An AI-Ready Multiplex Staining Dataset for Reproducible and Accurate Characterization of Tumor Immune Microenvironment.

We introduce a new AI-ready computational pathology dataset containing restained and co-registered digitized images from eight head-and-neck squamous cell carcinoma patients. Specifically, the same tumor sections were stained with the expensive multiplex immunofluorescence (mIF) assay first and then restained with cheaper multiplex immunohistochemistry (mIHC). This is a first public dataset that demonstrates the equivalence of these two staining methods which in turn allows several use cases; due to the equivalence, our cheaper mIHC staining protocol can offset the need for expensive mIF staining/scanning which requires highly-skilled lab technicians. As opposed to subjective and error-prone immune cell annotations from individual pathologists (disagreement > 50%) to drive SOTA deep learning approaches, this dataset provides objective immune and tumor cell annotations via mIF/mIHC restaining for more reproducible and accurate characterization of tumor immune microenvironment (e.g. for immunotherapy). We demonstrate the effectiveness of this dataset in three use cases: (1) IHC quantification of CD3/CD8 tumor-infiltrating lymphocytes via style transfer, (2) virtual translation of cheap mIHC stains to more expensive mIF stains, and (3) virtual tumor/immune cellular phenotyping on standard hematoxylin images. The dataset is available at https://github.com/nadeemlab/DeepLIIF.

An AI-Ready Multiplex Staining Dataset for Reproducible and Accurate Characterization of Tumor Immune Microenvironment.

We introduce a new AI-ready computational pathology dataset containing restained and co-registered digitized images from eight head-and-neck squamous cell carcinoma patients. Specifically, the same tumor sections were stained with the expensive multiplex immunofluorescence (mIF) assay first and then restained with cheaper multiplex immunohistochemistry (mIHC). This is a first public dataset that demonstrates the equivalence of these two staining methods which in turn allows several use cases; due to the equivalence, our cheaper mIHC staining protocol can offset the need for expensive mIF staining/scanning which requires highly-skilled lab technicians. As opposed to subjective and error-prone immune cell annotations from individual pathologists (disagreement > 50%) to drive SOTA deep learning approaches, this dataset provides objective immune and tumor cell annotations via mIF/mIHC restaining for more reproducible and accurate characterization of tumor immune microenvironment (e.g. for immunotherapy). We demonstrate the effectiveness of this dataset in three use cases: (1) IHC quantification of CD3/CD8 tumor-infiltrating lymphocytes via style transfer, (2) virtual translation of cheap mIHC stains to more expensive mIF stains, and (3) virtual tumor/immune cellular phenotyping on standard hematoxylin images. The dataset is available at \url{https://github.com/nadeemlab/DeepLIIF}.

Molecular Determinants of Thyroid Nodules with Indeterminate Cytology and RAS Mutations.

Background:RAS gene family mutations are the most prevalent in thyroid nodules with indeterminate cytology and are present in a wide spectrum of histological diagnoses. We evaluated differentially expressed genes and signaling pathways across the histological/clinical spectrum of RAS-mutant nodules to determine key molecular determinants associated with a high risk of malignancy. Methods: Sixty-one thyroid nodules with RAS mutations were identified. Based on the histological diagnosis and biological behavior, the nodules were grouped into five categories indicating their degree of malignancy: non-neoplastic appearance, benign neoplasm, indeterminate malignant potential, low-risk cancer, or high-risk cancer. Gene expression profiles of these nodules were determined using the NanoString PanCancer Pathways and IO 360 Panels, and Angiopoietin-2 level was determined by immunohistochemical staining. Results: The analysis of differentially expressed genes using the five categories as supervising parameters unearthed a significant correlation between the degree of malignancy and genes involved in cell cycle and apoptosis (BAX, CCNE2, CDKN2A, CDKN2B, CHEK1, E2F1, GSK3B, NFKB1, and PRKAR2A), PI3K pathway (CCNE2, CSF3, GSKB3, NFKB1, PPP2R2C, and SGK2), and stromal factors (ANGPT2 and DLL4). The expression of Angiopoietin-2 by immunohistochemistry also showed the same trend of increasing expression from non-neoplastic appearance to high-risk cancer (p < 0.0001). Conclusions: The gene expression analysis of RAS-mutant thyroid nodules suggests increasing upregulation of key oncogenic pathways depending on their degree of malignancy and supports the concept of a stepwise progression. The utility of ANGPT2 expression as a potential diagnostic biomarker warrants further evaluation.

Effects of Tobacco Smoking on the Tumor Immune Microenvironment in Head and Neck Squamous Cell Carcinoma.

PURPOSE: Patients with head and neck squamous cell carcinoma (HNSCC) who actively smoke during treatment have worse survival compared with never-smokers and former-smokers. We hypothesize the poor prognosis in tobacco smokers with HNSCC is, at least in part, due to ongoing suppression of immune response. We characterized the tumor immune microenvironment (TIM) of HNSCC in a retrospective cohort of 177 current, former, and never smokers. EXPERIMENTAL DESIGN: Tumor specimens were subjected to analysis of CD3, CD8, FOXP3, PD-1, PD-L1, and pancytokeratin by multiplex immunofluorescence, whole-exome sequencing, and RNA sequencing. Immune markers were measured in tumor core, tumor margin, and stroma. RESULTS: Our data indicate that current smokers have significantly lower numbers of CD8+ cytotoxic T cells and PD-L1+ cells in the TIM compared with never- and former-smokers. While tumor mutation burden and mutant allele tumor heterogeneity score do not associate with smoking status, gene-set enrichment analyses reveal significant suppression of IFNα and IFNγ response pathways in current smokers. Gene expression of canonical IFN response chemokines, CXCL9, CXCL10, and CXCL11, are lower in current smokers than in former smokers, suggesting a mechanism for the decreased immune cell migration to tumor sites. CONCLUSIONS: These results suggest active tobacco use in HNSCC has an immunosuppressive effect through inhibition of tumor infiltration of cytotoxic T cells, likely as a result of suppression of IFN response pathways. Our study highlights the importance of understanding the interaction between smoking and TIM in light of emerging immune modulators for cancer management.