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Transcriptional signatures of the BCL2 family for individualized acute myeloid leukaemia treatment
BACKGROUND: Although anti-apoptotic proteins of the B-cell lymphoma-2 (BCL2) family have been utilized as therapeutic targets in acute myeloid leukaemia (AML), their complicated regulatory networks make individualized therapy difficult. This study aimed to discover the transcriptional signatures of...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9520894/ https://www.ncbi.nlm.nih.gov/pubmed/36171613 http://dx.doi.org/10.1186/s13073-022-01115-w |
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author | Lee, Chansub Lee, Sungyoung Park, Eunchae Hong, Junshik Shin, Dong-Yeop Byun, Ja Min Yun, Hongseok Koh, Youngil Yoon, Sung-Soo |
author_facet | Lee, Chansub Lee, Sungyoung Park, Eunchae Hong, Junshik Shin, Dong-Yeop Byun, Ja Min Yun, Hongseok Koh, Youngil Yoon, Sung-Soo |
author_sort | Lee, Chansub |
collection | PubMed |
description | BACKGROUND: Although anti-apoptotic proteins of the B-cell lymphoma-2 (BCL2) family have been utilized as therapeutic targets in acute myeloid leukaemia (AML), their complicated regulatory networks make individualized therapy difficult. This study aimed to discover the transcriptional signatures of BCL2 family genes that reflect regulatory dynamics, which can guide individualized therapeutic strategies. METHODS: From three AML RNA-seq cohorts (BeatAML, LeuceGene, and TCGA; n = 451, 437, and 179, respectively), we constructed the BCL2 family signatures (BFSigs) by applying an innovative gene-set selection method reflecting biological knowledge followed by non-negative matrix factorization (NMF). To demonstrate the significance of the BFSigs, we conducted modelling to predict response to BCL2 family inhibitors, clustering, and functional enrichment analysis. Cross-platform validity of BFSigs was also confirmed using NanoString technology in a separate cohort of 47 patients. RESULTS: We established BFSigs labeled as the BCL2, MCL1/BCL2, and BFL1/MCL1 signatures that identify key anti-apoptotic proteins. Unsupervised clustering based on BFSig information consistently classified AML patients into three robust subtypes across different AML cohorts, implying the existence of biological entities revealed by the BFSig approach. Interestingly, each subtype has distinct enrichment patterns of major cancer pathways, including MAPK and mTORC1, which propose subtype-specific combination treatment with apoptosis modulating drugs. The BFSig-based classifier also predicted response to venetoclax with remarkable performance (area under the ROC curve, AUROC = 0.874), which was well-validated in an independent cohort (AUROC = 0.950). Lastly, we successfully confirmed the validity of BFSigs using NanoString technology. CONCLUSIONS: This study proposes BFSigs as a biomarker for the effective selection of apoptosis targeting treatments and cancer pathways to co-target in AML. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13073-022-01115-w. |
format | Online Article Text |
id | pubmed-9520894 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-95208942022-09-30 Transcriptional signatures of the BCL2 family for individualized acute myeloid leukaemia treatment Lee, Chansub Lee, Sungyoung Park, Eunchae Hong, Junshik Shin, Dong-Yeop Byun, Ja Min Yun, Hongseok Koh, Youngil Yoon, Sung-Soo Genome Med Research BACKGROUND: Although anti-apoptotic proteins of the B-cell lymphoma-2 (BCL2) family have been utilized as therapeutic targets in acute myeloid leukaemia (AML), their complicated regulatory networks make individualized therapy difficult. This study aimed to discover the transcriptional signatures of BCL2 family genes that reflect regulatory dynamics, which can guide individualized therapeutic strategies. METHODS: From three AML RNA-seq cohorts (BeatAML, LeuceGene, and TCGA; n = 451, 437, and 179, respectively), we constructed the BCL2 family signatures (BFSigs) by applying an innovative gene-set selection method reflecting biological knowledge followed by non-negative matrix factorization (NMF). To demonstrate the significance of the BFSigs, we conducted modelling to predict response to BCL2 family inhibitors, clustering, and functional enrichment analysis. Cross-platform validity of BFSigs was also confirmed using NanoString technology in a separate cohort of 47 patients. RESULTS: We established BFSigs labeled as the BCL2, MCL1/BCL2, and BFL1/MCL1 signatures that identify key anti-apoptotic proteins. Unsupervised clustering based on BFSig information consistently classified AML patients into three robust subtypes across different AML cohorts, implying the existence of biological entities revealed by the BFSig approach. Interestingly, each subtype has distinct enrichment patterns of major cancer pathways, including MAPK and mTORC1, which propose subtype-specific combination treatment with apoptosis modulating drugs. The BFSig-based classifier also predicted response to venetoclax with remarkable performance (area under the ROC curve, AUROC = 0.874), which was well-validated in an independent cohort (AUROC = 0.950). Lastly, we successfully confirmed the validity of BFSigs using NanoString technology. CONCLUSIONS: This study proposes BFSigs as a biomarker for the effective selection of apoptosis targeting treatments and cancer pathways to co-target in AML. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13073-022-01115-w. BioMed Central 2022-09-28 /pmc/articles/PMC9520894/ /pubmed/36171613 http://dx.doi.org/10.1186/s13073-022-01115-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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. |
spellingShingle | Research Lee, Chansub Lee, Sungyoung Park, Eunchae Hong, Junshik Shin, Dong-Yeop Byun, Ja Min Yun, Hongseok Koh, Youngil Yoon, Sung-Soo Transcriptional signatures of the BCL2 family for individualized acute myeloid leukaemia treatment |
title | Transcriptional signatures of the BCL2 family for individualized acute myeloid leukaemia treatment |
title_full | Transcriptional signatures of the BCL2 family for individualized acute myeloid leukaemia treatment |
title_fullStr | Transcriptional signatures of the BCL2 family for individualized acute myeloid leukaemia treatment |
title_full_unstemmed | Transcriptional signatures of the BCL2 family for individualized acute myeloid leukaemia treatment |
title_short | Transcriptional signatures of the BCL2 family for individualized acute myeloid leukaemia treatment |
title_sort | transcriptional signatures of the bcl2 family for individualized acute myeloid leukaemia treatment |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9520894/ https://www.ncbi.nlm.nih.gov/pubmed/36171613 http://dx.doi.org/10.1186/s13073-022-01115-w |
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