Biology subjects

Mottok, A.

Publications and source records attributed to Mottok, A..

4 recordsLinked to original sources

bioRxiv2021

Non-coding NFKBIZ 3' UTR mutations promote cell growth and resistance to targeted therapies in diffuse large B-cell lymphoma

Amplifications and non-coding 3' UTR mutations affecting NFKBIZ have been identified as recurrent genetic events in diffuse large B-cell lymphoma (DLBCL). We confirm the prevalence and pattern of NFKBIZ 3' UTR mutations in independent cohorts and determine they are enriched in the ABC subtype as well as the recently described novel BN2/C1/NOTCH2 classes of DLBCL. Presently, the effects of and mechanism by which non-coding mutations can act as cancer drivers has been relatively unexplored. Here, we provide a functional characterization of these non-coding NFKBIZ 3' UTR mutations. We demonstrate that the resulting elevated expression of I{kappa}B-{zeta} confers growth advantage in DLBCL cell lines and primary germinal center B-cells as well as nominate novel I{kappa}B-{zeta} target genes with potential therapeutic implications. The limited responses to targeted treatments in DLBCL, particularly those targeting the NF-{kappa}B axis, led us to investigate and confirm that NFKBIZ 3' UTR mutations affect response to therapeutics and suggest it may be a useful predictive biomarker. Statement of SignificanceThrough functional characterization we reveal that non-coding NFKBIZ 3' UTR mutations are a common driver in DLBCL, and mutation status may be a relevant biomarker to predict poor response to therapeutics targeting the NF-{kappa}B pathway.

cancer biology↗

bioRxiv2019

Selective inhibition of HDAC3 targets synthetic vulnerabilities and activates immune surveillance in lymphoma

CREBBP mutations are highly recurrent in B-cell lymphomas and either inactivate its histone acetyltransferase (HAT) domain or truncate the protein. Herein, we show that these two classes of mutations yield different degrees of disruption of the epigenome, with HAT mutations being more severe and associated with inferior clinical outcome. Genes perturbed by CREBBP mutation are direct targets of the BCL6/HDAC3 onco-repressor complex. Accordingly, we show that HDAC3 selective inhibitors fully reverse CREBBP mutant aberrant epigenetic programming resulting in: a) growth inhibition of lymphoma cells through induction of BCL6 target genes such as CDKN1A and b) restoration of immune surveillance due to induction of BCL6 repressed IFN pathway and antigen presentation genes. By reactivating these genes, exposure to HDAC3-i restored the ability of tumor infiltrating lymphocytes to kill DLBCL cells in an MHC II and MHC I dependent manner. Hence HDAC3-i represent a novel mechanism-based immune-epigenetic therapy for CREBBP mutant lymphomas. STATEMENT OF SIGNIFICANCEWe have leveraged the molecular characterization of different types of CREBBP mutations to define a rational approach for targeting these mutations through selective inhibition of HDAC3. This represents an attractive therapeutic avenue for targeting synthetic vulnerabilities in CREBBP mutant cells in tandem with promoting anti-tumor immunity.

cancer biology↗

bioRxiv2019

Probabilistic cell type assignment of single-cell transcriptomic data reveals spatiotemporal microenvironment dynamics in human cancers

Single-cell RNA sequencing (scRNA-seq) has transformed biomedical research, enabling decomposition of complex tissues into disaggregated, functionally distinct cell types. For many applications, investigators wish to identify cell types with known marker genes. Typically, such cell type assignments are performed through unsupervised clustering followed by manual annotation based on these marker genes, or via \"mapping\" procedures to existing data. However, the manual interpretation required in the former case scales poorly to large datasets, which are also often prone to batch effects, while existing data for purified cell types must be available for the latter. Furthermore, unsupervised clustering can be error-prone, leading to under- and over- clustering of the cell types of interest. To overcome these issues we present CellAssign, a probabilistic model that leverages prior knowledge of cell type marker genes to annotate scRNA-seq data into pre-defined and de novo cell types. CellAssign automates the process of assigning cells in a highly scalable manner across large datasets while simultaneously controlling for batch and patient effects. We demonstrate the analytical advantages of CellAssign through extensive simulations and exemplify real-world utility to profile the spatial dynamics of high-grade serous ovarian cancer and the temporal dynamics of follicular lymphoma. Our analysis reveals subclonal malignant phenotypes and points towards an evolutionary interplay between immune and cancer cell populations with cancer cells escaping immune recognition.

bioinformatics↗

bioRxiv2017

Genome-wide discovery of somatic coding and regulatory variants in Diffuse Large B-cell Lymphoma

Diffuse large B-cell lymphoma (DLBCL) is an aggressive cancer originating from mature B-cells. Many known driver mutations are over-represented in one of its two molecular subgroups, knowledge of which has aided in the development of therapeutics that target these features. The heterogeneity of DLBCL determined through prior genomic analysis suggests an incomplete understanding of its molecular aetiology, with a limited diversity of genetic events having thus far been attributed to the activated B-cell (ABC) subgroup. Through an integrative genomic analysis we uncovered genes and non-coding loci that are commonly mutated in DLBCL including putative regulatory sequences. We implicate recurrent mutations in the 3UTR of NFKBIZ as a novel mechanism of oncogene deregulation and found small amplifications associated with over-expression of FC-{gamma} receptor genes. These results inform on mechanisms of NF-{kappa}B pathway activation in ABC DLBCL and may reveal a high-risk population of patients that might not benefit from standard therapeutics.

genomics↗