Potentiating the radiation-induced type I interferon antitumoral immune response by ATM inhibition in pancreatic cancer.

Radiotherapy induces a type I interferon-mediated (T1IFN-mediated) antitumoral immune response that we hypothesized could be potentiated by a first-in-class ataxia telangiectasia mutated (ATM) inhibitor, leading to enhanced innate immune signaling, T1IFN expression, and sensitization to immunotherapy in pancreatic cancer. We evaluated the effects of AZD1390 or a structurally related compound, AZD0156, on innate immune signaling and found that both inhibitors enhanced radiation-induced T1IFN expression via the POLIII/RIG-I/MAVS pathway. In immunocompetent syngeneic mouse models of pancreatic cancer, ATM inhibitor enhanced radiation-induced antitumoral immune responses and sensitized tumors to anti-PD-L1, producing immunogenic memory and durable tumor control. Therapeutic responses were associated with increased intratumoral CD8+ T cell frequency and effector function. Tumor control was dependent on CD8+ T cells, as therapeutic efficacy was blunted in CD8+ T cell-depleted mice. Adaptive immune responses to combination therapy provided systemic control of contralateral tumors outside of the radiation field. Taken together, we show that a clinical candidate ATM inhibitor enhances radiation-induced T1IFN, leading to both innate and subsequent adaptive antitumoral immune responses and sensitization of otherwise resistant pancreatic cancer to immunotherapy.

A comprehensive patient-specific prediction model for temporomandibular joint osteoarthritis progression.

Temporomandibular joint osteoarthritis (TMJ OA) is a prevalent degenerative disease characterized by chronic pain and impaired jaw function. The complexity of TMJ OA has hindered the development of prognostic tools, posing a significant challenge in timely, patient-specific management. Addressing this gap, our research employs a comprehensive, multidimensional approach to advance TMJ OA prognostication. We conducted a prospective study with 106 subjects, 74 of whom were followed up after 2 to 3 y of conservative treatment. Central to our methodology is the development of an innovative, open-source predictive modeling framework, the Ensemble via Hierarchical Predictions through Nested cross-validation tool (EHPN). This framework synergistically integrates 18 feature selection, statistical, and machine learning methods to yield an accuracy of 0.87, with an area under the ROC curve of 0.72 and an F1 score of 0.82. Our study, beyond technical advancements, emphasizes the global impact of TMJ OA, recognizing its unique demographic occurrence. We highlight key factors influencing TMJ OA progression. Using SHAP analysis, we identified personalized prognostic predictors: lower values of headache, lower back pain, restless sleep, condyle high gray level-GL-run emphasis, articular fossa GL nonuniformity, and long-run low GL emphasis; and higher values of superior joint space, mouth opening, saliva Vascular-endothelium-growth-factor, Matrix-metalloproteinase-7, serum Epithelial-neutrophil-activating-peptide, and age indicate recovery likelihood. Our multidimensional and multimodal EHPN tool enhances clinicians' decision-making, offering a transformative translational infrastructure. The EHPN model stands as a significant contribution to precision medicine, offering a paradigm shift in the management of temporomandibular disorders and potentially influencing broader applications in personalized healthcare.

Heterogeneity in intrahepatic macrophage populations and druggable target expression in patients with steatotic liver disease-related fibrosis.

Background & Aims: Clinical trials for reducing fibrosis in steatotic liver disease (SLD) have targeted macrophages with variable results. We evaluated intrahepatic macrophages in patients with SLD to determine if activity scores or fibrosis stages influenced phenotypes and expression of druggable targets, such as CCR2 and galectin-3. Methods: Liver biopsies from controls or patients with minimal or advanced fibrosis were subject to gene expression analysis using nCounter to determine differences in macrophage-related genes (n = 30). To investigate variability among individual patients, we compared additional biopsies by staining them with multiplex antibody panels (CD68/CD14/CD16/CD163/Mac387 or CD163/CCR2/galectin-3/Mac387) followed by spectral imaging and spatial analysis. Algorithms that utilize deep learning/artificial intelligence were applied to create cell cluster plots, phenotype profile maps, and to determine levels of protein expression (n = 34). Results: Several genes known to be pro-fibrotic (e.g. CD206, TREM2, CD163, and ARG1) showed either no significant differences or significantly decreased with advanced fibrosis. Although marked variability in gene expression was observed in individual patients with cirrhosis, several druggable targets and their ligands (e.g. CCR2, CCR5, CCL2, CCL5, and LGALS3) were significantly increased when compared to patients with minimal fibrosis. Antibody panels identified populations that were significantly increased (e.g. Mac387+), decreased (e.g. CD14+), or enriched (e.g. interactions of Mac387) in patients that had progression of disease or advanced fibrosis. Despite heterogeneity in patients with SLD, several macrophage phenotypes and druggable targets showed a positive correlation with increasing NAFLD activity scores and fibrosis stages. Conclusions: Patients with SLD have markedly varied macrophage- and druggable target-related gene and protein expression in their livers. Several patients had relatively high expression, while others were like controls. Overall, patients with more advanced disease had significantly higher expression of CCR2 and galectin-3 at both the gene and protein levels. Impact and implications: Appreciating individual differences within the hepatic microenvironment of patients with SLD may be paramount to developing effective treatments. These results may explain why such a small percentage of patients have responded to macrophage-targeting therapies and provide additional support for precision medicine-guided treatment of chronic liver diseases.

Sirtuin 2 inhibition modulates chromatin landscapes genome-wide to induce senescence in ATRX-deficient malignant glioma.

BACKGROUND: Functional inactivation of ATRX characterizes large subgroups of malignant gliomas in adults and children. ATRX deficiency in glioma induces widespread chromatin remodeling, driving transcriptional shifts and oncogenic phenotypes. Effective strategies to therapeutically target these broad epigenomic sequelae remain undeveloped. METHODS: We utilized integrated multiomics and the Broad Institute Connectivity Map (CMAP) to identify drug candidates that could potentially revert ATRX-deficient transcriptional changes. We then employed disease-relevant experimental models to evaluate functional phenotypes, coupling these studies with epigenomic profiling to elucidate molecular mechanism(s). RESULTS: CMAP analysis and transcriptional/epigenomic profiling implicated the Class III HDAC Sirtuin2 (SIRT2) as a central mediator of ATRX-deficient cellular phenotypes and a driver of unfavorable prognosis in ATRX-deficient glioma. SIRT2 inhibitors reverted Atrx-deficient transcriptional signatures in murine neuroepithelial progenitor cells (mNPCs), impaired cell migration in Atrx/ATRX-deficient mNPCs and human glioma stem cells (GSCs), and increased expression of senescence markers in glioma models. Moreover, SIRT2 inhibition impaired growth and increased senescence in ATRX-deficient GSCs in vivo. These effects were accompanied by genome-wide shifts in enhancer-associated H3K27ac and H4K16ac marks, with the latter in particular demonstrating compelling transcriptional links to SIRT2-dependent phenotypic reversals. Motif analysis of these data identified the transcription factor KLF16 as a mediator of phenotype reversal in Atrx-deficient cells upon SIRT2 inhibition. CONCLUSIONS: Our findings indicate that SIRT2 inhibition selectively targets ATRX-deficient gliomas for senescence through global chromatin remodeling, while demonstrating more broadly a viable approach to combat complex epigenetic rewiring in cancer.

Low-parameter supervised learning models can discriminate pseudoprogression and true progression in non-perfusion-based MRI.

Discrimination of pseudoprogression and true progression is one challenge to the treatment of malignant gliomas. Although some techniques such as circulating tumor DNA (ctDNA) and perfusion-weighted imaging (PWI) demonstrate promise in distinguishing PsP from TP, we investigate robust and replicable alternatives to distinguish the two entities based on more widely-available media. In this study, we use low-parametric supervised learning techniques based on geographically-weighted regression (GWR) to investigate the utility of both conventional MRI sequences as well as a diffusion-weighted sequence (apparent diffusion coefficient or ADC) in the discrimination of PsP v TP. GWR applied to MRI modality pairs is a unique approach for small sample sizes and is a novel approach in this arena. From our analysis, all modality pairs involving ADC maps, and those involving post-contrast T1 regressed onto T2 showed potential promise. This work on ADC data adds to a growing body of research suggesting the predictive benefits of ADC, and suggests further research on the relationships between post-contrast T1 and T2.Clinical relevance- Few studies have investigated predictive potential of conventional MRI and ADC to detect PsP. Our study adds to the growing research on the topic and presents a new perspective to research by exploiting the utility of ADC in PsP v TP distinction. In addition, our GWR methodology for low-parametric supervised computer vision models demonstrates a unique approach for image processing of small sample sizes.

Unique characteristics of the tumor immune microenvironment in young patients with metastatic colorectal cancer.

Introduction: Metastatic colorectal cancer (mCRC) remains a common and highly morbid disease, with a recent increase in incidence in patients younger than 50 years. There is an acute need to better understand differences in tumor biology, molecular characteristics, and other age-related differences in the tumor microenvironment (TME). Methods: 111 patients undergoing curative-intent resection of colorectal liver metastases were stratified by age into those <50 years or >65 years old, and tumors were subjected to multiplex fluorescent immunohistochemistry (mfIHC) to characterize immune infiltration and cellular engagement. Results: There was no difference in infiltration or proportion of immune cells based upon age, but the younger cohort had a higher proportion of programmed death-ligand 1 (PD-L1)+ expressing antigen presenting cells (APCs) and demonstrated decreased intercellular distance and increased cellular engagement between tumor cells (TCs) and cytotoxic T lymphocytes (CTLs), and between TCs and APCs. These trends were independent of microsatellite instability in tumors. Discussion: Age-related differences in PD-L1 expression and cellular engagement in the tumor microenvironment of patients with mCRC, findings which were unrelated to microsatellite status, suggest a more active immune microenvironment in younger patients that may offer an opportunity for therapeutic intervention with immune based therapy.

DIMPLE: An R package to quantify, visualize, and model spatial cellular interactions from multiplex imaging with distance matrices.

A major challenge in the spatial analysis of multiplex imaging (MI) data is choosing how to measure cellular spatial interactions and how to relate them to patient outcomes. Existing methods to quantify cell-cell interactions do not scale to the rapidly evolving technical landscape, where both the number of unique cell types and the number of images in a dataset may be large. We propose a scalable analytical framework and accompanying R package, DIMPLE, to quantify, visualize, and model cell-cell interactions in the TME. By applying DIMPLE to publicly available MI data, we uncover statistically significant associations between image-level measures of cell-cell interactions and patient-level covariates.

Investigating Ecological Interactions in the Tumor Microenvironment using Joint Species Distribution Models for Point Patterns.

The tumor microenvironment (TME) is a complex and dynamic ecosystem that involves interactions between different cell types, such as cancer cells, immune cells, and stromal cells. These interactions can promote or inhibit tumor growth and affect response to therapy. Multitype Gibbs point process (MGPP) models are statistical models used to study the spatial distribution and interaction of different types of objects, such as the distribution of cell types in a tissue sample. Such models are potentially useful for investigating the spatial relationships between different cell types in the tumor microenvironment, but so far studies of the TME using cell-resolution imaging have been largely limited to spatial descriptive statistics. However, MGPP models have many advantages over descriptive statistics, such as uncertainty quantification, incorporation of multiple covariates and the ability to make predictions. In this paper, we describe and apply a previously developed MGPP method, the saturated pairwise interaction Gibbs point process model, to a publicly available multiplexed imaging dataset obtained from colorectal cancer patients. Importantly, we show how these methods can be used as joint species distribution models (JSDMs) to precisely frame and answer many relevant questions related to the ecology of the tumor microenvironment.

KRT17High/CXCL8+ tumor cells display both classical and basal features and regulate myeloid infiltration in the pancreatic cancer microenvironment.

PURPOSE: Pancreatic ductal adenocarcinoma (PDAC) is generally divided in two subtypes, classical and basal. Recently, single cell RNA sequencing has uncovered the co-existence of basal and classical cancer cells, as well as intermediary cancer cells, in individual tumors. The latter remains poorly understood; here, we sought to characterize them using a multimodal approach. EXPERIMENTAL DESIGN: We performed subtyping on a single cell RNA sequencing dataset containing 18 human PDAC samples to identify multiple intermediary subtypes. We generated patient-derived PDAC organoids for functional studies. We compared single cell profiling of matched blood and tumor samples to measure changes in the local and systemic immune microenvironment. We then leveraged longitudinally patient-matched blood to follow individual patients over the course of chemotherapy. RESULTS: We identified a cluster of KRT17-high intermediary cancer cells that uniquely express high levels of CXCL8 and other cytokines. The proportion of KRT17High/CXCL8+ cells in patient tumors correlated with intra-tumoral myeloid abundance, and, interestingly, high pro-tumor peripheral blood granulocytes, implicating local and systemic roles. Patient-derived organoids maintained KRT17High/CXCL8+cells and induced myeloid cell migration in an CXCL8-dependent manner. In our longitudinal studies, plasma CXCL8 decreased following chemotherapy in responsive patients, while CXCL8 persistence portended worse prognosis. CONCLUSIONS: Through single cell analysis of PDAC samples we identified KRT17High/CXCL8+ cancer cells as an intermediary subtype, marked by a unique cytokine profile and capable of influencing myeloid cells in the tumor microenvironment and systemically. The abundance of this cell population should be considered for patient stratification in precision immunotherapy.

Tumor radiogenomics in gliomas with Bayesian layered variable selection.

We propose a statistical framework to analyze radiological magnetic resonance imaging (MRI) and genomic data to identify the underlying radiogenomic associations in lower grade gliomas (LGG). We devise a novel imaging phenotype by dividing the tumor region into concentric spherical layers that mimics the tumor evolution process. MRI data within each layer is represented by voxel-intensity-based probability density functions which capture the complete information about tumor heterogeneity. Under a Riemannian-geometric framework these densities are mapped to a vector of principal component scores which act as imaging phenotypes. Subsequently, we build Bayesian variable selection models for each layer with the imaging phenotypes as the response and the genomic markers as predictors. Our novel hierarchical prior formulation incorporates the interior-to-exterior structure of the layers, and the correlation between the genomic markers. We employ a computationally-efficient Expectation-Maximization-based strategy for estimation. Simulation studies demonstrate the superior performance of our approach compared to other approaches. With a focus on the cancer driver genes in LGG, we discuss some biologically relevant findings. Genes implicated with survival and oncogenesis are identified as being associated with the spherical layers, which could potentially serve as early-stage diagnostic markers for disease monitoring, prior to routine invasive approaches. We provide a R package that can be used to deploy our framework to identify radiogenomic associations.

Association of graph-based spatial features with overall survival status of glioblastoma patients.

Glioblastoma is the most common malignant brain tumor with less than 15 months median survival. To aid prognosis, there is a need for decision tools that leverage diagnostic modalities such as MRI to inform survival. In this study, we examine higher-order spatial proximity characteristics from habitats and propose two graph-based methods (minimum spanning tree and graph run-length matrix) to characterize spatial heterogeneity over tumor MRI-derived intensity habitats and assess their relationships with overall survival as well as the immune signature status of patients with glioblastoma. A data set of 74 patients was studied based on the availability of post-contrast T1-weighted and T2-weighted fluid attenuated inversion recovery (FLAIR) image data in The Cancer Image Archive (TCIA). We assessed the predictive value of MST- and GRLM-derived features from 2D images for prediction of 12-month survival status and immune signature status of patients with glioblastoma via a receiver operating characteristic curve analysis. For 12-month survival prediction using MST-based method, sensitivity and specificity were 0.82 and 0.79 respectively. For GRLM-based method, sensitivity and specificity were 0.73 and 0.77 respectively. For immune status, sensitivity and specificity were 0.91 and 0.69, respectively, for the GRLM-based method with an immune effector. Our results show that the proposed MST- and GRLM-derived features are predictive of 12-month survival status as well as the immune signature status of patients with glioblastoma. To our knowledge, this is the first application of MST- and GRLM-based proximity analyses for the study of radiologically-defined tumor habitats in glioblastoma.

Clustering-based spatial analysis (CluSA) framework through graph neural network for chronic kidney disease prediction using histopathology images.

Machine learning applied to digital pathology has been increasingly used to assess kidney function and diagnose the underlying cause of chronic kidney disease (CKD). We developed a novel computational framework, clustering-based spatial analysis (CluSA), that leverages unsupervised learning to learn spatial relationships between local visual patterns in kidney tissue. This framework minimizes the need for time-consuming and impractical expert annotations. 107,471 histopathology images obtained from 172 biopsy cores were used in the clustering and in the deep learning model. To incorporate spatial information over the clustered image patterns on the biopsy sample, we spatially encoded clustered patterns with colors and performed spatial analysis through graph neural network. A random forest classifier with various groups of features were used to predict CKD. For predicting eGFR at the biopsy, we achieved a sensitivity of 0.97, specificity of 0.90, and accuracy of 0.95. AUC was 0.96. For predicting eGFR changes in one-year, we achieved a sensitivity of 0.83, specificity of 0.85, and accuracy of 0.84. AUC was 0.85. This study presents the first spatial analysis based on unsupervised machine learning algorithms. Without expert annotation, CluSA framework can not only accurately classify and predict the degree of kidney function at the biopsy and in one year, but also identify novel predictors of kidney function and renal prognosis.

LSD1 promotes prostate cancer reprogramming by repressing TP53 signaling independently of its demethylase function.

Lysine-specific demethylase 1 (LSD1) is a histone demethylase that promotes stemness and cell survival in cancers such as prostate cancer. Most prostate malignancies are adenocarcinomas with luminal differentiation. However, some tumors undergo cellular reprogramming to a more lethal subset termed neuroendocrine prostate cancer (NEPC) with neuronal differentiation. The frequency of NEPC is increasing since the widespread use of potent androgen receptor signaling inhibitors. Currently, there are no effective treatments for NEPC. We previously determined that LSD1 promotes survival of prostate adenocarcinoma tumors. However, the role of LSD1 in NEPC is unknown. Here, we determined that LSD1 is highly upregulated in NEPC versus adenocarcinoma patient tumors. LSD1 suppression with RNAi or allosteric LSD1 inhibitors - but not catalytic inhibitors - reduced NEPC cell survival. RNA-Seq analysis revealed that LSD1 represses pathways linked to luminal differentiation, and TP53 was the top reactivated pathway. We confirmed that LSD1 suppressed the TP53 pathway by reducing TP53 occupancy at target genes while LSD1's catalytic function was dispensable for this effect. Mechanistically, LSD1 inhibition disrupted LSD1-HDAC interactions, increasing histone acetylation at TP53 targets. Finally, LSD1 inhibition suppressed NEPC tumor growth in vivo. These findings suggest that blocking LSD1's noncatalytic function may be a promising treatment strategy for NEPC.

DIMPLE: AN R PACKAGE TO QUANTIFY, VISUALIZE, AND MODEL SPATIAL CELLULAR INTERACTIONS FROM MULTIPLEX IMAGING WITH DISTANCE MATRICES.

The tumor microenvironment (TME) is a complex ecosystem containing tumor cells, other surrounding cells, blood vessels, and extracellular matrix. Recent advances in multiplexed imaging technologies allow researchers to map several cellular markers in the TME at the single cell level while preserving their spatial locations. Evidence is mounting that cellular interactions in the TME can promote or inhibit tumor development and contribute to drug resistance. Current statistical approaches to quantify cell-cell interactions do not readily scale to the outputs of new imaging technologies which can distinguish many unique cell phenotypes in one image. We propose a scalable analytical framework and accompanying R package, DIMPLE, to quantify, visualize, and model cell-cell interactions in the TME. In application of DIMPLE to publicly available MI data, we uncover statistically significant associations between image-level measures of cell-cell interactions and patient-level covariates.

Efficient Inference of Spatially-Varying Gaussian Markov Random Fields With Applications in Gene Regulatory Networks.

In this paper, we study the problem of inferring spatially-varying Gaussian Markov random fields (SV-GMRF) where the goal is to learn a network of sparse, context-specific GMRFs representing network relationships between genes. An important application of SV-GMRFs is in inference of gene regulatory networks from spatially-resolved transcriptomics datasets. The current work on inference of SV-GMRFs are based on the regularized maximum likelihood estimation (MLE) and suffer from overwhelmingly high computational cost due to their highly nonlinear nature. To alleviate this challenge, we propose a simple and efficient optimization problem in lieu of MLE that comes equipped with strong statistical and computational guarantees. Our proposed optimization problem is extremely efficient in practice: we can solve instances of SV-GMRFs with more than 2 million variables in less than 2 minutes. We apply the developed framework to study how gene regulatory networks in Glioblastoma are spatially rewired within tissue, and identify prominent activity of the transcription factor HES4 and ribosomal proteins as characterizing the gene expression network in the tumor peri-vascular niche that is known to harbor treatment resistant stem cells.

SPARTIN: a Bayesian method for the quantification and characterization of cell type interactions in spatial pathology data.

Introduction: The acquisition of high-resolution digital pathology imaging data has sparked the development of methods to extract context-specific features from such complex data. In the context of cancer, this has led to increased exploration of the tumor microenvironment with respect to the presence and spatial composition of immune cells. Spatial statistical modeling of the immune microenvironment may yield insights into the role played by the immune system in the natural development of cancer as well as downstream therapeutic interventions. Methods: In this paper, we present SPatial Analysis of paRtitioned Tumor-Immune imagiNg (SPARTIN), a Bayesian method for the spatial quantification of immune cell infiltration from pathology images. SPARTIN uses Bayesian point processes to characterize a novel measure of local tumor-immune cell interaction, Cell Type Interaction Probability (CTIP). CTIP allows rigorous incorporation of uncertainty and is highly interpretable, both within and across biopsies, and can be used to assess associations with genomic and clinical features. Results: Through simulations, we show SPARTIN can accurately distinguish various patterns of cellular interactions as compared to existing methods. Using SPARTIN, we characterized the local spatial immune cell infiltration within and across 335 melanoma biopsies and evaluated their association with genomic, phenotypic, and clinical outcomes. We found that CTIP was significantly (negatively) associated with deconvolved immune cell prevalence scores including CD8+ T-Cells and Natural Killer cells. Furthermore, average CTIP scores differed significantly across previously established transcriptomic classes and significantly associated with survival outcomes. Discussion: SPARTIN provides a general framework for investigating spatial cellular interactions in high-resolution digital histopathology imaging data and its associations with patient level characteristics. The results of our analysis have potential implications relevant to both treatment and prognosis in the context of Skin Cutaneous Melanoma. The R-package for SPARTIN is available at https://github.com/bayesrx/SPARTIN along with a visualization tool for the images and results at: https://nateosher.github.io/SPARTIN.

Integration of Single-Cell RNA-Sequencing and Network Analysis to Investigate Mechanisms of Drug Resistance.

Innate resistance and therapeutic-driven development of resistance to anticancer drugs is a common complication of cancer therapy. Understanding mechanisms of drug resistance can lead to development of alternative therapies. One strategy is to subject drug-sensitive and drug-resistant variants to single-cell RNA-seq (scRNA-seq) and to subject the scRNA-seq data to network analysis to identify pathways associated with drug resistance. This protocol describes a computational analysis pipeline to study drug resistance by subjecting scRNA-seq expression data to Passing Attributes between Networks for Data Assimilation (PANDA), an integrative network analysis tool that incorporates protein-protein interactions (PPI) and transcription factor (TF)-binding motifs.

Increased Nerve Density Adversely Affects Outcome in Oral Cancer.

PURPOSE: Perineural invasion (PNI) in oral cavity squamous cell carcinoma (OSCC) is associated with poor survival. Because of the risk of recurrence, patients with PNI receive additional therapies after surgical resection. Mechanistic studies have shown that nerves in the tumor microenvironment promote aggressive tumor growth. Therefore, in this study, we evaluated whether nerve density (ND) influences tumor growth and patient survival. Moreover, we assessed the reliability of artificial intelligence (AI) in evaluating ND. EXPERIMENTAL DESIGN: To investigate whether increased ND in OSCC influences patient outcome, we performed survival analyses. Tissue sections of OSCC from 142 patients were stained with hematoxylin and eosin and IHC stains to detect nerves and tumor. ND within the tumor bulk and in the adjacent 2 mm was quantified; normalized ND (NND; bulk ND/adjacent ND) was calculated. The impact of ND on tumor growth was evaluated in chick chorioallantoic-dorsal root ganglia (CAM-DRG) and murine surgical denervation models. Cancer cells were grafted and tumor size quantified. Automated nerve detection, applying the Halo AI platform, was compared with manual assessment. RESULTS: Disease-specific survival decreased with higher intratumoral ND and NND in tongue SCC. Moreover, NND was associated with worst pattern-of-invasion and PNI. Increasing the number of DRG, in the CAM-DRG model, increased tumor size. Reduction of ND by denervation in a murine model decreased tumor growth. Automated and manual detection of nerves showed high concordance, with an F1 score of 0.977. CONCLUSIONS: High ND enhances tumor growth, and NND is an important prognostic factor that could influence treatment selection for aggressive OSCC. See related commentary by Hondermarck and Jiang, p. 2342.

Multiplex Immunohistochemistry and Immunofluorescence: A Practical Update for Pathologists.

Our understanding of the biology and management of human disease has undergone a remarkable evolution in recent decades. Improved understanding of the roles of complex immune populations in the tumor microenvironment has advanced our knowledge of antitumor immunity, and immunotherapy has radically improved outcomes for many advanced cancers. Digital pathology has unlocked new possibilities for the assessment and discovery of the tumor microenvironment, such as quantitative and spatial image analysis. Despite these advances, tissue-based evaluations for diagnosis and prognosis continue to rely on traditional practices, such as hematoxylin and eosin staining, supplemented by the assessment of single biomarkers largely using chromogenic immunohistochemistry (IHC). Such approaches are poorly suited to complex quantitative analyses and the simultaneous evaluation of multiple biomarkers. Thus, multiplex staining techniques have significant potential to improve diagnostic practice and immuno-oncology research. The different approaches to achieve multiplexed IHC and immunofluorescence are described in this study. Alternatives to multiplex immunofluorescence/IHC include epitope-based tissue mass spectrometry and digital spatial profiling (DSP), which require specialized platforms not available to most clinical laboratories. Virtual multiplexing, which involves digitally coregistering singleplex IHC stains performed on serial sections, is another alternative to multiplex staining. Regardless of the approach, analysis of multiplexed stains sequentially or simultaneously will benefit from standardized protocols and digital pathology workflows. Although this is a complex and rapidly advancing field, multiplex staining is now technically feasible for most clinical laboratories and may soon be leveraged for routine diagnostic use. This review provides an update on the current state of the art for tissue multiplexing, including the capabilities and limitations of different techniques, with an emphasis on potential relevance to clinical diagnostic practice.

Analysis of Donor Pancreata Defines the Transcriptomic Signature and Microenvironment of Early Neoplastic Lesions.

The adult healthy human pancreas has been poorly studied given the lack of indication to obtain tissue from the pancreas in the absence of disease and rapid postmortem degradation. We obtained pancreata from brain dead donors, thus avoiding any warm ischemia time. The 30 donors were diverse in age and race and had no known pancreas disease. Histopathologic analysis of the samples revealed pancreatic intraepithelial neoplasia (PanIN) lesions in most individuals irrespective of age. Using a combination of multiplex IHC, single-cell RNA sequencing, and spatial transcriptomics, we provide the first-ever characterization of the unique microenvironment of the adult human pancreas and of sporadic PanIN lesions. We compared healthy pancreata to pancreatic cancer and peritumoral tissue and observed distinct transcriptomic signatures in fibroblasts and, to a lesser extent, macrophages. PanIN epithelial cells from healthy pancreata were remarkably transcriptionally similar to cancer cells, suggesting that neoplastic pathways are initiated early in tumorigenesis. SIGNIFICANCE: Precursor lesions to pancreatic cancer are poorly characterized. We analyzed donor pancreata and discovered that precursor lesions are detected at a much higher rate than the incidence of pancreatic cancer, setting the stage for efforts to elucidate the microenvironmental and cell-intrinsic factors that restrain or, conversely, promote malignant progression. See related commentary by Hoffman and Dougan, p. 1288. This article is highlighted in the In This Issue feature, p. 1275.