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Abstract A065 Immunoliposome for Ewing sarcoma

2024· article· en· W4402266293 on OpenAlexaboutno aff
Daniel E. Panosyan, William S. Panosyan, Joseph L. Lasky

Bibliographic record

VenueCancer Research · 2024
Typearticle
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSarcomaEwing's sarcomaCancerCancer researchInternal medicinePathology

Abstract

fetched live from OpenAlex

Abstract Background. Metastatic and recurrent Ewing sarcoma (EWS) poses significant mortality risk in children and adolescents. Advances in precision medicine can help to mitigate dismal outcomes by designing targeted delivery of drugs for more effective eradication of cancer cells. This study aimed to identify a rationalized combination of antibody-covered immunoliposomes (IL) loaded with a small molecule against EWS. CD99 antigen is typically expressed on EWS cells and is used for diagnostics. However, CD99 is also expressed in normal tissues, and, if used for a targeted nanoparticle, its payload must be more damaging to EWS than normal cells. FDA-approved poly ADP ribose polymerase (PARP) inhibitors may present such an opportunity, since EWS is treated with DNA-damaging agents and PARP inhibition may enhance cell apoptosis. Methods. R2 genomics analysis platform (http://r2.amc.nl) was used to explore the potential of CD99-covered and PARP inhibitor loaded IL. Seven different databases were used for differential expression of CD99 and PARP1, two EWS databases and five normal tissue databases (B cells, endothelial cells, hematopoietic, lymphocytes, and various normal tissues). Kaplan-Meier analysis was conducted for prognostic significance of PARP1 overexpression in one of the EWS datasets containing survival data in R2. Lastly, comparative side effect profiling of variable PARP inhibitors is discussed and was utilized for selection of a candidate molecule for the presumed IL. Results. In two EWS datasets (Savola, n=117 & Surdez, n=79) CD99 expression was 2-3 times higher than 5 sets of normal tissues (n=637). Endothelial compartment has twice-higher CD99 compared to other normal tissues but ∼1.5 times lower CD99 and PARP1 than EWS. Recurrent/metastatic EWS has more PARP1 than primary tumors as seen in Savola dataset, p=0.02 (anova). Higher PARP1 expression was associated with worse event-free (EFS) and overall survivals (OS) further validating potential role of PARP inhibition in EWS. Ten-year survivals respectively for low versus high PARP1 expression were 36% and 14% for EFS (p=0.016), and 50% and 7% for OS (p<0.001). Normal hematopoietic/B-cell compartments have ≥2-times higher PARP1 than other datasets; therefore, for IL payload it is crucial to select an inhibitor with less lymphotoxicity. Discussion. Small molecule niraparib causes less lymphopenia compared to other PARP inhibitors, thus it would be a preferred candidate for the suggested nanoparticle. It has a molecular weight of 320g/mol, which should allow an ample amount to be packaged into a 100 nm IL. This size with PEG-linked attachment of monoclonal antibodies (mAbs) against CD99 may provide vascular permeability and tumor tropism as was seen in preclinical work of other ILs. Conclusion. ILs covered with PEG-linked anti-CD99 mAbs and loaded with niraparib may be developed as an adjuvant therapy for metastatic and recurrent EWS. Extensive preclinical testing will be required to ensure acceptable hematopoietic side effects and endothelial damage as seen with other targeted therapies. Citation Format: Daniel E. Panosyan, William S Panosyan, Joseph L. Lasky III. Immunoliposome for Ewing sarcoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr A065.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.159
GPT teacher head0.475
Teacher spread0.316 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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