Identification of chromosomal rearrangements in colorectal cancer

Curs 2014-2015Cancer research is continuously shedding light into these worldwide leading diseases. It is mandatory to have higher knowledge in cancer biology to consequently find out new candidate biomarkers and therapeutics. Among all of them, Colorectal cancer is the most commonly seen of human malignant cancers and has the third highest mortality rate[1]. Since the release of the first human genome sequence in 2004, new techniques have revolutionised the study of genetics and its possible applications. A broad type of studies has been carried out; being Single Nucleotide Polymorphisms and Copy Number Variants the most intensively studied analysis. However, other kinds of mutations involving larger parts of the genome, the so-called structural variants, have been substantially less analyzed due to technical limitations. High-throughput sequencing methods seem to have lowered these restrictions. In this study, gene fusions have been searched in whole exome sequencing samples taking 42 paired normal and cancer tissues. Beginning with short-read files obtained with the mentioned method, they have been aligned against a reference genome to later be analyzed with Breakdancer, a structural variant calling algorithm. After some filtering criteria performed in order to remove a high proportion of false positives, a highly probable list of 22 balanced structural variants (translocations and/or inversions) has been manually studied to get a final result of 20 chromosomal rearrangements, 8 of which are considered gene fusions. In addition, it has been found that one recurrent translocation seen in recent studies is indeed a false positive. Further studies taking into account these results may contribute to the findings of new biomarkers for certain subtypes of colorectal cancer.Director/a: Victor Moreno, co-director: Mireia Olivell

MorbiNet study. Hypothyroidism comorbidity networks in the adult general population

Purpose: Multimorbidity impacts quality of life. We constructed hypothyroidism comorbidity networks to identify positive and negative associations with other prevalent diseases. Methods: We analyzed data of 285,342 patients with hypothyroidism from 3,135,948 adults with multimorbidity in a population-based study in Catalonia, Spain, (period: 2006-2017). We constructed hypothyroidism comorbidity networks using logistic regression models, adjusted by age and sex, and for men and women separately. We considered relevant associations those with odds ratios (OR) >1.2 or <0.8 and p-value <1e-5 to identify coexistence greater (or smaller) than the expected by the prevalence of diseases. Multivariate models considering comorbidities were used to further adjust OR values. Results: The conditions associated included larynx cancer (adjusted OR: 2.48); congenital anomalies (2.26); thyroid cancer (2.13); hyperthyroidism (1.66), vitamin B12/folate deficiency anemia (1.57), and goiter (1.56). The network restricted to men had more connections (mental, cardiovascular, and neurological) and stronger associations with thyroid cancer (7.26 vs 2.55), congenital anomalies (5.11 vs 2.13), hyperthyroidism (4.46 vs 1.69), larynx cancer (3.55 vs 1.67), and goiter (3.94 vs 1.64). After adjustment for comorbidities, OR values were more similar in men and women. The strongest negative associations after adjusting for comorbidities were with HIV/AIDS (OR:0.71), and tobacco abuse (0.77). Conclusions: Networks show direct and indirect hypothyroidism multimorbidity associations. The strongest connections were thyroid and larynx cancer, congenital anomalies, hyperthyroidism, anemia, and goiter. Negative associations included HIV infection-AIDS and tobacco abuse. The network restricted to men had more and stronger associations, but not after adjusting for comorbidities suggesting important indirect interactions

GASVeM: A New Machine Learning Methodology for Multi-SNP Analysis of GWAS Data Based on Genetic Algorithms and Support Vector Machines

Genome-wide association studies (GWAS) are observational studies of a large set of genetic variants in an individual's sample in order to find if any of these variants are linked to a particular trait. In the last two decades, GWAS have contributed to several new discoveries in the field of genetics. This research presents a novel methodology to which GWAS can be applied to. It is mainly based on two machine learning methodologies, genetic algorithms and support vector machines. The database employed for the study consisted of information about 370,750 single-nucleotide polymorphisms belonging to 1076 cases of colorectal cancer and 973 controls. Ten pathways with different degrees of relationship with the trait under study were tested. The results obtained showed how the proposed methodology is able to detect relevant pathways for a certain trait: in this case, colorectal cancer

Novel insights into the molecular mechanisms underlying risk of colorectal cancer from smoking and red/processed meat carcinogens by modeling exposure in normal colon organoids

Tobacco smoke and red/processed meats are well-known risk factors for colorectal cancer (CRC). Most research has focused on studies of normal colon biopsies in epidemiologic studies or treatment of CRC cell lines in vitro. These studies are often constrained by challenges with accuracy of self-report data or, in the case of CRC cell lines, small sample sizes and lack of relationship to normal tissue at risk. In an attempt to address some of these limitations, we performed a 24-hour treatment of a representative carcinogens cocktail in 37 independent organoid lines derived from normal colon biopsies. Machine learning algorithms were applied to bulk RNA-sequencing and revealed cellular composition changes in colon organoids. We identified 738 differentially expressed genes in response to carcinogens exposure. Network analysis identified significantly different modules of co-expression, that included genes related to MSI-H tumor biology, and genes previously implicated in CRC through genome-wide association studies. Our study helps to better define the molecular effects of representative carcinogens from smoking and red/processed meat in normal colon epithelial cells and in the etiology of the MSI-H subtype of CRC, and suggests an overlap between molecular mechanisms involved in inherited and environmental CRC risk. Keywords: colon organoids; microsatellite instability; single-cell deconvolution; smoking; weighted gene co-expression network analysis

Identification of a Twelve-microRNA Signature with Prognostic Value in Stage II Microsatellite Stable Colon Cancer

Simple Summary Colorectal cancer (CRC) is one of the most prevalent cancers, and approximately a quarter of patients diagnosed at stage II exhibit a significant risk of recurrence. In this study, we successfully identified a microRNA (miRNA) signature allowing the recognition of patients at high recurrence risk. The validity of these findings has been confirmed through an entirely separate group of patients diagnosed with stage II microsatellite stability (MSS) colon adenocarcinoma (COAD). Most of the miRNAs present in the signature have demonstrated prognostic relevance in various other cancer types. Upon examining their gene targets, we discovered that some of these miRNAs are intricately involved in pivotal pathways of cancer progression. We aimed to identify and validate a set of miRNAs that could serve as a prognostic signature useful to determine the recurrence risk for patients with COAD. Small RNAs from tumors of 100 stage II, untreated, MSS colon cancer patients were sequenced for the discovery step. For this purpose, we built an miRNA score using an elastic net Cox regression model based on the disease-free survival status. Patients were grouped into high or low recurrence risk categories based on the median value of the score. We then validated these results in an independent sample of stage II microsatellite stable tumor tissues, with a hazard ratio of 3.24, (CI95% = 1.05-10.0) and a 10-year area under the receiver operating characteristic curve of 0.67. Functional analysis of the miRNAs present in the signature identified key pathways in cancer progression. In conclusion, the proposed signature of 12 miRNAs can contribute to improving the prediction of disease relapse in patients with stage II MSS colorectal cancer, and might be useful in deciding which patients may benefit from adjuvant chemotherapy

Identification of intergenerational epigenetic inheritance by whole genome DNA methylation analysis in trios

Genome-wide association studies have identified thousands of loci associated with common diseases and traits. However, a large fraction of heritability remains unexplained. Epigenetic modifications, such as the observed in DNA methylation have been proposed as a mechanism of intergenerational inheritance. To investigate the potential contribution of DNA methylation to the missing heritability, we analysed the methylomes of four healthy trios (two parents and one offspring) using whole genome bisulphite sequencing. Of the 1.5 million CpGs (19%) with over 20% variability between parents in at least one family and compatible with a Mendelian inheritance pattern, only 3488 CpGs (0.2%) lacked correlation with any SNP in the genome, marking them as potential sites for intergenerational epigenetic inheritance. These markers were distributed genome-wide, with some preference to be located in promoters. They displayed a bimodal distribution, being either fully methylated or unmethylated, and were often found at the boundaries of genomic regions with high/low GC content. This analysis provides a starting point for future investigations into the missing heritability of simple and complex traits

Genetic Effects on Transcriptome Profiles in Colon Epithelium Provide Functional Insights for Genetic Risk Loci

Background & aims: The association of genetic variation with tissue-specific gene expression and alternative splicing guides functional characterization of complex trait-associated loci and may suggest novel genes implicated in disease. Here, our aims were as follows: (1) to generate reference profiles of colon mucosa gene expression and alternative splicing and compare them across colon subsites (ascending, transverse, and descending), (2) to identify expression and splicing quantitative trait loci (QTLs), (3) to find traits for which identified QTLs contribute to single-nucleotide polymorphism (SNP)-based heritability, (4) to propose candidate effector genes, and (5) to provide a web-based visualization resource. Methods: We collected colonic mucosal biopsy specimens from 485 healthy adults and performed bulk RNA sequencing. We performed genome-wide SNP genotyping from blood leukocytes. Statistical approaches and bioinformatics software were used for QTL identification and downstream analyses. Results: We provided a complete quantification of gene expression and alternative splicing across colon subsites and described their differences. We identified thousands of expression and splicing QTLs and defined their enrichment at genome-wide regulatory regions. We found that part of the SNP-based heritability of diseases affecting colon tissue, such as colorectal cancer and inflammatory bowel disease, but also of diseases affecting other tissues, such as psychiatric conditions, can be explained by the identified QTLs. We provided candidate effector genes for multiple phenotypes. Finally, we provided the Colon Transcriptome Explorer web application. Conclusions: We provide a large characterization of gene expression and splicing across colon subsites. Our findings provide greater etiologic insight into complex traits and diseases influenced by transcriptomic changes in colon tissue

Meta-Analysis and Validation of a Colorectal Cancer Risk Prediction Model Using Deep Sequenced Fecal Metagenomes

Simple Summary Colorectal cancer (CRC) is the third most common cancer in the world. The gut microbiome, which includes a collection of microbes, is a potential modifiable risk factor. The study of the microbiome is complex and many issues remain unsolved despite the scientific efforts that have been recently made. The present study aimed to build a CRC predictive model performing a meta-analyses of previously published shotgun metagenomics data, and to validate it in a new study. For that purpose, 156 participants of a CRC screening program were recruited, with an even distribution of CRCs, high-risk colonic precancerous lesions, and a control group with normal colonic mucosa. We have identified a signature of 32 bacterial species that have a good predictive accuracy to identify CRC but not precancerous lesions. This suggests that the identified microbes that were enriched or depleted in CRC are merely a consequence of the tumor. The gut microbiome is a potential modifiable risk factor for colorectal cancer (CRC). We re-analyzed all eight previously published stool sequencing data and conducted an MWAS meta-analysis. We used cross-validated LASSO predictive models to identify a microbiome signature for predicting the risk of CRC and precancerous lesions. These models were validated in a new study, Colorectal Cancer Screening (COLSCREEN), including 156 participants that were recruited in a CRC screening context. The MWAS meta-analysis identified 95 bacterial species that were statistically significantly associated with CRC (FDR < 0.05). The LASSO CRC predictive model obtained an area under the receiver operating characteristic curve (aROC) of 0.81 (95%CI: 0.78-0.83) and the validation in the COLSCREEN dataset was 0.75 (95%CI: 0.66-0.84). This model selected a total of 32 species. The aROC of this CRC-trained model to predict precancerous lesions was 0.52 (95%CI: 0.41-0.63). We have identified a signature of 32 bacterial species that have a good predictive accuracy to identify CRC but not precancerous lesions, suggesting that the identified microbes that were enriched or depleted in CRC are merely a consequence of the tumor. Further studies should focus on CRC as well as precancerous lesions with the intent to implement a microbiome signature in CRC screening programs

COLONOMICS - integrative omics data of one hundred paired normal-tumoral samples from colon cancer patients

Colonomics is a multi-omics dataset that includes 250 samples: 50 samples from healthy colon mucosa donors and 100 paired samples from colon cancer patients (tumor/adjacent). From these samples, Colonomics project includes data from genotyping, DNA methylation, gene expression, whole exome sequencing and micro-RNAs (miRNAs) expression. It also includes data from copy number variation (CNV) from tumoral samples. In addition, clinical data from all these samples is available. The aims of the project were to explore and integrate these datasets to describe colon cancer at molecular level and to compare normal and tumoral tissues. Also, to improve screening by finding biomarkers for the diagnosis and prognosis of colon cancer. This project has its own website including four browsers allowing users to explore Colonomics datasets. Since generated data could be reuse for the scientific community for exploratory or validation purposes, here we describe omics datasets included in the Colonomics project as well as results from multi-omics layers integration

Heterozygote advantage at HLA class I and II loci and reduced risk of colorectal cancer

Objective: Reduced diversity at Human Leukocyte Antigen (HLA) loci may adversely affect the host's ability to recognize tumor neoantigens and subsequently increase disease burden. We hypothesized that increased heterozygosity at HLA loci is associated with a reduced risk of developing colorectal cancer (CRC). Methods: We imputed HLA class I and II four-digit alleles using genotype data from a population-based study of 5,406 cases and 4,635 controls from the Molecular Epidemiology of Colorectal Cancer Study (MECC). Heterozygosity at each HLA locus and the number of heterozygous genotypes at HLA class -I (A, B, and C) and HLA class -II loci (DQB1, DRB1, and DPB1) were quantified. Logistic regression analysis was used to estimate the risk of CRC associated with HLA heterozygosity. Individuals with homozygous genotypes for all loci served as the reference category, and the analyses were adjusted for sex, age, genotyping platform, and ancestry. Further, we investigated associations between HLA diversity and tumor-associated T cell repertoire features, as measured by tumor infiltrating lymphocytes (TILs; N=2,839) and immunosequencing (N=2,357). Results: Individuals with all heterozygous genotypes at all three class I genes had a reduced odds of CRC (OR: 0.74; 95% CI: 0.56-0.97, p= 0.031). A similar association was observed for class II loci, with an OR of 0.75 (95% CI: 0.60-0.95, p= 0.016). For class-I and class-II combined, individuals with all heterozygous genotypes had significantly lower odds of developing CRC (OR: 0.66, 95% CI: 0.49-0.87, p= 0.004) than those with 0 or one heterozygous genotype. HLA class I and/or II diversity was associated with higher T cell receptor (TCR) abundance and lower TCR clonality, but results were not statistically significant. Conclusion: Our findings support a heterozygote advantage for the HLA class-I and -II loci, indicating an important role for HLA genetic variability in the etiology of CRC