Abstract A009: Proteogenomic prioritization of immunotherapeutic targets in rhabdomyosarcoma nominate MEGF10 for preclinical development
Bibliographic record
Abstract
Abstract Background: Rhabdomyosarcoma (RMS) has a high unmet need in terms of precision therapy development as there are currently no approved immunotherapies or targeted therapies, and few in the developmental pipeline. Here we sought to identify cell surface oncoproteins as a target for novel RMS-directed immunotherapies. Methods: We first performed plasma membrane enrichment followed by mass spectrometry to define the cell surface landscape of 7 fusion(+) and 14 fusion(-) RMS patient-derived xenograft (PDX) models. The surfaceome data was filtered to only “high confidence” surface proteins by querying protein localization databases, Compartments (https://compartments.jensenlab.org/) and CIRFESS (https://gundrylab.shinyapps.io/cirfess/). We then developed a prioritization algorithm that uses a rank-product approach to score surface proteins. The input to the algorithm is a matrix that integrates multiple datasets to score the surface proteins based on their suitability to be an optimal immunotherapeutic target. In addition to the surfaceome data generated here, we also integrated matched RNA-sequencing data from eleven of the RMS PDX models, RNA-sequencing data from GTEx (n=15,253) and a recently developed normal tissue proteomics dataset (n=201) [Jiang. Cell. 2020], a list from Gene Ontology that included genes involved in muscle development pathways, and the gene dependency list for RMS in DepMap (https://depmap.org/portal/). Results: A total of 913 and 937 high confidence surface proteins were annotated from the mass spectrometry data for fusion(+) and fusion(−) samples, respectively. A dendrogram separated the surface protein profiles into two clusters based on fusion(+) and fusion(−) RMS subtypes, thus the algorithm was run separately on each subtype. Within the top 50% of prioritized targets, 88% and 86% of the targets overlapped and 12% and 14% were identified exclusively in fusion(+) and fusion(−) subtypes, respectively. ALK, a previously putative protein marker in fusion(+) RMS, scored in the top 10% of the fusion(+) targets based on the algorithm, and surprisingly we saw abundant ALK expression in 6/14 fusion(−) PDX samples. MEGF10, a novel target, was ranked as the top target for both fusion(+) and fusion(−) RMS. MEGF10 plays a role in cell adhesion, motility, and proliferation. It scored as a significant dependency in DepMap for RMS (p-value=0.0002). Based on RNA-sequencing and proteomics, MEGF10 shows no expression in most healthy tissues surveyed, with several orders of magnitude lower expression detected in RNASeq in muscle and brain tissue, but not in the proteomic datasets. Conclusion: Here, we defined the surfaceome of RMS, and found substantial overlap in surface proteins between fusion(+) and fusion(−) RMS subtypes. We validated previous observations that ALK is expressed in RMS, here verifying that the protein is expressed on the plasma membrane. MEGF10 appears to be a strong novel candidate target for RMS immunotherapies, and ongoing work to validate our proteogenomic findings will be reported. Citation Format: Rawan Shraim, Amber K. Weiner, Karina L. Conkrite, Alexander B. Radaoui, John M. Maris, Yael P. Mosse, Sharon J. Diskin, Ahmet Sacan, Benjamin A. Garcia, Peter J. Houghton, Raushan T. Kurmasheva, Poul Sorensen, Gregg B. Morin, Brian Mooney. Proteogenomic prioritization of immunotherapeutic targets in rhabdomyosarcoma nominate MEGF10 for preclinical development [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 A009.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".