Evidence map›Paper›PMID 36866958›Full record

Observational studyCancer science2023

Real-world therapeutic effectiveness of lorlatinib after alectinib in Japanese patients with ALK-positive non-small-cell lung cancer.

Yasushi Goto, Takehito Shukuya, Ai Murata, Hironori Kikkawa, Birol Emir, Robin Wiltshire, Satoru Miura

Open access · goldAbstract readObservational Study
In one paragraph

Observational study in Cancer science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
5.4field-weighted citation impact, top 4% 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

15 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.

  1. Pooled it
  2. Lorlatinib in patients withFuture oncology (London, England) · 2026
    Trial
  3. Review
  4. Efficacy and safety of second-line treatments in ALK mutation-positive advanced non-small cell lung cancer after second- or third-generation ALK inhibitors: Turkish Oncology Group real-life study (TOG study).Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
    Article
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  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
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  15. Observational
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

7 authors at 4 institutions in 2 countries.

Yasushi GotoDepartment of Thoracic Oncology, National Cancer Center Hospital, Tokyo, Japan.ORCID https://orcid.org/0000-0002-7437-5054
Takehito ShukuyaDepartment of Respiratory Medicine, Juntendo University, Tokyo, Japan.ORCID https://orcid.org/0000-0002-0972-4692
Ai MurataMedical Affairs, Oncology, Pfizer, Tokyo, Japan.
Hironori KikkawaMedical Affairs, Oncology, Pfizer, Tokyo, Japan.
Birol EmirGlobal Biometrics & Data Management, Pfizer Inc, New York, USA.
Robin WiltshireChief Medical Office, Oncology, Pfizer Inc, New York, USA.
Satoru MiuraDepartment of Internal Medicine, Niigata Cancer Center Hospital, Niigata, Japan.ORCID https://orcid.org/0000-0001-7868-6661
Pfizer (Japan) · JPPfizer (United States) · USJuntendo University · JPNiigata Cancer Center Hospital · JP

Funding

Pfizer
6 · The paper itself

Abstract

Alectinib, an anaplastic lymphoma kinase (ALK) tyrosine kinase inhibitor (TKI), is the recommended first-line treatment for ALK-positive non-small-cell lung cancer (NSCLC) in Japan. Lorlatinib was approved as a subsequent therapeutic option after progression while receiving ALK TKI treatment. However, data on the use of lorlatinib in the second- or third-line setting after alectinib failure are limited in Japanese patients. This retrospective real-world observational study investigated the clinical effectiveness of lorlatinib in second- or later-line settings after alectinib failure in Japanese patients. Clinical and demographic data collected in the Japan Medical Data Vision (MDV) database between December 2015 and March 2021 were used. Patients diagnosed with lung cancer who received lorlatinib following alectinib failure after the November 2018 marketing approval of lorlatinib in Japan were included. Of 1954 patients treated with alectinib, 221 were identified from the MDV database as receiving lorlatinib after November 2018. The median age of these patients was 62 years. Second-line lorlatinib treatment was reported for 154 patients (70%); third- or later-line lorlatinib treatment was reported for 67 patients (30%). The median duration of treatment (DOT) for all lorlatinib-treated patients was 161 days (95% confidence interval [CI], 126-248), and 83 patients (37.6%) continued treatment after data cut-off (March 31, 2021). Median DOTs of 147 days (95% CI, 113-242) and 244 days (95% CI, 109 to not reached) were reported with second-line and third- or later-line treatment, respectively. Consistent with clinical trial data, this real-world observational study supports data suggesting the effectiveness of lorlatinib after alectinib failure in Japanese patients.

Indexed as

Antineoplastic AgentsCarcinoma, Non-Small-Cell LungLung NeoplasmsAminopyridinesAnaplastic Lymphoma KinaseCarbazolesEast Asian PeopleHumansLactamsLactams, MacrocyclicMiddle AgedPiperidinesProtein Kinase InhibitorsProtein-Tyrosine KinasesPyrazolesRetrospective StudiesalectinibAminopyridinesAnaplastic Lymphoma KinaseAntineoplastic AgentsCarbazolesLactamsLactams, MacrocycliclorlatinibPiperidinesProtein Kinase InhibitorsProtein-Tyrosine KinasesPyrazolesanaplastic lymphoma kinasecarcinomaJapanlorlatinibnon-small-cell lung cancerreal-world clinical trials

Identifiers

PMID36866958
PMCPMC10236600
OpenAlexW4323036448

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.