Evidence map›Paper›PMID 34006997›Full record

ReviewNature reviews. Clinical oncology2021

Towards precision medicine for AML.

Hartmut Döhner, Andrew H Wei, Bob Löwenberg

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Clinical oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 183 papers.

0numbers the graph read from it
0cells of the map it votes in
183citing papers in PubMed
–field-weighted citation impact
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

183 citing papers in PubMed.

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123 more citing papers are in PubMed but not listed here.

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

3 authors.

Hartmut DöhnerDepartment of Internal Medicine III, University of Ulm, Ulm, Germany. hartmut.doehner@uniklinik-ulm.de.ORCID http://orcid.org/0000-0003-2116-5536
Andrew H WeiDepartment of Clinical Hematology, The Alfred Hospital and Monash University, Melbourne, VIC, Australia.
Bob LöwenbergDepartment of Hematology, Erasmus University Medical Center, Rotterdam, Netherlands.ORCID http://orcid.org/0000-0001-8982-5217

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With rapid advances in sequencing technologies, tremendous progress has been made in understanding the molecular pathogenesis of acute myeloid leukaemia (AML), thus revealing enormous genetic and clonal heterogeneity, and paving the way for precision medicine approaches. The successful development of precision medicine for patients with AML has been exemplified by the introduction of targeted FLT3, IDH1/IDH2 and BCL-2 inhibitors. When used as single agents, these inhibitors display moderate antileukaemic activity. However, augmented clinical activity has been demonstrated when they are administered in combination with drugs with broader mechanisms of action targeting epigenetic and/or other oncogenic signalling pathways or with conventional cytotoxic agents. The development of immunotherapies has been hampered by the expression of antigens that are expressed by both leukaemic and non-malignant haematopoietic progenitor cells; nonetheless, a diverse range of immunotherapies are now entering clinical development. This myriad of emerging agents also creates challenges, such as how to safely combine agents with different mechanisms of action, the need to circumvent primary and secondary resistance, and new challenges in future clinical trial design. In this Review, we discuss the current state of precision medicine for AML, including both the potential to improve patient outcomes and the related challenges.

Indexed as

Antineoplastic AgentsEpigenesis, GeneticHumansLeukemia, Myeloid, AcuteMolecular Targeted TherapyMutationPrecision MedicineAntineoplastic Agents

Identifiers

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.