Steven M Foltz, Yize Li, Lijun Yao, Nadezhda V Terekhanova, Amila Weerasinghe, Qingsong Gao, Guanlan Dong, Moses Schindler, Song Cao, Hua Sun, Reyka G Jayasinghe, Robert S Fulton, Catrina C Fronick, Justin King, Daniel R Kohnen, Mark A Fiala, Ken Chen, John F DiPersio, Ravi Vij, Li Ding
{"title":"利用链接读数对多发性骨髓瘤中的体细胞突变进行分期和单倍型扩展","authors":"Steven M Foltz, Yize Li, Lijun Yao, Nadezhda V Terekhanova, Amila Weerasinghe, Qingsong Gao, Guanlan Dong, Moses Schindler, Song Cao, Hua Sun, Reyka G Jayasinghe, Robert S Fulton, Catrina C Fronick, Justin King, Daniel R Kohnen, Mark A Fiala, Ken Chen, John F DiPersio, Ravi Vij, Li Ding","doi":"10.1101/2024.08.09.607342","DOIUrl":null,"url":null,"abstract":"Somatic mutation phasing informs our understanding of cancer-related events, like driver mutations. We generated linked-read whole genome sequencing data for 23 samples across disease stages from 14 multiple myeloma (MM) patients and systematically assigned somatic mutations to haplotypes using linked-reads. Here, we report the reconstructed cancer haplotypes and phase blocks from several MM samples and show how phase block length can be extended by integrating samples from the same individual. We also uncover phasing information in genes frequently mutated in MM, including DIS3, HIST1H1E, KRAS, NRAS, and TP53, phasing 79.4% of 20,705 high-confidence somatic mutations. In some cases, this enabled us to interpret clonal evolution models at higher resolution using pairs of phased somatic mutations. For example, our analysis of one patient suggested that two NRAS hotspot mutations occurred on the same haplotype but were independent events in different subclones. Given sufficient tumor purity and data quality, our framework illustrates how haplotype-aware analysis of somatic mutations in cancer can be beneficial for some cancer cases.","PeriodicalId":501307,"journal":{"name":"bioRxiv - Bioinformatics","volume":"59 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Somatic mutation phasing and haplotype extension using linked-reads in multiple myeloma\",\"authors\":\"Steven M Foltz, Yize Li, Lijun Yao, Nadezhda V Terekhanova, Amila Weerasinghe, Qingsong Gao, Guanlan Dong, Moses Schindler, Song Cao, Hua Sun, Reyka G Jayasinghe, Robert S Fulton, Catrina C Fronick, Justin King, Daniel R Kohnen, Mark A Fiala, Ken Chen, John F DiPersio, Ravi Vij, Li Ding\",\"doi\":\"10.1101/2024.08.09.607342\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Somatic mutation phasing informs our understanding of cancer-related events, like driver mutations. We generated linked-read whole genome sequencing data for 23 samples across disease stages from 14 multiple myeloma (MM) patients and systematically assigned somatic mutations to haplotypes using linked-reads. Here, we report the reconstructed cancer haplotypes and phase blocks from several MM samples and show how phase block length can be extended by integrating samples from the same individual. We also uncover phasing information in genes frequently mutated in MM, including DIS3, HIST1H1E, KRAS, NRAS, and TP53, phasing 79.4% of 20,705 high-confidence somatic mutations. In some cases, this enabled us to interpret clonal evolution models at higher resolution using pairs of phased somatic mutations. For example, our analysis of one patient suggested that two NRAS hotspot mutations occurred on the same haplotype but were independent events in different subclones. Given sufficient tumor purity and data quality, our framework illustrates how haplotype-aware analysis of somatic mutations in cancer can be beneficial for some cancer cases.\",\"PeriodicalId\":501307,\"journal\":{\"name\":\"bioRxiv - Bioinformatics\",\"volume\":\"59 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"bioRxiv - Bioinformatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1101/2024.08.09.607342\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"bioRxiv - Bioinformatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2024.08.09.607342","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
体细胞突变分期有助于我们了解癌症相关事件,如驱动突变。我们生成了来自 14 名多发性骨髓瘤(MM)患者的 23 个不同疾病分期样本的链接读数全基因组测序数据,并利用链接读数将体细胞突变系统地分配到单倍型中。在此,我们报告了从多个 MM 样本中重建的癌症单倍型和阶段块,并展示了如何通过整合来自同一个体的样本来扩展阶段块长度。我们还发现了在 MM 中经常发生突变的基因的相位信息,包括 DIS3、HIST1H1E、KRAS、NRAS 和 TP53,在 20705 个高置信度体细胞突变中,有 79.4% 的基因发生了相位突变。在某些情况下,这使我们能够利用成对的分期体细胞突变以更高的分辨率解释克隆进化模型。例如,我们对一名患者的分析表明,两个 NRAS 热点突变发生在同一个单倍型上,但在不同的亚克隆中是独立的事件。在肿瘤纯度和数据质量足够高的情况下,我们的框架说明了单体型感知的癌症体细胞突变分析对某些癌症病例的益处。
Somatic mutation phasing and haplotype extension using linked-reads in multiple myeloma
Somatic mutation phasing informs our understanding of cancer-related events, like driver mutations. We generated linked-read whole genome sequencing data for 23 samples across disease stages from 14 multiple myeloma (MM) patients and systematically assigned somatic mutations to haplotypes using linked-reads. Here, we report the reconstructed cancer haplotypes and phase blocks from several MM samples and show how phase block length can be extended by integrating samples from the same individual. We also uncover phasing information in genes frequently mutated in MM, including DIS3, HIST1H1E, KRAS, NRAS, and TP53, phasing 79.4% of 20,705 high-confidence somatic mutations. In some cases, this enabled us to interpret clonal evolution models at higher resolution using pairs of phased somatic mutations. For example, our analysis of one patient suggested that two NRAS hotspot mutations occurred on the same haplotype but were independent events in different subclones. Given sufficient tumor purity and data quality, our framework illustrates how haplotype-aware analysis of somatic mutations in cancer can be beneficial for some cancer cases.