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Record W4412626262 · doi:10.1038/s41523-025-00796-x

The untapped potential of radiation and immunotherapy for hormone receptor-positive breast cancer

2025· review· en· W4412626262 on OpenAlexaff
Matthew J. Fenton, Miki Yoneyama, Erik Wennerberg, Tom Lund, Andrew Tutt, Alan Melcher, Sandra Demaria, Navita Somaiah

Bibliographic record

Venuenpj Breast Cancer · 2025
Typereview
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsInstitute of Cancer Research
FundersNational Cancer InstituteOffice of Defense ProgramsCancer Research UKNational Institutes of HealthRoyal Marsden NHS Foundation TrustBreast Cancer Research FoundationU.S. Department of Defense
KeywordsBreast cancerImmunotherapyHormone receptorHormoneOncologyCancerMedicineInternal medicineReceptorCancer immunotherapyCancer research

Abstract

fetched live from OpenAlex

Hormone receptor-positive breast cancers are a diverse group of tumours, with only some responding well to immunotherapy. Alternative combination strategies such as radiotherapy present an exciting opportunity to improve immunotherapy responses. We review an intriguing overlap between the impact of oestrogen signalling and radiation on multiple signalling pathways and immune cells that may be exploited for therapeutic gains in breast cancer. This is synthesised with the pre-clinical data and clinical trial landscape supporting the use of combined radiation and immunotherapy to derive insights for future neo-adjuvant trial design.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.011
GPT teacher head0.315
Teacher spread0.304 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations10
Published2025
Admission routes1
Has abstractyes

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