Evidence map›Paper›PMID 30662002›Full record

ArticleEBioMedicine2019

Prediction of ALK mutations mediating ALK-TKIs resistance and drug re-purposing to overcome the resistance.

Koutaroh Okada, Mitsugu Araki, Takuya Sakashita, Biao Ma, Ryo Kanada, Noriko Yanagitani, Atsushi Horiike, Sumie Koike, Tomoko Oh-Hara, Kana Watanabe and 7 more

Open access · goldAbstract read
In one paragraph

Article in EBioMedicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers.

0numbers the graph read from it
0cells of the map it votes in
81citing papers in PubMed
13.3field-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

81 citing papers in PubMed, 125 citations in OpenAlex.

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21 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

17 authors at 6 institutions in 1 country.

Koutaroh OkadaDivision of Experimental Chemotherapy, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, Tokyo, Japan; Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan.
Mitsugu ArakiRIKEN Advanced Institute for Computational Science, 7-1-26 Minatojima-Minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan; Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan.
Takuya SakashitaDivision of Experimental Chemotherapy, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, Tokyo, Japan.
Biao MaResearch and Development Group for In Silico Drug Discovery, Pro-Cluster Kobe, Foundation for Biomedical Research and Innovation (FBRI), 6-3-5, Minatojima-Minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan.
Ryo KanadaRIKEN Compass to Healthy Life Research Complex Program, 6-3-5, Minatojima-Minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan.
Noriko YanagitaniDepartment of Thoracic Medical Oncology, The Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan.
Atsushi HoriikeDepartment of Thoracic Medical Oncology, The Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan.
Sumie KoikeDivision of Experimental Chemotherapy, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, Tokyo, Japan.
Tomoko Oh-HaraDivision of Experimental Chemotherapy, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, Tokyo, Japan.
Kana WatanabeDepartment of Respiratory Medicine, Miyagi Cancer Center, Miyagi, Japan.
Keiichi TamaiDivision of Cancer Stem Cell, Miyagi Cancer Center Research Institute, Miyagi, Japan.
Makoto MaemondoDepartment of Respiratory Medicine, Miyagi Cancer Center, Miyagi, Japan.
Makoto NishioDepartment of Thoracic Medical Oncology, The Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan.
Takeshi IshikawaDepartment of Molecular Microbiology and Immunology, Graduate School of Biomedical Sciences, Nagasaki University, 1-12-4 Sakamoto, Nagasaki 852-8523, Japan.
Yasushi OkunoRIKEN Advanced Institute for Computational Science, 7-1-26 Minatojima-Minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan; Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan.
Naoya FujitaDivision of Experimental Chemotherapy, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, Tokyo, Japan; Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan.
Ryohei KatayamaDivision of Experimental Chemotherapy, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, Tokyo, Japan. Electronic address: ryohei.katayama@jfcr.or.jp.
Japan Society of Chemotherapy · JPMiyagi Prefectural Hospital Organization · JPThe Cancer Institute Hospital · JPKyoto University · JPFoundation for Biomedical Research and Innovation · JPNagasaki University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlectinib has shown a greater efficacy to ALK-rearranged non-small-cell lung cancers in first-line setting; however, most patients relapse due to acquired resistance, such as secondary mutations in ALK including I1171N and G1202R. Although ceritinib or lorlatinib was shown to be effective to these resistant mutants, further resistance often emerges due to ALK-compound mutations in relapse patients following the use of ceritinib or lorlatinib. However, the drug for overcoming resistance has not been established yet.

methodsWe established lorlatinib-resistant cells harboring ALK-I1171N or -G1202R compound mutations by performing ENU mutagenesis screening or using an in vivo mouse model. We performed drug screening to overcome the lorlatinib-resistant ALK-compound mutations. To evaluate these resistances in silico, we developed a modified computational molecular dynamic simulation (MP-CAFEE).

findingsWe identified 14 lorlatinib-resistant ALK-compound mutants, including several mutants that were recently discovered in lorlatinib-resistant patients. Some of these compound mutants were found to be sensitive to early generation ALK-TKIs and several BCR-ABL inhibitors. Using our original computational simulation, we succeeded in demonstrating a clear linear correlation between binding free energy and in vitro experimental IC

interpretationWe discovered lorlatinib-resistant multiple ALK-compound mutations and an L1256F single mutation as well as the potential therapeutic strategies for these ALK mutations. Our original computational simulation to calculate the binding affinity may be applicable for predicting resistant mutations and for overcoming drug resistance in silico. FUND: This work was mainly supported by MEXT/JSPS KAKENHI Grants and AMED Grants.

Indexed as

Drug Resistance, NeoplasmMolecular Dynamics SimulationMutation, MissenseAminopyridinesAnaplastic Lymphoma KinaseAnimalsAntineoplastic AgentsCarcinoma, Non-Small-Cell LungCell Line, TumorHEK293 CellsHumansLactamsLactams, MacrocyclicLung NeoplasmsMiceMice, Inbred BALB CALK protein, humanAminopyridinesAnaplastic Lymphoma KinaseAntineoplastic AgentsceritinibLactamsLactams, MacrocycliclorlatinibProtein Kinase InhibitorsPyrazolesPyrimidinesSulfonesALK-rearranged lung cancerCompound mutationComputational simulationMP-CAFEEQuantum chemistryResistant mutation

Identifiers

PMID30662002
PMCPMC6441848
OpenAlexW2908885437

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.