Systems genetics analysis of cancer susceptibility: from mouse models to humans

From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine

Prostate cancer is the most common cancer in men worldwide and has a high mortality rate. The complex and heterogeneous development of prostate cancer has become a core obstacle in the treatment of prostate cancer. Simultaneously, the issues of overtreatment in early-stage diagnosis, oligometastasis and dormant tumor recognition, as well as personalized drug utilization, are also specific concerns that require attention in the clinical management of prostate cancer. Some typical genetic mutations have been proved to be associated with prostate cancer's initiation and progression. However, single-omic studies usually are not able to explain the causal relationship between molecular alterations and clinical phenotypes. Exploration from a systems genetics perspective is also lacking in this field, that is, the impact of gene network, the environmental factors, and even lifestyle behaviors on disease progression. At the meantime, current trend emphasizes the utilization of artificial intelligence (AI) and machine learning techniques to process extensive multidimensional data, including multi-omics. These technologies unveil the potential patterns, correlations, and insights related to diseases, thereby aiding the interpretable clinical decision making and applications, namely intelligent medicine. Therefore, there is a pressing need to integrate multidimensional data for identification of molecular subtypes, prediction of cancer progression and aggressiveness, along with perosonalized treatment performing. In this review, we systematically elaborated the landscape from molecular mechanism discovery of prostate cancer to clinical translational applications. We discussed the molecular profiles and clinical manifestations of prostate cancer heterogeneity, the identification of different states of prostate cancer, as well as corresponding precision medicine practices. Taking multi-omics fusion, systems genetics, and intelligence medicine as the main perspectives, the current research results and knowledge-driven research path of prostate cancer were summarized.

Intermediate Molecular Phenotypes to Identify Genetic Markers of Anthracycline-Induced Cardiotoxicity Risk

Cardiotoxicity due to anthracyclines (CDA) affects cancer patients, but we cannot predict who may suffer from this complication. CDA is a complex trait with a polygenic component that is mainly unidentified. We propose that levels of intermediate molecular phenotypes (IMPs) in the myocardium associated with histopathological damage could explain CDA susceptibility, so variants of genes encoding these IMPs could identify patients susceptible to this complication. Thus, a genetically heterogeneous cohort of mice (n = 165) generated by backcrossing were treated with doxorubicin and docetaxel. We quantified heart fibrosis using an Ariol slide scanner and intramyocardial levels of IMPs using multiplex bead arrays and QPCR. We identified quantitative trait loci linked to IMPs (ipQTLs) and cdaQTLs via linkage analysis. In three cancer patient cohorts, CDA was quantified using echocardiography or Cardiac Magnetic Resonance. CDA behaves as a complex trait in the mouse cohort. IMP levels in the myocardium were associated with CDA. ipQTLs integrated into genetic models with cdaQTLs account for more CDA phenotypic variation than that explained by cda-QTLs alone. Allelic forms of genes encoding IMPs associated with CDA in mice, including AKT1, MAPK14, MAPK8, STAT3, CAS3, and TP53, are genetic determinants of CDA in patients. Two genetic risk scores for pediatric patients (n = 71) and women with breast cancer (n = 420) were generated using machine-learning Least Absolute Shrinkage and Selection Operator (LASSO) regression. Thus, IMPs associated with heart damage identify genetic markers of CDA risk, thereby allowing more personalized patient management.

Intermediate molecular phenotypes to identify genetic markers of anthracycline-induced cardiotoxicity risk

Cardiotoxicity due to anthracyclines (CDA) affects cancer patients, but we cannot predict who may suffer from this complication. CDA is a complex disease whose polygenic component is mainly unidentified. We propose that levels of intermediate molecular phenotypes in the myocardium associated with histopathological damage could explain CDA susceptibility; so that variants of genes encoding these intermediate molecular phenotypes could identify patients susceptible to this complication. A genetically heterogeneous cohort of mice generated by backcrossing (N = 165) was treated with doxorubicin and docetaxel. Cardiac histopathological damage was measured by fibrosis and cardiomyocyte size by an Ariol slide scanner. We determine intramyocardial levels of intermediate molecular phenotypes of CDA associated with histopathological damage and quantitative trait loci (ipQTLs) linked to them. These ipQTLs seem to contribute to the missing heritability of CDA because they improve the heritability explained by QTL directly linked to CDA (cda-QTLs) through genetic models. Genes encoding these molecular subphenotypes were evaluated as genetic markers of CDA in three cancer patient cohorts (N = 517) whose cardiac damage was quantified by echocardiography or Cardiac Magnetic Resonance. Many SNPs associated with CDA were found using genetic models. LASSO multivariate regression identified two risk score models, one for pediatric cancer patients and the other for women with breast cancer. Molecular intermediate phenotypes associated with heart damage can identify genetic markers of CDA risk, thereby allowing a more personalized patient management. A similar strategy could be applied to identify genetic markers of other complex trait diseases.

Systems genetics analysis of oral squamous cell carcinoma susceptibility using the mouse model: current position and new perspective

Oral squamous cell carcinoma (OSCC) is one of the most common human malignancies with complex etiology and poor prognosis. Although environmental carcinogens and carcinogenic viruses are still considered the main etiologic factors for OSCC development, genetic factors obviously play a key role in the initiation and progression of this neoplasm, given that not all individuals exposed to carcinogens develop the same severity of the disease, if any. Identifying genetic loci modulating OSCC risk may have several important clinical implications, including early detection, prevention and developing new treatment strategies. Due to limitations in controlled and standardized genetic studies in humans, genetic components underlying susceptibility of OSCC development remain largely unknown. A combination of quantitative trait loci mapping in mice, with complementary association studies in humans, has the potential to discover novel cancer risk loci. As of today, a limited number of genetic analyses were applied on rodent models to locate novel genetic loci associated with human OSCC. Here, we discuss the current status of the mouse models use for dissecting the genetic basis of OSCC and highlight how systems genetics analysis using mouse models, may increase our understanding of human OSCC susceptibility.

Genetic characterization of early renal changes in a novel mouse model of diabetic kidney disease

Genetic factors influence susceptibility to diabetic kidney disease. Here we mapped genes mediating renal hypertrophic changes in response to diabetes. A survey of 15 mouse strains identified variation in diabetic kidney hypertrophy. Strains with greater (FVB/N(FVB)) and lesser (C57BL/6 (B6)) responses were crossed and diabetic F2 progeny were characterized. Kidney weights of diabetic F2 mice were broadly distributed. Quantitative trait locus analyses revealed diabetic mice with kidney weights in the upper quartile shared alleles on chromosomes (chr) 6 and 12; these loci were designated as Diabetic kidney hypertrophy (Dkh)-1 and -2. To confirm these loci, reciprocal congenic mice were generated with defined FVB chromosome segments on the B6 strain background (B6.Dkh1/2f) or vice versa (FVB.Dkh1/2b). Diabetic mice of the B6.Dkh1/2f congenic strain developed diabetic kidney hypertrophy, while the reciprocal FVB.Dkh1/2b congenic strain was protected. The chr6 locus contained the candidate gene; Ark1b3, coding aldose reductase; the FVB allele has a missense mutation in this gene. Microarray analysis identified differentially expressed genes between diabetic B6 and FVB mice. Thus, since the two loci identified by quantitative trait locus mapping are syntenic with regions identified for human diabetic kidney disease, the congenic strains we describe provide a valuable new resource to study diabetic kidney disease and test agents that may prevent it.

Discovering Condition-Specific Gene Co-Expression Patterns Using Gaussian Mixture Models: A Cancer Case Study

A gene co-expression network (GCN) describes associations between genes and points to genetic coordination of biochemical pathways. However, genetic correlations in a GCN are only detectable if they are present in the sampled conditions. With the increasing quantity of gene expression samples available in public repositories, there is greater potential for discovery of genetic correlations from a variety of biologically interesting conditions. However, even if gene correlations are present, their discovery can be masked by noise. Noise is introduced from natural variation (intrinsic and extrinsic), systematic variation (caused by sample measurement protocols and instruments), and algorithmic and statistical variation created by selection of data processing tools. A variety of published studies, approaches and methods attempt to address each of these contributions of variation to reduce noise. Here we describe an approach using Gaussian Mixture Models (GMMs) to address natural extrinsic (condition-specific) variation during network construction from mixed input conditions. To demonstrate utility, we build and analyze a condition-annotated GCN from a compendium of 2,016 mixed gene expression data sets from five tumor subtypes obtained from The Cancer Genome Atlas. Our results show that GMMs help discover tumor subtype specific gene co-expression patterns (modules) that are significantly enriched for clinical attributes.

Directing the Self-assembly of Tumour Spheroids by Bioprinting Cellular Heterogeneous Models within Alginate/Gelatin Hydrogels

Human tumour progression is a dynamic process involving diverse biological and biochemical events such as genetic mutation and selection in addition to physical, chemical, and mechanical events occurring between cells and the tumour microenvironment. Using 3D bioprinting we have developed a method to embed MDA-MB-231 triple negative breast cancer cells, and IMR-90 fibroblast cells, within a cross-linked alginate/gelatin matrix at specific initial locations relative to each other. After 7 days of co-culture the MDA-MB-231 cells begin to form multicellular tumour spheroids (MCTS) that increase in size and frequency over time. After ~15 days the IMR-90 stromal fibroblast cells migrate through a non-cellularized region of the hydrogel matrix and infiltrate the MDA-MB-231 spheroids creating mixed MDA-MB-231/IMR-90 MCTS. This study provides a proof-of-concept that biomimetic in vitro tissue co-culture models bioprinted with both breast cancer cells and fibroblasts will result in MCTS that can be maintained for durations of several weeks.

Multiscale analysis of the murine intestine for modeling human diseases

When functioning properly, the intestine is one of the key interfaces between the human body and its environment. It is responsible for extracting nutrients from our food and excreting our waste products. It provides an environment for a host of healthful microbes and serves as a first defense against pathogenic ones. These processes require tight homeostatic controls, which are provided by the interactions of a complex mix of epithelial, stromal, neural and immune cells, as well as the resident microflora. This homeostasis can be disrupted by invasive microbes, genetic lesions, and carcinogens, resulting in diseases such Clostridium difficile infection, inflammatory bowel disease (IBD) and cancer. Enormous strides have been made in understanding how this important organ functions in health and disease using everything from cell culture systems to animal models to human tissue samples. This has resulted in better therapies for all of these diseases, but there is still significant room for improvement. In the United States alone, 14,000 people per year die of C. difficile, up to 1.6 million people suffer from IBD, and more than 50,000 people die every year from colon cancer. Because these and other intestinal diseases arise from complex interactions between the different components of the gut ecosystem, we propose that systems approaches that address this complexity in an integrative manner may eventually lead to improved therapeutics that deliver lasting cures. This review will discuss the use of systems biology for studying intestinal diseases in vivo with particular emphasis on mouse models. Additionally, it will focus on established experimental techniques that have been used to drive this systems-level analysis, and emerging techniques that will push this field forward in the future.

Allele-specific imbalance mapping at human orthologs of mouse susceptibility to colon cancer (Scc) loci

Colorectal cancer (CRC) can be classified into different types. Chromosomal instable (CIN) colon cancers are thought to be the most common type of colon cancer. The risk of developing a CIN-related CRC is due in part to inherited risk factors. Genome-wide association studies have yielded over 40 single nucleotide polymorphisms (SNPs) associated with CRC risk, but these only account for a subset of risk alleles. Some of this missing heritability may be due to gene-gene interactions. We developed a strategy to identify interacting candidate genes/loci for CRC risk that utilizes both linkage and RNA-seq data from mouse models in combination with allele-specific imbalance (ASI) studies in human tumors. We applied our strategy to three previously identified CRC susceptibility loci in the mouse that show evidence of genetic interaction: Scc4, Scc5 and Scc13. 525 SNPs from genes showing differential expression in the mouse and/or a previous role in cancer from the literature were evaluated for allele-specific imbalance in 194 paired human normal/tumor DNAs from CIN-related CRCs. One hundred three SNPs showing suggestive evidence of ASI (31 variants with uncorrected p values < 0.05) were genotyped in a validation set of 296 paired DNAs. Two variants in SNX10 (SCC13) showed significant evidence of allelic selection after multiple comparisons testing. Future studies will evaluate the role of these variants in combination with interacting genetic partners in colon cancer risk in mouse and humans.

Chaperone Hsp47 Drives Malignant Growth and Invasion by Modulating an ECM Gene Network

The extracellular matrix (ECM) is a determining factor in the tumor microenvironment that restrains or promotes malignant growth. In this report, we show how the molecular chaperone protein Hsp47 functions as a nodal hub in regulating an ECM gene transcription network. A transcription network analysis showed that Hsp47 expression was activated during breast cancer development and progression. Hsp47 silencing reprogrammed human breast cancer cells to form growth-arrested and/or noninvasive structures in 3D cultures, and to limit tumor growth in xenograft assays by reducing deposition of collagen and fibronectin. Coexpression network analysis also showed that levels of microRNA(miR)-29b and -29c were inversely correlated with expression of Hsp47 and ECM network genes in human breast cancer tissues. We found that miR-29 repressed expression of Hsp47 along with multiple ECM network genes. Ectopic expression of miR-29b suppressed malignant phenotypes of breast cancer cells in 3D culture. Clinically, increased expression of Hsp47 and reduced levels of miR-29b and -29c were associated with poor survival outcomes in breast cancer patients. Our results show that Hsp47 is regulated by miR-29 during breast cancer development and progression, and that increased Hsp47 expression promotes cancer progression in part by enhancing deposition of ECM proteins.

Unraveling heterogeneous susceptibility and the evolution of breast cancer using a systems biology approach

BACKGROUND

An essential question in cancer is why individuals with the same disease have different clinical outcomes. Progress toward a more personalized medicine in cancer patients requires taking into account the underlying heterogeneity at different molecular levels.

RESULTS

Here, we present a model in which there are complex interactions at different cellular and systemic levels that account for the heterogeneity of susceptibility to and evolution of ERBB2-positive breast cancers. Our model is based on our analyses of a cohort of mice that are characterized by heterogeneous susceptibility to ERBB2-positive breast cancers. Our analysis reveals that there are similarities between ERBB2 tumors in humans and those of backcross mice at clinical, genomic, expression, and signaling levels. We also show that mice that have tumors with intrinsically high levels of active AKT and ERK are more resistant to tumor metastasis. Our findings suggest for the first time that a site-specific phosphorylation at the serine 473 residue of AKT1 modifies the capacity for tumors to disseminate. Finally, we present two predictive models that can explain the heterogeneous behavior of the disease in the mouse population when we consider simultaneously certain genetic markers, liver cell signaling and serum biomarkers that are identified before the onset of the disease.

CONCLUSIONS

Considering simultaneously tumor pathophenotypes and several molecular levels, we show the heterogeneous behavior of ERBB2-positive breast cancer in terms of disease progression. This and similar studies should help to better understand disease variability in patient populations.

Genetic study of complex diseases in the post-GWAS era

Genome-wide association studies (GWASs) have identified thousands of genes and genetic variants (mainly SNPs) that contribute to complex diseases in humans. Functional characterization and mechanistic elucidation of these SNPs and genes action are the next major challenge. It has been well established that SNPs altering the amino acids of protein-coding genes can drastically impact protein function, and play an important role in molecular pathogenesis. Functions of regulatory SNPs can be complex and elusive, and involve gene expression regulation through the effect on RNA splicing, transcription factor binding, DNA methylation and miRNA recruitment. In the present review, we summarize the recent progress in our understanding of functional consequences of GWAS-associated non-coding regulatory SNPs, and discuss the application of systems genetics and network biology in the interpretation of GWAS findings.

Mouse Tumor Biology (MTB): a database of mouse models for human cancer

The Mouse Tumor Biology (MTB; http://tumor.informatics.jax.org) database is a unique online compendium of mouse models for human cancer. MTB provides online access to expertly curated information on diverse mouse models for human cancer and interfaces for searching and visualizing data associated with these models. The information in MTB is designed to facilitate the selection of strains for cancer research and is a platform for mining data on tumor development and patterns of metastases. MTB curators acquire data through manual curation of peer-reviewed scientific literature and from direct submissions by researchers. Data in MTB are also obtained from other bioinformatics resources including PathBase, the Gene Expression Omnibus and ArrayExpress. Recent enhancements to MTB improve the association between mouse models and human genes commonly mutated in a variety of cancers as identified in large-scale cancer genomics studies, provide new interfaces for exploring regions of the mouse genome associated with cancer phenotypes and incorporate data and information related to Patient-Derived Xenograft models of human cancers.

New experimental models of skin homeostasis and diseases

Homeostasis, whose regulation at the molecular level is still poorly understood, is intimately related to the functions of epidermal stem cells. Five research groups have been brought together to work on new in vitro and in vivo skin models through the SkinModel-CM program, under the auspices of the Spanish Autonomous Community of Madrid. This project aims to analyze the functions of DNA methyltransferase 1, endoglin, and podoplanin in epidermal stem cell activity, homeostasis, and skin cancer. These new models include 3-dimensional organotypic cultures, immunodeficient skin-humanized mice, and genetically modified mice. Another aim of the program is to use skin-humanized mice to model dermatoses such as Gorlin syndrome and xeroderma pigmentosum in order to optimize new protocols for photodynamic therapy.

Principles and methods of integrative genomic analyses in cancer

Combined analyses of molecular data, such as DNA copy-number alteration, mRNA and protein expression, point to biological functions and molecular pathways being deregulated in multiple cancers. Genomic, metabolomic and clinical data from various solid cancers and model systems are emerging and can be used to identify novel patient subgroups for tailored therapy and monitoring. The integrative genomics methodologies that are used to interpret these data require expertise in different disciplines, such as biology, medicine, mathematics, statistics and bioinformatics, and they can seem daunting. The objectives, methods and computational tools of integrative genomics that are available to date are reviewed here, as is their implementation in cancer research.

Systems biology perspectives on the carcinogenic potential of radiation

This review focuses on recent experimental and modeling studies that attempt to define the physiological context in which high linear energy transfer (LET) radiation increases epithelial cancer risk and the efficiency with which it does so. Radiation carcinogenesis is a two-compartment problem: ionizing radiation can alter genomic sequence as a result of damage due to targeted effects (TE) from the interaction of energy and DNA; it can also alter phenotype and multicellular interactions that contribute to cancer by poorly understood non-targeted effects (NTE). Rather than being secondary to DNA damage and mutations that can initiate cancer, radiation NTE create the critical context in which to promote cancer. Systems biology modeling using comprehensive experimental data that integrates different levels of biological organization and time-scales is a means of identifying the key processes underlying the carcinogenic potential of high-LET radiation. We hypothesize that inflammation is a key process, and thus cancer susceptibility will depend on specific genetic predisposition to the type and duration of this response. Systems genetics using novel mouse models can be used to identify such determinants of susceptibility to cancer in radiation sensitive tissues following high-LET radiation. Improved understanding of radiation carcinogenesis achieved by defining the relative contribution of NTE carcinogenic effects and identifying the genetic determinants of the high-LET cancer susceptibility will help reduce uncertainties in radiation risk assessment.

Transcriptome analysis of psoriasis in a large case-control sample: RNA-seq provides insights into disease mechanisms

To increase our understanding of psoriasis, we utilized RNA-seq to assay the transcriptomes of lesional psoriatic and normal skin. We sequenced polyadenylated RNA-derived cDNAs from 92 psoriatic and 82 normal punch biopsies, generating an average of ~38 million single-end 80-bp reads per sample. Comparison of 42 samples examined by both RNA-seq and microarray revealed marked differences in sensitivity, with transcripts identified only by RNA-seq having much lower expression than those also identified by microarray. RNA-seq identified many more differentially expressed transcripts enriched in immune system processes. Weighted gene co-expression network analysis (WGCNA) revealed multiple modules of coordinately expressed epidermal differentiation genes, overlapping significantly with genes regulated by the long non-coding RNA TINCR, its target gene, staufen-1 (STAU1), the p63 target gene ZNF750, and its target KLF4. Other coordinately expressed modules were enriched for lymphoid and/or myeloid signature transcripts and genes induced by IL-17 in keratinocytes. Dermally-expressed genes were significantly down-regulated in psoriatic biopsies, most likely due to expansion of the epidermal compartment. These results demonstrate the power of WGCNA to elucidate gene regulatory circuits in psoriasis, and emphasize the influence of tissue architecture in both differential expression and co-expression analysis.

Systems genetics approaches to understand complex traits

Systems genetics is an approach to understand the flow of biological information that underlies complex traits. It uses a range of experimental and statistical methods to quantitate and integrate intermediate phenotypes, such as transcript, protein or metabolite levels, in populations that vary for traits of interest. Systems genetics studies have provided the first global view of the molecular architecture of complex traits and are useful for the identification of genes, pathways and networks that underlie common human diseases. Given the urgent need to understand how the thousands of loci that have been identified in genome-wide association studies contribute to disease susceptibility, systems genetics is likely to become an increasingly important approach to understanding both biology and disease.

The 5p12 breast cancer susceptibility locus affects MRPS30 expression in estrogen-receptor positive tumors

Genome-wide association studies have identified numerous loci linked to breast cancer susceptibility, but the mechanism by which variations at these loci influence susceptibility is usually unknown. Some variants are only associated with particular clinical subtypes of breast cancer. Understanding how and why these variants influence subtype-specific cancer risk contributes to our understanding of cancer etiology. We conducted a genome-wide expression Quantitative Trait Locus (eQTL) study in a discovery set of 287 breast tumors and 97 normal mammary tissue samples and a replication set of 235 breast tumors. We found that the risk-associated allele of rs7716600 in the 5p12 estrogen receptor-positive (ER-positive) susceptibility locus was associated with elevated expression of the nearby gene MRPS30 exclusively in ER-positive tumors. We replicated this finding in 235 independent tumors. Further, we showed the rs7716600 risk genotype was associated with decreased MRPS30 promoter methylation exclusively in ER-positive breast tumors. In vitro studies in MCF-7 cells carrying the protective genotype showed that estrogen stimulation decreased MRPS30 promoter chromatin availability and mRNA levels. In contrast, in 600MPE cells carrying the risk genotype, estrogen increased MRPS30 expression and did not affect promoter availability. Our data suggest the 5p12 risk allele affects MRPS30 expression in estrogen-responsive tumor cells after tumor initiation by a mechanism affecting chromatin availability. These studies emphasize that the genetic architecture of breast cancer is context-specific, and integrated analysis of gene expression and chromatin remodeling in normal and tumor tissues will be required to explain the mechanisms of risk alleles.

Integrating gene expression and epidemiological data for the discovery of genetic interactions associated with cancer risk

Dozens of common genetic variants associated with cancer risk have been identified through genome-wide association studies (GWASs). However, these variants only explain a modest fraction of the heritability of disease. The missing heritability has been attributed to several factors, among them the existence of genetic interactions (G × G). Systematic screens for G × G in model organisms have revealed their fundamental influence in complex phenotypes. In this scenario, G × G overlap significantly with other types of gene and/or protein relationships. Here, by integrating predicted G × G from GWAS data and complex- and context-defined gene coexpression profiles, we provide evidence for G × G associated with cancer risk. G × G predicted from a breast cancer GWAS dataset identified significant overlaps [relative enrichments (REs) of 8-36%, empirical P values < 0.05 to 10(-4)] with complex (non-linear) gene coexpression in breast tumors. The use of gene or protein data not specific for breast cancer did not reveal overlaps. According to the predicted G × G, experimental assays demonstrated functional interplay between lipoma-preferred partner and transforming growth factor-β signaling in the MCF10A non-tumorigenic mammary epithelial cell model. Next, integration of pancreatic tumor gene expression profiles with pancreatic cancer G × G predicted from a GWAS corroborated the observations made for breast cancer risk (REs of 25-59%). The method presented here can potentially support the identification of genetic interactions associated with cancer risk, providing novel mechanistic hypotheses for carcinogenesis.

Neuroblastoma: developmental biology, cancer genomics and immunotherapy

Neuroblastoma is a solid tumour that arises from the developing sympathetic nervous system. Over the past decade, our understanding of this disease has advanced tremendously. The future challenge is to apply the knowledge gained to developing risk-based therapies and, ultimately, improving outcome. In this Review we discuss the key discoveries in the developmental biology, molecular genetics and immunology of neuroblastoma, as well as new translational tools for bringing these promising scientific advances into the clinic.

Positional mapping and candidate gene analysis of the mouse Ccs3 locus that regulates differential susceptibility to carcinogen-induced colorectal cancer

The Ccs3 locus on mouse chromosome 3 regulates differential susceptibility of A/J (A, susceptible) and C57BL/6J (B6, resistant) mouse strains to chemically-induced colorectal cancer (CRC). Here, we report the high-resolution positional mapping of the gene underlying the Ccs3 effect. Using phenotype/genotype correlation in a series of 33 AcB/BcA recombinant congenic mouse strains, as well as in groups of backcross populations bearing unique recombinant chromosomes for the interval, and in subcongenic strains, we have delineated the maximum size of the Ccs3 physical interval to a ∼2.15 Mb segment. This interval contains 12 annotated transcripts. Sequencing of positional candidates in A and B6 identified many either low-priority coding changes or non-protein coding variants. We found a unique copy number variant (CNV) in intron 15 of the Nfkb1 gene. The CNV consists of two copies of a 54 bp sequence immediately adjacent to the exon 15 splice site, while only one copy is found in CRC-susceptible A. The Nfkb1 protein (p105/p50) expression is much reduced in A tumors compared to normal A colonic epithelium as analyzed by immunohistochemistry. Studies in primary macrophages from A and B6 mice demonstrate a marked differential activation of the NfκB pathway by lipopolysaccharide (kinetics of stimulation and maximum levels of phosphorylated IκBα), with a more robust activation being associated with resistance to CRC. NfκB has been previously implicated in regulating homeostasis and inflammatory response in the intestinal mucosa. The interval contains another positional candidate Slc39a8 that is differentially expressed in A vs B6 colons, and that has recently been associated in CRC tumor aggressiveness in humans.

Synthetic lethal screening with small-molecule inhibitors provides a pathway to rational combination therapies for melanoma

Recent data show that extracellular signals are transmitted through a network of proteins rather than hierarchical signaling pathways, suggesting that the inhibition of a single component of a canonical pathway is insufficient for the treatment of cancer. The biologic outcome of signaling through a network is inherently more robust and resistant to inhibition of a single network component. In this study, we conducted a functional chemical genetic screen to identify novel interactions between signaling inhibitors that would not be predicted on the basis of our current understanding of signaling networks. We screened over 300 drug combinations in nine melanoma cell lines and have identified pairs of compounds that show synergistic cytotoxicity. The synergistic cytotoxicities identified did not correlate with the known RAS and BRAF mutational status of the melanoma cell lines. Among the most robust results was synergy between sorafenib, a multikinase inhibitor with activity against RAF, and diclofenac, a nonsteroidal anti-inflammatory drug (NSAID). Drug substitution experiments using the NSAIDs celecoxib and ibuprofen or the MAP-ERK kinase inhibitor PD325901 and the RAF inhibitor RAF265 suggest that inhibition of COX and mitogen-activated protein kinase signaling are targets for the synergistic cytotoxicity of sorafenib and diclofenac. Cotreatment with sorafenib and diclofenac interrupts a positive feedback signaling loop involving extracellular signal-regulated kinase, cellular phospholipase A2, and COX. Genome-wide expression profiling shows synergy-specific downregulation of survival-related genes. This study has uncovered novel functional drug combinations and suggests that the underlying signaling networks that control responses to targeted agents can vary substantially, depending on unexplored components of the cell genotype.

The gene expression landscape of breast cancer is shaped by tumor protein p53 status and epithelial-mesenchymal transition

Introduction

Gene expression data derived from clinical cancer specimens provide an opportunity to characterize cancer-specific transcriptional programs. Here, we present an analysis delineating a correlation-based gene expression landscape of breast cancer that identifies modules with strong associations to breast cancer-specific and general tumor biology.

Methods

Modules of highly connected genes were extracted from a gene co-expression network that was constructed based on Pearson correlation, and module activities were then calculated using a pathway activity score. Functional annotations of modules were experimentally validated with an siRNA cell spot microarray system using the KPL-4 breast cancer cell line, and by using gene expression data from functional studies. Modules were derived using gene expression data representing 1,608 breast cancer samples and validated in data sets representing 971 independent breast cancer samples as well as 1,231 samples from other cancer forms.

Results

The initial co-expression network analysis resulted in the characterization of eight tightly regulated gene modules. Cell cycle genes were divided into two transcriptional programs, and experimental validation using an siRNA screen showed different functional roles for these programs during proliferation. The division of the two programs was found to act as a marker for tumor protein p53 (TP53) gene status in luminal breast cancer, with the two programs being separated only in luminal tumors with functional p53 (encoded by TP53). Moreover, a module containing fibroblast and stroma-related genes was highly expressed in fibroblasts, but was also up-regulated by overexpression of epithelial-mesenchymal transition factors such as transforming growth factor beta 1 (TGF-beta1) and Snail in immortalized human mammary epithelial cells. Strikingly, the stroma transcriptional program related to less malignant tumors for luminal disease and aggressive lymph node positive disease among basal-like tumors.

Conclusions

We have derived a robust gene expression landscape of breast cancer that reflects known subtypes as well as heterogeneity within these subtypes. By applying the modules to TP53-mutated samples we shed light on the biological consequences of non-functional p53 in otherwise low-proliferating luminal breast cancer. Furthermore, as in the case of the stroma module, we show that the biological and clinical interpretation of a set of co-regulated genes is subtype-dependent.

Deciphering the complexities of human diseases and disorders by coupling induced-pluripotent stem cells and systems genetics

The recent discovery that adult mouse and human somatic cells can be 'reprogrammed' to a state of pluripotency by ectopic expression of only a few transcription factors has already made a major impact on the biomedical community. For the first time, it is possible to study diseases on an individual patient basis, which may eventually lead to the realization of personalized medicine. The utility of induced-pluripotent stem cells (iPSCs) for modeling human diseases has greatly benefitted from established human embryonic stem cell (ESC) differentiation and tissue engineering protocols developed to generate many different cell and tissue types. The limited access to preimplantation genetic tested embryos and the difficulty in gene targeting human ESCs have restricted the use of human ESCs in modeling human disease. Afforded by iPSC technology is the ability to study disease pathogenesis as it unfolds during tissue morphogenesis. The complexities of molecular signaling and interplay with protein transduction during disease progression necessitate a systems approach to studying human diseases, whereby data can be statistically integrated by sorting out the signal to noise issues that arise from global biological changes associated with disease versus experimental noise. Using a systems approach, biomarkers can be identified that define the initiation or progression of disease and likewise can serve as putative therapeutic targets.

Treatment- and population-dependent activity patterns of behavioral and expression QTLs

Genetic control of gene expression and higher-order phenotypes is almost invariably dependent on environment and experimental conditions. We use two families of recombinant inbred strains of mice (LXS and BXD) to study treatment- and genotype-dependent control of hippocampal gene expression and behavioral phenotypes. We analyzed responses to all combinations of two experimental perturbations, ethanol and restraint stress, in both families, allowing for comparisons across 8 combinations of treatment and population. We introduce the concept of QTL activity patterns to characterize how associations between genomic loci and traits vary across treatments. We identified several significant behavioral QTLs and many expression QTLs (eQTLs). The behavioral QTLs are highly dependent on treatment and population. We classified eQTLs into three groups: cis-eQTLs (expression variation that maps to within 5 Mb of the cognate gene), syntenic trans-eQTLs (the gene and the QTL are on the same chromosome but not within 5 Mb), and non-syntenic trans-eQTLs (the gene and the QTL are on different chromosomes). We found that most non-syntenic trans-eQTLs were treatment-specific whereas both classes of syntenic eQTLs were more conserved across treatments. We also found there was a correlation between regions along the genome enriched for eQTLs and SNPs that were conserved across the LXS and BXD families. Genes with eQTLs that co-localized with the behavioral QTLs and displayed similar QTL activity patterns were identified as potential candidate genes associated with the phenotypes, yielding identification of novel genes as well as genes that have been previously associated with responses to ethanol.

Beyond genome-wide association studies: new strategies for identifying genetic determinants of hypertension

Genetic linkage and association methods have long been the most important tools for gene identification in humans. These approaches can either be hypothesis-based (i.e., candidate-gene studies) or hypothesis-free (i.e., genome-wide studies). The first part of this review offers an overview of the latest successes in gene finding for blood pressure (BP) and essential hypertension using these DNA sequence-based discovery techniques. We further emphasize the importance of post-genome-wide association study (post-GWAS) analysis, which aims to prioritize genetic variants for functional follow-up. Whole-genome next-generation sequencing will eventually be necessary to provide a more comprehensive picture of all DNA variants affecting BP and hypertension. The second part of this review discusses promising novel approaches that move beyond the DNA sequence and aim to discover BP genes that are differentially regulated by epigenetic mechanisms, including microRNAs, histone modification, and methylation.

Identification of miR-374a as a prognostic marker for survival in patients with early-stage nonsmall cell lung cancer

Lung cancer is one of the deadliest types of cancer proven by the poor survival and high relapse rates after surgery. Recently discovered microRNAs (miRNAs), small noncoding RNA molecules, play a crucial role in modulating gene expression networks and are directly involved in the progression of a number of human cancers. In this study, we analyzed the expression profile of 858 miRNAs in 38 Estonian nonsmall cell lung cancer (NSCLC) samples (Stage I and II) and 27 adjacent nontumorous tissue samples using Illumina miRNA arrays. We found that 39 miRNAs were up-regulated and 33 down-regulated significantly in tumors compared with normal lung tissue. We observed aberrant expression of several well-characterized tumorigenesis-related miRNAs, as well as a number of miRNAs whose function is currently unknown. We show that low expression of miR-374a in early-stage NSCLC is associated with poor patient survival. The combinatorial effect of the up- and down-regulated miRNAs is predicted to most significantly affect pathways associated with cell migration, differentiation and growth, and several signaling pathways that contribute to tumorigenesis. In conclusion, our results demonstrate that expression of miR-374a at early stages of NSCLC progression can serve as a prognostic marker for patient risk stratification and may be a promising therapeutic target for the treatment of lung cancer.

Discretization provides a conceptually simple tool to build expression networks

Biomarker identification, using network methods, depends on finding regular co-expression patterns; the overall connectivity is of greater importance than any single relationship. A second requirement is a simple algorithm for ranking patients on how relevant a gene-set is. For both of these requirements discretized data helps to first identify gene cliques, and then to stratify patients.

We explore a biologically intuitive discretization technique which codes genes as up- or down-regulated, with values close to the mean set as unchanged; this allows a richer description of relationships between genes than can be achieved by positive and negative correlation. We find a close agreement between our results and the template gene-interactions used to build synthetic microarray-like data by SynTReN, which synthesizes “microarray” data using known relationships which are successfully identified by our method.

We are able to split positive co-regulation into up-together and down-together and negative co-regulation is considered as directed up-down relationships. In some cases these exist in only one direction, with real data, but not with the synthetic data. We illustrate our approach using two studies on white blood cells and derived immortalized cell lines and compare the approach with standard correlation-based computations. No attempt is made to distinguish possible causal links as the search for biomarkers would be crippled by losing highly significant co-expression relationships. This contrasts with approaches like ARACNE and IRIS.

The method is illustrated with an analysis of gene-expression for energy metabolism pathways. For each discovered relationship we are able to identify the samples on which this is based in the discretized sample-gene matrix, along with a simplified view of the patterns of gene expression; this helps to dissect the gene-sample relevant to a research topic - identifying sets of co-regulated and anti-regulated genes and the samples or patients in which this relationship occurs.

Gene transcriptional networks integrate microenvironmental signals in human breast cancer

A significant amount of evidence shows that microenvironmental signals generated from extracellular matrix (ECM) molecules, soluble factors, and cell-cell adhesion complexes cooperate at the extra- and intracellular level. This synergetic action of microenvironmental cues is crucial for normal mammary gland development and breast malignancy. To explore how the microenvironmental genes coordinate in human breast cancer at the genome level, we have performed gene co-expression network analysis in three independent microarray datasets and identified two microenvironment networks in human breast cancer tissues. Network I represents crosstalk and cooperation of ECM microenvironment and soluble factors during breast malignancy. The correlated expression of cytokines, chemokines, and cell adhesion proteins in Network II implicates the coordinated action of these molecules in modulating the immune response in breast cancer tissues. These results suggest that microenvironmental cues are integrated with gene transcriptional networks to promote breast cancer development.

Cancer evolution and individual susceptibility

Cancer susceptibility is due to interactions between inherited genetic factors and exposure to environmental carcinogens. The genetic component is constituted mainly by weakly acting low-penetrance genetic variants that interact among themselves, as well as with the environment. These low susceptibility genes can be categorized into two main groups: one includes those that control intrinsic tumor cell activities (i.e. apoptosis, proliferation or DNA repair), and the other contains those that modulate the function of extrinsic tumor cell compartments (i.e. stroma, angiogenesis, or endocrine and immune systems). Genome-Wide Association Studies (GWAS) of human populations have identified numerous genetic loci linked with cancer risk and behavior, but nevertheless the major component of cancer heritability remains to be explained. One reason may be that GWAS cannot readily capture gene-gene or gene-environment interactions. Mouse model approaches offer an alternative or complementary strategy, because of our ability to control both the genetic and environmental components of risk. Recently developed genetic tools, including high-throughput technologies such as SNP, CGH and gene expression microarrays, have led to more powerful strategies for refining quantitative trait loci (QTL) and identifying the critical genes. In particular, the cross-species approaches will help to refine locations of QTLs, and reveal their genetic and environmental interactions. The identification of human tumor susceptibility genes and discovery of their roles in carcinogenesis will ultimately be important for the development of methods for prediction of risk, diagnosis, prevention and therapy for human cancers.

Network analysis of skin tumor progression identifies a rewired genetic architecture affecting inflammation and tumor susceptibility

BACKGROUND

Germline polymorphisms can influence gene expression networks in normal mammalian tissues and can affect disease susceptibility. We and others have shown that analysis of this genetic architecture can identify single genes and whole pathways that influence complex traits, including inflammation and cancer susceptibility. Whether germline variants affect gene expression in tumors that have undergone somatic alterations, and the extent to which these variants influence tumor progression, is unknown.

RESULTS

Using an integrated linkage and genomic analysis of a mouse model of skin cancer that produces both benign tumors and malignant carcinomas, we document major changes in germline control of gene expression during skin tumor development resulting from cell selection, somatic genetic events, and changes in the tumor microenvironment. The number of significant expression quantitative trait loci (eQTL) is progressively reduced in benign and malignant skin tumors when compared to normal skin. However, novel tumor-specific eQTL are detected for several genes associated with tumor susceptibility, including IL18 (Il18), Granzyme E (Gzme), Sprouty homolog 2 (Spry2), and Mitogen-activated protein kinase kinase 4 (Map2k4).

CONCLUSIONS

We conclude that the genetic architecture is substantially altered in tumors, and that eQTL analysis of tumors can identify host factors that influence the tumor microenvironment, mitogen-activated protein (MAP) kinase signaling, and cancer susceptibility.

Expression QTL modules as functional components underlying higher-order phenotypes

Systems genetics studies often involve the mapping of numerous regulatory relations between genetic loci and expression traits. These regulatory relations form a bipartite network consisting of genetic loci and expression phenotypes. Modular network organizations may arise from the pleiotropic and polygenic regulation of gene expression. Here we analyzed the expression QTL (eQTL) networks derived from expression genetic data of yeast and mouse liver and found 65 and 98 modules respectively. Computer simulation result showed that such modules rarely occurred in randomized networks with the same number of nodes and edges and same degree distribution. We also found significant within-module functional coherence. The analysis of genetic overlaps and the evidences from biomedical literature have linked some eQTL modules to physiological phenotypes. Functional coherence within the eQTL modules and genetic overlaps between the modules and physiological phenotypes suggests that eQTL modules may act as functional units underlying the higher-order phenotypes.

A genetic ensemble approach for gene-gene interaction identification

BACKGROUND

It has now become clear that gene-gene interactions and gene-environment interactions are ubiquitous and fundamental mechanisms for the development of complex diseases. Though a considerable effort has been put into developing statistical models and algorithmic strategies for identifying such interactions, the accurate identification of those genetic interactions has been proven to be very challenging.

METHODS

In this paper, we propose a new approach for identifying such gene-gene and gene-environment interactions underlying complex diseases. This is a hybrid algorithm and it combines genetic algorithm (GA) and an ensemble of classifiers (called genetic ensemble). Using this approach, the original problem of SNP interaction identification is converted into a data mining problem of combinatorial feature selection. By collecting various single nucleotide polymorphisms (SNP) subsets as well as environmental factors generated in multiple GA runs, patterns of gene-gene and gene-environment interactions can be extracted using a simple combinatorial ranking method. Also considered in this study is the idea of combining identification results obtained from multiple algorithms. A novel formula based on pairwise double fault is designed to quantify the degree of complementarity.

CONCLUSIONS

Our simulation study demonstrates that the proposed genetic ensemble algorithm has comparable identification power to Multifactor Dimensionality Reduction (MDR) and is slightly better than Polymorphism Interaction Analysis (PIA), which are the two most popular methods for gene-gene interaction identification. More importantly, the identification results generated by using our genetic ensemble algorithm are highly complementary to those obtained by PIA and MDR. Experimental results from our simulation studies and real world data application also confirm the effectiveness of the proposed genetic ensemble algorithm, as well as the potential benefits of combining identification results from different algorithms.

Cancer: more of polygenic disease and less of multiple mutations? A quantitative viewpoint

The focus of cancer research is on cancer-specific mutations, with most clinical trials involving targeted drugs. Huge numbers of DNA lesions and tumor resistance events, in each of the >10¹³ cells of a human individual, form a striking contrast to the low, and also very narrow, cancer incidence window (10⁻¹ -10⁰). A detailed consideration of these quantitative observations seems to question the present paradigm, while suggesting that a systemic regulatory network mechanism is a stronger determinant for overt cancer disease, as compared with cancer-specific gene products. If we shall ever achieve major improvements in survival, we must gain understanding of this systemic network, rather than targeting therapy to a limited set of molecules or mutations. This may give us new opportunities for development of highly potent therapeutic tools.

Our changing view of the genomic landscape of cancer

Sporadic tumours, which account for the majority of all human cancers, arise from the acquisition of somatic, genetic and epigenetic alterations leading to changes in gene sequence, structure, copy number and expression. Within the last decade, the availability of a complete sequence-based map of the human genome, coupled with significant technological advances, has revolutionized the search for somatic alterations in tumour genomes. Recent landmark studies, which resequenced all coding exons within breast, colorectal, brain and pancreatic cancers, have shed new light on the genomic landscape of cancer. Within a given tumour type there are many infrequently mutated genes and a few frequently mutated genes, resulting in incredible genetic heterogeneity. However, when the altered genes are placed into biological processes and biochemical pathways, this complexity is significantly reduced and shared pathways that are affected in significant numbers of tumours can be discerned. The advent of next-generation sequencing technologies has opened up the potential to resequence entire tumour genomes to interrogate protein-encoding genes, non-coding RNA genes, non-genic regions and the mitochondrial genome. During the next decade it is anticipated that the most common forms of human cancer will be systematically surveyed to identify the underlying somatic changes in gene copy number, sequence and expression. The resulting catalogues of somatic alterations will point to candidate cancer genes requiring further validation to determine whether they have a causal role in tumourigenesis. The hope is that this knowledge will fuel improvements in cancer diagnosis, prognosis and therapy, based on the specific molecular alterations that drive individual tumours. In this review, I will provide a historical perspective on the identification of somatic alterations in the pre- and post-genomic eras, with a particular emphasis on recent pioneering studies that have provided unprecedented insights into the genomic landscape of human cancer.