Abstract 3164: Somatic mutation analysis pipeline for exome-sequencing data identifies oncogenic STAT3 mutations in T-LGL leukemia.
Authors: Eldfors Samuli , Rajala Hanna LM , Ellonen Pekka , Andersson Emma I. , Lagström Sonja , Almusa Henrikki , Edgren Henrik , Lepistö Maija , Mattila Pirkko , Knowles Jonathan , Saarela Janna , Porkka Kimmo , Kallioniemi Olli , Mustjoki Satu , Heckman Caroline A. 
Published: August 2014

Exome-sequencing of matched tumor-normal sample pairs enables the identification of the full set of somatically acquired mutations that directly affect protein function in a given tumor. Inferring the functional significance of coding region mutations is relatively straightforward compared to mutations occurring in non-coding regions. Furthermore, exome sequencing enables sufficient sampling depth required for sensitive detection of somatic mutations in tumor samples. However, identification of novel driver mutations remains challenging due to the high levels of sequencing errors observed with current next generation sequencing technologies and due to passenger mutations which vastly out number driver mutations in any given tumor. Identification of novel driver mutations is especially challenging in the absence of cohort data.

To identify driving somatic events in exome sequence data, we developed an analytical pipeline utilizing a combination of 3rd party tools for sequence read processing and mutation calling. This was coupled to an annotation framework that aids prioritization of driver mutation candidates for downstream validation experiments. The annotation framework integrates prior knowledge from reference databases on known cancer genes, sequence level evolutionary conservation and functional consequence predictions. Gene expression data from RNA-seq of the tumor sample can also be integrated in the annotations.

To test this pipeline, we performed exome sequencing of a tumor-normal sample pair from an index patient diagnosed with T-cell derived large granular lymphocytic leukemia (T-LGL), which is a rare lymphoproliferative disease of previously unknown pathogenesis. The analysis pipeline identified a high-ranking candidate mutation in STAT3. Structural analysis indicated that the mutation resulted in a hydrophobic substitution at the SH2 dimerization interface, suggesting that the mutation stabilizes the active dimer form through increased hydrophobic interaction between monomers. Further validation experiments showed STAT3 SH2 hydrophobic substitutions to be recurrent in T-LGL and to result in constitutive STAT3 activation [1]. Application of the pipeline enabled identification of a novel class of oncogenic STAT3 SH2 domain mutations and established STAT3 as a key driver oncogene in T-LGL.

Citation Format: Samuli Eldfors, Hanna LM Rajala, Pekka Ellonen, Emma I. Andersson, Sonja Lagström, Henrikki Almusa, Henrik Edgren, Maija Lepistö, Pirkko Mattila, Jonathan Knowles, Janna Saarela, Kimmo Porkka, Olli Kallioniemi, Satu Mustjoki, Caroline A. Heckman. Somatic mutation analysis pipeline for exome-sequencing data identifies oncogenic STAT3 mutations in T-LGL leukemia. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3164. doi:10.1158/1538-7445.AM2013-3164