Compositions and methods for inducing viral mimicry in cancer cells

Abstract

The present disclosure provides methods for treating cancer in a subject in need thereof comprising administering to the subject an effective amount of inhibitors of LINE- 1 (LI) activity or ADAR1 inhibitors based on TP53 mutation status. The methods of the present technology increase immunogenic SINE-derived dsRNA levels, thus triggering an effective immune response. Also disclosed herein are methods for selecting cancer patients for combination therapy with (i) LI inhibitors or AD ARI inhibitors and (ii) immune checkpoint blockade (ICB) inhibitors based on TP53 mutation status.

Description

COMPOSITIONS AND METHODS FOR INDUCING VIRAL MIMICRYIN CANCER CELLSCROSS-REFERENCE TO RELATED PATENT APPLICATIONS10001] This application claims the benefit of and priority to U.S. Provisional Patent Application No. 63 / 467,566, filed May 18, 2023, the contents of which are incorporated herein by reference in their entireties.TECHNICAL FIELD

[0002] The present disclosure provides methods for treating cancer in a subject in need thereof comprising administering to the subject an effective amount of inhibitors of LINE- 1 (LI) activity or ADAR1 inhibitors based on TP53 mutation status. The methods of the present technology increase immunogenic SINE-derived dsRNA levels, thus triggering an effective immune response. Also disclosed herein are methods for selecting cancer patients for combination therapy with (i) L1 inhibitors or AD ARI inhibitors and (ii) immune checkpoint blockade (ICB) inhibitors based on TP53 mutation status.BACKGROUND

[0003] Overexpression of repetitive elements is an emerging hallmark of human cancers1. Diverse repeats can mimic viruses by replicating within the cancer genome through retrotransposition, or presenting pathogen-associated molecular patterns (PAMPs) to the pattern recognition receptors (PRRs) of the innate immune system2 5. Yet, how specific repeats affect tumor evolution and shape the tumor immune microenvironment (TME) in a pro- or anti- tumorigenic manner remains poorly defined.

[0004] Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease and the fourth leading cause of cancer-related deaths in the United States. Despite an increase in five-year patient survival from 8% to 11% during the past five years13, the disease remains largely incurable. Inflammatory processes have emerged as key mediators of pancreatic cancerdevelopment and progression, with inflammation associated with negative outcomes by forming a highly immune- suppressed TME14 15, implying tumors modulate the immune system to their selective advantage14,15. Like many other tumors, PDAC exhibits abnormal expression of repetitive elements, with high enrichment in the over 70% of cases with TP53 mutations11,16.

[0005] Accordingly, there is an urgent need for methods for treating refractory tumors that are resistant to conventional therapies such as immune checkpoint blockade therapy.SUMMARY OF THE PRESENT TECHNOLOGY

[0006] In one aspect, the present disclosure provides a method for treating cancer in a patient in need thereof comprising administering to the patient an effective amount of a LINE-1 (LI) inhibitor, wherein the patient comprises a tumor harboring a TP53 mutation. The TP53 mutation may be a missense mutation, a nonsense mutation, or a frameshift mutation. In an another aspect, the present disclosure provides a method for treating cancer in a patient in need thereof comprising administering to the patient an effective amount of an ADAR1 inhibitor, wherein the patient comprises tumors that do not harbor TP53 mutations. In yet another aspect, the present disclosure provides a method for treating cancer in a patient in need thereof comprising administering to the patient an effective amount of an AD ARI inhibitor and a LINE- 1 (L1) inhibitor, wherein the patient comprises tumors that include at least one TP53 mutant gene and at least one wildtype TP53 gene. The at least one TP53 mutant gene may comprise a missense mutation, a nonsense mutation, or a frameshift mutation.

[0007] In any of the preceding embodiments of the methods disclosed herein, the method further comprises administering an effective amount of an additional therapeutic agent, such as immune checkpoint blockade therapy.

[0008] In one aspect, the present disclosure provides a method for enhancing tumor sensitivity to immune checkpoint blockade therapy in a patient diagnosed with cancer comprising administering to the patient an effective amount of a LINE-1 (L1) inhibitor, wherein the patient comprises a tumor harboring a TP53 mutation. The TP53 mutation may be a missense mutation, a nonsense mutation, or a frameshift mutation. In one aspect, the present disclosure provides acethod for enhancing tumor sensitivity to immune checkpoint blockade therapy in a patient diagnosed with cancer comprising administering to the patient an effective amount of an AD ARI inhibitor, wherein the patient comprises tumors that do not harbor TP53 mutations.

[0009] In any and all embodiments of the methods disclosed herein, the AD ARI inhibitor is a small molecule or an AD ARI -specific inhibitory nucleic acid. Examples of AD ARI small molecule inhibitors include, but are not limited to, rebecsinib, GT0001X, AVA-ADR-001, CLAD-100, lithospermic acid, and Regaloside B. The ADAR 1 -specific inhibitory nucleic acid may be a siRNA, a shRNA, an antisense oligonucleotide, or a sgRNA.

[0010] In any and all embodiments of the methods disclosed herein, the L1 inhibitor is a small molecule or an Ll-specific inhibitory nucleic acid. Examples of L1 small molecule inhibitors include, but are not limited to, capsaicin, nucleoside reverse transcriptase inhibitors (e.g., lamivudine, adefovir, penci cl ovir, abacavir, stavudine, didanosine, zidovudine, emtricitabine, zalcitabine, tenofovir, entecavir, telbivudine, AZTTP, d4TTP, ddCTP, 3TCTP), NSC332395, and AD2 / ZINC89469886. The Ll-specific inhibitory nucleic acid may be a siRNA, a shRNA, an antisense oligonucleotide, or a sgRNA.

[0011] In some embodiments of the methods disclosed herein, the methods further comprise (a) detecting Short Interspersed Nuclear Elements (SINE) expression levels in a test sample obtained from the patient prior to treatment with the L1 inhibitor and (b) determining that the SINE expression levels in the test sample are reduced compared to a reference sample obtained from a healthy control patient or a predetermined threshold. In some embodiments, the patient exhibits elevated L1 retrotransposition activity compared to a healthy control patient or a predetermined threshold. In other embodiments of the methods disclosed herein, the methods further comprise (a) detecting SINE expression levels in a test sample obtained from the patient prior to treatment with the AD ARI inhibitor and (b) determining that the SINE expression levels in the test sample are elevated or comparable to a reference sample obtained from a healthy control patient or a predetermined threshold. The SINE expression levels may be detected via next-generation sequencing, RNA-Seq, PCR, real-time quantitative PCR (qPCR), digital PCR(dPCR), Southern blotting, Reverse transcriptase-PCR (RT-PCR), Northern blotting, microarray, dot or slot blots, in situ hybridization, or fluorescent in situ hybridization (FISH). In some embodiments, the patient exhibits L r1etrotransposition activity that is comparable to a healthy control patient or a predetermined threshold. Additionally or alternatively, in some embodiments, the SINE comprise at least one or more of SINE2 / SINE3, monomeric SINE1, AluJ, AluS, AluY and SINE-VNTR-Alu (SVA). In some embodiments, the test sample or reference sample includes a biopsied tissue, wherein the biopsied tissue is frozen, fresh, or Formalin-Fixed Paraffin-Embedded (FFPE).

[0012] In any and all embodiments of the methods disclosed herein, the L i1nhibitor or the AD ARI inhibitor is administered orally, topically, intranasally, systemically, intravenously, subcutaneously, intraperitoneally, intradermally, intraocularly, iontophoretically, transmucosally, or intramuscularly.

[0013] In any of the preceding embodiments of the methods disclosed herein, the cancer is pancreatic cancer or ovarian cancer.

[0014] Additionally or alternatively, in certain embodiments, the immune checkpoint blockade therapy comprises an anti-PD-1 antibody, an anti-PD-Ll antibody, an anti-PD-L2 antibody, an anti-CTLA-4 antibody, an anti-TIM3 antibody, an anti-4-lBB antibody, an anti- CD73 antibody, an anti-GITR antibody, or an anti-LAG-3 antibody. Examples of immune checkpoint inhibitors include, but are not limited to, cemiplimab, tremelimumab, ipilimumab, nivolumab, pidilizumab, lambrolizumab, pembrolizumab, envafolimab, atezolizumab, avelumab, durvalumab, dostarlimab, MPDL3280A, BMS-936559, MEDI-4736, MSB 00107180, inhibitory antibodies against LAG-3 (lymphocyte activation gene 3), TIM3 (T-cell immunoglobulin and mucin-3), B7-H3, B7-H4, TIGIT (T-cell immunoreceptor with Ig and ITIM domains), AMP -224, MDX-1105, arelumab, tremelimumab, IMP321, MGA271, BMS-986016, lirilumab, urelumab, PF-05082566, IPH2101, MEDI-6469, CP-870,893, Mogamulizumab, Varlilumab, Galiximab, AMP-514, AUNP 12, Indoximod, NLG-919, INCB024360, CD80,CD86, ICOS (inducible T-cell costimulatory), DLBCL (diffuse large B-cell lymphoma) inhibitors, BTLA (B and T lymphocyte attenuator), PDR001, and any combination thereof.

[0015] In any and all embodiments of the methods described herein, the patient exhibits elevated expression levels of SINE-derived double stranded RNAs (dsRNA) and / or elevated levels of one or more RIG-I associated type-I interferon genes. The one or more RIG-I associated type-I interferon genes may be selected from the group consisting of PELI1, HLA-G, CDKN1A, PIM1, CGAS, PSMA2, UPP1, RIPK2, PFKP, STAT2, IRF9, TNFAIP2, ISG20, SOCS1, SOCS7, DHX58, TRIM25, IFI30, IRF3, and HELZ2.

[0016] In another aspect, the present disclosure provides systems and methods for selecting cancer therapies based on double-stranded ribonucleic acid (dsRNA) force metrics associated with short interspersed nuclear elements (SINE). One or more processors coupled with memory may obtain, for a subject at risk of or diagnosed with cancer, a dataset identifying a plurality of RNA sequences from a biological sample of the subject, the plurality of RNA sequences comprising (i) a first RNA sequence corresponding to a first strand of a dsRNA and (ii) a second RNA sequence corresponding to a second strand of the dsRNA, the second strand complementary to and shorter than the first strand. The one or more processors may identify, using the dataset, (i) at least one SINE copy in the first RNA sequence, (ii) a first length of nucleotides of the first RNA sequence, and (iii) a second length of nucleotides of the second RNA sequence. The one or more processors may determine a probability of an occurrence of a pairable SINE associated with the at least one SINE copy on the dsRNA based on a (i) first plurality of frequencies for a corresponding first plurality of nucleotides that are within at least a first predetermined window of the first RNA sequence of the at least one SINE copy and (ii) a second plurality of frequencies for a corresponding second plurality of nucleotides that are within at least a second predetermined window of the second RNA sequence of the at least one SINE copy. The one or more processors may identify using at least the first predetermined window of the first RNA sequence, an insert length of the at least one SINE copy within the at least the first predetermined window. The one or more processors may generate a dsRNA force metric indicating a degree of potential of the at least one SINE copy to fold with the pairable SINE onthe dsRNA based on (i) the probability of the occurrence of the pairable SINE, (ii) the first length, (iii) the second length, and (iv) the insert length. The one or more processors may select, from a plurality of cancer therapies, a cancer therapy based on the dsRNA force metric relative to a threshold. The one or more processors may store, using one or more data structures, an association between the subject and the cancer therapy.

[0017] In some embodiments, the one or more processors may determine that the dsRNA force metric satisfies the threshold. The one or more processors may select the cancer therapy comprising immune checkpoint blockade (ICB) therapy, responsive to determining that the dsRNA force metric satisfies the threshold. In some embodiments, the one or more processors may determine that the dsRNA force metric does not satisfy the threshold. The one or more processors may select the cancer therapy comprising a combination therapy, responsive to determining that the dsRNA force metric does not satisfy the threshold. The combination therapy may include at least one of (i) a first combination comprising ICB therapy and Ll-inhibitor or (ii) a second combination comprising the ICB therapy and AD ARI inhibitor.

[0018] In some embodiments, the biological sample includes a biopsied tissue obtained from an organ infected with the cancer, wherein the biopsied tissue is at least one of frozen, fresh, or Formalin-Fixed Paraffin-Embedded (FFPE). In some embodiments, the one or more processors may remove, from the dataset, a portion of the plurality of RNA sequences corresponding to RNA sequencing reads with a quality score not satisfying a threshold score. In some embodiments, the one or more processors may filter, from the dataset, a portion of the plurality of RNA sequences corresponding to RNA sequencing reads with counts of repeats below a threshold count.

[0019] In some embodiments, the one or more processors may obtain, for the subject, a second dataset identifying genomic sequences derived from a second biological sample of the subject. The genomic sequences may be obtained via next-generation sequencing and comprise a plurality of sequencing reads. The one or more processors may identify, using the seconddataset, at least one p53 sequence in the genomic sequences of the subject. The at least one p53 sequence may include a plurality of non-silent p53 mutant sequences and a plurality of wildtype p53 sequences. The one or more processors may generate a wild-type p53 fitness metric based on transactivation levels of at least one p53 target gene induced by a wild-type p53 polypeptide encoded by the plurality of wild-type p53 sequences, a binding affinity of the wildtype p53 polypeptide to a promoter region of the at least one p53 target gene, and expression levels of the wild-type p53 polypeptide. The one or more processors may generate a maximum p53 fitness metric based on transactivation levels of the at least one p53 target gene caused by a complete deletion of p53 gene. The one or more processors may determine a mutant p53 fitness metric based on the wild-type p53 fitness metric, the maximum p53 fitness metric, and the mutated position of each p53 mutant sequence of the plurality of non-silent p53 mutant sequences. In some embodiments, the at least one p53 target gene may be WAF1, MDM2, BAX, hl433s, AIP1, GADD45, NOXA, or P53R2.

[0020] In some embodiments, the one or more processors may identify, using the second dataset, a retrotransposon (RT) activity metric corresponding to a RT indicating a level of RT activity in the genomic sequences of the subject. The RT activity may correspond to at least one of a long interspersed nuclear element- 1 (LINE-1), SINE, or a SINE- Variable number of tandem repeat (VNTR)-Alus (SVA). In some embodiments, the one or more processors may select the cancer therapy as a function of the dsRNA metric, the mutant p53 fitness metric, and the RT burden metric. In some embodiments, the biological sample may be obtained from at least one of ovary or pancreas. In some embodiments, the cancer may be selected from among ovarian cancer and pancreatic cancer.BRIEF DESCRIPTION OF THE DRAWINGS

[0021] FIGs. 1A-1D: SINE elements are co-expressed with RIG-I IFN signaling in PDAC autopsy cohort. FIG. 1A: Multiregional sample and multi-omics data overview of autopsy PDAC cohort. FIG. IB: Sample composition of the autopsy PDAC cohort: 214 total RNAseq samplew with 58 of which have matched WGS and 92 of which have matched WES.FIG. 1C: Pairwise correlation analysis of 140 interferon related genes expression in 214 samples (data not shown) revealed 4 co-expression clusters (left). KEGG pathway annotation of genes identified distinct functional pathways in each cluster (right). Cluster 4 (C4) exclusive pathways are highlighted in yellow. FIG. ID: Pearson correlation coefficient between median expression of genes in C4 (hereafter IFN signature) and repeats' expression in different classes across 214 samples. Benjamini -Hochberg corrected P values of t- test are labeled.

[0022] FIGs. 2A-2D: Younger SINE elements have stronger correlation with core IFN signature and are prone to form dsRNA. FIG. 2A: Pearson correlation between IFN signature and different SINE subtypes. SINE subtypes are colored based on their evolutionary age. Evolutionary timeline of SINEs: SINE2 / SINE3, SINE1 (Monomeric and dimeric AluJ, AluS and AluY, and SVA). FIG. 2B: Kimura distance of all copies of SINEs from the consensus sequence of the subfamily grouped by SINE evolutionary groups. FIG. 2C: Pearson correlation coefficients between IFN signature and locus specific expression of SINEs stratified by evolutionary age groups. FIG. 2D: dsRNA forces calculated for the human genome (hg38) that encompassing SINEs. (* p < 0.05, ** p < 0.01, *** p< 0.001, **** p < 0.0001 representing the t-test significance).

[0023] FIGs. 3A-3F: L1 retrotransposition activity is associated with mutant TP53 status and tracked with phylogenetic tumor evolutionary branching. FIG. 3A: Overview of de novo insertions of LINE1 in 74 samples from 8 patients based on Whole Genome Sequencing (WGS) data stratified by tumor sites (primary vs metastasis). Out of which, 31 samples from 5 patients have non-zero somatic L1 insertion. FIG. 3B: Insertion loci overview of 31 samples from 5 patients have non-zero somatic insertion detected. Track background is colored by the patient. Each dot represents one insertion locus. Position of each dot is proportion to number of samples share the same insertion from the same patient. For example, dots on the inner margin of each patient indicate the insertion is only found in one sample, dots on the outer margin indicate that insertion happened in all samples for that given patient. FIG. 3C: Oncoprint of SNPs with frequency over 10% across samples from the 5 patients. All three samples from patient MPAM05 have a frameshift deletion in TP53 at c.78delT. FIG. 3D: Boxplot of numberof L1 insertion in samples with different TP53 mutation status in PDAC autopsy cohort. Multiple dots per box indicate multiregional samples from the same patient. Given that the samples in each TP53 mutation background are not independent, no statistical test were applied. FIG. 3E: Boxplot of number of somatic L 1insertion in samples with wild type or TP53 mutants in PDAC biopsy cohort in COMPASS trial35, p-value representing the t-test significance of somatic L1 insertion difference between WT and TP53 mutant samples is labeled. FIG. 3F: Integrative analysis of L i1nsertion phenotypes and tumor evolution of patient MPAM04. Samples are boxed with colors representing the tissue type. Target site features is coded as red (target site duplication) and green (target site deletion).

[0024] FIGs. 4A-4D: Immunogenic SINE expression is anti-correlated with L1 retrotransposition and M2 macrophage infiltration. FIG. 4A: Density plots colored by quartiles of samples (n=27, excluding 3 samples from MPAM05 with an extremely high number of somatic L1 insertions) with different number of somatic L in1 sertions. Samples in quartile 4 (Q4) and quartile 1 (Q1) are selected to represent L1 insertion high and low samples for differential expression analysis shown as volcano plot below. The calculated LogsFoldChanges have been corrected for tumor purity (Methods). FIG. 4B: Volcano plot showing the differential expression of SINEs between samples with high (Q4) or low number (Q1) of somatic LI insertions. SINE subtypes are colored based on their evolutionary age. FIG. 4C: Cropped western blot of L1 ORFlp expression in 11 PDAC patient derived cell lines. Actin normalized L1 ORFlp intensity were ordered to select for the high and low L 1ORF1p expression cell lines. FIG. 4D: Comparison of change of SINEs expression between PDAC patient samples (Log2FC (high L1 insertions / low L1 insertions)) and in vitro cell lines Log2FC (high ORFlp expression / low ORFlp expression).

[0025] FIGs. 5A-5H: L1 retrotransposition activity suppresses immunogenic SINE expression likely via in-cis binding. FIG. 5A: Design of two short hairpin (sh)DNA sequences for knocking down of L1 ORF1p protein, and results expression of L1 ORF1p confirmed by western blot. GAPDH normalized ORF1p intensity were quantified. The knockdown experiment was performed in biological triplicates. FIG. 5B: An example scatter plot showingthe anti-association between L1 ORFlp expression and young SINE (AluYkl2) expression. FIG. 5C: Summarized compartmentalization of repeats that show significant negative (r < 0 & p < 0.05) or positive (r > 0 & p < 0.05) correlation with L O1RFlp expression in the L sh1DNA knocking down experiments. SINEs are exclusive and dominating the repeats that are negatively associated with L1 ORFlp expression. FIG. 5D: Immunofluorescent staining with dsRNA antibody (Jr2), L O1 RFlp antibody, and DAPI in L O1RFlp high (PDAC6, 8, 9) and L O1RFlp low (PANC-1, KP-4) cell lines. Intensities of L O1RFlp green signal and dsRNA red signal are quantified for each cell line and plotted on the right violin plots. FIG. 5E: RNA ImmunoPrecipitation sequencing (RIPseq) experimental setup and computational pipeline for detecting ORFlp and ORF2p binding transcripts in embryonal carcinoma cell line - N2102p. FIG. 5F: Violin plots show the distribution of dsRNA force assigned for each repeat copy (Method) grouped by whether it is bound by protein of interests - ORFlp and ORF2p detected in the RIPseq. p- values calculated by t-test are labeled (**** p.value < 0.00001). FIG. 5G: Composition of SINEs bound by both L O1RF Ip and ORF2p. SINE subtypes are colored based on their evolutionary age. FIG. 5H: Heatmap shows the expression (Z-score) of L O1RFlp and ORF2p binding SINEs in the RIPseq experiment.

[0026] FIGs. 6A-6F: ADAR1 expression and function are dampened in TP53 mutation. FIG. 6A: Violin plot shows the expression of AD ARI and MDM2 in different TP53 backgrounds in the autopsy PDAC cohort (p-values from Wilcoxon test are labeled). FIG. 6B: Violin plot shows the expression of SINE subgroups in different TP53 backgrounds in the autopsy PDAC cohort (p- values from Wilcoxon test are labeled). FIG. 6C: A-I editing index for four patients with known different somatic mutation of TP53 and number of somatic LI insertions. Inverted SINEs are SINE copies with an adjacent copy next in the opposite direction that can potentially form dsRNA when expressed; non-inverted SINEs are SINE copies that do not meet these criteria, p-values out of Wilcoxon test are labeled. FIG. 6D: A-I editing index for PDAC biopsy samples in COMPASS trial stratified by different TP53 mutation backgrounds, p-values calculated after Wilcoxon test are labeled. FIG. 6E: Scatter plot of A-I editing index as a function of predicted TP53 missense mutation fitness46. FIG. 6F: Schematic diagram showsdiff erent strategies tumor cells leverage to maintain pro- tumorigenic SINE expression in TP 53 WT (via ADAR1 editing) and TP53 mutant (via L b1inding) backgrounds. (* p < 0.05, ** p < 0.01, *** p< 0.001, **** p < 0.0001, representing the corresponding statistical significance).

[0027] FIG. 7A: Scatter plot of tumor purity estimated based on RNAseq data (x-axis, using ESTIMATE) and DNAseq data (y-axis, using FACETS). FIG. 7B: Gene Set Enrichment Analysis (GSEA) of HALLMARK gene sets. GSEA was applied to the gene list ranked by Log2FoldChanges between metastasis (n=90) and primary (n=124). The Log2FoldChanges have been corrected for tumor purity (Methods). FIG. 7C: Distribution of Pearson correlation coefficients between median expression of genes in each cluster and subfamily-based expression of repeats grouped and colored by classes. FIG. 7D: Scatter plot of correlation with IFN core signature as a function of base mean expression of different repeat classes. Spearman correlation coefficient and associated p value are labeled.

[0028] FIG. 8A: Heatmap of normalized and column-wise scaled SINE expression and IFN signature genes (C4) expression across all 214 samples. Color annotation for each sample (by row) is labeled for L1HS expression, tissue categories, patient ID, and sample site (primaiy / metastasis). 214 samples are clustered into three groups with either low (bottom cluster), medium (middle cluster), and high (top cluster) expression in L1HS, SINEs and IFN signature genes. FIG. 8B: Fraction of repeats from high (dsRNA force > 0.5) or low dsRNA force (dsRNA force < 0.05) regions in each evolutionary group. FIG. 8C: Violin plot comparing the correlation with RLR signature between SINEs with high and low dsRNA force (cutoff = 0.5). FIG. 8D: Scatter plot of immunogenicity of SINE elements (averaged correlation with IFN signature) and mean foldability (dsRNA force) of each SINE element across all loci in the genome 24

[0029] FIG. 9A: Immunohistochemistry staining for L O1 RF Ip in samples from rapid autopsy cohort. The manually quantified IHC score are labeled on the example images. DAPI are used for nuclear staining. FIG. 9B: Scatter plot of manually quantified IHC score of ORFlp expression as a function of the number of somatic L i1nsertions. FIG. 9C: Boxplot of numberof L1 insertion in samples collected from primary and metastatic sites in PDAC biopsy cohort in COMPASS trail35, p-values after t- test are labeled. FIGs. 9D-9E: Boxplot of number of LI insertion in samples with wild type and different types of TP53 mutants in PDAC biopsy cohort in COMPASS trail, p-values after t-test are labeled. FIG. 9F: Integrative analysis ofLl insertion phenotypes and tumor evolution of patient MPAM05. Samples are boxed with colors representing the tissue type. Target site features is coded as red (target site duplication) and green (target site deletion).

[0030] FIG. 10A: Volcano plot of Log2FC of repeats between samples with high LI insertions and low L1 insertions including three extreme samples from patient MPAM5. FIG. 10B: Volcano plot of LogiFC of core IFN gene, defined in FIG. 1C are repressed in samples with high L a1ctivities. FIG. 10C: Spearman correlation coefficients between number of somatic LI insertions and repeat expression grouped by repeat classes in autopsy PDAC cohort. FIG. 10D: Violin plot of spearman correlation between L in1sertions and expression of SINEs. SINE elements are colored based on their evolutionary time. FIG. 10E: Distribution of Pearson correlation coefficient between myeloids cell infiltration estimated using CIBERSORTx and repeats' expression in different classes across 214 samples. FIG. 10F: Violin plot of Pearson correlation coefficient between absolute M2 Macrophage infiltration estimated using CIBERSORTx and different repeats' expression. Benj ami ni -Hochberg corrected P values of t- test are labeled. Pearson correlation between absolute M2 Macrophage infiltration and expression of different SINE subtypes. SINE subtypes are colored based on their evolutionary age: SINE2 / SINE3, SINE1 (Monomeric and dimeric Alul, AluS and AluY, and SVA). FIG. 10G: Spearman correlation between L1 insertions and Meyloids cell infiltration. FIG. 10H: Barplot of ratios of reads mapped to SINEs and coding genes in COMPASS (n=203, polyA- selection) and autopsy PDAC cohort (n=214, rRNA depletion). FIG. 101: Scatter plot of tumor purity corrected Log2FoldChange of all SINE elements in PDAC patients with different LI insertions and their Log2FoldChange in PDAC cell lines with different L O1 RF Ip expression. FIG. 10J: Volcano plot of Log2FC ofrepeats with high O LR1 Flp and low O LR1 Flp expressing PDAC cell lines.

[0031] FIG. 11A: Pearson correlation coefficients between L1 ORF Ip expression normalized by GAPDH and repeat expression grouped by classes in L k1 nocking down in cell line PDAC6. FIG. 11B: Volcano plot of Pearson correlation coefficient between SINE expression and ORFlp expression in PDAC6 cell line with different L k1nocking downs. FIG. 11C: Heatmap shows the normalized and scaled expression (Z-score) of L O1RFlp and ORF2p binding repeats (colored by classes on columns) in the RIPseq experiment.

[0032] FIG. 12A: Heatmap of pairwise correlation among young SINE repeats (AluY and SVAs), L1 insertion, and expression of gene AD ARI and ZBP1. FIG. 12B: A-I editing index of inverted SINEs for patients with known different somatic mutation of TP53 stratified by tumor sites, p-values out of Wilcoxon test are labeled. FIG. 12C: A-I editing index for PDAC autopsy samples and COMPASS biopsy samples stratified by inverted and non-inverted SINEs. p-value calculated by Wilcoxon test are labeled. FIG. 12D: A-I editing index for PDAC biopsy samples in COMPASS trail stratified by tumor sites, p-value calculated by Wilcoxon test are labeled. FIG. 12E: Scatter plot of A-I editing index on inverted SINEs as a function of loglO based number of somatic L i1nsertions. (* p < 0.05, ** p < 0.01, *** p< 0.001, **** p < 0.0001, representing the corresponding statistical significance).

[0033] FIG. 13 depicts a block diagram of a system for cancer therapies based on double-stranded ribonucleic acid (dsRNA) force metrics associated with short interspersed nuclear elements (SINE), in accordance with an illustrative embodiment;

[0034] FIG. 14 depicts a block diagram of a process to derive metrics to select which cancer therapy to select in the system, in accordance with an illustrative embodiment;

[0035] FIG. 15 depict a flow diagram of a method of selecting cancer therapies based on double-stranded ribonucleic acid (dsRNA) force metrics associated with short interspersed nuclear elements (SINE), in accordance with an illustrative embodiment; and

[0036] FIG. 16 depicts a block diagram of a server system and a client computer system, in accordance with one or more implementations.DETAILED DESCRIPTION

[0037] It is to be appreciated that certain aspects, modes, embodiments, variations and features of the present methods are described below in various levels of detail in order to provide a substantial understanding of the present technology. It is to be understood that the present disclosure is not limited to particular uses, methods, reagents, compounds, compositions or biological systems, which can, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting.

[0038] In practicing the present methods, many conventional techniques in molecular biology, protein biochemistry, cell biology, immunology, microbiology and recombinant DNA are used. See, e.g., Sambrook and Russell eds. (2001) Molecular Cloning: A Laboratory Manual, 3rd edition; the series Ausubel et al. eds. (2007) Current Protocols in Molecular Biology, the series Methods in Enzymology (Academic Press, Inc., N.Y.); MacPherson et al. (1991) PCR 1: A Practical Approach (IRL Press at Oxford University Press); MacPherson et al. (1995) PCR 2: A Practical Approach, Harlow and Lane eds. (1999) Antibodies, A Laboratory Manual,' Freshney (2005) Culture of Animal Cells: A Manual of Basic Technique, 5th edition; Gait ed. (1984) Oligonucleotide Synthesis', U.S. Patent No. 4,683,195; Hames and Higgins eds. (1984) Nucleic Acid Hybridization,' Anderson (1999) Nucleic Acid Hybridization, Hames and Higgins eds. (1984) Transcription and Translation; Immobilized Cells and Enzymes (IRL Press (1986)); Perbal (1984) A Practical Guide to Molecular Cloning; Miller and Calos eds. (1987) Gene Transfer Vectors for Mammalian Cells (Cold Spring Harbor Laboratory); Makrides ed. (2003) Gene Transfer and Expression in Mammalian Cells; Mayer and Walker eds. (1987) Immunochemical Methods in Cell and Molecular Biology (Academic Press, London); and Herzenberg et al. eds (1996) Weir ’s Handbook of Experimental Immunology. Methods to detect and measure levels of polypeptide gene expression products (i.e ., gene translation level) are well- known in the art and include the use of polypeptide detection methods such as antibody detection and quantification techniques. (See also, Strachan & Read, Human Molecular Genetics, Second Edition. (John Wiley and Sons, Inc., NY, 1999)).

[0039] Here, whole genome and total transcriptome data were integrated from a unique autopsy cohort of multiregional samples collected in pancreatic ductal adenocarcinoma (PDAC) patients, into a comprehensive evolutionary analysis. As reported herein, a more recently evolved Short Interspersed Nuclear Elements (SINE), a family of retrotransposable repeats, are more likely to form immunostimulatory double-strand RNAs (dsRNAs). Consequently, younger SINEs are strongly co-regulated with RIG-I associated type-I interferon genes but anti -correlated with pro- tumorigenic macrophage infiltration. It was discovered that immunostimulatory SINE expression in tumors is regulated by either Long Interspersed Nuclear Elements 1 (LINE1 / L1) mobility or ADAR1 activity in a TP53 mutation dependent manner. Moreover, LI retrotransposition activity tracks with tumor evolution and is associated with TP53 mutation status. Altogether, these results suggest pancreatic tumors actively evolve to modulate immunogenic SINE stress and induce pro- tumorigenic inflammation. The integrative, evolutionary analysis therefore illustrates how dark matter genomic repeats enable tumors to coevolve with the TME by actively regulating viral mimicry to their selective advantage.

[0040] The present disclosure provides methods for enhancing the responsiveness of pancreatic tumors to immune checkpoint blockade (ICB) therapy by inhibiting L1 activity to maintain immunogenic SINE expression. Also disclosed herein are combination therapies that effectively target PDAC tumors was envisioned based on the status of their TP53 mutations, LI insertions and AD ARI activity, with the goal to increase immunogenic SINE-derived dsRNA to trigger an effective immune response and avoid low level, chronic activation of inflammatory signaling.

[0041] Following below are more detailed descriptions of various concepts related to, and embodiments of, systems and methods for selecting cancer therapies based on doublestranded ribonucleic acid (dsRNA) force metrics associated with short interspersed nuclear elements (SINE). It should be appreciated that various concepts introduced above and discussed in greater detail below may be implemented in any of numerous ways, as the disclosed concepts are not limited to any particular manner of implementation. Examples of specific implementations and applications are provided primarily for illustrative purposes.

[0042] Section B describes therapeutic methods of the present disclosure based onTP53 mutation status, AD ARI activity and LINE-1 activity

[0043] Section B describes systems and methods of selecting cancer therapies based on double-stranded ribonucleic acid (dsRNA) force metrics associated with short interspersed nuclear elements (SINE).

[0044] Section C describes a network environment and computing environment which may be useful for practicing various computing related embodiments described herein.Definitions

[0045] Before describing the disclosed embodiments in detail, it is to be understood that the present disclosure is not limited to particular compositions or biological systems, which can, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting.

[0046] The definitions of certain terms as used in this specification are provided below. Unless defined otherwise, all technical and scientific terms used herein generally have the same meaning as commonly understood by one of ordinary skill in the art to which the present technology belongs.

[0047] As used in this specification and the appended claims, the singular forms “a”, “an” and “the” include plural referents unless the content clearly dictates otherwise. For example, reference to “a cell” includes a combination of two or more cells, and the like. Generally, the nomenclature used herein and the laboratory procedures in cell culture, molecular genetics, organic chemistry, analytical chemistry and nucleic acid chemistry and hybridization described below are those well-known and commonly employed in the art.

[0048] As used herein, the term “about” in reference to a number is generally taken to include numbers that fall within a range of 1%, 5%, or 10% in either direction (greater than or less than) of the number unless otherwise stated or otherwise evident from the context (except where such number would be less than 0% or exceed 100% of a possible value).

[0049] As used herein, the “administration” of an agent or drug to a subject includes any route of introducing or delivering to a subject a compound to perform its intended function. Administration can be carried out by any suitable route, including orally, intranasally, parenterally (intravenously, intramuscularly, intraperitoneally, or subcutaneously), rectally, intrathecally, or topically. Administration includes self-administration and the administration by another.

[0050] The term “amino acid” refers to naturally occurring and non-naturally occurring amino acids, as well as amino acid analogs and amino acid mimetics that function in a manner similar to the naturally occurring amino acids. Naturally encoded amino acids are the 20 common amino acids (alanine, arginine, asparagine, aspartic acid, cysteine, glutamine, glutamic acid, glycine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, proline, serine, threonine, tryptophan, tyrosine, and valine) and pyrolysine and selenocysteine. Amino acid analogs refer to agents that have the same basic chemical structure as a naturally occurring amino acid, i.e., an a carbon that is bound to a hydrogen, a carboxyl group, an amino group, and an R group, such as, homoserine, norleucine, methionine sulfoxide, methionine methyl sulfonium. Such analogs have modified R groups (such as, norleucine) or modified peptide backbones, but retain the same basic chemical structure as a naturally occurring amino acid. In some embodiments, amino acids forming a polypeptide are in the D form. In some embodiments, the amino acids forming a polypeptide are in the L form. In some embodiments, a first plurality of amino acids forming a polypeptide are in the D form, and a second plurality of amino acids are in the L form. Amino acids are referred to herein by either their commonly known three letter symbols or by the one-letter symbols recommended by the IUPAC-IUB Biochemical Nomenclature Commission. Nucleotides, likewise, are referred to by their commonly accepted single-letter code.

[0051] The terms “cancer” or “tumor” are used interchangeably and refer to the presence of cells possessing characteristics typical of cancer-causing cells, such as uncontrolled proliferation, immortality, metastatic potential, rapid growth and proliferation rate, and certain characteristic morphological features. Cancer cells are often in the form of a tumor, but suchcells can exist alone within an animal, or can be a non-tumorigenic cancer cell. As used herein, the term “cancer” includes premalignant, as well as malignant cancers.

[0052] As used herein, “complementary” sequences refer to two nucleotide sequences which, when aligned anti -parallel to each other, contain multiple individual nucleotide bases which pair with each other. Paring of nucleotide bases forms hydrogen bonds and thus stabilizes the double strand structure formed by the complementary sequences. It is not necessary for every nucleotide base in two sequences to pair with each other for sequences to be considered “complementary”. Sequences may be considered complementary, for example, if at least 30%, 40%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 98%, 99%, or 100% of the nucleotide bases in two sequences pair with each other. In some embodiments, the term complementary refers to 100% of the nucleotide bases in two sequences pair with each other. In addition, sequences may still be considered “complementary” when the total lengths of the two sequences are significantly different from each other. For example, a primer of 15 nucleotides may be considered “complementary” to a longer polynucleotide containing hundreds of nucleotides if multiple individual nucleotide bases of the primer pair with nucleotide bases in the longer polynucleotide when the primer is aligned anti-parallel to a particular region of the longer polynucleotide. Nucleotide bases paring is known in the field, such as in DNA, the purine adenine (A) pairs with the pyrimidine thymine (T) and the pyrimidine cytosine (C) always pairs with the purine guanine (G); while in RNA, adenine (A) pairs with uracil (U) and guanine (G) pairs with cytosine (C). Further, the nucleotide bases aligned anti-parallel to each other in two complementary sequences, but not a pair, are referred to herein as a mismatch.10053] As used herein, a "control" is an alternative sample used in an experiment for comparison purpose. A control can be "positive" or "negative." For example, where the purpose of the experiment is to determine a correlation of the efficacy of a therapeutic agent for the treatment for a particular type of disease, a positive control (a compound or composition known to exhibit the desired therapeutic effect) and a negative control (a subject or a sample that does not receive the therapy or receives a placebo) are typically employed.

[0054] As used herein, the term “effective amount” refers to a quantity sufficient to achieve a desired therapeutic and / or prophylactic effect, e.g.. an amount which results in the prevention of, or a decrease in a disease or condition described herein or one or more signs or symptoms associated with a disease or condition described herein. In the context of therapeutic or prophylactic applications, the amount of a composition administered to the subject will vary depending on the composition, the degree, type, and severity of the disease and on the characteristics of the individual, such as general health, age, sex, body weight and tolerance to drugs. The skilled artisan will be able to determine appropriate dosages depending on these and other factors. The compositions can also be administered in combination with one or more additional therapeutic compounds. In the methods described herein, the therapeutic compositions may be administered to a subject having one or more signs or symptoms of a disease or condition described herein. As used herein, a "therapeutically effective amount" of a composition refers to composition levels in which the physiological effects of a disease or condition are ameliorated or eliminated. A therapeutically effective amount can be given in one or more administrations.

[0055] As used herein, “expression” includes one or more of the following: transcription of the gene into precursor mRNA; splicing and other processing of the precursor mRNA to produce mature mRNA; mRNA stability; translation of the mature mRNA into protein (including codon usage and tRNA availability); and glycosylation and / or other modifications of the translation product, if required for proper expression and function.

[0056] As used herein, the term “gene” means a segment of DNA that contains all the information for the regulated biosynthesis of an RNA product, including promoters, exons, introns, and other untranslated regions that control expression. Specifically, a gene refers to a DNA sequence that comprises regulatory and coding sequences necessary for the production of an RNA, which may have a non-coding function (e.g., a ribosomal or transfer RNA) or which may include a polypeptide or a polypeptide precursor. The RNA or polypeptide may be encoded by a full length coding sequence or by any portion of the coding sequence so long as the desired activity or function is retained. Although a sequence of the nucleic acids may be shown in the form of DNA, a person of ordinary skill in the art recognizes that the corresponding RNAsequence will have a similar sequence with the thymine being replaced by uracil, i.e., "T" is replaced with "U."

[0057] “Homology” or “identity” or “percent identity” or “similarity” refers to sequence similarity between two peptides or between two nucleic acid molecules. Homology can be determined by comparing a position in each sequence which may be aligned for purposes of comparison. When a position in the compared sequence is occupied by the same base or amino acid, then the molecules are homologous at that position. A degree of homology between sequences is a function of the number of matching or homologous positions shared by the sequences. A polynucleotide or polynucleotide region (or a polypeptide or polypeptide region) has a certain percentage (for example, at least 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 98% or 99%) of “sequence identity” to another sequence means that, when aligned, that percentage of bases (or amino acids) are the same in comparing the two sequences. This alignment and the percent homology or sequence identity can be determined using software programs known in the art. In some embodiments, default parameters are used for alignment. One alignment program is BLAST, using default parameters. In particular, programs are BLASTN and BLASTP, using the following default parameters: Genetic code=standard; filter=none; strand=both; cutoff=60; expect=10; Matrix=BLOSUM62; Descriptions=50 sequences; sort by =HIGH SCORE; Databases=non-redundant, GenBank+EMBL+DDBJ+PDB+GenBank CDS translations+SwissProtein+SPupdate+PIR. Details of these programs can be found at the National Center for Biotechnology Information. The percent homology between two amino acid sequences can be also determined using the algorithm of E. Meyers and W. Miller (Comput. Appl. Biosci., 4: 1 1-17 (1988)) which has been incorporated into the ALIGN program (version 2.0), using a PAM120 weight residue table, a gap length penalty of 12 and a gap penalty of 4. In addition, the percent homology between two amino acid sequences can be determined using the Needleman and Wunsch (J. Mol. Biol. 48:444-453 (1970)) algorithm which has been incorporated into the GAP program in the GCG software package (available at www.gcg.com), using either a Blossum 62 matrix or a PAM250 matrix, and a gap weight of 16, 14, 12, 10, 8, 6, or 4 and a length weight of 1, 2, 3, 4, 5, or 6. Biologically equivalent polynucleotides are thosehaving the specified percent homology and encoding a polypeptide having the same or similar biological activity. Two sequences are deemed “unrelated” or “non-homologous” if they share less than 40% identity, or less than 25% identity, with each other.

[0058] The term “hybridize” as used herein refers to a process where two substantially complementary nucleic acid strands (at least about 65% complementary over a stretch of at least 14 to 25 nucleotides, at least about 75%, or at least about 90% complementary) anneal to each other under appropriately stringent conditions to form a duplex or heteroduplex through formation of hydrogen bonds between complementary base pairs. Nucleic acid hybridization techniques are well known in the art. See, e.g., Sambrook, et al., 1989, Molecular Cloning: A Laboratory Manual , Second Edition, Cold Spring Harbor Press, Plainview, N.Y. Hybridization and the strength of hybridization (i.e., the strength of the association between the nucleic acids) is influenced by such factors as the degree of complementarity between the nucleic acids, stringency of the conditions involved, and the thermal melting point (Tm) of the formed hybrid. Those skilled in the art understand how to estimate and adjust the stringency of hybridization conditions such that sequences having at least a desired level of complementarity will stably hybridize, while those having lower complementarity will not. For examples of hybridization conditions and parameters, see, e.g., Sambrook, et al., 1989, Molecular Cloning: A Laboratory Manual, Second Edition, Cold Spring Harbor Press, Plainview, N.Y.; Ausubel, F. M. et al., 1994, Current Protocols in Molecular Biology, John Wiley & Sons, Secaucus, N.J. In some embodiments, specific hybridization occurs under stringent hybridization conditions. An oligonucleotide or polynucleotide (e.g., a probe or a primer) that is specific for a target nucleic acid will “hybridize” to the target nucleic acid under suitable conditions.

[0059] As used herein, “immune checkpoint inhibitor” or “immune checkpoint blocking agent” or “immune checkpoint blockade inhibitor” refers to molecules that completely or partially reduce, inhibit, interfere with, or modulate the activity of one or more checkpoint proteins. Checkpoint proteins regulate T-cell activation or function. Checkpoint proteins include, but are not limited to, CD28 receptor family members, CTLA-4 and its ligands CD80 and CD86; PD-1 and its ligands PD-L1 and PD-L2; LAG3, B7-H3, B7-H4, TIM.3, ICOS, IIDLBCL, BTLA or any combination of two or more of the foregoing. Non-limiting examples of immune checkpoint blocking agents contemplated for use herein include, but are not limited to, inhibitory antibodies against CD28 inhibitor such as CTLA-4 (cytotoxic T lymphocyte antigen 4) (e.g., ipilimumab), anti-PD-1 (programmed Death 1) inhibitory antibodies (e.g., nivolumab, pembrolizumab, pidilizumab, lambrolizumab), and anti-PD-Ll (Programmed death ligand 1) inhibitory antibodies (MPDL3280A, BMS-936559, MEDI4736, MSB 00107180), as well as inhibitory antibodies against LAG-3 (lymphocyte activation gene 3), TIM3 (T-cell immunoglobulin and mucin-3), B7-H3, TIGIT (T-cell immunoreceptor with Ig and ITIM domains), AMP -224, MDX-1105, arelumab, tremelimumab, IMP321, MGA271, BMS-986016, lirilumab, urelumab, PF-05082566, IPH2101, MEDI-6469, CP-870,893, Mogamulizumab, Varlilumab, Galiximab, AMP-514, AUNP 12, Indoximod, NLG-919, INCB024360, CD80, CD86, ICOS, DLBCL inhibitors, or BTLA, PDR001, and combinations thereof.

[0060] As used herein, “immune response” refers to the action of one or more of lymphocytes, antigen presenting cells, phagocytic cells, granulocytes, and soluble macromolecules produced by the above cells or the liver (including antibodies, cytokines, and complement) that results in selective damage to, destruction of, or elimination from the human body of cancerous cells, metastatic tumor cells, etc. An immune response may include a cellular response, such as a T-cell response that is an alteration (modulation, e.g., significant enhancement, stimulation, activation, impairment, or inhibition) of cellular, i.e., T-cell function. A T-cell response may include generation, proliferation or expansion, or stimulation of a particular type of T-cell, or subset of T-cells, for example, effector CD4+, CD4+helper, effector CD81, CD 81cytotoxic, or natural killer (NK) cells. Such T-cell subsets may be identified by detecting one or more cell receptors or cell surface molecules (e.g., CD or cluster of differentiation molecules). A T-cell response may also include altered expression (statistically significant increase or decrease) of a cellular factor, such as a soluble mediator (e.g., a cytokine, lymphokine, cytokine binding protein, or interleukin) that influences the differentiation or proliferation of other cells. For example, Type I interferon (IFN-a / p) is a critical regulator of the innate immunity (Huber et al., Immunology 132(4):466-474 (2011)). Animal and human studieshave shown a role for IFN-a / p in directly influencing the fate of both CD4+and CD8+T-cells during the initial phases of antigen recognition and anti-tumor immune response. IFN Type I is induced in response to activation of dendritic cells, in turn a sentinel of the innate immune system. An immune response may also include humoral (antibody) response.

[0061] As used herein, “oligonucleotide” refers to a molecule that has a sequence of nucleic acid bases on a backbone comprised mainly of identical monomer units at defined intervals. The bases are arranged on the backbone in such a way that they can bind with a nucleic acid having a sequence of bases that are complementary to the bases of the oligonucleotide. The most common oligonucleotides have a backbone of sugar phosphate units. A distinction may be made between oligodeoxyribonucleotides that do not have a hydroxyl group at the 2' position and oligoribonucleotides that have a hydroxyl group at the 2' position. Oligonucleotides may also include derivatives, in which the hydrogen of the hydroxyl group is replaced with organic groups, e.g., an allyl group. One or more bases of the oligonucleotide may also be modified to include a phosphorothioate bond (e.g., one of the two oxygen atoms in the phosphate backbone which is not involved in the internucleotide bridge, is replaced by a sulfur atom) to increase resistance to nuclease degradation. The exact size of the oligonucleotide will depend on many factors, which in turn depend on the ultimate function or use of the oligonucleotide. The oligonucleotide may be generated in any manner, including, for example, chemical synthesis, DNA replication, restriction endonuclease digestion of plasmids or phage DNA, reverse transcription, PCR, or a combination thereof. The oligonucleotide may be modified e.g., by addition of a methyl group, a biotin or digoxigenin moiety, a fluorescent tag or by using radioactive nucleotides.

[0062] As used herein, by “pharmaceutically acceptable” or “pharmacologically compatible” is meant a material that is not biologically or otherwise undesirable, e.g., the material may be incorporated into a pharmaceutical composition administered to a patient without causing any significant undesirable biological effects or interacting in a deleterious manner with any of the other components of the composition in which it is contained. Pharmaceutically acceptable carriers or excipients have preferably met the required standards of toxicological andmanufacturing testing and / or are included on the Inactive Ingredient Guide prepared by the U.S. Food and Drug administration.

[0063] The terms “polynucleotide”, “nucleic acid” and “oligonucleotide” are used interchangeably and refer to a polymeric form of nucleotides of any length, either deoxyribonucleotides or ribonucleotides or analogs thereof, in modified or unmodified form. Polynucleotides can have any three-dimensional structure and may perform any function, known or unknown. The following are non-limiting examples of polynucleotides: a gene or gene fragment (for example, a probe, primer, EST or SAGE tag), exons, introns, messenger RNA (mRNA), transfer RNA, ribosomal RNA, ribozymes, cDNA, recombinant polynucleotides, branched polynucleotides, plasmids, vectors, isolated DNA of any sequence, isolated RNA of any sequence, nucleic acid probes and primers. A polynucleotide can comprise modified nucleotides, such as methylated nucleotides and nucleotide analogs. If present, modifications to the nucleotide structure can be imparted before or after assembly of the polynucleotide. The sequence of nucleotides can be interrupted by non-nucleotide components. A polynucleotide can be further modified after polymerization, such as by conjugation with a labeling component. Unless otherwise specified or required, any embodiment of this disclosure that is a polynucleotide encompasses both the double-stranded form and each of two complementary single-stranded forms known or predicted to make up the double-stranded form. A polynucleotide is composed of a specific sequence of four nucleotide bases: adenine (A); cytosine (C); guanine (G); thymine (T); and uracil (U) for thymine when the polynucleotide is RNA. Thus, the term “polynucleotide sequence” is the alphabetical representation of a polynucleotide molecule. This alphabetical representation can be input into databases in a computer having a central processing unit and used for bioinformatics applications such as functional genomics and homology searching. Polynucleotides include, without limitation, single- and double-stranded DNA, DNA that is a mixture of single- and double-stranded regions, single- and double-stranded RNA, RNA that is mixture of single- and double-stranded regions, and hybrid molecules comprising DNA and RNA that may be single-stranded or, more typically, double-stranded or a mixture of single- and double-stranded regions. In addition, polynucleotide refers to tri pie- stranded regions comprisingRNA or DNA or both RNA and DNA. The term polynucleotide also includes DNAs or RNAs containing one or more modified bases and DNAs or RNAs with backbones modified for stability or for other reasons.

[0064] The terms “polypeptide,” “peptide,” and “protein” are used interchangeably herein to refer to a polymer of amino acid residues. The terms apply to naturally occurring amino acid polymers as well as amino acid polymers in which one or more amino acid residues are a non-naturally occurring amino acid, e.g., an amino acid analog. The terms encompass amino acid chains of any length, including full length proteins, wherein the amino acid residues are linked by covalent peptide bonds.

[0065] As used herein, “prevention” or “preventing” of a disorder or condition refers to a compound that, in a statistical sample, reduces the occurrence of the disorder or condition in the treated sample relative to an untreated control sample, or delays the onset of one or more symptoms of the disorder or condition relative to the untreated control sample.

[0066] As used herein, a “sample” refers to a substance that is being assayed for the presence of a mutation or alteration in a nucleic acid of interest. Processing methods to release or otherwise make available a nucleic acid for detection are well known in the art and may include steps of nucleic acid manipulation. A biological sample may be a body fluid or a tissue sample. In some cases, a biological sample may consist of or comprise blood, plasma, sera, urine, feces, epidermal sample, vaginal sample, skin sample, cheek swab, sperm, amniotic fluid, cultured cells, bone marrow sample, tumor biopsies, aspirate and / or chorionic villi, cultured cells, and the like. Fresh, fixed or frozen tissues may also be used. In one embodiment, the sample is preserved as a frozen sample or as formaldehyde- or paraformaldehyde-fixed paraffin-embedded (FFPE) tissue preparation. For example, the sample can be embedded in a matrix, e.g., an FFPE block or a frozen sample. Whole blood samples of about 0.5 to 5 ml collected with EDTA, ACD or heparin as anti -coagulant are suitable.

[0067] As used herein, the term “separate” therapeutic use refers to an administration of at least two active ingredients at the same time or at substantially the same time by different routes.

[0068] As used herein, the term “sequential” therapeutic use refers to administration of at least two active ingredients at different times, the administration route being identical or different. More particularly, sequential use refers to the whole administration of one of the active ingredients before administration of the other or others commences. It is thus possible to administer one of the active ingredients over several minutes, hours, or days before administering the other active ingredient or ingredients. There is no simultaneous treatment in this case.

[0069] As used herein, the term “simultaneous” therapeutic use refers to the administration of at least two active ingredients by the same route and at the same time or at substantially the same time.

[0070] The term “specific” as used herein in reference to an oligonucleotide means that the nucleotide sequence of the oligonucleotide has at least 12 bases of sequence identity with a portion of a target nucleic acid when the oligonucleotide and the target nucleic acid are aligned. An oligonucleotide that is specific for a target nucleic acid is one that, under the stringent hybridization or washing conditions, is capable of hybridizing to the target nucleic acid of interest and not substantially hybridizing to nucleic acids which are not of interest. Higher levels of sequence identity are desirable and include at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 98% sequence identity.

[0071] The term “stringent hybridization conditions” as used herein refers to hybridization conditions at least as stringent as the following: hybridization in 50% formamide, 5xSSC, 50 mM NaftPCh, pH 6.8, 0.5% SDS, 0.1 mg / mL sonicated salmon sperm DNA, and 5x Denhart's solution at 42° C. overnight; washing with 2x SSC, 0.1% SDS at 45° C; and washing with 0.2x SSC, 0.1% SDS at 45° C. In another example, stringent hybridization conditions should not allow for hybridization of two nucleic acids which differ over a stretch of 20 contiguous nucleotides by more than two bases.

[0072] As used herein, the terms “subject”, “patient”, or “individual” can be an individual organism, a vertebrate, a mammal, or a human. In some embodiments, the subject, patient or individual is a human.

[0073] “Treating” or “treatment” as used herein covers the treatment of a disease or disorder described herein, in a subject, such as a human, and includes: (i) inhibiting a disease or disorder, i.e., arresting its development; (ii) relieving a disease or disorder, i.e., causing regression of the disorder; (iii) slowing progression of the disorder; and / or (iv) inhibiting, relieving, or slowing progression of one or more symptoms of the disease or disorder. Therapeutic effects of treatment include, without limitation, inhibiting recurrence of disease, alleviation of symptoms, diminishment of any direct or indirect pathological consequences of the disease, preventing metastases, decreasing the rate of disease progression, amelioration or palliation of the disease state, and remission or improved prognosis. By “treating a cancer” is meant that the symptoms associated with the cancer are, e.g., alleviated, reduced, cured, or placed in a state of remission.

[0074] It is also to be appreciated that the various modes of treatment of diseases as described herein are intended to mean “substantial,” which includes total but also less than total treatment, and wherein some biologically or medically relevant result is achieved. The treatment may be a continuous prolonged treatment for a chronic disease or a single, or few time administrations for the treatment of an acute condition.Section A: Therapeutic Methods of the Present Technology Based on TP53 Mutation Status, AD ARI Activity and LINE-1 ActivityRetrotransposons

[0075] Repetitive elements account for approximately 54% of the human genome6, with more than half consisting of retrotransposable elements that can, when fully functional and activated, insert themselves into new genomic sites via a copy-and-paste mechanism6. SINE and LINE (Short / Long Interspersed Nuclear Elements) are two major classes of retrotransposable elements found in the human genome6. There are -500,000 copies of LINE-1 (or LI) occupying 21% of the human genome, but only -100 full-length human-specific L1 elements (LIHs) are capable of active and autonomous retrotransposition7. L -1encoded proteins also mobilize non- autonomous retrotransposons - and these retrotransposed SINEs, make up another 13% of human DNA1. Due to the deleterious nature of retrotransposition, multiple defense mechanisms,including epigenetic silencing and post-transcriptional degradation, have evolved to protect genomes8,9. These protective functions are often dysregulated in cancer10, resulting in the frequent observation of derepressed repeats in multiple cancer types11,12. The dysregulation of SINEs and L1 can induce “viral mimicry” by enabling both reverse transcription and the display of pathogen- associated molecular patterns (PAMPs) which have the potential to trigger inflammatory responses2 3. The work in ovarian and pancreatic cancer has found an enrichment of repeat RNAs in tumor-derived extracellular vesicles (EVs) capable of stimulating monocyte- derived macrophages, demonstrating these viral-like repeat sequences can, in principle, alter the tumor microenvironment (TME)12.LINE-1 Inhibitors and AD ARI Inhibitors

[0076] The present disclosure provides therapeutic agents that inhibit the activity or expression of LINE-1 (LI) or ADAR1. In some embodiments, the L1 inhibitor or AD ARI inhibitor is a small molecule, or an inhibitory nucleic acid (e.g., siRNA, antisense nucleic acid, shRNA, sgRNA, ribozymes). Examples of small molecule inhibitors of ADAR1 include, but are not limited to, rebecsinib, GT0001X, AVA-ADR-001, CL-AD-100, lithospermic acid, and Regaloside B. Examples of small molecule inhibitors of LINE-1 activity include capsaicin, nucleoside reverse transcriptase inhibitors (e.g., lamivudine, adefovir, penciclovir, abacavir, stavudine, didanosine, zidovudine, emtricitabine, zalcitabine, tenofovir, entecavir, telbivudine, AZTTP, d4TTP, ddCTP, 3TCTP), NSC332395, and AD2 / ZINC89469886.[0077| Exemplary mRNA sequences of LINE-1 and AD ARI are provided below, represented by SEQ ID NOs: 1-6.

[0078] U93569.1 Human L e1lement LI.20 p40 and putative pl50 genes, complete cds (SEQ ID NO: 1)CGAGGAGGAGCCAAGATGGCCGAATAGGAACAGCTCCGGTCTACAGCTCCCAGCGTGAGCGACGCAGAAG ACGGGTGATTTCTGCATTTCCATCTGAGGTACCGGGTTCATCTCACTAGGGAGTGCCAGACAGTGGGCGC AGGC CAGT GT GT GT GC GCACT GT GC GCGAGC C GAAGCAGGGC GAGGCAT T GC CT CACT T GGGAAGC GCAA GGGGT CAGGGAGT T C C CT T T CC GAGT CAAAGAAAGGGGT GAC GGAC GCAC CT GGAAAAT C GGGT CACT C C CACCCGAATATTGCGCTTTTCAGACCGGCTTAAAAAACGGCGCACCACGAGACTATATCCCACACCTGGC TCAGAGGGTCCTACGCCCACGG7XATCTCGCTGATTGCTAGCACAGCAGTCTGAGATC7XAACTGCAAGGCG

[0079] NM 001111.5 Homo sapiens adenosine deaminase RNA specific (ADAR), transcript variant 1, mRNA (SEQ ID NO: 2)

[0080] NM 015840.4 Homo sapiens adenosine deaminase RNA specific (ADAR), transcript variant 2, mRNA (SEQ ID NO: 3)

[0081] NM 015841.4 Homo sapiens adenosine deaminase RNA specific (ADAR), transcript variant 3, mRNA (SEQ ID NO: 4)(0082) NM_001025107.3 Homo sapiens adenosine deaminase RNA specific (ADAR), transcript variant 4, mRNA (SEQ ID NO: 5)

[0083] NM_001193495.2 Homo sapiens adenosine deaminase RNA specific (ADAR), transcript variant 5, mRNA (SEQ ID NO: 6)

[0084] In one aspect, the present disclosure provides L o1r AD ARI -specific inhibitory nucleic acids comprising a nucleic acid molecule, which is complementary to a portion of a nucleic acid sequence selected from the group consisting of SEQ ID NOs: 1-6.

[0085] The present disclosure also provides an antisense nucleic acid comprising a nucleic acid sequence that is complementary to and specifically hybridizes with a portion of any one of SEQ ID NOs: 1-6, thereby reducing or inhibiting expression of L or1 ADAR1. The antisense nucleic acid may be antisense RNA, or antisense DNA. Antisense nucleic acids based on the known L1 or AD ARI gene sequence can be readily designed and engineered using methods known in the art. In some embodiments, the antisense nucleic acid comprises the nucleic acid sequence of any one of SEQ ID NOs: 7-8 or a complement thereof.

[0086] Antisense nucleic acids are molecules which are complementary to a sense nucleic acid strand, e.g., complementary to the coding strand of a double-stranded DNA molecule (or cDNA) or complementary to an mRNA sequence. Accordingly, an antisense nucleic acid can form hydrogen bonds with a sense nucleic acid. The antisense nucleic acid can be complementary to an entire L1 or AD ARI coding strand, or to a portion thereof, e.g., all or partof the protein coding region (or open reading frame). In some embodiments, the antisense nucleic acid is an oligonucleotide which is complementary to only a portion of the mRNA coding region of L o1r AD ARI. In certain embodiments, an antisense nucleic acid molecule can be complementary to a noncoding region of the L o1r AD ARI coding strand. In some embodiments, the noncoding region refers to the 5' and 3’ untranslated regions that flank the coding region and are not translated into amino acids. For example, the antisense oligonucleotide can be complementary to the region surrounding the translation start site of LI or AD ARI . An antisense oligonucleotide can be, for example, about 5, 10, 15, 20, 25, 30, 35, 40, 45 or 50 nucleotides in length.

[0087] An antisense nucleic acid can be constructed using chemical synthesis and enzymatic ligation reactions using procedures known in the art. For example, an antisense nucleic acid (e.g., an antisense oligonucleotide) can be chemically synthesized using naturally occurring nucleotides or modified nucleotides designed to increase the biological stability of the molecules or to increase the physical stability of the duplex formed between the antisense and sense nucleic acids, e.g., phosphorothioate derivatives and acridine substituted nucleotides. Examples of modified nucleotides which can be used to generate the antisense nucleic acid include 5-fluorouracil, 5-bromouracil, 5-chlorouracil, 5-hodouracil, hypoxanthine, xanthine, 4- acetyl cytosine, 5-(carboxyhydroxylmethyl)uracil, 5-carboxymethylaminomethyl-2-thouridine, 5-carboxymethylaminometh-yluracil, dihydrouracil, beta-D-galactosylqueosine, inosine, N6- isopentenyladenine, 1-methylguanine, 1 -methylinosine, 2,2-dimethylguanine, 2-methyladenine, 2-methylguanine, 3 -methylcytosine, 5-metnylcytosine, N6-adenine, 7-methylguanine, 5- methylaminomethyluracil, 5-methoxyaminomethyl-2-thiouracil, beta-D-mannosylqueosine, 5'- methoxycarboxymethyluracil, 5-methoxyuracil, 2-methylthio-N6-isopenten-yladenine, uracil- 5-oxyacetic acid (v), wybutosine, pseudouracil, queosine, 2-thiocytosine, 5-methyl-2 -thiouracil, 2-thlouracil, 4-thiouracil, 5-methyluracil, uracil-5-oxyacetic acid methylester, uracil-5- cxyacetic acid (v), 5-methyl-2-thiouracil, 3-(3-amino-3-N-2-carboxypropyl) uracil, (acp3)w, and 2,6-diaminopurine. Alternatively, the antisense nucleic acid can be produced biologically using an expression vector into which a nucleic acid has been subcloned in an antisenseorientation (z.e., RNA transcribed from the inserted nucleic acid will be of an antisense orientation to a target nucleic acid of interest).

[0088] The antisense nucleic acid molecules may be administered to a subject or generated in situ such that they hybridize with or bind to cellular mRNA and / or genomic DNA encoding the protein of interest to thereby inhibit expression of the protein, e.g., by inhibiting transcription and / or translation. The hybridization can occur via Watson-Crick base pairing to form a stable duplex, or in the case of an antisense nucleic acid molecule which binds to DNA duplexes, through specific interactions in the major groove of the double helix.

[0089] In some embodiments, the antisense nucleic acid molecules are modified such that they specifically bind to receptors or antigens expressed on a selected cell surface, e.g., by linking the antisense nucleic acid molecules to peptides or antibodies which bind to cell surface receptors or antigens. In some embodiments, the antisense nucleic acid molecule is an alpha- anomeric nucleic acid molecule. An alpha-anomeric nucleic acid molecule forms specific double-stranded hybrids with complementary RNA in which, contrary to the usual P-units, the strands run parallel to each other (Gaultier et al., Nucleic Acids. Res. 15:6625-6641(1987)). The antisense nucleic acid molecule can also comprise a 2'-0 -methylribonucleotide (Inoue et al., Nucleic Acids Res. 15:6131-6148 (1987)) or a chimeric RNA-DNA analogue (Inoue et al., FEBSLett. 215:327-330 (1987)).

[0090] The present disclosure also provides a short hairpin RNA (shRNA) or small interfering RNA (siRNA) comprising a nucleic acid sequence that is complementary to and specifically hybridizes with a portion of any one of SEQ ID NOs: 1-6, thereby reducing or inhibiting expression of L1 or AD ARI . In some embodiments, the shRNA or siRNA is about 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28 or 29 base pairs in length. Double-stranded RNA (dsRNA) can induce sequence-specific post-transcriptional gene silencing (e.g., RNA interference (RNAi)) in many organisms such as C. elegans, Drosophila, plants, mammals, oocytes and early embryos. RNAi is a process that interferes with or significantly reduces the number of protein copies made by an mRNA. For example, a double-stranded siRNA or shRNA molecule is engineeredto complement and hybridize to a mRNA of a target gene. Following intracellular delivery, the siRNA or shRNA molecule associates with an RNA-induced silencing complex (RISC), which then binds and degrades a complementary target mRNA (such as mRNA of L or1 AD ARI). In some embodiments, the shRNA or siRNA comprises the nucleic acid sequence of any one of SEQ ID NOs: 7-8.

[0091] The present disclosure also provides a ribozyme comprising a nucleic acid sequence that is complementary to and specifically hybridizes with a portion of any one of SEQ ID NOs: 1-6 (LI or AD ARI mRNA), thereby reducing or inhibiting expression of L or1 AD ARI. Ribozymes are catalytic RNA molecules with ribonuclease activity which are capable of cleaving a complementary single-stranded nucleic acid, such as an mRNA. Thus, ribozymes (e.g., hammerhead ribozymes (described in Haselhoff and Gerlach, Nature 334:585-591 (1988))) can be used to catalytically cleave L o1r AD ARI transcripts, thereby inhibiting translation of L o1r AD ARI.

[0092] A ribozyme having specificity for an L o1r ADAR 1 -encoding nucleic acid can be designed based upon a nucleic acid sequence of L o1r AD ARI. For example, a derivative of a Tetrahymena L-19 IVS RNA can be constructed in which the nucleotide sequence of the active site is complementary to the nucleotide sequence to be cleaved in an L or1 AD ARI -encoding mRNA. See, e.g., U.S. Pat. No. 4,987,071 and U.S. Pat. No. 5,116,742. Alternatively, mRNA of an L1 or AD ARI can be used to select a catalytic RNA having a specific ribonuclease activity from a pool of RNA molecules. See, e.g., Bartel and Szostak (1993) Science 261 : 1411- 1418, incorporated herein by reference.

[0093] The present disclosure also provides a synthetic guide RNA (sgRNA) comprising a nucleic acid sequence that is complementary to and specifically hybridizes with a portion of any one of SEQ ID NOs: 1-6. Guide RNAs for use in CRISPR-Cas systems are typically generated as a single guide RNA comprising a crRNA segment and a tracrRNA segment. The crRNA segment and a tracrRNA segment can also be generated as separate RNA molecules. The crRNA segment comprises the targeting sequence that binds to a portion of any one ofSEQ ID NOs: 1-6, and a stem portion that hybridizes to a tracrRNA. The tracrRNA segment comprises a nucleotide sequence that is partially or completely complementary to the stem sequence of the crRNA and a nucleotide sequence that binds to the CRISPR enzyme. In some embodiments, the crRNA segment and the tracrRNA segment are provided as a single guide RNA. In some embodiments, the crRNA segment and the tracrRNA segment are provided as separate RNAs. The combination of the CRISPR enzyme with the crRNA and tracrRNA make up a functional CRISPR-Cas system. Exemplary CRISPR-Cas systems for targeting nucleic acids, are described, for example, in WO2015 / 089465.

[0094] In some embodiments, a synthetic guide RNA is a single RNA represented as comprising the following elements:5'-Xl-X2-Y-Z-3'

[0095] where XI and X2 represent the crRNA segment, where XI is the targeting sequence that binds to a portion of any one of SEQ ID NOs: 1-6, X2 is a stem sequence the hybridizes to a tracrRNA, Z represents a tracrRNA segment comprising a nucleotide sequence that is partially or completely complementary to X2, and Y represents a linker sequence. In some embodiments, the linker sequence comprises two or more nucleotides and links the crRNA and tracrRNA segments. In some embodiments, the linker sequence comprises 2, 3, 4, 5, 6, 7, 8, 9, 10 or more nucleotides. In some embodiments, the linker is the loop of the hairpin structure formed when the stem sequence hybridized with the tracrRNA.

[0096] In some embodiments, a synthetic guide RNA is provided as two separate RNAs where one RNA represents a crRNA segment: 5'-Xl-X2-3' where XI is the targeting sequence that binds to a portion of any one of SEQ ID NOs: 1-6, X2 is a stem sequence the hybridizes to a tracrRNA, and one RNA represents a tracrRNA segment, Z, that is a separate RNA from the crRNA segment and comprises a nucleotide sequence that is partially or completely complementary to X2 of the crRNA.

[0097] Exemplary crRNA stem sequences and tracrRNA sequences are provided, for example, in WO / 2015 / 089465, which is incorporated by reference herein. In general, a stem sequenceincludes any sequence that has sufficient complementarity with a complementary sequence in the tracrRNA to promote formation of a CRISPR complex at a target sequence, wherein the CRISPR complex comprises the stem sequence hybridized to the tracrRNA. In general, degree of complementarity is with reference to the optimal alignment of the stem and complementary sequence in the tracrRNA, along the length of the shorter of the two sequences. Optimal alignment may be determined by any suitable alignment algorithm, and may further account for secondary structures, such as self-complementarity within either the stem sequence or the complementary sequence in the tracrRNA. In some embodiments, the degree of complementarity between the stem sequence and the complementary sequence in the tracrRNA along the length of the shorter of the two when optimally aligned is about or more than about 25%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 97.5%, 99%, or higher. In some embodiments, the stem sequence is about or more than about 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 40, 50, or more nucleotides in length. In some embodiments, the stem sequence and complementary sequence in the tracrRNA are contained within a single RNA, such that hybridization between the two produces a transcript having a secondary structure, such as a hairpin. In some embodiments, the tracrRNA has additional complementary sequences that form hairpins. In some embodiments, the tracrRNA has at least two or more hairpins. In some embodiments, the tracrRNA has two, three, four or five hairpins. In some embodiments, the tracrRNA has at most five hairpins.

[0098] In a hairpin structure, the portion of the sequence 5' of the final “N” and upstream of the loop corresponds to the crRNA stem sequence, and the portion of the sequence 3' of the loop corresponds to the tracrRNA sequence. Further non-limiting examples of single polynucleotides comprising a guide sequence, a stem sequence, and a tracr sequence are as follows (listed 5' to 3'), where “N” represents a base of a guide sequence (e.g. a modified oligonucleotide provided herein), the first block of lower case letters represent stem sequence, and the second block of lower case letters represent the tracrRNA sequence, and the final poly- T sequence represents the transcription terminator: (a) NNNNNNNNNNNNNNNNNNNNgtttttgtactctcaagatttaGAAAtaaatcttgcagaagctacaaagataaggcttcatgccgaaatcaacaccctgtcattttatggcagggtgttttcgttatttaaTTTTTT (SEQ ID NO: 9); (b) NNNNNNNNNNNNNNNNNNNNgtttttgtactctcaGAAAtgcagaagctacaaagataaggcttcatgccg aaatcaacaccctgtcattttatggcagggtgttttcgttatttaaTTTTTT (SEQ ID NO: 10); (c) NNNNNNNNNNNNNNNNNNNNgtttttgtactctcaGAAAtgcagaagctacaaagataaggcttcatgccg aaatcaacaccctgtcattttatggcagggtgtTTTTTT (SEQ ID NO: 11); (d) NNNNNNNNNNNNNNNNNNNNgtttagagctaGAAAtagcaagttaaaataaggctagtccgttatcaactt gaaaaagtggcaccgagtcggtgcTTTTTT (SEQ ID NO: 12); (e) NNNNNNNNNNNNNNNNNNNNgtttagagctaGAAATAGcaagttaaaataaggctagtccgttatcaac ttgaaaaagtgTTTTTTT (SEQ ID NO: 13); and (f) NNNNNNNNNNNNNNNNNNNNgttttagagctagAAATAGcaagttaaaataaggctagtccgttatcaTT TTTTTT (SEQ ID NO: 14).

[0099] Selection of suitable oligonucleotides for use in as a targeting sequence in a CRISPR Cas system depends on several factors including the particular CRISPR enzyme to be used and the presence of corresponding proto-spacer adjacent motifs (PAMs) downstream of the target sequence in the target nucleic acid. The PAM sequences direct the cleavage of the target nucleic acid by the CRISPR enzyme. In some embodiments, a suitable PAM is 5'- NRG or 5'- NNGRR (where N is any Nucleotide) for SpCas9 or SaCas9 enzymes (or derived enzymes), respectively. Generally the PAM sequences should be present between about 1 to about 10 nucleotides of the target sequence to generate efficient cleavage of the target nucleic acid. Thus, when the guide RNA forms a complex with the CRISPR enzyme, the complex locates the target and PAM sequence, unwinds the DNA duplex, and the guide RNA anneals to the complementary sequence on the opposite strand. This enables the Cas9 nuclease to create a double-strand break.

[0100] A variety of CRISPR enzymes are available for use in conjunction with the disclosed guide RNAs of the present disclosure. In some embodiments, the CRISPR enzyme is a Type II CRISPR enzyme. In some embodiments, the CRISPR enzyme catalyzes DNA cleavage. In some embodiments, the CRISPR enzyme catalyzes RNA cleavage. In some embodiments, the CRISPR enzyme is any Cas9 protein, for instance any naturally-occurringbacterial Cas9 as well as any chimeras, mutants, homologs or orthologs. Non-limiting examples of Cas proteins include Cast, CaslB, Cas2, Cas3, Cas4, Cas5, Cas6, Cas7, Cas8, Cas9 (also known as Csnl and Csxl2), CaslO, Csyl, Csy2, Csy3, Csel, Cse2, Cscl, Csc2, Csa5, Csn2, Csm2, Csm3, Csm4, Csm5, Csm6, Cmrl, Cmr3, Cmr4, Cmr5, Cmr6, Csbl, Csb2, Csb3, Csxl7, Csxl4, CsxlO, Csxl6, CsaX, Csx3, Csxl, Csxl5, Csfl, Csf2, Csf3, Csf4, homologues thereof, or modified variants thereof. In some embodiments, the CRISPR enzyme cleaves both strands of the target nucleic acid at the Protospacer Adjacent Motif (PAM) site. In some embodiments, the CRISPR enzyme is a nickase, which cleaves only one strand of the target nucleic acid.Pharmaceutical Compositions[01011 In one aspect, the present disclosure provides pharmaceutical compositions comprising a L1 inhibitor and / or an AD ARI inhibitor.

[0102] The pharmaceutical compositions of the present disclosure may be prepared by any of the methods known in the pharmaceutical arts. The amount of active ingredient that can be combined with a carrier material to produce a single dosage form will vary depending upon the host being treated and the particular mode of administration. The amount of active ingredient that can be combined with a carrier material to produce a single dosage form will generally be that amount of the compound that produces a therapeutic effect. Generally, the amount of active compound will be in the range of about 0.1 to 99 percent, more typically, about 5 to 70 percent, and more typically, about 10 to 30 percent.

[0103] In some embodiments, pharmaceutical compositions of the present technology may contain one or more pharmaceutically-acceptable carriers, which as used herein, generally refers to a pharmaceutically-acceptable composition, such as a liquid or solid filler, diluent, excipient, manufacturing aid (e.g, lubricant, talc magnesium, calcium or zinc stearate, or steric acid), or solvent encapsulating material, useful for introducing the active agent into the body.

[0104] Examples of suitable aqueous and non-aqueous carriers that may be employed in the pharmaceutical compositions of the present technology include, for example, water, ethanol, polyols (such as glycerol, propylene glycol, polyethylene glycol, and the like), vegetable oils(such as olive oil), and injectable organic esters (such as ethyl oleate), and suitable mixtures thereof. Proper fluidity can be maintained, for example, by the use of coating materials, such as lecithin, by the maintenance of the required particle size in the case of dispersions, and by the use of surfactants.

[0105] In some embodiments, the formulations may include one or more of sugars, such as lactose, glucose and sucrose; starches, such as corn starch and potato starch; cellulose, and its derivatives, such as sodium carboxymethyl cellulose, ethyl cellulose and cellulose acetate; powdered tragacanth; malt; gelatin; talc; excipients, such as cocoa butter and suppository waxes; oils, such as peanut oil, cottonseed oil, safflower oil, sesame oil, olive oil, com oil and soybean oil; glycols, such as propylene glycol; polyols, such as glycerin, sorbitol, mannitol and polyethylene glycol; esters, such as ethyl oleate and ethyl laurate; agar; alginic acid; buffering agents, such as magnesium hydroxide and aluminum hydroxide; pyrogen-free water; isotonic saline; Ringer's solution; ethyl alcohol; pH buffered solutions; polyesters, polycarbonates and / or polyanhydrides; preservatives; glidants; fillers; and other non-toxic compatible substances employed in pharmaceutical formulations.

[0106] Various auxiliary agents, such as wetting agents, emulsifiers, lubricants (e.g., sodium lauryl sulfate and magnesium stearate), coloring agents, release agents, coating agents, sweetening agents, flavoring agents, preservative agents, and antioxidants can also be included in the pharmaceutical composition of the present technology. Some examples of pharmaceutically-acceptable antioxidants include: water soluble antioxidants, such as ascorbic acid, cysteine hydrochloride, sodium bisulfate, sodium metabisulfite, sodium sulfite, and the like; oil-soluble antioxidants, such as ascorbyl palmitate, butylated hydroxyanisole (BHA), butylated hydroxy toluene (BHT), lecithin, propyl gallate, alpha-tocopherol, and the like; and metal chelating agents, such as citric acid, ethylenediamine tetraacetic acid (EDTA), sorbitol, tartaric acid, phosphoric acid, and the like. In some embodiments, the pharmaceutical formulation includes an excipient selected from, for example, celluloses, liposomes, micelle-forming agents (e.g., bile acids), and polymeric carriers, e.g, polyesters and polyanhydrides. Suspensions, in addition to the active compounds, may contain suspending agents, such as, for example,ethoxylated isostearyl alcohols, polyoxyethylene sorbitol and sorbitan esters, microcrystalline cellulose, aluminum metahydroxide, bentonite, agar-agar and tragacanth, and mixtures thereof. Prevention of the action of microorganisms on the active compounds may be ensured by the inclusion of various antibacterial and antifungal agents, such as, for example, paraben, chlorobutanol, phenol sorbic acid, and the like. It may also be desirable to include isotonic agents, such as sugars, sodium chloride, and the like into the compositions. In addition, prolonged absorption of the injectable pharmaceutical form may be brought about by the inclusion of agents that delay absorption, such as aluminum monostearate and gelatin.Modes of Administration and Effective Dosages

[0107] Any method known to those in the art for contacting a cell, organ or tissue with one or more L i1nhibitors and / or AD ARI inhibitors disclosed herein may be employed. Suitable methods include in vitro, ex vivo, or in vivo methods. In vivo methods typically include the administration of one or more L1 inhibitors and / or AD ARI inhibitors to a mammal, suitably a human. When used in vivo for therapy, the one or more L i1nhibitors and / or AD ARI inhibitors described herein are administered to the subject in effective amounts (i.e., amounts that have desired therapeutic effect). Lhe dose and dosage regimen will depend upon the degree of the disease state of the subject, the characteristics of the particular L1 inhibitor and / or ADAR1 inhibitor used, e.g., its therapeutic index, and the subject’s history.

[0108] The effective amount may be determined during pre-clinical trials and clinical trials by methods familiar to physicians and clinicians. An effective amount of one or more LI inhibitors and / or AD ARI inhibitors useful in the methods may be administered to a mammal in need thereof by any of a number of well-known methods for administering pharmaceutical compounds. The L1 inhibitor and / or AD ARI inhibitor may be administered systemically or locally.

[0109] The one or more L i1nhibitors and / or AD ARI inhibitors described herein can be incorporated into pharmaceutical compositions for administration, singly or in combination, to a subject for the treatment or prevention of cancer (e g., pancreatic cancer, ovarian cancer).Such compositions typically include the active agent and a pharmaceutically acceptable carrier. As used herein the term “pharmaceutically acceptable carrier” includes saline, solvents, dispersion media, coatings, antibacterial and antifungal agents, isotonic and absorption delaying agents, and the like, compatible with pharmaceutical administration. Supplementary active compounds can also be incorporated into the compositions.

[0110] Pharmaceutical compositions are typically formulated to be compatible with its intended route of administration. Examples of routes of administration include parenteral (e.g., intravenous, intradermal, intraperitoneal or subcutaneous), oral, inhalation, transdermal (topical), intraocular, iontophoretic, and transmucosal administration. Solutions or suspensions used for parenteral, intradermal, or subcutaneous application can include the following components: a sterile diluent such as water for injection, saline solution, fixed oils, polyethylene glycols, glycerine, propylene glycol or other synthetic solvents; antibacterial agents such as benzyl alcohol or methyl parabens; antioxidants such as ascorbic acid or sodium bisulfite; chelating agents such as ethylenediaminetetraacetic acid; buffers such as acetates, citrates or phosphates and agents for the adjustment of tonicity such as sodium chloride or dextrose. pH can be adjusted with acids or bases, such as hydrochloric acid or sodium hydroxide. The parenteral preparation can be enclosed in ampoules, disposable syringes or multiple dose vials made of glass or plastic. For convenience of the patient or treating physician, the dosing formulation can be provided in a kit containing all necessary equipment (e.g, vials of drug, vials of diluent, syringes and needles) for a treatment course (e.g, 7 days of treatment).

[0111] Pharmaceutical compositions suitable for injectable use can include sterile aqueous solutions (where water soluble) or dispersions and sterile powders for the extemporaneous preparation of sterile injectable solutions or dispersion. For intravenous administration, suitable carriers include physiological saline, bacteriostatic water, CREMOPHOR EL™ (BASF, Parsippany, N.J.) or phosphate buffered saline (PBS). In all cases, a composition for parenteral administration must be sterile and should be fluid to the extent that easy syringabilityexists. It should be stable under the conditions of manufacture and storage and must be preserved against the contaminating action of microorganisms such as bacteria and fungi.

[0112] The pharmaceutical compositions having one or more L in1hibitors and / or AD ARI inhibitors disclosed herein can include a carrier, which can be a solvent or dispersion medium containing, for example, water, ethanol, polyol (e.g., glycerol, propylene glycol, and liquid polyethylene glycol, and the like), and suitable mixtures thereof. The proper fluidity can be maintained, for example, by the use of a coating such as lecithin, by the maintenance of the required particle size in the case of dispersion and by the use of surfactants. Prevention of the action of microorganisms can be achieved by various antibacterial and antifungal agents, for example, parabens, chlorobutanol, phenol, ascorbic acid, thiomerasol, and the like. Glutathione and other antioxidants can be included to prevent oxidation. In many cases, it will be advantageous to include isotonic agents, for example, sugars, polyalcohols such as mannitol, sorbitol, or sodium chloride in the composition. Prolonged absorption of the injectable compositions can be brought about by including in the composition an agent that delays absorption, for example, aluminum monostearate or gelatin.

[0113] Sterile injectable solutions can be prepared by incorporating the active compound in the required amount in an appropriate solvent with one or a combination of ingredients enumerated above, as required, followed by filtered sterilization. Generally, dispersions are prepared by incorporating the active compound into a sterile vehicle, which contains a basic dispersion medium and the required other ingredients from those enumerated above. In the case of sterile powders for the preparation of sterile injectable solutions, typical methods of preparation include vacuum drying and freeze drying, which can yield a powder of the active ingredient plus any additional desired ingredient from a previously sterile-filtered solution thereof.

[0114] Oral compositions generally include an inert diluent or an edible carrier. For the purpose of oral therapeutic administration, the active compound can be incorporated with excipients and used in the form of tablets, troches, or capsules, e.g., gelatin capsules. Oral compositions can also be prepared using a fluid carrier for use as a mouthwash.Pharmaceutically compatible binding agents, and / or adjuvant materials can be included as part of the composition. The tablets, pills, capsules, troches and the like can contain any of the following ingredients, or compounds of a similar nature: a binder such as microcrystalline cellulose, gum tragacanth or gelatin; an excipient such as starch or lactose, a disintegrating agent such as alginic acid, Primogel, or corn starch; a lubricant such as magnesium stearate or Sterotes; a glidant such as colloidal silicon dioxide; a sweetening agent such as sucrose or saccharin; or a flavoring agent such as peppermint, methyl salicylate, or orange flavoring.

[0115] For administration by inhalation, the compounds can be delivered in the form of an aerosol spray from a pressurized container or dispenser, which contains a suitable propellant, e.g., a gas such as carbon dioxide, or a nebulizer. Such methods include those described in U.S. Pat. No. 6,468,798.

[0116] Systemic administration of a therapeutic compound as described herein can also be by transmucosal or transdermal means. For transmucosal or transdermal administration, penetrants appropriate to the barrier to be permeated are used in the formulation. Such penetrants are generally known in the art, and include, for example, for transmucosal administration, detergents, bile salts, and fusidic acid derivatives. Transmucosal administration can be accomplished through the use of nasal sprays. For transdermal administration, the active compounds are formulated into ointments, salves, gels, or creams as generally known in the art. In one embodiment, transdermal administration may be performed by iontophoresis.

[0117] A therapeutic agent can be formulated in a carrier system. The carrier can be a colloidal system. The colloidal system can be a liposome, a phospholipid bilayer vehicle. In one embodiment, the therapeutic agent is encapsulated in a liposome while maintaining the agent’s structural integrity. One skilled in the art would appreciate that there are a variety of methods to prepare liposomes. (See Lichtenberg, et al., Methods Biochem. Anal., 33:337-462 (1988); Anselem, et al., Liposome Technology, CRC Press (1993)). Liposomal formulations can delay clearance and increase cellular uptake (See Reddy, Ann. Pharmacother., 34(7-8):915-923 (2000)). An active agent can also be loaded into a particle prepared from pharmaceuticallyacceptable ingredients including, but not limited to, soluble, insoluble, permeable, impermeable, biodegradable or gastroretentive polymers or liposomes. Such particles include, but are not limited to, nanoparticles, biodegradable nanoparticles, microparticles, biodegradable microparticles, nanospheres, biodegradable nanospheres, microspheres, biodegradable microspheres, capsules, emulsions, liposomes, micelles and viral vector systems.

[0118] The carrier can also be a polymer, e.g., a biodegradable, biocompatible polymer matrix. In one embodiment, the therapeutic agent can be embedded in the polymer matrix, while maintaining the agent’s structural integrity. The polymer may be natural, such as polypeptides, proteins or polysaccharides, or synthetic, such as poly a-hydroxy acids. Examples include carriers made of, e.g., collagen, fibronectin, elastin, cellulose acetate, cellulose nitrate, polysaccharide, fibrin, gelatin, and combinations thereof. In one embodiment, the polymer is poly-lactic acid (PLA) or copoly lactic / glycolic acid (PGLA). The polymeric matrices can be prepared and isolated in a variety of forms and sizes, including microspheres and nanospheres. Polymer formulations can lead to prolonged duration of therapeutic effect. (See Reddy, Ann. Pharmacother ., 34(7-8):915-923 (2000)). A polymer formulation for human growth hormone (hGH) has been used in clinical trials. (See Kozarich and Rich, Chemical Biology, 2:548-552 (1998)).

[0119] Examples of polymer microsphere sustained release formulations are described in PCT publication WO 99 / 15154 (Tracy, el al), U.S. Pat. Nos. 5,674,534 and 5,716,644 (both to Zale, et al ), PCT publication WO 96 / 40073 (Zale, et al ), and PCT publication WO 00 / 38651 (Shah, et al). U.S. Pat. Nos. 5,674,534 and 5,716,644 and PCT publication WO 96 / 40073 describe a polymeric matrix containing particles of erythropoietin that are stabilized against aggregation with a salt.

[0120] In some embodiments, the therapeutic compounds are prepared with carriers that will protect the therapeutic compounds against rapid elimination from the body, such as a controlled release formulation, including implants and microencapsulated delivery systems. Biodegradable, biocompatible polymers can be used, such as ethylene vinyl acetate,polyanhydrides, polyglycolic acid, collagen, polyorthoesters, and polylactic acid. Such formulations can be prepared using known techniques. The materials can also be obtained commercially, e.g., from Alza Corporation and Nova Pharmaceuticals, Inc. Liposomal suspensions (including liposomes targeted to specific cells with monoclonal antibodies to cellspecific antigens) can also be used as pharmaceutically acceptable carriers. These can be prepared according to methods known to those skilled in the art, for example, as described in U.S. Pat. No. 4,522,811.

[0121] The therapeutic compounds can also be formulated to enhance intracellular delivery. For example, liposomal delivery systems are known in the art, see, e.g., Chonn and Cullis, “Recent Advances in Liposome Drug Delivery Systems,” Current Opinion in Biotechnology 6:698-708 (1995); Weiner, “Liposomes for Protein Delivery: Selecting Manufacture and Development Processes,” Immunomethods, 4(3):201-9 (1994); and Gregoriadis, “Engineering Liposomes for Drug Delivery: Progress and Problems,” Trends BiotechnoL, 13(12):527-37 (1995). Mizguchi, et al., Cancer Lett., 100:63-69 (1996), describes the use of fusogenic liposomes to deliver a protein to cells both in vivo and in vitro.

[0122] Dosage, toxicity and therapeutic efficacy of any therapeutic agent can be determined by standard pharmaceutical procedures in cell cultures or experimental animals, e.g., for determining the LD50 (the dose lethal to 50% of the population) and the ED50 (the dose therapeutically effective in 50% of the population). The dose ratio between toxic and therapeutic effects is the therapeutic index and it can be expressed as the ratio LD50 / ED50. Compounds that exhibit high therapeutic indices are advantageous. While compounds that exhibit toxic side effects may be used, care should be taken to design a delivery system that targets such compounds to the site of affected tissue in order to minimize potential damage to uninfected cells and, thereby, reduce side effects.

[0123] The data obtained from the cell culture assays and animal studies can be used in formulating a range of dosage for use in humans. The dosage of such compounds may be within a range of circulating concentrations that include the ED50 with little or no toxicity.The dosage may vary within this range depending upon the dosage form employed and the route of administration utilized. For any compound used in the methods, the therapeutically effective dose can be estimated initially from cell culture assays. A dose can be formulated in animal models to achieve a circulating plasma concentration range that includes the IC50 (z.e., the concentration of the test compound which achieves a half-maximal inhibition of symptoms) as determined in cell culture. Such information can be used to determine useful doses in humans accurately. Levels in plasma may be measured, for example, by high performance liquid chromatography.

[0124] Typically, an effective amount of the one or more L in1hibitors and / or AD ARI inhibitors disclosed herein sufficient for achieving a therapeutic or prophylactic effect, range from about 0.000001 mg per kilogram body weight per day to about 10,000 mg per kilogram body weight per day. Suitably, the dosage ranges are from about 0.0001 mg per kilogram body weight per day to about 100 mg per kilogram body weight per day. For example, dosages can be 1 mg / kg body weight or 10 mg / kg body weight every day, every two days or every three days or within the range of 1-10 mg / kg every week, every two weeks or every three weeks. In one embodiment, a single dosage of the therapeutic compound ranges from 0.001-10,000 micrograms per kg body weight. In one embodiment, one or more L in1hibitor or AD ARI inhibitor concentrations in a carrier range from 0.2 to 2000 micrograms per delivered milliliter. An exemplary treatment regime entails administration once per day or once a week. In therapeutic applications, a relatively high dosage at relatively short intervals is sometimes required until progression of the disease is reduced or terminated, or until the subject shows partial or complete amelioration of symptoms of disease. Thereafter, the patient can be administered a prophylactic regime.

[0125] In some embodiments, a therapeutically effective amount of one or more L in1hibitors and / or AD ARI inhibitors may be defined as a concentration of inhibitor at the target tissue of IO’32to 10'6molar, e.g., approximately 10’7molar. This concentration may be delivered by systemic doses of 0.001 to 100 mg / kg or equivalent dose by body surface area. The schedule of doses would be optimized to maintain the therapeutic concentration at the target tissue, suchas by single daily or weekly administration, but also including continuous administration (e.g., parenteral infusion or transdermal application).

[0126] The skilled artisan will appreciate that certain factors may influence the dosage and timing required to effectively treat a subject, including but not limited to, the severity of the disease or disorder, previous treatments, the general health and / or age of the subject, and other diseases present. Moreover, treatment of a subject with a therapeutically effective amount of the therapeutic compositions described herein can include a single treatment or a series of treatments.

[0127] The mammal treated in accordance with the present methods can be any mammal, including, for example, farm animals, such as sheep, pigs, cows, and horses; pet animals, such as dogs and cats; laboratory animals, such as rats, mice and rabbits. In some embodiments, the mammal is a human.Therapeutic Methods of the Present Technology

[0128] In one aspect, the present disclosure provides a method for treating cancer in a patient in need thereof comprising administering to the patient an effective amount of a LINE-1 (LI) inhibitor, wherein the patient comprises a tumor harboring a TP53 mutation. The TP53 mutation may be a missense mutation, a nonsense mutation, or a frameshift mutation. In an another aspect, the present disclosure provides a method for treating cancer in a patient in need thereof comprising administering to the patient an effective amount of an ADAR1 inhibitor, wherein the patient comprises tumors that do not harbor TP53 mutations. In yet another aspect, the present disclosure provides a method for treating cancer in a patient in need thereof comprising administering to the patient an effective amount of an AD ARI inhibitor and a LINE- 1 (LI) inhibitor, wherein the patient comprises tumors that include at least one TP53 mutant gene and at least one wildtype TP53 gene. The at least one TP53 mutant gene may comprise a missense mutation, a nonsense mutation, or a frameshift mutation.

[0129] In any of the preceding embodiments of the methods disclosed herein, the method further comprises administering an effective amount of an additional therapeutic agent, such as immune checkpoint blockade therapy.

[0130] In one aspect, the present disclosure provides a method for enhancing tumor sensitivity to immune checkpoint blockade therapy in a patient diagnosed with cancer comprising administering to the patient an effective amount of a LINE-1 (LI) inhibitor, wherein the patient comprises a tumor harboring a TP53 mutation. The TP53 mutation may be a missense mutation, a nonsense mutation, or a frameshift mutation. In one aspect, the present disclosure provides a method for enhancing tumor sensitivity to immune checkpoint blockade therapy in a patient diagnosed with cancer comprising administering to the patient an effective amount of an AD ARI inhibitor, wherein the patient comprises tumors that do not harbor TP53 mutations.

[0131] In any and all embodiments of the methods disclosed herein, the AD ARI inhibitor is a small molecule or an AD ARI -specific inhibitory nucleic acid. Examples of AD ARI small molecule inhibitors include, but are not limited to, rebecsinib, GT0001X, AVA-ADR-001, CLAD-100, lithospermic acid, and Regaloside B. The AD ARI -specific inhibitory nucleic acid may be a siRNA, a shRNA, an antisense oligonucleotide, or a sgRNA.

[0132] In any and all embodiments of the methods disclosed herein, the L in1hibitor is a small molecule or an Ll-specific inhibitory nucleic acid. Examples of L1 small molecule inhibitors include, but are not limited to, capsaicin, nucleoside reverse transcriptase inhibitors (e.g., lamivudine, adefovir, penci cl ovir, abacavir, stavudine, didanosine, zidovudine, emtricitabine, zalcitabine, tenofovir, entecavir, telbivudine, AZTTP, d4TTP, ddCTP, 3TCTP), NSC332395, and AD2 / ZINC89469886. The Ll-specific inhibitory nucleic acid may be a siRNA, a shRNA, an antisense oligonucleotide, or a sgRNA.

[0133] In some embodiments of the methods disclosed herein, the methods further comprise (a) detecting Short Interspersed Nuclear Elements (SINE) expression levels in a test sample obtained from the patient prior to treatment with the i Lnh1ibitor and (b) determining that the SINE expression levels in the test sample are reduced compared to a reference sampleobtained from a healthy control patient or a predetermined threshold. In some embodiments, the patient exhibits elevated L r1etrotransposition activity compared to a healthy control patient or a predetermined threshold. In other embodiments of the methods disclosed herein, the methods further comprise (a) detecting SINE expression levels in a test sample obtained from the patient prior to treatment with the AD ARI inhibitor and (b) determining that the SINE expression levels in the test sample are elevated or comparable to a reference sample obtained from a healthy control patient or a predetermined threshold. The SINE expression levels may be detected via next-generation sequencing, RNA-Seq, PCR, real-time quantitative PCR (qPCR), digital PCR (dPCR), Southern blotting, Reverse transcriptase-PCR (RT-PCR), Northern blotting, microarray, dot or slot blots, in situ hybridization, or fluorescent in situ hybridization (FISH). In some embodiments, the patient exhibits L r1etrotransposition activity that is comparable to a healthy control patient or a predetermined threshold. Additionally or alternatively, in some embodiments, the SINE comprise at least one or more of SINE2 / SINE3, monomeric SINE1, AluJ, AluS, AluY and SINE-VNTR-Alu (SVA). In some embodiments, the test sample or reference sample includes a biopsied tissue, wherein the biopsied tissue is frozen, fresh, or Formalin-Fixed Paraffin-Embedded (FFPE).

[0134] In any and all embodiments of the methods disclosed herein, the L i1nhibitor or the AD ARI inhibitor is administered orally, topically, intranasally, systemically, intravenously, subcutaneously, intraperitoneally, intradermally, intraocularly, iontophoretically, transmucosally, or intramuscularly.

[0135] In any of the preceding embodiments of the methods disclosed herein, the cancer is pancreatic cancer or ovarian cancer.

[0136] Additionally or alternatively, in certain embodiments, the immune checkpoint blockade therapy comprises an anti-PD-1 antibody, an anti-PD-Ll antibody, an anti-PD-L2 antibody, an anti-CTLA-4 antibody, an anti-TIM3 antibody, an anti-4-lBB antibody, an anti- CD73 antibody, an anti-GITR antibody, or an anti-LAG-3 antibody. Examples of immune checkpoint inhibitors include, but are not limited to, cemiplimab, tremelimumab, ipilimumab,nivolumab, pidilizumab, lambrolizumab, pembrolizumab, envafolimab, atezolizumab, avelumab, durvalumab, dostarlimab, MPDL3280A, BMS-936559, MEDI-4736, MSB 00107180, inhibitory antibodies against LAG-3 (lymphocyte activation gene 3), TIM3 (T-cell immunoglobulin and mucin-3), B7-H3, B7-H4, TIGIT (T-cell immunoreceptor with Ig and ITIM domains), AMP -224, MDX-1105, arelumab, tremelimumab, IMP321, MGA271, BMS-986016, lirilumab, urelumab, PF-05082566, IPH2101, MEDI-6469, CP-870,893, Mogamulizumab, Varlilumab, Galiximab, AMP-514, AUNP 12, Indoximod, NLG-919, INCB024360, CD80, CD86, ICOS (inducible T-cell costimulatory), DLBCL (diffuse large B-cell lymphoma) inhibitors, BTLA (B and T lymphocyte attenuator), PDR001, and any combination thereof.

[0137] In any and all embodiments of the methods described herein, the patient exhibits elevated expression levels of SINE-derived double stranded RNAs (dsRNA) and / or elevated levels of one or more RIG-I associated type-I interferon genes. The one or more RIG-I associated type-I interferon genes may be selected from the group consisting of PELI1, HLA-G, CDKN1A, PIM1, CGAS, PSMA2, UPP1, RIPK2, PFKP, STAT2, IRF9, TNFAIP2, ISG20, SOCS1, SOCS7, DHX58, TRIM25, IFI30, IRF3, and HELZ2.

[0138] In therapeutic applications, compositions or medicaments comprising an LI inhibitor or ADAR1 inhibitor disclosed herein are administered to a subject suspected of, or already suffering from such a disease or condition (such as, pancreatic cancer, or ovarian cancer), in an amount sufficient to cure, or at least partially arrest, the symptoms of the disease, including its complications and intermediate pathological phenotypes in development of the disease.

[0139] In certain embodiments, subjects suffering from cancer (e.g., pancreatic cancer, ovarian cancer) that are treated with the L1 inhibitor or AD ARI inhibitor will show reduced cancer proliferation, amelioration of cancer symptoms, and / or increased survival compared to untreated subjects suffering from the same disease or condition.

[0140] For therapeutic applications, a composition comprising an i Lnh1ibitor or AD ARI inhibitor disclosed herein, is administered to the subject. In some embodiments, the i Lnh1ibitoror ADAR1 inhibitor is administered one, two, three, four, or five times per day. In some embodiments, the L1 inhibitor or AD ARI inhibitor is administered more than five times per day. Additionally or alternatively, in some embodiments, the L1 inhibitor or ADARl inhibitor is administered every day, every other day, every third day, every fourth day, every fifth day, or every sixth day. In some embodiments, the L1 inhibitor or AD ARI inhibitor is administered weekly, bi-weekly, tri -weekly, or monthly. In some embodiments, the L i1nhibitor or AD ARI inhibitor is administered for a period of one, two, three, four, or five weeks. In some embodiments, the L1 inhibitor or ADAR1 inhibitor is administered for six weeks or more. In some embodiments, the L1 inhibitor or ADAR1 inhibitor is administered for twelve weeks or more. In some embodiments, the L i1nhibitor or AD ARI inhibitor is administered for a period of less than one year. In some embodiments, the L in1hibitor or AD ARI inhibitor is administered for a period of more than one year. In some embodiments, the L in1hibitor or AD ARI inhibitor is administered throughout the subject’s life.

[0141] In some embodiments of the methods of the present technology, the L in1hibitor or ADARl inhibitor is administered daily for 1 week or more. In some embodiments of the methods of the present technology, the L1 inhibitor or ADARl inhibitor is administered daily for 2 weeks or more. In some embodiments of the methods of the present technology, the LI inhibitor or ADARl inhibitor is administered daily for 3 weeks or more. In some embodiments of the methods of the present technology, the L1 inhibitor or ADARl inhibitor is administered daily for 4 weeks or more. In some embodiments of the methods of the present technology, the LI inhibitor or ADARl inhibitor is administered daily for 6 weeks or more. In some embodiments of the methods of the present technology, the L i1nhibitor or ADARl inhibitor is administered daily for 12 weeks or more. In some embodiments, the L1 inhibitor or ADARl inhibitor is administered daily throughout the subject’s life.Combination Therapy

[0142] In some embodiments, one or more of the L in1hibitors and / or ADARl inhibitors disclosed herein may be combined with one or more additional therapies for the treatment of acancer. In some embodiments, the additional therapies comprise one or more immune checkpoint blocking agents. In some embodiments, the one or more immune checkpoint blocking agents may be an anti-PD-1 antibody, an anti-PD-Ll antibody, an anti-PD-L2 antibody, an anti-CTLA- 4 antibody, an anti-TIM3 antibody, an anti-4-lBB antibody, an anti-CD73 antibody, an anti- GITR antibody, or an anti-LAG-3 antibody. In some embodiments, the one or more immune checkpoint blocking agents are selected from the group consisting of cemiplimab, tremelimumab, ipilimumab, nivolumab, pidilizumab, lambrolizumab, pembrolizumab, envafolimab, atezolizumab, avelumab, durvalumab, dostarlimab, MPDL3280A, BMS-936559, MEDL4736, MSB 00107180, inhibitory antibodies against LAG-3 (lymphocyte activation gene 3), TIM3 (T- cell immunoglobulin and mucin-3), B7-H3, B7-H4, TIGIT (T-cell immunoreceptor with Ig and ITIM domains), AMP -224, MDX-1105, arelumab, tremelimumab, IMP321, MGA271, BMS- 986016, lirilumab, urelumab, PF-05082566, IPH2101, MEDL6469, CP-870,893, Mogamulizumab, Varlilumab, Galiximab, AMP-514, AUNP 12, Indoximod, NLG-919, INCB024360, CD80, CD86, ICOS (inducible T-cell costimulatory), DLBCL (diffuse large B- cell lymphoma) inhibitors, BTLA (B and T lymphocyte attenuator), PDR001, and any combination thereof.

[0143] Additionally or alternatively, in some embodiments, the one or more in Lh1ibitors and / or ADAR1 inhibitors disclosed herein may be separately, sequentially or simultaneously administered with at least one additional therapeutic agent selected from the group consisting of an anti-PD-1 antibody, an anti-PD-Ll antibody, an anti-PD-L2 antibody, an anti-CTLA-4 antibody, an anti-TIM3 antibody, an anti-4- IBB antibody, an anti-CD73 antibody, an anti-GITR antibody, or an anti-LAG-3 antibody.

[0144] In any case, the multiple therapeutic agents may be administered in any order or even simultaneously. If simultaneously, the multiple therapeutic agents may be provided in a single, unified form, or in multiple forms (by way of example only, either as a single pill or as two separate pills). One of the therapeutic agents may be given in multiple doses, or both may be given as multiple doses. If not simultaneous, the timing between the multiple doses may varyfrom more than zero weeks to less than four weeks. In addition, the combination methods, compositions and formulations are not to be limited to the use of only two agents.Kits

[0145] The present disclosure also provides kits for the treatment of cancer (e.g., pancreatic cancer, ovarian cancer), comprising one or more L1 inhibitors and / or ADAR1 inhibitors disclosed herein. Optionally, the above described components of the kits of the present technology are packed in suitable containers and labeled for the treatment of cancer (e.g., pancreatic cancer, ovarian cancer).

[0146] The above-mentioned components may be stored in unit or multi-dose containers, for example, sealed ampoules, vials, bottles, syringes, and test tubes, as an aqueous, preferably sterile, solution or as a lyophilized, preferably sterile, formulation for reconstitution. The kit may further comprise a second container which holds a diluent suitable for diluting the pharmaceutical composition towards a higher volume. Suitable diluents include, but are not limited to, the pharmaceutically acceptable excipient of the pharmaceutical composition and a saline solution. Furthermore, the kit may comprise instructions for diluting the pharmaceutical composition and / or instructions for administering the pharmaceutical composition, whether diluted or not. The containers may be formed from a variety of materials such as glass or plastic and may have a sterile access port (for example, the container may be an intravenous solution bag or a vial having a stopper which may be pierced by a hypodermic injection needle). The kit may further comprise more containers comprising a pharmaceutically acceptable buffer, such as phosphate-buffered saline, Ringer's solution and dextrose solution. It may further include other materials desirable from a commercial and user standpoint, including other buffers, diluents, filters, needles, syringes, culture medium for one or more of the suitable hosts. The kits may optionally include instructions customarily included in commercial packages of therapeutic or diagnostic products, that contain information about, for example, the indications, usage, dosage, manufacture, administration, contraindications and / or warnings concerning the use of such therapeutic or diagnostic products.

[0147] The kit can also comprise, e.g., a buffering agent, a preservative or a stabilizing agent. The kit can also contain a control sample or a series of control samples, which can be assayed and compared to the test sample. Each component of the kit can be enclosed within an individual container and all of the various containers can be within a single package, along with instructions for interpreting the results of the assays performed using the kit. The kits of the present technology may contain a written product on or in the kit container. The written product describes how to use the reagents contained in the kit. In certain embodiments, the use of the reagents can be according to the methods of the present technology.B. Systems and Methods for Selecting Cancer Therapies Based on Magnitudes and Frequencies of Retrotransposon (RT) Insertions in Subject Genomes

[0148] Referring now to FIG. 13, depicted is a block diagram of a system 1300 for cancer therapies based on double-stranded ribonucleic acid (dsRNA) force metrics associated with short interspersed nuclear elements (SINE). In brief overview, the system 1300 may include at least one data processing system 1305, at least one sequencing device (e.g., a nextgeneration sequencer) 1310, and at least one administrative device 1315, communicatively coupled with one another via at least one network 1320. The data processing system 1305 may include at least one dataset aggregator 1325, at least one RNA analyzer 1330, at least one genome analyzer 1335, at least one metric evaluator 1340, at least one output handler 1345, and at least one database 1350, among others. Each of the components in the system 1300 as detailed herein may be implemented using hardware (e.g., one or more processors coupled with memory), or a combination of hardware and software as detailed herein in Section C. Each of the components in the system 1300 may implement or execute the functionalities detailed herein, such as those described in Section A.

[0149] In further detail, the data processing system 1305 may (sometimes herein generally referred to as a computing system or a server) be any computing device including one or more processors coupled with memory and software and capable of performing the various processes and tasks described herein. The data processing system 1305 can be incommuni cation with the sequencing device 1310, the administrative device 1315, the database 1350, and other devices, via the network 1320. The data processing system 1305 may be situated, located, or otherwise associated with at least one server group. The server group may correspond to a data center, a branch office, or a site at which one or more servers corresponding to the data processing system 1305 is situated. On the data processing system 1305, the dataset aggregator 1325 may acquire or obtain RNA sequencing datasets or genome sequencing datasets from the sequencing device 1310. The RNA analyzer 1330 may process the RNA sequencing datasets to determine various RNA related metrics. The genome analyzer 1335 may process the genome sequencing datasets to determine various genome related metrics. The metric evaluator 1340 may use the RNA or genome related metrics to select cancer therapies. The output handler 1345 may generate outputs based on the selection of the cancer therapies or the metrics. The data processing system 1305 can perform or implement any of the functionalities detailed herein in conjunction with Section A.

[0150] The sequencing device 1310 may be any device to perform genome sequencing or RNA sequencing on samples taken from subjects. The sequencing device 1310 may be communicatively coupled with the data processing system 1305 and the administrative device 1315 via the network 1320. The sequencing device 1310 may be associated with an entity examining subjects or facilitating the derivation of genome and RNA sequencing from such subject. The sequencing device 1310 may perform RNA sequencing on an RNA sample to generate RNA sequencing datasets. The RNA sequencing performed may include, for example, Poly(A) Selection RNA-seq, Total RNA-seq, strand-specific RNA-seq, single-cell RNA-seq, Long-read RNA-seq, small RNA-seq, or RNA capture sequencing, among others. The genetic sequencing may be targeted to find retrotransposon insertion sites in the genomes of subjects. The genome sequencing dataset may include long reads (e.g., greater than 13000 base pairs) or short reads (e.g., between 50 to 300 base pairs). The RNA sequencing dataset may include long reads (e.g., greater than 13000 base pairs) or short reads (e.g., between 50 to 300 base pairs).

[0151] In some embodiments, the sequencing device 1310 may perform genetic sequencing on a deoxyribonucleic acid (DNA) sample taken from a subject to generate genome sequencing datasets. The genetic sequencing carried out may be a high throughput, massively parallel sequencing technique (sometimes herein referred to as next-generation sequencing), such as short-read or long-read whole genome sequencing (WGS), pyrosequencing, Reversible dye-terminator sequencing, SOLiD sequencing, Ion semiconductor sequencing, Helioscope single molecule sequencing, among others. The gene sequencing and RNA sequence may be maintained using one or more files according to a format (e.g., FASTQ, BCL, or VCF formats). Thus, the genomic sequences and RNA sequences obtained via next -gen eration sequencing comprise a plurality of sequencing reads.

[0152] The administrative device 1315 (sometimes herein referred to as an end user computing device) may be any computing device comprising one or more processors coupled with memory and software and capable of performing the various processes and tasks described herein. The administrative device 1315 may be in communication with the data processing system 1305 and the sequencing device 1310 via the network 1320. The administrative device 1315 may have at least one display. The administrative device 1315 may be associated with an entity (e.g., a clinician) examining the subject or genomic or RNA data from the subject. The display may present information about the subject provided by the data processing system 1305.

[0153] The database 1350 may store and maintain various resources and data associated with the data processing system 1305, the sequencing device 1310, and the administrative device 1315, among others. The database 1350 may include a database management system (DBMS) to arrange and organize the data maintained thereon. The database 1350 may be in communication with the data processing system 1305, the sequencing device 1310, and the administrative device 1315, via the network 1320. While running various operations, the data processing system 1305, the sequencing device 1310, and the administrative device 1315 may access the database 1350 to retrieve identified data therefrom. The data processing system1305, the sequencing device 1310, and the administrative device 1315 may also write data onto the database 1350 from running such operations.

[0154] Referring now to FIG. 14, depicted is a block diagram of a process 1400 to derive metrics to determine which cancer therapy to select in the system 1300. The process 1400 may include or correspond to operations performed in the system 1300 to obtain RNA and genome sequence data from subjects to derive metrics with which to select cancer therapies for the subjects. Under the process 1400, the sequencing device 1310 may execute, carry out, or otherwise perform genome sequencing and RNA sequencing on one or more biological samples obtained from at least one subject 1405. The subject 1405 may at risk of or diagnosed with cancer.

[0155] The biological samples may be obtained from any tissue or organ of the subject 1405, such as ovary and pancreas (e.g., head, neck, body, tail, or duct portion), among others. The cancer may include, for example, ovarian cancer (e.g., epithelial, germ cell tumor, and sex cord stromal tumor) and pancreatic cancer (e.g., pancreatic ductal adenocarcinoma, pancreatic acinar cell carcinoma, solid pseudopapillary tumor, adenosquamous carcinoma, pancreatic neuroendocrine tumor, and intraductal papillary mucinous neoplasm (IPMN)) , among others. In some embodiments, the one or more biological samples comprise the same tissue organ type. In some embodiments, at least one biological sample may be a biopsied tissue obtained from the organ infected with cancer. For example, when the subject 1405 is diagnosed with pancreatic cancer, a biopsy of the pancreas may be performed (e g., using a needle or surgery) to obtain the tissue sample. The biopsied tissue from the organ to be used by the sequencing device 1310 may be frozen, fresh, or formalin-fixed paraffin-embedded (FFPE), among others.

[0156] The sequencing device 1310 may perform RNA sequencing on at least one biological sample of the subject 1405 to output, produce, or otherwise generate at least one RNA sequence dataset 1410. The RNA sequence dataset 1410 may include a set of RNA sequences from the biological sample of the subject 1405. The set of RNA sequences may correspond to strands of a given double-stranded RNA (dsRNA). At least one RNA sequencemay correspond to a first strand of a double-stranded RNA. At least one other RNA sequence may correspond to a second strand of the dsRNA. The second strand may be complementary to the first strand. The second strand may be shorter in length than the first strand of the same dsRNA. For example, the first strand may correspond to the RNA strand with the SINE insertion, and as a result the first strand may be longer in length than the second strand. The SINE may be of one or more types, such as SINE1, SINE2, SINE 3, monomeric and dimeric AluJ elements, AluS elements, AluY elements, or SINE-Variable number of tandem repeat (VNTR)-Alus (SVA) elements, among others. One or more portions of the set of RNA sequences may include or identify transcriptions of SINE expressions. The RNA sequence dataset 1410 may include or identify metadata associated with the set of RNA sequences, such as an identifier (e.g., an anonymized identifier) for the subject 1405. With the generation, the sequencing device 1310 may provide, send, or otherwise transmit the RNA sequence dataset 1410 to the data processing system 1305.

[0157] In some embodiments, the sequencing device 1310 may perform genome sequencing on at least one biological sample of the subject 1405 to output, produce, or otherwise generate at least one genome sequence dataset 1415. The biological sample may be obtained from the organ affected by the cancer. The genome sequence dataset 1415 may include or identify a set of genomic sequences from the biological sample of the subject 1405. One or more portions of the genomic sequences may include or identify reads corresponding to RT insertion sites (e.g., long interspersed nuclear element-1 (LINE-1) elements). One or more portions of the genomic sequences may include or identify p53 genes. In some embodiments, the biological sample for the genome sequencing may differ from the biological sample for the RNA sequencing. In some embodiments, the biological sample for the genome sequencing may be from the same organ as the organ of the biological sample for RNA sequencing. For instance, two biological samples may be taken from the organ affected by the cancer. One biological sample may be used for the genome sequencing and the other biological sample may be used for the RNA sequencing. The genome sequence dataset 1415 may include or identify metadata associated with the set of genomic sequences, such as an identifier (e.g., ananonymized identifier) for the subject 1405. With the generation, the sequencing device 1310 may provide, send, or otherwise transmit the genome sequence dataset 1415 to the data processing system 1305.

[0158] The dataset aggregator 1325 executing on the data processing system 1305 may receive, identify, or otherwise obtain the RNA sequence dataset 1410 from the sequencing device 130. The RNA sequence dataset 1410 may identify or include the set of RNA sequences obtained from the biological sample of the subject 1405. Upon obtaining, the dataset aggregator 1325 may perform preprocessing (e.g., filtering or normalizations for quality control) on the genomic sequences identified in the RNA sequence dataset 1410. The preprocessing may be for quality control as detailed herein in Section A. In some embodiments, the dataset aggregator 1325 may delete, erase, or otherwise remove at least a portion of the RNA sequences corresponding to RNA sequencing reads with quality scores not satisfying (e.g., less than) a threshold score, from the RNA sequence dataset 1410. Conversely, the dataset aggregator 1325 may keep or maintain at least a remaining portion of the RNA sequences corresponding to RNA sequencing reads with quality scores satisfying (e.g., greater than or equal to) the threshold score in the RNA sequence dataset 1410. In some embodiments, the dataset aggregator 1325 may remove, erase, or otherwise filter a portion of the RNA sequences corresponding to RNA sequencing reads with counts of repeats not satisfying (e.g., less than) a threshold count, from the RNA sequence dataset 1410. Conversely, the dataset aggregator 1325 may keep or maintain a remaining portion of the RNA sequences corresponding to RNA sequencing reads with counts of repeats satisfying (e.g., greater than or equal to) the threshold count in the RNA sequence dataset. The filtered RNA sequence dataset 1410 may be used for additional processing by the data processing system 1305.

[0159] In some embodiments, the dataset aggregator 1325 executing on the data processing system 1305 may receive, identify, or otherwise obtain the genome sequence dataset 1415 from the sequencing device 1310. The genome sequence dataset 1415 may identify or include the set of genomic sequences of the biological sample from the subject 1405. With the obtaining of the genome sequence dataset 1415, the dataset aggregator 1325 may performfiltering (e.g., for quality control) on the genomic sequences identified in the genome sequence dataset 1415. In some embodiments, the dataset aggregator 1325 may delete, erase, or otherwise remove at least a portion of the genome sequences corresponding to genome sequencing reads with quality scores not satisfying (e.g., less than) a threshold score, from the genome sequence dataset 1415. Conversely, the dataset aggregator 1325 may keep or maintain at least a remaining portion of the genome sequences corresponding to genome sequencing reads with quality scores satisfying (e.g., greater than or equal to) the threshold score in the genome sequence dataset 1415. The filtered genome sequence dataset 1415 may be used for additional processing by the data processing system 1305.

[0160] The RNA analyzer 1330 executing on the data processing system 1305 may process or parse the RNA sequence dataset 1410. From parsing the RNA sequence dataset 1410, the RNA analyzer 1330 may select or identify the set of RNA sequences. From the set of RNA sequences, the RNA analyzer 1330 may extract, select, or otherwise identify at least one first RNA sequence corresponding to a first RNA strand of the dsRNA. The first RNA strand may correspond to the RNA strand with at least one SINE copy. In some embodiments, the RNA analyzer 1330 may identify the first RNA sequence as corresponding to the first RNA strand that includes the SINE copy. The RNA analyzer 1330 may also extract, select, or otherwise identify one or more second RNA sequences corresponding to second RNA strands each complementary to the first RNA strand of the dsRNA strand. Each of the other RNA sequences may correspond to a second RNA strand that is complementary to the first RNA strand. The second RNA strand may lack the SINE copy, and thus may be shorter than the first RNA strand.

[0161] Using the RNA sequence dataset 1410, the RNA analyzer 1330 may select, extract, or otherwise identify at least one SINE copy in the first RNA sequence of the set of RNA sequences. The SINE copy may include, for example, a SINE1 element, SINE2 element, SINE 3 element, monomeric and dimeric AluJ element, AluS element, AluY element, or SVA element, among others, in one or more orientations such as sense and antisense orientations . The SINE copy may correspond to at least a portion of the first RNA sequence. The SINEcopy may serve as a reference SINE element for which a pairable SINE copy on the first RNA strand or the second RNA strand in the dsRNA is to be found. In addition, using the RNA sequence dataset 1410, the RNA analyzer 1330 may determine or identify a length of nucleotides of the first RNA sequence. The length may indicate or correspond to a number of base pairs (or bases) of nucleotides of the first RNA sequence, including the SINE copy. In some embodiments, the RNA analyzer 1330 may calculate the length of nucleotides of the first RNA sequence in a predetermined window with the SINE copy. The predetermined window may range from 1500-5000 base pairs about the start and the end of the SINE element within the first RNA sequence.

[0162] In addition, the RNA analyzer 1330 may determine or identify a length of nucleotides of the second RNA sequence. The length may indicate or correspond to a number of base pairs (or bases) of nucleotides of the complementary second RNA sequence, which lacks the SINE copy. In some embodiments, the RNA analyzer 1330 may calculate the length of nucleotides of the second RNA sequence in a predetermined window associated with the SINE copy. The predetermined window may range from 1500-5000 base pairs about the start and the end of the SINE element. In some embodiments, the RNA analyzer 1330 may calculate, determine, or otherwise identify at least one insert length of the SINE copy. The insert length may indicate or correspond to a number of base pairs (or bases) of nucleotides of the SINE copy on the first RNA strand. In some embodiments, the RNA analyzer 1330 may identify the insert length within at least the predetermined window, using the predetermined window in the first RNA sequence of the RNA sequence dataset 1410.

[0163] The RNA analyzer 1330 may calculate, generate, or otherwise determine at least one probability of an occurrence of a pairable SINE associated with the at least one SINE copy on the dsRNA. The pairable SINE may be a SINE element that can combine or pair with the SINE copy, and may include, for example, a SINE1 element, SINE2 element, SINE 3 element, monomeric and dimeric AluJ element, AluS element, AluY element, or SVA element, among others. To determine the probability, the RNA analyzer 1330 may calculate, identify, or otherwise determine a first set of frequencies for a corresponding first set of nucleotides (e.g.,adenine (A), guanine (G), cytosine (C), thymine (T), or uracil (U)) in the first RNA sequence. In some embodiments, the first set of frequencies may be for the first set of nucleotides are within predetermined window of the first RNA sequence of the at least one SINE copy. In addition, the RNA analyzer 1330 may calculate, identify, or otherwise determine a second set of frequencies for a corresponding second set of complementary nucleotides in the second RNA sequence. In some embodiments, the second set of frequencies may be for the second set of nucleotides are within predetermined window of the second RNA sequence of the at least one SINE copy.

[0164] Using the first set of probabilities and second set of probabilities, the RNA analyzer 1330 may determine the probability of the occurrence of the pairable SINE associated with the at least one SINE copy on the dsRNA. The probability of the occurrence may indicate a likelihood that the pairable SINE associated with the at least one SINE copy is present on the dsRNA (e.g., on the first or second RNA strands) within the predetermined window (e.g., within 1500-5000 base pairs). For example, the RNA analyzer 1330 may determine the probability in accordance with the following formula:where a corresponds to the types of nucleotides (e.g., adenine (A), guanine (G), cytosine (C), thymine (T), or uracil (U)), f(σ) corresponds to the set of frequencies of the nucleotides for the first RNA strand, f(σcompl') corresponds to the set of frequencies of the nucleotides for the second RNA strand, and a corresponds to the probability of an occurrence of a pairable SINE associated with the at least one SINE copy.

[0165] Based on the probability of occurrence of the pairable SINE, the length of the first RNA strand, the length of the second RNA strand, and the insert length, the RNA analyzer 1330 may calculate, generate, or otherwise determine at least one dsRNA force metric 1420. The dsRNA force metric 1420 may indicate or correspond to a degree of potential of the at least one SINE copy to fold with the pairable SINE on the dsRNA. The dsRNA force metric1420 may be correlated with a mismatch or divergence between the length of the first RNA sequence corresponding to the first RNA strand with the SINE copy versus the length of the second RNA sequence corresponding to the second RNA strand. The RNA analyzer 1330 may determine the dsRNA force metric 1420 as a function of the probability of occurrence of the pairable SINE, the length of the first RNA strand, the length of the second RNA strand, and the insert length, among others. In some embodiments, the RNA analyzer 1330 may determine the dsRNA force metric 1420 using an adjustment factor (e.g., to account for certain pairs such as Watson-Crick and Wobble pairs as a complementary base pairs). For example, the RNA analyzer 1330 may solve for the following function to determine the dsRNA metric 1420:where xdscorresponds to the dsRNA metric 120, corresponds to the length of the secondRNA strand, L corresponds to the length of the first RNA strand, Licorresponds to the insert length of the SINE copy, and c is the adjustment factor (e.g., 2.2 or a value within 90% of 2.2), among others.

[0166] In conjunction, the genome analyzer 1335 executing on the data processing system 1305 may calculate, determine, or otherwise identify at least one RT activity metric 1425 using the genome sequence dataset 1415. The RT burden metric may identify or correspond to a level of RT activity in the set of genomic sequences in the biological sample from the subject 1405. The RT activity may comprise canonical insertions, inversioncontaining insertions, 5’ truncated insertions, full-length insertions, and 3’ transductions of an RT. The RT activity may include or correspond to, for example, one or more of a long interspersed nuclear element- 1 (LINE-1), a short interspersed nuclear element (SINE), or a SINE- Variable number of tandem repeat (VNTR)-Alus (SVA) within the genomic sequences of the host subject. To identify the RT burden metric, the genome analyzer 1335 may process or parse the set of genomic sequences identified by the genome sequence dataset 1415 (e.g., thefiltered genome sequence set 1410). From parsing, the genome analyzer 1335 may detect or identify a plurality of RT insertion sites using soft-clipped reads and / or discordant read pairs in the plurality of sequencing reads as a signal. The insertion sites may correspond to the RT activity (RT burden) within the genomic sequences of the subject.

[0167] Using the soft-clipped reads or discordant reads, the genome analyzer 1335 may identify or otherwise detect at least one of a long terminal repeat (LTR) retrotransposon (also referred herein as class I transposable LTR element), a non-LTR retrotransposon (e.g., also referred herein as class I non-LTR element), or DNA retrotransposon (e.g., also referred herein as class II element) in the set of genomic sequences of the subject. The occurrences of the LTR retrotransposon, non-LTR retrotransposon, or DNA retrotransposon may correspond to RT activity. In some embodiments, the genome analyzer 1335 may identify or detect insertions of at least one of LINE- 1, SINE, or SVA as the RT activity in the set of genomic sequences of the subject. In some embodiments, the genome analyzer 1335 may determine or identify an orientation of the RT (e.g., LINE-1) sequence for each insertion site detected in the genomic sequences of the subject. The orientation may include or correspond to at least one of singlestranded priming or an inversion-containing integration, among others.

[0168] Based on the RT activity detected in the genomic sequences of the subject, the genome analyzer 1335 may calculate, determine, or otherwise generate the RT activity metric 1425. The RT activity metric 1425 may be a numerical value indicating the level of RT activity within the genomic sequences of the biological sample. In general, the higher the RT activity, the higher the RT activity metric 1425 may be; conversely, the lower the RT activity, the lower the RT activity metric 1425 may be. In some embodiments, the genome analyzer 1335 may generate the RT activity metric 1425 based on the number of insertion sites detected in the set of genomic sequences from the genome sequence dataset 1415. In some embodiments, the genome analyzer 1335 may determine the RT metric 1420 based on a number of detected occurrences of the LTR retrotransposon, non-LTR retrotransposon, or DNA retrotransposon in the genomic sequences of the subject. In some embodiments, the genomeanalyzer 1335 may determine the RT metric 1420 based on a number of detected insertions of LINE-1, SINE, or SVA in the genomic sequences of the subject.

[0169] In addition, the genome analyzer 1335 may select, detect, or otherwise identify a set of p53 sequences in the genomic sequences of the subject using the genome sequence dataset 1415. Each p53 sequence may correspond to or include at least one of a set of non- silent p53 mutant sequences or a set of wild-type p53 sequences. The non-silent p53 mutant sequences may correspond to p53 somatic mutations, missense mutations, nonsense mutations, frameshift mutations copy number variations, structural variations (e.g., indels), translocations, or any other alterations among others. The wild-type p53 sequences may correspond to p53 genes without any such mutations, silent p53 mutant sequences or p53 mutant sequences in non-coding regions. The genome analyzer 1335 may parse or process the genomic sequences of the genome sequence dataset 1415 to identify the p53 sequences. From the set of p53 sequences, the genome analyzer 1335 may categorize, classify, or otherwise select a subset of the sequences as part of the set of non-silent p53 mutant sequences. The genome analyzer 1335 may select another subset of sequences as part of the wild-type p53 sequences. In some embodiments, silent p53 mutant sequences or p53 mutant sequences in non-coding regions are selected as part of the set of wild-type p53 sequences.

[0170] For each identified p53 sequence, the genome analyzer 1335 may calculate, generate, or otherwise determine at least one mutant p53 metric 1430. To determine, the genome analyzer 1335 may determine or identify a mutated position of each p53 mutant sequence within the p53 sequence using the genome sequence dataset 1415. The mutation may include, for example, a nonsense or frameshift mutation, among others, of the p53 gene. The position may be a numeric value corresponding to a location within the p53 gene sequence with the mutation. For example, the position may be an integer between 13 to 393, inclusive, as the p53 protein is 393 amino acids long, corresponding to the location of the first altered residue along the protein. In addition, the genome analyzer 1335 may calculate, generate, or otherwise determine a maximum p53 fitness metric. The maximum p53 fitness metric may correspond to transaction levels of at least one p53 target gene (e.g., WAF1, MDM2, BAX,hl433s, AIP1, GADD45, NOXA, and P53R) caused by a complete deletion of a p53 gene from the p53 gene sequence.

[0171] Furthermore, the genome analyzer 1335 may calculate, generate, or otherwise determine a wild-type p53 fitness metric. The wild-type p53 fitness metric may identify or correspond to a level of wild-type p53 concentrations and wild-type p53 affinities to DNA promoter regions of one or more p53 target genes. The determination of the wild-type p53 fitness metric may be based on one or more of transactivation levels of at least one p53 target gene (e.g., WAF1, MDM2, BAX, hl433s, AIP1, GADD45, NOXA, and P53R) induced by a wild-type p53 polypeptide encoded by the set of wild-type p53 sequences, a binding affinity of the wild-type p53 polypeptide to a promoter region of the at least one p53 target gene, and expression levels of the wild-type p53 polypeptide, among others. The binding affinity may correspond to strength of interaction of the wild-type p53 polypeptide to the promoter region of the p53 target gene. The expression level may correspond to a quantity of the wild-type p53 polypeptide in the p53 sequence.

[0172] Based on the mutated position of each p53 mutant sequence, the wild-type p53 fitness metric, and the maximum p53 metric, the genome analyzer 1335 may calculate, generate, or otherwise determine the mutant p53 metric 1430. The combination of the mutated position of each p53 mutant sequence, the wild-type p53 fitness metric, and the maximum p53 metric to determine the mutant p53 metric 1430 may be in accordance with the following:where FMTis the mutant p53 metric 1430, FWTis the fitness metric of wild-type p53, Fmnxis the maximum p53 fitness metric, and p is the position of the p53 mutant sequence (e.g., an integer value between 13 and 393, inclusive). The mutant p53 metric 1430 may identify or correspond to an oncogenic advantage of having a p53 mutation by the probability of mutant p53 not binding to transcriptional targets, factoring in the presence of wild-type p53 in the p53 sequences. In general, the higher the mutant p53 metric 1430, the more likely the mutant p53 isto not bind to target sites and by extension a higher loss of DNA-binding abilities. Conversely, the lower the mutant p53 metric 1430, the less likely the mutant p53 is to not bind to target sites and by extension a lower loss of DNA-binding abilities. The genome analyzer 1335 may repeat the operations detailed herein with respect to each identified p53 sequence of the set of genomic sequences in the genome sequence dataset 1415.

[0173] The metric evaluator 1340 executing on the data processing system 1305 may identify or select at least one cancer therapy from a set of candidate cancer therapies based on the dsRNA force metric 1420 relative to at least one threshold. The threshold may delineate, identify, or otherwise define a value for the dsRNA force metric 1420 to select one of the candidate cancer therapies. The candidate cancer therapies may include, for example, an immune checkpoint blockade (ICB) therapy or a combination therapy. The ICB therapy may include, for example, an anti- PD-1 antibody, an anti-PD-Ll antibody, an anti-PD-L2 antibody, an anti-CTLA-4 antibody, an anti-T!M3 antibody, an anti-TIGIT antibody, an anti-VlSTA antibody, an anti-B7-H3 antibody, an anti- BTLA antibody, an anti-CD73 antibody, or an anti- LAG-3 antibody, among others. The combination therapy may include the ICB therapy in addition to a molecularly targeted therapy. The molecularly targeted therapy may include, for example, L1 inhibitor, AD ARI inhibitor, or an MDM2 inhibitor, among others.

[0174] To select, the metric evaluator 1340 may compare the dsRNA force metric 1420 to the threshold. The dsRNA force metric 1420 may be within a range of values (e.g., between 0 to 1) and the threshold may be set to a fixed value (e.g., 0.4-0.6). If the dsRNA force metric 1420 satisfies (e.g., greater than or equal to) the threshold, the metric evaluator 1340 may select the ICB therapy as the cancer therapy. On the other hand, if the dsRNA force metric 1420 does not satisfy (e.g., less than) the threshold, the metric evaluator 1340 may select the combination therapy as the cancer therapy. The combination therapy may include, for example, a first combination including ICB therapy and L 1 inhibi tor or second combination including ICB therapy and AD ARI inhibitor, among others.

[0175] In some embodiments, the metric evaluator 1340 may select the at least one cancer therapy as a function of the dsRNA force metric 1420, the RT activity metric 1425, and the mutant p53 metric 1430, among others. The function may define or specify a combination of the dsRNA force metric 1420, the RT activity metric 1425, and the mutant p53 metric 1430. The function may include, for example, a weighted sum, an average, or a specified set of operations, among others. The function may account for the correlations among the dsRNA force metric 1420, the RT activity metric 1425, and the mutant p53 metric 1430. For instance, the function may specify a higher combined score, when the dsRNA force metric 1420, the RT activity metric 1425, and the mutant p53 metric 1430 are all relatively high. Conversely, the function may specify a lower combined score, when at least one of the dsRNA force metric 1420, the RT activity metric 1425, and the mutant p53 metric 1430 is relatively high.

[0176] Using the function, the metric evaluator 1340 may calculate, determine, or generate a composite score from the combination of the dsRNA force metric 1420, the RT activity metric 1425, and the mutant p53 metric 1430, among others. The metric evaluator 1340 may compare the composite score with the threshold. If the combined score satisfies (e.g., greater than or equal to) the threshold, the metric evaluator 1340 may select the ICB therapy as the cancer therapy. On the other hand, if the combined score does not satisfy (e.g., less than) the threshold, the metric evaluator 1340 may select the combination therapy as the cancer therapy. The combination therapy may include, for example, a first combination including ICB therapy and L i1nhibitor or a second combination including ICB therapy and AD ARI inhibitor, among others.

[0177] With the selection, the output handler 1345 may store and maintain an association between the subject 1405 and the selected cancer therapy using one or more data structures on the database 1350. The data structures may include, for example, a linked list, a tree, an array, a table, a matrix, a stack, a queue, or a heap, among others. In some embodiments, the output handler 1345 may store an association among two or more of the subject 1405, the dsRNA force metric 1420, the RT activity metric 1425, the mutant p53 fitnessmetric 1430, or the selected cancer therapy. The association may be stored and maintained as the one or more data structures on the database 1350.

[0178] In some embodiments, the output handler 1345 may provide, send, or otherwise transmit at least one instruction 1440 to the administrative device 1315. The output handler 1345 may generate the instruction 1440 to include information based on the association. For instance, the information may include an identifier of the subject 1405 (e.g., anonymized identifier), one or more metrics (e.g., the dsRNA force metric 1420, the RT activity metric 1425, the mutant p53 fitness metric 1430), and an identification of the selected cancer therapy, among others. The instruction 1440 may also include a script defining the presentation of the information on the administrative device 1315. With the generation, the output handler 1345 may send the instruction 1440 to the administrative device 1315. Upon receipt of the instruction 1440, the administrative device 1315 may display, render, or otherwise present the information. For instance, the administrative device 1315 may display the information via a graphical user interface of an application that is interfacing with the data processing system 1305. Using the information, a clinician examining the subject 1405 may make further determination as to which actions to take or which cancer therapy to administer to the subject 1405 to alleviate or treat the cancer.

[0179] In this manner, the data processing system 1305 may more accurately identify and quantify the dsRNA force metric 1420 in view of the presence of SINE copies in the RNA sequences identified by the RNA sequence dataset 1410. By processing and evaluating a predetermined window about detected SINE copies in the RNA sequences, the data processing system 1305 may significantly cut the consumption of amount of computer resources (e.g., processor and memory) in processing such sequences. In addition, the data processing system 1305 may be able to better identify RNA sequences corresponding to RNA strands with higher likelihood of forming double strands using the dsRNA force metric 1420. By leveraging the dsRNA force metric 1420 (along with the RT activity metric 1425 and the mutant p53 fitness metric 1430), the data processing system 1305 may be able to select cancer therapies with higher likelihood of affecting or treating the cancer in the subject 1405. There is anexpectation that such cancer therapies selected by the data processing system 1305 will better treat the cancer in the subject 1405.

[0180] Referring now to FIG. 15, depicted is a flow diagram of a method 1500 of selecting cancer therapies based on double-stranded ribonucleic acid (dsRNA) force metrics associated with short interspersed nuclear elements (SINE). The method 1500 can be implemented by any components detailed herein, such as the system 1300. Under the method 1500, a computing system may obtain an RNA sequence dataset (1505). The computing system may identify a SINE copy from at least one of the RNA sequences in the RNA sequence dataset (1510). The computing system may identify a length for each RNA strand in the RNA sequences (1515). The computing system may determine a probability of a pairable SINE (1520). The computing system may identify an insert length for the SINE copy (1525). The computing system may generate a dsRNA force metric based on the length of each RNA strength, the insert length, and the probability of the pairable SINE, among others (1530). The computing system may obtain genome sequence data (1535). The computing system may determine a TP53 mutant fitness metric (1540). The computing system may determine an RT activity metric (1545). The computing system may select a cancer therapy as a function of the dsRNA metric, the TP53 mutant metric, or the RT activity metric (1550). The computing system may provide an instruction based on the selected cancer therapy (1555).C. Computing and Network Environment

[0181] Various operations described herein can be implemented on computer systems. FIG. 16 shows a simplified block diagram of a representative server system 1600, client computing system 1614, and network 1626 usable to implement certain embodiments of the present disclosure. In various embodiments, server system 1600 or similar systems can implement services or servers described herein or portions thereof. Client computing system 1614 or similar systems can implement clients described herein. The system 1600 described herein can be similar to the server system 1600. Server system 1600 can have a modular design that incorporates a number of modules 1602 (e.g., blades in a blade server embodiment); whiletwo modules 1602 are shown, any number can be provided. Each module 1602 can include processing unit(s) 1604 and local storage 1606.

[0182] Processing unit(s) 1604 can include a single processor, which can have one or more cores, or multiple processors. In some embodiments, processing unit(s) 1604 can include a general -purpose primary processor as well as one or more special-purpose co-processors such as graphics processors, digital signal processors, or the like. In some embodiments, some or all processing units 1604 can be implemented using customized circuits, such as application specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs). In some embodiments, such integrated circuits execute instructions that are stored on the circuit itself. In other embodiments, processing unit(s) 1604 can execute instructions stored in local storage 1606. Any type of processors in any combination can be included in processing unit(s) 1604.

[0183] Local storage 1606 can include volatile storage media (e.g., DRAM, SRAM, SDRAM, or the like) and / or non-volatile storage media (e.g., magnetic or optical disk, flash memory, or the like). Storage media incorporated in local storage 1606 can be fixed, removable or upgradeable as desired. Local storage 1606 can be physically or logically divided into various subunits such as a system memory, a read-only memory (ROM), and a permanent storage device. The system memory can be a read-and-write memory device or a volatile read- and-write memory, such as dynamic random-access memory. The system memory can store some or all of the instructions and data that processing unit(s) 1604 need at runtime. The ROM can store static data and instructions that are needed by processing unit(s) 1604. The permanent storage device can be a non-volatile read-and-write memory device that can store instructions and data even when module 1602 is powered down. The term “storage medium” as used herein includes any medium in which data can be stored indefinitely (subject to overwriting, electrical disturbance, power loss, or the like) and does not include carrier waves and transitory electronic signals propagating wirelessly or over wired connections.

[0184] In some embodiments, local storage 1606 can store one or more software programs to be executed by processing unit(s) 1604, such as an operating system and / orprograms implementing various server functions such as functions of the system 500 of FIG. 5 or any other system described herein, or any other server(s) associated with system 500 or any other system described herein.

[0185] “Software” refers generally to sequences of instructions that, when executed by processing unit(s) 1604 cause server system 1600 (or portions thereof) to perform various operations, thus defining one or more specific machine embodiments that execute and perform the operations of the software programs. The instructions can be stored as firmware residing in read-only memory and / or program code stored in non-volatile storage media that can be read into volatile working memory for execution by processing unit(s) 1604. Software can be implemented as a single program or a collection of separate programs or program modules that interact as desired. From local storage 1606 (or non-local storage described below), processing unit(s) 1604 can retrieve program instructions to execute and data to process in order to execute various operations described above.

[0186] In some server systems 1600, multiple modules 1602 can be interconnected via a bus or other interconnect 1608, forming a local area network that supports communication between modules 1602 and other components of server system 1600. Interconnect 1608 can be implemented using various technologies including server racks, hubs, routers, etc.

[0187] A wide area network (WAN) interface 1610 can provide data communication capability between the local area network (interconnect 1608) and the network 1626, such as the Internet. Technologies can be used, including wired (e.g., Ethernet, IEEE 1602.3 standards) and / or wireless technologies (e.g., Wi-Fi, IEEE 1602.24 standards).

[0188] In some embodiments, local storage 1606 is intended to provide working memory for processing unit(s) 1604, providing fast access to programs and / or data to be processed while reducing traffic on interconnect 1608. Storage for larger quantities of data can be provided on the local area network by one or more mass storage subsystems 1612 that can be connected to interconnect 1608. Mass storage subsystem 1612 can be based on magnetic, optical, semiconductor, or other data storage media. Direct attached storage, storage areanetworks, network-attached storage, and the like can be used. Any data stores or other collections of data described herein as being produced, consumed, or maintained by a service or server can be stored in mass storage subsystem 1612. In some embodiments, additional data storage resources may be accessible via WAN interface 1610 (potentially with increased latency).

[0189] Server system 1600 can operate in response to requests received via WAN interface 1610. For example, one of modules 1602 can implement a supervisory function and assign discrete tasks to other modules 1602 in response to received requests. Work allocation techniques can be used. As requests are processed, results can be returned to the requester via WAN interface 1610. Such operation can generally be automated. Further, in some embodiments, WAN interface 1610 can connect multiple server systems 1600 to each other, providing scalable systems capable of managing high volumes of activity. Other techniques for managing server systems and server farms (collections of server systems that cooperate) can be used, including dynamic resource allocation and reallocation.

[0190] Server system 1600 can interact with various user-owned or user-operated devices via a wide-area network such as the Internet. An example of a user-operated device is shown in FIG. 16 as client computing system 1614. Client computing system 1614 can be implemented, for example, as a consumer device such as a smartphone, other mobile phone, tablet computer, wearable computing device (e.g., smart watch, eyeglasses), desktop computer, laptop computer, and so on.

[0191] For example, client computing system 1614 can communicate via WAN interface 1610. Client computing system 1614 can include computer components such as processing unit(s) 1616, storage device 1618, network interface 1620, user input device 1622, and user output device 1624. Client computing system 1614 can be a computing device implemented in a variety of form factors, such as a desktop computer, laptop computer, tablet computer, smartphone, other mobile computing device, wearable computing device, or the like.

[0192] Processing unit(s) 1616 and storage device 1618 can be similar to processing unit(s) 1604 and local storage 1606 described above. Suitable devices can be selected based on the demands to be placed on client computing system 1614; for example, client computing system 1614 can be implemented as a “thin” client with limited processing capability or as a high-powered computing device. Client computing system 1614 can be provisioned with program code executable by processing unit(s) 1616 to enable various interactions with server system 1600.

[0193] Network interface 1620 can provide a connection to the network 1626, such as a wide area network (e.g., the Internet) to which WAN interface 1610 of server system 1600 is also connected. In various embodiments, network interface 1620 can include a wired interface (e.g., Ethernet) and / or a wireless interface implementing various RF data communication standards such as Wi-Fi, Bluetooth, or cellular data network standards (e.g., 3G, 16G, LTE, etc.).

[0194] User input device 1622 can include any device (or devices) via which a user can provide signals to client computing system 1614; client computing system 1614 can interpret the signals as indicative of particular user requests or information. In various embodiments, user input device 1622 can include any or all of a keyboard, touch pad, touch screen, mouse or other pointing device, scroll wheel, click wheel, dial, button, switch, keypad, microphone, and so on.

[0195] User output device 1624 can include any device via which client computing system 1614 can provide information to a user. For example, user output device 1624 can include a display to present images generated by or delivered to client computing system 1614. The display can incorporate various image generation technologies, e.g., a liquid crystal display (LCD), light-emitting diode (LED) including organic light-emitting diodes (OLED), projection system, cathode ray tube (CRT), or the like, together with supporting electronics (e.g., digital- to-analog or analog-to-digital converters, signal processors, or the like). Some embodiments can include a device such as a touchscreen that function as both input and output device. Insome embodiments, other user output devices 1624 can be provided in addition to or instead of a display. Examples include indicator lights, speakers, tactile “display” devices, printers, and so on.

[0196] Some embodiments include electronic components, such as microprocessors, storage and memory that store computer program instructions in a computer-readable storage medium. Many of the features described in this specification can be implemented as processes that are specified as a set of program instructions encoded on a computer-readable storage medium. When these program instructions are executed by one or more processing units, they cause the processing unit(s) to perform various operation indicated in the program instructions. Examples of program instructions or computer code include machine code, such as is produced by a compiler, and files including higher-level code that are executed by a computer, an electronic component, or a microprocessor using an interpreter. Through suitable programming, processing unit(s) 1604 and 1616 can provide various functionality for server system 1600 and client computing system 1614, including any of the functionality described herein as being performed by a server or client, or other functionality.

[0197] It will be appreciated that server system 1600 and client computing system 1614 are illustrative and that variations and modifications are possible. Computer systems used in connection with embodiments of the present disclosure can have other capabilities not specifically described here. Further, while server system 1600 and client computing system 1614 are described with reference to particular blocks, it is to be understood that these blocks are defined for convenience of description and are not intended to imply a particular physical arrangement of component parts. For instance, different blocks can be but need not be located in the same facility, in the same server rack, or on the same motherboard. Further, the blocks need not correspond to physically distinct components. Blocks can be configured to perform various operations, e.g., by programming a processor or providing appropriate control circuitry, and various blocks might or might not be reconfigurable depending on how the initial configuration is obtained. Embodiments of the present disclosure can be realized in a variety ofapparatus including electronic devices implemented using any combination of circuitry and software.

[0198] While the disclosure has been described with respect to specific embodiments, one skilled in the art will recognize that numerous modifications are possible. Embodiments of the disclosure can be realized using a variety of computer systems and communication technologies including but not limited to the specific examples described herein. Embodiments of the present disclosure can be realized using any combination of dedicated components and / or programmable processors and / or other programmable devices. The various processes described herein can be implemented on the same processor or different processors in any combination. Where components are described as being configured to perform certain operations, such configuration can be accomplished, e.g., by designing electronic circuits to perform the operation, by programming programmable electronic circuits (such as microprocessors) to perform the operation, or any combination thereof. Further, while the embodiments described above may make reference to specific hardware and software components, those skilled in the art will appreciate that different combinations of hardware and / or software components may also be used and that particular operations described as being implemented in hardware might also be implemented in software or vice versa.

[0199] Computer programs incorporating various features of the present disclosure may be encoded and stored on various computer-readable storage media; suitable media include magnetic disk or tape, optical storage media such as compact disk (CD) or DVD (digital versatile disk), flash memory, and other non-transitory media. Computer-readable media encoded with the program code may be packaged with a compatible electronic device, or the program code may be provided separately from electronic devices (e.g., via Internet download or as a separately packaged computer-readable storage medium).

[0200] Thus, although the disclosure has been described with respect to specific embodiments, it will be appreciated that the disclosure is intended to cover all modifications and equivalents within the scope of the following claims.EXAMPLESExample 1: Experimental methods

[0201] PDAC cell lines. PDAC3, PDAC6, PDAC8, PDAC9, were generated from metastatic ascites fluid of pancreatic adenocarcinoma patients at the Massachusetts General Hospital (MGH) under a discarded tissue protocol in accordance with the Massachusetts General Hospital IRB protocol 2011P001236 and Dana-Farber Harvard Cancer Center IRB protocol 02- 240 as previously described52. PANC-1 (CRL-1469), MiaPaCa-2 (CRL-1420), Capan-1 (HTB- 79), KP4, YAPC, DAN-G and BxPC-3 (CRL-1687) were purchased from the American Type Culture collection (ATCC).

[0202] Western blot. PDAC cell lines were cultured into ultra-low attachment 6-well plate (Corning, USA, #3471) for 1 week. Cells were collected, washed, and lysed with lysis buffer containing 50 mM Tri-Cl (pH 6.8), 10% glycerol, 2% sodium dodecyl sulfate, 1 mM 1,4- dithiothreitol and Halt™ Protease and Phosphatase Inhibitor Cocktail (ThermoFisher Scientific, USA, #78440). The cell lysates were subjected to sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and then transferred to a 0.45 pm polyvinylidene difluoride (PVDF) membrane (Millipore, MA, USA, #IPVH00010). The membranes were blocked with 3% of bovine serum albumin (BSA, Sigma- Aldrich, # A2058) for 1 hour and applied with primary antibodies against LINE1 ORFlp (Millipore, USA, #MABC1152) and P-actin (Cell Signaling Technology, USA, #4970S) for overnight at 4 C. Membranes were washed with IX phosphate-buffered saline (PBS) containing 0.1% tween-20 (Sigma-Aldrich, USA, #P1379) (PBST) and incubated with horseradish peroxidase (HRP)- conjugated secondary antibodies (HRP-conjugated goat anti-rabbit (Cell Signaling Technology, # 7074S) and HRP -conjugated goat anti-mouse (Cell Signaling Technology, # 7076S). Signal was detected with enhanced chemiluminescence (SuperSignal™ West Pico PLUS Chemiluminescent Substrate, Thermo Scientific, #34577), and images were developed using G:BOX (SYNGENE).

[0203] Total RNA preparation and sequencing

[0204] PDAC autopsy cohort. Raw bam files of RNAseq data were kindly provided by the lacobuzio lab associated with their study17. Briefly, frozen sections were cut from samples for histological review and regions of interest were microdissected for extracting total RNA using TRIzol (Life Technologies) followed by Rneasy Plus Mini Kit (Qiagen). Each RNA sample was initially quantified by Qubit 2.0 Fluorometer (Thermo Fisher Scientific). Samples were additionally quantified by RiboGreen and assessed for quality control using an Agilent BioAnalyzer in the Integrated Genomics Core at MSKCC and 513 ng to 1.0 pg of total RNA with an RNA integrity number ranging 1.3-8.3 underwent ribosomal depletion and library preparation using the TruSeq Stranded Total RNA LT kit (Illumina, RS-122-1202) according to instructions provided by the manufacturer with eight cycles of PCR. Samples were barcoded and run on aHiSeq 4000 in a 100 bp per 100 bp or 125 bp per 125 bp paired end run, using the HiSeq 3000 / 4000 SBS kit (Illumina). On average, 94 million paired reads were generated per sample and 26% of the data were mapped to the transcriptome.

[0205] PDAC cell lines. Cells were cultured in 3D tumoursphere condition for 7 days then, cells were lysed, and total RNA was extracted using the miRNeasy Mini Kit (QIAGEN, cat# 217004). Libraries were prepared for sequencing using the SMARTer Stranded Total RNAseq Kit v2 (cat# 634413) - Pico Input Mammalian (Takara Bio USA). RNA samples were first synthesized into cDNA, and adaptors for Illumina sequencing were added using PCR. Then, the PCR products are purified using AMPure Beads, and ribosomal cDNA was removed. After depletion, the samples were further amplified using PCR, then purified again using AMPure Beads. Each sample was then qPCR quantified using KAPA Library Quantification Kit (KAPA Biosystems). 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.

[0206] Mapping

[0207] To re-align sequences to the pipeline, all bam files were first converted into fastq using samtools vl.9. RNA sequencing reads were 3’ trimmed for base quality less than 20 using skewer. Reads shorter than 40 after trimming were removed. Then the quality score of the remaining bases were sorted, and the quality at the 20th percentile was computed. Reads with quality at the 20th percentile less than 15 were discarded. Only paired reads which both pass the quality check were mapped to the reference genome (hg38) using STAR v2.7 with default parameters. Gene counts were assigned based on Gencode annotation using feature Counts (subread - 1.4) with the external Ensembl annotation. Repeats counts per element family were primarily quantified against RepeatMaster using featureCounts (subread - 1.4) and then adding the counts of the unassigned reads that mapped to Repbase consensus sequence. Repeat counts of a given family is the sum of mapped reads to RepeatMassker and unmapped reads against Repbase.

[0208] Counts filtering and normalization

[0209] 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 study18. Low-count gene / repeats in each sample with counts per million (CPM) smaller than 2 were removed before calculating the size factor. Log2 transformed CPM were used for downstream visualization and correlation analysis.

[0210] RNA based tumor purity estimation and differential expression analysis

[0211] ESTIMATE v2.0.019in R was used to infer the tumor purity from total transcriptome data in 214 human samples. Differential expressions of coding genes and repetitive elements were analyzed separately using DESeq2 vl.33.453by adding tumor purity as a covariate in formula design to minimize the bias caused by high stromal contamination in human samples or by default parameter for PDAC or RPE cell lines.

[0212] Gene set enrichment analysis (GSEA)

[0213] GSEA was applied on a gene list ranked by the differential expression on the basis of the methods described54. To test the gene set significance, an enrichment score is defined as the maximum distance from the middle of the ranked list. Gene set with positive enrichment score indicates upregulation with a positive log2FC, whereas the negative enrichment score is suggesting a downregulation with a negative log2FC. cluster Pro filer v4.O20was used to run this analysis against HALLMARK pathways in MSigDB collection.

[0214] dsRNA force and evolutionary distance calculation for each SINE copy in hg38

[0215] By searching for a pairable SINE (nearby SINE in the opposite direction) located upstream or downstream of the target SINE within a 3000-bp window. The likelihood of discovering a suitable matching copy and corresponding matched length between two SINEs was determined.

[0216] dsRNA force calculation

[0217] Given a set of nucleotide frequencies f(<f), the probability of observing a pair of complementary nucleotides is

[0218] From this quantity it can be computed the expected length Nds(L, 0) of the longest fully complementary strand of a sequence composed by L nucleotides sampled from the frequency distribution / (cr), obtaining after some approximations24

[0219] where for the correction constant c = -2.2 was obtained when considering both canonical Watson- Crick pairs and Wobble pairs as complementary basepairs. For a given RNA sequence, the dsRNA force xas. is introduced to fix mismatches from the expected values Nds.(L, 0) and the observed ones, Nd^s. Following Sulc et al., the equation was obtained

[0220] that can be used to compute the dsRNA force %ds for a RNA sequence of length L having length of the longest complementary strand equal to NdsJS.

[0221] In the case of SINE element, interest in computing the dsRNA force was found when one of the two complementary segments composing the longest complementary strand is constrained to be within the SINE insertion. To do that, windows of 3 kb around the insertion was considered, and use the equation

[0222] where Lt is the insert length. This equation is obtained again by following the approach used by Sulc et al.24, but considering that one of the two strands of the longest doublestrand segment must be in a subsequence of length L,.

[0223] RNA immunoprecipitation sequencing and data analysis

[0224] RNA extractions and coimmunoprecipitations (RIP) from EC cells - N2102Ep, Embryonal carcinoma cells were cultured at 37 °C in humidified incubators maintained with 7% CO2 atmosphere. N2102Ep Clone 2 / A6 cells (Merk, #06011803) were cultured in DMEM (high glucose, no sodium pyruvate; Thermo Fisher, #11965092), supplemented with 10% (v / v) FBS, Ixpenicillin / streptavidin and 2 mM Glutamine (Thermo Fisher, #25030024). Largescale growth,harvesting, cryomilling, and coIP was achieved as previously described5'' '’7, summarized as follows. aORFlp and aORF2p targeted coIPs used inhouse made58magnetic affinity media: for aORFlp [15 pg antibody / mg magnetic beads], the 4H1 monoclonal antibody (Millipore Sigma, #MABC1152) was used; for aORF2p [10 pg / mg magnetic beads] the clone 9 monoclonal antibody39was used. CoIPs were conducted using 100 mg cell powder, extracted at 25% (w:v) in 20 mM HEPES pH 7.4, 500 mM or 300 mM NaCl, 1% (v / v) Triton X100, lx protease inhibitors (Roche, #1187358001), and 1 :250 RNasin (Promega, #2515). Centrifugally clarified cell extracts were incubated with affinity medium (20 pl of slurry for aORFlp and aORF2p for 30 min at 4 °C. The solutions were made with nucleasefree H2O and experiments were conducted using nucleasefree tubes and pipette tips. Macromolecule extractions performed on these cell lines as described typically yielded between 450-500 pl of soluble extract at 6-8 mg / ml of protein as assessed by Bradford assay (Thermo Fisher, #23200). After target capture, washing the media was performed with the same solution without protease inhibitors and with RNase at 1 : 1000. RNAs were eluted from the affinity media after RIP with 250 pl of TRIzol Reagent (Thermo Fisher, #15596026). After adding chloroform to the TRIzol eluate, the separated aqueous phase (containing RNAs) was obtained using Phasemaker tubes (per manufacturer’s instructions; Thermo Fisher, #A33248), and was then combined with an equal volume of ethanol and further purified using a spin column according to the manufacturer’s instructions (Zymo Research, #R2060). Eluates from aORFlp and aORF2p coIPs were not treated with DNase I on-column, this was done during the sequencing library preparation (described, below). Purified nucleic acids from aORFlp and aORF2p coIPs were eluted in 6ul of nucleasefree water; in all cases 1 pl was used for quality analysis and the remainder conserved for RNAseq. Mock RNA coIP controls were prepared in an identical manner using naive polyclonal mouse IgG (control for aORFlp; Millipore Sigma, #15381) or naive polyclonal rabbit IgG (control for aORF2p: Innovative Research, #IRBIGGAP10MG). Total RNA controls were prepared by combining up to 35 pl of the clarified cell extracts with up to 500 pl of Trizol, vortex mixing for 1 min, then snap freezing in liquid nitrogen and then later proceeding as above.

[0225] cDNA library preparation and RNA-seq

[0226] aORFlp and aORF2p RIPseq: RNA extractions were quantified, and quality controlled using RNA Pico Chips (Cat. #50671513) on an Agilent 2100 BioAnalyzer. RNA-seq cDNA libraries were prepared 15 using the Trio RNA-seq Library Prep kit (Tecan, #0357A01) with Any Deplete Probe MixHuman rRNA (Tecan, #S02305). DNase treatment preceded cDNA synthesis. cDNA synthesis: 3 5ng of input RNA from aORFlp RIPs and mock IPs, 1 ng from aORF2p RIPs and mock IPs, and 50 ng of total RNA were used with 8 (2+6) cycles of predepletion PCR library amplification and 8 (2+6) cycles of post-depletion amplification; the libraries were purified using Agencourt AMPure XP beads (Beckmann Coulter), quantified by qPCR, and the size distribution was checked using the Agilent TapeStation 2200 system. Final libraries were sequenced, pair- end, at 50 bp read length on an Illumina NovaSeq 6000 vl .5 with 2% PhiX spike-in.

[0227] RIPseq read mapping and quantification

[0228] Reads were trimmed and quality checked using skewer39. 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, the mate had to be discarded as well. Quality filtered reads were mapped to annotated repeat loci in RepeatMasker using software: Quantifying Interspersed Repeat Expression (SQuIRE) (github.com / wyangl7 / SQuIRE)60. The SQuIRE pipeline first obtains reference annotation files from RepeatMasker, then aligns reads using STAT, and lastly, quantify locus specific repeat expression by redistributing multimapping read fractions in proportion to estimated TE expression with an expectation maximization algorithm.

[0229] Selection of RIPSeq enriched repeats / transcripts

[0230] Targeted protein enriched transcripts were selected by Log2(CoIP / mock) > 3, Log2(CoIP / totalRNA) > 3, and BenjaminiHochberg adjusted p-value < 0.05. Similarly targetprotein depleted transcripts were selected by Log2(CoIP / mock) < 3, Log2(CoIP / total RNA) < 3, and Benjamini- Hochberg adjusted p-value < 0.05.

[0231] DNA sequencing and mutation calls

[0232] Genomic DNA was extracted from each tissue using QIAamp DNA Mini Kits (Qiagen). WGS, WES and alignment were performed as previously described61. Briefly, an Illumina HiSeq 2000, HiSeq 2500, HiSeq 4000 or NovaSeq 6000 platform was used to target a coverage of 60* for WGS samples and 150* for WES samples. The resulting sequencing reads were analyzed in silico to assess quality, coverage, as well as alignment to the human reference genome (hgl9 or hg38) using BWA62. All WES samples and WGS in MP AM samples were aligned to hgl9, WGS data from PAM samples were aligned to hg38. After read deduplication, base quality recalibration and multiple sequence realignment were completed with the PICARD Suite and GATK v.3.1 (refs. 52,53) somatic single-nucleotide variants and insertion-deletion mutations were detected using Mutect v.1.1.6 and HaplotypeCaller v.2.463’64. The low-quality or poorly aligned reads were excluded from phylogenetic analysis. Filtering of called somatic mutations required each mutant to be observed in at least one neoplastic sample per patient with at least 5% variant allele frequency and with at least 20* coverage; correspondingly, each mutant had to have been observed in <2% of the reads (or fewer than two reads in total) of the matched normal sample with at least 10* coverage.

[0233] Evolutionary analysis

[0234] Phylogenies were derived for each set of samples by using Treeomics 1.7.937. Each phylogeny was rooted at the matched patient's normal sample and the leaves represented tumor samples. Treeomics employs a Bayesian inference model to account for error-prone sequencing and varying neoplastic cell content to calculate the probability that a specific variant is present or absent. The global optimal tree is based on mixed integer linear programming. All evolutionary analyses were performed on the basis of WGS depending on the sequencing dataavailability (FIG. 1A)61. Somatic alterations present in all analyzed samples of a PDAC were considered clonal, in a subset of samples or a single sample considered subclonal.

[0235] Whole Genome Inverted Repeats Analysis

[0236] The inverted-repeat pipeline4was applied on all repeat elements in the human hg38 genome by considering all repeat elements in the RepeatMasker list of elements which was downloaded from the UCSC database. All repeat loci that are within 3Kb distance were paired and aligned locally in the reverse complement manner by the Smith-Waterman algorithm using the exonerate tool (v.2.2.0)65. Two different penalty scores were applied for gap opening and extension which are a 12-penalty score for gap opening and a 4-penalty score for gap extension. Aligned pairs that are separated by distance greater than 3Kb or pairs that are aligned with alignment score less than 500 were discarded. There are about 795,058 repeat pairs that were aligned as inverted repeats pairs. Among these pairs that were aligned as inverted repeats, there are about 750,004 pairs formed by Alu elements.

[0237] A-I editing index

[0238] All possible RNA editing events were called using REDItools2.066against all annotated SINE loci in hg38. The estimated RNA editing events were selected for single- and multi- A-I nucleotide substitutions. All subset RNA editing profiles were then subjected to the following quality control measures to remove editing estimates that fell below the following thresholds: minimum average quality score (MeanQ) of 25; minimum depth of coverage of 10; and minimum average editing frequency of 1%. Only non-mitochondrial edits were subject to downstream analysis for clarity. The resulting subset of A-I editing events was used to calculate an A-I RNA Editing Index using the RNAE diting AnalysisTools R package67. Here, a normalized sample-specific global A-I RNA Editing index was calculated as the total proportion of edited reads over reads mapping to edited sites (Editing Read s / C overage).

[0239] Histology, IHC and IF

[0240] LINE-1 ORF Ip Immunohistochemistry

[0241] Tissues were fixed overnight in 10% formalin, embedded in paraffin, and cut into 5 pm sections. Slides were heated for 20 min at 55°C, deparaffmized, rehydrated with an alcohol series, and subjected to antigen retrieval with citrate buffer (Vector Laboratories Unmasking Solution, H-3300) for 15 min in a pressure cooker set on high. Sections were treated with 3% H2O2 (in PBS) for 15 min followed by a wash in deionized water, washed in PBS, then blocked in 5% BSA (in PBS). The following primary antibody was incubated overnight at 4°C in blocking buffer: LINE- 1 ORF Ip (ab230966, Abeam, 1 :200). Vector ImmPress anti-Rabbit HRP and ImmPact DAB kits (Vector Laboratories) were used for secondary detection. Tissues were then counterstained with Haematoxylin, dehydrated and mounted with Permount (Fisher). Images were acquired on a Axio Imager Z2 platform (Zeiss).

[0242] LINE-1 ORF Ip and dsRNA Immunofluorescence

[0243] PDAC cell lines were plated onto the Collagen type-I, rat-tail (50 pg / ml) coated 12 mm coverslips and allowed to adhere for at least 48 hrs before use in experiments. Cells were fixed for 15 min with freshly prepared 4% (w / v) paraformaldehyde in PBS, permeabilized with 0.5% (v / v) Triton X- 100 in PBS for 10 min. Then, cells were blocked with 2% BSA solution for 1 hr, incubated with primary antibodies (anti-dsRNA, clone rJ2; Sigma-Aldrich, cat# MABE1 134 and anti-LINE-1 ORFlp, clone 4H1; Sigma-Aldrich, cat#MABCl 152) with desired concentration (1:50) in blocking solution for 1 hr at room temperature. After three washes with 0.1% Triton X-100 in PBS (PBST) were further incubated with fluorophore-conjugated secondary antibodies (goat anti -Mouse IgG2a Cross-Adsorbed Secondary Antibody, Alexa Fluor 594, Thermo Scientific, cat# A-21135, goat anti-Mouse IgGl Cross-Adsorbed Secondary Antibody, Alexa Fluor 488, Thermo Scientific, cat# A-21121) for 1 hr at room temperature. Following additional three washes with PBST, DAPI solution was applied for 1 min then washed. Coverslips were mounted in Fluoromount-G® Mounting Medium (SouthemBiotech, cat# 0100-01) and imaged by fluorescence microscopy (Nikon). The fluorescence intensity of dsRNA and LINE-1 ORF1 was measured using Image J software.

[0244] LINE1 ORF Ip shRNA knocking down

[0245] Plasmid construction and generation of stable cell line. shRNA sequences targeting LINE-1 ORF168were cloned into pLKO.l-TRC cloning vector (Addgene, cat# 10878) using EcoRl and Agel restriction enzyme digestion. LINE-1 siRNA contained a pool of siRNA (Silencer Select AAGCAAAUGUUGAGAGAUUtt (SEQ ID NO: 7) and Stealth GAAAuGAAGCGAGAAGGGAAGuuuA (SEQ ID NO: 8)).

[0246] For lentiviral production, HEK293T cells were seeded at 80% density in a 10-cm tissue cell culture treated 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 according to manufacturer’s instructions. Conditioned medium containing recombinant lentivirus was collected and filtered through 0.45 pm filters. The LINE-1 ORF1 shRNA containing lentivirus medium was added to cells with 8 pg ml-1 of polybrene (Santa Cruz Biotechnology, cat# sc-134220). Approximately 48 hrs after infection, the cells were selected with 1 pg ml-1 puromycin (InvivoGen, cat# ant-pr-1).

[0247] Immunoblotting. PDAC cells were harvested, washed, and lysed with lysis buffer (50 mM Tri-Cl (pH 6.8), 10% glycerol, 2% sodium dodecyl sulfate, 1 mM 1 ,4-dithiothreitol and Halt™ Protease and Phosphatase Inhibitor Cocktail (ThermoFisher Scientific, USA, cat# 78440). Total protein concentration was determined by BCA protein assay (Thermo Fisher Scientific, cat# 23227). The cell lysates were performed to sodium dodecyl sulfate-polyaciylamide gel electrophoresis (SDS- PAGE) and then transferred to a polyvinylidene difluoride (PVDF, 0.45 pm) membrane (MilliporeSigma, 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-Aldrich, cat# P1379) (PBST) for 10 minutes 3times and incubated with horseradish peroxidase (HRP)-conjugated secondary antibodies. Signals were developed with enhanced chemiluminescence (SuperSignal™ West Pico PLUS Chemiluminescent Substrate, Thermo Scientific, cat# 34577), and images were detected using G:BOX (SYNGENE).

[0248] The following primary antibodies were used: anti-LINE-1 ORFlp (MilliporeSigma, cat# MABC1152), GAPDH (Cell Signaling Technology, cat# 2118) were used following the manufacturer s suggested protocols.

[0249] Statistics and reproducibility

[0250] All statistics and graphs were performed and made using R v4.0.3 and organized in adobe illustrator 2022. Parametric distributions were compared by a two-sided t-test, with correction using Fisher’s exact test for sample sizes <5. Nonparametric distributions were compared using Wilcoxon test and for analysis of contingency tables, a two-sided Fisher’s exact test was used. Each analysis is described in the Results. Statistical significance was considered if the adjusted P value (after Benjamini & Hochberg correction) was <0.05. The FDR q value was used for GSEA. No statistical method was used to predetermine sample size. No data were excluded from the analyses as long as the library and / or sequencing quality passed the criteria. The experiments were not randomized. The investigators were not blinded to allocation during experiments and outcome assessment except for review of histological slides.

[0251] Data availability

[0252] Data availability RNA and DNA sequence data for this study have been deposited at the European Genome-phenome Archive under accession number EGAS00001003974. All other data supporting the findings of this study are available from the corresponding author upon reasonable request.Example 2: SINE elements are co-expressed with RIG-I interferon signaling in PDAC autopsy cohort

[0253] Multi-omics data, including whole genome and total RNA sequencing collected from a rapid autopsy PDAC cohort17was integrated to characterize the role of repetitive elements in mediating tumor inflammation and evolution (FIG. 1A). According to available information, this is the largest group of total RNA sequencing human specimens in PDAC, a sequencing protocol necessary for unbiased assessment of repeats17. The cohort also covers a substantial number of samples from primary and metastatic sites from multiple tissues, allowing us to track tumor evolution within each patient. In total, whole transcriptome data was collected from 214 multiregional samples across 27 patients (FIG. IB). Additionally, 58 matched tumor samples from 8 patients were simultaneously subjected to whole genome sequencing (WGS) (FIG. IB), allowing us to, for the first time, untangle the interaction between repeat transcription, innate immunity, and tumor genome evolution.

[0254] To assess the relationship between repeats and innate immune signaling, repeat expression18was quantified, and evaluated differentially expressed genes and repeats in tumors collected from primary versus metastatic sites. Given the relatively high stromal content in PDAC even after laser microdissection, tumor purity corrected differential expression analyses were performed (Methods, FIG. 7A)19. Metastatic tumors were found to be enriched for multiple inflammatory signatures using Gene Set Enrichment Analysis20compared to primary tumors. Meanwhile, five immune related HALLMARK pathways including interferon alpha and gamma responses were upregulated in metastatic samples compared to primary tumors (FIG. 7B), suggesting a selective advantage for inflammatory states in metastasis.

[0255] Type I interferon pathways were focused on which have shown associations with repeat-mediated viral mimicry in multiple cancer types3,21. A list of 140 interferon genes (IFN) and interferon stimulated genes was curated (ISG) (Table A). Using unsupervised clustering on gene expression, four co-expression clusters were identified out of the 140 genes using pairwisecorrelation across all 214 samples (FIG. 1C). Further KEGG pathway annotation of genes identified distinct functional pathways enriched in each cluster. Notably, retinoic acid-inducible gene I (RIG-I) Like Receptor (RLRs) pathway and JAK-STAT signaling pathway are over- represented in one cluster - labelled C4.Table A|0256] Recent studies have revealed that both viral and host- derived RNAs can trigger RLR activation22 24, therefore it was sought out to identify the association between expression of specific repeat classes (SINE, LINE, LTR / ERVs, SAT, DNA) and different clusters. Among five repeat classes, SINEs show the most significant and strongest positive correlation with the RLR signature (median r = 0.53) (FIGs. ID, 7C), which is not affected by the absolute expression level of SINEs (FIG. 7D). The association between highly coregulated core RLR mediated IFN signatures and SINEs expression, supports the notion that SINEs25, and not Endogenous RetroVirus (ERVs / LTRs)5, are likely the primary binding substrate of RLRs in PDAC. The result is in line with a recent study which claims that a subtype of SINE - inverted Alu elements are the major substrates of MDA5, a known RLR in vitro3,4.Example 3: Newly evolved SINEs show stronger correlation with IFN signature and are prone to form dsRNA|0257] To dissect which SINE subfamilies are responsible for the strong interaction with RLR mediated signaling, The SINEs were stratified into six groups based on their evolutionary age (FIG. 2A, Methods)26 31. The positive correlation between RLR associated IFN signature and each SINE subgroup increased as the evolutionary age decreased, with AluY harboring the largest variations (FIG. 2A). Moreover, unbiased clustering of all genes defining the RLRsignature, and all SINE elements revealed two gene / repeat clusters, where younger SINEs are grouped together with 17 / 20 genes in the RLR signature (FIG. 8A). Altogether those results imply that younger SINEs have a stronger association with RLR signature in PDAC.

[0258] Then the relation between immunogenicity and evolutionary age at the level of single copies were investigated. The evolutionary age of each SINE copy was quantified by utilizing the evolutionary distance of each copy against its reference consensus sequence24(FIG. 2B). Consistent with the general association of younger SINEs and RLR, the association with RLR signature is significantly higher when SINEs are evolutionarily younger (FIG. 2C). Given that RLRs typically recognize and bind to double-stranded RNA (dsRNA) to initiate a downstream innate immune response32, it was hypothesized that younger SINEs possess a greater potential to generate dsRNAs than older SINEs. To test this hypothesis, the previously developed computational framework24was adapted for assessing the potential of a specific SINE copy to fold into a dsRNA with a nearby sequence (< 3000bp), which was referred to as the dsRNA force (Methods). SVAs, as a composite SINE, are excluded from this analysis as their sequence composition and size is quite different from other SINEs. The distribution of dsRNA forces is also evolutionary age depended, with younger groups - AluY and AluS harboring significantly higher dsRNA force than older groups (FIG. 2D). However, AluS have slightly higher fraction of dsRNA forming copies compared to AluY (FIG. 8B), likley indicating a secondary selection pressure on AluS in maintaining their capability to form dsRNA. SINEs with stronger predicated foldability (dsRNA force > 0.5) have significant stronger association with RLR signature (FIG. 8C). Moreover, the mean foldability of each SINE subfamily significantly correlated with the association with pre- defined core IFN signatures (r = 0.77, pval < 0.00001) (FIG. 8D). Taken together, the findings suggest that the expression of younger SINEs is more strongly linked to RLR-mediated innate immune responses due to their increased capacity to form dsRNAs.Example 4: L1 transposition frequency is associated with mutant TP53 status and continues throughout tumor evolution

[0259] L1 is the sole active autonomous retrotransposable element in the human genome, and its activity has the potential to influence the genomic evolution of cancer cells33. To precisely identify L1 insertions, a pipeline that utilizes soft-clipped reads and discordant read pairs derived from non-reference L1 insertion sites present only in tumor samples compared to matched normal samples was implemented. In total, 373 de novo L1 insertions were identified in 39 WGS samples (out of 74) from five patients (out of eight), with a substantially greater number of insertions identified in samples obtained from metastatic sites (FIG. 3A). Out of the 39 samples with detected de novo L1 insertions, only one was obtained from the primary site. No common insertions were detected across patients and the insertion sites overlap across different metastatic sites or patients were limited (FIG. 3B). One patient (MPAM05) exhibited nearly a tenfold increase in the number of de novo L1 insertions compared to the other patients (FIG. 3A), with exclusive nuclear L1 ORF Ip localization as assessed by immunohistochemistry (FIG. 9A). Consistent with the result from an orthogonal quantification method for L1 retrotransposition34, the expression level of the L1 ORF Ip, as scored by immunohistochemistry, correlated with the number of de novo insertions (r = 0.48), albeit not to a statistically significant degree (p = 0.054) (FIG. 9B). Different from patient MPAM05, IHC of L1 ORFlp from the other patients show a previously documented cytoplasmic localization (FIG. 9B).

[0260] Subsequently, potential genetic factors that may account for the significantly higher number of L1 insertions observed in patient MPAM05 were evaluated. No significant association was detected between the number of L1 insertions and either the COSMIC mutational signature or microsatellite instability. Notably, an analysis of SNP calls on the WGS data revealed a distinct frameshift deletion of TP53 in all 11 samples obtained from MPAM05 (FIG. 3C). The specific frameshift deletion eliminates the p53 DNA binding domain, resulting in a complete loss of function for the p53 protein. Upon comparing the frequency of L1 insertions across the 39 samples stratified by different TP53 mutation variants, an association between LIretrotransposition frequency and their TP53 mutation status (FIG. 3D) was observed. Samples with splice site mutations (MPAM06) had the lowest insertion frequency, which is comparable to patient PAM03 who has a wildtype TP53. However, samples with missense mutations (MPAM01& MPAM04) had a higher number of insertions. The highest insertion frequency was observed in samples with frameshift deletions - MPAM05 (FIG. 3D). To further validate the findings, the same analysis was conducted on WGS from an independent biopsy cohort from the COMPASS trial35. Consistently, metastatic samples had a higher number of somatic LI insertions compared to primary samples (FIG. 9C). Furthermore, samples harboring TP53 mutations tend to have a higher number of insertions than those without mutations, with higher number of insertions in los of function TP53 mutation types (FIGs. 3E, 9D-9E). Those results have fortified the role of p53 as surveyor of L1 mobility in PDAC, also corroborated in other different cancer types36.

[0261] To investigate the L1 re-integration trajectory along tumor evolution, The WGS data was utilized to reconstruct a phylogenetic tree of metastases and subclones based on their anatomical locations37. In patient MP AM04, all samples had three de novo insertions in common on chromosomes 1, 10, and 12. Samples within the same clade shared common insertions, as seen with samples PT2, PT9, PT10, PT11, which all had 6 common insertions located on different chromosomes (FIG. 3F). Similar concurrency was observed between tumor evolution and LI insertion in patient MPAM05 (FIG. 9F), where samples that are evolutionarily close share similar insertions. However, due to the limited samples, a similar comparison for samples from the other three patients was not able to be performed. The phylogenetic relationships of LI insertions were not confined to a shared anatomic location or tissue type, suggesting that LI activity does not follow a linear evolution along tumor progression. Instead, L in1sertions seem to be seeded by multiple prior locations and then disseminated to different anatomic locations. These results indicate that the majority of L1 insertions happened during the metastasis, which suggests a closer connection between L1 activity and tumor progression (e.g. metastasi s / tumor dissemination) rather than just during tumorigenesis (e.g. initiation / local progression).Example 5: Immunogenic SINE expression is anti-correlated with L1 retrotransposition and M2 macrophage infiltration

[0262] L1 retrotransposition is dependent on the expression of L1 proteins, such as ORFlp and ORF2p, as reintegration of L1 requires reverse transcription of L1 mRNA by LI proteins binding in cis. In addition, SINEs can utilize L p1roteins in trans to facilitate their own retrotransposition back into the genome38. Hence, the interplay between L1 retrotransposition, SINE expression, and the core RLR signatures linked to younger SINE elements was next explored. SINEs exhibited a unique pattern compared to other repeat classes (LINE, ERV / LTR, DNA, SAT) and were consistently downregulated in samples with high L1 retrotransposition (FIG. 4A) More importantly, the antagonistic relationship between L m1obility and SINEs was dependent on the evolutionary age, with younger SINEs (SVA, AluY, AluS) having a stronger inverse relationship (FIG. 4B). Including the three samples from patient MPAM05 did not change the observed trends (FIG. 10A), indicating the results are not biased by the presence of this patient’s high-integration samples. Consistently, the RLR-associated genes defined in FIG. 1C are also repressed in samples with high number of L1 insertions (FIG. 10B). In addition, SINEs showed the strongest negative ranked correlation with the number of L1 insertion compared to other types of repeats (FIG. 10C), and the evolutionary younger SINEs are negatively associated with L1 activity, while older SINEs mostly show positive or no strong association (FIG. 10D, blue / grey vs red dots). These results suggest increased LI retrotransposition may be selected for during tumor evolution due to its role in suppressing the immune response stimulated by dsRNA-forming SINEs.

[0263] Next, the relationship between the expression of different repeat classes and their associated immune infiltration was examined. CIBERSORTx was utilized to perform cell type deconvolution from bulk RNA sequencing data. Out of all myeloid cells, M2 macrophages show a clear separation among different repeat classes (FIG. 10E), a distinct evolutionary age dependent negative correlation between SINE expression and M2 macrophage infiltration (FIG. 10F), and subsequently a positive correlation between number of somatic L in1sertions and M2macrophage infiltration (FIG. 10G). Attempts were made to validate the observed correlation between the expression of immunogenic SINEs, L r1etrotransposition, and immune infiltration in the COMPASS trial. However, due to the implementation of polyA selection in the COMAPSS trial RNA sequencing protocol, the significantly decreased ratio of reads mapped to repeats and coding genes prevent a similar comparison (FIG. 10H)18.

[0264] To corroborate the observed antagonistic relationship between r Let1rotransposon activity and SINE expression, what we examined was SINE expression in 11 PDAC cell lines with varying levels of L1 ORF Ip expression quantified by western blot, as a proxy for LI mobility (FIG. 4C). Differential expression analysis was then performed between cell lines with low L1 ORFlp expression (PANC-1, KP-4, BxPC-3), and high L O1RFlp expression (PDAC8, DAN-G, YAPC) (data not shown). A consistent evolutionary age dependent downregulation of SINEs in samples with high L1 ORFlp expression (FIG. 101) was observed. Together, the findings revealed a strong negative correlation between younger SINEs and L1 activity (FIG. 4D). Notably, SINEs demonstrated the highest level of consistency among repeat classes in the differential expression analysis in PDAC cell lines and PDAC human samples when stratified by LI expression or activities (FIG. 10J), indicating a strong association between SINEs and LI compared to other repeat classes.Example 6: L1 ORFlp suppresses immunogenic SINE expression in vitro

[0265] To validate the direct repression of immunogenic SINE RNA by L I ORF Ip, two short hairpin (sh)RNA oligos (FIG. 5A) were designed and tested to knock down L1 ORFlp expression in PDAC6 cell line, who has relatively high L O1 RFlp expression (FIG. 4C). The knockdown experiment was performed in biological triplicates, and different knockdown efficiencies were observed among biological replicates and two shRNA oligos as revealed by western blot (FIG. 5A). The resulting distribution of samples with different L1 ORFlp expression levels was used to assess the impact of L O1 RFlp knockdown on repeat expression, calculating the correlation between GAPDH- normalized ORFlp intensity and repeat expressionacross all nine samples (FIG. 5B). In line with previous observations in the autopsy PDAC cohort and PDAC cell lines, SINEs showed the strongest negative correlation with L1 ORFlp protein expression (FIG. 11A), with younger SINEs being more prone to be repressed under high LI expression (FIG. 11B). Collectively, SINEs constitute half and of the repeats that are significantly and negatively correlated with L O1RFlp expression, while LINEs and LTRs were the only two classes of repats that showed significantly positive correlation with L1 ORFlp expression (FIG. 5C).[02661 It was then sought to verify whether the observed reduction of immunogenic SINE expression leads to a reduction in dsRNA production. Immunofluorescent staining with anti- dsRNA (rJ2 clone) antibody (Methods) was performed on five selected cell lines, considering cytoplasmic volume for the further analysis. Representative immunofluorescence images of cytoplasmic dsRNA have shown a significant reduction in high L1 ORFlp expressing PDAC cell lines (PDAC6, PDAC8, PDAC9) compared to low L1 ORFlp cell lines (KP-4, PANC-1) (data not shown). It was found that high L1 ORFlp expressing cell lines had a reduced signal from the dsRNA antibody rJ2 in immunofluorescent imaging analysis (FIG. 5D). Furthermore, a co- localization phenotype of L O1RFlp and dsRNA antibodies in the nucleus of cells carrying high L1 ORFlp expression was observed. These findings provide further evidence for a potential link between L1 activity and dsRNA accumulation in cancer cells.

[0267] The observed co-localization of L1 ORFlp and dsRNA in PDAC cell lines supports the hypothesis of a preferential binding between L1 proteins and SINEs with high immunogenicity due to a higher dsRNA force. To test this hypothesis, RNA ImmunoPrecipitation Sequencing (RIP- Seq) assays for both a-ORFlp and a-ORF2p of LI proteins were conducted in the embryonal carcinoma cell line N2102p (FIG. 5E), with detectable expression of ORF2p, a L1 ORF encodes the reverse transcriptase and endonuclease39. Transcripts enriched by co-IP were determined by differential expression analysis of RNA immunoprecipitated vs. total RNA and matched mock IP controls (Methods). L pr1otein binding showed a specific preference to LINEs and SINEs: repeats that co-IPed with both ORFlp andORF2p are mostly composed of LINEs and SINEs as expected (FIG. 11C). Overall, both SINE and LINE transcripts that bound ORFlp and ORF2p (Log2FC > 3, p.adj < 0.05) had significantly higher dsRNA force compared to the non-binding transcripts from the same repeat class, in contrast to DNA and LTRs (FIG. 5F). AluS comprises the majority of ORFlp and ORF2p binding SINEs (42%) (FIG. 5G & FIG. 5H). By comparing the composition of the SINEs that bound both ORFlp and ORF2p with those that did not bind, it was found that AluYs, the youngest SINE family member, are exclusively bound by both L pr1oteins (FIG. 5G).Example 7: Tumors converge on L1 or ADAR1 to suppress immunogenic SINEs

[0268] Several studies have demonstrated that ADAR1, an RNA deaminase enzyme, can suppress immunogenic double-stranded RNAs through Adenosine-Inosine editing, with inverted SINEs being a predominant substrate9 25 41 45. To better understand the interplay between LI, AD ARI, and their impact on immunogenic SINEs in tumor cells, AD ARI activity was examined from both transcriptional and functional perspectives. It was found that samples with mutated TP53 exhibit compromised expression of AD ARI, similar to the observed pattern for MDM2, a TP53 suppressor gene (FIG. 6A). However, no significant differences in immunogenic SINE expression (AluS, AluY, SVA) were found under different TP53 status (FIG. 6B). As expected, both ADAR1 expression and L1 insertion are anti-correlated with young / immunogenic SINEs (FIG. 12A). Interestingly, AD ARI expression and L ac1tivity are inversely correlated with each other (FIG. 12A), suggesting that ADAR1 governs immunogenic SINE expression independently of L 1.

[0269] To investigate the functional impact of ADAR1 on dsRNAs, A-G and T-C mismatched in RNAseq data were quantified as a readout of A-I editing. As predicted, inverted SINEs (Methods), have significantly higher A-I editing index compared to non-inverted SINEs in multiple patients (FIG. 6C). Interestingly, the A-I editing index is positively correlated with the total number of somatic L1 insertions and negatively correlated with TP 53 mutation status. For example, samples with more significant loss of function TP53 mutations tend to have moresomatic L1 insertions and lower A-I editing index (FIG. 6C). Stratifying samples based on different TP53 mutation status revealed that patients with attenuated TP53 mutations have higher A-I editing index in both primary and metastatic samples, with a stronger difference observed in metastasis (FIG. 12B) This association in the independent cohort from the COMPASS trial35was confirmed. As the COMPASS trail has a smaller number of reads mapped to SINEs due to its poly-A RNA- sequencing protocol (FIG. 12H), less A-I editing index was seen compared to the PDAC autopsy cohort (FIG. 12C). However, the discrepancy between inverted and noninverted SINEs was recapitulated (FIG. 12C). ADAR1 functionality was then compared in samples with different TP53 mutation statues and found that samples with weaker TP53 mutations or wildtype status had significantly higher A-I editing index than those with nonsense or frameshift deletions (FIG. 6D). Interestingly, primary samples had significantly higher A-I editing index compared to metastatic samples (FIG. 12D), which is opposite to the observed somatic L1 insertion trend (FIG. 9D). When comparing the A-I editing index of inverted SINEs with the number of somatic L1 insertions, a significant negative association was found in the rapid autopsy cohort (FIG. 12E). Those observations indicate that L1 mobility and ADAR1 functionality are complementary and mutually exclusive to each other.

[0270] To further explore the relation between AD ARI functionality and TP53 missense mutations more granular in the COMPASS trial, a previously developed method was used to compute the fitness of different TP53 missense mutations based on their oncogenicity46. A significant negative correlation was observed between predicted missense mutation fitness (oncogenicity) and A-I editing index in metastatic samples, further demonstrating ADAR1 functionality is dependent on the oncogenicity of TP53 mutations (FIG. 6E). Taken together, the findings suggest that L p1rotein can repress immunogenic SINEs while, in parallel, AD ARI can edit SINEs and ultimately lead to RNA degradation as an alternative immune surveillance mechanism when TP53 is largely functional (FIG. 6F). These parallel mechanisms of immunogenic SINE regulation are essentially mutually exclusive and converge on maintaining immunogenic SINE expression in a pro-tumorigenic manner in PDAC.

[0271] In this study, the interplay was shown between two classes of retrotransposons, and how their interaction is influenced by TP53 mutation status and RNA editing along tumor evolution in PDAC. The study revealed a core RIG-I Like Receptor (RLR) associated IFN signature associated with the expression of immunogenic SINE elements, such as the AluY and AluS families (FIG. 2A). The mechanism was computationally validated, whereby the transcripts of these younger Alu repeats have a higher likelihood to form dsRNA (FIG. 2D)24, acting as viral mimics that engage pattern recognition receptors, such as RIG-I and MDA5, which then upregulate immune responses47. To gain proliferative advantage a tumor cell has to balance a tradeoff between oncogenic potential and immunogenic cost46. Initially increased epigenetic plasticity in a tumor will trigger the acute expression of repeats which activate immune signaling48. SINE-induced innate immune responses will likely be initially detrimental to the tumor, and thus tumors adapt to keep SINE expression in check and shift to chronic pro- tumorigenic inflammation mediated by other pathways, such as DNA sensing pathways cGAS / STING49.

[0272] De novo L1 insertions in tumor samples were associated with the status of TP 53 mutations (FIG. 3D), supporting active differential surveillance of L m1obility by mutant TP 53 in tumor evolution in addition to the repressive role of TP 53 on a Lct1ivity36,50. The patient data support an antagonistic relationship between L a1ctivity and immunogenic SINE expression, in that high L1 insertion rates correlate with low immunogenic SINE expression (FIG. 4B). Consistently in PDAC cell lines, reducing the expression of L O1RFlp by shRNA resulted in an increase of SINE expression. The findings revealed a previously undescribed role for L1 in repressing anti- tumorigenic immunity elicited by SINEs. This hitherto unappreciated mechanism distinctly regulates immunogenic SINE expression in PDAC evolution: tumors evolve to escape the immunosurveillance by modulating the abundance of SINE RNAs via increased L1 activity. In contrast, for tumors with low L ac1tivity, AD ARI keeps immunogenic SINEs in check via RNA editing. AD ARI editing activity was highest in samples with wild type TP 53 and low L1 insertions and, conversely, lowest in samples with mutated TP53 and high LI insertions. A model was therefore proposed, whereby pancreatic tumors converge on regulationof dsRNA viral mimics by modulating their concentration via either L -1mediated removal or AD ARI editing (FIG. 6F). Reduced levels of SINE-derived dsRNAs will result in reduced RLR mediated acute inflammation while shaping an immune suppressive environment potentially by facilitating M2 macrophage infdtration (FIG. 10F) that is likely a result of chronic rather than acute inflammation.

[0273] As validated in an additional large cohort of PDAC tumors and analysis of PDAC cell lines, it was found immunostimulatory SINE expression is negatively regulated by LI retrotransposon activity in the setting of TP53 mutation while, in the setting of wildtype TP53 status, tumors utilize AD ARI as a response mechanism to SINEs. Both mechanisms highlight the multiple pathways tumor cells utilize to converge on repeat regulation to promote tumor growth via immune manipulation.EQUIVALENTS[0274J The present technology is not to be limited in terms of the particular embodiments described in this application, which are intended as single illustrations of individual aspects of the present technology. Many modifications and variations of this present technology can be made without departing from its spirit and scope, as will be apparent to those skilled in the art. Functionally equivalent methods and apparatuses within the scope of the present technology, in addition to those enumerated herein, will be apparent to those skilled in the art from the foregoing descriptions. Such modifications and variations are intended to fall within the scope of the present technology. It is to be understood that this present technology is not limited to particular methods, reagents, compounds compositions or biological systems, which can, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting.

[0275] In addition, where features or aspects of the disclosure are described in terms of Markush groups, those skilled in the art will recognize that the disclosure is also thereby described in terms of any individual member or subgroup of members of the Markush group.-I l l-

[0276] As will be understood by one skilled in the art, for any and all purposes, particularly in terms of providing a written description, all ranges disclosed herein also encompass any and all possible subranges and combinations of subranges thereof. Any listed range can be easily recognized as sufficiently describing and enabling the same range being broken down into at least equal halves, thirds, quarters, fifths, tenths, etc. As a non-limiting example, each range discussed herein can be readily broken down into a lower third, middle third and upper third, etc. As will also be understood by one skilled in the art all language such as “up to,” “at least,” “greater than,” “less than,” and the like, include the number recited and refer to ranges which can be subsequently broken down into subranges as discussed above. Finally, as will be understood by one skilled in the art, a range includes each individual member. Thus, for example, a group having 1-3 cells refers to groups having 1, 2, or 3 cells. Similarly, a group having 1-5 cells refers to groups having 1, 2, 3, 4, or 5 cells, and so forth.

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Claims

WHAT IS CLAIMED IS:

1. A method for treating cancer in a patient in need thereof comprising administering to the patient an effective amount of a LINE-1 (LI) inhibitor, wherein the patient comprises a tumor harboring a TP53 mutation, optionally wherein the TP53 mutation is a missense mutation, a nonsense mutation, or a frameshift mutation.

2. A method for treating cancer in a patient in need thereof comprising administering to the patient an effective amount of an AD ARI inhibitor, wherein the patient comprises tumors that do not harbor TP53 mutations.

3. A method for treating cancer in a patient in need thereof comprising administering to the patient an effective amount of an AD ARI inhibitor and a LINE-1 (LI) inhibitor, wherein the patient comprises tumors that include at least one TP53 mutant gene and at least one wildtype TP53 gene, optionally wherein the at least one TP53 mutant gene comprises a missense mutation, a nonsense mutation, or a frameshift mutation.

4. The method of any one of claims 1-3, further comprising administering an effective amount of an additional therapeutic agent, optionally wherein the additional therapeutic agent comprises immune checkpoint blockade therapy.

5. A method for enhancing tumor sensitivity to immune checkpoint blockade therapy in a patient diagnosed with cancer comprising administering to the patient an effective amount of a LINE-1 (LI) inhibitor, wherein the patient comprises a tumor harboring a TP53 mutation, optionally wherein the TP53 mutation is a missense mutation, a nonsense mutation, or a frameshift mutation.

6. A method for enhancing tumor sensitivity to immune checkpoint blockade therapy in a patient diagnosed with cancer comprising administering to the patient an effective amount of an AD ARI inhibitor, wherein the patient comprises tumors that do not harbor TP53 mutations.

7. The method of any one of claims 2-4 or 6, wherein the AD ARI inhibitor is a small molecule or an AD ARI -specific inhibitory nucleic acid.

8. The method of claim 7, wherein the small molecule is selected from the group consisting of rebecsinib, GT0001X, AVA-ADR-001, CL-AD-100, lithospermic acid, and Regaloside B.

9. The method of claim 7, wherein the AD ARI -specific inhibitory nucleic acid is a siRNA, a shRNA, an antisense oligonucleotide, or a sgRNA.

10. The method of any one of claims 1, 3-4 or 5, wherein the L in1hibitor is a small molecule or an L -1specific inhibitory nucleic acid.

11. The method of claim 10, wherein the small molecule is selected from the group consisting of capsaicin, nucleoside reverse transcriptase inhibitors (e.g., lamivudine, adefovir, penciclovir, abacavir, stavudine, didanosine, zidovudine, emtricitabine, zalcitabine, tenofovir, entecavir, telbivudine, AZTTP, d4TTP, ddCTP, 3TCTP), NSC332395, and AD2 / ZINC89469886.

12. The method of claim 11, wherein the Ll-specific inhibitory nucleic acid is a siRNA, a shRNA, an antisense oligonucleotide, or a sgRNA.

13. The method of any one of claims 1, 3-5, or 10-12, further comprising (a) detecting Short Interspersed Nuclear Elements (SINE) expression levels in a test sample obtained from the patient prior to treatment with the L i1nhibitor and (b) determining that the SINE expression levels in the test sample are reduced compared to a reference sample obtained from a healthy control patient or a predetermined threshold.

14. The method of any one of claims 2, 4 or 6-9, further comprising (a) detecting SINE expression levels in a test sample obtained from the patient prior to treatment with the AD ARI inhibitor and (b) determining that the SINE expression levels in the test sample are elevated orcomparable to a reference sample obtained from a healthy control patient or a predetermined threshold.

15. The method of claim 13 or 14, wherein the SINE expression levels are detected via next-generation sequencing, RNA-Seq, PCR, real-time quantitative PCR (qPCR), digital PCR (dPCR), Southern blotting, Reverse transcriptase-PCR (RT-PCR), Northern blotting, microarray, dot or slot blots, in situ hybridization, or fluorescent in situ hybridization (FISH).

16. The method of any one of claims 1, 3-5, 10-13 or 15, wherein the patient exhibits elevated L1 retrotransposition activity compared to a healthy control patient or a predetermined threshold.

17. The method of any one of claims 2, 4, 6-9, or 14-15, wherein the patient exhibits LI retrotransposition activity that is comparable to a healthy control patient or a predetermined threshold.

18. The method of any one of claims 13-17, wherein the SINE comprise at least one or more of SINE2 / SINE3, monomeric SINE1, AluJ, AluS, AluY and SINE-VNTR-Alu (SVA).

19. The method of any one of claims 1-18, wherein the L i1nhibitor or the AD ARI inhibitor is administered orally, topically, intranasally, systemically, intravenously, subcutaneously, intraperitoneally, intradermally, intraocularly, iontophoretically, transmucosally, or intramuscularly.

20. The method of any one of claims 1-19, wherein the cancer is pancreatic cancer or ovarian cancer.

21. The method of any one of claims 4-20, wherein the immune checkpoint blockade therapy comprises an anti-PD-1 antibody, an anti-PD-Ll antibody, an anti-PD-L2 antibody, an anti-CTLA-4 antibody, an anti-TIM3 antibody, an anti -4- IBB antibody, an anti-CD73 antibody, an anti-GITR antibody, or an anti-LAG-3 antibody.

22. The method of any one of claims 4-21, wherein the immune checkpoint blockade therapy comprises cemiplimab, tremelimumab, ipilimumab, nivolumab, pidilizumab, lambrolizumab, pembrolizumab, envafolimab, atezolizumab, avelumab, durvalumab, dostarlimab, MPDL3280A, BMS-936559, MEDI-4736, MSB 00107180, inhibitory antibodies against LAG-3 (lymphocyte activation gene 3), TIM3 (T-cell immunoglobulin and mucin-3), B7-H3, B7-H4, TIGIT (T-cell immunoreceptor with Ig and ITIM domains), AMP -224, MDX- 1105, arelumab, tremelimumab, IMP321, MGA271, BMS-986016, lirilumab, urelumab, PF- 05082566, IPH2101, MEDI-6469, CP-870,893, Mogamulizumab, Varlilumab, Galiximab, AMP-514, AUNP 12, Indoximod, NLG-919, INCB024360, CD80, CD86, ICOS (inducible T- cell costimulatory), DLBCL (diffuse large B-cell lymphoma) inhibitors, BTLA (B and T lymphocyte attenuator), PDR001, and any combination thereof.

23. The method of any one of claims 1-22, wherein the patient exhibits elevated expression levels of SINE-derived double stranded RNAs (dsRNA) and / or elevated levels of one or more RIG-I associated type-I interferon genes.

24. The method of claim 23, wherein the one or more RIG-I associated type-I interferon genes are selected from the group consisting of PELI1, HLA-G, CDKN1A, PIM1, CGAS, PSMA2, UPP1, RIPK2, PFKP, STAT2. IRF9, TNFAIP2, ISG20, SOCS1, SOCS7, DHX58, TRIM25, IFI30, IRF3, and HELZ2.

25. A method of selecting cancer therapies based on double-stranded ribonucleic acid (dsRNA) force metrics associated with short interspersed nuclear elements (SINE), comprising: obtaining, by one or more processors, for a subject at risk of or diagnosed with cancer, a dataset identifying a plurality of RNA sequences from a biological sample of the subject, the plurality of RNA sequences comprising (i) a first RNA sequence corresponding to a first strand of a dsRNA and (ii) a second RNA sequence corresponding to a second strand of the dsRNA, the second strand complementary to and shorter than the first strand;identifying, by the one or more processors, using the dataset, (i) at least one SINE copy in the first RNA sequence, (ii) a first length of nucleotides of the first RNA sequence, and (iii) a second length of nucleotides of the second RNA sequence; determining, by the one or more processors, a probability of an occurrence of a pairable SINE associated with the at least one SINE copy on the dsRNA based on a (i) first plurality of frequencies for a corresponding first plurality of nucleotides that are within at least a first predetermined window of the first RNA sequence of the at least one SINE copy and (ii) a second plurality of frequencies for a corresponding second plurality of nucleotides that are within at least a second predetermined window of the second RNA sequence of the at least one SINE copy; identifying, by the one or more processors, using at least the first predetermined window of the first RNA sequence, an insert length of the at least one SINE copy within the at least the first predetermined window; generating, by the one or more processors, a dsRNA force metric indicating a degree of potential of the at least one SINE copy to fold with the pairable SINE on the dsRNA based on (i) the probability of the occurrence of the pairable SINE, (ii) the first length, (iii) the second length, and (iv) the insert length; selecting, by the one or more processors, from a plurality of cancer therapies, a cancer therapy based on the dsRNA force metric relative to a threshold; and storing, by the one or more processors, using one or more data structures, an association between the subject and the cancer therapy.

26. The method of claim 25, further comprising determining, by the one or more processors, that the dsRNA force metric satisfies the threshold, and wherein selecting the cancer therapy further comprises selecting the cancer therapy comprising immune checkpoint blockade (ICB) therapy, responsive to determining that the dsRNA force metric satisfies the threshold.

27. The method of claim 25 or 26, further comprising determining, by the one or more processors, that the dsRNA force metric does not satisfy the threshold, and wherein selecting the cancer therapy further comprises selecting the cancer therapy comprising a combination therapy, responsive to determining that the dsRNA force metric does not satisfy the threshold, wherein the combination therapy comprises at least one of (i) a first combination comprising ICB therapy and L -1inhibitor or (ii) a second combination comprising the ICB therapy and AD ARI inhibitor.

28. The method of any one of claims 25-27, wherein the biological sample includes a biopsied tissue obtained from an organ infected with the cancer, wherein the biopsied tissue is at least one of frozen, fresh, or Formalin-Fixed Paraffin-Embedded (FFPE).

29. The method of any one of claims 25-28, further comprising removing, by the one or more processors, from the dataset, a portion of the plurality of RNA sequences corresponding to RNA sequencing reads with a quality score not satisfying a threshold score.

30. The method of any one of claims 25-29, further comprising filtering, by the one or more processors, from the dataset, a portion of the plurality of RNA sequences corresponding to RNA sequencing reads with counts of repeats below a threshold count.

31. The method of any one of claims 25-30, further comprising: obtaining, by the one or more processors, for the subject, a second dataset identifying genomic sequences derived from a second biological sample of the subject, wherein the genomic sequences are obtained via next-generation sequencing and comprise a plurality of sequencing reads; identifying, by the one or more processors, using the second dataset, at least one p53 sequence in the genomic sequences of the subject, the at least one p53 sequence including a plurality of non-silent p53 mutant sequences and a plurality of wild-type p53 sequences;generating, by the one or more processors, a wild-type p53 fitness metric based on transactivation levels of at least one p53 target gene induced by a wild-type p53 polypeptide encoded by the plurality of wild-type p53 sequences, a binding affinity of the wild-type p53 polypeptide to a promoter region of the at least one p53 target gene, and expression levels of the wild-type p53 polypeptide; generating, by the one or more processors, maximum p53 fitness metric based on transactivation levels of the at least one p53 target gene caused by a complete deletion of p53 gene; and determining, by the one or more processors, a mutant p53 fitness metric based on the wild-type p53 fitness metric, the maximum p53 fitness metric, and the mutated position of each p53 mutant sequence of the plurality of non-silent p53 mutant sequences.

32. The method of claim 31, wherein the at least one p53 target gene is WAF1, MDM2, BAX, hl433s, AIP1, GADD45, NOXA, or P53R2.

33. The method of claim 31 or 32, further comprising identifying, by the one or more processors, using the second dataset, a retrotransposon (RT) activity metric corresponding to a RT indicating a level of RT activity in the genomic sequences of the subject.

34. The method of claim 33, wherein the RT activity corresponds to at least one of a long interspersed nuclear element-1 (LINE-1), a short interspersed nuclear element (SINE), or a SINE-Variable number of tandem repeat (VNTR)-Alus (SVA).

35. The method of claim 33 or 34, wherein selecting the cancer therapy further comprises selecting the cancer therapy as a function of the dsRNA metric, the mutant p53 fitness metric, and the RT activity metric.

36. A system comprising one or more processors coupled with memory configured to perform any one or more of the methods of any one of claims 25-35.

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