MétaCan
Menu
Back to cohort
Record W2757910896 · doi:10.21037/apm.2017.09.04

Genetic biomarkers associated with pain flare and dexamethasone response following palliative radiotherapy in patients with painful bone metastases

2017· article· en· W2757910896 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 institutionsUniversity of TorontoPrincess Margaret Cancer CentreLunenfeld-Tanenbaum Research InstituteHealth Sciences CentreMount Sinai HospitalSinai Health SystemSunnybrook Health Science CentreQueen's University
Fundersnot available
KeywordsMedicineDexamethasoneFlareIncidence (geometry)Confidence intervalBone painGlucocorticoidInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: In patients who receive palliative radiation therapy (RT) for painful bone metastases, 40% experience a transient increase in pain known as a pain flare. Prophylactic dexamethasone has been shown to reduce pain flare incidence to 25%. We aimed to identify DNA biomarkers associated with pain flare and dexamethasone response. METHODS: Daily pain levels were recorded by 81 patients who received a single 8 Gy RT for painful bone metastases, of which 50 also received prophylactic dexamethasone. To identify single-nucleotide variants (SNVs), patient saliva samples obtained at day of RT were sequenced for 4,813 disease-associated genes, then filtered for genes associated with inflammation, radiation or immune response, and DNA damage. Significant SNVs (P<0.005) identified by the Cochran-Armitage trend test underwent the Penalized LASSO method with minimum Bayesian Information Criterion to select a multi-SNV model that jointly predicted pain flare, and pain flare despite prophylactic dexamethasone (dexamethasone response). The corresponding estimated effects of the multi-SNVs were used to drive the prognostic score of developing pain flare for each patient, who were divided into three risk groups of roughly equal sizes. RESULTS: Risk groups were significantly predictive of pain flare (P<0.0001) and dexamethasone response (P<0.0001). The high-risk patient groups had a 78% chance of developing pain flare, and pain flare despite dexamethasone [OR =24.6, 95% confidence interval (CI): 1.8-342.7, P=0.02]. The multivariable model for pain flare included 15 variants, with effect sizes ranging from -4.97 (NBPF1 rs3872309 C>T) to 5.54 (DNM2 10940838 A>C). The multivariable model for dexamethasone response included 6 variants, with effect sizes ranging from -1.03 (NBPF1 rs3872309 C>T) to 0.85 (TSEN54 rs62088470 C>G). CONCLUSIONS: Significant SNVs associated with pain flare were found in genes with functions in biosynthesis (DHODH, PECR), lipid excretion and metabolism (UGT2A1/2, VLDLR), and intracellular signalling (DNM2, SEC23A). Significant SNVs associated with dexamethasone response were from genes involved in extracellular matrix (HAS1, ADAMTS16) and cytoskeleton regulation (GAS2L2). Identification of SNVs predictive of pain flare and dexamethasone response enables targeted prophylactic therapy according to a patient's predisposed response.

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)0.005
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.010
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 head0.051
GPT teacher head0.345
Teacher spread0.294 · 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.

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

Citations12
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueAnnals of Palliative MedicineSame topicManagement of metastatic bone diseaseFrench-language works237,207