Evidence map›Paper›PMID 38831004›Full record

ArticleScientific reports2024

Strength of selection in lung tumors correlates with clinical features better than tumor mutation burden.

Ivan P Gorlov, Olga Y Gorlova, Spyridon Tsavachidis, Christopher I Amos

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Leveraging FracMinHash Containment for GenomicbioRxiv : the preprint server for biology · 2025
    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

4 authors.

Ivan P Gorlov *Institute for Clinical and Translational Research, Baylor College of Medicine, One Baylor Plaza, Mailstop: BCM451, Houston, TX, 77030, USA. ivan.gorlov@bcm.edu.
Olga Y Gorlova *Institute for Clinical and Translational Research, Baylor College of Medicine, One Baylor Plaza, Mailstop: BCM451, Houston, TX, 77030, USA.
Spyridon Tsavachidis *Institute for Clinical and Translational Research, Baylor College of Medicine, One Baylor Plaza, Mailstop: BCM451, Houston, TX, 77030, USA.
Christopher I AmosInstitute for Clinical and Translational Research, Baylor College of Medicine, One Baylor Plaza, Mailstop: BCM451, Houston, TX, 77030, USA.

Funding

Translating Molecular and Clinical Data to Population Lung Cancer Risk AssessmentU19CA203654 · NCI · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI Rayjean J. Hung · 2017 to 2026
$23.7M
Cancer Prevention and Research Institute of Texas RR170048NCI NIH HHS U19 CA203654NIH HHS U19CA203654
6 · The paper itself

Abstract

Single nucleotide substitutions are the most common type of somatic mutations in cancer genome. The goal of this study was to use publicly available somatic mutation data to quantify negative and positive selection in individual lung tumors and test how strength of directional and absolute selection is associated with clinical features. The analysis found a significant variation in strength of selection (both negative and positive) among tumors, with median selection tending to be negative even though tumors with strong positive selection also exist. Strength of selection estimated as the density of missense mutations relative to the density of silent mutations showed only a weak correlation with tumor mutation burden. In the "all histology together" analysis we found that absolute strength of selection was strongly correlated with all clinically relevant features analyzed. In histology-stratified analysis selection was strongest in small cell lung cancer. Selection in adenocarcinoma was somewhat higher compared to squamous cell carcinoma. The study suggests that somatic mutation- based quantifying of directional and absolute selection in individual tumors can be a useful biomarker of tumor aggressiveness.

Indexed as

Lung NeoplasmsMutationSelection, GeneticBiomarkers, TumorCarcinoma, Squamous CellHumansMutation, MissenseBiomarkers, Tumor

Identifiers

PMID38831004
PMCPMC11148192

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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.