Nicholas Cheng, Kimberly Skead, David Soave, Jocelyn Meng, Elias Gbeha, I. Lungu, Bernard Lam, S. Bratman, D. D. Carvalho, P. Awadalla
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引用次数: 0
Abstract
Cancer survival rates are significantly improved when detected at early stages, particularly when the tumor is still localized to the tissue of origin. However, effective screening tools for early cancer detection is currently limited to a subset of cancer types. The early development of human malignancies are difficult to observe as cancers are often detected once it becomes symptomatic, as such many cancer biomarker and evolution studies to date have primarily examined the genomics from solid tumor or liquid biopsies following a diagnosis. Investigating early tumor evolution in the pre-diagnosis context could allow us to better understand how to prevent or detect cancers in the earliest stage when survival rates are significantly higher, however this requires application of new technologies to biologics collected prior to a cancer diagnosis. Here, we leverage blood samples collected from participants in the Canadian Partnership for Tomorrow Project (CPTP), a longitudinal population cohort, prior to the onset of a cancer. Specifically, we utilize hybrid capture approaches to enrich for and characterize early mutations and methylation changes in circulating tumor DNA (ctDNA) of pre-cancer plasma samples collected from patients several months to years prior to clinical diagnosis. Here, we identify the earliest detectability of aberrant genetic and epigenetic events in ctDNA and describe the molecular evolution of these events at various stages prior to clinical detection of cancers. Further, we develop molecular biomarkers and implement machine learning tools to classify individuals with early cancers, and to develop risk scores from survival analyses predictive of cancer development up to 5 years prior to diagnosis. In our current study, we focus specifically on breast, prostate, lung and pancreatic cancer cases, and are extending this to pan-cancer applications in subsequent studies. Citation Format: Nicholas Cheng, Kimberly Skead, David Soave, Jocelyn Meng, Elias Gbeha, Ilinca Lungu, Bernard Lam, Scott Bratman, Daniel De Carvalho, Philip Awadalla. Leveraging cell-free methylome markers for early cancer detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2602.
如果在早期阶段检测到癌症,特别是当肿瘤仍局限于原发组织时,癌症存活率显著提高。然而,有效的早期癌症检测工具目前仅限于癌症类型的一个子集。人类恶性肿瘤的早期发展很难观察到,因为癌症通常在出现症状时才被发现,因为迄今为止许多癌症生物标志物和进化研究主要是在诊断后检查实体肿瘤或液体活检的基因组学。在诊断前研究早期肿瘤进化可以让我们更好地了解如何在生存率显著提高的早期阶段预防或检测癌症,然而这需要将新技术应用于癌症诊断前收集的生物制剂。在这里,我们利用从加拿大明天合作项目(CPTP)参与者收集的血液样本,这是一个纵向人群队列,在癌症发病之前。具体而言,我们利用混合捕获方法来丰富和表征临床诊断前数月至数年从患者收集的癌前血浆样本中循环肿瘤DNA (ctDNA)的早期突变和甲基化变化。在这里,我们确定了ctDNA中异常遗传和表观遗传事件的最早可检测性,并描述了这些事件在癌症临床检测之前的各个阶段的分子进化。此外,我们开发分子生物标志物并实施机器学习工具来对早期癌症患者进行分类,并从生存分析中开发风险评分,预测癌症在诊断前5年的发展。在我们目前的研究中,我们专注于乳腺癌,前列腺癌,肺癌和胰腺癌病例,并在后续研究中将其扩展到泛癌症应用。引文格式:Nicholas Cheng, Kimberly Skead, David Soave, Jocelyn Meng, Elias Gbeha, Ilinca Lungu, Bernard Lam, Scott Bratman, Daniel De Carvalho, Philip Awadalla。利用无细胞甲基组标记物进行早期癌症检测[摘要]。见:美国癌症研究协会2021年年会论文集;2021年4月10日至15日和5月17日至21日。费城(PA): AACR;癌症杂志,2021;81(13 -增刊):2602。