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Genetic prognostic and predictive markers in colorectal cancer

A Corrigendum to this article was published on 01 March 2011

This article has been updated

Key Points

  • The most studied markers of colorectal cancer prognosis and response to therapy are somatic mutations in KRAS, adenomatous polyposis coli(APC) and TP53. With the exception of KRAS mutations and their association with clinical resistance to epidermal growth factor receptor (EGFR)-specific antibody therapy, there is no compelling evidence that these markers have a role in clinical decision making.

  • Chromosomal instability is associated with a worse prognosis, and microsatellite instability with a better prognosis.

  • Germline polymorphisms have been described in the metabolic pathways of chemotherapeutic agents used in colorectal cancer — for example, 5-fluorouracil (5-FU) and irinotecan — which correlate with the degree of toxicity.

  • High-throughput expression and genotyping arrays are starting to generate novel markers and gene signatures that may be of use in the management of colorectal cancer. At present, these are not sufficiently validated to be clinically useful.

  • Linking the collection of tissue and germline DNA to well-designed clinical trials will increase our understanding of the mechanisms of poor prognosis, and with it our capacity to identify novel biomarkers.

Abstract

Despite many studies of the likely survival outcome of individual patients with colorectal cancer, our knowledge of this subject remains poor. Until recently, we had virtually no understanding of individual responses to therapy, but the discovery of the KRAS mutation as a marker of probable failure of epidermal growth factor receptor (EGFR)-targeted therapy is a first step in the tailoring of treatment to the individual. With the application of molecular analyses, as well as the ability to perform high-throughput screens, there has been an explosive increase in the number of markers thought to be associated with prognosis and treatment outcome in this disease. In this Review, we attempt to summarize the sometimes confusing findings, and critically assess those markers already in the public domain.

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Figure 1: Adenoma–carcinoma sequence model for chromosomal instability in colorectal cancer.
Figure 2: The epidermal growth factor receptor signalling pathway.
Figure 3: Venn diagram of chromosomal instability, microsatellite instability and CpG island methylator phenotype.
Figure 4: Pathways that affect 5-fluorouracil efficacy.
Figure 5: Trial schema to demonstrate the use of a predictive biomarker.

Change history

  • 26 February 2011

    This has been corrected on both the html and pdf versions.

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Acknowledgements

I.T. was funded by the Oxford Comprehensive Biomedical Research Centre.

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DATABASES

ClinicalTrials.gov 

E5202

National Cancer Institute Drug Dictionary 

5-FU

capecitabine

cetuximab

irinotecan

oxaliplatin

panitumumab

trastuzumab

Glossary

Dukes staging system

A staging system based on the depth of invasion of the primary tumour and the presence of lymph node metastasis. Originally described only for rectal cancer, it did not include distant metastasis or unresectable tumours, although both have been addressed in modifications since.

Prognostic marker

A marker that provides information about the natural history of the disease.

Predictive marker

A marker that provides information about the likelihood of response to a treatment.

AJCC staging system

Stage groupings based on the TNM system: depth of primary tumour invasion (T stage), presence and number of lymph node metastases (N stage) and presence of distant metastasis (M stage).

Quantitative marker

A continuous marker that can change its value many fold; for example, gene expression, which increases power for detection with fewer samples, provided there is no noise in the signal.

Discrete marker

A marker that can take one of several — forms for example, a single nucleotide polymorphism — and which may therefore require more samples than a quantitative marker for the same power, but is often more robust in its determination.

Test set

The set of patients in whom a hypothesis is generated, which is then validated in the validation set. The test set is necessary only in unbiased screening approaches, as hypothesis-driven discovery already has a hypothesis to test.

Validation set

The set of patients in whom the hypotheses generated in the test set are studied further, to prove or refute the validity of the initial hypothesis. This step is vital in unbiased approaches as otherwise the test set could merely report chance findings.

Linkage disequilibrium

Deviation from the association that would be expected between two genetic markers if they were subject to random recombination during meiosis, the likelihood of which (without linkage disequilibrium) is a function of the distance between the two markers.

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Walther, A., Johnstone, E., Swanton, C. et al. Genetic prognostic and predictive markers in colorectal cancer. Nat Rev Cancer 9, 489–499 (2009). https://doi.org/10.1038/nrc2645

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