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The recent success of checkpoint blockade therapies has established immunotherapy as one of the most promising treatments for melanoma. Nonetheless, a complete curative response following immunotherapy is observed only in a fraction of patients. To identify what factors limit the efficacy of immunotherapies, we established mouse models that cease to respond to immunotherapies once their tumors exceed a certain stage. Analysis of the immune systems of the organisms revealed that the numbers of tumor-infiltrating dendritic cells (TIDC) drastically decreased with time. Further, in contrast to the current paradigm, once melanoma was established, TIDC did not migrate into sentinel lymph nodes. Instead, they underwent local cell death due to excessive phagocytosis of lysosomes. Importantly, TIDC were required to license the cytotoxic activity of tumor CD8+ T cells, and in their absence, T cells did not lyse melanoma cells. Our results offer a paradigm shift regarding the role of TIDC and a framework to increase the efficacy of immunotherapies.Significance:This work redefines the role of monocyte-derived dendritic cells in melanoma and provides a novel strategy to increase the efficacy of T-cell–based immunotherapies in nonresponding individuals.

Proteins associated with myeloid dendritic cells positively correlate with improved survival of melanoma patients

Tumor-infiltrating dendritic cells promote, in a contact-dependant manner, secretion of lytic granules by CD8+ T cells.

Melanoma tumor cells secrete lysosomes which are taken up by dendritic cells

Cell numbers of multiple immune cell substes, and mainly dendritic cells, are decreased over time in melanoma tumors.

Tumor antigens are drained into sentinel lymph nodes independently of dendritic cell migration.

Higher doses of immunmotherapy do not induce tumor regression when administrated to large melanoma lesions.

Alterations in DNA methylation are one of the earliest, most common signatures of cancer development, making them ideal biomarkers for early detection. Methylation profiling of plasma cell-free DNA (cfDNA) has significant potential to expand the use of liquid biopsies to cancer screening. The EpiCheck platform combines methylation-sensitive restriction endonuclease (MSRE) digestion with whole genome sequencing to comprehensively map genome-wide DNA methylation changes with high fidelity. In this proof-of-concept study, MSRE-NGS was used to interrogate a liquid biopsy atlas focused on early-stage lung cancers to i) identify informative NGS methylation biomarkers in plasma, and ii) develop a machine learning method to discriminate between cancer and high-risk, non-cancer controls. The EpiCheck lung cancer atlas was constructed using biospecimens from academic (UBC, Vanderbilt, Cleveland Clinic) and commercial biobanks, and from a prospective multi-center study (NCT04968548). Plasma samples were collected from 93 high-risk primary lung cancer patients (62% stage I) and 90 high-risk individuals without cancer. Extracted DNA was digested with methylation-sensitive endonucleases and sequenced at an average depth of 600x. Methylation levels of ~6 million genomic loci were rank ordered using Student's t-test. Gene set enrichment analysis (GSEA) was performed on the top ranking 1000 differentially methylated loci. A logistic regression classifier with Lasso regularization was trained on 100,000 loci, and performance was examined by mean AUC using 5-fold cross-validation. A total of 23,130 loci exhibited significant differential methylation patterns between cancers and controls (p Citation Format: Dvir Netanely, Stephen Lam, Anna McGuire, Stephen Deppen, Eric Grogan, Fabien Maldonado, Michael Gieske, Joseph Seaman, Kimberly Rieger-Christ, Satish Kalanjeri, Luis Herrera, Nichole Tanner, Garrett B. Sherwood, Orna Savin, Shacade Danan, Sarah Zaouch, Nimrod Axelrad, Revital Knirsh, Ofir Shliefer, Keren Manor, Radha Duttagupta, Aharona Shuali, Peter J. Mazzone, Gerard A. Silvestri, Catherine A. Schnabel, Danny Frumkin, Adam Wasserstrom. Machine-learning-based epigenetic detection of early-stage lung cancers using the EpiCheck liquid biopsy platform. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5434.

Cutaneous melanoma tumors are heterogeneous and show diverse responses to treatment. Identification of robust molecular biomarkers for classifying melanoma tumors into clinically distinct and homogenous subtypes is crucial for improving the diagnosis and treatment of the disease. In this study, we present a classification of melanoma tumors into four subtypes with different survival profiles based on three distinct gene expression signatures: keratin, immune, and melanogenesis. The melanogenesis expression pattern includes several genes that are characteristic of the melanosome organelle and correlates with worse survival, suggesting the involvement of melanosomes in melanoma aggression. We experimentally validated the secretion of melanosomes into surrounding tissues by melanoma tumors, which potentially affects the lethality of metastasis. We propose a simple molecular decision tree classifier for predicting a tumor’s subtype based on representative genes from the three identified signatures. Key predictor genes were experimentally validated on melanoma samples taken from patients with varying survival outcomes. Our three-pattern approach for classifying melanoma tumors can contribute to advancing the understanding of melanoma variability and promote accurate diagnosis, prognostication, and treatment.

The recent success of checkpoint blockade therapies has established immunotherapy as one of the most promising treatments for melanoma. Nonetheless, a complete curative response following immunotherapy is observed only in a fraction of patients. To identify what factors limit the efficacy of immunotherapies, we established mouse models that cease to respond to immunotherapies once their tumors exceed a certain stage. Analysis of the immune systems of the organisms revealed that the numbers of tumor-infiltrating dendritic cells (TIDC) drastically decreased with time. Further, in contrast to the current paradigm, once melanoma was established, TIDC did not migrate into sentinel lymph nodes. Instead, they underwent local cell death due to excessive phagocytosis of lysosomes. Importantly, TIDC were required to license the cytotoxic activity of tumor CD8+ T cells, and in their absence, T cells did not lyse melanoma cells. Our results offer a paradigm shift regarding the role of TIDC and a framework to increase the efficacy of immunotherapies. Significance: This work redefines the role of monocyte-derived dendritic cells in melanoma and provides a novel strategy to increase the efficacy of T-cell–based immunotherapies in nonresponding individuals.

Background Analysis of large genomic datasets along with their accompanying clinical information has shown great promise in cancer research over the last decade. Such datasets typically include thousands of samples, each measured by one or several high-throughput technologies (‘omics’) and annotated with extensive clinical information. While instrumental for fulfilling the promise of personalized medicine, the analysis and visualization of such large datasets is challenging and necessitates programming skills and familiarity with a large array of software tools to be used for the various steps of the analysis. Results We developed PROMO (Profiler of Multi-Omic data), a friendly, fully interactive stand-alone software for analyzing large genomic cancer datasets together with their associated clinical information. The tool provides an array of built-in methods and algorithms for importing, preprocessing, visualizing, clustering, clinical label enrichment testing, and survival analysis that can be performed on a single or multi-omic dataset. The tool can be used for quick exploration and stratification of tumor samples taken from patients into clinically significant molecular subtypes. Identification of prognostic biomarkers and generation of simple subtype classifiers are additional important features. We review PROMO’s main features and demonstrate its analysis capabilities on a breast cancer cohort from TCGA. Conclusions PROMO provides a single integrated solution for swiftly performing a complete analysis of cancer genomic data for subtype discovery and biomarker identification without writing a single line of code, and can, therefore, make the analysis of these data much easier for cancer biologists and biomedical researchers. PROMO is freely available for download at http://acgt.cs.tau.ac.il/promo/.

Breast cancer is a heterogeneous disease comprising several biologically different types, exhibiting diverse responses to treatment. In the past years, gene expression profiling has led to definition of several “intrinsic subtypes” of breast cancer (basal-like, HER2-enriched, luminal-A, luminal-B and normal-like), and microarray based predictors such as PAM50 have been developed. Despite their advantage over traditional histopathological classification, precise identification of breast cancer subtypes, especially within the largest and highly variable luminal-A class, remains a challenge. In this study, we revisited the molecular classification of breast tumors using both expression and methylation data obtained from The Cancer Genome Atlas (TCGA). Unsupervised clustering was applied on 1148 and 679 breast cancer samples using RNA-Seq and DNA methylation data, respectively. Clusters were evaluated using clinical information and by comparison to PAM50 subtypes. Differentially expressed genes and differentially methylated CpGs were tested for enrichment using various annotation sets. Survival analysis was conducted on the identified clusters using the log-rank test and Cox proportional hazards model. The clusters in both expression and methylation datasets had only moderate agreement with PAM50 calls, while our partitioning of the luminal samples had better five-year prognostic value than the luminal-A/luminal-B assignment as called by PAM50. Our analysis partitioned the expression profiles of the luminal-A samples into two biologically distinct subgroups exhibiting differential expression of immune-related genes, with one subgroup carrying significantly higher risk for five-year recurrence. Analysis of the luminal-A samples using methylation data identified a cluster of patients with poorer survival, characterized by distinct hyper-methylation of developmental genes. Cox multivariate survival analysis confirmed the prognostic significance of the two partitions after adjustment for commonly used factors such as age and pathological stage. Modern genomic datasets reveal large heterogeneity among luminal breast tumors. Our analysis of these data provides two prognostic gene sets that dissect and explain tumor variability within the luminal-A subgroup, thus, contributing to the advancement of subtype-specific diagnosis and treatment.

Introduction Modern genomic datasets may include thousands of samples, each measured by several high-throughput technologies and described by extensive clinical information. Analysis and visualisation of such large multi-label multi-omic datasets pose significant challenges not easily met by existing bioinformatic tools. PROMO (Profiler of Multi-Omics data) is an interactive tool, designed to meet these challenges. Material and methods PROMO provides various data exploratory methods, enables applying clustering analysis on both samples and features and utilising various popular useful statistical tests including survival analysis and enrichment analyses of subject clinical parameters. Special multi-omic integrative features include joint multi-omic sample clustering and identification of inter-omic feature correlation. Results and discussions We will describe PROMO’s main capabilities and show how it can be used for analysing TCGA/GDC’s Breast Cancer datasets for tumour subtype detection and biomarker identification, as done for Luminal-A subtypes in Netanely et al. Breast Cancer Research 18:74 (2016). Conclusion PROMO provides researchers with an extensive array of tools for quick analysis of large multi-omic cancer datasets. PROMO is freely available for download at http://acgt.cs.tau.ac.il/promo

Dysfunction of the retinal pigmented epithelium (RPE) results in degeneration of photoreceptors and vision loss and is correlated with common blinding disorders in humans. Although many protein-coding genes are known to be expressed in RPEs and important for their development and maintenance, virtually nothing is known about the in vivo roles of non-protein coding transcripts in RPEs. The expression patterns of microRNAs (miRNAs) have been analyzed in a variety of ocular tissues, and few were implicated to play role in RPE based on studies in cell lines. Herein, through RPE specific conditional mutagenesis of Dicer1 or DGCR8, the importance of miRNA for RPE differentiation was uncovered. Interestingly, miRNAs were found to be dispensable for maintaining the RPE fate and survival, and yet they are essential for acquisition of important RPE properties such as the expression of genes involved in the visual cycle pathway, pigmentation and cell adhesion. Importantly miRNAs of the RPE were found to be required for maturation of the adjacent photoreceptors, specifically for the morphogenesis of the outer segments. The profiles of miRNA and mRNA altered in the Dicer1 deficient RPE point to a key role of miR-204 in regulation of RPE differentiation program in vivo and uncovers the importance of additional novel RPE miRNAs. The study exposes the combined regulatory activity of miRNAs of the RPE, which is required for RPE differentiation and for the development of the adjacent neuroretina.

We have analyzed gene expression data from 3 different kinds of samples: normal human tissues, human cancer cell lines and leukemic cells from lymphoid and myeloid leukemia pediatric patients. We have searched for genes that are over expressed in human cancer and also show specific patterns of tissue-dependent expression in normal tissues. Using the expression data of the normal tissues we identified 4346 genes with a high variability of expression, and clustered these genes according to their relative expression level. Of 91 stable clusters obtained, 24 clusters included genes preferentially expressed either only in hematopoietic tissues or in hematopoietic and 1-2 other tissues; 28 clusters included genes preferentially expressed in various non-hematopoietic tissues such as neuronal, testis, liver, kidney, muscle, lung, pancreas and placenta. Analysis of the expression levels of these 2 groups of genes in the human cancer cell lines and leukemias, identified genes that were highly expressed in cancer cells but not in their normal counterparts, and were thus over expressed in the cancers. The different cancer cell lines and leukemias varied in the number and identity of these over expressed genes. The results indicate that many genes that are over expressed in human cancer cells are specific to a variety of normal tissues, including normal tissues other than those from which the cancer originated. It is suggested that this general property of cancer cells plays a major role in determining the behavior of the cancers, including their metastatic potential., Comment: To appear in PNAS

Identifying active site residues strictly from protein three-dimensional structure is a difficult task, especially for proteins that have few or no homologues. We transformed protein structures into residue interaction graphs (RIGs), where amino acid residues are graph nodes and their interactions with each other are the graph edges. We found that active site, ligand-binding and evolutionary conserved residues, typically have high closeness values. Residues with high closeness values interact directly or by a few intermediates with all other residues of the protein. Combining closeness and surface accessibility identified active site residues in 70% of 178 representative structures. Detailed structural analysis of specific enzymes also located other types of functional residues. These include the substrate binding sites of acetylcholinesterases and subtilisin, and the regions whose structural changes activate MAP kinase and glycogen phosphorylase. Our approach uses single protein structures, and does not rely on sequence conservation, comparison to other similar structures or any prior knowledge. Residue closeness is distinct from various sequence and structure measures and can thus complement them in identifying key protein residues. Closeness integrates the effect of the entire protein on single residues. Such natural structural design may be evolutionary maintained to preserve interaction redundancy and contribute to optimal setting of functional sites.

Dysfunction of the retinal pigmented epithelium (RPE) results in degeneration of photoreceptors and vision loss and is correlated with common blinding disorders in humans. Although many protein-coding genes are known to be expressed in RPE and are important for its development and maintenance, virtually nothing is known about the in vivo roles of non-coding transcripts. The expression patterns of microRNAs (miRNAs) have been analyzed in a variety of ocular tissues, and a few were implicated to play role in RPE based on studies in cell lines. Here, through RPE-specific conditional mutagenesis of Dicer1 or Dgcr8 in mice, the importance of miRNAs for RPE differentiation was uncovered. miRNAs were found to be dispensable for maintaining RPE fate and survival, and yet they are essential for the acquisition of important RPE properties such as the expression of genes involved in the visual cycle pathway, pigmentation and cell adhesion. Importantly, miRNAs of the RPE are required for maturation of adjacent photoreceptors, specifically for the morphogenesis of the outer segments. The alterations in the miRNA and mRNA profiles in the Dicer1-deficient RPE point to a key role of miR-204 in regulation of the RPE differentiation program in vivo and uncover the importance of additional novel RPE miRNAs. This study reveals the combined regulatory activity of miRNAs that is required for RPE differentiation and for the development of the adjacent neuroretina.

One proposed strategy for bone regeneration involves ex vivo tissue engineering, accomplished using bone-forming cells, biodegradable scaffolds, and dynamic culture systems, with the goal of three-dimensional tissue formation. Rotating wall vessel bioreactors generate simulated microgravity conditions ex vivo, which lead to cell aggregation. Human mesenchymal stem cells (hMSCs) have been extensively investigated and shown to possess the potential to differentiate into several cell lineages. The goal of the present study was to evaluate the effect of simulated microgravity on all genes expressed in hMSCs, with the underlying hypothesis that many important pathways are affected during culture within a rotating wall vessel system. Gene expression was analyzed using a whole genome microarray and clustering with the aid of the National Institutes of Health's Database for Annotation, Visualization and Integrated Discovery database and gene ontology analysis. Our analysis showed 882 genes that were downregulated and 505 genes that were upregulated after exposure to simulated microgravity. Gene ontology clustering revealed a wide variety of affected genes with respect to cell compartment, biological process, and signaling pathway clusters. The data sets showed significant decreases in osteogenic and chondrogenic gene expression and an increase in adipogenic gene expression, indicating that ex vivo adipose tissue engineering may benefit from simulated microgravity. This finding was supported by an adipogenic differentiation assay. These data are essential for further understanding of ex vivo tissue engineering using hMSCs.

Aging is often associated with a decline in hippocampus-dependent spatial memory. Here, we show that functional cell-mediated immunity is required for the maintenance of hippocampus-dependent spatial memory. Sudden imposition of immune compromise in young mice caused spatial memory impairment, whereas immune reconstitution reversed memory deficit in immune-deficient mice. Analysis of hippocampal gene expression suggested that immune-dependent spatial memory performance was associated with the expression of insulin-like growth factor (Igf1) and of genes encoding proteins related to presynaptic activity (Syt10, Cplx2). We further showed that memory loss in aged mice could be attributed to age-related attenuation of the immune response and could be reversed by immune system activation. Homeostatic-driven proliferation of lymphocytes, which expands the existing T cell repertoire, restored spatial memory deficits in aged mice. Thus, our results identify a novel function of the immune system in the maintenance of spatial memory and suggest an original approach for arresting or reversing age-associated memory loss.

Using DNA microarray and cluster analysis of expressed genes in a cloned line (M1-t-p53) of myeloid leukemic cells, we have analyzed the expression of genes that are preferentially expressed in different normal tissues. Clustering of 547 highly expressed genes in these leukemic cells showed 38 genes preferentially expressed in normal hematopoietic tissues and 122 other genes preferentially expressed in different normal non-hematopoietic tissues including neuronal tissues, muscle, liver and testis. We have also analyzed the genes whose expression in the leukemic cells changed after activation of wild-type p53 and treatment with the cytokine interleukin 6 (IL-6) or the calcium mobilizer thapsigargin (TG). Out of 620 such genes in the leukemic cells that were differentially expressed in normal tissues, clustering showed 80 genes that were preferentially expressed in hematopoietic tissues and 132 genes in different normal non-hematopietic tissues that also included neuronal tissues, muscle, liver and testis. Activation of p53 and treatment with IL-6 or TG induced different changes in the genes preferentially expressed in these normal tissues. These myeloid leukemic cells thus express genes that are expressed in normal non-hematopoietic tissues, and various treatments can reprogram these cells to induce other such non-hematopoietic genes. The results indicate that these leukemic cells share with normal hematopoietic stem cells the plasticity of differentiation to different cell types. It is suggested that this reprogramming to induce in malignant cells genes that are expressed in different normal tissues may be of clinical value in therapy.

Melanoma is one of the deadliest human cancers, responsible for approximately 80% of skin cancer mortalities. The aggressiveness of melanoma is due to its capacity to proliferate and rapidly invade surrounding tissues, leading to metastases. A recent model suggests melanoma progresses by reversibly switching between proliferation and invasion transcriptional signatures. Recent studies show that cancer cells are more sensitive to microRNA (miRNA) perturbation than are non-cancer cells; however, the roles of miRNAs in melanoma plasticity remain unexplored. Here, we use the gene expression profiles of melanoma and normal melanocytes to characterize the transcription factor-miRNA relationship that modulates the proliferative and invasive programs of melanoma. We identified two sets of miRNAs that likely regulate these programs. Interestingly, one of the miRNAs involved in melanoma invasion is miR-211, a known target of the master regulator microphthalmia-associated transcription factor (MITF). We demonstrate that miR-211 contributes to melanoma adhesion by directly targeting a gene, NUAK1. Inhibition of miR-211 increases NUAK1 expression and decreases melanoma adhesion, whereas upregulation of miR-211 restores adhesion through NUAK1 repression. This study defines the MITF/miR-211 axis that inhibits the invasive program by blocking adhesion. Furthermore, we have identified NUAK1 as a potential target for the treatment of metastatic melanoma.