Evidence map›Paper›PMID 35016696›Full record

ArticleGenome medicine2022

A lepidic gene signature predicts patient prognosis and sensitivity to immunotherapy in lung adenocarcinoma.

Thinh T Nguyen, Hyun-Sung Lee, Bryan M Burt, Jia Wu, Jianjun Zhang, Christopher I Amos, Chao Cheng

Open access · goldAbstract read
In one paragraph

Article in Genome medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed
12.2field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

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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

37 citing papers in PubMed, 68 citations in OpenAlex.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Thinh T NguyenInstitute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, 77030, USA.
Hyun-Sung LeeDivision of General Thoracic Surgery, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, 77030, USA.
Bryan M BurtDivision of General Thoracic Surgery, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, 77030, USA.
Jia WuDepartment of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
Jianjun ZhangDepartment of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
Christopher I AmosInstitute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, 77030, USA. chris.amos@bcm.edu.
Chao ChengInstitute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, 77030, USA. chao.cheng@bcm.edu.ORCID 0000-0002-5002-3417
Baylor College of Medicine · USThe University of Texas MD Anderson Cancer Center · US

Funding

Proteomic determinants of response to checkpoint blockade in malignant pleuralmesotheliomaR37CA248478 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Bryan Michael Burt · 2021 to 2026
$2.9M
Computational Identification of new candidate drugs for lung cancer treatmentR21CA227996 · NCI · BAYLOR COLLEGE OF MEDICINE · PI CHENG, CHAO, ONEGA, TRACY · 2019 to 2020
$397k
NCI NIH HHS R21 CA227996NCI NIH HHS R37 CA248478
6 · The paper itself

Abstract

backgroundLung adenocarcinoma, the most common type of lung cancer, has a high level of morphologic heterogeneity and is composed of tumor cells of multiple histological subtypes. It has been reported that immune cell infiltration significantly impacts clinical outcomes of patients with lung adenocarcinoma. However, it is unclear whether histologic subtyping can reflect the tumor immune microenvironment, and whether histologic subtyping can be applied for therapeutic stratification of the current standard of care.

methodsWe inferred immune cell infiltration levels using a histological subtype-specific gene expression dataset. From differential gene expression analysis between different histological subtypes, we developed two gene signatures to computationally determine the relative abundance of lepidic and solid components (denoted as the L-score and S-score, respectively) in lung adenocarcinoma samples. These signatures enabled us to investigate the relationship between histological composition and clinical outcomes in lung adenocarcinoma using previously published datasets.

resultsWe found dramatic immunological differences among histological subtypes. Differential gene expression analysis showed that the lepidic and solid subtypes could be differentiated based on their gene expression patterns while the other subtypes shared similar gene expression patterns. Our results indicated that higher L-scores were associated with prolonged survival, and higher S-scores were associated with shortened survival. L-scores and S-scores were also correlated with global genomic features such as tumor mutation burdens and driver genomic events. Interestingly, we observed significantly decreased L-scores and increased S-scores in lung adenocarcinoma samples with EGFR gene amplification but not in samples with EGFR gene mutations. In lung cancer cell lines, we observed significant correlations between L-scores and cell sensitivity to a number of targeted drugs including EGFR inhibitors. Moreover, lung cancer patients with higher L-scores were more likely to benefit from immune checkpoint blockade therapy.

conclusionsOur findings provided further insights into evaluating histology composition in lung adenocarcinoma. The established signatures reflected that lepidic and solid subtypes in lung adenocarcinoma would be associated with prognosis, genomic features, and responses to targeted therapy and immunotherapy. The signatures therefore suggested potential clinical translation in predicting patient survival and treatment responses. In addition, our framework can be applied to other types of cancer with heterogeneous histological subtypes.

Indexed as

Adenocarcinoma of LungLung NeoplasmsHumansImmunotherapyMutationPrognosisTumor MicroenvironmentDrug sensitivityGene signatureGenomic alterationsHistologyImmunotherapyLung adenocarcinomaPrognosisTumor immune microenvironment

Identifiers

PMID35016696
PMCPMC8753834
OpenAlexW4205115378

What OpenQuestion holds

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LicenceCC BY
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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.