Analysing mutational heterogeneity to identify true cancer-associated genes

A recent blog post discussed the need to assess the pathogenicity of genetic variants to determine which mutations are truly causative and which are only background. This is highly important in the search for new cancer drugs as rapidly dividing tissues are more prone to accruing mutations. Large cancer genomic studies are identifying increasing numbers of apparently “significantly-mutated genes” across all major cancer types. However, these genes often include highly unlikely candidates. Analysis of 178 squamous lung cell carcinomas identified 450 significantly-mutated genes; almost a quarter of which were olfactory receptors (TCGA Research Network, 2012). It is necessary to be able to eliminate these false-positives and retain focus on true cancer-genes.

Significantly-mutated genes are identified as harbouring more mutations than expected given the average background mutation rate for the cancer type (Kan et al., 2010). Recent research from Lawrence et al., (2013) has determined significant background mutation frequency heterogeneity across cancer types and the genome as contributing to misidentification of “significantly-mutated” cancer-genes. To account for this heterogeneity they have developed MutSigCV – analytic software which incorporates more sample-specific mutation rates – to help identify which mutated genes are truly associated with cancer.

Lawrence et al. analysed 3,083 tumour-normal pairs across 27 tumour types, including clear cell and papillary Renal Cell Carcinoma (RCC), using whole-exome or whole-genome sequences. They identified 373,909 non-silent coding mutations over all, with an average of 4/Mb and median of 1.5/Mb per sample.

Comparison of individual patient mutation rates within and between cancer types identified variation over 1000-fold. Some variation is based on tumour origin tissue: melanoma and lung cancer samples, from tissues typically exposed to high levels of UV and smoke-based carcinogens respectively, exceeded 100 mutation/Mb compared to paediatric cancers, with less exposure to carcinogens, at around 0.1/Mb. However, there were also magnitudes of difference in variation between patients with the same cancer that could be due to inherited mutations, rather than acquired mutations, driving tumour development.

Lawrence et al. also found regional heterogeneity, up to 5-fold difference, in mutation rates across the genome. There was a strong correlation between high mutation and both low expression rate and late DNA replication. This correlation has been previously reported in germline cells potentially associated with low transcription-coupled repair levels (Fousteri & Mullenders, 2008) and a reduction in available nucleotides later in replication (Stamatoyannopoulous et al., 2009). An increased background mutation rate can explain a large number of the spurious squamous lung cell carcinoma cancer-genes identified as they are both low expression and late replicating genes.

Reanalysis of the original squamous lung cell carcinoma samples using MutSigCV software identified only 11 genes as significantly mutated (TCGA Research Network, 2012). These included genes previous reported to be associated with cancers and one novel gene – HLA-A – suggesting a role for immune evasion in tumourigenesis, an avenue that requires further follow up.

Lawrence et al. acknowledge that there are still other forms of heterogeneity that should be investigated including the co-occurrence of mutations in close proximity and heterogeneity across cancer cells within a tumour. The latter could be important in studies of diseases such as BHD where RCCs of multiple histologies develop and a high proportion of tumours are hybrids (Benusiglio et al., 2014). Although the main tumour driving force in BHD is the loss of FLCN, the development of different tumour types suggests additional mutations resulting in varied histologies. Identifying these mutations could help identify new treatment targets, but to do so it is necessary to separate true cancer-genes from background mutations.

  • Benusiglio PR, Giraud S, Deveaux S, Méjean A, Correas JM, Joly D, Timsit MO, Ferlicot S, Verkarre V, Abadie C, Chauveau D, Leroux D, Avril MF, Cordier JF, Richard S; French National Cancer Institute Inherited Predisposition to Kidney Cancer Network (2014). Renal cell tumour characteristics in patients with the Birt-Hogg-Dubé cancer susceptibility syndrome: a retrospective, multicentre study. Orphanet J Rare Dis. Oct 29;9:163. PMID: 25519458.
  • Cancer Genome Atlas Research Network (2012). Comprehensive genomic characterization of squamous cell lung cancers. Nature. Sep 27;489(7417):519-25 PMID: 22960745.
  • Fousteri M, Mullenders LH (2008). Transcription-coupled nucleotide excision repair in mammalian cells: molecular mechanisms and biological effects. Cell Res. Jan;18(1):73-84. Review. PMID: 18166977.
  • Kan Z, Jaiswal BS, Stinson J, Janakiraman V, Bhatt D, Stern HM, Yue P, Haverty PM, Bourgon R, Zheng J, Moorhead M, Chaudhuri S, Tomsho LP, Peters BA, Pujara K, Cordes S, Davis DP, Carlton VE, Yuan W, Li L, Wang W, Eigenbrot C, Kaminker JS, Eberhard DA, Waring P, Schuster SC, Modrusan Z, Zhang Z, Stokoe D, de Sauvage FJ, Faham M, Seshagiri S (2010). Diverse somatic mutation patterns and pathway alterations in human cancers. Nature. Aug 12;466(7308):869-73 PMID: 20668451.
  • Lawrence MS, Stojanov P, Polak P, Kryukov GV, Cibulskis K, Sivachenko A, Carter SL, Stewart C, Mermel CH, Roberts SA, Kiezun A, Hammerman PS, McKenna A, Drier Y, Zou L, Ramos AH, Pugh TJ, Stransky N, Helman E, Kim J, Sougnez C, Ambrogio L, Nickerson E, Shefler E, Cortés ML, Auclair D, Saksena G, Voet D, Noble M, DiCara D, Lin P, Lichtenstein L, Heiman DI, Fennell T, Imielinski M, Hernandez B, Hodis E, Baca S, Dulak AM, Lohr J, Landau DA, Wu CJ, Melendez-Zajgla J, Hidalgo-Miranda A, Koren A, McCarroll SA, Mora J, Lee RS, Crompton B, Onofrio R, Parkin M, Winckler W, Ardlie K, Gabriel SB, Roberts CW, Biegel JA, Stegmaier K, Bass AJ, Garraway LA, Meyerson M, Golub TR, Gordenin DA, Sunyaev S, Lander ES, & Getz G (2013). Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature, 499 (7457), 214-8 PMID: 23770567
  • Stamatoyannopoulos JA, Adzhubei I, Thurman RE, Kryukov GV, Mirkin SM, Sunyaev SR (2009). Human mutation rate associated with DNA replication timing. Nat Genet. Apr;41(4):393-5. PMID: 19287383.

Leave a Reply