Covering Hierarchical Dirichlet Mixture Models on binary data to enhance genomic stratifications in onco-hematology.

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.

Glioblastoma initiating cells are sensitive to histone demethylase inhibition due to epigenetic deregulation.

Tumor-initiating cells are a subpopulation of cells that have self-renewal capacity to regenerate a tumor. Here, we identify stem cell-like chromatin features in human glioblastoma initiating cells (GICs) and link them to a loss of the repressive histone H3 lysine 9 trimethylation (H3K9me3) mark. Increasing H3K9me3 levels by histone demethylase inhibition led to cell death in GICs but not in their differentiated counterparts. The induction of apoptosis was accompanied by a loss of the activating H3 lysine 9 acetylation (H3K9ac) modification and accumulation of DNA damage and downregulation of DNA damage response genes. Upon knockdown of histone demethylases, KDM4C and KDM7A both differentiation and DNA damage were induced. Thus, the H3K9me3-H3K9ac equilibrium is crucial for GIC viability and represents a chromatin feature that can be exploited to specifically target this tumor subpopulation.

Rearrangements involving 11q23.3/KMT2A in adult AML: mutational landscape and prognostic implications - a HARMONY study.

Balanced rearrangements involving the KMT2A gene (KMT2Ar) are recurrent genetic abnormalities in acute myeloid leukemia (AML), but there is lack of consensus regarding the prognostic impact of different fusion partners. Moreover, prognostic implications of gene mutations co-occurring with KMT2Ar are not established. From the HARMONY AML database 205 KMT2Ar adult patients were selected, 185 of whom had mutational information by a panel-based next-generation sequencing analysis. Overall survival (OS) was similar across the different translocations, including t(9;11)(p21.3;q23.3)/KMT2A::MLLT3 (p = 0.756). However, independent prognostic factors for OS in intensively treated patients were age >60 years (HR 2.1, p = 0.001), secondary AML (HR 2.2, p = 0.043), DNMT3A-mut (HR 2.1, p = 0.047) and KRAS-mut (HR 2.0, p = 0.005). In the subset of patients with de novo AML < 60 years, KRAS and TP53 were the prognostically most relevant mutated genes, as patients with a mutation of any of those two genes had a lower complete remission rate (50% vs 86%, p < 0.001) and inferior OS (median 7 vs 30 months, p < 0.001). Allogeneic hematopoietic stem cell transplantation in first complete remission was able to improve OS (p = 0.003). Our study highlights the importance of the mutational patterns in adult KMT2Ar AML and provides new insights into more accurate prognostic stratification of these patients.

Glioblastoma epigenome profiling identifies SOX10 as a master regulator of molecular tumour subtype.

Glioblastoma frequently exhibits therapy-associated subtype transitions to mesenchymal phenotypes with adverse prognosis. Here, we perform multi-omic profiling of 60 glioblastoma primary tumours and use orthogonal analysis of chromatin and RNA-derived gene regulatory networks to identify 38 subtype master regulators, whose cell population-specific activities we further map in published single-cell RNA sequencing data. These analyses identify the oligodendrocyte precursor marker and chromatin modifier SOX10 as a master regulator in RTK I-subtype tumours. In vitro functional studies demonstrate that SOX10 loss causes a subtype switch analogous to the proneural-mesenchymal transition observed in patients at the transcriptomic, epigenetic and phenotypic levels. SOX10 repression in an in vivo syngeneic graft glioblastoma mouse model results in increased tumour invasion, immune cell infiltration and significantly reduced survival, reminiscent of progressive human glioblastoma. These results identify SOX10 as a bona fide master regulator of the RTK I subtype, with both tumour cell-intrinsic and microenvironmental effects.