Skip to main content

Advertisement

Predicting cancer drivers: are we there yet?

Article metrics

Abstract

Genomic variants with a key role in causing cancer or affecting the response to cancertherapeutics need to be identified so that they can be targeted for therapy. The transFIC tool aimsto identify somatic point mutations that drive cancer in sequencing projects. This package isavailable as a web service, a stand-alone program and a website. It improves the functionalprediction scores generated by popular established prediction tools and will be useful to cancerresearchers.

See research article: http://genomemedicine.com/content/4/11/89

The functional impact of cancer-associated mutations

Mutations give rise to cancerous cells by affecting genes. For example, 'gain of function'mutations in oncogenes such as EGFR and KRAS promote tumor progression, and 'lossof function' mutations in the tumor-suppressor gene TP53 promote cancer by dysregulatingthe cell cycle. Mutations that provide a selective growth advantage to the cancer cell are called'driver' mutations. 'Passenger' mutations, by contrast, are present in cancer genomes but do notgive such a growth advantage.

Identifying driver genes is important for clinical applications. If certain mutations are presentin specific cancer-associated genes, then the cancer drugs that target these genes and theirrespective pathways might behave differently, thus affecting the treatment outcome. For example, theBRAF gene encodes a serine/threonine kinase and is known to contain activating somaticmutations in melanomas, colorectal cancer and other cancers [1]. In a metastatic colorectal cancer study, it was reported that none of the patients withBRAF mutations responded to treatment with the drugs panitumumab or cetuximab [2]. Thus, activating somatic mutations can affect drug sensitivities.

Identifying driver and passenger mutations

When sequencing a cancer genome, somatic single base substitutions can number in the tens ofthousands [3]. Sifting through these somatic single nucleotide variants (SNVs) to pin down the fewdriver mutations implicated in cancer is a challenge. Most researchers concentrate on the somaticmutations that cause missense changes in gene products. This focus helps to reduce the number ofmutations for further investigation.

To achieve the ultimate goal of distinguishing driver mutations from passenger mutations, oneapproach is to sequence many cancer samples and then identify the highly mutated genes and/or therecurrent mutations across all of the samples. The disadvantage of this approach is that many cancersamples need to be sequenced, and it is not straightforward to prioritize genes with a small numberof somatic mutations. To supplement this approach, one could look at the severity of the mutationsin the gene and assess whether they change the gene's function. It may be possible to detect drivergenes in addition to the frequently mutated genes by using this supplementary approach. In this way,some of the genes with a smaller number of mutations would gain stronger support as cancer-causinggenes as opposed to background noise [4].

In the past, researchers have used tools such as PolyPhen and SIFT to assess the effect ofmutations on protein function. Although these tools are generally useful, they are not trainedspecifically to identify driver mutations in cancer. Recently, prediction tools that evaluate whichmutations specifically drive cancer have been developed. In Table 1, we listsome of these publicly available cancer-specific tools. For example, CHASM [4] ranks somatic missense SNVs according to their putative tumorigenic impact. CHASM uses amachine-learning algorithm that has been trained on approximately 50 pre-computed features todistinguish drivers from passenger mutations. CHASM uses a specific passenger mutation rate for eachtype of cancer. Another example is CanPredict [5], which was one of the first tools for predicting cancer-associated mutations and appliesgene ontology knowledge.

Table 1 Available cancer missense mutation prediction tools

In this issue of Genome Medicine, Abel Gonzalez-Perez, Jordi Deu-Pons and NuriaLopez-Bigas [6] have developed a computational method called transFIC (TRANSformed Functional Impact forCancer) to predict somatic mutations that are putative drivers of tumorigenesis. The authors madethe initial observation that cancer-associated genes are less likely to have deleterious germlinevariation than genes that are not involved in cancer. Based on this observation, transFIC firstlooks at the scores generated by a missense prediction tool such as SIFT, PolyPhen-2,MutationAssessor [7] or CHASM. It then normalizes the initial prediction scores by taking into account agene's tolerance to deleterious germline variation. The transformed scores are used to rank thesomatic mutations that have functional effects, and mutations with higher transFIC scores areconsidered candidate cancer drivers. This process improves the performance of the original scoresfrom pre-existing tools, by approximately a twofold to sevenfold increase in the Matthew'scorrelation coefficient, on various datasets.

In summary, the transFIC prediction tool reported by Gonzalez-Perez et al. has manyuser-friendly features to discriminate cancer driver mutations from mutations that are neutral.TransFIC could be of great use to the cancer research community because it improves the functionalimpact scores of four well-known tools and uses these transformed scores to prioritize mutations.This tool also has the potential to be useful in cancer resequencing projects to predict thefunctional impact of somatic cancer mutations.

Beyond driver mutations

The validation of driver mutations will be easier in the future because lower sequencing costswill allow the deep sequencing of tumor samples. Because driver mutations are expected to occurearly in the development of cancer cells, these mutations will tend to be present at higherfrequencies in a cancer sample than passenger mutations, which occur later. Deep sequencing providesbetter estimates of mutation frequencies compared to sequencing at medium coverage and thereforedeep sequencing helps distinguish driver and passenger mutations.

After distinguishing these two types of mutations, it is crucial to pinpoint the keycancer-causing genes and pathways. Software packages such as MuSiC [8] and MutSig [9] aid in this step by prioritizing genes that are significantly mutated. These packagesidentify frequently mutated genes, pathways and gene families across a group of patients for variouscancer types, and they also highlight clinically relevant mutations. This entire process could allowbetter treatment. For example, the My Cancer Genome website [10] captures cancer variation and the reported drug responses for various cancers. Thisinformation can then make doctors aware of the outcome of a patient's drug response based on thepatient's genotype. Discovery and distribution of this knowledge will lead to improved personalizedcancer treatment.

Abbreviations

API:

Application Programming Interface

BAM:

binary SAM

BED:

Browser Extensible Data

CCDS:

Consensus CoDing Sequence Project

CHASM:

Cancer-specific High-throughput Annotation of SomaticMutations

COSMIC:

Catalogue of Somatic Mutations in Cancer

dbSNP:

Single Nucleotide Polymorphismdatabase

GO:

Gene Ontology

indel:

insertion/deletion

MAF:

Mutation Annotation Format

MuSiC:

Mutational Significance in Cancer

MutSig:

Mutation Significance

PolyPhen:

PolymorphismPhenotyping

RefSeq:

Reference Sequence

SIFT:

Sorting Intolerant From Tolerant

SNV:

singlenucleotide variant

transFIC:

TRANSformed Functional Impact for Cancer

UniProt:

Universal ProteinResource

URL:

Uniform Resource Locator.

References

  1. 1.

    Davies H, Bignell GR, Cox C, Stephens P, Edkins S, Clegg S, Teague J, Woffendin H, Garnett MJ, Bottomley W, Davis N, Dicks E, Ewing R, Floyd Y, Gray K, Hall S, Hawes R, Hughes J, Kosmidou V, Menzies A, Mould C, Parker A, Stevens C, Watt S, Hooper S, Wilson R, Jayatilake H, Gusterson BA, Cooper C, Shipley J, et al: Mutations of the BRAF gene in human cancer. Nature. 2002, 417: 949-954. 10.1038/nature00766.

  2. 2.

    Di Nicolantonio F, Martini M, Molinari F, Sartore-Bianchi A, Arena S, Saletti P, De Dosso S, Mazzucchelli L, Frattini M, Siena S, Bardelli A: Wild-type BRAF is required for response to panitumumab or cetuximab in metastaticcolorectal cancer. J Clin Oncol. 2008, 26: 5705-5712. 10.1200/JCO.2008.18.0786.

  3. 3.

    Pleasance ED, Cheetham RK, Stephens PJ, McBride DJ, Humphray SJ, Greenman CD, Varela I, Lin ML, Ordóñez GR, Bignell GR, Ye K, Alipaz J, Bauer MJ, Beare D, Butler A, Carter RJ, Chen L, Cox AJ, Edkins S, Kokko-Gonzales PI, Gormley NA, Grocock RJ, Haudenschild CD, Hims MM, James T, Jia M, Kingsbury Z, Leroy C, Marshall J, Menzies A, et al: A comprehensive catalogue of somatic mutations from a human cancer genome. Nature. 2010, 463: 191-196. 10.1038/nature08658.

  4. 4.

    Carter H, Chen S, Isik L, Tyekucheva S, Velculescu VE, Kinzler KW, Vogelstein B, Karchin R: Cancer-specific high-throughput annotation of somatic mutations: computational prediction ofdriver missense mutations. Cancer Res. 2009, 69: 6660-6667. 10.1158/0008-5472.CAN-09-1133.

  5. 5.

    Kaminker JS, Zhang Y, Watanabe C, Zhang Z: CanPredict: a computational tool for predicting cancer-associated missense mutations. Nucleic Acids Res. 2007, 35: W595-W598. 10.1093/nar/gkm405.

  6. 6.

    Gonzalez-Perez A, Deu-Pons J, Lopez-Bigas N: Improving the prediction of the functional impact of cancer mutations by baseline tolerancetransformation. Genome Med. 2012, 4: 89-10.1186/gm390.

  7. 7.

    Reva B, Antipin Y, Sander C: Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res. 2011, 39: e118-10.1093/nar/gkr407.

  8. 8.

    Dees ND, Zhang Q, Kandoth C, Wendl MC, Schierding W, Koboldt DC, Mooney TB, Callaway MB, Dooling D, Mardis ER, Wilson RK, Ding L: MuSiC: identifying mutational significance in cancer genomes. Genome Res. 2012, 22: 1589-1598. 10.1101/gr.134635.111.

  9. 9.

    Cancer Genome Analysis Tool. [https://confluence.broadinstitute.org/display/CGATools/MutSig]

  10. 10.

    Personalized Cancer Medicine Resource. [http://www.mycancergenome.org/]

Download references

Acknowledgements

We thank Dr Francesca Menghi and Dr Joyce Suling Lin for proof-reading. We apologize if we haveneglected to cite cancer tools or references because of space limitations.

Author information

Correspondence to Pauline C Ng.

Additional information

Competing interests

The authors declare that they have no competing interests.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Krishnan, V.G., Ng, P.C. Predicting cancer drivers: are we there yet?. Genome Med 4, 88 (2012) doi:10.1186/gm389

Download citation

Keywords

  • Cetuximab
  • Prediction Tool
  • Panitumumab
  • Driver Mutation
  • Cancer Driver