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Record W2565502203 · doi:10.1158/1538-7445.am2015-5289

Abstract 5289: The role of the immune system in lymph node positive ER+ breast cancer

2015· article· en· W2565502203 on OpenAlexaff
Jessica Cockburn, Amy Gillgrass, Anita Bane

Bibliographic record

VenueCancer Research · 2015
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsHamilton Health SciencesMcMaster University
Fundersnot available
KeywordsBreast cancerMedicineTissue microarrayLymph nodeImmune systemOncologyEstrogen receptorCancerPopulationImmunohistochemistryInternal medicineStage (stratigraphy)TamoxifenImmunologyBiology

Abstract

fetched live from OpenAlex

Abstract Estrogen receptor (ER) positive breast cancer (BC) accounts for 70% of BC diagnoses and is associated with a good outcome when treated with conventional therapies, including tamoxifen. However, there remains a sub-population of patients who undergo relapse within 10 years of diagnosis and do not respond well to standard therapies. While lymph node (LN) status is an important indicator of poor prognosis, additional information is needed to more accurately predict patients who will or will not relapse. Previous genomic studies from our lab have shown that immune response is an important feature of LN+ BCs that do not relapse. In addition, other studies have also examined the role of the immune system in ER+ BC, but due to varying study designs, it is unclear how immune response pertains to ER+ BC relapse. The goal of this project is to characterize immune response in ER+ BC by measuring levels of immune markers using immunohistochemistry (IHC) and RNA expression levels and comparing those to tumour characteristics. We have developed a cohort of retrospective ER+ BC patients with tumour samples available through the Hamilton Health Science tumour bank. Patients were selected for eligibility based on ER status, early stage disease, and having long term clinical follow-up. Clinical charts for each patient were reviewed and pathological, treatment, and outcome data were abstracted. Tumour blocks were obtained and sections stained for haematoxylin and eosin were marked for tumour boarders then used to construct Tissue microarrays (TMA) for IHC assays and RNA is to be extracted from each tumour block. 318 patient samples have been obtained that meet eligibility requirements. Among those, 163 are LN- and 110 are LN+, and the remainder have unknown LN status. Primary endpoint for this study is the development of distant metastasis. Roughly 10% of LN- patients developed distant metastasis within 10 years and roughly 20% of LN+ patients developed distant metastasis. In total, 14% of patients came to endpoint during the study period. TMAs were stained for pathological markers, including ER, PR, HER2, and Ki67 as well as immune markers such as CD8 and CD20. RNA expression levels for each of these were also determined and both were compared with clinical outcome. Here we present a retrospective cohort of ER+ BC patients with 10 years of clinical follow-up data that can be used for IHC and RNA analysis. Further, we have examined the association between immune response and prognosis in lymph node positive ER+ BC patients. Citation Format: Jessica G. Cockburn, Amy Gillgrass, Anita Bane. The role of the immune system in lymph node positive ER+ breast cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 5289. doi:10.1158/1538-7445.AM2015-5289

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)0.000
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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0060.001

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 head0.043
GPT teacher head0.370
Teacher spread0.327 · 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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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