Immune evasion is a hallmark of cancer, and therapies that restore immune surveillance have proven highly effective in cancers with high tumor mutation burden (TMB) (e.g., those with microsatellite instability (MSI)). Whether low TMB cancers, which are largely refractory to immunotherapy, harbor potentially immunogenic neoantigens remains unclear. Here, we show that tumors from all patients with microsatellite stable (MSS) colorectal cancer (CRC) express clonal predicted neoantigens despite low TMB. Unexpectedly, these neoantigens are broadly expressed at lower levels compared to those in MSI CRC. Using a versatile platform for modulating neoantigen expression in CRC organoids and transplantation into the distal colon of mice, we show that low expression precludes productive cross priming and drives immediate T cell dysfunction. Strikingly, experimental or therapeutic rescue of priming rendered T cells capable of controlling tumors with low neoantigen expression. These findings underscore a critical role of neoantigen expression level in immune evasion and therapy response.

DNA mismatch repair deficiency (MMRd) in human cancer is associated with high tumor mutational burden (TMB), frameshift mutation-derived neoantigens, increased T cell infiltration, and remarkable responsiveness to immune checkpoint blockade (ICB) therapy. Nevertheless, about half of MMRd tumors do not respond to ICB for unclear reasons. While tumor cell line transplant models of MMRd have reinforced the importance of TMB in immune response, critical questions remain regarding the role of immunosurveillance in the evolution of MMRd tumors induced in vivo. Here, we developed autochthonous mouse models of lung and colon cancer with highly efficient ablation of MMR genes via in vivo CRISPR/Cas9 targeting. Surprisingly, MMRd in these models did not result in increased immunogenicity or response to ICB. Mechanistically, we showed this lack of immunogenicity to be driven by profound intratumoral heterogeneity (ITH). Studies in animals depleted of T cells further demonstrated that immunosurveillance in MMRd tumors has no impact on TMB but shapes the clonal architecture of neoantigens by exacerbating ITH. These results provide important context for understanding immune evasion in cancers with high TMB and have major implications for therapies aimed at increasing TMB.

Genetically-engineered mouse models have greatly advanced our understanding of cancer, yet do not recapitulate the mutational complexity of human cancer, and likely lack neo-antigens capable of eliciting potent anti-tumor T cell responses. We developed a novel orthotopic organoid transplant model of colorectal cancer (CRC) harboring the strong T cell antigen SIINFEKL, and demonstrate the importance of antigen expression level in the anti-tumor T cell response. Although SIINFEKL low-expressing organoids (SIINLow) elicit an endogenous antigen-specific T cell response, the magnitude is substantially lower and kinetics delayed relative to SIINFEKL high-expressing organoids (SIINHi). Consistently, transplant of SIINHi results in rejection and T cell memory, while SIINLow results in tumor progression, terminally-exhausted T cells, and metastasis to the liver. We have shown that suboptimal T cell priming is the major factor underlying SIINLow tumor escape. Importantly, co-transplant of SIINHi and SIINLow organoids at distinct sites in the colon of the same animal results in complete rejection of both lines. In addition, co-transplant rescues the SIINLow tumor-infiltrating antigen-specific T cell response to a magnitude and quality comparable to that of SIINHi tumors. Single-cell RNA-sequencing of antigen-specific T cells from SIINHi and SIINLow tumors 8 days post-transplant revealed distinct clusters dominated by SIINLow-primed T cells, including a cluster enriched for immediate early response genes, Tox, and a number of immune checkpoints, indicative of early dysfunction. Collectively, our results establish the existence of a neo-antigen expression threshold at which T cell priming is limiting, resulting in attenuated magnitude and functionality of the T cell response, and tumor escape. To assess the therapeutic relevance of a poorly-expressed neo-antigen in CRC, we performed preclinical trials with immune checkpoint blockade and agonistic-CD40 (aCD40), which has been shown to potentiate T cell priming and response in poorly-immunogenic mouse and human pancreatic adenocarcinoma. While monotherapies showed only modest effects, the combination of checkpoint blockade and aCD40 resulted in an 80% response rate with a number of complete responses. In conclusion, antigen expression level is a critical determinant of T cell dysfunction, resulting from poor priming. Our results argue that targeting T cell priming may be a promising therapeutic strategy to invigorate anti-tumor immunity in human CRC, the majority of which remains refractory to immunotherapy. Citation Format: Peter Maxwell Kienitz Westcott, Nathan J. Sacks, Olivia Smith, Jason Schenkel, Zackery Ely, Daniel Zhang, Mary Clare Beytagh, William Hwang, George Eng, Jatin Roper, Omer Yilmaz, Tyler Jacks. T cell antigen expression levels govern progression and therapy response in a novel model of colon cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3447.

DNA mismatch repair deficiency (MMRd) is associated with high tumor mutational burden (TMB), increased T cell infiltration, and remarkable responsiveness to immune checkpoint blockade (ICB) therapy1. Nevertheless, about half of MMRd tumors do not respond to ICB for unclear reasons. While cell line transplant models of MMRd have reinforced the importance of TMB in immune response2,3, critical questions remain regarding the role of immunosurveillance in the evolution of MMRd tumors induced in vivo. Here, we developed autochthonous mouse models of lung and colon cancer with ablation of MMR via in vivo CRISPR/Cas9 targeting. Surprisingly, MMRd in these models did not increase T cell infiltration or response to ICB. Mechanistically, we showed this lack of immunogenicity to be driven by profound intratumoral heterogeneity. Studies in immune deficient animals further demonstrated that immunosurveillance in MMRd tumors has no impact on TMB but shapes the clonal architecture of neoantigens by exacerbating heterogeneity. These results provide important context for understanding immune evasion in cancers with high TMB and have major implications for therapies aimed at increasing TMB.

Lung cancer is the leading cause of cancer-related deaths worldwide, and genetically-engineered mouse models (GEMMs) of cancer provide important mechanistic and preclinical insights into this deadly disease. In particular, the “KP” model enables lung-specific inducible activation of oncogenic Kras G12D, and loss of Trp53, the two most common driver events of human non-small cell lung cancer (NSCLC). Importantly, the KP model is widely used and faithfully recapitulates molecular and histopathological features of the human disease, including progression from early hyperplasia and adenoma to invasive adenocarcinoma. However, the KP model results in multi-focal and heterogeneous tumor burden, and there is a need for improved tools to increase throughput and decrease subjectivity of tumor burden quantification and histopathological analyses. To this end, we trained a convolutional neural network (CNN) for semantic multi-class segmentation using the Aiforia(R) platform. The CNN was trained to classify and detect lung parenchyma, NSCLC tumors, and NSCLC tumor grades (grade 1-4). For supervised training, we used selected areas from 93 hematoxylin and eosin stained slides. For validation, we analyzed 34 slides completely independent of the CNN training. Tumor grades were manually annotated on the validation slides blinded to the CNN results. The overall F1 score of the CNN in grade classification was 98% and total area error 0.3%. The grade-specific F1-scores were 89%, 97%, 99%, and 98% for grades 1, 2, 3, and 4, respectively. Corresponding grade-specific total area errors were 0.4%, 0.2%, 0.4%, and 0.1%. Manual scoring independent of the training and CNN yielded similar tumor burden and grading results. In addition, the algorithm accurately recapitulates the increased tumor burden and grade seen in KP tumors harboring additional mutation of the tumor suppressor Keap1, and the delayed kinetics of KP tumors harboring a strong T cell antigen, in independent datasets. We have also extended this methodology to identification of tumors in a GEMM of small cell lung cancer, a distinct class of lung cancer with poor prognosis. In conclusion, we demonstrate that deep neural networks can be used for automated analysis and grading of preclinical models of lung cancer. We anticipate that this powerful technology will increase the throughput, sensitivity and reproducibility of hypothesis-driven studies of factors influencing tumor progression and immune response in mouse models of lung cancer. Citation Format: Peter Maxwell Kienitz Westcott, Tuomas Pitkänen, Sami Blom, Thomas Westerling, Tuomas Ropponen, Nathan Sacks, Katherine Wu, Roderick Bronson, Tuomas Tammela, Tyler Jacks. Deep neural network for automatic histopathologic analysis of murine lung tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4447.