Anàlisi molecular del càncer colorectal mitjançant un enfocament computacional integratiu

[cat] Durant aquests últims anys hem viscut el gran impacte de l'era post-genòmica, en la qual la seqüència del genoma humà ja és coneguda. En aquesta etapa, per exemple, s'ha pogut demostrar que el DNA no codificant, inicialment considerat com a no funcional, té un rol essencial en el funcionament de les cèl.lules humanes. Com a conseqüència d'aquest nou escenari, així com de la reducció del cost dels experiments a gran escala i la incorporació de la bioinformàtica com a eina essencial, la recerca en biologia molecular i salut pública ha modificat el seu enfocament clàssic per incorporar aquest canvi de paradigma. El càncer, una malaltia ja tractada per civilitzacions presents segles enrere, per primera vegada podrà ser estudiat des d'un punt de vista holístic a nivell cel.lular. Més específicament, en el cas del càncer colorectal, una malaltia altament heterogènia i molecularment molt complexa, seran de gran ajuda tots els avenços possibles en quant a tècniques de laboratori, tecnologia i noves disciplines. Durant els propers anys molt probablement s'incorporaran a la rutina clínica noves classificacions moleculars, que permetran prescriure un tractament més precís en comptes dels agents quimioteràpics utilitzats actualment amb la majoria dels pacients. També s'identificaran biomarcadors més acurats per al diagnòstic precoç i per predir millor el pronòstic de la malaltia. L'aprofundiment en les bases moleculars del càncer colorectal ens durà fins i tot a desenvolupar eines diagnòstiques basades en criteris moleculars, en substitució dels criteris clínics i patològics emprats majoritàriament a dia d'avui. Amb aquesta tesi el lector podrà endinsar-se en treballs concrets que intenten avançar en aquestes direccions. Els resultats es presenten com a compendi de tres articles científics publicats a revistes científiques internacionals. Tots tres tracten de càncer colorectal, i són el resultat de part del treball realitzat durant els últims anys a la Unitat de Biomarcadors i Susceptibilitat de l'Institut Català d'Oncologia.[eng] Over the last years we have lived the great impact of post-genomic era, in which the human genome was already known. In this stage, for example, it has been demonstrated that noncoding DNA, initially considered as non-functional DNA, has an essential role in human cells. As a result of this new scenario, as well as reducing the cost of large-scale experiments and incorporating the bioinformatics as an essential tool, the research in molecular biology and public health has changed its traditional approach to incorporate this paradigm shift. Cancer, a disease already treated by civilizations many centuries ago, for the first time can be studied from a holistic point of view at the cellular level. In particular, to study the colorectal cancer, a heterogeneous and complex disease, will be helpful all new developments in terms of laboratory techniques, technology and new disciplines. Over the next few years, likely will be incorporated new molecular classifications into the clinical routine, that will able to recommend a more precise medicine instead of the chemotherapeutic agents, currently used with the vast majority of patients. More accurate biomarkers for early diagnosis and to better predict the prognosis of the disease also will be identified. Deepening at the molecular basis of colorectal cancer will lead to even develop diagnostic tools based on the molecular information, replacing the clinical and pathological criteria currently used. In this thesis the reader will be introduced in specific works that try to advance in these directions. The results are based on a collection of three scientific articles published in international journals. These three articles speak about colorectal cancer and represent the work carried out during the last years at the Biomarkers and Susceptibility Unit of the Catalan Institute of Oncology

Large differences in global transcriptional regulatory programs of normal and tumor colon cells

Background: Dysregulation of transcriptional programs leads to cell malfunctioning and can have an impact in cancer development. Our study aims to characterize global differences between transcriptional regulatory programs of normal and tumor cells of the colon. Methods: Affymetrix Human Genome U219 expression arrays were used to assess gene expression in 100 samples of colon tumor and their paired adjacent normal mucosa. Transcriptional networks were reconstructed using ARACNe algorithm using 1,000 bootstrap replicates consolidated into a consensus network. Networks were compared regarding topology parameters and identified well-connected clusters. Functional enrichment was performed with SIGORA method. ENCODE ChIP-Seq data curated in the hmChIP database was used for in silico validation of the most prominent transcription factors. Results: The normal network contained 1,177 transcription factors, 5,466 target genes and 61,226 transcriptional interactions. A large loss of transcriptional interactions in the tumor network was observed (11,585; 81% reduction), which also contained fewer transcription factors (621; 47% reduction) and target genes (2,190; 60% reduction) than the normal network. Gene silencing was not a main determinant of this loss of regulatory activity, since the average gene expression was essentially conserved. Also, 91 transcription factors increased their connectivity in the tumor network. These genes revealed a tumor-specific emergent transcriptional regulatory program with significant functional enrichment related to colorectal cancer pathway. In addition, the analysis of clusters again identified subnetworks in the tumors enriched for cancer related pathways (immune response, Wnt signaling, DNA replication, cell adherence, apoptosis, DNA repair, among others). Also multiple metabolism pathways show differential clustering between the tumor and normal network. Conclusions: These findings will allow a better understanding of the transcriptional regulatory programs altered in colon cancer and could be an invaluable methodology to identify potential hubs with a relevant role in the field of cancer diagnosis, prognosis and therapy

Polymorphisms in alcohol metabolism genes ADH1B and ALDH2, alcohol consumption and colorectal cancer

Background: Colorectal cancer (CRC) is a leading cause of cancer death worldwide. Epidemiological risk factors for CRC included alcohol intake, which is mainly metabolized to acetaldehyde by alcohol dehydrogenase and further oxidized to acetate by aldehyde dehydrogenase; consequently, the role of genes in the alcohol metabolism pathways is of particular interest. The aim of this study is to analyze the association between SNPs in ADH1B and ALDH2 genes and CRC risk, and also the main effect of alcohol consumption on CRC risk in the study population. Methodology/Principal Findings: SNPs from ADH1B and ALDH2 genes, included in alcohol metabolism pathway, were genotyped in 1694 CRC cases and 1851 matched controls from the Molecular Epidemiology of Colorectal Cancer study. Information on clinicopathological characteristics, lifestyle and dietary habits were also obtained. Logistic regression and association analysis were conducted. A positive association between alcohol consumption and CRC risk was observed in male participants from the Molecular Epidemiology of Colorectal Cancer study (MECC) study (OR = 1.47; 95%CI = 1.18-1.81). Moreover, the SNPs rs1229984 in ADH1B gene was found to be associated with CRC risk: under the recessive model, the OR was 1.75 for A/A genotype (95%CI = 1.21-2.52; p-value = 0.0025). A path analysis based on structural equation modeling showed a direct effect of ADH1B gene polymorphisms on colorectal carcinogenesis and also an indirect effect mediated through alcohol consumption. Conclusions/Significance: Genetic polymorphisms in the alcohol metabolism pathways have a potential role in colorectal carcinogenesis, probably due to the differences in the ethanol metabolism and acetaldehyde oxidation of these enzyme variants

Identifying causal models between genetically regulated methylation patterns and gene expression in healthy colon tissue

Background: DNA methylation is involved in the regulation of gene expression and phenotypic variation, but the inter-relationship between genetic variation, DNA methylation and gene expression remains poorly understood. Here we combine the analysis of genetic variants related to methylation markers (methylation quantitative trait loci: mQTLs) and gene expression (expression quantitative trait loci: eQTLs) with methylation markers related to gene expression (expression quantitative trait methylation: eQTMs), to provide novel insights into the genetic/epigenetic architecture of colocalizing molecular markers. Results: Normal mucosa from 100 patients with colon cancer and 50 healthy donors included in the Colonomics project have been analyzed. Linear models have been used to find mQTLs and eQTMs within 1 Mb of the target gene. From 32,446 eQTLs previously detected, we found a total of 6850 SNPs, 114 CpGs and 52 genes interrelated, generating 13,987 significant combinations of co-occurring associations (meQTLs) after Bonferromi correction. Non-redundant meQTLs were 54, enriched in genes involved in metabolism of glucose and xenobiotics and immune system. SNPs in meQTLs were enriched in regulatory elements (enhancers and promoters) compared to random SNPs within 1 Mb of genes. Three colorectal cancer GWAS SNPs were related to methylation changes, and four SNPs were related to chemerin levels. Bayesian networks have been used to identify putative causal relationships among associated SNPs, CpG and gene expression triads. We identified that most of these combinations showed the canonical pathway of methylation markers causes gene expression variation (60.1%) or non-causal relationship between methylation and gene expression (33.9%); however, in up to 6% of these combinations, gene expression was causing variation in methylation markers. Conclusions: In this study we provided a characterization of the regulation between genetic variants and inter-dependent methylation markers and gene expression in a set of 150 healthy colon tissue samples. This is an important finding for the understanding of molecular susceptibility on colon-related complex diseases

Colon-specific eQTL analysis to inform on functional SNPs

BACKGROUND: Genome-wide association studies on colorectal cancer have identified more than 60 susceptibility loci, but for most of them there is no clear knowledge of functionality or the underlying gene responsible for the risk modification. Expression quantitative trail loci (eQTL) may provide functional information for such single nucleotide polymorphisms (SNPs). METHODS: We have performed detailed eQTL analysis specific for colon tissue on a series of 97 colon tumours, their paired adjacent normal mucosa and 47 colon mucosa samples donated by healthy individuals. R package MatrixEQTL was used to search for genome-wide cis-eQTL and trans-eQTL fitting linear models adjusted for age, gender and tissue type to rank transformed expression data. RESULTS: The cis-eQTL analyses has revealed 29,073 SNP-gene associations with permutation-adjusted P-values < 0.01. These correspond to 363 unique genes. The trans-eQTL analysis identified 10,665 significant SNP-gene associations, most of them in the same chromosome, further than 1 Mb of the gene. We provide a web tool to search for specific SNPs or genes. The tool calculates Pearson or Spearman correlation, and allows to select tissue type for analysis. Data and plots can be exported. CONCLUSIONS: This resource should be useful to prioritise SNPs for further functional studies and to identify relevant genes behind identified loci

Aberrant gene expression in mucosa adjacent to tumor reveals a molecular crosstalk in colon cancer

Background: A colorectal tumor is not an isolated entity growing in a restricted location of the body. The patient's gut environment constitutes the framework where the tumor evolves and this relationship promotes and includes a complex and tight correlation of the tumor with inflammation, blood vessels formation, nutrition, and gut microbiome composition. The tumor influence in the environment could both promote an anti-tumor or a pro-tumor response. Methods: A set of 98 paired adjacent mucosa and tumor tissues from colorectal cancer (CRC) patients and 50 colon mucosa from healthy donors (246 samples in total) were included in this work. RNA extracted from each sample was hybridized in Affymetrix chips Human Genome U219. Functional relationships between genes were inferred by means of systems biology using both transcriptional regulation networks (ARACNe algorithm) and protein-protein interaction networks (BIANA software). Results: Here we report a transcriptomic analysis revealing a number of genes activated in adjacent mucosa from CRC patients, not activated in mucosa from healthy donors. A functional analysis of these genes suggested that this active reaction of the adjacent mucosa was related to the presence of the tumor. Transcriptional and protein-interaction networks were used to further elucidate this response of normal gut in front of the tumor, revealing a crosstalk between proteins secreted by the tumor and receptors activated in the adjacent colon tissue; and vice versa. Remarkably, Slit family of proteins activated ROBO receptors in tumor whereas tumor-secreted proteins transduced a cellular signal finally activating AP-1 in adjacent tissue. Conclusions: The systems-level approach provides new insights into the micro-ecology of colorectal tumorogenesis. Disrupting this intricate molecular network of cell-cell communication and pro-inflammatory microenvironment could be a therapeutic target in CRC patient

Integrative transcriptome analysis of malignant pleural mesothelioma reveals a clinically relevant immune-based classification

Background: Malignant pleural mesothelioma (MPM) is a rare and aggressive neoplasia affecting the lung mesothelium. Immune checkpoint inhibitors (ICI) in MPM have not been extremely successful, likely due to poor identification of suitable candidate patients for the therapy. We aimed to identify cellular immune fractions associated with clinical outcome and classify patients with MPM based on their immune contexture. For each defined group, we sought for molecular specificities that could help further define our MPM classification at the genomic and transcriptomic level, as well as identify differential therapeutic strategies based on transcriptional signatures predictive of drug response. Methods: The abundance of 20 immune cell fractions in 516 MPM samples from 7 gene expression datasets was inferred using gene set variation analysis. Identification of clinically relevant fractions was performed with Cox proportional-hazards models adjusted for age, stage, sex, and tumor histology. Immune-based groups were defined based on the identified fractions. Results: T-helper 2 (TH2) and cytotoxic T (TC) cells were found to be consistently associated with overall survival. Three immune clusters (IG) were subsequently defined based on TH2 and TC immune infiltration levels: IG1 (54.5%) was characterized by high TH2 and low TC levels, IG2 (37%) had either low or high levels of both fractions, and IG3 (8.5%) was defined by low TH2 and high TC levels. IG1 and IG3 groups were associated with worse and better overall survival, respectively. While no differential genomic alterations were identified among immune groups, at the transcriptional level, IG1 samples showed upregulation of proliferation signatures, while IG3 samples presented upregulation of immune and inflammation-related pathways. Finally, the integration of gene expression with functional signatures of drug response showed that IG3 patients might be more likely to respond to ICI. Conclusions: This study identifies a novel immune-based signature with potential clinical relevance based on TH2 and TC levels, unveiling a fraction of patients with MPM with better prognosis and who might benefit from immune-based therapies. Molecular specificities of the different groups might be used to tailor specific potential therapies in the future

Comprehensive analysis of copy number aberrations in microsatellite stable colon cancer in view of stromal component

Background: Somatic copy number aberrations (CNA) are common acquired changes in cancer cells playing an important role in the progression of colon cancer (CRC). This study aimed to perform a characterization of CNA and their impact in gene expression.Methods: CNA were inferred from SNP array data in a series of 99 CRC. CNA events were calculated and used to assess the association between copy number dosage, clinical and molecular characteristics of the tumours, and gene expression changes. All analyses were adjusted for the quantity of stroma in each sample, that was inferred from gene expression data.Results: High heterogeneity among samples was observed, the proportion of altered genome ranged between 0.04 and 26.6%. Recurrent CNA regions with gains were frequent in chromosomes 7p, 8q, 13q, and 20 while 8p, 17p, and 18 cumulated loses. A significant positive correlation was observed between the number of somatic mutations and total CNA (Spearman r=0.42, P=0.006). Approximately 37% of genes located in CNA regions changed their level of expression, and the average partial correlation (adjusted for stromal content) with copy number was 0.54 (inter-quartile range 0.20 to 0.81). Altered genes showed enrichment in pathways relevant for colorectal cancer. Tumours classified as CMS2 and CMS4 by the consensus molecular subtyping showed higher frequency of CNA. Loses of one small region in 1p36.33, with gene CDK11B, were associated with poor prognosis. More than 66% of the recurrent CNA were validated in the TCGA data when analysed with the same procedure. Also 79% of the genes with altered expression in our data were validated in the TCGA.Conclusion: Though CNA are frequent events in MSS CRC, few focal recurrent regions were found. These aberrations have strong effects on gene expression and contribute to deregulate relevant cancer pathways. Due to the diploid nature of stromal cells, it is important to consider the purity of tumour samples to accurately calculate CNA events in CRC

Discovery and validation of new potential biomarkers for early detection of colon cancer

Background: accurate detection of characteristic proteins secreted by colon cancer tumor cells in biological fluids could serve as a biomarker for the disease. The aim of the present study was to identify and validate new serum biomarkers and demonstrate their potential usefulness for early diagnosis of colon cancer. Methods: the study was organized in three sequential phases: 1) biomarker discovery, 2) technical and biological validation, and 3) proof of concept to test the potential clinical use of selected biomarkers. A prioritized subset of the differentially-expressed genes between tissue types (50 colon mucosa from cancer-free individuals and 100 normal-tumor pairs from colon cancer patients) was validated and further tested in a series of serum samples from 80 colon cancer cases, 23 patients with adenoma and 77 cancer-free controls.Results: in the discovery phase, 505 unique candidate biomarkers were identified, with highly significant results and high capacity to discriminate between the different tissue types. After a subsequent prioritization, all tested genes (N = 23) were successfully validated in tissue, and one of them, COL10A1, showed relevant differences in serum protein levels between controls, patients with adenoma (p = 0.0083) and colon cancer cases (p = 3.2e-6). Conclusion: we present a sequential process for the identification and further validation of biomarkers for early detection of colon cancer that identifies COL10A1 protein levels in serum as a potential diagnostic candidate to detect both adenoma lesions and tumor. Impact:the use of a cheap serum test for colon cancer screening should improve its participation rates and contribute to decrease the burden of this disease

Clinical value of prognosis gene expression signatures in colorectal cancer: a systematic review

Introduction: the traditional staging system is inadequate to identify those patients with stage II colorectal cancer (CRC) at high risk of recurrence or with stage III CRC at low risk. A number of gene expression signatures to predict CRC prognosis have been proposed, but none is routinely used in the clinic. The aim of this work was to assess the prediction ability and potential clinical usefulness of these signatures in a series of independent datasets. Methods: a literature review identified 31 gene expression signatures that used gene expression data to predict prognosis in CRC tissue. The search was based on the PubMed database and was restricted to papers published from January 2004 to December 2011. Eleven CRC gene expression datasets with outcome information were identified and downloaded from public repositories. Random Forest classifier was used to build predictors from the gene lists. Matthews correlation coefficient was chosen as a measure of classification accuracy and its associated p-value was used to assess association with prognosis. For clinical usefulness evaluation, positive and negative post-tests probabilities were computed in stage II and III samples. Results: five gene signatures showed significant association with prognosis and provided reasonable prediction accuracy in their own training datasets. Nevertheless, all signatures showed low reproducibility in independent data. Stratified analyses by stage or microsatellite instability status showed significant association but limited discrimination ability, especially in stage II tumors. From a clinical perspective, the most predictive signatures showed a minor but significant improvement over the classical staging system. Conclusions: the published signatures show low prediction accuracy but moderate clinical usefulness. Although gene expression data may inform prognosis, better strategies for signature validation are needed to encourage their widespread use in the clinic