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Record W2567345722 · doi:10.1158/1538-7445.am2015-249

Abstract 249: Assessment of PD-L1 expression and tumor-associated lymphocytes in pediatric cancer tissues

2015· article· en· W2567345722 on OpenAlexaff
Robbie G. Majzner, Jason S. Simon, Joseph F. Grosso, Daniel Martínez, Bruce Pawel, Mariarita Santi-Vincini, Melinda S. Merchant, Poul H. Sorensen, Crystal L. Mackall, John M. Maris

Bibliographic record

VenueCancer Research · 2015
Typearticle
Languageen
FieldMedicine
TopicNeuroblastoma Research and Treatments
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsTissue microarrayCancerMedicineImmune systemNeuroblastomaLymphomaCD8Cancer researchImmunohistochemistryTumor microenvironmentMedulloblastomaPathologyImmunologyBiologyInternal medicineCell culture

Abstract

fetched live from OpenAlex

Abstract PD-1 signaling in the tumor microenvironment dampens immune responses to cancer and blocking this axis induces anti-tumor effects in several malignancies. Some studies have demonstrated increased efficacy of PD-1 blockade when tumor cells express PD-L1. Clinical studies of PD-1 blockade have not yet been conducted in pediatric patients and little is known regarding PD-L1 expression in common childhood cancers. We characterized PD-L1 expression and Tumor Associated Immune Cells (TAIC, lymphocytes and macrophages) in common pediatric cancers. Whole slide sections (n = 91) and tissue microarrays (n = 365) were evaluated by IHC for PD-L1 expression using the 28-8 anti-PD-L1 mAb in an automated Dako assay. PD-L1 expression was considered positive when at least 1% of tumor cells analyzed demonstrated plasma membrane staining. TAIC were also assessed for expression of PD-L1, and a subset of 60 tumors were assessed for CD3, CD4, CD8, CD45RO, PD-1, and FoxP3 expression on TAIC. Nine percent (N = 40) of evaluable tumors expressed PD-L1. Highest frequency histotypes comprised non-Hodgkin lymphoma (80%, 8/10), glioblastoma multiforme (30%, 6/20), and neuroblastoma (14%, 17/118). No PD-L1 staining was observed in Ewing sarcoma (0/20) or medulloblastoma (0/40). Despite a relatively low frequency of PD-L1+ pediatric cancers, the majority contained TAIC (73%, 334/456). Of these TAIC+ tumors, 73% of samples contained lymphocytes only, 25% contained lymphocytes and macrophages and 3% contained macrophages only. Twenty-one percent of TAIC+ samples demonstrated PD-L1 expression on TAIC, mostly comprising PD-L1 expression on macrophages (65%, 60/92), while lymphocytes expressed PD-L1 in only 7% (22/324). Taken together, PD-L1 was expressed in tumor and/or TAIC in 20% (90/456). Sixty samples were further analyzed to characterize TAIC, with 77% of the samples demonstrating infiltration of CD8+ T cells, most of which expressed CD45RO. Fox-P3 and PD-1 were expressed in 42% and 47% of the samples respectively. In summary, this provides the most comprehensive analysis of PD-L1 expression in pediatric associated cancer to date. Results show that a subset of pediatric cancers demonstrate tumor associated PD-L1 expression, while a much larger fraction demonstrate infiltration with tumor associated lymphocytes. Further preclinical and clinical investigation will define the predictive nature of PD-L1 expression in childhood cancers, but available data is not sufficient to exclude enrollment to clinical trials based on PD-L1 status. Citation Format: Robbie G. Majzner, Jason S. Simon, Joseph F. Grosso, Daniel Martinez, Bruce Pawel, Mariarita Santi-Vincini, Melinda S. Merchant, Poul Sorensen, Crystal L. Mackall, John M. Maris. Assessment of PD-L1 expression and tumor-associated lymphocytes in pediatric cancer tissues. [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 249. doi:10.1158/1538-7445.AM2015-249

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

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

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 head0.116
GPT teacher head0.480
Teacher spread0.363 · 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

Citations7
Published2015
Admission routes1
Has abstractyes

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