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Correction: SMARCA4 controls state plasticity in small cell lung cancer through regulation of neuroendocrine transcription factors and REST splicing
Journal of Hematology & Oncology ( IF 29.5 ) Pub Date : 2024-09-29 , DOI: 10.1186/s13045-024-01609-7
Esther Redin, Harsha Sridhar, Yingqian A. Zhan, Barbara Pereira Mello, Hong Zhong, Vidushi Durani, Amin Sabet, Parvathy Manoj, Irina Linkov, Juan Qiu, Richard P. Koche, Elisa de Stanchina, Maider Astorkia, Doron Betel, Álvaro Quintanal-Villalonga, Charles M. Rudin

Correction: Journal of Hematology & Oncology (2024) 17:58 https://doi.org/10.1186/s13045-024-01572-3


The original article mistakenly omitted numerous elements from the article figures due to an error in transferring the files at the proofing stage. The figures have since been updated to restore all missing elements of each affected figure (Figs. 1, 2, 3, 4, 5, 6).

Fig. 1
figure 1

SMARCA4 expression correlates with NE features in SCLC. A SMARCA4 mRNA levels in cell lines derived from 30 tumor types assessed using the Cancer Cell Line Encyclopedia (CCLE). Bars indicate the median expression per tumor type. B SMARCA4 mRNA levels in LUAD and SCLC specimens retrieved from Quintanal Villalonga et al. [27]. Student’s two-tailed unpaired t test. **p < 0.01. C Spearman correlation of SYP, CHGA, INSM1, YAP1 and REST with SMARCA4 mRNA levels in Rudin et al. and George et al. databases and CCLE[25, 26]. D SMARCA4 mRNA expression in low and high NE SCLC tumors in cell lines (CCLE) and clinical specimens (Rudin et al. and George et al.) [25, 26]. One-way ANOVA test followed by Bonferroni post-hoc test. ****p < 0.0001, ***p < 0.001, **p < 0.01. E Western blotting of ASCL1, NEUROD1, SYP and CHGA in isogenic cell lines derived from H82 and H146 expressing different combinations of shRNAs against SMARCA4 and/or SMARCA2. Expression of shRNAs from E was conditional of doxycycline treatment. Protein collection and blotting was performed after 14 days of doxycycline treatment. See also Fig. S1

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Fig. 2
figure 2

SMARCA4 inhibition suppresses the NE phenotype in SCLC. A Hockey-stick plots of DEGs in FHD-286-treated cells after 14 days (100 nM) versus control, untreated cells. (See Table S1). B Dot plots showing negative enrichment in selected neuronal and NE pathways analyzed by GSEA in RNAseq data from H82 and H146 cell lines treated with FHD-286 versus untreated. (See Table S1). C GSEA applying Zhang et al. NE gene signature [28] in H82 cell line treated with FHD-286 versus untreated. D Heatmaps showing the most significant confident targets (top 25 with TPMs > 2) of NEUROD1 (left) and ASCL1 (right) [7], in H82 (left) and H146 (right) bulk RNAseq (FHD-286 treated vs untreated). E Log2 fold change of Hippo pathway genes from data in A. Student’s two-tailed unpaired t test. ***p < 0.001, **p < 0.01. The mean ± SD is shown. F Log2 fold change of NOTCH pathway genes from data in A. Student’s two-tailed unpaired t test. ***p < 0.001, *p < 0.05. The mean ± SD is shown. G Western blotting of H524 (SCLC-N), H82 (SCLC-N), HCC33 (SCLC-N), H69 (SCLC-A), SHP77 (SCLC-A) and H146 (SCLC-A) cells after treatment with 100 nM of FHD-286 for 7 and 14 days. H t-SNE of Zhang NE signature and SMARCA4 levels applied to public scRNAseq data of 4 myc-driven murine (RPM) tumors [6]. I Scoring for Zhang NE signature and SMARCA4 projected in a pseudotime trajectory from early to late time points in a tumor from a Myc-driven murine SCLC model showing subtype plasticity [6]. See also Figs. S2, S3 and Table S1

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Fig. 3
figure 3

SMARCA4 inactivation alters chromatin accessibility in NE-high SCLC. A Heatmap showing ATACseq chromatin accessibility changes (FDR:0.01, FC > 1.5) in H82 and H146 cells after treatment with FHD-286 (100 nM, 14 days). B Enrichment of neuronal and NE HOMER transcription factor-binding DNA motifs in ATAC-seq peaks lost after treatment with FHD-286 (100 nM, 14 days). The percentage indicates the amount of target sequences with motif. C Genomic localization of lost and gained accessible sites upon FHD-286 treatment in H82 and H146 cells. D ATACseq genome tracks of NEUROD1, SYP and CHGA in H82 and H146 cells after treatment with FHD-286. Peaks with a significant reduction in chromatin accessibility are indicated with arrows. E Enrich analysis applied to all genes with lost sites (across all gene body) following FHD-286 treatment. Top 10 GO Biological processes enriched are shown. See also Fig. S4

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Fig. 4
figure 4

SMARCA4 binds to neuronal and NE lineage TF genes in SCLC. A Heatmap and metaplot showingSMARCA4 binding profile determined by ChIP-seq in 4 NE SCLC PDXs and a pooled input. The range under the map indicates the ChIP-seq signal intensity. B Metaplots of ASCL1 and NEUROD1 in all PDXs and input. Heatmaps showing the binding of SMARCA4 to ASCL1 and NEUROD1 gene bodies. The range indicates the normalized enrichment along the respective gene regions. C NE lineage TFs and gene promoter proximal regions (within 1 kb of TSS) bound by SMARCA4 in NE SCLC PDXs. D Dot plot of Poly-Enrich analysis applied to SMARCA4 ChIP-seq peaks. Fold enrichment refers to the fold increase in the signal for a particular gene relative to the background signal. The counts refer to the number of genes detected in the ChIP-seq data that are part of the indicated pathways. E Enrich analysis of 617 consensus genes selected by combining RNAseq from Fig. 2 and ChIP-seq data. See also Fig. S5E. F Enrichment analysis of TF-binding motifs in the SMARCA4 ChIP-seq data identified with HOMER. See also Figs. S5, S6 and Table S3

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Fig. 5
figure 5

SMARCA4 regulates SRRM4 expression to control splicing and activation of REST. A Venn diagram of ASCL1 and NEUROD1 published binding targets from Borromeo et al. [7] overlapping with genes downregulated by FHD-286 in H146 and H82 cells. B Western blots of H82 and H146 cells treated with FHD-286 for 14 days. C Metaplot of SMARCA4 ChIP-seq showing SMARCA4 binding to SRRM4 in 4 NE SCLC PDXs. Range indicates the fold enrichment with respect the input. ChIP-seq genome tracks at SRRM4 TSS. Graphs were obtained from IGV. D Correlation of SMARCA4 and SRRM4 mRNA levels in SCLC patients’ database. Spearman correlation. E Correlation analysis of SRRM4 and SMARCA4 in cancer cell lines retrieved from CCLE. Cell lines with both high SMARCA4 and SRRM4 mRNA levels are highlighted. F Merged ATAC-seq tracks of H82 and H146 parentals cells and FHD-286 treated cells (day 14) at SRRM4 gene locus visualized with IGV. G Graphical representation of REST genomic regions and spliced isoforms with the binding location of the different primers used for PCR. H PCR analysis of REST splicing isoforms using two pairs of primers (E2F1 + E4R1 and E1F1 + E4R1) that span N3c. I RT-qPCR of REST4 isoforms (S3, S7, S12) in H82, H146 and H524 treated with FHD-286 (14 days) versus untreated cells. The pair of primers E3N3c and E4R2 that recognizes all isoforms including exon N3c was used. Student’s two-tailed unpaired t test. ***p < 0.001. The mean ± SD is shown. J Enrich analysis applied to commonly and significantly downregulated genes in both H146 and H82 (n = 904) cell lines identified in the bulk-RNAseq (Fig. 2). See also Fig. S7

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Fig. 6
figure 6

SMARCA4/2 inhibition by FHD-286 induces ERBB signaling and sensitivity to afatinib in SCLC. A Proliferation curves of SCLC-A, -N, -P and -Y SCLC cell lines treated with FHD-286 for 96 h. The mean ± SD is shown. B Tumor growth of Lx151 and Lx95 SCLC PDXs implanted in NSG mice and treated with 1.5 mg/kg BID p.o. of FHD-286. Student’s two-tailed unpaired t test. ***p < 0.001. C IPA analysis on significantly upregulated genes in FHD-286-treated cells versus control untreated cells. D Immunoblot of ERBB family proteins in H146 and H82 cells after treatment with 100 nM of FHD-286 for 14 days. E Western blots of FHD-286 (100 nM) treated cells at the indicated times. F Synergy plots of FHD-286 and afatinib in NE SCLC cell lines. G Cell death quantification by flow cytometry at day 5 of H146 and H82 cells after treatment with FHD-286, afatinib or both. One way ANOVA followed by Bonferroni comparison test. ***p < 0.001, ****p < 0.0001. H Normalized tumor growth of Lx1042 (SCLC-N), Lx1322 (SCLC-P), Lx151 (SCLC-A) and Lx95 (SCLC-A) relative to day 1 of treatment. Two-way ANOVA followed by Bonferroni comparison test. *p < 0.05, **p < 0.01, ***p < 0.001. I Schematic representation of the role of SMARCA4 in sustaining the NE phenotype in SCLC

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Authors and Affiliations

  1. Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Esther Redin, Harsha Sridhar, Barbara Pereira Mello, Hong Zhong, Vidushi Durani, Amin Sabet, Parvathy Manoj, Álvaro Quintanal-Villalonga & Charles M. Rudin

  2. Center for Epigenetics Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Yingqian A. Zhan & Richard P. Koche

  3. Precision Pathology Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Irina Linkov

  4. Antitumor Assessment Core, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Juan Qiu & Elisa de Stanchina

  5. Weill Cornell Medicine Graduate School of Medical Sciences, New York, NY, USA

    Vidushi Durani & Charles M. Rudin

  6. Applied Bioinformatics Core, Weill Cornell Medicine, New York, NY, 10065, USA

    Maider Astorkia & Doron Betel

  7. Division of Hematology and Oncology, Department of Medicine, Weill Cornell Medicine, New York, NY, 10065, USA

    Doron Betel

  8. Department of Physiology, Biophysics and Systems Biology, Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, 10065, USA

    Doron Betel

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Redin, E., Sridhar, H., Zhan, Y.A. et al. Correction: SMARCA4 controls state plasticity in small cell lung cancer through regulation of neuroendocrine transcription factors and REST splicing. J Hematol Oncol 17, 89 (2024). https://doi.org/10.1186/s13045-024-01609-7

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中文翻译:


更正:SMARCA4 通过调节神经内分泌转录因子和 REST 剪接来控制小细胞肺癌的状态可塑性




由于在校对阶段传输文件时出错,原始文章错误地遗漏了文章图表中的许多元素。此后,这些数字被更新以恢复每个受影响数字的所有缺失元素(图 1、2、3、4、5、6)。

 图 1
figure 1


SMARCA4表达与 SCLC 中的 NE 特征相关。 ASMARCA4使用癌细胞系百科全书 (CCLE) 评估的 30 种肿瘤类型的细胞系中的 mRNA 水平。条形图表示每种肿瘤类型的中位表达。BSMARCA4 从 Quintanal Villalonga 等人那里提取的 LUAD 和 SCLC 标本中的 mRNA 水平 [27]。学生的双尾未配对 t 检验。**p < 0.01.SYP、CHGA、INSM1、YAP1RESTC Spearman 相关性与 Rudin 等人和 George 等人数据库和 CCLE 中 SMARCA4 mRNA 水平[25, 26]。DSMARCA4 细胞系 (CCLE) 和临床标本 (Rudin et al. 和 George et al.) 中低和高 NE SCLC 肿瘤中 mRNA 的表达[25, 26].单因素方差分析检验,然后是 Bonferroni 事后检验。p < 0.0001, ***p < 0.001, **p < 0.01.E 在表达针对 SMARCA4 和/或 SMARCA2 的不同 shRNA 组合的 H82 和 H146 来源的同基因细胞系中 ASCL1、NEUROD1、SYP 和 CHGA 的蛋白质印迹。E 中 shRNAs 的表达是多西环素处理的条件。多西环素治疗 14 天后进行蛋白质收集和印迹。另见图第 1 季

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 图 2
figure 2


SMARCA4抑制抑制 SCLC 中的 NE 表型。A 14 天 (100 nM) 后 FHD-286 处理的细胞与对照、未处理细胞的曲棍球棒图(见表 S1)。B 点图显示 GSEA 在用 FHD-286 处理与未处理的 H82 和 H146 细胞系的 RNAseq 数据中分析的选定神经元和 NE 通路的负富集。(见表 S1)。C GSEA 应用 Zhang et al. NE 基因特征 [28] 在用 FHD-286 处理的 H82 细胞系与未处理的 H82 细胞系中。D 热图显示了 H82(左)和 H146(右)大量 RNAseq(FHD-286 处理与未处理)中 NEUROD1(左)和 ASCL1(右)[7] 中最重要的置信靶标(TPM > 2 的前 25 个)。E Log Hippo 通路基因的2 倍变化来自 A. Student 的双尾未配对 t 检验中的数据。p < 0.001, **p < 0.01.显示 SD ±平均值。F Log 2 倍 NOTCH 通路基因与 A 中的数据。学生的双尾未配对 t 检验。p < 0.001, *p < 0.05.显示 SD ±平均值。 100 nM FHD-286 处理 7 天和 14 天后,对 H524 (SCLC-N)、H82 (SCLC-N)、HCC33 (SCLC-N)、H69 (SCLC-A)、SHP77 (SCLC-A) 和 H146 (SCLC-A) 细胞进行 G 蛋白质印迹。Zhang NE 特征的 H t-SNE 和 SMARCA4 水平应用于 4 种 myc 驱动的小鼠 (RPM) 肿瘤的公共 scRNAseq 数据 [6]。I 对 Zhang NE 特征进行评分,并且 SMARCA4 在 Myc 驱动的小鼠 SCLC 模型中以伪时间轨迹投影,从早期到晚期的时间点,来自 Myc 驱动的小鼠 SCLC 模型,显示亚型可塑性 [6]。另见图 S2、S3 和表 S1

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 图 3
figure 3


SMARCA4 失活会改变 NE 高 SCLC 中的染色质可及性。图显示 FHD-286 处理(100 nM,14 天)后 H82 和 H146 细胞中 ATACseq 染色质可及性变化(FDR:0.01,FC > 1.5)。B 用 FHD-286 处理后丢失的 ATAC-seq 峰中神经元和 NE HOMER 转录因子结合 DNA 基序的富集 (100 nM,14 天)。百分比表示带有基序的靶序列的数量。C FHD-286 处理后 H82 和 H146 细胞中丢失和获得的可及位点的基因组定位。D ATACseq 基因组追踪 FHD-286 处理后 H82 和 H146 细胞中 NEUROD1SYPCHGA。染色质可及性显著降低的峰用箭头表示。E Enriched 分析应用于 FHD-286 处理后所有位点缺失的基因 (跨所有基因体)。显示了富集的前 10 个 GO 生物过程。另见图第 4 季

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 图 4
figure 4


SMARCA4 与 SCLC 中的神经元和 NE 谱系 TF 基因结合。图和元图显示了 ChIP-seq 在 4 个 NE SCLC PDX 和混合输入中确定的SMARCA4结合谱。图下方的范围表示 ChIP-seq 信号强度。B 所有 PDX 和输入中 ASCL1NEUROD1 的元图。显示 SMARCA4 与 ASCL1 和 NEUROD1 基因体结合的热图。该范围表示沿相应基因区域的归一化富集。C NE 谱系 TF 和基因启动子近端区域(TSS 的 1 kb 以内)在 NE SCLC PDX 中被 SMARCA4 结合。应用于 SMARCA4 ChIP-seq 峰的 Poly-Rich 分析的 D 点图。倍数富集是指特定基因的信号相对于背景信号的倍数增加。计数是指在 ChIP-seq 数据中检测到的基因数量,这些基因是指定通路的一部分。E 通过结合图 2 中的 RNAseq 和 ChIP-seq 数据,对选择的 617 个共有基因进行富集分析。另见图 S5E。F 用 HOMER 鉴定的 SMARCA4 ChIP-seq 数据中 TF 结合基序的富集分析。另见图 S5、S6 和表 S3

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 图 5
figure 5


SMARCA4调节 SRRM4 表达以控制 REST 的剪接和激活。ASCL1 的维恩图和NEUROD1 Borromeo 等人 [7] 发表的结合靶标与 H146 和 H82 细胞中 FHD-286 下调的基因重叠。B 用 FHD-286 处理 14 天的 H82 和 H146 细胞的 Western 印迹。SMARCA4 ChIP-seq 的 C 元图显示 4 个 NE SCLC PDX 中与 SRRM4 的SMARCA4结合。Range 表示相对于输入的倍数富集。ChIP-seq 基因组在 SRRM4 TSS 处追踪。图表来自 IGV。D SCLC 患者数据库中 SMARCA4SRRM4 mRNA 水平的相关性。Spearman 相关性。E 从 CCLE 检索的癌细胞系中 SRRM4SMARCA4 的相关性分析。突出显示了具有高 SMARCA4SRRM4 mRNA 水平的细胞系。F 用 IGV 可视化的 SRRM4 基因位点合并 H82 和 H146 父系细胞和 FHD-286 处理细胞(第 14 天)的 ATAC-seq 轨迹。G REST 基因组区域和剪接亚型的图形表示,以及用于 PCR 的不同引物的结合位置。使用跨越 N3c 的两对引物(E2F1 + E4R1 和 E1F1 + E4R1)对 REST 剪接亚型进行 H PCR 分析。I 用 FHD-286 处理(14 天)的 H82、H146 和 H524 中 REST4 亚型(S3、S7、S12)与未处理细胞的 RT-qPCR。使用识别包括外显子 N3c 在内的所有亚型的引物 E3N3c 和 E4R2。学生的双尾未配对 t 检验。p < 0.001.显示 SD ±平均值。 J Enriched 分析应用于 mass-RNAseq 中鉴定的 H146 和 H82 (n = 904) 细胞系中常见且显著下调的基因(图 2)。另见图S7系列

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 图 6
figure 6


FHD-286 的 SMARCA4/2 抑制诱导 SCLC 中 ERBB 信号传导和对阿法替尼的敏感性。A FHD-286 处理 96 h 的 SCLC-A 、 -N 、 -P 和 -Y SCLC 细胞系的增殖曲线。显示 SD ±平均值。B 植入 NSG 小鼠并用 1.5 mg/kg BID 口服 FHD-286 处理的 Lx151 和 Lx95 SCLC PDX 的肿瘤生长。学生的双尾未配对 t 检验。p < 0.001.C 对 FHD-286 处理的细胞与对照未处理细胞中显着上调的基因进行 IPA 分析。D 用 100 nM FHD-286 处理 14 天后 H146 和 H82 细胞中 ERBB 家族蛋白的免疫印迹。指定时间对 FHD-286 (100 nM) 处理的细胞进行 E 蛋白质印迹。F FHD-286 和 afatinib 在 NE SCLC 细胞系中的协同图。用 FHD-286、阿法替尼或两者处理后,在第 5 天通过流式细胞术对 H146 和 H82 细胞进行 G 细胞死亡定量。单向方差分析后跟 Bonferroni 比较检验。p < 0.001, ****p < 0.0001.H 相对于治疗第 1 天,Lx1042 (SCLC-N)、Lx1322 (SCLC-P)、Lx151 (SCLC-A) 和 Lx95 (SCLC-A) 的肿瘤生长正常化。双向方差分析,然后是 Bonferroni 比较检验。*p < 0.05, **p < 0.01, ***p < 0.001.I SMARCA4 在维持 SCLC 中 NE 表型中的作用示意图

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 作者和单位


  1. 美国纽约州纽约市纪念斯隆凯特琳癌症中心医学部


    Esther Redin, Harsha Sridhar, Barbara Pereira Mello, Hong Zhong, Vidushi Durani, Amin Sabet, Parvathy Manoj, Álvaro Quintanal-Villalonga & Charles M. Rudin


  2. 美国纽约州纽约市纪念斯隆凯特琳癌症中心表观遗传学研究中心

    Yingqian A. Zhan & Richard P. Koche


  3. 美国纽约州纽约市纪念斯隆凯特琳癌症中心精准病理学中心

     伊琳娜·林科夫


  4. 美国纽约州纽约市纪念斯隆凯特琳癌症中心抗肿瘤评估核心


    邱娟 & Elisa de Stanchina


  5. 威尔康奈尔医学研究生院,美国纽约州纽约市

    Vidushi Durani & Charles M. Rudin


  6. 威尔康奈尔医学院应用生物信息学核心,纽约州纽约市,10065,美国

    Maider Astorkia & Doron Betel


  7. 威尔康奈尔医学院医学系血液肿瘤科, 纽约, NY, 10065, 美国

     多伦·贝特尔


  8. 威尔康奈尔医学院计算生物医学研究所生理学、生物物理学和系统生物学系,纽约,纽约,10065,美国

     多伦·贝特尔

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Redin, E., Sridhar, H., Zhan, Y.A. 等人。更正:SMARCA4 通过调节神经内分泌转录因子和 REST 剪接来控制小细胞肺癌的状态可塑性。J Hematol Oncol17, 89 (2024)。https://doi.org/10.1186/s13045-024-01609-7

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