Gene expression signature for recurrence in stage III colorectal cancers

Profiles of alternative splicing in colorectal cancer and their clinical significance: A study based on large-scale sequencing data

Background

Alternative splicing (AS), as a potent and pervasive mechanism of transcriptional regulatory, expands the genome's coding capacity and involves in the initiation and progression of cancer. Systematic analysis of alternative splicing in colorectal cancer (CRC) is lacking and greatly needed.

Methods

RNA-Seq data and corresponding clinical information of CRC cohort were downloaded from the TCGA data portal. Then, a java application, known as SpliceSeq, was used to evaluate the RNA splicing patterns and calculate the Percent Spliced In (PSI) value. Differently expressed AS events (DEAS) were identified based on PSI value between paired CRC and adjacent tissues. DEAS and its splicing networks were further analyzed by bioinformatics methods. Kaplan-Meier, Cox proportional regression and unsupervised clustering analysis were used to evaluate the association between DEAS and patients' clinical features.

Results

After strict filtering, a total of 34,334 AS events were identified, among which 421 AS events were found expressed differently. Parent genes of these DEAS play a important role in regulating CRC-related processes such as protein kinase activity (FDR<0.0001), PI3K-Akt signaling pathway (FDR = 0.0024) and p53 signaling pathway (FDR = 0.0143). 37 DEAS events were found to be associated with OS, and 68 DEAS events were found to be associated with DFS. Stratifying patients according to the PSI value of AT in CXCL12 and RI in CSTF3 formed significant Kaplan-Meier curves in both OS and DFS survival analysis. Unsupervised clustering analysis using DEAS revealed four clusters with distinct survival patterns, and associated with consensus molecular subtypes.

Conclusions

Large differences of AS events in CRC appear to exist, and these differences are likely to be important determinants of both prognosis and biological regulation. Our identified CRC-related AS events and uncovered splicing networks are valuable in deciphering the underlying mechanisms of AS in CRC, and provide clues of therapeutic targets to further validations.

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.

The Application of Gene Expression Profiling in Predictions of Occult Lymph Node Metastasis in Colorectal Cancer Patients

A key factor in determining the likely outcome for a patient with colorectal cancer is whether or not the tumour has metastasised to the lymph nodes-information which is also important in assessing any possibilities of lymph node resection so as to improve survival. In this review we perform a wide-range assessment of literature relating to recent developments in gene expression profiling (GEP) of the primary tumour, to determine their utility in assessing node status. A set of characteristic genes seems to be involved in the prediction of lymph node metastasis (LNM) in colorectal patients. Hence, GEP is applicable in personalised/individualised/tailored therapies and provides insights into developing novel therapeutic targets. Not only is GEP useful in prediction of LNM, but it also allows classification based on differences such as sample size, target gene expression, and examination method.

Standardising RNA profiling based biomarker application in cancer-The need for robust control of technical variables

Histopathology-based staging of colorectal cancer (CRC) has utility in assessing the prognosis of patient subtypes, but as yet cannot accurately predict individual patient's treatment response. Transcriptomics approaches, using array based or next generation sequencing (NGS) platforms, of formalin fixed paraffin embedded tissue can be harnessed to develop multi-gene biomarkers for predicting both prognosis and treatment response, leading to stratification of treatment. While transcriptomics can shape future biomarker development, currently <1% of published biomarkers become clinically validated tests, often due to poor study design or lack of independent validation. In this review of a large number of CRC transcriptional studies, we identify recurrent sources of technical variability that encompass collection, preservation and storage of malignant tissue, nucleic acid extraction, methods to quantitate RNA transcripts and data analysis pipelines. We propose a series of defined steps for removal of these confounding issues, to ultimately aid in the development of more robust clinical biomarkers.

Genomic Profiling of Thyroid Cancer Reveals a Role for Thyroglobulin in Metastasis

Papillary thyroid carcinoma (PTC) has a wide geographic variation in incidence; it is most common in Saudi Arabia, where it is only second to breast cancer as the most common cancer among females. Genomic profiling of PTC from Saudi Arabia has not been attempted previously. We performed whole-exome sequencing of 101 PTC samples and the corresponding genomic DNA to identify genes with recurrent somatic mutations, then sequenced these genes by using a next-generation gene-panel approach in an additional 785 samples. In addition to BRAF, N-RAS, and H-RAS, which have previously been shown to be recurrently mutated in PTC, our analysis highlights additional genes, including thyroglobulin (TG), which harbored somatic mutations in 3% of the entire cohort. Surprisingly, although TG mutations were not exclusive to mutations in the RAS-MAP kinase pathway, their presence was associated with a significantly worse clinical outcome, which suggests a pathogenic role beyond driving initial oncogenesis. Analysis of metastatic PTC tissue revealed significant enrichment for TG mutations (p < 0.001), including events of apparent clonal expansion. Our results suggest a previously unknown role of TG somatic mutations in the pathogenesis of PTC and its malignant evolution.

Therapeutic innovations in inflammatory bowel diseases

Inflammatory bowel disease (IBD) is a spectrum of complex multifactorial immune disorders characterized by chronic inflammation of the gut. Significant advances have been made in unraveling the pathogenesis of this disease spectrum, which have spurred the discovery of new therapeutic targets and strategies. In this review, we highlight the emerging new classes of IBD therapeutics under clinical evaluation and their method of action, including JAK inhibitors, anti-SMAD7 oligonucleotides, and cell-based therapies. Moreover, we discuss how an approach based on unique molecular insights in a given patient will, in the future, lead to a truly individualized/tailored disease management, starting at diagnosis, aiding in prognosis, and resulting in a personalized therapeutic approach.

Gene expression profiling-derived immunohistochemistry signature with high prognostic value in colorectal carcinoma

OBJECTIVE

Gene expression profiling provides an opportunity to develop robust prognostic markers of colorectal carcinoma (CRC). However, the markers have not been applied for clinical decision making. We aimed to develop an immunohistochemistry signature using microarray data for predicting CRC prognosis.

DESIGN

We evaluated 25 CRC gene signatures in independent microarray datasets with prognosis information and constructed a subnetwork using signatures with high concordance and repeatable prognostic values. Tumours were examined immunohistochemically for the expression of network-centric and the top overlapping molecules. Prognostic values were assessed in 682 patients from Shanghai, China (training cohort) and validated in 343 patients from Guangzhou, China (validation cohort). Median follow-up duration was 58 months. All p values are two-sided.

RESULTS

Five signatures were selected to construct a subnetwork. The expression of GRB2, PTPN11, ITGB1 and POSTN in cancer cells, each significantly associated with disease-free survival, were selected to construct an immunohistochemistry signature. Patients were dichotomised into high-risk and low-risk subgroups with an optimal risk score (1.55). Compared with low-risk patients, high-risk patients had shorter disease-specific survival (DSS) in the training (HR=6.62; 95% CI 3.70 to 11.85) and validation cohorts (HR=3.53; 95% CI 2.13 to 5.84) in multivariate Cox analyses. The signature better predicted DSS than did tumour-node-metastasis staging in both cohorts. In those who received postoperative chemotherapy, high-risk score predicted shorter DSS in the training (HR=6.35; 95% CI 3.55 to 11.36) and validation cohorts (HR=5.56; 95% CI 2.25 to 13.71).

CONCLUSIONS

Our immunohistochemistry signature may be clinically practical for personalised prediction of CRC prognosis.

Anticipating the clinical use of prognostic gene expression-based tests for colon cancer stage II and III: is Godot finally arriving?

PURPOSE

According to current recommendations for adjuvant treatment, patients with colon cancer stage II are not routinely offered chemotherapy, unless considered to have a high risk of relapse based on specific clinicopathological parameters. Following these criteria, it is challenging to identify the subgroup of patients that will benefit the most from adjuvant treatment. Contrarily, patients with colon cancer stage III are routinely offered chemotherapy, but due to expected adverse effects and frailty, elderly patients are often excluded from standard protocols. Colon cancer is a disease of the elderly and accordingly, there is a large subgroup of patients for which guidelines for adjuvant treatment remain less clear. In these two clinical settings, improved risk stratification has great potential impact on patient care, anticipating that high-risk patients will benefit from chemotherapy. However, microsatellite instability is the only molecular prognostic marker recommended for clinical use.

EXPERIMENTAL DESIGN

In this perspective, we provide an updated view on the status and clinical potential of the many proposed prognostic gene expression-based tests for colon cancer stage II and III.

RESULTS

The main limitation for clinical implementation is lack of prospective validation. For patients with stage II, highly promising tests have been identified and clinical trials are ongoing. For elderly patients with stage III, the value of such tests has received less focus, but promising early results have been shown.

CONCLUSION

Although awaiting results from prospective trials, improved risk assessment for patients with stage II and III is likely to be achieved in the foreseeable future.

Recent advances: Personalised use of current Crohn's disease therapeutic options

Several aspects of the management of Crohn's disease (CD) are shared between patients. The goal of all therapies should be to achieve clinical and endoscopic remission in a timely manner to avoid disease progression and abdominal resections. The way this goal is achieved may differ and predicting how the disease will evolve, what the most appropriate therapy with the highest chance of success will be, how long a therapy needs to be continued, and what the intensity of follow-up should be are more difficult questions and require an individualised approach. Clinical parameters have been suggested to aid in the therapeutic decision process but lack specificity. Although much promise has been put in molecular markers, these have not yet found their way to the clinic. More recently, clinicians have started to gain interest in drug level monitoring to adapt doses of immunomodulators and/or anti-tumour necrosis factor antibodies in an individualised manner. An increasing number of studies show that therapeutic drug monitoring can help physicians to improve and personalise the management of their patients. What is needed now are pharmaco-economic studies showing that personalised management of CD is cost effective.

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

Introduction

The traditional staging system is inadequate to identify those patients with stage II colorectal cancer (CRC) at high risk of recurrence or with stage III CRC at low risk. A number of gene expression signatures to predict CRC prognosis have been proposed, but none is routinely used in the clinic. The aim of this work was to assess the prediction ability and potential clinical usefulness of these signatures in a series of independent datasets.

Methods

A literature review identified 31 gene expression signatures that used gene expression data to predict prognosis in CRC tissue. The search was based on the PubMed database and was restricted to papers published from January 2004 to December 2011. Eleven CRC gene expression datasets with outcome information were identified and downloaded from public repositories. Random Forest classifier was used to build predictors from the gene lists. Matthews correlation coefficient was chosen as a measure of classification accuracy and its associated p-value was used to assess association with prognosis. For clinical usefulness evaluation, positive and negative post-tests probabilities were computed in stage II and III samples.

Results

Five gene signatures showed significant association with prognosis and provided reasonable prediction accuracy in their own training datasets. Nevertheless, all signatures showed low reproducibility in independent data. Stratified analyses by stage or microsatellite instability status showed significant association but limited discrimination ability, especially in stage II tumors. From a clinical perspective, the most predictive signatures showed a minor but significant improvement over the classical staging system.

Conclusions

The published signatures show low prediction accuracy but moderate clinical usefulness. Although gene expression data may inform prognosis, better strategies for signature validation are needed to encourage their widespread use in the clinic.

High-throughput arrays identify distinct genetic profiles associated with lymph node involvement in rectal cancer

BACKGROUND

Preoperative clinical diagnosis of lymph node involvement guides treatment decisions for rectal cancer. Unfortunately, clinical staging still suffers from a lack of accuracy.

OBJECTIVE

The aim of this study was to evaluate objective genetic differences in primary rectal cancers with and without associated lymph node metastasis.

DESIGN

cDNA microarrays were generated from fresh-frozen tumors. Normalized data underwent global unsupervised hierarchical clustering analysis, and discriminating genes were mapped. Top discriminating genes were compared between stage II and III rectal cancers by use of an empirical Bayes 2 group t test with the Statistical Analysis of Microarrays and the Reproducibility-Optimized Test Statistic software separately to guide data reduction and deal with the difficulties of simultaneous statistical inference. Ingenuity Pathways Analysis software was used to analyze discriminating genes in terms of function and biological processes.

PATIENTS

Fifty-five patients with stage II and 22 patients with stage III rectal adenocarcinomas not treated with chemoradiation were included.

RESULTS

Two major unsupervised clusters emerged representing stage II and III cancers. In 1 cluster, 11 of 12 patients (92%) had stage III cancer; in the other cluster, 54 of 65 patients (83%) had stage II (p < 0.001). Five significantly differentially expressed genes characterized the stage III cluster: interleukin-8, 3-hydroxy-3-methylglutaryl coenzyme A synthase, carbonic anhydrase, ubiquitin, and cystatin (all p < 0.05). Of the 12 patients with differential expression of the 5 marker genes, only one had stage II cancer. Fifty-four of 55 stage II patients clustered with alternative expression patterns of the predictor genes. Differentially expressed genes are related to cancer-associated processes, pathways, and networks.

LIMITATIONS

The identified gene signatures have not yet been validated in independent patient populations.

CONCLUSIONS

Distinct gene expression signatures from primary rectal adenocarcinomas can help differentiate the presence or absence of lymph node metastases. These data are informative, and validation of this gene signature may provide a novel approach for more appropriate individualized treatment selection.

Chromosomal instability (CIN) phenotype, CIN high or CIN low, predicts survival for colorectal cancer

PURPOSE

To examine whether chromosomal instability (CIN) phenotype, determined by the severity of CIN, can predict survival for stages II and III colorectal cancer (CRC).

PATIENTS AND METHODS

We determined microsatellite instability (MSI) and loss of heterozygosity (LOH) status in 1,103 patients (training [n = 845] and validation [n = 258] sets with stages II and III CRC). The LOH ratio was defined as the frequency of LOH in chromosomes 2p, 5q, 17p, and 18q. According to the LOH ratio, non-MSI high tumors were classified as CIN high (LOH ratio ≥ 33%) or CIN low (LOH ratio < 33%). CIN-high tumors were subclassified as CIN high (mild type; LOH ratio < 75%) or CIN high (severe type; LOH ratio ≥ 75%). We used microarrays to identify a gene signature that could classify the CIN phenotype and evaluated its ability to predict prognosis.

RESULTS

CIN high showed the worst survival (P < .001), whereas there was no significant difference between CIN low and MSI high. CIN high (severe type) showed poorer survival than CIN high (mild type; P < .001). Multivariate analysis revealed that CIN phenotype was an independent risk factor for disease-free and overall survival, respectively, in both the training (P < .001 and P = .0155) and validation sets (P < .001 and P = .0076). Microarray analysis also revealed that survival was significantly poorer in those with the CIN-high than in the CIN-low gene signature (P = .0203). In a validation of 290 independent CRCs (GSE14333), the CIN-high gene signature showed significantly poorer survival than the CIN-low signature (P = .0047).

CONCLUSION

The CIN phenotype is a predictive marker for survival and may be used to select high-risk patients with stages II and III CRC.

Computational analysis of mRNA expression profiles identifies microRNA-29a/c as predictor of colorectal cancer early recurrence

Colorectal cancer (CRC) is one of the leading malignant cancers with a rapid increase in incidence and mortality. The recurrences of CRC after curative resection are sometimes unavoidable and often take place within the first year after surgery. MicroRNAs may serve as biomarkers to predict early recurrence of CRC, but identifying them from over 1,400 known human microRNAs is challenging and costly. An alternative approach is to analyze existing expression data of messenger RNAs (mRNAs) because generally speaking the expression levels of microRNAs and their target mRNAs are inversely correlated. In this study, we extracted six mRNA expression data of CRC in four studies (GSE12032, GSE17538, GSE4526 and GSE17181) from the gene expression omnibus (GEO). We inferred microRNA expression profiles and performed computational analysis to identify microRNAs associated with CRC recurrence using the IMRE method based on the MicroCosm database that includes 568,071 microRNA-target connections between 711 microRNAs and 20,884 gene targets. Two microRNAs, miR-29a and miR-29c, were disclosed and further meta-analysis of the six mRNA expression datasets showed that these two microRNAs were highly significant based on the Fisher p-value combination (p = 9.14 × 10(-9) for miR-29a and p = 1.14 × 10(-6) for miR-29c). Furthermore, these two microRNAs were experimentally tested in 78 human CRC samples to validate their effect on early recurrence. Our empirical results showed that the two microRNAs were significantly down-regulated (p = 0.007 for miR-29a and p = 0.007 for miR-29c) in the early-recurrence patients. This study shows the feasibility of using mRNA profiles to indicate microRNAs. We also shows miR-29a/c could be potential biomarkers for CRC early recurrence.

Gene expression profiling of CD8+ T cells predicts prognosis in patients with Crohn disease and ulcerative colitis

Crohn disease (CD) and ulcerative colitis (UC) are increasingly common, chronic forms of inflammatory bowel disease. The behavior of these diseases varies unpredictably among patients. Identification of reliable prognostic biomarkers would enable treatment to be personalized so that patients destined to experience aggressive disease could receive appropriately potent therapies from diagnosis, while those who will experience more indolent disease are not exposed to the risks and side effects of unnecessary immunosuppression. Using transcriptional profiling of circulating T cells isolated from patients with CD and UC, we identified analogous CD8+ T cell transcriptional signatures that divided patients into 2 otherwise indistinguishable subgroups. In both UC and CD, patients in these subgroups subsequently experienced very different disease courses. A substantially higher incidence of frequently relapsing disease was experienced by those patients in the subgroup defined by elevated expression of genes involved in antigen-dependent T cell responses, including signaling initiated by both IL-7 and TCR ligation - pathways previously associated with prognosis in unrelated autoimmune diseases. No equivalent correlation was observed with CD4+ T cell gene expression. This suggests that the course of otherwise distinct autoimmune and inflammatory conditions may be influenced by common pathways and identifies what we believe to be the first biomarker that can predict prognosis in both UC and CD from diagnosis, a major step toward personalized therapy.

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.

Phosphorylated AKT expression is associated with PIK3CA mutation, low stage, and favorable outcome in 717 colorectal cancers

BACKGROUND

AKT (AKT1, AKT2, and AKT3) was a downstream effector of phosphatidylinositide-3-kinase (PI3K) and played crucial roles in protein synthesis, cellular metabolism, survival, and proliferation. The PI3K/AKT pathway was commonly activated in human cancers and was recognized as a potential target for anticancer therapy. Nonetheless, clinical, molecular, or prognostic features of AKT-activated colon cancer remained uncertain.

METHODS

Using a database of 717 colon and rectal cancers in the Nurses' Health Study and the Health Professionals Follow-up Study, Ser473 phosphorylated AKT (p-AKT) expression was detected in 448 (62%) tumors by immunohistochemistry. Cox proportional hazards model was used to compute mortality hazards ratio (HR), adjusting for clinical and tumoral features, including PIK3CA, KRAS, BRAF, microsatellite instability (MSI), CpG island methylator phenotype (CIMP), LINE-1 methylation, TP53 (p53), and FASN (fatty acid synthase).

RESULTS

Tumor p-AKT expression was associated with PIK3CA mutation (odds ratio [OR], 1.77; 95% confidence interval [CI], 1.12-2.80; P = .015). p-AKT expression was significantly associated with longer colorectal cancer-specific survival in Kaplan-Meier analysis (log-rank P = .0005), univariate Cox regression (HR, 0.62; 95% CI, 0.47-0.82; P = .0006) and multivariate analysis (adjusted HR, 0.75; 95% CI, 0.56-0.99; P = .048) adjusting for clinical and molecular variables including PIK3CA, MSI, CIMP and LINE-1 hypomethylation. p-AKT expression was inversely associated with high stage (III-IV) (adjusted OR, 0.63; 95% CI, 0.45-0.88, P = .0071).

CONCLUSIONS

p-AKT expression in colorectal cancer is associated with low stage and good prognosis. p-AKT may serve as a tissue biomarker to identify patients with superior prognosis and a possible therapeutic target (analogous to estrogen receptor ESR1 in breast cancer).

The frequency of KRAS mutation detection in human colon carcinoma is influenced by the sensitivity of assay methodology: a comparison between direct sequencing and real-time PCR

PURPOSE

Kirsten rat sarcoma (KRAS) gene mutations occur early in the progression of colorectal adenoma to carcinoma. The mutation status of the KRAS gene determines the benefits of molecular targeting drugs in patients with advanced colorectal cancer, although many methods are available to detect such mutations. The purpose of this study was to evaluate the influence of assay sensitivity on the detection frequency of mutated genes.

METHODS

Colorectal tumors in 224 colorectal cancer patients were characterized for KRAS mutations using PCR amplification following by direct sequencing as well as a peptide nucleic acid (PNA)-clamp real-time PCR-based assay.

RESULTS

KRAS mutations were observed in 32.1% (72/224) patients by direct sequencing, and 43.3% (97/224) by PNA-clamp PCR. The chi-square test revealed that the difference in the frequency of KRAS mutations determined by direct sequencing and PNA-clamped PCR (threshold for 1% detection) was statistically significant (p<0.015).

CONCLUSIONS

Our data suggest that assay method sensitivity clearly influences the detection frequency of mutated genes. As more sensitive assays detect more mutated genes in clinical samples, this must be taken into consideration when determining KRAS gene status in clinical practice.

Increasing the number of thyroid lesions classes in microarray analysis improves the relevance of diagnostic markers

Background

Genetic markers for thyroid cancers identified by microarray analysis have offered limited predictive accuracy so far because of the few classes of thyroid lesions usually taken into account. To improve diagnostic relevance, we have simultaneously analyzed microarray data from six public datasets covering a total of 347 thyroid tissue samples representing 12 histological classes of follicular lesions and normal thyroid tissue. Our own dataset, containing about half the thyroid tissue samples, included all categories of thyroid lesions.

Methodology/Principal Findings

Classifier predictions were strongly affected by similarities between classes and by the number of classes in the training sets. In each dataset, sample prediction was improved by separating the samples into three groups according to class similarities. The cross-validation of differential genes revealed four clusters with functional enrichments. The analysis of six of these genes (APOD, APOE, CLGN, CRABP1, SDHA and TIMP1) in 49 new samples showed consistent gene and protein profiles with the class similarities observed. Focusing on four subclasses of follicular tumor, we explored the diagnostic potential of 12 selected markers (CASP10, CDH16, CLGN, CRABP1, HMGB2, ALPL2, ADAMTS2, CABIN1, ALDH1A3, USP13, NR2F2, KRTHB5) by real-time quantitative RT-PCR on 32 other new samples. The gene expression profiles of follicular tumors were examined with reference to the mutational status of the Pax8-PPARγ, TSHR, GNAS and NRAS genes.

Conclusion/Significance

We show that diagnostic tools defined on the basis of microarray data are more relevant when a large number of samples and tissue classes are used. Taking into account the relationships between the thyroid tumor pathologies, together with the main biological functions and pathways involved, improved the diagnostic accuracy of the samples. Our approach was particularly relevant for the classification of microfollicular adenomas.