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Leeomic: A comprehensive proteomic analysis towards discovery of predictive patterns of protein expression to ribociclib sensitivity and resistance—A compLEEment-1 Canadian correlative sub-study.

2019· article· en· W2947012155 on OpenAlexaffabout
Stephen Chia, Jan‐Willem Henning, Ellen Warner, Xinni Song, Anil A. Joy, Nadia Califaretti, Christine Desbiens, Juan Pablo Zarate, Sina Haftchenary, Sabrina R. Perri, Gregg B. Morin

Bibliographic record

VenueJournal of Clinical Oncology · 2019
Typearticle
Languageen
FieldMedicine
TopicAdvanced Breast Cancer Therapies
Canadian institutionsNovartis (Canada)Grand River HospitalUniversité LavalUniversity of AlbertaBC Cancer AgencyOttawa HospitalHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineBreast cancerOncologyProteomeInternal medicinePopulationProteomicsCancerLetrozoleMetastatic breast cancerBiomarkerBioinformaticsBiologyTamoxifenGenetics

Abstract

fetched live from OpenAlex

TPS3170 Background: Despite developments in the treatment of advanced hormone receptor positive (HR+), human epidermal growth factor receptor 2 negative (HER2-) breast cancer, primary or acquired resistance eventually occurs in all cases and there is still very limited understanding of the mechanisms of resistance to therapy. LEEOMIC is a sub-study of the main CompLEEment-1 ( N = 3255 patients enrolled, CLEE011A2404 v03) trial, an open-label, phase 3b study evaluating ribociclib + letrozole as first-line therapy in an expanded advanced breast cancer patient population which recruited over 250 Canadian patients. The purpose of this Canadian correlative sample collection study is to explore the mechanisms of response and resistance to ribociclib in combination with letrozole through proteomic and ctDNA analysis. Methods: The British Columbia Cancer Research Centre team developed a novel and optimized MS/MS platform called SP3-Clinical Tissue Proteomics (SP3-CTP) to perform in-depth proteome profiling ( > 8,000 proteins) from formalin fixed paraffin embedded (FFPE) material (10-micron section). SP3-CTP analysis of the proteome of the study patients who did not achieve clinical benefit (primary resistance: progression within 3 months of treatment) will be compared to the proteome of the sub-group of prolonged responders (time to progression of 22 months or more) in order to identify biomarkers that can predict response or de-novo resistance to therapy. Archival tumor biopsies (primary or metastatic) collected from the study will be submitted for proteomic analysis to identify proteomic expression levels that may serve as predictor of response. It is anticipated that over 150 samples will be collected. If available, blood samples taken at time of progression or end of treatment will also be analyzed for ctDNA for genetic profiling and to study if there is any correlation between genetic mutations and response or resistance to therapy. Currently, both tissue and blood samples are being collected and no analysis has been conducted thus far. Clinical trial information: NCT03613220.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.067
Threshold uncertainty score0.946

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.055
GPT teacher head0.402
Teacher spread0.347 · 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 teacher head, 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

Citations0
Published2019
Admission routes2
Has abstractyes

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