Assessment of programmed death‐ligand 1 expression and tumor‐associated immune cells in pediatric cancer tissues
Bibliographic record
Abstract
BACKGROUND: Programmed death 1 (PD-1) signaling in the tumor microenvironment dampens immune responses to cancer, and blocking this axis induces antitumor effects in several malignancies. Clinical studies of PD-1 blockade are only now being initiated in pediatric patients, and little is known regarding programmed death-ligand 1 (PD-L1) expression in common childhood cancers. The authors characterized PD-L1 expression and tumor-associated immune cells (TAICs) (lymphocytes and macrophages) in common pediatric cancers. METHODS: Whole slide sections and tissue microarrays were evaluated by immunohistochemistry for PD-L1 expression and for the presence of TAICs. TAICs were also screened for PD-L1 expression. RESULTS: Thirty-nine of 451 evaluable tumors (9%) expressed PD-L1 in at least 1% of tumor cells. The highest frequency histotypes comprised Burkitt lymphoma (80%; 8 of 10 tumors), glioblastoma multiforme (36%; 5 of 14 tumors), and neuroblastoma (14%; 17 of 118 tumors). PD-L1 staining was associated with inferior survival among patients with neuroblastoma (P = .004). Seventy-four percent of tumors contained lymphocytes and/or macrophages. Macrophages were significantly more likely to be identified in PD-L1-positive versus PD-L1-negative tumors (P < .001). CONCLUSIONS: A subset of diagnostic pediatric cancers exhibit PD-L1 expression, whereas a much larger fraction demonstrates infiltration with tumor-associated lymphocytes. PD-L1 expression may be a biomarker for poor outcome in neuroblastoma. Further preclinical and clinical investigation will define the predictive nature of PD-L1 expression in childhood cancers both at diagnosis and after exposure to chemoradiotherapy. Cancer 2017;123:3807-3815. © 2017 American Cancer Society.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".