Evidence map›Paper›PMID 34402912›Full record

ArticleJournal of the National Cancer Institute2022

Clinical Implications of Inter- and Intratumor Heterogeneity of Immune Cell Markers in Lung Cancer.

Wei Zhao, Bin Zhu, Amy Hutchinson, Angela Cecilia Pesatori, Dario Consonni, Neil E Caporaso, Tongwu Zhang, Difei Wang, Jianxin Shi, Maria Teresa Landi

Abstract read
In one paragraph

Article in Journal of the National Cancer Institute, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
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

12 citing papers in PubMed.

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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

10 authors.

Wei ZhaoIntegrative Tumor Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA.
Bin ZhuBiostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA.ORCID 0000-0003-0172-5516
Amy HutchinsonCancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Frederick, MD, USA.ORCID 0000-0002-4962-2711
Angela Cecilia PesatoriDepartment of Clinical Sciences and Community Health, University of Milan, Milan, Italy.ORCID 0000-0002-0261-3252
Dario ConsonniEpidemiology Unit, Fondazione IRCCS Ca' Granda-Ospedale Maggiore Policlinico, Milan, Italy.ORCID 0000-0002-8935-3843
Neil E CaporasoOccupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA.
Tongwu ZhangIntegrative Tumor Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA.ORCID 0000-0003-2124-2706
Difei WangCancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Frederick, MD, USA.ORCID 0000-0003-4088-3859
Jianxin ShiBiostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA.
Maria Teresa LandiIntegrative Tumor Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA.ORCID 0000-0003-4507-329X

Funding

Studies of Genetic and Environmental EpidemiologyZIACP101231 · NCI · DIVISION OF CANCER EPIDEMIOLOGY AND GENETICS · PI LANDI, MARIA · 2018 to 2025
$23.6M
6 · The paper itself

Abstract

backgroundImmune cell transcriptome signatures have been widely used to study the lung tumor microenvironment (TME). However, it is unclear to what extent the immune cell composition in the lung TME varies across histological and molecular subtypes (intertumor heterogeneity [inter-TH]) and within tumors (intratumor heterogeneity [ITH]) and whether ITH has any prognostic relevance.

methodsUsing RNA sequencing in 269 tumor samples from 160 lung cancer patients, we quantified the inter-TH of immune gene expression and immune cell abundance and evaluated the association of median immune cell abundance with clinical and pathological features and overall survival. In 39 tumors with 132 multiregion samples, we also analyzed the ITH of immune cell abundance in relation to overall survival using a variance-weighted multivariate Cox model not biased by the number of samples per tumor.

resultsSubstantial inter-TH of 14 immune cell types was observed even within the same histological and molecular subtypes, but early stage tumors had higher lymphocyte infiltration across all tumor types. In multiregion samples, an unbiased estimate of low ITH of overall immune cell composition (hazard ratio [HR] = 0.40, 95% confidence interval [CI] = 0.21 to 0.78; P = .007), dendritic cells (HR = 0.24, 95% CI = 0.096 to 0.58; P = .002), and macrophages (HR = 0.50, 95% CI = 0.30 to 0.84; P = .009) was strongly associated with poor survival. The ITH of 3 markers, including CD163 and CD68 (macrophages) and CCL13 (dendritic cells), was enough to characterize the ITH of the corresponding immune cell abundances and its association with overall survival.

conclusionThis study suggests that lack of immune cell diversity may facilitate tumor evasion and progression. ITH inferred from CCL13, CD163, and CD68 could be used as a prognostic tool in clinical practice.

Indexed as

Lung NeoplasmsHumansPrognosisSequence Analysis, RNATranscriptomeTumor Microenvironment

Identifiers

PMID34402912
PMCPMC8826638

What OpenQuestion holds

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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.