Evidence map›Paper›PMID 35460942›Full record

ArticleTranslational oncology2022

Improved prediction of radiation pneumonitis by combining biological and radiobiological parameters using a data-driven Bayesian network analysis.

Tonaye Hinton, David Karnak, Ming Tang, Ralph Jiang, Yi Luo, Philip Boonstra, Yilun Sun, Derek J Nancarrow, Erin Sandford, Paramita Ray and 12 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Translational oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.2field-weighted citation impact, top 24% of its field
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

4 citing papers in PubMed, 8 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Review
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

22 authors at 2 institutions in 1 country.

Tonaye HintonDepartment of Radiation Oncology, Medical School, The University of Michigan Medical School, University of Michigan, Ann Arbor, MI 48109-2026, USA.
David KarnakDepartment of Radiation Oncology, Medical School, The University of Michigan Medical School, University of Michigan, Ann Arbor, MI 48109-2026, USA.
Ming TangDepartment of Radiation Oncology, Medical School, The University of Michigan Medical School, University of Michigan, Ann Arbor, MI 48109-2026, USA; Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
Ralph JiangDepartment of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
Yi LuoDepartment of Radiation Oncology, Medical School, The University of Michigan Medical School, University of Michigan, Ann Arbor, MI 48109-2026, USA.
Philip BoonstraDepartment of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
Yilun SunDepartment of Radiation Oncology, Medical School, The University of Michigan Medical School, University of Michigan, Ann Arbor, MI 48109-2026, USA; Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
Derek J NancarrowDepartment of Surgery, Division of Hematology-Oncology, Department of Internal Medicine, Medical School, University of Michigan, Ann Arbor, MI, USA.
Erin SandfordDivision of Hematology and Oncology, Department of Internal Medicine, Henry Ford Cancer Institute/Henry Ford Hospital, Detroit, MI, USA.
Paramita RayDepartment of Radiation Oncology, Medical School, The University of Michigan Medical School, University of Michigan, Ann Arbor, MI 48109-2026, USA.
Christopher MaurinoDepartment of Radiation Oncology, Medical School, The University of Michigan Medical School, University of Michigan, Ann Arbor, MI 48109-2026, USA.
Martha MatuszakDepartment of Radiation Oncology, Medical School, The University of Michigan Medical School, University of Michigan, Ann Arbor, MI 48109-2026, USA.
Matthew J SchipperDepartment of Radiation Oncology, Medical School, The University of Michigan Medical School, University of Michigan, Ann Arbor, MI 48109-2026, USA; Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
Michael D GreenDepartment of Radiation Oncology, Medical School, The University of Michigan Medical School, University of Michigan, Ann Arbor, MI 48109-2026, USA.
Gregory A YanikDivision of Hematology and Oncology, Department of Internal Medicine, Henry Ford Cancer Institute/Henry Ford Hospital, Detroit, MI, USA.
Muneesh TewariDivision of Hematology and Oncology, Department of Internal Medicine, Henry Ford Cancer Institute/Henry Ford Hospital, Detroit, MI, USA.
Issam El NaqaDepartment of Radiation Oncology, Medical School, The University of Michigan Medical School, University of Michigan, Ann Arbor, MI 48109-2026, USA.
Caitlin A SchonewolfDepartment of Radiation Oncology, Medical School, The University of Michigan Medical School, University of Michigan, Ann Arbor, MI 48109-2026, USA.
Randall Ten HakenDepartment of Radiation Oncology, Medical School, The University of Michigan Medical School, University of Michigan, Ann Arbor, MI 48109-2026, USA.
Shruti JollyDepartment of Radiation Oncology, Medical School, The University of Michigan Medical School, University of Michigan, Ann Arbor, MI 48109-2026, USA.
Theodore S LawrenceDepartment of Radiation Oncology, Medical School, The University of Michigan Medical School, University of Michigan, Ann Arbor, MI 48109-2026, USA.
Dipankar RayDepartment of Radiation Oncology, Medical School, The University of Michigan Medical School, University of Michigan, Ann Arbor, MI 48109-2026, USA. Electronic address: dipray@umich.edu.
University of Michigan · USHenry Ford Hospital · US

Funding

TREATMENT VERIFICATION AND TREATMENT PLAN REFINEMENTP01CA059827 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI LAWRENCE, THEODORE S, TEN HAKEN, RANDALL K · 1993 to 2018
$37.9M
Optimal Decision Making in Radiotherapy Using Panomics AnalyticsR01CA233487 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI EL NAQA, ISSAM M. · 2019 to 2023
$2.4M
NCI NIH HHS P01 CA059827NCI NIH HHS R01 CA233487
6 · The paper itself

Abstract

Grade 2 and higher radiation pneumonitis (RP2) is a potentially fatal toxicity that limits efficacy of radiation therapy (RT). We wished to identify a combined biomarker signature of circulating miRNAs and cytokines which, along with radiobiological and clinical parameters, may better predict a targetable RP2 pathway. In a prospective clinical trial of response-adapted RT for patients (n = 39) with locally advanced non-small cell lung cancer, we analyzed patients' plasma, collected pre- and during RT, for microRNAs (miRNAs) and cytokines using array and multiplex enzyme linked immunosorbent assay (ELISA), respectively. Interactions between candidate biomarkers, radiobiological, and clinical parameters were analyzed using data-driven Bayesian network (DD-BN) analysis. We identified alterations in specific miRNAs (miR-532, -99b and -495, let-7c, -451 and -139-3p) correlating with lung toxicity. High levels of soluble tumor necrosis factor alpha receptor 1 (sTNFR1) were detected in a majority of lung cancer patients. However, among RP patients, within 2 weeks of RT initiation, we noted a trend of temporary decline in sTNFR1 (a physiological scavenger of TNFα) and ADAM17 (a shedding protease that cleaves both membrane-bound TNFα and TNFR1) levels. Cytokine signature identified activation of inflammatory pathway. Using DD-BN we combined miRNA and cytokine data along with generalized equivalent uniform dose (gEUD) to identify pathways with better accuracy of predicting RP2 as compared to either miRNA or cytokines alone. This signature suggests that activation of the TNFα-NFκB inflammatory pathway plays a key role in RP which could be specifically ameliorated by etanercept rather than current therapy of non-specific leukotoxic corticosteroids.

Indexed as

Data-driven Bayesian network (DD-BN) analysisInflammatory cytokinesMicroRNA (miRNA)Radiation pneumonitisTumor necrosis factor alpha (TNFα) signaling, nuclear factor kappa B (NFκB)

Identifiers

PMID35460942
PMCPMC9046881
OpenAlexW4224287317

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.