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Record W2757702989 · doi:10.21037/apm.2017.09.01

Genetic biomarkers associated with changes in quality of life and pain following palliative radiotherapy in patients with bone metastases

2017· article· en· W2757702989 on OpenAlexaff
Anthony Furfari, Bo Wan, Keyue Ding, Andrew Wong, Liting Zhu, Andrea Bezjak, Rebecca Wong, Carolyn F. Wilson, Carlo DeAngelis, Azar Azad, Edward Chow, George S. Charames

Bibliographic record

VenueAnnals of Palliative Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicManagement of metastatic bone disease
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoQueen's UniversityLunenfeld-Tanenbaum Research InstituteHealth Sciences CentreMount Sinai HospitalSinai Health SystemSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineQuality of life (healthcare)Palliative careRadiation therapyIntensive care medicineInternal medicineOncologyNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Patients with bone metastases undergoing palliative radiation therapy (RT) may experience changes in both the functional and symptomatic aspects of quality of life (QOL). The European Organization of Cancer Research and Treatment (EORTC) QOL Questionnaire Core-15 Palliative (QLQ-C15-PAL) is a validated questionnaire employed to assess QOL specifically in palliative patients. Our study aimed to identify single-nucleotide variant (SNV) genetic biomarkers associated with changes in QOL and pain. METHODS: Fifty-two patients who received a single 8-Gy RT for painful bone metastases completed the EORTC QOL-C15-PAL questionnaire prior to randomization and at 42-day post RT. Saliva samples obtained at day of RT were sequenced, and SNVs from genes involved in inflammation, radiation response, immune response, DNA damage, or QOL were assessed for association with changes in global QOL or the pain scale items using the Cochran-Armitage trend test. The penalized LASSO method with minimum Bayesian information criterion was used to select a multi-SNV model out of significant SNVs (P<0.005) and to produce prognostic scores for patients that categorized them into risk groups of low, middle, and high. RESULTS: The multivariable model predicting global QOL included 14 SNVs, of which HS1BP3 rs35579164 G:C and ABCA1 rs2230805 C>T had the largest positive and negative effect sizes, respectively (HS1BP3: 8.21, ABCA1: -3.44). The model for the response of QOL pain item included 8 SNVs, of which PLAUR rs4760 A>G and ELAC2rs11545302 had the largest positive and negative effect sizes, respectively (PLAUR: 5.23; ELAC: -3.84). The patients' risk groups were highly predictive of QOL response (P<0.0001) and pain item response (P<0.0001). In logistic regression analysis accounting for baseline factors of gender and primary cancer site, the global QOL risk group predicts pain response after RT [OR: 2.1, 95% confidence interval (CI): 1.2-3.9, P=0.015], but the QOL pain item risk group did not (OR: 0.93; 95% CI: 0.5-1.6, P=0.79). The multi-SNVs model included SNVs from genes involved in metabolism, membrane transport, cell cycle control, ciliary structure, and gene expression regulation. CONCLUSIONS: SNVs were significantly associated with changes in global QOL of global domain and pain item in patients with bone metastases. Identification of genetic biomarkers predictive of QOL items may allow patients and health care providers anticipate and better address the needs of the palliative cancer patient population.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.802

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.094
GPT teacher head0.378
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2017
Admission routes1
Has abstractyes

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