Proximogram-A multi-omics network-based framework to capture tissue heterogeneity integrating single-cell omics and spatial profiling.

The increasing availability of patient-derived multimodal biological data for various diseases has opened up avenues for finding the optimal methods for jointly leveraging the information extracted in a customizable and scalable manner. Here, we propose the Proximogram, a graph-based representation that provides a joint construct for embedding independently obtained omics and spatial data. To evaluate the representation, we generated proximograms from 2 distinct biological sources, namely, multiplexed immunofluorescence images and single-cell RNA-seq data obtained from patients across two pancreatic diseases that include normal and chronic Pancreatitis (CP) and pancreatic ductal adenocarcinoma (PDAC). The generated proximograms were used as inputs to 2 distinct graph deep-learning models. The improved classification results over simpler spatial-data-based input graphs point to the increased discriminatory power obtained by integrating structural information from single-cell ligand-receptor signaling data and the spatial architecture of cells in each disease class, which can help point to markers of high diagnostic significance.

Inflammation-induced epigenetic imprinting regulates intestinal stem cells.

It remains unknown whether and how intestinal stem cells (ISCs) adapt to inflammatory exposure and whether the adaptation leaves scars that will affect their subsequent regeneration. We investigated the consequences of inflammation on Lgr5+ ISCs in well-defined clinically relevant models of acute gastrointestinal graft-versus-host disease (GI GVHD). Utilizing single-cell transcriptomics, as well as organoid, metabolic, epigenomic, and in vivo models, we found that Lgr5+ ISCs undergo metabolic changes that lead to the accumulation of succinate, which reprograms their epigenome. These changes reduced the ability of ISCs to differentiate and regenerate ex vivo in serial organoid cultures and also in vivo following serial transplantation. Furthermore, ISCs demonstrated a reduced capacity for in vivo regeneration despite resolution of the initial inflammatory exposure, demonstrating the persistence of the maladaptive impact induced by the inflammatory encounter. Thus, inflammation imprints the epigenome of ISCs in a manner that persists and affects their sensitivity to adapt to future stress or challenges.

Multimodal Machine Learning in Image-Based and Clinical Biomedicine: Survey and Prospects.

Machine learning (ML) applications in medical artificial intelligence (AI) systems have shifted from traditional and statistical methods to increasing application of deep learning models. This survey navigates the current landscape of multimodal ML, focusing on its profound impact on medical image analysis and clinical decision support systems. Emphasizing challenges and innovations in addressing multimodal representation, fusion, translation, alignment, and co-learning, the paper explores the transformative potential of multimodal models for clinical predictions. It also highlights the need for principled assessments and practical implementation of such models, bringing attention to the dynamics between decision support systems and healthcare providers and personnel. Despite advancements, challenges such as data biases and the scarcity of "big data" in many biomedical domains persist. We conclude with a discussion on principled innovation and collaborative efforts to further the mission of seamless integration of multimodal ML models into biomedical practice.

Complement C3d enables protective immunity capable of distinguishing spontaneously transformed from non-transformed cells.

Immune-surveillance depends in part on the recognition of peptide variants by T cell antigen receptors. Given that both normal B cells and malignant B cells accumulate mutations we chose a murine model of multiple myeloma to test conditions to induce cell-mediated immunity targeting malignant plasma cell (PC) clones but sparing of normal PCs. Revealing a novel function for intracellular C3d, we discovered that C3d engaged T cell responses against malignant plasma cells in the bone marrow of mice that had developed multiple myeloma spontaneously. Our results show that C3d internalized by cells augments immune surveillance by several mechanisms. In one, C3d induces a master transcription regulator, E2f1, to increase the expression of long non-coding (lnc) RNAs, to generate peptides for MHC-I presentation and increase MHC-I expression. In another, C3d increases expression of RNAs encoding ribosomal proteins linked to processing of defective ribosomal products (DRiPs) that arise from non-canonical translation and known to promote immunosurveillance. Cancer cells are uniquely susceptible to increased expression and presentation of mutant peptides given the extent of protein misfolding and accumulation of somatic mutations. Accordingly, although C3d can be internalized by any cell, C3d preferentially targets malignant clones by evoking specific T cell mediated immunity (CMI) and sparing most non-transformed polyclonal B cells and plasma cells with lower mutation loads. Malignant plasma cell deletion was blocked by cyclosporin or by CD8 depletion confirming that endogenous T cells mediated malignant clone clearance. Besides the potential for therapeutic application our results highlight how intracellular C3d modifies cellular metabolism to augment immune surveillance. One Sentence Summary: We show that intracellular soluble fragment 3d of complement (C3d) induces regression of spontaneous multiple myeloma in mice reducing tumor burden by 10 fold, after 8 weeks. C3d enables cell-mediated immunity to target multiple myeloma clones sparing non-transformed polyclonal B cells and plasma cells with lower mutation loads. We show that C3d increases the expression of ribosomal subunits associated with the translation of defective ribosomal products (DRiPs). C3d also decreases expression of protein arginine methyl transferase (PRMT) 5 which in turn relieves E2f1 repression increasing the expression of Lnc RNAs and derived peptides that evoke anti-tumor cellular immunity. The approach increases MHC-I expression by tumor cells and generates a CMI response that overcomes tumor immune-evasion strategies. Significance: Tumors are immunogenic in part because of somatic mutations that originate novel peptides that once presented on MHC engage cell-mediated immunity (CMI). However, in spite of the higher mutation load most tumors evade immunity. We discovered that a component of the complement system (C3d) overcomes tumor immune evasion by augmenting expression of ribosomal proteins and lncRNAs linked to the presentation of novel peptides by tumor cells. C3d induced CMI targets cancer cells sparing non transformed cells uncovering a novel function for complement in immune surveillance.

Oncogenic KRAS-dependent stromal interleukin-33 directs the pancreatic microenvironment to promote tumor growth.

Pancreatic cancer is characterized by an extensive fibroinflammatory microenvironment. During carcinogenesis, normal stromal cells are converted to cytokine-high cancer associated fibroblasts (CAFs). The mechanisms underlying this conversion, including regulation and function of fibroblast-derived cytokines, are poorly understood. Thus, efforts to target CAFs therapeutically have so far failed. Here, we show that signals from epithelial cells expressing oncogenic KRAS -a hallmark pancreatic cancer mutation- activate fibroblast autocrine signaling, which drives expression of the cytokine interleukin-33 (IL-33). Stromal IL-33 expression remains high and dependent on epithelial KRAS throughout carcinogenesis; in turn, environmental stress induces IL-33 secretion. Using compartment-specific IL-33 knockout mice, we observed that lack of stromal IL-33 leads to profound reprogramming of multiple components of the pancreatic tumor microenvironment, including CAFs, myeloid cells and lymphocytes. Notably, loss of stromal IL-33 leads to an increase in CD8+ T cell infiltration and activation, and, ultimately, reduced tumor growth.

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.

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.

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.

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.

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.

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.

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.