Pathway-based evaluation in early onset colorectal cancer suggests focal adhesion and immunosuppression along with epithelial-mesenchymal transition

Insights into the molecular mechanisms and signalling pathways of epithelial to mesenchymal transition (EMT) in colorectal cancer: A systematic review and bioinformatic analysis of gene expression

Colorectal cancer (CRC) is ranked as the second leading cause of mortality worldwide, mainly due to metastasis. Epithelial to mesenchymal transition (EMT) is a complex cellular process that drives CRC metastasis, regulated by changes in EMT-associated gene expression. However, while numerous genes have been identified as EMT regulators through various in vivo and in vitro studies, little is known about the genes that are differentially expressed in CRC tumour tissue and their signalling pathway in regulating EMT. Using an integration of systematic search and bioinformatic analysis, gene expression profiles of CRC tumour tissues were compared to non-tumour adjacent tissues to identify differentially expressed genes (DEGs), followed by performing systematic review on common identified DEGs. Fifty-eight common DEGs were identified from the analysis of 82 tumour tissue samples obtained from four gene expression datasets (NCBI GEO). These DEGS were then systematically searched for their roles in modulating EMT in CRC based on previously published studies. Following this, 10 common DEGs (CXCL1, CXCL8, MMP1, MMP3, MMP7, TACSTD2, VIP, HPGD, ABCG2, CLCA4) were included in this study and subsequently subjected to further bioinformatic analysis. Their roles and functions in modulating EMT in CRC were discussed in this review. This study enhances our understanding of the molecular mechanisms underlying EMT and uncovers potential candidate genes and pathways that could be targeted in CRC.

Early Onset Metastatic Colorectal Cancer: Current Insights and Clinical Management of a Rising Condition

Despite a recent overall decrease in colorectal cancer (CRC) incidence and mortality, there has been a significant rise in CRC diagnoses in young adults. Early onset colorectal cancer (EOCRC) is defined as CRC diagnosed before the age of 50. Possible predisposing conditions include not only genetic syndromes but also other risk factors, such as microbiome alteration, antibiotic exposure, obesity, diabetes mellitus, and inflammatory bowel disease. EOCRC tends to be diagnosed later than in the older counterpart because of a lack of awareness and the fact that screening for CRC usually starts at the age of 50. Furthermore, CRC in young adults seems to be related to unique molecular features and more aggressive clinical behavior. This paper aims to provide an in-depth review of this poorly understood subject, with a comprehensive review of the state of the art and considerations for future perspectives.

Systems biology and OMIC data integration to understand gastrointestinal cancers

Gastrointestinal (GI) cancers are a set of diverse diseases affecting many parts/ organs. The five most frequent GI cancer types are esophageal, gastric cancer (GC), liver cancer, pancreatic cancer, and colorectal cancer (CRC); together, they give rise to 5 million new cases and cause the death of 3.5 million people annually. We provide information about molecular changes crucial to tumorigenesis and the behavior and prognosis. During the formation of cancer cells, the genomic changes are microsatellite instability with multiple chromosomal arrangements in GC and CRC. The genomically stable subtype is observed in GC and pancreatic cancer. Besides these genomic subtypes, CRC has epigenetic modification (hypermethylation) associated with a poor prognosis. The pathway information highlights the functions shared by GI cancers such as apoptosis; focal adhesion; and the p21-activated kinase, phosphoinositide 3-kinase/Akt, transforming growth factor beta, and Toll-like receptor signaling pathways. These pathways show survival, cell proliferation, and cell motility. In addition, the immune response and inflammation are also essential elements in the shared functions. We also retrieved information on protein-protein interaction from the STRING database, and found that proteins Akt1, catenin beta 1 (CTNNB1), E1A binding protein P300, tumor protein p53 (TP53), and TP53 binding protein 1 (TP53BP1) are central nodes in the network. The protein expression of these genes is associated with overall survival in some GI cancers. The low TP53BP1 expression in CRC, high EP300 expression in esophageal cancer, and increased expression of Akt1/TP53 or low CTNNB1 expression in GC are associated with a poor prognosis. The Kaplan Meier plotter database also confirmed the association between expression of the five central genes and GC survival rates. In conclusion, GI cancers are very diverse at the molecular level. However, the shared mutations and protein pathways might be used to understand better and reveal diagnostic/prognostic or drug targets.

SEPTIN9-SDC2-VIM methylation signature as a biomarker for the early diagnosis of colorectal cancer

The accurate detection of colorectal cancer (CRC) at its initial stage can reduce mortality. However, the broad application of endoscopy has been limited due to the invasive procedure and patient noncompliance. Liquid biopsy with subsequent mapping of methylation in specific cell-free DNA (cfDNA) may represent an alternative approach for early diagnosis. In this study, we have developed a minimal-invasive blood-based test for detection of precancerous lesions and early-stage CRC. Using TCGA M450K methylation data, we identified candidate methylation sites with the highest Fold Change (FC) for three genes (SEPTIN9, SDC2 and VIM), which were selected from previous studies. Based on logistic regression models, we developed a 3-gene methylation signature for CRC diagnosis with high accuracy (Sensitivity =0.959, Specificity =1, AUC =0.997). Using independent public databases and data from blood samples, this model has demonstrated superior performance. The AUC was 0.919-1 and 0.905-0.916 in public tissue database for CRC and blood sample data, respectively. Thus, our proposed 3-gene methylation signature has a more reliable performance than other methods. Furthermore, signal enhancement effect of 3-gene methylation signature can improve the accuracy of early diagnosis for CRC, which demonstrates the potential for clinical application.

Prenatal opioid-exposed infant extracellular miRNA signature obtained at birth predicts severity of neonatal opioid withdrawal syndrome

Prenatal opioid exposure (POE) is commonly associated with neonatal opioid withdrawal syndrome (NOWS), which is characterized by a broad variability in symptoms and severity. Currently there are no diagnostic tools to reliably predict which infants will develop severe NOWS, while risk stratification would allow for proactive decisions about appropriate clinical monitoring and interventions. The aim of this prospective cohort study was to assess if extracellular microRNAs (miRNAs) in umbilical cord plasma of infants with POE could predict NOWS severity. Participants (n = 58) consisted of pregnant women receiving medications for opioid use disorder and their infants. NOWS severity was operationalized as the need for pharmacologic treatment and prolonged hospitalization (≥ 14 days). Cord blood miRNAs were assessed using semi-quantitative qRT-PCR arrays. Receiver operating characteristic curves and area under the curve (AUC) were estimated. The expression of three miRNAs (miR-128-3p, miR-30c-5p, miR-421) predicted need for pharmacologic treatment (AUC: 0.85) and prolonged hospitalization (AUC: 0.90). Predictive validity improved after two miRNAs (let-7d-5p, miR-584-5p) were added to the need for pharmacologic treatment model (AUC: 0.94) and another two miRNAs (let-7b-5p, miR-10-5p) to the prolonged hospitalization model (AUC: 0.99). Infant cord blood extracellular miRNAs can proactively identify opioid-exposed neonates at high-risk for developing severe NOWS.

Alpha-1 Antitrypsin Induces Epithelial-to-Mesenchymal Transition, Endothelial-to-Mesenchymal Transition, and Drug Resistance in Lung Cancer Cells

Purpose

Alpha-1 antitrypsin (A1AT) is a secreted protein that plays an important role in various diseases. However, the role of A1AT in non-small cell lung cancer is obscure.

Materials and Methods

A1AT expression in non-small cell lung cancer was analyzed using quantitative reverse transcription PCR, Western blotting (WB), immunohistochemistry (IHC), and ELISA. WB and IF were used to analyze markers of epithelial-to-mesenchymal transition (EMT), EndoMT, and cancer stem cell (CSC). Transwell and cell wound healing assays were used to analyze migration and invasion abilities. Colony formation and CCK-8 assays were used to analyze cell proliferation following cisplatin treatment.

Results

A1AT expression was higher in lung cancer samples than in normal tissues and the increased expression was correlated with poor overall survival of patients. In vitro experiments showed that A1AT overexpressed by plasmid transfection significantly promoted migration, invasion, EMT, EndoMT, stemness, and colony formation in lung cancer cell lines, as opposed to A1AT downregulation by siRNA transfection, which significantly inhibited all these variables.

Conclusion

A1AT is a novel therapeutic target and might be associated with tumor metastasis in lung carcinoma.

Identification and Validation of Prognostically Relevant Gene Signature in Melanoma

Background

Currently, effective genetic markers are limited to predict the clinical outcome of melanoma. High-throughput multiomics sequencing data have provided a valuable approach for the identification of genes associated with cancer prognosis.

Method

The multidimensional data of melanoma patients, including clinical, genomic, and transcriptomic data, were obtained from The Cancer Genome Atlas (TCGA). These samples were then randomly divided into two groups, one for training dataset and the other for validation dataset. In order to select reliable biomarkers, we screened prognosis-related genes, copy number variation genes, and SNP variation genes and integrated these genes to further select features using random forests in the training dataset. We screened for robust biomarkers and established a gene-related prognostic model. Finally, we verified the selected biomarkers in the test sets (GSE19234 and GSE65904) and on clinical samples extracted from melanoma patients using qRT-PCR and immunohistochemistry analysis.

Results

We obtained 1569 prognostic-related genes and 1101 copy-amplification, 1093 copy-deletions, and 92 significant mutations in genomic variants. These genomic variant genes were closely related to the development of tumors and genes that integrate genomic variation. A total of 141 candidate genes were obtained from prognosis-related genes. Six characteristic genes (IQCE, RFX6, GPAA1, BAHCC1, CLEC2B, and AGAP2) were selected by random forest feature selection, many of which have been reported to be associated with tumor progression. Cox regression analysis was used to establish a 6-gene signature. Experimental verification with qRT-PCR and immunohistochemical staining proved that these selected genes were indeed expressed at a significantly higher level compared with the normal tissues. This signature comprised an independent prognostic factor for melanoma patients.

Conclusions

We constructed a 6-gene signature (IQCE, RFX6, GPAA1, BAHCC1, CLEC2B, and AGAP2) as a novel prognostic marker for predicting the survival of melanoma patients.

A comparison of mechanistic signaling pathway activity analysis methods

Understanding the aspects of cell functionality that account for disease mechanisms or drug modes of action is a main challenge for precision medicine. Classical gene-based approaches ignore the modular nature of most human traits, whereas conventional pathway enrichment approaches produce only illustrative results of limited practical utility. Recently, a family of new methods has emerged that change the focus from the whole pathways to the definition of elementary subpathways within them that have any mechanistic significance and to the study of their activities. Thus, mechanistic pathway activity (MPA) methods constitute a new paradigm that allows recoding poorly informative genomic measurements into cell activity quantitative values and relate them to phenotypes. Here we provide a review on the MPA methods available and explain their contribution to systems medicine approaches for addressing challenges in the diagnostic and treatment of complex diseases.

Inhibition of ZIP4 reverses epithelial-to-mesenchymal transition and enhances the radiosensitivity in human nasopharyngeal carcinoma cells

ZIP4 is a zinc transporter involved in epithelial cell morphology and migration in various cancers. In the epithelial-to-mesenchymal transition (EMT), epithelial cells transition into mesenchymal cells. The EMT plays a crucial role in invasiveness and metastasis during tumorigenesis. The aim of this study was to investigate the role of ZIP4 in the invasiveness and radiosensitivity of human nasopharyngeal carcinoma (NPC). In this study, results from 99 human patients with NPC showed that ZIP4 expression levels significantly correlated with a higher TN (tumor, lymph node) classification, as well as shorter overall survival (OS), recurrence-free survival (RFS), and distant metastasis-free survival (DMFS). Forced overexpression of ZIP4 promoted the migration and invasion of C666-1 cells through regulation of the EMT process. In contrast, ZIP4 silencing by lentivirus-mediated shRNA inhibited the EMT and metastasis of C666-1 cells in vitro and in vivo. Importantly, protein microarray analyses showed that downregulation of ZIP4 in C666-1 cells resulted in the decreased abundance of phosphoinositide 3-kinase (PI3K) p85 (Tyr607), phosphorylated (p)-Akt (Ser473), phosphorylated (p)-Akt (Thr308), and phosphorylated glycogen synthase kinase 3β (pGSK3β; Ser9). These data suggest that ZIP4 induces the EMT and promotes migration and invasion via the PI3K/Akt signaling pathway in NPC. Moreover, ZIP4 silencing significantly enhanced radiation-induced apoptosis and growth inhibition of human C666-1 cells in vitro and enhanced the antitumor activity of ionizing radiation (IR), leading to tumor growth inhibition in vivo. These results demonstrate that ZIP4 is a novel prognostic factor for malignant NPC progression. More importantly, targeting ZIP4, along with radiotherapy, may be an effective new treatment for NPC.

Genomes of early onset prostate cancer

PURPOSE OF REVIEW

Prostate cancer is a disease of the elderly but a clinically relevant subset occurs early in life. In the current review, we discuss recent findings and the current understanding of the molecular underpinnings associated with early-onset prostate cancer (PCa) and the evidence supporting age-specific differences in the cancer genomes.

RECENT FINDINGS

Recent surveys of PCa patient cohorts have provided novel age-dependent links between germline and somatic aberrations which points to differences in the molecular cause and treatment options.

SUMMARY

Identifying the earliest molecular alterations in PCa can provide insight into the cause of the disease and biomarkers for patient risk stratification. Genomic aberrations of early-onset PCas display several patterns distinct from late-onset PCa genomes, suggesting age-dependent pathomechanisms involving alterations in the androgen receptor pathway.

Cancer Transcriptome Dataset Analysis: Comparing Methods of Pathway and Gene Regulatory Network-Based Cluster Identification

Cancer transcriptome analysis is one of the leading areas of Big Data science, biomarker, and pharmaceutical discovery, not to forget personalized medicine. Yet, cancer transcriptomics and postgenomic medicine require innovation in bioinformatics as well as comparison of the performance of available algorithms. In this data analytics context, the value of network generation and algorithms has been widely underscored for addressing the salient questions in cancer pathogenesis. Analysis of cancer trancriptome often results in complicated networks where identification of network modularity remains critical, for example, in delineating the "druggable" molecular targets. Network clustering is useful, but depends on the network topology in and of itself. Notably, the performance of different network-generating tools for network cluster (NC) identification has been little investigated to date. Hence, using gastric cancer (GC) transcriptomic datasets, we compared two algorithms for generating pathway versus gene regulatory network-based NCs, showing that the pathway-based approach better agrees with a reference set of cancer-functional contexts. Finally, by applying pathway-based NC identification to GC transcriptome datasets, we describe cancer NCs that associate with candidate therapeutic targets and biomarkers in GC. These observations collectively inform future research on cancer transcriptomics, drug discovery, and rational development of new analysis tools for optimal harnessing of omics data.

Benchmarking pathway interaction network for colorectal cancer to identify dysregulated pathways

Different pathways act synergistically to participate in many biological processes. Thus, the purpose of our study was to extract dysregulated pathways to investigate the pathogenesis of colorectal cancer (CRC) based on the functional dependency among pathways. Protein-protein interaction (PPI) information and pathway data were retrieved from STRING and Reactome databases, respectively. After genes were aligned to the pathways, each pathway activity was calculated using the principal component analysis (PCA) method, and the seed pathway was discovered. Subsequently, we constructed the pathway interaction network (PIN), where each node represented a biological pathway based on gene expression profile, PPI data, as well as pathways. Dysregulated pathways were then selected from the PIN according to classification performance and seed pathway. A PIN including 11,960 interactions was constructed to identify dysregulated pathways. Interestingly, the interaction of mRNA splicing and mRNA splicing-major pathway had the highest score of 719.8167. Maximum change of the activity score between CRC and normal samples appeared in the pathway of DNA replication, which was selected as the seed pathway. Starting with this seed pathway, a pathway set containing 30 dysregulated pathways was obtained with an area under the curve score of 0.8598. The pathway of mRNA splicing, mRNA splicing-major pathway, and RNA polymerase I had the maximum genes of 107. Moreover, we found that these 30 pathways had crosstalks with each other. The results suggest that these dysregulated pathways might be used as biomarkers to diagnose CRC.

Databases and tools for constructing signal transduction networks in cancer

Traditionally, biologists have devoted their careers to studying individual biological entities of their own interest, partly due to lack of available data regarding that entity. Large, high-throughput data, too complex for conventional processing methods (i.e., “big data”), has accumulated in cancer biology, which is freely available in public data repositories. Such challenges urge biologists to inspect their biological entities of interest using novel approaches, firstly including repository data retrieval. Essentially, these revolutionary changes demand new interpretations of huge datasets at a systems-level, by so called “systems biology”. One of the representative applications of systems biology is to generate a biological network from high-throughput big data, providing a global map of molecular events associated with specific phenotype changes. In this review, we introduce the repositories of cancer big data and cutting-edge systems biology tools for network generation, and improved identification of therapeutic targets.

Gene Expression Differences in Prostate Cancers between Young and Old Men

Prostate cancer incidence is increasing in younger men. We investigated whether men diagnosed with Gleason 7 (3+4) T2 prostate cancer at younger ages (≤ 45 years, young cohort) had different mRNA and miRNA expression profiles than men diagnosed at older ages (71–74 years, older cohort). We identified differentially expressed genes (DEGs) related to tumor-normal differences between the cohorts. Subsequent pathway analysis of DEGs revealed that the young cohort had significantly more pronounced inflammatory and immune responses to tumor development compared to the older cohort. Further supporting a role of inflammation-induced immune-suppression in the development of early-onset prostate cancer, we observed significant up-regulation of CTLA4 and IDO1/TDO2 pathways in tumors of the young cohort. Moreover, over-expression of CTLA4 and IDO1 was significantly associated with biochemical recurrence. Our results provide clues on the mechanisms of tumor development and point to potential biomarkers for early detection and treatment for prostate cancer in young men.

The incidence of prostate cancer is increasing in young men, and young men are more likely to develop more aggressive prostate cancers than older men. These findings suggest biological differences between prostate cancers that develop in young men and in older men; yet little data and few studies on men diagnosed under age 50 years exist. In this study, we investigated whether men diagnosed with prostate cancer at young ages (≤ age 45 years) had different gene expression profiles than men diagnosed at older ages (71–74 years). We found that inflammatory and immune-related pathways were up-regulated in the young group as compared to the older group, suggesting fundamental differences in tumor development. Moreover, 21% of the young group, compared to 8% of the older group, had biochemical recurrence of prostate cancer–a surprising result given that both groups were diagnosed in early stages of disease (all T2, Gleason 7 (3+4). The recurrence in the young group was associated with over-expression of two genes involved in immune regulation. After validation in a larger dataset, these may provide clues for potential biomarkers to test for monitoring which young patients are likely to progress.

Novel mouse model recapitulates genome and transcriptome alterations in human colorectal carcinomas

Human colorectal carcinomas are defined by a nonrandom distribution of genomic imbalances that are characteristic for this disease. Often, these imbalances affect entire chromosomes. Understanding the role of these aneuploidies for carcinogenesis is of utmost importance. Currently, established transgenic mice do not recapitulate the pathognonomic genome aberration profile of human colorectal carcinomas. We have developed a novel model based on the spontaneous transformation of murine colon epithelial cells. During this process, cells progress through stages of pre-immortalization, immortalization and, finally, transformation, and result in tumors when injected into immunocompromised mice. We analyzed our model for genome and transcriptome alterations using ArrayCGH, spectral karyotyping (SKY), and array based gene expression profiling. ArrayCGH revealed a recurrent pattern of genomic imbalances. These results were confirmed by SKY. Comparing these imbalances with orthologous maps of human chromosomes revealed a remarkable overlap. We observed focal deletions of the tumor suppressor genes Trp53 and Cdkn2a/p16. High-level focal genomic amplification included the locus harboring the oncogene Mdm2, which was confirmed by FISH in the form of double minute chromosomes. Array-based global gene expression revealed distinct differences between the sequential steps of spontaneous transformation. Gene expression changes showed significant similarities with human colorectal carcinomas. Pathways most prominently affected included genes involved in chromosomal instability and in epithelial to mesenchymal transition. Our novel mouse model therefore recapitulates the most prominent genome and transcriptome alterations in human colorectal cancer, and might serve as a valuable tool for understanding the dynamic process of tumorigenesis, and for preclinical drug testing. © 2016 Wiley Periodicals, Inc.

Using activation status of signaling pathways as mechanism-based biomarkers to predict drug sensitivity

Many complex traits, as drug response, are associated with changes in biological pathways rather than being caused by single gene alterations. Here, a predictive framework is presented in which gene expression data are recoded into activity statuses of signal transduction circuits (sub-pathways within signaling pathways that connect receptor proteins to final effector proteins that trigger cell actions). Such activity values are used as features by a prediction algorithm which can efficiently predict a continuous variable such as the IC50 value. The main advantage of this prediction method is that the features selected by the predictor, the signaling circuits, are themselves rich-informative, mechanism-based biomarkers which provide insight into or drug molecular mechanisms of action (MoA).

Network Comparison of Inflammation in Colorectal Cancer and Alzheimer's Disease

Recently, a large clinical study revealed an inverse correlation of individual risk of cancer versus Alzheimer's disease (AD). However, no explanation exists for this anticorrelation at the molecular level; however, inflammation is crucial to the pathogenesis of both diseases, necessitating a need to understand differing signaling usage during inflammatory responses distinct to both diseases. Using a subpathway analysis approach, we identified numerous well-known and previously unknown pathways enriched in datasets from both diseases. Here, we present the quantitative importance of the inflammatory response in the two disease pathologies and summarize signal transduction pathways common to both diseases that are affected by inflammation.

Assessing the impact of mutations found in next generation sequencing data over human signaling pathways

Modern sequencing technologies produce increasingly detailed data on genomic variation. However, conventional methods for relating either individual variants or mutated genes to phenotypes present known limitations given the complex, multigenic nature of many diseases or traits. Here we present PATHiVar, a web-based tool that integrates genomic variation data with gene expression tissue information. PATHiVar constitutes a new generation of genomic data analysis methods that allow studying variants found in next generation sequencing experiment in the context of signaling pathways. Simple Boolean models of pathways provide detailed descriptions of the impact of mutations in cell functionality so as, recurrences in functionality failures can easily be related to diseases, even if they are produced by mutations in different genes. Patterns of changes in signal transmission circuits, often unpredictable from individual genes mutated, correspond to patterns of affected functionalities that can be related to complex traits such as disease progression, drug response, etc. PATHiVar is available at: http://pathivar.babelomics.org.

Identification of key genes associated with colorectal cancer based on the transcriptional network

Colorectal cancer (CRC) is among the most lethal human cancers, but the mechanism of the cancer is still unclear enough. We aimed to explore the key genes in CRC progression. The gene expression profile (GSE4183) of CRC was obtained from Gene Expression Omnibus database which included 8 normal samples, 15 adenoma samples, 15 CRC samples and 15 inflammatory bowel disease (IBD) samples. Thereinto, 8 normal, 15 adenoma, and 15 CRC samples were chosen for our research. The differentially expressed genes (DEGs) in normal vs. adenoma, normal vs. CRC, and adenoma vs. CRC, were identified using the Wilcoxon test method in R respectively. The interactive network of DEGs was constructed to select the significant modules using the Pearson's correlation. Meanwhile, transcriptional network of DEGs was also constructed using the g: Profiler. Totally, 2,741 DEGs in normal vs. adenoma, 1,484 DEGs in normal vs. CRC, and 396 DEGs in adenoma vs. CRC were identified. Moreover, function analysis of DEGs in each group showed FcR-mediated phagocytosis pathway in module 1, cardiac muscle contraction pathway in module 6, and Jak-STAT signaling pathway in module 19 were also enriched. Furthermore, MZF1 and AP2 were the transcription factor in module 6, with the target SP1, while SP1 was also a transcription in module 20. DEGs like NCF1, AKT, SP1, AP2, MZF1, and TPM might be used as specific biomarkers in CRC development. Therapy targeting on the functions of these key genes might provide novel perspective for CRC treatment.

A pathway-based approach for identifying biomarkers of tumor progression to trastuzumab-resistant breast cancer

Although trastuzumab is a successful targeted therapy for breast cancer patients with tumors expressing HER2 (ERBB2), many patients eventually progress to drug resistance. Here, we identified subpathways differentially expressed between trastuzumab-resistant vs. -sensitive breast cancer cells, in conjunction with additional transcriptomic preclinical and clinical gene datasets, to rigorously identify overexpressed, resistance-associated genes. From this approach, we identified 32 genes reproducibly upregulated in trastuzumab resistance. 25 genes were upregulated in drug-resistant JIMT-1 cells, which also downregulated HER2 protein by >80% in the presence of trastuzumab. 24 genes were downregulated in trastuzumab-sensitive SKBR3 cells. Trastuzumab sensitivity was restored by siRNA knockdown of these genes in the resistant cells, and overexpression of 5 of the 25 genes was found in at least one of five refractory HER2 + breast cancer. In summary, our rigorous computational approach, followed by experimental validation, significantly implicate ATF4, CHEK2, ENAH, ICOSLG, and RAD51 as potential biomarkers of trastuzumab resistance. These results provide further proof-of-concept of our methodology for successfully identifying potential biomarkers and druggable signal pathways involved in tumor progression to drug resistance.

Understanding disease mechanisms with models of signaling pathway activities

BACKGROUND

Understanding the aspects of the cell functionality that account for disease or drug action mechanisms is one of the main challenges in the analysis of genomic data and is on the basis of the future implementation of precision medicine.

RESULTS

Here we propose a simple probabilistic model in which signaling pathways are separated into elementary sub-pathways or signal transmission circuits (which ultimately trigger cell functions) and then transforms gene expression measurements into probabilities of activation of such signal transmission circuits. Using this model, differential activation of such circuits between biological conditions can be estimated. Thus, circuit activation statuses can be interpreted as biomarkers that discriminate among the compared conditions. This type of mechanism-based biomarkers accounts for cell functional activities and can easily be associated to disease or drug action mechanisms. The accuracy of the proposed model is demonstrated with simulations and real datasets.

CONCLUSIONS

The proposed model provides detailed information that enables the interpretation disease mechanisms as a consequence of the complex combinations of altered gene expression values. Moreover, it offers a framework for suggesting possible ways of therapeutic intervention in a pathologically perturbed system.

Sporadic early-onset colorectal cancer is a specific sub-type of cancer: a morphological, molecular and genetics study

Sporadic early onset colorectal carcinoma (EOCRC) which has by definition no identified hereditary predisposition is a growing problem that remains poorly understood. Molecular analysis could improve identification of distinct sub-types of colorectal cancers (CRC) with therapeutic implications and thus can help establish that sporadic EOCRC is a distinct entity. From 954 patients resected for CRC at our institution, 98 patients were selected. Patients aged 45–60 years were excluded to help define “young” and “old” groups. Thirty-nine cases of sporadic EOCRC (patients≤45 years with microsatellite stable tumors) were compared to both microsatellite stable tumors from older patients (36 cases, patients>60 years) and to groups of patients with microsatellite instability. Each group was tested for TP53, KRAS, BRAF, PIK3CA mutations and the presence of a methylator phenotype. Gene expression profiles were also used for pathway analysis. Compared to microsatellite stable CRC from old patients, sporadic EOCRC were characterized by distal location, frequent synchronous metastases and infrequent synchronous adenomas but did not have specific morphological characteristics. A familial history of CRC was more common in sporadic EOCRC patients despite a lack of identified hereditary conditions (p = 0.013). Genetic studies also showed the absence of BRAF mutations (p = 0.022) and the methylator phenotype (p = 0.005) in sporadic EOCRC compared to older patients. Gene expression analysis implicated key pathways such as Wnt/beta catenin, MAP Kinase, growth factor signaling (EGFR, HGF, PDGF) and the TNFR1 pathway in sporadic EOCRC. Wnt/beta catenin signaling activation was confirmed by aberrant nuclear beta catenin immunostaining (p = 0.01). This study strongly suggests that sporadic EOCRC is a distinct clinico-molecular entity presenting as a distal and aggressive disease associated with chromosome instability. Furthermore, several signaling pathways including the TNFR1 pathway have been identified as potential biomarkers for both the diagnosis and treatment of this disease.

PATHOME: an algorithm for accurately detecting differentially expressed subpathways

The translation of high-throughput gene expression data into biologically meaningful information remains a bottleneck. We developed a novel computational algorithm, PATHOME, for detecting differentially expressed biological pathways. This algorithm employs straightforward statistical tests to evaluate the significance of differential expression patterns along subpathways. Applying it to gene expression data sets of gastric cancer (GC), we compared its performance with those of other leading programs. Based on a literature-driven reference set, PATHOME showed greater consistency in identifying known cancer-related pathways. For the WNT pathway uniquely identified by PATHOME, we validated its involvement in gastric carcinogenesis through experimental perturbation of both cell lines and animal models. We identified HNF4α-WNT5A regulation in the cross-talk between the AMPK metabolic pathway and the WNT signaling pathway, and further identified WNT5A as a potential therapeutic target for GC. We have demonstrated PATHOME to be a powerful tool, with improved sensitivity for identifying disease-related dysregulated pathways.

Harnessing the complexity of gene expression data from cancer: from single gene to structural pathway methods

High-dimensional gene expression data provide a rich source of information because they capture the expression level of genes in dynamic states that reflect the biological functioning of a cell. For this reason, such data are suitable to reveal systems related properties inside a cell, e.g., in order to elucidate molecular mechanisms of complex diseases like breast or prostate cancer. However, this is not only strongly dependent on the sample size and the correlation structure of a data set, but also on the statistical hypotheses tested. Many different approaches have been developed over the years to analyze gene expression data to (I) identify changes in single genes, (II) identify changes in gene sets or pathways, and (III) identify changes in the correlation structure in pathways. In this paper, we review statistical methods for all three types of approaches, including subtypes, in the context of cancer data and provide links to software implementations and tools and address also the general problem of multiple hypotheses testing. Further, we provide recommendations for the selection of such analysis methods.

The Functional Genomics Network in the evolution of biological text mining over the past decade

Different programs of The European Science Foundation (ESF) have contributed significantly to connect researchers in Europe and beyond through several initiatives. This support was particularly relevant for the development of the areas related with extracting information from papers (text-mining) because it supported the field in its early phases long before it was recognized by the community. We review the historical development of text mining research and how it was introduced in bioinformatics. Specific applications in (functional) genomics are described like it's integration in genome annotation pipelines and the support to the analysis of high-throughput genomics experimental data, and we highlight the activities of evaluation of methods and benchmarking for which the ESF programme support was instrumental.