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Covering hierarchical dirichlet mixture models on binary data to enhance genomic stratifications in onco-hematology

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Item Type:Article
Title:Covering hierarchical dirichlet mixture models on binary data to enhance genomic stratifications in onco-hematology
Creators Name:Dall'Olio, D. and Sträng, E. and Turki, A.T. and Tettero, J.M. and Barbus, M. and Schulze-Rath, R. and Elicegui, J.M. and Matteuzzi, T. and Merlotti, A. and Carota, L. and Sala, C. and Della Porta, M.G. and Giampieri, E. and Hernández-Rivas, J.M. and Bullinger, L. and Castellani, G.
Abstract:Onco-hematological studies are increasingly adopting statistical mixture models to support the advancement of the genomically-driven classification systems for blood cancer. Targeting enhanced patients stratification based on the sole role of molecular biology attracted much interest and contributes to bring personalized medicine closer to reality. In onco-hematology, Hierarchical Dirichlet Mixture Models (HDMM) have become one of the preferred method to cluster the genomics data, that include the presence or absence of gene mutations and cytogenetics anomalies, into components. This work unfolds the standard workflow used in onco-hematology to improve patient stratification and proposes alternative approaches to characterize the components and to assign patient to them, as they are crucial tasks usually supported by a priori clinical knowledge. We propose (a) to compute the parameters of the multinomial components of the HDMM or (b) to estimate the parameters of the HDMM components as if they were Multivariate Fisher's Non-Central Hypergeometric (MFNCH) distributions. Then, our approach to perform patients assignments to the HDMM components is designed to essentially determine for each patient its most likely component. We show on simulated data that the patients assignment using the MFNCH-based approach can be superior, if not comparable, to using the multinomial-based approach. Lastly, we illustrate on real Acute Myeloid Leukemia data how the utilization of MFNCH-based approach emerges as a good trade-off between the rigorous multinomial-based characterization of the HDMM components and the common refinement of them based on a priori clinical knowledge.
Keywords:Acute Myeloid Leukemia, Chromosome Aberrations, Genomics, Hematology
Source:PLoS Computational Biology
Publisher:Public Library of Science
Page Range:e1011299
Date:2 February 2024
Official Publication:https://doi.org/10.1371/journal.pcbi.1011299
PubMed:View item in PubMed

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