The endogenous ligand-activated aryl hydrocarbon receptor (AHR) plays an important role in numerous biologic processes. As the known number of AHR-mediated processes grows, so too does the importance of determining what endogenous AHR ligands are produced, how their production is regulated, and what biologic consequences ensue. Consequently, our studies were designed primarily to determine whether ER⁻/PR⁻/Her2⁻ breast cancer cells have the potential to produce endogenous AHR ligands and, if so, how production of these ligands is controlled. We postulated that: 1) malignant cells produce tryptophan-derived AHR ligand(s) through the kynurenine pathway; 2) these metabolites have the potential to drive AHR-dependent breast cancer migration; 3) the AHR controls expression of a rate-limiting kynurenine pathway enzyme(s) in a closed amplification loop; and 4) environmental AHR ligands mimic the effects of endogenous ligands. Data presented in this work indicate that primary human breast cancers, and their metastases, express high levels of AHR and tryptophan-2,3-dioxygenase (TDO); representative ER⁻/PR⁻/Her2⁻ cell lines express TDO and produce sufficient intracellular kynurenine and xanthurenic acid concentrations to chronically activate the AHR. TDO overexpression, or excess kynurenine or xanthurenic acid, accelerates migration in an AHR-dependent fashion. Environmental AHR ligands 2,3,7,8-tetrachlorodibenzo[p]dioxin and benzo[a]pyrene mimic this effect. AHR knockdown or inhibition significantly reduces TDO2 expression. These studies identify, for the first time, a positive amplification loop in which AHR-dependent TDO2 expression contributes to endogenous AHR ligand production. The net biologic effect of AHR activation by endogenous ligands, which can be mimicked by environmental ligands, is an increase in tumor cell migration, a measure of tumor aggressiveness.

Biology has become a prime area for the deployment of deep learning and artificial intelligence (AI), enabled largely by the massive data sets that the field can generate. Key to most AI tasks is the availability of a sufficiently large, labeled data set with which to train AI models. In the context of microscopy, it is easy to generate image data sets containing millions of cells and structures. However, it is challenging to obtain large-scale high-quality annotations for AI models. Here, we present HALS (Human-Augmenting Labeling System), a human-in-the-loop data labeling AI, which begins uninitialized and learns annotations from a human, in real-time. Using a multi-part AI composed of three deep learning models, HALS learns from just a few examples and immediately decreases the workload of the annotator, while increasing the quality of their annotations. Using a highly repetitive use-case—annotating cell types—and running experiments with seven pathologists—experts at the microscopic analysis of biological specimens—we demonstrate a manual work reduction of 90.60%, and an average data-quality boost of 4.34%, measured across four use-cases and two tissue stain types.

Scientific understanding of how the immune microenvironment interacts with renal cell carcinoma (RCC) has substantially increased over the last decade as a result of research investigations and applying immunotherapies, which modulate how the immune system targets and eliminates RCC tumor cells. Clinically, immune checkpoint inhibitor therapy (ICI) has revolutionized the treatment of advanced clear cell RCC because of improved outcomes compared to targeted molecular therapies. From an immunologic perspective, RCC is particularly interesting because tumors are known to be highly inflamed, but the mechanisms underlying the inflammation of the tumor immune microenvironment are atypical and not well described. While technological advances in gene sequencing and cellular imaging have enabled precise characterization of RCC immune cell phenotypes, multiple theories have been suggested regarding the functional significance of immune infiltration in RCC progression. The purpose of this review is to describe the general concepts of the anti-tumor immune response and to provide a detailed summary of the current understanding of the immune response to RCC tumor development and progression. This article describes immune cell phenotypes that have been reported in the RCC microenvironment and discusses the application of RCC immunophenotyping to predict response to ICI therapy and patient survival.

Predicting which patients will progress to metastatic disease after surgery for non-metastatic clear cell renal cell carcinoma (ccRCC) is difficult; however, recent data suggest that tumor immune cell infiltration could be used as a biomarker. We evaluated the quantity and type of immune cells infiltrating ccRCC tumors for associations with metastatic progression following attempted curative surgery. We quantified immune cell densities in the tumor microenvironment and validated our findings in two independent patient cohorts with multi-region sampling to investigate the impact of heterogeneity on prognostic accuracy. For non-metastatic ccRCC, increased CD8⁺ T cell infiltration was associated with a reduced likelihood of progression to metastatic disease. Interestingly, patients who progressed to metastatic disease also had increased percentages of exhausted CD8⁺ T cells. Finally, we evaluated the spatial heterogeneity of the immune infiltration and demonstrated that patients without metastatic progression had CD8⁺ T cells in closer proximity to ccRCC cells. These data strengthen the evidence for CD8⁺ T cell infiltration as a prognostic biomarker in non-metastatic ccRCC and demonstrate that multi-region sampling may be necessary to fully characterize immune infiltration within heterogeneous tumors. Tumor CD8⁺ T cell infiltration should be investigated as a biomarker in adjuvant systemic therapy clinical trials for high-risk non-metastatic RCC.