Cell surface proteomics of patient-derived malignant rhabdoid tumor organoids identifies ROBO1 as potential CAR T cell target for pediatric solid tumors
Bibliographic record
Abstract
Abstract Malignant rhabdoid tumors are highly aggressive pediatric malignancies with limited treatment options and poor outcomes. To expand therapeutic options, we employed mass spectrometry-based cell surface proteomics on malignant rhabdoid tumor and patient-matched normal kidney organoids to identify potential CAR T cell targets. Integrating these findings with transcriptomics and protein expression data, we revealed ROBO1 as a promising target, showing strong and uniform expression across malignant rhabdoid and other pediatric tumors. ROBO1-targeted CAR T cells displayed potent anti-tumor activity in vitro , effectively eliminating tumor cells in co-cultures with organoids from malignant rhabdoid tumors, rhabdomyosarcoma, and neuroblastoma. In vivo, ROBO1 CAR T cells infiltrated tumors, induced potent tumor regression and significantly increased survival in malignant rhabdoid tumor-bearing mice. These findings establish ROBO1 as a compelling therapeutic target for CAR T cell therapy and offer a promising approach to address the critical need for effective treatments in high-risk pediatric solid tumors. Statement of significance This study identifies ROBO1 as a promising CAR T cell target for pediatric solid tumors. Using patient-derived tumor organoids and tissues, we demonstrate strong ROBO1 expression across several tumor entities and robust anti-tumor efficacy of ROBO1-targeted CAR T cells in vitro and in vivo , underscoring its potential for clinical translation.
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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 | 0.000 |
| 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".