000167949 001__ 167949
000167949 005__ 20240229133556.0
000167949 0247_ $$2doi$$a10.3324/haematol.2020.251561
000167949 0247_ $$2pmid$$apmid:33730841
000167949 0247_ $$2ISSN$$a0390-6078
000167949 0247_ $$2ISSN$$a1592-8721
000167949 0247_ $$2altmetric$$aaltmetric:102004041
000167949 037__ $$aDKFZ-2021-00660
000167949 041__ $$aEnglish
000167949 082__ $$a610
000167949 1001_ $$aBloehdorn, Johannes$$b0
000167949 245__ $$aIntegrative prognostic models predict long-term survival after immunochemotherapy in chronic lymphocytic leukemia patients.
000167949 260__ $$aPavia$$bFerrata Storti Foundation64433$$c2022
000167949 3367_ $$2DRIVER$$aarticle
000167949 3367_ $$2DataCite$$aOutput Types/Journal article
000167949 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1678888090_29492
000167949 3367_ $$2BibTeX$$aARTICLE
000167949 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000167949 3367_ $$00$$2EndNote$$aJournal Article
000167949 500__ $$a2022Volume 107(3):615-624 / #LA:C060#
000167949 520__ $$aChemoimmunotherapy with fludarabine, cyclophosphamide and rituximab can induce longterm remissions in patients with chronic lymphocytic leukemia. Treatment efficacy with Bruton's tyrosine kinase inhibitors was found similar to fludarabine, cyclophosphamide and rituximab in untreated chronic lymphocytic leukemia patients with a mutated immunoglobulin heavy chain variable gene. To identify patients who specifically benefit from fludarabine, cyclophosphamide and rituximab, we developed integrative models including established prognostic parameters and gene expression profiling. Gene expression profiling was conducted on n=337 CLL8 trial samples, 'core' probe sets were summarized on gene levels and RMA normalized. Prognostic models were built using penalized Cox proportional hazards models with the smoothly clipped absolute deviation penalty. We identified a prognostic signature of less than a dozen genes, which substituted for established prognostic factors, including TP53 and immunoglobulin heavy chain variable gene mutation status. Independent prognostic impact was confirmed for treatment, β2-microglobulin and del(17p) regarding overall survival and for treatment, del(11q), del(17p) and SF3B1 mutation for progression-free survival. The combination of independent prognostic and gene expression profiling variables performed equal to models including only established non-gene expression profiling variables. Gene expression profiling variables showed higher prognostic accuracy for patients with long progression-free survival compared to categorical variables like the immunoglobulin heavy chain variable gene mutation status and reliably predicted overall survival in CLL8 and an independent cohort. Gene expression profiling based prognostic models can help to identify patients who specifically benefit from fludarabine, cyclophosphamide and rituximab treatment. The CLL8 trial is registered under EUDRACT- 2004-004938-14 and ClinicalTrials.gov Identifier NCT00281918.
000167949 536__ $$0G:(DE-HGF)POF4-313$$a313 - Krebsrisikofaktoren und Prävention (POF4-313)$$cPOF4-313$$fPOF IV$$x0
000167949 588__ $$aDataset connected to CrossRef, PubMed,
000167949 7001_ $$0P:(DE-He78)5a7a75d1b29b770f98f1bb2062fc3df9$$aKrzykalla, Julia$$b1
000167949 7001_ $$aHolzmann, Karlheinz$$b2
000167949 7001_ $$aGerhardinger, Andreas$$b3
000167949 7001_ $$aJebaraj, Billy Michael Chelliah$$b4
000167949 7001_ $$aBahlo, Jasmin$$b5
000167949 7001_ $$aHumphrey, Kathryn$$b6
000167949 7001_ $$aTausch, Eugen$$b7
000167949 7001_ $$aRobrecht, Sandra$$b8
000167949 7001_ $$0P:(DE-He78)833c06a995d272b78f3a20df3eba6e9e$$aMertens, Daniel$$b9
000167949 7001_ $$aSchneider, Christof$$b10
000167949 7001_ $$aFischer, Kirsten$$b11
000167949 7001_ $$aHallek, Michael$$b12
000167949 7001_ $$aDöhner, Hartmut$$b13
000167949 7001_ $$0P:(DE-He78)e15dfa1260625c69d6690a197392a994$$aBenner, Axel$$b14$$eLast author
000167949 7001_ $$aStilgenbauer, Stephan$$b15
000167949 773__ $$0PERI:(DE-600)2805244-4$$a10.3324/haematol.2020.251561$$n3$$p615-624$$tHaematologica$$v107$$x1592-8721$$y2022
000167949 909CO $$ooai:inrepo02.dkfz.de:167949$$pVDB
000167949 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)5a7a75d1b29b770f98f1bb2062fc3df9$$aDeutsches Krebsforschungszentrum$$b1$$kDKFZ
000167949 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)833c06a995d272b78f3a20df3eba6e9e$$aDeutsches Krebsforschungszentrum$$b9$$kDKFZ
000167949 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)e15dfa1260625c69d6690a197392a994$$aDeutsches Krebsforschungszentrum$$b14$$kDKFZ
000167949 9131_ $$0G:(DE-HGF)POF4-313$$1G:(DE-HGF)POF4-310$$2G:(DE-HGF)POF4-300$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lKrebsforschung$$vKrebsrisikofaktoren und Prävention$$x0
000167949 9130_ $$0G:(DE-HGF)POF3-319H$$1G:(DE-HGF)POF3-310$$2G:(DE-HGF)POF3-300$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lKrebsforschung$$vAddenda$$x0
000167949 9141_ $$y2021
000167949 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2021-01-26
000167949 915__ $$0StatID:(DE-HGF)1190$$2StatID$$aDBCoverage$$bBiological Abstracts$$d2021-01-26
000167949 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2021-01-26
000167949 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2021-01-26
000167949 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2021-01-26
000167949 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2021-01-26
000167949 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2022-11-23
000167949 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2022-11-23
000167949 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2020-08-11T15:43:53Z
000167949 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2020-08-11T15:43:53Z
000167949 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Peer review$$d2020-08-11T15:43:53Z
000167949 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2022-11-23
000167949 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2022-11-23
000167949 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews$$d2022-11-23
000167949 915__ $$0StatID:(DE-HGF)1110$$2StatID$$aDBCoverage$$bCurrent Contents - Clinical Medicine$$d2022-11-23
000167949 915__ $$0StatID:(DE-HGF)1030$$2StatID$$aDBCoverage$$bCurrent Contents - Life Sciences$$d2022-11-23
000167949 9202_ $$0I:(DE-He78)C060-20160331$$kC060$$lC060 Biostatistik$$x0
000167949 9201_ $$0I:(DE-He78)C060-20160331$$kC060$$lC060 Biostatistik$$x0
000167949 9201_ $$0I:(DE-He78)B061-20160331$$kB061$$lB061 Mechanismen der Leukämogenese$$x1
000167949 980__ $$ajournal
000167949 980__ $$aVDB
000167949 980__ $$aI:(DE-He78)C060-20160331
000167949 980__ $$aI:(DE-He78)B061-20160331
000167949 980__ $$aUNRESTRICTED