Evidence map›Paper›PMID 32472084›Full record

ArticleLeukemia2020

Analysis of chronic myeloid leukaemia during deep molecular response by genomic PCR: a traffic light stratification model with impact on treatment-free remission.

Katerina Machova Polakova, Hana Zizkova, Jan Zuna, Eliska Motlova, Lenka Hovorkova, Andrea Gottschalk, Ingmar Glauche, Jitka Koblihova, Pavla Pecherkova, Hana Klamova and 13 more

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In one paragraph

Article in Leukemia, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
2.7field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 30 citations in OpenAlex.

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  16. The New ELN Recommendations for Treating CML.Journal of clinical medicine · 2020
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

23 authors at 7 institutions in 4 countries.

Katerina Machova PolakovaInstitute of Hematology and Blood Transfusion, Prague, Czech Republic. katerina.machova@uhkt.cz.ORCID http://orcid.org/0000-0002-7398-5555
Hana ZizkovaInstitute of Hematology and Blood Transfusion, Prague, Czech Republic.
Jan ZunaCLIP, Department of Paediatric Haematology and Oncology, Second Faculty of Medicine, Charles University and University Hospital Motol, Prague, Czech Republic.
Eliska MotlovaInstitute of Hematology and Blood Transfusion, Prague, Czech Republic.
Lenka HovorkovaCLIP, Department of Paediatric Haematology and Oncology, Second Faculty of Medicine, Charles University and University Hospital Motol, Prague, Czech Republic.
Andrea GottschalkInstitute for Medical Informatics and Biometry (IMB), Carl Gustav Carus Faculty of Medicine, TU Dresden, Dresden, Germany.
Ingmar GlaucheInstitute for Medical Informatics and Biometry (IMB), Carl Gustav Carus Faculty of Medicine, TU Dresden, Dresden, Germany.ORCID http://orcid.org/0000-0002-2524-1199
Jitka KoblihovaInstitute of Hematology and Blood Transfusion, Prague, Czech Republic.
Pavla PecherkovaInstitute of Hematology and Blood Transfusion, Prague, Czech Republic.
Hana KlamovaInstitute of Hematology and Blood Transfusion, Prague, Czech Republic.
Marketa Stastna MarkovaInstitute of Hematology and Blood Transfusion, Prague, Czech Republic.
Dana SrbovaInstitute of Hematology and Blood Transfusion, Prague, Czech Republic.
Adela BenesovaInstitute of Hematology and Blood Transfusion, Prague, Czech Republic.
Vaclava PolivkovaInstitute of Hematology and Blood Transfusion, Prague, Czech Republic.
Tomas JurcekCenter of Molecular Biology and Gene Therapy, Internal Hematology and Oncology Clinic, Faculty Hospital Brno and Faculty of Medicine, Masaryk University, Brno, Czech Republic.
Daniela ZackovaInternal Hematology and Oncology Clinic, Faculty Hospital Brno and Faculty of Medicine, Masaryk University, Brno, Czech Republic.
Jiri MayerInternal Hematology and Oncology Clinic, Faculty Hospital Brno and Faculty of Medicine, Masaryk University, Brno, Czech Republic.
Thomas ErnstAbteilung Hämatologie/Onkologie, Klinik für Innere Medizin II, University of Jena, Jena, Germany.
Francois X MahonBERGONIE Institute BORDEAUX, INSERM U1218 University of Bordeaux, Bordeaux, France.
Susanne SausseleDepartment of Haematology and Oncology, University Hospital Mannheim, Heidelberg University, Mannheim, Germany.
Ingo RoederInstitute for Medical Informatics and Biometry (IMB), Carl Gustav Carus Faculty of Medicine, TU Dresden, Dresden, Germany.ORCID http://orcid.org/0000-0002-6741-0608
Nicholas C P CrossWessex Regional Genetics Laboratory, Salisbury NHS Foundation Trust, Salisbury and Faculty of Medicine, University of Southampton, Southampton, UK.ORCID http://orcid.org/0000-0001-5481-2555
Andreas HochhausAbteilung Hämatologie/Onkologie, Klinik für Innere Medizin II, University of Jena, Jena, Germany.
Institute of Haematology and Blood Transfusion · CZCharles University · CZMasaryk University · CZZimmer Biomet (Germany) · DEHeidelberg University · DEUniversité de Bordeaux · FRWessex Regional Genetics Laboratory · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This work investigated patient-specific genomic BCR-ABL1 fusions as markers of measurable residual disease (MRD) in chronic myeloid leukaemia, with a focus on relevance to treatment-free remission (TFR) after achievement of deep molecular response (DMR) on tyrosine kinase inhibitor (TKI) therapy. DNA and mRNA BCR-ABL1 measurements by qPCR were compared in 2189 samples (129 patients) and by digital PCR in 1279 sample (62 patients). A high correlation was found at levels of disease above MR4, but there was a poor correlation for samples during DMR. A combination of DNA and RNA MRD measurements resulted in a better prediction of molecular relapse-free survival (MRFS) after TKI stop (n = 17) or scheduled interruption (n = 25). At 18 months after treatment cessation, patients with stopped or interrupted TKI therapy who were DNA negative/RNA negative during DMR maintenance (green group) had an MRFS of 80% and 100%, respectively, compared with those who were DNA positive/RNA negative (MRFS = 57% and 67%, respectively; yellow group) or DNA positive/RNA positive (MRFS = 20% for both cohorts; red group). Thus, we propose a "traffic light" stratification as a TFR predictor based on DNA and mRNA BCR-ABL1 measurements during DMR maintenance before TKI cessation.

Indexed as

AdultAgedFemaleFusion Proteins, bcr-ablHumansLeukemia, Myelogenous, Chronic, BCR-ABL PositiveMaleMiddle AgedNeoplasm, ResidualPolymerase Chain ReactionProtein Kinase InhibitorsProtein-Tyrosine KinasesRemission InductionRNA, MessengerWithholding TreatmentBCR-ABL1 fusion protein, humanFusion Proteins, bcr-ablProtein Kinase InhibitorsProtein-Tyrosine KinasesRNA, Messenger

Identifiers

PMID32472084
OpenAlexW3029060690

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.