The Hunt of cancer genes : statistical inference of cancer risk and driver genes using next generation sequencing data
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Document Version
Author
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Ossowski, Stephan
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Pages
148 p.
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Citation
Sušak, H. The Hunt of cancer genes : statistical inference of cancer risk and driver genes using next generation sequencing data. Universitat Pompeu Fabra; 2017. handle: http://hdl.handle.net/10803/664504
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Citation
Doctoral program
Programa de doctorat en Biomedicina
Abstract
Els distints projectes internacionals de seqüenciació de càncer duts a terme en els últims anys han generat catàlegs complets d’alteracions trobades en els genomes tumorals, així com informació de variants germinals per a milers d'individus. En aquesta tesi descrivim dos mètodes estadístics aprofitant aquestes bases de dades per tal d’entendre millor la iniciació i la progressió dels tumors, i la contribució de variants genètiques al risc de desenvolupar càncer al llarg de la vida. El primer mètode, anomenat cDriver, es basa en un model d’inferència Bayesià que utilitza múltiples senyals de la selecció positiva que ocorre en els genomes tumorals per tal de predir els gens driver del càncer. En aquest mètode, hem inclòs la fracció de cèl·lules tumorals com a nova senyal de la selecció positiva a nivell cel·lular. Aquesta es basa en la hipòtesi que les cèl·lules que adquireixen mutacions ventajoses proliferaran i s’expandiran clonalment més ràpidament. Per avaluar cDriver, aquest es va comparar amb els mètodes més utilitzats per a la predicció de gens driver actuals. L’anàlisi es va dur a terme amb conjunts de dades de tres càncer diferents i els resultats van ser iguals o millors que els obtinguts per les eines més competitives en el tema. El segon mètode, anomenat REWAS, és un marc de treball per l’estudi d’associació de variants rares (RVAS) amb l'objectiu de millorar la identificació dels gens de predisposició al càncer. Tot i això, REWAS es pot aplicar a qualsevol estudi cas-control de malalties complexes. Per una altra part, a més d'integrar mètodes RVAS ben establerts, hem desenvolupat un nou mètode d'inferència Bayesiana RVAS basat en Integrated Nested Laplace Approximation (BATI). També demostrem que BATI mostra millors resultats que altres mètodes en dades simulades amb soroll de fons real, especialment quan el context biològic (p.e. variants amb impacte funcional) està disponible or quan les variants de risc expliquen en total poca variància fenotípica.
International cancer sequencing projects have generated comprehensive catalogs of alterations found in tumor genomes, as well as germline variant data for thousands of individuals. In this thesis, we describe two statistical methods exploiting these rich datasets in order to better understand tumor initiation, tumor progression and the contribution of genetic variants to the lifetime risk of developing cancer. The first method, a Bayesian inference model named cDriver, utilizes multiple signatures of positive selection acting on tumor genomes to predict cancer driver genes. Cancer cell fraction is introduced as a novel signature of positive selection on a cellular level, based on the hypothesis that cells obtaining additional advantageous driver mutations will undergo rapid proliferation and clonal expansion. We benchmarked cDriver against state of the art driver prediction methods on three cancer datasets demonstrating equal or better performance than the best competing tool. The second method, termed REWAS is a comprehensive framework for rare-variant association studies (RVAS) aiming at improving identification of cancer predisposition genes. Nonetheless, REWAS is readily applicable to any case-control study of complex diseases. Besides integrating well-established RVAS methods, we developed a novel Bayesian inference RVAS method (BATI) based on Integrated Nested Laplace Approximation (INLA). We demonstrate that BATI outperforms other methods on realistic simulated datasets, especially when meaningful biological context (e.g. functional impact of variants) is available or when risk variants in sum explain low phenotypic variance. Both methods developed during my thesis have the potential to facilitate personalized medicine and oncology through identification of novel therapeutic targets and identification of genetic predisposition facilitating prevention and early diagnosis of cancer.
International cancer sequencing projects have generated comprehensive catalogs of alterations found in tumor genomes, as well as germline variant data for thousands of individuals. In this thesis, we describe two statistical methods exploiting these rich datasets in order to better understand tumor initiation, tumor progression and the contribution of genetic variants to the lifetime risk of developing cancer. The first method, a Bayesian inference model named cDriver, utilizes multiple signatures of positive selection acting on tumor genomes to predict cancer driver genes. Cancer cell fraction is introduced as a novel signature of positive selection on a cellular level, based on the hypothesis that cells obtaining additional advantageous driver mutations will undergo rapid proliferation and clonal expansion. We benchmarked cDriver against state of the art driver prediction methods on three cancer datasets demonstrating equal or better performance than the best competing tool. The second method, termed REWAS is a comprehensive framework for rare-variant association studies (RVAS) aiming at improving identification of cancer predisposition genes. Nonetheless, REWAS is readily applicable to any case-control study of complex diseases. Besides integrating well-established RVAS methods, we developed a novel Bayesian inference RVAS method (BATI) based on Integrated Nested Laplace Approximation (INLA). We demonstrate that BATI outperforms other methods on realistic simulated datasets, especially when meaningful biological context (e.g. functional impact of variants) is available or when risk variants in sum explain low phenotypic variance. Both methods developed during my thesis have the potential to facilitate personalized medicine and oncology through identification of novel therapeutic targets and identification of genetic predisposition facilitating prevention and early diagnosis of cancer.
Keywords
Gens causal del càncer, Evolució del càncer, Prova d'associació, Trets complexos, Variants rares, Cancer driver genes, Cancer evolution, Association test, Coomplex trait, Rare variants
Subjects
616 - Pathology. Clinical medicine. Oncology
Publisher
Universitat Pompeu Fabra
