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Gene signature predictor of dose-response to prostate radiation: Validation of PORTOS in phase III trials.

2025· article· en· W4407699651 on OpenAlexaff
Shuang Zhao, Hyunnam Ryu, J. Proudfoot, Elai Davicioni, Jeff M. Michalski, Daniel E. Spratt, Stefanie Hayoz, Jeffry Simko, Howard M. Sandler, Alan Pollack, Matthew Parliament, Ian S. Dayes, Rohann Correa, Theodore Karrison, William A. Hall, Daniel M. Aebersold, Felix Y. Feng, Pirus Ghadjar, Phuoc T. Tran, Alan Dal Pra

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsLondon Health Sciences CentreJuravinski Cancer CentreUniversity of Alberta
Fundersnot available
KeywordsMedicineGene signatureProstate cancerProstateOncologySignature (topology)Radiation doseInternal medicineGeneNuclear medicineCancerGene expressionGeneticsBiology

Abstract

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308 Background: NRG/RTOG 0126 and SAKK 09/10 were phase III randomized trials examining whether higher dose resulted in better response/outcomes in PCa patients following definitive and post-operative RT, respectively. RTOG 0126 showed a benefit for RT dose escalation (DE) from 70.2Gy to 79.2Gy though SAKK 09/10 did not show a benefit from 64Gy to 70Gy. We hypothesized that a previously developed 24-gene prostate cancer RT gene expression score (PORTOS) could distinguish patients who benefited from RT DE in both trials. Methods: PORTOS scores were calculated on biopsy samples in RTOG 0126 and prostatectomy samples in SAKK 09/10 as published. Since the original PORTOS cutoffs were in the post-op setting, we utilized tertile score groups in RTOG 0126, whereas the published PORTOS cutoffs were used for SAKK 09/10. The primary objective was to evaluate PORTOS as a predictive biomarker for the benefit of RT DE on biochemical failure (BF) via the Phoenix criteria in RTOG 0126 (N=215) and clinical progression-free survival (CFPS) in SAKK 09/10 (n=226). In addition, we also investigated clinical and molecular correlates of PORTOS in large real-world datasets of 31,107 prostate biopsy samples and 42,407 radical prostatectomy samples. Results: In RTOG 0126, in patients with lower tertile PORTOS scores, there was no difference in Phoenix BF (sHR 1.14 [0.54-2.40], P=0.73). However, for patients in the middle and higher tertile PORTOS score range, there was a significant benefit for RT DE for Phoenix BF (middle PORTOS: sHR 0.45 [0.22-0.90], P=0.02; higher PORTOS: sHR 0.30 [0.12-0.75], P=0.009). An interaction test indicated a significant difference in benefit for DE between higher and lower PORTOS groups (P=0.048). Similarly in the post-op SAKK 09/10 trial, only patients in the higher PORTOS score group benefited from RT DE (CPFS HR 0.19 [0.05-0.70]; P=0.01), with a significant biomarker-treatment interaction between lower vs. higher PORTOS and treatment arm (P=0.003). Interestingly, PORTOS was not consistently associated with clinicopathologic variables in either trial or in the large real-world biopsy or prostatectomy datasets. Biologically, in the real-world datasets, PORTOS was modestly associated with hypoxia signatures consistent with its role in radio-resistance, and strongly associated with immune signatures and molecular subtypes. Conclusions: In two phase III randomized trials, we have validated that PORTOS can identify patients who benefit as well as those that do not benefit from RT DE for localized PCa and provides the first randomized evidence for any biomarker to be able to predict RT dose response. PORTOS could be used to personalize radiation dose for patients with prostate cancer clinically, and allow selection of patients most likely to benefit from RT DE while sparing others from the potential increased risk of 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)0.011
metaresearch head score (Gemma)0.011
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 score0.011
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.055
GPT teacher head0.490
Teacher spread0.435 · 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".

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Citations3
Published2025
Admission routes1
Has abstractyes

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