MétaCan
Menu
← Back to cohort

PORTOS gene signature as a predictor of risk of adverse events after dose-escalated vs. lower-dose prostate radiation therapy in NRG/RTOG 0126.

2025· article· en· W4407700582 on OpenAlexaff
Karen E. Hoffman, Sophia C. Kamran, Hyunnam Ryu, James A. Proudfoot, Elai Davicioni, Paul L. Nguyen, Stephanie L. Pugh, Daniel E. Spratt, Jeff M. Michalski, Matthew Parliament, Ian S. Dayes, Rohann Correa, John M. Robertson, Elizabeth Gore, Desiree E. Doncals, Éric Vigneault, Luís Souhami, Felix Y. Feng, Phuoc T. Tran, Shuang Zhao

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsLondon Health Sciences CentreJuravinski Cancer CentreMcGill University Health CentreCentre hospitalier universitaire de QuébecUniversity of Alberta
Fundersnot available
KeywordsMedicineProstate cancerRadiation therapyAdverse effectOncologyInternal medicineProstateRadiation doseNuclear medicineCancer

Abstract

fetched live from OpenAlex

375 Background: Dose-escalated radiation therapy is standard treatment for patients with prostate cancer. Dose-escalation improves cancer control but also increases the risk of treatment adverse effects. We hypothesized RNA-based tumor gene expression recapitulates normal tissue gene expression and therefore could identify patients at increased risk of adverse events after dose-escalated radiation. We specifically evaluated the 24-gene PORTOS score which characterizes response to DNA damage and radiation. Methods: PORTOS scores were calculated from biopsy samples obtained from 215 patients treated on the NRG/RTOG 0126 clinical trial that randomized patients with intermediate-risk prostate cancer between 70.2 Gy and 79.2 Gy delivered in 1.8 Gy fractions. In this trial, adverse events were categorized using RTOG criteria. Fine-Gray multivariable analysis of continuous and categorical PORTOS (tertiles) were used to calculate subdistribution hazard ratios (sHR), treating death without events as a competing risk, adjusting for age. Results: Median age was 70 years [IQR 65-74]. Fifty percent received 70.2 Gy (n=107), 50% received 79.2 Gy (n=108) and median follow up was 12.8 years. Patient and treatment characteristics were well balanced across treatment arms (all p>0.05) and across PORTOS groups (all p>0.05). Forty-five percent (n=97) of patients experienced grade 2 or higher adverse events after treatment. In patients receiving standard dose 70.2 Gy radiation, PORTOS was not associated with grade 2 or higher adverse events. However, in patients receiving dose-escalated 79.2 Gy radiation, higher PORTOS score was associated with a higher rate of grade 2 or higher adverse events (sHR = 1.12 [95% CI 1.03-1.22], p=0.01). There was a statistically significant interaction between continuous PORTOS scores and treatment arm for grade 2 or higher adverse events (p=0.01). Regarding treatment arm effects by PORTOS tertile, we observed that for patients with higher tertile PORTOS scores, dose-escalated radiation is more likely to cause grade 2 or higher adverse events compared to lower-dose radiation (sHR = 2.15 [1.04 - 4.44], p = 0.04; five-year cumulative incidence adverse events of 61% after 79.2 Gy vs. 36% after 70.2 Gy). In contrast, risk of grade 2 or higher adverse events was similar after treatment with dose-escalated vs. lower-dose radiation for patients with lower (p=0.41) or mid-tertile (p=0.78) PORTOS scores. Conclusions: HigherPORTOS scores were associated with an increased risk of adverse events after administration of dose-escalated radiation compared to standard-dose radiation. PORTOS is the first radiation sensitivity biomarker to be validated for toxicity with data from a phase III randomized trial and could be used to help personalize radiation therapy dose for patients to limit risk of treatment toxicity.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.386
Teacher spread0.371 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Clinical Oncology→Same topicAdvanced Radiotherapy Techniques→French-language works237,207→