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推断转移性癌症的简约迁移史。

Inferring parsimonious migration histories for metastatic cancers.

机构信息

Department of Computer Science, Princeton University, Princeton, NJ, USA.

Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.

出版信息

Nat Genet. 2018 May;50(5):718-726. doi: 10.1038/s41588-018-0106-z. Epub 2018 Apr 26.

Abstract

Metastasis is the migration of cancerous cells from a primary tumor to other anatomical sites. Although metastasis was long thought to result from monoclonal seeding, or single cellular migrations, recent phylogenetic analyses of metastatic cancers have reported complex patterns of cellular migrations between sites, including polyclonal migrations and reseeding. However, accurate determination of migration patterns from somatic mutation data is complicated by intratumor heterogeneity and discordance between clonal lineage and cellular migration. We introduce MACHINA, a multi-objective optimization algorithm that jointly infers clonal lineages and parsimonious migration histories of metastatic cancers from DNA sequencing data. MACHINA analysis of data from multiple cancers shows that migration patterns are often not uniquely determined from sequencing data alone and that complicated migration patterns among primary tumors and metastases may be less prevalent than previously reported. MACHINA's rigorous analysis of migration histories will aid in studies of the drivers of metastasis.

摘要

转移是癌细胞从原发性肿瘤迁移到其他解剖部位的过程。虽然转移长期以来被认为是源于克隆播种,即单个细胞的迁移,但最近对转移性癌症的系统发育分析报告了在不同部位之间存在复杂的细胞迁移模式,包括多克隆迁移和再播种。然而,从体细胞突变数据准确推断迁移模式受到肿瘤内异质性和克隆谱系与细胞迁移之间不匹配的影响。我们引入了 MACHINA,这是一种多目标优化算法,可以从 DNA 测序数据中联合推断出转移性癌症的克隆谱系和简约的迁移历史。对来自多种癌症的数据进行 MACHINA 分析表明,迁移模式通常不能仅从测序数据单独确定,而且原发性肿瘤和转移瘤之间复杂的迁移模式可能比之前报道的要少见。MACHINA 对迁移历史的严格分析将有助于研究转移的驱动因素。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aae8/6103651/82be2ba2e221/nihms-950403-f0001.jpg

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