Identification of key microRNAs as predictive biomarkers of Nilotinib response in chronic myeloid leukemia: a sub-analysis of the ENESTxtnd clinical trial
Despite the effectiveness of tyrosine kinase inhibitors (TKIs) against chronic myeloid leukemia (CML), they are not usually curative as some patients develop drug-resistance or are at risk of disease relapse when treatment is discontinued. Studies have demonstrated that primitive CML cells display u...
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| Veröffentlicht in: | Leukemia 2022-10, Vol.36 (10), p.2443-2452 |
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| creator | Yen, Ryan Grasedieck, Sarah Wu, Andrew Lin, Hanyang Su, Jiechuang Rothe, Katharina Nakamoto, Helen Forrest, Donna L. Eaves, Connie J. Jiang, Xiaoyan |
| description | Despite the effectiveness of tyrosine kinase inhibitors (TKIs) against chronic myeloid leukemia (CML), they are not usually curative as some patients develop drug-resistance or are at risk of disease relapse when treatment is discontinued. Studies have demonstrated that primitive CML cells display unique miRNA profiles in response to TKI treatment. However, the utility of miRNAs in predicting treatment response is not yet conclusive. Here, we analyzed differentially expressed miRNAs in CD34
+
CML cells pre- and post-nilotinib (NL) therapy from 58 patients enrolled in the Canadian sub-analysis of the ENESTxtnd phase IIIb clinical trial which correlated with sensitivity of CD34
+
cells to NL treatment in in vitro colony-forming cell (CFC) assays. We performed Cox Proportional Hazard (CoxPH) analysis and applied machine learning algorithms to generate multivariate miRNA panels which can predict NL response at treatment-naïve or post-treatment time points. We demonstrated that a combination of miR-145 and miR-708 are effective predictors of NL response in treatment-naïve patients whereas miR-150 and miR-185 were significant classifiers at 1-month and 3-month post-NL therapy. Interestingly, incorporation of NL-CFC output in these panels enhanced predictive performance. Thus, this novel predictive model may be developed into a prognostic tool for use in the clinic. |
| doi_str_mv | 10.1038/s41375-022-01680-4 |
| format | Article |
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+
CML cells pre- and post-nilotinib (NL) therapy from 58 patients enrolled in the Canadian sub-analysis of the ENESTxtnd phase IIIb clinical trial which correlated with sensitivity of CD34
+
cells to NL treatment in in vitro colony-forming cell (CFC) assays. We performed Cox Proportional Hazard (CoxPH) analysis and applied machine learning algorithms to generate multivariate miRNA panels which can predict NL response at treatment-naïve or post-treatment time points. We demonstrated that a combination of miR-145 and miR-708 are effective predictors of NL response in treatment-naïve patients whereas miR-150 and miR-185 were significant classifiers at 1-month and 3-month post-NL therapy. Interestingly, incorporation of NL-CFC output in these panels enhanced predictive performance. Thus, this novel predictive model may be developed into a prognostic tool for use in the clinic.</description><identifier>ISSN: 0887-6924</identifier><identifier>EISSN: 1476-5551</identifier><identifier>DOI: 10.1038/s41375-022-01680-4</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>13/100 ; 13/31 ; 38/61 ; 45/77 ; 692/308 ; 692/308/575 ; Algorithms ; Biomarkers ; Cancer Research ; CD34 antigen ; Chronic myeloid leukemia ; Clinical trials ; Colony-forming cells ; Critical Care Medicine ; Drug resistance ; Health risks ; Health services ; Hematology ; Inhibitor drugs ; Intensive ; Internal Medicine ; Kinases ; Leukemia ; Machine learning ; Medicine ; Medicine & Public Health ; MicroRNAs ; miRNA ; Myeloid leukemia ; Oncology ; Panels ; Patients ; Performance prediction ; Prediction models ; Protein-tyrosine kinase ; Risk assessment ; Targeted cancer therapy ; Tyrosine</subject><ispartof>Leukemia, 2022-10, Vol.36 (10), p.2443-2452</ispartof><rights>The Author(s), under exclusive licence to Springer Nature Limited 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c352t-5ed6c40e67a5cae058464274f1beff02b0ed303c5bedcb851a5922aa4e4a1dc43</citedby><cites>FETCH-LOGICAL-c352t-5ed6c40e67a5cae058464274f1beff02b0ed303c5bedcb851a5922aa4e4a1dc43</cites><orcidid>0000-0002-8721-5295 ; 0000-0003-0694-1496 ; 0000-0002-3452-1926 ; 0000-0002-8974-7340</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1038/s41375-022-01680-4$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1038/s41375-022-01680-4$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,777,781,27905,27906,41469,42538,51300</link.rule.ids></links><search><creatorcontrib>Yen, Ryan</creatorcontrib><creatorcontrib>Grasedieck, Sarah</creatorcontrib><creatorcontrib>Wu, Andrew</creatorcontrib><creatorcontrib>Lin, Hanyang</creatorcontrib><creatorcontrib>Su, Jiechuang</creatorcontrib><creatorcontrib>Rothe, Katharina</creatorcontrib><creatorcontrib>Nakamoto, Helen</creatorcontrib><creatorcontrib>Forrest, Donna L.</creatorcontrib><creatorcontrib>Eaves, Connie J.</creatorcontrib><creatorcontrib>Jiang, Xiaoyan</creatorcontrib><title>Identification of key microRNAs as predictive biomarkers of Nilotinib response in chronic myeloid leukemia: a sub-analysis of the ENESTxtnd clinical trial</title><title>Leukemia</title><addtitle>Leukemia</addtitle><description>Despite the effectiveness of tyrosine kinase inhibitors (TKIs) against chronic myeloid leukemia (CML), they are not usually curative as some patients develop drug-resistance or are at risk of disease relapse when treatment is discontinued. Studies have demonstrated that primitive CML cells display unique miRNA profiles in response to TKI treatment. However, the utility of miRNAs in predicting treatment response is not yet conclusive. Here, we analyzed differentially expressed miRNAs in CD34
+
CML cells pre- and post-nilotinib (NL) therapy from 58 patients enrolled in the Canadian sub-analysis of the ENESTxtnd phase IIIb clinical trial which correlated with sensitivity of CD34
+
cells to NL treatment in in vitro colony-forming cell (CFC) assays. We performed Cox Proportional Hazard (CoxPH) analysis and applied machine learning algorithms to generate multivariate miRNA panels which can predict NL response at treatment-naïve or post-treatment time points. We demonstrated that a combination of miR-145 and miR-708 are effective predictors of NL response in treatment-naïve patients whereas miR-150 and miR-185 were significant classifiers at 1-month and 3-month post-NL therapy. Interestingly, incorporation of NL-CFC output in these panels enhanced predictive performance. Thus, this novel predictive model may be developed into a prognostic tool for use in the clinic.</description><subject>13/100</subject><subject>13/31</subject><subject>38/61</subject><subject>45/77</subject><subject>692/308</subject><subject>692/308/575</subject><subject>Algorithms</subject><subject>Biomarkers</subject><subject>Cancer Research</subject><subject>CD34 antigen</subject><subject>Chronic myeloid leukemia</subject><subject>Clinical trials</subject><subject>Colony-forming cells</subject><subject>Critical Care Medicine</subject><subject>Drug resistance</subject><subject>Health risks</subject><subject>Health services</subject><subject>Hematology</subject><subject>Inhibitor drugs</subject><subject>Intensive</subject><subject>Internal Medicine</subject><subject>Kinases</subject><subject>Leukemia</subject><subject>Machine learning</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>MicroRNAs</subject><subject>miRNA</subject><subject>Myeloid 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Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><jtitle>Leukemia</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yen, Ryan</au><au>Grasedieck, Sarah</au><au>Wu, Andrew</au><au>Lin, Hanyang</au><au>Su, Jiechuang</au><au>Rothe, Katharina</au><au>Nakamoto, Helen</au><au>Forrest, Donna L.</au><au>Eaves, Connie J.</au><au>Jiang, Xiaoyan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identification of key microRNAs as predictive biomarkers of Nilotinib response in chronic myeloid leukemia: a sub-analysis of the ENESTxtnd clinical trial</atitle><jtitle>Leukemia</jtitle><stitle>Leukemia</stitle><date>2022-10-01</date><risdate>2022</risdate><volume>36</volume><issue>10</issue><spage>2443</spage><epage>2452</epage><pages>2443-2452</pages><issn>0887-6924</issn><eissn>1476-5551</eissn><abstract>Despite the effectiveness of tyrosine kinase inhibitors (TKIs) against chronic myeloid leukemia (CML), they are not usually curative as some patients develop drug-resistance or are at risk of disease relapse when treatment is discontinued. Studies have demonstrated that primitive CML cells display unique miRNA profiles in response to TKI treatment. However, the utility of miRNAs in predicting treatment response is not yet conclusive. Here, we analyzed differentially expressed miRNAs in CD34
+
CML cells pre- and post-nilotinib (NL) therapy from 58 patients enrolled in the Canadian sub-analysis of the ENESTxtnd phase IIIb clinical trial which correlated with sensitivity of CD34
+
cells to NL treatment in in vitro colony-forming cell (CFC) assays. We performed Cox Proportional Hazard (CoxPH) analysis and applied machine learning algorithms to generate multivariate miRNA panels which can predict NL response at treatment-naïve or post-treatment time points. We demonstrated that a combination of miR-145 and miR-708 are effective predictors of NL response in treatment-naïve patients whereas miR-150 and miR-185 were significant classifiers at 1-month and 3-month post-NL therapy. Interestingly, incorporation of NL-CFC output in these panels enhanced predictive performance. Thus, this novel predictive model may be developed into a prognostic tool for use in the clinic.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><doi>10.1038/s41375-022-01680-4</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-8721-5295</orcidid><orcidid>https://orcid.org/0000-0003-0694-1496</orcidid><orcidid>https://orcid.org/0000-0002-3452-1926</orcidid><orcidid>https://orcid.org/0000-0002-8974-7340</orcidid></addata></record> |
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| subjects | 13/100 13/31 38/61 45/77 692/308 692/308/575 Algorithms Biomarkers Cancer Research CD34 antigen Chronic myeloid leukemia Clinical trials Colony-forming cells Critical Care Medicine Drug resistance Health risks Health services Hematology Inhibitor drugs Intensive Internal Medicine Kinases Leukemia Machine learning Medicine Medicine & Public Health MicroRNAs miRNA Myeloid leukemia Oncology Panels Patients Performance prediction Prediction models Protein-tyrosine kinase Risk assessment Targeted cancer therapy Tyrosine |
| title | Identification of key microRNAs as predictive biomarkers of Nilotinib response in chronic myeloid leukemia: a sub-analysis of the ENESTxtnd clinical trial |
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