Stage II colon cancer prognosis prediction by tumor gene expression profiling

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.

ColoGuidePro: a prognostic 7-gene expression signature for stage III colorectal cancer patients

PURPOSE

Improved prognostic stratification of patients with stage II and III colorectal cancer is warranted for postoperative clinical decision making. This study was conducted to develop a clinically feasible and robust prognostic classifier for these patients independent of adjuvant treatment.

EXPERIMENTAL DESIGN

Global gene expression profiles from altogether 387 stage II and III colorectal cancer tissue samples from three independent patient series were included in the study. ColoGuidePro, a seven-gene prognostic classifier, was developed from a selected Norwegian learning series (n = 95; no adjuvant treatment) using lasso-penalized multivariate survival modeling with cross-validation.

RESULTS

The expression signature significantly stratified patients in a consecutive Norwegian test series, in which patients were treated according to current standards [HR, 2.9 (1.1-7.5); P = 0.03; n = 77] and an external validation series [HR, 3.7 (2.0-6.8); P < 0.001; n = 215] according to survival. ColoGuidePro was also an independent predictor of prognosis in multivariate models including tumor stage in both series (HR, ≥ 3.1; P ≤ 0.03). In the validation series, which consisted of patients from other populations (United States and Australia), 5-year relapse-free survival was significantly predicted for stage III patients only (P < 0.001; n = 107). Here, prognostic stratification was independent of adjuvant treatment (P = 0.001).

CONCLUSIONS

We present ColoGuidePro, a prognostic classifier developed for patients with stage II and III colorectal cancer. The test is suitable for transfer to clinical use and has best prognostic prediction potential for stage III patients.

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

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

Guidelines for biomarker testing in colorectal carcinoma (CRC): a national consensus of the Spanish Society of Pathology (SEAP) and the Spanish Society of Medical Oncology (SEOM)

This consensus statement, conceived as a joint initiative of the Spanish Society of Pathology and the Spanish Society of Medical Oncology, makes diagnostic and treatment recommendations for the management of patients with hereditary, localised and advanced CRC based on the current scientific evidence on biomarker use. This consensus statement thus provides an opportunity to improve healthcare efficiency and resource use, which will benefit these patients. Based on the currently available data on this subject, this expert group recommends testing for microsatellite instability (MSI) in patients with localised CRC, as this is a strong predictive factor for deciding on adjuvant treatment. However, although the ColoPrint(®) and Oncotype Dx(®) gene expression signatures have been shown to have prognostic value, no consensus yet exists concerning their use in clinical practice. For advanced CRC, it is essential to test for KRAS mutation status before administering an anti-EGFR treatment, such as cetuximab or panitumumab. However, testing for other biomarkers, such as BRAF, EGFR, PI3K and PTEN mutations, should not be done routinely, because this does not influence treatment planning at the present time. Other important issues addressed include organisational requirements and the quality controls needed for proper testing of these biomarkers as well as the legal implications to be borne in mind when testing some biomarkers.

A network-based gene expression signature informs prognosis and treatment for colorectal cancer patients

Background

Several studies have reported gene expression signatures that predict recurrence risk in stage II and III colorectal cancer (CRC) patients with minimal gene membership overlap and undefined biological relevance. The goal of this study was to investigate biological themes underlying these signatures, to infer genes of potential mechanistic importance to the CRC recurrence phenotype and to test whether accurate prognostic models can be developed using mechanistically important genes.

Methods and Findings

We investigated eight published CRC gene expression signatures and found no functional convergence in Gene Ontology enrichment analysis. Using a random walk-based approach, we integrated these signatures and publicly available somatic mutation data on a protein-protein interaction network and inferred 487 genes that were plausible candidate molecular underpinnings for the CRC recurrence phenotype. We named the list of 487 genes a NEM signature because it integrated information from Network, Expression, and Mutation. The signature showed significant enrichment in four biological processes closely related to cancer pathophysiology and provided good coverage of known oncogenes, tumor suppressors, and CRC-related signaling pathways. A NEM signature-based Survival Support Vector Machine prognostic model was trained using a microarray gene expression dataset and tested on an independent dataset. The model-based scores showed a 75.7% concordance with the real survival data and separated patients into two groups with significantly different relapse-free survival (p = 0.002). Similar results were obtained with reversed training and testing datasets (p = 0.007). Furthermore, adjuvant chemotherapy was significantly associated with prolonged survival of the high-risk patients (p = 0.006), but not beneficial to the low-risk patients (p = 0.491).

Conclusions

The NEM signature not only reflects CRC biology but also informs patient prognosis and treatment response. Thus, the network-based data integration method provides a convergence between biological relevance and clinical usefulness in gene signature development.

Gene Signatures in Stage II Colon Cancer: A Clinical Review

Understanding the biology of cancer is the key to understanding its behavior. Stage II colon cancers represent a unique treatment challenge for medical oncologists because they contain a very heterogeneous group of tumors with a wide range of recurrence risks after resection. Defining these differences in biology can help to explain differences in behavior. To this end, gene signatures have been developed to define various prognostic groups beyond the clinicopathologic features alone. Adjuvant chemotherapy for stage II colon cancers as a group has not shown survival advantage in clinical trials. Future research to develop gene signatures to predict a group that will benefit from adjuvant chemotherapy will be helpful in the clinical decision-making process. The purpose of this review is to present the prognostic gene signatures currently available for use, those in development, and their utility in stratifying recurrence risk in stage II colon cancer patients.

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

Background

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

Methods

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

Results

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

Conclusions

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

Cancer core modules identification through genomic and transcriptomic changes correlation detection at network level

BACKGROUND

Identification of driver mutations among numerous genomic alternations remains a critical challenge to the elucidation of the underlying mechanisms of cancer. Because driver mutations by definition are associated with a greater number of cancer phenotypes compared to other mutations, we hypothesized that driver mutations could more easily be identified once the genotype-phenotype correlations are detected across tumor samples.

RESULTS

In this study, we describe a novel network analysis to identify the driver mutation through integrating both cancer genomes and transcriptomes. Our method successfully identified a significant genotype-phenotype change correlation in all six solid tumor types and revealed core modules that contain both significantly enriched somatic mutations and aberrant expression changes specific to tumor development. Moreover, we found that the majority of these core modules contained well known cancer driver mutations, and that their mutated genes tended to occur at hub genes with central regulatory roles. In these mutated genes, the majority were cancer-type specific and exhibited a closer relationship within the same cancer type rather than across cancer types. The remaining mutated genes that exist in multiple cancer types led to two cancer type clusters, one cluster consisted of three neural derived or related cancer types, and the other cluster consisted of two adenoma cancer types.

CONCLUSIONS

Our approach can successfully identify the candidate drivers from the core modules. Comprehensive network analysis on the core modules potentially provides critical insights into convergent cancer development in different organs.

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

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

The importance of knowing when to stop. A sequential stopping rule for component-wise gradient boosting

OBJECTIVES

Component-wise boosting algorithms have evolved into a popular estimation scheme in biomedical regression settings. The iteration number of these algorithms is the most important tuning parameter to optimize their performance. To date, no fully automated strategy for determining the optimal stopping iteration of boosting algorithms has been proposed.

METHODS

We propose a fully data-driven sequential stopping rule for boosting algorithms. It combines resampling methods with a modified version of an earlier stopping approach that depends on AIC-based information criteria. The new "subsampling after AIC" stopping rule is applied to component-wise gradient boosting algorithms.

RESULTS

The newly developed sequential stopping rule outperformed earlier approaches if applied to both simulated and real data. Specifically, it improved purely AIC-based methods when used for the microarray-based prediction of the recurrence of metastases for stage II colon cancer patients.

CONCLUSIONS

The proposed sequential stopping rule for boosting algorithms can help to identify the optimal stopping iteration already during the fitting process of the algorithm, at least for the most common loss functions.

Colorectal adenoma to carcinoma progression is accompanied by changes in gene expression associated with ageing, chromosomal instability, and fatty acid metabolism

Background

Colorectal cancer develops in a multi-step manner from normal epithelium, through a pre-malignant lesion (so-called adenoma), into a malignant lesion (carcinoma), which invades surrounding tissues and eventually can spread systemically (metastasis). It is estimated that only about 5% of adenomas do progress to a carcinoma.

Aim

The present study aimed to unravel the biology of adenoma to carcinoma progression by mRNA expression profiling, and to identify candidate biomarkers for adenomas that are truly at high risk of progression.

Methods

Genome-wide mRNA expression profiles were obtained from a series of 37 colorectal adenomas and 31 colorectal carcinomas using oligonucleotide microarrays. Differentially expressed genes were validated in an independent colorectal gene expression data set. Gene Set Enrichment Analysis (GSEA) was used to identify altered expression of sets of genes associated with specific biological processes, in order to better understand the biology of colorectal adenoma to carcinoma progression.

Results

mRNA expression of 248 genes was significantly different, of which 96 were upregulated and 152 downregulated in carcinomas compared to adenomas. Classification of adenomas and carcinomas using the expression of these genes showed to be very accurate, also when tested in an independent expression data set. Gene-sets associated with ageing (which is related to senescence) and chromosomal instability were upregulated, and a gene-set associated with fatty acid metabolism was downregulated in carcinomas compared to adenomas. Moreover, gene-sets associated with chromosomal location revealed chromosome 4q22 loss and chromosome 20q gain of gene-set expression as being relevant in this progression.

Concluding remark

These data are consistent with the notion that adenomas and carcinomas are distinct biological entities. Disruption of specific biological processes like senescence (ageing), maintenance of chromosomal instability and altered metabolism, are key factors in the progression from adenoma to carcinoma.

Electronic supplementary material

The online version of this article (doi:10.1007/s13402-011-0065-1) contains supplementary material, which is available to authorized users.

Low expression of Granzyme B in colorectal cancer is associated with signs of early metastastic invasion

AIMS

Tumour-infiltrating forkhead box P3 (FoxP3+ ) regulatory T cells (T(regs) ) have stronger prognostic significance than cytotoxic CD8+ T cells in colorectal cancer (CRC). Because there is evidence that some tumour-infiltrating CD8+ T cells may be inactive, the present study aimed to investigate the prognostic significance of Granzyme B, one of the major effector molecules of T cells.

METHODS AND RESULTS

A tissue microarray containing 963 CRCs was stained immunohistochemically for Granzyme B and the level of expression quantified by digital image analysis. Granzyme B expression was higher in tumours with microsatellite instability (P < 0.0001), a dense lymphocytic infiltrate (P < 0.0001) and location in the proximal colon (P = 0.009), but lower in tumours with vascular invasion (P = 0.007), perineural invasion (P =0.041) and positive nodal status (P < 0.001). Elevated expression of Granzyme B was associated with improved survival on univariate analysis (hazard ratio = 0.65; 95% confidence interval 0.51-0.84; P = 0.001), but not in a multivariate model that included stage, vascular invasion and FoxP3+ T(reg) cell density.

CONCLUSIONS

Low expression of Granzyme B was associated with early signs of metastasis in CRC. The stronger prognostic significance of FoxP3+ T(regs) is in keeping with animal models that suggest these cells act as gatekeepers for the release of Granzyme B from CD8+ T cells.

Is there currently an established role for the use of predictive or prognostic molecular markers in the management of colorectal cancer? A point/counterpoint

The term "personalized oncology" means different things to the oncologist than to the patient. But fundamentally, the phrase creates the expectation that decisions can be informed by the unique features of the patient and patient's cancer. Much like determining antibiotic sensitivities in urinary tract infections, the oncologist is expected to choose the right treatment(s), for each individual patient. Numerous methods can be used to "personalize" management decisions, although truly useful biomarkers continue to escape our grasp. Positron Emission Tomography in patients with GI stromal tumors or genotyping of c-kit in chronic myelogenous leukemia cells can guide the use of imatinib, these scenarios represent a minority of patients. The promise of individualized therapy, however, has led to the commercialization of numerous assays to probe patient's genetic make-up and that of the tumor. Breast cancer management has benefitted from the analysis of gene recurrence scores. More recently the analysis of germline or tumor-associated mutations in non-small cell lung cancer and melanoma has led to clinically meaningful molecular subsets of these diseases, guiding the successful targeting of such cancers with small-molecule inhibitors. Despite the high incidence of colorectal cancer and our relatively long-standing grasp of the molecular pathways in colorectal carcinogenesis, the management of these patients remains mostly empiric and movement toward "personalization" has been slow and incremental. Now, however, molecular imaging and commercial assays for genetic makeup of tumor specimens has put the oncologist and oncologic surgeon in the crossfire with patients and families who believe the era of "personalization" is here.

Multiwalled carbon nanotube-induced gene signatures in the mouse lung: potential predictive value for human lung cancer risk and prognosis

Concerns over the potential for multiwalled carbon nanotubes (MWCNT) to induce lung carcinogenesis have emerged. This study sought to (1) identify gene expression signatures in the mouse lungs following pharyngeal aspiration of well-dispersed MWCNT and (2) determine if these genes were associated with human lung cancer risk and progression. Genome-wide mRNA expression profiles were analyzed in mouse lungs (n = 160) exposed to 0, 10, 20, 40, or 80 μg of MWCNT by pharyngeal aspiration at 1, 7, 28, and 56 d postexposure. By using pairwise statistical analysis of microarray (SAM) and linear modeling, 24 genes were selected, which have significant changes in at least two time points, have a more than 1.5-fold change at all doses, and are significant in the linear model for the dose or the interaction of time and dose. Additionally, a 38-gene set was identified as related to cancer from 330 genes differentially expressed at d 56 postexposure in functional pathway analysis. Using the expression profiles of the cancer-related gene set in 8 mice at d 56 postexposure to 10 μg of MWCNT, a nearest centroid classification accurately predicts human lung cancer survival with a significant hazard ratio in training set (n = 256) and test set (n = 186). Furthermore, both gene signatures were associated with human lung cancer risk (n = 164) with significant odds ratios. These results may lead to development of a surveillance approach for early detection of lung cancer and prognosis associated with MWCNT in the workplace.

A systems biology approach identifies SART1 as a novel determinant of both 5-fluorouracil and SN38 drug resistance in colorectal cancer

Chemotherapy response rates for advanced colorectal cancer remain disappointingly low, primarily because of drug resistance, so there is an urgent need to improve current treatment strategies. To identify novel determinants of resistance to the clinically relevant drugs 5-fluorouracil (5-FU) and SN38 (the active metabolite of irinotecan), transcriptional profiling experiments were carried out on pretreatment metastatic colorectal cancer biopsies and HCT116 parental and chemotherapy-resistant cell line models using a disease-specific DNA microarray. To enrich for potential chemoresistance-determining genes, an unsupervised bioinformatics approach was used, and 50 genes were selected and then functionally assessed using custom-designed short interfering RNA (siRNA) screens. In the primary siRNA screen, silencing of 21 genes sensitized HCT116 cells to either 5-FU or SN38 treatment. Three genes (RAPGEF2, PTRF, and SART1) were selected for further analysis in a panel of 5 colorectal cancer cell lines. Silencing SART1 sensitized all 5 cell lines to 5-FU treatment and 4/5 cell lines to SN38 treatment. However, silencing of RAPGEF2 or PTRF had no significant effect on 5-FU or SN38 sensitivity in the wider cell line panel. Further functional analysis of SART1 showed that its silencing induced apoptosis that was caspase-8 dependent. Furthermore, silencing of SART1 led to a downregulation of the caspase-8 inhibitor, c-FLIP, which we have previously shown is a key determinant of drug resistance in colorectal cancer. This study shows the power of systems biology approaches for identifying novel genes that regulate drug resistance and identifies SART1 as a previously unidentified regulator of c-FLIP and drug-induced activation of caspase-8.

Validation study of a quantitative multigene reverse transcriptase-polymerase chain reaction assay for assessment of recurrence risk in patients with stage II colon cancer

PURPOSE

We developed quantitative gene expression assays to assess recurrence risk and benefits from chemotherapy in patients with stage II colon cancer.

PATIENTS AND METHODS

We sought validation by using RNA extracted from fixed paraffin-embedded primary colon tumor blocks from 1,436 patients with stage II colon cancer in the QUASAR (Quick and Simple and Reliable) study of adjuvant fluoropyrimidine chemotherapy versus surgery alone. A recurrence score (RS) and a treatment score (TS) were calculated from gene expression levels of 13 cancer-related genes (n = 7 recurrence genes and n = 6 treatment benefit genes) and from five reference genes with prespecified algorithms. Cox proportional hazards regression models and log-rank methods were used to analyze the relationship between the RS and risk of recurrence in patients treated with surgery alone and between TS and benefits of chemotherapy.

RESULTS

Risk of recurrence was significantly associated with RS (hazard ratio [HR] per interquartile range, 1.38; 95% CI, 1.11 to 1.74; P = .004). Recurrence risks at 3 years were 12%, 18%, and 22% for predefined low, intermediate, and high recurrence risk groups, respectively. T stage (HR, 1.94; P < .001) and mismatch repair (MMR) status (HR, 0.31; P < .001) were the strongest histopathologic prognostic factors. The continuous RS was associated with risk of recurrence (P = .006) beyond these and other covariates. There was no trend for increased benefit from chemotherapy at higher TS (P = .95).

CONCLUSION

The continuous 12-gene RS has been validated in a prospective study for assessment of recurrence risk in patients with stage II colon cancer after surgery and provides prognostic value that complements T stage and MMR. The TS was not predictive of chemotherapy benefit.

Challenges in the management of stage II colon cancer

Approximately one third of patients diagnosed with early-stage colon cancer will present with lymph node involvement (stage III) and about one quarter with transmural bowel wall invasion but negative lymph nodes (stage II). Adjuvant chemotherapy targets micrometastatic disease to improve disease-free (DFS) and overall survival (OS). While beneficial for stage III patients, the role of adjuvant chemotherapy is unestablished in stage II disease. This likely relates to the improved outcome of these patients, and the difficulties in developing studies with sufficient power to document benefit in this patient population. However, recent investigation also suggests that molecular differences may exist between stage II and III cancers and within stage II patients. Validated pathologic prognostic markers are useful at identifying stage II patients at high risk for recurrence for whom the benefit from adjuvant chemotherapy may be greater. Such high-risk features include higher T stage (T4 v T3), suboptimal lymph node retrieval, presence of lymphovascular invasion, bowel obstruction, or bowel perforation, and poorly differentiated histology. However, for the majority of patients who do not carry any of these adverse features and are classified as "average-risk" stage II patients, the benefit of adjuvant chemotherapy remains unproven. Emerging understanding of the underlying biology of stage II colon cancer has identified molecular markers that may change this paradigm and improve our risk assessment and treatment choices for stage II disease. Assessment of microsatellite stability (MSI), which serves as a marker for DNA mismatch repair (MMR) system function, has emerged as a useful tool for risk stratification of patients with stage II colon cancer. Patients with high frequency of MSI have been shown to have increased OS and limited benefit from 5-fluorouracil (5-FU)-based chemotherapy. Additional research is necessary to clearly define the most appropriate way to use this marker and others in routine clinical practice.

Prediction of metastasis and recurrence in colorectal cancer based on gene expression analysis: ready for the clinic?

Cancers of the colon and rectum, which rank among the most frequent human tumors, are currently treated by surgical resection in locally restricted tumor stages. However, disease recurrence and formation of local and distant metastasis frequently occur even in cases with successful curative resection of the primary tumor (R0). Recent technological advances in molecular diagnostic analysis have led to a wealth of knowledge about the changes in gene transcription in all stages of colorectal tumors. Differential gene expression, or transcriptome analysis, has been proposed by many groups to predict disease recurrence, clinical outcome, and also response to therapy, in addition to the well-established clinico-pathological factors. However, the clinical usability of gene expression profiling as a reliable and robust prognostic tool that allows evidence-based clinical decisions is currently under debate. In this review, we will discuss the most recent data on the prognostic significance and potential clinical application of genome wide expression analysis in colorectal cancer.

Molecular profiles and clinical outcome of stage UICC II colon cancer patients

PURPOSE

Published multigene classifiers suggesting outcome prediction for patients with stage UICC II colon cancer have not been translated into a clinical application so far. Therefore, we aimed at validating own and published gene expression signatures employing methods which enable their reconstruction in routine diagnostic specimens.

METHODS

Immunohistochemistry was applied to 68 stage UICC II colon cancers to determine the protein expression of previously published prognostic classifier genes (CDH17, LAT, CA2, EMR3, and TNFRSF11A). RNA from macrodissected tumor samples from 53 of these 68 patients was profiled on Affymetrix GeneChips (HG-U133 Plus 2.0). Prognostic signatures were generated by "nearest shrunken centroids" with cross-validation. Previously published gene signatures were applied to our data set using "global tests" and leave-one-out cross-validation

RESULTS

Correlation of protein expression with clinical outcome failed to separate patients with disease-free follow-up (group DF) and relapse (group R). Although gene expression profiling allowed the identification of differentially expressed genes ("DF" vs. "R"), a stable classification/prognosis signature was not discernable. Furthermore, the application of previously published gene signatures to our data was unable to predict clinical outcome (prediction rate 75.5% and 64.2%; n.s.). T-stage was the only independent prognostic factor for relapse with established clinical and pathological parameters including microsatellite status (multivariate analysis).

CONCLUSIONS

Our protein and gene expression analyses do not support application of molecular classifiers for prediction of clinical outcome in current routine diagnostic as a basis for patient-orientated therapy in stage UICC II colon cancer. Further studies are needed to develop prognosis signatures applicable in patient care.

Molecular alterations associated with liver metastases development in colorectal cancer patients

Background:

Understanding the molecular biology of colorectal cancer (CRC) provides opportunities for effective personalised patient management. We evaluated whether chromosomal aberrations, mutations in the PI(3)K signalling pathway and the CpG-island methylator phenotype (CIMP) in primary colorectal tumours can predict liver metastases.

Methods:

Formalin-fixed paraffin-embedded material from primary colorectal tumours of three different groups were investigated: patients with CRC without metastases (M0, n=39), patients who were treated with hyperthermal intraperitoneal chemotherapy for CRC metastases confined to the peritoneum (PM, n=46) and those who had isolated hepatic perfusion for CRC metastases confined to the liver (LM, n=48).

Results:

All samples were analysed for DNA copy number changes, PIK3CA, KRAS, BRAF mutations, CIMP and microsatellite instability. The primary CRCs of the LM group had significantly higher frequency of amplified chromosome 20q (P=0.003), significantly fewer mutations in the PI(3)K signalling pathway (P=0.003) and fewer CIMP high tumours (P=0.05). There was a strong inverse correlation between 20q and the PI(3)K pathway mutations.

Conclusion:

The development of CRC liver metastases is associated with amplification of chromosome 20q and not driven by mutations in the PI(3)K signalling pathway.

Development of molecular biomarkers in individualized treatment of colorectal cancer

Colorectal cancer is a leading cause of cancer mortality despite recent expansion of treatment options in metastatic colorectal cancer (mCRC). Our knowledge about key signaling pathways in colorectal tumors has contributed to the identification of specific molecular markers of response to targeted agents. In this review we discuss well-established and potential predictive biomarkers of benefit with epidermal growth factor receptor (EGFR) inhibitors. Data derived from multiple phase III trials have indicated that KRAS mutations can be considered a highly specific negative biomarker of response to anti-EGFR monoclonal antibodies. Other molecular aberrations in pathways downstream of EGFR such as BRAF, NRAS, and PIK3CA mutations, and PTEN loss are also reviewed. Moreover biomarkers of efficacy to classic chemotherapeutic agents as well as recent advances regarding high-throughput technologies and circulating tumor cells are also considered. Personalized cancer medicine in the mCRC scenario seems to be near reality, but validation of many biomarkers in prospective clinical trials is urgently warranted.

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.

Proteome changes in the initial bacterial colonist during ecological succession in an acid mine drainage biofilm community

Proteomes of acid mine drainage biofilms at different stages of ecological succession were examined to understand microbial responses to changing community membership. We evaluated the degree of reproducibility of the community proteomes between samples of the same growth stage and found stable and predictable protein abundance patterns across time and sampling space, allowing for a set of 50 classifier proteins to be identified for use in predicting growth stages of undefined communities. Additionally, physiological changes in the dominant species, Leptospirillum Group II, were analysed as biofilms mature. During early growth stages, this population responds to abiotic stresses related to growth on the acid mine drainage solution. Enzymes involved in protein synthesis, cell division and utilization of 1- and 2-carbon compounds were more abundant in early growth stages, suggesting rapid growth and a reorganization of metabolism during biofilm initiation. As biofilms thicken and diversify, external stresses arise from competition for dwindling resources, which may inhibit cell division of Leptospirillum Group II through the SOS response. This population also represses translation and synthesizes more complex carbohydrates and amino acids in mature biofilms. These findings provide unprecedented insight into the physiological changes that may result from competitive interactions within communities in natural environments.

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.

A 12-gene genomic instability signature predicts clinical outcomes in multiple cancer types

BACKGROUND AND AIMS

Genomic instability, as reflected in specific chromosomal aneuploidies and variation in the nuclear DNA content, is a defining feature of human carcinomas. It is solidly established that the degree of genomic instability influences clinical outcome. We have recently identified a 12-gene expression signature that discerned genomically stable from unstable breast carcinomas. This gene expression signature was also useful to predict, with high accuracy, the clinical course in independent multiple published breast cancer cohorts. From a biological point of view, this result confirmed the central role of genomic instability for a tumor's ability to adapt to external challenges and selective pressure, and hence for continued survival fitness. This prompted us to investigate whether this genomic instability signature could also predict clinical outcome in other cancer types of epithelial origin, including colorectal tumors, non-small cell lung carcinomas, and ovarian cancer.

RESULTS

The results show that the gene expression signature that defines genomic instability and poor outcome in breast cancer contributes significantly more accurate (p<0.05 compared with random prediction) prognostic information in multiple cancer types independent of established clinical parameters. The 12-gene genomic instability signature stratified patients into high- and low-risk groups with distinct postoperative survival in three non-small cell lung cancer cohorts (n=637) in Kaplan-Meier analyses (log-rank p<0.05). It predicted recurrence in colon cancer patients (n=92) with an overall accuracy greater than 69% (p=0.04) in cross-cohort validation. It quantified relapse-free survival in ovarian cancer (n=124; log-rank p<0.05). Functional pathway analysis revealed interactions between the 12 signature genes and well-known cancer hallmarks.

CONCLUSION

The degree of genomic instability has diagnostic and prognostic implications. It is tempting to speculate that pursuing genomic instability therapeutically could provide entry points for a target that is unique to cancer cells.

Molecular profiling of colon tumors: the search for clinically relevant biomarkers of progression, prognosis, therapeutics, and predisposition

If properly translated to clinical use, our knowledge about biomarkers may lead to a more effective way of combating colorectal cancer (CRC). Biomarkers are biomolecular, genetic, or cytogenetic attributes indicative of the disease's progression, predisposition, prognosis, or therapeutic options. For CRC, these include chromosomal instability, mutations in KRAS and TP53, loss of 18q, and elevated level of carcinoembryonic antigen (CEA), which are all associated with poor prognosis. The prognostic significance of 18q loss can be attributed to reduced expression of SMAD4, or DCC, although the chromosomal arm is actually heavily populated by genes whose downregulation correlate to worse survival. Potentially, identification of prognostic biomarkers can help the oncologist decide whether adjuvant chemotherapy is necessary after surgery. Testing for therapeutic biomarkers can be necessary if targeted therapeutics are being considered. The identification of highly penetrant predisposition markers (such as mutations in APC and MLH1) can be a lifesaver for carrier individuals, who would then have to undergo colonoscopy at an earlier age. Even sporadic CRCs may have some hereditary components, according to recent studies. Genome-wide association studies (using SNP arrays) showed that polymorphisms of certain genes can have subtle influence on CRC predisposition. Our own SNP array-based analysis suggested that long stretches of germline homozygosity (autozygosity), indicative of consanguinity, may also factor in CRC predisposition.

The colorectal cancer disease-specific transcriptome may facilitate the discovery of more biologically and clinically relevant information

BACKGROUND

To date, there are no clinically reliable predictive markers of response to the current treatment regimens for advanced colorectal cancer. The aim of the current study was to compare and assess the power of transcriptional profiling using a generic microarray and a disease-specific transcriptome-based microarray. We also examined the biological and clinical relevance of the disease-specific transcriptome.

METHODS

DNA microarray profiling was carried out on isogenic sensitive and 5-FU-resistant HCT116 colorectal cancer cell lines using the Affymetrix HG-U133 Plus2.0 array and the Almac Diagnostics Colorectal cancer disease specific Research tool. In addition, DNA microarray profiling was also carried out on pre-treatment metastatic colorectal cancer biopsies using the colorectal cancer disease specific Research tool. The two microarray platforms were compared based on detection of probesets and biological information.

RESULTS

The results demonstrated that the disease-specific transcriptome-based microarray was able to out-perform the generic genomic-based microarray on a number of levels including detection of transcripts and pathway analysis. In addition, the disease-specific microarray contains a high percentage of antisense transcripts and further analysis demonstrated that a number of these exist in sense:antisense pairs. Comparison between cell line models and metastatic CRC patient biopsies further demonstrated that a number of the identified sense:antisense pairs were also detected in CRC patient biopsies, suggesting potential clinical relevance.

CONCLUSIONS

Analysis from our in vitro and clinical experiments has demonstrated that many transcripts exist in sense:antisense pairs including IGF2BP2, which may have a direct regulatory function in the context of colorectal cancer. While the functional relevance of the antisense transcripts has been established by many studies, their functional role is currently unclear; however, the numbers that have been detected by the disease-specific microarray would suggest that they may be important regulatory transcripts. This study has demonstrated the power of a disease-specific transcriptome-based approach and highlighted the potential novel biologically and clinically relevant information that is gained when using such a methodology.

Specific genomic aberrations in primary colorectal cancer are associated with liver metastases

BACKGROUND

Accurate staging of colorectal cancer (CRC) with clinicopathological parameters is important for predicting prognosis and guiding treatment but provides no information about organ site of metastases. Patterns of genomic aberrations in primary colorectal tumors may reveal a chromosomal signature for organ specific metastases.

METHODS

Array Comparative Genomic Hybridization (aCGH) was employed to asses DNA copy number changes in primary colorectal tumors of three distinctive patient groups. This included formalin-fixed, paraffin-embedded tissue of patients who developed liver metastases (LM; n = 36), metastases (PM; n = 37) and a group that remained metastases-free (M0; n = 25).A novel statistical method for identifying recurrent copy number changes, KC-SMART, was used to find specific locations of genomic aberrations specific for various groups. We created a classifier for organ specific metastases based on the aCGH data using Prediction Analysis for Microarrays (PAM).

RESULTS

Specifically in the tumors of primary CRC patients who subsequently developed liver metastasis, KC-SMART analysis identified genomic aberrations on chromosome 20q. LM-PAM, a shrunken centroids classifier for liver metastases occurrence, was able to distinguish the LM group from the other groups (M0&PM) with 80% accuracy (78% sensitivity and 86% specificity). The classification is predominantly based on chromosome 20q aberrations.

CONCLUSION

Liver specific CRC metastases may be predicted with a high accuracy based on specific genomic aberrations in the primary CRC tumor. The ability to predict the site of metastases is important for improvement of personalized patient management.

Elevated preoperative neutrophil to lymphocyte ratio predicts risk of recurrence following curative resection for stage IIA colon cancer

BACKGROUND AND OBJECTIVES

Adjuvant chemotherapy for stage II colon cancer remains controversial but may be considered for patients with high-risk features. Recent studies have shown that elevated neutrophil to lymphocyte ratio (NLR) is a worse prognostic factor and a predictor of response to chemotherapy in patients with advanced colorectal cancer. The purpose of this study was to evaluate whether NLR predicts risk of recurrence in patients with stage IIA colon cancer undergoing curative resection without adjuvant chemotherapy.

METHODS

We retrospectively reviewed 141 consecutive patients with stage IIA colon cancer treated with curative surgery alone from 2002 to 2006. NLR, as well as demographics, clinical, histopathologic, and laboratory data were analyzed. Univariate and multivariate analyses were conducted to identify prognostic factors associated with recurrent-free survival (RFS).

RESULTS

Cox's regression analysis demonstrated that elevated NLR (>4) (hazard ratio, 4.88; P < 0.01) and less lymph node sampling (<15 lymph nodes; hazard ratio, 3.80; P < 0.05) were adverse prognostic factors for RFS. The 5-year RFS was 91.4% (95% CI, 88.6-94.2%) for patients with normal NLR and 63.8% (51.1-76.3%) for patients with elevated NLR. The 5-year RFS for patients with 0, 1, and 2 of the identified risk factors was 95.1%, 87.4%, and 33.3%, respectively (P < 0.001).

CONCLUSIONS

Elevated preoperative NLR is an independent predictor of worse RFS for patients with stage IIA colon cancer and a potential biomarker to identify candidates for adjuvant chemotherapy.

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

PURPOSE

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

PATIENTS AND METHODS

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

RESULTS

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

CONCLUSION

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

A breast cancer prognostic signature predicts clinical outcomes in multiple tumor types

Epidemiological studies indicate an increased risk of subsequent primary ovarian cancer from women with breast cancer. We have recently identified a 28-gene expression signature that predicts, with high accuracy, the clinical course in a large population of breast cancer patients. This prognostic gene signature also accurately predicts response to chemotherapy commonly used for treating breast cancer, including CMF, Tamoxifen, Paclitaxel, Docetaxel and Doxorubicin (Adriamycin), in a panel of 60 cancer cell lines of nine different tissue origins. This prompted us to investigate whether this prognostic gene signature could also predict clinical outcome in other cancer types of epithelial origins, including ovarian cancer (n=124), colon tumors (n=74) and lung adenocarcinomas (n=442). The results show that the gene expression signature contributes significantly more accurate (P<0.05; compared with random prediction) prognostic information in multiple cancer types independent of established clinical parameters. Furthermore, the functional pathway analysis with curated database delineated a biological network with tight connections between the signature genes and numerous well established cancer hallmarks, indicating important roles of this prognostic gene signature in tumor genesis and progression.

Referral patterns and adjuvant chemotherapy use in patients with stage II colon cancer

BACKGROUND

Little is known about the actual rate of use of adjuvant chemotherapy in stage II colon cancer and about referral patterns that give patients access to this treatment.

PATIENTS AND METHODS

We searched the tumor registry at Baylor University Medical Center at Dallas to identify patients with stage II colon cancer who underwent resection between 1995 and 2003. The rates of referral to medical oncology and adjuvant chemotherapy use were calculated and potential predictive variables were analyzed using univariate and multivariate techniques.

RESULTS

We identified 287 patients with stage II colon cancer. A total of 160 patients (56%) were referred to a medical oncologist. Eighty patients (28%) received adjuvant chemotherapy. Age < 50 years, private insurance status, lower comorbidity score, higher T stage, and poor tumor differentiation were significant predictors of adjuvant chemotherapy use (P <or= .05).

CONCLUSION

Variability and controversy exist over the use of adjuvant chemotherapy in patients with stage II colon cancer. Our study suggests many patients are not referred to a medical oncologist and may not be fully informed of all treatment options. Referral patterns become more important as a better understanding of recurrence risk is achieved and patient selection for adjuvant chemotherapy is optimized.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

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

Relationship between tumor gene expression and recurrence in four independent studies of patients with stage II/III colon cancer treated with surgery alone or surgery plus adjuvant fluorouracil plus leucovorin

PURPOSE

These studies were conducted to determine the relationship between quantitative tumor gene expression and risk of cancer recurrence in patients with stage II or III colon cancer treated with surgery alone or surgery plus fluorouracil (FU) and leucovorin (LV) to develop multigene algorithms to quantify the risk of recurrence as well as the likelihood of differential treatment benefit of FU/LV adjuvant chemotherapy for individual patients.

PATIENTS AND METHODS

We performed quantitative reverse transcription polymerase chain reaction (RT-qPCR) on RNA extracted from fixed, paraffin-embedded (FPE) tumor blocks from patients with stage II or III colon cancer who were treated with surgery alone (n = 270 from National Surgical Adjuvant Breast and Bowel Project [NSABP] C-01/C-02 and n = 765 from Cleveland Clinic [CC]) or surgery plus FU/LV (n = 308 from NSABP C-04 and n = 508 from NSABP C-06). Overall, 761 candidate genes were studied in C-01/C-02 and C-04, and a subset of 375 genes was studied in CC/C-06.

RESULTS

A combined analysis of the four studies identified 48 genes significantly associated with risk of recurrence and 66 genes significantly associated with FU/LV benefit (with four genes in common). Seven recurrence-risk genes, six FU/LV-benefit genes, and five reference genes were selected, and algorithms were developed to identify groups of patients with low, intermediate, and high likelihood of recurrence and benefit from FU/LV.

CONCLUSION

RT-qPCR of FPE colon cancer tissue applied to four large independent populations has been used to develop multigene algorithms for estimating recurrence risk and benefit from FU/LV. These algorithms are being independently validated, and their clinical utility is being evaluated in the Quick and Simple and Reliable (QUASAR) study.

Targeted therapies as adjuvant treatment for early-stage colorectal cancer: first impressions and clinical questions

Survival rates for metastatic colorectal cancer (CRC) have increased considerably over the years largely because of the application of optimized chemotherapy regimens. More recently, the addition of the targeted agents bevacizumab, cetuximab, and panitumumab in advanced disease has also demonstrated clinical benefit. Adjuvant chemotherapy is the standard of care in patients with early-stage CRC who are at high risk of recurrence. One challenge is how to apply targeted agents in early-stage CRC. Initial results from the NSABP-08 trial in CRC, the first report of bevacizumab in the adjuvant treatment of solid cancers, are disappointing. Nevertheless, the results of several important phase III trials of targeted adjuvant therapy are awaited. Until then, the use of targeted agents in early-stage CRC cannot be recommended. The identification of biomarkers to select patients who might derive clinical benefit is crucial in determining the full potential of targeted agents in the adjuvant setting. In this review, we discuss the biologic rationale for targeted therapies in early-stage CRC, the current clinical trials, and the clinical challenges facing the success of these agents in the adjuvant setting.

Prognostic and predictive biomarkers in resected colon cancer: current status and future perspectives for integrating genomics into biomarker discovery

The number of agents that are potentially effective in the adjuvant treatment of locally advanced resectable colon cancer is increasing. Consequently, it is important to ascertain which subgroups of patients will benefit from a specific treatment. Despite more than two decades of research into the molecular genetics of colon cancer, there is a lack of prognostic and predictive molecular biomarkers with proven utility in this setting. A secondary objective of the Pan European Trials in Adjuvant Colon Cancer-3 trial, which compared irinotecan in combination with 5-fluorouracil and leucovorin in the postoperative treatment of stage III and stage II colon cancer patients, was to undertake a translational research study to assess a panel of putative prognostic and predictive markers in a large colon cancer patient cohort. The Cancer and Leukemia Group B 89803 trial, in a similar design, also investigated the use of prognostic and predictive biomarkers in this setting. In this article, the authors, who are coinvestigators from these trials and performed similar investigations of biomarker discovery in the adjuvant treatment of colon cancer, review the current status of biomarker research in this field, drawing on their experiences and considering future strategies for biomarker discovery in the postgenomic era.

Experimentally derived metastasis gene expression profile predicts recurrence and death in patients with colon cancer

BACKGROUND & AIMS

Staging inadequately predicts metastatic risk in patients with colon cancer. We used a gene expression profile derived from invasive, murine colon cancer cells that were highly metastatic in an immunocompetent mouse model to identify patients with colon cancer at risk of recurrence.

METHODS

This phase 1, exploratory biomarker study used 55 patients with colorectal cancer from Vanderbilt Medical Center (VMC) as the training dataset and 177 patients from the Moffitt Cancer Center as the independent dataset. The metastasis-associated gene expression profile developed from the mouse model was refined with comparative functional genomics in the VMC gene expression profiles to identify a 34-gene classifier associated with high risk of metastasis and death from colon cancer. A metastasis score derived from the biologically based classifier was tested in the Moffitt dataset.

RESULTS

A high score was significantly associated with increased risk of metastasis and death from colon cancer across all pathologic stages and specifically in stage II and stage III patients. The metastasis score was shown to independently predict risk of cancer recurrence and death in univariate and multivariate models. For example, among stage III patients, a high score translated to increased relative risk of cancer recurrence (hazard ratio, 4.7; 95% confidence interval, 1.566-14.05). Furthermore, the metastasis score identified patients with stage III disease whose 5-year recurrence-free survival was >88% and for whom adjuvant chemotherapy did not increase survival time.

CONCLUSION

A gene expression profile identified from an experimental model of colon cancer metastasis predicted cancer recurrence and death, independently of conventional measures, in patients with colon cancer.

A 'metastasis-prone' signature for early-stage mismatch-repair proficient sporadic colorectal cancer patients and its implications for possible therapeutics

Metastasis is the major cause of cancer mortality. We aimed to find a metastasis-prone signature for early stage mismatch-repair proficient sporadic colorectal cancer (CRC) patients for better prognosis and informed use of adjuvant chemotherapy. The genome-wide expression profiles of 82 age-, ethnicity- and tissue-matched patients and healthy controls were analyzed using the Affymetrix U133 Plus 2 array. Metastasis-negative patients have 5 years or more of follow-up. A 10 x 10 two-level nested cross-validation design was used with several families of classification models to identify the optimal predictor for metastasis. The best classification model yielded a 54 gene-set (74 probe sets) with an estimated prediction accuracy of 71%. The specificity, sensitivity, negative and positive predictive values of the signature are 0.88, 0.58, 0.84 and 0.65, respectively, indicating that the gene-set can improve prognosis for early stage sporadic CRC patients. These 54 genes, including node molecules YWHAB, MAP3K5, LMNA, APP, GNAQ, F3, NFATC2, and TGM2, integrate multiple bio-functions in various compartments into an intricate molecular network, suggesting that cell-wide perturbations are involved in metastasis transformation. Further, querying the ;Connectivity Map' with a subset (70%) of these genes shows that Gly-His-Lys and securinine could reverse the differential expressions of these genes significantly, suggesting that they have combinatorial therapeutic effect on the metastasis-prone patients. These two perturbagens promote wound-healing, extracellular matrix remodeling and macrophage activation thus highlighting the importance of these pathways in metastasis suppression for early-stage CRC.