The spatial structure of the tumor immune microenvironment can explain and predict patient response in high-grade serous carcinoma.

Ovarian cancer is the deadliest gynecological malignancy, and therapeutic options and mortality rates over the last three decades have largely not changed. Recent studies indicate that the composition of the tumor immune microenvironment (TIME) influences patient outcomes. To improve spatial understanding of the TIME, we performed multiplexed ion beam imaging on 83 human high-grade serous carcinoma tumor samples, identifying about 160,000 cells across 23 cell types. For 77 of these samples meeting inclusion criteria, we generated composition features based on cell type proportions, spatial features based on the distances between cell types, and spatial network features representing cell interactions and cell clustering patterns, which we linked to traditional clinical and immunohistochemical variables and patient overall survival (OS) and progression-free survival (PFS) outcomes. Among these features, we found several significant univariate correlations, including B-cell contact with M1 macrophages (OS hazard ratio HR=0.696, p=0.011, PFS HR=0.734, p=0.039). We then used high-dimensional random forest models to evaluate out-of-sample predictive performance for OS and PFS outcomes and to derive relative feature importance scores for each feature. The top model for predicting low or high PFS used TIME composition and spatial features and achieved an average AUC (area under the receiver-operating characteristic curve) score of 0.71. The results demonstrate the importance of spatial structure in understanding how the TIME contributes to treatment outcomes. Furthermore, the present study provides a generalizable roadmap for spatial analyses of the TIME in ovarian cancer research.

Targeting Tryptophan Catabolism in Ovarian Cancer to Attenuate Macrophage Infiltration and PD-L1 Expression.

High-grade serous carcinoma (HGSC) of the fallopian tube, ovary, and peritoneum is the most common type of ovarian cancer and is predicted to be immunogenic because the presence of tumor-infiltrating lymphocytes conveys a better prognosis. However, the efficacy of immunotherapies has been limited because of the immune-suppressed tumor microenvironment (TME). Tumor metabolism and immune-suppressive metabolites directly affect immune cell function through the depletion of nutrients and activation of immune-suppressive transcriptional programs. Tryptophan (TRP) catabolism is a contributor to HGSC disease progression. Two structurally distinct rate-limiting TRP catabolizing enzymes, indoleamine 2,3-dioxygenase 1 (IDO1) and tryptophan 2,3-dioxygenase 2 (TDO2), evolved separately to catabolize TRP. IDO1/TDO2 are aberrantly expressed in carcinomas and metabolize TRP into the immune-suppressive metabolite kynurenine (KYN), which can engage the aryl hydrocarbon receptor to drive immunosuppressive transcriptional programs. To date, IDO inhibitors tested in clinical trials have had limited efficacy, but those inhibitors did not target TDO2, and we find that HGSC cell lines and clinical outcomes are more dependent on TDO2 than IDO1. To identify inflammatory HGSC cancers with poor prognosis, we stratified patient ascites samples by IL6 status, which correlates with poor prognosis. Metabolomics revealed that IL6-high patient samples had enriched KYN. TDO2 knockdown significantly inhibited HGSC growth and TRP catabolism. The orally available dual IDO1/TDO2 inhibitor, AT-0174, significantly inhibited tumor progression, reduced tumor-associated macrophages, and reduced expression of immune-suppressive proteins on immune and tumor cells. These studies demonstrate the importance of TDO2 and the therapeutic potential of AT-0174 to overcome an immune-suppressed TME. SIGNIFICANCE: Developing strategies to improve response to chemotherapy is essential to extending disease-free intervals for patients with HGSC of the fallopian tube, ovary, and peritoneum. In this article, we demonstrate that targeting TRP catabolism, particularly with dual inhibition of TDO2 and IDO1, attenuates the immune-suppressive microenvironment and, when combined with chemotherapy, extends survival compared with chemotherapy alone.

Investigating the immunological function of alpha-2-glycoprotein 1, zinc-binding in regulating tumor response in the breast cancer microenvironment.

BACKGROUND: Alpha-2-glycoprotein 1, zinc-binding (ZAG), a secreted protein encoded by the AZGP1 gene, is structurally similar to HLA class I. Despite its presumed immunological function, little is known about its role in tumor immunity. In this study, we thus aimed to determine the relationship between the expression of AZGP1/ZAG and the immunological profiles of breast cancer tissues at both the gene and protein level. METHODS: Using a publicly available gene expression dataset from a large-scale breast cancer cohort, we conducted gene set enrichment analysis (GSEA) to screen the biological processes associated with AZGP1. We analyzed the correlation between AZGP1 expression and immune cell composition in breast cancer tissues, estimated using CIBERSORTx. Previously, we evaluated the infiltration of 11 types of immune cells for 45 breast cancer tissues using flow cytometry (FCM). ZAG expression was evaluated by immunohistochemistry on these specimens and analyzed for its relationship with immune cell infiltration. The action of ZAG in M1/M2 polarization models using primary cultures of human peripheral blood mononuclear cells (PBMC)-derived macrophage (Mφ) was analyzed based on the expression of M1/M2 markers (CD86, CD80/CD163, MRC1) and HLA class I/II by FCM. RESULTS: AZGP1 expression was negatively correlated with multiple immunological processes and specific immune cell infiltration including Mφ M1 using GSEA and CIBERSORTx. ZAG expression was associated with decreased infiltration of monocytes/macrophages, non-classical monocytes, and myeloid-derived suppressor cells in tumor tissues assessed using FCM. In in vitro analyses, ZAG decreased the expression of CD80, CD163, MRC1, and HLA classes I/II in the M1 polarization model and the expression of CD163 and MRC1 in the M2 polarization model. CONCLUSION: ZAG is suggested to be a novel immunoregulatory factor affecting the Mφ phenotype in breast cancer tissues.

The spatial structure of the tumor immune microenvironment can explain and predict patient response in high-grade serous carcinoma.

Despite ovarian cancer being the deadliest gynecological malignancy, there has been little change to therapeutic options and mortality rates over the last three decades. Recent studies indicate that the composition of the tumor immune microenvironment (TIME) influences patient outcomes but are limited by a lack of spatial understanding. We performed multiplexed ion beam imaging (MIBI) on 83 human high-grade serous carcinoma tumors - one of the largest protein-based, spatially-intact, single-cell resolution tumor datasets assembled - and used statistical and machine learning approaches to connect features of the TIME spatial organization to patient outcomes. Along with traditional clinical/immunohistochemical attributes and indicators of TIME composition, we found that several features of TIME spatial organization had significant univariate correlations and/or high relative importance in high-dimensional predictive models. The top performing predictive model for patient progression-free survival (PFS) used a combination of TIME composition and spatial features. Results demonstrate the importance of spatial structure in understanding how the TIME contributes to treatment outcomes. Furthermore, the present study provides a generalizable roadmap for spatial analyses of the TIME in ovarian cancer research.