Evidence map›Paper›PMID 24056683›Full record

ArticleCancer discovery2013

Individualized systems medicine strategy to tailor treatments for patients with chemorefractory acute myeloid leukemia.

Tea Pemovska, Mika Kontro, Bhagwan Yadav, Henrik Edgren, Samuli Eldfors, Agnieszka Szwajda, Henrikki Almusa, Maxim M Bespalov, Pekka Ellonen, Erkki Elonen and 26 more

Open access · bronzeAbstract read
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In one paragraph

Article in Cancer discovery, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 231 papers.

0numbers the graph read from it
0cells of the map it votes in
231citing papers in PubMed
15.8field-weighted citation impact, top 1% 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

231 citing papers in PubMed, 403 citations in OpenAlex.

  1. Trial
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  7. Harmonization and integration of pharmacogenomics screens.Bioinformatics (Oxford, England) · 2026
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  12. A Pan-Cancer Ex Vivo Drug Screen Atlas for Functional Precision Oncology.bioRxiv : the preprint server for biology · 2026
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  14. Article
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171 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

36 authors at 9 institutions in 2 countries.

Tea Pemovska1Institute for Molecular Medicine Finland, FIMM; 2Hematology Research Unit Helsinki, Helsinki University Central Hospital, University of Helsinki, Helsinki; 3Department of Clinical Chemistry and TYKSLAB, Turku University Central Hospital, University of Turku, Turku; 4Department of Internal Medicine, Tampere University Hospital, Tampere, Finland; 5Department of Clinical Science, Hematology Section, University of Bergen; and 6Department of Internal Medicine, Hematology Section, Haukeland University Hospital, Bergen, Norway.
Mika Kontro
Bhagwan Yadav
Henrik Edgren
Samuli Eldfors
Agnieszka Szwajda
Henrikki Almusa
Maxim M Bespalov
Pekka Ellonen
Erkki Elonen
Bjørn T Gjertsen
Riikka Karjalainen
Evgeny Kulesskiy
Sonja Lagström
Anna Lehto
Maija Lepistö
Tuija Lundán
Muntasir Mamun Majumder
Jesus M Lopez Marti
Pirkko Mattila
Astrid Murumägi
Satu Mustjoki
Aino Palva
Alun Parsons
Tero Pirttinen
Maria E Rämet
Minna Suvela
Laura Turunen
Imre Västrik
Maija Wolf
Jonathan Knowles
Tero Aittokallio
Caroline A Heckman
Kimmo Porkka
Olli Kallioniemi
Krister Wennerberg
University of Bergen · NOUniversity of Turku · FIHelsinki University Hospital · FITampere University · FITampere University Hospital · FIUniversity of Helsinki · FIHaukeland University Hospital · NOInstitute for Molecular Medicine Finland · FITurku University Hospital · FI

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

unlabelledWe present an individualized systems medicine (ISM) approach to optimize cancer drug therapies one patient at a time. ISM is based on (i) molecular profiling and ex vivo drug sensitivity and resistance testing (DSRT) of patients' cancer cells to 187 oncology drugs, (ii) clinical implementation of therapies predicted to be effective, and (iii) studying consecutive samples from the treated patients to understand the basis of resistance. Here, application of ISM to 28 samples from patients with acute myeloid leukemia (AML) uncovered five major taxonomic drug-response subtypes based on DSRT profiles, some with distinct genomic features (e.g., MLL gene fusions in subgroup IV and FLT3-ITD mutations in subgroup V). Therapy based on DSRT resulted in several clinical responses. After progression under DSRT-guided therapies, AML cells displayed significant clonal evolution and novel genomic changes potentially explaining resistance, whereas ex vivo DSRT data showed resistance to the clinically applied drugs and new vulnerabilities to previously ineffective drugs. SIGNIFICANCE: Here, we demonstrate an ISM strategy to optimize safe and effective personalized cancer therapies for individual patients as well as to understand and predict disease evolution and the next line of therapy. This approach could facilitate systematic drug repositioning of approved targeted drugs as well as help to prioritize and de-risk emerging drugs for clinical testing.

Indexed as

Antineoplastic AgentsDisease ProgressionDrug RepositioningDrug Resistance, NeoplasmGene Expression ProfilingGenome, HumanHumansLeukemia, Myeloid, AcutePrecision MedicineSignal TransductionTreatment OutcomeAntineoplastic Agents

Identifiers

PMID24056683
OpenAlexW2115517051

What OpenQuestion holds

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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.