PO.BCS01.12 · 生物信息与计算
一种用于ALT检测的集成机器学习方法揭示了缺乏可成药靶点的儿童癌症中的治疗脆弱性
An ensemble machine learning approach to ALT detection revals therapeutic vulnerabilities in pediatric cancers lacking actionable drug targets
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
端粒替代延长（ALT）通过一种基于重组的机制在15-20%的癌症中维持端粒，并与不良结局相关。ALT常由ATRX/DAXX功能缺失突变驱动，并富集于包括骨肉瘤、神经母细胞瘤和软组织肉瘤在内的儿童恶性肿瘤中——这些癌症治疗选择有限。当前的ALT检测方法通量低，无法扩展至大型队列，阻碍了对ALT特异性脆弱性的系统性发现。我们开发了ALTitude，一个基于全基因组测序（WGS）的集成机器学习框架，用于预测ALT状态。我们利用从儿童癌症依赖性加速器（Pediatric Cancer Dependencies Accelerator）和癌症依赖性图谱（DepMap）资源中超过1,000个癌细胞系提取的端粒相关基因组特征，针对正交的ALT检测方法训练并验证了该模型。我们将ALTitude预测与DepMap中的全基因组CRISPR功能缺失筛选数据整合，以识别ALT特异性依赖性。ALTitude在不同癌症类型中实现了ALT分类的高准确性，为一个功能特征密集刻画的细胞系panel提供了首个可扩展的ALT图谱。与CRISPR必需性筛选的整合揭示SMARCAL1是ATRX/DAXX突变型ALT阳性骨肉瘤、软组织肉瘤和神经母细胞瘤中的顶级选择性依赖。SMARCAL1含有一个与ATRX远缘旁系同源的解旋酶结构域，并在DNA损伤应答期间解决端粒处停滞的复制叉，提示ALT端粒处复制应激增加使SMARCAL1功能成为必需。这项工作建立了一种从WGS数据推断ALT状态的可扩展方法，并系统性地将ALT生物学与癌细胞系模型中的治疗脆弱性联系起来。通过将SMARCAL1鉴定为ALT阳性细胞系中的选择性依赖，我们为具有挑战性的儿童癌症类型提名了新的治疗靶点。我们的方法展示了如何将源自基因组数据的特征与功能筛选及其他组学数据整合，从而揭示对侵袭性癌症亚型驱动因素的新见解。
查看英文原文 English abstract
Alternative lengthening of telomeres (ALT) maintains telomeres in 15-20% of cancers through a recombination-based mechanism associated with poor outcomes. ALT is frequently driven by ATRX/DAXX loss-of-function mutations and is enriched in pediatric malignancies including osteosarcoma, neuroblastoma, and soft tissue sarcomas-cancers with limited therapeutic options. Current ALT detection methods are low-throughput and not scalable to large cohorts, impeding systematic discovery of ALT-specific vulnerabilities. We developed ALTitude, a whole-genome sequencing (WGS)-based ensemble machine learning framework to predict ALT status. Using telomere-relevant genomic features extracted from >1,000 cancer cell lines in the Pediatric Cancer Dependencies Accelerator and Cancer Dependency Map (DepMap) resources. We trained and validated the model against orthogonal ALT detection methods. We integrated ALTitude predictions with genome-wide CRISPR loss-of-function screening data in DepMap to identify ALT-specific dependencies. ALTitude achieved high accuracy in ALT classification across diverse cancer types, providing the first scalable map of ALT in a densely functionally characterized cell line panel. Integration with CRISPR essentiality screens revealed SMARCAL1 as a top selective dependency in ATRX/DAXX mutant ALT - positive osteosarcoma, soft tissue sarcomas, and neuroblastoma. SMARCAL1 harbors a helicase domain distantly paralogous to ATRX and resolves stalled replication forks at telomeres during DNA damage response, suggesting that increased replication stress at ALT telomeres creates essentiality for SMARCAL1 function. This work establishes a scalable method to infer ALT status from WGS data and systematically connects ALT biology to therapeutic vulnerabilities in cancer cell line models. By identifying SMARCAL1 as selective dependency in ALT-positive cell lines, we nominate novel therapeutic targets for challenging pediatric cancer types. Our approach demonstrates how integrating features derived from genomic data with functional screening and other 'omics data can reveal novel insights into drivers across aggressive cancer subtypes.
利益披露 Disclosure
D. Bennett, None..
M. Weirdl, None.