Numéro spécial : analyse de mélanges
A mixture model to characterize genomic alterations of tumors
[Un modèle de mélange pour caractériser les altérations génomiques tumorales]
Journal de la société française de statistique, Tome 160 (2019) no. 1, pp. 130-148.

Characterizing the genomic copy number alterations (CNA) in cancer is of major importance in order to develop personalized medicine. Single nucleotide polymorphism (SNP) arrays are still in use to measure CNA profiles. Among the methods for SNP-array analysis, the Genome Alteration Print (GAP) by Popova et al, based on a preliminary segmentation of SNP-array profiles, uses a deterministic approach to infer the absolute copy numbers profile. We develop a probabilistic model for GAP and define a Gaussian mixture model where centers are constrained to belong to a frame depending on unknown parameters such as the proportion of normal tissue. The estimation is performed using an expectation-maximization (EM) algorithm to recover the parameters characterizing the genomic alterations as well as the most probable copy number change of each segment and the unknown proportion of normal tissue. We claim to deduce the tumor ploidy from penalized model selection criterion. Our model is tested on simulated and real data.

La caractérisation des altérations du nombre de copies dans le génome est d’importance capitale pour développer une médecine personnalisée en cancérologie. Les puces à SNPs (Single Nucleotide Polymorphism), une variante de puce à ADN, sont toujours utilisées pour mesurer les profils d’altération du nombre de copies. Parmi les méthodes d’analyse de profil de SNPs, la méthode GAP (Genome Alteration Print) de Popova et al, basée sur une segmentation préliminaire de profils issus de puces SNPs, utilise une approche déterministe pour déterminer le profil du nombre absolu de copies. Nous développons un modèle probabiliste pour la méthode GAP et définissons un modèle de mélange gaussien dont les centres sont contraints d’appartenir à un réseau dépendant de paramètres inconnus tels que la proportion de tissu tumoral dans le prélèvement. L’estimation est effectuée à l’aide d’un algorithme EM (expectation-maximization) permettant d’accéder non seulement aux paramètres mais aussi au nombre altéré de copies le plus probable sur chaque segment ainsi que la proportion tumorale inconnue. Nous proposons de déduire la ploïdie tumorale en utilisant un critère pénalisé de choix de modèle. Notre modèle est testé sur des données simulées et appliqué à un exemple de données de cancer du côlon.

Mots clés : mixture model, EM algorithm, BIC criterion, slope heuristics, cancer, GAP method, SNP-array
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     author = {Keribin, Christine and Liu, Yi and Popova, Tatiana and Rozenholc, Yves},
     title = {A mixture model to characterize genomic alterations of tumors},
     journal = {Journal de la soci\'et\'e fran\c{c}aise de statistique},
     pages = {130--148},
     publisher = {Soci\'et\'e fran\c{c}aise de statistique},
     volume = {160},
     number = {1},
     year = {2019},
     zbl = {1417.62316},
     mrnumber = {3928543},
     language = {en},
     url = {http://www.numdam.org/item/JSFS_2019__160_1_130_0/}
}
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Keribin, Christine; Liu, Yi; Popova, Tatiana; Rozenholc, Yves. A mixture model to characterize genomic alterations of tumors. Journal de la société française de statistique, Tome 160 (2019) no. 1, pp. 130-148. http://www.numdam.org/item/JSFS_2019__160_1_130_0/

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