Evidence map›Paper›PMID 33882406›Full record

ArticleLung cancer (Amsterdam, Netherlands)2021

Evaluation of disease staging and chemotherapeutic response in non-small cell lung cancer from patient tumor-derived metabolomic data.

Hunter A Miller, Xinmin Yin, Susan A Smith, Xiaoling Hu, Xiang Zhang, Jun Yan, Donald M Miller, Victor H van Berkel, Hermann B Frieboes

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Lung cancer (Amsterdam, Netherlands), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
–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

24 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  6. Liquid biopsy techniques and lung cancer: diagnosis, monitoring and evaluation.Journal of experimental & clinical cancer research : CR · 2024
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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

9 authors.

Hunter A MillerDepartment of Pharmacology and Toxicology, University of Louisville, United States.
Xinmin YinDepartment of Chemistry, University of Louisville, United States.
Susan A SmithDepartment of Surgery, University of Louisville, United States.
Xiaoling HuJames Graham Brown Cancer Center, University of Louisville, United States; Division of Immunotherapy, Department of Surgery, University of Louisville, United States.
Xiang ZhangDepartment of Chemistry, University of Louisville, United States.
Jun YanDepartment of Pharmacology and Toxicology, University of Louisville, United States; James Graham Brown Cancer Center, University of Louisville, United States; Division of Immunotherapy, Department of Surgery, University of Louisville, United States; Department of Microbiology and Immunology, University of Louisville, United States.
Donald M MillerDepartment of Pharmacology and Toxicology, University of Louisville, United States; James Graham Brown Cancer Center, University of Louisville, United States; Department of Medicine, University of Louisville, United States.
Victor H van BerkelJames Graham Brown Cancer Center, University of Louisville, United States; Department of Cardiovascular and Thoracic Surgery, University of Louisville, United States.
Hermann B FrieboesDepartment of Pharmacology and Toxicology, University of Louisville, United States; James Graham Brown Cancer Center, University of Louisville, United States; Department of Bioengineering, University of Louisville, United States; Center for Predictive Medicine, University of Louisville, United States. Electronic address: hbfrie01@louisville.edu.

Funding

Integrated framework to predict chemotherapy response post lung tumor resectionR15CA203605 · NCI · UNIVERSITY OF LOUISVILLE · PI FRIEBOES, HERMANN · 2016 to 2016
$462k
NCI NIH HHS R15 CA203605
6 · The paper itself

Abstract

objectivesDespite extensive effort, the search for clinically-relevant metabolite biomarkers for early detection, disease monitoring, and outcome prediction in lung cancer remains unfulfilled. Although biofluid evaluation has been explored, the complexity inherent in metabolite data and the dynamic discrepancy between metabolites in biofluids vs. tumor tissue have prevented conclusive results. This proof-of-concept study explored models predictive of staging and chemotherapy response based on metabolomic analysis of fresh, patient-derived non-small cell lung cancer (NSCLC) core biopsies. MATERIALS AND

methodsSamples (n = 36) were evaluated with high-resolution 2DLC-MS/MS and

resultsThe models predicted patient classifications in the validation subsets with AUC (95 % CI): DC vs. PD (SVM), 0.970(0.961-0.979); CR/PR vs. SD/PD (PLS-DA), 0.880(0.865-0.895); stage I/II/III vs. IV (SVM), 0.902(0.880-0.924). Highest performing model was SVM for DC vs. PD (balanced accuracy = 0.92; kappa = 0.74).

conclusionThis study illustrates a comprehensive evaluation of patient tumor-specific metabolic profiles, with the potential to identify disease stage and predict response to first-line chemotherapy.

Indexed as

Carcinoma, Non-Small-Cell LungLung NeoplasmsHumansMetabolomicsPrognosisTandem Mass SpectrometryChemotherapyLung cancerMachine learningMetabolomicsPersonalized medicine

Identifiers

PMID33882406
PMCPMC8138715

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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.