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Principal Investigator: Benjamin Greenbaum
Organization: SLOAN-KETTERING INST CAN RESEARCH
Fiscal Year: 2024
Award: $617,031
Funding agency: National Cancer Institute
PROJECT SUMMARY
Transcriptional dysregulation in tumors can induce the abundant expression of repetitive elements in
cancerous cells compared to normal tissues, where they are often transcriptionally silent. Such transcripts
have been associated with better outcomes to cancer immunotherapies, as they can modulate the tumor
immune microenvironment and generate an under-quantified source of tumor neoantigens. Therefore, it has
been hypothesized that the aberrant transcription of repeat RNA is both a critical mechanism for initiating the
immune response in the tumor microenvironment and an untapped source of potential therapeutic targets.
Using a set of approaches from statistical physics, our team predicted repetitive element RNA directly
stimulates receptors of the innate immune system, confirmed this hypothesis in a key subset of immune cells,
and showed repeat expression can correlate with response to checkpoint blockade immunotherapies. Repeat
RNA is therefore both a novel biomarker for the innate immune response in cancer and a potential therapeutic
target to modulate tumor immunity.
We will utilize a set of tools, developed by our team, from statistical physics to characterize repeat RNA
recognition by innate immune receptors in silico and their role in tumor-immune co-evolution, both with and
without the application of immunotherapy (Aim 1). Next, we will characterize the spatial context of repeat
RNAs in the tumor immune microenvironment and the co-localization of predicted immunostimulatory RNA with
activation of immune signaling, along with in depth immune-phenotyping of the state of the immune
microenvironment in vivo (Aim 2). Finally, we will perform functional validation of our predictions on human
immune cells to validate mechanisms of recognition and the specific immune subsets responsible for repeat
recognition via a set of in vitro assays (Aim 3). Our goal is to use approaches from statistical physics to
quantify the role of repetitive elements in tumor immunology, their rules of recognition by innate immune
receptors and their part in facilitating cytolytic T cell activity. In doing so we will combine novel RNA detection
technologies to study their spatial distribution and localization in cancers; state of the art immune-phenotyping;
and mathematical models to characterize their direct role in tumor evolution. We hypothesize that our approach
from statistical physics will identify the key structural and sequence features of repeat mediated immune
activation in solid tumors and shed light on their specific consequences for tumor evolution and therapeutic
efficacy.
Terms: <Antigen-Presenting Cells><Antigens><Archives><Assay><Bioassay><Biological Assay><Blood Plasma><Cancer Patient><Cancer cell line><Cancerous><Cancers><Cell Body><Cell-Mediated Lympholytic Cells><Cells><Clinical><Colon Cancer><Colon Carcinoma><Colorectal Cancer><Cytolytic T-Cell><Cytotoxic T Cell><Cytotoxic T-Lymphocytes><DNA Molecular Biology><Dendritic Cells><Detection><Double-Stranded RNA><Epigenetic><Epigenetic Change><Epigenetic Mechanism><Epigenetic Process><Evolution><Family><Gene Transcription><Genetic Transcription><Goals><History><Human><Immune><Immune Cell Activation><Immune Markers><Immune mediated therapy><Immune response><Immune signaling><Immune system><Immunes><Immunochemical Immunologic><Immunohistochemistry><Immunohistochemistry Cell/Tissue><Immunohistochemistry Staining Method><Immunologic><Immunologic Markers><Immunologic Receptors><Immunological><Immunological Receptors><Immunological response><Immunologically><Immunologically Directed Therapy><Immunologics><Immunotherapy><In Situ Hybridization><In Vitro><Individual><Innate Immune Response><Innate Immune System><Length><Link><Machine Learning><Macrophage><Malignant Neoplasms><Malignant Pancreatic Neoplasm><Malignant Tumor><Malignant Tumor of the Prostate><Malignant neoplasm of pancreas><Malignant neoplasm of prostate><Malignant prostatic tumor><Math Models><Measures><Mediating><Methods><Modeling><Modern Man><Molecular Biology><Mφ><Non-Polyadenylated RNA><Normal Tissue><Normal tissue morphology><Oncornaviruses><Oncovirinae><Oncoviruses><Outcome><Pancreas Cancer><Pancreatic Cancer><Pathway interactions><Patients><Pattern><Phenotype><Physics><Plasma><Plasma Serum><Probabilistic Models><Probability Models><Procedures><Production><Prognosis><Property><Prostate CA><Prostate Cancer><Prostate malignancy><Prostatic Cancer><RNA><RNA Expression><RNA Gene Products><RNA Seq><RNA Tumor Viruses><RNA analysis><RNA sequencing><RNAseq><Receptor Protein><Recording of previous events><Repetitive Element><Repetitive Regions><Repetitive Sequence><Reticuloendothelial System, Serum, Plasma><Ribonucleic Acid><Role><Sampling><Solid Neoplasm><Solid Tumor><Source><Spatial Distribution><Statistical Models><Structure><System><Technology><Transcript><Transcription><Treatment Efficacy><Tumor Immunity><Validation><Veiled Cells><Virus><accessory cell><anti-cancer immunotherapy><anti-tumor immunity><anticancer immunotherapy><antitumor immunity><cancer immunity><cancer immunology><cancer immunotherapy><cancer in the colon><cancer microenvironment><check point blockade><checkpoint blockade><cohort><cost><cytokine><design><designing><dsRNA><epigenetically><exosome><experience><fitness><histories><host response><immune activation><immune check point blockade><immune checkpoint blockade><immune microenvironment><immune receptor><immune system response><immune therapeutic approach><immune therapeutic interventions><immune therapeutic regimens><immune therapeutic strategy><immune therapy><immune-based biomarkers><immune-based cancer therapies><immune-based therapies><immune-based treatments><immuno therapy><immunogen><immunological biomarkers><immunological markers><immunoresponse><immunosuppressive microenvironment><immunosuppressive tumor microenvironment><immunotherapy for cancer><immunotherapy of cancer><in silico><in situ Hybridization Genetics><in situ Hybridization Staining Method><in vitro Assay><in vivo><interdisciplinary approach><intervention efficacy><killer T cell><liquid biopsy><machine based learning><malignancy><mathematic model><mathematical model><mathematical modeling><mimicry><multidisciplinary approach><neo-antigen><neo-epitopes><neoantigens><neoepitopes><neoplasm immunology><neoplasm/cancer><new marker><novel><novel biomarker><novel marker><pancreatic malignancy><pathogen><pathway><patient subclass><patient subcluster><patient subgroups><patient subpopulations><patient subsets><patient subtypes><receptor><response><response to therapy><response to treatment><role model><single molecule><social role><statistical linear mixed models><statistical linear models><stem><therapeutic efficacy><therapeutic response><therapeutic target><therapy efficacy><therapy response><tool><transcriptome sequencing><transcriptomic sequencing><transcriptomics><treatment response><treatment responsiveness><tumor><tumor immune microenvironment><tumor immunology><tumor microenvironment><tumor-immune system interactions><uptake><validations>