Oncocytic intraductal carcinoma of salivary glands: a distinct variant with TRIM33-RET fusions and BRAF V600E mutations.

AIMS: Salivary gland intraductal carcinoma (IDC) is a complex ductal neoplasm surrounded by a layer of myoepithelial cells. Recent insights have shown that there are three different types: intercalated duct-like, with frequent NCOA4-RET fusions; apocrine, with salivary duct carcinoma-like mutations; and mixed intercalated duct-like/apocrine, with RET fusions, including TRIM27-RET. In addition, an oncocytic IDC has been described, but it remains unclear whether it represents a fourth variant or simply oncocytic metaplasia of another IDC type. Our aim was to more completely characterize oncocytic IDC. METHODS AND RESULTS: Six IDCs with oncocytic changes were retrieved from the authors' archives, from three men and three women ranging in age from 45 to 75 years (mean, 63 years). Five arose in the parotid gland, with one in an accessory parotid gland. Four patients with follow-up were free of disease after 1-23 months. Several immunostains (S100, mammaglobin, androgen receptor, and p63/p40) and molecular tools (RNA sequencing, RET fluorescence in-situ hybridisation, BRAF V600E VE1 immunohistochemistry, and Sanger sequencing) were applied. Histologically, the tumours were variably cystic with solid intracystic nodules often difficult to recognise as intraductal. In all, tumour ducts were positive for S100 and mammaglobin, negative for androgen receptor, and completely surrounded by myoepithelial cells positive for p63/p40. Molecular analysis revealed TRIM33-RET in two of six cases, NCOA4-RET in one of six cases, and BRAF V600E in two of six cases. One case had no identifiable alterations. CONCLUSIONS: Oncocytic IDC shares similarities with intercalated duct-like IDC. Although additional verification is needed, the oncocytic variant appears to be sufficiently unique to be now regarded as the fourth distinct subtype of IDC. Because of its indolent nature, oncocytic IDC should be distinguished from histological mimics.

An Aryl Hydrocarbon Receptor-Mediated Amplification Loop That Enforces Cell Migration in ER-/PR-/Her2- Human Breast Cancer Cells.

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.

Non-Metastatic Clear Cell Renal Cell Carcinoma Immune Cell Infiltration Heterogeneity and Prognostic Ability in Patients Following Surgery.

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.

Understanding the Tumor Immune Microenvironment in Renal Cell Carcinoma.

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.

Stress Keratin 17 Is a Predictive Biomarker Inversely Associated with Response to Immune Check-Point Blockade in Head and Neck Squamous Cell Carcinomas and Beyond.

Low response rates in immune check-point blockade (ICB)-treated head and neck squamous cell carcinoma (HNSCC) drive a critical need for robust, clinically validated predictive biomarkers. Our group previously showed that stress keratin 17 (CK17) suppresses macrophage-mediated CXCL9/CXCL10 chemokine signaling involved in attracting activated CD8+ T cells into tumors, correlating with decreased response rate to pembrolizumab-based therapy in a pilot cohort of ICB-treated HNSCC (n = 26). Here, we performed an expanded analysis of the predictive value of CK17 in ICB-treated HNSCC according to the REMARK criteria and investigated the gene expression profiles associated with high CK17 expression. Pretreatment samples from pembrolizumab-treated HNSCC patients were stained via immunohistochemistry using a CK17 monoclonal antibody (n = 48) and subjected to spatial transcriptomic profiling (n = 8). Our findings were validated in an independent retrospective cohort (n = 22). CK17 RNA expression in pembrolizumab-treated patients with various cancer types was investigated for predictive significance. Of the 48 patients (60% male, median age of 61.5 years), 21 (44%) were CK17 high, and 27 (56%) were CK17 low. A total of 17 patients (35%, 77% CK17 low) had disease control, while 31 patients (65%, 45% CK17 low) had progressive disease. High CK17 expression was associated with a lack of disease control (p = 0.037), shorter time to treatment failure (p = 0.025), and progression-free survival (PFS, p = 0.004), but not overall survival (OS, p = 0.06). A high CK17 expression was associated with lack of disease control in an independent validation cohort (p = 0.011). PD-L1 expression did not correlate with CK17 expression or clinical outcome. CK17 RNA expression was predictive of PFS and OS in 552 pembrolizumab-treated cancer patients. Our findings indicate that high CK17 expression may predict resistance to ICB in HNSCC patients and beyond.

Intraductal carcinoma of the salivary gland with NCOA4-RET: expanding the morphologic spectrum and an algorithmic diagnostic approach.

After the publication of the 2017 World Health Organization Classification of Head and Neck Tumours, there has been increasing interest in the classification of newly categorized intraductal carcinomas. Intraductal carcinoma (IC) is an indolent tumor, typically arising in the parotid gland, with an intact myoepithelial layer and a cystic, papillary, often cribriform architecture. Early studies of IC identified a heterogeneous group of molecular alterations driving neoplasia, and recent studies have defined three primary morphological/immunohistochemical variants, subsequently linking these morphologic variants with defined molecular signatures. Although studies to date have pointed toward distinct molecular alterations after histological classification, this study used a novel approach, focusing primarily on six cases of IC with NCOA4-RET gene rearrangement as determined by next-generation sequencing and describing the spectrum of clinicopathologic findings within that molecularly-defined group, among them a unique association between the NCOA4-RET fusion and hybrid variant IC and the first case of IC arising in association with a pleomorphic adenoma. RET-rearranged IC show histological and immunohistochemical overlap with the more widely recognized secretory carcinoma, including low-grade morphology, a lumen-forming or microcystic growth pattern, and co-expression of S100, SOX10, and mammaglobin, findings undoubtedly leading to misdiagnosis. Typically regarded to have ETV6-NTRK3 fusions, secretory carcinomas may alternatively arise with RET fusions as well. Adding our cohort of six NCOA4-RET fusion-positive IC compared with four cases of secretory carcinoma with ETV6-RET fusions and a single case of fusion-negative IC with salivary duct carcinoma-like genetics, we propose a diagnostic algorithm that integrates histological elements, including atypia and invasiveness, and the likelihood of specific molecular alterations to increase diagnostic accuracy in what can be a very subtle diagnosis with important clinical implications.

Biological data annotation via a human-augmenting AI-based labeling system.

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.