with gene expression data provides insights about the broader context in which genetic variants associated with an increased risk of developing prostate cancer operate.

Cancer biomarker discovery: current status and future perspectives

PURPOSE

Cancer is a multigene disease which arises as a result of mutational and epigenetic changes coupled with activation of complex signaling networks. The use of biomarkers for early cancer detection, staging and individualization of therapy might improve patient care. A few fundamental issues such as tumor heterogeneity, a highly dynamic nature of the intrinsic and extrinsic determinants of radio- and chemoresistance, along with the plasticity and diversity of cancer stem cells (CSC) make biomarker development a challenging task. In this review we outline the preclinical strategies of cancer biomarker discovery including genomic, proteomic, metabolomic and microRNomic profiling, comparative genome hybridization (CGH), single nucleotide polymorphism (SNP) analysis, high throughput screening (HTS) and next generation sequencing (NGS). Other promising approaches such as assessment of circulating tumor cells (CTC), analysis of CSC-specific markers and cell-free circulating tumor DNA (ctDNA) are also discussed.

CONCLUSIONS

The emergence of powerful proteomic and genomic technologies in conjunction with advanced bioinformatic tools allows the simultaneous analysis of thousands of biological molecules. These techniques yield the discovery of new tumor signatures, which are sensitive and specific enough for early cancer detection, for monitoring disease progression and for proper treatment selection, paving the way to individualized cancer treatment.

Comprehensive assessment and network analysis of the emerging genetic susceptibility landscape of prostate cancer

Background

Recent advances in high-throughput genotyping have made possible identification of genetic variants associated with increased risk of developing prostate cancer using genome-wide associations studies (GWAS). However, the broader context in which the identified genetic variants operate is poorly understood. Here we present a comprehensive assessment, network, and pathway analysis of the emerging genetic susceptibility landscape of prostate cancer.

Methods

We created a comprehensive catalog of genetic variants and associated genes by mining published reports and accompanying websites hosting supplementary data on GWAS. We then performed network and pathway analysis using single nucleotide polymorphism (SNP)-containing genes to identify gene regulatory networks and pathways enriched for genetic variants.

Results

We identified multiple gene networks and pathways enriched for genetic variants including IGF-1, androgen biosynthesis and androgen signaling pathways, and the molecular mechanisms of cancer. The results provide putative functional bridges between GWAS findings and gene regulatory networks and biological pathways.

Resolving breast cancer heterogeneity by searching reliable protein cancer biomarkers in the breast fluid secretome

BACKGROUND

One of the major goals in cancer research is to find and evaluate the early presence of biomarkers in human fluids and tissues. To resolve the complex cell heterogeneity of a tumor mass, it will be useful to characterize the intricate biomolecular composition of tumor microenvironment (the so called cancer secretome), validating secreted proteins as early biomarkers of cancer initiation and progression. This approach is not broadly applicable because of the paucity of well validated and FDA-approved biomarkers and because most of the candidate biomarkers are mainly organ-specific rather than tumor-specific. For these reasons, there is an urgent need to identify and validate a panel of biomarker combinations for early detection of human tumors. This is especially important for breast cancer, the cancer spread most worldwide among women. It is well known that patients with early diagnosed breast cancer live longer, require less extensive treatment and fare better than patients with more aggressive and/or advanced disease.

RESULTS

In the frame of searching breast cancer biomarkers (especially using nipple aspirate fluid mirroring breast microenvironment), studies have highlighted an optimal combination of well-known biomarkers: uPA + PAI-1 + TF. When individually investigated they did not show perfect accuracy in predicting the presence of breast cancer, whereas the triple combination has been demonstrated to be highly predictive of pre-cancer and/or cancerous conditions, approaching 97-100% accuracy.

CONCLUSION

Despite the heterogeneous composition of breast cancer and the difficulties to find specific breast cancer biomolecules, the noninvasive analysis of the nipple aspirate fluid secretome may significantly improve the discovery of promising biomarkers, helping also the differentiation among benign and invasive breast diseases, opening new frontiers in early oncoproteomics.

Novel Integrative Genomics Approach for Associating GWAS Information with Intrinsic Subtypes of Breast Cancer

Genome-wide association studies (GWAS) have achieved great success in identifying common variants associated with increased risk of developing breast cancer. However, GWAS do not typically provide information about the broader context in which genetic variants operate in different subtypes of breast cancer. The objective of this study was to determine whether genes containing single nucleotide polymorphisms (SNPs, herein called genetic variants) are associated with different subtypes of breast cancer. Additionally, we sought to identify gene regulator networks and biological pathways enriched for these genetic variants. Using supervised analysis, we identified 201 genes that were significantly associated with the six intrinsic subtypes of breast cancer. The results demonstrate that integrative genomics analysis is a powerful approach for linking GWAS information to distinct disease states and provide insights about the broader context in which genetic variants operate in different subtypes of breast cancer.

Neurobiology meets genomic science: the promise of human-induced pluripotent stem cells

The recent introduction of the induced pluripotent stem cell technology has made possible the derivation of neuronal cells from somatic cells obtained from human individuals. This in turn has opened new areas of investigation that can potentially bridge the gap between neuroscience and psychopathology. For the first time we can study the cell biology and genetics of neurons derived from any individual. Furthermore, by recapitulating in vitro the developmental steps whereby stem cells give rise to neuronal cells, we can now hope to understand factors that control typical and atypical development. We can begin to explore how human genes and their variants are transcribed into messenger RNAs within developing neurons and how these gene transcripts control the biology of developing cells. Thus, human-induced pluripotent stem cells have the potential to uncover not only what aspects of development are uniquely human but also variations in the series of events necessary for normal human brain development that predispose to psychopathology.

An Integrative Genomics Approach for Associating GWAS Information with Triple-Negative Breast Cancer

Genome-wide association studies (GWAS) have identified genetic variants associated with an increased risk of developing breast cancer. However, the association of genetic variants and their associated genes with the most aggressive subset of breast cancer, the triple-negative breast cancer (TNBC), remains a central puzzle in molecular epidemiology. The objective of this study was to determine whether genes containing single nucleotide polymorphisms (SNPs) associated with an increased risk of developing breast cancer are connected to and could stratify different subtypes of TNBC. Additionally, we sought to identify molecular pathways and networks involved in TNBC. We performed integrative genomics analysis, combining information from GWAS studies involving over 400,000 cases and over 400,000 controls, with gene expression data derived from 124 breast cancer patients classified as TNBC (at the time of diagnosis) and 142 cancer-free controls. Analysis of GWAS reports produced 500 SNPs mapped to 188 genes. We identified a signature of 159 functionally related SNP-containing genes which were significantly (P <10⁻⁵) associated with and stratified TNBC. Additionally, we identified 97 genes which were functionally related to, and had similar patterns of expression profiles, SNP-containing genes. Network modeling and pathway prediction revealed multi-gene pathways including p53, NFkB, BRCA, apoptosis, DNA repair, DNA mismatch, and excision repair pathways enriched for SNPs mapped to genes significantly associated with TNBC. The results provide convincing evidence that integrating GWAS information with gene expression data provides a unified and powerful approach for biomarker discovery in TNBC.

Reverse engineering biomolecular systems using -omic data: challenges, progress and opportunities

Recent advances in high-throughput biotechnologies have led to the rapid growing research interest in reverse engineering of biomolecular systems (REBMS). 'Data-driven' approaches, i.e. data mining, can be used to extract patterns from large volumes of biochemical data at molecular-level resolution while 'design-driven' approaches, i.e. systems modeling, can be used to simulate emergent system properties. Consequently, both data- and design-driven approaches applied to -omic data may lead to novel insights in reverse engineering biological systems that could not be expected before using low-throughput platforms. However, there exist several challenges in this fast growing field of reverse engineering biomolecular systems: (i) to integrate heterogeneous biochemical data for data mining, (ii) to combine top-down and bottom-up approaches for systems modeling and (iii) to validate system models experimentally. In addition to reviewing progress made by the community and opportunities encountered in addressing these challenges, we explore the emerging field of synthetic biology, which is an exciting approach to validate and analyze theoretical system models directly through experimental synthesis, i.e. analysis-by-synthesis. The ultimate goal is to address the present and future challenges in reverse engineering biomolecular systems (REBMS) using integrated workflow of data mining, systems modeling and synthetic biology.

Antiestrogen Resistance and the Application of Systems Biology

Understanding the molecular changes that drive an acquired antiestrogen resistance phenotype is of major clinical relevance. Previous methodologies for addressing this question have taken a single gene/pathway approach and the resulting gains have been limited in terms of their clinical impact. Recent systems biology approaches allow for the integration of data from high throughput "-omics" technologies. We highlight recent advances in the field of antiestrogen resistance with a focus on transcriptomics, proteomics and methylomics.

Integrative Analysis of Response to Tamoxifen Treatment in ER-Positive Breast Cancer Using GWAS Information and Transcription Profiling

Variable response and resistance to tamoxifen treatment in breast cancer patients remains a major clinical problem. To determine whether genes and biological pathways containing SNPs associated with risk for breast cancer are dysregulated in response to tamoxifen treatment, we performed analysis combining information from 43 genome-wide association studies with gene expression data from 298 ER⁺ breast cancer patients treated with tamoxifen and 125 ER⁺ controls. We identified 95 genes which distinguished tamoxifen treated patients from controls. Additionally, we identified 54 genes which stratified tamoxifen treated patients into two distinct groups. We identified biological pathways containing SNPs associated with risk for breast cancer, which were dysregulated in response to tamoxifen treatment. Key pathways identified included the apoptosis, P53, NFkB, DNA repair and cell cycle pathways. Combining GWAS with transcription profiling provides a unified approach for associating GWAS findings with response to drug treatment and identification of potential drug targets.