CKLF and IL1B transcript levels at diagnosis are predictive of relapse in children with pre‐B‐cell acute lymphoblastic leukaemia
Summary Disease relapse is the greatest cause of treatment failure in paediatric B‐cell acute lymphoblastic leukaemia (B‐ALL). Current risk stratifications fail to capture all patients at risk of relapse. Herein, we used a machine‐learning approach to identify B‐ALL blast‐secreted factors that are a...
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| Veröffentlicht in: | British journal of haematology 2021-04, Vol.193 (1), p.171-175 |
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| container_title | British journal of haematology |
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| creator | Fitter, Stephen Bradey, Alanah L. Kok, Chung Hoow Noll, Jacqueline E. Wilczek, Vicki J. Venn, Nicola C. Law, Tamara Paisitkriangkrai, Sakrapee Story, Colin Saunders, Lynda Dalla Pozza, Luciano Marshall, Glenn M. White, Deborah L. Sutton, Rosemary Zannettino, Andrew C. W. Revesz, Tamas |
| description | Summary
Disease relapse is the greatest cause of treatment failure in paediatric B‐cell acute lymphoblastic leukaemia (B‐ALL). Current risk stratifications fail to capture all patients at risk of relapse. Herein, we used a machine‐learning approach to identify B‐ALL blast‐secreted factors that are associated with poor survival outcomes. Using this approach, we identified a two‐gene expression signature (CKLF and IL1B) that allowed identification of high‐risk patients at diagnosis. This two‐gene expression signature enhances the predictive value of current at diagnosis or end‐of‐induction risk stratification suggesting the model can be applied continuously to help guide implementation of risk‐adapted therapies. |
| doi_str_mv | 10.1111/bjh.17161 |
| format | Article |
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Disease relapse is the greatest cause of treatment failure in paediatric B‐cell acute lymphoblastic leukaemia (B‐ALL). Current risk stratifications fail to capture all patients at risk of relapse. Herein, we used a machine‐learning approach to identify B‐ALL blast‐secreted factors that are associated with poor survival outcomes. Using this approach, we identified a two‐gene expression signature (CKLF and IL1B) that allowed identification of high‐risk patients at diagnosis. This two‐gene expression signature enhances the predictive value of current at diagnosis or end‐of‐induction risk stratification suggesting the model can be applied continuously to help guide implementation of risk‐adapted therapies.</description><identifier>ISSN: 0007-1048</identifier><identifier>EISSN: 1365-2141</identifier><identifier>DOI: 10.1111/bjh.17161</identifier><identifier>PMID: 33620089</identifier><language>eng</language><publisher>England: Blackwell Publishing Ltd</publisher><subject>Acute lymphoblastic leukemia ; Diagnosis ; Gene expression ; Hematology ; Interleukin 1 ; Learning algorithms ; Leukemia ; Transcription</subject><ispartof>British journal of haematology, 2021-04, Vol.193 (1), p.171-175</ispartof><rights>2021 British Society for Haematology and John Wiley & Sons Ltd</rights><rights>2021 British Society for Haematology and John Wiley & Sons Ltd.</rights><rights>Copyright © 2021 John Wiley & Sons Ltd</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3881-dcbc39ae69c702ebc354dc021bd832f8783be32eb646e8368510e80ca8f43af03</citedby><cites>FETCH-LOGICAL-c3881-dcbc39ae69c702ebc354dc021bd832f8783be32eb646e8368510e80ca8f43af03</cites><orcidid>0000-0003-1663-6807 ; 0000-0002-6646-6167 ; 0000-0002-3181-7852</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fbjh.17161$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fbjh.17161$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,1433,27924,27925,45574,45575,46409,46833</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33620089$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Fitter, Stephen</creatorcontrib><creatorcontrib>Bradey, Alanah L.</creatorcontrib><creatorcontrib>Kok, Chung Hoow</creatorcontrib><creatorcontrib>Noll, Jacqueline E.</creatorcontrib><creatorcontrib>Wilczek, Vicki J.</creatorcontrib><creatorcontrib>Venn, Nicola C.</creatorcontrib><creatorcontrib>Law, Tamara</creatorcontrib><creatorcontrib>Paisitkriangkrai, Sakrapee</creatorcontrib><creatorcontrib>Story, Colin</creatorcontrib><creatorcontrib>Saunders, Lynda</creatorcontrib><creatorcontrib>Dalla Pozza, Luciano</creatorcontrib><creatorcontrib>Marshall, Glenn M.</creatorcontrib><creatorcontrib>White, Deborah L.</creatorcontrib><creatorcontrib>Sutton, Rosemary</creatorcontrib><creatorcontrib>Zannettino, Andrew C. W.</creatorcontrib><creatorcontrib>Revesz, Tamas</creatorcontrib><title>CKLF and IL1B transcript levels at diagnosis are predictive of relapse in children with pre‐B‐cell acute lymphoblastic leukaemia</title><title>British journal of haematology</title><addtitle>Br J Haematol</addtitle><description>Summary
Disease relapse is the greatest cause of treatment failure in paediatric B‐cell acute lymphoblastic leukaemia (B‐ALL). Current risk stratifications fail to capture all patients at risk of relapse. Herein, we used a machine‐learning approach to identify B‐ALL blast‐secreted factors that are associated with poor survival outcomes. Using this approach, we identified a two‐gene expression signature (CKLF and IL1B) that allowed identification of high‐risk patients at diagnosis. This two‐gene expression signature enhances the predictive value of current at diagnosis or end‐of‐induction risk stratification suggesting the model can be applied continuously to help guide implementation of risk‐adapted therapies.</description><subject>Acute lymphoblastic leukemia</subject><subject>Diagnosis</subject><subject>Gene expression</subject><subject>Hematology</subject><subject>Interleukin 1</subject><subject>Learning algorithms</subject><subject>Leukemia</subject><subject>Transcription</subject><issn>0007-1048</issn><issn>1365-2141</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp1kc9u1DAQxi1ERZeFAy-ALHEph7RjO3GcI7ui9M9KXOBsOc6E9eL8wU5a7Y0DD8Az8iR42cKhEiONRp_mp29G-gh5xeCcpbqod9tzVjLJnpAFE7LIOMvZU7IAgDJjkKtT8jzGHQATULBn5FQIyQFUtSA_1rebS2r6hl5v2IpOwfTRBjdO1OMd-kjNRBtnvvRDdEkEpGPAxtnJ3SEdWhrQmzEidT21W-ebgD29d9P2gP36_nOV2qL31Nh5Qur33bgdam_i5Gy6MH812Dnzgpy0xkd8-TCX5PPl-0_rq2zz8cP1-t0ms0IpljW2tqIyKCtbAsckiryxwFndKMFbVSpRo0gLmUtUQqqCASqwRrW5MC2IJTk7-o5h-DZjnHTn4uE90-MwR83zikvJSl4l9M0jdDfMoU_faV5AxRWIVEvy9kjZMMQYsNVjcJ0Je81AH6LRKRr9J5rEvn5wnOsOm3_k3ywScHEE7p3H_f-d9Orm6mj5G9X9mf4</recordid><startdate>202104</startdate><enddate>202104</enddate><creator>Fitter, Stephen</creator><creator>Bradey, Alanah L.</creator><creator>Kok, Chung Hoow</creator><creator>Noll, Jacqueline E.</creator><creator>Wilczek, Vicki J.</creator><creator>Venn, Nicola C.</creator><creator>Law, Tamara</creator><creator>Paisitkriangkrai, Sakrapee</creator><creator>Story, Colin</creator><creator>Saunders, Lynda</creator><creator>Dalla Pozza, Luciano</creator><creator>Marshall, Glenn M.</creator><creator>White, Deborah L.</creator><creator>Sutton, Rosemary</creator><creator>Zannettino, Andrew C. W.</creator><creator>Revesz, Tamas</creator><general>Blackwell Publishing Ltd</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7T5</scope><scope>H94</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-1663-6807</orcidid><orcidid>https://orcid.org/0000-0002-6646-6167</orcidid><orcidid>https://orcid.org/0000-0002-3181-7852</orcidid></search><sort><creationdate>202104</creationdate><title>CKLF and IL1B transcript levels at diagnosis are predictive of relapse in children with pre‐B‐cell acute lymphoblastic leukaemia</title><author>Fitter, Stephen ; Bradey, Alanah L. ; Kok, Chung Hoow ; Noll, Jacqueline E. ; Wilczek, Vicki J. ; Venn, Nicola C. ; Law, Tamara ; Paisitkriangkrai, Sakrapee ; Story, Colin ; Saunders, Lynda ; Dalla Pozza, Luciano ; Marshall, Glenn M. ; White, Deborah L. ; Sutton, Rosemary ; Zannettino, Andrew C. 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W.</creatorcontrib><creatorcontrib>Revesz, Tamas</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Immunology Abstracts</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>British journal of haematology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fitter, Stephen</au><au>Bradey, Alanah L.</au><au>Kok, Chung Hoow</au><au>Noll, Jacqueline E.</au><au>Wilczek, Vicki J.</au><au>Venn, Nicola C.</au><au>Law, Tamara</au><au>Paisitkriangkrai, Sakrapee</au><au>Story, Colin</au><au>Saunders, Lynda</au><au>Dalla Pozza, Luciano</au><au>Marshall, Glenn M.</au><au>White, Deborah L.</au><au>Sutton, Rosemary</au><au>Zannettino, Andrew C. 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Disease relapse is the greatest cause of treatment failure in paediatric B‐cell acute lymphoblastic leukaemia (B‐ALL). Current risk stratifications fail to capture all patients at risk of relapse. Herein, we used a machine‐learning approach to identify B‐ALL blast‐secreted factors that are associated with poor survival outcomes. Using this approach, we identified a two‐gene expression signature (CKLF and IL1B) that allowed identification of high‐risk patients at diagnosis. This two‐gene expression signature enhances the predictive value of current at diagnosis or end‐of‐induction risk stratification suggesting the model can be applied continuously to help guide implementation of risk‐adapted therapies.</abstract><cop>England</cop><pub>Blackwell Publishing Ltd</pub><pmid>33620089</pmid><doi>10.1111/bjh.17161</doi><tpages>5</tpages><orcidid>https://orcid.org/0000-0003-1663-6807</orcidid><orcidid>https://orcid.org/0000-0002-6646-6167</orcidid><orcidid>https://orcid.org/0000-0002-3181-7852</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Acute lymphoblastic leukemia Diagnosis Gene expression Hematology Interleukin 1 Learning algorithms Leukemia Transcription |
| title | CKLF and IL1B transcript levels at diagnosis are predictive of relapse in children with pre‐B‐cell acute lymphoblastic leukaemia |
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