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Reflex MammaPrint testing on breast core biopsies: A single center experience.

2023· article· en· W4379284520 on OpenAlexaff
Colleen Kerrigan, Nicole Look Hong, Amanda Roberts, Sharon Nofech‐Mozes, Tanya Jorden, Sonal Gandhi

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineBreast cancerInternal medicineOncologyCancerRetrospective cohort study

Abstract

fetched live from OpenAlex

e12581 Background: Breast cancer genomic assays are well established in the adjuvant setting, but still evolving in the neoadjuvant setting. Recent data shows that MammaPrint (MP)/BluePrint (BP) genomic assays in the neoadjuvant setting can reclassify hormone receptor positive (HR+), HER2 negative (HER2-) tumors into intrinsic phenotypes with corresponding responses to neoadjuvant therapy (NAC). We performed reflex MP/BP assays on invasive breast cancer core biopsies and evaluated the relationships between clinical risk, molecular risk and intrinsic phenotype. Methods: 150 consecutive HR+, HER2- invasive breast cancers were identified by core biopsy in a tertiary care cancer center and underwent reflex MP/BP testing by an external central laboratory between July 2021 and Jan 2022. Retrospective review was completed on patient demographics and tumor characteristics; descriptive statistics were performed. Results: Of 150 newly diagnosed HR+ HER2- breast cancers, 141 successfully had a reflex MP/BP test performed; 9 were not performed due to technical feasibility or clerical error. Of the 141 patients with a completed MP/BP assay, 7 had recurrent disease, and 8 had metastatic disease, these were excluded. Patients were categorized as clinically high (cHR) or low risk (cLR) using the criteria from the MINDACT trial and as molecularly high risk (MP HR) or low risk (MP LR) per their MP results. We then grouped patients with discordant clinical and molecular risk. We then examined clinical features and NAC receipt amongst these clinical and molecular risk groups. Conclusions: There was discordance between clinical and molecular risk in 38% of our cohort, 20% were cLR/MP and 18% were cHR/MP LR. The decision to give NAC was primarily driven by clinical risk as only 1 patient with cLR/MP HR received NAC. Amongst cHR/MP LR 87% did not get NAC, but this appears to have been a clinical decision as only 7 of these patients had a pre-operative oncology visit at which MP/BP was mentioned. Most (72%) of > cN1 patients received NAC, of the 5 who didn’t, 4 were in the MP LR group and had a pre-operative oncology visit where MP was mentioned. In this select group the genomic assay may have influenced the decision to withhold NAC. Reflex genomic assays for HR+ HER2- breast cancer may not be useful; the decision to integrate molecular risk into the decision for NAC is multifactorial and stage of disease may be most impactful. Further work is needed in this area. [Table: see text] [Table: see text]

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)0.002
metaresearch head score (Gemma)0.005
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 score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.289
GPT teacher head0.486
Teacher spread0.198 · 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
Published2023
Admission routes1
Has abstractyes

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