Table 3.

Acquisition strategies and main methods for neoantigens

Category	Introduction	Main method	Feasibility	Application scenarios
Direct acquisition of neoantigens	Sampling and screening of an existing population of tumor tissue, usually from biopsy or surgical resection Aim: to find available targets in their naturally occurring antigen pools	Immunoprecipitation-MS [102–105]	Patient tumor tissues	To capture primary tumor p-MHC
		Affinity-tag extraction [106]	Animal tumor tissues with specific MHC type tagged	To precisely extract known and neo-antigens in situ
		RNA-seq and WGS [107, 108]	Patient tumor tissues	To obtain complete serial sequence information of one patient
		peptide-MHC libraries [109]	Specific TCR or acquired T cells, and constructed vector libraries	To undifferentiately screen one TCR- recognizable known epitopes
		MANAFEST, T-SCAN [110, 111]	Specific TCR	To high-throughput screen recognizable epitopes
		SABR [112]	Specific TCR	To screen homologous epitopes
		Trogocytosis [114]	T cells fluorescently labeled with membrane proteins	To trace target cells binding and then sequence involved TCRs
		Hansolo system [115]	Patient T cells and immortalized B cell lines	To construct unbiased mutanome minigene recognizable library of the patient
Predictive modeling of neoantigens	Acquisition of patient's MHC molecular profile (individual-specific MHC typing) In silico analysis and prediction of deliverable epitopes combined with simulation of realistic multi-step parameter optimization, with attention to distortion or overestimation of the predicted epitope library Aim: capture of possible key antigens for usable TCR design	TCR and antigen prediction 1. Personalized information and MHC typing 2. Computerized prediction models: i. HLA typing ii. mutation typing and calling iii. HLA binding prediction iv. TCR prediction v. TCR priority vi. TCR-recognizing HLA screen [135] 3. Design of the corresponding TCR at the optimal epitope-MHC	Databases for in silico pre-analysis: • whole-genome sequencing and WES • RNA-seq • proteomics • MS	To predict epitopes and also exclude self-reactive antigens on a large-scale, use sequence information and select models