The accepted paradigm for both cellular and anti-tumor immunity relies upon tumor cell killing by CD8+ T cells recognizing cognate antigens presented in the context of target cell major histocompatibility complex (MHC) class I (MHC-I) molecules. Likewise, a classically described mechanism of tumor immune escape is tumor MHC-I downregulation. Here, we report that CD8+ T cells maintain the capacity to kill tumor cells that are entirely devoid of MHC-I expression. This capacity proves to be dependent instead on interactions between T cell natural killer group 2D (NKG2D) and tumor NKG2D ligands (NKG2DLs), the latter of which are highly expressed on MHC-loss variants. Necessarily, tumor cell killing in these instances is antigen independent, although prior T cell antigen-specific activation is required and can be furnished by myeloid cells or even neighboring MHC-replete tumor cells. In this manner, adaptive priming can beget innate killing. These mechanisms are active in vivo in mice as well as in vitro in human tumor systems and are obviated by NKG2D knockout or blockade. These studies challenge the long-advanced notion that downregulation of MHC-I is a viable means of tumor immune escape and instead identify the NKG2D–NKG2DL axis as a therapeutic target for enhancing T cell-dependent anti-tumor immunity against MHC-loss variants.

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We recently developed a monocyte-based cellular vaccine platform for cancer treatment. In contrast to the traditional utilization of monocytes as precursors to generate dendritic cells (DC) for vaccination purposes, we find that freshly isolated monocytes with no differentiation process can be loaded with tumor antigens (Ag) and trigger robust antitumor cytotoxic T lymphocyte (CTL) responses. In this chapter, we describe methods to prepare, administer, and evaluate murine Ly-6C hi monocyte-based cellular vaccines for their therapeutic efficacy. This includes procedures for isolation, purity determination, Ag loading, administration of bone marrow (BM)-derived monocytes, as well as methods to determine vaccine efficacy through the examination of Ag-specific CD8 + T cell expansion and antitumor responses in murine melanoma models. As a vaccine platform, undifferentiated monocytes can be easily adapted to different tumor models with a multitude of target antigens. The method described here seeks to facilitate preclinical research of monocyte-based vaccination as a strategy for cancer immunotherapy.

Background There is progress to be made in building artificially intelligent systems to detect abnormalities that are not only accurate but can handle the true breadth of findings that radiologists encounter in body (chest, abdomen, and pelvis) computed tomography (CT). Currently, the major bottleneck for developing multi-disease classifiers is a lack of manually annotated data. The purpose of this work was to develop high throughput multi-label annotators for body CT reports that can be applied across a variety of abnormalities, organs, and disease states thereby mitigating the need for human annotation. Methods We used a dictionary approach to develop rule-based algorithms (RBA) for extraction of disease labels from radiology text reports. We targeted three organ systems (lungs/pleura, liver/gallbladder, kidneys/ureters) with four diseases per system based on their prevalence in our dataset. To expand the algorithms beyond pre-defined keywords, attention-guided recurrent neural networks (RNN) were trained using the RBA-extracted labels to classify reports as being positive for one or more diseases or normal for each organ system. Alternative effects on disease classification performance were evaluated using random initialization or pre-trained embedding as well as different sizes of training datasets. The RBA was tested on a subset of 2158 manually labeled reports and performance was reported as accuracy and F-score. The RNN was tested against a test set of 48,758 reports labeled by RBA and performance was reported as area under the receiver operating characteristic curve (AUC), with 95% CIs calculated using the DeLong method. Results Manual validation of the RBA confirmed 91–99% accuracy across the 15 different labels. Our models extracted disease labels from 261,229 radiology reports of 112,501 unique subjects. Pre-trained models outperformed random initialization across all diseases. As the training dataset size was reduced, performance was robust except for a few diseases with a relatively small number of cases. Pre-trained classification AUCs reached > 0.95 for all four disease outcomes and normality across all three organ systems. Conclusions Our label-extracting pipeline was able to encompass a variety of cases and diseases in body CT reports by generalizing beyond strict rules with exceptional accuracy. The method described can be easily adapted to enable automated labeling of hospital-scale medical data sets for training image-based disease classifiers.

Major Histocompatibility Complex (MHC) Class I downregulation is a well described mechanism of tumor immune escape, posing a challenge for T cell based immunotherapies including immune checkpoint blockade (ICB). Recent studies, however, have demonstrated mixed roles of MHC Class 1 and the critical component beta-2-microglobulin (β2m) expression in cancer progression and ICB response, with some studies showing inactivation of antigen presentation to be associated with resistance to ICB and others showing low β2m expression to be associated with favorable prognosis. Glioblastoma (GBM) in particular expresses little or no MHC Class 1 and patients remain unresponsive to ICB. We thus sought to evaluate the role of MHC Class 1 in ICB, given that we have previously demonstrated that combination ICB with anti-PD-1 and co-stimulation with 4-1BB agonism has marked efficacy against intracranial murine glioma tumors in a CD8 T cell dependent manner. Surprisingly, in a CT2A murine glioma tumor line expressing the antigen TRP2 and lacking cell surface MHC I (CT2A-TRP2-β2mKO), the efficacy of combination 4-1BB and PD-1 therapy (ICB) was re-demonstrated in a CD8 dependent manner, independent of NK cells, CD4 T cells, and B cells. Furthermore, the efficacy of immunotherapy against intracranial CT2A-TRP2-β2mKO was demonstrated to be antigen dependent, with an adoptive lymphocyte transfer (ALT) of TRP2 TCR transduced T cells (TRP2 T cells) into a CD8KO mouse sufficient to eliminate CT2A-TRP2-β2mKO in the setting of ICB. Additionally, an ALT of TRP2 T cells did not kill CT2A-β2mKO tumors in the setting of ICB, while OT-1 mice whose CD8+ T cells primarily recognize OVA peptide with CT2A-TRP2-β2mKO tumors did not respond to ICB. In vitro studies revealed that TRP2 T cells demonstrated anti-tumor cytotoxicity against MHC Class I negative CT2A-TRP2-β2mKO tumor cells in the presence of TRP2 loaded bone marrow derived macrophages (TRP2 Mφ), but neither cell type was individually sufficient to induce tumor cell death, while the combination of TRP2 T cells and TRP2 Mφ demonstrated no cytotoxicity against CT2A-β2mKO tumors. Transwell experiments in which TRP2 Mφ and CT2A-TRP2-β2mKO tumor cells were separated by a 0.5µm membrane permeable to T cells but not Mφ or tumor cells revealed that contact between TRP2 Mφ and tumor cells was not necessary to induce T cell dependent killing. Indeed, tumor-bearing β2mKO bone marrow chimeras lacking MHC class 1 on hematopoeitically derived cells did not respond to ICB, highlighting the importance of antigen presentation from myeloid cells. The mechanism of killing was found to be dependent on interferon gamma (IFNγ), as IFNγKO mice did not respond to ICB. These findings demonstrate that tumors with low MHC Class 1 expression may still be targeted by T cell dependent immunotherapies such as ICB when antigen presentation can occur from myeloid cells. Citation Format: Emily Lerner, Vincent D'Anniballe, William Tomaszewski, Jonathan Perera, Xiuyu Cui, Jessica Waibl-Polania, Daniel Wilkinson, Michael D. Gunn, Peter E. Fecci, Karolina Woroniecka. CD8 T cell mediated killing of MHC class 1 negative tumors requires antigen presenting myeloid cells and interferon gamma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1378.