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Abstract OT2-03-01: RNA disruption assay (RDA) - Breast cancer response evaluation for individualized therapy (brevity/brevity-02)

2022· article· en· W4220851987 on OpenAlexaff
Maureen Trudeau, Joke Tio, Foluso O. Ademuyiwa, Thierry Petit, Bryan T. Hennessy, Marina Elena Cazzaniga, Daniele Generali

Bibliographic record

VenueCancer Research · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineBreast cancerOncologyChemotherapyNeoadjuvant therapyInternal medicineStage (stratigraphy)Clinical trialCancerBiopsySurgery

Abstract

fetched live from OpenAlex

Abstract Background & Rationale Identifying non-responders to chemotherapy with reliable predictive tests is crucial to response-guided neoadjuvant therapy. BREVITY aims to support RDA as a response assessment tool in breast cancer.RDA is based on RNA disruption, a qualitative and quantitative alteration of ribosomal RNA that correlates with chemotherapy response. RDA stratifies patients into 3 zones: non-response zone 1, partial-response zone 2 and response zone 3. Survival data in MA.22 trial showed significantly superior 5-year DFS KM-curves for zone 3 patients compared to zones 1 & 2. MA.22 and other studies indicated a potentially valuable clinical utility to be pursued. Trial Design. Study type: Prospective 2-phase interventional study. Samples: 2 core needle biopsy specimens taken at 2 time points during chemotherapy. 1 Follow-up: 60 months from date of study enrolment. Incl. Criteria. •Women 18+ years old and able to provide informed consent. •Newly diagnosed clinical stage I, II or III breast cancer with complete surgical excision after neoadjuvant therapy as treatment goal. •Confirmation of invasive breast cancer of any subtype or grade, tumor size > 1cm. •Scheduled for neoadjuvant chemotherapy +/- antibodies and +/- other drugs according to SoC. •Willing to have research core needle biopsies at 2 timepoints during neoadjuvant treatment. Excl. Criteria. •Prior local or systemic therapy for current breast cancer. •Participation in another interventional clinical trial with experimental drugs during neoadjuvant therapy. •Stage IV breast cancer. •Bilateral, multifocal or multicentric breast cancer. •Prior malignant disease except curatively treated basalioma of the skin or pTis of the cervix uteri. •Pregnancy or breast feeding. Trial Objectives & Endpoints Statistical Methods. Phase 1: training set will be used to estimate and visualize operating characteristics as functions of RDI. These include NPV, PPV, sensitivity, specificity, and related concepts. Suitable cut-offs will be determined that optimize NPV of zone 1 and PPV of zone 3. Phase 1 powered to estimate 25th RDI percentile as candidate for cut-off c1 with 9.5% precision E. Phase 2: analysis by interval-estimation of performance characteristics on an independent validation set, given cut-offs selected in phase 1. Primary target of validation is NPV of zone 1 that corresponds to RDA’s main clinical utility. PPV of zone 3 to be established for selected subgroups. Sample size for validation chosen to yield 95% CI estimate for NPV with 5.25% precision. Present and Target Accrual. Present AccrualTarget Accrual(must be fully evaluable). Phase 1 (Training set):71 (target 80). Phase 2 (Validation set):454 Other Contacts. Sponsor: Rna Diagnostics Inc Biopsy Collection Time Points for RDABiopsy(2 specimens)1st core biopsy2nd core biopsyTiming35 +/- 4 daysIf no drug change:55 +/- 5 days. If drug change:2-3 weeks after start of new drugs:•3-weekly: at 16 +/- 2 days•Bi-weekly: at day of 2nd dose•Weekly: at day of 4th dose Primary ObjectivesDescriptionEndpointsi.1.Phase 1: Determine 2 RDI cut-offs to have a diagnostic test optimized in terms of predictive values2.Phase 2: Establish performance characteristics for first cut-off in terms of NPVNPVPPVSecondary Objectivesi.Assess NPV in subgroups by subtypeNPVii.Assess PPV in HER2+PPViii.Compare pCR prevalence and DFS in zones 1-3 for all patients and by subgrouppCRDFSv.Test for association btw. cancer subgroups and test resultTest result (zones 1-3)vi.Compare RDA and other biomarker results (Ki67, tumor grade) Citation Format: Maureen Trudeau, Joke Tio, Foluso Ademuyiwa, Thierry Petit, Bryan Hennessy, Marina Cazzaniga, Daniele Generali. RNA disruption assay (RDA) - Breast cancer response evaluation for individualized therapy (brevity/brevity-02) [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr OT2-03-01.

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)0.012
metaresearch head score (Gemma)0.009
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 score0.012
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.003

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 head0.119
GPT teacher head0.460
Teacher spread0.341 · 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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