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Record W2795140780 · doi:10.3390/cancers10040102

EBV+ and MSI Gastric Cancers Harbor High PD-L1/PD-1 Expression and High CD8+ Intratumoral Lymphocytes

2018· article· en· W2795140780 on OpenAlexaff
Simona Rosa, Nora Sahnane, Maria Grazia Tibiletti, Francesca Magnoli, Alessandro Vanoli, Fausto Sessa, Anna Maria Chiaravalli

Bibliographic record

VenueCancers · 2018
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsUniversity Hospital Foundation
Fundersnot available
KeywordsCancer researchPD-L1MedicineCD8Cytotoxic T cellMicrosatellite instabilityCancerImmune systemChemistryInternal medicineImmunotherapyImmunologyGeneBiochemistry

Abstract

fetched live from OpenAlex

Both EBV+ and MSI gastric cancers (GCs) have high lymphoid infiltration which is rare in MSS/EBV− cancers. PD-L1/PD-1 interaction leads to a down-regulated immune response and it is one of the most promising targets for gastric cancer immunotherapy. PD-L1/PD-1 and CD8 expression were immunohistochemically investigated in a series of 169 FFPE GCs, including 33 EBV+, 59 MSI and 77 MSS/EBV− cases. PD-L1 membrane immunoreactivity in more than 5% of tumor cells was present in 31/169 GCs and was associated with high levels of CD8 intraepithelial lymphocytes (TILs; p < 0.001). PD-L1+ cases were mainly poorly differentiated (71%), intestinal type (85%) and high lymphoid response (HLR; 90%) tumors. PD-L1 expression was only present in EBV⁺ (46%), MSI (24%) and rare MSS/EBV− (3%) GCs with high CD8+ TILs (p < 0.001). Despite being associated with a better prognosis both in the whole series (p < 0.05) and in the MSI subset, PD-L1 is not an independent prognostic factor. PD-L1 gene amplification was detected in 3/17 cases, including 2/7 EBV+ and 1/8 MSI GC. PD-1⁺ TILs were significantly higher in EBV⁺ than MSI and MSS/EBV− cases. PD-L1/PD-1 pathway is selectively activated in HLR GCs and could be considered an emerging therapeutic target, particularly for EBV and MSI GCs.

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 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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score0.960

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.011
GPT teacher head0.249
Teacher spread0.238 · 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 designBench or experimental
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

Citations69
Published2018
Admission routes1
Has abstractyes

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