Abstract IA011: Identifying tumor-restricted target antigens for adoptive cellular immunotherapy to treat Ewing Sarcoma using multi-omic discovery platforms
Bibliographic record
Abstract
Abstract Background: In the last four decades, no significant improvement in survival rates for children with Ewing Sarcoma (EWS) has been made. Less than 30% of patients with metastatic disease are long- term survivors and standard therapy incorporates dose-intensive cytotoxic chemotherapy risking lifelong late effects. Engineered T cells (TCRs/CARs) can mediate impressive clinical anti-tumor activity and would represent a promising treatment option for patients. Development of potent and safe adoptive cellular immunotherapies (ACT) relies on antigents that are absent from any normal, vital tissue in the body to avoid on-target/off-tumor recognition, which conventional methods often fail to identify. Therefore, novel pipelines for rational target antigen discovery are critical for advancing ACT for the treatment of the EWS. Methods: To identify tumor antigens that meet the requirement for ACT targets, we performed differential gene expression analysis of compiled transcriptomes from EWS tumors (n=120) and normal tissues (n=42). Bioinformatic cell-surface annotation identified top differentially expressed targets best suited for CAR T cells (Heitzeneder et. al, JNCI 2019). However, more than 70% of the human proteome represent intracellular molecules, a target-pool accessible to TCRs through presentation of peptides via MHC. To empirically determine the immunopeptidome, we profiled EWS cells by immunoprecipitation with a pan-MHC-I antibody, followed by liquid chromatography and tandem mass spectrometry (LC-MS/MS). To identify peptide-MHC antigens best suited for TCRs, this data was incorporated with the differential tumor-to-normal transcriptome. Results: We identified 584 genes as overexpressed in EWS(LogFC>1, P<.01) compared to normal tissue. A total of 24 genes showed high abundance in EWS (AVElog2[TPM+1]>5) and low normal tissue expression (AVElog2[TPM+1]<2). Of those, 11 were predicted to be cell surface-associated and possess the potential to serve as CAR T-cell targets. This identified pregnancy-associated plasma protein-A (PAPP-A), a placental antigen expressed at the maternal-fetal interface (Heitzeneder et al, JNCI 2019). Immunopeptidome analysis of HLA-A*0201+EW8 cells showed that peptides of ~30% of the top 24 genes were presented by MHC-I. No neoantigenic peptides caused by common mutations in EWS or the EWS-Fli1 breakpoint region were found. Instead, immunopeptidome analysis identified oncofetal and cancer-testis antigens for TCR-based treatment approaches. Conclusions: Truly tumor-specific antigens are rare in pediatric solid tumors, yet, they often manifest stalled fetal developmental programs and continue to express placental, oncofetal and cancer testis antigens. Those represent an attractive pool of target antigens for the development of ACT to treat EWS, which is otherwise characterized by lofe immunogenicity. Comprehensive analysis of the surfaceome and immunopeptidome has proven an efficient method to identify such target antigens and facilitates the preclinical development of CAR-T cell and TCR-based immunotherapies. Citation Format: Sabine Heitzeneder. Identifying tumor-restricted target antigens for adoptive cellular immunotherapy to treat Ewing Sarcoma using multi-omic discovery platforms [abstract]. In: Proceedings of the AACR Special Conference: Sarcomas; 2022 May 9-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(18_Suppl):Abstract nr IA011.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".