[PMC free article] [PubMed] [Google Scholar] 16. by additional T cell subsets have received less attention. Here we display spontaneous and immunotherapy-induced anti-tumor reactions require the activity of both tumor antigen specific CD8+ and CD4+ T cells, actually in tumors that do not communicate MHC class II. Additionally, tumor cell manifestation of MHC class II-restricted antigens is required at the site of successful rejection, indicating that CD4+ T cell activation must also happen in the tumor microenvironment. These findings suggest that MHC class II-restricted neoantigens have a key function in the anti-tumor response that is nonoverlapping with that of MHC class I-restricted neoantigens and therefore need to be regarded as when identifying individuals who will most benefit from immunotherapy. Immune checkpoint therapy (ICT) demonstrates remarkable clinical effectiveness in subsets of malignancy individuals but many fail to develop durable reactions2C4. Although MHC class I (MHC-I)-restricted neoantigens are important focuses on of tumor-specific CD8+ cytotoxic T lymphocytes (CTL) during successful ICT in both mice and humans5C12, current methods to forecast patient response to ICT are TMCB imprecise and additional or better prognostic signals are needed13C17. The influence of MHC class II (MHC-II)-restricted CD4+ T cell reactions to tumor neoantigens during immunotherapy offers only recently been tackled18,19. While some reports display that effective tumor immunity can occur in the absence of CD4+ T cell help, most indicate that CD4+ T cells play important roles in generating Rabbit Polyclonal to Tubulin beta tumor-specific CD8+ T cells20C25. However, since it has proven difficult to identify tumor-specific mutations that function as neoantigens for CD4+ T cells using existing MHC-II antigen prediction algorithms, considerable uncertainty remains as to whether rigid tumor specificity in the CD4+ T cell TMCB compartment is required during spontaneous or ICT-induced anti-tumor responses26,24,27 especially for tumors that do not express MHC-II. Herein we use the well characterized, MHC-II-negative T3 methylcholanthrene (MCA)-induced sarcoma collection that grows progressively in wild-type (WT) mice but is usually rejected following ICT in a CD4+ and CD8+ T cell dependent manner9. Although we have identified point mutations in laminin- subunit 4 (G1254VLAMA4; mLAMA4) and asparagine-linked glycosylation 8 glucosyltransferase (A506TALG8; mALG8) as major MHC-I neoantigens in T3, the identities of T3-specific MHC-II antigens remain unknown9. Using newly developed predictive algorithms, we identify an N710Y somatic point mutation in integrin-1 (mITGB1) as a major MHC-II neoantigen of TMCB T3 sarcoma cells. Employing nonimmunogenic oncogene-driven sarcoma cells (KP9025) that lack mutational neoantigens, we demonstrate that co-expression of single MHC-I and MHC-II T3 neoantigens renders KP9025 cells susceptible to ICT. We find comparable requirements for vaccines that drive rejection of T3 tumors. In mice bearing contralateral KP.mLAMA4.mITGB1 and KP.mLAMA4 tumors, ICT induces rejection of tumors expressing both neoantigens but not tumors expressing mLAMA4 only, indicating that co-expression of both MHC-I and MHC-II neoantigens at the tumor site is necessary for successful ICT. These results show that expression of MHC-II neoantigens in tumors is usually a critical determinant of responsiveness to ICT, personalized malignancy vaccines and potentially other immunotherapies. Predicting MHC-II neoantigens with hmMHC The best currently available methods for predicting MHC-II restricted neoantigens rely on tools (netMHCII-2.3 and netMHCIIpan-3.2) that are inaccurate partially due to the open structure of the MHC-II binding groove leading to significant epitope length variability18,26. Moreover, the existing tools cannot be re-trained on new data. We therefore developed a hidden Markov model-based MHC binding predictor (hmMHC, Extended Data Fig. 1a) that inherently accommodates peptide sequences of variable length and is qualified on recent Immune Epitope Database (IEDB) content (Extended Data Fig. 1bCd). Validation analyses showed hmMHC to be superior to other predictors since it displays substantially higher sensitivity for high specificity values (Extended Data Physique 2aCb). Using hmMHC, we calculated the likelihood of each of the 700 missense mutations expressed in T3 (Supplementary Data 1) being offered by I-Ab and processed our results by prioritizing candidates based on I-Ab binding affinity, mutant:wild type I-Ab binding ratios, and transcript large quantity (Fig. 1a, Extended Data Fig. 3a)18. Open in a separate window Physique 1: N710Y Itgb1 (mITGB1) is usually a major MHC class II-restricted neoantigen of T3 sarcoma cells.(a) hmMHC predictions of MHC-II neoantigens expressed in T3 sarcoma cells. Potential neoantigens were filtered as in Extended Data Fig. 3a and those meeting the strong binder threshold are shown as expression level (FPKM) and neoepitope ratio (NER). Strong binders are those with ?10logOdds 26.21. Green collection: high expression cutoff (FPKM=89.1). Blue collection: high NER cutoff (NER=6.55)..