Identification of a biomarker panel for colorectal cancer diagnosis

Kelch-like proteins in the gastrointestinal tumors

Gastrointestinal tumors have become a worldwide health problem with high morbidity and poor clinical outcomes. Chemotherapy and surgery, the main treatment methods, are still far from meeting the treatment needs of patients, and targeted therapy is in urgent need of development. Recently, emerging evidence suggests that kelch-like (KLHL) proteins play essential roles in maintaining proteostasis and are involved in the progression of various cancers, functioning as adaptors in the E3 ligase complex and promoting the specific degradation of substrates. Therefore, KLHL proteins should be taken into consideration for targeted therapy strategy discovery. This review summarizes the current knowledge of KLHL proteins in gastrointestinal tumors and discusses the potential of KLHL proteins as potential drug targets and prognostic biomarkers.

Simple Isothermal and Label-Free Strategy for Colorectal Cancer Potential Biomarker miR-625-5p Detection

miRNA is considered a novel biomarker for cancer diagnosis and due to its low level in vivo, the development of new detection methods for it has become a research hotspot in recent years. Here, we firstly found that miR-625-5p was significantly upregulated in colorectal cancer tissues by means of differential expression analysis of the dbDEMC database and clinical validation. Subsequently, it was found that miR-625-5p promoted cell proliferation and migration but inhibited apoptosis through phenotypic experiments; thus, we initially identified miR-625-5p as a potential biomarker for colorectal cancer. Moreover, in order to monitor slight changes in the miR-625-5p level, we developed a novel detection method for it based on strand displacement amplification (SDA). In this system, a hairpin was designed to recognize and pair with miR-625-5p, which was used as a primer to initiate SDA, and a large number of complementary DNAs were generated via cyclic amplification, followed by the addition of SYBR Gold to achieve quantitative analysis of miR-625-5p. Moreover, this method showed a good response to miR-625-5p with a detection limit of 8.6 pM and a dynamic range of 0.01 to 200 nM, and the specificity of it was verified using a set of other miRNAs as an interference. Finally, we set up different concentrations of biologic samples for detection to verify the practicability of the method. The results of this study indicate that this detection method has great potential in clinical diagnosis.

Multi-label Learning for the Diagnosis of Cancer and Identification of Novel Biomarkers with High-throughput Omics

Background:

The advancement of bioinformatics and machine learning has facilitated the diagnosis of cancer and the discovery of omics-based biomarkers.

Objective:

Our study employed a novel data-driven approach to classifying the normal samples and different types of gastrointestinal cancer samples, to find potential biomarkers for effective diagnosis and prognosis assessment of gastrointestinal cancer patients.

Methods:

Different feature selection methods were used, and the diagnostic performance of the proposed biosignatures was benchmarked using support vector machine (SVM) and random forest (RF) models.

Results:

All models showed satisfactory performance in which Multilabel-RF appeared to be the best. The accuracy of the Multilabel-RF based model was 83.12%, with precision, recall, F1, and Hamming- Loss of 79.70%, 68.31%, 0.7357 and 0.1688, respectively. Moreover, proposed biomarker signatures were highly associated with multifaceted hallmarks in cancer. Functional enrichment analysis and impact of the biomarker candidates in the prognosis of the patients were also examined.

Conclusion:

We successfully introduced a solid workflow based on multi-label learning with High- Throughput Omics for diagnosis of cancer and identification of novel biomarkers. Novel transcriptome biosignatures that may improve the diagnostic accuracy in gastrointestinal cancer are introduced for further validations in various clinical settings.

ENC1 Facilitates Colorectal Carcinoma Tumorigenesis and Metastasis via JAK2/STAT5/AKT Axis-Mediated Epithelial Mesenchymal Transition and Stemness

Ectodermal neural cortex 1 (ENC1) is an actin-binding protein and has been known to be upregulated in several cancers, but the molecular mechanisms through which it contributes to the pathology of CRC have largely been elusive. We utilized data mining and validated the aberrant expression of ENC1, following which phenotypic traits of malignancy were assessed in vitro. Ruxolitinib was used as a surrogate to compare the effects of ENC1 expression and silencing on the JAK-STAT-AKT pathway. In vivo models were employed to confirm the in vitro observations. Computation analysis, strengthened by in situ and in vitro data, confirmed the overexpression of ENC1 in CRC and predicted a poor prognosis, while enhanced cell proliferation, invasion, migration, EMT, and stemness were associated with ENC1 overexpression. Silencing of ENC1 downregulated the phenotypes. Additionally, silencing of ENC1 significantly reduced the activation of JAK2 and consequent activation of STAT5 and AKT comparable to ruxolitinib inhibition of JAK2. Silencing of ENC1 resulted in lesser tumor volumes and fewer numbers of tumors, in vivo. These data suggest that ENC1 induces CRC through the JAK2-STAT5-AKT axis. ENC1 is a suitable diagnostic marker for CRC detection, and ENC1 targeting therapies may suppress CRC progression.

Diagnostic Value of Combinatorial Markers in Colorectal Carcinoma

Objectives: Blood-based tests have been shown to be an effective strategy for colorectal cancer (CRC) detection in screening programs. This study was aimed to test the performance of 20 blood markers including tumor antigens, inflammatory markers, and apolipoproteins as well as their combinations.

Methods: In total 203 healthy volunteers and 102 patients with CRC were enrolled into the study. Differences between healthy and cancer subjects were evaluated using Wilcoxon rank-sum test. Several multivariate classification algorithms were employed using information about different combinations of biomarkers altered in CRC patients as well as age and gender of the subjects; random sub-sampling cross-validation was done to overcome overfitting problem. Diagnostic performance of single biomarkers and multivariate classification models was evaluated by receiver operating characteristic (ROC) analysis.

Results: Of 20 biomarkers, 16 were significantly different between the groups (p-value ≤ 0.001); ApoA1, ApoA2 and ApoA4 levels were decreased, whereas levels of tumor antigens (e.g. carcinoembriogenic antigen) and inflammatory markers (e.g., C-reactive protein) were increased in CRC patients vs. healthy subjects. Combinatorial markers including information about all 16 significant analytes, age and gender of patients, demonstrated better performance over single biomarkers with average accuracy on test datasets ≥95% and area under ROC curve (AUROC) ≥98%.

Conclusions: Combinatorial approach was shown to be a valid strategy to improve performance of blood-based CRC diagnostics. Further evaluation of the proposed models in screening programs will be performed to gain a better understanding of their diagnostic value.

Plasma Lysyl-tRNA Synthetase 1 (KARS1) as a Novel Diagnostic and Monitoring Biomarker for Colorectal Cancer

Colorectal cancer (CRC) is one of the leading causes of world cancer deaths. To improve the survival rate of CRC, diagnosis and post-operative monitoring is necessary. Currently, biomarkers are used for CRC diagnosis and prognosis. However, these biomarkers have limitations of specificity and sensitivity. Levels of plasma lysyl-tRNA synthetase (KARS1), which was reported to be secreted from colon cancer cells by stimuli, along with other secreted aminoacyl-tRNA synthetases (ARSs), were analyzed in CRC and compared with the currently used biomarkers. The KARS1 levels of CRC patients (n = 164) plasma were shown to be higher than those of healthy volunteers (n = 32). The diagnostic values of plasma KARS1 were also evaluated by receiving operating characteristic (ROC) curve. Compared with other biomarkers and ARSs, KARS1 showed the best diagnostic value for CRC. The cancer specificity and burden correlation of plasma KARS1 level were validated using azoxymethane (AOM)/dextran sodium sulfate (DSS) model, and paired pre- and post-surgery CRC patient plasma. In the AOM/DSS model, the plasma level of KARS1 showed high correlation with number of polyps, but not for inflammation. Using paired pre- and post-surgery CRC plasma samples (n = 60), the plasma level of KARS1 was significantly decreased in post-surgery samples. Based on these evidence, KARS1, a surrogate biomarker reflecting CRC burden, can be used as a novel diagnostic and post-operative monitoring biomarker for CRC.

High-Throughput Omics and Statistical Learning Integration for the Discovery and Validation of Novel Diagnostic Signatures in Colorectal Cancer

The advancement of bioinformatics and machine learning has facilitated the discovery and validation of omics-based biomarkers. This study employed a novel approach combining multi-platform transcriptomics and cutting-edge algorithms to introduce novel signatures for accurate diagnosis of colorectal cancer (CRC). Different random forests (RF)-based feature selection methods including the area under the curve (AUC)-RF, Boruta, and Vita were used and the diagnostic performance of the proposed biosignatures was benchmarked using RF, logistic regression, naïve Bayes, and k-nearest neighbors models. All models showed satisfactory performance in which RF appeared to be the best. For instance, regarding the RF model, the following were observed: mean accuracy 0.998 (standard deviation (SD) < 0.003), mean specificity 0.999 (SD < 0.003), and mean sensitivity 0.998 (SD < 0.004). Moreover, proposed biomarker signatures were highly associated with multifaceted hallmarks in cancer. Some biomarkers were found to be enriched in epithelial cell signaling in Helicobacter pylori infection and inflammatory processes. The overexpression of TGFBI and S100A2 was associated with poor disease-free survival while the down-regulation of NR5A2, SLC4A4, and CD177 was linked to worse overall survival of the patients. In conclusion, novel transcriptome signatures to improve the diagnostic accuracy in CRC are introduced for further validations in various clinical settings.

Identification of differential metabolic characteristics between tumor and normal tissue from colorectal cancer patients by gas chromatography-mass spectrometry

Colorectal cancer (CRC) is one of the most common human malignancies and encompasses cancers of the colon and rectum. Although the gold-standard colonoscopy screening method is effective in detecting CRC, this method is invasive and can result in severe complications for patients. The purpose of this study was to determine differences in metabolites between CRC and matched adjacent nontumor tissues from CRC patients, to identify potential biomarkers that may be informative and developed screening methods. Metabolomic analysis was performed on clinically localized CRC tissue and matched adjacent nontumor tissue from 20 CRC patients. Unsupervised analysis, supervised analysis, univariate analysis and pathway analysis were used to identify potential metabolic biomarkers of CRC. The levels of 25 metabolites in CRC tissues were significantly altered compared with the matched adjacent nontumor tissues. Four metabolites (lactic acid, alanine, phosphate and aspartic acid) demonstrated good area under the curve of receiver-operator characteristic with acceptable sensitivities and specificities, indicating their potential as important biomarkers for CRC. Alterations of amino acid metabolism and enhanced glycolysis may be major factors in the development and progression of CRC. Lactic acid, alanine, phosphate, and aspartic acid could be effective diagnostic indicators for CRC.

Quantitative proteomic profiling of paired cancerous and normal colon epithelial cells isolated freshly from colorectal cancer patients

PURPOSE

The heterogeneous structure in tumor tissues from colorectal cancer (CRC) patients excludes an informative comparison between tumors and adjacent normal tissues. Here, we develop and apply a strategy to compare paired cancerous (CEC) versus normal (NEC) epithelial cells enriched from patients and discover potential biomarkers and therapeutic targets for CRC.

EXPERIMENTAL DESIGN

CEC and NEC cells are respectively isolated from five different tumor and normal locations in the resected colon tissue from each patient (N = 12 patients) using an optimized epithelial cell adhesion molecule (EpCAM)-based enrichment approach. An ion current-based quantitative method is employed to perform comparative proteomic analysis for each patient.

RESULTS

A total of 458 altered proteins that are common among >75% of patients are observed and selected for further investigation. Besides known findings such as deregulation of mitochondrial function, tricarboxylic acid cycle, and RNA post-transcriptional modification, functional analysis further revealed RAN signaling pathway, small nucleolar ribonucleoproteins (snoRNPs), and infection by RNA viruses are altered in CEC cells. A selection of the altered proteins of interest is validated by immunohistochemistry analyses.

CONCLUSION AND CLINICAL RELEVANCE

The informative comparison between matched CEC and NEC enhances our understanding of molecular mechanisms of CRC development and provides biomarker candidates and new pathways for therapeutic intervention.

KRAS mutant colorectal cancer gene signatures identified angiotensin II receptor blockers as potential therapies

Colorectal cancer (CRC) is a life-threatening disease with high prevalence and mortality worldwide. The KRAS oncogene is mutated in approximately 40% of CRCs. While antibody based EGFR inhibitors (cetuximab and panitumumab) represent a major treatment strategy for advanced KRAS wild type (KRAS-WT) CRCs, there still remains no effective therapeutic course for advanced KRAS mutant (KRAS-MT) CRC patients.In this study, we employed a novel and comprehensive approach of gene expression connectivity mapping (GECM) to identify candidate compounds to target KRAS-MT tumors. We first created a combined KRAS-MT gene signature with 248 ranked significant genes using 677 CRC clinical samples. A series of 248 sub-signatures was then created containing an increasing number of the top ranked genes. As an input to GECM analysis, each sub-signature was translated into a statistically significant therapeutic drugs list, which was finally combined to obtain a single list of significant drugs.We identify four antihypertensive angiotensin II receptor blockers (ARBs) within the top 30 significant drugs indicating that these drugs have a mechanism of action that can alter the KRAS-MT CRC oncogenic signaling. A hypergeometric test (p-value = 6.57 × 10-6) confirmed that ARBs are significantly enriched in our results. These findings support the hypothesis that ARB antihypertensive drugs may directly block KRAS signaling resulting in improvement in patient outcome or, through a reversion to a KRAS wild-type phenotype, improve the response to anti-EGFR treatment. Antihypertensive angiotensin II receptor blockers are therefore worth further investigation as potential therapeutic candidates in this difficult category of advanced colorectal cancers.

Mass Spectrometry-Based Analysis for the Discovery and Validation of Potential Colorectal Cancer Stool Biomarkers

Colorectal cancer (CRC) is the third leading cause of cancer mortality for both men and women, and the second leading cause of cancer death for men and women combined. If detected early, before metastasis has occurred, survival following surgical resection of the tumor is >90%. Early detection is therefore critical for effective disease surveillance. Unfortunately, current biomarker assays lack the necessary sensitivity and specificity for reliable early disease detection. Development of new robust, non- or minimally invasive specific and sensitive biomarkers or panels with improved compliance and performance is therefore urgently required. The use of fecal samples offers several advantages over other clinical biospecimens (e.g., plasma or serum) as a source of CRC biomarkers, including: collection is noninvasive, the test can be performed at home, one is not sample limited, and the stool effectively samples the entire length of the inner bowel wall contents (including tumor) as it passes down the gastrointestinal tract. Recent advances in mass spectrometry now facilitate both the targeted discovery and validation of potential CRC biomarkers. We describe, herein, detailed protocols that can be used to mine deeply into the fecal proteome to reveal candidate proteins, identify proteotypic/unitypic peptides (i.e., peptides found in only a single known human protein that serve to identify that protein) suitable for sensitive and specific quantitative multiplexed analysis, and undertake high-throughput analysis of clinical samples. Finally, we discuss future directions that may further position this technology to support the current switch in translation research toward personalized medicine.

Chasing the personalized medicine dream through biomarker validation in colorectal cancer

Colorectal cancer (CRC) is a major health burden worldwide. The optimal approach to the diagnosis, management, and treatment of CRC involves multidisciplinary and integrated management practices. The field is rapidly changing because of recent advancements in delineating the molecular basis of tumorigenesis, introduction of targeted therapy, varied patient response to mainstay chemotherapeutics, biological drugs, and the effective combination regimes being used for treatment. Recent meta-analysis studies, which tend to establish few clinically useful predictor biomarkers, identify inconsistent results and limitations of the trials. Therefore, molecular pathological epidemiology discipline initiatives are promising. Here, we provide an overview of the potential of biomarker validation for personalized medicine by focusing largely on metastatic (m)CRC. We also highlight new candidate predictive and prognostic biomarkers.

Novel Biomarker Candidates for Colorectal Cancer Metastasis: A Meta-analysis of In Vitro Studies

Colorectal cancer (CRC) is one of the most common and lethal cancers. Although numerous studies have evaluated potential biomarkers for early diagnosis, current biomarkers have failed to reach an acceptable level of accuracy for distant metastasis. In this paper, we performed a gene set meta-analysis of in vitro microarray studies and combined the results from this study with previously published proteomic data to validate and suggest prognostic candidates for CRC metastasis. Two microarray data sets included found 21 significant genes. Of these significant genes, ALDOA, IL8 (CXCL8), and PARP4 had strong potential as prognostic candidates. LAMB2, MCM7, CXCL23A, SERPINA3, ABCA3, ALDH3A2, and POLR2I also have potential. Other candidates were more controversial, possibly because of the biologic heterogeneity of tumor cells, which is a major obstacle to predicting metastasis. In conclusion, we demonstrated a meta-analysis approach and successfully suggested ten biomarker candidates for future investigation.

Comparative analysis of copy number variations in ulcerative colitis associated and sporadic colorectal neoplasia

Background

The incidence of and mortality from colorectal cancers (CRC) can be reduced by early detection. Currently there is a lack of established markers to detect early neoplastic changes. We aimed to identify the copy number variations (CNVs) and the associated genes which could be potential markers for the detection of neoplasia in both ulcerative colitis-associated neoplasia (UC-CRN) and sporadic colorectal neoplasia (S-CRN).

Methods

We employed array comparative genome hybridization (aCGH) to identify CNVs in tissue samples of UC nonprogressor, progressor and sporadic CRC. Select genes within these CNV regions as a panel of markers were validated using quantitative real time PCR (qRT-PCR) method along with the microsatellite instability (MSI) in an independent cohort of samples. Immunohistochemistry (IHC) analysis was also performed.

Results

Integrated analysis showed 10 overlapping CNV regions between UC-Progressor and S-CRN, with the 8q and 12p regions showing greater overlap. The qRT-PCR based panel of MYC, MYCN, CCND1, CCND2, EGFR and FNDC3A was successful in detecting neoplasia with an overall accuracy of 54 % in S-CRN compared to that of 29 % in UC neoplastic samples. IHC study showed that p53 and CCND1 were significantly overexpressed with an increasing frequency from pre-neoplastic to neoplastic stages. EGFR and AMACR were expressed only in the neoplastic conditions.

Conclusion

CNVs that are common and unique to both UC-associated and sporadic colorectal neoplasm could be the key players driving carcinogenesis. Comparative analysis of CNVs provides testable driver aberrations but needs further evaluation in larger cohorts of samples. These markers may help in developing more effective neoplasia-detection strategies during screening and surveillance programs.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-016-2303-4) contains supplementary material, which is available to authorized users.

Voxel-Based Diagnosis of Alzheimer's Disease Using Classifier Ensembles

Functional magnetic resonance imaging (fMRI) is one of the most promising noninvasive techniques for early Alzheimer's disease (AD) diagnosis. In this paper, we explore the application of different machine learning techniques to the classification of fMRI data for this purpose. The functional images were first preprocessed using the statistical parametric mapping toolbox to output individual maps of statistically activated voxels. A fast filter was applied afterwards to select voxels commonly activated across demented and nondemented groups. Four feature ranking selection techniques were embedded into a wrapper scheme using an inner-outer loop for the selection of relevant voxels. The wrapper approach was guided by the performance of six pattern recognition models, three of which were ensemble classifiers based on stochastic searches. Final classification performance was assessed from the nested internal and external cross-validation loops taking several voxel sets ordered by importance. Numerical performance was evaluated using statistical tests, and the best combination of voxel selection and classification reached a 97.14% average accuracy. Results repeatedly pointed out Brodmann regions with distinct activation patterns between demented and nondemented profiles, indicating that the machine learning analysis described is a powerful method to detect differences in several brain regions between both groups.

Risk Prediction Models for Colorectal Cancer: A Systematic Review

Colorectal cancer is the second leading cause of cancer-related death in Europe and the United States. Survival is strongly related to stage at diagnosis and population-based screening reduces colorectal cancer incidence and mortality. Stratifying the population by risk offers the potential to improve the efficiency of screening. In this systematic review we searched Medline, EMBASE, and the Cochrane Library for primary research studies reporting or validating models to predict future risk of primary colorectal cancer for asymptomatic individuals. A total of 12,808 papers were identified from the literature search and nine through citation searching. Fifty-two risk models were included. Where reported (n = 37), half the models had acceptable-to-good discrimination (the area under the receiver operating characteristic curve, AUROC >0.7) in the derivation sample. Calibration was less commonly assessed (n = 21), but overall acceptable. In external validation studies, 10 models showed acceptable discrimination (AUROC 0.71-0.78). These include two with only three variables (age, gender, and BMI; age, gender, and family history of colorectal cancer). A small number of prediction models developed from case-control studies of genetic biomarkers also show some promise but require further external validation using population-based samples. Further research should focus on the feasibility and impact of incorporating such models into stratified screening programmes.

Machine learning for biomarker identification in cancer research - developments toward its clinical application

The patterns identified from the systematically collected molecular profiles of patient tumor samples, along with clinical metadata, can assist personalized treatments for effective management of cancer patients with similar molecular subtypes. There is an unmet need to develop computational algorithms for cancer diagnosis, prognosis and therapeutics that can identify complex patterns and help in classifications based on plethora of emerging cancer research outcomes in public domain. Machine learning, a branch of artificial intelligence, holds a great potential for pattern recognition in cryptic cancer datasets, as evident from recent literature survey. In this review, we focus on the current status of machine learning applications in cancer research, highlighting trends and analyzing major achievements, roadblocks and challenges toward its implementation in clinics.

Gene-expression analysis of a colorectal cancer-specific discriminatory transcript set on formalin-fixed, paraffin-embedded (FFPE) tissue samples

BACKGROUND

A recently published transcript set is suitable for gene expression-based discrimination of normal colonic and colorectal cancer (CRC) biopsy samples. Our aim was to test the discriminatory power of the CRC-specific transcript set on independent biopsies and on formalin-fixed, paraffin-embedded (FFPE) tissue samples.

METHODS

Total RNA isolations were performed with the automated MagNA Pure 96 Cellular RNA Large Volume Kit (Roche) from fresh frozen biopsies stored in RNALater (CRC (n = 15) and healthy colonic (n = 15)), furthermore from FFPE specimens including CRC (n = 15) and normal adjacent tissue (NAT) (n = 15) specimens next to the tumor. After quality and quantity measurements, gene expression analysis of a colorectal cancer-specific marker set with 11 genes (CA7, COL12A1, CXCL1, CXCL2, CHI3L1, GREM1, IL1B, IL1RN, IL8, MMP3, SLC5A7) was performed with array real-time PCR using Transcriptor First Strand cDNA Synthesis Kit (Roche) and RealTime ready assays on LightCycler480 System (Roche). In situ hybridization for two selected transcripts (CA7, CXCL1) was performed on NAT (n = 3), adenoma (n = 3) and CRC (n = 3) FFPE samples.

RESULTS

Although analytical parameters of automatically isolated RNA samples showed differences between fresh frozen biopsy and FFPE samples, both quantity and the quality enabled their application in gene expression analyses. CRC and normal fresh frozen biopsy samples could be distinguished with 93.3% sensitivity and 86.7% specificity and FFPE samples with 96.7 and 70.0%, respectively. In situ hybridization could confirm the upregulation of CXCL1 and downregulation of CA7 in colorectal adenomas and tumors compared to healthy controls.

CONCLUSION

According to our results, gene expression analysis of the analyzed colorectal cancer-specific marker set can also be performed from FFPE tissue material. With the addition of an automated workflow, this marker set may enhance the objective classification of colorectal neoplasias in the routine procedure in the future.

Large scale systematic proteomic quantification from non-metastatic to metastatic colorectal cancer

A systematic proteomic quantification of formalin-fixed, paraffin-embedded (FFPE) colorectal cancer tissues from stage I to stage IIIC was performed in large scale. 1017 proteins were identified with 338 proteins in quantitative changes by label free method, while 341 proteins were quantified with significant expression changes among 6294 proteins by iTRAQ method. We found that proteins related to migration expression increased and those for binding and adherent decreased during the colorectal cancer development according to the gene ontology (GO) annotation and ingenuity pathway analysis (IPA). The integrin alpha 5 (ITA5) in integrin family was focused, which was consistent with the metastasis related pathway. The expression level of ITA5 decreased in metastasis tissues and the result has been further verified by Western blotting. Another two cell migration related proteins vitronectin (VTN) and actin-related protein (ARP3) were also proved to be up-regulated by both mass spectrometry (MS) based quantification results and Western blotting. Up to now, our result shows one of the largest dataset in colorectal cancer proteomics research. Our strategy reveals a disease driven omics-pattern for the metastasis colorectal cancer.

The prognostic value of the stem-like group in colorectal cancer using a panel of immunohistochemistry markers

Colorectal cancer (CRC) is the second leading cause of cancer-related deaths in the Western world. It is becoming increasingly clear that CRC is a diverse disease, as exemplified by the identification of subgroups of CRC tumours that are driven by distinct biology. Recently, a number of studies have begun to define panels of diagnostically relevant markers to align patients into individual subgroups in an attempt to give information on prognosis and treatment response. We examined the immunohistochemical expression profile of 18 markers, each representing a putative role in cancer development, in 493 primary colorectal carcinomas using tissue microarrays. Through unsupervised clustering in stage II cancers, we identified two cluster groups that are broadly defined by inflammatory or immune-related factors (CD3, CD8, COX-2 and FOXP3) and stem-like factors (CD44, LGR5, SOX2, OCT4). The expression of the stem-like group markers was associated with a significantly worse prognosis compared to cases with lower expression. In addition, patients classified in the stem-like subgroup displayed a trend towards a benefit from adjuvant treatment. The biologically relevant and poor prognostic stem-like group could also be identified in early stage I cancers, suggesting a potential opportunity for the identification of aggressive tumors at a very early stage of the disease.

A novel mixed integer programming for multi-biomarker panel identification by distinguishing malignant from benign colorectal tumors

Multi-biomarker panels can capture the nonlinear synergy among biomarkers and they are important to aid in the early diagnosis and ultimately battle complex diseases. However, identification of these multi-biomarker panels from case and control data is challenging. For example, the exhaustive search method is computationally infeasible when the data dimension is high. Here, we propose a novel method, MILP_k, to identify serum-based multi-biomarker panel to distinguish colorectal cancers (CRC) from benign colorectal tumors. Specifically, the multi-biomarker panel detection problem is modeled by a mixed integer programming to maximize the classification accuracy. Then we measured the serum profiling data for 101 CRC patients and 95 benign patients. The 61 biomarkers were analyzed individually and further their combinations by our method. We discovered 4 biomarkers as the optimal small multi-biomarker panel, including known CRC biomarkers CEA and IL-10 as well as novel biomarkers IMA and NSE. This multi-biomarker panel obtains leave-one-out cross-validation (LOOCV) accuracy to 0.7857 by nearest centroid classifier. An independent test of this panel by support vector machine (SVM) with threefold cross validation gets an AUC 0.8438. This greatly improves the predictive accuracy by 20% over the single best biomarker. Further extension of this 4-biomarker panel to a larger 13-biomarker panel improves the LOOCV to 0.8673 with independent AUC 0.8437. Comparison with the exhaustive search method shows that our method dramatically reduces the searching time by 1000-fold. Experiments on the early cancer stage samples reveal two panel of biomarkers and show promising accuracy. The proposed method allows us to select the subset of biomarkers with best accuracy to distinguish case and control samples given the number of selected biomarkers. Both receiver operating characteristic curve and precision-recall curve show our method's consistent performance gain in accuracy. Our method also shows its advantage in capturing synergy among selected biomarkers. The multi-biomarker panel far outperforms the simple combination of best single features. Close investigation of the multi-biomarker panel illustrates that our method possesses the ability to remove redundancy and reveals complementary biomarker combinations. In addition, our method is efficient and can select multi-biomarker panel with more than 5 biomarkers, for which the exhaustive methods fail. In conclusion, we propose a promising model to improve the clinical data interpretability and to serve as a useful tool for other complex disease studies. Our small multi-biomarker panel, CEA, IL-10, IMA, and NSE, may provide insights on the disease status of colorectal diseases. The implementation of our method in MATLAB is available via the website: http://doc.aporc.org/wiki/MILP_k.

Improved stool DNA integrity method for early colorectal cancer diagnosis

BACKGROUND

DNA integrity analysis could represent an alternative approach to the early detection of colorectal cancer. Previously, fluorescence long DNA (FL-DNA) in stools was extracted using a manual approach and analyzed by capillary electrophoresis assay (CE FL-DNA). We aimed to improve diagnostic accuracy using a simpler and more standardized method [Real Time PCR FL-DNA (RT FL-DNA)] for the detection of early malignant lesions in a population undergoing colorectal cancer screening.

METHODS

From 241 stool samples, DNA was extracted using manual and semiautomatic extraction systems and analyzed using FL-DNA tests by CE and RT assays. The RT FL-DNA approach showed slightly higher sensitivity and specificity compared with the CE FL-DNA method. Furthermore, we compared the RT FL-DNA approach with the iFOBT report.

RESULTS

Nonparametric ranking statistics were used to analyze the relationship between the median values of RT FL-DNA and the clinicohistopathologic characteristics. The median values of both variables were significantly higher in patients with cancer than in patients with noncancerous lesions. According to the Fagan nomogram results, the iFOBT and FL-DNA methods provided more accurate diagnostic information and were able to identify subgroups at varying risks of cancer.

CONCLUSIONS

The combination of the semiautomatic extraction system and RT FL-DNA analysis improved the quality of DNA extracted from stool samples.

IMPACT

RT FL-DNA shows great potential for colorectal cancer diagnosis as it is a reliable and relatively easy analysis to perform on routinely processed stool samples in combination with iFOBT.

Atomic force microscopic detection enabling multiplexed low-cycle-number quantitative polymerase chain reaction for biomarker assays

Quantitative polymerase chain reaction is the current "golden standard" for quantification of nucleic acids; however, its utility is constrained by an inability to easily and reliably detect multiple targets in a single reaction. We have successfully overcome this problem with a novel combination of two widely used approaches: target-specific multiplex amplification with 15 cycles of polymerase chain reaction (PCR), followed by single-molecule detection of amplicons with atomic force microscopy (AFM). In test experiments comparing the relative expression of ten transcripts in two different human total RNA samples, we find good agreement between our single reaction, multiplexed PCR/AFM data, and data from 20 individual singleplex quantitative PCR reactions. This technique can be applied to virtually any analytical problem requiring sensitive measurement concentrations of multiple nucleic acid targets.

Identification of a circulating microRNA signature for colorectal cancer detection

Prognosis of patients with colorectal cancer (CRC) is generally poor because of the lack of simple, convenient, and noninvasive tools for CRC detection at the early stage. The discovery of microRNAs (miRNAs) and their different expression profiles among different kinds of diseases has opened a new avenue for tumor diagnosis. We built a serum microRNA expression profile signature and tested its specificity and sensitivity as a biomarker in the diagnosis of CRC. We also studied its possible role in monitoring the progression of CRC. We conducted a two phase case-control test to identify serum miRNAs as biomarkers for CRC diagnosis. Using quantitative reverse transcription polymerase chain reactions, we tested ten candidate miRNAs in a training set (30 CRCs vs 30 controls). Risk score analysis was used to evaluate the diagnostic value of the serum miRNA profiling system. Other independent samples, including 83 CRCs and 59 controls, were used to validate the diagnostic model. In the training set, six serum miRNAs (miR-21, let-7g, miR-31, miR-92a, miR-181b, and miR-203) had significantly different expression levels between the CRCs and healthy controls. Risk score analysis demonstrated that the six-miRNA-based biomarker signature had high sensitivity and specificity for distinguishing the CRC samples from cancer-free controls. The areas under the receiver operating characteristic (ROC) curve of the six-miRNA signature profiles were 0.900 and 0.923 for the two sets of serum samples, respectively. However, for the same serum samples, the areas under the ROC curve used by the tumor markers carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) were only 0.649 and 0.598, respectively. The expression levels of the six serum miRNAs were also correlated with CRC progression. Thus, the identified six-miRNA signature can be used as a noninvasive biomarker for the diagnosis of CRC, with relatively high sensitivity and specificity.

Circulating microRNA biomarkers for glioma and predicting response to therapy

The need for glioma biomarkers with improved sensitivity and specificity has sparked research into short non-coding RNA known as microRNA (miRNA). Altered miRNA biogenesis and expression in glioma plays a vital role in important signaling pathways associated with a range of tumor characteristics including gliomagenesis, invasion, and malignancy. This review will discuss current research into the role of miRNA in glioma and altered miRNA expression in biofluids as candidate biomarkers with a particular focus on glioblastoma, the most malignant form of glioma. The isolation and characterization of miRNA using cellular and molecular biology techniques from the circulation of glioma patients could potentially be used for improved diagnosis, prognosis, and treatment decisions. We aim to highlight the links between research into miRNA function, their use as biomarkers, and how these biomarkers can be used to predict response to therapy. Furthermore, increased understanding of miRNA in glioma biology through biomarker research has led to the development of miRNA therapeutics which could restore normal miRNA expression and function and improve the prognosis of glioma patients. A panel of important miRNA biomarkers for glioma in various biofluids discovered to date has been summarized here. There is still a need, however, to standardize techniques for biomarker characterization to bring us closer to clinically relevant miRNA-based diagnostic and therapeutic signatures. A clinically validated biomarker panel has potential to improve time to diagnosis, predicting response to treatment and ultimately the prognosis of glioma patients.

A Bayesian Approach for the Cox Proportional Hazards Model with Covariates Subject to Detection Limit

The research on biomarkers has been limited in its effectiveness because biomarker levels can only be measured within the thresholds of assays and laboratory instruments, a challenge referred to as a detection limit (DL) problem. In this paper, we propose a Bayesian approach to the Cox proportional hazards model with explanatory variables subject to lower, upper, or interval DLs. We demonstrate that by formulating the time-to-event outcome using the Poisson density with counting process notation, implementing the proposed approach in the OpenBUGS and JAGS is straightforward. We have conducted extensive simulations to compare the proposed Bayesian approach to the other four commonly used methods and to evaluate its robustness with respect to the distribution assumption of the biomarkers. The proposed Bayesian approach and other methods were applied to an acute lung injury study, in which a panel of cytokine biomarkers was studied for the biomarkers’ association with ventilation-free survival.

Machine learning approach for the outcome prediction of temporal lobe epilepsy surgery

Epilepsy surgery is effective in reducing both the number and frequency of seizures, particularly in temporal lobe epilepsy (TLE). Nevertheless, a significant proportion of these patients continue suffering seizures after surgery. Here we used a machine learning approach to predict the outcome of epilepsy surgery based on supervised classification data mining taking into account not only the common clinical variables, but also pathological and neuropsychological evaluations. We have generated models capable of predicting whether a patient with TLE secondary to hippocampal sclerosis will fully recover from epilepsy or not. The machine learning analysis revealed that outcome could be predicted with an estimated accuracy of almost 90% using some clinical and neuropsychological features. Importantly, not all the features were needed to perform the prediction; some of them proved to be irrelevant to the prognosis. Personality style was found to be one of the key features to predict the outcome. Although we examined relatively few cases, findings were verified across all data, showing that the machine learning approach described in the present study may be a powerful method. Since neuropsychological assessment of epileptic patients is a standard protocol in the pre-surgical evaluation, we propose to include these specific psychological tests and machine learning tools to improve the selection of candidates for epilepsy surgery.

Stable feature selection and classification algorithms for multiclass microarray data

Background

Recent studies suggest that gene expression profiles are a promising alternative for clinical cancer classification. One major problem in applying DNA microarrays for classification is the dimension of obtained data sets. In this paper we propose a multiclass gene selection method based on Partial Least Squares (PLS) for selecting genes for classification. The new idea is to solve multiclass selection problem with the PLS method and decomposition to a set of two-class sub-problems: one versus rest (OvR) and one versus one (OvO). We use OvR and OvO two-class decomposition for other recently published gene selection method. Ranked gene lists are highly unstable in the sense that a small change of the data set often leads to big changes in the obtained ordered lists. In this paper, we take a look at the assessment of stability of the proposed methods. We use the linear support vector machines (SVM) technique in different variants: one versus one, one versus rest, multiclass SVM (MSVM) and the linear discriminant analysis (LDA) as a classifier. We use balanced bootstrap to estimate the prediction error and to test the variability of the obtained ordered lists.

Results

This paper focuses on effective identification of informative genes. As a result, a new strategy to find a small subset of significant genes is designed. Our results on real multiclass cancer data show that our method has a very high accuracy rate for different combinations of classification methods, giving concurrently very stable feature rankings.

Conclusions

This paper shows that the proposed strategies can improve the performance of selected gene sets substantially. OvR and OvO techniques applied to existing gene selection methods improve results as well. The presented method allows to obtain a more reliable classifier with less classifier error. In the same time the method generates more stable ordered feature lists in comparison with existing methods.

Reviewers

This article was reviewed by Prof Marek Kimmel, Dr Hans Binder (nominated by Dr Tomasz Lipniacki) and Dr Yuriy Gusev