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Abstract P4-10-29: Identifying novel molecular markers of response to trastuzumab in metastatic breast cancer

2020· article· en· W3006105063 on OpenAlexaffabout
Katarzyna J. Jerzak, Danielle Desautels, Phillip Blanchette, Jane Bayani, Sharon Nofech‐Mozes, Martin J. Yaffe, Gregory R. Pond, Kathleen I. Pritchard, John M.S. Bartlett

Bibliographic record

VenueCancer Research · 2020
Typearticle
Languageen
FieldMedicine
TopicAdvanced Breast Cancer Therapies
Canadian institutionsWestern UniversityMcMaster UniversityOntario Institute for Cancer ResearchCancerCare ManitobaUniversity of Toronto
Fundersnot available
KeywordsMedicineTrastuzumabOncologyInterquartile rangeInternal medicineBreast cancerMetastatic breast cancerCancerProportional hazards modelCohortRetrospective cohort study

Abstract

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Abstract Background: The survival of women with HER2+ metastatic breast cancer (MBC) has improved with the introduction of the anti-HER2 antibody trastuzumab, but responses vary greatly among individuals. A predictive signature of response (and lack thereof) to trastuzumab may help personalize therapy for women with HER2+ MBC and enable the discovery of new “druggable” targets. Methods: In this retrospective cohort study, 256 patients with HER2+ MBC at the Sunnybrook Odette Cancer Centre were identified sequentially from January 1999 to December 2013 using the Cancer Care Ontario Registry. In total, 53 patients (21%) had archived metastatic tissue available for analysis, 29 (55%) of whom had samples obtained <3 years prior to the initiation of trastuzumab. Nanostring molecular testing was used to measure the expression of commonly known oncogenes and gene signatures using the Nanostring technologies nCounter Analysis System TM (Seattle, Washington, USA). Descriptive statistics were used to summarize patient results and prognostic scores. Patients were categorized into short-term survivors (died within 12 months) and long-term survivors (survived >30 months after starting trastuzumab). Results were compared between these two groups of patients using a Fisher’s exact test (dichotomous groups) and Wilcoxon rank sum tests (continuous scores). Univariable Cox regression models were evaluated using all patients, to evaluate if risk scores were prognostic of overall survival (OS). Statistical significance was set at the p=0.05 level and all tests were two-sided. Results: The median patient age was 60 years (interquartile range 53-65 years), 24% (n=7) had de-novo MBC and the majority of patients (52%) had ER or PR positive tumors. Among 29 included patients, the median OS was 23.4 months (95%CI 10.3-34.3 months); 10 patients died within 12 months and 10 patients survived >30 months after starting trastuzumab-based therapy. A median of 155 days (range 4-812) elapsed between metastatic biopsy and initiation of trastuzumab. The distribution of the Prosigna ROR Score, Basal Cor, HER2 Cor, Luminal A Cor, Luminal B Cor, Mammaprint Subtype/Risk Group, Oncotype Risk Score/Risk Group, IHC4 mRNA Risk Group and GGI Risk Score/Risk Group did not differ between the short- and long-term survivors. However, the Mammaprint Predictor Score was significantly higher [mean 0.34 (std 5.50) versus -4.95 (std 5.24), p=0.033] and the IHC4 Risk Score was significantly lower among long-term as compared to short-term survivors [mean 99.3 (std 67.1) versus 160.3 (std 40.9), p=0.044]. The association between molecular gene signatures and OS are described in Table 1. Conclusion: Several gene signatures were associated with OS in this exploratory analysis of women with HER2+ MBC who received trastuzumab-based therapy. The Mammaprint Predictor and the IHC4 Risk Scores differed among short- versus long-term survivors, warranting validation in an independent dataset. Table 1. Univariable Survival Results (n=29)ComparatorHR (95% CI)p-valueProsigna ROR Score/ 10 units1.23 (0.92, 1.65)0.17ProsignaHER2 vs Other1.25 (0.50, 3.13)0.64Basal Cor/ unit3.61 (1.10, 11.78)0.034Her2 Cor/ unit0.99 (0.15, 6.40)0.99Luminal A Cor/ unit0.22 (0.05, 0.95)0.042Luminal B Cor/ unit0.26 (0.06, 1.06)0.060Mammaprint Predictor Score/ unit1.10 (1.02, 1.19)0.015Mammaprint SubtypeHER2 vs Other0.85 (0.37, 2.00)0.72Mammaprint RiskHigh vs Low4.20 (1.37, 12.86)0.012Oncotype Risk Score/ 10 units1.27 (0.95, 1.70)0.11Oncotype Risk GroupHigh vs Intermediate4.83 (0.60, 39.07)0.14IHC4 Risk Score/ 10 units1.09 (1.00, 1.18)0.046IHC4 mRNA Risk GroupHigh vs Intermediate4.83 (0.60, 39.07)0.14GGI Risk Score/ 10 units1.63 (1.02, 2.61)0.043GGI Risk GroupHigh vs Low5.14 (1.13, 23.51)0.035 Citation Format: Katarzyna Joanna Jerzak, Danielle N Desautels, Phillip S Blanchette, Jane Bayani, Sharon Nofech-Mozes, Martin J Yaffe, Gregory Pond, Kathleen I Pritchard, John MS Bartlett. Identifying novel molecular markers of response to trastuzumab in metastatic breast cancer [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P4-10-29.

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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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.507
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.121
GPT teacher head0.450
Teacher spread0.329 · 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.

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
Published2020
Admission routes2
Has abstractyes

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