Irshad, Sheeba

Abstract The paradigm in the field of oncolytic virotherapy proposes that tumor cell killing by an oncolytic virus (OV) culminates in the priming of antitumor CD8+ T cells. However, this ignores the impact a highly immunodominant antiviral response against the OV has on the subdominant antitumor response, particularly with regard to the naturally low affinity of tumor antigens to their cognate T-cell receptors. To investigate this experimentally, we first studied the interaction between the two responses in B16 and MC38 tumor models, both expressing ovalbumin (OVA). Contrary to the accepted paradigm, the inflammatory vesicular stomatitis virus (VSV) failed to expand OVA-specific T cells in vivo unless armed with tumor-associated antigen (TAA). In fact, treatment with the control VSV led to the ablation of antitumor T cells and a shift toward dysfunction as determined by the upregulation of markers, including LAG3 and CD39, correlating with lower rates of survival. Combination with anti–PD-1 immune checkpoint blockade therapy significantly improved survival only when VSV expressed TAA. VSV-expressing altered peptide ligand variants of tumor antigens, which evade mechanisms of central tolerance, expanded near self-antigen–specific T cells with the ability to cross-react with tolerized tumor antigens, thus providing an antitumor effector population capable of proliferating in the presence of a potent antiviral response. Taken together, these findings offer a rational approach to OV design which preserves acute antitumor therapy provided by antiviral innate and T-cell responses while promoting the induction of tumor-specific T cells. Significance: Oncolytic viruses can induce an antiviral T-cell response that occludes an antitumor response, challenging the paradigm in the field and elucidating strategies to optimize the efficacy of oncolytic viroimmunotherapy.

Abstract T-cell immune tolerance is established in part through the activity of the autoimmune regulator (AIRE) transcription factor in medullary thymic epithelial cells (mTEC) of the thymus. AIRE induces expression of peripheral tissue–specific self-antigens for presentation to naïve T cells to promote activation/deletion of autoreactive T cells. This traditional role of AIRE in mTECs is to prevent autoimmunity. Herein, we demonstrate that tumors mimic the role of AIRE in mTECs to evade immune rejection. We found that AIRE induced a profile of “selfness” at the RNA and protein levels which, when presented on major histocompatibility complexes (MHC), shielded the tumor from inherently self-tolerized T cells. Moreover, we describe an in vivo immunotherapy in which engineered changes in AIRE expression in tumor cells altered their profile of selfness, exposing both AIRE-modified and parental unmodified tumor cells to T-cell attack. Therefore, by resetting the immunologic selfness of cancer cells, this AIRE-mediated immunotherapy (i) converted a highly tolerized T-cell compartment into a tumor-reactive T-cell population; (ii) conferred upon nonimmunogenic tumors de novo sensitivity to immune checkpoint blockade; (iii) removed the need to identify potentially immunogenic tumor-associated antigens as targets for generation of T-cell responses; and (iv) lead to potent T cell–mediated rejection of aggressive, immunologically cold, nonimmunogenic tumors. Patient RNA sequencing data showed that the expression of AIRE predicted response to immune therapies with a strong correlation between AIRE expression and markers of T-cell receptor signaling, suggesting that our studies have therapeutic translational value.

Abstract T cell immune tolerance is established in part through the activity of the Auto-immune Regulator (AIRE) transcription factor in the medullary Thymic Epithelial Cells (mTEC) of the thymus. AIRE induces expression of SELF peripheral tissue-specific antigens for presentation to naïve T cells to promote activation/deletion of potentially autoreactive T cells. We show, for the first time to our knowledge, that tumors mimic the role of AIRE in mTEC to evade immune rejection. Thus, by expressing a broad range of SELF epitopes against which minimal functional T cell reactivities exist because of thymic deletion, AIRE acts as a master controller of SELFNESS, effectively cloaking the tumor from T cell attack. Moreover, we describe a completely novel immunotherapy in which engineered changes in AIRE expression in tumor cells alters their profile of SELFNESS, exposing both AIRE-modified, and parental unmodified, tumor cells to T cell attack. Consistent with our studies, patient RNAseq shows expression of AIRE predicts response to immune therapies with a strong correlation between AIRE expression and markers of TCR signaling. Therefore, by re-setting the immunological SELFNESS of cancer cells, this novel AIRE-mediated immunotherapy 1). converts a highly tolerized T cell compartment into a heteroclitic tumor-reactive T cell population; 2) confers de novo sensitivity to immune checkpoint blockade upon non-immunogenic tumors; 3). completely removes the need to identify potentially immunogenic tumor-associated antigens as targets for generation of de novo CD8+ and helper CD4+ T cell responses; and 4) leads to potent T cell-mediated rejection of aggressive, immunologically cold, non-immunogenic tumors.

Abstract Recent advances in the field of immuno‐oncology have brought transformative changes in the management of cancer patients. The immune profile of tumours has been found to have key value in predicting disease prognosis and treatment response in various cancers. Multiplex immunohistochemistry and immunofluorescence have emerged as potent tools for the simultaneous detection of multiple protein biomarkers in a single tissue section, thereby expanding opportunities for molecular and immune profiling while preserving tissue samples. By establishing the phenotype of individual tumour cells when distributed within a mixed cell population, the identification of clinically relevant biomarkers with high‐throughput multiplex immunophenotyping of tumour samples has great potential to guide appropriate treatment choices. Moreover, the emergence of novel multi‐marker imaging approaches can now provide unprecedented insights into the tumour microenvironment, including the potential interplay between various cell types. However, there are significant challenges to widespread integration of these technologies in daily research and clinical practice. This review addresses the challenges and potential solutions within a structured framework of action from a regulatory and clinical trial perspective. New developments within the field of immunophenotyping using multiplexed tissue imaging platforms and associated digital pathology are also described, with a specific focus on translational implications across different subtypes of cancer. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Abstract Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector‐based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well‐described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland.

AbstractThe clinical significance of the tumor‐immune interaction in breast cancer is now established, and tumor‐infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple‐negative (estrogen receptor, progesterone receptor, and HER2‐negative) breast cancer and HER2‐positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state‐of‐the‐art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false‐positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in‐depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple‐negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.