Beyond genome-wide association studies: genetic heterogeneity and individual predisposition to cancer

Discovering predisposing genes for hereditary breast cancer using deep learning

Breast cancer (BC) is the most common malignancy affecting Western women today. It is estimated that as many as 10% of BC cases can be attributed to germline variants. However, the genetic basis of the majority of familial BC cases has yet to be identified. Discovering predisposing genes contributing to familial BC is challenging due to their presumed rarity, low penetrance, and complex biological mechanisms. Here, we focused on an analysis of rare missense variants in a cohort of 12 families of Middle Eastern origins characterized by a high incidence of BC cases. We devised a novel, high-throughput, variant analysis pipeline adapted for family studies, which aims to analyze variants at the protein level by employing state-of-the-art machine learning models and three-dimensional protein structural analysis. Using our pipeline, we analyzed 1218 rare missense variants that are shared between affected family members and classified 80 genes as candidate pathogenic. Among these genes, we found significant functional enrichment in peroxisomal and mitochondrial biological pathways which segregated across seven families in the study and covered diverse ethnic groups. We present multiple evidence that peroxisomal and mitochondrial pathways play an important, yet underappreciated, role in both germline BC predisposition and BC survival.

Germline determinants of aberrant signaling pathways in cancer

Cancer is a complex disease influenced by a heterogeneous landscape of both germline genetic variants and somatic aberrations. While there is growing evidence suggesting an interplay between germline and somatic variants, and a substantial number of somatic aberrations in specific pathways are now recognized as hallmarks in many well-known forms of cancer, the interaction landscape between germline variants and the aberration of those pathways in cancer remains largely unexplored. Utilizing over 8500 human samples across 33 cancer types characterized by TCGA and considering binary traits defined using a large collection of somatic aberration profiles across ten well-known oncogenic signaling pathways, we conducted a series of GWAS and identified genome-wide and suggestive associations involving 276 SNPs. Among these, 94 SNPs revealed cis-eQTL links with cancer-related genes or with genes functionally correlated with the corresponding traits' oncogenic pathways. GWAS summary statistics for all tested traits were then used to construct a set of polygenic scores employing a customized computational strategy. Polygenic scores for 24 traits demonstrated significant performance and were validated using data from PCAWG and CCLE datasets. These scores showed prognostic value for clinical variables and exhibited significant effectiveness in classifying patients into specific cancer subtypes or stratifying patients with cancer-specific aggressive phenotypes. Overall, we demonstrate that germline genetics can describe patients' genetic liability to develop specific cancer molecular and clinical profiles.

Standard deviation of CT radiomic features among malignancies in each individual: prognostic ability in lung cancer patients

PURPOSE

It was reported that individual heterogeneity among malignancies (IHAM) might correlate well to the prognosis of lung cancer; however, seldom radiomic study is on this field. Standard deviation (SD) in statistics could scale average amount of variability of a variable; therefore, we used SD of CT feature (FeatureSD) among primary tumor and malignant lymph nodes (LNs) in an individual to represent IHAM, and its prognostic ability was explored.

METHODS

The enrolled patients who had accepted PET/CT scans were selected from our previous study (ClinicalTrials.gov, NCT03648151). The patients had primary tumor and at least one LN, and standardized uptake value of LN higher than 2.0 and 2.5 were enrolled as the cohort 1 (n = 94) and 2 (n = 88), respectively. FeatureSD from the combined or thin-section CT were calculated among primary tumor and malignant LNs in each patient, and were separately selected by the survival XGBoost method. Finally, their prognostic ability was compared to the significant patient characteristics identified by the Cox regression.

RESULTS

In the univariate and multi-variate Cox analysis, surgery, target therapy, and TNM stage were significantly against OS in the both cohorts. In the survival XGBoost analysis of the thin-section CT dataset, none FeatureSD could be repeatably ranked on the top list of the both cohorts. For the combined CT dataset, only one FeatureSD ranked in the top three of both cohorts, but the three significant factors in the Cox regression were not even on the list. Both in the cohort 1 and 2, C-index of the model composed of the three factors could be improved by integrating the continuous FeatureSD; furthermore, that of each factor was obviously lower than FeatureSD.

CONCLUSION

Standard deviation of CT features among malignant foci within an individual was a powerful prognostic factor in vivo for lung cancer patients.

Molecular methods for colorectal cancer screening: Progress with next-generation sequencing evolution

Currently, colorectal cancer (CRC) represents the third most common malignancy and the second most deadly cancer worldwide, with a higher incidence in developed countries. Like other solid tumors, CRC is a heterogeneous genomic disease in which various alterations, such as point mutations, genomic rearrangements, gene fusions or chromosomal copy number alterations, can contribute to the disease development. However, because of its orderly natural history, easily accessible onset location and high lifetime incidence, CRC is ideally suited for preventive intervention, but the many screening efforts of the last decades have been compromised by performance limitations and low penetrance of the standard screening tools. The advent of next-generation sequencing (NGS) has both facilitated the identification of previously unrecognized CRC features such as its relationship with gut microbial pathogens and revolutionized the speed and throughput of cataloguing CRC-related genomic alterations. Hence, in this review, we summarized the several diagnostic tools used for CRC screening in the past and the present, focusing on recent NGS approaches and their revolutionary role in the identification of novel genomic CRC characteristics, the advancement of understanding the CRC carcinogenesis and the screening of clinically actionable targets for personalized medicine.

Using whole-genome sequencing (WGS) to plot colorectal cancer-related gut microbiota in a population with varied geography

BACKGROUND

Colorectal cancer (CRC) is a multifactorial disease with genetic and environmental factors. Regional differences in risk factors are an important reason for the different incidences of CRC in different regions.

OBJECTIVE

The goal was to clarify the intestinal microbial composition and structure of CRC patients in different regions and construct CRC risk prediction models based on regional differences.

METHODS

A metagenomic dataset of 601 samples from 6 countries in the GMrepo and NCBI databases was collected. All whole-genome sequencing (WGS) data were annotated for species by MetaPhlAn2. We obtained the relative abundance of species composition at the species level and genus level. The MicrobiotaProcess package was used to visualize species composition and PCA. LEfSe analysis was used to analyze the differences in the datasets in each region. Spearman correlation analysis was performed for CRC differential species. Finally, the CRC risk prediction model was constructed and verified in each regional dataset.

RESULTS

The composition of the intestinal bacterial community varied in different regions. Differential intestinal bacteria of CRC in different regions are inconsistent. There was a common diversity of bacteria in all six countries, such as Peptostreptococcus stomatis and Fusobacterium nucleatum at the species level. Peptostreptococcus stomatis (species level) and Peptostreptococcus (genus level) are important CRC-related bacteria that are related to other bacteria in different regions. Region has little influence on the accuracy of the CRC risk prediction model. Peptostreptococcus stomatis is an important variable in CRC risk prediction models in all regions.

CONCLUSION

Peptostreptococcus stomatis is a common high-risk pathogen of CRC worldwide, and it is an important variable in CRC risk prediction models in all regions. However, regional differences in intestinal bacteria had no significant impact on the accuracy of the CRC risk prediction model.

Advancing the Genetics of Lewy Body Disorders with Disease-Modifying Treatments in Mind

In this article, a caveat for advancing the genetics of Lewy body disorders is raised, given the nosological controversy about whether to consider dementia with Lewy bodies (DLB) and Parkinson's disease (PD) as one entity or two separate entities. Using the framework of the sufficient and component causes model of causation, as further developed into an evolution-based model of causation, it is proposed that a disease of complex etiology is defined as having a relatively high degree of sharing of the component causes (a genetic or environmental factor), that is, a low degree of heterogeneity of the sufficient causes. Based on this definition, only if the sharing of component causes within each of two diseases is similar to their combined sharing can lumping be warranted. However, it is not known whether the separate and combined sharing are similar before conducting the etiologic studies. This means that lumping DLB and PD can be counterproductive as it can decrease the ability to detect component causes despite the potential benefit of conducting studies with larger sample sizes. In turn, this is relevant to the development of disease-modifying treatments, because non-overlapping causal genetic factors may result in distinct pathogenetic pathways providing promising targets for interventions.

Genome-wide analysis of genetic predisposition to common polygenic cancers

Lung, breast, prostate, and colorectal cancers are among the most common and fatal malignancies worldwide. They are mainly caused by multifactorial mechanisms and are genetically heterogeneous. We investigated the genetic architecture of these cancers through genome-wide association, pathway-based, and summary-based transcriptome-/methylome-wide association analyses using three independent cohorts. Our genome-wide association analyses identified the associations of 33 single-nucleotide polymorphisms (SNPs) at P < 5E - 06, of which 32 SNPs were not previously reported and did not have proxy variants within their ± 1 Mb flanking regions. Moreover, other polymorphisms mapped to their closest genes were not previously associated with the same cancers at P < 5E - 06. Our pathway enrichment analyses revealed associations of 32 pathways; mainly related to the immune system, DNA replication/transcription, and chromosomal organization; with the studied cancers. Also, 60 probes were associated with these cancers in our transcriptome-wide and methylome-wide analyses. The ± 1 Mb flanking regions of most probes had not attained P < 5E - 06 in genome-wide association studies. The genes corresponding to the significant probes can be considered as potential targets for further functional studies. Two genes (i.e., CDC14A and PMEL) demonstrated stronger evidence of associations with lung cancer as they had significant probes in both transcriptome-wide and methylome-wide association analyses. The novel cancer-associated SNPs and genes identified here would advance our understanding of the genetic heterogeneity of the common cancers.

An Evolution-Based Model of Causation for Aging-Related Diseases and Intrinsic Mortality: Explanatory Properties and Implications for Healthy Aging

Aging-related diseases are the most prevalent diseases in advanced countries nowadays, accounting for a substantial proportion of mortality. We describe the explanatory properties of an evolution-based model of causation (EBMC) applicable to aging-related diseases and intrinsic mortality. The EBMC takes the sufficient and component causes model of causation as a starting point and develops it using evolutionary and statistical theories. Genetic component causes are classified as “early-onset” or “late-onset” and environmental component causes as “evolutionarily conserved” or “evolutionarily recent.” Genetic and environmental component causes are considered to occur as random events following time-to-event distributions, and sufficient causes are classified according to whether or not their time-to-event distributions are “molded” by the declining force of natural selection with increasing age. We obtain for each of these two groups different time-to-event distributions for disease incidence or intrinsic mortality asymptotically (i.e., for a large number of sufficient causes). The EBMC provides explanations for observations about aging-related diseases concerning the penetrance of genetic risk variants, the age of onset of monogenic vs. sporadic forms, the meaning of “age as a risk factor,” the relation between frequency and age of onset, and the emergence of diseases associated with the modern Western lifestyle. The EBMC also provides an explanation of the Gompertz mortality model at the fundamental level of genetic causes and involving evolutionary biology. Implications for healthy aging are examined under the scenarios of health promotion and postponed aging. Most importantly from a public health standpoint, the EBMC implies that primary prevention through changes in lifestyle and reduction of environmental exposures is paramount in promoting healthy aging.

High-throughput proteomics and AI for cancer biomarker discovery

Biomarkers are assayed to assess biological and pathological status. Recent advances in high-throughput proteomic technology provide opportunities for developing next generation biomarkers for clinical practice aided by artificial intelligence (AI) based techniques. We summarize the advances and limitations of cancer biomarkers based on genomic and transcriptomic analysis, as well as classical antibody-based methodologies. Then we review recent progresses in mass spectrometry (MS)-based proteomics in terms of sample preparation, peptide fractionation by liquid chromatography (LC) and mass spectrometric data acquisition. We highlight applications of AI techniques in high-throughput clinical studies as compared with clinical decisions based on singular features. This review sets out our approach for discovering clinical biomarkers in studies using proteomic big data technology conjoined with computational and statistical methods.

Fecal Bacteria as Non-Invasive Biomarkers for Colorectal Adenocarcinoma

Colorectal adenocarcinoma (CRC) ranks one of the five most lethal malignant tumors both in China and worldwide. Early diagnosis and treatment of CRC could substantially increase the survival rate. Emerging evidence has revealed the importance of gut microbiome on CRC, thus fecal microbial community could be termed as a potential screen for non-invasive diagnosis. Importantly, few numbers of bacteria genus as non-invasive biomarkers with high sensitivity and specificity causing less cost would be benefitted more in clinical compared with the whole microbial community analysis. Here we analyzed the gut microbiome between CRC patients and healthy people using 16s rRNA sequencing showing the divergence of microbial composition between case and control. Furthermore, ExtraTrees classifier was performed for the classification of CRC gut microbiome and heathy control, and 13 bacteria were screened as biomarkers for CRC. In addition, 13 biomarkers including 12 bacteria genera and FOBT showed an outstanding sensitivity and specificity for discrimination of CRC patients from healthy controls. This method could be used as a non-invasive method for CRC early diagnosis.

Genetic association studies of alterations in protein function expose recessive effects on cancer predisposition

The characterization of germline genetic variation affecting cancer risk, known as cancer predisposition, is fundamental to preventive and personalized medicine. Studies of genetic cancer predisposition typically identify significant genomic regions based on family-based cohorts or genome-wide association studies (GWAS). However, the results of such studies rarely provide biological insight or functional interpretation. In this study, we conducted a comprehensive analysis of cancer predisposition in the UK Biobank cohort using a new gene-based method for detecting protein-coding genes that are functionally interpretable. Specifically, we conducted proteome-wide association studies (PWAS) to identify genetic associations mediated by alterations to protein function. With PWAS, we identified 110 significant gene-cancer associations in 70 unique genomic regions across nine cancer types and pan-cancer. In 48 of the 110 PWAS associations (44%), estimated gene damage is associated with reduced rather than elevated cancer risk, suggesting a protective effect. Together with standard GWAS, we implicated 145 unique genomic loci with cancer risk. While most of these genomic regions are supported by external evidence, our results also highlight many novel loci. Based on the capacity of PWAS to detect non-additive genetic effects, we found that 46% of the PWAS-significant cancer regions exhibited exclusive recessive inheritance. These results highlight the importance of recessive genetic effects, without relying on familial studies. Finally, we show that many of the detected genes exert substantial cancer risk in the studied cohort determined by a quantitative functional description, suggesting their relevance for diagnosis and genetic consulting.

Prevalence of cancer susceptibility variants in patients with multiple Lynch syndrome related cancers

Along with early-onset cancers, multiple primary cancers (MPCs) are likely resulting from increased genetic susceptibility; however, the associated predisposition genes or prevalence of the pathogenic variants genes in MPC patients are often unknown. We screened 71 patients with MPC of the stomach, colorectal, and endometrium, sequencing 65 cancer predisposition genes. A subset of 19 patients with early-onset MPC of stomach and colorectum were further evaluated for variants in cancer related genes using both normal and tumor whole exome sequencing. Among 71 patients with MPCs, variants classified to be pathogenic were observed in 15 (21.1%) patients and affected Lynch Syndrome (LS) genes: MLH1 (n = 10), MSH6 (n = 2), PMS2 (n = 2), and MSH2 (n = 1). All carriers had tumors with high microsatellite instability and 13 of them (86.7%) were early-onset, consistent with LS. In 19 patients with early-onset MPCs, loss of function (LoF) variants in RECQL5 were more prevalent in non-LS MPC than in matched sporadic cancer patients (OR = 31.6, 2.73–1700.6, p = 0.001). Additionally, there were high-confidence LoF variants at FANCG and CASP8 in two patients accompanied by somatic loss of heterozygosity in tumor, respectively. The results suggest that genetic screening should be considered for synchronous cancers and metachronous MPCs of the LS tumor spectrum, particularly in early-onset. Susceptibility variants in non-LS genes for MPC patients may exist, but evidence for their role is more elusive than for LS patients.

How to Better Understand the Influence of Host Genetics on Developing an Effective Immune Response to Thoracic Cancers

Thoracic cancers pose a significant global health burden. Immune checkpoint blockade therapies have improved treatment outcomes, but durable responses remain limited. Understanding how the host immune system interacts with a developing tumor is essential for the rational development of improved treatments for thoracic malignancies. Recent technical advances have improved our understanding of the mutational burden of cancer cells and changes in cancer-specific gene expression, providing a detailed understanding of the complex biology underpinning tumor-host interactions. While there has been much focus on the genetic alterations associated with cancer cells and how they may impact treatment outcomes, how host genetics affects cancer development is also critical and will greatly determine treatment response. Genome-wide association studies (GWAS) have identified genetic variants associated with cancer predisposition. This approach has successfully identified host genetic risk factors associated with common thoracic cancers like lung cancer, but is less effective for rare cancers like malignant mesothelioma. To assess how host genetics impacts rare thoracic cancers, we used the Collaborative Cross (CC); a powerful murine genetic resource designed to maximize genetic diversity and rapidly identify genes associated with any biological trait. We are using the CC in conjunction with our asbestos-induced MexTAg mouse model, to identify host genes associated with mesothelioma development. Once genes that moderate tumor development and progression are known, human homologues can be identified and human datasets interrogated to validate their association with disease outcome. Furthermore, our CC-MexTAg animal model enables in-depth study of the tumor microenvironment, allowing the correlation of immune cell infiltration and gene expression signatures with disease development. This strategy provides a detailed picture of the underlying biological pathways associated with mesothelioma susceptibility and progression; knowledge that is crucial for the rational development of new diagnostic and therapeutic strategies. Here we discuss the influence of host genetics on developing an effective immune response to thoracic cancers. We highlight current knowledge gaps, and with a focus on mesothelioma, describe the development and application of the CC-MexTAg to overcome limitations and illustrate how the knowledge gained from this unique study will inform the rational design of future treatments of mesothelioma.

The Shared Genetic Architectures Between Lung Cancer and Multiple Polygenic Phenotypes in Genome-Wide Association Studies

BACKGROUND

Prior genome-wide association studies have identified numerous lung cancer risk loci and reveal substantial etiologic heterogeneity across histologic subtypes. Analyzing the shared genetic architecture underlying variation in complex traits can elucidate common genetic etiologies across phenotypes. Exploring pairwise genetic correlations between lung cancer and other polygenic traits can reveal the common genetic etiology of correlated phenotypes.

METHODS

Using cross-trait linkage disequilibrium score regression, we estimated the pairwise genetic correlation and heritability between lung cancer and multiple traits using publicly available summary statistics. Identified genetic relationships were also examined after excluding genomic regions known to be associated with smoking behaviors, a major risk factor for lung cancer.

RESULTS

We observed several traits showing moderate single nucleotide polymorphism-based heritability and significant genetic correlations with lung cancer. We observed highly significant correlations between the genetic architectures of lung cancer and emphysema/chronic bronchitis across all histologic subtypes, as well as among lung cancer occurring among smokers. Our analyses revealed highly significant positive correlations between lung cancer and paternal history of lung cancer. We also observed a strong negative correlation with parental longevity. We observed consistent directions in genetic patterns after excluding genomic regions associated with smoking behaviors.

CONCLUSIONS

This study identifies numerous phenotypic traits that share genomic architecture with lung carcinogenesis and are not fully accounted for by known smoking-associated genomic loci.

IMPACT

These findings provide new insights into the etiology of lung cancer by identifying traits that are genetically correlated with increased risk of lung cancer.

The PI3K/mTOR Pathway Is Targeted by Rare Germline Variants in Patients with Both Melanoma and Renal Cell Carcinoma

Background: Malignant melanoma and RCC have different embryonic origins, no common lifestyle risk factors but intriguingly share biological properties such as immune regulation and radioresistance. An excess risk of malignant melanoma is observed in RCC patients and vice versa. This bidirectional association is poorly understood, and hypothetic genetic co-susceptibility remains largely unexplored. Results: We hereby provide a clinical and genetic description of a series of 125 cases affected by both malignant melanoma and RCC. Clinical germline mutation testing identified a pathogenic variant in a melanoma and/or RCC predisposing gene in 17/125 cases (13.6%). This included mutually exclusive variants in MITF (p.E318K locus, N = 9 cases), BAP1 (N = 3), CDKN2A (N = 2), FLCN (N = 2), and PTEN (N = 1). A subset of 46 early-onset cases, without underlying germline variation, was whole-exome sequenced. In this series, thirteen genes were significantly enriched in mostly exclusive rare variants predicted to be deleterious, compared to 19,751 controls of similar ancestry. The observed variation mainly consisted of novel or low-frequency variants (<0.01%) within genes displaying strong evolutionary mutational constraints along the PI3K/mTOR pathway, including PIK3CD, NFRKB, EP300, MTOR, and related epigenetic modifier SETD2. The screening of independently processed germline exomes from The Cancer Genome Atlas confirmed an association with melanoma and RCC but not with cancers of established differing etiology such as lung cancers. Conclusions: Our study highlights that an exome-wide case-control enrichment approach may better characterize the rare variant-based missing heritability of multiple primary cancers. In our series, the co-occurrence of malignant melanoma and RCC was associated with germline variation in the PI3K/mTOR signaling cascade, with potential relevance for early diagnostic and clinical management.

Genome-Wide Analysis of Sex Disparities in the Genetic Architecture of Lung and Colorectal Cancers

Almost all complex disorders have manifested epidemiological and clinical sex disparities which might partially arise from sex-specific genetic mechanisms. Addressing such differences can be important from a precision medicine perspective which aims to make medical interventions more personalized and effective. We investigated sex-specific genetic associations with colorectal (CRCa) and lung (LCa) cancers using genome-wide single-nucleotide polymorphisms (SNPs) data from three independent datasets. The genome-wide association analyses revealed that 33 SNPs were associated with CRCa/LCa at P < 5.0 × 10⁻⁶ neither males or females. Of these, 26 SNPs had sex-specific effects as their effect sizes were statistically different between the two sexes at a Bonferroni-adjusted significance level of 0.0015. None had proxy SNPs within their ±1 Mb regions and the closest genes to 32 SNPs were not previously associated with the corresponding cancers. The pathway enrichment analyses demonstrated the associations of 35 pathways with CRCa or LCa which were mostly implicated in immune system responses, cell cycle, and chromosome stability. The significant pathways were mostly enriched in either males or females. Our findings provided novel insights into the potential sex-specific genetic heterogeneity of CRCa and LCa at SNP and pathway levels.

Correlation between polymorphism of TYMS gene and toxicity response to treatment with 5-fluoruracil and capecitabine

Tumorigenesis is a multiphasic process in which genetic alterations guide the progressive transformation in cancer cells1. In order to evaluate the possible correlation between some gene variants and the risk of the toxicity development onset, two of the polymorphisms of the thymidylate synthase (TYMS), rs34743033 (2R/3R) and rs16430 (DEL/INS) were investigated. We enrolled in our study 47 patients from the Hospital of Sicily. Our preliminary findings suggest that there could be a linkage between the genotypes discussed and the development of the toxicity following the chemotherapy treatment. These results need to be confirmed by further studies, however this short paper offers some initial insight into the relationships between genetic background and the better outcome for patients.

Set-based genetic association and interaction tests for survival outcomes based on weighted V statistics

With advancements in high-throughout technologies, studies have been conducted to investigate the role of massive genetic variants in human diseases. While set-based tests have been developed for binary and continuous disease outcomes, there are few computationally efficient set-based tests available for time-to-event outcomes. To facilitate the genetic association and interaction analyses of time-to-event outcomes, We develop a suite of multivariant tests based on weighted V statistics with or without considering potential genetic heterogeneity. In addition to the computation efficiency and nice asymptotic properties, all the new tests can deal with left truncation and competing risks in the survival data, and adjust for covariates. Simulation studies show that the new tests run faster, are more accurate in small samples, and account for confounding effect better than the existing multivariant survival tests. When the genetic effect is heterogeneous across individuals/subpopulations, the association test considering genetic heterogeneity is more powerful than the existing tests that do not account for genetic heterogeneity. Using the new methods, we perform a genome-wide association analysis of the genotype and age-to-Alzheimer's data from the Rush Memory and Aging Project and the Religious Orders Study. The analysis identifies two genes, APOE and APOC1, associated with age to Alzheimer's disease onset.

Expanding cancer predisposition genes with ultra-rare cancer-exclusive human variations

It is estimated that up to 10% of cancer incidents are attributed to inherited genetic alterations. Despite extensive research, there are still gaps in our understanding of genetic predisposition to cancer. It was theorized that ultra-rare variants partially account for the missing heritable component. We harness the UK BioBank dataset of ~ 500,000 individuals, 14% of which were diagnosed with cancer, to detect ultra-rare, possibly high-penetrance cancer predisposition variants. We report on 115 cancer-exclusive ultra-rare variations and nominate 26 variants with additional independent evidence as cancer predisposition variants. We conclude that population cohorts are valuable source for expanding the collection of novel cancer predisposition genes.

Dissecting the heritable risk of breast cancer: From statistical methods to susceptibility genes

Decades of research have shown that rare highly penetrant mutations can promote tumorigenesis, but it is still unclear whether variants observed at high-frequency in the broader population could modulate the risk of developing cancer. Genome-wide Association Studies (GWAS) have generated a wealth of data linking single nucleotide polymorphisms (SNPs) to increased cancer risk, but the effect of these mutations are usually subtle, leaving most of cancer heritability unexplained. Understanding the role of high-frequency mutations in cancer can provide new intervention points for early diagnostics, patient stratification and treatment in malignancies with high prevalence, such as breast cancer. Here we review state-of-the-art methods to study cancer heritability using GWAS data and provide an updated map of breast cancer susceptibility loci at the SNP and gene level.

Identification of genetic polymorphisms modulating nausea and vomiting in two series of opioid-treated cancer patients

Nausea and vomiting are often associated with opioid analgesia in cancer patients; however, only a subset of patients develop such side effects. Here, we tested the hypothesis that the occurrence of nausea and vomiting is modulated by the genetic background of the patients. Whole exome sequencing of DNA pools from patients with either low (n = 937) or high (n = 557) nausea and vomiting intensity, recruited in the European Pharmacogenetic Opioid Study, revealed a preliminary association of 53 polymorphisms. PCR-based genotyping of 45 of these polymorphisms in the individual patients of the same series confirmed the association for six SNPs in AIM1L, CLCC1, MUC16, PDE3A, POM121L2, and ZNF165 genes. Genotyping of the same 45 polymorphisms in 264 patients of the Italian CERP study, also treated with opioids for cancer pain, instead confirmed the association for two SNPs in ZNF568 and PDE3A genes. Only one SNP, rs12305038 in PDE3A, was confirmed in both series, although with opposite effects of the minor allele on the investigated phenotype. Overall, our findings suggest that genetic factors are indeed associated with nausea and vomiting in opioid-treated cancer patients, but the role of individual polymorphisms may be weak.

Clonal Evolution of TP53 c.375+1G>A Mutation in Pre- and Post- Neo-Adjuvant Chemotherapy (NACT) Tumor Samples in High-Grade Serous Ovarian Cancer (HGSOC)

Carboplatin/paclitaxel is the reference regimen in the treatment of advanced high-grade serous ovarian cancer (HGSOC) in neo-adjuvant chemotherapy (NACT) before interval debulking surgery (IDS). To identify new genetic markers of platinum-resistance, next-generation sequencing (NGS) analysis of 26 cancer-genes was performed on paired matched pre- and post-NACT tumor and blood samples in a patient with stage IV HGSOC treated with NACT-IDS, showing platinum-refractory/resistance and poor prognosis. Only the TP53 c.375+1G>A somatic mutation was identified in both tumor samples. This variant, associated with aberrant splicing, was in trans configuration with the 72Arg allele of the known germline polymorphism TP53 c.215C>G (p. Pro72Arg). In the post-NACT tumor sample we observed the complete expansion of the TP53 c.375+1G>A driver mutant clone with somatic loss of the treatment-sensitive 72Arg allele. NGS results were confirmed with Sanger method and immunostaining for p53, BRCA1, p16, WT1, and Ki-67 markers were evaluated. This study showed that (i) the splice mutation in TP53 was present as an early driver mutation at diagnosis; (ii) the mutational profile was shared in pre- and post-NACT tumor samples; (iii) the complete expansion of a single dominant mutant clone through loss of heterozygosity (LOH) had occurred, suggesting a possible mechanism of platinum-resistance in HGSOC under the pressure of NACT.

Genomic Regions Associated with IgE Levels against Culicoides spp. Antigens in Three Horse Breeds

Insect bite hypersensitivity (IBH), which is a cutaneous allergic reaction to antigens from Culicoides spp., is the most prevalent skin disorder in horses. Misdiagnosis is possible, as IBH is usually diagnosed based on clinical signs. Our study is the first to employ IgE levels against several recombinant Culicoides spp. allergens as an objective, independent, and quantitative phenotype to improve the power to detect genetic variants that underlie IBH. Genotypes of 200 Shetland ponies, 127 Icelandic horses, and 223 Belgian Warmblood horses were analyzed while using a mixed model approach. No single-nucleotide polymorphism (SNP) passed the Bonferroni corrected significance threshold, but several regions were identified within and across breeds, which confirmed previously identified regions of interest and, in addition, identifying new regions of interest. Allergen-specific IgE levels are a continuous and objective phenotype that allow for more powerful analyses when compared to a case-control set-up, as more significant associations were obtained. However, the use of a higher density array seems necessary to fully employ the use of IgE levels as a phenotype. While these results still require validation in a large independent dataset, the use of allergen-specific IgE levels showed value as an objective and continuous phenotype that can deepen our understanding of the biology underlying IBH.

Identifying Multi-Omics Causers and Causal Pathways for Complex Traits

The central dogma of molecular biology delineates a unidirectional causal flow, i.e., DNA → RNA → protein → trait. Genome-wide association studies, next-generation sequencing association studies, and their meta-analyses have successfully identified ~12,000 susceptibility genetic variants that are associated with a broad array of human physiological traits. However, such conventional association studies ignore the mediate causers (i.e., RNA, protein) and the unidirectional causal pathway. Such studies may not be ideally powerful; and the genetic variants identified may not necessarily be genuine causal variants. In this article, we model the central dogma by a mediate causal model and analytically prove that the more remote an omics level is from a physiological trait, the smaller the magnitude of their correlation is. Under both random and extreme sampling schemes, we numerically demonstrate that the proteome-trait correlation test is more powerful than the transcriptome-trait correlation test, which in turn is more powerful than the genotype-trait association test. In conclusion, integrating RNA and protein expressions with DNA data and causal inference are necessary to gain a full understanding of how genetic causal variants contribute to phenotype variations.

Pathway-based subnetworks enable cross-disease biomarker discovery

Biomarkers lie at the heart of precision medicine. Surprisingly, while rapid genomic profiling is becoming ubiquitous, the development of biomarkers usually involves the application of bespoke techniques that cannot be directly applied to other datasets. There is an urgent need for a systematic methodology to create biologically-interpretable molecular models that robustly predict key phenotypes. Here we present SIMMS (Subnetwork Integration for Multi-Modal Signatures): an algorithm that fragments pathways into functional modules and uses these to predict phenotypes. We apply SIMMS to multiple data types across five diseases, and in each it reproducibly identifies known and novel subtypes, and makes superior predictions to the best bespoke approaches. To demonstrate its ability on a new dataset, we profile 33 genes/nodes of the PI3K pathway in 1734 FFPE breast tumors and create a four-subnetwork prediction model. This model out-performs a clinically-validated molecular test in an independent cohort of 1742 patients. SIMMS is generic and enables systematic data integration for robust biomarker discovery.

Functional variants in the low-density lipoprotein receptor gene are associated with clear cell renal cell carcinoma susceptibility

Recent studies indicate that abnormal levels of low-density lipoprotein (LDL), which is an important component of dyslipidaemia, are associated with alterations to cancer risk, including that of renal cell carcinoma (RCC). Single nucleotide polymorphisms at microRNA-binding sites contribute to cancer susceptibility and progression by affecting the messenger RNA (mRNA) function of target genes. In this case-control study, we examined the frequency of six potentially functional single nucleotide polymorphisms in the LDL receptor gene (LDLR) in 1004 clear cell RCC (ccRCC) patients and 1065 cancer-free subjects. Logistic regression analyses estimated odds ratios (ORs) and 95% confidence intervals (CIs). The association between genetic variants and levels of LDLR mRNA and protein was also evaluated. Compared with the CC genotype, multivariate logistic regression analysis showed that the LDLR rs2738464 variant GG genotype was associated with a significantly decreased ccRCC risk (P = 0.002, OR: 0.605, 95% CI: 0.439-0.833). Further functional experiments showed that the rs2738464 variant G allele affected miR-330 regulation of the LDLR 3'-untranslated region (UTR), increasing LDLR mRNA levels in patient kidney tissues. These findings suggest that LDLR rs2738464 may affect the affinity of miR-330 binding to the LDLR 3'-UTR, thus regulating LDLR expression and contributing to ccRCC risk.

Verification of Three-Phase Dependency Analysis Bayesian Network Learning Method for Maize Carotenoid Gene Mining

Background and Objective

Mining the genes related to maize carotenoid components is important to improve the carotenoid content and the quality of maize.

Methods

On the basis of using the entropy estimation method with Gaussian kernel probability density estimator, we use the three-phase dependency analysis (TPDA) Bayesian network structure learning method to construct the network of maize gene and carotenoid components traits.

Results

In the case of using two discretization methods and setting different discretization values, we compare the learning effect and efficiency of 10 kinds of Bayesian network structure learning methods. The method is verified and analyzed on the maize dataset of global germplasm collection with 527 elite inbred lines.

Conclusions

The result confirmed the effectiveness of the TPDA method, which outperforms significantly another 9 kinds of Bayesian network learning methods. It is an efficient method of mining genes for maize carotenoid components traits. The parameters obtained by experiments will help carry out practical gene mining effectively in the future.

Complex genetic control of lung tumorigenesis in resistant mice strains

The SM/J mouse strain is resistant to chemically‐induced lung tumorigenesis despite having a haplotype, in the pulmonary adenoma susceptibility locus (Pas1) locus, that confers tumor susceptibility in other strains. To clarify this inconsistent genotype‐phenotype correlation, we crossed SM/J mice with another resistant strain and conducted genome‐wide linkage analysis in the (C57BL/6J × SM/J)F2 progeny exposed to urethane to induce lung tumors. Overall, >80% of F2 mice of both sexes developed from 1 to 20 lung tumors. Genotyping of 372 F2 mice for 744 informative non‐redundant SNPs dispersed over all autosomal chromosomes revealed four quantitative trait loci (QTLs) affecting lung tumor multiplicity, on chromosomes 3 (near rs13477379), 15 (rs6285067), 17 (rs33373629) and 18 (rs3706601), all with logarithm of the odds (LOD) scores >5. Four QTLs modulated total lung tumor volume, on chromosome 3 (rs13477379), 10 (rs13480702), 15 (rs6285067) and 17 (rs3682923), all with LOD scores >4. No QTL modulating lung tumor multiplicity or total volume was detected in Pas1 on chromosome 6. The present study demonstrates that the SM/J strain carries, at the Pas1 locus, the resistance allele: a finding that will facilitate identification of the Pas1 causal element. More generally, it demonstrates that lung tumorigenesis is under complex polygenic control even in a pedigree with low susceptibility to this neoplasia, suggesting that the genetics of lung tumorigenesis is much more complex than evidenced by the pulmonary adenoma susceptibility and resistance loci that have, so far, been mapped in a small number of crosses between a few inbred strains.

Interaction Landscape of Inherited Polymorphisms with Somatic Events in Cancer

Recent studies have characterized the extensive somatic alterations that arise during cancer. However, the somatic evolution of a tumor may be significantly affected by inherited polymorphisms carried in the germline. Here, we analyze genomic data for 5,954 tumors to reveal and systematically validate 412 genetic interactions between germline polymorphisms and major somatic events, including tumor formation in specific tissues and alteration of specific cancer genes. Among germline-somatic interactions, we found germline variants in RBFOX1 that increased incidence of SF3B1 somatic mutation by 8-fold via functional alterations in RNA splicing. Similarly, 19p13.3 variants were associated with a 4-fold increased likelihood of somatic mutations in PTEN. In support of this association, we found that PTEN knockdown sensitizes the MTOR pathway to high expression of the 19p13.3 gene GNA11 Finally, we observed that stratifying patients by germline polymorphisms exposed distinct somatic mutation landscapes, implicating new cancer genes. This study creates a validated resource of inherited variants that govern where and how cancer develops, opening avenues for prevention research.Significance: This study systematically identifies germline variants that directly affect tumor evolution, either by dramatically increasing alteration frequency of specific cancer genes or by influencing the site where a tumor develops. Cancer Discovery; 7(4); 410-23. ©2017 AACR.See related commentary by Geeleher and Huang, p. 354This article is highlighted in the In This Issue feature, p. 339.

Metagenomic analysis of faecal microbiome as a tool towards targeted non-invasive biomarkers for colorectal cancer

OBJECTIVE

To evaluate the potential for diagnosing colorectal cancer (CRC) from faecal metagenomes.

DESIGN

We performed metagenome-wide association studies on faecal samples from 74 patients with CRC and 54 controls from China, and validated the results in 16 patients and 24 controls from Denmark. We further validated the biomarkers in two published cohorts from France and Austria. Finally, we employed targeted quantitative PCR (qPCR) assays to evaluate diagnostic potential of selected biomarkers in an independent Chinese cohort of 47 patients and 109 controls.

RESULTS

Besides confirming known associations of Fusobacterium nucleatum and Peptostreptococcus stomatis with CRC, we found significant associations with several species, including Parvimonas micra and Solobacterium moorei. We identified 20 microbial gene markers that differentiated CRC and control microbiomes, and validated 4 markers in the Danish cohort. In the French and Austrian cohorts, these four genes distinguished CRC metagenomes from controls with areas under the receiver-operating curve (AUC) of 0.72 and 0.77, respectively. qPCR measurements of two of these genes accurately classified patients with CRC in the independent Chinese cohort with AUC=0.84 and OR of 23. These genes were enriched in early-stage (I-II) patient microbiomes, highlighting the potential for using faecal metagenomic biomarkers for early diagnosis of CRC.

CONCLUSIONS

We present the first metagenomic profiling study of CRC faecal microbiomes to discover and validate microbial biomarkers in ethnically different cohorts, and to independently validate selected biomarkers using an affordable clinically relevant technology. Our study thus takes a step further towards affordable non-invasive early diagnostic biomarkers for CRC from faecal samples.

Major milestones in translational oncology

Translational oncology represents a bridge between basic research and clinical practice in cancer medicine. Today, translational research in oncology benefits from an abundance of knowledge resulting from genome-scale studies regarding the molecular pathways involved in tumorigenesis. In this Forum article, we highlight the state of the art of translational oncology in five major cancer types. We illustrate the use of molecular profiling to subtype colorectal cancer for both diagnosis and treatment, and summarize the results of a nationwide screening program for ovarian cancer based on detection of a tumor biomarker in serum. Additionally, we discuss how circulating tumor DNA can be assayed to safely monitor breast cancer over the course of treatment, and report on how therapy with immune checkpoint inhibitors is proving effective in advanced lung cancer. Finally, we summarize efforts to use molecular profiling of prostate cancer biopsy specimens to support treatment decisions. Despite encouraging early successes, we cannot disregard the complex genetics of individual susceptibility to cancer nor the enormous complexity of the somatic changes observed in tumors, which urge particular attention to the development of personalized therapies.

Expression quantitative trait analysis reveals fine germline transcript regulation in mouse lung tumors

Gene expression modulates cellular functions in both physiologic and pathologic conditions. Herein, we carried out a genetic linkage study on the transcriptome of lung tumors induced by urethane in an (A/J x C57BL/6)F4 intercross population, whose individual lung tumor multiplicity (Nlung) is linked to the genotype at the Pulmonary adenoma susceptibility 1 (Pas1) locus. We found that expression levels of 1179 and 1579 genes are modulated by an expression quantitative trait locus (eQTL) in cis and in trans, respectively (LOD score > 5). Of note, the genomic area surrounding and including the Pas1 locus regulated 14 genes in cis and 857 genes in trans. In lung tumors of the same (A/J x C57BL/6)F4 mice, we found 1124 genes whose transcript levels associated with Nlung (FDR < 0.001). The expression levels of about a third of these genes (n = 401) were regulated by the genotype at the Pas1 locus. Pathway analysis of the sets of genes associated with Nlung and regulated by Pas1 revealed a set of 14 recurrently represented genes that are components or targets of the Ras-Erk and Pi3k-Akt signaling pathways. Altogether our results illustrate the architecture of germline control of gene expression in mouse lung cancer: they highlight the importance of Pas1 as a tumor-modifier locus, attribute to it a novel role as a major regulator of transcription in lung tumor nodules and strengthen the candidacy of the Kras gene as the effector of this locus.

Discovering causal interactions using Bayesian network scoring and information gain

BACKGROUND

The problem of learning causal influences from data has recently attracted much attention. Standard statistical methods can have difficulty learning discrete causes, which interacting to affect a target, because the assumptions in these methods often do not model discrete causal relationships well. An important task then is to learn such interactions from data. Motivated by the problem of learning epistatic interactions from datasets developed in genome-wide association studies (GWAS), researchers conceived new methods for learning discrete interactions. However, many of these methods do not differentiate a model representing a true interaction from a model representing non-interacting causes with strong individual affects. The recent algorithm MBS-IGain addresses this difficulty by using Bayesian network learning and information gain to discover interactions from high-dimensional datasets. However, MBS-IGain requires marginal effects to detect interactions containing more than two causes. If the dataset is not high-dimensional, we can avoid this shortcoming by doing an exhaustive search.

RESULTS

We develop Exhaustive-IGain, which is like MBS-IGain but does an exhaustive search. We compare the performance of Exhaustive-IGain to MBS-IGain using low-dimensional simulated datasets based on interactions with marginal effects and ones based on interactions without marginal effects. Their performance is similar on the datasets based on marginal effects. However, Exhaustive-IGain compellingly outperforms MBS-IGain on the datasets based on 3 and 4-cause interactions without marginal effects. We apply Exhaustive-IGain to investigate how clinical variables interact to affect breast cancer survival, and obtain results that agree with judgements of a breast cancer oncologist.

CONCLUSIONS

We conclude that the combined use of information gain and Bayesian network scoring enables us to discover higher order interactions with no marginal effects if we perform an exhaustive search. We further conclude that Exhaustive-IGain can be effective when applied to real data.

Binomial Mixture Model Based Association Testing to Account for Genetic Heterogeneity for GWAS

Genome-wide association studies (GWAS) have confirmed the ubiquitous existence of genetic heterogeneity for common disease: multiple common genetic variants have been identified to be associated, while many more are yet expected to be uncovered. However, the single SNP (single-nucleotide polymorphism) based trend test (or its variants) that has been dominantly used in GWAS is based on contrasting the allele frequency difference between the case and control groups, completely ignoring possible genetic heterogeneity. In spite of the widely accepted notion of genetic heterogeneity, we are not aware of any previous attempt to apply genetic heterogeneity motivated methods in GWAS. Here, to explicitly account for unknown genetic heterogeneity, we applied a mixture model based single-SNP test to the Wellcome Trust Case Control Consortium (WTCCC) GWAS data with traits of Crohn's disease, bipolar disease, coronary artery disease, and type 2 diabetes, identifying much larger numbers of significant SNPs and risk loci for each trait than those of the popular trend test, demonstrating potential power gain of the mixture model based test.

A weighted U statistic for association analyses considering genetic heterogeneity

Converging evidence suggests that common complex diseases with the same or similar clinical manifestations could have different underlying genetic etiologies. While current research interests have shifted toward uncovering rare variants and structural variations predisposing to human diseases, the impact of heterogeneity in genetic studies of complex diseases has been largely overlooked. Most of the existing statistical methods assume the disease under investigation has a homogeneous genetic effect and could, therefore, have low power if the disease undergoes heterogeneous pathophysiological and etiological processes. In this paper, we propose a heterogeneity-weighted U (HWU) method for association analyses considering genetic heterogeneity. HWU can be applied to various types of phenotypes (e.g., binary and continuous) and is computationally efficient for high-dimensional genetic data. Through simulations, we showed the advantage of HWU when the underlying genetic etiology of a disease was heterogeneous, as well as the robustness of HWU against different model assumptions (e.g., phenotype distributions). Using HWU, we conducted a genome-wide analysis of nicotine dependence from the Study of Addiction: Genetics and Environments dataset. The genome-wide analysis of nearly one million genetic markers took 7h, identifying heterogeneous effects of two new genes (i.e., CYP3A5 and IKBKB) on nicotine dependence. Copyright © 2016 John Wiley & Sons, Ltd.

Multiple primary colorectal cancer: Individual or familial predisposition?

Colorectal carcinoma (CRC) is one of the most frequent cancers. Along the surface of the large bowel, several foci of CRC may appear simultaneously or over the time. The development of at least two different tumours has been defined as multiple primary CRC (MPCRC): When more than one tumour is diagnosed at the same time, it is known as synchronous CRC (SCRC), while when a second neoplasm is diagnosed some time after the resection and/or diagnosis of the first lesion, it is called metachronous CRC (MCRC). Multiple issues can promote the development of MPCRC, ranging from different personal factors, such as environmental exposure, to familial predisposition due to hereditary factors. However, most studies do not distinguish this dichotomy. High- and low-pentrance genetic variants are involved in MPCRC. An increased risk for MPCRC has been described in Lynch syndrome, familial adenomatous polyposis, and serrated polyposis. Non-syndromic familial CRCs should also be considered as risk factors for MPCRC. Environmental factors can promote damage to colon mucosae that enable the concurrence of MPCRC. Epigenetics are thought to play a major role in the carcinogenesis of sporadic MPCRC. The methylation state of the DNA depends on multiple environmental factors (e.g., smoking and eating foods cooked at high temperatures), and this can contribute to increasing the MPCRC rate. Certain clinical features may also suggest individual predisposition for MPCRC. Different etiopathogenic factors are suspected to be involved in SCRC and MCRC, and different familial vs individual factors may be implicated. MCRC seems to follow a familial pattern, whereas individual factors are more important in SCRC. Further studies must be carried out to know the molecular basis of risks for MPCRC in order to modify, if necessary, its clinical management, especially from a preventive point of view.

Learning Predictive Interactions Using Information Gain and Bayesian Network Scoring

BACKGROUND

The problems of correlation and classification are long-standing in the fields of statistics and machine learning, and techniques have been developed to address these problems. We are now in the era of high-dimensional data, which is data that can concern billions of variables. These data present new challenges. In particular, it is difficult to discover predictive variables, when each variable has little marginal effect. An example concerns Genome-wide Association Studies (GWAS) datasets, which involve millions of single nucleotide polymorphism (SNPs), where some of the SNPs interact epistatically to affect disease status. Towards determining these interacting SNPs, researchers developed techniques that addressed this specific problem. However, the problem is more general, and so these techniques are applicable to other problems concerning interactions. A difficulty with many of these techniques is that they do not distinguish whether a learned interaction is actually an interaction or whether it involves several variables with strong marginal effects.

METHODOLOGY/FINDINGS

We address this problem using information gain and Bayesian network scoring. First, we identify candidate interactions by determining whether together variables provide more information than they do separately. Then we use Bayesian network scoring to see if a candidate interaction really is a likely model. Our strategy is called MBS-IGain. Using 100 simulated datasets and a real GWAS Alzheimer's dataset, we investigated the performance of MBS-IGain.

CONCLUSIONS/SIGNIFICANCE

When analyzing the simulated datasets, MBS-IGain substantially out-performed nine previous methods at locating interacting predictors, and at identifying interactions exactly. When analyzing the real Alzheimer's dataset, we obtained new results and results that substantiated previous findings. We conclude that MBS-IGain is highly effective at finding interactions in high-dimensional datasets. This result is significant because we have increasingly abundant high-dimensional data in many domains, and to learn causes and perform prediction/classification using these data, we often must first identify interactions.

Evaluation of a two-stage framework for prediction using big genomic data

We are in the era of abundant 'big' or 'high-dimensional' data. These data afford us the opportunity to discover predictors of an event of interest, and to estimate occurrence of the event based on values of these predictors. For example, 'genome-wide association studies' examine millions of single-nucleotide polymorphisms (SNPs), along with disease status. We can learn SNPs that affect disease status from these data sets, and use the knowledge learned to predict disease likelihood. Owing to the large number of features, it is difficult for many prediction methods to use all the features directly. The ReliefF algorithm ranks a set of features in terms of how well they predict a target. It can be used to identify good predictors, which can then be provided to a prediction method. We compared the performance of eight prediction methods when predicting binary outcomes using high-dimensional discrete data sets. We performed two-stage prediction, where ReliefF is used in the first stage to identify good predictors. Bayesian network (BN)-based methods performed best overall. Furthermore, ReliefF did not improve their performance. The BN-based methods use the Bayesian Dirichlet Equivalent Uniform score to evaluate candidate models, and use BN inference algorithms to perform prediction. This score and these algorithms were developed for discrete variables. This perhaps explains why they perform better in this domain. Many prediction methods are available, and researchers have little reason for choosing one over the other in the domain of binary prediction using high-dimensional data sets. Our results indicate that the best choices overall are BN-based methods.

Rapid dissemination of RET-transgene-driven melanoma in the presence of non-obese diabetic alleles: Critical roles of Dectin-1 and Nitric-oxide synthase type 2

Mice transgenic for the RET oncogene provide a remarkable model for investigating the mechanisms underlying the promotion and the development of melanoma. This model was established on the C57BL/6 genetic background. In the present study, we investigated an effect of the strongly proinflammatory and autoimmune genetic makeup of the non-obese diabetic (NOD) strain. We bred (NODxB6)F1 mice and backcrossed them with NOD mice. F1 mice and mice at subsequent generations of backcrossing showed marked acceleration of tumor development, in particular with a more frequent and earlier extension of the primary uveal melanoma. In close relation with this severe evolution, we observed a profound drop in Dectin-1 expression on CD11b(+)Ly6G(+) granulocytic myeloid cells correlating with an expansion of CD4(+)Foxp3(+) T regulatory cell and of interferon(IFN)γ-producing CD8(+) T cell subsets in tumors. IFNγ is a major inducer of the type 2 nitric-oxide synthase (Nos2) gene whose products are known to be tumorigenic. Germline inactivation of the Nos2 gene was associated with a dramatically improved tumor prognosis and a restoration of Dectin-1 expression on myeloid cells. Moreover, in vivo treatment of (NODxB6)F1.RET(+) mice with curdlan, a glucose polymer that binds Dectin-1, prevented tumor extension and was associated with marked reduction of the CD4(+)Foxp3(+) T cell subset. These observations highlight the (NODxB6)F1.RET(+) mice as a new model to investigate the role of the immune system in the host-tumor relationship and point to Dectin-1 and Nos2 as potentially promising therapeutic targets.

XRCC1 Arg194Trp and Arg399Gln polymorphisms and risk of extrahepatic cholangiocarcinoma: a hospital-based case-control study in China

BACKGROUND

Extrahepatic cholangiocarcinoma (ECCA) is a rare but devastating malignancy. Up to 90% of patients presenting with ECCA have no identifiable risk factors. The base excision repair (BER) pathway has a principal role in the repair of mutations caused by oxidized or reduced bases. The XRCC1 is one of the key proteins in the BER pathway. In this study, we investigated the influence of XRCC1 Arg194Trp and Arg399Gln polymorphisms on ECCA incidence.

METHODS

The study included 189 ECCA patients and 216 controls. Genotypes were detected by polymerase chain reaction restriction fragment length polymorphism (PCR-RFLP) method.

RESULTS

For codon 194, the genotype frequencies of C/C, T/C and T/T were 51.3, 43.4 and 5.3%, respectively, in the ECCA cases compared with 54.2, 38.9 and 6.9%, respectively, in the controls. No statistically significant differences were observed in the genotype frequencies of codon 194 between the two groups compared to the control (TC, OR: 0.85, 95% CI: 0.57-1.28, TT, OR: 1.24, 95% CI: 0.54-2.89, TC+TT, OR: 0.89, 95% CI: 0.60-1.32). For codon 399, the genotype frequencies of G/G, G/A and A/A were 54.0, 37.0 and 9.0%, respectively, in the ECCA cases compared with 56.1, 39.8 and 4.1%, respectively, in the controls. No statistically significant differences were observed in the genotype frequencies codon 399 between the two groups compared to the control (GA, OR: 1.04, 95% CI: 0.69-1.56, AA, OR: 0.45, 95% CI: 0.19-1.04, GA+AA, OR: 0.92, 95% CI: 0.62-1.36). Meanwhile, no statistically significant differences were found in the haplotype and risk of developing ECCA compared to the control (CA, OR: 0.83, 95% CI: 0.49-1.39, TG, OR: 0.96, 95% CI: 0.58-1.60, TA, OR: 0.83, 95% CI: 0.38-1.82).

CONCLUSION

The present study suggested that Arg194Trp and Arg399Gln polymorphism in the DNA repair gene XRCC1 was not statistically associated with risk of ECCA. It would be necessary to confirm these findings in a large sample size and multiethnic population study in future.

The Ser326Cys polymorphism of hOGG1 is associated with intrahepatic cholangiocarcinoma susceptibility in a Chinese population

OBJECTIVE

Intrahepatic cholangiocarcinoma is a rare disease whose etiology is far from clear, the Ser326Cys polymorphism in human 8-hydroxyguanine glycosylase (hOGG1) has been shown associated with various cancers, however, the association of Ser326Cys (rsl052133) polymorphism and intrahepatic cholangiocarcinoma susceptibility has not been clarified. The purpose of this study is to investigate whether this polymorphism is related to the genetic susceptibility of intrahepatic cholangiocarcinoma.

METHODS

A total 150 patients and 150 normal people were included in this study, the Ser326Cys polymorphisms in each group were genotyped using PCR-RFLP method.

RESULTS

We found that individuals carrying Cys/Cys genotype were exposed to higher riskof intrahepatic cholangiocarcinoma (OR=2.924, 95% CI=1.475-5.780) compared with the individuals with wild type genotype Ser/Ser. Further analysis revealed that male individuals carrying Cys/Cys genotype also had increased risk (OR=2.762, 95% CI=1.233-6.173), whereas no significant difference was observed in female group.

CONCLUSIONS

Therefore, our data indicates that the Ser326Cys (rs1052133) polymorphism is associated with intrahepatic cholangiocarcinoma susceptibility, and it shows preference in male population.

Polymorphisms in the kinesin-like factor 1 B gene and risk of epithelial ovarian cancer in Eastern Chinese women

The kinesin-like factor 1 B (KIF1B) gene plays an important role in the process of apoptosis and the transformation and progression of malignant cells. Genetic variations in KIF1B may contribute to risk of epithelial ovarian cancer (EOC). In this study of 1,324 EOC patients and 1,386 cancer-free female controls, we investigated associations between two potentially functional single nucleotide polymorphisms in KIF1B and EOC risk by the conditional logistic regression analysis. General linear regression model was used to evaluate the correlation between the number of variant alleles and KIF1B mRNA expression levels. We found that the rs17401966 variant AG/GG genotypes were significantly associated with a decreased risk of EOC (adjusted odds ratio (OR) = 0.81, 95 % confidence interval (CI) = 0.68-0.97), compared with the AA genotype, but no associations were observed for rs1002076. Women who carried both rs17401966 AG/GG and rs1002076 AG/AA genotypes of KIF1B had a 0.82-fold decreased risk (adjusted 95 % CI = 0.69-0.97), compared with others. Additionally, there was no evidence of possible interactions between about-mentioned co-variants. Further genotype-phenotype correlation analysis indicated that the number of rs17401966 variant G allele was significantly associated with KIF1B mRNA expression levels (P for GLM = 0.003 and 0.001 in all and Chinese subjects, respectively), with GG carriers having the lowest level of KIF1B mRNA expression. Taken together, the rs17401966 polymorphism likely regulates KIF1B mRNA expression and thus may be associated with EOC risk in Eastern Chinese women. Larger, independent studies are warranted to validate our findings.

ACN9 and alcohol dependence: family-based association analysis in multiplex alcohol dependence families

A previous genome-wide linkage study of alcohol dependence (AD) in the Pittsburgh-based multiplex family study found suggestive evidence for linkage on Chromosome 7q, a region in which the ACN9 gene is located. Using the same two generation Pittsburgh family data in which linkage was found, data for a third generation was added. The expanded sample included 133 pedigrees with 995 individuals. Finer mapping was undertaken using six SNPs extending from rs1917939 to rs13475 with minor allele frequency (MAF) ≥0.15 and pair-wise linkage disequilibrium (LD) of r(2) <0.8 using the HapMap CEU population. Binary affection status, visual, and auditory P300 data were tested for family-based association. Family-based analyses found all six SNPs associated with affected status. Three SNPs are located upstream of the gene, two SNPs are within intron 1 and one is in Exon 4. FBAT P-values for the six SNPs ranged between 0.05 and 0.0005. Haplotype analysis revealed one four-SNP block formed by rs10499934, rs7794886, rs12056091, and rs13475 with an overall significant association at P = 0.0008. Analysis of visual P300 amplitude data, a known endophenotype of alcohol dependence risk, revealed a significant association for SNPs within intron 1 and exon 4 under a dominant model of transmission. Family-based association analysis shows the ACN9 gene significantly associated with alcohol dependence and P300 amplitude variation. The potential importance of the ACN9 gene for AD risk may be related to its role in gluconeogenesis which may be involved in the regulation of alcohol metabolism.

Extensive genomic variability of knops blood group polymorphisms is associated with sickle cell disease in Africa

Sickle cell disease (SCD) is a multisystem disorder characterized by chronic hemolytic anemia, vaso-occlusive crises, and marked variability in disease severity. Patients require transfusions to manage disease complications, with complements, directed by complement regulatory genes (CR1) and its polymorphisms, implicated in the development of alloantibodies. We hypothesize that CR1 polymorphisms affect complement regulation and function, leading to adverse outcome in SCD. To this end, we determined the genomic diversity of complement regulatory genes by examining single nucleotide polymorphisms associated with Knops blood group antigens. Genomic DNA samples from 130 SCD cases and 356 control Africans, 331 SCD cases and 497 control African Americans, and 254 Caucasians were obtained and analyzed, utilizing a PCR-RFLP (polymerase chain reaction-restriction fragment length polymorphism) assay. Analyzing for ethnic diversity, we found significant differences in the genotypic and allelic frequencies of Sl1/Sl2 (rs17047661) and McCa/b (rs17047660) polymorphisms between Africans, African Americans, and Caucasians (P < 0.05). The homozygote mutant variants had significantly higher frequencies in Africans and African Americans but were insignificant in Caucasians (80.2% and 59.6% vs 5.9% for Sl1/2; and 36% and 24% vs 1.8% for McCa/b). With SCD, we did not detect any difference among cases and controls either in Africa or in the United States. However, we found significant difference in genotypic (P < 0.0001) and allelic frequencies (P < 0.0001) of Sl1/Sl2 (rs17047661) and McCa/b (rs17047660) polymorphisms between SCD groups from Africa and the United States. There was no difference in haplotype frequencies of these polymorphisms among or between groups. The higher frequency of CR1 homozygote mutant variants in Africa but not United States indicates a potential pathogenic role, possibly associated with complicated disease pathophysiology in the former and potentially protective in the latter. The difference between sickle cell groups suggests potential genetic drift or founder effect imposed on the disease in the United States, but not in Africa, and a possible confirmation of the ancestral susceptibility hypothesis. The lower haplotype frequencies among sickle cell and control populations in the United States may be due to the admixture and the dilution of African genetic ancestry in the African American population.

LEAP: biomarker inference through learning and evaluating association patterns

Single nucleotide polymorphism (SNP) high-dimensional datasets are available from Genome Wide Association Studies (GWAS). Such data provide researchers opportunities to investigate the complex genetic basis of diseases. Much of genetic risk might be due to undiscovered epistatic interactions, which are interactions in which combination of several genes affect disease. Research aimed at discovering interacting SNPs from GWAS datasets proceeded in two directions. First, tools were developed to evaluate candidate interactions. Second, algorithms were developed to search over the space of candidate interactions. Another problem when learning interacting SNPs, which has not received much attention, is evaluating how likely it is that the learned SNPs are associated with the disease. A complete system should provide this information as well. We develop such a system. Our system, called LEAP, includes a new heuristic search algorithm for learning interacting SNPs, and a Bayesian network based algorithm for computing the probability of their association. We evaluated the performance of LEAP using 100 1,000-SNP simulated datasets, each of which contains 15 SNPs involved in interactions. When learning interacting SNPs from these datasets, LEAP outperformed seven others methods. Furthermore, only SNPs involved in interactions were found to be probable. We also used LEAP to analyze real Alzheimer's disease and breast cancer GWAS datasets. We obtained interesting and new results from the Alzheimer's dataset, but limited results from the breast cancer dataset. We conclude that our results support that LEAP is a useful tool for extracting candidate interacting SNPs from high-dimensional datasets and determining their probability.

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.

Identification of Stmm3 locus conferring resistance to late-stage chemically induced skin papillomas on mouse chromosome 4 by congenic mapping and allele-specific alteration analysis

Genome-wide association studies have revealed that many low-penetrance cancer susceptibility loci are located throughout the genome; however, a very limited number of genes have been identified so far. Using a forward genetics approach to map such loci in a mouse skin cancer model, we previously identified strong genetic loci conferring resistance to chemically induced skin papillomas on chromosome 4 and 7 with a large number of [(FVB/N × MSM/Ms) F₁ × FVB/N] backcross mice. In this report, we describe a combination of congenic mapping and allele-specific alteration analysis of the loci on chromosome 4. We used linkage analysis and a congenic mouse strain, FVB.MSM-Stmm3 to refine the location of Stmm3 (Skin tumor modifier of MSM 3) locus within a physical interval of about 34 Mb on distal chromosome 4. In addition, we used patterns of allele-specific imbalances in tumors from N₂ and N₁₀ congenic mice to narrow down further the region of Stmm3 locus to a physical distance of about 25 Mb. Furthermore, immunohistochemical analysis showed papillomas from congenic mice had less proliferative activity. These results suggest that Stmm3 responsible genes may have an influence on papilloma formation in the two-stage skin carcinogenesis by regulating papilloma growth rather than development.

Etiologic field effect: reappraisal of the field effect concept in cancer predisposition and progression

The term 'field effect' (also known as field defect, field cancerization, or field carcinogenesis) has been used to describe a field of cellular and molecular alteration, which predisposes to the development of neoplasms within that territory. We explore an expanded, integrative concept, 'etiologic field effect', which asserts that various etiologic factors (the exposome including dietary, lifestyle, environmental, microbial, hormonal, and genetic factors) and their interactions (the interactome) contribute to a tissue microenvironmental milieu that constitutes a 'field of susceptibility' to neoplasia initiation, evolution, and progression. Importantly, etiological fields predate the acquisition of molecular aberrations commonly considered to indicate presence of filed effect. Inspired by molecular pathological epidemiology (MPE) research, which examines the influence of etiologic factors on cellular and molecular alterations during disease course, an etiologically focused approach to field effect can: (1) broaden the horizons of our inquiry into cancer susceptibility and progression at molecular, cellular, and environmental levels, during all stages of tumor evolution; (2) embrace host-environment-tumor interactions (including gene-environment interactions) occurring in the tumor microenvironment; and, (3) help explain intriguing observations, such as shared molecular features between bilateral primary breast carcinomas, and between synchronous colorectal cancers, where similar molecular changes are absent from intervening normal colon. MPE research has identified a number of endogenous and environmental exposures which can influence not only molecular signatures in the genome, epigenome, transcriptome, proteome, metabolome and interactome, but also host immunity and tumor behavior. We anticipate that future technological advances will allow the development of in vivo biosensors capable of detecting and quantifying 'etiologic field effect' as abnormal network pathology patterns of cellular and microenvironmental responses to endogenous and exogenous exposures. Through an 'etiologic field effect' paradigm, and holistic systems pathology (systems biology) approaches to cancer biology, we can improve personalized prevention and treatment strategies for precision medicine.