Integrative analysis of drug response and clinical outcome in acute myeloid leukemia
Acute myeloid leukemia (AML) is a cancer of myeloid-lineage cells with limited therapeutic options. We previously combined ex vivo drug sensitivity with genomic, transcriptomic, and clinical annotations for a large cohort of AML patients, which facilitated discovery of functional genomic correlates....
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| Vydáno v: | Cancer cell Ročník 40; číslo 8; s. 850 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
United States
08.08.2022
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| Témata: | |
| ISSN: | 1878-3686, 1878-3686 |
| On-line přístup: | Zjistit podrobnosti o přístupu |
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| Abstract | Acute myeloid leukemia (AML) is a cancer of myeloid-lineage cells with limited therapeutic options. We previously combined ex vivo drug sensitivity with genomic, transcriptomic, and clinical annotations for a large cohort of AML patients, which facilitated discovery of functional genomic correlates. Here, we present a dataset that has been harmonized with our initial report to yield a cumulative cohort of 805 patients (942 specimens). We show strong cross-cohort concordance and identify features of drug response. Further, deconvoluting transcriptomic data shows that drug sensitivity is governed broadly by AML cell differentiation state, sometimes conditionally affecting other correlates of response. Finally, modeling of clinical outcome reveals a single gene, PEAR1, to be among the strongest predictors of patient survival, especially for young patients. Collectively, this report expands a large functional genomic resource, offers avenues for mechanistic exploration and drug development, and reveals tools for predicting outcome in AML. |
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| AbstractList | Acute myeloid leukemia (AML) is a cancer of myeloid-lineage cells with limited therapeutic options. We previously combined ex vivo drug sensitivity with genomic, transcriptomic, and clinical annotations for a large cohort of AML patients, which facilitated discovery of functional genomic correlates. Here, we present a dataset that has been harmonized with our initial report to yield a cumulative cohort of 805 patients (942 specimens). We show strong cross-cohort concordance and identify features of drug response. Further, deconvoluting transcriptomic data shows that drug sensitivity is governed broadly by AML cell differentiation state, sometimes conditionally affecting other correlates of response. Finally, modeling of clinical outcome reveals a single gene, PEAR1, to be among the strongest predictors of patient survival, especially for young patients. Collectively, this report expands a large functional genomic resource, offers avenues for mechanistic exploration and drug development, and reveals tools for predicting outcome in AML. Acute myeloid leukemia (AML) is a cancer of myeloid-lineage cells with limited therapeutic options. We previously combined ex vivo drug sensitivity with genomic, transcriptomic, and clinical annotations for a large cohort of AML patients, which facilitated discovery of functional genomic correlates. Here, we present a dataset that has been harmonized with our initial report to yield a cumulative cohort of 805 patients (942 specimens). We show strong cross-cohort concordance and identify features of drug response. Further, deconvoluting transcriptomic data shows that drug sensitivity is governed broadly by AML cell differentiation state, sometimes conditionally affecting other correlates of response. Finally, modeling of clinical outcome reveals a single gene, PEAR1, to be among the strongest predictors of patient survival, especially for young patients. Collectively, this report expands a large functional genomic resource, offers avenues for mechanistic exploration and drug development, and reveals tools for predicting outcome in AML.Acute myeloid leukemia (AML) is a cancer of myeloid-lineage cells with limited therapeutic options. We previously combined ex vivo drug sensitivity with genomic, transcriptomic, and clinical annotations for a large cohort of AML patients, which facilitated discovery of functional genomic correlates. Here, we present a dataset that has been harmonized with our initial report to yield a cumulative cohort of 805 patients (942 specimens). We show strong cross-cohort concordance and identify features of drug response. Further, deconvoluting transcriptomic data shows that drug sensitivity is governed broadly by AML cell differentiation state, sometimes conditionally affecting other correlates of response. Finally, modeling of clinical outcome reveals a single gene, PEAR1, to be among the strongest predictors of patient survival, especially for young patients. Collectively, this report expands a large functional genomic resource, offers avenues for mechanistic exploration and drug development, and reveals tools for predicting outcome in AML. |
| Author | Dao, Kim-Hien T Sweeney, Tyler Liu, Selina Qiuying Minnier, Jessica Ramsdill, Justin Deininger, Michael W Harrelson, Heath Traer, Elie Wu, Guanming Jordan, Craig T Benton, Erik Hagler, Stuart Cohen, Aaron M Loriaux, Marc M Blucher, Aurora d'Almeida, Amanda McWeeney, Shannon K Schultz, Anna Reister Weir, Scott J Bryant, Jade Lawhead, Matt Brown, Jordana Luty, Samuel Druker, Brian J Lin, Chenwei Mori, Motomi Braun, Theodore P Watts, Justin M Wilmot, Beth Swords, Ronan T Zhang, Haijiao Tognon, Cristina E Thapa, Aashis Hourigan, Christopher S Pollyea, Daniel A Maxson, Julia E Ho, Hibery Romine, Kyle A Kaempf, Andy Leonard, Jessica T Bottomly, Daniel Carlos, Amy Junio, Brian Abel, Melissa Monteblanco, Andrea Smith, Rebecca L Spurgeon, Stephen E Sampson, David A Morrow, Quinlan Nelson, Dylan Danilov, Alexey Rogers, Alexandra Henson, Rachel Joshi, Sunil K Saultz, Jennifer N Long, Nicola Martinez, Micaela E Johnson, Kara Lin, Tara L Kurtz, Stephen E Borate, Uma Kosaka, Yoko Christy, Stephen Ryabinin, Peter Lind, Evan F Martinez, Jacqueline Thiel-Klare, Karin |
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Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR 97239, USA. Electronic address: drukerb@ohsu.edu – sequence: 93 givenname: Shannon K surname: McWeeney fullname: McWeeney, Shannon K email: mcweeney@ohsu.edu organization: Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA; Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, OR 97239, USA. Electronic address: mcweeney@ohsu.edu – sequence: 94 givenname: Jeffrey W surname: Tyner fullname: Tyner, Jeffrey W email: tynerj@ohsu.edu organization: Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA; Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR 97239, USA. Electronic address: tynerj@ohsu.edu |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35868306$$D View this record in MEDLINE/PubMed |
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| Keywords | targeted therapy JEDI leukemia stem cell eigengene LSC17 monocyte MEGF12 hematologic malignancy cell state |
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| Title | Integrative analysis of drug response and clinical outcome in acute myeloid leukemia |
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