A genomic approach to colon cancer risk stratification yields biologic insights into therapeutic opportunities

Systems Medicine Design based on Systems Biology Approaches and Deep Neural Network for Gastric Cancer

Gastric cancer (GC) is the third leading cause of cancer death in the world. It is associated with the stimulation of microenvironment, aberrant epigenetic modification, and chronic inflammation. However, few researches discuss the GC molecular progression mechanisms from the perspective of the system level. In this study, we proposed a systems medicine design procedure to identify essential biomarkers and find corresponding drugs for GC. At first, we did big database mining to construct candidate protein-protein interaction network (PPIN) and candidate gene regulation network (GRN). Second, by leveraging the next-generation sequencing (NGS) data, we performed system modeling and applied system identification and model selection to obtain real genome-wide genetic and epigenetic networks (GWGENs). To make the real GWGENs easy to analyze, the principal network projection method was used to extract the core signaling pathways denoted by KEGG pathways. Subsequently, based on the identified biomarkers, we trained a deep neural network of drug-target interaction (DeepDTI) with supervised learning and filtered our candidate drugs considering drug regulation ability and drug sensitivity. With the proposed systematic strategy, we not only shed the light on the progression of GC but also suggested potential multiple-molecule drugs efficiently.

Systems Medicine Design for Triple-Negative Breast Cancer and Non-Triple-Negative Breast Cancer Based on Systems Identification and Carcinogenic Mechanisms

Triple-negative breast cancer (TNBC) is a heterogeneous subtype of breast cancers with poor prognosis. The etiology of triple-negative breast cancer (TNBC) is involved in various biological signal cascades and multifactorial aberrations of genetic, epigenetic and microenvironment. New therapeutic for TNBC is urgently needed because surgery and chemotherapy are the only available modalities nowadays. A better understanding of the molecular mechanisms would be a great challenge because they are triggered by cascade signaling pathways, genetic and epigenetic regulations, and drug–target interactions. This would allow the design of multi-molecule drugs for the TNBC and non-TNBC. In this study, in terms of systems biology approaches, we proposed a systematic procedure for systems medicine design toward TNBC and non-TNBC. For systems biology approaches, we constructed a candidate genome-wide genetic and epigenetic network (GWGEN) by big databases mining and identified real GWGENs of TNBC and non-TNBC assisting with corresponding microarray data by system identification and model order selection methods. After that, we applied the principal network projection (PNP) approach to obtain the core signaling pathways denoted by KEGG pathway of TNBC and non-TNBC. Comparing core signaling pathways of TNBC and non-TNBC, essential carcinogenic biomarkers resulting in multiple cellular dysfunctions including cell proliferation, autophagy, immune response, apoptosis, metastasis, angiogenesis, epithelial-mesenchymal transition (EMT), and cell differentiation could be found. In order to propose potential candidate drugs for the selected biomarkers, we designed filters considering toxicity and regulation ability. With the proposed systematic procedure, we not only shed a light on the differences between carcinogenetic molecular mechanisms of TNBC and non-TNBC but also efficiently proposed candidate multi-molecule drugs including resveratrol, sirolimus, and prednisolone for TNBC and resveratrol, sirolimus, carbamazepine, and verapamil for non-TNBC.

Identification of phenothiazine as an ETV1‑targeting agent in gastrointestinal stromal tumors using the Connectivity Map

Gastrointestinal stromal tumors (GISTs) are gastrointestinal tract sarcomas that commonly contain a mutation in the tyrosine kinases, KIT and platelet‑derived growth factor receptor A (PDGFRA). Imatinib, sunitinib and regorafenib are all effective tyrosine kinase inhibitors; however, acquired resistance is inevitable. The E26 variant 1 (ETV1) pathway has been found to be a key downstream effector of KIT and is therefore a reasonable therapeutic target for this disease. In this study, we explored the potential agents targeting ETV1 in GISTs by uploading an ETV1 knockout gene signature of GIST cell lines to the pattern‑matching software 'Connectivity Map'. The activity and mechanisms of identified agents were examined using an in vitro model. Four drugs were identified: Suberanilohydroxamic acid and trichostatin [two histone deacetylase inhibitors (HDACIs)] and trifluoperazine and thioridazine (two phenothiazine‑class drugs). Western blot analysis demonstrated that all four drugs had ETV1‑downregulating effects. As HDACIs have been previously studied in GISTs, we focused on phenothiazine. Phenothiazine was found to exert cytotoxicity and to induce apoptosis and autophagy in GISTs. Treatment with phenothiazine had little effect on the KIT/AKT/mammalian target of rapamycin (mTOR) pathway, but instead upregulated extracellular‑signal‑regulated kinase (ERK) activity. A combination of phenothiazine and a MEK inhibitor had a synergistic cytotoxic effect on GISTs. Western blot analysis indicated that ELK1 and early growth response 1 (EGR1) were activated/upregulated following phenothiazine treatment, and the MEK inhibitor/phenothiazine combination downregulated the ERK/ELK1/EGR1 pathway, resulting in diminished autophagy, as well as enhanced apoptosis. On the whole, the findings of this study established phenothiazine as a novel class of therapeutic agents in GIST treatment and demonstrate that a combination of phenothiazine and MEK inhibitor has great potential for use in the treatment of GISTs.

Nomogram Integrating Genomics with Clinicopathologic Features Improves Prognosis Prediction for Colorectal Cancer

The current tumor staging system is insufficient for predicting the outcomes for patients with colorectal cancer because of its phenotypic and genomic heterogeneity. Integrating gene expression signatures with clinicopathologic factors may yield a predictive accuracy exceeding that of the currently available system. Twenty-seven signatures that used gene expression data to predict colorectal cancer prognosis were identified and re-analyzed using bioinformatic methods. Next, clinically annotated colorectal cancer samples (n = 1710) with the corresponding expression profiles, that predicted a patient's probability of cancer recurrence, were pooled to evaluate their prognostic values and establish a clinicopathologic-genomic nomogram. Only 2 of the 27 signatures evaluated showed a significant association with prognosis and provided a reasonable prediction accuracy in the pooled cohort (HR, 2.46; 95% CI, 1.183-5.132, P < 0.001; AUC, 60.83; HR, 2.33; 95% CI, 1.218-4.453, P < 0.001; AUC, 71.34). By integrating the above signatures with prognostic clinicopathologic features, a clinicopathologic-genomic nomogram was cautiously constructed. The nomogram successfully stratified colorectal cancer patients into three risk groups with remarkably different DFS rates and further stratified stage II and III patients into distinct risk subgroups. Importantly, among patients receiving chemotherapy, the nomogram determined that those in the intermediate- (HR, 0.98; 95% CI, 0.255-0.679, P < 0.001) and high-risk (HR, 0.67; 95% CI, 0.469-0.957, P = 0.028) groups had favorable responses.Implications: These findings offer evidence that genomic data provide independent and complementary prognostic information, and incorporation of this information refines the prognosis of colorectal cancer. Mol Cancer Res; 16(9); 1373-84. ©2018 AACR.

What (not) to expect when classifying rare events

When building classifiers, it is natural to require that the classifier correctly estimates the event probability (Constraint 1), that it has equal sensitivity and specificity (Constraint 2) or that it has equal positive and negative predictive values (Constraint 3). We prove that in the balanced case, where there is equal proportion of events and non-events, any classifier that satisfies one of these constraints will always satisfy all. Such unbiasedness of events and non-events is much more difficult to achieve in the case of rare events, i.e. the situation in which the proportion of events is (much) smaller than 0.5. Here, we prove that it is impossible to meet all three constraints unless the classifier achieves perfect predictions. Any non-perfect classifier can only satisfy at most one constraint, and satisfying one constraint implies violating the other two constraints in a specific direction. Our results have implications for classifiers optimized using g-means or F1-measure, which tend to satisfy Constraints 2 and 1, respectively. Our results are derived from basic probability theory and illustrated with simulations based on some frequently used classifiers.

Gene expression profiling to predict recurrence of advanced squamous cell carcinoma of the tongue: discovery and external validation

OBJECTIVES

To establish a prognostic signature for locally advanced tongue squamous cell carcinoma (TSCC) patients treated with surgery.

RESULTS

In the discovery study, unsupervised hierarchical clustering analysis identified two clusters which differentiated the Kaplan-Meier curves of RFS [median RFS, 111 days vs. not reached; log-rank test, P = 0.023]. The 30 genes identified were combined into a dichotomous PI. In the validation cohort, classification according to the PI was associated with RFS [median RFS, 754 days vs. not reached; log-rank test, P = 0.026 in GSE31056] and DSS [median DSS, 540 days vs. not reached; log-rank test, P = 0.046 in GSE42743 and 443 days vs. not reached; P < 0.001 in GSE41613]. Among genes, positive immunohistochemical staining of cytokeratin 4 was associated with favorable prognostic values for RFS (hazard ratio (HR), 0.591, P = 0.045) and DSS (HR, 0.333, P = 0.004).

MATERIALS AND METHODS

We conducted gene expression profiling of 26 clinicopathologically homogeneous advanced TSCC tissue samples using cDNA microarray as a discovery study. Candidate genes were screened using clustering analysis and univariate Cox regression analysis for relapse-free survival (RFS). These were combined into a prognostic index (PI), which was validated using three public microarray datasets of tongue and oral cancer (123 patients). Some genes identified in discovery were immunohistochemically examined for protein expression in another 127 TSCC patients.

CONCLUSION

We identified robust molecular markers that showed significant associations with prognosis in TSCC patients. Gene expression profiling data were successfully converted to protein expression profiling data.

Associating transcriptional modules with colon cancer survival through weighted gene co-expression network analysis

BACKGROUND

Colon cancer (CC) is a heterogeneous disease influenced by complex gene networks. As such, the relationship between networks and CC should be elucidated to obtain further insights into tumour biology.

RESULTS

Weighted gene co-expression network analysis, a powerful technique used to extract co-expressed gene networks from mRNA expressions, was conducted to identify 11 co-regulated modules in a discovery dataset with 461 patients. A transcriptional module enriched in cell cycle processes was correlated with the recurrence-free survival of the CC patients in the discovery (HR = 0.59; 95% CI = 0.42-0.81) and validation (HR = 0.51; 95% CI = 0.25-1.05) datasets. The prognostic potential of the hub gene Centromere Protein-A (CENPA) was also identified and the upregulation of this gene was associated with good survival. Another cell cycle phase-related gene module was correlated with the survival of the patients with a KRAS mutation CC subtype. The downregulation of several genes, including those found in this co-expression module, such as cyclin-dependent kinase 1 (CDK1), was associated with poor survival.

CONCLUSION

Network-based approaches may facilitate the discovery of biomarkers for the prognosis of a subset of patients with stage II or III CC, these approaches may also help direct personalised therapies.

Drug discovery using clinical outcome-based Connectivity Mapping: application to ovarian cancer

Background

Epithelial ovarian cancer (EOC) is the fifth leading cause of cancer death among women in the United States (5 % of cancer deaths). The standard treatment for patients with advanced EOC is initial debulking surgery followed by carboplatin-paclitaxel combination chemotherapy. Unfortunately, with chemotherapy most patients relapse and die resulting in a five-year overall survival around 45 %. Thus, finding novel therapeutics for treating EOC is essential. Connectivity Mapping (CMAP) has been used widely in cancer drug discovery and generally has relied on cancer cell line gene expression and drug phenotype data. Therefore, we took a CMAP approach based on tumor information and clinical endpoints from high grade serous EOC patients.

Methods

We determined tumor gene expression signatures (e.g., sets of genes) associated with time to recurrence (with and without adjustment for additional clinical covariates) among patients within TCGA (n = 407) and, separately, from the Mayo Clinic (n = 326). Each gene signature was inputted into CMAP software (Broad Institute) to determine a set of drugs for which our signature “matches” the “reference” signature, and drugs that overlapped between the CMAP analyses and the two studies were carried forward for validation studies involving drug screens on a set of 10 EOC cell lines.

Results

Of the 11 drugs carried forward, five (mitoxantrone, podophyllotoxin, wortmannin, doxorubicin, and 17-AAG) were known a priori to be cytotoxics and were indeed shown to effect EOC cell viability.

Conclusions

Future research is needed to investigate the use of these CMAP and similar analyses for determining combination therapies that might work synergistically to kill cancer cells and to apply this in silico bioinformatics approach using clinical outcomes to other cancer drug screening studies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3149-5) contains supplementary material, which is available to authorized users.

Comparison of Nanostring nCounter® Data on FFPE Colon Cancer Samples and Affymetrix Microarray Data on Matched Frozen Tissues

The prognosis of colorectal cancer (CRC) stage II and III patients remains a challenge due to the difficulties of finding robust biomarkers suitable for testing clinical samples. The majority of published gene signatures of CRC have been generated on fresh frozen colorectal tissues. Because collection of frozen tissue is not practical for routine surgical pathology practice, a clinical test that improves prognostic capabilities beyond standard pathological staging of colon cancer will need to be designed for formalin-fixed paraffin-embedded (FFPE) tissues. The NanoString nCounter^® platform is a gene expression analysis tool developed for use with FFPE-derived samples. We designed a custom nCounter^® codeset based on elements from multiple published fresh frozen tissue microarray-based prognostic gene signatures for colon cancer, and we used this platform to systematically compare gene expression data from FFPE with matched microarray array data from frozen tissues. Our results show moderate correlation of gene expression between two platforms and discovery of a small subset of genes as candidate biomarkers for colon cancer prognosis that are detectable and quantifiable in FFPE tissue sections.

Comparative study of joint analysis of microarray gene expression data in survival prediction and risk assessment of breast cancer patients

Microarray gene expression data sets are jointly analyzed to increase statistical power. They could either be merged together or analyzed by meta-analysis. For a given ensemble of data sets, it cannot be foreseen which of these paradigms, merging or meta-analysis, works better. In this article, three joint analysis methods, Z -score normalization, ComBat and the inverse normal method (meta-analysis) were selected for survival prognosis and risk assessment of breast cancer patients. The methods were applied to eight microarray gene expression data sets, totaling 1324 patients with two clinical endpoints, overall survival and relapse-free survival. The performance derived from the joint analysis methods was evaluated using Cox regression for survival analysis and independent validation used as bias estimation. Overall, Z -score normalization had a better performance than ComBat and meta-analysis. Higher Area Under the Receiver Operating Characteristic curve and hazard ratio were also obtained when independent validation was used as bias estimation. With a lower time and memory complexity, Z -score normalization is a simple method for joint analysis of microarray gene expression data sets. The derived findings suggest further assessment of this method in future survival prediction and cancer classification applications.

Gene-Set Local Hierarchical Clustering (GSLHC)--A Gene Set-Based Approach for Characterizing Bioactive Compounds in Terms of Biological Functional Groups

Gene-set-based analysis (GSA), which uses the relative importance of functional gene-sets, or molecular signatures, as units for analysis of genome-wide gene expression data, has exhibited major advantages with respect to greater accuracy, robustness, and biological relevance, over individual gene analysis (IGA), which uses log-ratios of individual genes for analysis. Yet IGA remains the dominant mode of analysis of gene expression data. The Connectivity Map (CMap), an extensive database on genomic profiles of effects of drugs and small molecules and widely used for studies related to repurposed drug discovery, has been mostly employed in IGA mode. Here, we constructed a GSA-based version of CMap, Gene-Set Connectivity Map (GSCMap), in which all the genomic profiles in CMap are converted, using gene-sets from the Molecular Signatures Database, to functional profiles. We showed that GSCMap essentially eliminated cell-type dependence, a weakness of CMap in IGA mode, and yielded significantly better performance on sample clustering and drug-target association. As a first application of GSCMap we constructed the platform Gene-Set Local Hierarchical Clustering (GSLHC) for discovering insights on coordinated actions of biological functions and facilitating classification of heterogeneous subtypes on drug-driven responses. GSLHC was shown to tightly clustered drugs of known similar properties. We used GSLHC to identify the therapeutic properties and putative targets of 18 compounds of previously unknown characteristics listed in CMap, eight of which suggest anti-cancer activities. The GSLHC website http://cloudr.ncu.edu.tw/gslhc/ contains 1,857 local hierarchical clusters accessible by querying 555 of the 1,309 drugs and small molecules listed in CMap. We expect GSCMap and GSLHC to be widely useful in providing new insights in the biological effect of bioactive compounds, in drug repurposing, and in function-based classification of complex diseases.

Capillary nano-immunoassays: advancing quantitative proteomics analysis, biomarker assessment, and molecular diagnostics

There is an emerging demand for the use of molecular profiling to facilitate biomarker identification and development, and to stratify patients for more efficient treatment decisions with reduced adverse effects. In the past decade, great strides have been made to advance genomic, transcriptomic and proteomic approaches to address these demands. While there has been much progress with these large scale approaches, profiling at the protein level still faces challenges due to limitations in clinical sample size, poor reproducibility, unreliable quantitation, and lack of assay robustness. A novel automated capillary nano-immunoassay (CNIA) technology has been developed. This technology offers precise and accurate measurement of proteins and their post-translational modifications using either charge-based or size-based separation formats. The system not only uses ultralow nanogram levels of protein but also allows multi-analyte analysis using a parallel single-analyte format for increased sensitivity and specificity. The high sensitivity and excellent reproducibility of this technology make it particularly powerful for analysis of clinical samples. Furthermore, the system can distinguish and detect specific protein post-translational modifications that conventional Western blot and other immunoassays cannot easily capture. This review will summarize and evaluate the latest progress to optimize the CNIA system for comprehensive, quantitative protein and signaling event characterization. It will also discuss how the technology has been successfully applied in both discovery research and clinical studies, for signaling pathway dissection, proteomic biomarker assessment, targeted treatment evaluation and quantitative proteomic analysis. Lastly, a comparison of this novel system with other conventional immuno-assay platforms is performed.

A reproducible approach to high-throughput biological data acquisition and integration

Modern biological research requires rapid, complex, and reproducible integration of multiple experimental results generated both internally and externally (e.g., from public repositories). Although large systematic meta-analyses are among the most effective approaches both for clinical biomarker discovery and for computational inference of biomolecular mechanisms, identifying, acquiring, and integrating relevant experimental results from multiple sources for a given study can be time-consuming and error-prone. To enable efficient and reproducible integration of diverse experimental results, we developed a novel approach for standardized acquisition and analysis of high-throughput and heterogeneous biological data. This allowed, first, novel biomolecular network reconstruction in human prostate cancer, which correctly recovered and extended the NFκB signaling pathway. Next, we investigated host-microbiome interactions. In less than an hour of analysis time, the system retrieved data and integrated six germ-free murine intestinal gene expression datasets to identify the genes most influenced by the gut microbiota, which comprised a set of immune-response and carbohydrate metabolism processes. Finally, we constructed integrated functional interaction networks to compare connectivity of peptide secretion pathways in the model organisms Escherichia coli, Bacillus subtilis, and Pseudomonas aeruginosa.

A novel monoclonal antibody against human GRK6 antigen

G protein-coupled receptor kinase 6 (GRK6) plays a universal role in receptor desensitization, by acting as a receptor-G protein interface, thereby affecting serine/threonine kinases. In this study, a 20-aa-long peptide of human GRK6 C-terminus domain was synthesized and covalently coupled to keyhole limpet hemocyanin (KLH). A mouse monoclonal antibody against human GRK6 (anti-GRK6 MAb) was successfully prepared through hybridoma technique by immunizing BALB/c mice with synthesized GRK6426-446-KLH peptides. A high specificity and affinity strain of hybridoma 5D12 were established. The titer of the purified anti-GRK6 MAb was 1.28 × 10(6) measured by indirect ELISA. Western blot and immunocytochemistry experiments were also applied to characterize the antibody specificity. Antibody absorption assays showed that the anti-GRK6 MAb can be blocked by GRK6426-446 peptides. These results indicated that the antibody could bind to GRK6 antigen specifically. This MAb provides valuable support for further studies on the functional properties of GRK6.

A genomic signature approach to rescue ΔF508-cystic fibrosis transmembrane conductance regulator biosynthesis and function

The most common cystic fibrosis (CF) mutation, ΔF508, causes protein misfolding, leading to proteosomal degradation. We recently showed that expression of miR-138 enhances CF transmembrane conductance regulator (CFTR) biogenesis and partially rescues ΔF508-CFTR function in CF airway epithelia. We hypothesized that a genomic signature approach can be used to identify new bioactive small molecules affecting ΔF508-CFTR rescue. The Connectivity Map was used to identify 27 small molecules with potential to restore ΔF508-CFTR function in airway epithelia. The molecules were screened in vitro for efficacy in improving ΔF508-CFTR trafficking, maturation, and chloride current. We identified four small molecules that partially restore ΔF508-CFTR function in primary CF airway epithelia. Of these, pyridostigmine showed cooperativity with corrector compound 18 in improving ΔF508-CFTR function. There are few CF therapies based on new molecular insights. Querying the Connectivity Map with relevant genomic signatures offers a method to identify new candidates for rescuing ΔF508-CFTR function.

Recent advances in personalized colorectal cancer research

Colorectal cancer is one of the most prevalent cancers and a leading cause ofcancer-related death. It is also curable if detected early. The prognosis for metastatic colorectal cancer remains poor and resistance to chemotherapy is still a major obstacle in effective treatment. While many patients do not clinically benefit from chemotherapy, others experience adverse reactions resulting in dose modifications or treatment withdrawal, thereby reducing treatment efficacy. Researchefforts attempt to identify reliable biomarkers which will guide clinicians in decision making, while matching suitable therapeutic regimens. We here review currently known molecular biomarkers used for the personalized treatment of colorectal cancer.

Functional Module Connectivity Map (FMCM): a framework for searching repurposed drug compounds for systems treatment of cancer and an application to colorectal adenocarcinoma

Drug repurposing has become an increasingly attractive approach to drug development owing to the ever-growing cost of new drug discovery and frequent withdrawal of successful drugs caused by side effect issues. Here, we devised Functional Module Connectivity Map (FMCM) for the discovery of repurposed drug compounds for systems treatment of complex diseases, and applied it to colorectal adenocarcinoma. FMCM used multiple functional gene modules to query the Connectivity Map (CMap). The functional modules were built around hub genes identified, through a gene selection by trend-of-disease-progression (GSToP) procedure, from condition-specific gene-gene interaction networks constructed from sets of cohort gene expression microarrays. The candidate drug compounds were restricted to drugs exhibiting predicted minimal intracellular harmful side effects. We tested FMCM against the common practice of selecting drugs using a genomic signature represented by a single set of individual genes to query CMap (IGCM), and found FMCM to have higher robustness, accuracy, specificity, and reproducibility in identifying known anti-cancer agents. Among the 46 drug candidates selected by FMCM for colorectal adenocarcinoma treatment, 65% had literature support for association with anti-cancer activities, and 60% of the drugs predicted to have harmful effects on cancer had been reported to be associated with carcinogens/immune suppressors. Compounds were formed from the selected drug candidates where in each compound the component drugs collectively were beneficial to all the functional modules while no single component drug was harmful to any of the modules. In cell viability tests, we identified four candidate drugs: GW-8510, etacrynic acid, ginkgolide A, and 6-azathymine, as having high inhibitory activities against cancer cells. Through microarray experiments we confirmed the novel functional links predicted for three candidate drugs: phenoxybenzamine (broad effects), GW-8510 (cell cycle), and imipenem (immune system). We believe FMCM can be usefully applied to repurposed drug discovery for systems treatment of other types of cancer and other complex diseases.

BRCA-Monet: a breast cancer specific drug treatment mode-of-action network for treatment effective prediction using large scale microarray database

BACKGROUND

Connectivity map (cMap) is a recent developed dataset and algorithm for uncovering and understanding the treatment effect of small molecules on different cancer cell lines. It is widely used but there are still remaining challenges for accurate predictions.

METHOD

Here, we propose BRCA-MoNet, a network of drug mode of action (MoA) specific to breast cancer, which is constructed based on the cMap dataset. A drug signature selection algorithm fitting the characteristic of cMap data, a quality control scheme as well as a novel query algorithm based on BRCA-MoNet are developed for more effective prediction of drug effects.

RESULT

BRCA-MoNet was applied to three independent data sets obtained from the GEO database: Estrodial treated MCF7 cell line, BMS-754807 treated MCF7 cell line, and a breast cancer patient microarray dataset. In the first case, BRCA-MoNet could identify drug MoAs likely to share same and reverse treatment effect. In the second case, the result demonstrated the potential of BRCA-MoNet to reposition drugs and predict treatment effects for drugs not in cMap data. In the third case, a possible procedure of personalized drug selection is showcased.

CONCLUSIONS

The results clearly demonstrated that the proposed BRCA-MoNet approach can provide increased prediction power to cMap and thus will be useful for identification of new therapeutic candidates.

Preoperative serum CA 72.4 as prognostic factor of recurrence and death, especially at TNM stage II, for colorectal cancer

Background

Nowadays, evaluation of colorectal cancer prognosis and decision-making for treatment continues to be based primarily on TNM tumour stage. Administration of adjuvant chemotherapy is especially challenging for stage II patients that can have very different disease-related outcomes. Therefore, more reliable prognostic markers need to be developed to improve the selection of stage II patients at high risk for recurrence. Our purpose is to assess the prognostic value of preoperative serum CA 72.4 to improve the risk stratification of CRC patients.

Methods

Preoperative sera collected from 71 unselected patients between January 1994 and February 1997 was assayed for CA 72.4 and CEA levels. Patients were followed-up for at least 30 months or until relapse. Survival curves were estimated by the Kaplan-Meier method and the prognostic value was determined using Log-Rank test and Cox regression analysis.

Results

Preoperative CA 72.4 levels above 7 U/mL correlate with a worse prognosis, with associated recurrence and death percentages exceeding the displayed by CEA. In a multivariate analysis, its combination with CEA proved the most important independent factor predicting survival. Remarkably, at stage II CA 72.4 also discriminates better than CEA those patients that will relapse or die from those with a favourable prognosis; however, CEA has not a negligible effect on survival.

Conclusions

The most outstanding finding of the present work is the correct classification of nearly every patient with bad prognosis (relapse or death) at TNM stage II when CEA and CA 72.4 are used altogether. This could improve the decision-making involved in the treatment of stage II colon cancer. Certainly further large-scale studies must be performed to determine whether CA 72.4 can be effectively used in the clinical setting.

Comparison of prognostic genomic predictors in colorectal cancer

Background

Although several prognostic genomic predictors have been identified from independent studies, it remains unclear whether these predictors are actually concordant with respect to their predictions for individual patients and which predictor performs best. We compared five prognostic genomic predictors, the V7RHS, the ColoGuideEx, the Meta163, the OncoDX, and the MDA114, in terms of predicting disease-free survival in two independent cohorts of patients with colorectal cancer.

Study Design

Using original classification algorithms, we tested the predictions of five genomic predictors for disease-free survival in two cohorts of patients with colorectal cancer (n = 229 and n = 168) and evaluated concordance of predictors in predicting outcomes for individual patients.

Results

We found that only two predictors, OncoDX and MDA114, demonstrated robust performance in identifying patients with poor prognosis in 2 independent cohorts. These two predictors also had modest but significant concordance of predicted outcome (r>0.3, P<0.001 in both cohorts).

Conclusions

Further validation of developed genomic predictors is necessary. Despite the limited number of genes shared by OncoDX and MDA114, individual-patient outcomes predicted by these two predictors were significantly concordant.

Re-overcoming barriers in translating biomarkers to clinical practice

IMPORTANCE OF THE FIELD

Recently, there has been growing evidence for the concept of personalized medicine as the implementation of genomic and molecular information in the delivery of healthcare. In parallel, the identification of biomarkers has become of enormous significance as a prerequisite for individualized intervention regimens.

AREAS COVERED IN THIS REVIEW

Biomarkers are developed to improve prevention, diagnosis or therapeutic outcome of a given disease. Although each application reveals distinct developmental strategies, evidence-based approval of new biomarkers is important for the success of new drugs, diagnostic tests or recommendations in preventive medicine. Current hurdles to bringing biomarkers into clinical practice are reviewed, thereby focusing on adequate approaches to overcome these limitations in the future.

WHAT THE READER WILL GAIN

The reader will get an introduction to strategies resolving actual barriers in clinical biomarker development.

TAKE HOME MESSAGE

The identification of evidence-based biomarkers is crucial for the success of individualized therapeutic approaches. Developmental strategies have to be adapted to clinical need, thereby focusing on biomarker validation in clinical settings as well as on the establishment of standardized biomarker test systems for routine application. Consortia have been established bringing together representatives of government, academia and industry to improve future biomarker development.

Molecular biomarkers of colorectal cancer: prognostic and predictive tools for clinical practice

Colorectal cancer remains one of the most common types of cancer and leading causes of cancer death worldwide. Although we have made steady progress in chemotherapy and targeted therapy, evidence suggests that the majority of patients undergoing drug therapy experience severe, debilitating, and even lethal adverse drug events which considerably outweigh the benefits. The identification of suitable biomarkers will allow clinicians to deliver the most appropriate drugs to specific patients and spare them ineffective and expensive treatments. Prognostic and predictive biomarkers have been the subjects of many published papers, but few have been widely incorporated into clinical practice. Here, we want to review recent biomarker data related to colorectal cancer, which may have been ready for clinical use.

A data similarity-based strategy for meta-analysis of transcriptional profiles in cancer

Background

Robust transcriptional signatures in cancer can be identified by data similarity-driven meta-analysis of gene expression profiles. An unbiased data integration and interrogation strategy has not previously been available.

Methods and Findings

We implemented and performed a large meta-analysis of breast cancer gene expression profiles from 223 datasets containing 10,581 human breast cancer samples using a novel data similarity-based approach (iterative EXALT). Cancer gene expression signatures extracted from individual datasets were clustered by data similarity and consolidated into a meta-signature with a recurrent and concordant gene expression pattern. A retrospective survival analysis was performed to evaluate the predictive power of a novel meta-signature deduced from transcriptional profiling studies of human breast cancer. Validation cohorts consisting of 6,011 breast cancer patients from 21 different breast cancer datasets and 1,110 patients with other malignancies (lung and prostate cancer) were used to test the robustness of our findings. During the iterative EXALT analysis, 633 signatures were grouped by their data similarity and formed 121 signature clusters. From the 121 signature clusters, we identified a unique meta-signature (BRmet50) based on a cluster of 11 signatures sharing a phenotype related to highly aggressive breast cancer. In patients with breast cancer, there was a significant association between BRmet50 and disease outcome, and the prognostic power of BRmet50 was independent of common clinical and pathologic covariates. Furthermore, the prognostic value of BRmet50 was not specific to breast cancer, as it also predicted survival in prostate and lung cancers.

Conclusions

We have established and implemented a novel data similarity-driven meta-analysis strategy. Using this approach, we identified a transcriptional meta-signature (BRmet50) in breast cancer, and the prognostic performance of BRmet50 was robust and applicable across a wide range of cancer-patient populations.

Subtypes of primary colorectal tumors correlate with response to targeted treatment in colorectal cell lines

Background

Colorectal cancer (CRC) is a heterogeneous and biologically poorly understood disease. To tailor CRC treatment, it is essential to first model this heterogeneity by defining subtypes of patients with homogeneous biological and clinical characteristics and second match these subtypes to cell lines for which extensive pharmacological data is available, thus linking targeted therapies to patients most likely to respond to treatment.

Methods

We applied a new unsupervised, iterative approach to stratify CRC tumor samples into subtypes based on genome-wide mRNA expression data. By applying this stratification to several CRC cell line panels and integrating pharmacological response data, we generated hypotheses regarding the targeted treatment of different subtypes.

Results

In agreement with earlier studies, the two dominant CRC subtypes are highly correlated with a gene expression signature of epithelial-mesenchymal-transition (EMT). Notably, further dividing these two subtypes using iNMF (iterative Non-negative Matrix Factorization) revealed five subtypes that exhibit activation of specific signaling pathways, and show significant differences in clinical and molecular characteristics. Importantly, we were able to validate the stratification on independent, published datasets comprising over 1600 samples. Application of this stratification to four CRC cell line panels comprising 74 different cell lines, showed that the tumor subtypes are well represented in available CRC cell line panels. Pharmacological response data for targeted inhibitors of SRC, WNT, GSK3b, aurora kinase, PI3 kinase, and mTOR, showed significant differences in sensitivity across cell lines assigned to different subtypes. Importantly, some of these differences in sensitivity were in concordance with high expression of the targets or activation of the corresponding pathways in primary tumor samples of the same subtype.

Conclusions

The stratification presented here is robust, captures important features of CRC, and offers valuable insight into functional differences between CRC subtypes. By matching the identified subtypes to cell line panels that have been pharmacologically characterized, it opens up new possibilities for the development and application of targeted therapies for defined CRC patient sub-populations.

Prognostic factors assessed for 15,096 patients with colon cancer in stages I and II

BACKGROUND

We focused on the risk factors for poor outcome after curative resection of a colon cancer in UICC stages I and II based on the data of the Germany-wide quality assurance study "colon/rectum cancer (primary tumor)." In some countries, all stage II colon cancer patients are encouraged to participate in a clinical trial. We feel that this approach is too broad.

METHODS

Using the data of 15,096 patients operated on from January 1, 2000 to December 31, 2004, the following factors were analyzed with the Cox regression model: age, comorbidities, ASA score, gender, localization of the tumor (left colon vs. right colon), perioperative complications (yes/no), pT stage, grading (G1/G2 vs. G3/G4), L-status (lymph vessels invasion yes/no), and V-status (venous invasion yes/no).

RESULTS

The probability of a local relapse in stages I and II was 1.5 and 4.6%, respectively, or distant metastases 4.7 and 10.2%, respectively. Only pT stage [hazard ratio (HR) for pT1 = 1, pT2 = 1.821, pT3 = 2.735, and pT4 = 5.881], L-status (HR for L1 = 1.393), age (HR per year = 1.021), as well as ASA score IV (HR = 4.536) had significant influence on tumor-free survival.

CONCLUSIONS

Despite favorable prognosis and R0 resection, a small percentage of patients will still relapse. The most important risk factor comprising the tumor-free survival is the pT stage followed by L-status and age. These results should be taken into consideration when determining the course for adjuvant chemotherapy, especially if the course includes the recommendation of clinical trial participation for stage II colon cancer patients after an R0 resection.

Recent approaches to identifying biomarkers for high-risk stage II colon cancer

The use of adjuvant chemotherapy for stage II colon cancer remains controversial. The accurate assessment of the risk factors associated with recurrence in patients with stage II disease is the key to identifying the patients that are most likely to benefit from adjuvant chemotherapy. Recent guidelines advocate that adjuvant chemotherapy for high-risk stage II colon cancer should take into account factors such as the T stage, number of lymph nodes examined, tumor differentiation, and tumor perforation. In addition to these clinicopathological factors, there has also been intense interest in the identification of new prognostic or predictive biomarkers that can improve outcomes through better patient classification and selection for adjuvant chemotherapy. Recent advances in the field of molecular genetics have led to the identification of specific biomarkers involved in colorectal cancer progression, whereas gene expression microarray technology has led to the identification of molecular profiles able to predict recurrence or benefit from adjuvant chemotherapy. However, none of these has yet been validated in large prospective clinical trials. In this article, we review the current status of prognostic and predictive biomarkers for stage II colon cancer and provide an update on the most recent developments.

Identification and validation of gene expression models that predict clinical outcome in patients with early-stage laryngeal cancer

BACKGROUND

Despite improvement in therapeutic techniques, patients with early-stage laryngeal cancer still recur after treatment. Gene expression prognostic models could suggest which of these patients would be more appropriate for testing adjuvant strategies.

MATERIALS AND METHODS

Expression profiling using whole-genome DASL arrays was carried out on 56 formalin-fixed paraffin-embedded tumor samples of patients with early-stage laryngeal cancer. We split the samples into a training and a validation set. Using the supervised principal components survival analysis in the first cohort, we identified gene expression profiles that predict the risk of recurrence. These profiles were then validated in an independent cohort.

RESULTS

Gene models comprising different number of genes identified a subgroup of patients who were at high risk of recurrence. Of these, the best prognostic model distinguished between a high- and a low-risk group (log-rank P<0.005). The prognostic value of this model was reproduced in the validation cohort (median disease-free survival: 38 versus 161 months, log-rank P=0.018), hazard ratio=5.19 (95% confidence interval 1.14-23.57, P<0.05).

CONCLUSIONS

We have identified gene expression prognostic models that can refine the estimation of a patient's risk of recurrence. These findings, if further validated, should aid in patient stratification for testing adjuvant treatment strategies.

Colon cancer molecular subtypes identified by expression profiling and associated to stroma, mucinous type and different clinical behavior

Background

Colon cancer patients with the same stage show diverse clinical behavior due to tumor heterogeneity. We aimed to discover distinct classes of tumors based on microarray expression patterns, to analyze whether the molecular classification correlated with the histopathological stages or other clinical parameters and to study differences in the survival.

Methods

Hierarchical clustering was performed for class discovery in 88 colon tumors (stages I to IV). Pathways analysis and correlations between clinical parameters and our classification were analyzed. Tumor subtypes were validated using an external set of 78 patients. A 167 gene signature associated to the main subtype was generated using the 3-Nearest-Neighbor method. Coincidences with other prognostic predictors were assesed.

Results

Hierarchical clustering identified four robust tumor subtypes with biologically and clinically distinct behavior. Stromal components (p < 0.001), nuclear β-catenin (p = 0.021), mucinous histology (p = 0.001), microsatellite-instability (p = 0.039) and BRAF mutations (p < 0.001) were associated to this classification but it was independent of Dukes stages (p = 0.646). Molecular subtypes were established from stage I. High-stroma-subtype showed increased levels of genes and altered pathways distinctive of tumour-associated-stroma and components of the extracellular matrix in contrast to Low-stroma-subtype. Mucinous-subtype was reflected by the increased expression of trefoil factors and mucins as well as by a higher proportion of MSI and BRAF mutations. Tumor subtypes were validated using an external set of 78 patients. A 167 gene signature associated to the Low-stroma-subtype distinguished low risk patients from high risk patients in the external cohort (Dukes B and C:HR = 8.56(2.53-29.01); Dukes B,C and D:HR = 1.87(1.07-3.25)). Eight different reported survival gene signatures segregated our tumors into two groups the Low-stroma-subtype and the other tumor subtypes.

Conclusions

We have identified novel molecular subtypes in colon cancer with distinct biological and clinical behavior that are established from the initiation of the tumor. Tumor microenvironment is important for the classification and for the malignant power of the tumor. Differential gene sets and biological pathways characterize each tumor subtype reflecting underlying mechanisms of carcinogenesis that may be used for the selection of targeted therapeutic procedures. This classification may contribute to an improvement in the management of the patients with CRC and to a more comprehensive prognosis.

Chemocentric informatics approach to drug discovery: identification and experimental validation of selective estrogen receptor modulators as ligands of 5-hydroxytryptamine-6 receptors and as potential cognition enhancers

We have devised a chemocentric informatics methodology for drug discovery integrating independent approaches to mining biomolecular databases. As a proof of concept, we have searched for novel putative cognition enhancers. First, we generated Quantitative Structure-Activity Relationship (QSAR) models of compounds binding to 5-hydroxytryptamine-6 receptor (5-HT(6)R), a known target for cognition enhancers, and employed these models for virtual screening to identify putative 5-HT(6)R actives. Second, we queried chemogenomics data from the Connectivity Map ( http://www.broad.mit.edu/cmap/ ) with the gene expression profile signatures of Alzheimer's disease patients to identify compounds putatively linked to the disease. Thirteen common hits were tested in 5-HT(6)R radioligand binding assays and ten were confirmed as actives. Four of them were known selective estrogen receptor modulators that were never reported as 5-HT(6)R ligands. Furthermore, nine of the confirmed actives were reported elsewhere to have memory-enhancing effects. The approaches discussed herein can be used broadly to identify novel drug-target-disease associations.

Overexpression of CIAPIN1 inhibited pancreatic cancer cell proliferation and was associated with good prognosis in pancreatic cancer

Cytokine-induced antiapoptotic molecule (CIAPIN1), a newly identified apoptosis inhibitor, has been found to participate in the process of proliferation and tumorigenicity for several cancers. The aim of this study was to evaluate the prognostic value of CIAPIN1 in pancreatic cancer and to probe its function in pancreatic carcinogenesis. We found that CIAPIN1 protein was absent or reduced in pancreatic cancer cell lines. There was also a loss or decrease in CIAPIN1 expression in 118 cases of pancreatic cancer tissues as compared with that in 82 cases of normal pancreatic tissues. In a Cox proportional hazards model, CIAPIN1 expression independently predicted better survival (P<0.0001). Adenoviral-mediated restoration of CIAPIN1 expression greatly repressed the proliferation of pancreatic cancer cell in vitro and suppressed the tumorigenicity of pancreatic cancer cell in Balb/c nude mice. Our data also revealed that inhibition of pancreatic cancer cells proliferation by enforcing CIAPIN1 expression at least partly through delaying cell cycle progression and inducing cell apoptosis. In summary, our work revealed a novel function of CIAPIN1, which might possibly be used as an independent prognostic factor and a potential therapeutic target for pancreatic cancer.

High accordance in prognosis prediction of colorectal cancer across independent datasets by multi-gene module expression profiles

A considerable portion of patients with colorectal cancer have a high risk of disease recurrence after surgery. These patients can be identified by analyzing the expression profiles of signature genes in tumors. But there is no consensus on which genes should be used and the performance of specific set of signature genes varies greatly with different datasets, impeding their implementation in the routine clinical application. Instead of using individual genes, here we identified functional multi-gene modules with significant expression changes between recurrent and recurrence-free tumors, used them as the signatures for predicting colorectal cancer recurrence in multiple datasets that were collected independently and profiled on different microarray platforms. The multi-gene modules we identified have a significant enrichment of known genes and biological processes relevant to cancer development, including genes from the chemokine pathway. Most strikingly, they recruited a significant enrichment of somatic mutations found in colorectal cancer. These results confirmed the functional relevance of these modules for colorectal cancer development. Further, these functional modules from different datasets overlapped significantly. Finally, we demonstrated that, leveraging above information of these modules, our module based classifier avoided arbitrary fitting the classifier function and screening the signatures using the training data, and achieved more consistency in prognosis prediction across three independent datasets, which holds even using very small training sets of tumors.

Proteomics-based strategy to delineate the molecular mechanisms of RhoGDI2-induced metastasis and drug resistance in gastric cancer

Rho GDP dissociation inhibitor 2 (RhoGDI2) was initially identified as a regulator of the Rho family of GTPases. Our recent works suggest that RhoGDI2 promotes tumor growth and malignant progression, as well as enhances chemoresistance in gastric cancer. Here, we delineate the mechanism by which RhoGDI2 promotes gastric cancer cell invasion and chemoresistance using two-dimensional gel electrophoresis (2-DE) on proteins derived from a RhoGDI2-overexpressing SNU-484 human gastric cancer cell line and control cells. Differentially expressed proteins were identified using matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF-MS). In total, 47 differential protein spots were identified; 33 were upregulated, and 14 were downregulated by RhoGDI2 overexpression. Upregulation of SAE1, Cathepsin D, Cofilin1, CIAPIN1, and PAK2 proteins was validated by Western blot analysis. Loss-of-function analysis using small interference RNA (siRNA) directed against candidate genes reveals the need for CIAPIN1 and PAK2 in RhoGDI2-induced cancer cell invasion and Cathepsin D and PAK2 in RhoGDI2-mediated chemoresistance in gastric cancer cells. These data extend our understanding of the genes that act downstream of RhoGDI2 during the progression of gastric cancer and the acquisition of chemoresistance.

Discovery and preclinical validation of drug indications using compendia of public gene expression data

The application of established drug compounds to new therapeutic indications, known as drug repositioning, offers several advantages over traditional drug development, including reduced development costs and shorter paths to approval. Recent approaches to drug repositioning use high-throughput experimental approaches to assess a compound's potential therapeutic qualities. Here, we present a systematic computational approach to predict novel therapeutic indications on the basis of comprehensive testing of molecular signatures in drug-disease pairs. We integrated gene expression measurements from 100 diseases and gene expression measurements on 164 drug compounds, yielding predicted therapeutic potentials for these drugs. We recovered many known drug and disease relationships using computationally derived therapeutic potentials and also predict many new indications for these 164 drugs. We experimentally validated a prediction for the antiulcer drug cimetidine as a candidate therapeutic in the treatment of lung adenocarcinoma, and demonstrate its efficacy both in vitro and in vivo using mouse xenograft models. This computational method provides a systematic approach for repositioning established drugs to treat a wide range of human diseases.

Design of clinical trials for biomarker research in oncology

The developmental pathway from discovery to clinical practice for biomarkers and biomarker-directed therapies is complex. While several issues need careful consideration, two critical issues that surround the validation of biomarkers are the choice of clinical trial design (which is based on the strength of the preliminary evidence and marker prevalence) and the biomarker assay related issues surrounding the marker assessment methods such as the reliability and reproducibility of the assay. This review focuses on trial designs for marker validation, both in the setting of early phase trials for initial validation, as well as in the context of larger definitive trials. Designs for biomarker validation are broadly classified as retrospective (i.e., using data from previously well-conducted, randomized, controlled trials) or prospective (enrichment, allcomers or adaptive). We believe that the systematic evaluation and implementation of these design strategies are essential to accelerate the clinical validation of biomarker-guided therapy, thereby taking us a step closer to the goal of personalized medicine.

Predictive and prognostic molecular markers for cancer medicine

Over the last 10 years there has been an explosion of information about the molecular biology of cancer. A challenge in oncology is to translate this information into advances in patient care. While there are well-formed routes for translating new molecular information into drug therapy, the routes for translating new information into sensitive and specific diagnostic, prognostic and predictive tests are still being developed. Similarly, the science of using tumor molecular profiles to select clinical trial participants or to optimize therapy for individual patients is still in its infancy. This review will summarize the current technologies for predicting treatment response and prognosis in cancer medicine, and outline what the future may hold. It will also highlight the potential importance of methods that can integrate molecular, histopathological and clinical information into a synergistic understanding of tumor progression. While these possibilities are without doubt exciting, significant challenges remain if we are to implement them with a strong evidence base in a widely available and cost-effective manner.

Gene expression-based chemical genomics identifies potential therapeutic drugs in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is an aggressive tumor with a poor prognosis. Currently, only sorafenib is approved by the FDA for advanced HCC treatment; therefore, there is an urgent need to discover candidate therapeutic drugs for HCC. We hypothesized that if a drug signature could reverse, at least in part, the gene expression signature of HCC, it might have the potential to inhibit HCC-related pathways and thereby treat HCC. To test this hypothesis, we first built an integrative platform, the "Encyclopedia of Hepatocellular Carcinoma genes Online 2", dubbed EHCO2, to systematically collect, organize and compare the publicly available data from HCC studies. The resulting collection includes a total of 4,020 genes. To systematically query the Connectivity Map (CMap), which includes 6,100 drug-mediated expression profiles, we further designed various gene signature selection and enrichment methods, including a randomization technique, majority vote, and clique analysis. Subsequently, 28 out of 50 prioritized drugs, including tanespimycin, trichostatin A, thioguanosine, and several anti-psychotic drugs with anti-tumor activities, were validated via MTT cell viability assays and clonogenic assays in HCC cell lines. To accelerate their future clinical use, possibly through drug-repurposing, we selected two well-established drugs to test in mice, chlorpromazine and trifluoperazine. Both drugs inhibited orthotopic liver tumor growth. In conclusion, we successfully discovered and validated existing drugs for potential HCC therapeutic use with the pipeline of Connectivity Map analysis and lab verification, thereby suggesting the usefulness of this procedure to accelerate drug repurposing for HCC treatment.

A quantitative proteomic approach of the different stages of colorectal cancer establishes OLFM4 as a new nonmetastatic tumor marker

Expression profiles represent new molecular tools that are useful to characterize the successive steps of tumor progression and the prediction of recurrence or chemotherapy response. In this study, we have used quantitative proteomic analysis to compare different stages of colorectal cancer. A combination of laser microdissection, OFFGEL separation, iTRAQ labeling, and MALDI-TOF/TOF MS was used to explore the proteome of 28 colorectal cancer tissues. Two software packages were used for identification and quantification of differentially expressed proteins: Protein Pilot and iQuantitator. Based on ∼1,190,702 MS/MS spectra, a total of 3138 proteins were identified, which represents the largest database of colorectal cancer realized to date and demonstrates the value of our quantitative proteomic approach. In this way, individual protein expression and variation have been identified for each patient and for each colorectal dysplasia and cancer stage (stages I-IV). A total of 555 proteins presenting a significant fold change were quantified in the different stages, and this differential expression correlated with immunohistochemistry results reported in the Human Protein Atlas database. To identify a candidate biomarker of the early stages of colorectal cancer, we focused our study on secreted proteins. In this way, we identified olfactomedin-4, which was overexpressed in adenomas and in early stages of colorectal tumors. This early stage overexpression was confirmed by immunohistochemistry in 126 paraffin-embedded tissues. Our results also indicate that OLFM4 is regulated by the Ras-NF-κB2 pathway, one of the main oncogenic pathways deregulated in colorectal tumors.

Deletion of chromosome 4q predicts outcome in stage II colon cancer patients

BACKGROUND

Around 30% of all stage II colon cancer patients will relapse and die of their disease. At present no objective parameters to identify high-risk stage II colon cancer patients, who will benefit from adjuvant chemotherapy, have been established. With traditional histopathological features definition of high-risk stage II colon cancer patients is inaccurate. Therefore more objective and robust markers for prediction of relapse are needed. DNA copy number aberrations have proven to be robust prognostic markers, but have not yet been investigated for this specific group of patients. The aim of the present study was to identify chromosomal aberrations that can predict relapse of tumor in patients with stage II colon cancer.

MATERIALS AND METHODS

DNA was isolated from 40 formaldehyde fixed paraffin embedded stage II colon cancer samples with extensive clinicopathological data. Samples were hybridized using Comparative Genomic Hybridization (CGH) arrays to determine DNA copy number changes and microsatellite stability was determined by PCR. To analyze differences between stage II colon cancer patients with and without relapse of tumor a Wilcoxon rank-sum test was implemented with multiple testing correction.

RESULTS

Stage II colon cancers of patients who had relapse of disease showed significantly more losses on chromosomes 4, 5, 15q, 17q and 18q. In the microsatellite stable (MSS) subgroup (n = 28), only loss of chromosome 4q22.1-4q35.2 was significantly associated with disease relapse (P < 0.05, FDR < 0.15). No differences in clinicopathological characteristics between patients with and without relapse were observed.

CONCLUSION

In the present series of MSS stage II colon cancer patients losses on 4q22.1-4q35.2 were associated with worse outcome and these genomic alterations may aid in selecting patients for adjuvant therapy.

The role of bioinformatics in studying rheumatic and autoimmune disorders

In the past decade, the availability and abundance of individual-level molecular data, such as gene expression, proteomics and sequence data, has enabled the use of integrative computational approaches to pose and answer novel questions about disease. In this article, we discuss several examples of applications of bioinformatics techniques to study autoimmune and rheumatic disorders. We focus our discussion on how integrative techniques can be applied to analyze gene expression and genetic variation data across different diseases, and discuss the implications of such analyses. We also outline current challenges and future directions of these approaches. We show that integrative computational methods are essential for translational research and provide a powerful opportunity to improve human health by refining the current knowledge about diagnostics, therapeutics and mechanisms of disease pathogenesis.

Genetic-based biomarkers and next-generation sequencing: the future of personalized care in colorectal cancer

The past 5 years have witnessed extraordinary advances in the field of DNA sequencing technology. What once took years to accomplish with Sanger sequencing can now be accomplished in a matter of days with next-generation sequencing (NGS) technology. This has allowed researchers to sequence individual genomes and match combinations of mutations with specific diseases. As cancer is inherently a disease of the genome, it is not surprising to see NGS technology already being applied to cancer research with promises of greater understanding of carcinogenesis. While the task of deciphering the cancer genomic code remains ongoing, we are already beginning to see the application of genetic-based testing in the area of colorectal cancer. In this article we will provide an overview of current colorectal cancer genetic-based biomarkers, namely mutations and other genetic alterations in cancer genome DNA, discuss recent advances in NGS technology and speculate on future directions for the application of NGS technology to colorectal cancer diagnosis and treatment.

Consensus pathways implicated in prognosis of colorectal cancer identified through systematic enrichment analysis of gene expression profiling studies

Background

A large number of gene expression profiling (GEP) studies on prognosis of colorectal cancer (CRC) has been performed, but no reliable gene signature for prediction of CRC prognosis has been found. Bioinformatic enrichment tools are a powerful approach to identify biological processes in high-throughput data analysis.

Principal Findings

We have for the first time collected the results from the 23 so far published independent GEP studies on CRC prognosis. In these 23 studies, 1475 unique, mapped genes were identified, from which 124 (8.4%) were reported in at least two studies, with 54 of them showing consisting direction in expression change between the single studies. Using these data, we attempted to overcome the lack of reproducibility observed in the genes reported in individual GEP studies by carrying out a pathway-based enrichment analysis. We used up to ten tools for overrepresentation analysis of Gene Ontology (GO) categories or Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in each of the three gene lists (1475, 124 and 54 genes). This strategy, based on testing multiple tools, allowed us to identify the oxidative phosphorylation chain and the extracellular matrix receptor interaction categories, as well as a general category related to cell proliferation and apoptosis, as the only significantly and consistently overrepresented pathways in the three gene lists, which were reported by several enrichment tools.

Conclusions

Our pathway-based enrichment analysis of 23 independent gene expression profiling studies on prognosis of CRC identified significantly and consistently overrepresented prognostic categories for CRC. These overrepresented categories have been functionally clearly related with cancer progression, and deserve further investigation.

Prognostic and predictive markers in stage II colon cancer: is there a role for gene expression profiling?

Conventional clinical and pathologic risk factors in stage II colon cancer provide limited prognostic information and do not predict response to adjuvant 5-fluorouracil-based chemotherapy. New prognostic and predictive biomarkers are needed to identify patients with highest recurrence risk who will receive the greatest absolute risk reduction from adjuvant chemotherapy. We review below the evidence for conventional risk factors in patients with node-negative colon cancer, followed by a discussion of promising new molecular and genetic markers in this malignancy. Gene expression profiling is an emerging tool with both prognostic and predictive potential in oncology. For patients with stage II colon cancer, the Oncotype DX Colon Cancer test is now commercially available as a prognostic marker, and the ColoPrint assay is expected to be released later this year. Current evidence for both of these assays is described below, concluding with a discussion of potential future directions for gene expression profiling in colon cancer risk stratification and treatment decision making.

Caveats and pitfalls of ROC analysis in clinical microarray research (and how to avoid them)

The receiver operating characteristic (ROC) has emerged as the gold standard for assessing and comparing the performance of classifiers in a wide range of disciplines including the life sciences. ROC curves are frequently summarized in a single scalar, the area under the curve (AUC). This article discusses the caveats and pitfalls of ROC analysis in clinical microarray research, particularly in relation to (i) the interpretation of AUC (especially a value close to 0.5); (ii) model comparisons based on AUC; (iii) the differences between ranking and classification; (iv) effects due to multiple hypotheses testing; (v) the importance of confidence intervals for AUC; and (vi) the choice of the appropriate performance metric. With a discussion of illustrative examples and concrete real-world studies, this article highlights critical misconceptions that can profoundly impact the conclusions about the observed performance.

ENCODE tiling array analysis identifies differentially expressed annotated and novel 5' capped RNAs in hepatitis C infected liver

Microarray studies of chronic hepatitis C infection have provided valuable information regarding the host response to viral infection. However, recent studies of the human transcriptome indicate pervasive transcription in previously unannotated regions of the genome and that many RNA transcripts have short or lack 3′ poly(A) ends. We hypothesized that using ENCODE tiling arrays (1% of the genome) in combination with affinity purifying Pol II RNAs by their unique 5′ m⁷GpppN cap would identify previously undescribed annotated and unannotated genes that are differentially expressed in liver during hepatitis C virus (HCV) infection. Both 5′-capped and poly(A)+ populations of RNA were analyzed using ENCODE tiling arrays. Sixty-four annotated genes were significantly increased in HCV cirrhotic as compared to control liver; twenty-seven (42%) of these genes were identified only by analyzing 5′ capped RNA. Thirty-one annotated genes were significantly decreased; sixteen (50%) of these were identified only by analyzing 5′ capped RNA. Bioinformatic analysis showed that capped RNA produced more consistent results, provided a more extensive expression profile of intronic regions and identified upregulated Pol II transcriptionally active regions in unannotated areas of the genome in HCV cirrhotic liver. Two of these regions were verified by PCR and RACE analysis. qPCR analysis of liver biopsy specimens demonstrated that these unannotated transcripts, as well as IRF1, TRIM22 and MET, were also upregulated in hepatitis C with mild inflammation and no fibrosis. The analysis of 5′ capped RNA in combination with ENCODE tiling arrays provides additional gene expression information and identifies novel upregulated Pol II transcripts not previously described in HCV infected liver. This approach, particularly when combined with new RNA sequencing technologies, should also be useful in further defining Pol II transcripts differentially regulated in specific disease states and in studying RNAs regulated by changes in pre-mRNA splicing or 3′ polyadenylation status.

Systematic analyses of genes associated with radiosensitizing effect by celecoxib, a specific cyclooxygenase-2 inhibitor

To investigate genes regulated by COX-2 or a COX-2 specific inhibitor, celecoxib, in irradiated cancer cells, we analyzed changes in gene expression using complementary DNA microarray following celecoxib or combined celecoxib and ionizing radiation (IR) treatment in a stable COX-2 knockdown A549 (AS) and a mock cell line (AN). Thirty-six genes were differentially expressed by COX-2 knockdown. Celecoxib changed the expressions of 40 and 69 genes in AN and AS cells, respectively. Twenty-seven genes were synchronously regulated by COX-2 and celecoxib. Among these, celecoxib regulated ras homolog gene family B and mitosin protein expression in a COX-2 dependent manner, especially in irradiated cells. In addition, we identified 11 genes that changed by more than 1.5 times the expected additive values after celecoxib and IR treatment. The current study may provide evidence that COX-2 or celecoxib regulates various intracellular functions in addition to their enzymatic activity regulation. We also identified candidate molecules that may be responsible for COX-2-dependent radiosensitization by celecoxib.

Gene expression signature to improve prognosis prediction of stage II and III colorectal cancer

PURPOSE

This study aims to develop a robust gene expression classifier that can predict disease relapse in patients with early-stage colorectal cancer (CRC).

PATIENTS AND METHODS

Fresh frozen tumor tissue from 188 patients with stage I to IV CRC undergoing surgery was analyzed using Agilent 44K oligonucleotide arrays. Median follow-up time was 65.1 months, and the majority of patients (83.6%) did not receive adjuvant chemotherapy. A nearest mean classifier was developed using a cross-validation procedure to score all genes for their association with 5-year distant metastasis-free survival.

RESULTS

An optimal set of 18 genes was identified and used to construct a prognostic classifier (ColoPrint). The signature was validated on an independent set of 206 samples from patients with stage I, II, and III CRC. The signature classified 60% of patients as low risk and 40% as high risk. Five-year relapse-free survival rates were 87.6% (95% CI, 81.5% to 93.7%) and 67.2% (95% CI, 55.4% to 79.0%) for low- and high-risk patients, respectively, with a hazard ratio (HR) of 2.5 (95% CI, 1.33 to 4.73; P = .005). In multivariate analysis, the signature remained one of the most significant prognostic factors, with an HR of 2.69 (95% CI, 1.41 to 5.14; P = .003). In patients with stage II CRC, the signature had an HR of 3.34 (P = .017) and was superior to American Society of Clinical Oncology criteria in assessing the risk of cancer recurrence without prescreening for microsatellite instability (MSI).

CONCLUSION

ColoPrint significantly improves the prognostic accuracy of pathologic factors and MSI in patients with stage II and III CRC and facilitates the identification of patients with stage II disease who may be safely managed without chemotherapy.

Integration of genetic signature and TNM staging system for predicting the relapse of locally advanced colorectal cancer

PURPOSE

To identify potential genetic markers in treated stage II-III colorectal cancer patients and predict 3-year tumor relapse using statistical models based on important clinical factors and significant genetic markers.

METHODS

Gene expression profiling by cDNA-mediated Annealing, Selection, extension and Ligation assay was performed in a prospectively collected 95 stage II-III colorectal cancer patients with Fluorouracil-based adjuvant chemotherapy. We studied the gene expression level of 502 genes for patients with different outcomes. The prognostic effect of genetic signature was evaluated in multivariate analysis. We further integrated the genetic signature to clinical Classification of Malignant Tumors (TNM) staging system for predicting of 3-year tumor relapse.

RESULTS

An 8-gene signature was identified to well discriminate patients with different treatment outcomes. An integrated risk factor, which including 8-gene signature and TNM staging has been developed. ROC curve revealed that our integrated risk factor was better than genetic signature or current sixth edition TNM staging system alone.

CONCLUSIONS

Our 8-gene signature was promising in predicting 3-year disease-free survival rate for locally advanced colorectal cancer. The integrated risk factor, which combining genetic signature with clinical TNM staging system may further improve the outcome prediction.

Antibody-based proteomics: fast-tracking molecular diagnostics in oncology

The effective implementation of personalized cancer therapeutic regimens depends on the successful identification and translation of informative biomarkers to aid clinical decision making. Antibody-based proteomics occupies a pivotal space in the cancer biomarker discovery and validation pipeline, facilitating the high-throughput evaluation of candidate markers. Although the clinical utility of these emerging technologies remains to be established, the traditional use of antibodies as affinity reagents in clinical diagnostic and predictive assays suggests that the rapid translation of such approaches is an achievable goal. Furthermore, in combination with, or as alternatives to, genomic and transcriptomic methods for patient stratification, antibody-based proteomics approaches offer the promise of additional insight into cancer disease states. In this Review, we discuss the current status of antibody-based proteomics and its contribution to the development of new assays that are crucial for the realization of individualized cancer therapy.