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Abstract P2-23-17: Programmed death ligand-1 expression in triple-negative breast cancer from Nigeria

2023· article· en· W4322774054 on OpenAlexaffabout
Olalekan Olasehinde, Funmilola Wuraola, Aleksandra Kajetanowicz, Gilllian Bethune, Marcia Edelweiss, Peter Ntiamoah, Oluwole Odujoko, Avinash Sharma, Victoria L. Mango, T. Peter Kingham, Olusegun Isaac Alatise, Gregory Knapp

Bibliographic record

VenueCancer Research · 2023
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsDalhousie University
Fundersnot available
KeywordsTriple-negative breast cancerMedicineBreast cancerIncidence (geometry)ImmunohistochemistryCancerOncologyEstrogen receptorInternal medicinePathology

Abstract

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Abstract Introduction: The incidence of triple negative breast cancer (TNBC) in West Africa appears to mirror the higher incidence of the disease among African American women in the United States. However, there remains a paucity of molecular data on TNBC from sub-Saharan Africa, despite the emergence of effective immunotherapies. Methods: Consecutive patients diagnosed with invasive breast cancer between March 2018-Jan 2020 were identified from a prospective clinical database and paired biobank at Obafemi Awolowo University Teaching Hospital (OAUTH). All specimens were processed and fixed in formalin within 60 minutes of excision by a trained pathologist. Tissue sections (4μm thick) representative of tumor were selected for routine evaluation of estrogen, progesterone and human epidermal growth factor receptor-2 expression by immunohistochemistry. This was performed at OAUTH with adequate controls according to the ASCO/CAP guidelines and verified at an outside institution for quality assurance. Additional sections from the FFPE blocks of TNBC specimens were further stained using the Dako PharmDx 22C3 PD-L1 commercial assay according to manufacturer protocols at Dalhousie University. External on-slide controls included tonsil, PD-L1 negative TNBC, and PD-L1 positive TNBC. PD-L1 expression was scored using the combined positive score (CPS), which is the number of 22C3 staining tumour cells, lymphocytes, and macrophages divided by the number of viable tumour cells, multiplied by 100. The threshold for a positive result was a CPS of ≥10 as per manufacturer instructions and institutional protocol. Research ethics board approval as well as data and material transfer agreements between institutions was obtained for this study. Results: From 85 cases, 32 were TNBC (37.6%). The mean age and BMI were 49.6±SD 7.7 and 26.2±5.7, respectively. The majority of patients presented with locally advanced disease (64.2% Stage III, 14.2% Stage IV). Seventy-nine percent (78.5%) of patients received an average of five cycles of neoadjuvant chemotherapy (SD 2.3). From 32 TNBC specimens, 27 had available FFPE tissue blocks, and of those, 24 had interpretable PD-L1 IHC for inclusion in the analysis. A total of 37.5% (9/24) of cases demonstrated a CPS ≥10. Conclusions: Over a third of breast cancer specimens in this Nigerian cohort were triple negative, 37.5% of which had PD-L1 CPS scores of ≥10. These results suggest a large proportion of patients in Nigeria may benefit from access to immunotherapy. This is the first reported incidence of PD-L1 expression in breast cancer from sub-Saharan Africa. Citation Format: Olalekan Olasehinde, Funmilola Wuraola, Aleksandra Kajetanowicz, Gilllian Bethune, Marcia Edelweiss, Peter Ntiamoah, Oluwole Odujoko, Avinash Sharma, Victoria Mango, Peter Kingham, Olusegun Alatise, Gregory Knapp. Programmed death ligand-1 expression in triple-negative breast cancer from Nigeria [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-23-17.

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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.096
GPT teacher head0.428
Teacher spread0.333 · 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
Published2023
Admission routes2
Has abstractyes

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