Evidence map›Paper›PMID 31839882›Full record

ArticleOncotarget2019

Survival correlation of immune response in human cancers.

Yuexin Liu

Abstract read
In one paragraph

Article in Oncotarget, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
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  3. 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

1 author.

Yuexin LiuDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Scott Kopetz · 1985 to 2026
$290.8M
Targeting the PI3K Signaling Pathway in Endometrial CarcinomaP50CA098258 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI BODURKA, DIANE · 2003 to 2020
$32.0M
NCI NIH HHS P30 CA016672NCI NIH HHS P50 CA098258
6 · The paper itself

Abstract

backgroundThe clinical benefit of immune response is largely unknown. We systematically explored the correlation of immune response with patient outcome in human cancers.

resultsThe global immune gene signature was primarily located on the plasma membrane with a high gene density at 6p21 and 1q23-1q24. Immune responses varied with a wide range in human cancers. A total of 11 cancer types exhibited significant correlation of immune response with overall survival. Higher immune response was significantly associated with longer overall survival in 7 types and with shorter overall survival in 4 types. In addition, 11 cancer types exhibited significant correlation of immune response with progression-free interval. Higher immune response was significantly associated with longer progression-free interval in 7 types and with shorter progression-free interval in 4 types.

methodsThe Ingenuity Knowledge Base and human genome assembly GRCh38 were used to annotate the immune gene signature by cellular components and genomic coordinates, respectively. We devised an mRNA-based metric of pre-existing immune conditions by using the gene signature, and calculated the metric for 10,062 The Cancer Genome Atlas tumor samples across 32 different cancer types. The Kaplan-Meier method was used to evaluate the overall survival and progression-free interval differences between dichotomic groups stratified by the median metric for each cancer type.

conclusionsImmune responses have different impacts on patient outcome in different human cancers. Prospective verification is needed before the findings can be applied for clinical trial development.

Indexed as

cancerimmune responseimmunogenicityoverall survivalprogression-free interval

Identifiers

PMID31839882
PMCPMC6901338

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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.