Targeting of intracellular oncoproteins with peptide-centric CARs

The majority of oncogenic drivers are intracellular proteins, constraining their immunotherapeutic targeting to mutated peptides (neoantigens) presented by individual human leukocyte antigen (HLA) allotypes1. However, most cancers have a modest mutational burden that is insufficient for generating responses using neoantigen-based therapies2,3. Neuroblastoma is a paediatric cancer that harbours few mutations and is instead driven by epigenetically deregulated transcriptional networks4. Here we show that the neuroblastoma immunopeptidome is enriched with peptides derived from proteins essential for tumorigenesis. We focused on targeting the unmutated peptide QYNPIRTTF discovered on HLA-A*24:02, which is derived from the neuroblastoma-dependency gene and master transcriptional regulator PHOX2B. To target QYNPIRTTF, we developed peptide-centric chimeric antigen receptors (PC-CARs) through a counter panning strategy using predicted potentially cross-reactive peptides. We further proposed that PC-CARs can recognize peptides on additional HLA allotypes when presenting a similar overall molecular surface. Informed by our computational modelling results, we show that PHOX2B PC-CARs also recognize QYNPIRTTF presented by HLA-A*23:01, the most common non-A2 allele in people with African ancestry. Finally, we demonstrate potent and specific killing of neuroblastoma cells expressing these HLAs in vitro and complete tumour regression in mice. These data suggest that PC-CARs have the potential to expand the pool of immunotherapeutic targets to include non-immunogenic intracellular oncoproteins and allow targeting through additional HLA allotypes in a clinical setting.


Statistics
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A description of all covariates tested
A description of any assumptions or corrections, such as tests of normality and adjustment for multiple comparisons A full description of the statistical parameters including central tendency (e.g.means) or other basic estimates (e.g.regression coefficient) AND variation (e.g. standard deviation) or associated estimates of uncertainty (e.g.confidence intervals) For null hypothesis testing, the test statistic (e.g.F, t, r) with confidence intervals, effect sizes, degrees of freedom and P value noted Give P values as exact values whenever suitable.
For Bayesian analysis, information on the choice of priors and Markov chain Monte Carlo settings For hierarchical and complex designs, identification of the appropriate level for tests and full reporting of outcomes Estimates of effect sizes (e.g.Cohen's d, Pearson's r), indicating how they were calculated Our web collection on statistics for biologists contains articles on many of the points above.

Software and code
Policy information about availability of computer code Data collection Flow cytometry data was collected using CytExpert (Beckman Coulter) and FACSDiva ( v8, BD Biosciences).FACSAria Fusion (BD Biosciences)   was used for cell sorting.10x Genomics 5' V(D)J Kits were used on the Chromium machine and sequenced using the lllumina MiSeq.Orbitrap Fusion Lumos (Thermo Fisher Scientific) was used for mass spectrometry.BLltzTM system (ForteBio, USA) was used for binding assays.lncucyte ZOOM and S3 (Essence BioScience) was used in T cell cytotoxicity assays.Aperio CS-O slide scanner (Leica Biosystems) was used in scanning IHC slides.Sanger sequencing was performed at CHOP NapCore.

Data analysis
Flow cytometry analysis was performed using FlowJo (v10.7.l,BD Biosciences), R Studio, and Microsoft Excel were used to analyze data.Cellranger VDJ was used to analyze single-cell TCR data.SequestHT algorithm in the Proteome Discoverer (v2.1 and v2.4,ThermoFisher) software was used for LC/MS/MS analysis.NetMHC-4.0was used in pMHC binding predictions.PH LAT 1.1 was used for HLA typing.Blitz Pro TM software was used to analyze scFv binding data.PyMOL v2.4.1 and RosettaMHC were used for structural modeling.DNA constructs and sequencing data analyzed using SnapGene v5.2 and Benchling.Gene Ontology analyses were performed using PANTHER (v16.0).Crystallization model building and refinement were performed using COOT and Phenix (v1.19.2), respectively.Our algorithms ShinyNAP and sCRAP are described in methods and referenced, and have been made available to reviewers at: https://marisshiny.research.chop.edu/sCRAP/For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and reviewers.We strongly encourage code deposition in a community repository (e.g.GitHub).See the Nature Portfolio guidelines for submitting code & software for further information.

Eukaryotic cell lines
Policy information about cell lines Cell line source(s) SK-N-AS, SK-N-FI, and NB-SD neuroblastoma cancer cell lines were obtained from the Maris Lab cell line bank.Other human cancer cell lines, including 293T (human embryonic kidney), Jurkat (acute T cell leukemia), SW620 (Dukes' type C, colorectal adenocarcinoma), HEPG2 (hepatocellular carcinoma), and KATO Ill (gastric carcinoma) were obtained from American Type Culture Collection (ATCC).Platinum-A (Plat-A) cells were obtained from Cell Biolabs.Primary human T cells were obtained from anonymous donors through the Human Immunology Core at the Perelman School of Medicine at the University of Pennsylvania (Philadelphia, Pennsylvania).