用深度学习分析肝癌突变数据，分出五类亚型，助力精准治疗。
DLSOM: A Deep learning-based strategy for liver cancer subtyping
- 用堆叠自编码器将突变数据降维到三个特征，实现稳健聚类。
- 发现五种亚型，突变负荷差异显著，其中SC1和SC2最高，SC3最低。
- 揭示各亚型特有突变特征，适合研究肝癌机制与个体化治疗者参考。
肝癌是全球癌症死亡的主要原因之一，其高度的基因异质性给诊断和治疗带来挑战。本研究提出DLSOM，一种基于深度学习的框架，利用堆叠自编码器分析1,139例肝癌样本的全部体细胞突变图谱，涵盖20,356个蛋白编码基因。通过将高维突变数据转换为三个低维特征，DLSOM实现了稳定的聚类，识别出五种具有独特突变、功能和生物学特征的肝癌亚型。亚型SC1和SC2表现出更高的突变负荷，而SC3最低，反映了突变异质性。新发现及COSMIC相关突变特征揭示了亚型特异性的分子机制，包括超突变与化疗耐药的关联。功能分析进一步验证了各亚型的生物学意义。该综合性框架推动了肝癌精准医学的发展，支持开发亚型特异性诊断工具、生物标志物和疗法，展示了深度学习在应对癌症复杂性方面的潜力。
原文摘要 · Abstract (English)
Liver cancer is a leading cause of cancer-related mortality worldwide, with its high genetic heterogeneity complicating diagnosis and treatment. This study introduces DLSOM, a deep learning framework utilizing stacked autoencoders to analyze the complete somatic mutation landscape of 1,139 liver cancer samples, covering 20,356 protein-coding genes. By transforming high-dimensional mutation data into three low-dimensional features, DLSOM enables robust clustering and identifies five distinct liver cancer subtypes with unique mutational, functional, and biological profiles. Subtypes SC1 and SC2 exhibit higher mutational loads, while SC3 has the lowest, reflecting mutational heterogeneity. Novel and COSMIC-associated mutational signatures reveal subtype-specific molecular mechanisms, including links to hypermutation and chemotherapy resistance. Functional analyses further highlight the biological relevance of each subtype. This comprehensive framework advances precision medicine in liver cancer by enabling the development of subtype-specific diagnostics, biomarkers, and therapies, showcasing the potential of deep learning in addressing cancer complexity.
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