Evidence map›Paper›PMID 34128337›Full record

ArticleThoracic cancer2021

Neoantigen load as a prognostic and predictive marker for stage II/III non-small cell lung cancer in Chinese patients.

Lei Gong, Ronghui He, Yanjun Xu, Taobo Luo, Kaixiu Jin, Wuzhou Yuan, Zengguang Zheng, Lanxuan Liu, Zebin Liang, Ao Li and 2 more

Open access · goldAbstract read
In one paragraph

Article in Thoracic cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 2 pooled it
0.7field-weighted citation impact, top 39% 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

10 citing papers in PubMed, 2 syntheses or guidelines pooled it, 12 citations in OpenAlex.

  1. Pooled it
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  6. Review
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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

12 authors at 4 institutions in 2 countries.

Lei GongDepartment of Medical Oncology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital) and Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China.
Ronghui HeHangzhou YITU Healthcare Technology Co., Ltd., Hangzhou, China.
Yanjun XuDepartment of Medical Oncology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital) and Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China.
Taobo LuoDepartment of Thoracic Surgery, The Cancer Hospital of the University of Chinese Academy of Sciences(Zhejiang Cancer Hospital) and Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China.
Kaixiu JinHangzhou YITU Healthcare Technology Co., Ltd., Hangzhou, China.
Wuzhou YuanMingma Technologies Co., Ltd., Shanghai, China.
Zengguang ZhengDepartment of Pathology, Tthe Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital) and Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China.
Lanxuan LiuHangzhou YITU Healthcare Technology Co., Ltd., Hangzhou, China.
Zebin LiangHangzhou YITU Healthcare Technology Co., Ltd., Hangzhou, China.
Ao LiMingma Technologies Co., Ltd., Shanghai, China.
Zhiguo ZhengZhejiang Cancer Research Institute, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital) and Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China.
Hui LiDepartment of Medical Oncology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital) and Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China.ORCID 0000-0001-8164-1624
Zhejiang Cancer Hospital · CNHangzhou DAC Biotech (China) · CNCancer Hospital of Chinese Academy of Medical Sciences · CNSigma Technologies (United States) · US

Funding

the Natural Science Foundation of Zhejiang Province LQ21H160004Zhejiang Medical technology program 202143913
6 · The paper itself

Abstract

backgroundThe prognosis for patients with stage II/III non-small cell lung cancer (NSCLC) is unsatisfactory, even after complete tumor resection and adjuvant chemotherapy. Here, we assessed the prognostic and predictive value of immunogenomic signatures for stage II/III NSCLC in Chinese patients.

methodsA total of 91 paired resected stage II/III NSCLC and normal tissues, including 47 squamous cell lung carcinomas (SCC) and 44 lung adenocarcinomas (ADC), were collected and analyzed using whole exome sequencing (WES) to identify immunogenomic signatures for association with clinicopathological variables and disease-free survival (DFS).

resultsHigher neoantigen load (NAL, >2 neoantigens/Mb) exhibited better DFS for SCC patients (p = 0.021) but not ADC patients. A benefit from adjuvant chemotherapy was correlated with lower NAL (≤2 neoantigens/Mb) (p = 0.009). However, tumor mutation burden (TMB), mutations of individual gene, oncogene pathways, and antigen presentation machinery genes, and human leukocyte antigen (HLA)-I number and HLA-I loss of heterozygosity (LOH) had no prognostic or predictive value for DFS of SCC or ADC patients.

conclusionsNAL is a useful biomarker for lung SCC prognosis and prediction of chemotherapy responses in Chinese patients. The predictive value of NAL for adjuvant immunotherapy should be further explored in patients with resected NSCLC.

Indexed as

AgedAntigens, NeoplasmBiomarkers, TumorCarcinoma, Non-Small-Cell LungChinaExome SequencingFemaleHumansLung NeoplasmsMaleMiddle AgedPrognosisRetrospective StudiesAntigens, NeoplasmBiomarkers, Tumorbiomarkerneoantigen loadNSCLCprognosiswhole exome sequencing

Identifiers

PMID34128337
PMCPMC8327700
OpenAlexW3117309378

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

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.