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Abstract P3-14-11: Microarray data analysis and long term outcomes of NCIC-CTG MA.22: Neoadjuvant epirubicin and docetaxel with pegfilgrastim support for locally advanced breast cancer

2013· article· en· W2062382244 on OpenAlexaff
Maureen Trudeau, JA Chapman, Bingqi Guo, M. Clemons, Rebecca Dent, Robert S. de Jong, Harriette Kahn, LE Shepherd, Kathleen I. Pritchard, Junnan Xu, P. O’Brien, Amadeo M. Parissenti

Bibliographic record

VenueCancer Research · 2013
Typearticle
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsHealth Sciences CentreOttawa HospitalSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineDocetaxelOncologyEpirubicinInternal medicineBreast cancerTissue microarrayCohortCancerSignificance analysis of microarraysMicroarrayGene expressionBiologyGene

Abstract

fetched live from OpenAlex

Abstract Background: 93 patients were enrolled in sequential phase 1 and 2 trials of epirubicin (E) and docetaxel (D) given at 3 (cohort A) or 2 weekly (cohort B) intervals. We previously reported clinical (93%) and pathologic response rates (pCR 7%) as well as the association of fall in tumour RNA integrity (RIN) with response (ASCO 2010). Here we report the results of microarray analysis of tumor specimens pre-and mid-treatment to determine genes which are differentially expressed in different groups. Methods: 6 core biopsies were collected for all patients pre-, mid- and post-treatment with ED. 3 cores were used for standard pathologic assessment while 3 were used for gene expression assessment using Agilent full genome microarrays. Pre- and mid-treatment cores were used with Agilent Feature Extraction Software to assess microarrays; and baseline continuous (% positive) immunohistochemical ER, PR, HER2, and Topo2 were investigated by schedule and dose. RNAs with RIN > = 5.0 were subjected to microarray analysis. NIH BRB array tools were used for investigations of differential gene expressions. K-M curves were generated for the phase 2 cohorts for disease free (DFS) and event free survival (EFS), and for ER− PR− and ER or PR+ subgroups. Results: Of the 93 patients, 47 were in cohort A and 46 in cohort B. Median overall survival was 6.34 years on study. DFS at 50 months was 55% for A (phase 2) and 67% for B (phase 2), while EFS was 55% for A and 63% for B. For ER or PR+ DFS and EFS were 60% and for ER− PR− DFS and EFS were 63%. 134 arrays were available in total: 57 from A, 68 from B with 11 reference breast tumour RNAs for standardization. Patients with and without microarrays were not significantly different. Pre-treatment, we found 3 differentially expressed genes in A and 6 in B between patients who did and did not have RIN > = 5.0 at mid-treatment. Comparing CR to non-CR (PR, SD, PD), 40 genes were found for A and 2 genes for B. Many genes were also differentially expressed in A and B when analyzed by pathologic factors ER, PR, HER2, Topo2, schedule and dose. Mid-treatment, 4,365 genes in A and 18,770 genes in B were significantly different from pre-treatment. Conclusion: At 50 months, DFS was 55% for 3 weekly and 67% for 2 weekly schedules of ED. The genes identified in each cohort pretreatment will be investigated further for relevance to predicting sensitivity or resistance to E or D. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P3-14-11.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.042
GPT teacher head0.410
Teacher spread0.368 · 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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Citations0
Published2013
Admission routes1
Has abstractyes

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