Tumor cells can be recognized through tumor surface antigens by immune cells and antibodies, which therefore can be used as drug targets for chimeric antigen receptor-T (CAR-T) therapies and antibody drug conjugates (ADCs). In this study, we aimed to identify novel tumor-specific antigens as targets for more effective and safer CAR-T cell therapies and ADCs. Here, we performed differential expression analysis of pan-cancer data obtained from the Cancer Genome Atlas (TCGA), and then performed a series of conditional screenings including Cox regression analysis, Pearson correlation analysis, and risk-score calculation to find tumor-specific cell membrane genes. A tumor tissue-specific and highly expressed gene set containing 3919 genes from 17 cancer types was obtained. Moreover, the prognostic roles of these genes and the functions of these highly expressed membrane proteins were assessed. Notably, 427, 584, 431 and 578 genes were identified as risk factors for LIHC, KIRC, UCEC, and KIRP, respectively. Functional enrichment analysis indicated that these tumor-specific surface proteins might confer tumor cells the ability to invade and metastasize. Furthermore, correlation analysis displayed that most overexpressed membrane proteins were positively correlated to each other. In addition, 371 target membrane protein-coding genes were sifted out by excluding proteins expressed in normal tissues. Apart from the identification of well-validated genes such as GPC3, MSLN and EGFR in the literature, we further confirmed the differential protein expression of 23 proteins: ADD2, DEF6, DOK3, ENO2, FMNL1, MICALL2, PARVG, PSTPIP1, FERMT1, PLEK2, CD109, GNG4, MAPT, OSBPL3, PLXNA1, ROBO1, SLC16A3, SLC26A6, SRGAP2, and TMEM65 in four types of tumors. In summary, our findings reveal novel tumor-specific antigens, which could be potentially used for next-generation CAR-T cell therapies and ADC discovery.

The distribution and extent of immune cell infiltration into solid tumors play pivotal roles in cancer immunology and therapy. Here we introduced an immune long non-coding RNA (lncRNA) signature-based method (ILnc), for estimating the abundance of 14 immune cell types from lncRNA transcriptome data. Performance evaluation through pure immune cell data shows that our lncRNA signature sets can be more accurate than protein-coding gene signatures. We found that lncRNA signatures are significantly enriched to immune functions and pathways, such as immune response and T cell activation. In addition, the expression of these lncRNAs is significantly correlated with expression of marker genes in corresponding immune cells. Application of ILnc in 33 cancer types provides a global view of immune infiltration across cancers and we found that the abundance of most immune cells is significantly associated with patient clinical signatures. Finally, we identified six immune subtypes spanning cancer tissue types which were characterized by differences in immune cell infiltration, homologous recombination deficiency (HRD), expression of immune checkpoint genes, and prognosis. Altogether, these results demonstrate that ILnc is a powerful and exhibits broad utility for cancer researchers to estimate tumor immune infiltration, which will be a valuable tool for precise classification and clinical prediction.