Evidence map›Paper›PMID 31937321›Full record

ArticleJournal of translational medicine2020

Identification of a small mutation panel of coding sequences to predict the efficacy of immunotherapy for lung adenocarcinoma.

Ying Li, Wenbin Jiang, Tianhao Li, Mengyue Li, Xin Li, Zheyang Zhang, Sainan Zhang, Yixin Liu, Wenyuan Zhao, Yunyan Gu and 3 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of translational medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.7field-weighted citation impact, top 14% 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

2 citing papers in PubMed, 21 citations in OpenAlex.

  1. Article
  2. Article
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

13 authors at 2 institutions in 1 country.

Ying LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
Wenbin JiangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
Tianhao LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
Mengyue LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
Xin LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
Zheyang ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
Sainan ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
Yixin LiuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
Wenyuan ZhaoCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
Yunyan GuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
Lishuang QiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China. qilishuang7@ems.hrbmu.edu.cn.
Lu AoDepartment of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350001, China. lukey@fjmu.edu.cn.
Zheng GuoCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China. guoz@ems.hrbmu.edu.cn.ORCID 0000-0003-4466-6026
Harbin Medical University · CNFujian Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundImmune checkpoint inhibitors are effective in some cases of lung adenocarcinoma (LUAD). Whole-exome sequencing has revealed that the tumour mutation burden (TMB) is associated with clinical benefits among patients from immune checkpoint inhibitors. Several commercial mutation panels have been developed for estimating the TMB regardless of the cancer type. However, different cancer types have different mutational landscapes; hence, this study aimed to develop a small cancer-type-specific mutation panel for high-accuracy estimation of the TMB of LUAD patients.

methodsWe developed a small cancer-type-specific mutation panel based on coding sequences (CDSs) rather than genes, for LUAD patients. Using somatic CDSs mutation data from 486 LUAD patients in The Cancer Genome Atlas (TCGA) database, we pre-selected a set of CDSs with mutation states significantly correlated with the TMB, from which we selected a CDS mutation panel with a panel-score most significantly correlated with the TMB, using a genetic algorithm.

resultsA mutation panel containing 106 CDSs of 100 genes with only 0.34 Mb was developed, whose length was much shorter than current commercial mutation panels of 0.80-0.92 Mb. The correlation of this panel with the TMB was validated in two independent LUAD datasets with progression-free survival data for patients treated with nivolumab plus ipilimumab and pembrolizumab immunotherapies, respectively. In both test datasets, survival analyses revealed that patients with a high TMB predicted via the 106-CDS mutation panel with a cut-point of 6.20 mutations per megabase, median panel score in the training dataset, had a significantly longer progression-free survival than those with a low predicted TMB (log-rank p = 0.0018, HR = 3.35, 95% CI 1.51-7.42; log-rank p = 0.0020, HR = 5.06, 95% CI 1.63-15.69). This small panel better predicted the efficacy of immunotherapy than current commercial mutation panels.

conclusionsThe small-CDS mutation panel of only 0.34 Mb is superior to current commercial mutation panels and can better predict the efficacy of immunotherapy for LUAD patients, and its low cost and time-intensiveness make it more suitable for clinical applications.

Indexed as

Adenocarcinoma of LungImmunotherapyLung NeoplasmsBiomarkers, TumorFemaleHumansMaleMutationBiomarkers, TumorCDS mutation panelClinical applicationImmunotherapyLung adenocarcinomaTumour mutation burden

Identifiers

PMID31937321
PMCPMC6961230
OpenAlexW3007737802

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.