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Record W4400280878 · doi:10.1158/1055-9965.epi-24-0390

Contribution of Prediagnostic Host Factors to Shaping the Stromal Microenvironment of Breast Cancer among Sub-Saharan African Women

2024· article· en· W4400280878 on OpenAlexaff
Mustapha Abubakar, Thomas U. Ahearn, Máire A. Duggan, Scott M. Lawrence, Ernest Adjei, Joe‐Nat Clegg‐Lamptey, Joel Yarney, Beatrice Wiafe‐Addai, Baffour Awuah, Seth Wiafe, Kofi Nyarko, Francis Aitpillah, Daniel Ansong, Stephen M. Hewitt, Louise A. Brinton, Jonine D. Figueroa, Montserrat García‐Closas, Lawrence Edusei, Nicholas Titiloye

Bibliographic record

VenueCancer Epidemiology Biomarkers & Prevention · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsCalgary Laboratory ServicesUniversity of Calgary
FundersDivision of Cancer Epidemiology and Genetics, National Cancer InstituteNational Cancer InstituteNational Institutes of HealthUniversity of GhanaMemorial Sloan-Kettering Cancer Center
KeywordsBreast cancerStromal cellMedicineInternal medicineOncologyHost factorsFamily historyCancerImmunology

Abstract

fetched live from OpenAlex

BACKGROUND: The stromal microenvironment (SME) is integral to breast cancer biology, impacting metastatic proclivity and treatment response. Emerging data indicate that host factors may impact the SME, but the relationship between prediagnostic host factors and SME phenotype remains poorly characterized, particularly among women of African ancestry. METHODS: We conducted a case-only analysis involving 792 patients with breast cancer (17-84 years) from the Ghana Breast Health Study. High-accuracy machine-learning algorithms were applied to standard H&E-stained images to characterize SME phenotypes [including percent tumor-associated connective tissue stroma, Ta-CTS (%); tumor-associated stromal cellular density, Ta-SCD (%)]. Associations between prediagnostic host factors and SME phenotypes were assessed in multivariable linear regression models. RESULTS: Decreasing Ta-CTS and increasing Ta-SCD were associated with aggressive, mostly high-grade tumors (P-value < 0.001). Several prediagnostic host factors were associated with Ta-SCD independently of tumor characteristics. Compared with nulliparous women, parous women had higher levels of Ta-SCD [mean (standard deviation, SD) = 31.3% (7.6%) vs. 28.9% (7.1%); P-value = 0.01]. Similarly, women with a positive family history of breast cancer had higher levels of Ta-SCD than those without family history [mean (SD) = 33.0% (7.5%)] vs. 30.9% (7.6%); P-value = 0.03]. Conversely, increasing body size was associated with decreasing Ta-SCD [mean (SD) = 31.6% (7.4%), 31.4% (7.3%), and 30.1% (8.0%) for slight, average, and large body sizes, respectively; P-value = 0.005]. CONCLUSIONS: Epidemiological risk factors were associated with varying degrees of stromal cellularity in tumors, independently of clinicopathological characteristics. IMPACT: The findings raise the possibility that epidemiological risk factors may partly influence tumor biology via the stromal microenvironment. See related In the Spotlight, p. 459.

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)0.002
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.299
Teacher spread0.280 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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