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Record W4313546351 · doi:10.1200/jco.22.00655

Development and Validation of a Genomic Profile for the Omission of Local Adjuvant Radiation in Breast Cancer

2023· article· en· W4313546351 on OpenAlexafffund
Martin Sjöström, Anthony Fyles, Fei‐Fei Liu, David R. McCready, Wei Shi, Katrina Rey‐McIntyre, S. Laura Chang, Felix Y. Feng, Corey Speers, Lori J. Pierce, Erik Holmberg, Mårten Fernö, Per Malmström, Per Karlsson

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
FundersCanadian Institutes of Health ResearchGunnar Nilssons CancerstiftelseLunds UniversitetCancerfonden
KeywordsMedicineBreast cancerOncologyInternal medicineCohortHazard ratioProportional hazards modelBreast-conserving surgeryTamoxifenRadiation therapyAdjuvantEstrogen receptorCancerMastectomyConfidence interval

Abstract

fetched live from OpenAlex

PURPOSE Adjuvant radiotherapy (RT) is used for women with early-stage invasive breast cancer treated with breast-conserving surgery. However, some women with low risk of recurrence may safely be spared RT. This study aimed to identify these women using a molecular-based approach. METHODS We analyzed two randomized trials of women with node-negative invasive breast cancer to ± RT following breast-conserving surgery: SweBCG91-RT (stage I-II, no adjuvant systemic therapy) and Princess Margaret (age 50 years or older, T1-T2, adjuvant tamoxifen). Transcriptome-wide profiling was performed (Affymetrix Human Exon 1.0 ST microarray). Patients with estrogen receptor–positive/human epidermal growth factor receptor 2–negative tumors and with gene expression data were included. The SweBCG91-RT cohort was divided into training (N = 243) and validation (N = 354) cohorts. A 16-gene signature named Profile for the Omission of Local Adjuvant Radiation (POLAR) was trained to predict locoregional recurrence (LRR) using elastic net regression. POLAR was then validated in the SweBCG91-RT validation cohort and the Princess Margaret cohort (N = 132). RESULTS Patients categorized as POLAR low-risk without RT had a 10-year LRR of 6% (95% CI, 2 to 16) and 7% (0 to 27) in SweBCG91-RT and Princess Margaret cohorts, respectively. There was no significant benefit from RT in POLAR low-risk patients (hazard ratio [HR], 1.1 [0.39 to 3.4], P = .81, and HR, 1.5 [0.14 to 16], P = .74, respectively). Patients categorized as POLAR high-risk had a significant decreased risk of LRR with RT (HR, 0.43 [0.24 to 0.78], P = .0055, and HR, 0.25 [0.07 to 0.92], P = .038, respectively). An exploratory analysis testing for interaction between RT and POLAR in the combined validation cohort was performed ( P = .066). CONCLUSION The novel POLAR genomic signature on the basis of LRR biology may identify patients with a low risk of LRR despite not receiving RT, and thus may be candidates for RT omission.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.068
GPT teacher head0.425
Teacher spread0.357 · 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 designBench or experimental
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

Citations62
Published2023
Admission routes2
Has abstractyes

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