Methods for suppressing line-1 ORF1 in cancer cells

Abstract

The present disclosure provides a method for suppressing LINE-1 ORF1 expression in a cancer cell. The method comprises administering to the cancer cell an agent that reduces expression of LINE-1 ORF1, wherein the agent is selected from shRNA, siRNA, antisense oligonucleotides, or a CRISPR construct targeting LINE-1 ORF1. The method reduces tumorsphere growth, increases interferon-stimulated gene expression, decreases epithelial-mesenchymal transition, enhances sensitivity to KRAS inhibitors, and alters the tumor microenvironment in cancer cells, particularly pancreatic ductal adenocarcinoma cells. The disclosure also provides compositions comprising the LINE-1 ORF1 suppressing agents for treating cancer.

Description

Atty. Dkt. No.: 29539-0860WO1METHODS FOR SUPPRESSING LINE-1 ORF1 IN CANCER CELLS CROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims the benefit of U.S. Provisional Application Nos.63 / 756,478, filed on February 10, 2025, and 63 / 809,971, filed on May 21, 2025. The contents of these applications are hereby incorporated by reference in their entireties.SEQUENCE LISTING

[0002] This application contains a Sequence Listing that has been submitted electronically as an XML file named “29539-0860 WO 1 ST26 SL.XML.” The XML file, created on February 5, 2026. is 75,898 bytes in size. The material in the XML file is hereby incorporated by reference in its entirety.FIELD OF THE INVENTION

[0003] The present disclosure relates to methods for suppressing LINE-1 ORF1 expression in cancer cells, and more particularly to therapeutic approaches for treating cancer by targeting LINE-1 ORF1 using RNA interference and gene editing techniques.FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0004] This invention was made with government support under Grant Nos.R01CA240924, U01CA228963, and R01CA235412, each awarded by the National Institutes of Health. The government has certain rights in the invention.BACKGROUND

[0005] Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal forms of cancer, with a poor prognosis and limited treatment options. Despite advances in cancer therapy, the 5-year survival rate for PDAC patients remains low. The aggressive nature of PDAC and its resistance to conventional therapies pose significant challenges in developing effective treatments.

[0006] LINE-1 is a retrotransposon that comprises a substantial portion of the human genome. While normally repressed in somatic cells, LINE-1 expression is often dysregulated in various cancers, including PDAC. LINE-1 includes two open reading frames, ORF1 and ORF2, which encode proteins necessary for retrotransposition. The ORF1 protein (ORFlp) isAtty. Dkt. No.: 29539-0860WO1an RNA-binding protein that forms ribonucleoprotein complexes with LINE-1 RNA.Elevated expression of LINE- 1 ORF Ip has been observed in multiple cancer types, suggesting a potential role in tumor progression.

[0007] The development of RNA interference (RNAi) technologies, including short hairpin RNA (shRNA) and small interfering RNA (siRNA), has enabled targeted suppression of gene expression. These tools have proven valuable for investigating gene function and have shown promise as potential therapeutic agents. Additionally, the emergence of CRISPR-Cas9 gene editing technology has opened new avenues for modulating gene expression in cancer cells.

[0008] There is an ongoing need for new approaches to understand and target the molecular mechanisms underlying PDAC progression and therapy resistance. Investigating the role of LINE- 1 ORF1 in cancer biology and exploring strategies to modulate its expression can contribute to the development of novel therapeutic interventions for PDAC and other cancers.SUMMARY

[0009] This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary’ is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

[0010] The role of LINE-1 ORFlp in preventing repeat RNA sensing and activation of the innate immune response was examined. In the context of short hairpin RNA (shRNA)-mediated suppression of ORFlp, there was an observed induction of interferon-stimulated gene expression, which was dependent on RIG-I / MAVS as the primary patern recognition receptor (PRR) signaling effectors responsible for detecting repeat RNAs. ORFlp was shown to interact with the RNA helicase MOV10 in processing bodies (PBs) to facilitate the processing of double-stranded RNA (dsRNA) species. This innate immune response led to cell death in tumorspheres and xenografts. Notably, pancreatic ductal adenocarcinoma (PDAC) cell lines with low ORFlp expression lacked RIG-I or MAVS, effectively rescuing tumor cells from innate immune activation. Additionally, a relationship between ORFlp expression and epithelial-mesenchymal transition (EMT) was demonstrated in cell and mouse models, aligning with findings in human PDAC primary' tumors that correlate with worsened survival. Collectively, these findings highlight converging mechanisms of innate immuneAtty. Dkt. No.: 29539-0860WO1evasion employed by both cancer and viruses, contributing to a more aggressive phenotype in PDAC.

[0011] This study uncovers PD AC-specific mechanisms that dampen immune responses to viral-repeat RNA via LINE-1 ORFlp. The research addresses the challenge of understanding how cancer cells evade innate immune detection, particularly in pancreatic ductal adenocarcinoma (PDAC). By investigating the role of LINE-1 ORFlp, the study reveals a potential solution to this problem. Suppression of ORFlp activates antiviral responses, reducing tumor growth and epithelial-mesenchymal transition (EMT). This finding suggests that targeting ORFlp can provide a therapeutic approach for PDAC treatment. Additionally, the study demonstrates that high ORFlp expression correlates with poor prognosis, highlighting its potential as a therapeutic target for PDAC. This correlation can offer prognostic value and further emphasizes the importance of developing strategies to modulate ORFlp expression in cancer cells.

[0012] According to an aspect of the present disclosure, a method for suppressing LINE-1 ORF 1 expression in a cancer cell is provided. The method includes administering to a cancer cell an agent that reduces expression of LINE-1 ORF1, wherein the agent is selected from shRNA, siRNA, antisense oligonucleotides, or a CRISPR construct targeting LINE-1 ORF1.

[0013] According to another aspect of the present disclosure, a method of treating cancer is provided. The method includes administering to a subject in need thereof an effective amount of an agent that reduces expression of LINE-1 ORF1 in cancer cells, wherein the agent is selected from shRNA, siRNA, antisense oligonucleotides, or a CRISPR construct targeting LINE-1 ORF1.

[0014] In some instances, a method of enhancing cancer cell sensitivity to KRAS inhibitors is provided. The method includes administering to cancer cells an agent that reduces expression of LINE-1 ORF1 in combination with a KRAS inhibitor.

[0015] According to another aspect of the present disclosure, a method of inducing innate immune responses in cancer cells is provided. The method includes administering to cancer cells an agent that reduces expression of LINE- 1 ORF1 or LINE-1 ORFlp, wherein the reduction in LINE-1 ORF1 expression activates RIG-I / MAVS-dependent interferon responses, and / or wherein the suppression of LINE-1 ORFlp activates innate immune responses through dsRNA sensors RIG-I and MAVS.Atty. Dkt. No.: 29539-0860WO1

[0016] In some instances, a method of reducing epithelial-mesenchymal transition in cancer cells is provided. The method includes administering to cancer cells an agent that reduces expression of LINE- 1 ORF1.

[0017] According to another aspect of the present disclosure, a method of identifying cancer cells with increased sensitivity to LINE-1 ORF1 or LINE-1 ORFlp suppression is provided. The method includes detecting expression levels of RIG-I and MAVS in cancer cells, wherein reduced expression of RIG-I or MAVS indicates increased sensitivity to LINE-1 ORF1 suppression or detecting expression levels of pattern recognition receptors (PRRs) in pancreatic ductal adenocarcinoma (PDAC) cell lines, wherein reduced expression of PRRs indicates increased sensitivity to LINE-1 ORFlp suppression.

[0018] In some instances, a method of reducing growth of a tumor is provided. The method includes administering to a tumor an agent that reduces expression of LINE-1 ORFlp, wherein the reduction in LINE-1 ORFlp expression decreases tumorsphere formation and xenograft growth, and promotes an epithelial cell state.

[0019] According to another aspect of the present disclosure, a method of spatially- analyzing RNA methylation status in a biological sample is provided. The method includes contacting the biological sample with a substrate, permeabilizing the biological sample, and detecting methylated RNA or complement thereof using spatial analysis methods.

[0020] According to other aspects of the present disclosure, the method can include one or more of the following features. The spatial analysis methods can comprise in situ sequencing, spatial transcriptomics, or fluorescence in situ hybridization. The biological sample can be a tissue section. Detecting methylated RNA can comprise using antibodies specific to methylated RNA. The method can further comprise imaging the biological sample to obtain spatial information. The substrate can comprise a capture probe array. The capture probe array can comprise capture probes, each capture probe having a capture domain that can hybridize to a target in the biological sample and a spatial barcode that can uniquely identify- a location on the array. The method can further comprise reverse transcribing the methylated RNA to generate cDNA. The spatial analysis methods can provide single-cell resolution of RNA methylation status. The method can further comprise computationally analyzing the spatial distribution of methylated RNA. The biological sample can be derived from a tumor. The method can further comprise correlating the spatial distribution of methylated RNA with histological features of the biological sample.

[0021] According to other aspects of the present disclosure, the cancer cell can be a pancreatic cancer cell. The agent can be an shRNA targeting LINE-1 ORF I. The cancer cellsAtty. Dkt. No.: 29539-0860WO1can be pancreatic ductal adenocarcinoma (PDAC) cells. The agent can reduce tumorsphere growth of the cancer cells. The agent can increase interferon-stimulated gene expression in the cancer cells. The interferon-stimulated gene expression can be dependent on RIG-I / MAVS signaling. The agent can decrease epithelial-mesenchymal transition (EMT) gene expression in the cancer cells. The method can further comprise administering a KRAS inhibitor. The combination of the agent and the KRAS inhibitor can result in a synergistic reduction in cancer cell growth. The agent can reduce tumor growth in vivo. The agent can increase tumor cell apoptosis in vivo. The agent can alter the tumor microenvironment in vivo. Altering the tumor microenvironment can include reducing myofibroblastic cancer-associated fibroblasts. The agent can be administered systemically. The agent can be administered locally to a tumor site. The agent can be encapsulated in a nanoparticle deliver}’ system. The method can further comprise monitoring LINE-1 ORF1 expression levels in the cancer cells. The cancer cells can be from a solid tumor. The cancer cells can be from a hematological malignancy. The method can further comprise administering an immunotherapy agent. The agent can be administered in combination with chemotherapy. The agent can be administered in combination with radiation therapy. The method can further comprise monitoring innate immune responses in the cancer cells. The agent can reduce cancer stem cell properties in the cancer cells. The method can further comprise analyzing gene expression changes in the cancer cells following administration of the agent. The agent can modulate DNA damage response pathways in the cancer cells. The method can further comprise assessing cancer cell invasiveness following administration of the agent. The agent can alter metabolic pathways in the cancer cells. The method can further comprise monitoring cell cycle progression in the cancer cells following administration of the agent. The agent can modulate autophagy in the cancer cells. The method can further comprise assessing angiogenesis following administration of the agent. The agent can alter epigenetic modifications in the cancer cells. The method can further comprise monitoring cytokine production by the cancer cells following administration of the agent. The agent can modulate cellular senescence in the cancer cells. The method can further comprise assessing cancer cell migration following administration of the agent. The agent can alter extracellular matrix composition in the tumor microenvironment. The method can further comprise monitoring T cell infiltration into the tumor following administration of the agent. The agent can modulate cancer cell metabolism. The method can further comprise assessing cancer cell differentiation state following administration of the agent. The agent can alter telomerase activity in the cancer cells. The method can further comprise monitoring circulating tumor DNA levelsAtty. Dkt. No.: 29539-0860WO1follow ing administration of the agent. The agent can modulate cancer cell plasticity. The method can further comprise assessing tumor-associated macrophage polarization following administration of the agent. The agent can alter cancer cell dormancy. The method can further comprise monitoring cancer cell heterogeneity follow ing administration of the agent. The agent can modulate exosome production by the cancer cells. The method can further comprise assessing cancer cell response to hypoxic conditions following administration of the agent. The agent can alter cancer cell interactions with the extracellular matrix. The method can further comprise monitoring natural killer cell activity against the cancer cells following administration of the agent. The agent can modulate cancer cell resistance to apoptosis.

[0022] In some instances, a composition for suppressing LINE-1 ORF1 expression in cancer cells is provided. The composition includes an agent that reduces expression of LINE-1 ORF1, wherein the agent is selected from the group consisting of shRNA, siRNA, antisense oligonucleotides, and CRISPR constructs targeting LINE-1 ORF1.

[0023] In some instances, the composition can include one or more of the following features. The shRNA can be capable of reducing expression of LINE-1 ORF1 protein (ORF Ip) when administered to cancer cells. The shRNA can target a sequence within the open reading frame 1 (ORF1) of LINE-1. The cancer cells can be pancreatic ductal adenocarcinoma (PDAC) cells. The shRNA can be capable of reducing tumorsphere growth of the cancer cells when administered. The shRNA can be capable of increasing interferon-stimulated gene expression in the cancer cells when administered. The interferon-stimulated gene expression can be dependent on RIG-I / MAVS signaling. The shRNA can be capable of decreasing epithelial-mesenchymal transition (EMT) gene expression in the cancer cells when administered. The composition can further comprise a KRAS inhibitor. The combination of the shRNA and the KRAS inhibitor can result in a synergistic reduction in cancer cell growth when administered to cancer cells. The shRNA can be capable of reducing tumor growth in vivo when administered. The shRNA can be capable of increasing tumor cell apoptosis w hen administered in vivo. The shRNA can be capable of altering the tumor microenvironment when administered in vivo, including reducing myofibroblastic cancer-associated fibroblasts. The shRNA can be encapsulated in a nanoparticle delivery system. The composition can further comprise an immunotherapy agent. The shRNA can be capable of reducing cancer stem cell properties in the cancer cells when administered. The shRNA can be capable of modulating DNA damage response pathways in the cancer cells when administered. The shRNA can be capable of altering metabolic pathways in the cancer cells when administered. The shRNA can be capable of modulating autophagy in the cancer cellsAtty. Dkt. No.: 29539-0860WO1when administered. The shRNA can be capable of altering epigenetic modifications in the cancer cells when administered. The shRNA can be capable of modulating cellular senescence in the cancer cells when administered. The shRNA can be capable of altering extracellular matrix composition in the tumor microenvironment when administered in vivo. The shRNA can be capable of modulating cancer cell plasticity when administered.

[0024] All publications, patents, patent applications, and information available on the internet and mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, patent application, or item of information was specifically and individually indicated to be incorporated by reference. To the extent publications, patents, patent applications, and items of information incorporated by reference contradict the disclosure contained in the specification, the specification is intended to supersede and / or take precedence over any such contradictory material.

[0025] In some instances throughout the application, published references are referred to by number. These numbers and complete references are provided at the end of the specification.

[0026] Where values are described in terms of ranges, it should be understood that the description includes the disclosure of all possible sub-ranges within such ranges, as well as specific numerical values that fall within such ranges irrespective of whether a specific numerical value or specific sub-range is expressly stated.

[0027] The term “each,’7when used in reference to a collection of items, is intended to identity' an individual item in the collection but does not necessarily refer to every item in the collection, unless expressly stated otherwise, or unless the context of the usage clearly indicates otherwise.

[0028] Alter, alters, altering, and the like refers to an increase or a decrease in expression of a metabolite, such as protein or RNA.

[0029] Various embodiments of the features of this disclosure are described herein. However, it should be understood that such embodiments are provided merely by way of example, and numerous variations, changes, and substitutions can occur to those skilled in the art without departing from the scope of this disclosure. It should also be understood that various alternatives to the specific embodiments described herein are also within the scope of this disclosure.

[0030] The foregoing general description of the illustrative embodiments and the following detailed description thereof are merely exemplary’ aspects of the teachings of this disclosure and are not restrictive.Atty. Dkt. No.: 29539-0860WO1BRIEF DESCRIPTION OF FIGURES

[0031] Non-limiting and non-exhaustive examples are described with reference to the following figures.

[0032] FIGs. 1A-1K show LINE-1 ORFlp loss results in decreased tumorsphere size in vitro and induced innate immune response. FIG. 1A shows differential gene expression between 2D (adherent) and 3D (suspension condition) in PDAC3 and PDAC6 cell lines. Significant repeat RNAs are labelled. FIG. IB shows viral immune evasion mechanisms; Hide, Mask, Hit (adapted from Zinzula and Tramontane (22)). FIG. 1C shows representative images of IHC with LINE-1 ORFlp antibody. T; tumor gland. S; stroma. Scale bar, 200 pm. The histological score (H-score) was calculated by a 1 x percentage of weak staining) + (2 x percentage of moderate staining) + (3 x percentage of strong staining) within the target region. Normal pancreas (2V=9), Tumor (7V=87), Tumor gland ( V=48), Stroma (JV=48). FIGs. ID- IF show screening of LINE-1 ORFlp expression in 2 NF, 3 CAF, and 19 PDAC cell lines. FIG. IF shows subcellular localization of LINE-1 ORFlp in CAF-1, PDAC3 and PDAC6 cell lines. The number of ORFlp foci was quantified. Statistical significance was determined by one-way ANOVA followed by Tukey’s multiple comparison. ***p<0.005. Scale bar, 20 pm. FIG. 1G-1H show the effect of ORFlp loss in 2D proliferation over 7 days and 3D tumorsphere growth. Statistical significance was assessed by unpaired student's / -test. ***p<0.005. Scale bar, 100 pm. FIG. II shows volcano plot depicting differential gene expression in PDAC3 and PDAC6 shNTC vs. shORFl. FIG. 1J shows gene ontology (MF; molecular function, BP; biological process, CC; cellular component) analysis of differentially expressed genes (DEGs) by ORFlp knockdown. FIG. IK shows 3D tumorsphere grow th in PDAC3 cells upon Nectrostatin-1 (Nec-1) and Z-VAD-FMK (VAD) inhibitor treatment with indicated concentration (10, 25, 50 pM). Statistical significance was determined by one-way ANOVA compared to shORFl with DMSO followed by Dunnett’s multiple comparison. *** ?<0.005.

[0033] FIGs. 2A-2F show LINE-1 ORFlp function in PDAC and other cell lines. FIG.2A shows a HALO classifier showing tissue compartments; tumor gland or stroma and the intensity of LINE- 1 ORFlp. The percentage of ORFlp positive cells in indicated compartments is plotted. FIG. 2B shows shRNA sequences targeting LINE-1 ORFlp.Validation of ORFlp knockdown efficiency in PDAC3 and PDAC6 cells. FIGs.2C-2D show ORFlp knockdown validation and 3D tumorsphere growth following shORFl lentiviral infection in each PDAC cell line. Statistical significance was assessed by unpaired student’sAtty. Dkt. No.: 29539-0860WO1t-test. FIG.2E shows GSEA comparing shNTC and shORFl in PDAC3 and PDAC6 cell lines. FIG.2F shows STRING protein-protein interactions (PPI) of DEGs in both PDAC3 and PDAC6 ORF Ip knockdown cells. Only connected nodes are shown, with an interaction score threshold set at high confidence (0.7).

[0034] FIGs. 3A-3G show titration of Dox concentration to rescue ORFlp expression and evaluation of micronuclei-mediated STING activation. FIG. 3A shows qRT-PCR of LINE-1 ORF1 and ORF2 in ORF1 knockdown PDAC3 and PDAC6 cell lines (3D tumorsphere). FIG. 3B shows relative luciferase activity in PDAC3 and PDAC6 cells grown in 3D tumorsphere condition upon treatment with indicated NRTIs (10 pM of 3TC, 10 pM of d4T, 10 pM of FTC, 10 pM of TDF) for 7 days. Statistical significance was determined by one-way ANOVA followed by Tukey’s multiple comparison. *p<0.05. ***p<0.005, n.s. nonsignificant. FIG.3C shows confirmation of ORFlp expression by Dox treatment in ORF1 knockdown cell lines grown in 2D. FIG. 3D shows ORFlp expression at varying Dox concentration in 3D tumorsphere condition. FIG.3E shows a heatmap showing relative innate immune-related gene expression in ORF1 knockdown cell lines upon Dox treatment for 7 days under 3D tumorsphere condition. FIG. 3F shows a comparison of IFN- and inflammation-related genes between 2D and 3D conditions. FIG.3G shows validation of RIG-I, MDA5, MAVS and STING CRISPR / Cas9 knockout in PDAC3 WT cells.

[0035] FIGs. 4A-4G show LINE-1 ORFlp protects PDAC cells from repeat RNA-induced antiviral response. FIG. 4A shows generation of doxycycline (Dox) inducible ORFlp expression in ORF1 knockdown cell lines. Validation of ORFlp restoration in ORFlp knockdown PDAC3 cells by Dox treatment with 20 ng mF1in 3D tumorsphere condition. Heatmap representing the relative expression of innate immune-related genes in ORFlp rescue cell lines and bar graph depicts average of fold change of these genes. EV; empty vector. FIGs.4B-4C shows dissecting the pathogen recognition receptor (PRR) pathway with CRISPR-Cas9 KO cell lines in shORFl PDAC3 and PDAC6 cells in tumorsphere condition. Heatmap showing the relative gene expression in indicated cell lines.FIG. 4D shows quantification of % cells with micronuclei in shNTC and shORFl PDAC cell lines growing under 2D and 3D conditions. Immunoblots of yH2AX (marker for DNA damage), LINE-1 ORFlp and GAPDH in PDAC cell lines cultured in 2D or 3D tumorsphere conditions. Scale bar, 10 pm. FIG. 4E shows a heatmap displaying the innate immune-related gene expression in PRR KO cells in 3D tumorsphrere condition. FIG. 4F shows dsRNA dot assays in PDAC cell lines. MB, methylene blue staining for loading control. FIG.Atty. Dkt. No.: 29539-0860WO14G shows screening of PRR expression in various PDAC cell lines. Metastatic PDAC cell lines are highlighted (ORFlp-high and ORFlp-low / deficient).

[0036] FIGs. 5A-5F show RIP-seq reveals LINE-1 ORFlp association with repeat RNAs. FIGs. 5A-5B shows schema of RIP-seq. Validation of LINE-1 ORFlp precipitation efficiency by Western blotting and qRT-PCR analysis using ORF1- and ORF2-specific primers. FIG. 5C shows a volcano plot showing locus-specific ORFlp-bound repeat RNAs (DNA transposon, LINE, LTR and SINE). Only ORFlp-bound RNAs are highlighted (Log2FC>l, adjusted -value<0.05). Pie chart showing composition of repeat RNA classes found in ORFlp precipitates in PDAC3, PDAC6 and PDAC8 cell lines. FIG. 5D shows a circos plot summarizing the chromosomal distribution of repeat RNAs. The outermost circle depicts the ideograms of each chromosome. Bar plots represent the BaseMean expression of LINE (top) and SINE (bottom) in PDAC3, PDAC6 and PDAC8 (outer to inner) and overlaps across cell lines. FIG. 5E shows a violin plots represent the distribution of dsRNA force assigned for each ORFlp-bound repeat copy (Log2FC>l, adjusted p- value<0.05). FIG. 5F shows RIP qRT-PCR analysis in PA-TU-8988T and Mia PACA2 cell lines in 3D tumorsphere condition. Statistical significance was assessed by two-way ANOVA followed by Tukey’s multiple comparison.

[0037] FIGs. 6A-6H show ORFlp knockdown sensitizes repeat RNA-induced IFN response. FIG.6A shows counts of HSATII and GAPDH transcripts from total -RNA-seq.FIG. 6B is a box plot showing Log2-transformed trimmed-mean of M-values (TMM) counts of HSATII RNA between input and ORFlp precipitates. FIG. 6C shows qRT-PCR analysis of LINE-1 (ORF1, ORF2), SINE (AluSg, AluSx, AluYh6), ERV / LTR (HERVK) upon HSATII RNA transfection (2D condition). FIG. 6D shows phase contrast image showing cellular toxicity under the HSATII RNA transfection (250 fmol) for 24 hrs. Protein lysates extracted from PDAC3 cell lines were further analyzed to measure caspase-3-dependent apoptosis. Scale bar, 100 pm. FIG. 6E-6F show- IFNB1 expression and innate immunerelated gene expression upon GFP mRNA or in vitro transcribed HSATII RNA transfection (250 fmol) in shNTC and shORFl PDAC3 and PDAC6 cell lines (2D condition). Statistical significance was assessed by two-way ANOVA followed by Tukey's multiple comparison.FIGs. 6G-6H show poly(I:C) immunogenic response upon ORFlp depletion. qRT-PCR of IFNB1 expression following Poly(I:C) transfection at 0.1 and 1 ng ml-1 for 24 hrs (2D condition). Heatmap displaying relative expression of innate immune-related genes.Statistical significance was determined by two-way ANOVA followed by Bonferrom's post-hoc test.Atty. Dkt. No.: 29539-0860WO1

[0038] FIGs. 7A-7J show ORF Ip associates with MOVIO RNA helicase enzyme in PBs. FIG. 7A shows maximum intensity’ projection (MIP) images with z-stack (every 0.5 pm) using confocal microscopy in PDAC6 cell line grown in 3D tumorsphere for 3 days. Signal profiles of LSM14A, LINE-1 ORF Ip, and DAPI. Scale bar, 10 pm. FIG. 7B shows quantification of number and area of LSM14A+PBs per cell from high ORFlp (Q4) and low ORFlp (QI) cells. High- and low-ORFlp-expressing cells are marked. Scale bar, 20 pm. FIG. 7C shows qRT-PCR analysis of INFLAMED gene expression in shNTC and sh SM / 4 / l PDAC cell line. Statistical significance was assessed by two-way ANOVA followed by Bonferroni ’s multiple comparisons. FIG. 7D shows representative image showing cytoplasmic ORFlp foci in s LSM14A knockdoyvn PDAC6 cell lines. FIG. 7E shows identification of MOVIO as an ORFlp-interacting protein in PBs.Immunoprecipitation (IP) analysis with anti-ORFlp antibody in PDAC3 and PDAC6 cell lines grown in 3D tumorsphere condition. MOVlO-ORFlp interaction is highlighted (*). FIG. 7F shows MIP images representing co-localization of MOVIO and ORFlp in the cytoplasm with foci. Scale bar, 10 pm. FIGs. 7G-7H shows analysis of 2D (left; PDAC3, right; PDAC6) and 3D tumorsphere growth in assigned cell line. Statistical significance was determined by one-way ANOVA followed by Tukey’s post- hoc test. Scale bar, 100 pm. FIG. 71 shows a volcano plot showing differential coding gene expression following MO VI 0 knockdow n in PDAC6 cell line. FIG. 7 J show s a Venn diagram of overlapping upregulated genes in both shORFl (Fig. 1G) and shMOVIO knockdown cell lines. Gene ontology analysis of commonly upregulated genes (7V=35).

[0039] FIGs. 8A-8J show ORFlp cellular localization and evaluation of RNA editing protein function. FIG.8A shows the Pearson correlation coefficient between ORFlp intensity and area of LSM14A+ foci. FIG. 8B shows cell viability’ in 3D tumorsphere condition in shNTC and shLSM14A PDAC cell lines. FIG.8C shows immunoprecipitation of LINE-1 ORFlp in PDAC6 cell line cultured in 2D and 3D conditions. Immunoprecipitated proteins were analyzed by immunoblotting with anti-MOVIO and anti-ORFlp antibodies. FIG. 8D shows subcellular fractionation of shNTC and shORFl PDAC cell lines. C; cytoplasm, N; nucleus. GAPDH and Lamin A / C were used as markers for cytoplasm and nucleus, respectively. FIGs. 8E-8F show generation of MOVIO knockdown (#2) and ORF1-MOV10 double knockdown cell lines. Validation of MOVIO and METTL3 knockdoy n efficiency. FIGs. 8G, 8H, and 81 show' generation of METTL3 knockdoyvn cell lines. Cell viability in 3D tumorsphere condition was assessed using CellTiter-Glo assay. Statistical significance was determined by one-way ANOVA followed by Tukey’s post-hoc test.Atty. Dkt. No.: 29539-0860WO1***p<0.005. Scale bar, 100 pm. FIG. 8J shows a Venn diagram of overlapping down-regulated genes in both shORFl (Fig. 1G) and shMOVIO knockdown cell lines. Gene ontology analysis of commonly downregulated genes (N=44).

[0040] FIGs. 9A-9C show EM-related gene expression and response to drugs upon ORF1 knockdown. FIG. 9A shows qRT-PCR analysis with EMT-related genes (QM genes; FN1, ACTA2, SERPINE1, E genes; KRT19, KRT7, MUC1, CDH1) in various PDAC cell lines (PDAC8. PDAC9, PA-TU-8988T, YAPC. MGH937 and MGH1473) grown in 3D tumorspheres for 7 days. FIG. 9B shows growth inhibition dose-response of PDAC cell lines to Pan-RAS-IN-1 for 7 days in 3D tumorsphere condition. FIG. 9C shows a heatmap of repeat RNA expression in PDAC3 cell lines upon KRAS-IN-1 treatment for 7 days.

[0041] FIGs. 10A-10H show knockdown of LINE-1 ORFlp shifts a gain of epithelial phenotype and increases KRAS inhibitor efficacy. FIG. 10A shows qRT-PCR analysis with EMT-related genes (QM genes; FN1, ACTA2, SERPINE1, E genes; KRT19, KRT7, MUC1, CDHL) in PDAC3 and PDAC6 cell lines grown in 3D tumorspheres for 7 days. Statistical significance was determined by unpaired student's r-test. FIG. 10B shows CDH1 expression upon Dox- inducible ORFlp expression in shORFl knockdown PDAC cell lines. FIG. 10C shows growth inhibition dose response of PDAC cell lines to Pan-KRAS-IN-1 for 7 days in 3D tumorsphere condition. FIGs. 10D-10E shows viability upon varying KRAS-IN-1 concentration in shNTC and shORFl PDAC3 (D) and PDAC6 (E) cell lines. FIG. 10F shows a heatmap of Loewe scores (SynergyFinder) for shORFl and Pan-KRAS-IN-1 in PDAC3 (D) and PDAC6 (E) cell lines. Scores>10 were considered synergistic. FIG. 10G shows quantification of single spheroid using Incucyte real-time analysis in PDAC3 (upper) and PDAC6 (lower). Cells were seeded in a ULA round bottom 96-well plate (3,000 cells / well) and allowed to form spheroids for 1 day. Spheroids were subsequently treated with DMSO or Pan-RAS-IN-1 for 9 days. Statistical significance was determined by two-way ANOVA followed by Tukey’s post-hoc test. FIG. 10H shows qRT-PCR analysis of IFN-related genes upon 10 nM of Pan-KRAS-IN-1 in PDAC3 shNTC and shORFl knockdown cell lines. Statistical significance was determined by one-way ANOVA followed by Tukey 's post-hoc test.

[0042] FIGs. 11A-11D show that ORF1 knockdown in vivo suppresses tumor progression by altering the TME. FIG. 11A shows representative images of LINE-1 ORFlp IHC and quantification of protein intensity (strong, moderate, and w eak expression).Percentage of LINE-1 ORFlp (strong and weak) intensity across shNTC and shORFl mouse xenografts. Scale bar, 2 mm, 60 pm (zoomed). FIG. 11B shows the average number ofAtty. Dkt. No.: 29539-0860WO1Epi+QM-, Epi+QM+, Epi-QM+ cells in tumor xenografts. FIG. 11C shows the whole images of tumor xenografts for EMT quantification. FIG. 11D shows the total area and H-score of INFLAMED+, Acta2+, and 116+ were analyzed using HALO imaging software. Statistical significance was determined by the Mann- Whitney test.

[0043] FIGs. 12A-12I show ORF1 knockdown in vivo suppresses tumor growth and alters the TME. FIG. 12A shows a schematic diagram illustrating the experimental process.FIG. 12B shows tumor growth was monitored by measuring luciferase activity every 7 days from day 0 to day 56 after injection. Statistical significance was assessed using two-way ANOVA with Bonferroni ’s post-hoc test. FIG. 12C shows tumor weights in mice w ere measured 56 days after injection. FIG. 12D shows the percentage of Ki-67 and Bcl-2 positive cancer cells. FIG. 12E shows representative images of mouse xenografts stained by RNA-ISH for Epi and QM markers. Scale bars, 200 pm. Cell phenotypes (Epi QM", Epi+QM+, Epi" QM+) and total number of cells were enumerated within each hexagon tile (50 pm). Finally, cell phenoty pes normalized by the total number of cells in each tile, w ere compared for NTC and ORF I groups using a Wilcoxon rank-sum test. FIG. 12F shows representative H&E staining images from shNTC and shORFl xenografts. FIG. 12G shows representative images of Masson’s trichrome staining for collagen content. Graphs depict the percentage of fibrotic area and the Pearson correlation coefficient betw een ORFlp expression and tissue fibrosis. Scale bar: 2 mm, 200 pm (zoomed image). FIG. 12H shows representative fluorescence images show multiplexed RNA-ISH (pooled probes for human immune-related genes:INFLAMED including CXCL1, OAS1, IFIT3; IL6; ACTA2) combined with PanCK IHC. The number and percentage of PanCK+INFLAMED+, ACTA2+, IL6+, and ACTA2+IL6+ cells were analyzed using HALO imaging software. Statistical significance was determined by the Mann-Whitney test. Scale bar, 100 pm. FIG. 121 shows a schema of LINE- 1 ORFlp function in PDAC shows that cancer-specific expression of LINE-1 ORFlp sequesters viral repeat RNA in PBs, where it masks these repeat RNAs through association with MOV10 helicase to avoid PRR sensing. Suppression of ORFlp leads to RIG-I / MAVS-dependent IFN responses, which in turn trigger caspase-mediated cell apoptosis and enhance the efficacy of KRASi treatment.

[0044] FIGs. 13A-13H show spatial transcriptomic analysis reveals distinct tumor phenotype and TME associated with LINE-1 ORFlp expression. FIG. 13A shows a schema of CosMx Spatial Molecular Imager (SMI) analysis based on LINE-1 ORFlp expression. FIG. 13B shows representative image of ORFlp manual scoring. The bar chart shows the number of FOVs assigned to LINE-1 ORFlp low, high, and NA (missing core). FIG. 13CAtty. Dkt. No.: 29539-0860WO1shows a UMAP plot showing the diversity of cell types in low and high ORF Ip tumor microenvironment. FIG. 13D shows a volcano plot showing differential gene expression of LINE-1 ORF Ip high versus low expression in cancer cells analyzed by CosMx SMI. FIG.13E shows GO:BP and KEGG pathway analysis of genes enriched in high ORFlp cancer cells. FIG. 13F shows neighborhood enrichment analysis between cell clusters in spatial coordinates. Proximity scores were defined as the log odds ratios for the tendency of the cell tvpe in the column to fall in the ten nearest neighbors of the cell type in the row. VSMC; vascular smooth muscle cell, IPN; intrapancreatic neuron. FIG. 13G shows quantification of EMT in human PDAC TMA. Statistical significance was determined using a Wilcoxon ranksum test (ORFlp low N=44, ORFlp high N=37). Scale bar, 100 pm, 50 pm (zoomed image). AvgRnd: Average rounded. FIG. 13H shows scoring of LINE-1 ORFlp expression from 131 patients across 15 TMAs. Scale bar, 300 pm. Kaplan-Meier plot shows the probability of overall survival (cutoff 36 months) in LINE-1 ORFlp low (1+, 2+) versus high (3+, 4+) expressors.

[0045] FIGs. 14A-14D provide analysis of single-cell spatial neighborhoods. FIG. 14A shows cell type composition of LINE-1 ORFlp low and high. IPN: Intrapancreatic neuron. VSMC: vascular smooth muscle cell. FIG. 14B shows a volcano plot showing DEGs between LINE-1 ORFlp high and low expression in CAF analyzed by CosMx SMI. FIGs.14C and 14D shows GO:BP and KEGG pathway analysis of genes enriched in CAFs located near high ORFlp cancer cells.DETAILED DESCRIPTIONIntroduction

[0046] The present disclosure addresses the challenge of effectively targeting and suppressing LINE-1 ORF1 expression in cancer cells, particularly in pancreatic ductal adenocarcinoma (PDAC). By providing methods and compositions for reducing LINE-1 ORF1 expression, this disclosure offers potential solutions for enhancing cancer treatment efficacy and overcoming therapy resistance.

[0047] The compositions and methods disclosed in this application can offer several advantages for cancer diagnosis and treatment. By targeting LINE-1 OREL these approaches can provide a mechanism for modulating cancer cell behavior and tumor grow th. In some aspects, the suppression of LINE- 1 ORF1 can enhance the efficacy of existing cancer therapies, such as KRAS inhibitors, potentially leading to improved treatment outcomes. TheAtty. Dkt. No.: 29539-0860WO1methods described herein can also allow for the identification of cancer cells with increased sensitivity to LINE-1 ORF1 suppression, which could facilitate personalized treatment strategies. Additionally, the spatial analysis techniques disclosed can enable more precise characterization of tumor heterogeneity and microenvironment, potentially improving diagnostic accuracy and treatment planning. These advantages can collectively contribute to the development of more effective and targeted cancer therapies, particularly for aggressive cancers like PDAC that currently have limited treatment options.

[0048] The present disclosure relates to methods and compositions for suppressing LINE-1 ORF1 expression in cancer cells. In some cases, the methods can involve administering to cancer cells an agent that reduces expression of LINE- 1 OREL The agent can be selected from shRNA, siRNA, antisense oligonucleotides, or a CRISPR construct targeting LINE-1 ORF1.

[0049] In some cases, the cancer cells can be pancreatic cancer cells, such as pancreatic ductal adenocarcinoma (PDAC) cells. The methods and compositions can be applicable to cancer cells from solid tumors or hematological malignancies.

[0050] The disclosure also provides methods for spatially analyzing RNA methylation status in biological samples. These methods can involve contacting a biological sample with a substrate, permeabilizing the biological sample, and detecting methylated RNA or complement thereof using spatial analysis methods. In some cases, the biological sample can be a tissue section. The biological sample can be derived from a tumor.

[0051] The methods described herein can reduce expression of LINE-1 ORF1 or LINE-1 ORF Ip in cancer cells. This reduction in expression can lead to various effects on cancer cell behavior and tumor grow th, as described in more detail in subsequent sections.

[0052] The methods described herein can reduce expression of LINE- 1 ORF1 or LINE-1 ORFlp in cancer cells. This reduction in expression can lead to various effects on cancer cell behavior and tumor growth, as described in more detail in subsequent sections. In some aspects, both RNA and protein levels of LINE-1 ORF1 can be detected to assess the efficacy of the suppression methods. The detection of LINE-1 ORF1 RNA can be performed using techniques such as qRT-PCR or RNA sequencing, while LINE-1 ORF1 protein levels can be analyzed through methods including Western blotting or immunofluorescence microscopy.Atty. Dkt. No.: 29539-0860WO1Compositions

[0053] In some cases, the methods for suppressing LINE-1 ORF1 expression in cancer cells can utilize short hairpin RNA (shRNA) inhibitors. The shRNA can be designed to target specific sequences within the LINE-1 ORF1 region.

[0054] In some cases, the shRNA can be encoded in plasmid vectors for delivery to cancer cells. The plasmid vectors can contain promoter sequences to drive expression of the shRNA in target cells.

[0055] The shRNA targeting LINE-1 ORF1 can be encapsulated in a nanoparticle delivery system to enhance cellular uptake and protect the RNA from degradation. The nanoparticle system can comprise lipid or polymer-based nanoparticles. In some cases, the vectors can include selectable markers, such as antibiotic resistance genes, to facilitate the selection of successfully transduced cells. The vectors can also contain fluorescent reporter genes, such as GFP or RFP, allowing for visual confirmation of vector expression and potential cell sorting. In some aspects, the vectors can incorporate inducible promoter systems, such as tetracycline-responsive elements, enabling controlled expression of the shRNA. The vectors can include multiple cloning sites to allow for easy insertion of different shRNA sequences or additional genetic elements. In some cases, the vectors can contain origins of replication compatible with both prokaryotic and eukaryotic systems, facilitating large-scale production in bacteria and subsequent use in mammalian cells. The vectors can also include nuclear localization signals to enhance nuclear entry and expression of the shRNA construct. In some aspects, the vectors can incorporate scaffold / matrix attachment regions (S / MARs) to improve transgene expression and reduce silencing effects. The vector design can include insulator sequences to minimize position effects and enhance consistent expression across different genomic integration sites.

[0056] Design of plasmids is provided in the Examples section.

[0057] In some instances, the genes and targets are those listed in Table 1 below.

[0058] Table 1: shRNA targets and sequences.>Atty. Dkt. No.: 29539-0860WO1

[0059] In some cases, small interfering RNA (siRNA) technology can be employed to suppress LINE-1 ORF1 expression in cancer cells. siRNA molecules are short doublestranded RNA sequences designed to specifically target and degrade complementary messenger RNA (mRNA) through the RNA interference (RNAi) pathway. The use of siRNA can provide a potent and specific method for reducing LINE-1 ORF 1 expression at the post-transcriptional level.

[0060] The design of siRNA sequences targeting LINE-1 ORF1 can involve careful consideration of factors such as GC content, seed region composition, and potential off-target effects. Computational algorithms and empirical testing can be used to identify highly effective siRNA sequences that maximize target knockdown while minimizing unintended effects on other genes. In some aspects, multiple siRNA sequences targeting different regions of the LINE-1 ORF1 transcript can be employed to enhance knockdown efficiency and reduce the likelihood of escape mutations.

[0061] Delivery of siRNA molecules to cancer cells can be achieved through various methods, including lipid-based transfection reagents, electroporation, or nanoparticle formulations. The choice of delivery' method can depend on factors such as the specific cancer cell type, the desired duration of knockdown, and the experimental or therapeuticAtty. Dkt. No.: 29539-0860WO1context. In some cases, chemical modifications to the siRNA molecules, such as 2'-O-methyl or phosphorothioate linkages, can be incorporated to enhance stability’ and reduce immunogenicity.

[0062] The effects of siRNA-mediated LINE-1 ORF1 suppression can be assessed through a combination of molecular and functional assays. These can include quantitative PCR to measure target mRNA levels, Western blotting to assess protein expression, and various cellular assays to evaluate phenotypic changes such as proliferation, migration, or sensitivity to therapeutic agents. In some aspects, genome-wide expression profiling can be performed to comprehensively characterize the downstream effects of LINE- 1 ORF1 knockdown on cancer cell biology.

[0063] For potential therapeutic applications, the development of siRNA-based approaches targeting LINE-1 ORF1 can involve optimization of dosing regimens, evaluation of combination strategies with other cancer therapies, and assessment of potential resistance mechanisms. In vivo studies using animal models can be conducted to evaluate the efficacy and safety of siRNA-mediated LINE-1 ORF1 suppression in more complex biological systems. These studies can provide valuable insights into the potential of siRNA technology as a therapeutic strategy for targeting LINE-1 ORF1 in cancer.

[0064] In some cases, CRISPR-based compositions can be utilized for suppressing LINE-1 ORF1 expression in cancer cells. These compositions can include CRISPR-Cas9 or other CRISPR-associated nucleases engineered to target specific sequences within the LINE-1 ORF1 locus. The CRISPR system can be delivered to cancer cells using viral vectors, such as lentivirus or adeno-associated virus (AAV), or non-viral methods like lipid nanoparticles or electroporation.

[0065] The CRISPR-based compositions can include guide RNAs (gRNAs) designed to direct the Cas nuclease to the LINE-1 ORF1 genomic locus. Multiple gRNAs targeting different regions of the LINE-1 ORF1 sequence can be employed to enhance knockout efficiency and reduce the likelihood of escape mutations. In some aspects, the CRISPR system can be modified to include enhanced specificity variants of Cas nucleases, such as high-fidelity Cas9 or Casl2a, to minimize potential off-target effects.

[0066] Inducible CRISPR systems can be developed to allow for temporal control of LINE-1 ORF1 suppression. These systems can incorporate doxycycline-responsive promoters or other inducible elements to regulate the expression of the Cas nuclease or gRNAs. In some cases, the CRISPR components can be delivered separately, with the Cas nuclease stablyAtty. Dkt. No.: 29539-0860WO1integrated into the genome and the gRNAs delivered transiently, allowing for flexible targeting of different genomic loci.

[0067] The efficacy of CRISPR-mediated LINE-1 ORF1 suppression can be evaluated using a combination of genomic, transcriptomic, and proteomic analyses. These can include targeted sequencing to assess editing efficiency, RNA-seq to examine global transcriptional changes, and Western blotting or immunofluorescence to measure LINE-1 ORF1 protein levels. In some aspects, functional assays can be performed to characterize the phenotypic effects of CRISPR-mediated LINE-1 ORF1 knockout on cancer cell behavior, including proliferation, migration, and tumorsphere formation capacity.

[0068] In some cases, lentiviral vectors can be used to deliver LINE-1 ORF 1 -targeting agents to cancer cells. Lentiviruses are a subclass of retroviruses that can efficiently transduce both dividing and non-dividing cells, making them versatile tools for gene delivery.Lentiviral vectors can be engineered to express shRNA, CRISPR components, or other nucleic acid-based inhibitors targeting LINE-1 OREL These vectors can provide stable, longterm expression of the therapeutic pay load, potentially leading to sustained suppression of LINE-1 ORF1 in target cancer cells.

[0069] Adenoviral vectors represent another option for delivering LINE-1 ORF1 inhibitors. Adenoviruses are non-enveloped viruses with a double-stranded DNA genome that can accommodate large transgene inserts. Adenoviral vectors can offer high transduction efficiency and robust transgene expression, albeit typically for a shorter duration compared to lentiviral vectors. In some applications, the transient nature of adenoviral -mediated expression can be advantageous for controlling the timing and duration of LINE-1 ORF1 suppression in cancer cells.

[0070] Adeno-associated vims (AAV) vectors can also be employed for delivering therapeutic agents targeting LINE-1 ORF1. AAV is a small, non-pathogenic virus that can transduce a wide range of cell types and tissues. AAV vectors can offer the advantage of low' immunogenicity and the potential for long-term transgene expression. Different AAV serotypes can be selected to enhance tropism for specific cancer cell types or tissues. The choice of viral vector can depend on factors such as the target cell type, desired duration of expression, and safety considerations in the context of cancer therapy.Atty. Dkt. No.: 29539-0860WO1Methods

[0071] In some cases, disclosed are methods of using the compositions that are disclosed herein. For instance, disclosed is methods for suppressing LINE-1 ORF1 expression in a cancer cell can involve administering an agent that reduces expression of LINE-1 ORF1. The agent can be selected from shRNA, siRNA, antisense oligonucleotides, or a CRISPR construct targeting LINE-1 ORFE The cancer cells can be from a solid tumor or a hematological malignancy. In some cases, the agent can be encapsulated in a nanoparticle delivery system to enhance cellular uptake and protect from degradation.

[0072] A method of treating cancer can comprise administering to a subject an effective amount of an agent that reduces expression of LINE-1 ORF1 in cancer cells. The agent can be selected from shRNA, siRNA, antisense oligonucleotides, or a CRISPR construct targeting LINE-1 ORF1. The methods can be applicable to treating solid tumors or hematological malignancies. In some cases, the agent can be administered systemically or locally to a tumor site.

[0073] In some cases, a method of enhancing cancer cell sensitivity to KRAS inhibitors can involve administering to cancer cells an agent that reduces expression of LINE-1 ORF1 in combination with a KRAS inhibitor. The combination can result in a synergistic reduction in cancer cell growth. The methods can be applicable to cancer types with frequent KRAS mutations, such as pancreatic cancer.

[0074] A method of inducing innate immune responses in cancer cells can comprise administering an agent that reduces expression of LINE-1 ORF1 or LINE-1 ORF Ip. The reduction in LINE-1 ORF1 expression can activate RIG-I / MAVS-dependent interferon responses. In some cases, the suppression of LINE-1 ORFlp can activate innate immune responses through dsRNA sensors RIG-I and MAVS. In some cases, the methods can involve monitoring the activation of innate immune pathw ays following LINE-1 ORF1 or LINE-1 ORFlp suppression. This can include assessing the expression levels of interferon-stimulated genes, measuring the production of pro-inflammatory cytokines, or evaluating the phosphorylation status of key signaling molecules in the RIG-I / MAVS pathway. The induction of innate immune responses can contribute to the anti -tumor effects of LINE- 1 ORF1 suppression. Additionally, LINE-1 ORFlp knockdown can lead to decreased 3D tumorsphere growth, w hich can be indicative of reduced cancer stem cell-like properties. This effect on tumorsphere formation can be particularly pronounced in certain cancer ty pes or cellular contexts. The combination of innate immune activation and reduced tumorsphereAtty. Dkt. No.: 29539-0860WO1growth can provide multiple mechanisms by which LINE-1 0RF1 suppression exerts its anticancer effects.

[0075] In some cases, a method of reducing epithelial-mesenchymal transition in cancer cells can involve administering an agent that reduces expression of LINE-1 ORF1. The methods can decrease expression of mesenchymal genes and increase expression of epithelial genes in cancer cells. This can potentially reduce cancer cell invasiveness and metastatic potential.

[0076] A method of identifying cancer cells with increased sensitivity to LINE-1 ORF1 or LINE-1 ORFlp suppression can comprise detecting expression levels of RIG-I and MAVS in cancer cells. Reduced expression of RIG-I or MAVS can indicate increased sensitivity to LINE-1 ORF1 suppression. In some cases, the methods can involve detecting expression levels of pattern recognition receptors (PRRs) in pancreatic ductal adenocarcinoma (PDAC) cell lines, where reduced expression of PRRs can indicate increased sensitivity to LINE-1 ORFlp suppression.

[0077] In some cases, a method of reducing growth of a tumor can comprise administering to the tumor an agent that reduces expression of LINE- 1 ORFlp. The reduction in LINE-1 ORFlp expression can decrease tumorsphere formation and xenograft grow th. The methods can also promote an epithelial cell state in cancer cells. The agent can be administered systemically or locally to the tumor site, and can be encapsulated in a nanoparticle delivery system in some cases. Suppression of ORFlp can induce repeat RNA-mediated interferon (IFN) responses through the RIG-I / MAVS pathw ay in pancreatic ductal adenocarcinoma (PDAC) cells. This activation of the innate immune response can contribute to the anti -tumor effects observed with ORFlp suppression. In some aspects, the induction of IFN responses can be associated with increased expression of interferon-stimulated genes and pro-inflammatory cytokines. The RIG-I / MAVS -dependent signaling cascade can lead to the phosphorylation and activation of downstream effectors, such as IRF3 and NF-KB, which can further amplify the antiviral-like state in cancer cells. Additionally, the release of repeat RNAs upon ORFlp suppression can serve as pathogen-associated molecular patterns (PAMPs) that are recognized by pattern recognition receptors, further enhancing the innate immune response in PDAC cells.

[0078] In some cases, a method of reducing growth of a tumor can comprise administering to the tumor an agent that reduces expression of LINE-1 ORFlp. The reduction in LINE-1 ORFlp expression can decrease tumorsphere formation and xenograft growth. The methods can also promote an epithelial cell state in cancer cells. The agent can beAtty. Dkt. No.: 29539-0860WO1administered systemically or locally to the tumor site, and can be encapsulated in a nanoparticle delivery system in some cases.

[0079] In some cases, the agent that reduces expression of LINE-1 ORF1 can be administered systemically to treat cancer. Systemic delivery can involve intravenous inj ection, allowing the agent to circulate throughout the body and potentially target cancer cells in multiple locations. This approach can be particularly useful for treating metastatic cancers or hematological malignancies. Systemic administration can also allow for repeated dosing to maintain therapeutic levels of the agent over time. The agent can be formulated in a way that enhances its stability in circulation and promotes its uptake by target cancer cells.

[0080] Local delivery to a tumor site can be employed in some cases to achieve high concentrations of the LINE-1 ORF 1 -targeting agent in the tumor microenvironment. This approach can involve direct intratumoral injection, implantation of drug-eluting devices, or application of the agent during surgical resection. Local delivery can reduce systemic exposure and potential off-target effects while maximizing the therapeutic impact on the tumor. In some aspects, local delivery can be combined with imaging guidance to ensure accurate placement of the agent within the tumor mass.

[0081] The choice between systemic and local delivery can depend on factors such as tumor type, location, and stage of disease. In some cases, a combination of systemic and local delivery' approaches can be used to maximize therapeutic efficacy. For example, systemic administration can be used to target circulating tumor cells or micrometastases, while local delivery to the primary tumor site can help to reduce tumor burden and alter the local microenvironment.

[0082] In some cases, identification of tumors suitable for the methods or treatments targeting LINE-1 ORF1 can involve molecular profiling techniques. This can include nextgeneration sequencing approaches to assess LINE-1 ORF1 expression levels in tumor samples. RNA sequencing or quantitative PCR methods can be employed to quantify LINE-1 ORF1 transcript abundance. Additionally, proteomic analyses such as mass spectrometry' or Western blotting can be used to evaluate LINE-1 ORF1 protein levels in tumor tissues.

[0083] Imaging-based approaches can also be utilized to identify tumors amenable to LINE-1 ORFl-targeted therapies. Positron emission tomography (PET) imaging with radiolabeled probes specific for LINE-1 ORF1 can allow for non-invasive detection and monitoring of LINE- 1 ORF1 expression in tumors. In some aspects, magnetic resonance imaging (MRI) techniques coupled with LINE-1 ORFl-targeted contrast agents can provideAtty. Dkt. No.: 29539-0860WO1spatial information on LINE-1 ORF1 distribution within tumor masses. These imaging modalities can facilitate patient selection and treatment response assessment.

[0084] Liquid biopsy techniques can offer another avenue for identifying tumors suitable for LINE-1 ORF 1 -directed interventions. Analysis of circulating tumor DNA (ctDNA) in blood samples can reveal LINE-1 ORF1 copy number alterations or methylation patterns indicative of elevated LINE-1 activity. In some cases, detection of LINE-1 ORF1 protein or RNA in circulating tumor cells or extracellular vesicles can serve as biomarkers for tumors likely to respond to LINE-1 ORF1 suppression strategies. These minimally invasive approaches can allow for longitudinal monitoring of LINE- 1 ORF1 status throughout treatment.

[0085] Functional assays can be employed to assess tumor dependence on LINE-1 ORF1 activity. Ex vivo culture of patient-derived tumor organoids or xenografts can enable testing of LINE-1 ORF1 inhibitors to identify responsive tumor types. In some aspects, CRISPR-Cas9 screening approaches in patient-derived cell lines can reveal synthetic lethal interactions with LINE-1 ORF1, highlighting tumor subtypes particularly vulnerable to its suppression. These functional studies can complement molecular and imaging data to guide patient selection for LINE-1 ORFl-targeted therapies.

[0086] Integration of multi-omics data can enhance tumor identification for LINE-1 ORF 1 -directed treatments. Machine learning algorithms can be applied to combine genomic, transcriptomic. proteomic, and metabolomic profiles with clinical data to develop predictive models of tumor response to LINE-1 ORF1 suppression. In some cases, these computational approaches can uncover novel biomarkers or tumor subtypes associated with LINE-1 ORF1 dependence. The resulting integrative analyses can improve patient stratification and inform personalized treatment strategies targeting LINE-1 ORF1 in cancer.

[0087] In some cases, the agent that reduces expression of LINE-1 ORF1 can decrease tumorsphere growth of cancer cells. This effect can be particularly pronounced in three-dimensional culture conditions, which more closely mimic the in vivo tumor environment. The reduction in tumorsphere formation can indicate a decrease in cancer stem cell-like properties and self-renewal capacity.

[0088] The agent can increase interferon-stimulated gene expression in cancer cells. This upregulation of interferon response genes can contribute to enhanced anti-tumor immunity and increased sensitivity to other therapeutic interventions. In some aspects, the interferon-stimulated gene expression can be dependent on RIG-I / MAVS signaling pathways, suggesting activation of innate immune sensing mechanisms.7Atty. Dkt. No.: 29539-0860WO1

[0089] Administration of the LINE-1 ORF 1 -targeting agent can result in decreased epithelial-mesenchymal transition (EMT) gene expression in cancer cells. This shift in gene expression profiles can be associated with reduced invasiveness and metastatic potential. The agent can promote a more epithelial phenotype, potentially increasing sensitivity to certain chemotherapeutic agents.

[0090] In vivo studies can demonstrate that the agent reduces tumor growth in animal models. This growth inhibition can be observed across multiple cancer types and can be dosedependent. The agent can also increase tumor cell apoptosis in vivo, contributing to overall tumor regression and improved survival outcomes in preclinical models.

[0091] The tumor microenvironment can be altered by administration of the LINE-1 ORF 1 -targeting agent. These changes can include reductions in myofibroblastic cancer-associated fibroblasts, which are known to promote tumor growth and therapy resistance. Modulation of the tumor stroma can enhance drug delivery and efficacy of combination therapies.

[0092] In some cases, the agent can reduce cancer stem cell properties in treated cells. This can be evidenced by decreased expression of sternness markers, reduced sphere-forming ability, and diminished tumor-initiating capacity in limiting dilution assays. Targeting cancer stem cell populations can help prevent tumor recurrence and metastasis.

[0093] The agent can modulate DNA damage response pathways in cancer cells. This could involve alterations in the expression or activity of key DNA repair proteins, potentially sensitizing cancer cells to DNA-damaging therapies such as radiation or certain chemotherapeutics. The precise effects can depend on the specific cancer type and genetic background.

[0094] Metabolic pathways in cancer cells can be altered by the LINE-1 ORF 1 -targeting agent. These changes can include shifts in glucose metabolism, lipid synthesis, or mitochondrial function. Modulation of cancer cell metabolism can contribute to reduced proliferation and increased sensitivity to metabolic stress.

[0095] In some aspects, the agent can modulate autophagy in cancer cells. This could involve changes in the expression of autophagy -related genes or alterations in autophagic flux. The impact on autophagy can influence cancer cell survival, particularly under stress conditions or in response to other therapies.

[0096] Epigenetic modifications in cancer cells can be altered by the LINE-1 ORF1-targeting agent. This can include changes in DNA methylation patterns, histoneAtty. Dkt. No.: 29539-0860WO1modifications, or chromatin remodeling. Epigenetic reprogramming can contribute to the observed changes in gene expression and cellular phenotype.

[0097] Epigenetic modifications in cancer cells can be altered by the LINE-1 ORF1-targeting agent. This can include changes in DNA methylation patterns, such as hypomethylation of specific gene promoters or global changes in methylation status. Histone modifications can also be affected, potentially involving alterations in acetylation, methylation, or phosphorylation of histone tails. These changes can impact chromatin accessibility and gene expression. The agent can influence the activity or expression of epigenetic regulators like DNA methyltransferases, histone deacetylases, or histone methyltransferases. In some cases, the LINE-1 ORF 1 -targeting agent can modulate the recruitment of chromatin remodeling complexes to specific genomic loci, affecting nucleosome positioning and gene regulation. These epigenetic alterations can lead to the reactivation of tumor suppressor genes or the silencing of oncogenes. The epigenetic reprogramming induced by the agent can contribute to changes in cellular plasticity', potentially affecting cancer cell differentiation states or therapy resistance. In some aspects, the epigenetic effects of LINE-1 ORF1 suppression can extend beyond individual genes to impact higher-order chromatin organization and nuclear architecture. These epigenetic changes can persist through cell divisions, potentially leading to long-term alterations in cancer cell behavior and phenoty pe.

[0098] The methods can include monitoring LINE-1 ORF1 expression levels in the cancer cells following administration of the agent. This can involve techniques such as quantitative PCR, RNA sequencing, or Western blotting to assess changes in LINE-1 ORF1 mRNA or protein levels over time. Monitoring expression levels can provide insights into the durability’ of the suppression effect and inform dosing strategies.

[0099] In some cases, the methods can involve monitoring innate immune responses in the cancer cells after treatment. This can include assessing the expression of interferon-stimulated genes, activation of pattern recognition receptors, or production of inflammatory cytokines. Flow cytometry or multiplex cytokine assays can be used to evaluate changes in immune-related markers at the cellular level.

[0100] The methods can include analyzing gene expression changes in the cancer cells following administration of the agent. RNA sequencing or microarray analysis can be performed to identity' global transcriptional changes induced by LINE-1 ORF1 suppression. This can reveal alterations in key signaling pathways or cellular processes affected by the treatment.Atty. Dkt. No.: 29539-0860WO1

[0101] The methods disclosed herein can impact multiple cellular pathways in cancer cells. DNA damage response pathways can be modulated, potentially altering the expression or activity of key repair proteins and sensitizing cells to certain therapies. Metabolic pathways, including glucose metabolism, lipid synthesis, and mitochondrial function, can be affected, potentially contributing to reduced proliferation and increased metabolic stress. Autophagy pathways can be altered, which could influence cancer cell survival under various conditions. Epigenetic pathways can be impacted, leading to changes in DNA methylation patterns, histone modifications, and chromatin remodeling, potentially affecting gene expression profiles. Innate immune signaling path ays, particularly those involving RIG-I / MAVS, can be activated, contributing to enhanced anti -tumor immunity. Cell death pathways, including apoptosis and potentially necroptosis, can be modulated, affecting overall tumor cell survival. Additionally, pathways involved in epithelial-mesenchymal transition can be altered, potentially influencing cancer cell invasiveness and metastatic potential.

[0102] Assessing cancer cell invasiveness following administration of the agent can be part of the method. This can involve in vitro invasion assays using Matrigel-coated transwell chambers or 3D spheroid invasion models. Changes in the expression of invasion-related genes or proteins can also be evaluated.

[0103] The methods can include monitoring cell cycle progression in the cancer cells following administration of the agent. Flow cytometry analysis of DNA content or expression of cell cycle markers can be used to assess potential changes in cell cycle distribution or arrest induced by LINE-1 ORF1 suppression. In some cases, real-time imaging techniques can be employed to track individual cell division events and measure cell cycle duration. The agent can alter the expression or activity of key cell cycle regulators, such as cyclins. cyclin-dependent kinases, or checkpoint proteins. These changes in cell cycle dynamics can contribute to the observed effects on cancer cell proliferation and tumor growth.

[0104] In some aspects, the methods can involve assessing angiogenesis following administration of the agent. This can include evaluating the expression of angiogenic factors, such as VEGF. or examining tumor vasculature in animal models using immunohistochemistry or intravital microscopy techniques. The agent can modulate the secretion of pro- or anti-angiogenic factors by cancer cells, potentially altering the tumor's ability' to recruit new blood vessels. In some cases, the effects on angiogenesis can be assessed using in vitro endothelial cell tube formation assays or ex vivo aortic ring assays. Changes in tumor vasculature can impact drug delivery and overall tumor grow th kinetics.Atty. Dkt. No.: 29539-0860WO1

[0105] Monitoring cytokine production by the cancer cells following administration of the agent can be part of the method. Multiplex cytokine assays or ELISA can be used to measure changes in the secretion of pro-inflammatory or immunosuppressive factors by treated cancer cells. In some aspects, single-cell cytokine profding techniques can be employed to capture heterogeneity' in cytokine production wi thin the tumor cell population. The agent can alter the balance of pro- and anti-inflammatory cytokines, potentially reshaping the tumor immune microenvironment. These changes in cytokine profiles can influence immune cell recruitment and activation within the tumor.

[0106] The methods can include assessing cancer cell migration following administration of the agent. Wound healing assays, time-lapse microscopy, or transwell migration assays can be employed to evaluate potential changes in cancer cell motility induced by LINE-1 ORF1 suppression. In some cases, microfluidic devices can be used to create chemotactic gradients and assess directional migration capabilities of treated cancer cells. The agent can affect the expression or activity' of proteins involved in cell adhesion and cytoskeletal remodeling, which are critical for cell migration. These changes in migratory behavior can have implications for tumor invasion and metastasis.

[0107] In some cases, the methods can involve monitoring T cell infiltration into the tumor following administration of the agent. Immunohistochemistry or flow cytometry' analysis of tumor samples can be used to assess changes in T cell populations within the tumor microenvironment. Multiplex immunofluorescence imaging techniques can allow for spatial analysis of T cell distribution and their interactions with tumor cells. The agent can indirectly affect T cell recruitment by modulating the expression of chemokines or adhesion molecules by cancer cells. Changes in T cell infdtration can correlate with alterations in antitumor immune responses and overall treatment efficacy.

[0108] Assessing cancer cell differentiation state following administration of the agent can be part of the method. This can involve examining the expression of differentiation markers or evaluating morphological changes indicative of altered cellular differentiation. Single-cell RNA sequencing can be used to capture the full spectrum of differentiation states within the tumor cell population. The agent can influence the activity of key transcription factors or signaling pathways that regulate cell fate decisions. Changes in differentiation state can impact tumor heterogeneity and response to therapy.

[0109] The methods can include monitoring circulating tumor DNA levels following administration of the agent. Liquid biopsy techniques can be used to detect and quantity tumor-specific DNA alterations in blood samples, potentially providing a non-invasive meansAtty. Dkt. No.: 29539-0860WO1of assessing treatment response. Digital PCR or next-generation sequencing approaches can be employed to detect rare mutant alleles in circulation. The agent can affect the rate of tumor cell death or DNA release, influencing the levels of circulating tumor DNA. Monitoring circulating tumor DNA can provide real-time insights into tumor burden and treatment efficacy.

[0110] In some aspects, the methods can involve assessing tumor-associated macrophage polarization following administration of the agent. Flow cytometry or immunohistochemistry can be used to evaluate changes in macrophage phenotypes (e.g., Ml vs. M2) within the tumor microenvironment. Single-cell RNA sequencing of isolated tumor-associated macrophages can reveal subtle shifts in activation states or functional programs. The agent can indirectly influence macrophage polarization by altering the cytokine milieu produced by cancer cells. Changes in macrophage phenotypes can have significant implications for tumor progression and response to immunotherapies.

[0111] Monitoring cancer cell heterogeneity following administration of the agent can be part of the method. Single-cell RNA sequencing or multiparameter flow cytometry can be employed to assess changes in cellular subpopulations or the emergence of resistant clones. Spatial transcriptomics techniques can provide insights into the spatial organization of distinct cellular states within the tumor. The agent can differentially affect various subpopulations of cancer cells, potentially altering the clonal composition of the tumor. Understanding changes in tumor heterogeneity can be critical for predicting long-term treatment outcomes and designing combination therapies.

[0112] The methods can include assessing cancer cell response to hypoxic conditions following administration of the agent. This can involve evaluating the expression of hypoxiainducible factors or assessing cell viability under low oxygen conditions in vitro. Oxygensensitive fluorescent probes can be used to map hypoxic regions within tumors in vivo. The agent can modulate cellular adaptation mechanisms to hypoxia, such as metabolic reprogramming or angiogenic signaling. Changes in hypoxia response can influence tumor growth patterns and sensitivity to certain therapies.

[0113] In some cases, the methods can involve monitoring natural killer cell activity against the cancer cells following administration of the agent. Cytotoxicity assays or flow cytometry -based killing assays can be used to assess potential changes in NK cell-mediated tumor cell recognition and elimination. Live-cell imaging techniques can allow for real-time visualization of NK cell-tumor cell interactions. The agent can alter the expression of NK cell activating or inhibitory ligands on cancer cells, potentially modulating their susceptibility toAtty. Dkt. No.: 29539-0860WO1NK cell-mediated killing. Enhanced NK cell activity can contribute to improved anti-tumor immune responses following treatment.

[0114] A method of spatially analyzing RNA methylation status in a biological sample can involve contacting the biological sample with a substrate, permeabilizing the biological sample, and detecting methylated RNA or complement thereof using spatial analysis methods. The spatial analysis methods can comprise in situ sequencing, spatial transcriptomics, or fluorescence in situ hybridization. In some cases, the biological sample can be a tissue section.

[0115] Detecting methylated RNA can involve using antibodies specific to methylated RNA. The methods can include imaging the biological sample to obtain spatial information. In some aspects, the substrate can comprise a capture probe array. The capture probe array can comprise capture probes, each capture probe having a capture domain that can hybridize to a target in the biological sample and a spatial barcode that can uniquely identify a location on the array.

[0116] The methods can include reverse transcribing the methylated RNA to generate cDNA. In some cases, the spatial analysis methods can provide single-cell resolution of RNA methylation status. The methods can involve computationally analyzing the spatial distribution of methylated RNA. This analysis can allow for the correlation of spatial distribution of methylated RNA with histological features of the biological sample.

[0117] The spatial analysis of RNA methylation status can provide insights into the heterogeneity of methylation patterns within complex tissues. This approach can reveal cell type-specific methylation profiles and their relationship to cellular function or disease states. In some aspects, the methods can enable the identification of rare cell populations with distinct methylation signatures that can be missed by bulk analysis methods.

[0118] Integration of spatial RNA methylation data with other spatially resolved omics data, such as gene expression or protein localization, can provide a more comprehensive understanding of cellular states and tissue organization. This multi-modal approach can reveal relationships between RNA methylation patterns and other molecular features at specific tissue locations. The spatial context provided by this methods can be particularly valuable for understanding the role of RNA methylation in processes such as development, disease progression, or response to therapeutic interv entions.

[0119] The suppression of LINE- 1 ORF Ip in pancreatic ductal adenocarcinoma (PDAC) cells can lead to a shift towards a more epithelial cell state. This transition can be characterized by increased expression of epithelial markers and decreased expression ofAtty. Dkt. No.: 29539-0860WO1mesenchymal genes. In some cases, the loss of LINE-1 ORFlp can result in reduced tumor cell invasiveness and metastatic potential. The epithelial shift induced by LINE-1 ORFlp suppression can enhance the sensitivity of PDAC cells to KRAS inhibitors. This increased sensitivity can be due to alterations in cellular signaling pathways or changes in the tumor microenvironment. The combination of LINE-1 ORFlp suppression and KRAS inhibition can provide a synergistic effect in reducing PDAC tumor growth and improving therapeutic outcomes. Thus, the methods herein include administering a KRAS inhibitor in combination with the LINE-1 ORF 1 -targeting agent. This combination therapy approach can exploit potential synergistic effects between KRAS pathway inhibition and LINE-1 ORF1 suppression. In some cases, the combination of the agent and the KRAS inhibitor can result in a greater reduction in cancer cell growth compared to either treatment alone. The synergistic effect can be observed in both in vitro and in vivo models, potentially leading to enhanced tumor regression and improved therapeutic outcomes.

[0120] The knockdow n of LINE-1 ORFlp can enhance tumor cell apoptosis through multiple mechanisms. In some cases, the reduction of ORFlp expression can lead to increased activation of pro-apoptotic signaling pathways, such as those mediated by caspases or Bcl-2 family proteins. This enhanced apoptotic response can be particularly pronounced in 3D tumor models or in vivo xenografts, where the loss of ORFlp can disrupt critical survival signals. Additionally, the suppression of ORFlp can sensitize cancer cells to various forms of cellular stress, including oxidative stress, DNA damage, or nutrient deprivation, thereby lowering the threshold for apoptosis induction. The increased tumor cell apoptosis observed with ORFlp knockdown can contribute to reduced tumor growth and improved responses to conventional therapies.

[0121] ORF Ip suppression can also induce significant changes in the tumor microenvironment, particularly with respect to fibrotic alterations. In some aspects, the loss of ORFlp in cancer cells can modulate the secretion of factors that influence stromal cell behavior, potentially leading to increased activation of fibroblasts and enhanced deposition of extracellular matrix components. This fibrotic remodeling can be characterized by increased collagen production, altered matrix stiffness, and changes in the composition of proteoglycans and glycosaminoglycans within the tumor stroma. The fibrotic changes induced by ORFlp knockdown can have complex effects on tumor progression, potentially limiting tumor cell invasion in some contexts while creating barriers to drug delivery in others. Understanding the interplay between ORFlp suppression, tumor cell apoptosis, andAtty. Dkt. No.: 29539-0860WO1stromal remodeling can provide insights into optimizing therapeutic strategies that target LINE-1 ORF Ip in cancer.

[0122] Administering an immunotherapy agent in conjunction with the LINE-1 ORF1-targeting agent can also be part of the method. This combination can leverage the potential immuno modulator)' effects of LINE- 1 ORF1 suppression to enhance the efficacy of immunotherapies such as immune checkpoint inhibitors or adoptive cell therapies. The combination can lead to increased tumor infiltration by immune cells, altered cytokine profiles within the tumor microenvironment, or enhanced recognition of cancer cells by the immune system.

[0123] In some aspects, the LINE-1 ORF 1 -targeting agent can be administered in combination with chemotherapy. This approach can take advantage of potential chemosensitizing effects of LINE- 1 ORF1 suppression. The combination can enhance the cytotoxic effects of chemotherapeutic agents, potentially allowing for dose reductions or overcoming resistance mechanisms. The specific chemotherapy regimens used in combination can depend on the cancer ty pe and standard of care protocols.

[0124] The methods can also involve administering the LINE-1 ORF 1 -targeting agent in combination with radiation therapy. This combination strategy can exploit potential radiosensitizing effects of LINE-1 ORF1 suppression. The agent can enhance DNA damage responses or alter cellular repair mechanisms, potentially increasing the effectiveness of radiotherapy. In some cases, the combination can allow for reduced radiation doses while maintaining therapeutic efficacy, potentially minimizing side effects associated with radiation treatment.Samples

[0125] The methods can be applied to various species, including but not limited to any mammal, including but not limited to humans, non-human primates, rodents (e.g., mice, rats), canines, felines, and livestock animals. In certain embodiments, the subject is a human. In other embodiments, the subject may be a non-human mammal, such as a rodent model used for research purposes. The compositions and methods described herein are intended to be applicable across a range of mammalian species, particularly those commonly used in preclinical or clinical studies.

[0126] The agent that reduces expression of LINE- 1 ORF1 can be administered to various cell types. These can include in vitro cell lines such as established cancer cell lines,Atty. Dkt. No.: 29539-0860WO1primary cell cultures derived from patient samples, or genetically modified cell lines. The methods can also be applied to ex vivo cells, which can be isolated from patient tissues or animal models and treated outside the body before potential reintroduction. In some aspects, the methods can be used on three-dimensional cell culture models such as organoids or spheroids, which can better recapitulate certain aspects of in vivo tissue architecture.

[0127] The methods can be applied to a diverse range of pancreatic ductal adenocarcinoma (PDAC) cell lines, such as PANC-1, MIA PaCa-2, BxPC-3, AsPC-1.PANC-1 , KP-4, MGH1222, MGH1312, Mia PACA2, and MGH1247. Patient-derived PDAC cell lines, which can better represent the heterogeneity of clinical tumors, can also be utilized in this application. In some cases, genetically engineered PDAC cell lines with specific mutations or gene modifications can be employed to study the effects of LINE- 1 ORF1 suppression in different genetic contexts. Additionally, PDAC cells isolated from patient-derived xenografts or organoids can provide valuable models for investigating the impact of LINE-1 ORF1 targeting agents in more physiologically relevant systems.

[0128] The cancer cells targeted by the methods can encompass a wide range of cancer ty pes. These can include, but are not limited to, breast cancer, lung cancer, colorectal cancer, prostate cancer, ovarian cancer, melanoma, leukemia, lymphoma, brain tumors, liver cancer, and renal cell carcinoma. The methods can be applicable to both solid tumors and hematological malignancies. In some cases, the approach can be tailored to specific molecular subtypes within these broader cancer categories.

[0129] Pancreatic cancers can be a particular focus for the application of this method. This can include pancreatic ductal adenocarcinoma (PDAC), which is often characterized by aggressive growth and poor prognosis. The methods can also be relevant for other pancreatic cancer types such as acinar cell carcinoma, pancreatic neuroendocrine tumors, or intraductal papillary mucinous neoplasms. In some aspects, the LINE-1 ORF 1 -targeting approach can be investigated in the context of early-stage pancreatic lesions or in combination with standard-of-care treatments for pancreatic cancer. The potential impact on pancreatic cancer stem cells, tumor microenvironment, or metastatic processes can be of particular interest in this cancer type.

[0130] The methods can be applied to biological samples derived from various types of tumors, including both solid tumors and hematological malignancies. In some cases, the cancer cells targeted by the methods can be pancreatic cancer cells, such as those from pancreatic ductal adenocarcinoma (PDAC). PDAC cells can be of particular interest due to the aggressive nature and poor prognosis associated with this cancer type. The methods canAtty. Dkt. No.: 29539-0860WO1also be applicable to other solid tumors, such as breast, lung, colorectal, or prostate cancers, as well as hematological malignancies like leukemia or lymphoma. The versatility of the approach allows for potential application across a wide range of cancer t pes, with the specific choice of cancer cells or tumor samples depending on the research objectives or clinical context. In some aspects, the method can be adapted to address the unique characteristics and challenges associated with different tumor types or cancer cell populations.Additional Methods

[0131] Cell viability assays can be performed to assess the effects of LINE-1 ORF1 suppression on cancer cell survival and proliferation. These assays can include colorimetric methods such as MTT or WST-1, luminescence-based approaches like CellTiter-Glo, or flow cytometry -based techniques using fluorescent dyes. In some cases, real-time cell analysis systems can be employed to monitor cell growth and viability continuously over extended periods.

[0132] hnmunohistochemistry (IHC) and in situ hybridization (ISH) techniques can be utilized to evaluate LINE-1 ORF1 expression and localization within tumor tissues. IHC can detect ORF1 protein levels and distribution, while ISH can reveal LINE-1 RNA expression patterns. These methods can provide spatial information about ORF1 expression in relation to other cellular markers or tissue structures.

[0133] Various imaging modalities can be employed to assess tumor response to LINE-1 ORF 1 -targeting treatments. These can include magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), or ultrasound. In some cases, molecular imaging approaches using targeted contrast agents or radiotracers can be developed to specifically visualize LINE-1 ORF1 expression or activity in vivo.

[0134] Immunofluorescence (IF) microscopy can be used to examine the subcellular localization of LINE- 1 ORF1 protein and its potential interactions with other cellular components. This technique can allow for co-localization studies with markers of specific organelles or cellular structures. In some aspects, live-cell IF imaging can be performed to track dynamic changes in ORF1 localization or expression in response to treatments.

[0135] PCR-based methods, including endpoint PCR and quantitative PCR (qPCR), can be employed to measure LINE-1 expression levels and copy number variations. These techniques can be particularly useful for detecting changes in LINE-1 transcription orAtty. Dkt. No.: 29539-0860WO1genomic insertions following treatment. In some cases, digital PCR approaches can provide absolute quantification of LINE- 1 copy numbers or rare LINE-1 variants.

[0136] RNA immunoprecipitation (RIP) assays can be conducted to identify RNA species that interact with LINE-1 ORF1 protein. This technique can reveal potential regulatory interactions between ORF1 and other cellular RNAs. In some aspects, RIP followed by sequencing (RIP-seq) can provide a genome-wide view of ORF 1 -RNA interactions. RIP-seq can provide valuable insights into the RNA species that associate with LINE-1 ORFlp. This technique can reveal a preferential binding of ORFlp to various repeat RNA elements, including LINE-1 transcripts themselves as well as other retrotransposon-derived RNAs. The analysis of RIP-seq data can uncover specific sequence motifs or structural features that facilitate ORFlp-RNA interactions. In some cases, the binding patterns observed through RIP-seq can correlate with the genomic distribution of LINE-1 elements and their potential impact on gene regulation. The integration of RIP-seq results with other genomic and transcriptomic data can offer a more comprehensive understanding of how LINE-1 ORFlp influences cellular RNA metabolism and gene expression programs in cancer cells.

[0137] Total RNA sequencing can be performed to assess global transcriptional changes induced by LINE-1 ORF1 suppression. This approach can uncover alterations in gene expression patterns, alternative splicing events, or non-coding RNA expression that can contribute to the cellular response to ORF1 targeting.

[0138] Spatial transcriptomics techniques can be applied to analyze gene expression patterns within the context of tissue architecture. These methods can reveal spatial relationships between LINE-1 ORF1 expression and other molecular features of the tumor microenvironment. In some cases, spatial transcriptomics can be combined with protein detection methods to provide multi-omic spatial profiling of tumor tissues.

[0139] In vitro RNA synthesis can be used to generate LINE-1 RNA or other relevant transcripts for functional studies. These synthetic RNAs can be employed in various assays to investigate the molecular mechanisms of LINE- 1 ORF1 function or to develop RNA-based therapeutic approaches.

[0140] Immunoprecipitation experiments can be conducted to identify protein-protein interactions involving LINE-1 ORF1. This technique can reveal potential binding partners or complexes that mediate ORF1 function in cancer cells. In some aspects, mass spectrometrybased approaches can be used to characterize the ORF1 interactome comprehensively.Atty. Dkt. No.: 29539-0860WO1

[0141] Immunoblotting (Western blotting) can be performed to quantify LINE-1 ORF1 protein levels and assess post-translational modifications. This methods can be useful for monitoring changes in ORF1 expression or processing in response to treatments. In some cases, multiplexed immunoblotting techniques can allow for simultaneous detection of multiple proteins or modifications. The methods can involve analyzing various targets related to innate immune sensing and signaling pathways. These can include pattern recognition receptors such as RIG-I (retinoic acid-inducible gene I) and MDA5 (melanoma differentiation-associated protein 5), which can detect viral and endogenous double-stranded RNA. The mitochondrial antiviral-signaling protein (MAVS) can also be examined, as it plays a crucial role in transmitting signals from RIG-I and MDA5 to downstream effectors. Additionally, the stimulator of interferon genes (STING) protein can be analyzed to assess its involvement in DNA sensing pathways and potential crosstalk with RNA-mediated immune responses in the context of LINE- 1 ORF1 suppression.

[0142] dsRNA dot blot assays can be employed to detect and quantify double-stranded RNA species that can be associated with LINE-1 activity. This technique can provide insights into the potential immunostimulatory effects of LINE- 1 expression or the efficacy of treatments targeting LINE-1 transcripts.

[0143] Subcellular fractionation methods can be used to isolate and analyze different cellular compartments for LINE-1 ORF1 localization or activity. This approach can reveal compartment-specific functions or interactions of ORF1 protein. In some aspects, fractionation can be combined with other analytical techniques such as immunoblotting or mass spectrometry to provide a detailed view of ORF1 distribution and function within the cell.EXAMPLESExample 1: Materials / MethodsMouse model

[0144] All animal experiments were approved by the IACIC of Massachusetts General Hospital. NOD / SCID / gamma-c (NSG; NOD.Cg-PrkdcscidI12rgtmlWjl / SzJ, Jackson Laboratories) mice w ere used for xenograft model. All 24 mice were female and 4-6-week-old to minimize variation in tumor growth due to sex. Mice were kept anesthetized during all procedures. Matrigel (50 pl; Coming, cat# 354263) and DMEM (1:1) containing 100,000 PDAC6 GFP-luciferase expressing cancer cell was injected into subcutaneous. After injection, tumors were monitored weekly using in vivo luciferase imaging on the IVISAtty. Dkt. No.: 29539-0860WO1Lumina platform (PerkinElmer / Caliper) for 8 weeks. Tumor grow th was estimated by measuring bioluminescent signal (photonflux) from the abdominal region of interest 10 minutes after intraperitoneal injection of 150 pl of luciferin.Cell Culture

[0145] Patient-derived PDAC cell lines (PDAC3, PDAC6, PDAC8 and PDAC9) were isolated from metastatic ascites from patients under a discarded tissue protocol in accordance with the Massachusetts General Hospital (MGH) IRB protocol 2011 POO 1236 as previously described (59). MGH937, MGH1222, MGH1247, MGH1275, MGH1312, MGH1473 primary PDAC cell lines were gift from Dr. Andrew Liss (MGH). Primary CAF line (CAF-1) was derived from PDAC tumor tissue collected in accordance with the Massachusetts General Hospital (MGH) IRB protocol 2003P001289 as previously described (59). PANC- 1 (cat# CRL-1469), MIA PaCa-2 (cat# CRL-1420), Capan-1 (cat# HTB-79), BxPC-3 (cat# CRL-1687)

[0146] were purchased from the American Type Culture Collection (ATCC). KP-4, YAPC, DAN-G were obtained from the Center for Molecular Therapeutics in the MGH Cancer Center. AsPC-L SLUT-2 and PA-TU- 8988T were obtained from Dr. Christopher Ott (MGH, Harvard Medical School). CAF-1, PDAC3, PDAC6, PDAC8, PDAC9, PANC-1, MIA PaCa-2, PA-TU-8988T cells were cultured in DMEM (GibcoTM, cat# 11995065), KP-4 and Capan-1 in IMDM (GibcoTM, cat# 12440-053), YAPC, DAN-G, BxPC-3. SU86.86,

[0147] AsPC-1 and SUIT-2 in RPMH640 (GibcoTM, cat# 11875093) supplemented with 10% (v / v) FBS and antibiotics (100 U ml-I penicillin, 100 pg ml-1 streptomycin;GibcoTM, cat# 15140122), all from GibcoTM. Cells were incubated at 37°C in the incubator supplied with 5% of CO2. Supernatants of cell lines were collected and regularly tested for mycoplasma contamination with the MycoAlertTM PLUS mycoplasma detection kit (Lonza, cat# LT07-705).Plasmids construction

[0148] Short hairpin RNA (shRNA) sequences were designed from Sigma predesigned shRNA or adopted from paper (35). shRNAs targeting shORFl-1 (5’-AAGCAAATGTTGAGAGATT-3’; SEQ ID NO: 1), shORFl-2 (5 -GAAATGAAGCGAGAAGGGAAG-3’; SEQ ID NO: 2), shLSM14A (5 -GCCGATGAAGAAGATCCACTT-3’; SEQ ID NO: 3) were cloned into pLKO.l-puro-TRC cloning vector (Addgene, cat# 10878). shRNAs targeting shMETTL3-l (5’-GCCAAGGAACAATCCATTGTT-3’; SEQ ID NO: 4), shMETTL3-2 (5’-GCCTTAACATTGCCCACTGAT-3’; SEQ ID NO: 5), shMOVlO-1 (5’-Atty. Dkt. No.: 29539-0860WO1CGTTACTGCATCACCAAACTT-3’; SEQ ID NO: 6), shMOV10-2 (5’-GCTGACCTTCAAGGTGAACTT-3’; SEQ ID NO: 7) were cloned into pLKO.l-blast (Addgene, cat# 26655) by Agel and EcoRI restriction enzyme digestion. Single guide RNA (sgRNA) targeting DDX58 (sgDDX58-l: 5 -GGGTCTTCCGGATATAATCC-3’; SEQ ID NO: 8) IFIH1 (sgIFIHl-1: 5 - CGAATTCCCGAGTCCAACCA-3'; SEQ ID NO: 9, MAVS (sgMAVS-3: 5 -CTGTGAGCTAGTTGATCTCG-3’; SEQ ID NO: 10) were cloned into lentiCRISPR v2 (Addgene, cat# 52961) using BsmBI (Thermo Fisher Scientific, cat# FD0454) digestion and used previous work. sgRNA targeting STING1 (sgSTINGl-1 :GTACCCAATGTAGTATGACC; SEQ ID NO: 11) was purchased from GenScript. To construct doxycycline-inducible synthetic ORFeus expression vectors, ORFeus fragments was obtained by PCR from pLD563 plasmid (gift from Martin Taylor) and inserted into the Gateway entry vector using pCRTM8 / GW / TOPO TA cloning kit (InvitrogenTM, cat# K2500-20). ORFeus (Forward: 5’- ATGGGCAAGAAGCAGAAC-3’; SEQ ID NO: 12, Reverse: 5’- TTACATCTTGGCGTGGTT-3’; SEQ ID NO: 13). Cloning orientation was verified by Sanger sequencing. Then, ORFeus partial fragments are transferred into Tet-on lentiviral vector pCW57.1 (Addgene, cat# 41393) destination vector by standard gateway based in vitro recombination using Gateway TM LR clonaseTM II Enzyme mix (InvitrogenTM, cat# 11791020) according to the manufacturer’s protocol.Lentivirus transduction and generation of stable cell line

[0149] HEK293T cells were seeded at 80% density in a 10 cm tissue culture dish and transfected with the 6 pg of expression plasmid and packaging plasmids 2 pg of pMD2.G (Addgene, cat# 12259) and 4 pg of psPAX2 (Addgene, cat# 12260) using Lipofectamine 2000 Transfection reagent (InvitrogenTM, cat# 11668019) according to manufacturer's instructions. Conditioned medium containing recombinant lentivirus was collected and filtered through 0.45 pm filters. The lentivirus containing medium was added to cells with 8 pg ml-1 of Polybrene (Santa Cruz Biotechnology7, cat# sc-134220). Approximately 24-48 hrs after infection, the cells were selected with 1 pg ml-1 puromycin (InvivoGen, cat# ant-pr-1) or 1 pg ml-1 blasticidin (Invivogen, cat# ant-bl-05) for 2-5 days.Cell viability assay

[0150] Cell viability assays were conducted under both adherent (2D) and tumorsphere (3D) conditions. Cells were plated at a concentration of 3,000 cells per well in 96-well ultra-low-attachment plates (Coming, cat# 7007) or 1,000 cells per well in tissue culture-treated 96-well plates. Cells were treated with the indicated drug for the specified number of days.Atty. Dkt. No.: 29539-0860WO1Cell viability was measured using the CellTiter-Glo reagent (Promega, cat# G9683) according to the manufacturer's recommendations, and luminescence was measured with the Spectramax i3X (Molecular Devices) plate reader. Percent cell / tumorsphere viability was expressed as luminescence units normalized to the shNTC or DMSO control. The following drugs were used in this study: Pan-KRAS-IN-1 (MedChem Express, cat# HY-148098), Pan-RAS-IN-1 (Selleck Chemicals, cat# El 157), d4T (Stavudine; Selleck Chemicals, cat# SI 398), 3TC (Lamivudine; Selleck Chemicals, cat# SI 706), FTC (Emtricitabine; Selleck Chemicals, cat# S1704), TDF (Tenofovir; Selleck Chemicals, cat# SI 401 ), Z-VAD-FMK (Selleck Chemicals, cat# S7023), Necrostatin-1 (Selleck Chemicals, cat# S8037).IHC and RNA-ISH Staining:

[0151] All chromogenic IHC and RNA-ISH stains were performed on a Leica Bond RX automated Stainer. Fluorescent multiplex ISH-IF stain was performed manually. For LINE-1 ORFlp IHC, FFPE tissue sections were stained with Anti-LINE-1 ORFlp Antibody, clone 4H1 (Sigma- Aldrich, cat# MABC1152) labelled with DAB chromogen Leica Bond Poly mer Refine Detection DS9800) using Citrate-based pH 6 epitope retrieval condition. For EMT RNA-ISH, PDAC6 xenograft FFPE sections were stained with RNAScope 2.5 LS Probe Pancreatic Cancer Pool Epi-Cl, QM-C2 (ACD Bio, cat# 321762) labelled with DAB chromogen (Leica Bond Polymer Refine Detection, cat# DS9800), fast red chromogen (BOND Polymer Refine Red Detection, cat# DS9390) and RNA-ISH kit (RNAscope® 2.5 LS Duplex Reagent Kit- (red / brown) using EDTA-based pH 9 epitope retrieval condition. For fluorescence multiplex RNA-ISH combined with PanCK IHC, experiments were performed according to the manufacturer instructions. To deparaffinize, FFPE tumor sections were incubated at 60°C for 1 hr, which was followed by two 5 min incubations in xylene, and two 2 min incubations in 100% ethanol at room temperature. RNAscope hydrogen peroxide was added to the tumor sections and incubated for 10 min at room temperature. The slides were then washed 3 times. To perform target retrieval, slides were pre-boiled for 10 sec to acclimate prior to being submerged in IX Target Retrieval Reagent for 15 min at >99°C. Following boiling, slides were washed, and then transferred to 100% ethanol for 3 min at room temperature. The slides were then dried at room temperature for 5 min. Once dry, a hydrophobic pen (ImmEdge® Hydrophobic Barrier Pen, Vector Laboratories, cat# H-4000) was used to create a hydrophobic barrier surrounding the tumor section. RNAscope protease plus was then applied and incubated at 40°C for 30 min in a pre- warmed HybEZ Humidity Control Tray, then washed off. A mix of the RNAscope® probes (ACD Bio, C1-CXCL2: cat# 300040; OAS1: cat# 1029931-C1; IFIT3: cat# 851491, C2-I16: cat# 315891-C2, C3-Atty. Dkt. No.: 29539-0860WO1Acta2: cat# 319531-C3) was prepared according to instructions from the manufacturer, applied to the tumor sections, and incubated for 2 hr at 40°C, then washed in wash buffer. Following probe incubation, RNAscope multiplex FL V2 Amp 1 was dropped to cover the section, and the slide was incubated at 40°C for 30 min, then washed with wash buffer. Next, HRP-C1 was added to the tumor section, incubated at 40°C for 15 min, and then washed off in wash buffer. Opal dyes (Akoya Biosciences; Opal 520; cat# FP1487001KT, Opal 570; cat# FP1488001KT, Opal 620; cat# FP1495001KT, Opal 690; cat# FP 1497001 KT) were diluted in dilution buffer (Opal 520, 620, 570 - 1 :750, Opal 690 - 1 :100), and the fluorophore was added after its respective probe and incubated at 40°C for 30 min. This step was followed by washing and HRP blocker incubation for 15 min at 40°C. These steps were repeated with each following Amp, probe and corresponding fluorophore. After completion of RNA-ISH, slides are incubated with pre-blocking solution at room temperature for 30 min and further incubated with anti- PanCK primary antibody (Agilent Technologies, cat# M351501-2, 1:500) for 1 hr at room temperature. Slides w ere then washed, and the Opal Anti-Ms+Rb HRP (Akoya Biosciences, cat# ARH1001EA) was applied for 30 min at room temperature. Slides were washed again, and the opal 620 dye was applied for 10 min at room temperature. Following this, the slides were washed and DAPI staining was applied for 30 sec, then removed. A cover slip was then applied with ProLong Gold Antifade Mountant solution (Invitrogen, cat# P36930).Image acquisition:

[0152] For chromogenic stains, whole slide images of stained slides were acquired at 40x (0.26 pm) resolution using a MoticEasyScan Infinity digital pathology scanner. For fluorescence multiplex RNA-ISH combined with PanCK, whole slide images of stained slides were acquired at 40x (0.25 pm) resolution using an Akoya PhenoImager HT multispectral system. Exposures were set to avoid saturated pixels.

[0153] Image quantification: All images were quantified using the HALO image analysis platform (Indica Labs). Tumor regions w ere verified by a pathologist (B.P.) and annotated to exclude large folds, debris, fat, muscle and normal mouse skin tissue from quantification.

[0154] For LINE-1 ORFlp IHC quantification in human PDAC TMAs, each TMA core was manually annotated and quantified using the HALO image analysis platform (Indica Labs). HALO Al DenseNet convolutional neural network classifiers were trained to distinguish tumor glands from stroma within the annotated tumor regions. Nuclear detection and cell segmentation were performed using the default Al nuclear segmentation algorithmAtty. Dkt. No.: 29539-0860WO1on the hematoxylin color-deconvolved image component. Halo module Multiplex IHC v3.4.9 module was used to quantify the percentage of LINE- 1 ORF Ip IHC positive epithelial cells.

[0155] For EMT ISH and LINE-1 ORFlp IHC correlation in human PDAC TMAs, the LINE-1 ORFlp IHC stain was manually scored per TMA core on an increasing scale from 1 to 4 by a pathologist (B.P.). ORFlp score was calculated per patient by averaging the score across cores, which were distinct tissue samples, and rounding to the nearest integer. Scores 1 and 2 were considered to be ORFlp low, and scores 3 and 4 were considered to be ORFlp high. The EMT dual ISH stain was also quantified per TMA core using the HALO.Annotated tumor regions were verified by a pathologist (B.P.) with exclusions of large folds, debris, fat, muscle, and normal tissue from quantification. HALO Al DenseNet convolutional neural network classifiers were trained to distinguish tumor glands from stroma within the annotated tumor regions. Nuclear detection and cell segmentation were performed using the default Al nuclear segmentation algorithm on the hematoxylin color-deconvolved image component. HALO ISH IHC module v.3.1.4 was optimized to quantify the ISH markers. Positivity of each ISH stain was determined based on signal intensity and staining pattern of the color-deconvolved image component of each chromogen (DAB- epithelial probe pool, FastRed - mesenchymal probe pool). Cells that were double-positive for epithelial (Epi) and mesenchymal stains were considered quasi-mesenchymal (QM). The percentage of QM cells out of Epi cells (% QM of Epi) per TMA core was calculated. Percent QM of Epi per patient was calculated by averaging the percentage across cores for a patient. Percent QM of Epi was then compared between LINE-1 ORFlp low and LINE-1 ORFlp high samples using a Wilcoxon test, both per core and per patient.

[0156] For LINE-1 ORFlp IHC area quantification in mouse xenografts, HALO Area Quantification module v.2.4.3 was used to determine the proportion of annotated tumor area positive for LINE-1 ORFlp. Color deconvolution of Hematoxylin and chromogen stains as well as thresholds for LINE-1 ORFlp positivity were manually validated in each image. For EMT dual RNA-ISH cell enumeration, HALO Al DenseNet convolutional neural network classifiers were trained to distinguish tumor glands from stroma within the annotated tumor regions for each stain. HALO Multiplex IHC module v.3.4.9 was optimized to quantify the EMT dual ISH stain. The IHC stains were quantified with HALO Multiplex IHC module v.3.1.4. Cells were segmented based on the Hematoxylin color-deconvolved image component. Marker positivity7was determined based on signal intensity and area coverage within the nuclear and cytoplasmic compartments of the color-deconvolved image component for each chromogen. Cell phenotypes were also defined using multiple markers (EMT dualAtty. Dkt. No.: 29539-0860WO1ISH: Epi+QM+, Epi+QM-, Epi-QM+). This quantification was performed within the tumor gland-classified regions. Data tables containing cell phenotype, signal intensities and area analyzed were exported for statistical analyses. For tumor heterogeneity using hexagon tessellation, to test whether NTC vs ORF1 tumor samples had distinct differences in EMT expression, an approach was used that quantified EMT cell phenoty pes within individual tiles of a hexagon tessellation overlaid on the annotated tumor region. Output object data from HALO segmentation analysis, which contains the bounding box coordinates of each cell, was converted to the cell center in micron coordinate space (0.268 microns / pixel). A hexagon tessellation, with side length of 50 microns, was then created using the R sf package. Tiles that did not contain any cells were excluded. Plots of the hexagon tiling for each sample was generated for visual inspection. Cell phenotypes (Epi+QM-, Epi+QM+, Epi-QM+) and total number of cells were enumerated within each hexagon tile. Finally, cell phenotypes, normalized by the total number of cells in each tile, were compared for NTC and ORF1 groups using a Wilcoxon rank-sum test. For Masson’s trichrome area quantification, a HALO Al DenseNet classifier was trained to discriminate collagen-rich regions from tumor cells and subsequently determine their area proportions in each image. For multiplex ISH combined with PanCK positive cell enumeration, cell segmentation and phenotyping were performed within annotated tumor regions using the HALO FISH-IF module v2.2.5. Nuclear detection was performed using the default Al nuclear segmentation algorithm based on the DAPI channel. Cells were categorized as PanCK+ based on the channel's signal intensity within the nuclear and cytoplasmic compartments. The positivity of ISH channels w as determined based on signal intensity and dot size within a cell, with brighter and larger dots having more weight. The autofluorescence channel was employed as an exclusion marker to reduce false positives from folds, debris and necrotic regions. Cell phenotypes were also defined using multiple marker criteria (e.g. PanCK+INFLAMED+, ACTA2+, I16+). Marker positivity thresholds were manually validated for cell phenotyping quality in each sample. HALO and HALO Al versions: HALO v3.6.4134.137 and HALO Al 3.6.4134.Immunofluorescence staining and confocal microscopy

[0157] To analyze cells in the 3D tumorsphere condition. PDAC cells were plated into T75 cm2 ultra-low attachment (ULA) flasks with DMEM medium at 1x106 cells per flask and grown for 2-3 days before harvesting. Cells were collected and dissociated as a single cell by trypsinization, fixed with 4% (w / v) paraformaldehyde in PBS for 15 min, then resuspended in PBS to yield a concentration of 1x106 cells ml-1. A hydrophobic pen was used to create a hydrophobic barrier on the slides, and 300 pl of each sample were aliquotedAtty. Dkt. No.: 29539-0860WO1into the appropriate wells of the double cytology funnel (Fisher Scientific, cat# 10-356). Once the cells had adhered to the slide, the slides are stored in PBS up to 1 week at 4oC. For the immunofluorescent staining, cells were permeabilized with 0.5% (v / v) Triton X-100 in PBS for 10 minutes, then blocked with 2% BSA solution for 1 hr. Following this, the cells were incubated w ith a primary' antibody of desired concentration (diluted in blocking solution) for 1 hr at room temperature. Following three washes with 0.1% Triton X-100 in PBS (PBST). cells were incubated with fluorophore-conjugated secondary antibodies (FITC-AffiniPure goat anti-rabbit, Jackson ImmunoResearch, cat# 111-095-003, Goat Anti-Mouse IgG H&L (Alexa Fluor® 594, abeam, cat# ab 150120) for 1 hr at room temperature. After another three w ashes in 0.1% Triton X-100 in PBS, DAPI solution was applied for 30 s, then washed three times. Coverslips were then mounted with Fluoromount-G® Mounting Medium (SouthemBiotech, cat# 0100-01). Five images were captured per sample condition at a magnification of 63X using a Zeiss LSM 710 confocal laser microscope with z-stacking 0.5 pm distance. Then images were further processed by maximum intensity projection using Zeiss Zen software. The following primary’ antibodies were used: anti-LSM14A (Thermo Scientific, cat# PA5-53680), anti-LlNE-1 ORFlp (EMD Millipore, cat# MABC1152), anti-MOV10 (Abeam, cat# ab80613). For the 2D samples, cells were plated on the Collagen type-I, rat-tail (50 pg ml-1) coated 12 mm coverslips and allow ed to adhere for at least 24 hrs before use in experiments.qRT-PCR

[0158] Total RNA extraction was performed using the miRNeasy Mini Kit (QIAGEN, cat# ) according to the manufacturer’s instructions. Total 1 pg of RNA was reverse transcribed using TaqMan™ Reverse Transcription Reagents (Invitrogen™, cat# ). qRT-PCR was conducted using the PowerUpTM SYBRTM Green Master Mix (Applied Biosystems™, cat# ). The following primers shown in Table 2 were used.

[0159] Table 2. qPCR primers.Atty. Dkt. No.: 29539-0860WO1Atty. Dkt. No.: 29539-0860WO1

[0160] Reactions were performed on a QuantStudio thermocycler. The level of gene expression was calculated based on the 2AACTmethod and normalized to the Ct value of GAPDH as endogenous control.RNA immunoprecipitation (RIP) sequencing

[0161] To identify ORFlp binding RNA, we performed RIP-seq using Magna RIPTM, RNA-binding protein immunoprecipitation Kit (Millipore, cat# 17-700) following manufacturer’s instructions. PDAC3, PDAC6, PDAC8, PA-TU-8988T and Mia PACA-2 cells were plated into the T75 cm2 ultra-low attachment (ULA) flasks containing DMEM medium at 2x106 cells per flask. After 7 days, the cells were collected and washed with cold PBS. For RIP, cells were lysed using 100 pl of RIP lysis buffer (150 mM KC1, 25 mM Tris-HC1 pH 7.4, 5 mM EDTA, 0.5% NP-40) per IP reaction including protease inhibitor cocktail and RNase inhibitor followed by centrifugation 14,000 rpm for 10 min at 4oC. 50 pl (0.5 mg) of protein A / G magnetic beads was washed twice with RIP wash buffer. For each sample, theAtty. Dkt. No.: 29539-0860WO1beads were incubated with 5 ig of mouse monoclonal anti-LINE-1 ORFlp antibody and mouse normal IgG in 100 pl of RIP wash buffer at room temperature with rotation for 30 minutes. After incubation, the antibody-conjugated beads were washed with RIP wash buffer twice. The beads were resuspended in 900 pl of RIP immunoprecipitation buffer containing 0.5 M EDTA, RNase inhibitor. 100 pl of protein lysates were added and incubated at 4oC for overnight with gentle rotation. After washing the beads with ice-cold RIP wash buffer the RNA binding proteins were digested with proteinase K at 55oC for 30 min with shaking. Purified RNA was isolated following the manufacturer’s instructions and reverse transcribed for qRT-PCR and RNA sequencing. RIP-seq read mapping and quantification: Reads were trimmed and quality checked using a skew er. Briefly, ends of the reads were trimmed to remove Ns and bases with quality less than 20. After that, the quality scores of the remaining bases were sorted, and the quality at the 20th percentile was computed. Reads were discarded if its quality at the 20th percentile was less than 15. In addition, reads shorter than 40 bases after trimming were discarded. If at least 1 of the reads in the pair failed the quality check and had to be discarded, we discarded the mate as well. Quality filtered reads were mapped to annotated repeat loci in RepeatMasker using software: Quantify ing Interspersed Repeat Expression (SQuIRE) (github.conVwyangl7 / SQuIRE) (60). The SQuIRE pipeline first obtains reference annotation files from RepeatMasker, then aligns reads using STAT, and lastly, quantifies locus specific repeat expression by redistributing multimapping read fractions in proportion to estimated TE expression with an expectation maximization algorithm. Selection of RIP-seq enriched repeats / transcripts: LINE-1 ORFlp enriched transcripts were selected by Log2(ORFlp RIP / input)>l, and Benjamini-Hochberg adjusted p-value<0.05. Similarly target protein depleted transcripts were selected by Log2(ORFlp RIP / input)<-l, and Benjamini-Hochberg adjusted p-value<0.05. dsRNA force calculation for repeats: To estimate xds for a given repeat locus, we intersect each repeat locus annotated in RepeatMasker with the complementary pairs of sequences within each 3kb genomic window s that have calculated dsRNA force (Sulc et al, bioRxiv, 2023). The start and end coordinates of the corresponding complementary sequence must be 100% overlap with a given repeat. Then, the calculated dsRNA force for that given sequence was assigned to the maximally overlapping repeat. Statistical analysis and visualization: Expression of repeats and coding genes was normalized across samples using trimmed-mean of M-values (TMM) in edgeR. The size factor for each sample was calculated using calcNormF actors based on coding genes alone as described in the previous study (60). Low-count gene / repeats in each sample with counts per million (CPM) smaller than 2 were removed. Log2 -transformed CPM were usedAtty. Dkt. No.: 29539-0860WO1for downstream visualization in heatmaps. In heatmaps, only repeats with standard deviation greater than 2 across all samples were plotted. Differential expressions of coding genes and repetitive elements were analyzed separately using DESeq2 v 1.33.4.Total RNA sequencing

[0162] Total RNA was extracted using the miRNeasy Mini Kit. Cell sample libraries were prepared for sequencing using the SMARTer Stranded Total RNA-seq Kit v2 (cat# 634413) - Pico Input Mammalian (Takara Bio USA). RNA samples were first converted into cDNA, then adaptors for Illumina sequencing were added through PCR. The PCR products are purified using AMPure Beads (Beckman Coulter, cat# A63881), then ribosomal cDNA was depleted. After depletion, the samples were further amplified using PCR, then purified again using AMPure Beads. Each sample was then qPCR quantified using a KAPA Library Quantification Kit (Roche, cat# 07960140001) and Phix Control Kit V3 (Illumina, cat# FC-110- 3001). Samples were then pooled together and quantified again using the KAPA Library Quantification Kit. The Pooled library was then sequenced using the NextSeq 1000 Sequencing Kit.CosMx spatial molecular imager (SMI) transcriptomic data analysis

[0163] Spatial transcriptomic data was generated and previously reported (33). From four tissue microarrays (total N=94 FOV), we manually scored the intensity7of LINE-1 ORF Ip and categorized each FOV into two groups; ORF Ip low (1+2+) and ORF Ip high (3+4+). Then, differential expression testing was performed using the R library smiDE (Vasconcelos et al, bioRxiv, 2024). We used the package to fit a negative binomial regression for each gene, predicting raw expression from high / low biopsy LINE-1 ORFlp, adjusting for potential contamination from segmentation errors, including log (total counts) as an offset term to normalize the raw expression values, and including biopsy ID as a random effect. Genes at high risk of contamination from neighboring cells were excluded, as described in (Vasconcelos & McGuire). Cell proximity scores w ere calculated as follows. For each pair of cell types A and B, we recorded a 2x2 table of whether cells had cell type A and whether cells' nearest 10 neighbors included any of cell type B. The cell proximity score for cell types A and B was then defined as the log odds ratio calculated from this table.In vitro transcribed (IVT) RNA synthesis

[0164] pCRII-TOPO-HSATII (chromosome 10) plasmid was previously used for IVT RNA synthesis (24). HSATII Fragments were amplified by PCR with Ml 3 forward / reverse primers and subjected to in vitro transcription with SP6 (MAXIscriptTM SP6 transcription kit. Thermo Scientific, cat# AM1308) following manufacturer’s instruction. This included aAtty. Dkt. No.: 29539-0860WO1Turbo DNase digestion after RNA transcription to remove DNA. Then, in vitro transcribed repeat RNAs were purified with Megaclear™ Transcription Clean-Up Kit (Thermo Fisher Scientific, cat# AM1908) and quantified.Immunoprecipitation

[0165] PDAC3 and PDAC6 cells were plated into the T75 cm2 ultra-low attachment (ULA) flasks containing DMEM medium at 2x106 cells per flask. After 3-5 days, the cells were collected and washed with cold- PBS. For IP reaction, cells were lysed with RIPA buffer (50 mM Tris-HCl pH7.4, 150 mM sodium chloride, 0.5% sodium deoxycholate, 0.1% SDS, 1%NP-4O) (Boston BioProducts, cat# BP-115) containing Halt™ Protease and Phosphatase Inhibitor Cocktail (PIC; ThermoFisher Scientific, USA, cat# 78440) for 1 hr at 4oC. Then, lysed suspension 5-10 times through aneedle (27G ‘A inch; BD, cat# 305109) attached to a 1 ml syringe. After centrifugation at 12,000 x g for 10 min to pellet the cell debris, the supernatant was collected and quantified using Bradford (Sigma-Aldrich, cat# B6916). 50 pl (0.5 mg) of Pierce Protein A / G Magnetic beads (ThermoFisher Scientific, cat# 88802) was washed twice with RIPA buffer. For each sample, the beads were incubated with 5 pg of mouse monoclonal anti-LlNE-1 ORFlp antibody and mouse normal IgGin 100 pl of RIPA buffer for 30 minutes at room temperature. After incubation, the antibody-conjugated beads were washed with RIPA buffer twice. The beads were resuspended in 100 pl of RIPA buffer containing PIC and the same total amount of protein was used for each immunoprecipitation. Magnetic beads conjugated to the anti-mouse IgG and anti-LINE-1 ORFlp was added to the supernatant and RIPA buffer to make up 1 ml reaction volume and incubated at 4oC for overnight with gentle rotation. The beads were then washed five times with 500 pl of the RIPA buffer. The ORFlp protein complex bound was eluted using IX SDS sample buffer and boiled at lOOoC for 10 min.Immunoblotting

[0166] Cells were collected, washed, and lysed with lysis buffer (50 mM Tri-Cl (pH 6.8), 10% glycerol, 2% SDS, 1 mM 1,4-dithiothreitol (DTT) and Halt™ Protease and Phosphatase Inhibitor Cocktail. Total protein concentration was determined by BCA protein assay (Thermo Fisher Scientific, cat# 23227). The cell lysates were subjected to sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and then transferred to a polyvinylidene difluoride membrane (Millipore Sigma, MA, USA, cat# IPVH00010). The membranes were blocked with 3% of bovine serum albumin (BSA, Sigma- Aldrich, cat# A2058) for 1 hr and applied with primary antibodies for overnight at 4°C. Membranes were washed with IX phosphate- buffered saline (PBS) containing 0.1% Tween-20 (Sigma-Atty. Dkt. No.: 29539-0860WO1Aldrich, cat# P1379) (PBST) for 10 minutes 3 times and incubated with horseradish peroxidase (HRP)-conjugated secondary antibodies. Signal was detected with enhanced chemiluminescence (ECL; SuperSignal™ West Pico PLUS Chemiluminescent Substrate, Thermo Scientific, cat# 34577, SuperSignal™ West Femto Maximum Sensitivity Substrate, Thermo Scientific, cat# 34094), and images were developed using G:BOX (SYNGENE). The following primary antibodies were used: anti-cleaved caspase 3 (D175; Cell Signaling Technology, cat# 9661), anti- H2AX (SI 39; Cell Signaling Technology, cat# 9718), anti-MDA5 (Cell Signaling Technology, cat# 5321), anti-RIG-T (Cell Signaling Technology, cat# 3743), anti-MAVS (Cell Signaling Technology, cat# 3993), anti-TBKl (Cell Signaling Technology, cat# 3504), anti-phospho-TBKl (S172; Cell Signaling Technology, cat# 5483), anti-MOVIO (Abeam, cat# ab80613, Novus Biologicals, cat# NBP2-94867), anti-METTL3 (Cell Signaling Technology, cat# 86132S), anti-ADARl (Cell Signaling Technology, cat# 81284S), anti- LINE-1 ORFlp (EMD Millipore, cat# MABC1152), anti-LSM14A (Thermo Scientific, cat# PA5-53680), anti-Lamin A / C (Cell Signaling Technology, cat# 2032), anti-GAPDH (Cell Signaling Technology’, cat# 2118). For secondary’ antibodies, HRP-conjugated goat anti-rabbit (Cell Signaling Technology, cat# 7074) and HRP-conjugated goat anti-mouse (Cell Signaling Technology, cat# 7076) were used following the manufacturer’s suggested protocols.dsRNA dot blot assay

[0167] RNA was isolated from PDAC cell lines and dilute to 400 ng pl-1. The 4 pl RNA was applied to a 0.45 um nylon transfer membrane (WhatmanTM, cat# 10416230), then membrane was completely dried, then washed in PBST three times 5 min each, blocked with 3% BSA in PBST for 30 min. The membrane was incubated with primary' anti-dsRNA (clone rJ2, 1:1000, EMD Millipore. cat# MABE1134) for overnight at4°C, then washed in PBST 3x10 min, incubated in HRP-conjugated goat anti-mouse secondary antibody (1:5000) for 2 hrs at room temperature, washed in PBST 3x10 min, and visualized using ECL solution. Then, membranes were stained with 0.02% (w / v) methylene blue in 0.3 M sodium acetate (pH 5.5). As a control, RNase III (New’ England Biolabs, cat# M0245S) and RNase H (New England Biolabs, cat# M0297S) treated RNA were used.Subcelhilar fractionation

[0168] PDAC 3 shNTC and shORFl-2 cells w ere plated into T75 cm2 ULA flask at 1x106 cells for 7 days. Then, cells were collected and washed with cold-PBS. The nuclear and cytoplasmic fraction was performed using Nuclear / cytoplasmic fractionation kit (Abeam, cat# ab289882) as manufacturer’s instructions. Briefly, cells were lysed with cytosolAtty. Dkt. No.: 29539-0860WO1extraction buffer A containing DTT and protease inhibitor and vortex vigorously for 15 sec and further incubate on ice for 10 min. Ice-cold cytosol extraction buffer B was added and vortexed, then centrifuged for 5 min at 14,000 x g. The supernatant (cytoplasmic extract) fraction was immediately transferred to a clear pre-chilled tube. Then, the pellet was resuspended in 50 pl of ice-cold nuclear extraction buffer, mixed and vortexed for 15 sec every 10 min for a total 40 min. The nuclear fraction was separated by centrifugation at 16.000 x g for 10 min. 4x SDS sample buffer was added and boiled for SDS-PAGE.Statistical analysis

[0169] For all experiments p-values were calculated using PRISM9 GraphPad unpaired two-tailed student’s t- test, one-way ANOVA or two-way ANOVA, Mann-Whitney test, Wilcoxon rank-sum test, and p-values of less than 0.05 were considered significant.Data availability statement

[0170] RNA-seq data for shORFl and shMOVIO are available at NCBI GEO database (GSE287105). RIP-seq for PDAC3, PDAC6 and PDAC8 are also available at NCBI GEO database (GSE287102). The remaining data generated in this study are available upon request to the corresponding author.Example 2: LINE-1 ORFlp knockdown induces innate immune response and decreased 3D tumorsphere growth.

[0171] Our prior studies have demonstrated significantly higher expression of repeat RNA species in 3D tumorsphere suspension culture compared to 2D adherent conditions (24,25), which we again observed in our PDAC cell lines through total RNA-seq (FIG. 1A).Given the viral mimicry of repeat RNAs, we hypothesized that cancer cells require a mechanism to evade IFN response driven by innate immune sensing normally used to detect viral pathogens (FIG. IB). LINE-1 ORFlp has been detected at high levels across a variety of cancers including PDAC (11). IHC for LINE-1 ORFlp revealed expression that was restricted to tumor glands and not in normal pancreas or the responding stromal microenvironment (FIG. 1C, FIG.2A).

[0172] We screened for ORFlp expression in a panel of normal fibroblasts (MRC-9; lung fibroblast, NF; pancreas fibroblast), cancer-associated fibroblasts (CAFs; CAF-1, CAF-2, CAF-3), and 19 PDAC cell lines collected from primary7tumors, metastatic liver, and ascites. This showed that the majority of PDAC cell lines, regardless of their origin, expressed ORFlp, which was not present in any fibroblast cell lines (FIGs. 1D-1E).Atty. Dkt. No.: 29539-0860WO1Although nuclear localization of LINE- 1 -encoded proteins (ORFlp and ORF2p) is required for retrotransposition, LINE-1 ORFlp was primarily observed as punctate cytoplasmic foci in PDAC3 and PDAC6 cells compared to NF and CAF (CAF-L CAF-2, CAF-3) cell lines using immunofluorescent staining (FIG. IF). The presence of ORFlp in these cytoplasmic compartments points to potential functional activity' independent of retrotransposition.

[0173] Our prior work demonstrated that we could suppress LINE-1 ORFlp with shRNA in patient-derived PDAC cell lines (PDAC3 and PDAC6), which resulted in a significant reduction in 3D tumorsphere growth (21). However, anti-growth effects were not seen in these same cell lines grow n in standard 2D adherent culture (FIGs. 1G-1H, FIG. 2B), which suggested differences linked with the higher levels of repeat RNAs found in 3D versus 2D culture (FIG. 1A). Tumorsphere 3D growth across multiple PDAC cell lines was significantly affected by ORFlp suppression (FIGs. 2C-2D), particularly in those with high ORFlp expression.

[0174] Using total RNA-seq, we further characterized transcriptomic changes from ORFlp suppression, revealing distinct transcriptional profiles (FIG. II). Gene set enrichment analysis (GSEA) in ORFlp knockdown cell lines identified positive enrichment of IFN response, JAK-STAT signaling pathway and TNF-alpha signaling pathway7with negative enrichment of epithelial-mesenchymal transition (EMT) and E2F target genes (FIGs. 2E-2F).

[0175] Of note, cellular component (Gene Ontology'; GO:CC) analysis by g:ProfilerR revealed differential gene set enrichment in genes from the extracellular region (G0:0005576), which suggests potential effects of ORFlp suppression on tumor cell driven paracrine effects (FIG. 1J).

[0176] Given the relationship of innate immune response with loss of ORFlp, we evaluated if the tumorsphere effect observed was driven by apoptosis or necroptosis. We observed that decreased tumorsphere growth in LINE-1 ORFlp knockdown PDAC cells was rescued by pan-caspase inhibitor (Z- VAD-FMK; VAD) treatment, but not a necroptosis inhibitor (Necrostatin-1; Nec-1), suggesting that ORFlp reduction induces caspase-mediated cell apoptosis in 3D tumorsphere condition (FIG. IK).Example 3: Suppression of ORFlp induces repeat RNA-mediated IFN responses through RIG-I / MAVS pathway in PDAC

[0177] LINE-1 encodes bicistronic transcripts essential for the production of ORFlp and ORF2p. In shORFl knockdown cell lines, we found that shRNA targeting ORF1 also decreased ORF2 transcripts (FIG. 3A). To determine if the effects of ORFlp suppressionAtty. Dkt. No.: 29539-0860WO1were independent of LINE- 1 ORF2p reverse transcriptase activity, we treated PDAC cell lines with a panel of nucleoside reverse transcriptase inhibitors (NRTIs; Stavudine: d4T, Lamivudine: 3TC, Emtricitabine: FTC, Tenofovir: TDF) that are known to inhibit ORF2p activity (26). Both single and combinational NRTI treatment showed modest effects on tumorsphere growth compared to DMSO, but the effects were unable to phenocopy the more significant reduction in tumorsphere growth from shORF 1 in PDAC cell lines (FIG. 3B). This supported the contribution of ORFlp in PDAC survival irrespective of retrotransposition.

[0178] Next, we generated doxycycline (Dox)-inducible shRNA resistant ORFlp (ORFeus) cell lines in the shORFl PDAC3 cell line to further investigate ORFlp specific function. The synthetic ORFeus encodes identical amino acids with native ORF1 but differs in nucleotide sequences (27), which enables ORFlp to be expressed in the stable shORFl cell line (FIG. 4A, FIG.3C). Given the viral mimicry of LINE-1 RNA itself (28-30), we titrated Dox concentration to induce ORFlp expression to physiologically relevant endogenous ORFlp levels. The 20 ng ml-1 of Dox demonstrated reduced gene expression associated with an innate immune response consistent with rescue of the phenotype (FIG. 4A, FIG. 3D). However, at 100 ng ml-1 of Dox, this result was reversed, which is presumably due to an excess of ORFeus mRNA or protein stimulating an innate immune response (FIG. 3E). Altogether, these results demonstrate that ORFlp depletion leads to increased apoptosis and induces an IFN response.

[0179] Viral mimicry of repeat RNAs has been shown to involve PRR recognition leading to induction of IFN response and IFN-stimulated gene (ISG) expression (5,31). To investigate the specific PRR responsible for activating the IFN response in ORFlp knockdown cell lines, we utilized CRISPR-Cas9 mediated ablation of representative PRRs including RIG-I (DDX58), MDA5 (IFIH1), MAVS (MAVS), and STING (STING1) in ORFlp knockdown PDAC3 and PDAC6 cells (FIG. 4B). Depletion of RIG-I / MAVS had the strongest effect while MDA5 had partial effects in suppressing innate immune-related gene expression in ORFlp knockdown cells (FIG. 4C, left: PDAC3, right: PDAC6).

[0180] Ablation of STING also strongly suppress innate immune gene response in PDAC cell lines, but a potential confounder for cGAS / STING-mediated IFN response is the contribution of micronuclei as a source of cytoplasmic DNA (32). To evaluate this possibility, we quantified micronuclei using fluorescence microscopy of DAPI signal and found elevated micronuclei in 3D tumorspheres compared to 2D culture with concordant increased yH2AX intensity for DNA double strand breaks (FIG. 4D). Similarly, activation ofAtty. Dkt. No.: 29539-0860WO1the IFN response in 3D tumorspheres (FIG.3F) was suppressed by STING depletion in wild type (WT) PDAC3, which demonstrates that STING-mediated IFN response is shared in both shNTC and shORF 1 cell lines in response to cytoplasmic DNA from different sources including micronuclei and potential reverse transcriptional products (FIG. 4E, FIG. 3G).

[0181] In summan-, these results indicate that RIG- I / MAVS are the major PRRs involved in activation of the IFN response in the setting of ORFlp loss in PDAC cells.Interestingly. ORFlp-low expressing PDAC cell lines (AsPC-1, PANC-1, KP-4, MGH1222. MGH1312, Mia PACA2, MGH1247) also exhibited high levels of dsRNA as assessed by dot blotting (FIG. 4F). Additionally, PANC-1 was found to tolerate repeat RNA-mediated antiviral response measured by apoptosis and IFNB1 expression in prior work (33), which suggests that ORFlp-low cancer cells adopt alternative mechanisms in tolerating repeat dsRNA-induced IFN signaling.

[0182] Immunoblot of PRR expression (RIG-I, MDA5, MAVS, STING) across PDAC cell lines found a striking loss of one or more PRRs in ORFlp-low PDAC cell lines (Mia PACA2, Capan-1, BxPC3. KP-4, PANC-1, PA-TU- 8988T18988T, SUIT-2, AsPC-1), which implies that PRR loss is an alternative mechanism to tolerate repeats which would otherwise induce an antiviral response (FIG. 4G). Overall, our results demonstrate that ORFlp shields repeat RNA from activating PRR-induced cancer cell death (Hide) and / or suppresses PRR expression (Hit).Example 4: RNA immunoprecipitation sequencing (RIP-seq) reveals LINE-1 ORFlp association with repeat RNAs

[0183] We next focused on understanding the potential function of ORF Ip on the dsRNA-mediated immune response. First, we used an ORFlp monoclonal antibody to conduct RIP-seq in three PDAC cell lines (PDAC3, PDAC6, and PDAC8) grown in suspension for 7 days to understand the composition of RNA species directly bound by ORFlp (FIG. 1A, FIG. 5A). The efficiency of ORFlp pull-down was confirmed by Western blotting (FIG. 5B). Extracted ORFlp-bound RNA was reverse-transcribed for qRT-PCR analysis or used for library construction for RNA sequencing. We observed the enrichment of LINE-1 ORF1 and ORF2 RNAs in the ORFlp precipitates compared to input or IgG indicating successful precipitation of native LINE-1 ORFlp ribonucleoproteins (RNPs) (FIG.5B)

[0184] RNAs extracted from input and ORFlp precipitates ere subjected to paired-end sequencing and reads were mapped to annotated repeat loci in RepeatMasker (see methods).Atty. Dkt. No.: 29539-0860WO1With the threshold Log2FC>l and p-adj<0.05 compared to input, RIP-seq demonstrates enrichment of a vast amount of repeat RNAs including DNA, LINE, LTR and SINE across all three PDAC cell lines (PDAC3, 6, 8) (FIG. 5C). As expected, LIHs was enriched in the ORFlp precipitates compared input. LINE and SINE elements were the predominant repeat RNA species enriched on ORFlp in all cell lines (FIG. 5C). Notably, we found certain recurring LINE and SINE sites in the human genome were recurrently enriched in all 3 cell lines (FIG. 5D). For example, specific regions of LINE elements on chromosomes 3, 7 10.1 1, 13, and 22 and SINE elements on chromosomes 6, 15, and 20 were found in all 3 cell lines.

[0185] Given the RIG-I / MAVS dependency on IFN response in ORF1 knockdown cell lines, we assessed for dsRNA-forming capability that we have defined as a dsRNA force (21). This revealed highest dsRNA forces in the SINE elements bound to ORFlp in all 3 cell lines (FIG. 5E). We repeated this experiment in two commercially available PDAC cell lines (PA-TU-8988T, MiaPACA2) that express low LINE-1 ORFlp (FIG. 5E). RIP qRT-PCR analysis showed an enrichment of ORF1 and ORF2 RNAs in ORFlp precipitates in both cell lines as expected. However, Mia PACA2 showed less enrichment of other repeat RNAs (AluYh6, AluYg6, AluSx, AluSg, HERVK-env), which was not observed in PA-TU-8988T cell line (FIG. 5F).

[0186] PA-TU-8988T cells had a 50% reduction in 3D tumorsphere growth with ORFlp knockdown compared to a more modest reduction of 15% in Mia PACA2 shORFl cell lines (FIG. 2D). This indicates that ORFlp binding of repeat RNA species is associated with enhanced survival in 3D tumorsphere conditions. Altogether, these results support ORFlp function as a molecular sponge for dsRNA-forming repeat RNAs to "shield" from PRR sensing and improved survival in 3D cultures.

[0187] Aberrant expression of HSATII RNA in 3D tumorsphere condition (FIG. 1A, FIG. 6A), leads to RLR-mediated IFN response (33). Nonetheless, we did not observe enrichment of HSATII species with ORFlp, which supports that ORFlp effects are downstream of HSATII RNA induction of dsRNA repeat species (33), including Alu species (AluSg, AluSx, AluYg6), LINE-1 ORF1 and ORF2 RNA, and the endogenous retrovirus HERVK (FIGs. 6B-6C).

[0188] To test the sensitivity of shNTC and shORFl cell lines to dsRNA induced by HSATII in vitro-transcribed (IVT) RNA, we introduced HSATII species into our PDAC cell lines (PDAC3 and PDAC6) in standard 2D adherent culture, given the lower basal expression levels of repeat RNA. PDAC3 and PDAC6 shORFl -2 cell lines grown in 2D cultureAtty. Dkt. No.: 29539-0860WO1transfected with HSATII IVT RNAs showed a robust induction of p-TBKl and apoptosis (cleaved caspase-3, C1-CASP3) and associated induction of IFNB1 gene expression (FIGs.6D, 6E, and 6F).

[0189] To evaluate if this ORFlp response was specific to repeat RNAs, we used well known PAMPs including lipopolysaccharide (LPS) and synthetic dsRNApolyinosinic: poly cytidylic acid [Poly(LC)]. Due to a lack of Toll-like receptor 4 (TLR4) expression in PDAC cell lines (34), we were unable to detect significant induction of LPS-mediated TFNB1 and TSG expression. In contrast, Poly (I:C) transfection elicited a strong immune response in PDAC cell lines, including IFNB1 and ISG expression to a similar extent in both WT and ORFlp knockdown cell lines (FIG. 6G-6H), indicating ORFlp shielding is specific for repeat RNA.Example 5: ORFlp Associates with MOV10 RNA Helicase in PBs

[0190] ORFlp is known to bind with rnRNAs enriched in PBs (35), which are cytoplasmic RNP granules primarily composed of translationally repressed rnRNAs, mediated by inducing microRNA (miRNA)- mediated mRNA silencing and a nonsense-mediated rnRNA decay pathway containing deadenylases, decapping enzymes, and exonucleases (36). To confirm cellular localization of ORFlp, we conducted immunofluorescence staining with an anti-LSM14A antibody, a canonical PB marker (37). Confocal microscopy images with Z-stacks taken at 0.5 pm intervals show co-localization of ORFlp and LSM14A in the cytoplasm in a punctate pattern (FIG. 7A). Measurements of PBs revealed a significant reduction in both the size and number of PBs in PDAC6 cells that express low' ORFlp (Quartile 1; QI) (FIG. 7B). The intensity of ORFlp and the area of LSM14A+ PBs are significantly correlated at the individual cell level (FIG. 8A). To further dissect the relationship of ORFlp with PBs, we attempted to reduce cytoplasmic PBs by generating shLSM14A cell lines (FIG. 7C). Knockdown of LSM14A significantly' suppressed 3D tumorsphere growths along w ith increased inflamed gene expression (e.g. MX1, IL6, IFI27 ISG20), which phenocopied our ORFlp knockdown cell lines (FIG. 7C, FIG. 8B). To observe the cytoplasmic localization of ORFlp upon LSM14A loss, we performed immunofluorescent staining in 2D condition to obtain sufficient cytoplasmic visualization as ORFlp localization in PBs was found in both 2D and 3D conditions.Although LSM14A knockdown slightly increased ORFlp levels, presumably due to post-transcriptional regulation (mRNA decay) in PBs (FIG. 7C), ORFlp failed to form cytoplasmic foci (FIG. 7D). These results suggest that LSM14A mediated assembly ofAtty. Dkt. No.: 29539-0860WO1cytoplasmic RNP granules is necessary' for ORF Ip subcellular localization in PBs, which serves to shield and process viral repeat RNA.

[0191] Previous study characterized ORF Ip interacting proteins by Mass Spectrometric assay and identified high confidence interactors, including ZCCHC3, TROVE2 and MOV 10 (27). Overlapping these proteins with known proteins localized in the PB (Gene Ontology'; GO:CC p-body), we identified MOVIO as a candidate gene important for ORFlp function in PBs (FIG. 7E, Table 3)Atty. Dkt. No.: 29539-0860WO1

[0192] Table 3: List of proteins localized in PBs.Atty. Dkt. No.: 29539-0860WO1Atty. Dkt. No.: 29539-0860WO1ZFP36 ring finger protein-like 1

[0193] MOVIO is a 5'-3’ RNA helicase that has ATP-dependent DNA or RNA "structure remodeling” activity including double-stranded nucleic acid unwinding and is involved in miRNA-mediated RNA silencing (38). In the PDAC cell lines, we verified interaction of MOVlO-ORFlp by immunoprecipitation in both 2D and 3D culture conditions (FIG. 7E, FIG. 8C). Notably, other viral RNA processing enzymes including m6A methyltransferase (METTL3) and RNA-specific adenosine deaminase 1 (AD ARI) known to affect PRR signaling (39-41) were not found to interact with ORFlp by immunoprecipitation (FIG. 7E). This MOVlO-ORFlp interaction was verified by immunofluorescent staining demonstrating cytoplasmic foci (FIG. 7F), and there was no change in cy toplasmic and nuclear levels of MOVIO in the ORF1 knockdown cell lines (FIG.8D). Next, we examined MOVIO function by using lentiviral shRNA-mediated knockdown cell lines, which showed no effect on ORFlp levels (FIGs.8E-8F). There was a significant effect on 3D tumorsphere growth in shMOVIO PDAC3 and PDAC6 cell lines with some modest grow th inhibition of only shMOVIO PDAC62D culture (FIG. 7G-7H). There were also modest additive effects of ORF 1 -MOV 10 double knockdown in PDAC6 cell lines supporting overlapping effects of shORFl and shMOVIO in PDAC. Knockdown of METTL3 inhibited 3D tumorsphere growth analogous to ORF1 knockdown, and this result is augmented in ORF1;METTL3 double- knockdown cell lines (FIGs. 8G-8I), supporting independent effects of METTL3 and ORFlp suppression. To demonstrate if MOVIO knockdown manifests similar transcriptional signatures to the ORF1 knockdown cell line, we conducted total RNA-seq analysis in shMOV 10 PDAC6 cells (FIG. 71). Differentially expressed protein-coding genes in the MOVIO knockdown (Log2FC>±0.4, p-adj<0.05) were enriched for viral infection-related pathways and innate immune-related genes including IFIT1, IFIH1. CXCL2, MX1, RSAD1, DDX58 (FIG. 71). In contrast. FN1 is downregulated with MOVIO suppression. GO:BP analysis of differentially expressed genes in shORFl and shMOVIO cell lines revealed 35 shared up-regulated and 44 shared down-regulated genes. Overlapping up-regulated genes were enriched for IFN response and inflammatory response-related genes, while down-regulated genes were enriched for collagen fibril organization and cell adhesion-related genes (FIG. 7 J, FIG. 8J). Together, these results suggest ORFlp binds repeat RNA and coding RNAs to be processed by MOVIO in PBs as an adaptive mechanism to protect PDAC cells from overactive PRR signaling.Atty. Dkt. No.: 29539-0860WO1Example 6: LINE-1 ORF Ip Loss Shifts PDAC to Epithelial Cell States and Enhances KRAS Inhibitor Efficacy

[0194] Given the correlation of EMT-related genes with LINE-1 ORFlp expression (FIG. 2E), we sought to investigate the biological implications of LINE- 1 ORFlp in PDAC functional models.

[0195] To validate our sequencing results, we performed qRT-PCR analysis of EMT genes (quasi -mesenchymal genes (QM); FN1, ACTA2, SERPINE1, epithelial genes (E); KRT19, KRT7, MUC1, CDH1), showing gain of epithelial signatures upon ORFlp knockdown (FIG. 10A). Moreover, CDH1 expression was rescued by inducing Dox-inducible ORFlp in shORFl knockdown PDAC3 cell line (FIG. 10B). Suppression of ORF1 in 6 other PDAC cell lines was more heterogeneous for changes in EMT-related gene expression, but there was increase of at least 1 E gene or decrease of at least 1 QM gene in all cell lines (FIG. 9A).

[0196] KRAS inhibitors (KRASi) have become an exciting therapeutic modality being evaluated in PDAC given the high prevalence (-90%) of oncogenic KRAS mutations (42). Our PDAC cell lines harbor KRAS mutations G12D (PDAC9), G12V (PDAC3, PDAC8) and Q61H (PDAC6). Given the reactivation of innate immune response by KRASi (43,44), we hypothesized that ORFlp knockdown could enhance PDAC cell line sensitivity to KRASi. We performed a dose-response curve analysis of a pan-KRASi (Pan-KRAS-IN-1) in PDAC cell lines. All PDAC cell lines tested were sensitive to Pan-KRAS-IN-1 compared to Pan-RAS- IN-1 (FIG. 10C, FIG. 9B). We then evaluated the effect of ORF1 suppression on Pan-KRAS-IN-1 response (FIGs. 10D-10E) and found synergistic effects compared to shRNA or drug treatment alone. PDAC3 had a calculated mean Loewe synergy score of 12.02 (p-value=9.64e-13) and PDAC6 had a score of 12.91 (p-value=5.02e-16) (FIG. 10F).

[0197] To measure tumorsphere growth effects over time, we monitored size every 6 hrs by Incucyte live-cell imaging. This revealed clear reduction in tumorsphere size with Pan-KRAS-IN-1 inhibitor treatment in shORFl knockdown PDAC cell lines (FIG. 10G). We tested if KRAS inhibition amplifies IFN response in shORFl knockdown cell lines, and indeed qRT-PCR analysis revealed increased IFN-related gene expression upon Pan-KRAS-IN-1 treatment in shORFl PDAC3 cells by inducing endogenous repeat RNA expression (FIG. 10H, FIG. 9C). Altogether, our results suggest targeting ORFlp could enhance the efficacy of KRAS inhibitors by boosting cancer- intrinsic innate immune responses.Atty. Dkt. No.: 29539-0860WO1Example 7: ORF Ip Knockdown Enhances Tumor Cell Apoptosis and Fibrotic Changes in the Tumor Microenvironment

[0198] To delineate the relevance of ORF Ip in vivo, we generated luciferase-expressing PDAC6 shNTC and shORFl cells. These cell lines were subcutaneously injected into immunodeficient NOD scid gamma (NSG) mice and tumor grow th was monitored every' week for eight weeks (FIG. 12A). PDAC6 shORFl had significantly slower tumor growth compared to shNTC (FIGs. 12B-12C). Tumors were harvested after eight weeks and ORFlp suppression in shORFl PDAC6 compared to shNTC PDAC6 was validated by IHC (FIG. 11A)

[0199] Dual IHC analysis revealed no changes in proliferation marker Ki-67, but a significantly higher level of apoptosis regulator Bel -2 in shORFl PDAC6 tumors compared to shNTC (FIG. 12D). Based on our in-vitro data shown in FIGs. 10A-10H, we next employed our previously developed EMT RNA in situ hybridization (RNA-ISH) for human PDAC (45), which uses a dual-color RNA-ISH for epithelial genes (Epi: CDH1, EPCAM, KRT5, KRT7, KRT8, KRT19) and quasi-mesenchymal genes (QM: SERPINE1, FN1, CDH2). EMT RNA-ISH of PDAC xenografts revealed shORFl compared to shNTC PDAC6 xenografts had significant reduction in the normalized QM+ cells (mean=shNTC; 9.5 vs shORFl; 4.5; p=<2e-16) with gains in the normalized number of Epi+ only cells per tile (mean=shNTC; 0.9168 vs shORFl; 0.9194; p=7.7e-08) (FIG. 12E, FIGs. 11B-11C). These findings are consistent with decreased EMT and gain of apoptosis regulator Bcl-2 with the suppression of LINE-1 ORF1.

[0200] Distinct subtypes of the tumor microenvironment (subTME) have been described in PDAC, categorized as deserted subTME and reactive subTME, characterized respectively by enrichment in the extracellular matrix (ECM)-signaling pathway and enrichment in the growth factor / innate immune signaling pathway (46). To evaluate whether the loss of ORF Ip mediates an intrinsic IFN and pro- inflammatory response in cancer cells, potentially altering the ECM composition and subTME, w e conducted histological analyses using H&E and Masson’s trichrome staining on xenografts (FIGs. 12F-12G).

[0201] Our findings reveal a significantly greater extent of fibrotic tissue in shORF 1 cases compared to shNTC cases, characterized by an increase in collagen and / or myxoid material-rich extracellular matrix consistent with a deserted subTME (FIG. 12G).Furthermore, ORFlp expression by IHC was inversely correlated with the extent of fibrosis (Pearson r=-0.5977, p=0.0076) (FIG. 12G). To gain deeper insight into the alterations within the TME resulting from ORFlp depletion, considering potential effects of ORFlpAtty. Dkt. No.: 29539-0860WO1suppression on PD AC-driven paracrine effects (FIG. 12H), we utilized 3-plex RNA in-situ hybridization (RNA-ISH) to evaluate CAF phenotypes and innate immune gene activation. This targeted myofibroblastic CAF (myCAF) and inflammatory CAF (iCAF) specific markers (Acta2 for myCAF and 116 for iCAF) (47,48), immune-related genes (INFLAMED; CXCL2, OAS1, IFIT3), combined with Pan cytokeratin (PanCK) IHC. Fluorescence RNA-ISH revealed significant differences in INFLAMED gene expression in PanCK+ cancer cells and CAF populations between shNTC and shORFl tumors. Analysis of PanCK+INFLAMED+ cells showed a significant decrease in shORFl compared to shNTC xenografts (FIG. 12H, FIG. HD) Furthermore, our results demonstrated a marked reduction in the number of Acta2+ myCAFs, but not il6+ iCAFs, in the tumor stroma in shORFl compared to shNTC xenografts (FIG. 12H, FIG. HD). In summary, our in vivo experiments reveal diminished tumor growth with associated decreased EMT activity and higher iCAF to myCAF proportions with fibrosis. These findings underscore the significant functional impact of ORFlp on tumor development and progression in vivo (FIG. 121).

[0202] Example 8: Spatial Transcriptomic Analysis Reveals Distinct Intratumoral TME Based on LINE-1 ORFlp

[0203] Finally, to investigate the clinical relevance of ORFlp expression in PDAC patients, we re- analyzed our previously reported CosMx spatial molecular imager (SMI) transcriptomic data (four TMAs; 1, 28, 31, 32), based on ORFlp intensity assessed through IHC scoring (FIG. 13A). From a total of 94 fields of view (FOV), 51 FOVs were assigned to the low (1+2+) group, and 24 FOVs were assigned to the high (3+4+) group, 19 of FOVs was not classified (FIGs. 13B-13C, FIGs. 14A-14D; Table 4)Atty. Dkt. No.: 29539-0860WO1

[0204] Table 4: Classification of FOVs based on LINE-1 ORFlp expressionAtty. Dkt. No.: 29539-0860WO1&

[0205] UMAP (Uniform Manifold Approximation and Projection) analysis showed the population of cell types in each group, and the percentage of cell populations (per core) were visualized (FIG. 13C, FIG. 14A) Differential gene expression analysis in cancer cells revealed increased expression of keratins found in squamous epithelial cells (KRT4, KRT13, KRT15), STAT4, SOX2, ACTA2, DDR1, and DNA repair and cell cycle checkpoint-related genes (ATR, CHEK1, CHEK2, BRCA1) in association with high ORF Ip tumors (FIG. 13D).In contrast, the low ORF Ip group showed increased expression of BID (a pro-apoptotic member of the Bcl-2 protein family) and EZH2 (methyltransferase) (FIG. 13D). GO: BP analysis and KEGG pathway revealed enrichment of cell proliferation and inflammatory signaling pathway in high ORFlp cancer cells (FIG. 13E). In CAFs, inflammatory CAF (iCAF) signatures (LIF, SOD2, MT2A, IL11) and ISGs (OAS3, IFIH1) were enriched in CAFs located near high ORFlp cancer cells (FIGs. 14B-14D).

[0206] Spatial organization and cellular interactions play important roles in driving tumor progression and therapeutic resistance in PDAC (46,49). To address how the spatialAtty. Dkt. No.: 29539-0860WO1distribution of LINE-1 ORF Ip expressing cancer cells contributes to tumor architecture, we performed neighborhood enrichment analysis between cell clusters in CosMx SMI data, measured as a log odds ratio (see Example 1; FIG. 13F). Notably, we observed enrichment of Schwann cells, vascular smooth muscle cells (VSMC), intrapancreatic neurons (IPN) and adipocytes near high ORF1 cancer cells. In contrast, there was a decrease in T cell, B cell, and plasma cell populations near high ORF1 cancer cells indicating an immunologically cold tumor phenotype. These findings indicate that intratumoral subTMEs differed widely in composition and displayed distinct regional variation in immune milieus based on LINE-1 ORFlp cancer cell expression. Finally, we evaluated the relationship of LINE-1 ORFlp expression with EMT in our PDAC samples. Using the established EMT RNA-ISH probes used in FIG. 12E, quantification of EMT showed LINE-1 ORFlp association with gain of QM states (FIG. 13G), which was consistent with high squamous epithelial cell keratin expression from our CosMx analysis. Additionally, we assessed the relationship of LINE-1 ORFlp expression with overall survival after surgery (cutoff 36 months) in a cohort of 131 resectable PDAC patients (FIG. 13H). Kaplan-Meier analysis revealed LINE-1 ORFlp high

[0207] expressors (3+ and 4+) compared to low expressors (1+ and 2+) had significantly worsened survival post- surgery (Log-rank p=0.046; FIG. 13H). Overall, these results demonstrate that the cancer cell-specific expression of LINE-1 ORFlp as a prognostic marker in human PDAC.

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Claims

Atty. Dkt. No.: 29539-0860WO1What is claimed is:

1. A method for treating cancer, the method comprising administering to a subject in need thereof an agent that reduces expression of Long Interspersed Nuclear Element- 1 open reading frame-1 (LINE-1 ORF1) in a cancer cell of the subject, wherein the agent is selected from short hairpin RNA (shRNA). small interfering RNA (siRNA), antisense oligonucleotides, or a Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) construct targeting LINE- 1 OREL2. The method of claim 1. wherein the agent comprises a sequence selected from SEQ ID NO: 1 to SEQ ID NO: 13, or a complement thereof.

3. The method of claim 1 or 2, wherein the cancer cell is a pancreatic cancer cell.

4. The method of any one of claims 1-3, wherein the cancer cell is a pancreatic ductal adenocarcinoma (PDAC) cell.

5. The method of any one of claims 1-4, wherein administering the agent results in one or more of the following:(a) suppresses LINE-1 ORF1 expression in the cancer cell;(b) enhances sensitivity of the cancer cell to a Kirsten rat sarcoma virus (KRAS) inhibitor;(c) induce an innate immune response in the cancer cell;(d) activates a retinoic acid-inducible gene I / mitochondrial antiviral signaling protein (RIG-I / MAVS)-dependent interferon response;(e) activates an innate immune response through double-stranded RNA (dsRNA) sensors RIG-I and MAVS;(I) reduces epithelial-mesenchymal transition in the cancer cell;(g) decreases tumorsphere formation and xenograft growth;(h) promotes an epithelial cell state;(i) modulates a DNA damage response pathway in the cancer cell;(j) modulates autophagy in the cancer cell;(k) alters epigenetic modifications in the cancer cell;Atty. Dkt. No.: 29539-0860WO1(l) modulates cellular senescence in the cancer cell;(m) alters extracellular matrix composition in the tumor microenvironment;(n) alters telomerase activity in the cancer cell;(o) modulates cancer cell plasticity;(p) alters cancer cell dormancy;(q) modulates exosome production by the cancer cell;(r) alters cancer cell interactions with the extracellular matrix; and / or(s) modulates cancer cell resistance to apoptosis.

6. The method of any one of claims 1-5, further comprising administering to the subject a KRAS inhibitor.

7. The method of any one of claims 1-6, further comprising:detecting expression levels of RIG-I and MAVS in the cancer cell, wherein reduced expression of RIG-I or MAVS indicates increased sensitivity to LINE-1 ORF1 suppression or detecting expression levels of pattern recognition receptors (PRRs) in pancreatic ductal adenocarcinoma (PDAC) cell lines, wherein reduced expression of PRRs indicates increased sensitivity to LINE-1 ORF1 suppression.

8. The method of claim 7, wherein the detecting step is performed prior to the administering step.

9. A method of spatially analyzing RNA methylation status in a biological sample, the method comprising:(a) contacting the biological sample with a substrate;(b) permeabilizing the biological sample; and(c) detecting methylated RNA or complement thereof using spatial analysis methods.

10. The method of claim 9, wherein the spatial analysis methods comprise in situ sequencing, spatial transcriptomics, or fluorescence in situ hybridization.

11. The method of claim 9 or 10, wherein the biological sample is a tissue section comprising a pancreatic cancer cell.Atty. Dkt. No.: 29539-0860WO112. The method of any one of claims 9-11, wherein detecting methylated RNA comprises using antibodies specific to methylated RNA.

13. The method of any one of claims 9-12, further comprising imaging the biological sample to obtain spatial information.

14. The method of any one of claims 9-13, wherein the substrate comprises a capture probe array, wherein the capture probe array comprises capture probes, each capture probe having a capture domain that can hybridize to a target in the biological sample and a spatial barcode that can uniquely identify a location on the array.

15. The method of any one of claims 1-14, wherein the LINE-1 ORF1 is LINE-1 ORF1 RNA.

16. The method of any one of claims 1-14, wherein the LINE-1 ORF1 is LINE-1 ORF1 protein.

17. The method of any one of claims 1-16. wherein the agent is an shRNA targeting LINE-1 ORFE18. The method of any one of claims 1-17, wherein the agent:(a) reduces tumor grow th in vivo relative to a tumor in a subject who is not treated with the agent;(b) increases tumor cell apoptosis in vivo relative to a tumor in a subject who is not treated with the agent; and / or(c) reduces myofibroblastic cancer-associated fibroblasts.

19. The method of any one of claims 1-18. wherein the agent is administered systemically.

20. The method of any one of claims 1-18, wherein the agent is administered locally to a tumor site.Atty. Dkt. No.: 29539-0860WO121. The method of any one of claims 1-20. wherein the agent is encapsulated in a nanoparticle delivery system.

22. The method of any one of claims 1-21, further comprising administering:(a) an immunotherapy agent;(b) chemotherapy; and / or(c) radiation therapy.

23. A composition for suppressing LINE-1 ORF1 expression in a cancer cell, comprising: an agent that reduces expression of LINE-1 ORF1, wherein the agent is selected from the group consisting of shRNA, siRNA, antisense oligonucleotides, and CRISPR constructs targeting LINE- 1 ORF1.

24. The composition of claim 23, wherein the agent comprises a sequence selected from SEQ ID NO: 1 to SEQ ID NO:

13. or a complement thereof.

25. The composition of claim 23 or 24, wherein the shRNA targets a sequence within the ORF1 of LINE- 1.

26. The composition of any one of claims 23-25, wherein the cancer cell is a PDAC cell.

27. The composition of any one of claims 23-26, further comprising a KRAS inhibitor.

28. The composition of any one of claims 23-27, wherein the combination of the shRNA and the KRAS inhibitor results in a synergistic reduction in cancer cell growth when administered to the cancer cell.

29. The composition of any one of claims 23-28, wherein the shRNA is encapsulated in a nanoparticle delivery system.

30. The composition of any one of claims 23-29, further comprising an immunotherapy agent.Atty. Dkt. No.: 29539-0860WO131. The composition of any one of claims 23-30, wherein the LINE-1 ORF1 is LINE-1 ORF1 RNA.

32. The composition of any one of claims 23-30, wherein the LINE-1 ORF1 is LINE-1 ORF1 protein.

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