Colorectal Cancer Dataset Research Articles

Significance of BMI1 and FSCN1 expression in colorectal cancer.

Background/Aims:Colorectal cancer (CRC) is the third most common type of cancer in terms of incidence and the fourth in cause of death world-wide, underscoring the need to identify novel biomarkers for early diagnosis, as well as improved disease stratification and treatment choices.Patients and Methods:The Gene Expression Omnibus (GSE21510) and the Cancer Genome Atlas (TCGA) CRC datasets were utilized in the current study. GeneSpring 13.0 was used for normalization and analysis. The log-rank test was used to compare the outcome between expression groups.Result:Significant upregulation of BMI1 (2.3 FC, P = 3.7 × 10-18) and FSCN1 (1.3 FC,P = 4.7 × 10-3) was observed in CRC. High BMI1 expression was associated with reduced overall survival (OS) [Hazard ratio (HR), 1.87; 95% CI. 1.17–3.03; P = 0.009] and reduced disease-free survival (DFS) [HR, 162; 95% CI 1.01–2.63;P = 0.045]. Similarly, high expression of FSCN1 was associated with reduced OS (HR, 2.0; 95% CI, 1.24–3.2; P = 0.0044) and reduced DFS (HR, 1.60; 95% CI, 0.99–2.57;P = 0.055). Importantly, BMI1high/FSCN1high patients experienced the worst OS (HR, 3.17; 95% CI, 1.77–6.15; P = 0.0002) and DFS (HR, 2.34; 95% CI, 1.27–4.67,P = 0.0078). Using pathway analyses, tumors overexpressing BMI1 were enriched in zinc finger proteins and genes involved in DNA binding and regulation of transcription, whereas tumors expressing FSCN1 were enriched in genes involved in cell migration.Conclusion:Our data revealed poor OS and DFS in CRC patients overexpressing BMI1 or FSCN1 and suggest that these two markers in combination may represent superior prognostic marker to either one. Targeting BMI1 and FSCN1 may also provide potential therapeutic opportunity in CRC.

Integration and Analysis of Heterogeneous Colorectal Cancer Data for Translational Research.

Cancer is the number one cause of death in Australia with colorectal cancer being the second most common cancer type. The translation of cancer research into clinical practice is hindered by the lack of integration of heterogeneous and autonomous data from various data sources. Integration of heterogeneous data can offer researchers a comprehensive source for biospecimen identification, hypothesis formulation, hypothesis validation, cohort discovery and biomarker discovery. Alongside the increasing prominence of big data, various translational research tools such as tranSMART have emerged that can converge and analyse different types of data. In this study, we show the integration of different data types from a significant Australian colorectal cancer cohort. Additionally, colorectal cancer datasets from The Cancer Genome Atlas were also integrated for comparison. These integrated data are accessible via http://www.tcrn.unsw.edu.au/transmart. The use of translational research tools for data integration can provide a cost-effective and rapid approach to translational cancer research.

Design of Decision Support System in the Metastatic Colorectal Cancer Data Set and Its Application

Developing the system which will help doctors with the result to be obtained from the medical data sets by realizing the design of the medical decision support system in which data mining methods are used is the primary objective of this study. The survivability condition of metastatic colorectal cancer disease has been predicted using data mining methods in the developed system. In the process of data mining, after the phase of data preprocessing, Support Vector Machines, Naive Bayes, Decision Trees, Artificial Neural Networks, Multilayer Perceptron, Logistic Regression algorithms have been used. In the study, two different medical decision support system models, classifying prediction and hybrid models, have been developed, and the results obtained from the two models have been compared after being examined. While the most successful algorithm is Support Vector Machines in Classifying Prediction Model in which only classification algorithms are applied, it is Decision Trees and Artificial Neural Networks which are the most successful algorithms with an accuracy rate of 100 per cent in Hybrid Prediction Model. In consequence of the classifying processes, when the accuracy rates of the models are examined, it is seen that while the accuracy rate of Classifying Prediction Model is 65-70%, this rate reaches 95-100% in Hybrid Prediction Model. It has been seen that accuracy rates have elicited very high and very close values for all of the algorithms with the realized hybrid structure, that is, with clustering, in the applications of the classifying algorithms combined.

A novel gene ranking method using Wilcoxon rank sum test and genetic algorithm

Over the last few decades, a large amount of research work has been carried on genomic data. The cancer disease make cells in specific tissues in the body undergo uncontrolled division which results in the malignant growth or tumour. Today, DNA microarray technologies allow us to simultaneously monitor the expression pattern of thousands of genes. Microarray gene expression data are characterised by a very high dimensionality (genes), and a relatively small number of samples (observations). If one wants to identify all those genes from these thousands of gene expressions which are responsible for the disease like cancer, then it is useful to rank the genes. In this paper, we have proposed a novel gene ranking method based on Wilcoxon Rank Sum Test and genetic algorithm. WRST has been used for reducing dimensionality and genetic algorithm for finding out those differentially expressed genes. The final subset of genes has been cross-validated using k fold LOOCV (k varied for different dataset) method and thereafter used for classification of data using SVM with linear kernel. At first the proposed method has been applied on two relatively new benchmark datasets, like GDS4382 colorectal cancer dataset and GDS4794 small cell lung cancer dataset and the results show that the proposed method can reach up to 100% classification accuracy with very few dominant genes, which indirectly validates the biological and statistical significance of the proposed method. After that it is also applied on five real-life datasets and the results are compared with one of the recent state of the art approach on the basis of % of Accuracy, Sensitivity, and Specificity etc.

Expression profile of innate immune receptors, NLRs and AIM2, in human colorectal cancer: correlation with cancer stages and inflammasome components.

NLRs (nucleotide-binding domain leucine-rich repeat proteins or NOD-like receptors) are regulators of inflammation and immunity. A subgroup of NLRs and the innate immune receptor, AIM2 (absent-in-melanoma 2), can induce the assembly of a large caspase-1 activating complex called the inflammasome. Other NLRs regulate key signaling pathways such as NF-kB and MAPK. Since inflammation is a central component of colorectal cancer (CRC), this work was undertaken to analyze NLR and AIM2 expression in human CRC by combining bioinformatics analysis and experimental verification using clinical tissue samples. Additional experiments analyzed the association of (i) gene expression and cancer staging, and (ii) gene expression among inflammasome components.Ten public CRC datasets from the Oncomine® Platform were analyzed. Genes analyzed include NLRP1, NLRP3, NLRP6, NLRP12, NLRC3, NLRC4, NLRC5, NOD1, NOD2 and AIM2. Additionally, forty case-matched cancer samples and adjacent healthy control tissues isolated from a cohort of Chinese CRC patients were profiled.Three patterns of gene expression in CRC are shown. The expression of NLRC3, a checkpoint of inflammation, and the inflammasome components NLRP1, NLRP3, NLRC4 and AIM2 were reduced in CRC. NOD1 and NOD2 expression was increased in CRC, while NLRC5, NLRP6 and NLRP12 showed little difference compared to controls. Reduced expression of NLRC3 in CRC was verified in all available databases analyzed and confirmed with our patient cohort. Furthermore, the extent of NLRC3 and AIM2 gene reduction was correlated with cancer progression. This report reveals the potential value of NLR and AIM2 genes as biomarkers of CRC and cancer progression.

Connectivity mapping using a combined gene signature from multiple colorectal cancer datasets identified candidate drugs including existing chemotherapies.

BackgroundWhile the discovery of new drugs is a complex, lengthy and costly process, identifying new uses for existing drugs is a cost-effective approach to therapeutic discovery. Connectivity mapping integrates gene expression profiling with advanced algorithms to connect genes, diseases and small molecule compounds and has been applied in a large number of studies to identify potential drugs, particularly to facilitate drug repurposing. Colorectal cancer (CRC) is a commonly diagnosed cancer with high mortality rates, presenting a worldwide health problem. With the advancement of high throughput omics technologies, a number of large scale gene expression profiling studies have been conducted on CRCs, providing multiple datasets in gene expression data repositories. In this work, we systematically apply gene expression connectivity mapping to multiple CRC datasets to identify candidate therapeutics to this disease.ResultsWe developed a robust method to compile a combined gene signature for colorectal cancer across multiple datasets. Connectivity mapping analysis with this signature of 148 genes identified 10 candidate compounds, including irinotecan and etoposide, which are chemotherapy drugs currently used to treat CRCs. These results indicate that we have discovered high quality connections between the CRC disease state and the candidate compounds, and that the gene signature we created may be used as a potential therapeutic target in treating the disease. The method we proposed is highly effective in generating quality gene signature through multiple datasets; the publication of the combined CRC gene signature and the list of candidate compounds from this work will benefit both cancer and systems biology research communities for further development and investigations.

Abstract 2140: Dual Wnt and EGFR-MAPK dependency of BRAFV600E-mutant colorectal cancer

Abstract Aberrant Wnt pathway activation due to inactivating mutations in the gene encoding RNF43 (an E3 ubiquitin ligase that promotes degradation of the Wnt receptors Frizzled and LRP6) may contribute to the unresponsiveness of BRAFV600E-mutant colorectal cancer (CRC) to BRAF inhibitors. Analysis of The Cancer Genome Atlas (TCGA) CRC data set reveals a striking co-occurrence of the BRAFV600E mutation and truncating mutations in RNF43. RNF43 mutations are likely to be functionally significant, as RNF43 mutations and mutations in the β-catenin destruction complex component APC are almost completely mutually exclusive. The vast majority of BRAFV600E;RNF43-mutant CRCs are hypermutable [microsatellite instability (MSI)-high phenotype]. The mismatch repair deficiency in these tumors may directly contribute to RNF43 mutagenesis, as RNF43 mutations tend to be small insertions/deletions in homopolymeric tracts. To determine if RNF43 mutations confer Wnt dependency in BRAFV600E-mutant CRC, we treated three BRAFV600E;RNF43-mutant CRC patient-derived xenograft (PDX) models with the porcupine inhibitor WNT974 (formerly LGK974), which blocks the palmitoylation and secretion of Wnt ligands. Single agent WNT974 anti-tumor activity was observed in 2/3 PDX models, and correlated with decreased tumor cell proliferation and mucinous differentiation. Single agent anti-tumor activity with the BRAF inhibitor LGX818 was also observed in 2/3 PDX models. No single agent anti-tumor activity was observed with the EGFR inhibitor cetuximab. The double combinations of WNT974+LGX818 and LGX818+cetuximab, and the triple combination of WNT974+LGX818+cetuximab were efficacious in all three BRAFV600E;RNF43-mutant CRC PDX models. In summary, the Wnt pathway and the EGFR-MAPK pathway may jointly promote tumorigenesis of BRAFV600E-mutant CRC, providing a strong rationale to treat patients with BRAFV600E-mutant CRCs harboring upstream Wnt pathway mutations with combinations of WNT974, LGX818 and/or cetuximab. Citation Format: Youzhen Wang, Michael Palmer, Savina Jaeger, Linda Bagdasarian, Shumei Qiu, Steve Woolfenden, Ronald Meyer, Guizhi Yang, John Green, Shifeng Pan, Jun Liu, Hui Gao, Z. Alexander Cao, Andrea Myers, Margaret E. McLaughlin. Dual Wnt and EGFR-MAPK dependency of BRAFV600E-mutant colorectal cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2140. doi:10.1158/1538-7445.AM2015-2140

Subpathway Analysis based on Signaling-Pathway Impact Analysis of Signaling Pathway.

Pathway analysis is a common approach to gain insight from biological experiments. Signaling-pathway impact analysis (SPIA) is one such method and combines both the classical enrichment analysis and the actual perturbation on a given pathway. Because this method focuses on a single pathway, its resolution generally is not very high because the differentially expressed genes may be enriched in a local region of the pathway. In the present work, to identify cancer-related pathways, we incorporated a recent subpathway analysis method into the SPIA method to form the “sub-SPIA method.” The original subpathway analysis uses the k-clique structure to define a subpathway. However, it is not sufficiently flexible to capture subpathways with complex structure and usually results in many overlapping subpathways. We therefore propose using the minimal-spanning-tree structure to find a subpathway. We apply this approach to colorectal cancer and lung cancer datasets, and our results show that sub-SPIA can identify many significant pathways associated with each specific cancer that other methods miss. Based on the entire pathway network in the Kyoto Encyclopedia of Genes and Genomes, we find that the pathways identified by sub-SPIA not only have the largest average degree, but also are more closely connected than those identified by other methods. This result suggests that the abnormality signal propagating through them might be responsible for the specific cancer or disease.

A Clinical Parameters-Based Model Predicts Anastomotic Leakage After a Laparoscopic Total Mesorectal Excision

Anastomotic leakage after colorectal surgery is a major and life-threatening complication that occurs more frequently than expected. Intraoperative judgment in predicting potential leakage has shown extremely low sensitivity and specificity. The lack of a model for predicting anastomotic leakage might explain this insufficient judgment. We aimed to propose a clinical parameters-based model to predict anastomotic leakage after laparoscopic total mesorectal excision (TME).This study was a retrospective analysis of a prospectively designed colorectal cancer dataset. In total, 1968 patients with a laparoscopic TME were enrolled from November 1, 2010, to March 20, 2014. The independent risk factors for anastomotic leakage were identified, from which the parameters-based model for leakage was developed.Anastomotic leakage was noted in 63 patients (3.2%). Male sex, a low level of anastomosis, intraoperative blood loss, diabetes, the duration time of the surgery, and low temperature were significantly associated by the bivariate analysis and the Cochran–Mantel–Haenszel test with an increased risk. From these factors, the logistic regression model identified the following 4 independent predictors: male sex (risk ratio [RR] = 1.85, 95% confidence interval [CI]: 1.13–4.87), diabetes (RR = 2.08, 95% CI: 1.19–5.8), a lower anastomosis level (RR = 3.41, 95% CI: 1.17–6.71), and a high volume of blood loss (RR = 1.03, 95% CI: 1.01–1.05). The locally weighted scatterplot smoothing regression showed an anastomosis within 5 cm from the anus and intraoperative blood loss of >100 mL as the cutoff values for a significantly increased risk of leakage. Based on these independent factors, a parameters-based model was established by the regression coefficients. The high and low-risk groups were classified according to scores of 3–5 and 0–2, with leakage rates of 8.57% and 1.66%, respectively (P < 0.001).This parameters-based model could predict the risk of anastomotic leakage following laparoscopic rectal cancer. After further validation, this model might facilitate the intraoperative recognition of high-risk patients to perform defunctional stomas.

PWE-346 Lynch syndrome and application of the rcpath colorectal cancer dataset in the united kingdom

<h3>Introduction</h3> Lynch syndrome is estimated to cause over 1,000 cases of colorectal cancer in the UK every year. Individuals that have Lynch syndrome have a lifetime risk of developing colorectal cancer or other Lynch associated cancers of &gt;35%. Yet fewer than 5% of individuals with the condition have been diagnosed in the UK. The Royal College of Pathologists (RCPath) cancer dataset (July 2014) mandates the use of immunohistochemistry or other testing for molecular features of Lynch syndrome in colorectal cancer or Lynch associated cancer patients under the age of 50, at the time of diagnosis. Performing this type of screening test for Lynch syndrome identifies those at greater risk of recurrence as well as family members at extended risk through early cascade testing. <h3>Method</h3> The strategy was developed by Bowel Cancer UK with advice from the Medical Advisory Board of the charity. We identified all NHS hospital trusts in England, health boards in Scotland and Wales and health and social care trusts in Northern Ireland. A freedom of information (FOI) request was submitted to each of these bodies in which a number of questions were asked to establish whether they were implementing the RCPath core dataset requirement, with regard to mismatch repair (MMR) testing for Lynch syndrome. As this was an FOI request respondents were required to respond within 20 working days. <h3>Results</h3> In England, 78% of trusts responded, 100% responded in Scotland and Northern Ireland; one health board in Wales did not respond. In England and Scotland 50% of hospital trusts and health boards currently do not follow the RCPath cancer dataset. 4 health boards in Wales do not, and all health and social care trusts in Northern Ireland stated they perform MMR testing to identify suspected Lynch syndrome patients. In England only 28 of those that did not perform testing stated they were intending to implement the dataset and two in Wales. Scotland is in the process of developing a nationally agreed protocol for implementation of the dataset. We found that among trusts and health boards that do test for Lynch syndrome many do not yet have in place a systematic reflex service, as mandated by RCPath. <h3>Conclusion</h3> Our findings show that the RCPath dataset may have already had some impact on the application of MMR testing for Lynch syndrome in patients diagnosed under the age of 50. However there is considerable variation in approach. We recommend that: England and Wales carry out a nationwide initiative to look at ways of delivering a consistent approach to reflex testing for Lynch syndrome; that all CCGs commission to reflect the RCPath cancer dataset thus ensuring providers are compliant with the dataset. <h3>Disclosure of interest</h3> None Declared.

Cd11b(+) myeloid cells support hepatic metastasis through down-regulation of angiopoietin-like 7 in cancer cells.

Myeloid cells are known to mediate metastatic progression. Here, we attempted to elucidate the mechanisms underlying these effects by identifying gene expression alterations in cancer cells forming hepatic metastases after myeloid cell depletion. Hepatic metastases are heavily infiltrated by CD11b(+) myeloid cells. We established hepatic metastases in transgenic CD11b-diphtheria toxin receptor mice by intrasplenic injection of MC38 colon and Lewis lung carcinoma cells before depleting myeloid cells with diphtheria toxin. Myeloid cell depletion inhibited metastatic growth with a marked diminishment of tumor vasculature. Expression of ANGPTL7 (angiopoietin-like 7), a protein not previously linked to metastasis, was highly up-regulated in cancer cells after myeloid cell depletion. This effect was duplicated in tissue culture, where coculture of cancer cells with tumor-conditioned myeloid cells from liver metastases or myeloid cell conditioned media down-regulated ANGPTL7 expression. Analogous to myeloid cell depletion, overexpression of ANGPTL7 in cancer cells significantly reduced hepatic metastasis formation and angiogenesis. We found that ANGPTL7 itself has strong antiangiogenic effects in vitro. Furthermore, analysis of The Cancer Genome Atlas colorectal and breast cancer data sets revealed striking ANGPTL7 underexpression in cancerous compared to normal tissues. Also, ANGPTL7 was down-regulated in metastatic liver colonies of colorectal cancer patients compared to their adjacent liver tissue. Myeloid cells promote liver metastasis by down-regulating ANGPTL7 expression in cancer cells; our findings implicate ANGPTL7 as a mediator of metastatic progression and a potential target for interference with liver metastases.

PGTools: A Software Suite for Proteogenomic Data Analysis and Visualization.

We describe PGTools, an open source software suite for analysis and visualization of proteogenomic data. PGTools comprises applications, libraries, customized databases, and visualization tools for analysis of mass-spectrometry data using combined proteomic and genomic backgrounds. A single command is sufficient to search databases, calculate false discovery rates, group and annotate proteins, generate peptide databases from RNA-Seq transcripts, identify altered proteins associated with cancer, and visualize genome scale peptide data sets using sophisticated visualization tools. We experimentally confirm a subset of proteogenomic peptides in human PANC-1 cells and demonstrate the utility of PGTools using a colorectal cancer data set that led to the identification of 203 novel protein coding regions missed by conventional proteomic approaches. PGTools should be equally useful for individual proteogenomic investigations as well as international initiatives such as chromosome-centric Human Proteome Project (C-HPP). PGTools is available at http://qcmg.org/bioinformatics/PGTools.

Transposon mutagenesis identifies genes and evolutionary forces driving gastrointestinal tract tumor progression.

To provide a more comprehensive understanding of the genes and evolutionary forces driving colorectal cancer (CRC) progression, we performed Sleeping Beauty (SB) transposon mutagenesis screens in mice carrying sensitizing mutations in genes that act at different stages of tumor progression. This approach allowed us to identify a set of genes that appear to be highly relevant for CRC and to provide a better understanding of the evolutionary forces and systems properties of CRC. We also identified six genes driving malignant tumor progression and a new human CRC tumor-suppressor gene, ZNF292, that might also function in other types of cancer. Our comprehensive CRC data set provides a resource with which to develop new therapies for treating CRC.

Biological Network Module-Based Model for the Analysis of Differential Expression in Shotgun Proteomics

Protein differential expression analysis plays an important role in the understanding of molecular mechanisms as well as the pathogenesis of complex diseases. With the rapid development of mass spectrometry, shotgun proteomics using spectral counts has become a prevailing method for the quantitative analysis of complex protein mixtures. Existing methods in differential proteomics expression typically carry out analysis at the single-protein level. However, it is well-known that proteins interact with each other when they function in biological processes. In this study, focusing on biological network modules, we proposed a negative binomial generalized linear model for differential expression analysis of spectral count data in shotgun proteomics. In order to show the efficacy of the model in protein expression analysis at the level of protein modules, we conducted two simulation studies using synthetic data sets generated from theoretical distribution of count data and a real data set with shuffled counts. Then, we applied our method to a colorectal cancer data set and a nonsmall cell lung cancer data set. When compared with single-protein analysis methods, the results showed that module-based statistical model which takes account of the interactions among proteins led to more effective identification of subtle but coordinated changes at the systems level.

The role of linkage disequilibrium in case-only studies of gene-environment interactions.

Gene-environment (G×E) interactions have been invoked to account, at least in part, for the gap between the known heritability of common human diseases and the phenotypic variation hitherto explained by genetic variants. Noteworthy in this context, a case-only (CO) design has been proposed in the past as a means to detect G×E interactions possibly more efficiently than by using classical case-control and cohort designs. So far, however, most CO studies have followed a candidate (or single) gene approach, and the genome-wide utility of the CO design is still more or less unknown. In particular, the way in which linkage disequilibrium (LD) impacts upon the chance to detect G×E interaction through the analysis of proxy markers has not been studied in much detail before. Therefore, we systematically assessed the power to indirectly detect a given G×E interaction through exploiting LD in a CO design. Our simulations revealed a strong relationship between LD and detection power that was subsequently validated in a real colorectal cancer data set.

A novel method for detecting association between DNA methylation and diseases using spatial information.

DNA methylation may represent an important contributor to the missing heritability described in complex trait genetics. However, technology to measure DNA methylation has outpaced statistical methods for analysis. Taking advantage of the recent finding that methylated sites cluster together, we propose a Spatial Clustering Method (SCM) to detect differentially methylated regions (DMRs) in the genome in case and control studies using spatial location information. This new method compares the distribution of distances in cases and controls between DNA methylation marks in the genomic region of interest. A statistic is computed based on these distances. Proper type I error rate is maintained and statistical significance is evaluated using permutation test. The effectiveness of the SCM we propose is evaluated by a simulation study. By simulating a simple disease model, we demonstrate that SCM has good power to detect DMRs associated with the disease. Finally, we applied the SCM to an exploratory analysis of chromosome 14 from a colorectal cancer data set and identified statistically significant genomic regions. Identification of these regions should lead to a better understanding of methylated sites and their contribution to disease. The SCM can be used as a reliable statistical method for the identification of DMRs associated with disease states in exploratory epigenetic analyses.

ShatterProof: operational detection and quantification of chromothripsis

BackgroundChromothripsis, a newly discovered type of complex genomic rearrangement, has been implicated in the evolution of several types of cancers. To date, it has been described in bone cancer, SHH-medulloblastoma and acute myeloid leukemia, amongst others, however there are still no formal or automated methods for detecting or annotating it in high throughput sequencing data. As such, findings of chromothripsis are difficult to compare and many cases likely escape detection altogether.ResultsWe introduce ShatterProof, a software tool for detecting and quantifying chromothriptic events. ShatterProof takes structural variation calls (translocations, copy-number variations, short insertions and loss of heterozygosity) produced by any algorithm and using an operational definition of chromothripsis performs robust statistical tests to accurately predict the presence and location of chromothriptic events. Validation of our tool was conducted using clinical data sets including matched normal, prostate cancer samples in addition to the colorectal cancer and SCLC data sets used in the original description of chromothripsis.ConclusionsShatterProof is computationally efficient, having low memory requirements and near linear computation time. This allows it to become a standard component of sequencing analysis pipelines, enabling researchers to routinely and accurately assess samples for chromothripsis. Source code and documentation can be found at http://search.cpan.org/~sgovind/Shatterproof.

A network-based, integrative approach to identify genes with aberrant co-methylation in colorectal cancer

Epigenetic changes, including aberrations in DNA methylation, are a common hallmark of many cancers. The identification and interpretation of epigenetic changes associated with cancers may benefit from integration with protein interactomes. Based on the assumption that genes implicated in a specific tumor phenotype will show high aberrant co-methylation patterns with their interacting partners, we propose an integrated approach to uncover cancer-associated genes by integrating a DNA methylome with an interactome. Aberrant co-methylated interactions were first identified in the specific cancer, and genes were then prioritized based on their enrichment in aberrant co-methylation. By applying this to a large-scale colorectal cancer (CRC) dataset, the proposed method increases the power to capture known genes. More importantly, genes possessing high aberrant co-methylation patterns, located at the topological center of the original protein-protein interaction network (PPIN), affect several cancer-associated pathways and form hotspots that are frequently hijacked in cancer. Additionally, the top-ranked candidate genes may also be useful as an indicator of CRC diagnosis and prognosis. Five fold cross-validation of the top-ranked genes in diagnosis reveals that it can achieve an area under the receiver operating characteristic (ROC) curve ranging from 82.2% to 98.4% in three independent datasets. Five of these genes form a core repressive module. CCNA1 and ESR1 in particular are evidently silenced by promoter hypermethylation in CRC cell lines and tissues, whose re-expression markedly suppresses tumor cell survival and clonogenicity. These results show that the network-centric method could identify novel disease biomarkers and model how oncogenic lesions mediate epigenetic changes, providing important insights into tumorigenesis.

Model-independent evaluation of tumor markers and a logistic-tree approach to diagnostic decision support.

Sensitivity and specificity of using individual tumor markers hardly meet the clinical requirement. This challenge gave rise to many efforts, e.g., combing multiple tumor markers and employing machine learning algorithms. However, results from different studies are often inconsistent, which are partially attributed to the use of different evaluation criteria. Also, the wide use of model-dependent validation leads to high possibility of data overfitting when complex models are used for diagnosis. We propose two model-independent criteria, namely, area under the curve (AUC) and Relief to evaluate the diagnostic values of individual and multiple tumor markers, respectively. For diagnostic decision support, we propose the use of logistic-tree which combines decision tree and logistic regression. Application on a colorectal cancer dataset shows that the proposed evaluation criteria produce results that are consistent with current knowledge. Furthermore, the simple and highly interpretable logistic-tree has diagnostic performance that is competitive with other complex models.

Modeling RAS phenotype in colorectal cancer uncovers novel molecular traits of RAS dependency and improves prediction of response to targeted agents in patients.

KRAS wild-type status is an imperfect predictor of sensitivity to anti-EGF receptor (EGFR) monoclonal antibodies in colorectal cancer, motivating efforts to identify novel molecular aberrations driving RAS. This study aimed to build a quantitative readout of RAS pathway activity to (i) uncover molecular surrogates of RAS activity specific to colorectal cancer, (ii) improve the prediction of cetuximab response in patients, and (iii) suggest new treatment strategies. A model of RAS pathway activity was trained in a large colorectal cancer dataset and validated in three independent colorectal cancer patient datasets. Novel molecular traits were inferred from The Cancer Genome Atlas colorectal cancer data. The ability of the RAS model to predict resistance to cetuximab was tested in mouse xenografts and three independent patient cohorts. Drug sensitivity correlations between our model and large cell line compendiums were performed. The performance of the RAS model was remarkably robust across three validation datasets. (i) Our model confirmed the heterogeneity of the RAS phenotype in KRAS wild-type patients, and suggests novel molecular traits driving its phenotype (e.g., MED12 loss, FBXW7 mutation, MAP2K4 mutation). (ii) It improved the prediction of response and progression-free survival (HR, 2.0; P < 0.01) to cetuximab compared with KRAS mutation (xenograft and patient cohorts). (iii) Our model consistently predicted sensitivity to MAP-ERK kinase (MEK) inhibitors (P < 0.01) in two cell panel screens. Modeling the RAS phenotype in colorectal cancer allows for the robust interrogation of RAS pathway activity across cell lines, xenografts, and patient cohorts. It demonstrates clinical utility in predicting response to anti-EGFR agents and MEK inhibitors.