Spatially Resolved Transcriptomics Enables Dissection of Genetic Heterogeneity in Stage III Cutaneous Malignant Melanoma
Spatially Resolved Transcriptomics Enables Dissection of Genetic Heterogeneity in Stage III Cutaneous Malignant Melanoma
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DOI：
10.1158/0008-5472.can-18-0747
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发表时间：
2018-10-15
期刊：
影响因子：
11.2
通讯作者：
Lundeberg, Joakim
中科院分区：
文献类型：
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作者：
Thrane, Kim;Eriksson, Hanna;Lundeberg, Joakim
皮肤恶性黑色素瘤（黑色素瘤）的特点是高突变负荷，广泛的肿瘤间和肿瘤内的遗传异质性，和复杂的肿瘤微环境（TME）的相互作用。进一步了解黑色素瘤的潜在机制对于理解肿瘤进展和治疗反应至关重要。在这里，我们将空间转录组学（ST）技术应用于黑色素瘤淋巴结活检，并成功地对超过2，200个组织域的转录组进行了测序。去卷积结合传统的方法对全转录组数据进行降维，使我们能够可视化组织内的转录景观，并识别与特定组织学实体相关的基因表达谱。我们的无监督分析揭示了黑色素瘤转移瘤的复杂的空间组成，通过形态学注释并不明显。每个活检显示不同的基因表达谱，并包括在一个单一的肿瘤区域内共存的多个黑色素瘤签名的例子，以及根据其空间位置和基因表达谱特征的淋巴组织的共享配置文件。靠近肿瘤区域的淋巴区显示出特异性表达模式，这可能反映了TME，这是充分理解肿瘤进展的关键组成部分。总之，使用ST技术生成基因表达谱揭示了黑色素瘤转移的详细情况。这应该激励研究人员将空间信息整合到分析中，旨在确定潜在的肿瘤进展和治疗outcome.Significance的因素：应用ST技术在黑色素瘤淋巴结转移的基因表达谱揭示了一个复杂的转录景观的空间背景下，这是必不可少的了解肿瘤进展和治疗结果的多个组成部分。(C)2018年AACR。
Cutaneous malignant melanoma (melanoma) is characterized by a high mutational load, extensive intertumoral and intratumoral genetic heterogeneity, and complex tumor microenvironment (TME) interactions. Further insights into the mechanisms underlying melanoma are crucial for understanding tumor progression and responses to treatment. Here we adapted the technology of spatial transcriptomics (ST) to melanoma lymph node biopsies and successfully sequenced the transcriptomes of over 2,200 tissue domains. Deconvolution combined with traditional approaches for dimensional reduction of transcriptome-wide data enabled us to both visualize the transcriptional landscape within the tissue and identify gene expression profiles linked to specific histologic entities. Our unsupervised analysis revealed a complex spatial intratumoral composition of melanoma metastases that was not evident through morphologic annotation. Each biopsy showed distinct gene expression profiles and included examples of the coexistence of multiple melanoma signatures within a single tumor region as well as shared profiles for lymphoid tissue characterized according to their spatial location and gene expression profiles. The lymphoid area in close proximity to the tumor region displayed a specific expression pattern, which may reflect the TME, a key component to fully understanding tumor progression. In conclusion, using the ST technology to generate gene expression profiles reveals a detailed landscape of melanoma metastases. This should inspire researchers to integrate spatial information into analyses aiming to identify the factors underlying tumor progression and therapy outcome.Significance: Applying ST technology to gene expression profiling in melanoma lymph node metastases reveals a complex transcriptional landscape in a spatial context, which is essential for understanding the multiple components of tumor progression and therapy outcome. (C) 2018 AACR.