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      Single-cell multiomics reveal the scale of multilayered adaptations enabling CLL relapse during venetoclax therapy

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          Key Points

          • Multiple independent but recurring genetic and epigenetic changes drive venetoclax resistance, with marked NF-κB activation ubiquitous.

          • NF-κB activation is apparent within the first year of therapy, and most changes in CLL cells are sustained by ongoing venetoclax therapy.

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          Abstract

          Venetoclax (VEN) inhibits the prosurvival protein BCL2 to induce apoptosis and is a standard therapy for chronic lymphocytic leukemia (CLL), delivering high complete remission rates and prolonged progression-free survival in relapsed CLL but with eventual loss of efficacy. A spectrum of subclonal genetic changes associated with VEN resistance has now been described. To fully understand clinical resistance to VEN, we combined single-cell short- and long-read RNA-sequencing to reveal the previously unappreciated scale of genetic and epigenetic changes underpinning acquired VEN resistance. These appear to be multilayered. One layer comprises changes in the BCL2 family of apoptosis regulators, especially the prosurvival family members. This includes previously described mutations in BCL2 and amplification of the MCL1 gene but is heterogeneous across and within individual patient leukemias. Changes in the proapoptotic genes are notably uncommon, except for single cases with subclonal losses of BAX or NOXA. Much more prominent was universal MCL1 gene upregulation. This was driven by an overlying layer of emergent NF-κB (nuclear factor kappa B) activation, which persisted in circulating cells during VEN therapy. We discovered that MCL1 could be a direct transcriptional target of NF-κB. Both the switch to alternative prosurvival factors and NF-κB activation largely dissipate following VEN discontinuation. Our studies reveal the extent of plasticity of CLL cells in their ability to evade VEN-induced apoptosis. Importantly, these findings pinpoint new approaches to circumvent VEN resistance and provide a specific biological justification for the strategy of VEN discontinuation once a maximal response is achieved rather than maintaining long-term selective pressure with the drug.

          Abstract

          Two complementary articles shed new light on resistance to venetoclax in lymphoid malignancies. Thijssen et al use single-cell studies to reveal the multilayered nature of the mechanisms underpinning the recurrence of chronic lymphocytic leukemia in patients on long-term venetoclax, identifying a range of recurring genetic and epigenetic changes in apoptotic regulators. Overlying this heterogeneity, heightened expression of MCL1 driven by NF-κB is ubiquitous but reversible upon drug discontinuation. Thomalla and colleagues use B-lineage cell lines and patient samples to elegantly demonstrate how methylation and silencing of PUMA, a pro-apoptotic, causes failure of venetoclax. Both articles provide clinically applicable suggestions for circumventing emergent resistance to venetoclax.

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          Most cited references48

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            Comprehensive Integration of Single-Cell Data

            Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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              Dimensionality reduction for visualizing single-cell data using UMAP

              Advances in single-cell technologies have enabled high-resolution dissection of tissue composition. Several tools for dimensionality reduction are available to analyze the large number of parameters generated in single-cell studies. Recently, a nonlinear dimensionality-reduction technique, uniform manifold approximation and projection (UMAP), was developed for the analysis of any type of high-dimensional data. Here we apply it to biological data, using three well-characterized mass cytometry and single-cell RNA sequencing datasets. Comparing the performance of UMAP with five other tools, we find that UMAP provides the fastest run times, highest reproducibility and the most meaningful organization of cell clusters. The work highlights the use of UMAP for improved visualization and interpretation of single-cell data.
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                Author and article information

                Contributors
                Journal
                Blood
                Blood
                Blood
                The American Society of Hematology
                0006-4971
                1528-0020
                20 June 2022
                17 November 2022
                20 June 2022
                : 140
                : 20
                : 2127-2141
                Affiliations
                [1 ]The Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia
                [2 ]Department of Medical Biology, University of Melbourne, Melbourne, Australia
                [3 ]Department of Clinical Haematology, Peter MacCallum Cancer Centre and Royal Melbourne Hospital, Melbourne, Australia
                [4 ]Department of Medicine, University of Melbourne, Melbourne, Australia
                [5 ]Department of Pathology, Peter MacCallum Cancer Centre, Melbourne, Australia
                [6 ]Faculty of Medicine, Dentistry, and Health Sciences, University of Melbourne, Melbourne, Australia
                Author notes
                []Correspondence: David C. S. Huang, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3052, Australia; huang_d@ 123456wehi.edu.au
                [∗∗ ]Andrew W. Roberts, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3052, Australia; roberts@ 123456wehi.edu.au
                Article
                S0006-4971(22)00801-1
                10.1182/blood.2022016040
                10653037
                35709339
                e0c4bb8c-1d53-4e4d-9907-68903b1a2805
                © 2022 by The American Society of Hematology. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0), permitting only noncommercial, nonderivative use with attribution. All other rights reserved.

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 22 February 2022
                : 6 June 2022
                Categories
                Lymphoid Neoplasia

                Hematology
                Hematology

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