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Abstract P5-05-01: Prevalance of crown-like structures of the breast, a histologic biomarker linked to obesity: A retrospective study of 99 cases

2016· article· en· W2403657692 on OpenAlexaff
MC Chang, Zohreh Eslami, Marguerite Ennis, PJ Goodwin

Bibliographic record

VenueCancer Research · 2016
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Disease and Adiposity
Canadian institutionsUniversity of TorontoStatistics CanadaMount Sinai Hospital
Fundersnot available
KeywordsMedicineBreast cancerInternal medicineFat necrosisAdipose tissueCohortCLs upper limitsObesityPathologyCancerBiomarkerOncologyGastroenterologyBiology

Abstract

fetched live from OpenAlex

Abstract Introduction: Breast cancer risk is multifactorial, and depends partly on obesity and related metabolic imbalances, including inflammation. Obesity is increasing worldwide, and is a known cancer risk albeit with complex mechanisms. Previous reports (Morris et al., 2011; Iyengar et al., 2015) indicate that local inflammation can be seen histologically as a rings of macrophages around necrotic adipocytes ("crown-like structures of the breast", CLS-B). Our goal was to determine the prevalence of CLS-B in routine specimens from a cohort of patients with known BMI. Methods: We retrieved archival H&E slides from a breast cancer cohort (N=99) previously characterized for BMI and fasting plasma/serum metabolic factors. Two pathologists reviewed all available sections of white adipose tissue not adjacent to tumour (median 7 blocks/case), excluding fat necrosis and mastitis, blinded to correlative data/BMI. We recorded the presence/absence and numbers of CLS-B, defined as a continuous ring of macrophages surrounding an adipocyte. Paraffin blocks were available in a subset (N=72) and a representative block was immunostained for CD68 to highlight CLS-B. For all cases, the average fat vacuole size was determined by digital image analysis (NIH ImageJ Software). We performed correlative statistics between CLS-B status and clinical data (χ2, Wilcoxon rank-sum tests). Results: CLS-B were present in 37 of 99 cases (37%). When present the total number of CLS-B ranged from 1 to 18 (mean=4.3, median=3). CLS-B were detected in 7/10 (70%) patients with BMI >30 vs. 30/89 (34%) with BMI ≤ 30 (p=0.02). CLS-B also trended to higher prevalence in women over 60 compared to women under 60 (12/20, 60% vs. 25/79, 32%, p = 0.063). There was no significant association of CLS-B status with tumor T- and N-stage or grade (all P>0.4). The median C-reactive protein in the group with CLS-B was 1.5 mg/L vs. 0.8 mg/L in the group without CLS-B (P=0.10) There was no significant association of CLS-B with insulin, glucose, HOMA, leptin, adiponectin, total cholesterol, triglycerides, HDL cholesterol, LDL cholesterol, or IGF-1 (all P>0.27). The average fat globule area determined by image analysis correlated significantly with BMI (Spearman correlation 0.54, p<0.0001) but not to the presence of CLS-B (p=0.102). Within the subset immunostained for CD68, 32/72 (44%) had CLS-B on the original H&E sections, whereas 13/72 (18%) had CLS-B on the representative CD68-stained section. This corresponded to a false negative in 22/59 (37%) CD68-negative cases, and increased detection in 3/13 of the CD68-positive cases. Conclusion: In our cohort, obesity is correlated with elevated tissue inflammation as seen by the presence of CLS-B, but CLS-B is not correlated with metabolic markers. CLS-B are well appreciated on routine H&E sections; however, more work is needed to find a practical approach to both ancillary testing (e.g. CD68) and quantitation. Our work independently confirms the association of CLS-B with obesity, and supports the concept that CLS-B is a tissue biomarker of obesity-related inflammation. (Z.E. was co-principal author.) Citation Format: Chang MC, Eslami Z, Ennis M, Goodwin PJ. Prevalance of crown-like structures of the breast, a histologic biomarker linked to obesity: A retrospective study of 99 cases. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P5-05-01.

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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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.374
Threshold uncertainty score0.279

Codex and Gemma teacher scores by category

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

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 head0.065
GPT teacher head0.379
Teacher spread0.314 · 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".

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Citations1
Published2016
Admission routes1
Has abstractyes

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