Evidence map›Paper›PMID 33800260›Full record

ArticleJournal of personalized medicine2021

Telomere Length Dynamics and Chromosomal Instability for Predicting Individual Radiosensitivity and Risk via Machine Learning.

Jared J Luxton, Miles J McKenna, Aidan M Lewis, Lynn E Taylor, Sameer G Jhavar, Gregory P Swanson, Susan M Bailey

Abstract read
In one paragraph

Article in Journal of personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

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

  1. Pooled it
  2. Review
  3. Article
  4. Review
  5. Article
  6. Article
  7. Review
  8. Machine Learning & Molecular Radiation Tumor Biomarkers.Seminars in radiation oncology · 2023
    Review
  9. Role of telomere length in human carcinogenesis (Review).International journal of oncology · 2023
    Review
  10. Article
  11. Article
  12. Radiation Biomarkers: Silver Bullet, or Wild Goose Chase?Journal of personalized medicine · 2021
    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

7 authors.

Jared J LuxtonDepartment of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO 80523, USA.
Miles J McKennaDepartment of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO 80523, USA.
Aidan M LewisDepartment of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO 80523, USA.
Lynn E TaylorDepartment of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO 80523, USA.
Sameer G JhavarBaylor Scott & White Medical Center, Temple, TX 76508, USA.
Gregory P SwansonBaylor Scott & White Medical Center, Temple, TX 76508, USA.ORCID 0000-0002-7959-1493
Susan M BaileyDepartment of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO 80523, USA.

Funding

Colorado Office of Economic Development and International Trade (OEDIT) Advanced Industry (AI) Bioscience Proof of Concept (POC) award
6 · The paper itself

Abstract

The ability to predict a cancer patient's response to radiotherapy and risk of developing adverse late health effects would greatly improve personalized treatment regimens and individual outcomes. Telomeres represent a compelling biomarker of individual radiosensitivity and risk, as exposure can result in dysfunctional telomere pathologies that coincidentally overlap with many radiation-induced late effects, ranging from degenerative conditions like fibrosis and cardiovascular disease to proliferative pathologies like cancer. Here, telomere length was longitudinally assessed in a cohort of fifteen prostate cancer patients undergoing Intensity Modulated Radiation Therapy (IMRT) utilizing Telomere Fluorescence in situ Hybridization (Telo-FISH). To evaluate genome instability and enhance predictions for individual patient risk of secondary malignancy, chromosome aberrations were assessed utilizing directional Genomic Hybridization (dGH) for high-resolution inversion detection. We present the first implementation of individual telomere length data in a machine learning model, XGBoost, trained on pre-radiotherapy (baseline) and in vitro exposed (4 Gy γ-rays) telomere length measurements, to predict post radiotherapy telomeric outcomes, which together with chromosomal instability provide insight into individual radiosensitivity and risk for radiation-induced late effects.

Indexed as

chromosomal instabilityIMRTindividual radiosensitivityinversionslate effectsmachine learningpersonalized medicineprostate cancertelomeres

Identifiers

PMID33800260
PMCPMC8002073

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.