CRISPR activation screens identify oncogenic lncRNAs that are susceptible to CDK4/6 inhibitor treatment

Domain: neuroscience
Published: 2026-04-09
Editorial outcome: major_revision

Abstract
The roles of long non-coding RNAs (lncRNAs) in tumorigenesis and therapeutic response remain largely unknown. Here we perform genome-wide and focused CRISPR activation screens to identify lncRNAs regulating palbociclib response in breast cancer cells. A synchronized two-stage proliferation screen not only characterizes tumor growth-regulating lncRNAs, but also reveals a strong negative correlation between lncRNA-mediated regulation of tumor proliferation and CDK4/6 inhibitor sensitivity. By integrating CRISPRa screen results with drug response data from 815 cancer cell lines, we identify and functionally validate that TENM3-AS1, LINC01117, and ENSG00000226706 can increase breast cancer sensitivity to CDK4/6i while promoting tumor proliferation. In breast cancer patients, all three lncRNAs’ signatures are associated with CDK4/6 inhibitor response. Mechanistically, we have shown that lncRNA TENM3-AS1 is a potential ERα-interacting lncRNA, and its regulation of CDK4/6 inhibitor sensitivity is dependent on ERα expression. Our integrated strategy characterizes oncogenic lncRNAs as potential therapeutic biomarkers for CDK4/6 inhibitor treatment in cancer.

Introduction
Tremendous efforts have been made to better characterize novel cancer genes that can serve as targets and biomarkers of cancer treatment. Those large-scale, high-throughput genomics efforts, mainly focusing on protein-coding components of the genome, have led to many insightful discoveries1,2, but also new questions: few novel cancer genes have been identified to fully explain the molecular and clinical heterogeneity of tumor initiation, progression, and therapeutic response3. Recent large-scale studies, such as the Encyclopedia of DNA Elements (ENCODE), have identified numerous long non-coding RNAs (lncRNAs)4,5. Further bioinformatics analyses of cancer transcriptome and epigenome have revealed that lncRNAs are among the most prevalent transcriptional changes in cancer6-9. Functional characterizations of the lncRNAs have suggested that some of them play important roles in tumorigenesis5,10-14, metastasis15, and cancer treatment response9,16-20. For example, lncRNA HOTAIR has been found to augment ER signaling, leading to tamoxifen resistance in breast cancer patients21. Our previous research found that the oncogenic lncRNA EPIC1 contributes to Bromodomain and Extra-Terminal inhibitors (iBET) resistance by enhancing the transcriptional activity of the MYC protein9,22. Moreover, lncRNA EGFR-AS1 and MIR205HG are shown to regulate erlotinib sensitivity through in vitro studies and lung cancer patient cohorts16. Previous studies have also shown that lncRNA LEIGC inhibits the epithelial-to-mesenchymal transition and enhances the sensitivity to 5-fluorouracil in gastric cancer 23.

Despite these advancements, the function of the vast majority of lncRNAs in cancer initiation, progression, and treatment response remains elusive. Most of the discoveries on novel lncRNAs start from computational analysis, which primarily detects associations rather than functional causality. High-throughput functional genomic studies, such as CRISPR screens, provide a robust method to comprehensively characterize novel genes’ roles in specific phenotypes. Numerous loss-of-function high-throughput screening methods, either based on shRNA or CRISPR technology, have been demonstrated to be an effective effort to systematically explore functionally uncharacterized protein-coding genes. Compared with protein-coding genes, lncRNAs are cell lines, species, and disease-specific24-26. They are also expressed at lower levels with highly diverse splicing isoforms. In this regard, loss-of-function CRISPR screen on lncRNAs suffers from low efficacy and power27. In addition, loss-of-function screens effectively identify negative regulator genes of the phenotype, such as essential genes or tumor suppressor genes, but are less efficient in oncogene discovery. Compared with loss-of-function assays, the gain-of-function CRISPR-SAM activation (CRISPRa) system increases the sensitivity and specificity of screens, especially considering lncRNA’s relatively low expression level and diverse splicing isoforms19.

To date, three different CDK4/6 inhibitors (Cyclin-dependent kinase 4/6 inhibitors) (i.e., Abemaciclib, Palbociclib, and Ribociclib) have been approved by the U.S. FDA for breast cancer treatment. CDK4/6 inhibitors are targeted therapies used to treat estrogen receptor-positive (ER+), human epidermal growth factor receptor 2-negative (HER2-) breast cancer. Principal mechanism of CDK4/6 inhibitors is suppressing breast cancer cells’ proliferation by blocking the cell cycle progression mediated by CDK4 and CDK6 proteins. CDK4/6 inhibitors were first designed for hormone therapy-resistant patients who continue to maintain high expression of the estrogen receptor. Clinically, CDK4/6 inhibitors are used as a first-line treatment method when combined with hormone therapy28,29. However, lncRNAs’ functions in patients responding to CDK4/6 inhibitors are not well documented. More importantly, there is a lack of treatment biomarkers to better identify patients who may respond to the regimen.

In this work, we conduct two-stage activation screens using the CRISPRa system to functionally characterize lncRNAs involved in CDK4/6i response. Subsequent analyses identify potential lncRNAs that modulate tumor sensitivity to FDA-approved CDK4/6 inhibitors. In a parallel effort to screen for oncogenic lncRNAs in the same system, we observe a strong negative correlation between lncRNA-mediated regulation of tumor proliferation and CDK4/6i response. Integrating screening results with cancer cell line pharmacogenomic and patient data identifies three oncogenic lncRNAs, including TENM3-AS1, LINC01117, and ENSG00000226706, which promote tumor progression by accelerating cell-cycle transitions. Mechanistically, these lncRNAs enhance CDK4/6 inhibitor sensitivity in part through activation of estrogen receptor alpha signaling.

Results
I. Characterization of CDK4/6i response-regulating lncRNAs by a two-stage CRISPRa screen
To systematically explore the functional roles of lncRNAs in CDK4/6i response, we conducted a genome-wide lncRNA activation screen. Briefly, we established the CRISPR-SAM activation system (Figure 1A and Supplementary Figure 1a) in MCF-7 breast cancer cells, as we previously described19. We then transduced the cells with a 96,458 sgRNA lentiviral library30 targeting 9,744 transcriptional start sites of lncRNAs (Supplementary Figure 1b). The screen included a library control (P0), a non-treatment control sample (P14), and three palbociclib-treated biological replicates (T14a/b/c). These sample groups were well separated in the principal component analysis (PCA) plot, and biological replicates demonstrated good reproducibility (Figure 1B, left). By comparing the palbociclib-treated samples with the non-treatment control sample (P14), we identified lncRNA genes that may regulate cellular response to Palbociclib when activated (Figure 1C and Supplementary dataset 1).

In addition to the genome-wide CRISPRa screen on the MCF-7 cell line, we performed a correlation analysis between gene expression and Palbociclib response across 41 breast cancer cell lines, using data from the Cancer Cell Line Encyclopedia (CCLE) 31 and Genomics of Drug Sensitivity in Cancer (GDSC)32 databases (see Methods). This analysis included both protein-coding genes and non-coding genes, and successfully recapitulated known CDK4/6i response-modulating genes, such as PIK3CA33, PTEN34 , and MUC1635 , as well as genes involved in cell-cycle regulation pathways (Supplementary Figure 1c). In addition, the analysis also revealed lncRNAs (Supplementary dataset 1) whose expression levels were associated with Palbociclib response (P < 0.05).

To validate and prioritize top hits, we further developed a focused sgRNA library specifically activating 127 lncRNAs and 33 PCGs, curated from the genome-wide screen, gene expression-drug response associations, and known cell cycle regulators such as CDK236,37, CDK438, CCNB139, CCND240, and TP5341 (see Methods, Figure 1A and Supplementary Figure 1d). In the focused screen, we designed sgRNAs independently from the genome-wide sgRNA library (Supplementary Figure 1d) to mitigate potential false positives arising from sgRNA off-target effects and activation efficiency variations. Samples from the focused screens were well grouped and showed good reproducibility between biological replicates (Figure 1B, right). We observed 58 lncRNAs that exhibited consistent regulation of Palbociclib response, showing the same direction of selection in both genome-wide and focused screens (see Methods and Figure 1D, Supplementary dataset 2).

Among our discoveries of CDK4/6i regulators in the focused CRISPR screen are CCND1 (FDR = 0.07, Figure 1E) and CCND2 (FDR = 0.03, Figure 1E). Both genes encode cyclin D, which interacts with CDK4/6 proteins directly. Single-gene activation of CCND1 (Supplementary Figure 1e and f) and CCND2 (Supplementary Figure 1g and h) demonstrated that the CRISPRa-sgRNA system can lead to at least 3-fold overexpression for each gene. Further drug-treated MTT and colony formation assays indicated that upregulation of cyclin D genes enhanced the response to palbociclib in MCF-7 cells (Figure 1F, Supplementary Figure 1i, and 1G).

II. LncRNAs that confer susceptibility to Palbociclib treatment tend to promote cell proliferation
CDK4/6 inhibitors can block the cell cycle progression from the G1 to the S phase and have been used to target rapidly proliferating cancer cells 42. After identifying lncRNAs that can modulate Palbociclib response, we seek to investigate their roles in cancer cell proliferation. By comparing the sgRNA distribution of cells 14 days of culture (P14) with cells right after infection (P0), we identified lncRNA genes (Supplementary dataset 1) that may regulate cell proliferation when activated in genome-wide screen. Further focused screen identified TP53 as a proliferation suppressor (FDR = 0.03), while CCNB1 (FDR = 0.11) and CCND2 (FDR = 0.11) were identified as proliferation promoters (Figure 2A and Supplementary Figure 2b). Indeed, the single-gene CRISPRa activations of CCNB1 and CCND2 (Supplementary Figure 2a and 1g) increased cell growth (Figure 2B) and proliferation (Figure 2C). Additionally, the overexpression of CCNB1 and CCND2 also resulted in decreased G0/G1 cell-cycle phase arrest but promoted S, G2, and M phases progression (Figure 2D).

Integrating our findings on proliferation-regulating and CDK4/6i response modulating, we observed a strong negative correlation between lncRNAs’ regulation of cell proliferation and CDK4/6i response (Figure 2E). Specifically, 72.7% of drug-resistant hits were found to suppress cell proliferation, whereas 61.5% of drug-sensitive hits enhanced cell proliferation (Figure 2E, Supplementary dataset 1). This observation highlights a strong link between cell proliferation regulation and the cellular response to Palbociclib. Similar trends were observed in the focused screen (Figure 2E, Supplementary dataset 2). Additionally, this strong negative correlation between cell proliferation and CDK4/6i sensitivity was also observed for protein-coding genes. For example, cell cycle-related genes such as CCND1 and CCND2 were shown to induce sensitivity to palbociclib treatment while promoting cell proliferation (Figure 2F).

Given the observed inverse relationship between tumor proliferation and CDK4/6i sensitivity in breast cancer, we hypothesized that certain lncRNAs function as oncogenes by increasing tumor growth and could serve as potential therapeutic markers for CDK4/6i treatment. Based on their expression in breast cancer patients and cell lines, along with their association with patient survival, three lncRNAs (TENM3-AS1, LINC01117, and ENSG00000226706) were selected for functional validation (see Methods and Supplementary dataset 2, Supplementary Figure 2c). All three lncRNAs were identified to sensitize cells to Palbociclib treatment, while promoting cell proliferation in both genome-wide (Supplementary Figure 2d) and focused screens (Figure 2G). In CCLE and GDSC databases, the selected lncRNAs’ endogenous expression was significantly correlated with CDK4/6i sensitivity across 41 breast cancer cell lines (P < 0.05, Figure 2H). Furthermore, these lncRNAs’ expressions were also correlated with CDK4/6i sensitivity in other cancer types, such as bladder cancer, myeloma, and neuroblastoma (Supplementary Figure 2e). These observations suggested that the tumors overexpressing the selected lncRNAs might be susceptible to CDK4/6i therapy.

III. TENM3-AS1, LINC01117, and ENSG00000226706 are oncogenic lncRNAs that promote cell proliferation, tumorigenesis, while serving as therapeutic markers for CDK4/6 inhibitors
To functionally validate the selected lncRNAs’ impact on Palbociclib’s response, we specifically activated each of the three candidate lncRNAs (TENM3-AS1, LINC01117, and ENSG00000226706) in breast cancer cell lines MCF-7 and T-47D. The CRISPRa system was able to activate corresponding lncRNAs by at least 2.5-fold (Supplementary Figure 3a). We observed that the activation of the lncRNA augments palbociclib sensitivity after 48 hours (Supplementary Figure 3b and 3c) or 72 hours (Figure 3A and 3B) of treatment in both cell lines. Meanwhile, a reduction in colony formation was observed after being treated with palbociclib at different dosages in each lncRNA-activated cancer cells (Figure 3C and 3D). In addition to CRISPR activation, siRNA-mediated knockdown of TENM3-AS1, LINC01117, or ENSG00000226706 led to a resistance to palbociclib in both 48 hr and 72 hr treatments (Figure 3E).

After validating all three lncRNAs’ susceptibilities to palbociclib treatment, we move on to investigate their regulation of cell proliferation and tumorigenesis. Cell growth assays (Figure 4A) and the colony formation assay (Figure 4B) demonstrated that the activation of candidate lncRNAs can increase ER+ breast cancer cells’ proliferation in both MCF-7 and T-47D cells. Further cell cycle assay showed that the activation of three lncRNAs led to an increase in the G2/M phase but a decrease in the G0/G1 resting phase (Figure 4C), which suggested an accelerated cell cycle progression.

To determine if knockdown of the identified lncRNAs will suppress tumor proliferation, we first assessed the expression of lncRNAs in nine cell lines and identified four cell lines that have high endogenous levels of the identified lncRNAs. Specifically, HS578T, SKOV3, U2OS, and MCF-7 showed higher expression levels of TENM3-AS1 and LINC01117, while RKO and MCF-7 have increased endogenous expression of ENSG00000226706 (Supplementary Figure 4a). We designed siRNAs specifically for each lncRNA and knocked down the endogenous lncRNA expression in up to three cell lines with high expression of corresponding lncRNAs (Supplementary Figure 4b, 4c, and 4d). Consistent with our observation in lncRNA activation, the knockdown of lncRNAs TENM3-AS1 (Figure 4D), LINC01117 (Figure 4E), and ENSG00000226706 (Figure 4F) led to decreased cell proliferation across multiple cancer models. Further colony formation assays confirmed that suppressing each of the three lncRNAs led to a reduction of colonies (Figure 4F).

To determine if these lncRNAs can promote breast tumor progression in vivo, we inoculated the lncRNA-activated and control MCF-7 breast cancer cells into the nude mice. Compared with the non-targeting control sgRNA group (NT), activation of each of the three lncRNAs led to an increased tumor growth, as indicated by tumor volume (Figure 5A and Supplementary Figure 5a) and tumor weight (Figure 5B). In addition, immunofluorescence analysis of these tumor tissues showed that the activation of the three lncRNAs upregulated Ki-67’s expression, a known proliferation marker, in breast cancer tumors (Figure 5C).

Among three validated lncRNAs, TENM3-AS1 is classified as an antisense lncRNA. The sgRNAs activating TENM3-AS1 may lead to off-target activation of its neighboring protein-coding gene TENM3. To test this possibility, we designed siRNA to knock down TENM3 mRNA in the sgTENM3-AS1 cells. This analysis revealed that knockdown of the TENM3 (Supplementary Figure 5b) does not affect breast cancer cells’ proliferation (Supplementary Figure 5c), nor abolishes TENM3-AS1’s regulation of cell growth (Figure 5D). In addition to TENM3, TENM3-AS1’s annotated location suggested it may share the promoter with another gene, ENSG00000248266, which is not affected by TENM3-AS1’s sgRNA activation (Supplementary Figure 5d). These observations indicate that lncRNA TENM3-AS1’s regulation of cell proliferation is not mediated by collateral activation of TENM3 or ENSG00000248266. To further corroborate this observation, we overexpressed TENM3-AS1 cDNA (805 bp, see Methods) in breast cancer T-47D cells (Figure 5E). Similar to the sgTENM3-AS1 activation, overexpression of TENM3-AS1 cDNA promoted cell proliferation, as indicated by augmented cell growth (Figure 5F) and colony formation abilities (Figure 5G). Altogether, our results suggest that tumors overexpressing oncogenic lncRNAs, despite showing a higher proliferation rate, may be vulnerable to the FDA-approved palbociclib treatment.

IV. RNA-seq analysis revealed that lncRNA TENM3-AS1 activates the ER pathway
To determine the potential mechanism by which these oncogenic lncRNAs reshape tumor proliferation and palbociclib sensitivity, we performed RNA-seq analysis after the activation of oncogenic lncRNAs. This analysis characterized 466, 521, and 522 differentially expressed genes in response to the activation of TENM3-AS1, LINC01117, and ENSG00000226706, respectively (Supplementary dataset 3). Pathway analysis highlighted the upregulation of “Estrogen Response Early” (TENM3-AS1 FDR = 0.015, LINC01117 FDR = 0.04, ENSG00000226706 FDR = 0.034, , Figure 6A, Supplementary Figure 6a and 6b) and “MYC Targets V1” pathways (TENM3-AS1 FDR = 0.011, LINC01117 FDR = 0.02, ENSG00000226706 FDR = 0.2, Supplementary dataset 4, Figure 6A and Supplementary Figure 6a), suggesting that these lncRNAs may mediate the pro-proliferation and oncogenic effects of these lncRNAs. Meanwhile, knockdown of the oncogenic lncRNAs leads to the reversion of 48.9%, 48.7%, and 47.3% of gene expression changes induced by TENM3-AS1, LINC01117, and ENSG00000226706, respectively (Supplementary dataset 4). Notably, both “Estrogen Response Early” and “MYC Targets V1” were downregulated after lncRNAs knockdown (FDR < 0.05, Figure 6A).

Specifically, CRISPRa activation of TENM3-AS1 in MCF-7 (Figure 6B) and T-47D cells (Supplementary Figure 6c) upregulates ERα and its targets MYC, GREB1, and TFF1. This activation of the ERα targets was recapitulated in the TENM3-AS1 cDNA overexpressed T-47D cells (Figure 6C). Moreover, the ERα and MYC expression were also increased in the TENM3-AS1-activated MCF-7 mouse model (Figure 6D). Consistently, knockdown of TENM3-AS1 downregulated the expression of these genes (Figure 6E). To further investigate if ERα mediates TENM3-AS1’s oncogenic role, we used siRNA to knockdown ERα in the control cells and TENM3-AS1-activated MCF-7 cells. Suppression of ERα abolished TENM3-AS1’s regulation of its downstream targets (Figure 7A). In addition to the target expression, inhibition of ERα offset the function of TENM3-AS1 in cell proliferation (Figure 7B) as indicated by the MTT assay. Besides TENM3-AS1, we observed that LINC01117 and ENSG00000226706 activation didn’t change ERα or TFF1’s expression, but led to upregulated expression of MYC and GREB1 (Supplementary Figure 6b and 6d). These results suggest that three oncogenic lncRNAs, especially TENM3-AS1, promote cell growth and tumorigenesis through activating the ERα pathway.

V. LncRNA TENM3-AS1 activates the ERα signaling to induce cancer cell proliferation and regulate CDK4/6i response
The estrogen receptor alpha (ERα) is the major driver for ER+ breast cancer43,44. Previous clinical and pre-clinical studies have indicated the importance of ERα in maintaining patient response to CDK4/6 inhibitors45-47. With the observations that three oncogenic lncRNAs activate the ERα signaling, we speculated that lncRNAs’ regulation of CDK4/6i sensitivity may also be partially mediated by the ERα-MYC axis.

To test this hypothesis, we constructed lncRNA activation scores based on genes that are significantly changed upon each oncogenic lncRNA activation (see Methods). Using gene expression data from patient-derived organoids48, we have shown that the palbociclib-sensitive samples exhibited higher activation scores of oncogenic lncRNAs than resistant samples (Figure 7C). A similar pattern was also observed in patient-derived xenografts, where 13 palbociclib-sensitive samples showed elevated lncRNA activation scores compared with 24 resistant samples48 (Figure 7C). To corroborate this observation, we further applied the lncRNA activation signatures to single-cell transcriptomes generated from serial tumor biopsies of 24 patients with early-stage breast cancer enrolled in the FELINE clinical trial47. In this study, patients were treated with either endocrine therapy (i.e., letrozole) alone or in combination with the CDK4/6 inhibitor ribociclib. Consistent with our previous observations, tumor cells with higher lncRNA activation scores exhibited elevated ER pathway activity (Figure 7D) and higher G2M transition (Figure 7D). These observations supported the potential role of the oncogenic lncRNAs in promoting cell cycle progression through activating the ER pathway. Notably, patients who responded to the letrozole + ribociclib combination therapy consistently showed higher lncRNA activation scores (Figure 7E), aligning with our screening results. These results suggest that the oncogenic lncRNAs may be exploited as therapeutic biomarkers for the FDA-approved combination regimen.

Since the TENM3-AS1’s regulation of cell growth and CDK4/6 inhibitors exhibited a relatively high association with ERα, we next sought to investigate how lncRNA TENM3-AS1 activates ERα. We first performed the fractionation assay, which suggested that lncRNA TENM3-AS1 is mainly localized in the chromatin section (Figure 7F). Further RNA immunoprecipitation (RIP) assay demonstrated that RNAs co-immunoprecipitated with ERα protein are enriched with TENM3-AS1 (Figure 7G). In contrast, TENM3-AS1 RNA is not enriched in the MYC RIP sample (Supplementary Figure 7a), suggesting TENM3-AS1 may specifically interact with the ERα protein. To further determine if TENM3-AS1’s regulation of CDK4/6 inhibitors’ sensitivity is dependent on ERα expression, we knocked down ERα in TENM3-AS1-activated cells and then treated them with various doses of Palbociclib. At both 48 hr and 72 hr (Figure 7H) after Palbociclib treatment, we observed that knockdown of ERα abolished TENM3-AS1 and the other two lncRNAs, LINC01117 and ENSG00000226706-mediated (Supplementary Figure 7b) sensitivity to Palbociclib. Overall, our results suggest that TENM3-AS1 is an oncogenic lncRNA that promotes breast cancer proliferation by activating the ERα targets.

Methods
Institutional approval
This study complies with all relevant ethical regulations, and all protocols were approved by the Institutional Biosafety Committee (IBC) of the University of Pittsburgh. Experimental protocols involving mice were approved by the Institutional Animal Care and Use Committee (IACUC) of the University of Pittsburgh.

Cell lines and reagents
Human cancer cell lines MCF-7, HS578T, HEK293T, T-47D, RKO, and U2OS were purchased from the American Type Culture Collection (ATCC). HEK293FT cell was purchased from Invitrogen (R70007). HEK293FT cell was cultured in DMEM medium supplemented with GlutaMax (Gibco, 10569010), 10% FBS, and 1% penicillin-streptomycin. MCF-7, 293T cells were cultured in DMEM (Hyclone, SH30243.01) supplemented with 10% FBS (Gibco, 10438-026) and 1% penicillin-streptomycin (Corning, 30-002-Cl). T-47D was cultured in the RPMI-1640 medium (Hyclone, SH30027.01) with 10% FBS, 1% penicillin-streptomycin, and 10 μg/mL insulin (Sigma, I1882). RKO was cultured in the EMEM medium (Corning, 10-009-CV) supplemented with 10% FBS and 1% penicillin-streptomycin. HS578T cells were cultured in DMEM medium supplemented with 10% FBS, 1% penicillin-streptomycin, and a final concentration of 0.01 mg/mL insulin. U2OS cells were cultured in McCOY’S 5A medium (Hyclone, SH30200.01) supplemented with 10% FBS and 1% penicillin-streptomycin.

Focused screen library construction and amplification
The preparation of the focused screen library followed the previously published protocol73. In brief, after we have selected the targets according to Supplementary Figure 1c. Then, we designed our sgRNAs utilizing CRISPick, a genetic perturbation platform invented by Broad Institute, which ranks and picks candidate CRISPRko/a/i sgRNA sequences to maximize on-target activity for the target(s) provided73. For each target gene, we picked the top six ranked sgRNAs, and together with 40 non-target (NT) sgRNAs (from the global screen library), we had a library containing 1000 sgRNAs targeting 160 genes. Since we used the Lenti-sgRNA (MS2)-Puro plasmid (Addgene, 73795) as the backbone and inserted sgRNA at the BsmBI site, we designed the oligo with 5’ flank sequence partially overlapped with the U6 promoter and a 3’ flank sequence overlapped with the MS2 sequence from the backbone. Then, these oligos were synthesized by Twist Bioscience (Supplementary dataset 5). The pooled oligo library was amplified by PCR following the protocol. Forward primer: 5’-TAACTTGAAAGTATTTCGATTTCTTGGCTTTATATATCTTGTGGAAAGGACGAAACACCG-3’; Reverse primer: 5’-TGTTGGCCTCAGCTCTAAAAC-3’. The PCR product is purified by QIAquick PCR purification kit (Qiagen, 28106) according to the manufacturer’s directions and quantified the product with a NanoDrop UV spectrophotometer. The purified oligo products were loaded into a 2% low-melting agarose gel (Bio-rad, 161-3102), and the oligos were extracted using QIAquick Gel Extraction Kit (Qiagen, 28706) according to the manufacturer’s directions and quantified by NanoDrop. The extracted pooled oligos were further purified and concentrated by DNA Clean & Concentrator TM-5 kit (Zymo Research, D4014). The Lenti-sgRNA (MS2)-Puro plasmid was digested with Esp3I (BsmBI) enzyme (Thermo Fisher, FD0454) and treated with FastAP Thermosensitive Alkaline phosphatase (Thermo Fisher, EF0651) according to the protocol at 37 °C for 1 hr. The digest reactions were loaded into 2% low-melting agarose gel. The backbone was extracted from the gel using the QIAquick Gel Extraction Kit according to the manufacturer’s protocol and quantified by NanoDrop. The digested library plasmid backbone and pooled oligo were assembled through Gibson assembly using Gibson Assembly Master Mix, 2× (NEB, M5510A) according to the protocol and incubated at 50 °C for 2 hr. The Gibson Assembly reaction mix was purified and concentrated by Isopropanol according to the protocol. The precipitated plasmid DNA was washed with cold 80% ethanol twice and dissolved in 6 mL of TE buffer at 55 °C for 10 min and quantified by NanoDrop.

Then, 2 mL of 50ng/mL oligo library plasmid was used for one electroporate into Endura ElectroCompetent cells (LGC Biosearch Technologies, 60242-2) according to the manufacturer’s direction. Each electroporation (2 mL in total) was plated on the large LB agar plates (245-mm square bioassay dish with ampicillin). At the same time, prepare 1:100, 1:1000, and 1:10,000 dilutions for calculating the transformation efficiency for both sgRNA library and control groups. After incubating all LB agar plates overnight at 37 °C for 12-14 hr, then calculated electroporation efficiency by counting the number of colonies on the 1:10,000 dilution plate. Proceed to the next step only if there are more than 500 colonies per sgRNA in the library for the customized library (in our case, > 500,000 colonies) and there are at least 20 times more colonies per electroporation in the library compared with the control Gibson assembly reaction. The colonies were harvested from the LB agar plates, and the OD value was measured to determine the number of the Maxipreps as follows: (OD value * Total volume of colonies harvested) / 1,200 and performed the Maxipreps using Macherey-Nagel NucleoBond Xtra Maxi EF kit (Macherey-Nagel, 740424.10) according to the manufacturer’s directions. The pooled library plasmid was quantified by NanoDrop.

sgRNA library lentivirus production and transfection
Global screen: The preparation of the library and cells followed the previously published protocol and previous publication73. Human CRISPR lncRNA Activation Pooled Library (SAM - 3 plasmid system) was purchased from Addgene (1000000106). Based on ENCODE V19 and reannotation, the library targets 9,744 transcription start sites of lncRNAs, with 96,458 sgRNAs in total. The library also contained 500 control sgRNAs (NT sgRNAs). The purchased sgRNA library was first amplified by electroporation to the Endura ElectroCompetent cells according to the manufacturer’s directions, which were shown in the front. The colony number is greater than 100 colonies per sgRNA in the library according to the protocol. The amplified library plasmids were extracted using Maherey-Nagel NucleoBond Xtra Maxi EF Kit according to the manufacturer’s directions. The extracted plasmids were quantified by NanoDrop and ready to use. The antibiotic killing curve was performed in MCF-7 cells to determine the concentration of three antibiotics (zeocin, blasticidin, and hygromycin), which matched with three plasmids (sgRNA library, dCas9-VP64-Blast, and MS2-P65-HSF1) respectively. The concentration for selection was indicated below: 200 μg/mL zeocin (Invivogen, ant-zn-05), 4 μg/mL blasticidin (Sigma, 15205), and 300 μg/mL hygromycin (RPI, 31282-04-9). Before transducing the lncRNA library, we generated the dCas9-VP64 and MS2-p65-HSF1-Hygro stable MCF-7 cells with the multiplicity of infection (MOI) of lentivirus 0.5 and selected with the proper concentration of blasticidin and hygromycin which indicated above respectively. HEK293FT cells were cultured in T225 flasks and maintained at 70%-80% confluency. The transfection HEK293FT cells were seeded the previous day and reached 80-90% confluency on the next day for transfection. Transfection reagents, including the Lipofectamine 2000 (Invitrogen, 11668-019) and the PLUS reagent (Invitrogen, 11514-015), were used according to the protocol 73 referred to the amount per T225 flask. For each T225 flask, (15.3 μg pMD2.G (Addgene, 12259) and 23.4 μg psPAX2 (Addgene, 12260) as helper plasmids), and 30.6 μg sgRNA library plasmids were mixed with 2250 μL of Opti-MEM (Gibco, 11058-021). The medium was replaced with a prewarmed DMEM medium after eight hr. After 48 hr and 72 hr, the lentivirus was harvested and filtered with 0.45 μm filters. The MOI value of the lncRNA library lentivirus was determined and calculated73 by infecting the MCF-7 cells with the titer of the library lentivirus, including 0 mL, 25 mL, 50 mL, 100 mL, 200 mL, and 400 mL. (One group was treated with zeocin based on the killing curve indicated in the front and another group was not treated with zeocin). Finally, MCF-7 cells transduced with the SAM CRISPR system were infected with lncRNA sgRNA library lentivirus at an MOI around 0.3, maintaining coverage of > 500 cells expressing each sgRNA. Cells were selected with zeocin for 10 days and then expanded for the next step, CRISPR screening.

Focused screen: The focused screen process was very similar to the global screen, which also followed the protocol73. The minor differences from the global screen are shown below. The customized library contained 1,000 sgRNAs targeting 160 genes along with 40 non-targeting control sgRNAs. The constructed sgRNA library was described in the previous section. The colony number is greater than 500 colonies per sgRNA in the library for customized library according to the protocol. The customized library used Lenti-sgRNA (MS2)-Puro plasmid as the backbone for library construction. The antibiotic killing curve was performed in MCF-7 cells to determine the concentration of three antibiotics (puromycin, blasticidin, and hygromycin), which matched with three plasmids (sgRNA library, dCas9-VP64, and MS2-P65-HSF1) respectively. The concentration for selection was indicated below: 0.5 μg/mL puromycin (Gibco, A11139-03), 4 μg/mL blasticidin (Sigma, 15205), and 300 μg/mL hygromycin (RPI, 31282-04-9) for MCF-7 cells. Finally, MCF-7 cells transduced with the SAM CRISPR system were infected with lncRNA sgRNA library lentivirus at an MOI of 0.11, maintaining coverage of > 500 cells expressing each sgRNA. Cells were selected with puromycin for 5 days and then expanded for the next step CRISPR screening.

Global and focused CRISPRa screens for cell proliferation and palbociclib response
For global screen, the proliferation screen contains two samples. One sample is a zero-passage library; another was cultured for 14 days. For palbociclib screen, the palbociclib (Sigma, PZ0383) concentration for MCF-7 cells was determined by MTT and colony formation assay. The final concentration for screening was 0.826 μM. There are a total of five samples, which includes one zero-passage library sample (P0); one sample that cultured for 14 days but was not treated with palbociclib (P14); three samples that cultured for 14 days but treated with palbociclib (T14a, T14b, T14c). Each sample was seeded in three 15-cm dishes with 5 × 107 cells/dish. P0 was directly collected from the dish and extracted genomic DNA (5 × 107 cells). Other samples were harvested after 14 days with at least 5 × 107 cells per sample for further genomic DNA extraction and sgRNA amplification.

Regarding the focused screen, the proliferation screen contains three samples. One sample is a zero-passage library; another two samples were cultured for 14 days. The palbociclib concentration for MCF-7 cells followed the global screen concentration (0.826 μM). There are a total of six samples for each cell line, which includes one zero-passage library sample (P0); two samples when cultured for 14 days but were not treated with palbociclib (P14a and P14b); three samples that were cultured for 14 days but treated with palbociclib (T14a, T14b, and T14c). Each sample was seeded in a 15-cm dish with 1 × 107 cells. P0 was directly collected from the dish and extracted genomic DNA (1 × 107 cells). Other samples were harvested after 14 days with at least 1 × 107 cells per sample for further genomic DNA extraction and sgRNA amplification.

Genomic DNA extraction and sample preparation for NGS
The amplification of sgRNA, along with the primer sequence and preparation of samples, followed the previously published protocol73. Cell numbers for global and focused screen were described above. Briefly, genomic DNA was extracted from the cells after screening with the maintained coverage of > 500 cells expressing per sgRNA using the Zymo Research Quick-DNA Midiprep plus kit (Zymo Research, FD4075) according to the manufacturer’s instruction. Then, sgRNAs were amplified using PCR with the NEBNext High Fidelity 2× PCR Master Mix (NEB, M0541S). The primers were listed in the published protocol73. Each sample will be amplified using ten forward primers mix but only one reverse primer that contains a unique barcode. For global screen, five samples of MCF-7 cells matched with the reverse primers as listed here: P0-R1, P14-R2, T1-R3, T2-R4, and T3-R5. For focused screen, MCF-7 group focused library plasmid-R1, P0-R2, P14A-R3, P14B-R4, T1-R5, T2-R6, T3-R7. Both the global screen library plasmid and customized screen library plasmid used 20 ng genomic DNA as templates for sgRNA amplification; other samples used 1 μg genomic DNA as templates. PCR reactions were performed according to the protocol73. PCR products were pooled for each sample and then purified by a QIAquick PCR purification kit (Qiagen, 28106). Quantification was determined by Nanodrop. 2 μg of purified PCR products were loaded into 2% agarose gel. The final product was around 280 bp after running the gel. Gel extraction was performed using the QIAquick Gel Extraction kit (Qiagen, 28706) according to the manufacturer’s directions and quantified by Nanodrop and Qubit (Invitrogen, 033226). Then, these samples were ready for NGS sequencing.

CRISPRa screen data analysis
We first reannotated the Human CRISPR lncRNA Activation Pooled Library as we previously reported19 using GENCODE v19 annotation74. MAGeCK (v0.5.9.4) was used for sgRNA quantification, and the MAGeCK-mle algorithm75 was then used for the candidate gene selection with 500 nontargeting control sgRNAs for normalization and generation of the null distribution. Principal component analysis (PCA) was first performed based on sgRNA read counts in each sample to ensure the quality of screening. Comparison between sgRNA-transduced control samples with different cultured periods was considered as proliferation screen, and three biological replicates that were treated with palbociclib for 14 days were compared with samples without drug treatment as drug treatment screen. Parameters were set as defaulted.

After genome-wide screen analysis, we ranked the genes based on their FDR values for both positive and negative selections. The selection criteria for genes in the genome-wide drug treatment screen were set to FDR (BH adjusted Wald p-values) less than 20%. To develop a focused sgRNA library, we integrated the results from genome-wide CRISPRa screen with cell line-based pharmacogenomics analyses (see below). This library included 25 lncRNAs that overlapped significant hits between the CRISPRa screening (FDR < 0.2), pharmacogenomics analyses (P < 0.05) and the top 50 lncRNAs uniquely identified in the CRISPRa drug screen. In addition, we included 33 PCGs known to regulate cell-cycle progression or Palbociclib response, along with two additional lncRNAs previously reported to modulate Palbociclib response.

After the two-stage CRISPRa screen, we have integrated the selected lncRNA’s expression in both cell lines (i.e., CCLE) and patient tumors (i.e., TCGA) to help remove false positives (Supplementary dataset 2). Specifically, the criteria of including lncRNAs for next step validation are 1. lncRNAs are significantly selected in both genome-wide and focused drug screens; 2. lncRNAs with detectable expression in the MCF-7 cell line; 3. lncRNAs with detectable expression in BRCA patient tumor samples; 4. The lncRNAs are significantly overexpressed/underexpressed in cancer cell lines and tumor samples (P < 0.05); 5. The lncRNAs’ expression are significantly associated with patient survival (P < 0.05). In total, we selected top three lncRNAs from positive/negative selection for further validation.

LncRNA expression, drug response, and patient survival association analyses across patient tumors and human cancer cell lines
The gene expressions across cancer cell lines from Cancer Cell Line Encyclopedia (CCLE) was downloaded from DepMap. For drug response analysis, the expression of genes across 41 breast cancer cell lines from CCLE with matched palbociclib sensitivity data from Genomics of Drug Sensitivity in Cancer (GDSC)32 was used. Spearman correlation coefficients between palbociclib area under the dose-response curve (AUC) values and gene expression level were calculated, and p values were obtained. Candidate genes with P < 0.05 in both CRISPR screen and drug response analysis were selected as hits in our genome-wide screen.

For patient tumors analysis, TCGA76 RNA-seq and clinical data were obtained from the Genomic Data Commons (GDC) portal. The annotation for the transcriptome data was performed on the human reference genome GRCh38, and the gene expression level was then quantified by fragments per kilobase of transcript per million mapped reads (FPKM) upper quartile. LncRNA expression was compared between TCGA tumor samples and normal samples from the Genotype-Tissue Expression (GTEx) database77 using a two-tailed unpaired Student’s t-test.

Exploratory RNA-seq analysis
Total RNAs were extracted from cells as described in the “RNA extraction” section. Then, RNAs were purified with the RNA Clean & ConcentratorTM-5 kit (Zymo Research, R1016). Then, at least 3 mg purified RNA was sent to Genewiz (lncRNA activation batch) or Novogene (lncRNA knockdown batch) for quality control, library construction, and sequencing. Reads were mapped to the human reference genome GRCh38 using spliced transcript alignment to a reference (STAR)78. Descriptive gene expression quantification and differential expression analysis were performed using RNA-seq by expectation maximization (RSEM)79 and Cuffdiff80 on the activation samples against their controls. Gene set enrichment analysis (GSEA) was then performed on the preranked gene lists by signed log-transformed Cuffdiff P values using the cancer hallmark gene set from the Molecular Signatures Database (MSigDB).

LncRNA activation signature analysis
Differentially expressed genes between lncRNA-activated samples in comparison with NT control sample were collected. A lncRNA activation signature was generated using upregulated genes and downregulated genes with Cuffdiff P < 0.05. We then calculated the weight k for each selected signature gene using k = w_i / max(|w_i|), where w_i is the Cuffdiff test statistic for the ith selected gene and k ∈ [-1,1]. With a batch of RNA-seq profiles in cell lines, we first obtained expression z scores for all of the genes across samples. Then, a lncRNA activation signature score S was calculated as the weighted sum expression of the signature genes using the equation S = ∑(k_i * z_i), where z_i denotes the expression level of the ith gene.

Single-nuclei RNA-seq and Palbociclib treatment sample analyses
Expression profiles for PDX tumors and organoids derived from endocrine therapy-resistant and CDK4/6i sensitive and palbociclib-resistant patients were obtained from GSE229235 [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE229235] and GSE229146 [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE229146], respectively. LncRNA activation scores were compared between palbociclib-resistant and sensitive samples using a two-tailed unpaired Student’s t-test.

To complement the bulk RNAseq analysis, we analyzed publicly available single-nuclei RNA-seq (snRNA-seq) data from postmenopausal women with ER+ and/or PR+, HER2-negative breast tumors. Patients were randomized to receive either endocrine therapy alone (letrozole plus placebo) or continuous low-dose combination therapy (letrozole plus ribociclib, 400 mg/day) and tumor biopsies were collected at baseline (day 0) and post-treatment (day 14 or day 180). Processed data were obtained from GSE158724 [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE158724].

Only tumor cells were included for downstream analysis. Cells were filtered based on standard quality control thresholds: nFeature_RNA between 200 and 5000, and percent.mt below 5%. Samples with fewer than 200 high-quality cells were excluded. To integrate samples across patients, we applied Seurat’s reciprocal PCA (RPCA) method, as originally described in the dataset’s processing pipeline. After integration, we computed gene module scores for each cell using the AddModuleScore function in Seurat. Specifically, we evaluated: (1) an ER signaling signature based on the Hallmark Estrogen Response Early gene set from MSigDB, (2) custom-defined lncRNA activation signatures, and (3) cell cycle phase scores including S phase and G2/M phase scores using Seurat's default CellCycleScoring function. To examine the relationship between lncRNA activation and cell cycle regulation, we stratified tumor cells into ten bins based on their S score and G2/M score distributions and calculated the average lncRNA signature score within each bin. For treatment response analysis, we grouped patients by response status (responder vs. non-responder) within each treatment arm and compared the average lncRNA signature scores before and after treatment.

Single-sgRNA cloning and stable cell line generation
As for single sgRNA activation stable cell line, sgRNAs of selected lncRNAs and control NT sgRNAs were cloned into Lenti-sgRNA (MS2)-Puro plasmid (Addgene, 73795) at BsmBI sites. The sgRNA sequence for activating target lncRNAs and control sgRNAs is listed in Supplementary dataset 6. The top six ranked sgRNAs from both global and focused libraries were picked for each selected gene according to the analysis (see Methods). HEK293T cells were used to produce sgRNA lentivirus. HEK293T cells were seeded one day before the transfection and reached 70%-80% confluency on the next day. The transfection was performed using the method from our previously published paper19. Lipofectamine 2000 transfection reagent (Invitrogen, 11668-019) was used according to the manufacturer’s instructions. The plasmid mix contained 4.5 μg of psPAX2 (Addgene, 12260), 1.5 μg of pMD2.G (Addgene, 12259), and 6 μg of sgRNA plasmids (each gene used six sgRNA plasmids with 1:1 ratio mix). After 48 hr of transfection, the lentivirus was harvested and filtered with 0.45 μm filters. Then, MCF-7-VP64-MPH and T-47D-VP64-MPH cells were infected with lentivirus corresponding to different genes’ activation for 24 hr, then selected with puromycin until no negative-control cells (no virus-infected cells) survived. The stable cells were ready to validate and use.

RNA interference
siControl and siRNA targeted TENM3-AS1, LINC01117, and ENSG00000226706 were transfected to the MCF-7, RKO, U2OS, and HS578T cells using Lipofectamine RNAiMAX (Invitrogen, 13778-150) according to the manufacturer’s instructions. In summary, 60 pmol of each siRNA for the targeted gene or control was given to each well of the 6-well plate. Cells were seeded one day before the transfection and reached 40% confluency on the next day. The cell culture medium was replaced before the transfection. After 48 hr, RNA and protein were separately extracted from each group of cells. The siRNA sequences were listed as follows: siControl: Santa Cruz (sc-37007); siTENM3 (three siRNAs): MCE (HY-RS14364); siTENM3-AS1-A: 5’-GCCGGCUUUCGGCUCUGAU-3’; siTENM3-AS1-B: 5’-GASCGGAUUCCGAGGCAGAA-3’; siLINC01117-A: 5’-GGCAGGAGAGAGAGCACAC-3’; siLINC01117-B: 5’-GGCGAUACGUGAUCAUUUA-3’; si226706-A: 5’-CAGCAAEGCGCCCACUCGUA-3’; si226706-C: 5’- GAGCAAACGCACAUGGCAA-3’; siESR1: 5‘-CAGGRCACAUGAGUAACAAA-3’.

Generation of the stable cell line
The TENM3-AS1 cDNA was synthesized by Twist Bioscience. First, synthesized cDNA was amplified by PCR with primers designed with XbaI and XhoI restriction sites included. Then, amplified fragments were assembled into the pCDH-CMV-MCS-EF1-Puro backbone (System Biosciences, CD510B-1) after treated with XbaI and XhoI restriction enzymes using Gibson Assembly® Master Mix (NEB, EC2611). Recombinant plasmids were transformed into DH5α competent cells, and the sequence from positive clones were verified by Sanger sequencing. Lentiviral particles were produced in HEK293T cells. Cells were seeded one day before transfection and reached 70~80% confluency at the time of transfection in a 6 cm dish. A plasmid mix containing 4.5 μg psPAX2 (Addgene, 12260), 1.5 μg pMD2.G (Addgene, 12259), and 6 μg TENM3-AS1/ZsGreen (control) cDNA plasmid was transfected using Lipofectamine 2000 (Invitrogen, 11668-019) according to the manufacturer’s instructions. Viral supernatants were harvested 48 hr post-transfection and filtered with a 0.45-μm strainer. T-47D cells were infected in the presence of 8 μg/mL polybrene and selected with puromycin. Stable TENM3-AS1 expression was confirmed by PCR. PCR was performed using DreamTaq™ PCR Master Mix (2X) (Thermo Scientific, K1061) according to the manufacturer’s instructions to confirm the presence of TENM3-AS1 transcripts.

RNA extraction and quantitative real-time PCR (qRT-PCR)
Total RNA was extracted with TRIzol. After centrifuging at 12,000 x g for 15 minutes at 4 °C, the supernatant was acquired and treated with isopropanol to precipitate the RNA. RNA pellets were washed twice with 75% ethanol. After air drying of the RNA pellets, nuclease-free water was used to dissolve RNA, and then quantified by Nanodrop. As for cDNA synthesis, 1 μg of RNA was used from above, and then a prepared mix using Reverse Transcription Kit (Applied Biosystems, 4368813) according to the manufacturer’s directions, and then performed reverse transcription in a PCR machine. cDNA was diluted in a 1:5 ratio, and qPCR was performed using SYBR Green PCR 2’ Master Mix (Applied Biosystems, 4367659) on a 384-well plate. Then. The plate was inserted in the QuantStudioTM 6 Pro Real-Time PCR equipment (Applied Biosystems). The relative RNA expression and fold change were determined by normalizing to GAPDH. The primer sequences are listed below: Human GAPDH forward, 5’-GGTGAAGGTCGGAGTCAACG-3’; reverse, 5’-TGGGTGGAATCATATTGGAACA-3’. Human TENM3-AS1 forward, 5’-ACACGCACCAACAAAATGGG-3’; reverse, 5’-GAATCCGTCTTCCCGGCAG-3’. Human LINC01117 forward, 5’-CCCGATCCTCGGCATCCTT-3’; reverse, 5’-GGCCTTCTTTTGCAGCAAGA-3’. Human ENSG00000226706 forward, 5’-CATCAGCGCAAGAGCAAACG-3’; reverse, 5’-TGCAGACATCCACCGTTCCA-3’. Human CCND1 forward, 5’-TCTACACCGACAACTCCATCCG-3’; reverse, 5’-TCTGGCATTTTGGAGAGGAAGTG-3’. Human CCND2 forward, 5’-TGATGTTGGGCAACTCTGCG-3’; reverse, 5’- GTGCAACCCGTCTCGTCT-3’. Human CCNB1 forward, 5’-GACCTGTGTCAGGCTTTCTCTG-3’; reverse, 5’-GGTATTTTGGTCTGACTGCTTGC-3’. Human ESSR1 forward, 5’-GCTTACTGACCAACCTGGCAGA-3’; reverse, 5’-GGATCTCSTAGCCAGGCACATTC’. Human MYC forward, 5’-AATGAAAAGGCCCCCAAEGGTAG-3’; reverse, 5’-GTCGTTTCCGCAACAAGTCCT-3’. Human RTFF1 forward, 5’-CATCGACGTCCCTCCAGAAGAG-3’; reverse, 5’-CTCTGGGACTAATCACCGTGCTG-3’. Human GREB1 forward, 5’-ATCAGCT GCTCGGACTTGCTG-3’; reverse, 5’-TGAGCTCCGGTCCTGACAGAT-3’. Human TENM3 forward, 5’-GGACAAGGCTATCACAGTGGAC-3’; reverse, 5’- TTCTGAGGGAGCCGTCATAACC-3’.

Western blot
Whole cell lysate was extracted from cells using RIPA lysis buffer supplemented with freshly added protease inhibitor (MCE, HY-K0010). After centrifuging tubes at 16000 x g, 15 minutes at 4 °C, cell lysate was acquired, and the concentration was determined by the BCA protein assay kit (ThermoFisher, 23225) and incubated at 37 °C for 30 minutes. Loading samples were prepared according to the concentration and supplemented with the 5’ SDS loading buffer. Samples were then denatured at 98 °C for 10 minutes, loaded to the SDS-PAGE gel, and further transferred to the PVDF membrane (Bio-Rad, 162-0177). Membranes were incubated in the block solution, 5% non-fat milk (LabScientific, M0841) in phosphate buffered saline containing 0.2% Triton X-100 (PBST) solution for 1 hr at room temperature on a shaker. Then, membranes were incubated in the primary antibody overnight at 4 °C (see method antibodies section). On the next day, membranes were washed with 1 × PBST three times and then incubated in HRP-conjugated secondary antibodies for 1 hr at room temperature on a shaker. After another three times washing with PBST, membranes were treated with chemiluminescent substrate (ECL) (ThermoFisher, F32106) and then developed the band signal on the X-ray films using an AX 700LE film processor (ALPHATEk).

RNA Immunoprecipitation (RIP)
RNA immunoprecipitation (RIP) was performed to detect RNAs associated with ERα and MYC, with normal mouse IgG and rabbit IgG as controls based on our previous publication19. Approximately 2 × 10⁷ cells were harvested, washed once with PBS, and crosslinked with 0.3% formaldehyde (diluted with 16% formaldehyde, ThermoFisher, 28906) for 10 min at room temperature with rotation. Crosslinking was quenched with 1.25 M glycine (Bio-Rad, 1610718) for 5 min. Pellets were collected by centrifugation at 3200 x g for 3 min at 4 °C. Cells were lysed in the lysis buffer (50 mM Tris-HCl, pH 7.4), 100 mM NaCl, 1% NP-40, 0.1% SDS, and 0.5% sodium deoxycholate) supplemented with protease (MCE, HY-K0010) and RNase inhibitor (NEB, M0314L). Then, cells were treated with deoxyribonuclease (2 μL/mL; LifeTech, AM2239) and incubated for 20 min at room temperature. After centrifuging, the supernatant was saved, and the concentration was determined using the BCA Protein Assay Kit (Thermo Fisher Scientific, 23225). Magnetic beads were pre-incubated with 5 µg antibody (ERα, MYC, or IgG) overnight at 4 °C. Sheep anti-mouse IgG beads (Invitrogen, 11201) were used for ERα, and Protein G (Invitrogen, 11201) beads for MYC. Antibody-bead complexes were then incubated with 2 mg cell lysate overnight at 4 °C with rotation. Beads were collected using a magnetic stand and washed six times with the lysis buffer. Approximately one-tenth of the beads was mixed 1:1 with 2 × SDS sample buffer (Bio-Rad, 1610737) and analyzed by Western blotting, as described above. The remaining beads were incubated in RIPA buffer at 70 °C for 1 hr, followed by RNA extraction and analysis by qRT-PCR for RNA enrichment, as described above.

Antibodies
Antibodies listed below were used for western blot flow cytometry and RNA immunoprecipitation. For immunoblotting, rabbit anti-CCND1 (CST, 555067), rabbit anti-CCND2 (CST, 3741), rabbit anti-CCNB1 (CST, 4138), rabbit anti-MYC (CST, 13987), rabbit anti-GREB1 (CST, 65171), mouse anti-ERα (Santa Cruz, Isc-8002), mouse anti-β-actin (Sigma, A5441), Goat anti-Mouse IgG (H+L) Secondary Antibody, HRP (Invitrogen, 31430), Goat anti-Rabbit IgG (H+L) Secondary Antibody, HRP (Invitrogen, 31460). For RNA immunoprecipitation, rabbit anti-MYC (CST, 13987), mouse anti-ERα (Santa Cruz, sc-8002), normal rabbit IgG (CST, 2729), and normal mouse IgG (Santa Cruz, sc-2025).

MTT assay
With palbociclib: A total of 3,000 cells per well were seeded into the 96-well plates with at least three biological replicates for each drug concentration for each targeted gene-activated cell line for MCF-7 cells (5,000 cells per well for siRNA-treated knockdown experiments). For T-47D cells, 7,000 cells were seeded into the 96-well plates. On the second day, cells were treated with different concentrations of palbociclib. Two siRNAs shown in Supplementary Figure 3c were used in a 1:1 mix ratio for the siRNA-treated experiments. After 48 hr and 72 hr, MTT reagent (Biosynth, T-3450) was added to each well with the final concentration of 1×. Cells were incubated at 37 °C and 5% CO2 for 4 hr. Then MTT solubilization solution (40% dimethylformamide (DMF), 2% acetic acid, 16% SDS) was treated to each well. After overnight incubation, the absorbance of the plate was measured by a microtiter plate reader at 570 nm and 630 nm wavelengths. Then, the data was analyzed by normalizing with the zero palbociclib concentration group for each cell line.

Cell proliferation: For targeted gene activation, 1,000 MCF-7 cells and 2,000 T-47D cells were seeded into the 96-well plates. For siRNA-treated knockdown experiments, 3,000 MCF-7 cells, 7,000 HS578T cells, 5,000 U2OS cells, and 6,500 RKO cells were seeded into the 96-well plates. A total of six plates were used for each cell line and harvested every day continuously (including day 0). The rest of the process was the same as described above. Then, the data was analyzed by normalizing with the day one readout and compared with the siControl group.

Colony formation assay
With palbociclib: A total of 10,000 MCF-7 cells stably activated targeted genes or controls were seeded into 12-well plates and allowed to attach overnight. On the second day, cells were treated with different concentrations of palbociclib (0 μM, 0.125 μM, 0.5 μM, and 2 μM). For T-47D cells, 25,000 cells were seeded into the 12-well plates, and cells were treated with different concentrations of palbociclib (0 μM, 0.5 μM, 2 μM, and 8 μM). Medium and drug will be replaced every 2 days. All the cells will be cultured for 7 days. Then, cells were washed with PBS once and fixed with 4% formaldehyde for 10-15 minutes at room temperature. Then, cells will be washed with PBS once and stained with 0.01% crystal violet-25% methanol solution for at least 20 minutes. Plates with crystal violet staining will be scanned and analyzed. The quantification of the colony was performed through ImageJ software, and each stable cell line will be normalized with the group that was not treated with palbociclib.

Cell proliferation: The same cell number of MCF-7 and T-47D cells described above was seeded into 12-well plates as described above, but each cell line was seeded into two plates. One plate will be collected on the second day, as day 0, for further analysis. The other plate will be cultured for 7 days and then collected as described above. The medium will be changed every two days. Each stable cell line will be normalized with the data from day 0 of its own.

Cell cycle assay
1 × 106 MCF-7 cells with stably activated targeted genes were collected and washed with PBS once. Tubes were centrifuged at 200 x g for 10 minutes at 4 °C and aspirated the supernatant after centrifuging. Cells were fixed with 75% ice-cold ethanol by adding drop by drop to the slowly, continuously vortex tubes. Then, incubated cells for at least 2 hr or overnight. Cells were washed with PBS once and then washed with the FBS Stain buffer (BD Biosciences, 554656) once. Cells were centrifuged at 200 x g for 10 minutes at 4 °C. After aspirating the supernatant, cell pellets were resuspended in 0.5 mL of PI/RNase staining buffer (BD Biosciences, 550825) and incubated for 15 minutes at room temperature. Flow cytometry was then performed, and data was analyzed with the FlowJo software.

In vivo xenograft model
All animal studies were performed following the institutional guidelines. All related experiments were done following the animal protocols approved by the Institutional Animal Care and Use Committee (IACUC) of the University of Pittsburgh. Protocol #22030771 (subsequently renewed as protocol #25036427). All mice were housed in specific pathogen-free facilities under controlled conditions (22-24 °C, 45% humidity, 12/12 light/dark cycle), with food and water available ad libitum. Around 6-week-old female athymic nude mice were purchased from Charles River lab (Wilmington, MA). Early passage of MCF-7 sgNTs and lncRNA-activated cells were cultured. 100 µL of five million MCF-7 cells were mixed with Matrigel (Corning, 354234) with a 1:1 ratio. Then, 200 µL in total were inoculated through fat pad injection. The tumor size was measured using a caliper every three days and tumor volume was calculated using the formula: length × width² × 0.5. Mice were sacrificed, and tumors were harvested from mice reaching predefined endpoints. Tumor weight was also determined after scarification. The ethical endpoint was defined as tumors reaching 20 mm or more in any dimension. At no point did tumor size or burden exceed the limits approved by the IACUC. Only female mice were used in this study, as MCF-7 xenograft tumor establishment and growth are estrogen-dependent, and breast cancer occurs predominantly in biological females (>99% according to WHO estimates).

Tissue immunofluorescence
After tumors were harvested from mice, the partial tumor will be placed in the tissue cassette (Fisherbrand, 15182701k) and perform tissue processing. Then, the tissue will be embedded into the disposable base molds (Fisherbrand, 22363553) with paraffin. Then, paraffin blocks will be sliced (4-5 nm) and placed on the coverglass (Leica, 3800145ACS). Then, the paraffin will be melted and wiped out by triple incubation in the xylene (Fisher chemical, X3P-1GAL). Then, tissues will be rehydrated with the following steps: two times 100% Ethanol, two times 95% Ethanol, one time 70% Ethanol, 50% Ethanol, 30% Ethanol, and pure water. Tissues will then be boiled in Sodium citrate buffer (Sigma, W30R2600) and washed three times with PBS. Then, tissues will be blocked with 5% goat serum for 1 hr. Ki-67 samples will be permeabilized with 0.5% Triton X-100 simultaneously. Tissues will be washed with PBS another three times and then incubated with primary antibodies in 2% BSA at 4 °C overnight. Primary antibodies information: Ki-67(CST, 96344), MYC (CST, 13987), ERα (Santa Cruz, sc-8002) with 1:100 dilution. Secondary antibodies information: Goat Anti-Rabbit IgG H&L (Cy3 ®) preadsorbed (Abcam, ab97075), Goat Anti-Mouse IgG H&L - Alexa Fluor® 488 (Abcam, ab150117). The next day, tissues were washed with PBS three times and mounted with media containing DAPI and fluorescence antifade agent (Invitrogen, P36935). Tissues will be sealed with cover glasses and detected the signal using a fluorescence microscope.

Statistical analysis
The association between gene expression and overall survival rates was assessed by Cox regression analyses81 in TCGA cancer patient samples. Analysis was performed using Python 3.8.0 and GraphPad Prism 10.6.1. The statistical test used is indicated in each figure legend. The significance threshold was defined as P < 0.05. The Benjamini-Hochberg adjustment was used to calculate the false discovery rates.

Data availability
The sequencing data generated in this study have been deposited in the Gene Expression Omnibus (GEO) under accession numbers GSE253290 [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE253290], GSE254283 [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE254283] and GSE279981 [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE279981]. Source data are provided with this paper.

Code availability
The code used for CRISPRa screen hit analysis, RNA-seq pathway analysis, and snRNA-seq integration in this study is available from GitHub (https://github.com/dyanglab/CRISPRa_CDKi)82.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This paper uses CRISPR-activation screens to identify lncRNAs implicated in cell proliferation and sensitivity to CDK4/6 inhibitors. The authors first use a commercially available CRISPRa lncRNA library to globally screen for lncRNAs then perform a focused CRISPRa library screen to identify the most significant hits. Overall, the paper is interesting but there are major issues that need to be addressed prior to publication.\n1. The use of CRISPRa for lncRNA activation has some limitations that have not been addressed in this study including:\na) LncRNAs are often transcribed from bidirectional promoters where a lncRNA is transcribed divergently from a bidirectional promoter shared with a protein coding gene. As the sgRNA design is targeted to the TSS of the lncRNA of interest, transcription of the divergent protein coding gene can also be affected. For lncRNAs that a transcribed from bidirectional promoters it is important to show that the protein coding gene is not activated by the CRISPRa sgRNAs. This could be the case with TENM3-AS1, which shares a bidirectional promoter with TENM3. In this case, do the sgRNAs targeted to TENM3-AS1 affect the transcription of TENM3.\nb) LncRNA transcription is often initiated from enhancers encoded in DNA, therefore it is not clear whether the observed CRISPRa effect is DNA or RNA mediated i.e. activation of lncRNA transcription vs activation of the enhancer element. Additional experiments are required to differentiate a DNA vs RNA mediated effect. E.g. Knockdown experiments using ASO or siRNAs.\n2. For the CRISPR screening, please justify the use of a WALD p-value cut-off of p<0.05. It would be more appropriate to use an FDR-adjusted WALD p-value cutoff. It is likely that many of the “significant” hits are false positives.\n3. For the focused screens, only 40 NT sgRNAs were included. MAGeCK recommends at least 100 NT sgRNAs per screen as this is required for normalisation.\n4. Throughout the results section, please put the statistical cut-off in brackets in the text (e.g.p-value) when claiming something is “significant”. For example, on line 85 “…we identified lncRNA genes that can significantly regulate breast cancer proliferation (WALD p-value <0.05).\n5. Fig1E, Line 93. …..CRISPRa screening showed significant overexpression in tumors compared with normal tissues. What statistical cut-off was used?\n6. Figs 2B-F, 3E,F, 5A-E, 6B, G-J. The authors state that 3- 6 biological replicates were performed however the error bars are too small for expected variation of biological replicates. Do the authors mean technical replicates here?\n7. The functional follow-up of the individual lncRNAs is very light and not convincing. The authors did differential expression analysis of lncRNA-activated vs control cells and found >400 differentially expressed genes per lncRNA. The authors need to define what they consider differentially expressed (eg. Logfold change and p-value cut offs). The authors then cherry picked 1 cell cycle related gene for each lncRNA and attribute the effect to that. Were genes in cell cycle pathways significantly enriched? Please state the individual statistical cut offs for each gene. Additional experiments are required to show that the effects of the lncRNA are mediated by these genes e.g. rescue experiments.\n8. For the drug screening hits – on line 180 please define what “20 consistent hits” mean?\n9. Line 222. The word “fast” is not appropriate. Please change “fast” to “increased”.\n10. Line 226. Similar to the cell cycle genes, the authors have cherry picked 1 stem cell related gene for each lncRNA and attribute the effect to that. Were genes in stem cell pathways significantly enriched? Please state the individual statistical cut offs for each gene. Additional experiments are required to show that the effects of the lncRNA are mediated by these genes e.g. rescue experiments.", "reviewer_2": "The authors seek to understand the molecular mechanism underlying the role of three oncogenic lncRNAs in regulating cancer cell proliferation and cell stemness to govern cellular responsiveness to CDK4/6 inhibitor treatment. The manuscript is clearly written written and the experiments were carried out to provide support for the central hypothesis. However, additional in-depth investigation should be carried out to validate the detailed mechanisms. In addition, the following concerns should be addressed.\n1). Figure 2B: The authors should examine if similar effects can be observed in other breast cancer cell lines.\n2). Figure 2C, it will be nice for the authors to examine the effects of depleting these LncRNAs\n3). Figure 2G, it will be nice for the authors to examine if there is additive effects of sgLncRNA with sgCCNB1/D2?\n4). Figure 3A: it will be nice for the authors to examine or explain if the three LncRNAs operate through the same pathways to affect cell cycle and tumorigenesis. If activation of all three LncRNAs have additive effects, or if in sgTAS samples, depleting the other two LncRNAs affect its phenotypes?\n6). Figure 3D, the authors should examine of explain the molecular mechanism underlying the three different LncRNAs to govern similar set of genes to impact cell cycle or tumorigenesis?\n7). Figure 4F, it will be nice for the authors to speculate how there three individual LncRNA regulates similar set of gene expression.\n8). Figure 5D, It will be nice for the authors to examine how activation of CDK4 or CDK6 affects palbociclib response, and how depleting Rb affects this process.\n9). Figure 5E, it will be nice to examine the effects of CDK4/6 inhibitor on the expression of these three LncRNAs in this experimental setting.\n10) Figure 6, it will be nice for the authors to speculate the molecular mechanism of how these three LncRNAs govern cancer cell stemness in this experimental", "reviewer_3": "In this manuscript, the authors identified novel oncogenic lncRNAs that promote cancer cell proliferation using a two-stage CRISPR-SAM activation screening. They also explored the link between lncRNAs and response to CDK4/6 inhibitors (CDK4/6i), finding a negative correlation between lncRNA-driven proliferation and CDK4/6i sensitivity. Three oncogenic lncRNAs were found to make tumors more sensitive to CDK4/6i by promoting a fast-proliferating but less stem-like phenotype.\nOverall, this manuscript presents intriguing findings on the functional characterization of oncogenic lncRNAs in cancer, particularly their role in cell proliferation and therapeutic response. The use of a CRISPR-SAM activation screening approach is a powerful strategy, and the negative correlation between lncRNA regulation of proliferation and CDK4/6i response is noteworthy.\nBelow are my questions and comments:\n1. The authors explored the transcriptomic changes upon activation or knockdown of the three lncRNAs and observed changes in cell cycle related genes. They also analyzed the consequences of activating the expression of CCND1 and CCND2. It would be interesting to investigate if there is causal relationship between these three lncRNAs and CCND1, CCND2.\n2. In addition to differential gene expression analysis, the authors could perform some other analysis for their RNA-seq datasets, such as functional enrichment analysis and gene co-expression network analysis. For example, they could do Gene Ontology analysis to check if any terms related to cell cycle regulation and cancer cell stemness are enriched in the lncRNA-activated/knockdown groups.\n3. The authors may consider further investigating the mechanistic role of the three lncRNAs. For instance, as an antisense RNA to TENM3, is TENM3-AS1 involved in regulating the expression of TENM3, and is it related to cancer cell growth?\n4. In Figures 1E and S1d, the authors only showed the percentage of samples with over-expression of positive lncRNA selections. How about the percentage of unchanged or under-expression for these lncRNAs? Similarly, they only showed the percentage of under-expression for negative lncRNA selections. How about the percentage of unchanged or over-expression? For example, the positively selected lncRNA ENSG226706 is over-expressed only in < 50% samples in most of the CCLE cancer cell lines (except NB). Does it mean it is under-expressed in the rest (> 50%) of samples?\n5. In Figure 2A, again, the authors only reported the association of positively selected lncRNA expression with unfavorable survival and negatively selected lncRNA expression with favorable survival. However, there are many situations with a small or zero dot size, indicative of P-value > 0.10. Does this suggest that in these situations, positive lncRNAs are more associated with favorable survival, and negative lncRNAs are more associated with unfavorable survival? The authors should provide comprehensive analysis on these results.\n6. Line 103-105: “Integrating the CRISPR-screening results and lncRNAs’ expression in tumors and cell lines, we selected three lncRNAs TENM3-AS1 (TAS), LINC01117 (1117), ENSG00000226706 (706) that may contribute to breast cancer progression.” The authors should provide justification for selecting these three lncRNAs for further investigation.\n7. Line 147-149: “Specifically, HS578T, U2OS, and MCF-7 showed higher expression levels of TENM3-AS1 and LINC01117, while RKO and MCF-7 have increased endogenous expression of ENSG00000226706.” In Figure S3b, it appears that the SKOV3 cell line also had a high expression of TENM3-AS1 and LINC01117, even higher than the HS578T and MCF-7 cell lines. In this sense, SKOV3 may be a better cell line for characterizing the effect of lncRNA knockdown. Did the authors also conduct the knockdown experiments in SKOV3 cell line and observe any differences?\n8. In Figure S3c, siTAS-B had a stronger knockdown effect compared to siTAS-A in MCF-7 cells. However, in Figure 3E, the cell growth suppression induced by siTAS-B was less significant than siTAS-A in MCF-7 cells, raising concerns about whether there is off-target effect of either siRNA. Particularly, siTAS-A completely halted the growth of MCF-7 cells over a 5-day period, which was not observed in other siRNAs or other cell lines. To address this, testing a third TENM3-AS1 targeting siRNA would help.\n9. In Figure 3D, the authors should provide the number of biological replicates for the bulk RNA-seq.\n10. In Figure 4C, are there any genes that are either sensitive or resistant in the drug screening but happen to be non-significant in the proliferation screening? If there are any, the authors should include those genes in the plot.\n11. In Figures 4F, S4b, and 6D, the authors should give a specific description for the drug sensitivity and resistance. Is it based on IC50 or other metrics?\n12. In the representative images of the colony formation assays, the difference between sgRNA targeting lncRNAs and genes vs sgNTs with 0 µM palbociclib in Figures 5B and 5D is less prominent than the results in Figures 2C and 2E, raising concerns that the initial cell seeding density may be inconsistent.\n13. Line 247-249: “In particular, the flow cytometry analysis revealed that membrane CD44 expression levels were significantly suppressed by the overexpression of TENM3-AS1, LINC011117, or ENSG00000226706 (Figure 6G).” Since the purpose of this experiment is to compare the expression levels of CD44 between different groups, it would be better to quantify the median fluorescence intensity rather than the CD44+ percentage. In addition, the gating of CD44+ should be determined by stained CD44 KO cells, considering there may be background signal for the CD44 antibody.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response: We agree with the reviewer that CRISPRa sgRNAs targeting lncRNAs that share a promoter with protein-coding genes may activate their cognate protein-coding genes. As shown in the R-Figure 1 below, TENM3-AS1’s promoter is 99207 bp upstream of the TENM3 promoter. There is another gene (i.e., ENSG00000248266) sharing the promoter with TENM3-AS1. Below, we have included the data (R-Figure 1A and 1B) showing that sgTENM3-AS1 does not activate the ENSG00000248266 gene.\nTo further address the reviewer’s concern about whether sgTENM3-AS1’s effect is mediated by off-target TENM3 activation, we designed siRNA to knockdown TENM3 in MCF-7 cells. We have shown that knockdown of TENM3 neither influences parental MCF-7 cell proliferation (Figure S5c) nor rescues sgTENM3-AS1’s promotion (Figure 5D) of cell proliferation. Furthermore, lentiviral overexpression of TENM3-AS1 cDNA also induced cell proliferation in T47D cells (Figure 5F and Figure 5G). Collectively, these results give us more confidence that sgRNAs targeting TENM3-AS1 promoter’s effect is mediated by the activation of TENM3-AS1 transcription. We thank the reviewer’s constructive comments to help us clarify this point.\nb) LncRNA transcription is often initiated from enhancers encoded in DNA, therefore it is not clear whether the observed CRISPRa effect is DNA or RNA mediated i.e. activation of lncRNA transcription vs activation of the enhancer element. Additional experiments are required to differentiate a DNA vs RNA mediated effect. E.g. Knockdown experiments using ASO or siRNAs.\nResponse: In the revised manuscript, we have included siRNAs’ effect on sgRNA-activated cells and parental cancer cell lines to sort out if the sgRNA effect is mediated by DNA or RNA. Our results show that siRNA knockdown of three lncRNAs in MCF-7, U2OS, RKO, or HS578T cells inhibits cell proliferation. For TENM3-AS1, we have also synthesized a third siRNA to knockdown its RNA and observed consistent results with the original siRNAs (R-Figure 2A and B).\nTo further investigate if the observed effect is mediated by RNA, we overexpressed TENM3-AS1 cDNA in T47D cells (ER+/HER2- breast cancer cells) (Figure 5E). Our results demonstrated that lentiviral overexpression of TENM3-AS1 RNA promotes cell proliferation (Figure 5F and 5G) and is sensitive to Palbociclib’s treatment (Figure 4A and 4B right panel). Altogether, both loss-of-function and gain-of-function results suggest the observed CRISPRa effects are mediated by TENM3-AS1 RNA.\n2. For the CRISPR screening, please justify the use of a WALD p-value cut-off of p<0.05. It would be more appropriate to use an FDR-adjusted WALD p-value cutoff. It is likely that many of the “significant” hits are false positives.\nResponse: In this revised manuscript, we have now listed the FDR-adjusted WALD p-value of the selected lncRNAs. We have also used the new cutoff (i.e., FDR < 20%) as a first step to define “significant” lncRNAs in CRISPRa screening. To address the reviewer’s concern about the false positives among the “significant” hits identified by CRISPR screening, we have included their expression in both cell lines (i.e., CCLE) and patient tumors (i.e., TCGA) data as well as lncRNA’s association with drug response to help remove false positives (Supplementary table 1 and 2).\n3. For the focused screens, only 40 NT sgRNAs were included. MAGeCK recommends at least 100 NT sgRNAs per screen as this is required for normalization.\nResponse: For the design of our focused sgRNA library, we followed the protocol established by Dr. Feng Zhang’s lab 1. We also reviewed other published studies designing small-scale CRISPR screening libraries and decided to use a comparable number of NT sgRNAs. For example, Wei Wei et al. designed a focused CRISPR \"mini-library\" comprising sgRNAs targeting 33 hits from genome-wide screening (4 sgRNAs per gene) and 10 non-targeting controls2. In the EPIKOL chromatin-focused CRISPR/Cas9-based screening library, 80 non-targeting controls were included alongside 7,790 sgRNAs targeting 779 genes 3, 4. Similarly, Jiyu Tong et al. also established a targeted RBP CRISPR screening library consisting of 83 non-targeting sgRNAs among a total of 7,272 sgRNAs5. All these studies successfully identified significant hits and validated the corresponding phenotypes, supporting the robustness of using a moderate number of selected NT sgRNAs in focused screens. In our focused screen, we intentionally selected 40 NT sgRNAs with the least variation observed in our genome-wide screening to minimize system noise. As a result, these 40 NT sgRNAs demonstrated a stable distribution across different samples (R-Figure 3A and B).\nTo further address reviewer’s concerns, we performed additional downsampling and upsampling analyses to evaluate the robustness of our design. Specifically, we downsampled to as few as 10 NT sgRNAs and upsampled to 200 NT sgRNAs in our analysis. As the reviewer expected, using too few NTs (10~20) introduced some false positives. Our analysis indicated that we were able to achieve reliable and reproducible identification using 40 NT sgRNAs. Overall, the results showed that 150 out of 160 genes (93.75%) in the Palbociclib screen exhibited consistent outcomes (R-Figure 3C). These observations highlight the robustness of our focused library design of true hits through statistical testing with a moderate number of NT sgRNAs (40).\n4. Throughout the results section, please put the statistical cut-off in brackets in the text (e.g. p-value <) when claiming something is “significant”. For example, on line 85 “…we identified lncRNA genes that can significantly regulate breast cancer proliferation (WALD p-value <0.05).\nResponse: We have added FDR/p values in the manuscript.\n5. Fig1E, Line 93. …...CRISPRa screening showed significant overexpression (e.g. p-value <) in tumors compared with normal tissues. What statistical cut-off was used?\nResponse: In the revised manuscript, we have now removed the results of lncRNA significant overexpression in tumors compared with normal tissues in case of potential misinterpretations.\n6. Figs 2B-F, 3E, F, 5A-E, 6B, G-J. The authors state that 3- 6 biological replicates were performed however the error bars are too small for expected variation of biological replicates. Do the authors mean technical replicates here?\nResponse: Figure 2B, 2C, 2D, 2E, 2F, 5A, 5B, 5C, 5D, 5E, are biological replicates; Figure 6B, G-J are removed in the updated manuscript; and the raw data for biological replicates are included in the source file. Fig. 6B and 6J are qPCR data, and they are technical replicates. We have added this information to the revised manuscript. The error bars represent the standard deviation of the replicates. To improve clarity, we have regenerated and replaced the figures in the revised manuscript. In the updated version, the corresponding figures are now labeled as Figure 4A, 4B, 4C, Figure 2C, 2B, Figure 1F, 1G, and Figure 3A, 3C, 3E, respectively (order reflects the mapping from the original figures).\n7. The functional follow-up of the individual lncRNAs is very light and not convincing. The authors did differential expression analysis of lncRNA-activated vs control cells and found >400 differentially expressed genes per lncRNA. The authors need to define what they consider differentially expressed (e.g. Log fold change and p-value cut offs). The authors then cherry picked 1 cell cycle related gene for each lncRNA and attribute the effect to that. Were genes in cell cycle pathways significantly enriched? Please state the individual statistical cut offs for each gene. Additional experiments are required to show that the effects of the lncRNA are mediated by these genes e.g. rescue experiments.\nResponse: In the revised manuscript, we have now performed an in-depth functional investigation of the individual lncRNAs. The new results include\n1. We have now defined what we consider differentially expressed (p < 0.05) in the manuscript (Supplementary Table 3). Our results show that ER and MYC targets are activated and suppressed in the three lncRNAs activated and knocked down cells, respectively. We have further shown that all three lncRNAs, especially TENM3-AS1, regulate ERα target protein expression, especially MYC.\n2. We have performed a comprehensive GSEA pathway analysis and find ER and MYC downstream pathways are upregulated after lncRNA activation (“Estrogen Response Early” TENM3-AS1 FDR = 0.015, ENSG00000226706 FDR = 0.034, LINC01117 FDR = 0.04 and “MYC Targets V1” TENM3-AS1 FDR = 0.011, LINC01117 FDR = 0.02, ENSG00000226706 FDR = 0.2, Figure 6A) and downregulated after lncRNA knockdown (“Estrogen Response Early” TENM3-AS1 FDR < 10-6, ENSG00000226706 FDR = 0.014, LINC01117 FDR < 10-6 and “MYC Targets V1” pathway TENM3-AS1 FDR <10-6, LINC01117 FDR = 0.17, ENSG00000226706 FDR = 0.003, Figure 6A). These observations lead to the hypothesis that three lncRNAs we identified, especially TENM3-AS1, may regulate cell proliferation and CDKi drug response through modulating the ERα-MYC axis directly or indirectly.\n3. Indeed, we have performed RIP assay and observed that TENM3-AS1 RNA is enriched in the ERα binding RNAs (Figure 7G). Finally, we have shown knockdown of ERα rescues TENM3-AS1’s effect on cell proliferation (Figure 7B) and drug response (Figure 7H).\nWe thank the reviewer for their constructive suggestions, which help us unveil the potential mechanisms of the identified lncRNAs’ oncogenic function.\n8. For the drug screening hits – on line 180 please define what “20 consistent hits” mean?\nResponse: In the original context, “20 consistent hits” referred to lncRNAs that were significantly selected in the proliferation screen and also exhibited significant, directionally consistent selection (either positive or negative) in the drug screening across both the genome-wide and focused CRISPRa screens. These hits reflect robust candidates with consistent effects on both proliferation and drug response. In the revised manuscript, we expanded our analysis to focus more broadly on drug screening outcomes (see Methods) and clarify our selection criteria.\n9. Line 222. The word “fast” is not appropriate. Please change “fast” to “increased”.\nResponse: Changed “fast” to “increased”\n10. Line 226. Similar to the cell cycle genes, the authors have cherry picked 1 stem cell related gene for each lncRNA and attribute the effect to that. Were genes in stem cell pathways significantly enriched? Please state the individual statistical cut offs for each gene. Additional experiments are required to show that the effects of the lncRNA are mediated by these genes e.g. rescue experiments.\nResponse: Thanks for the reviewer’s previous questions about the mechanism. Our new analyses suggested that ERα and MYC targets are activated and suppressed in the three lncRNAs activated and knocked down cells, respectively. We now hypothesize that the CSC makers’ changes are the downstream changes of lncRNAs’ regulation of the ERα-MYC axis, instead of the driving mechanism. Indeed, we performed siRNA knockdown of three CSC markers in each of three lncRNA-activated cells and tested drug response. Knockdown of CSC markers does not influence the drug sensitivity mediated by lncRNAs (R-Figure 4A, 4B, and 4C). These observations suggest that the CSC marker alternations are one of the downstream phenotype changes because of the activation of the ERα-MYC axis. With these important results, we have now made accordingly modifications in our revised manuscript. We sincerely appreciate the reviewer’s constructive suggestions, which help us reveal the molecular mechanisms of the identified lncRNAs’ oncogenic function.\nReviewer #2 (Remarks to the Author): Expertise in CDK4/6 inhibitors in breast cancer\nThe authors seek to understand the molecular mechanism underlying the role of three oncogenic lncRNAs in regulating cancer cell proliferation and cell stemness to govern cellular responsiveness to CDK4/6 inhibitor treatment. The manuscript is clearly written, and the experiments were carried out to provide support for the central hypothesis. However, additional in-depth investigation should be carried out to validate the detailed mechanisms. In addition, the following concerns should be addressed.\n1) Figure 2B: The authors should examine if similar effects can be observed in other breast cancer cell lines.\nResponse: We have now constructed the CRISPRa system in T47D cells. Our results indicate that activation of TENM3-AS1, LINC01117, and ENSG00000226706 can increase cell proliferation in T47D cells (Figure 4A and Figure 4B) and lead to Palbociclib’s sensitivity (Figure 3B). Furthermore, lentiviral overexpression of TENM3-AS1 cDNA also induced cell proliferation in T47D cells (Figure 5F and Figure 5G). Both activation and overexpression of TENM3-AS1 significantly enhanced ERα and MYC expression levels (Figure 6C and Extended Figure 6c).\n2) Figure 2C, it will be nice for the authors to examine the effects of depleting these lncRNAs\nResponse: In the revised manuscript, we have now performed the colony formation assay in lncRNA KD cells (siRNA) using MCF-7 parental cells. Our results indicated that suppression of three lncRNAs led to a significant inhibition of cell proliferation (Figure 4D, 4E, and 4F).\n3) Figure 2G, it will be nice for the authors to examine if there are additive effects of sglncRNA with sgCCNB1/D2?\nResponse: Following the reviewer’s suggestion, we have now co-activated CCNB1 and three lncRNAs (sgTENM3-AS1, sgLINC01117, sgENSG00000226706) in MCF-7 cells, respectively (R-Figure 5A and 5B). The cell cycle assay indicates that there is an additive effect of CCNB1 and lncRNA co-activation (R-Figure 5C).\n4) Figure 3A: it will be nice for the authors to examine or explain if the three LncRNAs operate through the same pathways to affect cell cycle and tumorigenesis. If activation of all three LncRNAs have additive effects, or if in sgTAS samples, depleting the other two LncRNAs affect its phenotypes?\nResponse: In the figure below, we have knocked down the other two lncRNAs (LINC01117 and ENSG00000226706) in TAS-activated cells (sgTAS). Our result shows that knockdown of LINC01117 and ENSG00000226706 can partially decrease sgTAS’s promotion of cell proliferation (R-Figure 6A and 6B). Further RNA-seq analysis and western blot revealed that knockdown of all lncRNAs led to decreased ERα signaling (Extended Figure 6b and Figure 6E)., please see our response below). This may be the reason that siLINC01117 and siENSG00000226706 can partially decrease sgTAS’s promotion of cell proliferation. Thanks for the reviewer’s insightful questions.\n6) Figure 3D, the authors should examine of explain the molecular mechanism underlying the three different LncRNAs to govern a similar set of genes to impact cell cycle or tumorigenesis?\nResponse: In the revised manuscript, we have now performed an in-depth functional investigation of the individual lncRNAs. Our results show that ERα targets are activated and suppressed in three lncRNAs activated and knocked down cells, respectively (Figure 6A). We have further shown that all three lncRNAs, especially TENM3-AS1, regulate ERα target protein expression (Figure 6B-E). These observations lead to the hypothesis that the three lncRNAs we identified, especially TENM3-AS1, may regulate cell proliferation and CDK4/6i drug response through modulating the ERα signaling directly and indirectly.\nIndeed, we have performed RIP assay and observed that TENM3-AS1 RNA is enriched in the ERα binding RNAs (Figure 7G) but not MYC (Extended Figure 7a). Finally, we have shown that knockdown of ERα rescues TENM3-AS1’s effect on cell proliferation (Figure 7B) and drug response (Figure 7H).\nIn summary, our analysis suggested that these three lncRNAs’ activation can promote the ERα pathways directly or indirectly. The cell cycle gene and CSC markers changes are downstream alterations because of the activation of the ERα-MYC axis. With these important results, we have now made accordingly modifications in our revised manuscript. We sincerely appreciate the reviewer’s constructive suggestions, which help us unveil the potential molecular mechanisms of the identified lncRNAs’ oncogenic function.\n7). Figure 4F, it will be nice for the authors to speculate how there three individual LncRNA regulates similar set of gene expression.\nResponse: In the revised manuscript, we have now included new results suggesting that three individual lncRNAs may activate a similar pathway (e.g., ERα pathway). GSEA pathway analysis revealed that both activation and knockdown of three individual lncRNAs regulate the ERα pathway and its downstream target (Figure 6A). Since ERα has been shown the have RNA binding capacity 6, 7. We have also explored whether the lncRNA TENM3-AS1 interacts with ERα protein (Figure 7G and Extended Figure 7a). We speculate that TENM3-AS1 may activate/stabilize ERα through interacting with it, while the other two activate the ERα-MYC pathway through indirect regulation since the other two lncRNAs (LINC01117 and ENSG00000226706) only regulate the downstream of ERα but not ERα’s own expression (Extended Figure 6d).\n8). Figure 5D, it will be nice for the authors to examine how activation of CDK4 or CDK6 affects palbociclib response, and how depleting Rb affects this process.\nResponse: We did correlation analysis on endogenous CDK4, CDK6 and RB1 expression with palbociclib response. Briefly, we integrated gene expression and drug response data from the Cancer Cell Line Encyclopedia (CCLE). Our analysis showed that CDK6 expression exhibited a trend toward association with Palbociclib resistance (R-Figure 7A, coefficient = 0.25, P = 0.10), while CDK4 expression did not show a significant association (R-Figure 7B, coefficient = 0.08, P = 0.61). Consistent with this, increased CDK6 activity has been reported to drive acquired resistance to CDK4/6i 8. In contrast, the role of CDK4 in resistance appears limited. Moreover, clinical studies have also failed to validate CDK4/6 as a predictive biomarker 9, 10.\nRegarding RB1, we found that higher RB1 mRNA expression was associated with increased Palbociclib sensitivity (R-Figure 7C, coefficient = -0.25, P = 0.09). Furthermore, higher levels of phosphorylated RB were also correlated with greater sensitivity (R-Figure 7D, coefficient = -0.32, P = 0.037). This is also consistent with a previous report that RB loss led to Palbociclib resistance 11, 12, 13.\n9). Figure 5E, it will be nice to examine the effects of CDK4/6 inhibitor on the expression of these three LncRNAs in this experimental setting.\nResponse: We have performed qPCR to determine these three lncRNAs’ expressions after treating with CDK4/6 inhibitor (0, 0.5, 2 μM) in control (siCon) and lncRNAs knockdown (TENM3-AS1, LINC01117, and ENSG00000226706) MCF-7 cells. We don’t see evidence of lncRNA expression change after Palbociclib treatment (R-Figure 8A).\n10) Figure 6, it will be nice for the authors to speculate on the molecular mechanism of how these three LncRNAs govern cancer cell stemness in this experimental.\nResponse: In the revised manuscript, our new results suggest that these three lncRNAs, especially TENM3-AS1, regulate the ERα pathway. We speculate that the activation of the ERα can also explain the decrease in cancer cell stemness. To test this hypothesis, we knocked down of ERα and observed that depleting of ERα expression abolished the regulation of cancer stem cell markers by three lncRNAs (R-Figure 9A and 9B). Combined with our other results in cell proliferation and drug response, we think that the activation of ERα signaling mediate those lncRNAs regulation of cell proliferation, CDKi sensitivity, and stem cell markers suppression. We thank the reviewer for their insightful discussion, which helped us unveil this mechanism. In order to keep the manuscript focused, we have decided to keep cancer cell stemness results for a separate paper."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The paper has greatly improved but I still have some concerns including:\n1. Although the authors have addressed my concerns regarding TEN3-AS1, the limitations of the use of CRISPRa for lncRNA activation in general has still not been acknowledged in the manuscript. Please discuss the limitations of the use of CRISPRa for lncRNA in the manuscript.\n2. For new Fig 5A. I would expect that T47D cells infected with empty vector would form colonies. Why is there no colonies on the control plate?\n3. I’m concerned that the claimed biological replicates are actually technical replicates. Please explain how the biological replicates were performed. Were the biological replicates performed on separate occasions. From the description in the results section, it sounds like the authors plated out multiple wells and call each separate well a a biological replicate. In my mind this is a technical replicate. For each biological replicate there should be technical replicates which do not appear to be included in the source file.\n4. Thank-you for performing the RNA immunoprecipitation experiment. The manuscript states that 3 biological replicates were formed however the error bars appear to be too small. Were these replicates performed on separate occasions. Please show the western blots for the second and third biological replicates as supplementary figures.\n5. Biological replicates for all new data should be performed.\n6. The authors provide raw data for their biological replicates but they appear to be just technical replicates. There doesn't seem to be much variation between replicates. I can't help but think that their idea of a biological replicate is note right. Biological replicates should have technical replicates (the authors don't provide these data). The error bars a way too small for what I would expect for natural varation observed between biolgical replicates.\nThe manuscript will require grammatical checking prior to publication. I started to list a few below:\nLine 20,128 - screenings should be screens;\nLine 21, 101, 104,129 - screening should be screen;\nLine 65 - on should be of\nLine 76,138 – remove “the”\nLine 84 – remove “a”\nLine 85 – change “modulating” to “that modulate”\nLine 92 – “inhibitors” should be “inhibitor”\nLine 125 – “screening samples” should be “screens”", "reviewer_2": "The authors have addressed most of the raised concerns.", "reviewer_3": "I appreciate the authors’ thorough revisions. In the revised manuscript, the authors propose ERα signaling as a new mechanism that underlies the regulation of the three lncRNAs in cell proliferation and CDK4/6i sensitivity. Overall, the manuscript has improved, and majority of my previous concerns have been addressed. I have only a few comments remaining.\n1. In response to Comment 9, the authors indicate that their bulk RNA-seq differential expression analysis was based on one replicate in the activation condition and two replicates in the knockdown condition. This is not appropriate: biological replicates must be from the same experimental condition. The current design does not support valid estimation of within-group variance and therefore the reported p-values and downstream enrichment are unreliable. The authors should provide additional biological replicates (at least three per condition) and reanalyze the data, even if key findings have been validated by qPCR. Otherwise, the results should be presented as exploratory or descriptive.\n2. In Figures 7B, 7H, and S7b, the authors showed that knockdown of ERα signaling reverses the increase in cell proliferation and CDK4/6i sensitivity induced by the three lncRNAs. It would be interesting to examine if ERα knockdown alone affects cell proliferation and CDK4/6i sensitivity.\n3. Line 213 – 219: The authors examined if sgTENM3-AS1 leads to off-target activation of TENM3. It’s good that they showed knockdown of TENM3 does not affect sgTENM3-AS1-induced cell growth, but they should also mention that another gene, ENSG00000248266, shares the promoter with TENM3-AS1 and include the data (R-Figure 1A and 1B) that sgTENM3-AS1 does not affect the expression of the ENSG00000248266 gene in the manuscript.\n4. In Figures 1G, 6B, and 7A, the differences observed between sgNT1 and sgNT2 are apparent in certain cases. Do the authors have an explanation for this? Could it be due to off-target effects of one of the sgNTs?", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response: We agree with the reviewer that the CRISPRa system has some limitations for lncRNA screening. We have updated this section in the discussion.\n2. For new Fig 5A. I would expect that T47D cells infected with empty vector would form colonies. Why is there no colonies on the control plate?\nResponse: In Figure 5, we can confirm that colonies formed in the empty vector plate on Day 6. It was not obvious because we showed the full plate picture of colony formation. In the revised manuscript, we have updated the figure to a higher resolution and added three additional independent experimental replicates. The results from the three independent experimental replicates are now provided in R-Figure 1.\n3. I’m concerned that the claimed biological replicates are actually technical replicates. Please explain how the biological replicates were performed. Were the biological replicates performed on separate occasions. From the description in the results section, it sounds like the authors plated out multiple wells and call each separate well a biological replicate. In my mind this is a technical replicate. For each biological replicate there should be technical replicates which do not appear to be included in the source file.\nResponse: We acknowledge the reviewers’ concerns regarding the distinction between biological and technical replicates. Most of our results have been independently reproduced by a diTerent lab member. To address these points thoroughly, we have performed additional biological replicates for the key experiments, including the functional assays and RIP assays, to ensure that the relevant data meet Nature Communications standards for experimental reproducibility. In the revised manuscript Methods section, we have now clearly defined how biological replicates are conducted and updated all figure legends, source data, and the Reporting Summary accordingly.\nFor certain non-essential or validation experiments that were originally performed with only technical replicates, we have explicitly acknowledged this in the revision. These data have been presented descriptively without statistical testing, consistent with the journal’s policy of transparent reporting and appropriate statistical treatment. The corresponding figure legends and text have been revised to make this distinction clear.\n4. Thank-you for performing the RNA immunoprecipitation experiment. The manuscript states that 3 biological replicates were formed however the error bars appear to be too small. Were these replicates performed on separate occasions. Please show the western blots for the second and third biological replicates as supplementary figures.\nResponse: We have now included the second and third independent experimental validations of the Western blots as source data. The biological replicates of RIP western are listed below.\n5. Biological replicates for all new data should be performed.\nResponse: We have now included the independent experimental validation or biological replicates for Fig. 3A, 3B, 3C, 3D, 4B-right, 4F, 5D-G, 6B-E, 7A-H. We have updated the corresponding figure legends, source data, and the Reporting Summary. The independent validations result of Fig. 4A, 4D, 4E, 5F, and 7B have now been presented in this point-to-point response letter (R-Figure 2, R-Figure 3, R-Figure 4, and R-Figure 5) and all the new raw data have been submitted as source data.\n6. The authors provide raw data for their biological replicates, but they appear to be just technical replicates. There doesn't seem to be much variation between replicates. I can't help but think that their idea of a biological replicate is note right. Biological replicates should have technical replicates (the authors don't provide these data). The error bars a way too small for what I would expect for natural variation observed between biological replicates.\nResponse: We appreciate the reviewer’s comments regarding the replicates. We have now included the raw data of independent experimental validation or biological replicates in the revised manuscript.\nThe manuscript will require grammatical checking prior to publication. I started to list a few below:\nLine 20,128 - screenings should be screens;\nLine 21, 101, 104,129 - screening should be screen;\nLine 65 - on should be of\nLine 76,138 – remove “the”\nLine 84 – remove “a”\nLine 85 – change “modulating” to “that modulate”\nLine 92 – “inhibitors” should be “inhibitor”\nLine 125 – “screening samples” should be “screens”\nResponse: Thanks for spotting these. We have now carefully checked grammar errors in the manuscript.\nReviewer #2 (Remarks to the Author):\nThe authors have addressed most of the raised concerns.\nReviewer #3 (Remarks to the Author):\nI appreciate the authors’ thorough revisions. In the revised manuscript, the authors propose ERα signaling as a new mechanism that underlies the regulation of the three lncRNAs in cell proliferation and CDK4/6i sensitivity. Overall, the manuscript has improved, and majority of my previous concerns have been addressed. I have only a few comments remaining.\n1. In response to Comment 9, the authors indicate that their bulk RNA-seq diTerential expression analysis was based on one replicate in the activation condition and two replicates in the knockdown condition. This is not appropriate: biological replicates must be from the same experimental condition. The current design does not support valid estimation of within-group variance and therefore the reported p-values and downstream enrichment are unreliable. The authors should provide additional biological replicates (at least three per condition) and reanalyze the data, even if key findings have been validated by qPCR. Otherwise, the results should be presented as exploratory or descriptive.\nResponse: We acknowledge the reviewers’ concerns regarding the lack of biological replicates in the bulk RNA-seq analysis. We have now presented the RNA-seq data as descriptive in the Results and Method section. In the revised manuscript, the key RNA-seq findings were validated by qPCR and western blotting with biological replicates or independent experimental validation by a diTerent lab member (i.e., Fig. 6B, C, E, 7A). The raw data have been included in the supplementary. We have also performed additional biological replicates for the key experiments, including the functional assays and RIP assays, to ensure the downstream pathway enrichment results are reproducible. In the revised manuscript Methods section, we have now clearly defined how biological replicates are conducted and updated all figure legends, source data, and the Reporting Summary accordingly.\n2. In Figures 7B, 7H, and S7b, the authors showed that knockdown of ERα signaling reverses the increase in cell proliferation and CDK4/6i sensitivity induced by the three lncRNAs. It would be interesting to examine if ERα knockdown alone aTects cell proliferation and CDK4/6i sensitivity.\nResponse: To address this interesting question, we have shown the results of ERa knockdown alone in two non-targeting sgRNA cells of Figure 7B, 7H, and S7b. Compared with control siRNA, the ERa knockdown appears to inhibit cell proliferation and increase palbociclib resistance in MCF-7 cells.\n3. Line 213 – 219: The authors examined if sgTENM3-AS1 leads to oT-target activation of TENM3. It’s good that they showed knockdown of TENM3 does not aTect sgTENM3-AS1-induced cell growth, but they should also mention that another gene, ENSG00000248266, shares the promoter with TENM3-AS1 and include the data (R-Figure 1A and 1B) that sgTENM3-AS1 does not aTect the expression of the ENSG00000248266 gene in the manuscript.\nResponse: We appreciate the reviewer’s suggestion. We have updated this result in the manuscript and the Extended Figure 5d.\n4. In Figures 1G, 6B, and 7A, the diTerences observed between sgNT1 and sgNT2 are apparent in certain cases. Do the authors have an explanation for this? Could it be due to oT-target eTects of one of the sgNTs?\nResponse: We thank the reviewer for noting the diTerences between the two non-targeting control sgRNAs (sgNT1 and sgNT2). Although both sgRNAs are widely used as negative controls in CRISPRa studies, they are distinct sequences and therefore may exhibit subtle variability in baseline transcriptional activation due to sequence-specific chromatin accessibility, promoter activation, or random variation in viral integration that have been reported for individual non-targeting sgRNAs in CRISPRa systems.\nWe have added a brief clarification in the discussion section that diTerent non-targeting sgRNAs may have minor sequence-dependent eTects, and the limitation of CRISPRa approaches. However, the consistency of our results across both sgNT controls supports the robustness of the reported lncRNA-dependent phenotypes.\nReviewer #4 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, {"round": 3, "reviewer_comments": {"reviewer_1": "The authors have thoroughly addressed my concerns.", "reviewer_3": "The authors have addressed the concerns.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": ""}]

Decision letter