Abstract B27: Phenotypic plasticity and heterogeneity in small cell lung cancer (SCLC): Novel molecular subtypes and potential for targeted therapy.
Abstract B27: Phenotypic plasticity and heterogeneity in small cell lung cancer (SCLC): Novel molecular subtypes and potential for targeted therapy.
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DOI：
10.1158/1078-0432.14aacriaslc-b27
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发表时间：
2014-01
影响因子：
11.5
通讯作者：
A. Udyavar;M. Hoeksema;K. Diggins;J. Irish;P. Massion;V. Quaranta
中科院分区：
文献类型：
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作者：
A. Udyavar;M. Hoeksema;K. Diggins;J. Irish;P. Massion;V. Quaranta
背景资料：SCLC（占肺癌的15%）表现出：1）快速生长和早期致命性转移; 2）神经内分泌特征; 3）对化疗和放疗的高初始反应性; 4）侵袭性复发，5年患者生存率为5%。基因表达和突变谱的努力，以确定致癌突变，基因扩增或签名与临床实用性在SCLC迄今为止是没有成果的。此外，SCLC的预后或诊断标志物很少。因此，迫切需要研究SCLC中的分子亚型和致癌驱动因素。我们假设，解除管制的网络，而不是单个基因，驱动SCLC表型。结果如下：我们之前发现了一个小细胞肺癌特异性基因共表达网络，（蓝色模块，通过加权基因共表达网络分析-WGCNA）从肺癌患者数据集，并导出SCLC特异性中枢网络（SSHN）特征，其：1）在基因组和蛋白质组独立数据集中将SCLC与其他肺癌类型和正常肺分离; 2）在患者标本和培养的细胞系中鉴定出2种SSHN高表达和低表达的SCLC亚型。脾酪氨酸激酶（SYK）被验证为一种亚型的候选致癌驱动因子，因为SYK靶向的小干扰RNA通过增加高SYK表达的SCLC细胞系中的死亡而显著降低活力。由于缺乏更大的SCLC患者数据集，我们现在将SSHN分类器应用于来自癌细胞系百科全书（CCLE）的53个SCLC细胞系，并验证了SSHN-高亚型和低亚型。从这个更大的数据集来看，很明显SSHN定义的亚型并不是完全分开的。相反，它们通过逐渐的中间阴影连接。通过将WGCNA应用于来自CCLE的SCLC细胞系，这种分级变得更加清晰，其鉴定了2个基因共表达模块-Blue和Turquoise，其与来自上述患者数据集的模块重叠。蓝色模块富含神经内分泌信号传导，绿松石富含间质粘附相关通路。2个模块（MEblue、MEturquoise）的特征基因表达是反相关的，并且所有53个SCLC细胞系沿着该反相关对角线分布。神经内分泌标志物CD56的表达在该对角线一端的细胞（MEblue-高细胞系）中最高，并且朝向另一端（MEturquoise-高细胞系）降低，而间充质标志物CD44具有相反的趋势。用viSNE可视化的多维流式细胞术数据表明，SCLC细胞系就几种另外的细胞表面和细胞质标志物而言是异质的，并且通常存在倾向于与神经内分泌相关的这些标志物的表达梯度（例如，SYK）至间充质（例如，TGF β受体II）表型梯度。最后，在表型谱的神经内分泌端（MEblue-高），细胞悬浮生长，而它们朝向间充质端（MEturquoise-高）变得越来越粘附。结论：我们的数据提供了强有力的证据，在SCLC异质性表型空间，可能定义不同的亚型。这种异质性先前在人SCLC中未被怀疑，尽管在SCLC的遗传小鼠模型中报道了其证据{Calbo J，www.example.com，Cancer Cell，2011}。人类SCLC细胞系沿着神经内分泌到间充质分化梯度的分类也应该适用于人类肿瘤，因为WGCNA网络分类器重叠。然而，需要对患者进行进一步研究，以证明不同SCLC亚型的存在，并探索它们对生物标志物和靶向治疗的转化价值。引文格式：Akshata Ramrao Udyavar，Megan Hoeksema，Kirsten Diggins，Jonathan Irish，Pierre P. Massion，Vito Quaranta。小细胞肺癌（SCLC）的表型可塑性和异质性：新型分子亚型和靶向治疗的潜力。[摘要]。在：AACR-IASLC肺癌分子起源联合会议论文集; 2014年1月6日至9日;加利福尼亚州圣地亚哥。Philadelphia（PA）：AACR; Clin Cancer Res 2014; 20（2Suppl）：Abstract nr B27.
Background: SCLC (15% of lung cancers) exhibits: 1) rapid growth and early fatal metastasis; 2) neuroendocrine features; 3) high initial responsiveness to chemotherapy and radiation; 4) aggressive recurrence with 5% 5-year patient survival. Gene expression and mutation profiling efforts to identify oncogenic mutations, gene amplifications or signatures with clinical utility in SCLC have thus far been unfruitful. In addition, prognostic or diagnostic markers for SCLC are scarce. Hence, there is a dire need for investigating molecular subtypes and oncogenic drivers in SCLC. We hypothesize that deregulated networks, rather than single genes, drive SCLC phenotype. Results: We previously identified a SCLC-specific gene co-expression network (Blue module, by Weighted Gene Co-expression Network Analysis - WGCNA) from a lung cancer patient dataset, and derived a SCLC-specific hub network (SSHN) signature that: 1) separated SCLC from other lung cancer types and normal lung in both genomic and proteomic independent datasets; 2) identified 2 SCLC subtypes with high and low SSHN expression in both patient specimens and cultured cell lines. Spleen tyrosine kinase (SYK) was validated as a candidate oncogenic driver of one subtype, as SYK targeted small-interfering RNA significantly decreased viability via increased death in high SYK-expressing SCLC cell lines. Due to the lack of larger SCLC patient datasets, we have now applied the SSHN classifier to the 53 SCLC cell lines from the Cancer Cell Line Encyclopedia (CCLE) and validated the SSHN-high and low subtypes. From this larger dataset, it is evident that the SSHN-defined subtypes are not totally separate. Rather, they are connected by gradual intermediate shades. This gradation became clearer by applying WGCNA to SCLC cell lines from CCLE, which identified 2 gene co-expression modules – Blue and Turquoise, that overlap with modules from patient datasets described above. The Blue module is enriched in neuroendocrine signaling, the Turquoise in mesenchymal adhesion-related pathways. Eigengene expression of the 2 modules (MEblue, MEturquoise) is anti-correlated, and all 53 SCLC cell lines are distributed along this anti-correlation diagonal. Expression of the neuroendocrine marker CD56 is highest in cells at one end of this diagonal (MEblue-high cell lines), and decreases towards the other end (MEturquoise-high cell lines), whereas the mesenchymal marker CD44 has an opposite trend. Multi-dimensional flow cytometry data, visualized with viSNE, indicated that SCLC cell lines are heterogeneous with respect to several additional cell surface and cytoplasmic markers and that, in general, there is a gradient of expression of these markers that tends to correlate with the neuroendocrine (e.g., SYK) to mesenchymal (e.g., TGFbeta receptor II) phenotype gradient. Finally, at the neuroendocrine end of the phenotypic spectrum (MEblue-high) cells grow in suspension, whereas they become increasingly adherent towards the mesenchymal end (MEturquoise-high). Conclusion: Our data provide strong evidence for a heterogeneous phenotypic space in SCLC that may define distinct subtypes. This heterogeneity was previously unsuspected in human SCLC, although evidence for it was reported in genetic mouse models of SCLC {Calbo J, et.al, Cancer Cell, 2011}. Classification of human SCLC cell lines along a neuroendocrine to mesenchymal differentiation gradient should apply to human tumors as well, since the WGCNA network classifiers overlap. However, further studies in patients are warranted to prove the existence of distinct SCLC subtypes, as well as to probe their translational value for biomarkers and targeted treatment. Citation Format: Akshata Ramrao Udyavar, Megan Hoeksema, Kirsten Diggins, Jonathan Irish, Pierre P. Massion, Vito Quaranta. Phenotypic plasticity and heterogeneity in small cell lung cancer (SCLC): Novel molecular subtypes and potential for targeted therapy. [abstract]. In: Proceedings of the AACR-IASLC Joint Conference on Molecular Origins of Lung Cancer; 2014 Jan 6-9; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2014;20(2Suppl):Abstract nr B27.