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Prostate cancer genomics: towards a new understanding

Nature Reviews Genetics volume 10, pages 77–82 (2009)Cite this article

Abstract

Recent genetics and genomics studies of prostate cancer have helped to clarify the genetic basis of this common but complex disease. Genome-wide studies have detected numerous variants associated with disease as well as common gene fusions and expression 'signatures' in prostate tumours. On the basis of these results, some advocate gene-based individualized screening for prostate cancer, although such testing might only be worthwhile to distinguish disease aggressiveness. Lessons learned from these studies provide strategies for further deciphering the genetic causes of prostate cancer and other diseases.

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Figure 1: Limitations of using multiple associated variants to predict an individual's risk of prostate cancer.
Figure 2: Etiologic evolution of prostate cancer and key steps affected by germline variants and somatic fusions.

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Acknowledgements

Thanks to E. Jorgenson, I. Cheng and the anonymous referees for comments on this article. This work was supported by National Institutes of Health grants CA88164 and CA127298.

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Authors and Affiliations

  1. John S. Witte is at the Department of Epidemiology and Biostatistics, Institute for Human Genetics, University of California, San Francisco, 513 Parnassus Avenue, Room S965, San Francisco, California 94143-0794, USA. jwitte@ucsf.edu,

    John S. Witte

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FURTHER INFORMATION

The Witte Laboratory

Cancer Genetic Markers of Susceptibility (CGEMS) study

NHGRI Catalog of Published Genome-Wide Association Studies

International HapMap Project

Glossary

Admixture study

An approach for localizing genetic regions that might contain causal variants by correlating the level of individuals' admixture with disease. This type of analysis requires fewer markers than GWA studies, but necessitates a recently admixed population and variation in disease rates across ancestral populations.

Area under the receiver operating characteristic curve

(AUC). The receiver operating characteristic curve for a predictor (for example, a genetic test) plots the proportion of cases correctly identified by the test versus the proportion of controls incorrectly classified as cases. The AUC indicates the probability that a factor (for example, a genotype) will predict a higher risk of disease in a randomly selected case than in a control.

Cancer outlier profile analysis

(COPA). A method for detecting gene expression pattern outliers in subsets of samples. It is used to distinguish potential oncogenic chromosomal changes.

Expression array study

An examination of the expression of all known genes. This type of analysis is commonly used to determine profiles or 'signatures' of overexpressed and underexpressed genes in diseased versus normal samples.

Genome-wide association (GWA) study

An investigation of the association between common genetic variation and disease. This type of analysis requires a dense set of markers (for example, SNPs) that capture a substantial proportion of common variation across the genome, and large numbers of study subjects.

Linkage analysis

A method for localizing chromosomal regions that harbour causal variants by studying the co-segregation of genetic makers and disease within families. When a marker is commonly observed with the disease, the causal variant might be in the general proximity of the marker.

Negative predictive value

(NPV). The probability that an individual with a negative screening test is truly negative (for example, unaffected).

Population attributable risk

(PAR). The disease incidence in a population that is attributable to a particular risk factor. GWA studies commonly report the PAR percentage to estimate what proportion of the disease is explained by associated variants.

Positive predictive value

(PPV). Measures how well a screening or diagnostic test distinguishes true positives: it is the probability that an individual who tests positive is truly positive (for example, affected).

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Witte, J. Prostate cancer genomics: towards a new understanding. Nat Rev Genet 10, 77–82 (2009). https://doi.org/10.1038/nrg2507

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