Stage II colon cancer prognosis prediction by tumor gene expression profiling

Constructing a prognostic immune-related lncRNA model for colon cancer

Colon cancer is a common digestive tract tumor. Although many gene prognostic indicators have been used to predict the prognosis of colon cancer patients, the accuracy of these prognostic indicators is still uncertain. Thus, it is necessary to construct a model for the prognostic analysis of colon cancer. We downloaded the original transcriptome data of colon cancer and performed a differential coexpression analysis of immune-related genes to obtain different immune-related long noncoding RNAs, which were paired as differentially expressed immune-related lncRNA pairs (DEirlncRNAPs). Then, the 1-year overall survival rate receiver operating characteristic curve was calculated, and the Akaike information criterion value was evaluated to determine the maximum inflection point, which was used as the cutoff point to identify groups of colon cancer patients at high and low risk for death. Subsequently, the optimal prediction model was established. Finally, we used the patients' survival times, clinicopathological features, tumor infiltrating immune cells, chemotherapy responses, and immunosuppressive biomarkers to verify the DEirlncRNAP model. Seventy-one DEirlncRNAPs were obtained to build the risk assessment model. The patients were divided into a high-risk group and a low-risk group according to the cutoff point. Then, the DEirlncRNAP model was verified using patient survival times, clinicopathological features, tumor-infiltrating immune cells, chemotherapy responses, and immunosuppressive biomarkers. A new DEirlncRNAP model for predicting the prognosis of colon cancer patients was established, which could reveal new insights into the relationships of colon cancer with tumor-infiltrating immune cells and antitumor immunotherapy.

Prognostic Nomogram of Prognosis-Related Genes and Clinicopathological Characteristics to Predict the 5-Year Survival Rate of Colon Cancer Patients

Background: The Cancer Genome Atlas (TCGA) has established a genome-wide gene expression profile, increasing our understanding of the impact of tumor heredity on clinical outcomes. The aim of this study was to construct a nomogram using data from the TCGA regarding prognosis-related genes and clinicopathological characteristics to predict the 5-years survival rate of colon cancer (CC) patients. Methods: Kaplan-Meier and Cox regression analyses were used to identify genes associated with the 5-years survival rate of CC patients. Cox regression was used to analyze the relationship between the clinicopathological features and prognostic genes and overall survival rates in patients with CC and to identify independent risk factors for the prognosis of CC patients. A nomogram for predicting the 5-years survival rate of CC patients was constructed by R software. Results: A total of eight genes (KCNJ14, CILP2, ATP6V1G2, GABRD, RIMKLB, SIX2, PLEKHA8P1, and MPP2) related to the 5-years survival of rate CC patients were identified. Age, stage, and PLEKHA8P1 were independent risk factors for the 5-years survival rate in patients with CC. The accuracy, sensitivity and specificity of the nomogram model constructed by age, TNM staging, and PLEKHA8P1 for predicting the 5-years survival of rate CC patients were 83.3, 83.97, and 85.79%, respectively. Conclusion: The nomogram can correctly predict the 5-year survival rate of patients with CC, thus aiding the individualized decision-making process for patients with CC.

Transglutaminase 2 promotes tumorigenicity of colon cancer cells by inactivation of the tumor suppressor p53

Despite a high clinical need for the treatment of colorectal carcinoma (CRC) as the second leading cause of cancer-related deaths, targeted therapies are still limited. The multifunctional enzyme Transglutaminase 2 (TGM2), which harbors transamidation and GTPase activity, has been implicated in the development and progression of different types of human cancers. However, the mechanism and role of TGM2 in colorectal cancer are poorly understood. Here, we present TGM2 as a promising drug target.In primary patient material of CRC patients, we detected an increased expression and enzymatic activity of TGM2 in colon cancer tissue in comparison to matched normal colon mucosa cells. The genetic ablation of TGM2 in CRC cell lines using shRNAs or CRISPR/Cas9 inhibited cell expansion and tumorsphere formation. In vivo, tumor initiation and growth were reduced upon genetic knockdown of TGM2 in xenotransplantations. TGM2 ablation led to the induction of Caspase-3-driven apoptosis in CRC cells. Functional rescue experiments with TGM2 variants revealed that the transamidation activity is critical for the pro-survival function of TGM2. Transcriptomic and protein-protein interaction analyses applying various methods including super-resolution and time-lapse microscopy showed that TGM2 directly binds to the tumor suppressor p53, leading to its inactivation and escape of apoptosis induction.We demonstrate here that TGM2 is an essential survival factor in CRC, highlighting the therapeutic potential of TGM2 inhibitors in CRC patients with high TGM2 expression. The inactivation of p53 by TGM2 binding indicates a general anti-apoptotic function, which may be relevant in cancers beyond CRC.

Prognostic implications of an autophagy-based signature in colorectal cancer

BACKGROUND

The heterogeneity of colorectal cancer (CRC) poses a significant challenge to the precise treatment of patients. CRC has been divided into 4 consensus molecular subtypes (CMSs) with distinct biological and clinical characteristics, of which CMS4 has the mesenchymal identity and the highest relapse rate. Autophagy plays a vital role in CRC development and therapeutic response.

METHODS

The gene expression profiles collected from 6 datasets were applied to this study. Network analysis was applied to integrate the subtype-specific molecular modalities and autophagy signature to establish an autophagy-based prognostic signature for CRC (APSCRC).

RESULTS

Network analysis revealed that 6 prognostic autophagy genes (VAMP7, DLC1, FKBP1B, PEA15, PEX14, and DNAJB1) predominantly regulated the mesenchymal modalities of CRC. The APSCRC was constructed by these 6 core genes and applied for risk calculation. Patients were divided into high- and low-risk groups based on APSCRC score in all cohorts. Patients within the high-risk group showed an unfavorable prognosis. In multivariate analysis, the APSCRC remained an independent predictor of prognosis. Moreover, the APSCRC achieved higher prognostic power than commercialized multigene signatures.

CONCLUSIONS

We proposed and validated an autophagy-based signature, which is a promising prognostic biomarker of CRC patients. Further prospective studies are warranted to test and validate its efficiency for clinical application.

Integrated analysis identifies an immune-based prognostic signature for the mesenchymal identity in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) has been divided into 4 consensus molecular subtypes (CMSs), of which CMS4 has the mesenchymal identity and the highest relapse rate. Our goal is to develop a prognostic signature by integrating the immune system and mesenchymal modalities involved in CMS4.

METHODS

The gene expression profiles collected from 5 public datasets were applied to this study, including 1280 samples totally. Network analysis was applied to integrate the mesenchymal modalities and immune signature to establish an immune-based prognostic signature for CRC (IPSCRC).

RESULTS

We identified 6 immune genes as key factors of CMS4 and established the IPSCRC. The IPSCRC could significantly divide patients into high- and low- risk groups in terms of relapse-free survival (RFS) and overall survival (OS) and in discovery (RFS: P < .0001) and 4 independent validation sets (RFS range: P = .01 to <.0001; OS range: P = .02-.0004). After stage stratification, the IPSCRC could still distinguish poor prognosis patients in discovery (RFS: P = .04) and validation cohorts (RFS range: P = .04-.007) within stage II in terms of RFS. Further, in multivariate analysis, the IPSCRC remained an independent predictor of prognosis. Moreover, Macrophage M2 was significantly enriched in the high-risk group, while plasma cells enriched in the low-risk group.

CONCLUSION

We propose an immune-based signature identified by network analysis, which is a promising prognostic biomarker and help for the selection of CRC patients who might benefit from more rigorous therapies. Further prospective studies are warranted to test and validate its efficiency for clinical application.

Glandular orientation and shape determined by computational pathology could identify aggressive tumor for early colon carcinoma: a triple-center study

BACKGROUND

Identifying the early-stage colon adenocarcinoma (ECA) patients who have lower risk cancer vs. the higher risk cancer could improve disease prognosis. Our study aimed to explore whether the glandular morphological features determined by computational pathology could identify high risk cancer in ECA via H&E images digitally.

METHODS

532 ECA patients retrospectively from 2 independent data centers, as well as 113 from The Cancer Genome Atlas (TCGA), were enrolled in this study. Four tissue microarrays (TMAs) were constructed across ECA hematoxylin and eosin (H&E) stained slides. 797 quantitative glandular morphometric features were extracted and 5 most prognostic features were identified using minimum redundancy maximum relevance to construct an image classifier. The image classifier was evaluated on D2/D3 = 223, D4 = 46, D5 = 113. The expression of Ki67 and serum CEA levels were scored on D3, aiming to explore the correlations between image classifier and immunohistochemistry data and serum CEA levels. The roles of clinicopathological data and ECAHBC were evaluated by univariate and multivariate analyses for prognostic value.

RESULTS

The image classifier could predict ECA recurrence (accuracy of 88.1%). ECA histomorphometric-based image classifier (ECAHBC) was an independent prognostic factor for poorer disease-specific survival [DSS, (HR = 9.65, 95% CI 2.15-43.12, P = 0.003)]. Significant correlations were observed between ECAHBC-positive patients and positivity of Ki67 labeling index (Ki67Li) and serum CEA.

CONCLUSION

Glandular orientation and shape could predict the high risk cancer in ECA and contribute to precision oncology. Computational pathology is emerging as a viable and objective means of identifying predictive biomarkers for cancer patients.

Development and validation of an immune-related gene pairs signature in colorectal cancer

Although the outcome of colorectal cancer (CRC) patients has improved significantly with the recent implementation of annual screening programs, reliable prognostic biomarkers are still needed due to the disease heterogeneity. Increasing pieces of evidence revealed an association between immune signature and CRC prognosis. Thus, we aim to build a robust immune-related gene pairs (IRGPs) signature that can estimate prognosis for CRC. Gene expression profiles and clinical information of CRC patients were collected from six public cohorts, divided into training cohort (n = 565) and five independent validation cohorts (n = 572, 290, 90 177 and 68, respectively). Within 1534 immune genes, a 19 IRGPs signature consisting of 36 unique genes was constructed which was significantly associated with the survival. In the validation cohorts, the IRGPs signature significantly stratified patients into high- vs low-risk groups in terms of prognosis across and within subpopulations with early stages disease and was prognostic in univariate and multivariate analyses. Several biological processes, including response to bacterium, were enriched among genes in the IRGPs signature. Macrophage M2 and mast cells were significantly higher in the high-risk risk group compared with the low-risk group. The IRGPs signature achieved a higher accuracy than commercialized multigene signatures for estimation of survival. When integrated with clinical factors such as sex and stage, the composite clinical and IRGPs signature showed improved prognostic accuracy relative to IRGPs signatures alone. In short, we developed a robust IRGPs signature for estimating prognosis in CRC, including early-stage disease, providing new insights into the identification of CRC patients with a high risk of mortality.

Prognosis Prediction of Colorectal Cancer Using Gene Expression Profiles

Background: Investigation on prognostic markers for colorectal cancer (CRC) deserves efforts, but data from China are scarce. This study aimed to build a prognostic algorithm using differentially expressed gene (DEG) profiles and to compare it with the TNM staging system in their predictive accuracy for CRC prognosis in Chinese patients. Methods: DEGs in six paired tumor and corresponding normal tissues were determined using RNA-Sequencing. Subsequently, matched tumor and normal tissues from 127 Chinese patients were assayed for further validation. Univariate and multivariate Cox regressions were used to identify informative DEGs. A predictive index (PI) was derived as a linear combination of the products of the DEGs and their Cox regression coefficients. The combined predictive accuracy of the DEGs-based PI and tumors' TNM stages was also examined by a logistic regression model including the two predictors. The predictive performance was evaluated with the area under the receiver operating characteristics (AUCs). Results: Out of 75 candidate DEGs, we identified 10 DEGs showing statistically significant associations with CRC survival. A PI based on these 10 DEGs (PI-10) predicted CRC survival probability more accurately than the TNM staging system [AUCs for 3-year survival probability 0.73 (95% confidence interval: 0.64, 0.81) vs. 0.68 (0.59, 0.76)] but comparable to a simplified PI (PI-5) using five DEGs (LOC646627, BEST4, KLF9, ATP6V1A, and DNMT3B). The predictive accuracy was improved further by combining PI-5 and the TNM staging system [AUC for 3-year survival probability: 0.72 (0.63, 0.80)]. Conclusion: Prognosis prediction based on informative DEGs might yield a higher predictive accuracy in CRC prognosis than the TNM staging system does.

Transcriptional subtyping and CD8 immunohistochemistry identifies poor prognosis stage II/III colorectal cancer patients who benefit from adjuvant chemotherapy

Purpose

Transcriptomic profiling of colorectal cancer (CRC) has led to the identification of four consensus molecular subtypes (CMS1 to 4) that have prognostic value in stage II and III disease. More recently, the Colorectal Cancer Intrinsic Subtypes (CRIS) classification system has helped to define the biology specific to the epithelial component of colorectal tumors; however, the clinical value of these classification systems in the prediction of response to standard-of-care adjuvant chemotherapy remains unknown.

Patients and Methods

Using samples from four European sites, we assembled a novel cohort of patients with stage II and III CRC (n = 156 samples) and performed transcriptomic profiling and targeted sequencing and generated a tissue microarray to enable integrated multiomics analyses. We also accessed data from two published cohorts of patients with stage II and III CRC: GSE39582 and GSE14333 (n = 479 and n = 185 samples, respectively).

Results

The epithelial-rich CMS2 subtype of CRC benefitted significantly from treatment with adjuvant chemotherapy in both stage II and III disease (P = .02 and P < .001, respectively), whereas the CMS3 subtype significantly benefitted in stage III only (P = .001). After CRIS substratification of CMS2, we observed that only the CRIS-C subtype significantly benefitted from treatment with adjuvant chemotherapy in stage II and III disease (P = .0081 and P < .001, respectively), whereas the CRIS-D subtype significantly benefitted in stage III only (P = .0034). We also observed that CRIS-C patients with low levels of CD8⁺ tumor-infiltrating lymphocytes were most at risk for relapse in both stage II and III disease (log-rank P = .0031; hazard ratio, 12.18 [95% CI, 1.51 to 98.58]).

Conclusion

Patient stratification using a combination of transcriptional subtyping and CD8 immunohistochemistry analyses is capable of identifying patients with poor prognostic stage II and III disease who benefit from adjuvant standard-of-care chemotherapy. These findings are particularly relevant for patients with stage II disease, where the overall benefit of adjuvant chemotherapy is marginal.

Gene pair based prognostic signature for colorectal colon cancer

BACKGROUND

The identification of high-risk colorectal cancer (CRC) patient is key to individualized treatment after surgery and reliable prognostic biomarkers are needed identifying high-risk CRC patients.

METHODS

We developed a gene pair based prognostic signature that could can the prognosis risk in patients with CRC. This study retrospectively analyzed 4 public CRC datasets, and 1123 patients with CRC were divided into a training cohort (n = 300) and 3 independent validation cohorts (n = 507, 226, and 90 patients).

RESULTS

A signature of 9 prognosis-related gene pairs (PRGPs) consisting of 17 unique genes was constructed. Then, a PRGP index (PRGPI) was constructed and divided patients into high- and low-risk groups according to the signature score. Patients in the high-risk group showed a poorer relapse-free survival than the low-risk group in both the training cohort [hazard ratio (HR) range, 4.6, 95% confidence interval (95% CI), 2.55-8.32; P < .0001] and meta-validation set (hazard ratio range, 4.09, 95% CI, 1.99-8.39; P < .0001). The PRGPI signature achieved a higher accuracy [mean concordance index (C-index): 0.6∼0.74] than a commercialized molecular signature (mean C-index, 0.48∼0.56) for estimation of relapse-free survival in comparable validation sets.

CONCLUSION

The gene pair based prognostic signature is a promising biomarker for estimating relapse-free survival of CRC.

Hypoxic tumor microenvironment activates GLI2 via HIF-1α and TGF-β2 to promote chemoresistance in colorectal cancer

Colorectal cancer patients often relapse after chemotherapy, owing to the survival of stem or progenitor cells referred to as cancer stem cells (CSCs). Although tumor stromal factors are known to contribute to chemoresistance, it remains not fully understood how CSCs in the hypoxic tumor microenvironment escape the chemotherapy. Here, we report that hypoxia-inducible factor (HIF-1α) and cancer-associated fibroblasts (CAFs)-secreted TGF-β2 converge to activate the expression of hedgehog transcription factor GLI2 in CSCs, resulting in increased stemness/dedifferentiation and intrinsic resistance to chemotherapy. Genetic or small-molecule inhibitor-based ablation of HIF-1α/TGF-β2-mediated GLI2 signaling effectively reversed the chemoresistance caused by the tumor microenvironment. Importantly, high expression levels of HIF-1α/TGF-β2/GLI2 correlated robustly with the patient relapse following chemotherapy, highlighting a potential biomarker and therapeutic target for chemoresistance in colorectal cancer. Our study thus uncovers a molecular mechanism by which hypoxic colorectal tumor microenvironment promotes cancer cell stemness and resistance to chemotherapy and suggests a potentially targeted treatment approach to mitigating chemoresistance.

Identification of a gene expression signature predicting survival in oral cavity squamous cell carcinoma using Monte Carlo cross validation

OBJECTIVES

This study aims to identify a robust signature that performs well in predicting overall survival across tumor phenotypes and treatment strata, and validates the application of Monte Carlo cross validation (MCCV) as a means of identifying molecular signatures when utilizing small and highly heterogeneous datasets.

MATERIALS AND METHODS

RNA sequence gene expression data for 264 patient tumors were acquired from The Cancer Genome Atlas (TCGA). 100 iterations of Monte Carlo cross validation were applied to differential expression and Cox model validation. The association between the gene signature risk score and overall survival was measured using Kaplan-Meier survival curves, univariate, and multivariable Cox regression analyses.

RESULTS

Pathway analysis findings indicate that ligand-gated ion channel pathways are the most significantly enriched with the genes in the aggregated signature. The aggregated signature described in this study is predictive of overall survival in oral cancer patients across demographic and treatment strata.

CONCLUSION

This study reinforces previous findings supporting the role of ion channel gating, interleukin, calcitonin receptor, and keratinization pathways in tumor progression and treatment response in oral cancer. These results strengthen the argument that differential expression of genes within these pathways reduces tumor susceptibility to treatment. Conducting differential gene expression (DGE) with Monte Carlo cross validation, as this study describes, offers a potential solution to decreasing the variability in DGE results across future studies that are reliant upon highly heterogeneous datasets. This improves the ability of studies reliant upon similarly structured datasets to reach results that are reproducible.

A network-based predictive gene-expression signature for adjuvant chemotherapy benefit in stage II colorectal cancer

Background

The clinical benefit of adjuvant chemotherapy for stage II colorectal cancer (CRC) is controversial. This study aimed to explore novel gene signature to predict outcome benefit of postoperative 5-Fu-based therapy in stage II CRC.

Methods

Gene-expression profiles of stage II CRCs from two datasets with 5-Fu-based adjuvant chemotherapy (training dataset, n = 212; validation dataset, n = 85) were analyzed to identify the indicator. A systemic approach by integrating gene-expression and protein-protein interaction (PPI) network was implemented to develop the predictive signature. Kaplan-Meier curves and Cox proportional hazards model were used to determine the survival benefit of adjuvant chemotherapy. Experiments with shRNA knock-down were carried out to confirm the signature identified in this study.

Results

In the training dataset, we identified 44 PPI sub-modules, by which we separate patients into two clusters (1 and 2) having different chemotherapeutic benefit. A predictor of 11 PPI sub-modules (11-PPI-Mod) was established to discriminate the two sub-groups, with an overall accuracy of 90.1%. This signature was independently validated in an external validation dataset. Kaplan-Meier curves showed an improved outcome for patients who received adjuvant chemotherapy in Cluster 1 sub-group, but even worse survival for those in Cluster 2 sub-group. Similar results were found in both the training and the validation dataset. Multivariate Cox regression revealed an interaction effect between 11-PPI-Mod signature and adjuvant therapy treatment in the training dataset (RFS, p = 0.007; OS, p = 0.006) and the validation dataset (RFS, p = 0.002). From the signature, we found that PTGES gene was up-regulated in CRC cells which were more resistant to 5-Fu. Knock-down of PTGES indicated a growth inhibition and up-regulation of apoptotic markers induced by 5-Fu in CRC cells.

Conclusions

Only a small proportion of stage II CRC patients could benefit from adjuvant therapy. The 11-PPI-Mod as a potential predictor could be helpful to distinguish this sub-group with favorable outcome.

Electronic supplementary material

The online version of this article (10.1186/s12885-017-3821-4) contains supplementary material, which is available to authorized users.

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.

A subset of platinum-containing chemotherapeutic agents kills cells by inducing ribosome biogenesis stress

Cisplatin and its platinum analogs, carboplatin and oxaliplatin, are some of the most widely used cancer chemotherapeutics. Although cisplatin and carboplatin are used primarily in germ cell, breast and lung malignancies, oxaliplatin is instead used almost exclusively to treat colorectal and other gastrointestinal cancers. Here we utilize a unique, multi-platform genetic approach to study the mechanism of action of these clinically established platinum anti-cancer agents, as well as more recently developed cisplatin analogs. We show that oxaliplatin, unlike cisplatin and carboplatin, does not kill cells through the DNA-damage response. Rather, oxaliplatin kills cells by inducing ribosome biogenesis stress. This difference in drug mechanism explains the distinct clinical implementation of oxaliplatin relative to cisplatin, and it might enable mechanistically informed selection of distinct platinum drugs for distinct malignancies. These data highlight the functional diversity of core components of front-line cancer therapy and the potential benefits of applying a mechanism-based rationale to the use of our current arsenal of anti-cancer drugs.

Colorectal Carcinoma: A General Overview and Future Perspectives in Colorectal Cancer

Colorectal cancer (CRC) is the third most common cancer and the fourth most common cause of cancer-related death. Most cases of CRC are detected in Western countries, with its incidence increasing year by year. The probability of suffering from colorectal cancer is about 4%–5% and the risk for developing CRC is associated with personal features or habits such as age, chronic disease history and lifestyle. In this context, the gut microbiota has a relevant role, and dysbiosis situations can induce colonic carcinogenesis through a chronic inflammation mechanism. Some of the bacteria responsible for this multiphase process include Fusobacterium spp, Bacteroides fragilis and enteropathogenic Escherichia coli. CRC is caused by mutations that target oncogenes, tumour suppressor genes and genes related to DNA repair mechanisms. Depending on the origin of the mutation, colorectal carcinomas can be classified as sporadic (70%); inherited (5%) and familial (25%). The pathogenic mechanisms leading to this situation can be included in three types, namely chromosomal instability (CIN), microsatellite instability (MSI) and CpG island methylator phenotype (CIMP). Within these types of CRC, common mutations, chromosomal changes and translocations have been reported to affect important pathways (WNT, MAPK/PI3K, TGF-β, TP53), and mutations; in particular, genes such as c-MYC, KRAS, BRAF, PIK3CA, PTEN, SMAD2 and SMAD4 can be used as predictive markers for patient outcome. In addition to gene mutations, alterations in ncRNAs, such as lncRNA or miRNA, can also contribute to different steps of the carcinogenesis process and have a predictive value when used as biomarkers. In consequence, different panels of genes and mRNA are being developed to improve prognosis and treatment selection. The choice of first-line treatment in CRC follows a multimodal approach based on tumour-related characteristics and usually comprises surgical resection followed by chemotherapy combined with monoclonal antibodies or proteins against vascular endothelial growth factor (VEGF) and epidermal growth receptor (EGFR). Besides traditional chemotherapy, alternative therapies (such as agarose tumour macrobeads, anti-inflammatory drugs, probiotics, and gold-based drugs) are currently being studied to increase treatment effectiveness and reduce side effects.

Mutant-Allele Tumor Heterogeneity Scores Correlate With Risk of Metastases in Colon Cancer

BACKGROUND

Colorectal cancer is a leading cause of cancer-related mortality, has a very broad mutational spectrum, and there is no clinically available biomarker that can predict which patients with stage II or stage III colorectal cancer will develop metastatic disease.

PATIENTS AND METHODS

We used a targeted next-generation sequencing approach to analyze the mutational spectra in stage II and III colon cancer patient samples.

RESULTS

Amidst a broad range of acquired mutations and variants, we found evidence of tumor heterogeneity that distinguished the tumors in different groups. When heterogeneity was quantified using the Mutant-Allele Tumor Heterogeneity (MATH) score, there was a strong correlation between higher MATH score and risk of metastases.

CONCLUSIONS

Measures of tumor heterogeneity might be useful biomarkers for identifying patients with colon cancer who are at risk of developing metastases. This might allow for more specific, tailored follow-up and adjuvant therapies after standard surgery.

Emergency presenting colon cancer is an independent predictor of adverse disease-free survival

Twenty percent of colon cancers present as an emergency. However, the association between emergency presentation and disease-free survival (DFS) remains uncertain. Consecutive patients who underwent elective (CC) and emergent (eCC) resection for colon cancer were included in the analysis. Survival outcomes were compared between the 2 groups in univariate/multivariate analyses. A total of 439 patients underwent colonic resection for colon cancer during the interval 2000-2010; 97 (22.1%) presented as an emergency. eCC tumors were more often located at the splenic flexure (P = 0.017) and descending colon (P = 0.004). The eCC group displayed features of more advanced disease with a higher proportion of T4 (P = 0.009), N2 tumors (P < 0.01) and lymphovascular invasion (P< 0.01). eCC was associated with adverse locoregional recurrence (P = 0.02) and adverse DFS (P < 0.01 ) on univariate analysis. eCC remained an independent predictor of adverse locoregional recurrence (HR 1.86, 95% CI 1.50-3.30, P = 0.03) and DFS (HR 1.30, 95% CI 0.88-1.92, P = 0.05) on multivariate analysis. eCC was not associated with adverse overall survival and systemic recurrence. eCC is an independent predictor of adverse locoregional recurrence and DFS.

Evaluation of frozen tissue-derived prognostic gene expression signatures in FFPE colorectal cancer samples

Defining molecular features that can predict the recurrence of colorectal cancer (CRC) for stage II-III patients remains challenging in cancer research. Most available clinical samples are Formalin-Fixed, Paraffin-Embedded (FFPE). NanoString nCounter® and Affymetrix GeneChip® Human Transcriptome Array 2.0 (HTA) are the two platforms marketed for high-throughput gene expression profiling for FFPE samples. In this study, to evaluate the gene expression of frozen tissue-derived prognostic signatures in FFPE CRC samples, we evaluated the expression of 516 genes from published frozen tissue-derived prognostic signatures in 42 FFPE CRC samples measured by both platforms. Based on HTA platform-derived data, we identified both gene (99 individual genes, FDR < 0.05) and gene set (four of the six reported multi-gene signatures with sufficient information for evaluation, P < 0.05) expression differences associated with survival outcomes. Using nCounter platform-derived data, one of the six multi-gene signatures (P < 0.05) but no individual gene was associated with survival outcomes. Our study indicated that sufficiently high quality RNA could be obtained from FFPE tumor tissues to detect frozen tissue-derived prognostic gene expression signatures for CRC patients.

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.

CDX2 as a Prognostic Biomarker in Stage II and Stage III Colon Cancer

Background The identification of high-risk stage II colon cancers is key to the selection of patients who require adjuvant treatment after surgery. Microarray-based multigene-expression signatures derived from stem cells and progenitor cells hold promise, but they are difficult to use in clinical practice. Methods We used a new bioinformatics approach to search for biomarkers of colon epithelial differentiation across gene-expression arrays and then ranked candidate genes according to the availability of clinical-grade diagnostic assays. With the use of subgroup analysis involving independent and retrospective cohorts of patients with stage II or stage III colon cancer, the top candidate gene was tested for its association with disease-free survival and a benefit from adjuvant chemotherapy. Results The transcription factor CDX2 ranked first in our screening test. A group of 87 of 2115 tumor samples (4.1%) lacked CDX2 expression. In the discovery data set, which included 466 patients, the rate of 5-year disease-free survival was lower among the 32 patients (6.9%) with CDX2-negative colon cancers than among the 434 (93.1%) with CDX2-positive colon cancers (hazard ratio for disease recurrence, 3.44; 95% confidence interval [CI], 1.60 to 7.38; P=0.002). In the validation data set, which included 314 patients, the rate of 5-year disease-free survival was lower among the 38 patients (12.1%) with CDX2 protein-negative colon cancers than among the 276 (87.9%) with CDX2 protein-positive colon cancers (hazard ratio, 2.42; 95% CI, 1.36 to 4.29; P=0.003). In both these groups, these findings were independent of the patient's age, sex, and tumor stage and grade. Among patients with stage II cancer, the difference in 5-year disease-free survival was significant both in the discovery data set (49% among 15 patients with CDX2-negative tumors vs. 87% among 191 patients with CDX2-positive tumors, P=0.003) and in the validation data set (51% among 15 patients with CDX2-negative tumors vs. 80% among 106 patients with CDX2-positive tumors, P=0.004). In a pooled database of all patient cohorts, the rate of 5-year disease-free survival was higher among 23 patients with stage II CDX2-negative tumors who were treated with adjuvant chemotherapy than among 25 who were not treated with adjuvant chemotherapy (91% vs. 56%, P=0.006). Conclusions Lack of CDX2 expression identified a subgroup of patients with high-risk stage II colon cancer who appeared to benefit from adjuvant chemotherapy. (Funded by the National Comprehensive Cancer Network, the National Institutes of Health, and others.).

ColoLipidGene: signature of lipid metabolism-related genes to predict prognosis in stage-II colon cancer patients

Lipid metabolism plays an essential role in carcinogenesis due to the requirements of tumoral cells to sustain increased structural, energetic and biosynthetic precursor demands for cell proliferation. We investigated the association between expression of lipid metabolism-related genes and clinical outcome in intermediate-stage colon cancer patients with the aim of identifying a metabolic profile associated with greater malignancy and increased risk of relapse. Expression profile of 70 lipid metabolism-related genes was determined in 77 patients with stage II colon cancer. Cox regression analyses using c-index methodology was applied to identify a metabolic-related signature associated to prognosis. The metabolic signature was further confirmed in two independent validation sets of 120 patients and additionally, in a group of 264 patients from a public database. The combined analysis of these 4 genes, ABCA1, ACSL1, AGPAT1 and SCD, constitutes a metabolic-signature (ColoLipidGene) able to accurately stratify stage II colon cancer patients with 5-fold higher risk of relapse with strong statistical power in the four independent groups of patients. The identification of a group of 4 genes that predict survival in intermediate-stage colon cancer patients allows delineation of a high-risk group that may benefit from adjuvant therapy, and avoids the toxic and unnecessary chemotherapy in patients classified as low-risk group.

Identifying molecular subtypes in human colon cancer using gene expression and DNA methylation microarray data

Identifying colon cancer subtypes based on molecular signatures may allow for a more rational, patient-specific approach to therapy in the future. Classifications using gene expression data have been attempted before with little concordance between the different studies carried out. In this study we aimed to uncover subtypes of colon cancer that have distinct biological characteristics and identify a set of novel biomarkers which could best reflect the clinical and/or biological characteristics of each subtype. Clustering analysis and discriminant analysis were utilized to discover the subtypes in two different molecular levels on 153 colon cancer samples from The Cancer Genome Atlas (TCGA) Data Portal. At gene expression level, we identified two major subtypes, ECL1 (expression cluster 1) and ECL2 (expression cluster 2) and a list of signature genes. Due to the heterogeneity of colon cancer, the subtype ECL1 can be further subdivided into three nested subclasses, and HOTAIR were found upregulated in subclass 2. At DNA methylation level, we uncovered three major subtypes, MCL1 (methylation cluster 1), MCL2 (methylation cluster 2) and MCL3 (methylation cluster 3). We found only three subtypes of CpG island methylator phenotype (CIMP) in colon cancer instead of the four subtypes in the previous reports, and we found no sufficient evidence to subdivide MCL3 into two distinct subgroups.

SOX4 is a potential prognostic factor in human cancers: a systematic review and meta-analysis

OBJECTIVE

The aim of the this study was to analyze the status of sex-determining region Y-related high-mobility group box 4 (SOX4) expression in varied human cancers and its correlation with overall survival in patients with human cancers.

METHODS

To observe initially the expression status of SOX4 in twenty kinds of human cancers at protein database (The Human Protein Atlas). We systematically and carefully searched the studies from electronic databases and seriously identified according to eligibility criteria. The correlation between SOX4 expression and overall survival in human cancers was evaluated through Review Manager.

RESULTS

We found that SOX4 expression was significantly positive in most types of human cancer tissues, and the positive rate of SOX4 expression was about 78 % in overall cancer tissues. Furthermore, a total of 10 studies which included 1348 cancer patients were included in the final analysis. Meta-analysis showed that SOX4 overexpression was correlated with a poor overall survival and the pooled hazard ratio (HR), and corresponding 95 % confidence interval (CI) was 1.67 (95 % CI 1.01-2.78). From subgroup analyses, we present evidence that SOX4 overexpression was an unfavorable prognostic factor for colorectal cancer patients' recurrence-free survival and gastric cancer patients' overall survival, and the pooled HRs (95 % CI) were 1.73 (95 % CI 1.04-2.88) and 3.74 (95 % CI 1.04-13.45), respectively.

CONCLUSIONS

In summary, SOX4 is a potential prognostic biomarker in human cancers.

Quantitative proteomic analysis of paired colorectal cancer and non-tumorigenic tissues reveals signature proteins and perturbed pathways involved in CRC progression and metastasis

Modern proteomics has proven instrumental in our understanding of the molecular deregulations associated with the development and progression of cancer. Herein, we profile membrane-enriched proteome of tumor and adjacent normal tissues from eight CRC patients using label-free nanoLC-MS/MS-based quantitative proteomics and advanced pathway analysis. Of the 948 identified proteins, 184 proteins were differentially expressed (P<0.05, fold change>1.5) between the tumor and non-tumor tissue (69 up-regulated and 115 down-regulated in tumor tissues). The CRC tumor and non-tumor tissues clustered tightly in separate groups using hierarchical cluster analysis of the differentially expressed proteins, indicating a strong CRC-association of this proteome subset. Specifically, cancer associated proteins such as FN1, TNC, DEFA1, ITGB2, MLEC, CDH17, EZR and pathways including actin cytoskeleton and RhoGDI signaling were deregulated. Stage-specific proteome signatures were identified including up-regulated ribosomal proteins and down-regulated annexin proteins in early stage CRC. Finally, EGFR(+) CRC tissues showed an EGFR-dependent down-regulation of cell adhesion molecules, relative to EGFR(-) tissues. Taken together, this study provides a detailed map of the altered proteome and associated protein pathways in CRC, which enhances our mechanistic understanding of CRC biology and opens avenues for a knowledge-driven search for candidate CRC protein markers.

Immunoprofiling for prognostic assessment of colon cancer: a novel complement to ultrastaging

BACKGROUND

Although AJCC/TNM staging remains the gold standard for prognostic assessment of colon cancer, stage-specific outcomes vary. We therefore prospectively evaluated the prognostic role of immunoprofiling.

METHODS

Our cohort included 35 patients from an ongoing prospective trial of ultrastaging for colon cancer. Specimens were analyzed for T cell markers (CD3, CD4, CD8, and FoxP3). The number of tumor-infiltrating lymphocytes was analyzed at the tumor's margin and center and correlated with AJCC/TNM stage, clinicopathologic variables, and disease-free survival.

RESULTS

There was a significant inverse association between number of CD3(+) cells in the tumor center and tumor stage (P = 0.05). The tumor center/margin ratio of CD3(+) cells also showed an inverse but non-significant relationship with nodal involvement (P = 0.07). Body mass index was inversely associated with numbers of CD3(+)(P = 0.04) and CD8(+)(P = 0.02) cells. Longer disease-free survival was correlated with higher CD8+ counts (P = 0.07), lower CD4(+)/CD8(+) ratios (P = 0.008), and higher CD8(+)/FoxP3(+) ratios (P = 0.02).

CONCLUSIONS

This is the first prospective validation of immunoprofiling in patients whose colon cancer is staged with strict surgical and pathology quality measures. The apparent correlation between immunophenotypic response and clinical outcome warrants evaluation in a larger prospective trial.

Colorectal Cancer Biomarkers: Where Are We Now?

Colorectal cancer is one of the major causes of cancer-related death in the Western world. Patient survival is highly dependent on the tumor stage at the time of diagnosis. Reduced sensitivity to chemotherapy is still a major obstacle in effective treatment of advanced disease. Due to the fact that colorectal cancer is mostly asymptomatic until it progresses to advanced stages, the implementation of screening programs aimed at early detection is essential to reduce incidence and mortality rates. Current screening and diagnostic methods range from semi-invasive procedures such as colonoscopy to noninvasive stool-based tests. The combination of the absence of symptoms, the semi-invasive nature of currently used methods, and the suboptimal accuracy of fecal blood tests results in colorectal cancer diagnosis at advanced stages in a significant number of individuals. Alterations in gene expression leading to colorectal carcinogenesis are reflected in dysregulated levels of nucleic acids and proteins, which can be used for the development of novel, minimally invasive molecular biomarkers. The purpose of this review is to discuss the commercially available colorectal cancer molecular diagnostic methods as well as to highlight some of the new candidate predictive and prognostic molecular markers for tumor, stool, and blood samples.

Transcript profiling distinguishes complete treatment responders with locally advanced cervical cancer

Cervical cancer (CC) mortality is a major public health concern since it is the second cause of cancer-related deaths among women. Patients diagnosed with locally advanced CC (LACC) have an important rate of recurrence and treatment failure. Conventional treatment for LACC is based on chemotherapy and radiotherapy; however, up to 40% of patients will not respond to conventional treatment; hence, we searched for a prognostic gene signature able to discriminate patients who do not respond to the conventional treatment employed to treat LACC. Tumor biopsies were profiled with genome-wide high-density expression microarrays. Class prediction was performed in tumor tissues and the resultant gene signature was validated by quantitative reverse transcription–polymerase chain reaction. A 27-predictive gene profile was identified through its association with pathologic response. The 27-gene profile was validated in an independent set of patients and was able to distinguish between patients diagnosed as no response versus complete response. Gene expression analysis revealed two distinct groups of tumors diagnosed as LACC. Our findings could provide a strategy to select patients who would benefit from neoadjuvant radiochemotherapy-based treatment.

Personalized identification of altered pathways in cancer using accumulated normal tissue data

MOTIVATION

Identifying altered pathways in an individual is important for understanding disease mechanisms and for the future application of custom therapeutic decisions. Existing pathway analysis techniques are mainly focused on discovering altered pathways between normal and cancer groups and are not suitable for identifying the pathway aberrance that may occur in an individual sample. A simple way to identify individual's pathway aberrance is to compare normal and tumor data from the same individual. However, the matched normal data from the same individual are often unavailable in clinical situation. Therefore, we suggest a new approach for the personalized identification of altered pathways, making special use of accumulated normal data in cases when a patient's matched normal data are unavailable. The philosophy behind our method is to quantify the aberrance of an individual sample's pathway by comparing it with accumulated normal samples. We propose and examine personalized extensions of pathway statistics, overrepresentation analysis and functional class scoring, to generate individualized pathway aberrance score.

RESULTS

Collected microarray data of normal tissue of lung and colon mucosa are served as reference to investigate a number of cancer individuals of lung adenocarcinoma (LUAD) and colon cancer, respectively. Our method concurrently captures known facts of cancer survival pathways and identifies the pathway aberrances that represent cancer differentiation status and survival. It also provides more improved validation rate of survival-related pathways than when a single cancer sample is interpreted in the context of cancer-only cohort. In addition, our method is useful in classifying unknown samples into cancer or normal groups. Particularly, we identified 'amino acid synthesis and interconversion' pathway is a good indicator of LUAD (Area Under the Curve (AUC) 0.982 at independent validation). Clinical importance of the method is providing pathway interpretation of single cancer, even though its matched normal data are unavailable.

AVAILABILITY AND IMPLEMENTATION

The method was implemented using the R software, available at our Web site: http://bibs.snu.ac.kr/ipas.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genomic classifier ColoPrint predicts recurrence in stage II colorectal cancer patients more accurately than clinical factors

BACKGROUND

Approximately 20% of patients with stage II colorectal cancer will experience a relapse. Current clinical-pathologic stratification factors do not allow clear identification of these high-risk patients. ColoPrint (Agendia, Amsterdam, The Netherlands, http://www.agendia.com) is a gene expression classifier that distinguishes patients with low or high risk of disease relapse.

METHODS

ColoPrint was developed using whole-genome expression data and validated in several independent validation cohorts. Stage II patients from these studies were pooled (n = 416), and ColoPrint was compared with clinical risk factors described in the National Comprehensive Cancer Network (NCCN) 2013 Guidelines for Colon Cancer. Median follow-up was 81 months. Most patients (70%) did not receive adjuvant chemotherapy. Risk of relapse (ROR) was defined as survival until first event of recurrence or death from cancer.

RESULTS

In the pooled stage II data set, ColoPrint identified 63% of patients as low risk with a 5-year ROR of 10%, whereas high-risk patients (37%) had a 5-year ROR of 21%, with a hazard ratio (HR) of 2.16 (p = .004). This remained significant in a multivariate model that included number of lymph nodes retrieved and microsatellite instability. In the T3 microsatellite-stable subgroup (n = 301), ColoPrint classified 59% of patients as low risk with a 5-year ROR of 9.9%. High-risk patients (31%) had a 22.4% ROR (HR: 2.41; p = .005). In contrast, the NCCN clinical high-risk factors were unable to distinguish high- and low-risk patients (15% vs. 13% ROR; p = .55).

CONCLUSION

ColoPrint significantly improved prognostic accuracy independent of microsatellite status or clinical variables, facilitating the identification of patients at higher risk who might be considered for additional treatment.

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.

The sirtuin 3 expression profile is associated with pathological and clinical outcomes in colon cancer patients

AIM

To investigate the correlation between Sirtuin 3 (SIRT3) expression and the clinical outcome of patients with colon cancer.

METHODS

The tumor specimens from 127 patients with colon cancer were obtained for SIRT3 immunohistochemical staining. Patients were followed up. In in vitro study, SIRT3 gene was inhibited to observe the effects of SIRT3 on the biological behavior of cultured colon cells.

RESULTS

The SIRT3 expression level was found to be significantly associated with the lymph node metastasis (P < 0.001) and tumor stages (P < 0.001). The colon cancer-specific survival was 64.6% among patients with high SIRT3 expressions and 88.6% among patients with low SIRT3 expressions (log-rank P = 0.016). The overall survival was 80.2% among patients with low SIRT3 expressions and 55.9% among patients with high SIRT3 expressions (log-rank P = 0.002). In vitro study showed that silencing of SIRT3 gene inhibited the proliferation, invasion, and cells migration but increased the apoptosis in the cultured colon cell lines.

CONCLUSION

This study provides evidence supporting that SIRT3 is closely associated with the clinical outcomes of colon cancer. SIRT3 may be considered as a marker for colon cancer.

Nodal promotes the self-renewal of human colon cancer stem cells via an autocrine manner through Smad2/3 signaling pathway

Colorectal cancer is one of the most common and fatal tumors. However, molecular mechanisms underlying carcinogenesis of colorectal cancer remain largely undefined. Here, we explored the expression and function of Nodal in colon cancer stem cells (CCSCs). Nodal and its receptors were present in numerous human colorectal cancer cell lines. NODAL and ALK-4 were coexpressed in human colon cancerous tissues, and NODAL, CD24, and CD44, markers for CCSCs, were expressed at higher levels in human colon cancerous tissues than adjacent noncancerous colon tissues. Human CCSCs were isolated by magnetic activated cell sorting using anti-CD24 and anti-CD44. Nodal transcript and protein were hardly detectable in CD44- or CD24-negative human colorectal cancer cell lines, whereas Nodal and its receptors were present in CCSCs. Notably, Nodal facilitated spheroid formation of human CCSCs, and phosphorylation of Smad2 and Smad3 was activated by Nodal in cells of spheres derived from human CCSCs. Collectively, these results suggest that Nodal promotes the self-renewal of human CCSCs and mediate carcinogenesis of human colorectal cancer via an autocrine manner through Smad2/3 pathway. This study provides a novel insight into molecular mechanisms controlling fate of human CCSCs and offers new targets for gene therapy of human colorectal cancer.

Concerted down-regulation of immune-system related genes predicts metastasis in colorectal carcinoma

Background

This study aimed at the identification of prognostic gene expression markers in early primary colorectal carcinomas without metastasis at the time point of surgery by analyzing genome-wide gene expression profiles using oligonucleotide microarrays.

Methods

Cryo-conserved tumor specimens from 45 patients with early colorectal cancers were examined, with the majority of them being UICC stage II or earlier and with a follow-up time of 41–115 months. Gene expression profiling was performed using Whole Human Genome 4x44K Oligonucleotide Microarrays. Validation of microarray data was performed on five of the genes in a smaller cohort.

Results

Using a novel algorithm based on the recursive application of support vector machines (SVMs), we selected a signature of 44 probes that discriminated between patients developing later metastasis and patients with a good prognosis. Interestingly, almost half of the genes was related to the patients’ immune response and showed reduced expression in the metastatic cases.

Conclusions

Whereas up to now gene signatures containing genes with various biological functions have been described for prediction of metastasis in CRC, in this study metastasis could be well predicted by a set of gene expression markers consisting exclusively of genes related to the MHC class II complex involved in immune response. Thus, our data emphasize that the proper function of a comprehensive network of immune response genes is of vital importance for the survival of colorectal cancer patients.

High miR-21 expression from FFPE tissues is associated with poor survival and response to adjuvant chemotherapy in colon cancer

Colon cancer (CC) is a leading cause of cancer mortality. Novel biomarkers are needed to identify CC patients at high risk of recurrence and those who may benefit from therapeutic intervention. The aim of this study is to investigate if miR-21 expression from RNA isolated from formalin-fixed paraffin-embedded (FFPE) tissue sections is associated with prognosis and therapeutic outcome for patients with CC. The expression of miR-21 was measured by quantitative reverse transcriptase-polymerase chain reaction in a Japanese cohort (stage I-IV, n = 156) and a German cohort (stage II, n = 145). High miR-21 expression in tumors was associated with poor survival in both the stage II/III Japanese (p = 0.0008) and stage II German (p = 0.047) cohorts. These associations were independent of other clinical covariates in multivariable models. Receipt of adjuvant chemotherapy was not beneficial in patients with high miR-21 in either cohort. In the Japanese cohort, high miR-21 expression was significantly associated with poor therapeutic outcome (p = 0.0001) and adjuvant therapy was associated with improved survival in patients with low miR-21 (p = 0.001). These results suggest that miR-21 is a promising biomarker to identify patients with poor prognosis and can be accurately measured in FFPE tissues. The expression of miR-21 may also identify patients who will benefit from adjuvant chemotherapy.

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.

Biomarkers in precision therapy in colorectal cancer

Colorectal cancer (CRC) is the most commonly diagnosed cancer in Europe. Because CRC is also a major cause of cancer-related deaths worldwide, a lot of research has been focused on the discovery and development of biomarkers to improve the diagnostic process and to predict treatment outcomes. Up till now only a few biomarkers are recommended by expert panels. Current TNM criteria, however, cause substantial under- and overtreatment of CRC patients. Consequently, there is a growing need for new and efficient biomarkers to ensure optimal treatment allocation. An ideal biomarker should be easily translated into clinical practice, to identify patients who can be spared from treatment or benefit from therapy, ultimately resulting in precision medicine in the future. In this review we aim to provide an overview of a number of frequently studied biomarkers in CRC and, at the same time, we will emphasize the challenges and controversies that withhold the clinical introduction of these biomarkers. We will discuss both prognostic and predictive markers of chemotherapy, aspirin therapy as well as overall therapy toxicity. Currently, only mutant KRAS, mutant BRAF, MSI and the Oncotype DX® Colon Cancer Assay are used in clinical practice. Other biomarker studies showed insufficient evidence to be introduced into clinical practice. Divergent patient selection criteria, absence of validation studies and a large number of single biomarker studies are possibly responsible. We therefore recommend that future studies focus on combining key markers, rather than analysing single markers, standardizing study protocols, and validate the results in independent study cohorts, followed by prospective clinical trials.

Independent validation of a prognostic genomic signature (ColoPrint) for patients with stage II colon cancer

OBJECTIVES

The aim of this study was to independently validate a genomic signature developed both to assess recurrence risk in stage II patients and to assist in treatment decisions.

BACKGROUND

Adjuvant therapy is recommended for high-risk patients with stage II colon cancer, but better tools to assess the patients' prognosis accurately are still required.

METHODS

Previously, an 18-gene signature had been developed and validated on an independent cohort, using full genome microarrays. In this study, the gene signature was translated and validated as a robust diagnostic test (ColoPrint), using customized 8-pack arrays. In addition, clinical validation of the diagnostic ColoPrint assay was performed on 135 patients who underwent curative resection (R0) for colon cancer stage II in Munich. Fresh-frozen tissue, microsatellite instability status, clinical parameters, and follow-up data for all patients were available. The diagnostic ColoPrint readout was determined blindly from the clinical data.

RESULTS

ColoPrint identified most stage II patients (73.3%) as at low risk. The 5-year distant-metastasis free survival was 94.9% for low-risk patients and 80.6% for high-risk patients. In multivariable analysis, ColoPrint was the only significant parameter to predict the development of distant metastasis with a hazard ratio of 4.28 (95% confidence interval, 1.36-13.50; P = 0.013). Clinical risk parameters from the American Society of Clinical Oncology (ASCO) recommendation did not add power to the ColoPrint classification. Technical validation of ColoPrint confirmed stability and reproducibility of the diagnostic platform.

CONCLUSIONS

ColoPrint is able to predict the development of distant metastasis of patients with stage II colon cancer and facilitates the identification of patients who may be safely managed without chemotherapy.

Molecular markers in colorectal cancer: clinical relevance in stage II colon cancer

SUMMARY Colorectal cancer is the second most common cause of cancer death in developed countries. Adjuvant chemotherapy is standard for stage III colorectal cancer but its use in stage II is controversial. Several clinicopathological factors have been described to define a high-risk group among stage II colon cancers, which can aid the selection of patients who may benefit from chemotherapy. Local tumor invasion (T4), high histological grade, obstruction and perforation at diagnosis, and number of lymph nodes removed are the most widely accepted factors. Several molecular factors have been also investigated as prognostic candidate biomarkers. DNA ploidy, KRAS and TP53 mutations, thymidylate synthase, dihydropyrimidine dehydrogenase, thymidine phosphorylase, loss of heterozygosity on chromosome 18q and microsatellite instability have been widely investigated. The aim of this review is to analyze the current evidence and clinical applications of the classical molecular biomarkers as well as new ones such as BRAF, circulating tumor cells, genome expression signatures and DNA methylation.

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.

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.

Gene expression profiles in stages II and III colon cancers: application of a 128-gene signature

PURPOSE

A 128-gene signature has been proposed to predict outcome in patients with stages II and III colorectal cancers. In the present study, we aimed to reproduce and validate the 128-gene signature in external and independent material.

METHODS

Gene expression data from the original material were retrieved from the Gene Expression Omnibus (GEO) (n = 111) in addition to a Danish data set (n = 37). All patients had stages II and III colon cancers. A Prediction Analysis of Microarray classifier, based on the 128-gene signature and the original training set of stage I (n = 65) and stage IV (n = 76) colon cancers, was reproduced. The stages II and III colon cancers were subsequently classified as either stage I-like (good prognosis) or stage IV-like (poor prognosis) and assessed by the 36 months cumulative incidence of relapse.

RESULTS

In the GEO data set, results were reproducible in stage III, as patients predicted to be stage I-like had a significant lower risk of relapse than patients predicted as stage IV-like (P = 0.04, Gray test). Results were not reproducible in stage II patients (P > 0.05, Gray test). In the Danish data set, two of four stage III patients with relapse were correctly predicted as stage IV-like, and the remaining patients were predicted as stage I-like and unclassifiable, respectively. Stage II patients could not be stratified.

CONCLUSIONS

The 128-gene signature showed reproducibility in stage III colon cancer, but could not predict recurrence in stage II. Individual patient predictions in an independent Danish material were unsatisfactory. Additional validation in larger cohorts is warranted.

Biomarkers in early-stage colorectal cancer: ready for prime time?

BACKGROUND/AIMS

During the last two decades, hundreds of reports have detailed putative prognostic and predictive biomarkers for colorectal cancer (CRC). However, the majority of these studies have been small and retrospective, reporting results that are highly likely to represent false positives. Consequently, their relevance to clinical practice requires definition.

METHODS

Review of published literature on CRC biomarkers, focusing on early-stage disease.

RESULTS

Although most putative biomarkers have failed to be validated in subsequent studies, level I evidence now indicates that tumour microsatellite instability can be used to identify a cohort of patients with stage IIA disease at low risk of relapse who can be spared adjuvant chemotherapy. Emerging data suggest that gene expression arrays may have a role in selecting patients with stage IIA disease and mismatch repair-proficient tumours for chemotherapy following tumour resection.

CONCLUSION

Despite the profusion of biomarker literature, only mismatch repair status can be recommended as routine in current clinical practice. High-quality, adequately powered studies are essential to accurately define the utility of existing and putative biomarkers, and to support their rational application in the clinic.

Towards a "Lyon molecular signature" to individualize the treatment of rectal cancer. Prognostic analysis of a prospective cohort of 94 rectal cancers T1-2-3 Nx MO to be the basis of a molecular signature

PURPOSE

In 1998 a translational research was initiated in Lyon aiming at identifying a prognostic "biomolecular signature" in rectal cancer. This paper presents the clinical outcome of the patients included in this study.

PATIENTS AND METHODS

A total of 94 patients were included between 1998 and 2001. A staging with rectoscopy and biopsies was performed before treatment. In case of surgery, the operative specimen was analysed to evaluate the pathological response. There were two types of treatment: neoadjuvant radiotherapy (with or without concurrent chemotherapy) followed by surgery (76 cases) and radiotherapy alone with 'contactherapy' often associated with external beam radiotherapy (18 patients).

RESULTS

The patients had a mean age of 63years. Stage was T1: 4, T2: 24, T3: 65 and T4: 1. The overall survival of the 94 patients was 62% at 8years with a rate of distant metastases of 29%. Rate of local recurrence at 8years was 6% in the neoadjuvant group and 16% in the radiotherapy group with an overall 8years survival in both groups respectively: 64% and 53%. There was a trend towards more metastases in cT3, tumour diameter above 4cm, circumferential extension. There was a significant increase in the risk of metastases for ypT3, ypN1-2 and Dworak score 1-2-3. In multivariate analysis ypT3 was significantly associated with a high rate of metastases (55%; P=0.0003).

CONCLUSION

The rate of distant metastases is a major prognostic factor. These clinical results will serve as the base line to identify a "biomolecular signature" which could complement the TN(M) classification.