The HSP90-MYC-CDK9 network drives therapeutic resistance in mantle cell lymphoma

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Abstract

Brexucabtagene autoleucel CAR-T therapy is highly efficacious in overcoming resistance to Bruton’s tyrosine kinase inhibitors (BTKi) in mantle cell lymphoma. However, many patients relapse post CAR-T therapy with dismal outcomes. To dissect the underlying mechanisms of sequential resistance to BTKi and CAR-T therapy, we performed single-cell RNA sequencing analysis for 66 samples from 25 patients treated with BTKi and/or CAR-T therapy and conducted in-depth bioinformatics™ analysis. Our analysis revealed that MYC activity progressively increased with sequential resistance. HSP90AB1 (Heat shock protein 90 alpha family class B member 1), a MYC target, was identified as early driver of CAR-T resistance. CDK9 (Cyclin-dependent kinase 9), another MYC target, was significantly upregulated in Dual-R samples. Both HSP90AB1 and CDK9 expression were correlated with MYC activity levels. Pharmaceutical co-targeting of HSP90 and CDK9 synergistically diminished MYC activity, leading to potent anti-MCL activity. Collectively, our study revealed that HSP90-MYC-CDK9 network is the primary driving force of therapeutic resistance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40164-024-00484-9.

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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Michael Love, Wolfgang Huber, Simon Anders (2014)

In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.

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Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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Comprehensive Integration of Single-Cell Data

Tim Stuart, Andrew Butler, Paul Hoffman … (2019)

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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Author and article information

Contributors

Vivian Jiang: cjiang@mdanderson.org

Zhongming Zhao: Zhongming.Zhao@uth.tmc.edu

Lukas M. Simon: Lukas.Simon@bcm.edu

Michael Wang: miwang@mdanderson.org

Journal

Journal ID (nlm-ta): Exp Hematol Oncol

Journal ID (iso-abbrev): Exp Hematol Oncol

Title: Experimental Hematology & Oncology

Publisher: BioMed Central (London )

ISSN (Electronic): 2162-3619

Publication date (Electronic): 7 February 2024

Publication date PMC-release: 7 February 2024

Publication date Collection: 2024

Volume: 13

Electronic Location Identifier: 14

Affiliations

[1 ]Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, ( https://ror.org/03gds6c39) Houston, TX 77030 USA

[2 ]Department of Lymphoma and Myeloma, The University of Texas MD Anderson Cancer Center, ( https://ror.org/04twxam07) Houston, TX USA

[3 ]Department of Pharmacology and Toxicology, University of Texas Medical Branch, ( https://ror.org/016tfm930) Galveston, TX 77555 USA

[4 ]Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, ( https://ror.org/02pttbw34) Houston, TX 77030 USA

[5 ]GRID grid.240145.6, ISNI 0000 0001 2291 4776, MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, ; Houston, TX 77030 USA

[6 ]Therapeutic Innovation Center, Baylor College of Medicine, ( https://ror.org/02pttbw34) Houston, TX 77030 USA

[7 ]Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, ( https://ror.org/04twxam07) Houston, TX USA

Article

Publisher ID: 484

DOI: 10.1186/s40164-024-00484-9

PMC ID: 10848414

PubMed ID: 38326887

SO-VID: 10668304-615e-4684-9b42-2307ebf6820e

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 9 October 2023

Date accepted : 25 January 2024

Funding

Funded by: NIH

Award ID: R01LM012806

Award Recipient : Zhongming Zhao

Funded by: CPRIT

Award ID: RP180734

Award Recipient : Zhongming Zhao

Funded by: MD Anderson B-cell Lymphoma Moon Shot Project

Funded by: The Gary Rogers Foundation

Funded by: Kinder Foundation

Funded by: FundRef http://dx.doi.org/10.13039/100001064, Cullen Foundation;

Funded by: start-up research funds by MD Anderson Cancer Center

Funded by: NIH-funded Cancer Center Support Grant (CCSG)

Award ID: P30 CA016672

Award Recipient : Michael Wang

Funded by: NIH Core Grant for the Sequencing and Microarray Facility

Award ID: CA016672

Award Recipient : Michael Wang

Custom metadata

ScienceOpen disciplines: Oncology & Radiotherapy

Keywords: mantle cell lymphoma,resistance,car-t therapy,single-cell rna sequencing

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ScienceOpen disciplines: Oncology & Radiotherapy

Keywords: mantle cell lymphoma, resistance, car-t therapy, single-cell rna sequencing

The HSP90-MYC-CDK9 network drives therapeutic resistance in mantle cell lymphoma

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Abstract

Supplementary Information

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Cancer Immunotherapy

Most cited references 42

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

Comprehensive Integration of Single-Cell Data

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