COMPOSITIONS AND METHODS USING lncRNAs

Abstract

Compositions and methods for treating cancer are provided. In certain embodiments, the method includes reducing the level of a long non-coding RNA in a subject.

Description

[0001] NYG-LIPP-228.PCT

[0002] COMPOSITIONS AND METHODS USING IncRNAS

[0003] STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0004] This invention was made with government support under Grant Numbers DP2HG010099, R01HG012790, R01CA279135, R01CA218668, R01AI176601 and R01HL168247 awarded by the National Institutes of Health. The government has certain rights in this invention.

[0005] BACKGROUND OF THE INVENTION

[0006] The human genome is pervasively transcribed into RNA and encodes thousands of long noncoding RNAs (IncRNAs) that are often spliced and polyadenylated but not translated into proteinsl ?. Of annotated IncRNAs, very few (<1%) have been linked with a clear functional role4In those rare cases, IncRNAs have been found to sequester microRNAs5, block translation6, form biomolecular condensates7, encode micropeptides8'9, and regulate proteins or RNA10 1’. Their low sequence conservation12 n, low abundance14 15. and cell type-specific expression16make it challenging to distinguish them from unstable transcriptional noise17Even though genome-wide bioinformatic analyses and comparative sequencing studies have identified conserved IncRNAs18 19, suggesting possible functional roles, follow-up experimental validation has been limited to low-throughput studies, focusing on one IncRNA at a time4

[0007] Recently, CRISPR-Cas9 pooled screens using CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa) have been applied to identify functional IncRNAs20,21. While valuable, Cas9-based approaches often suffer from unintended on-target activity — that is, binding at the intended genomic locus but perturbing additional nearby genes. Moreover, DNA-based perturbation of a IncRNA locus might also suppress functional DNA elements unrelated to the IncRNA transcript22.

[0008] SUMMARY OF THE INVENTION

[0009] Various compositions and methods are provided for treating cancer. In one aspect a method of treating cancer in a subject includes reducing the level of a long non-coding RNA (IncRNA) in the subject. In certain embodiments, the IncRNA is selected from Table 1 and NYG-LIPP-228.PCT

[0010] Table 2. In certain embodiments, the IncRNA is reduced using siRNA or an ASO. In certain embodiments, the siRNA is selected from those of Table 5.

[0011] In another aspect, a method of treating cancer in a subject comprising increasing the level of a long non-coding RNA (IncRNA) in the subject. In certain embodiments, the IncRNA is selected from Table 3. In certain embodiments, the IncRNA level is increased by administering a nucleic acid comprising the IncRNA sequence.

[0012] In another aspect, a method of treating cancer in a subject includes reducing the level of a gene expression product of a gene selected from Table 4 in the subject. In certain embodiments, the method includes administering a small molecule, ASO, siRNA, or antibody that reduces the level of the gene expression product.

[0013] In another aspect, a method of treating cancer in a subject includes reducing the level of a long non-coding RNA (IncRNA) and the level of in the subject a gene expression product of a gene selected from Table 4.

[0014] In another aspect, a composition is provided that includes an antisense oligo or siRNA against an IncRNA of Table 1. 2, or PCG of Table 4. In one embodiment, the siRNA is selected from those of Table 5.

[0015] Still other aspects and advantages of these compositions and methods are described further in the following detailed description of the preferred embodiments thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] FIG. 1 A - FIG. 1 E show transcriptome-scale RNA-targeting CRISPR screens to identify essential IncRNAs in human cells. (FIG. 1A) Overview of the Casl3-based loss-of- function screens to identify' essential long noncoding RNAs (IncRNAs). lincRNA - long intergenic noncoding RNA, asRNA - antisense RNA. gRNA - guide RNA. (FIG. IB) Fold- change (FC) of gRNAs targeting IncRNAs in two independent biological replicate pooled screens in HAP1 cells at 14 days after Casl3 induction. Color denotes the number of Casl3 gRNAs. (FIG. 1C) Fold-change (Day 14 vs. Day 0) of five individual gRNAs (pink lines) targeting the indicated genes. The shaded region indicates the 95% confidence interval computed using the distribution of non-targeting (NT) gRNAs. The diamond denotes the mean fold-change of the five gRNAs in HAP1 cells. (FIG. ID) Ranking of IncRNAs via robust rank aggregation (RRA) in HAP1 screens, based on consistent depletion of five individual guide RNA (gRNA) targeting the same gene. (FIG. IE) Essential (RRA P < 0.05) and non-essential IncRNAs from the Casl3 screens in the five cell lines. In panels FIG. IB and FIG. 1C, the dashed lines indicate the 95% confidence interval for NT gRNAs. NYG-LIPP-228.PCT

[0017] FIG. 2A - FIG. 21. Distinct and common essential IncRNAs across five cell lines.

[0018] (FIG. 2A) Enrichment of essential IncRNAs over non-essential IncRNAs for genomic position to nearest protein-coding gene (left) and evolutionary age (right). For evolutionary age, my a denotes million years ago. The odds ratio is determined by a Fisher’s exact test with the significance given by the dot size (dark outline indicates P < 0.05). (FIG. 2B) Distribution of shared, partially shared and cell-type specific essential IncRNAs (left) and protein-coding genes (PCGs) (right) across all five cell lines. Shared genes are essential in all cell lines, and partially shared genes are essential in two to four cell lines. Numbers in parentheses indicate the number of essential genes in each cell line. (FIG. 2C) The proportion of essential IncRNAs and PCGs in each cell line. Fisher’s exact test for essential IncRNAs compared to essential PCGs for each essentiality category (see categories in FIG. 2B). (FIG. 2D) Foldchange (Day 14 vs. Day 0) of cell-type specific, partially shared, and shared essential IncRNAs in HAP1 cells after Casl3 induction. (FIG. 2E) Expression of cell-type specific, partially shared, and shared essential IncRNAs in HAP1 cells. (FIG. 2F) Pearson correlation of essential (upper) and non-essential IncRNAs (lower) expression across five cell lines.

[0019] (FIG. 2G) Fold-change (Day 14 vs. Day 0, x-axis) and RNA-seq reads / expression (y-axis) for two cell-specific essential and one nonessential IncRNAs. (FIG. 2H) A GFP-labeled competition assay to quantify the impact of knock-dow n of essential IncRNAs. (FIG. 21) Representative images of HAP 1 (left) and flow cytometry of THP1 (right) cells transduced with individual gRNAs targeting highly expressed IncRNAs indicated in panel G six days after Cast 3 induction. Survival of GFP+ cells transduced with three nonoverlapping gRNAs per gene normalized to the non-targeting (NT) gRNAs (right). Each green circle denotes a single gRNA and single transduction replicate. The diamonds denote the mean survival (n = 6 experiments with three gRNAs from two independent transductions). The dashed lines indicate the 95% confidence interval (CI) for NT gRNAs. Statistical significance was determined by a Student’s / -test. Scale bar for HAP1 images: 200 pm. In FIG. 2D and FIG. 2E, box plots indicate the median 25th and 75th percentiles, while whiskers are 1.5 times the interquartile range and statistical significance was determined by a two-sided Mann- Whitney U test. FIG. 3A - FIG. 3F show knockdown of shared essential IncRNAs reduces cell survival. (FIG. 3A) Essentiality, expression levels and genomic classification of shared essential IncRNAs ordered by their median essentiality (n = 5 cell lines). For comparison, six non-essential IncRNAs are included at the bottom of the heatmap. For some IncRNAs. a prior CRISPRi screen examined essentiality and we have indicated those IncRNAs found to be NYG-LIPP-228.PCT essential in any cell line screened (n = 7 cell lines screened with CRISPRi). (FIG. 3B) Representative images of HAP 1 cells transduced with individual gRNAs targeting shared essential IncRNAs indicated in panel A four days after Casl3 induction (left). Survival of GFP+ cells transduced with three non-overlapping gRNAs per gene normalized to the non- targeting (NT) gRNAs in HAP1, MDA-MB-231 and HEK293FT (right). Each green circle denotes a single gRNA and single transduction replicate. The diamonds denote the mean survival (n = 6 experiments with three gRNAs from two independent transductions). The gray shaded area indicate the 95% confidence interval (CI) for NT gRNAs. (FIG. 3C) Fluorescence ubiquitination cell cycle indicator (FUCCI) assay to measure cell fraction in different cell cycle phases. (FIG. 3D) Median distribution of MDA-MB-231 cells transduced with indicated gRNAs in cell cycle phases G1 (red), S (green) and G2-M (yellow) 24, 48 and 72 hours after Cast 3 induction (n = 54 images per perturbation and timepoint with 9 images per biological replicate and 6 biological replicates per perturbation). P values from the predominantly enriched cell cycle phase (determined for each IncRNA individually) were computed by a Mann- Whitney U test to test for differences from cells transduced with nontargeting gRNAs. (FIG. 3E) Apoptosis assay using Annexin V staining. (FIG. 3F) Representative images of MDA-MB-231 cells transduced with gRNAs targeting shared essential IncRNAs at 72 hours after Casl3 induction (left). Annexin V+ cells were quantified and normalized to the total cell area (right). Each pink circle denotes a single gRNA and single transduction replicate. The diamonds denote the mean survival (n = 54 images per perturbation with 9 images per biological replicate and 6 biological replicates per perturbation). In panels FIG. 3B and FIG. 3F, statistical significance w as determined by a Student’s / -test. Scale bar: 200 pm.

[0020] FIG. 4A - FIG. 41 show nearest protein-coding genes of essential IncRNAs are often not essential. (FIG. 4A) Number of essential IncRNAs and protein-coding genes (PCGs) across five cell lines. (FIG. 4B) Alluvial diagram of IncRNA-PCG pairs, depicting pairs where only the IncRNA is essential, where only the PCG is essential and where both the IncRNA and nearest PCG are essential. Numbers in parentheses indicates IncRNA-PCG pairs with at least one essential gene in each cell line. (FIG. 4C) Fold-change (Day 14 vs. Day 0) of IncRNAs and PCGs in each IncRNA-PCG pair in HAP1 cells after Casl3 induction. The pairs are separated by those pairs where only the IncRNA is essential (left), where only the PCG is essential (middle), and where both the IncRNA and nearest PCG are essential (right). (FIG. 4D) Examples of IncRNA-PCG pairs where one or both genes are essential. Fold change of five individual gRNAs targeting the indicated genes with the 95% confidence NYG-LIPP-228.PCT interval (range) of non-targeting gRNAs (gray). The diamond denotes the mean of the five gRNAs. (FIG. 4E) Representative images of HAP1 cells transduced with individual gRNAs targeting indicated genes five days after Casl3 induction left). Survival of GFP+ cells transduced with three nonoverlapping gRNAs per gene normalized to the median of non- targeting (NT) gRNAs (right). Each green circle denotes a single gRNA and single transduction replicate. The diamonds denote the mean survival (n = 6 experiments with three gRNAs from two independent transductions). The dashed lines indicate the 95% confidence interval (CI) for NT gRNAs. Statistical significance was determined by a Student’s / -test. Scale bar: 200 pm. (FIG. 4F) Essentiality of the closest PCGs (left) and the distance between IncRNAs and closest PCGs (right) for the shared essential IncRNAs. Orange boxes indicate that the closest PCG is essential. For distances, pink lines denote a distance of less than 1 kb between the IncRNA and PCG and blue lines denote a distance greater than 1 kb. (FIG. 4G) Key mechanistic differences in knock-down of IncRNAs and nearby genes with DNA- targeting CRISPRi or RNA-targeting Cast 3 (lipper). The proportion of essential closest PCGs for IncRNAs identified in this study and a prior IncRNA pooled CRISPRi screen (lower)20. Dot size corresponds to the number of essential IncRNAs identified. Common cell lines used in both studies (HEK293FT, K562, MDA-MB-231) are labeled in pink, and the study-specific cell lines are labeled m yellow. (FIG. 4H) The fraction of essential IncRNAs identified in the CRISPRi study and the present study, categorized by the distance to their nearest PCG and whether they were identified as essential in the DNA-targeting (CRISPRi) study, this RNA-targeting (Casl 3) study or both studies. (FIG. 41) The fraction of essential IncRNAs identified in both DNA- and RNA-targeting studies, categorized by essentiality level (left) and all essential IncRNAs (right) from this (RNA-targeting) study.

[0021] FIG. 5 A - FIG. 5D show single-cell trans criptomics after Casl 3 perturbation (CaRPool-seq) of essential IncRNAs identifies shared cellular pathways for proliferation. (FIG. 5A) Schematic of Casl3 RNA Perturb-seq (CaRPool-seq) using guide RNA (gRNA) arrays that encode two gRNAs that target the same gene (IncRNA or protein-coding). Each array also contains a barcode gRNA (bcgRNA) to enable identification of the gRNA array using single-cell sequencing. (FIG. 5B) Correlation between fold-change (FC) from the transcriptome-scale pooled screen (Day 14 vs. Day 0) and the CaRPool-seq pooled screen (Day 12 vs. Day 0) for 50 essential IncRNAs (purple) and 21 protein-coding genes (orange and green) in MDA-MB-231 cells. (FIG. 5C) Single-cell mRNA expression heatmap with the 25 most differentially downregulated genes for each IncRNA perturbation in MDA-MB-231 cells (Padj < 0.05). For each IncRNA, transcripts with the lowest median DepMap scores are NYG-LIPP-228.PCT labeled (n = 3 most essential transcripts per IncRNA and median over 1,095 DepMap cell lines). (FIG. 5D) Normalized enrichment scores (NES) from gene set enrichment analysis (GSEA) for 21 perturbed protein-coding genes (PCGs) (left) and 50 perturbed essential IncRNAs (middle) in MDA-MB-231 cells. The number of IncRNAs with the indicated pathway (MSigDB Hallmark 75) enriched or depleted (Padj < 0.05, black dots) in CaRPool- seq from MDA-MB-231 and HAP1 cells (right). Pathways categorized as proliferation or apoptosis are labeled (far left column)15. The fold-change in gRNA array abundance from the pooled readout of the MDA-MB-231 CaRPool-seq is shown at the bottom.

[0022] FIG. 6A - FIG. 6F show essential IncRNAs are expressed broadly across different tissues and at early stages of development. (FIG. 6A) Transcriptome profiles for each tissue at various developmental time points from recent developmental atlases of IncRNA and protein-coding gene (PCG) expression (n = 182 tissue samples) 19,76. (FIG. 6B) Empirical cumulative distributions of tissue-specificity' left) and time specificity (right) indices for essential and non-essential IncRNAs (two-sided Mann- Whitney U test). (FIG. 6C) The proportion of dynamic IncRNAs for essential and non-essential IncRNAs (Fisher’s exact test in comparison to non-essential IncRNAs). (FIG. 6D) The median expression of dynamic shared essential (purple) and non-essential (turquoise) IncRNAs at different developmental time points in each tissue. The heatmaps below provide annotations for the relative abundance of proliferation markers PCNA and MK167. (FIG. 6E) Gene set enrichment analysis (GSEA) of co-expressed PCGs for essential and non-essential IncRNAs across tissues, represented by the median normalized enrichment scores (NES) across 50 GSEA hallmark pathways. (FIG. 6F) NES of proliferation- or apoptosis-associated pathways (a subset of MSigDB Hallmark pathways 75) for each of the shared essential IncRNAs in brain (left). The fraction of shared essential IncRNAs with proliferation- or apoptosis-associated pathways enriched or depleted ( adj < 0.05, black dots) across different tissues (right).

[0023] FIG. 7A - FIG. 7G show essential IncRNAs are differentially expressed in tumors and correlate with survival. (FIG. 7A) Computational workflow to identify functional IncRNAs across 29 cancer types from The Cancer Genome Atlas (TCGA). (FIG. 7B) Median expression of essential and non-essential IncRNAs in primary tumors from 29 cancer types (two-sided Mann- Whitney U test). (FIG. 7C) The fraction of differentially expressed

[0024] IncRNAs in primary' tumors compared to normal tissue across 22 cancer types (bottom). The pie chart (top) separates differentially expressed essential IncRNAs by expression level: High (increased expression in tumor). Low (decreased expression in tumor), and Mix (varying expression in different cancer types). The number of cancer types with significant differential NYG-LIPP-228.PCT expression for the example IncRNAs is indicated in parentheses (Fisher's exact test). (FIG. 7D) Gene set enrichment analysis (GSEA) of co-expressed PCGs for essential and non- essential IncRNAs, represented by the median normalized enrichment scores (NES) across 50 MSigDB Hallmark pathways75. Marker size represents the fraction of IncRNAs within each respective group. Only enriched pathways in the top quartile (by NES) are plotted. (FIG. 7E) The proportion of IncRNAs associated with better or worse overall survival or progression- free survival. (FIG. 7F) Survival analysis coefficients for TCGA tumor types for the 46 shared essential IncRNAs. LncRNAs with negative coefficients (pink') are associated with worse survival outcomes (increased hazard). LncRNAs with positive coefficients (green) are associated with better survival outcomes (decreased hazard). Rectangles denote P < 0.05 and black dots denote / ’adj < 0.05 (logrank test). (FIG. 7G) Kaplan-Meier progression-free (left) or overall survival (right) estimates for patients with prostate adenocarcinoma (PRAD), low- grade glioma (LGG), kidney renal clear cell carcinoma (KIRC), and uterine corpus endometrial carcinoma (UCEC) stratified by the expression of SLC16A1-AS1 (BH-adjusted logrank test). Sample sizes for high and low expression groups: LGG: n = 257 (high), n = 259 (low). KIRC: n = 265 (high), n = 265 (low). UCEC: n = 272 (high), n = 263 (low).

[0025] FIG. 8 A - FIG. 8J show Casl3 IncRNA library design and gRNA depletion. (FIG.

[0026] 8 A) Pooled cloning for the lentiviral Cast 3 guide RNA (gRNA) library'. (FIG. 8B) Distribution of long noncoding RNAs (IncRNAs) in the Cast 3 library by their genomic classification (position relative to neighboring protein-coding gene [PCGs], in gray in the legend). (FIG. 8C) Distances between IncRNAs in the Casl3 library and their closest PCGs in the Casl3 library. (FIG. 8D) Generation of monoclonal Casl3 cell lines via lentiviral transduction and clonal isolation. (FIG. 8E) Differential gene expression of HAP 1, HEK293FT, K562, and THP1 cells engineered with Casl3 (and induced with doxycycline for 7 days) compared to respective parental cell lines (n = 8 expression libraries with 4 cell lines with and without Cast 3). Each point represents the average value of one transcript over eight experiments, with significance determined by a log2(fold change)>l and Padj. < 0.05. (FIG.

[0027] 8F- FIG. 8G) Fold-change (FC) of gRNAs targeting IncRNAs (FIG. 8F) and PCGs (FIG. 8G) in two independent replicates for HAPL HEK293FT. K562, MDA-MB-231. and THP1 cells at 14 days after Cast 3 induction (compared to Day 0, prior to Cast 3 induction). Dots are colored by the number of gRNAs with the indicated fold-change. Pearson correlation between biological replicates was calculated for day 7 (rd7) and day 14 (rd!4). (FIG. 8H) Fold-change (Day 14 vs. Day 0) of five individual gRNAs (pink lines) targeting the indicated IncRNAs with the density of non-targeting (NT) gRNAs (gray). The diamond denotes the NYG-LIPP-228.PCT mean fold-change of the five gRNAs. (FIG. 81) Fold-change (Day 14 vs. Day 0) of five individual gRNAs pink) targeting the indicated PCGs with the density of NT gRNAs. The diamond denotes the mean fold-change of the five gRNAs (left). The DepMap essentiality scores for the indicated PCG (right) the diamond indicates the median DepMap score (n = 1,095 cell lines). (FIG. 8J) Consistency among gRNAs as a function of gene (IncRNA and

[0028] PCG) depletion. For each gene, the gene fold-change (Day 14 vs. Day 0) is defined as the mean of the five most depleted gRNAs. Each point in the plot denotes all genes with the same or less gene-fold change than the indicated cutoff (x-axis). The mean fraction of depleted gRNAs (y-axis) is the mean fraction of gRNAs that are more depleted than the 97.5th percentile (dashed line) of the non-targeting (NT) guide RNAs. This mean is taken over all genes with the same or less gene-fold change. The number of genes used for each point is denoted by the color scale. In panels FIG. 8F- FIG. 81, the gray areas indicate the 95% confidence interval using the distribution of NT gRNAs.

[0029] FIG. 9A - FIG. 9G show identification of essential IncRNAs and correlation analysis of Cast 3 screens compared with DepMap scores. (FIG. 9A) Robust rank aggregation (RRA) of IncRNAs in the HEK293FT, K562, MDA-MB-231, and THP1 screens, based on consistent depletion of five gRNAs at 14 days after Casl3 induction. (FIG. 9B) Essential IncRNAs (RRA < 0.05, Day 14) at 7 days after Casl3 induction. (FIG. 9C) Correlation between foldchange (Day 14 vs. Day 0) of 4,390 protein-coding genes (PCGs) from the Cast 3 screens (this study) and DepMap scores from Cas9 perturbation studies 1. Dot colors represent gene counts. (FIG. 9D) Fold-change of PCGs from the Casl 3 screens (this study), grouped by their essentiality status defined by DepMap. (FIG. 9E) Correlation coefficients for expression of PCGs or IncRNAs across five cell lines. (FIG. 9F) Correlation of Casl3 screen fold-change (Day 14 vs Day 0) with the expression of essential IncRNAs (pink) and PCGs (orange) in the five cell lines. (FIG. 9G) Correlation of DepMap scores from Cas9-based knockout screens with the expression of essential PCGs identified in Cast 3 screens in the five cell lines. In panels FIG. 9F and FIG. 9G, gray areas indicate the 95% confidence interval for regression fit to the respective gene fold-changes.

[0030] FIG. 10A - FIG. 10E show expression of essential IncRNAs and protein-coding genes (PCGs) across five cell lines. (FIG. 10A- FIG. 10B) Expression distribution of expressed IncRNAs (FIG. 10A) and expressed protein-coding genes (PCGs) (FIG. 10B) from Casl3 screens across the five cell lines used in this study. (FIG. 10C- FIG. 10D) The proportion of for essential and non-essential genes binned by quartile of expression (QI: lowest expression; Q4: highest expression) for IncRNAs (FIG. 10C) and PCGs (FIG. 10D) (Fisher’s exact test in NYG-LIPP-228.PCT comparison to nonessential IncRNAs (FIG. IOC) and to non-essential PCGs (FIG. 10D)). (FIG. 10E) The proportion of lowest-expressed genes (QI) for essential IncRNAs and essential PCGs in five cell lines (Fisher’s exact test).

[0031] FIG. 11 A - FIG. I ll show distinct and shared essential IncRNAs across five cell lines. (FIG. 11 A) Essential IncRNAs labeled by essentiality (shared, partially shared, and cell-type specific) and cell lines. (FIG. 11B) Overlap of essential IncRNAs between cell lines. (FIG. 11C) Fold-change (Day 14 vs. Day 0) of cell-type specific, partially shared, and shared essential IncRNAs in HEK293FT, K562, MDA-MB-231, and THP1 cells. (FIG. HD) Expression of cell-type specific, partially shared, and shared essential IncRNAs in HEK293FT, K562, MDA-MB-231, and THP1 cells. (FIG. 1 IE) Gating strategy for competitive growth assay in THP1 cells. Cells are gated for singlets and live cells prior to GFP quantification. (FIG. 1 IF) Expression distribution of IncRNAs expressed in HAP1 and targeted in the Casl3 screen. The four indicated IncRNAs are highly expressed (highest expression quartile, Q4) and used in panels FIG. 11G - FIG. 1 II. (FIG. 11G) Fold-change (Day 14 vs. Day 0) of five individual gRNAs (pink lines)' targeting the highly-expressed IncRNAs in HAP1 cells (expression shown in panel F). The diamond denotes the mean foldchange of the five gRNAs. (FIG. 11H) RNA knockdow n for the indicated highly-expressed IncRNAs. RNA knockdown is measured using RT-qPCR relative to cells transduced with a non-targeting gRNA as a control (gRNA 1) and was normalized lo ACTB (twotailed Student’s t test; mean ± SD, n = 3 different gRNAs for each target IncRNA with two biological replicates per gRNA). (FIG. I l l) Representative images of HAP 1 cells transduced with individual gRNAs targeting the indicated highly-expressed IncRNAs on the day of (left) and five days after Casl3 induction (middle). Survival of GFP+ cells transduced with three non-overlapping gRNAs per gene normalized to the non-targeting (NT) gRNAs (right). Each green circle denotes a single gRNA and single transduction replicate. The diamonds denote the mean survival (n = 6 experiments with three gRNAs from two independent transductions). Statistical significance was determined by a Student’s / -test. Scale bar: 200 pm. In panels FIG. 11G and FIG. 1 II, the gray areas indicate the 95% confidence interval computed using the distribution of NT gRNAs. FIG. 12A - FIG. 12L show shared essential IncRNAs in proliferation, cell cycle and apoptosis using Cast 3 knockdown and RNA interference. (FIG. 12 A) Distribution of genomic location (relative to nearby protein-coding genes, in gray in the legend) for non- essential. cell-type specific, partially shared, and shared essential IncRNAs. (FIG. 12B) Fraction of divergent bi-directional transcripts (left) or isolated intergenic transcripts (right) NYG-LIPP-228.PCT for non-essential, cell-type specific, partially shared, and shared essential IncRNAs. Statistical significance was assessed using Fisher’s exact test relative to non-essential IncRNAs. (FIG. 12C) RNA knockdown for the indicated shared essential IncRNAs at 24 hours after Cast 3 induction. RNA knockdown is measured using RT-qPCR relative to cells transduced with a non-targeting (NT) gRNA as a control (gRNA 1) and w as normalized to ACTB (two-tailed Student's t test; mean ± SD, n = 3 different gRNAs for each target IncRNA with two biological replicates per gRNA). (FIG. 12D) Representative images of MDA-MB- 231 (left) and HEK293FT (right) cells transduced with individual gRNAs targeting shared essential IncRNAs five days after Casl3 induction. Scale bar: 200 pm. (FIG. 12E) Schematic of the lentiviral fluorescence ubiquitination cell cycle indicator (FUCCI) to measure changes in cell cycle. (FIG. 12F) Distribution of MDA-MB-231 cells in cell cycle phases G1 (red). S (green) and G2-M (yellow) transduced with NT gRNAs and exposed to dinaciclib (0.5 pM), doxorubicin (1 pM) for 24 hours (n = 54 images per perturbation and timepoint with 9 images per biological replicate and 6 biological replicates per perturbation). P values from the predominantly enriched cell cycle phase (determined for each condition individually) were computed by a Mann- Whitney U test to test for differences from cells transduced with NT gRNAs. (FIG. 12G) Representative images of MDA-MB-231 cells transduced with NT gRNAs and treated with dinaciclib and doxorubicin for 24h. (FIG. 12H) Representative images of perturbed MDA-MB-231 cells at 48 hours after Casl3 induction. In panels FIG. 12G and FIG. 12H, red - cells undergoing G1 phase, green - S phase, yellow - G2-M phase.

[0032] Scale bars, 50 pm. (FIG. 121) Fold-change in cell number (indicated time point vs. 0 hours) of MDA-MB-231 cells perturbed with targeting guides compared to NT gRNAs at 0, 24, 48 and 72 hours after Casl3 induction (left). Normalized cell count (to cells transduced with NT gRNAs) at 72 hours after Cast 3 induction (n = 6 biological replicates per perturbation) (right). (FIG. 12J) Schematic of RNA interference-mediated transcript knockdown using short-interfering RNA (siRNA) pools with 3 siRNAs per IncRNA target followed by proliferation and apoptosis assay s. (FIG. 12K) RNA knockdow n of the indicated shared essential IncRNAs by siRNA pools is measured using RT-qPCR at 48 hours, relative to cells transfected with a control siRNA (targeting luciferase, CTRL) and normalized to ACTB (one- way ANOVA with Tukey’s HSD post-hoc test 7(7. 20)= 88.5, p = 1.2 xl0-13; mean ± SD, n = 2 biological replicates per siRNA pool). (FIG. 12L) Representative images of HEK293FT cells transfected with siRNA pools targeting indicated genes at 24 hours and at 84 hours after transfection (left). Proliferation of cells transfected with siRNA pools normalized to the median of CTRL siRNA transfections (middle). Annexin V+ cells were quantified and NYG-LIPP-228.PCT normalized to the total cell area (right). Each green or pink circle denotes a single transfection replicate. The diamonds denote the mean survival (n = 54 images per siRNA pool with 9 images per biological replicate and 6 biological replicates per siRNA pool). The dashed lines indicate the 95% confidence interval for CTRL siRNA transfections. Scale bar: 200 pm. In panels FIG. 121 and FIG. 12L statistical significance was determined by a

[0033] Student’s / -test.

[0034] FIG. 13A - FIG. 13F from essential IncRNAs act independently of nearest proteincoding genes (PCGs). (FIG. 13A) Minimal correlation between the fold-changes (Day 14 vs. Day 0) of IncRNAs and their closest PCGs in the Casl3 screens for the indicated cell lines. Color scale indicates the number of IncRNA-PCG pairs (n = 264 [HAP1], 278 [HEK293FT], 213 [K562], 250 [MDA-MB-231], 262 [THP1] IncRNA-PCG pairs). (FIG. 13B) Minimal correlation between the transcript expression of IncRNAs and their closest PCGs in the indicated cell lines. Color scale indicates the number of IncRNA-PCG pairs. In panels FIG. 13A and FIG. 13B. gray areas indicate the 95% CI for regression line fit. (FIG. 13C - FIG. 13F) Fold-change (Day 14 vs. Day 0) of IncRNAs and PCGs in the Casl3 screens for

[0035] HEK293FT (FIG. 13C), K562 (FIG. 13D), MDA-MB-231 (FIG. 13E), and THP1 (FIG. 13F) cells. In each cell line, we have separated the pairs by those where only the IncRNA is essential (left), only the PCG is essential (middle), and where both the IncRNA and PCG are essential (right).

[0036] FIG. 14A - FIG. 14J show essentiality of nearby genes using linear distance and three-dimensional looping from Hi-C and H3K27ac HiChlP. (FIG. 14A) Hi-C to identify topologically associating domains (TADs) containing IncRNAs and H3K27ac HiChlP to identify’ distal protein-coding genes (PCGs) that contact with IncRNAs. (FIG. 14B) Number of IncRNAs that are either located in the same TADs as one or more essential PCGs (red) or in a TAD without any essential PCGs (gray) in HAP1 / KBM7 and K562 cells. (FIG. 14C) H3K27ac HiChlP read correlation in HAP1 cells (n = 2 biological replicates). (FIG. 14D) Average genome-wide contact probabilities at different genomic distances between H3K27ac HiChlP anchors in HAP1 cells. Dashed line denotes the slope. (FIG. 14E) Percent of IncRNAs with contact to PCGs in the HAP1 H3K27ac HiChlP. The IncRNAs are categorized by essentiality (shared, partially shared, cell-type specific, or non-essential). The HiChlP contacts for each IncRNA are colored by the type of PCG contacted: no PCG contact detected, only non-essential PCG contact detected (no essential PCG contact present), or essential PCG contact detected. (FIG. 14F) Distance between the transcription start sites of NYG-LIPP-228.PCT essential IncRNAs and their essential PCGs as given by H3K27ac HiChIP contact (y-axis). Only essential IncRNAs contacting essential PCGs from HAP1 H3K27ac HiChIP are shown (xaxis). Dot size indicates the DepMap score of the contacting essential PCGs. The ten most essential PCGs (lowest DepMap scores) are labeled. (FIG. 14G) RNA knockdown for the indicated genes (IncRNAs and their closest PCGs) at 24 hours after Cast 3 induction. RNA knockdown is measured using RT-qPCR relative to cells transduced with a non-targeting (NT) gRNA as a control (gRNA 1) and was normalized to ACTB (two-tailed Student's t test: mean ± SD, n = 3 different gRNAs for each target IncRNA with two biological replicates per gRNA). (FIG. 14H - FIG. 14J) Examples of IncRNAPCG pairs (left) where both genes (FIG. 14H) are essential, only the IncRNA is essential (FIG. 141), or only the PCG is essential (FIG.

[0037] 14J) in HAP1 Casl3 screens. Fold-change (Day 14 vs. Day 0) of five individual gRNAs (lines) targeting the indicated genes is shown with the 95% confidence interval of NT gRNAs (gray1) from the HAP1 Casl3 screens. The diamond denotes the mean fold-change of the five gRNAs (left). The DepMap essentiality7scores for each PCG in the IncRNA-PCG pair (diamond indicates the median DepMap score, n = 1,095 cell lines) (right).

[0038] FIG. 15A - FIG. 15 show additional analysis of single-cell transcriptomics after Casl3 perturbation (CaRPool-seq) in MDA-MB-231 and CaRPool-seq in a second cell line, HAP1. (FIG. 15A) Fold-change (Day 12 vs. Day 0, CaRPool-seq pooled screen) of three individual guide RNA (gRNA) arrays (purple lines) targeting the indicated essential IncRNAs. The shaded region indicates the 95% confidence interval computed using the distribution of non-targeting (NT) gRNA arrays. The diamond denotes the mean fold-change of the three gRNA arrays in MDA-MB-231 cells. (FIG. 15 A) Depletion of the protein-coding genes (PCGs) from the CaRPool-seq pooled screen(pooled screen fold-change, y-axis) and single-cell mRNA expression of the closest PCGs after IncRNA perturbation in MDA-MB- 231 cells (PCG fold-change, x-axis). The PCG fold-change (single-cell mRNA expression) was calculated by comparing the PCG expression in cells with IncRNA perturbations to the PCG expression from cells with NT perturbations. Differentially expressed PCGs (P < 0.05) are labeled in pink. (FIG. 15 A) Single-cell mRNA expression heatmap with the 25 most differentially upregulated genes for each IncRNA perturbation in MDA-MB-231 cells (Padj < 0.05). Most depleted PCGs from a prior genome-scale overexpression screen2 are labeled

[0039] (M = 3 most depleted PCGs per IncRNA). (FIG. 15D) The DepMap essentiality7scores for the indicated downregulated PCGs labeled in FIG. 5C (diamond indicates the median DepMap score, n = 1,095 cell lines). The solid line indicates the DepMap score in MDA-MB-231 cells. (FIG. 15E) Single-cell mRNA expression heatmap with the 25 most differentially NYG-LIPP-228.PCT downregulated lipper and upregulated lower genes for each IncRNA perturbation in HAP1 cells {Padj < 0.05). The genes labeled were chosen based on median DepMap scores for the downregulated PCGs {n = 3 most essential transcripts per IncRNA and median over 1,095 DepMap cell lines) or a prior genome scale overexpression screen for the upregulated PCGs {n = 3 most depleted PCGs per IncRNA). (FIG. 15F) The DepMap essentiality scores for the indicated downregulated PCGs labeled in FIG. 15E (diamond indicates the median DepMap score, n = 1,095 cell lines).

[0040] FIG. 16A - FIG. 16E show transcriptomic profiling after Casl3 perturbation of essential IncRNAs identifies differential expression of hypoxia- and DNA repair-related genes. (FIG. 16A) Normalized enrichment scores (NES) from gene set enrichment analysis (GSEA)3 for 21 perturbed protein-coding genes (PCGs) {left} and 50 perturbed essential IncRNAs {middle} in HAP1 cells. Pathways are from MSigDB Hallmark4 {right}. Pathways categorized as proliferation or apoptosis are labeled (far left column}. (FIG. 16B) Principal component analysis of bulk mRNA-seq data from MDA-MB-231 cells transduced with guide RNAs targeting the indicated essential IncRNAs (n = 2 biological replicates). (FIG. 16C) Correlation of GSEA NES from bulk mRNA-seq and single-cell CaRPool-seq after perturbation of the indicated IncRNAs. (FIG. 16D) Hypoxia gene set (MSigDB hallmark pathways4) NES for essential IncRNA perturbations in MDA-MB-231 cells. The essential IncRNAs are categorized by the fluorescence ubiquitination cell cycle indicator (FUCCI) assay after IncRNA knockdown (also in MDA-MB-231 cells, see FIG. 3D). Using FUCCI, we categorized cells after knockdown of these essential IncRNAs as inducing either G1 or G2-M cell-cycle phase accumulation. (FIG. 16E) Gene expression fold-change of genes involved in the p53 pathway and NES of the specified pathways for the 50 perturbed IncRNAs from CaRPool-seq in MDA-MB-231 cells. Gene expression fold-changes are calculated using IncRNA-perturbed cells versus cells that received a non-targeting gRNA array.

[0041] FIG. 17A - FIG. 17F show expression of essential IncRNAs in human primary tissues across development. (FIG. 17A) Number of donors for each RNA-sequencing library’ categorized by tissue and timepoint from recent developmental atlases of IncRNA and protein-coding gene (PCG) expression (n = 182 tissue samples) 5,6. (FIG. 17B) Median expression of non-essential and essential (cell-type specific, partially shared, shared) IncRNAs in prenatal and postnatal brain, heart, kidney, and liver tissues. The diamond denotes the mean expression of each group. The fold change was derived by comparing the mean expression of essential IncRNA groups compared to non-essential IncRNAs. (FIG. NYG-LIPP-228.PCT

[0042] 17C) Maximum expression (across brain, heart, kidney, and liver) of essential IncRNAs and expressionmatched non-essential IncRNAs in human developmental samples. (FIG. 17D) The fraction of dynamic IncRNAs for essential and expression-matched non-essential IncRNAs (Fishe’s exact test). (FIG. 17E) Expression of dynamic shared (purple), partially shared (pink), cel I -type specific (light pink) essential and non-essential (turquoise) IncRNAs at different developmental timepoints in each tissue. Each dot indicates the median across IncRNAs of the indicated essentiality and tissue. Shaded regions denote 95% confidence interval. (FIG. 17F) Mean expression of indicated dynamic shared essential IncRNAs in brain development (n = 1 - 4 brain tissue samples per timepoint). Error bars denote minimum and maximum values. In panels FIG. 17B and FIG. 17C, box plots indicate the median 25th and 75th percentiles, while whiskers are 1.5 times the interquartile range and statistical significance was determined by a two-sided Mann- Whitney U test. In FIG. 17E and FIG. 17F, the dashed line indicates birth / newbom timepoint.

[0043] FIG. 18A - FIG. 18B show identifying co-expressed protein-coding genes and pathways with essential IncRNAs in human primary tissues across development. (FIG. 18 A) Correlation of shared (purple), partially shared (pink), cell-type specific (light pink) essential and non-essential (turquoise) IncRNAs with proliferation markers PCNA and MKI67 in brain, heart, kidney, and liver development. Box plots indicate the median 25th and 75th percentiles, while whiskers are 1.5 times the interquartile range. (FIG. 18B) Normalized enrichment scores from gene set enrichment analysis (GSEA)3 for co-expressed proteincoding genes of shared essential IncRNAs in brain, heart, kidney, and liver developmental tissues (left). The fraction of shared essential IncRNAs with the indicated pathway (MSigDB Hallmark)4 enriched or depleted is shown on the right.

[0044] FIG. 19A - FIG. 19J show expression of essential IncRNAs in TCGA tumor and normal tissues. (FIG. 19A) The Cancer Genome Atlas (TCGA) primary tumor (upper) and normal tissue (lower) samples with RNA-sequencing across the 29 cancer types analyzed in this study7. Breast invasive carcinoma (BRCA) primary' tumor samples by subtype (right). (FIG. 19B) Median expression of essential and non-essential IncRNAs (n = 1 - 113 samples per tissue type. 22 normal solid tissue types in TCGA). (FIG. 19C) Median fold-change of differentially expressed IncRNAs in primary' tumors compared to matched normal tissues in different cancers (Wald test). (FIG. 19D) Normalized enrichment scores from gene set enrichment analysis (GSEA)3 for MSigDB Hallmark4 pathways using the co-expressed (in primary tumors) protein-coding genes of shared essential IncRNAs (n = 8,878 tumors). (FIG. 19E) Survival analysis (Cox regression coefficients) of PCGs identified in the Cast 3 screens NYG-LIPP-228.PCT as essential across all cell lines. PCGs with negative coefficients (pink) are associated with decreased survival. Conversely, PCGs with positive coefficients (green) are associated with better survival. Significance was given by a BH-adjusted logrank test. (FIG. 19F) Maximum expression of essential IncRNAs and expression-matched non-essential IncRNAs across TCGA primary tumor samples (n = 8,878 tumors, two-sided Mann-Whitney U test). (FIG.

[0045] 19G) The proportion of essential and expression-matched non-essential IncRNAs associated with better or worse overall survival or progression-free survival. (FIG. 19H) Median expression of essential and non-essential IncRNAs in BRCA basal samples (upper, n = 186 tumors) and normal breast tissue samples (lower, n = 113 normal breast tissues). (FIG. 191) The fraction of differentially expressed IncRNAs in breast invasive carcinoma (BRCA) basal samples compared to normal breast tissue samples (right). The pie charts (left) separate differentially expressed IncRNAs: Up (increased expression in BRCA basal) and down (decreased expression in BRCA basal). (FIG. 19J) The proportion of IncRNAs associated with better or worse overall survival or progression free survival in BRCA basal. In panels FIG. 19G, FIG. 191, and FIG. 19J, the statistical significance is determined by Fisher’s exact test relative to non-essential IncRNAs. In panels FIG. 19F and FIG. 19H, box plots indicate the median 25th and 75th percentiles, while whiskers are 1.5 times the interquartile range.

[0046] DETAILED DESCRIPTION Methods and compositions are provided to treat cancer in a subject in need thereof.

[0047] Mammalian genomes host a diverse array of RNA that includes protein-coding and noncoding transcripts. However, the functional roles of most long noncoding RNAs (IncRNAs) remain elusive. Using massively parallel RNA-targeting CRISPR-Casl3 screens, we probed how loss of -6,200 IncRNAs impacts cell fitness across five human cell lines and identified 778 IncRNAs with either context-specific or broad essentiality. We observed a high level of consistency between distinct guide RNAs targeting the same IncRNA in the pooled screens and confirm their essentiality' with individual perturbations. We find that the overwhelming majority’ of essential IncRNAs operate independently of their nearest proteincoding genes. Using transcriptome profiling in single cells, we discover that essential IncRNAs modulate key cellular pathways for proliferation and that their loss can impair cell cycle progression and drive apoptosis. Many essential IncRNAs demonstrate dynamic expression across tissues during development and, using -9,000 primary tumors, we pinpoint those IncRNAs whose expression in tumors correlates with survival, yielding neyv biomarkers and potential therapeutic targets. This transcriptome-yvide survey of functional IncRNAs NYG-LIPP-228.PCT advances our understanding of noncoding transcripts and demonstrates the potential of transcriptome-scale noncoding screens with Casl3.

[0048] A. Description of Terms and Components of the Methods and Compositions Technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs and by reference to published texts, which provide one skilled in the art with a general guide to many of the terms used in the present application. The definitions contained in this specification are provided for clarity in describing the components and compositions herein and are not intended to limit the claimed invention.

[0049] “CRISPR'’ or Clustered regularly interspaced short palindromic repeats genome editing techniques are useful for many types of genetic research, as well as treatment of diseases or disease conditions caused by malfunctioning or dysfunctioning genes. CRISPR is a gene editing system. In general, engineered CRISPR systems contain two components: a guide RNA (gRNA or sgRNA) and a CRISPR-associated endonuclease (Cas protein). The gRNA is a short synthetic RNA composed of a scaffold sequence necessary for Cas-binding and a user-defined ~20 nucleotide spacer that defines the genomic target to be modified. When the gRNA and the Cas protein are expressed in the cell, the genomic target sequence to which they bind can be modified by an insertion or deletion or permanently disrupted. Additional information on CRISPR is provided in more detail in the Addgene CRISPR online guide (www.addgene.org / guides / crispr / ) among multiple other known publications. See, also, U.S. Pat. Nos. 8,999,641, 8,993,233, 8,945,839, 8,932,814, 8,906,616, 8,895,308, 8,889,418, 8,889,356, 8,871,445, 8,865.406, 8,795,965, 8,771,945 and 8.697,359; US Patent

[0050] Publications US 2014-0310830, US 2014-0287938 Al, US 2014-0273234 Al, US2014- 0273232 Al, US 2014-0273231, US 2014-0256046 Al, US 2014-0248702 Al, US 2014-

[0051] 0242700 Al, US 2014-0242699 Al, US 2014-0242664 Al, US 2014-0234972 Al, US 2014- 0227787 Al, US 2014-0189896 Al, US 2014-0186958, US 2014-0186919 Al, US 2014- 0186843 Al. US 2014-0179770 Al and US 2014-0179006 Al, US 2014-0170753: European Patents EP 2 784 162 Bl and EP 2 771 468 Bl; European Patent Applications EP 2 771 468 (EP13818570.7), EP 2 764 103 (EP 13824232.6), and EP 2 784 162 (EP14170383.5); and

[0052] PCT Patent Publications PCT Patent Publications WO 2014 / 093661, WO 2014 / 093694, WO 2014 / 093595, WO 2014 / 093718, WO 2014 / 093709, WO 2014 / 093622, WO 2014 / 093635. WO 2014 / 093655. WO 2014 / 093712, WO2014 / 093701, WO2014 / 018423. WO 2014 / 204723, WO 2014 / 204724, WO 2014 / 204725, WO 2014 / 204726, WO 2014 / 204727, NYG-LIPP-228.PCT

[0053] WO 2014 / 204728, WO 2014 / 204729, and WO2016 / 028682. These documents are all incorporated by reference to provide additional general information on CRISPR-Cas systems, components thereof, and delivery of such components, including methods, materials, delivery vehicles, vectors, particles, AAV, and making and using thereof, including as to amounts and formulations, some of which are useful in the present method and compositions or kits.

[0054] The term “Gene” is used in accordance with its customary meaning in the art. A gene is a sequence of nucleotides forming part of a chromosome, the order of which determines the order of monomers in a polypeptide or nucleic acid molecule which a cell (or virus) may synthesize. The term “Protein Coding Gene” (PCG) refers to a gene that codes for a protein with any variety of function. The term Gene also includes coding sequences for long noncoding RNA (IncRNA) which are non-protein coding transcripts of more than 200 nucleotides. The term IncRNAs encompasses RNA polymerase I (Pol I), Pol II and Pol III transcribed RNAs, and RNAs from processed introns.

[0055] The term “Gene Product” refers to a sequence encoded by an identified gene having known function and / or activity. The Gene Product includes without limitation, proteins, polypeptides, RNA, fragments, isoforms, homologous proteins, oligopeptides, homodimers, heterodimers, protein variants, modified proteins, derivatives, analogs, and fusion proteins, among others.

[0056] The term “Expression System” or “Delivery System” as used herein refers to the components and techniques for delivery of an exogenous Gene to, or expression of an exogenous Gene in, a mammalian cell. These systems can include in vitro ex vivo or in vivo delivery. In one embodiment, a viral delivery system, which can also be used for in vivo deliver^' involves inserting the Gene into a lentiviral transfer vector. Packaging and envelope plasmids provide the necessary’ components to make lentiviral particles. This well-known expression system can also provide stable tunable expression of the Gene, including in vivo expression. In another frequently used viral expression system, the Gene can be inserted in an AAV transfer vector and used to generate AAV particles. Other non-viral delivery systems include plasmid expression vectors using a promoter that is constitutive (such as CMV, EF l alpha, CBh) or inducible (such as Tet-ON); or using a U6 promoter for gRNA can be used to transiently or stably express the Gene in a mammalian cell. In yet another embodiment, RNA delivery of a Gene may be accomplished by in vitro transcription, which are then delivered to target cells through microinjection or electroporation. In another embodiment, lipid nanoparticles (LNPs) are preferred, which predominantly target the liver. NYG-LIPP-228.PCT

[0057] Messenger RNA (mRNA) encoding a Gene is encapsulated into LNPs to shuttle these sequences to the liver.

[0058] ■‘Lipid nanoparticle (LNPs)” generally refer to particles comprised of cholesterol (aids in stability and promotes membrane fusion), a phospholipid (which provides structure to the LNP bilayer and also may aid in endosomal escape), a polyethylene glycol (PEG) derivative (which reduces LNP aggregation and “shields” the LNP from non-specific endocytosis by immune cells), and an ionizable lipid (complexes negatively charged RNA and enhances endosomal escape), which form the LNP -forming composition. See, e g., Fenton et al, Bioinspired Alkenyl Amino Alcohol Ionizable Lipid Materials for Highly Potent in vivo mRNA Delivery, Adv Mater. 2016 Apr 20; 28(15): 2939-2943, which is incorporated herein by reference.

[0059] The terms “administering” and “administration” refer to the process by which a therapeutically effective amount of a compound, agent or composition contemplated herein is delivered to a cell or subject for research or treatment purposes. Multiple techniques of administering a compound exist in the art including, but not limited to, intravenous, oral, aerosol, parenteral, ophthalmic, pulmonary and topical administration. Guidance for preparing pharmaceutical compositions may be found, for example, in Remington: The Science and Practice of Pharmacy, (20th ed.) ed. A. R. Gennaro A. R., 2000, Lippincott Williams & Wilkins. Compositions are administered in accordance with good medical practices taking into account the subject's clinical condition, the site and method of administration, dosage, patient age, sex, body weight, and other factors known to physicians.

[0060] “Decrease”, “reduce”, “inhibit”, “down -regulate" are all used herein generally to refer to a decrease by a statistically significant amount. The decrease can be, for example, a decrease by at least 10% as compared to a reference level, for example a decrease by at least about 20%, or at least about 30%, or at least about 40%, or at least about 50%, or at least about 60%, or at least about 70%, or at least about 80%, or at least about 90% or up to and including a 100% decrease (e.g. absent level or non-detectable level as compared to a reference level), or any decrease between 10-100% as compared to a reference level. The decrease or inhibition may be a decrease in activity, interaction, expression, function, response, condition, disease, or other biological parameter. This can include but is not limited to the complete ablation of the activity, interaction, expression, function, response, condition or disease.

[0061] “Activate”, “stimulate”, “over-express” “up-regulate” are all used herein generally to refer to an increase by a statistically significant amount. The increase can be, for example, an NYG-LIPP-228.PCT increase by at least 10% as compared to a reference level, for example an increase by at least about 20%. or at least about 30%, or at least about 40%, or at least about 50%, or at least about 60%, or at least about 70%, or at least about 80%, or at least about 90% or up to and including a 100% increase (e.g. absent level or non-detectable level as compared to a reference level), or any increase between 10-100% as compared to a reference level. The increase or activation may be an increase in activity, interaction, expression, function, response, condition, disease, or other biological parameter.

[0062] An '‘effective amount” refers to the amount of an agent that is sufficient to effect beneficial or desired results. The therapeutically effective amount may vary depending upon one or more of the subject and disease condition being treated, the weight and age of the subject, the severity of the disease condition, the manner of administration and the like, which can readily be determined by one of ordinary skill in the art. The term also applies to a dose that may vary depending on one or more of: the particular agent chosen, the dosing regimen to be followed, whether it is administered in combination with other compounds, timing of administration, the tissue to be imaged, and the physical delivery system in which it is carried. As used herein, the effective amount of a composition containing an Inhibitor, and / or an Activator and / or Combined composition, as disclosed herein, is that effective to increase the efficiency of a selected precise gene repair of a target gene. Such results include, without limitation, the treatment of a disease or condition disclosed herein as determined by any means suitable in the art.

[0063] “Pharmaceutically acceptable” refers to those compounds, agents, materials, compositions, and / or dosage forms which are, within the scope of sound medical judgment, suitable for use in contact with the tissues of human beings and animals without excessive toxicity, irritation, allergic response, or other problems or complications commensurate with a reasonable benefit / risk ratio.

[0064] “Pharmaceutically acceptable carrier” includes any of the standard pharmaceutical carriers, such as a phosphate buffered saline solution, water, emulsions such as an oil / w atcr or water / oil emulsion, and various types of wetting agents. The term also includes any of the agents approved by a regulatory agency such as the FDA or listed in the US Pharmacopeia for use in animals, including humans.

[0065] The terms “subject”, “individual” or “patient” refer, interchangeably, to a warmblooded animal such as a mammal. In particular, the term refers to a human. A subject, individual or patient may be afflicted with, or suspected of having, or being pre-disposed to a genetically -mediated disease as described herein. The term also includes animals bred for NYG-LIPP-228.PCT food, as pets, or for study including horses, cows, sheep, poultry, fish, pigs, cats, dogs, and zoo animals, goats, apes (e.g., gorilla or chimpanzee), and rodents such as rats and mice.

[0066] The term '‘cancer” refers to any number of conditions in which abnormal cells divide without control. Embodiments of various cancers include but are not limited to carcinomas, melanomas, lymphomas, sarcomas, blastomas, leukemias, myelomas, osteosarcomas and neural tumors. In certain embodiments, the cancer is breast, ovarian, pancreatic or prostate cancer. In certain embodiments, the cancer is a blood cancer, such as leukemia, lymphoma, or myeloma. Certain cancers are referred to herein using the terminology used in the Cancer Genome Atlas (TCGA). BRCA refers to Breast Invasive Carcinoma, or breast cancer which originates in breast tissues, ty pically in the lining of the ducts or lobules. LUAD refers to Lung Adenocarcinoma, a type of lung cancer that starts in glandular cells (often in the outer parts of the lung). BLCA refers to Bladder Urothelial Carcinoma a common type of bladder cancer, originating from the urothelial cells lining the bladder. KIRP refers to Kidney Renal Papillary Cell Carcinoma, a type of kidney cancer originating in the renal tubules, characterized by papillary (finger-like) structures. UCEC refers to Uterine Corpus Endometrial Carcinoma, a type of cancer that arises from the lining of the uterus (endometrium). KICH refers to Kidney Chromophobe Renal Cell Carcinoma, a type of kidney cancer, which starts in cells called chromophobe cells of the kidney. KIRC refers to Kidney Renal Clear Cell Carcinoma, the most common type of kidney cancer, originating in the kidney’s renal cortex and characterized by clear cells. READ refers to Rectum Adenocarcinoma, a cancer that starts in the rectum, typically originating from glandular epithelial cells. THCA refers to Thyroid Carcinoma, a type of cancer that begins in the thyroid gland, often papillary or follicular thyroid carcinoma. PRAD refers to Prostate Adenocarcinoma, a common type of prostate cancer originating in the glandular tissue of the prostate. CHOL refers to Cholangiocarcinoma, a cancer that forms in the bile ducts, inside or outside the liver. HNSC refers to Head and Neck Squamous Cell Carcinoma, a cancer that arises from the squamous cells lining the head and neck region (including the mouth, throat, and larynx). LUSC refers to Lung Squamous Cell Carcinoma, a subtype of lung cancer that begins in the squamous cells, which line the airways of the lungs. COAD refers to Colon Adenocarcinoma, a type of colorectal cancer that originates in the colon’s glandular cells. SARC refers to Sarcoma, a broad category of cancers arising from connective tissues, such as bone, muscle, fat, or cartilage. LIHC refers to Liver Hepatocellular Carcinoma, the most common form of liver cancer, originating in the hepatocytes. Gliomas are neuroepithelial tumors originating from the supporting glial cells of the central nervous system (CNS). Low- NYG-LIPP-228.PCT grade gliomas (LGGs) are a diverse group of primary brain tumors that often arise in young, otherwise healthy patients and generally have an indolent course with longer-term survival in comparison with high-grade gliomas. Kidney renal clear cell carcinoma (KIRC) is the most common type of kidney cancer and is associated with a poor prognosis and high mortality rate.

[0067] Cell lines discussed herein. HAP1 is a haploid human cell line derived from a chronic myelogenous leukemia (CML) patient. These cells are unique because they are haploid (i.e.. they have only one set of chromosomes), making them useful for gene knockout studies, genetic screening, and CRISPR-Cas9-based experiments. HEK293FT are derived from human embry onic kidney (HEK) cells, specifically a variant of the HEK293 line that has been transfected to express the SV40 large T antigen. These cells grow quickly and transfect efficiently, making them popular for producing recombinant proteins and viral vectors. K562 is a human immortalized myelogenous leukemia cell line, derived from a female CML patient. K562 cells are multipotent and can differentiate into various blood cell types under specific conditions. They are erythroid progenitor cells and are capable of producing hemoglobin. MDA-MB-231 is A human breast cancer cell line derived from a pleural effusion of a patient with metastatic adenocarcinoma. These cells are highly invasive and represent the triple-negative subty pe of breast cancer (i.e., negative for estrogen receptor, progesterone receptor, and HER2). THP-1 is a human monocy tic leukemia cell line derived from a 1 -year-old male with acute monocytic leukemia. THP-1 cells resemble monocytes and can differentiate into macrophage-like cells when treated with agents like PMA (phorbol 12- myristate 13-acetate).

[0068] As used herein, an “Antisense Oligonucleotide” (ASO) refers to a chemically synthesized, single-stranded oligomeric compound, typically comprising deoxyribonucleotides (DNA), ribonucleotides (RNA), or analogs thereof, that is designed to hybridize in a sequence-specific manner to a target nucleic acid sequence, such as mRNA, pre-mRNA, or other RNA molecules, through complementary' base pairing. The binding of the ASO to the target sequence interferes with the expression of a gene by blocking translation, splicing, or causing degradation of the target RNA, thereby modulating gene expression. ASOs are typically between 10 and 30 nucleotides, though other ranges may be specified. ASOs often include chemical modifications (e.g., phosphorothioate backbones, 2'- O-methyl modifications) to enhance stability, binding affinity, or cellular uptake.

[0069] As used herein. “Small Interfering RNA” (siRNA) refers to a double-stranded RNA molecule, typically 19 to 30 nucleotides in length, consisting of a guide strand and a NYG-LIPP-228.PCT passenger strand. The guide strand is complementary to a specific target RNA (RNA) sequence, which it directs for degradation or translational inhibition via the RNA-induced silencing complex (RISC). Upon binding to the target RNA, the siRNA induces gene silencing through sequence-specific cleavage or repression of translation, thereby downregulating the expression of the corresponding gene.

[0070] As used herein and in the appended claims, the singular forms “a”, “an”, and “the” include plural reference unless the context clearly dictates otherwise. As such, the terms “a” (or “an”), “one or more,” and “at least one” are used interchangeably herein.

[0071] As used herein, the words “comprising” (and any form of comprising, such as “comprise” and “comprises”), “having” (and any form of having, such as “have” and “has”), “including” (and any form of including, such as “includes” and “include”) or “containing” (and any form of containing, such as “contains” and “contain”) are inclusive or open-ended and do not exclude additional, unrecited elements or method steps.

[0072] The words “consist”, “consisting”, and its variants, are to be interpreted exclusively, rather than inclusively, i.e., to exclude components or steps not specifically recited.

[0073] As used herein, the term “about” means a variability of plus or minus 10 % from the reference given, unless otherwise specified.

[0074] B. Long Non-Coding RNA (IncRNA) As described herein, IncRNA were identified that were determined to be essential for viability in one or more cell types. The function of these IncRNA was previously unknown. Here, we also show that upregulation of certain IncRNA either had a protective or detrimental effect in survival one or more cancer types.

[0075] In one embodiment, a method for treating cancer in a subject is provided. In certain embodiments, the method includes reducing the level of an IncRNA in the subject. In certain embodiments, the IncRNA is selected from those of Table 1. In Table 1, a 1 indicates essentiality of that IncRNA in the identified cell type, and 0 indicates non-essentiality. NYG-LIPP-228.PCT NYG-LIPP-228.PCT NYG-LIPP-228.PCT NYG-LIPP-228.PCT NYG-LIPP-228.PCT

[0076] In certain embodiments, the IncRNA is selected from Table 2. NYG-LIPP-228.PCT

[0077] As noted above, in certain cases it was determined that upregulation of an IncRNA had a positive effect on overall survival of a subject having cancer. Thus, in certain embodiments, a method of treating cancer is provided that includes increasing the level of an IncRNA in a subject. In certain embodiments, the IncRNA is selected from Table 3. NYG-LIPP-228.PCT

[0078] C. Partner Coding Genes (PCG)

[0079] In one embodiment, a method of treating cancer is provided that includes reducing the level of both an IncRNA and its PCG. In certain embodiments, the IncRNA is selected from Table 1, 2 or 3, and the PCG is selected from Table 4. In Table 4, a 1 indicates essentiality of

[0080] 5 that PCG in the identified cell type, and 0 indicates non-essentiality. NYG-LIPP-228.PCT NYG-LIPP-228.PCT NYG-LIPP-228.PCT NYG-LIPP-228.PCT NYG-LIPP-228.PCT NYG-LIPP-228.PCT NYG-LIPP-228.PCT

[0081] D. Compositions and Kits

[0082] Provided herein are compositions and kits useful for modulating the IncRNA and PCG as described herein. In certain embodiments, the composition includes an antisense

[0083] 5 oligonucleotide (ASO) capable of binding a selected target IncRNA or PCG gene product. In certain embodiments, the composition includes two, 3, 4, 5. 6, 7, 8. 9, 10 or more ASOs.

[0084] An antisense oligonucleotide is a single stranded ribonucleic acid or deoxyribonucleic acid complementary to a chosen sequence. Antisense oligonucleotides may target a specific, complementary, coding or non-coding, nucleic acid. Depending on the antisense

[0085] 10 oligonucleotide used, the binding of the oligonucleotide to its target nucleic acid sequence may or may not activate RNAse H. In some embodiments, the antisense oligonucleotide activates RNAse H, which degrades the target nucleic acid. Methods of making antisense oligonucleotides are known in the art, and ASOs are available commercially.

[0086] The antisense oligonucleotide of the invention may be a deoxyribonucleotide

[0087] 15 oligonucleotide or a ribonucleotide oligonucleotide. The antisense oligonucleotide may be any length provided it binds selectively to the intended gene product. In general, the antisense oligonucleotide may be from 8, 10 or 12 nucleotides in length up to 20, 30, or 50 nucleotides in length. The term "antisense oligonucleotide” includes the physiologically and pharmaceutically acceptable salts thereof: i.e.. salts that retain the desired biological activity

[0088] 20 of the parent compound and do not impart undesired toxicological effects thereto. Examples of such salts are (a) salts formed with cations such as sodium, potassium, NH4+, magnesium, NYG-LIPP-228.PCT calcium, poly amines such as spermine and spermidine, etc.; (b) acid addition salts formed with inorganic acids, for example hydrochloric acid, hydrobromic acid, sulfuric acid, phosphoric acid, nitric acid and the like; (c) salts formed with organic acids such as, for example, acetic acid, oxalic acid, tartaric acid, succinic acid, maleic acid, fumaric acid, gluconic acid, citric acid, malic acid, ascorbic acid, benzoic acid, tannic acid, palmitic acid, alginic acid, poly glutamic acid, naphthalenesulfonic acid, methanesulfonic acid, p- toluenesulfonic acid, naphthalenedisulfonic acid, polygalacturonic acid, and the like; and (d) salts formed from elemental anions such as chlorine, bromine, and iodine.

[0089] ASOs may be designed for the IncRNA and PCG described herein using techniques known in the art and the sequences of the target gene products. See, e.g., Aartsma-Rus A, et al. Guidelines for antisense oligonucleotide design and insight into splice-modulating mechanisms. Mol Ther. 2009 Mar;17(3):548-53. doi: 10.1038 / mt.2008.205. Epub 2008 Sep 23. PMID: 18813282; PMCID: PMC2835096.

[0090] In certain embodiments, the composition includes an siRNA capable of binding a selected target IncRNA or PCG gene product. In certain embodiments, the composition includes two, 3, 4, 5, 6, 7, 8, 9, 10 or more siRNA.

[0091] The siRNA can be obtained using a number of techniques known to those of skill in the art. For example, the siRNA can be chemically synthesized or recombinantly produced using methods known in the art, such as the Drosophila in vitro system described in U.S. published application 2002 / 0086356 of Tuschl et al., the entire disclosure of which is herein incorporated by reference.

[0092] Preferably, the siRNA are chemically synthesized using appropriately protected ribonucleoside phosphoramidites and a conventional DNA / RNA synthesizer. The siRNA can be synthesized as two separate, complementary RNA molecules, or as a single RNA molecule with two complementary regions.

[0093] Alternatively, siRNA can also be expressed from recombinant circular or linear DNA plasmids using any suitable promoter. Suitable promoters for expressing siRNA from a plasmid include, for example, the U6 or Hl RNA pol III promoter sequences and the cytomegalovirus promoter. Selection of other suitable promoters is within the skill in the art. The recombinant plasmids can also comprise inducible or regulatable promoters for expression of the siRNA in a particular tissue or in a particular intracellular environment.

[0094] The siRNA expressed from recombinant plasmids can either be isolated from cultured cell expression systems by standard techniques, or can be expressed intracellularly.

[0095] Exemplary siRNA are set forth in Table 5. NYG-LIPP-228.PCT NYG-LIPP-228.PCT NYG-LIPP-228.PCT NYG-LIPP-228.PCT NYG-LIPP-228.PCT NYG-LIPP-228.PCT NYG-LIPP-228.PCT NYG-LIPP-228.PCT

[0096] The siRNA or ASO may be modified to provide enhanced delivery to the subject.

[0097] Such modifications include, without limitation, PS-ASOs, ribose sugar modifications, nucleobase modifications, briged nucleic acids, and alternative backbones. See, e.g., Gagliardi M, Ashizawa AT. The Challenges and Strategies of Antisense Oligonucleotide Drug Delivery. Biomedicines. 2021 Apr 16;9(4):433. doi: 10.3390 / biomedicines9040433. PMID: 33923688; PMCID: PMC8072990. NYG-LIPP-228.PCT

[0098] In another aspect, provided is a composition that includes a nucleic acid encoding an IncRNA of Table 3. Such nucleic acid may delivered via any suitable method including, gene gun, electroporation, viral vector, exosome, or liposome.

[0099] In certain embodiments, provided herein are pharmaceutical compositions that include a pharmaceutically acceptable carrier and an siRNA or ASO as described. The siRNA or ASO can be administered to the subject by any means suitable for delivering the siRNA to cancer cells. For example, the siRNA can be administered by gene gun. electroporation, or by other suitable parenteral or enteral administration routes. Naked nucleic acids, spherical nucleic acids, exosomes, and liposomes may be used for delivery7. Suitable enteral administration routes include oral, rectal, or intranasal delivery.

[0100] Suitable parenteral administration routes include intravascular administration (e.g. intravenous bolus injection, intravenous infusion, intra-arterial bolus injection, intra-arterial infusion and catheter instillation into the vasculature); peri- and intra-tissue administration (e.g., peri-tumoral and intra-tumoral injection, intra-retinal injection or subretinal injection); subcutaneous injection or deposition including subcutaneous infusion (such as by osmotic pumps); direct (e.g., topical), and inhalation.

[0101] The siRNA or ASO can be administered in a single dose or in multiple doses. Where the administration of the siRNA or ASO is by infusion, the infusion can be a single sustained dose or can be delivered by multiple infusions. One skilled in the art can also readily determine an appropriate dosage regimen for administering the siRNA or ASO to a given subject. For example, the siRNA or ASO can be administered to the subject once, such as by a single injection or deposition at or near a tumor. Alternatively, the siRNA or ASO can be administered to a subject multiple times daily or weekly. For example, the siRNA or ASO can be administered to a subject once weekly for a period of from about three to about twenty-eight weeks, more preferably from about seven to about ten weeks. In a preferred dosage regimen, the siRNA or ASO is injected at or near the site of neovascularization (e.g., intravitreally) once a week for seven weeks. It is understood that periodic administrations of the siRNA or ASO for an indefinite length of time may be necessary. Where a dosage regimen comprises multiple administrations or the administration of two or more siRNA or ASO, each of which comprise a different target sequence, it is understood that the effective amount of siRNA or ASO administered to the subject can comprise the total amount of siRNA administered over the entire dosage regimen. NYG-LIPP-228.PCT

[0102] The siRNA or ASO are preferably formulated as pharmaceutical compositions prior to administering to a subject, according to techniques known in the art. Pharmaceutical compositions of the present invention are characterized as being at least sterile and pyrogen- free. As used herein, “pharmaceutical formulations” include formulations for human and veterinary use. Methods for preparing pharmaceutical compositions are within the skill in the art, for example as described in Remington's Pharmaceutical Science, 17th ed.. Mack Publishing Company, Easton, Pa. (1985), the entire disclosure of which is herein incorporated by reference.

[0103] The present pharmaceutical formulations comprise an siRNA or ASO (e.g., 0.1 to 90% by weight), or a physiologically acceptable salt thereof, mixed with a physiologically acceptable carrier medium. Preferred physiologically acceptable carrier media are water, buffered water, normal saline, 0.4% saline, 0.3% glycine, hyaluronic acid and the like. Pharmaceutical compositions can also comprise conventional pharmaceutical excipients and / or additives. Suitable pharmaceutical excipients include stabilizers, antioxidants, osmolality adjusting agents, buffers, and pH adjusting agents. Suitable additives include physiologically biocompatible buffers (e.g., tromethamine hydrochloride), additions of chelants (such as, for example, DTPA or DTPA-bisamide) or calcium chelate complexes (as for example calcium DTPA, CaNaDTPA-bisamide), or, optionally, additions of calcium or sodium salts (for example, calcium chloride, calcium ascorbate, calcium gluconate or calcium lactate). Pharmaceutical compositions can be packaged for use in liquid form, or can be lyophilized.

[0104] The siRNA or ASO may be administered in combination with a second pharmaceutical agent, e.g., a chemotherapeutic agent. E. Therapeutic Methods

[0105] The compositions described above can be used to treat cancer in a subject in need thereof. The methods include administering an agent that reduces the level of an IncRNA, PCG. or both, to the subject. Further provided is a method of treatment of cancer, comprising contacting a cell of with an ASO or siRNA that targets an IncRNA or PCG as described herein, a vector, or a (pharmaceutical) composition as described herein.

[0106] G. Examples

[0107] The examples below encompass any and all variations that become evident as a result of the teaching provided herein. NYG-LIPP-228.PCT

[0108] EXAMPLE 1 - ORIGINAL CRISPR INHIBITION AND ACTIVATION SCREENS Transcriptome-scale Cast 3 screens for essential IncRNAs

[0109] Using the RNA-targeting CRISPR-Casl3 nuclease26, we systematically identified essential IncRNAs and, via targeting of nearby protein-coding genes (PCGs), also discerned whether these PCGs were similarly essential. Using an atlas of IncRNA expression across 7 organs and 26 developmental stages (4 weeks post-conception to old age)19, we designed the Cast 3 11 bran ■ to target all IncRNAs expressed at 5 RPKM or more in at least one organ or donor across all stages (n = 297 human tissue samples). We also targeted an additional -2,500 IncRNAs from a recent pooled genetic screen using a DNA-targeting CRISPR and those present in a manually curated database of IncRNAs (IncRNAdb)20,27In total, we designed a library of -75,000 guide RNAs (gRNAs) to target 6,199 IncRNAs and 4,390 PCGs with -8 gRNAs each (FIG. 10, Figure 8AC, Table SI A-B).

[0110] We engineered five human cell lines, HAP1, HEK293FT, K562, MDA-MB-231 and THP1, to express the nuclear-localized 7( / xCasl3d effector under doxycycline-inducible control (Figure 8D). RNA sequencing of the parental and C as 13 -engineered cell lines indicated no significant differences in gene expression post-induction (Figure 8E, Table S1C- D). Next, we transduced each cell line with the lentiviral gRNA library' at a low multiplicity - of-infection to ensure each cell received only a single perturbation and induced Cast 3 expression via doxycycline addition. We harvested genomic DNA from these cells at 0, 7, and 14 days post-Casl 3 induction and computed changes in gRNA abundance via amplicon sequencing (n = 20 transcriptome-scale datasets with two biological replicates for each cell line / timepoint) (Figure 1A). We found strong agreement betw een the two independent transduction replicates for each cell line (Figure IB, Figure 8FG, Table S2A-E). We computed changes in gRNA abundance between an early time point (the day of

[0111] Casl3 induction, which we term Day 0) and 14 days after Casl3 induction. We detected consistent depletion among gRNAs targeting IncRNAs with established roles in cell survival, proliferation and differentiation, such as MA L ATI 28 wd NEATl 29, as well as essential IncRNAs not described in prior literature (Figure 1C, Figure 8H). In all cell lines, we also observed consistent depletion of gRNAs that target known essential protein-coding genes, as identified by Cas9-based knockout screens performed in more than 1,000 cancer cell lines by the DepMap Consortium (Figure 81). Given that we designed multiple gRNAs to target each gene (IncRNA or protein-coding), we assessed the depletion of distinct gRNAs targeting the same gene (Figure 8J) and computed a gene-level ranking using robust-rank aggregation NYG-LIPP-228.PCT

[0112] (RRA) (Figure ID, Figure 9A, Table S2F-J). Many of the highly-depleted (essential) IncRNAs were already identified as essential at day 7 after Cast 3 induction (Figure 9B).

[0113] As a quality control measure, we compared the change in abundance of Casl3 gRNAs designed to target 4,390 PCGs targeted in the library with Cas9-based knockout screens from the DepMap Consortium30. The enrichment / depletion of these gRNAs demonstrated a high correlation with DepMap datasets utilizing DNA-targeting perturbations (0.59 < r < 0.72, Figure 9C), suggesting that Casl3 and Cas9 can both identify essential PCGs and do so in a comparable manner. The PCGs commonly classified as essential by DepMap were also depleted in the RNA-targeting CRISPR screens (Figure 9D). For each cell line, we performed stranded total RNA-sequencing after ribosomal RNA depletion to quantify the expression of IncRNAs and PCGs (Table S 1C-D). Approximately 10% of the expressed IncRNAs exhibited significant depletion in each cell line (Figure IE), a proportion similar to essential PCGs in the human genome found using genome-scale Cas9 knockout screens31 34. As others have found13-35, the correlation of transcript expression among IncRNAs is generally lower than that among PCGs across cell lines (Figure 9E). In each cell line, we also found a lower correlation between expression and essentiality — using both our Casl3 screens (for IncRNAs and PCGs) and Cas9-based DepMap screens (for PCGs) (Figure 9F-G). After stratifying IncRNAs and PCGs into expression quartiles, we compared the enrichment of essential genes in each quartile (Figure 10A-D). For both IncRNAs and PCGs. we found that highly expressed genes (Q4) are more likely to be essential compared to lowly-expressed (QI) genes, although the majority of highly-expressed genes (-86% IncRNAs, -60% PCGs) are not essential. However, we also observed a key difference between IncRNAs and PCGs: We identified a higher fraction of essential IncRNAs in the lower expression quartiles (QI) compared to PCGs (Figure 10E). This suggests that IncRNAs not only exhibit lower expression correlation between cell lines but also function effectively at lower expression levels.

[0114] Distinct and common essential IncRNAs for proliferation

[0115] Using our large-scale pooled transcriptome screens, we compared IncRNAs across the five cell lines to identify shared and cell-specific essential IncRNAs. We identified a total of 778 IncRNAs that were essential in one or more cell lines. As a group, we found that essential IncRNAs were enriched for divergent bi-directional transcripts and tended to have fewer isolated intergenic transcripts (Figure 2A). Additionally, there were fewer human- NYG-LIPP-228.PCT specific transcripts than expected and a larger fraction of older (180 Mya) transcripts, indicating the essential IncRNAs are more likely to be evolutionarily conserved (Figure 2A).

[0116] Among the 778 essential IncRNAs, we found that 61% (477 essential IncRNAs) were cell-type specific (essential in only one cell line), 33% (255) were partially shared (essential in two to four cell lines), and 6% (46) were shared essentials (essential in all five cell lines) (Figure 11 A-B). Compared to PCGs, essential IncRNAs were more cell-type-specific with less overlap between cell lines (Fisher's exact test P < 0.05. Figures 2B-C). The shared essential IncRNAs — those essential in all 5 cell lines from our study — exhibited higher depletion in the pooled screens than partially shared or cell-type-specific essential IncRNAs (Figure 2D, Figure 11C). They also had higher transcript abundance compared to cell-type specific essential IncRNAs, which was consistently observed across multiple cell lines (Figure 2E, Figure 1 ID).

[0117] In general, cell-type specific — but essential — IncRNAs have a lower correlation in expression across cell lines compared to shared essential IncRNAs (Figure 2F). For instance, the IncRNAs XLOC 044949 showed variable expression levels across cell lines and were specifically depleted in HAP1 and THP1, respectively (Figure 2G). To rigorously test cell-type specific functions, we designed an orthogonal assay using individual (arrayed) knockdown and time-lapse imaging (Figure 2H, Table S3A). We targeted each IncRNA with three independent gRNAs. Our results indicate clear cell-type specific depletion: XLOC 013548 showed essentiality exclusively in HAP1 cells and XLOC 044949 solely in THP1 cells. Importantly, XLOC 026046 — a non-essential IncRNA — did not show any depletion in HAP1 or THP1 cells (Figure 21, Figure 1 IE). This is in agreement with a previous (CRISPRi-based) study of essential IncRNAs that found similar cell typedependent effects 20. To directly demonstrate that IncRNA expression alone does not determine essentiality, we targeted two essential (XLOC 037681 and XLOC _005888~) and two non- essential IncRNAs (XLOC 044281 and XLOC 000008) with similarly high expression using three Cast 3 gRNAs in HAP1 cells (Figure 1 IF, Table S3). After confirming transcript knockdown, we found that only cells with perturbations targeting essential IncRNAs deplete after four days in a competitive growth assay compared to cells transduced with control (nontargeting) gRNAs (Figure 11G-I). This reinforces our prior findings that differences in growth and gRNA depletion are not due to higher levels of off-target, trans cleavage resulting from targeting of highly-expressed IncRNAs but instead reflect differential essentiality36. NYG-LIPP-228.PCT

[0118] Shared essential IncRNAs are required for cell proliferation

[0119] Next, we proceeded to examine the 46 shared essential IncRNAs in greater detail (Figure 3 A). This core set includes MALAT1 , an abundant IncRNA that has previously been shown to regulate cell motility and cancer metastasis37 40. and MIR17HG, a microRNA host gene IncRNA that promotes cancer progression41 42In addition, the shared essential IncRNAs include several IncRNAs that have not been previously described in the literature or identified in prior functional genomic studies. Only six of the 46 shared essential IncRNAs were found in a previous study of essential IncRNAs, and, for those IncRNAs, they were ty pically found in only one of the cell lines profiled20. Among the shared essential IncRNAs, the majority are antisense RNAs — either antisense to a PCG or divergent bi-directional transcripts (Figure 12A-B). For seven of the shared essential IncRNAs. we cloned three distinct gRNAs to perturb them and confirmed significant on-target knockdown of all transcripts in HAP1, HEK293FT, and MDAMB- 231 cells (Figure 12C, Table S3A-B). Next, we measured cell grow th via the same competitive growth assay. Individual knockdowns resulted in a significant reduction in cells carrying essential IncRNA perturbations compared to cells transduced with control (non targeting) gRNAs, including for well-known essential IncRNAs like MALAT1 28 and MIR! 7 HG 43 (Figure 3B, Figure 12D).

[0120] To better understand mechanisms underlying essential IncRNAs, we measured how' IncRNA perturbations impact cell cycle progression and apoptosis. To monitor cell cycle changes, we used a fluorescence ubiquitin cell cycle indicator (FUCCI) in MDA-MB-231 cells (Figure 3C, Figure 12E-G)44,45.We found that five of the seven shared-essential IncRNAs, including MALA Tl, induced a significant accumulation of cells in the G2-M phase, while two, XLOC 047988 and XI. OC 015918. led to cell accumulation in the G1 phase

[0121] (Figure 3D, Figure 12H, Table S3C), in agreement with the findings from competitive growth assays (Figure 121). We also analyzed the rate of apoptosis using Annexin V staining and noted that knockdow n of essential IncRNAs consistently increased apoptosis but to differing degrees for each IncRNA (Figure 3E-F).

[0122] Specifically, the knockdown of XLOC 032153. XLOC 016044, and XLOC 008323 resulted in a higher level of apoptosis than knockdown MAI. ATI (Figure 3F). For these seven shared essential IncRNAs, we also performed an orthogonal, non-CRISPR knockdown by targeting each IncRNA with pools of three siRNAs (Figure 12J, Table S3D). After confirming efficient transcript knockdown by RNAi, we found similar reductions in cell proliferation and increases in apoptosis (Figure 12K-L). These individual perturbations of shared or cell-type specific essential as well as non-essential IncRNAs confirm our pooled NYG-LIPP-228.PCT screen findings, recapitulating the varying degrees of depletion observed in different cell lines in the initial pooled screens.

[0123] Essential IncRNAs act independently of nearest PCGs A long-standing mechanistic question is whether IncRNAs act locally (e.g. ANRIL suppresses transcription of nearby PCGs and XIST silences genes on the X chromosome)46,47or have more global effects. To reveal whether the essentiality of IncRNAs and PCGs depends on their genomic proximity to each other, we examined 5,452 pairs of IncRNA and their nearest PCGs. Through our massively -parallel screens, each gene in the pair (IncRNA and PCG) was knocked-down via Casl3 in five cell lines. By perturbing IncRNAs and PCGs at the RNA-level, our approach has the advantage of being able to target genes with greater specificity (e.g., transcript-specific and strand specific) than DNA-targeting approaches, which can modulate multiples genes if they are in close proximity20,21.

[0124] In this manner, we were able to identify hundreds of essential genes within these pairs across all cell lines (Figure 4A). The asymmetric distribution between essential IncRNAs and PCGs suggests that most often one — but not both — genes are crucial for cell proliferation. Interestingly, we found that across all cell lines there is only a small fraction of IncRNA-PCG pairs in which both genes are essential (Figure 4B, Figure 13A-B). For example, in HAP1 cells, we identified many more IncRNA-PCG pairs where either the IncRNA or PCG was essential (590 pairs) and substantially fewer where both the IncRNA and its nearest PCG were essential (54 pairs) (Figure 4C, Figure 13C-F).

[0125] To validate this observation, we selected three IncRNAs with an antisense orientation to nearby PCGs with either one essential gene (IncRNA or PCG) or both classified as essential (Figure 4D). In all three cases, we targeted each gene in the pair using three independent gRNAs, confirmed strand-specific on-target knockdown and monitored cell grow th via the same imaging-based assay as before (Figure 4E, Figure 14G). For all 3 pairs (6 genes), we confirmed the depletion / essentiality as observed in the pooled screen. For example, we found that the PCG WARS2 — located in an antisense orientation adjacent to the essential IncRNA XLOC 001366 — was dispensable and its depletion had no impact on growth compared to cells receiving control (non-targeting) perturbations. For this pair, only the IncRNA was essential and, when the IncRNAXLOC 001366 was targeted by Casl3, it led to a significant reduction in proliferation. In contrast, we found that the IncRNA

[0126] XLOC 020797 — located in an antisense orientation adjacent to the essential PCG SRSF1 — was dispensable. Interestingly, XLOC 020797 was identified as essential in a prior CRISPRi NYG-LIPP-228.PCT screen 20, which may be a misclassification given the close proximity between the transcription start sites of XLOC 020797 and SRSF1 (<1 kb), the overlapping transcripts and the essentiality of SRSFJ. This nuanced interplay highlights the relative autonomy of essential IncRNAs and their nearby PCGs. For the third IncRNA-PCG pair (SLC16A1-AS1 and S C 16A 1). both the IncRNA and the nearby PCG were essential in the HAP1 pooled screens. We confirmed these results in the competition assay, showing that knockdown of the IncRNA (SLC16A1-AS1) and the PCG (SLC16A1) reduces cell proliferation when compared to non-targeting controls, although SLC16A1-AS1 knockdown results in a more profound phenot pic effect (Figure 4E, Figure 14G). This is consistent with DepMap classification 30, which designates SLC16A1 as strongly selective (essential in multiple cell lines). Among the 43 shared essential IncRNAs with a paired PCG perturbation in our library, the nearest PCG is essential for 21 of them in at least one cell line and only for four of them in all cell lines (Figure 4F). In 15 out of 21 instances where the nearest PCG is essential, the IncRNA is located very close (<1 kb) to the nearest PCG.

[0127] Given this minimal overlap between essential IncRNAs and neighboring PCGs, we wondered whether IncRNA loci may instead contact other (essential) PCGs in the native, three-dimensional genome. Using chromosome conformation capture (Hi-C) datasets48, we found that 53 - 62% of essential IncRNAs are found in the same Topologically Associating Domains (TADs) that contain essential PCGs from DepMap screens (Figure 14A-B). To quantify direct contacts, we performed H3K27ac HiChIP, a sequencing-based proximity assay to detect genome elements in close contact with PCG promoters, in HAP1 cells. We found that only a minority of essential IncRNAs (4 - 11%) directly contact essential PCGs (Figure 14C-F). These results suggest that most essential IncRNAs do not exert their influence on cell fitness through the modulation of nearby essential PCGs: Essential IncRNAs (when defined by essentiality of the transcript itself) are not transcriptional bystanders but rather co-evolved genes, required for proliferation of human cells.

[0128] Next, we assessed the ability of CRISPR-Casl3 to identify IncRNAs where the transcript itself is essential — and not neighboring PCGs — by comparing our set of essential IncRNAs with those from a set of CRISPRi-based IncRNA screens20. Compared to the CRISPRi screens, the RNA targeting (Cast 3) screens identify a smaller fraction of essential IncRNAs that are close to essential PCGs (Figure 4G). When comparing essential IncRNAs, the ones identified solely in the prior CRISPRi screens are enriched for IncRNAs located in close proximity (<1 kb) to a nearby PCG (Figure 4H). Given that CRISPRi cannot selectively NYG-LIPP-228.PCT target specific transcripts when transcription start sites are in close proximity49, these results suggest that RNA-targeting CRISPRs avoid confounding effects of neighboring essential PCGs (false positives). For instance, we identified IncRNA-PCG pairs in close proximity, where either both genes were essential, with the PCG also identified by Cas9-based knockout screens (Figure 14H), or only the IncRNA was essential (Figure 141). We further investigated putative false positive hits from the CRISPRi screen — cases where the IncRNA is close to an essential PCG — and were able to resolve several complex loci at the transcript level (Figure 14J). In total, we identified 725 essential IncRNAs that were not reported in the previous genome-scale CRISPRi study (Figure 41). In this manner, Casl3 targeting can reveal the functional autonomy of essential IncRNAs from nearby PCGs and reduce false positives in complex loci containing essential PCGs.

[0129] Single-cell sequencing identifies proliferation-associated pathways

[0130] Next, we sought to understand the mechanisms underlying how individual essential IncRNAs contribute to cell proliferation. To this end, we used Casl3 RNA Perturb-seq (CaRPool-seq) to couple pooled CRISPR-Casl3 perturbations with a transcriptomic readout in single cells50. This method incorporates the direct capture of barcode gRNAs (bcgRNAs) within gRNA arrays, facilitating the identification of specific perturbations in single cells. We designed a new pooled Casl3 library targeting 50 essential IncRNAs and 21 PCGs with three individual gRNA arrays for each target. Each array included two distinct gRNAs that target the same gene (IncRNA or PCG) and a corresponding bcgRNA to identify the array (Figure 5 A). After transducing the pooled library into MDA-MB-231, we analyzed the gRNA array representation and found strong correlation with the prior transcriptome-wide screen (Figure 5B. Figure 15 A). We then transduced both MDA-MB-231 and HAP1 cells with this focused library and performed single-cell RNA-seq. After sequencing and analysis, we obtained 5,933 single MDA-MB-231 and 6,606 single HAP1 cells. For a subset of the IncRNAs, we also targeted their closest PCGs. We found minimal impact on the expression of closest PCGs when the corresponding essential IncRNAs were perturbed (14 out of 16 cases showed no differential expression, Figure 15B). However, we identified distinct expression patterns triggered by essential IncRNA perturbations in both MDA-MB-231 and HAP1 cells (Figure 5C, Figure 15C-F). For example, in MDA-MB-231 cells, w e found downregulation of known essential genes, such as the translation initiation factor EIF2B3, inhibitor of apoptosis family member survivin (encoded by BIRC5) and mitotic spindle regulator A URKB (Figure 5C). NYG-LIPP-228.PCT

[0131] Concurrently, we observed upregulation in genes associated with reduced proliferation, as identified in a recent overexpression screen (Figure 15C, E) 51.

[0132] Gene set enrichment analysis (GSEA) revealed unique pathways modulated by different perturbations (Figure 5D). Reassuringly, we noted a pronounced downregulation of the MYC (normalized enrichment score [NES] = -6.00) and mTOR (NES = -2.96) pathways following perturbations of MYC andMTOR. respectively. Additionally, perturbations of essential genes, whether IncRNAs or PCGs. consistently upregulated the tumor-suppressive p53 pathway, while downregulating pathways associated with proliferation such as mitotic spindle organization and cell cycle checkpoints (e.g., E2F targets and G2M checkpoint). Overall, changes in pathways were consistent across both cell lines — MDA-MB-231 and HAP1 — that were used in the CaRPool-seq (Figure 5D, Figure 16A). We also performed bulk stranded mRNA-sequencing for five essential IncRNAs targeted in our CaRPool-seq library and found that pathw ay enrichment in the bulk RNA-seq was correlated with our single-cell data (Figure 16B-C). Despite the global similarities in gene expression and proliferation pathway changes upon knockdown of essential IncRNAs, we found that most pathways remain unaffected by respective IncRNA perturbations (Figure 5D, Figure 16A). We also identified that specific subsets of IncRNAs have distinct effects on certain pathways, such as the oxidative phosphorylation. Interestingly, the two essential IncRNAs that induced G1 phase accumulation in our cell cycle analyses — XLOC 047988 and XLOC 015918 — also led to increased enrichment of genes associated with hypoxia (Figure 16D). Tn line with these findings, prior work has shown that hypoxia induces G1 arrest52. Furthermore, while nearly all IncRNA perturbations upregulated the p53 pathw ay, we observed direct TP 53 transcript upregulation only in a subset of these cases (Figure 16E). In others, we noted a downregulation of MDM2. a post-translational repressor of p53, which can lead to altered p53 protein expression53. Despite the consistent upregulation of TP53 and its well-known role in DNA repair and apoptosis, we found that only a subset of essential IncRNA perturbations upregulate genes involved in DNA repair. Taken together, our transcriptome profiling in MDA-MB-231 and HAP 1 cells revealed several common and distinct mechanisms related to cell survival and proliferation, further reinforcing their role as essential IncRNAs as identified in the initial pooled screens.

[0133] Essential IncRNA expression during development NYG-LIPP-228.PCT

[0134] Given recent efforts to comprehensively map IncRNA expression across development and in different tissues (Figure 6A, Figure 17A) 19. we wondered whether essential IncRNAs may have distinct patterns of expression in vivo. Essential IncRNAs showed 5- to 7-fold increased expression across organs and developmental stages in vivo (Figure 17B, Mann Whitney U. P < 0.05). We also found that essential IncRNAs tend to exhibit widespread expression across different tissues and are broadly expressed at various time points during development (Figure 6B). Non-essential IncRNAs, on the other hand, show more restricted expression across different tissues and developmental periods (Figure 6B), suggesting specialized functions distinct from cell survival / proliferation. Interestingly, we also observed a significant enrichment of developmentally dynamic transcripts — those exhibiting differential expression across developmental stages — in essential IncRNAs (Fisher’s exact test, P < 0.05, Figure 6C, Figure 17C-D). By profiling their expression across different developmental stages in brain, heart, liver, and kidney, we found that those essential IncRNAs with dynamic expression profiles are most highly expressed in the early stages of human development with decreased expression in later stages (Figure 6D. Figure 10E-F). These IncRNAs may be important during embryonic development, where cell proliferation is high. In contrast, non-essential IncRNAs tended to show an opposite pattern: lower expression levels in early development and higher expression at later stages.

[0135] In human development, we found a stronger correlation between shared essential IncRNAs and genes that serve as markers of proliferation, such as PCNA and MKI67 (Figure 18A). Therefore, we examined co-expression patterns between each IncRNA targeted in our Cast 3 library' and all PCGs throughout development (from 4 weeks post-conception to old age). We found that coexpressed PCGs for the shared essential IncRNAs were enriched for genes involved in cell proliferation, such as cell cycle, MYC targets and mitotic spindle organization during brain, heart, liver and kidney development (Figure 6E, Figure 18B). In brain development, 61% of the shared essential IncRNAs (28 of the 46) are co-expressed with PCGs in proliferation pathways; we found similar enrichments across all organs (Figure 6F, Figure 18B). Overall, essential IncRNAs have higher expression in vivo than non-essential IncRNAs and tend to be more highly expressed during early stages of development.

[0136] Essential IncRNAs in cancer progression

[0137] Given the strong association with proliferation, we hypothesized that essential IncRNAs may also play a role in cancer progression. Previous genome-scale CRISPR screens to knock-out PCGs found that cancer-associated genes are over-represented in essential PCGs NYG-LIPP-228.PCT

[0138] 54,55, suggesting a fundamental link between cell viability and tumorigenesis. We delved into the role of essential IncRNAs in human tumors by re-aligning RNA-sequencing data from 8,878 primary tumors and 686 matched normal tissues spanning 29 cancer types from The Cancer Genome Atlas (TCGA) to a reference IncRNA transcriptome (Figure 7A, Figure 19A). We found that essential IncRNAs displayed elevated expression in both tumors and matched normal tissues — with greater increases for more essential IncRNAs (Figure 7B, Figure 19B).

[0139] When comparing tumor versus normal tissues (Figure 18C), we found that essential IncRNAs are more often differentially expressed in tumors compared to non-essential IncRNAs (Figure 7C, Fisher’s exact test, P < 0.05). Among the 164 differentially expressed essential IncRNAs. 76 were upregulated, 78 IncRNAs were downregulated, and 10 exhibited mixed expression patterns in various tumors. Reassuringly, this group identified several IncRNAs with established roles in cancer progression. For example, MALAT1, which was significantly downregulated in breast cancer, colon cancer, lung cancer and endometrial cancer, has previously been shown to have decreased expression in breast cancer metastases 38,40. Similarly, KCNQ1OT1 was significantly upregulated in colon and prostate cancer in our analyses, as previously shown.

[0140] To understand the functional roles of essential IncRNAs, we identified PCGs with correlated expression patterns in TCGA primary tumors for each IncRNA. We found that the co-expressed PCGs of essential IncRNAs were enriched for cell proliferation and that the enrichment was greatest for shared essential IncRNAs (Figure 7D). For example, oncogenic IncRNAs OIP5-AS1 (XLOC 016293) and ZFAS1 (XLOC 030849) are co-expressed with PCGs in MYC, E2F, cell cycle checkpoint, and mTOR pathways (Figure 19D), and have been shown to promote tumorigenesis in multiple tumor types 58-61. Other IncRNAs, such as CALCRL-AS1 (XLOC 027685) and LINC02821 (XLOC 010832), are co-expressed with PCGs that are in immune-related pathways (Figure 19D), suggesting distinct co-expressed genes and functional roles for different essential IncRNAs.

[0141] To further assess the clinical relevance of essential IncRNA expression, we categorized tumors as either IncRNA-high or IncRNA-low based on IncRNA expression, and then examined whether these groups showed significant differences in overall or progression- free survival. Each cancer cohort w as analyzed individually to mitigate bias arising from the distinct features of various cancer types. We found that 34 out of the 46 shared essential IncRNAs were associated with improved or worsened survival (Figure 7E-F, Figure 19E-G). For instance, increased expression of SLC16A1-AS1 is correlated with worse overall survival NYG-LIPP-228.PCT in three cancer types (glioma, renal clear cell carcinoma, and uterine endometrial carcinoma) and worse progression free survival in two cancer types (glioma and prostate adenocarcinoma) (Figure 7G). This agrees with our in vitro data, showing that knock-down of SLC16A1-AS1 (XLOC 001308) is lethal in leukemia cells (Figure 4E). When more closely matching the tumor samples to the cells with Cast 3 perturbations (breast invasive carcinomas and MDA-MB-231 pooled screens), we found similar results for essential IncRNAs for expression and survival analyses (Figure 19H-J). These analyses link essential IncRNAs to cancer progression and yield new transcriptomic biomarkers that can be tied to function via perturbation screens.

[0142] Discussion

[0143] The recent advent of RNA-targeting CRISPR nucleases and pooled screens using these tools have made it possible to perturb thousands of noncoding RNAs in a precise manner and profile their function 23,62. Since the discovery of pervasive transcription in the genome more than a decade ago 1.2.63, one striking puzzle has been to identify the functional role, if any, of the thousands of transcribed IncRNAs. Here, our transcriptome-scale RNA- targeting CRISPR screens provide a functional survey at the RNA level and pinpoint which IncRNAs are required across five human cell lines. This systematic approach, combined with comprehensive validation of the pooled screens and integration with multiple independent datasets, suggests a role for IncRNAs as important players in cell proliferation, human development, and cancer.

[0144] Out of more than 6,000 targeted IncRNAs, we identified 778 IncRNAs as essential for cell viability, with 61% as cell -type-specific essential IncRNAs and 39% as essential in multiple cell lines. For essential IncRNAs, we found that, in most cases, their nearest PCGs were dispensable, suggesting that these IncRNAs do not function exclusively through the regulation of nearby PCGs. Even for those IncRNAs that do regulate nearby PCGs, it can be challenging to distinguish regulatory activity' due to DNA c / .s-regulatory elements from regulation due to the transcript itself.

[0145] For example, a recent study at the Lockd-Cdknlb locus in mouse erythroblasts reconciled phenoty pic differences between insertion of a poly-adenylation signal near the beginning of the IncRNA Lockd to abrogate transcription (no change in Cdknlb expression) and deletion of the IncRNA (reduced Cdknlb expression): Regulation of Cdknlb expression was mediated entirely by a DNA cis-regulatory element at the 5 ’end of Lockd without any involvement of the transcript 22. This example — along with several others from our study NYG-LIPP-228.PCT

[0146] — demonstrates the unmet need for precise, scalable perturbations at the RNA level to avoid potential confounders from nearby PCGs or genomic cA-regulatory elements.

[0147] By examining the expression and distribution of IncRNAs in vivo, we found that essential IncRNAs are highly expressed early in development, which contrasts with non- essential IncRNAs that are more highly expressed in later stages of development and with greater tissue-specificity. We also extended our analyses from organismal development to disease states using primary tumors from 29 human cancers. After re-aligning expression data from -9,000 TCGA primary tumors to a IncRNA-inclusive reference transcriptome, we were able to show that essential IncRNAs are more often differentially expressed in tumors than other IncRNAs and that nearly all of the shared essential IncRNAs can serve as biomarkers for survival in certain tumor types.

[0148] The prospect of designing ASOs to inhibit functional IncRNAs in tumorigenesis opens an unexplored avenue for therapeutic intervention. The essential IncRNAs identified in this study, implicated in various tumor phenotypes, emerge as targets with clinical applications 72.

[0149] The framework of transcriptome-scale RNA-targeting CRISPR screens established in this study is broadly applicable and not limited to IncRNAs. It can be directly applied to other noncoding RNAs, including enhancer RNAs and circular RNAs 73,74, for interrogating the functional contribution of noncoding transcripts. As we have shown, a major advantage of Cast 3 and RNAtargeting CRISPR nucleases is their ability to dissect complex and gene- dense loci situated in close proximity to PCGs. Overall, transcriptome-wide Casl 3 pooled transcriptomic screens represent a powerful tool for the systematic investigation of the functional contributions of noncoding transcripts and pave the way to identify functional IncRNAs for any phenotype or disease.

[0150] METHODS DETAILS

[0151] Monoclonal Casl3 cell line generation and cell culture

[0152] MDA-MB-231 and K562 cell lines were acquired from American Type Culture Collection (HTB- 26 and CCL-243, respectively). Monoclonal doxycycline-inducible / fyi'Cas 13d MDA-MB-231 and K562 cells were generated by transducing cells with a lentivirus produced using pLentiRNACRISPR_007 (Addgene 138149) at a low multiplicity of infection (MOI < 0.1) and selected with 5 pg / ml of blasticidin S (A.G. Scientific B-1247). Single-cell colonies were isolated by low-density plating and then expression of HA-tagged Casl3 was confirmed by immunoblot using an anti-HA peptide antibody (Cell Signaling NYG-LIPP-228.PCT

[0153] Technology' 2367S). Monoclonal doxycy clineinducible RfxCas 13d-NLS HEK293FT cells were obtained from Wessels and Mendez-Mancilla et al 23. 7?y5cCasl3d-NLS HAP1 cells were obtained from Guo et al 24, and / / / xCas I 3d-NLS THP1 cells were obtained from Wessels and Mendez-Mancilla et al 50. HEK293FT and MDA-MB-231 cells were cultured in DIO medium: Dulbecco's

[0154] Modified Eagle Medium with high glucose and stabilized L-glutamine (Cytiva SH30022.01) supplemented with 10% Serum Plus II (Sigma- Aldrich 14009C) and 5 pg / ml blasticidin.

[0155] HAP1 and K562 cells were cultured in 110 medium: Iscove’s Modified Dulbecco’s Medium wi th L-glutamine (Cytiva SH30228.FS) supplemented with 10% Serum Plus II and 5 pg / ml blasticidin. THP-1 cells were cultured in R10 medium: HyClone RPMI 1640 Medium

[0156] (Cytiva SH30255.FS) supplemented with 10% Serum Plus II and 5 pg / ml blasticidin. All cells were incubated at 37 °C with 5% carbon dioxide.

[0157] Transcriptome-scale and CaRPool-seq Casl3 libraries: Design and cloning

[0158] To rationally select IncRNAs to perturb, we used a developmental atlas profiling 31,687 IncRNAs across developmental stages and different species 19 and essential IncRNAs identified in prior CRISPRi screens 20. We first filtered out lowly expressed IncRNAs, which we defined as those expressed that were not expressed above at least 5 reads per kilobase of transcript per million mapped reads (RPKM) in at least one sample from the developmental atlas. To identify matching IncRNAs between the developmental atlas and CRISPRi screen annotations, we used the primary transcription start site (TSS) from the CRISPRi screens. We extended the TSS by 500 bp in each direction and intersected it with the first exon of the IncRNA annotations from the developmental atlas in a strand-specific manner. In total, the final library- included 6,199 IncRNA targets, 4,309 closest protein-coding gene (PCG) targets, and 100 essential gene targets (positive controls). The 100 essential genes yvere selected as those that yvere essential in all CRISPR-Cas9 screens from Hart et al. 34 For each IncRNA, yve used annotations (e.g., genomic location, time- and tissue specificity, dynamic / non- dynamic) provided by the developmental atlas 19.

[0159] For each IncRNA / PCG, yve selected the transcript with the highest isoform expression and designed optimized gRNAs using a machine learning model for Cas t 3 gRNA design that previously was trained on thousands of gRNAs (http: / / casl3design.nygenome.org) 23. For each IncRNA / PCG, yve selected 8 gRNAs from the highest (or second-highest as needed) efficacy quartile (as given by cas!3design) and made sure that the selected gRNAs had no secondary target sites in the human transcriptome with 0 to 2 mismatches to the cognate site 24. Wherever possible, yve aimed to select 2 gRNAs from each of the first three exons of the NYG-LIPP-228.PCT

[0160] IncRNA / PCG to minimize false negatives due to alternative exon usage / splicing. We also embedded 1,000 non-targeting gRNAs as negative controls, which we ensured had 3 or more mismatches to any other transcripts (hgl 9). In total, the library included 75,065 gRNAs. Each gRNA was flanked with constant regions (for PCR amplification and Gibson cloning) and synthesized as 106mer single-stranded oligonucleotides (Twist Biosciences).

[0161] For library cloning, we amplified pooled oligonucleotides (Twist) using a nested PCR. PCR1 amplifies the oligo pool and PCR2 adds overhangs for Gibson cloning. For PCR1, the oligo pool was amplified using Q5 High-Fidelity DNA Polymerase (NEB M0491L) with 0.2 pl of enzyme and 1 ng (1 ng / l) of the oligo pool per reaction in four 20 pl reaction: 98 °C for 30s, 7* (98 °C for 10 s, 63 °C for 10 s, 72 °C for 15 s), 72 °C for 1 min.

[0162] For PCR1 1 pl of the following primers (10 pM) were used: 5 -TAGAAGGTCTATGTTCGCCA-3'

[0163] 5 -TAACGAGTCCTAAACGGGAT-3'

[0164] After PCR1 , the replicate reactions were combined. Then, for PCR2, 2 pl of pooled PCR1 product was used in each (20-pl) PCR2 reaction. In total, we performed 26 PCR2 reactions (20-pl each) with 7 amplification cycles using Q5 High-Fidelity DNA Polymerase (NEB M0491) as follows: 98 °C for 30s, 7* (98 °C for 10 s, 63 °C for 15 s, 72 °C for 15 s), 72 °C for 1 min. For PCR2 1 pl of the following primers (10 pM) were used: 5'- TATATATCTTGTGGAAAGGACGAAACACCGAACCCCTACCAACTGGTCGGGGTTT GA A AC-3'

[0165] 5'-

[0166] ACTGACGGGCACCGGAGCCAATTCCCACTCCTTTCAAGACCTAGCTAGCGAATTC AAAAA-3' The PCR2 amplicon was purified using the DNA Clean & Concentrator (Zymo

[0167] D4014). The purified amplicon was Gibson-cloned into a puromycin-resistant lentiviral vector with an enhanced Casl3 direct repeat (DR1), pLentiRNAGuide_001 (Addgene 138150). For that, 40 pg plasmid was digested using 10 pl Esp3I (Thermo FD0454) at 37 °C for 2 hours and dephosphorylated using 10 pl FastAP (Thermo EF0651) at 37 °C for 30 minutes. The digested plasmid backbone was gelpurified using a 1% E-Gel (Thermo G401001) and QiaQuick Gel Extraction Kit (Qiagen 28704). We performed three 20 pl Gibson reactions using 10 pl 2* Gibson Assembly Master Mix (NEB E2611L): Each Gibson reaction included 500 ng digested and dephosphorylated pLentiRNAGuide 001 plasmid and 80 ng PCR2 amplicon at 50 °C for 1 hour. We purified the plasmid library using isopropanol NYG-LIPP-228.PCT precipitation at room temperature for 15 minutes and transformed the purified library into Endura electrocompetent cells (Lucigen 60242-2) with >1,000 colonies per construct. Following library purification using the MAXI Fast-Ion Plasmid Purification Kit (IBI Scientific IB47125), we verified successful cloning via Illumina sequencing (MiSeq) with a 90: 10 ratio (90th percentile / lOth percentile crRNA read ratio) of 1.9 and a recovery rate of

[0168] 99.94%.

[0169] For each target gene in the CaRPool-seq library, we picked the three most depleted gRNAs from the transcriptome-scale screen that target expressed exons. The gRNAs chosen were nonoverlapping. Since every gRNA array includes two targeting gRNAs, we paired each selected gRNA (from the transcriptome-scale screen) with a newly-designed gRNA using the TIGER gRNA design tool 79 and an array-specific barcode gRNA (bcgRNA). We designed three individual gRNA arrays for 50 essential IncRNAs and 18 closest proteincoding genes (PCGs). We also designed gRNA arrays targeting three known essential genesMYC, MTOR and HSPA9). We added 10 control (non-targeting) gRNA arrays. For the bcgRNAs, we designed random 15mer sequences with a hamming distance greater than four from any other 15mer. The 225mer singlestranded oligonucleotides were designed in the following way: PCR-handle:BsmBI:gRNAl:DR:gRNA2:DR:TruSeq-PCR-handle:barcode:LguI:PCR-handle DR indicates wild-type 36nt7( / xCasl3d direct repeat. Pooled oligonucleotides were synthesized as an oPool (IDT). The pool was amplified using Q5 High-Fidelity

[0170] DNA Polymerase (NEB M0491 L) using 0.5 pl of enzyme and 20 ng (1 ng / pl) of the oligo pool in a single 50pl reaction: 98 °C for 30s, 8x (98 °C for 10 s, 60 °C for 15 s, 72 °C for 15 s), 72 °C for 1 min. For the PCR 2.5 pl of the following primers (10 pM) w ere used: 5 -TAGAAGGTCTATGTTCGCCA-3' 5'-TAACGAGTCCTAAACGGGAT-3'

[0171] The amplicon was purified using a 2.0* solid phase reversible immobilization (SPRI) clean-up. For digestion, all purified PCR product was digested in a 20 pl reaction with Ipl each of Esp3 and Lgul at 37 °C for 2 hours. We purified the digested amplicon using a 2. Ox SPRI cleanup. The purified amplicon was cloned into a puromycin-resistant lentiviral vector with a 5’ enhanced Casl3 direct repeat (DR1) and 3’ stabilizing pseudoknot element (evopreQl), pLentiRNAGuide_003 (Addgene 192505). For that, 5 pg pLentiRNAGuide_003 plasmid was digested using 2.5 pl Esp3I (Thermo FD0454) and 2.5 pl Lgul (Thermo FD1934) at 37 °C for 2 hours and dephosphorylated using 2.5 pl FastAP (Thermo EF0651) at 37 °C for 30 minutes. The digested plasmid backbone was gel-purified using a 2% E-Gel NYG-LIPP-228.PCT

[0172] (Thermo G401002) and QiaQuick Gel Extraction Kit (Qiagen 28704). All of the PCR product was ligated into Esp3\ / .gi / I -digested and dephosphorylated pLentiRNAGuide_003 in four 20 pl reactions using per reaction: 1 pl T7 DNA ligase (NEB M0318), 25 ng digested plasmid and 2x Rapid Ligase Buffer (Enzymatics B1010L) for 15 minutes at room temperature. Ligation products were purified using a 2x SPRI cleanup and transformed into Endura electrocompetent cells (Lucigen 60242-2) with > 10.000 colonies per construct.

[0173] Following library purification using the MAXI Fast-Ion Plasmid Purification Kit (IBI Scientific IB47125), the complete library representation with minimal bias (90th percentile / lOth percentile rRNA read ratio 11.8), and correct gRNA array to barcode linkage (88.3%) was verified by sequencing (Illumina MiSeq).

[0174] Pooled lentiviral production

[0175] For both pooled libraries, lentivirus was produced by transfecting the pooled transfer plasmid with packaging plasmids psPAX2 (Addgene 12260) and pMD2.G (Addgene 12259) using linear polyethylenimine MW25000 (Polysciences 23966). We seeded ten million HEK293FT cells per 10 cm dish and transfected them with 60 pl polyethylenimine, 9.2 pg plasmid pool, 6.4 pg psPAX2 and 4.4 pg pMD2.G. Three days post-transfection, the viral supernatant was collected, filtered through a 0.45-pm filter, and stored at -80 °C until further use. The amount of lentivirus used for transduction was titrated to result in 30-40% transduction efficiency, to minimize the probability of multiple gRNAs being introduced into a single cell.

[0176] Pooled Casl3 library CRISPR screens

[0177] We conducted pooled Casl3d screens following established protocols 23,50. Briefly, Casl3dexpressing cells were transduced with the library lentivirus through separate infection replicates by spinfection at 1000 rpm for 1 hour at 37 °C, followed by overnight incubation. After 24 hours, new media with 1 pg / ml puromycin (Invivogen ant-pr-1) was added. Puromycin selection was completed within 48 hours for all cell lines, except for THP1. Because THP1 required an extended selection time, we maintained it in R10 but with 10% Tet-system approved serum (Gibco A4736201) substituted for Serum Plus II and with 1 pg / ml puromycin. THP1 cells took approximately two weeks for full selection (using an in- line non-transduced control).

[0178] Following puromycin selection, / / / xCas 13d expression was induced by replenishing the growth medium containing 1 pg / ml puromycin, 5 pg / ml blasticidin and 1 pg / ml doxycycline. Cells were passaged every 2 to 4 days and split as needed, ensuring a guide representation of >l,000x. Samples with a guide abundance of 1,000-fold were harvested at NYG-LIPP-228.PCT

[0179] 0, 7, and 14 days post-Casl3d induction. Genomic DNA was isolated from cell pellets via a modified salting out procedure 104. For that, 12 ml of NK Lysis Buffer (50 mM Tris, 50 mM EDTA, 1% SDS, pH 8) and 60 pl of 20 mg / ml Proteinase K (QIAGEN 19131) were added to 80 million cells and incubated at 55 °C overnight. The next day, 6 pl of 100 mg / ml RNase A (A.G. Scientific R-2000) was added to the lysed sample, which was then inverted 25 times and incubated at 37 °C for 30 min. Samples were cooled on ice before addition of 4 ml of pre-chilled 7.5M ammonium acetate (Sigma A1542) to precipitate proteins. After adding ammonium acetate, the samples were vortexed and centrifuged at 4,000 x g for 10 minutes. After the spin, 12 ml isopropanol was added to the collected supernatant, inverted 50 times and centrifuged at 4,000 x g for 10 minutes. The supernatant was discarded, 12 ml of freshly prepared 70% ethanol was added, the tube was inverted 10 times, and then centrifuged at 4,000 x g for 10 minutes. The supernatant was discarded, and remaining ethanol was removed.

[0180] After air dry ing for 30 minutes, 500 pl of 0.2* TE buffer (Sigma 93283) was added, the tube was incubated at 65 °C for 1 hour and at room temperature overnight to fully resuspend the DNA. The next day, the gDNA samples were vortexed briefly. The gDNA concentration was measured using a Nanodrop (Thermo). We excluded one biological replicate (K562 day 0) due to low recovery7after gDNA extraction. We amplified gRNA cassettes and prepared them for sequencing using a two-step PCR. PCR1 was performed to amplify a region containing the crRNA cassette in the lentiviral genomic integrant using TaqB polymerase (Enzymatics P7250L). We performed 70 PCR1 reactions for each gDNA sample using 5 pg gDNA per 100 pl PCR1 reaction as follows: 94 °C for 3 min, 20* (94 °C for 10 s, 55 °C for 30 s, 68 °C for 45 s), 68 °C for 5 min. For PCR1 the following primers were used: 5 -GAGGGCCTATTTCCCATGATTC-3'

[0181] 5 -GTTGCGAAAAAGAACGTTCACGG-3'

[0182] We then combined PCR1 products for the same sample together before PCR2, which was done to incorporate Illumina adaptors using Q5 High-Fidelity DNA Polymerase (NEB M0491). We performed 10 PCR2 reactions for each sample using 10 pl unpurified PCR1 product per 50 pl reaction as follows: 98 °C for 30 s, 6-10x (98 °C for 10 s, 63 °C for 30 s, 72 °C for 45 s), 72 °C for 5 min. For PCR2 the following primers was used:

[0183] 5'-

[0184] AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGA TCT(Nl-9)(BC8)TCTTGTGGAAAGGACGAAACACCG-3' NYG-LIPP-228.PCT

[0185] 5'-

[0186] CAAGCAGAAGACGGCATACGAGAT(BC8)GTGACTGGAGTTCAGACGTGTGCTCTT CCGATCT(Nl-9)GAGCCAATTCCCACTCCTTTCAAG-3' where N is a stagger of 1 to 9 nucleotides and BC is a barcode of 8 nucleotides. The resulting amplicons from PCR2 (-270 bp) were pooled and then purified using double-sided SPRI beads clean up (Beckman

[0187] B23317) or gel extracted using a QiaQuick Gel Extraction kit (Qiagen 28704). For doublesided SPRI clean up. the amplicon was first incubated with 0.6x SPRI to remove larger fragments (>350 bp bound to beads and removed), the supernatant was then transferred and further incubated with 0.8x SPRI beads (<200 bp in supernatant and removed). The concentration of the purified PCR amplicon was quantified using Qubit dsDNA HS Assay Kit (Thermo Q32851) and sequenced on an Illumina NextSeq 500 using a single-end 150 cycle read.

[0188] Single-cell sequencing coupled with Casl3 perturbations (CciRPool-seq)

[0189] / / xCas 13d-expressing MDA-MB-231 and HAP1 cells were maintained in their respective media (D10 for MDA-MB-231 and 110 for HAP1), except 10% Tet-system approved serum (Gibco A4736201) was substituted for Serum Plus II. We transduced the CaRPool-seq library at a low multiplicity of infection (MOI < 0.2) by spinfection at 1000 x g for 1 hour at 37 °C, followed by overnight incubation. After 24 hours, fresh media with 1 pg / ml puromycin was added. Puromycin selection was completed within 72 hours for both cell lines and cells were collected for CaRPoolseq readouts at 48 hours after Casl3-induction (1 pg / ml doxycycline). The experiment was conducted using one lane per cell line of the 1 OX Genomics 3’ kit (Chromium Single Cell 3’ Gene Expression v3.1 with feature barcoding technology for CRISPR screening, 10000127, 10000268 and 10000262). For pooled screen readouts, the cells were passaged every 2 days, ensuring a guide representation of >10.000x. Five million cells were harvested at 0 and 12 days post-Casl3 induction. The libraries for bulk readout were prepared as described in Pooled Casl3 library CRISPR screens.

[0190] Single-cell sequencing library preparation for bcgRNAs and cDNA followed the 10X Genomics manual (CG000316 Rev D) with following modifications to allow for capture of bcgRNAs in our Cast 3 CRISPR array configuration type X as previously described 50. After elution of GEM-RT in 34 pL (Step 2.1-s.), we added 1 pL ADT additive primer (0.4 pM) 5'-CCTTGGCACCCGAGAATTCC-3'to increase bcgRNA yield during cDNA amplification. The cDNA and bcgRNAs were purified using SPRI cleanup as indicated in steps 2.3 A (Pellet Cleanup, 3' GEX) and 2.3B (Transferred Supernatant Cleanup, CRISPR screening library). NYG-LIPP-228.PCT respectively. 45 pL of the purified bcgRNAs was used to construct the bcgRNA library' through PCR amplification that adds Illumina P5 and P7 handles and an i7 index to the bcgRNA amplicon. We performed 12 cycles of PCR amplification (PCR1) in 100 pl reactions using 50 pl of 2* KAPA Hifi PCR Mastermix (Roche 07958935001) and 45 pl of bcgRNA PCR template as follows: 95 °C for 3 min, 12x (95 °C for 20 s, 60 °C for 8 s, 72 °C for 8 s), 72 °C for 1 min). For the PCR 2.5 pl of the following primers (10 pM) were used: 5'-

[0191] AATGATACGGCGACCACCGAGATCTACACTCGTCGGCAGCGTCAGATGTGTATA AGAGACAG-3' 5'-

[0192] CAAGCAGAAGACGGCATACGAGAT(BC)8GTGACTGGAGTTCCTTGGCACCCGAG AATTCCA-3' where BC is a barcode of 8 nucleotides. After PCR product purification using a 1.6x SPRI cleanup, we performed 4 more cycles of PCR amplification using P5 and P7 primers in 100 pl reactions using 50 pl of 2 KAPA Hifi PCR Mastermix (Roche 07958935001) and 45 pl of PCR1 product as follows: 95 °C for 3 min, 4x (95 °C for 20 s, 60 °C for 8 s, 72 °C for 8 s), 72 °C for 1 min). For the PCR 2.5 pl of the following primers (10 pM) were used:

[0193] 5 - AATGATACGGCGACCACCGA-3’

[0194] 5 - CAAGCAGAAGACGGCATACGAGA-3’ After purification using a 1.6x SPRI cleanup, bcgRNA and cDNA libraries were quantified using the Qubit dsDNA HS Assay Kit (Thermo Q32851 ). The average amplicon size of each library' was quantified using a Bioanalyzer 2100 using the High Sensitivity DNA Kit (Agilent 5067-4626). Using the average amplicon size and concentration, we then diluted both libraries appropriately for sequencing. We mixed the libraries in suitable proportion to achieve -45,000 reads per cell for the cDNA library and -5,000 reads per cell for the bcgRNA library. After mixing, the pooled CaRPool-seq libraries were sequenced on an NovaSeq 6000 (Illumina SI flow cell) using 28 cycles for read 1 and 91 cycles for read 2. Arrayed gRNA cloning and lentiviral production

[0195] For the competitive cell growth assays, we used a RfxCasl3d guide-only vector that also expresses GFP and puromycin resistance, pLentiRNAGuide_004 (Addgene 223175). For all validation assays, we cloned three individual gRNAs per gene into this construct using Esp3I sites and produced corresponding lentiviruses. In brief, we seeded one million HEK293FT cells per 6-well and transfected them with 7.5 pl polyethylenimine linear MW 25000 (Polysciences), 1.5 pg gRNA cloned pLentiRNAGuide_004, 1.25 pg psPAX2 and 0.5 NYG-LIPP-228.PCT ig pMD2.G. Two days post-transfection, the viral supernatant was collected, filtered through a 0.45-pm filter, and monoclonal Casl3 cell lines were transduced at a low MOI (~0.5).

[0196] Competitive cell growth assays

[0197] We performed two independent transductions for each gRNA and selected transduced cells with 1 g / ml puromycin for 3 days. Selected GFP-positive cells were then co-cultured with parental cells for 24 hours and the ratio of GFP-positive cells was determined using a live cell imaging system (Incucyte S3) at 20* magnification for HAP1. MDA-MB-231 and HEK293FT cells. After that, 1 pg / ml doxycycline was added to induce Cast 3 expression. Over the course of 4 - 6 days, the ratio of GFP-positive to GFP-negative cells was observed by taking nine images per transduction. We then determined relative survival by normalizing each ratios to 1) the initial time point prior to Cast 3 induction and 2) the median of cell mixtures containing cells transduced with three different non-targeting (negative control) gRNAs. Representative endpoint images show confluence masks of GFP-positive and GFP- negative cells (Incucyte Live Cell Analysis software). For competition assays in THP1. GFP quantification was performed using flow cytometry (Sony SH800S) six days after mixing with parental cells and Casl3 induction (1 pg / ml doxycycline). THP1 cells ere gated by forward and side scatter and signal intensity to remove potential multiplets and additionally gated for living cells using Fixable Violet Dead Cell Stain exclusion (Thermo L34963). For each sample, we analyzed the distribution of GFP-negative and GFPpositive cells from 9,000 gated (singlets and live) cells. We then determined relative survival by normalizing each ratio to the median of cell mixtures containing cells transduced with three different non-targeting (negative control) gRNAs. Cell cycle analyses

[0198] We used a fluorescence ubiquitin cell cycle indicator (FUCCI)-based reporter system to determine cell cycle changes using live-cell imaging. For that, we cloned the FUCCI cassette (AzaleaB5-hCdtl(l / 100)-P2A-h2-3-hGem(l / 110)) from tFUCCI(CA)5 (Addgene 153521) into a lentiviral plasmid driven by a short EF-la promoter (EFS). We modified the backbone from pLentiRNAGuide_001 (Addgene 138150) by replacing the U6-gRNA and EFS-puromycin cassettes with an EFS promoter and a downstream multiple cloning site (MCS). We digested pLentiRNAGuide_001 with Pacl and Apa restriction enzymes and amplified EFS-MCS and WPRE-LTR cassettes with Gibson overhangs using Q5 High- Fidelity DNA Polymerase (NEB M0491L) using 0.5 pl of enzyme and 10 ng pLentiRNAGuide 001 plasmid in 50 pl reactions: 98°C for 30 s, 25* (98 °C for 10 s, 60 °C NYG-LIPP-228.PCT for 15 s, 72 °C for 30 s), 72 °C for 1 min. For the PCRs 2.5 JJ.1 of the following primers (10 pM) were used: EFS-MCS 5'- ACAGCAGAGATCCAGTTTGGTTAATTAATCTTGAAAGGAGTGGGAATTGACTCCG -3'

[0199] 5'-

[0200] CGTGAATTCTCGGATCCGCTAGCCGTCTCCTCTAGTTAGCCAGCCGGTCCTGTGTT

[0201] CTGGCGGC AAACC -3 ' WPRE-LTR

[0202] 5'-

[0203] AGCGGATCCGAGAATTCACGGTACCCGTCTCTAAGGACTCTCGCGTTAAGTCGAC AATCAACCT-3'

[0204] 5 -CTGATCAGCGGGTTTAAACGGGCCCTGCTAGAGATTTTCCACACT-3' We assembled the final plasmid using the digested pLentiRNAGuide_001 plasmid and both PCR products by Gibson cloning. Then, we cloned the FUCCI cassette into this lentiviral plasmid using BamH\ (Thermo FD0055) and Kpnl (Thermo FD0524) restriction sites and termed the plasmid pLentiFUCCI(CA)5 (Addgene 223176). We produced a corresponding lentivirus, as described above, and transduced monoclonal Cas 13 -expressing MDA-MB-231 cells. For all cell cycle assays, we cloned individual gRNAs targeting IncRNAs into pLentiRNAGuide_001 (Addgene 138150) as described in the Arrayed gRNA cloning and lentiviral production section.

[0205] MDA-MB-231 Cas 13 cells expressing the FUCCI reporter were transduced at a low MOI (~0.5) with these lentiviruses. We performed two independent transductions for each gRNA and selected transduced cells with 1 pg / ml puromycin for three days. Then we seeded 2,000 into three wells of a 96-well plate (Coming 3904) per transduction and monitored cell cycle changes for 84 hours using live imaging (Incucyte S3) by taking nine images per well at 20x magnification. We induced Casl3 expression by addition of 1 pg / ml doxycycline 12 hours after seeding. For the last 24 hours of the experiment, a subset of cells transduced with non-targeting gRNAs were treated with compounds that inhibit cell cycle progression, the CDK1 / 2 / 5 / 9 inhibitor dinaciclib (0.5 pM, MedChemExpress HY-10492) and the DNA damage-inducing agent doxorubicin (1 pM, MedChemExpress HY-15142), which trigger Gl-S and G2-M arrest, respectively. NYG-LIPP-228.PCT

[0206] Images were analyzed using the Incucyte Cell-by-Cell Analysis Software Module (Sartorius 9600-0031). Intensity of the FUCCI signal was measured per cell by masking each cell for the red and green fluorescence channels using a top-hat masking strategy restricted to the mean cell area. Classification of cells into high-ret / (Gl), high-green (S phase) and high- red I high-green (G2-M) was done within the Cell-by-Cell Analysis Software based on the distribution of red and green intensities across cells. We computed the percentage of cells with high or low levels of either or both red and green signal intensity and the mean number of cells per well. To test if there were differences in the distribution of the high-re (Gl), high-greew (S phase) and high-rec / / high-greew (G2-M) populations between the cells perturbed with non-targeting gRNAs and with gRNAs targeting specific IncRNAs, we conducted a Mann- Whitney U test comparing each IncRNA targeting gRNA to the control (non-targeting) gRNA. The cell counts per well at 24-, 48- or 72-hour timepoints were normalized to the timepoint right after addition of doxycycline (0 hour). To evaluate changes in cell viability at the endpoint, the cell counts per well for each perturbation were normalized to the median cell count for the cells transduced with control (non-targeting) gRNA. Apoptosis assays

[0207] 7? / rCasl3d-expressing MDA-MB-231 cells were transduced in two biological replicates with lentiviruses produced from corresponding pLentiRNAGuide_001, as described in Cell cycle analyses. We selected the transduced cells for three days using 1 pg / ml puromycin. Then, we seeded 2,000 cells into three wells of a 96-well plate (Coming 3904) per transduction and cultured the cells in media supplemented with Annexin V (Sartorius 4642, final concentration 5 pM Annexin V). At 72 hours, we acquired nine images per well at 20* magnification (Incucyte S3). We determined the relative area of Annexin V-positive cells by measuring the area of Annexin Vpositive cells and the total cell area. Representative endpoint images show the confluence masks of total and Annexin V-positive cells (Incucyte Live Cell Analysis software).

[0208] RNA interference

[0209] The small interfering RNAs (siRNAs) were designed using the IDT Custom Dicer- Substrate siRNA (DsiRNA) tool

[0210] (https: / / www.idtdna.com / site / order / tool / index / DSIRNA_CUSTOM). A DsiRNA targeting the coding sequence of Renilla luciferase was selected as a negative control (CTRL). For the IncRNAs, sequences targeting 1 - 3 different exons were chosen. For each target IncRNA, we pooled three individual DsiRNAs (IDT) at equimolar ratios to ensure robust knockdown and NYG-LIPP-228.PCT reduce off-target effects. We seeded 250,000 HEK293FT cells in 12-well plates and transfected each well with 25 nM DsiRNA pools using 4.5 pl Lipofectamine RNAiMAX (Thermo 13778075). Twenty-four hours later, 2,000 cells were seeded in 96-well plates and monitored for 60 hours in media supplemented with Annexin V (Sartorius 4642, final concentration 5 pM Annexin V). At 72 hours, we acquired nine images per well at 20x magnification (Incucyte S3). We determined the proliferation of cells transfected with IncRN Atargeting siRNA pools by normalizing the cell area to 1) the initial time point (24 hours after transfection) and 2) the median of cells transfected with CTRL DsiRNA (median computed over 54 images with 9 images per biological replicate and 6 biological replicates for CTRL DsiRNA). For apoptosis, we determined the relative area of Annexin V-positive cells by measuring the area of Annexin V-positive cells and the total cell area. Representative endpoint images show the confluence masks of total and Annexin V-positive cells (Incucyte Live Cell Analysis software).

[0211] RNA-sequencing For cell line expression profiles, total RNA-sequencing libraries were prepared from parental and 7? / xCasl3d-expressing HAP1, K562, HEK293FT and THP1 cells using the KAPA Total RNA-seq kit with RiboErase (Roche) seven days after doxycycline induction (1 pg / ml). Both parental HAP1, K562, HEK293FT and THP1 cells and / / xCas l 3d-expressing HAP1, K562, HEK293FT and THP1 cells were treated with doxycycline. RNA extraction. library preparation and sequencing (Illumina NovaSeq 2500) were performed by the New York Genome Center’s Sequencing Platform. Total RNA-seq files for MDA-MB-231 were downloaded as fastq from the RNA Atlas 77. For differential gene expression after IncRNA perturbations (bulk mRNA-seq), RfxCas 13dexpressing MDA-MB-231 cells were transduced in two biological replicates with lentiviruses produced from corresponding pLentiRNAGuide_004, as described in Arrayed gRNA cloning and lentiviral production. We chose the same gRNAs used in the cell cycle (FUCCI) and apoptosis (Annexin V) assays. We selected the transduced cells for three days (1 pg / ml puromycin) and induced Casl3 expression by 1 pg / ml doxycycline for 48 hours. Total RNA was isolated using the Direct-zol RNA Purification Kit (Zymo R2062) with DNasel treatment and quantified using the Qubit RNA XR Assay Kit (Thermo Q10210). To generate strand-specific mRNA-seq libraries, we used the Stranded mRNA Prep kit (Illumina 20040532) with RNA UD Indexes Set A (Illumina 20040553) and followed the manufacturer’s protocol using 1 pg total RNA as input for mRNA capture. Libraries were quantified using the Qubit dsDNA HS Assay Kit (Thermo Q32851) and the High Sensitivity DNA Kit (Agilent 5067-4626) using a Bioanalyzer 2100 NYG-LIPP-228.PCT

[0212] (Agilent), as described in CaRPool-seq experiments. Pooled libraries (bulk mRNA-seq) were sequenced using a NextSeqSOO (Illumina).

[0213] H3K27ac AQuA-HiChIP

[0214] We prepared H3K27ac HiChIP libraries using a modified protocol with a mouse spike-in control 105 and two biological replicates. Briefly, we fixed ten million HAP1 cells in 1% formaldehyde for 10 minutes and quenched to a final concentration of 125 nM glycine. The cells were lysed in 0.5% SDS, quenched with 10% Triton X-100, and digested with 200 units Mbo] (NEB R0147M) at 37°C for 2 hours to produce blunt ends. After heat inactivation at 62°C for 20 minutes, the blunt DNA overhangs were biotinylated using DNA Polymerase I Large (Klenow) Fragment (NEB M0210L), 288 pM biotin-dATP (Thermo 19524016). dTTP, dGTP, and dCTP at 37°C for 1 hour. Biotinylated products were ligated using T4 DNA ligase (NEB B0202S) at room temperature for 4 hours. Nuclei were spun down, resuspended in nuclear lysis buffer (50 mM Tris-HCl pH 7.5, 10 mM EDTA, 1% SDS) and sonicated using a Covaris LE220 with the following conditions: Fill level 10, PIP 450, Duty7factor 30, CPB 200. We incubated the sheared DNA with Dynabeads Protein A (Thermo 10001D) for 2 hours at 4 °C. We then placed the tubes on a magnet and the supernatant was kept. We performed immunoprecipitation with a cross-species reactive H3K27ac antibody (Active Motif 39133). The samples were incubated with the antibody overnight at 4 °C. Dynabeads Protein A (Thermo 1000 ID) were added to the samples and incubated for 2 hours at 4°C to bind the DNA with H3K27ac antibodies. The beads with bound DNA were washed, the DNA was eluted, and the DNA was treated with Proteinase K (NEB P8107S). We purified the samples using DNA Clean & Concentrator (Zymo D4014). Biotin capture was performed with Dynabeads M-280 Streptavidin (Thermo 11205D), followed by library7preparation 105. We purified the amplified libraries with Sample Purification Beads (Illumina). Libraries were sequenced to generate 100-200 million read pairs per replicate (Illumina NextSeq 500). Quantitative reverse-transcription PCR (RT-qPCR)

[0215] To measure the target expression after IncRNA perturbations, RfxCas 13d-expressing cells were transduced in two biological replicates with lentiviruses produced from corresponding pLentiRNAGuide_004. as described in Arrayed gRNA cloning and lentiviral production. We selected the transduced cells for three days (1 pg / ml puromycin) and then induced Cast 3 expression by 1 pg / ml doxycycline. At 24 hours after Casl3 induction, total RNA w as isolated using the Direct-zol RNA Purification Kit (Zymo R2062) with DNasel treatment. For cDNA synthesis, 1 pg total RNA served as a template using RevertAid Reverse Transcriptase (Thermo EP0442) and random hexamer primers. RT-qPCR was NYG-LIPP-228.PCT performed using a QuantStudio 5 (Applied Biosystems) with Luna Universal qPCR Master Mix (NEB M3003E) and 2.5 pl 1 / 20-diluted cDNA as template in 5 pl reactions: 95 °C for 60 s, 45 x (95 °C for 15 s, 60 °C for 30 s). The relative transcript abundance was normalized ioACTB and control (non-targeting) gRNAs (DDCt method).

[0216] QUANTIFICATION AND STATISTICAL ANALYSIS

[0217] Pooled screen analysis

[0218] Quality of the raw fastq fdes was assessed using MultiQC 106. We processed reads from pooled Casl3d screens following established pipelines 23,50. In brief, reads were de- multiplexed based on Illumina i7 barcodes and custom i5 barcodes. We trimmed reads to the expected gRNA length by identifying known anchor sequences relative to the guide sequence. We did this using Cutadapt (v.1.13) 80 with the following parameters: -g CTGGTCGGGGTTTGAAAC -e 0.2 -O 5 -discarduntrimmed and -a TTTTTGAATTCGCTAGCT -e 0.1 -O 5 -minimum-length 15 —discarduntrimmed. We aligned processed reads to the designed crRNA reference using bowtie (v.1.1.2)

[0219] 81 allowing for up to three mismatches (parameters: -v 1 -m 3 -best -q). The raw gRNA counts were normalized using median-of-ratios (geometric mean), similar to DESeq2 87, and batch correction was applied using combat from the SVA R package (v.3.34.0) 82. We removed nonreproducible technical outliers by pair-wise linear regression for each sample, collecting residuals, and taking the median value for each gRNA across biological replicates. To calculate the correlation between biological replicates, we log-transformed the count ratios between time points (Day 14 or Day 7) after Casl3-induction and the corresponding early time point (Day 0). To identify essential genes, the mean log2 foldchange was determined using the five most depleted gRNAs per target gene and robust rank aggregation (RRA, vl.2. 1) 83. As a negative control, RRA analysis of randomized gRNAs and genes, repeated ten times, yielded no significant hits. For downstream screen analyses, we only considered expressed genes (TPM > 0) based on total RNA-seq in each cell line. For essential IncRNAs. we determined enrichment for genomic class and evolutionary age using Fisher’s exact test over all IncRNAs in the Cast 3 library. Total RNA-sequencing analyses

[0220] We aligned total RNA-seq fastq reads from five cell lines (HAP1, HEK293FT, K562, MDA-MB- 231 and THP1). Gene expression for protein-coding genes (PCGs) was determined by aligning to the GENCODE GROG 8 reference genome (GRCh38.dl.vdl.fa.tar) with the GENCODE v36 reference gene annotation NYG-LIPP-228.PCT

[0221] (gencode. v36.annotation.gtf.gz) using the STAR aligner 84. Gene expression for long noncoding RNAs (IncRNAs) was determined by aligning to the GENCODE GRCh38 reference genome (GRCh38.dl.vdl.fa.tar) with the Sarropoulos et al. IncRNA developmental atlas annotation (human. IncRNA. gtf) using the STAR aligner. For that, we first converted the IncRNA atlas from hgl9 to hg38 using UCSC Lift Genome Annotations (https: / / genome.ucsc.edu / cgi-bin / hgLiftOver).

[0222] For both alignments, quantification was conducted using RSEM 85. To identify differentially expressed genes (DEGs) between parental and Casl3-engineered cell lines for HAP1, HEK293FT, K562, the RSEM files were imported and summarized into matrices for gene-level analysis using the tximport R package 86. We applied the Wald test within the DESeq2 (v.3. 19) R package 87, utilizing an experimental design formula of ~0 + cell line + casl3_engineered to zerocenter the data. The inclusion criteria for the analysis mandated that genes possess a minimum of 10 reads across all samples We then categorized genes as differentially expressed if they had an adjusted P value < 0.05. H3K27ac AQuA-HiChIP and Hi-C analysis

[0223] HiChIP paired end reads were mapped to hg38 genome using HiC-Pro 96 with default settings, which included removing duplicate reads, identifying valid interactions, and generating contact maps. Valid pairs supported by at least three reads and spanning distances greater than 5 kb were preserved and binned into 5 kb bins. We used pybedtools (v.0.10.0) 97.98 to intersect valid pairs with the promoter regions of expressed IncRNAs in HAP1 cells and protein-coding genes (PCGs).

[0224] Promoter regions were defined as } 2 kb of the transcription start site from the most abundant transcript. To focus our analyses on PCGs beyond the closest genes in 2D, we removed any contacts between IncRNAs and their closest (2D) PCG. To identify protein- coding genes localized in the same topologically associating domain (TAD) as essential IncRNAs, we intersected the promoter regions of expressed essential IncRNAs with bed files containing TAD boundaries from the Hi-C data Browser 78 (http: / / 3dgenome.fsm.northwestem.edu / view.php) using pybedtools (v.0.10.0) 97,98. For TADs in HAP1 cells, we used its parental cell line, KBM7. Essential PCGs were identified as those with a median DepMap score of less than -0.5 across all cell lines (DepMap release 23Q2).

[0225] CaRPool-seq analysis: Pooled screen

[0226] For the CaRPool-seq pooled screen readout, we first identified all reads containing the end of the U6 promoter (last 24 nucleotides) and the first direct repeat, allowing up to three NYG-LIPP-228.PCT mismatches for each sequence. For these reads, we then searched for the barcode gRNA, allowing for up to one mismatch. The processed reads were aligned to the barcode / library reference using bowtie (v. 1. 1.2) 81 with the following parameters: -v 1 -m 1 -best -strata. Raw counts were normalized using a median of ratios methods as in DESeq2 87. For each barcode guide RNA (bcgRNA), we calculated the mean fold-change for the three gRNA arrays for each gene across the two replicates (Day 0 vs Day 12).

[0227] CaRPool-seq analysis: Single-cell

[0228] Sequencing reads from the cDNA library' were mapped to a pre-built reference (refdata-gex- GRCh38-2024-A, downloaded from https: / / www.10xgenomics.com / support / software / cellranger / downloads) using lOx Genomics Cell Ranger (v.6.0.0) 89. Barcode guide RNA (bcgRNA) library reads were concurrently mapped to a barcode reference using Cellranger count function with the following parameter: -expect-cells=13000 -nosecondary — chemistry=SC3Pv. The resulting count matrices were then used as input into the Seurat R package (v.4. 1. 1) 90 to perform all downstream analyses.

[0229] We performed an initial quality control step to preserve only those cells of sufficiently high quality: Cells with high mitochondrial gene content (> 20%) and low or high number of genes detected (<1000 and > 8000) were removed from the analysis. The median number of detected genes per cell was 5,457 for HAP1 and 4,974 for MDA-MB-231, with a median of unique molecular identifiers (UMIs) of 23,817 and 22,654 per cell, respectively. RNA counts were log normalized using the NormalizeData function. We used the FindVariableFeatures function to identify the top 2,000 most variable features for scaling the data using the ScaleData function with the following parameters: vars. to. regress = c(“nCount_RNA”). Guide array identity for cells in the CaRPool-seq pool was assigned based on bcgRNA UMI counts, which were normalized using the centered log-ratio transformation approach, with margin = 2 (normalizing across cells). A gRNA array was considered detected if it had > 3 UMI counts. The median UMI counts per bcgRNA was 134.5, and 95% of cells had > 1 bcgRNA.

[0230] We observed that 46% of the cells had more than one detected bcgRNA (49% had one bcgRNA, 30% had two bcgRNAs, and 11% had three bcgRNAs). To maximize cell recovery, we retained cells with multiple bcgRNA detected by comparing UMI counts for the bcgRNA with the highest UMI count (gl) to the second detected bcgRNA (g2). We assigned gl, as previously described 50. if: (1) gl had between 5 and 9 UMI counts and g2 had 0 or 1 UMI count, or (2) gl had more than 9 UMI counts, gl / (g 1 + g2) was greater than 0.8, and g2 had NYG-LIPP-228.PCT fewer than 11 UMI counts. After this step, we obtained 6,606 HAP1 and 5,933 MDA-MB- 231 single cells. Cells with gl < 3 were considered negative and all other cells were considered bcgRNA multiplets and were discarded.

[0231] To identify target gene perturbations that lead to transcriptomic changes, we used FindMarkers to find differentially-expressed genes between non-targeting cells and cells that belonged to a targeted gene class with the following parameters: pseudocount. use=0.01, logfc.threshold=0.01, test. use- ‘wilcox”. The mean expression of each gene was calculated using AverageExpression. The number of differentially-expressed genes per perturbation was calculated using adjusted P < 0.05. For the heatmaps in Figures 5C, S8C, and S8E, we selected the nine IncRNA perturbations that resulted in the highest number of differentially expressed genes for display and re-normalized data including only these perturbations and the non-targeting control. Using this re-normalized data, for each perturbation, we identified up to 25 of the most significantly differentially expressed proteincoding genes (adjusted P < 0.05). Among the downregulated genes, those with the lowest DepMap scores (release 23Q2) were highlighted. For the upregulated genes, we highlighted those with the lowest fold-change in an overexpression screen for the proliferation of human mammary epithelial cells (HMEC) 51.

[0232] Bulk mRNA-seq processing

[0233] Quality of the raw fastq files was assessed using MultiQC 106. Sequencing adapters were clipped off using Cutadapt (v2. 10) 80 with the following parameters: -q 20 -O 7 -m 20 - -trim-n. The processed sequencing reads were aligned to a pre-defined reference (UCSC hg38, downloaded from https: / / daehwankimlab.github.io / hisat2 / download / ) using HiSat2 (v2. 1.0) 92 with the following parameters: -q -dta -k 5. Alignments in the obtained bam files were sorted and indexed using samtools (vl.9) 101. FeatureCounts (v2.0.4) 91 was used for summarizing genemapped reads with the following parameters: -p -s 0. GENCODE v36 (gencode. v36.annotation.gtf.gz) served as annotation basis. Differential gene expression between

[0234] IncRNA perturbations and cells transduced with non-targeting gRNAs was determined using DESeq2 (v.3. 19) 87 with the two biological replicates as condition in the design formula. Developmental gene expression analyses

[0235] To analyze gene expression during human development, we used data from two recently published studies 19,76. For each organ, we compared the expression (in RPKM) of IncRNAs or PCGs in prenatal and postnatal tissues and calculated median expression values NYG-LIPP-228.PCT for each time point across samples and for all genes in the respective groups based on essentiality in our Cast 3 screens.

[0236] Tissue- and time-specificity indices were previously determined for each corresponding gene in these studies 19,76: They range between 0 for broad expression and 1 for restricted expression. We also compared the dynamics in expression (significant temporal alterations during development) across four tissues (brain, heart, kidney, and liver) for different sets of IncRNAs. as classified by essentiality (shared, partially shared and cell-type- specific essential, as well as non-essential IncRNAs). For co-expression studies, we computed the correlation between the expression of each IncRNA and that of each PCG at matched time points and donors. This was done using the log2 -transformed expression data (log2 (RPKM+1)).

[0237] Tumor gene expression and survival analyses

[0238] Fastq files of TCGA RNA-seq samples across 29 cancer types were accessed from database of Genopytes and Phenotypes (dbGaP) via accession number phs000178.vl l.p8, and downloaded from NIH Genomic Data Commons Data Portal (https: / / portal.gdc.cancer.gov / ). These 29 cancer types included in this study are adrenocortical carcinoma (ACC), bladder urothelial carcinoma (BLCA), brain lower grade glioma (LGG), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), lymphoid neoplasm diffuse large B cell lymphoma (DLBC), mesothelioma (MESO), pancreatic adenocarcinoma (PAAD), pheochromocytoma and paraganglioma (PCPG), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), sarcoma (SARC), skin cutaneous melanoma (SKCM), testicular germ cell tumors (TGCT), thymoma (THYM), thyroid carcinoma (THCA), uterine carcinosarcoma (UCS), uterine corpus endometrial carcinoma (UCEC), and uveal melanoma (UVM). The sample set consists of 9,564 total TCGA samples, including 8,878 primary tumor samples and 686 normal tissue samples.

[0239] Fastq reads were aligned to the GENCODE GRCh38 reference genome (GRCh38.dl.vdl.fa.tar) with the GENCODE v36 reference gene annotation (gencode.v36.annotation.gtf.gz) using the STAR aligner 84. Gene expression for long noncoding RNAs (IncRNAs) was determined by aligning to the GENCODE GRCh38 NYG-LIPP-228.PCT reference genome (GRCh38.dl.vdl.fa.tar) with the Sarropoulos et al. IncRNA developmental atlas annotation (human.lncRNA.gtf) using the STAR aligner. For that, we first converted the IncRNA atlas from hgl9 to hg38 using UCSC Lift Genome Annotations (https: / / genome.ucsc.edu / cgi-bin / hgLiftOver). For both alignments, quantification was conducted using RSEM 85. The GRCh38 reference genome and the GENCODE v36 annotation file were downloaded from https: / / gdc.cancer.gov / about-data / gdc-data- processing / gdcreference- files. Subsequently, RSEM files were imported and summarized into matrices in R for gene-level analysis, utilizing the tximport R package 86. For the identification of differentially expressed genes (DEGs), we applied the standardized Wald test within the DESeq2 (v.3. 19) function 87, utilizing an experimental design formula of ~0 + sample type to zero-center the data. The inclusion criteria for the analysis mandated that genes possess a minimum of 10 reads across all samples We then categorized genes as differentially expressed if had an adjusted P value < 0.2. DEG analysis was only conducted for 22 cancer Npes where normal tissue samples were available. For survival analysis, primary tumor samples were stratified based on the expression levels of the corresponding IncRNAs: samples with TPM values exceeding the median were assigned to the high group, while those with TPM values less than or equal to the median were assigned to the low group. The survival probability7for both progression-free survival and overall survival was subsequently computed for each group using the survival (v.3.2.7) and survminer (v.0.4.9) R packages. We assessed the association of the respective IncRNAs with changes in overall or progression-free survival using the logrank test, and the results yvere adjusted for the large number of comparisons using Benjamini -Hochberg correction ( adj < 0.05). After identifying these, we assessed the significance of the proportion in each category of essential IncRNAs relative to nonessential IncRNAs using a Fisher’s exact test. For co-expression studies, we matched our annotated IncRNA and existing PCG expression data (https: / / portal.gdc.cancer.gov / ) for each individual sample based on the provided TCGA identifier. We determined the Pearson correlation for each IncRNA with every7PCG on log2- transformed data.

[0240] Matched expression IncRNA analyses To control for the effect of maximum expression of essential and non-essential

[0241] IncRNAs in developmental tissues or tumor samples, yve generated sets of expression- matched IncRNAs. For direct matching, we paired essential IncRNAs with their closest non- essential counterparts by sampling without replacement. For the 778 essential IncRNAs, we matched 683 unique nonessential IncRNAs based on their closest expression levels in NYG-LIPP-228.PCT developmental tissues and 690 unique non-essential IncRNAs based on their closest expression levels in tumor samples.

[0242] Gene set enrichment analyses

[0243] Gene set enrichment analyses (GSEA) were conducted on pre-ranked lists using the R package clusterProfiler (v4.10.0)88 using MSigDB (v2023.2)107, 108 gene sets for 50

[0244] Hallmarks pathways (geneSetFile = h.all.v2023. l.Hs. symbols. gmt) using the GSEA function with the following parameters: geneList = geneList. TERM2GENE = geneSetFile. exponent = 0, pAdjustMethod = "‘fdr”, pvalueCutoff = 1, by = “fgsea”. For the geneList, proteincoding genes (PCGs) were ranked based on either their fold-change (log2) following IncRNA perturbations in single-cell or bulk mRNA-seqs or their co-expression (pearson correlation coefficient) with the specified IncRNAs across human organ development or in primary human tumors. P values were adjusted for multiple comparisons using Benjamini -Hochberg correction.

[0245] To categorize the 50 Hallmark pathw ays, we annotated the following hallmarks as proliferation pathways, as described recently 75: G2M checkpoint, E2F targets, Mitotic spindle organization, MYC targets (yl and v2), and the p53 pathway.

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[0355] Proc. Natl. Acad. Sci. U. S. A. 102, 15545-15550. NYG-LIPP-228.PCT

[0356] The present invention is not to be limited in scope by the specific embodiments described herein, since such embodiments are intended as but single illustrations of one aspect of the invention and any functionally equivalent embodiments are within the scope of this invention. Indeed, various modifications of the invention in addition to those shown and described herein will become apparent to those skilled in the art from the foregoing description and accompanying drawings. Such modifications are intended to fall within the scope of the appended claims.

[0357] All publications, patents and patent applications referred to herein are incorporated by reference in their entirety to the same extent as if each individual publication, patent or patent application was specifically and individually indicated to be incorporated by reference in its entirety. The citation of any reference herein is not an admission that such reference is available as prior art to the instant invention.

Claims

NYG-LIPP-228.PCTCLAIMS:

1. A method of treating cancer in a subject comprising reducing the level of a long noncoding RNA (IncRNA) in the subject.

2. The method of claim 1, wherein the IncRNA is selected from Table 1.

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

4. The method of claim 1 or claim 2, wherein the cancer is a breast cancer.

5. The method of claim 1, wherein the IncRNA is selected from Table 2.

6. The method of claim 1, wherein the cancer is kidney renal cell carcinoma (KIRC).

7. The method of claim 1, wherein the cancer is a low-grade glioma (LGG).

8. The method of any one of claims 1 to 5, wherein the IncRNA is reduced using siRNA or an ASO.

9. The method of claim 8, wherein the siRNA is selected from those of Table 5.

10. A method of treating cancer in a subject comprising increasing the level of a long noncoding RNA (IncRNA) in the subject.

11. The method of claim 1 , wherein the IncRNA is selected from Table 3.

12. The method of claim 1 or claim 2, wherein the cancer is a blood cancer.

13. The method of claim 1 or claim 2, wherein the cancer is a breast cancer.

14. The method of any one of claims 10 to 14, wherein the IncRNA level is increased by administering a nucleic acid comprising the IncRNA sequence.

15. The method of claim 14, wherein the nucleic acid is an mRNA molecule.

16. The method of claim 14, wherein the nucleic acid is comprised in a viral vector.

17. A method of treating cancer in a subject comprising reducing the level of a gene expression product of a gene selected from Table 4 in the subject.

18. The method of claim 17, comprising administering a small molecule, ASO. siRNA, or antibody that reduces the level of the gene expression product.

19. A method of treating cancer in a subject comprising reducing the level of a long noncoding RNA (IncRNA) and the level of in the subject a gene expression product of a gene selected from Table 4.

20. The method of claim 19, wherein the IncRNA and gene are located in close proximity on a chromosome of the subject.

21. The method of claim 19 or 20, wherein the IncRNA is selected from Table 1 or 2.

22. A composition comprising an antisense oligo or siRNA against an IncRNA of Table 1, 2, or gene of Table 4.

23. The composition of claim 22, wherein the siRNA is selected from those of Table 5.

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