PO.MD01.01 · 分子诊断与数据
由人工智能增强的整合空间多组学分析揭示南加州患者早发性结直肠癌中与祖先相关的分子特征
Integrative spatial multi-omics profiling enhanced by artificial intelligence reveals ancestry-associated molecular features in early-onset colorectal cancer among Southern California patients
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
引言：结直肠癌（CRC）是全球第三常见的恶性肿瘤，也是癌症相关死亡的第二大原因。尽管在许多高收入国家整体CRC发病率已趋于稳定，但早发性CRC（EOCRC；<50岁）仍在持续上升。这一增长在我们的服务区域——加州大洛杉矶地区尤为明显。尽管存在这一趋势，人们对这一高危人群知之甚少，限制了对与祖先相关的生物学因素和肿瘤微环境（TME）特征的洞察。
方法：共分析了来自我们NIH Cancer Moonshot COPECC PE-CGS网络患者以及公共数据库（包括AACR Project GENIE数据库）的2,730例结直肠癌（CRC）肿瘤样本。采用高分辨率空间转录组学（10x Genomics Visium HD），结合全外显子组测序（WES）和RNA测序（RNA-seq），评估区域基因表达模式。使用SpaCET量化区室特异性特征，重点关注CRC相关基因和通路。临床与分子数据集经过协调，并通过AI驱动的多组学平台进行分析，实现对基因组、转录组和临床特征的自然语言探索。
结果：EOCRC肿瘤与1000 Genomes中利马秘鲁人（1KG-PEL）参考人群显示出高中位遗传相似性。关键CRC相关突变在EOCRC中更为频繁，尤其在与1KG-PEL相似性更强的患者中。整合分析揭示了服务区队列中EOCRC与晚发性CRC之间与祖先相关的基因表达差异。空间转录组学显示恶性、免疫和基质区域间通路活性存在显著差异，EOCRC呈现出独特的区室特异性模式。
结论：我们服务区域人群中的EOCRC以与祖先相关的基因组改变和CRC相关通路中显著的空间异质性为特征。这些发现强调了祖先信息导向的CRC分子分析对推进精准肿瘤学的重要性。
查看英文原文 English abstract
Introduction: Colorectal cancer (CRC) is the third most common malignancy and the second leading cause of cancer-related mortality worldwide. Although overall CRC incidence has stabilized in many high-income countries, early-onset CRC (EOCRC; <50 years) continues to rise. This increase is especially noticeable in our catchment area, the greater Los Angeles, CA region. Despite this trend, little is known about this population at risk, limiting insight into ancestry-associated biological factors and tumor microenvironment (TME) features.
Methods: A total of 2,730 colorectal cancer (CRC) tumor samples were analyzed from patients in our NIH Cancer Moonshot COPECC PE-CGS Network and from public data repositories, including the AACR Project GENIE database. High-resolution spatial transcriptomics (10x Genomics Visium HD), together with whole-exome sequencing (WES) and RNA sequencing (RNA-seq), was used to assess regional gene expression patterns. Compartment-specific signatures were quantified using SpaCET, focusing on CRC-related genes and pathways. Clinical and molecular datasets were harmonized and analyzed through an AI-driven multi-omics platform, enabling natural-language-based exploration of genomic, transcriptomic, and clinical features.
Results: EOCRC tumors showed a high median genetic similarity to the 1000 Genomes Peruvian-in-Lima (1KG-PEL) reference population. Key CRC-associated mutations were more frequent in EOCRC, particularly among patients with stronger 1KG-PEL-like similarity. Integrated analyses revealed ancestry-associated differences in gene expression between EOCRC and late-onset CRC within the catchment cohort. Spatial transcriptomics demonstrated marked variation in pathway activity across malignant, immune, and stromal regions, with EOCRC displaying distinct compartment-specific patterns.
Conclusions: EOCRC in populations from our catchment area is defined by ancestry-associated genomic alterations and notable spatial heterogeneity in CRC-relevant pathways. These findings underscore the importance of ancestry-informed CRC molecular profiling to advance precision oncology.
利益披露 Disclosure
F. G. Carranza, None..
B. Waldrup, None..
Y. Jin, None..
Y. Amzaleg, None..
D. Craig, None..
J. Carpten, None..
E. I. Velazquez Villarreal, None.