Prediction of inherited genomic susceptibility to 20 common cancer types by a supervised machine-learning method.

Prevention and early intervention are the most effective ways of avoiding or minimizing psychological, physical, and financial suffering from cancer. However, such proactive action requires the ability to predict the individual's susceptibility to cancer with a measure of probability. Of the triad of cancer-causing factors (inherited genomic susceptibility, environmental factors, and lifestyle factors), the inherited genomic component may be derivable from the recent public availability of a large body of whole-genome variation data. However, genome-wide association studies have so far showed limited success in predicting the inherited susceptibility to common cancers. We present here a multiple classification approach for predicting individuals' inherited genomic susceptibility to acquire the most likely phenotype among a panel of 20 major common cancer types plus 1 "healthy" type by application of a supervised machine-learning method under competing conditions among the cohorts of the 21 types. This approach suggests that, depending on the phenotypes of 5,919 individuals of "white" ethnic population in this study, (i) the portion of the cohort of a cancer type who acquired the observed type due to mostly inherited genomic susceptibility factors ranges from about 33 to 88% (or its corollary: the portion due to mostly environmental and lifestyle factors ranges from 12 to 67%), and (ii) on an individual level, the method also predicts individuals' inherited genomic susceptibility to acquire the other types ranked with associated probabilities. These probabilities may provide practical information for individuals, heath professionals, and health policymakers related to prevention and/or early intervention of cancer

DNA methylation profiling to assess pathogenicity of BRCA1 unclassified variants in breast cancer

Germline pathogenic mutations in BRCA1 increase risk of developing breast cancer. Screening for mutations in BRCA1 frequently identifies sequence variants of unknown pathogenicity and recent work has aimed to develop methods for determining pathogenicity. We previously observed that tumor DNA methylation can differentiate BRCA1-mutated from BRCA1-wild type tumors. We hypothesized that we could predict pathogenicity of variants based on DNA methylation profiles of tumors that had arisen in carriers of unclassified variants. We selected 150 FFPE breast tumor DNA samples [47 BRCA1 pathogenic mutation carriers, 65 BRCAx (BRCA1-wild type), 38 BRCA1 test variants] and analyzed a subset (n=54) using the Illumina 450K methylation platform, using the remaining samples for bisulphite pyrosequencing validation. Three validated markers (BACH2, C8orf31, and LOC654342) were combined with sequence bioinformatics in a model to predict pathogenicity of 27 variants (independent test set). Predictions were compared with standard multifactorial likelihood analysis. Prediction was consistent for c.5194-12G>A (IVS 19-12 G>A) (P>0.99); 13 variants were considered not pathogenic or likely not pathogenic using both approaches. We conclude that tumor DNA methylation data alone has potential to be used in prediction of BRCA1 variant pathogenicity but is not independent of estrogen receptor status and grade, which are used in current multifactorial models to predict pathogenicity

Genome-Wide Expression of Azoospermia Testes Demonstrates a Specific Profile and Implicates ART3 in Genetic Susceptibility

Infertility affects about one in six couples attempting pregnancy, with the man responsible in approximately half of the cases. Because the pathophysiology underlying azoospermia is not elucidated, most male infertility is diagnosed as idiopathic. Genome-wide gene expression analyses with microarray on testis specimens from 47 non-obstructive azoospermia (NOA) and 11 obstructive azoospermia (OA) patients were performed, and 2,611 transcripts that preferentially included genes relevant to gametogenesis and reproduction according to Gene Ontology classification were found to be differentially expressed. Using a set of 945 of the 2,611 transcripts without missing data, NOA was further categorized into three classes using the non-negative matrix factorization method. Two of the three subclasses were different from the OA group in Johnsen's score, FSH level, and/or LH level, while there were no significant differences between the other subclass and the OA group. In addition, the 52 genes showing high statistical difference between NOA subclasses (p < 0.01 with Tukey's post hoc test) were subjected to allelic association analyses to identify genetic susceptibilities. After two rounds of screening, SNPs of the ADP-ribosyltransferase 3 gene (ART3) were associated with NOA with highest significance with ART3-SNP25 (rs6836703; p = 0.0025) in 442 NOA patients and 475 fertile men. Haplotypes with five SNPs were constructed, and the most common haplotype was found to be under-represented in patients (NOA 26.6% versus control 35.3%, p = 0.000073). Individuals having the most common haplotype showed an elevated level of testosterone, suggesting a protective effect of the haplotype on spermatogenesis. Thus, genome-wide gene expression analyses were used to identify genes involved in the pathogenesis of NOA, and ART3 was subsequently identified as a susceptibility gene for NOA. These findings clarify the molecular pathophysiology of NOA and suggest a novel therapeutic target in the treatment of NOA

Glioblastoma and the search for non-hypothesis driven combination therapeutics in academia

Glioblastoma (GBM) remains a cancer of high unmet clinical need. Current standard of care for GBM, consisting of maximal surgical resection, followed by ionisation radiation (IR) plus concomitant and adjuvant temozolomide (TMZ), provides less than 15-month survival benefit. Efforts by conventional drug discovery to improve overall survival have failed to overcome challenges presented by inherent tumor heterogeneity, therapeutic resistance attributed to GBM stem cells, and tumor niches supporting self-renewal. In this review we describe the steps academic researchers are taking to address these limitations in high throughput screening programs to identify novel GBM combinatorial targets. We detail how they are implementing more physiologically relevant phenotypic assays which better recapitulate key areas of disease biology coupled with more focussed libraries of small compounds, such as drug repurposing, target discovery, pharmacologically active and novel, more comprehensive anti-cancer target-annotated compound libraries. Herein, we discuss the rationale for current GBM combination trials and the need for more systematic and transparent strategies for identification, validation and prioritisation of combinations that lead to clinical trials. Finally, we make specific recommendations to the preclinical, small compound screening paradigm that could increase the likelihood of identifying tractable, combinatorial, small molecule inhibitors and better drug targets specific to GBM.Peer reviewe

흡연 관련 후성유전학 지표 발굴 연구

학위논문(박사)--서울대학교 대학원 :보건대학원 보건학과(보건학전공),2019. 8. 성주헌.흡연은 폐/심혈관계 질환 및 폐암 등 여러 질환들에 대한 교정 가능한 (modifiable) 위험 요인 임에도 불구하고, 흡연의 기여 사망률은 전세계적으로 11.5 % 에 달한다. 이러한 흡연 관련 건강영향을 평가하기 위해서는 정확한 흡연 노출 및 노출량 측정이 선행되어야 한다. 흡연 노출 평가에 이용되는 대표적인 방법에는 자가 보고 (self-report) 기반 설문 도구와 더불어, 코티닌 (cotinine) 과 같은 체내의 흡연 관련 대사체의 농도를 측정하는 등 생체 지표 (biomarker) 들을 이용하는 방법이 있으나, 최근 흡연 노출만을 제한적으로 반영한다는 한계점을 가진다. 이에 따라, 현재 뿐만 아니라 과거 흡연 노출을 반영하는 지속성 및 안정성을 보이는 지표들을 발굴하기 위하여 흡연 관련 후성유전 연구가 활발히 진행되고 있다. 후성유전 (Epigenetics) 은 DNA 염기서열 상의 변화 없이 유전자의 발현에 영향을 주는 유전적 현상을 가리키며, DNA 메틸화 (DNA methylation) 는 유전적인 요인 뿐만 아니라 생애 전반에 걸쳐 노출되는 여러가지 환경적인 요인들에 의해서 결정되는 대표적인 후성유전학적 지표이다. DNA 메틸화는 가변적인 특성 때문에 특정한 환경적 요인에 의한 특이적인 DNA 메틸화 변화를 발굴하기 위해서는 여러 가지 잠재 교란 요인들이 통제되어야 한다. 이에 따라, 본 연구는 유전 및 환경적 요인에 의한 교호 작용을 통제할 수 있는 일란성 쌍둥이 및 그 직계 가족들의 샘플을 이용하여 흡연 노출에 대해 특이적으로 변화하는 DNA 메틸화 지표를 발굴하고자 수행되었다. 먼저, 전장 후성유전체 연관 분석을 통해 흡연 여부에 따른 쌍둥이 간의 DNA 메틸화 차이를 관찰하는 분석을 수행하였다. 나아가, DNA 메틸화 수준의 변화와 연관된 단일염기성다형성 (Single nucleotide polymorphisms, SNP) 변이를 찾는 mQTL (methylation quantitative loci) 분석을 수행하였다. 최종적으로, 연관 분석에서 발굴된 흡연 관련 DNA 메틸화 지표들을 바탕으로 검증 데이터 (validation set) 의 각 샘플들에 대하여 DNA 메틸화 점수를 부여하여, DNA 메틸화 기반 점수의 흡연 예측 능력을 평가하였다. DNA 메틸화 분석을 위해 한국인 가족-쌍둥이 (KHT) 코호트 및 호주의 Australian Mammographic Density Twins and Sisters Study (AMDTSS) 코호트로 부터 각각 534명 및 132명의 대상자들이 포함되었다. KHT 코호트와 AMDTSS 코호트에서 각각 156쌍, 66 쌍의 일란성 쌍둥이 대상자들이 분석에 포함되었다. 말초 혈액 백혈구 샘플에서 DNA 를 추출한 다음, KHT 의 385 명의 대상자 및 AMDTSS 의 모든 대상자들의 샘플은 Illumina 사의 Infinium HumanMethylation 450 BeadChip 로 어세이하여 유전체 내 약 450,000 개 이상의 DNA 메틸화 위치에 대한 DNA 메틸화 수준의 데이터를 얻었으며, 총 149 명의 KHT 코호트의 샘플들은 Illumina 사의 Infinium MethylationEPIC BeadChip 로 어세이하여 유전체 내 총 850,000개 이상의 위치에 대한 DNA 메틸화 정도를 측정하였다. R 소프트웨어의 생물정보학 관련 패키지들을 이용하여 기존 대규모 메타 연구에서 밝혀진 18,000 개 가량의 흡연 관련 DNA 메틸화 지표에 대해 일란성 쌍둥이 내의 DNA 메틸화의 차이를 평가하고, 나아가 KHT 및 AMDTSS 코호트의 결과로 메타 분석을 수행하였다. 또한, 약 18,000 개의 각 DNA 메틸화 지표에 대해서 ±1Mb 위치 내의 SNP 과의 연관성을 검정하는 mQTL 분석을 수행하였다. 나아가, DNA 메틸화 점수는 크게 다음과 같이 총 3가지 모형에 대한 흡연 예측 능력을 평가 및 비교하였다. (1) 유의 수준 5X10-5 미만의 흡연과의 연관성을 보인 DNA 메틸화 지표들로 구성된 세트, (2) 유의 수준 0.05 미만의 DNA 메틸화 지표 중 mQTL의 영향을 받는 지표들을 제거한 나머지 지표들로 구성된 세트 및 (3) mQTL의 영향을 고려하지 않은 세트에 대해 DNA 메틸화 점수를 계산하여 흡연 여부에 대한 예측력을 평가하였다. 후성유전체 연관 메타 분석에 따르면, AHRR (cg23576855), ALPPL2 (cg21566642, cg01940273, cg05951221), MYO1G (cg12803068) 와 F2RL3 (cg03636183) 등의 유전자좌 내의 CpG 위치에서 DNA 메틸화 수준의 차이가 관찰되었다. mQTL 분석에서는 기존 연구에서 밝혀진 흡연 관련 DNA 메틸화 지표 중 약 19.6%가 적어도 하나의 근위 단일염기성다형성과 연관이 있는 것으로 확인되었다. 상위 연관 지표들로 계산되 DNA 메틸화 점수는 흡연 여부에 대한 예측력 (AUC) 은 검증 데이터 세트에 따라 약 0.84~0.92으로 계산되었다. 유의 수준 0.05 미만의 지표 중 mQTL 의 영향을 받는 DNA 메틸화 지표들을 제거한 세트의 AUC는 0.65~0.78, mQTL 과의 연관성을 고려하지 않은 세트의 AUC 는 0.61~0.75 에 비해 통계적으로 유의한 수준으로 높았다. 본 연구는 일란성 쌍둥이 및 가족 연구를 바탕으로 기존에 알려진 흡연 관련 후정유전학 지표들 중에 유전적 변이를 받는 지표들과 흡연 노출을 특이적으로 반영하는 지표들을 구분하고, 각 지표들의 흡연에 대한 예측 성능을 비교하였다. 나아가, 흡연 관련 DNA 메틸화 지표는 정확하게 흡연 노출력을 평가하고, 흡연 관련 건강 영향 평가에 활용될 것으로 기대된다.Introduction: Mounting evidence suggests that both genetics and environments shape DNA methylation (DNAm) status throughout lifetime. Little is known about reproducible DNAm changes that are specifically induced by environmental exposure. This study aimed to identify exposure-specific DNAm changes, particularly due to smoking. We first investigated smoking-associated DNAm changes in monozygotic (MZ) twins. CpG sites (CpGs) associated with smoking were subsequently examined for possible genetic control by methylation quantitative loci (mQTL). Finally, we evaluated DNAm score using smoking-associated CpGs for prediction of smoking. Methods: We obtained peripheral blood DNAm data of 385 samples (95 pairs of MZ twins for the discovery set and 195 non-MZ twin first-degree relatives for the validation set) from the Korean Healthy Twin (KHT) using Illuminas HumanMethylation450 array. An additional validation set of 149 samples (61 pairs of MZ twins and their first degree relatives) from the KHT were analyzed using Illuminas Infinium MethylationEPIC BeadChip array. We also obtained peripheral blood DNAm data of 479 individuals (66 pairs of MZ twins for the discovery set and 347 non-MZ twins for the validation set) from the Australian Mammographic Density Twins and Sisters Study (AMDTSS), using Illuminas Infinium HumanMethylation450 array. We tested associations between smoking and DNAm changes across >18,000 CpGs that were previously reported to be associated with smoking. After assessing study-specific smoking-associated CpGs, we meta-analyzed the two studies. To identify genetic control over DNAm, we performed methylation quantitative loci (mQTLs) analyses using the KHT genotype data of a total of 289 individuals, followed by subsequent examinations of whether those mQTLs are associated with smoking. Finally, we computed weighted DNAm score to assess its performance for prediction of smoking. Results: In the KHT MZ twins, 8 CpGs were significantly associated with high-dose smoking exposure (≥10 pack-years) at the suggestive significance threshold of p18,000 smoking-related CpGs were significantly associated with at least one proximal SNP (cis-mQTL) at Bonferroni-corrected p<0.05. 185 (22.1%) out of the smoking-associated 838 CpGs (meta-analysis of associations p<0.05) were associated with cis-mQTLs (Bonferrnoi-corrected p<0.05). None of the significant mQTLs were associated with smoking. DNAm score based on smoking-associated CpGs (p<5e-5) was computed for prediction of smoking, yielding an AUC of 0.917, 0.895 and 0.84 for the KHT I and II and the AMDTSS validation sets, respectively. With the exclusion of mQTL-associated CpGs (association p<0.05 with smoking), AUC has significantly improved (0.745 to 0.777, 0.7 to 0.734 and 0.61 to 0.646 for the KHT I and II and the AMDTSS validation sets, respectively). Discussion: We found epigenetic signatures of smoking across multiple loci including AHRR, 2q37.1, MYO1G and F2RL3. ~20% of the previously reported smoking-associated CpGs were under significant genetic control. DNAm score using the most significant CpGs was informative of predicting high-dose ever-smoking status. CpGs that are independent of effects of mQTLs showed superior performance in predicting smoking. A set of DNAm-associated markers may serve as a stable biomarker of exposure to smoking.Abstract 1 List of Tables 7 List of Figures 9 I. Introduction 11 1. Epigenetics 11 1.1 Overview of Epigenetics 11 1.2 DNA Methylation 12 1.3 Genome-wide DNA Methylation Profiling 13 1.4 Epigenome-wide Association Study (EWAS) 15 2. Epidemiology of Smoking: Health Consequences and Assessment of Exposure to Smoking 17 2.1 Health Consequences of Exposure to Smoking 17 2.2 Assessment of Exposure to Smoking: Biomarkers of Smoking 18 3. Objectives 21 II. Mini-review: Normalization and Cell-type Heterogeneity Deconvolution 23 1. Normalization 23 1.1 Within-array Normalization 24 1.2 Between-array Normalization 25 2. Cell-type Heterogeneity and Deconvolution 26 2.1 Reference-based Cell-type Deconvolution 26 2.2 Reference-free Cell-type Deconvolution 27 2.3 Choice of Cell-type Deconvolution Algorithms 28 III. Profiling Smoking-Associated DNA Methylation Changes 30 1. Material and Methods 30 1.1 Study Design and Population 30 1.2 DNA Methylation Data 31 1.3 Genotype Data 33 1.4 Genome-wide DNA Methylation Associations with Smoking 34 1.5 mQTL Analysis 35 2. Results 38 2.1 Characteristics of the Study Population 38 2.2 Smoking-associated DNA Methylation Changes 39 2.3 Genetic Influences of Smoking-associated DNA Methylation Changes 43 3. Discussion 45 IV. Prediction of Exposure to Smoking Using DNA Methylation Score 76 1. Material and Methods 76 1.1 DNA Methylation-based Score 76 1.2 Assessment of Performance of DNAm-based Score 79 2. Results 81 2.1 DNA Methylation Score by Smoking Status 81 2.2 Prediction of Smoking Exposure using DNA Methylation Score 84 2.3 Improvement of Prediction of Smoking Using DNA Methylation Score by Marker Sets 85 3. Discussion 88 V. References 113 VI. Abstract in Korean (국문 초록) iDocto

THREE METHODS TO INCREASE THE LIKELY TO IDENTIFY GENE INVOLVED IN COMPLEX DISEASE

The large part of human pathology is composed by complex disease, such as heart disease, obesity, cancer, diabetes, and many common psychiatric and neurological conditions. The common feature of all these conditions is the multifactorial etiology that involves both genetic and environmental factors. The common disease-common variant (CDCV) hypothesis posits that common, interacting alleles underlie most common diseases, in association with environmental factors. Furthermore, according to the thrift genotype, such alleles have been subjected to selective pressure, mainly those involved in metabolic disease such as T2DM and obesity. Although the concept of gene-environment interaction is central to ecogenetics, and has long been recognized by geneticists (Haldane 1946), there are relatively few detailed descriptions of gene–environment interaction in biomedical literature. This lacking may be explained by difficulties in collecting environmental information of enough quality and by great difficulties in analyze them. Indeed, when the number of factors to analyze is large, become overwhelming the course of dimensionality and the multiple testing problems. In the present thesis the hypothesis that knowledge-driven approaches may improve the ability to identify genes involved in complex disease was checked. Three approaches have been presented, each of them leading to the identification of a factor or of a interaction of factors. As the study a complex disease is composed by three steps: (1) selection of candidate genes, (2) collecting of genetic and non-genetic information and (3) statistical analysis of data, it is showed that each of these steps may be improved by consideration of the biological background. The first study, regarded the possibility to exploit evolutionary information to identify genes involved in type 2 diabetes. This hypothesis was based on the thrifty genotype hypothesis. A gene was identified, ACO1, and was successfully associated to the disease. In the second study, we analyses the case of a gene, PPAGγ that have been inconsistency associated with obesity. We hypothesized that the inconsistence of association may be due to its relationship with environment. Then we jointly analyzed the genotype of the gene and comprehensive nutritional information about a cohort and proved an interaction. The genotype of PPARγ modulated the response to the diet. Ala-carriers gained more weight than ProPro individuals when had the same caloric intake. In the third study, we implemented a software tool to create simulated populations based on gene-environment interactions. The system was based on genetic information to simulate realistic populations. We used these simulated populations to collect information on statistical methods more frequently used to study case-controls samples. Afterward, we built an ensemble of these methods and applied it to a real sample. We showed that ensemble had better performances of each single methods in condition of small sample size

Evolutionary analysis of mammalian genomes and associations to human disease

Statistical models of DNA sequence evolution for analysing protein-coding genes can be used to estimate rates of molecular evolution and to detect signals of natural selection. Genes that have undergone positive selection during evolution are indicative of functional adaptations that drive species differences. Genes that underwent positive selection during the evolution of humans and four mammals used to model human diseases (mouse, rat, chimpanzee and dog) were identified, using maximum likelihood methods. I show that genes under positive selection during human evolution are implicated in diseases such as epithelial cancers, schizophrenia, autoimmune diseases and Alzheimer’s disease. Comparisons of humans with great apes have shown such diseases to display biomedical disease differences, such as varying degrees of pathology, differing symptomatology or rates of incidence. The chimpanzee lineage was found to have more adaptive genes than any of the other lineages. In addition, evidence was found to support the hypothesis that positively selected genes tend to interact with each other. This is the first such evidence to be detected among mammalian genes and may be important in identifying molecular pathways causative of species differences. The genome scan analysis spurred an in*depth evolutionary analysis of the nuclear receptors, a family of transcription factors. 12 of the 48 nuclear receptors were found to be under positive selection in mammalia. The androgen receptor was found to have undergone positive selection along the human lineage. Positively selected sites were found to be present in the major activation domain, which has implications for ligand recognition and binding. Studying the evolution of genes which are associated with biomedical disease differences between species is an important way to gain insight into the molecular causes of diseases and may provide a method to predict when animal models do not mirror human biology