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Record W2565843701 · doi:10.1158/1538-7445.am2015-2344

Abstract 2344: The PD-1:PD-L1 pathway in the context of the osteosarcoma tumor microenvironment

2015· article· en· W2565843701 on OpenAlexaff
Pratistha Koirala, Jonathan Gill, Michael Roth, Sajida Piperdi, Amy Park, Vincent Poon, Michael A. Fremed, Bang H. Hoang, Richard Görlick

Bibliographic record

VenueCancer Research · 2015
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsImmune systemTumor microenvironmentCancer researchPD-L1CancerTumor-infiltrating lymphocytesMedicineAntibodyContext (archaeology)ImmunologyBiologyImmunotherapyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background PD-1 is a transmembrane protein found on immune cells including T cells, B cells, natural killer (NK) T cells, activated monocytes, and dendritic cells. Its interaction with its ligand, PD-L1, plays a role in the suppression of the immune system and is important in many processes such as autoimmunity and both central and peripheral tolerance. Cancer cells can hijack the PD-1:PD-L1 pathway in order to evade clearance by the immune system—a mechanism that is evident in many solid tumors, including breast, ovarian, renal cell carcinoma, colorectal, and lung cancers. Inhibition of the PD-1:PD-L1 pathway with both PD-1 and PD-L1 inhibitors have shown varying levels of efficacy, and provide proof of concept—that modulating the PD- 1:PD-L1 pathway can tip the balance of immune response in favor of the patient, working to eliminate the tumor. We seek to determine the expression of PD-L1 in osteosarcoma (OS), specifically in relation to the prescience of tumor infiltrating lymphocytes (TILs). By displaying expression of PD-L1 and the presence of TILs we hope to subsequently target the PD-1:PD-L1 pathway in OS. Methods Paraffin imbedded and sectioned patient primary OS tumor samples were provided by IRB approved protocols at Montefiore Medical Center and Memorial Sloan Kettering Cancer Center. Antibodies to identify various immune cells were first validated and optimized using immune tissues and subsequently used to stain tumor sample. Next, RNA was extracted from patient OS tumor samples and used to generate cDNA. PD-L1 expression was determined using quantitative real time PCR. Protein was also extracted and will be used to confirm PD-L1 expression using western blotting. Results A variety of TILs are found in OS patient tumor samples. We see expression of T cells (CD3) and T cell subsets—helper (CD4), cytotoxic (CD8), and natural killer (CD56) T cells. There is also robust expression of B cells (CD20) and macrophages (CD68). Furthermore, the OS tumors displayed varying degrees of PD-L1 positivity, with some tumors displaying greatly increased expression, some showing moderate elevation, and others showing low to no changes in PD-L1 expression. Conclusions and Future Directions OS tumors display varying levels of PD-L1 positivity, suggesting that the PD-1:PD-L1 pathway can be modulated for immunotherapeutic purposes. Importantly, we also demonstrated that a subset of immune cells, potentially expressing PD-1, is present in some tumors. Cell lines derived from tumors expressing low, moderate, and high levels of PD-L1 are being cultured. These cell lines will be used to test inhibition of the PD-1:PD-L1 pathway and its impact on their tumorigenic properties. Citation Format: Pratistha Koirala, Jonathan Gill, Michael Roth, Sajida Piperdi, Amy Park, Vincent Poon, Michael Fremed, Bang Hoang, Richard Gorlick. The PD-1:PD-L1 pathway in the context of the osteosarcoma tumor microenvironment. [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 2344. doi:10.1158/1538-7445.AM2015-2344

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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

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 head0.101
GPT teacher head0.374
Teacher spread0.273 · 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 designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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