Predictive and prognostic biomarkers in colorectal cancer

Abstract

Colorectal cancer is the third most common cancer and the second leading cause of cancer-related death in the United States. Three quarters of patients diagnosed with colorectal cancer will have early stage disease and despite surgical resection for curative intent, approximately 20%–25% of patients will recur with their disease within five years. The other 25% will present with advanced or metastatic disease and clinicians are faced with the challenge of choosing the most appropriate therapy for individual patients. Despite multiple treatment options which are now available and concomitant improvements in survival, advanced colorectal cancer remains universally fatal. The challenge for clinicians is to identify prognostic and predictive biomarkers that can assist in tailoring available treatments for an individual patient to improve clinical outcomes. This review will summarize current and future biomarkers in colorectal cancer and discuss their utility in managing patient care.

Introduction

Colorectal cancer is the third most common cancer in the world with approximately 150000 new cases in the United States each year, and ranks second only behind lung cancer as the leading cause of cancer-related deaths (Jemal et al., 2010). At initial diagnosis, approximately 20% of patients will have distant metastasis, and another 25%-30% of patients will develop metastasis despite curative intent surgery(August et al., 1985; André et al., 1999). Currently, while the use of chemotherapy in this setting can palliate symptoms and improve survival, it cannot cure patients. If left untreated, patients with metastatic colorectal cancer have an overall survival of 6-9 months, but with combination therapy, survival can be improved to greater than 20 months (Hurwitz et al., 2004; Saltz et al., 2008; Douillard et al., 2010).

The practice of oncology continually faces an immense challenge of matching the right therapeutic regimen with the right patient in addition to balancing relative benefit with therapeutic risk to achieve the most favorable outcome. This challenge is often daunting with only marginal success rates in many advanced disease contexts, which likely reflects the enormous complexity of the disease process coupled with an inability to properly guide the use of available therapeutics. Thus, the search for biomarkers that can help guide treatment and improve clinical outcomes has become a main thrust of oncology over the past few years.

In this context, two relevant concepts are critical. Biomarkers can be categorized as prognostic markers, which indicate the likelihood of outcome regardless of the specific treatment the patient receives, and predictive markers, which indicate the likelihood of response to a specific therapy (Atkinson et al., 2001; Fine and Amler, 2009). Both predictive and prognostic biomarkers are now becoming an integral part of the treatment of cancer patients. Second, while initial reports of a biomarker can be encouraging, several biomarkers have resulted in inconsistent conclusions with respect to subsequent validation studies. This need for consistency in biomarker development has resulted in the development of guidelines for the reporting of biomarker studies. These guidelines provide information about the study design, pre-planned hypotheses, patient and specimen characteristics, assay methods and statistical analysis methods (REMARK) (McShane et al., 2006a, 2006b). Adherence to these concepts and guidelines should help contribute to development of clinically relevant biomarkers that can be readily incorporated into treatment decisions.

In this review, we will examine the current predictive and prognostic biomarkers of colorectal cancer and the role of new and emerging technology that will help identify new biomarkers that will change how we treat colorectal cancer.

Current prognostic and predictive biomarkers

The TNM staging system currently still remains the gold standard for providing prognostic information and guiding treatment in colorectal cancer (Greene et al., 2002). This staging system places an emphasis on the depth of invasion of the tumor (T) and lymph node metastasis (N) for prognostic implication, but lacks any predictive capabilities. Patients with stage I disease will have less than a 5% chance of recurrence compared to stage II patients who have a 20%-40% risk of developing recurrence and stage III patients who have a risk as high as 70% (Gray et al., 2007). Patients with stage I disease do not need benefit from adjuvant chemotherapy while patients with stage IV disease are recommended for palliative chemotherapy. Patients with stage III disease are recommended to receive adjuvant therapy to reduce the risk of recurrence (Wolmark et al., 1988). However, it is controversial and indeterminate at this time whether patients with stage II disease derive any benefit from adjuvant therapy and it highlights the need for molecular markers to provide prognostic and predictive information.

Biomarkers currently in clinical practice

Carcinoembryonic antigen (CEA)

The carcinoembryonic antigen (CEA) was first described by Gold and Freedman in 1965 and is a glycoprotein that is present in approximately 90% of colorectal cancer. CEA is a member of the immunoglobulin supergene family and is located on chromosome 19q (Thompson et al., 1991). CEA is one of the most widely used clinical tumor marker and also the most frequently used marker in colorectal cancer.

As a prognostic marker, CEA has been used to detect early recurrence during follow-up after surgical resection and a high preoperative CEA level suggests an advanced disease either locally or with a distant metastasis (Wanebo et al., 1978; Chapman et al., 1998; Wichmann et al., 2000; Ishizuka et al., 2001; Wiratkapun et al., 2001). Although CEA has poor sensitivity (60%) for detection of locoregional recurrence, the sensitivity (20%-90%) and specificity (35%-90%) for detecting distal recurrence suggests that CEA is a much better prognostic marker for detecting metastatic disease (Fletcher, 1986; Harrison et al., 1997). Furthermore, CEA appears to be the most useful in detecting liver metastasis and studies have shown that the sensitivity and specificity of detecting liver metastasis is>90% (Arnaud et al., 1980; Pietra et al., 1998).

CEA is also a potential predictive biomarker for monitoring response to chemotherapy. Several studies have shown that patients who exhibited a decrease in CEA while on chemotherapy had a better overall survival compared with those whose CEA concentrations failed to decrease (Sugarbaker, 1976; Begent, 1984). However, there is no study showing that CEA testing in patients undergoing chemotherapy for advanced colorectal cancer has an impact on survival, quality of life, or cost of care (Panel, 1996). Despite this limitation, current ASCO guidelines released in 2006 recommend measuring CEA every 1-3 months in patients receiving active chemotherapy for metastatic disease (Locker et al., 2006).

Microsatellite instability

Microsatellites are repeated sequences of DNA typically ranging from 1 to 6 bps in length and are common occurrences in the human genome. When there is a defect in the mismatch repair (MMR) system, errors in these regions (MSI, microsatellite instability) can occur during replication. Approximately 15% of sporadic colorectal cancers demonstrate microsatellite instability (MSI), in which tumors contain insertion-deletion mutations, most commonly in short tandem repeated nucleotides (Aaltonen et al., 1993; Ionov et al., 1993). This tumor phenotype is also often referred to as the MMR deficient (dMMR or MSI-H) phenotype and is caused by germline mutations to one of a number of MMR genes (MLH1, MSH2, MSH3, MSH6, PMS1 and PMS2) (Grady, 2004).

In the clinical setting, MSI has been implicated as a prognostic marker in the adjuvant setting. Multiple retrospective studies have shown that patients with MSI-H (or dMMR) colorectal cancers have higher survival rates than those without MSI-H or MSS (microsatellite stable) tumors (Halling et al., 1999; Gryfe et al., 2000; Ribic et al., 2003). Furthermore, in a meta-analysis of 32 trials with over 7500 patients, patients with MSI-H tumors had significantly improved prognosis than those with MSS tumors (Popat et al., 2005). However, the prognostic implication of MSI may be more relevant in stage II patients as recent analysis of the PETACC-3 study by Roth et al. at the ASCO 2009 meeting demonstrated the prognostic value of MSI status was more significant in patients with stage II disease than in stage III cases (Roth et al., 2009).

In contrast, the use of MSI tumor status as a predictive marker of adjuvant therapy is more controversial. The majority of the studies suggest that patients with MSI-H tumors do not benefit from 5-FU-based adjuvant therapy, compared with patients with MSS tumors (Ribic et al., 2003; Jover et al., 2009; Sargent et al., 2010). However, recent analysis by Tejpar et al. (2009) showed an improvement in disease free survival (DFS) for patients with MSI-H treated with 5-FU adjuvant therapy than those with MSS tumors, and the recent CALGB 89803 study reported a higher 5-year DFS rate in stage III colon cancer patients with MSI-H tumors treated with irinotecan plus 5-FU than in patients with MSS tumors (Tejpar et al., 2009; Bertagnolli et al., 2009). More work is needed to tease out whether patients with MSI tumors predict benefit from adjuvant chemotherapy. Current ASCO guidelines do not suggest offering adjuvant chemotherapy to stage II patients unless there are high risk features present. What is not clear is if microsatellite status trumps other high risk features in predicting benefit or lack thereof to chemotherapy.

KRAS

KRAS, a member of the RAS family of genes, is an oncogene that encodes a GTPase that is involved in many signal transduction pathways such as the epidermal growth factor receptor (EGFR) signaling pathway. Located on chromosome 12, KRAS has been found to be mutated in 40%-50% of colorectal cancer with 90% of the mutation occurring in codons 12 or 13 (Fearon and Vogelstein, 1990; Kressner et al., 1998; Andreyev et al., 1998; Andreyev et al., 2001).

The use of KRAS as a prognostic marker was first evaluated in The RASCAL study where 2721 patients were evaluated for KRAS mutation and its implication in clinical outcome. There was not any association between tumor location, tumor stage, and pattern of recurrence and the presence of a KRAS mutation, but in the multivariate analysis, DFS was decreased in patients with a KRAS mutation (Andreyev et al., 1998). A follow-up study (RASCAL II) in 2001, confirmed the findings that the KRAS mutation had significant negative impact on disease-free survival (Andreyev et al., 2001).

Recently, KRAS has been implicated both as a prognostic and predictive biomarker in metastatic colorectal cancer and is routinely being used in the clinical setting to identify patients who may benefit from anti-EGFR therapy. The CRYSTAL trial demonstrated that patients with wild-type KRAS receiving FOLFIRI+ cetuximab showed a statistically significant difference in progression free survival and overall response compared to patients whose tumor harbored a KRAS mutation (Kohne et al., 2009). Similarly, the OPUS trial showed an improvement in DFS and overall response in patients with wild-type KRAS tumor receiving FOLFOX4+ cetuximab compared to patients with KRAS mutant tumor (Bokemeyer et al., 2010). Finally, these observations have now been prospectively validated in Phase III trials (Amado et al., 2008; Karapetis et al., 2008) and current recommendation is that KRAS testing should be performed prior to incorporating an anti-EGFR therapy in treating patients with metastatic colorectal cancer. A recent development is that patients with KRAS substitution G13D in exon 13 of their gene may derive clinical benefit from cetuximab therapy, highlighting that all mutations with the KRAS gene may not predict resistance to therapy. Formal guidelines regarding treatment of patients with this substitution have not been released (De Roock et al., 2010).

Other potentially clinically relevant biomarkers

Loss of heterozygosity (LOH) of chromosome 17p and 18q

The short arm of chromosome 17 (17p) and the long arm of chromosome 18 (18q) are deleted in approximately 70% of colorectal cancers (Vogelstein et al., 1988; Khine et al., 1994; Boland et al., 1995). Genes located in these regions include DCC, Smad2, Smad7, Smad4 (chromosome 18q), and p53 (chromosome 17p) which have been shown to be involved in the pathogenesis of colorectal cancer (Boland et al., 1995). LOH at chromosome 18q has been associated with poorer prognosis in early stage colorectal cancer, presumably due to a loss of DCC, and a meta-analysis of 18q LOH/DCC suggests that these patients may benefit from adjuvant therapy even in early stage disease (Fearon et al., 1990; Shibata et al., 1996; Sun et al., 1999; Popat and Houlston, 2005). However, a recent study suggested no differences in prognosis of 18q LOH in MSI tumors suggesting that 18q LOH is only a key marker for patients with MSS (Ogino et al., 2009). Despite evidence that patients with 18q LOH have a poorer prognosis, there are currently no recommendations for treating stage II patients with these chromosomal changes. This question is currently being addressed in a trial by the Eastern Cooperative Oncology Group (ECOG5202) investigating the utility of incorporating high risk molecular features such as 18q LOH information into chemotherapy decision making by randomizing patients with either MSS/18q loss or MSI-L/18q loss to FOLFOX or FOLFOX+ bevacizumab.

BRAF kinase

BRAF is a member of the RAF kinase family of serine/threonine-specific protein kinases and is involved in regulating the mitogen-activated protein (MAP) kinase/extracellular signal-regulated protein kinase (ERK) signaling pathway. The activating mutation of BRAF (V600E) is seen in approximately 10% of colorectal cancer and is mutually exclusive of the KRAS mutation (Minoo et al., 2007). Similar to KRAS mutation, patients with tumors containing the BRAF mutation appeared to have worse prognosis than those with WT tumor (Tol et al., 2009). As a predictive biomarker, retrospective analysis suggests that BRAF mutant tumors also do not respond to anti-EFGR therapy, although the numbers of BRAF mutant tumors in these studies are small (<15) (Di Nicolantonio et al., 2008; Loupakis et al., 2009). In contrast, the recent CRYSTAL study did not predict resistance to anti-EGFR therapy in the BRAF mutant population (Kohne et al., 2009) and therefore, further studies are needed to evaluate the use of BRAF as a prognostic and predictive biomarker.

Phosphatidylinositol 3 (PI3) kinase pathway

PI3 kinases are a family of related intracellular signal transducer enzymes involved in cellular functions such as cell growth, proliferation, differentiation, motility, survival and intracellular trafficking. The major mutation of the PI3 kinase pathway in colorectal cancer is the PIK3CA mutation or loss of PTEN (Samuels et al., 2004). Both mutations may coexist with either the KRAS or BRAF mutation, but the clinical impact of mutation in the PI3 kinase still remains controversial. Small studies have shown that PIK3CA mutation and PTEN loss is associated with a lack of response to anti-EGFR therapy (Sartore-Bianchi et al., 2009) while other studies have shown that this is no relationship between PIK3CA mutation and anti-EGFR therapy (Prenen et al., 2009). Currently, there are no recommendations for exclusion of anti-EGFR based therapy based on PI3 kinase mutation status.

Non-genetic biomarkers

Gene expression profiling

Studies in colorectal cancer were among the first reports utilizing expression array technology. Working in the Martin Lipkin laboratory, Leonard Augenlicht studied genetic changes between normal and neoplastic colon samples and found he could detect alterations in gene expression that could distinguish normal colonic epithelium from cancerous samples (Augenlicht et al., 1987). Since that time, gene expression profiling has advanced significantly and has the potential to complement currently available pathologic and biochemical markers to identify discrete biologically relevant phenotypes among individual tumor samples (Nevins and Potti, 2007). New statistical and computational methods provide structure in gene array data that gives a snapshot of gene activity which can be used to describe a phenotype for the individual patient (Golub, 200l; Golub et al., 1999). These techniques have begun to transform tumor characterization from an observational molecular science to a data-intensive quantitative genomic science, and such tools are being used to uncover patterns and trends that cannot only distinguish between biological phenotypes (such as recurrence risk (Wang et al., 2004; Lin et al., 2007; Barrier et al., 2007; Salazar et al., 2011) and treatment response predictors (Nishioka et al., 2011)) but also hold the potential to help guide existing therapies and discover new therapeutic targets (Perou et al., 2000; Rosenwald et al., 2002; Shipp et al., 2002).

In colorectal cancer, the use of gene expression profiling has been used as prognostic markers to identify patients with early stage colorectal cancer who are at high risk for recurrence (Frederiksen et al., 2003; Wang et al., 2004; Bertucci et al., 2004; Lin et al., 2007; Barrier et al., 2007). As these studies are limited by its small sample size (<100) and the use of fresh frozen tissue, they are not currently clinically applicable. However, a recent study (Salazar et al., 2011) and presentation using gene expression profiling was presented by Rosenberg et al. (Gastrointestinal Cancer Symposium, 2011) demonstrated that an 18 gene predictor was prognostic and validated in three independent data sets with each data set consisting of more than 200 patients. Unfortunately, all these studies were not predictive and the ideal clinical biomarker for early stage colorectal cancer would be one that is both prognostic and predictive.

Real time polymerase chain reaction (RT-PCR)

More recently, RT-PCR assays using formalin embedded tissue have been developed to amplify and simultaneously quantify a targeted RNA molecule. These techniques use a widely accepted method for the detection and quantification of mRNA of interest in a given sample. These techniques are being used as an extension of microarray studies outlined above to quantitate specific gene expression levels in tumor samples. Although they provide more information, genomic signatures from microarray studies are limited by the need to use RNA from freshly collected cell lines or tumor tissue to minimize the potential for RNA degradation during processing. However, procurement of fresh tissues in the context of clinical trials or clinical practice is currently highly impractical. In contrast, formalin-fixed paraffin-embedded (FFPE) samples are nearly universally available, have minimal acquisition costs, and confer no risk to the patient.

Currently, a RT-PCR based assay is available as a clinical diagnostic tool. The OncotypeDx Colorectal Recurrence Assay is a 12 gene assay that that has been validated for the assessment of risk of recurrence following surgery in stage II colon cancer patients (O’Connell et al., 2010). This multi-gene assay examines a colon cancer patient’s tumor at the molecular level in order to provide information about the biology of each tumor and the likelihood of recurrence following surgical resection. However, similar to the gene expression assays described above, although this current assay is prognostic, its clinical benefit of identifying patient who will benefit from adjuvant therapy remains unclear.

Other biomarkers

Several serum biomarkers are currently under investigation as potential tools to aid in both colorectal cancer detection, prediction of stage of disease at diagnosis and risk of relapse. These include hTERT, CK-19, and CK-20 which have been studied using several methodologies including a novel colorimetric membrane assay to detect multiple mRNA transcripts in circulating tumor cells in the blood of patients (Yeh et al., 2006; Koch et al., 2006; Uen et al., 2007; Wang et al., 2007). Additional work has identified guanylylcyclase C (GCC) as a highly specific and sensitive marker for colorectal cancer (Buc et al., 2005). Techniques including RT-PCR for GCC on patients with colorectal cancer are able to detect occult lymph node micrometastases as well as the presence of CTCs in patient blood. In some of these analyses, positive findings were associated with increased risk for metastatic disease and disease recurrence (Frick et al., 2005; Mejia and Waldman, 2008; Carlson, 2009). The work above, however, has yet to be fully incorporated into routine clinical practice and must be validated in larger sets of patient cohorts before they are clinically applicable.

However, major limitations of molecular clinical analysis of tissues include limited availability of samples, reagents and the ease of procedures. One potential solution is the development of tissue microarray (TMA), which allows for rapid visualization of molecular targets in thousands of tissue specimens at a time at the DNA, RNA or protein level and has the potential as a high throughput screen for genomic-based diagnostics and drug discovery (Kallioniemi et al., 2001).

Conclusions

Despite the advances discussed above in predictive and prognostic biomarkers, many questions still remain only partially answered for the clinician caring for patients with colorectal cancer. For example, which patients with stage II disease will derive significant benefit from adjuvant chemotherapy and who should remain untreated? In patients with metastatic disease, who will derive benefit from a particular chemotherapeutic/targeted regimen? These and other questions reflect our as yet limited understanding of the complex underlying biology of colorectal cancer. It has become clear that tumorigenesisis a result of complex interactions between multiple signaling pathways within a tumor. With the discovery of new drugs targeting these individual pathways, it is more critical than ever to understand the roadmaps of these signaling highways within a tumor to better identify which patient will derive the most benefit from specific targeted therapy. The advances such as gene expression profiling discussed in this review are beginning to provide tantalizing clues and insights that may help us simplify these issues into truly user-friendly biomarkers that will help inform therapeutic decision making. Until then, the above review summarizes the current armamentarium for practicing oncologists who continue to struggle with these difficult decisions.

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