Systematic lncRNA mapping to genome-wide co-essential pathways uncovers cancer dependency on uncharacterized lncRNAs

Quantification of gene dependency across hundreds of cell lines using genome-scale CRISPR screens have revealed co-essential pathways/modules and critical functions of uncharacterized genes. In contrast to protein-coding genes, robust CRISPR-based loss-of-function screens are lacking for long non-coding RNAs (lncRNAs), which are key regulators of many cellular process, leaving many essential lncRNAs unidentified and uninvestigated. Integrating copy- number, epigenetic, and transcriptomic data of >800 cancer cell lines with CRISPR-derived co-essential pathways, our method recapitulates known essential lncRNAs and predicts proliferation/growth dependency of 289 poorly characterized lncRNAs. Analyzing lncRNA dependencies across ten cancer types and their expression alteration by diverse growth- inhibitors across cell types, we prioritize 30 high-confidence pan-cancer proliferation/growth- regulating lncRNAs. Further evaluating two previously uncharacterized top proliferation-suppressive lncRNAs (PSLR-1, PSLR-2) showed they are transcriptionally regulated by p53, induced by multiple cancer treatments, and significantly correlate to increased cancer patient survival. These lncRNAs modulate G2 cell cycle-regulating genes within the FOXM1 transcriptional network, inducing a G2 arrest and inhibiting proliferation and colony formation. Collectively, our results serve as a powerful resource for exploring lncRNA-mediated regulation of cellular fitness in cancer, circumventing current limitations in lncRNA research.

). Using two independent statistical tests 126 (hypergeometric based enrichment analysis and a thousand randomization test), we estimated 127 whether a lncRNA regulates a module with higher confidence (Methods section; Figure 1C). 7 The regulating lncRNAs were ranked for each co-essential module based on their 137 average regression coefficients with the module members. These rankings summarize  Tables 7 and 8). Using a comprehensive PubMed search with the 145 keywords 'lncRNA' and 'cancer', we determined that over 66% of the identified lncRNAs 146 have not been studied in cancer. Another 10% of the lncRNAs are poorly characterized, as they 147 have only been mentioned in a single study, including those with exclusively prediction results 148 (Supplementary Table 9). Collectively, our results reveal many unknown significant 149 associations between lncRNA and cancer cell proliferation/growth regulatory networks. 150 To validate our predictions, we evaluated lncRNAs from the small nucleolar RNA host   Figure 2C). Therefore, the observed alterations of the proliferation/growth inducing and 204 suppressing lncRNAs suggest they are selected for during tumorigenesis to allow for cancer 205 cell proliferation/growth. 206 We also determined that 91% of known proliferation inducing lncRNAs (highlighted 207 in Figure 1E) have potentially induced proliferation/growth in a higher number of TCGA 208 cancer types by regulating a higher number of Hallmark proliferation genesets ( Figure 2D).

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Similarly, four of five known p53-regulated tumor-suppressor lncRNA show a negative impact 210 on a higher number of proliferation genesets across TCGA cancer types ( Figure 1E and Figure   211 2D). We determined 30 uncharacterized or poorly characterized lncRNAs that potentially 212 regulate at least five Hallmark proliferation-linked genesets across five or more TCGA cancer 213 types ( Figure 2E). Their reproducible association with proliferation in both CCLE and TCGA 214 datasets indicates they have high-confidence proliferation regulation potential that has not been 215 addressed previously.

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Identifying proliferation/growth-regulating lncRNAs altered frequently by growth inhibitors 218 By analyzing seven RNA-seq datasets, we identified the proliferation/growth-219 regulating lncRNAs altered at least two-fold in cells treated by one of the proliferation 220 inhibiting DNA damaging stimuli (doxorubicin, 5-fluorouracil, gamma radiation) compared to 221 control (Supplementary Table 11). We determined proliferation/growth suppressing and 222 inducing lncRNAs were preferentially elevated and reduced, respectively ( Figure 3A). These  (Methods sections; Supplementary Table 11). Compared with the control cells, Nutlin 229 treated cells showed a preferential up and down-regulation (at least two-fold) of the 230 proliferation/growth-suppressing and inducing lncRNAs, respectively, similar to what we 231 observed from the cells treated by the DNA damaging proliferation inhibitors ( Figure 3A).

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Furthermore, we observed reproducible expression patterns in Nutlin treated single-cell RNA-233 seq profiles with p53 wild-type cells, but not for cells with mutant p53 ( Figure 3B). Our results 234 suggest that our identified proliferation/growth-regulating lncRNAs have notable crosstalk 235 with p53-mediated tumor-suppressive pathways. 236 We determined that 7-32% (average=17.2%) of the identified lncRNAs have >2-fold  Table 14). Consistent reduction or increased 249 expression of these proliferation/growth-inducing or suppressing, respectively, lncRNAs  With the analysis of 17 p53 ChIP-seq datasets (Supplementary Table 15), we 261 investigated whether the 13 lncRNAs that were consistently up-regulated following treatment 262 with proliferation inhibitors have the potential to be directly transcriptionally regulated by p53. 263 We determined that a significantly (P=1.65x10 -9 ; Wilcoxon rank-sum test) higher number of 264 ChIP-seq datasets had p53-binding sites within 10 kilobases from the transcription start site of 265 these lncRNAs compared with the remaining lncRNAs (background data) annotated in    Table   282 11); therefore, our results from the high-throughput data analysis were validated in a 283 completely independent cell type. These data indicate that previously uncharacterized lncRNA 284 are regulated by p53.  287 To gain a greater understanding of PSLR-1 and -2, we evaluated their expression levels 288 in our own LUAD patient samples. Both lncRNAs were significantly (P<1.23x10 -7 ; t-test) 289 reduced in LUAD patient samples compared to normal lung tissue, indicating suppression of    337 To further verify whether PSLR-1 and PSLR-2 have growth suppressive effects in   357 To gain insight into the PSLR-induced G2 cell cycle arrest ( Figure 5D), we determined 358 from our RNA-seq data which genes in the G2 regulatory sub-network that were indicated in 359 the KEGG cell cycle pathway had significant alterations following increased expression of the 360 PSLRs ( Figure 6A). There was reduced expression of nine genes (blue squares; median fold-   systems-level analysis aiming to capture specific co-essential modules whose alterations are 445 associated with significant modulation of hallmark proliferation signatures, which could 446 directly impact cellular fitness in cancer.

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In the co-essential network modules, the p53 tumor suppressor is negatively associated      581 We evaluated 14 RNA-seq datasets from Gene Expression Omnibus (GEO) (Barrett et al.,         RNA-seq data: The RNA-sequencing data we generated will be deposited to Gene Expression 718 Omnibus and NCBI Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra) databases. 719 We make extensive use of previously published algorithms and datasets as described in the          ectopically express PSLR-1, PSLR-2, MALAT1, or empty vector control. A) Viability was determined by Trypan Blue dye exclusion 48hr after plating. Graphs represent the average ± SD from 4 independent experiments for both cell lines. B) Intracellular staining for phospho-Histone H3 was performed 2 independent times, in duplicate, 48hr after plating. One representative experiment is shown with quantification (top; ± SEM) and representative flow plots (bottom).

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Colcemid was used to arrest cells in mitosis and serves as a positive control.