Abstract A13: Immunogenomic approaches to optimize immunotherapeutic targeting of neuroblastoma
Bibliographic record
Abstract
Abstract Neuroblastoma (NB) is the most common extracranial solid cancer in children. Although multimodal therapies with differentiating agents and immunotherapy with anti-GD2 antibody and GM-CSF have shown promising results, it remains deadly in approximately 50% of patients with high-risk disease. Chimeric antigen receptor T-cell therapies (CAR-T) have been found to be effective in treating refractory and relapsed leukemia and lymphoma, and two of them have been recently approved by the FDA. However, current CARs frequently lose efficacy due to T-cell exhaustion, and CARs against solid tumor antigens often lack enough specificity due to a low incidence of somatic mutations resulting in a paucity of tumor neoantigens. There have not been effective CAR-T therapies against other solid cancers to date, although many clinical trials are under way. Therefore, we attempted to develop a high-throughput way of identifying optimal CART cell binders that show activation and expansion in the presence of targets but lack of exhaustion. We previously identified two cell surface cancer-associated antigens, GPC2 (Glypican-2) and CD276 (B7-H3), both highly expressed in NB tumor cells but expressed at low or undetectable levels in normal organs. 14 established binders as well as novel binders targeting these two antigens were cloned into CAR lentiviral constructs and then were separately transduced into T cells to develop 14 CAR-T cells using a 2nd-generation design. All 14 CAR-T cells were pooled and cocultured with CD276/GPC2-expressing NB cancer cells (target cells) for 24 hr. To identify the effective GPC2 or CD276-specific targeting CAR-T cells, we utilized a combined proteomics and transcriptomics method for every single CAR-T cell to quantify RNA and protein at the same. Cocultured CAR-T cells were examined for their activation, exhaustion, cytotoxicity state and distinguished different cell types by staining with CITE-seq (Cellular Indexing of Transcriptomes and Epitopes by Sequencing) antibodies, and then molecularly barcoded using 10X Genomics platform for single-cell RNA-sequencing (scRNA-seq). The data are currently being analyzed and will be presented. Using this method, we will be able to identify which of the CARs are enriched and have an activated T-cell signature, and lack exhaustion marks as determined by the CITE-seq and RNAseq analyses. Finally, top candidate binders for each antigen will be developed into “AND” or “OR” CARs and will be tested in in vitro and in vivo models of NB. Thus, we will develop a high-throughput way to identify high-affinity functional binders against tumor cell surface antigens. This study also will provide novel immunogenomics methods of CARs optimization for development of highly effective immunotherapies against NB and other cancers. Citation Format: Meijie Tian, Adam Tai-Chi Cheuk, Jeetendra Kumar, Young K. Song, Sivasish Sindiri, Nan Li, Christopher M. Dower, Mitchell Ho, Brad St. Croix, Javed Khan. Immunogenomic approaches to optimize immunotherapeutic targeting of neuroblastoma [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr A13.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".