PO.CL12.03 · 临床研究

Cloud-based computational framework for individualized genomic analysis in pediatric acute lymphoblastic leukemia: A nationwide multi-center real-world clinical study

编号 5283 展板 3 时间 4/21 09:00–12:00 区域 Section 44 主讲 Han Wang, PhD

分会场 Epigenetics, Cytogenetics, and Clinical Molecular Genetics

该海报暂无可访问的完整资料 AACR 官方页面 ↗

作者与单位

Han Wang¹, Jiaoyang Cai¹, Jie Yu², Shaoyan Hu³, Yongjun Fang⁴, Ju Gao⁵, Jian Li⁶, Hua Jiang⁷, Xiuli Ju⁸, Sixi Liu⁹, Wenyong Kuang¹⁰, Runming Jin¹¹, Liangchun Yang¹², Xuedong Wu¹³, Xiaowen Zhai¹⁴, Qun Hu¹⁵, Hui Jiang¹⁶, Ningling Wang¹⁷, Chi Kong Li¹⁸, Lirong Sun¹⁹, Jiao Jin²⁰, Chun Li²¹, Changda Liang²², Yan Dai²³, Kaili Pan²⁴, Hao Xiong²⁵, Ching-Hon Pui²⁶, Shuhong Shen²⁷, Yu Liu¹

¹Shanghai Children’s Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China,²Chongqing Medical University Affiliated Children's Hospital, Chongqing, China,³Children's Hospital of Soochow Universit, Suzhou, China,⁴Nanjing Children's Hospital Affiliated to Nanjing Medical University, Nanjing, China,⁵West China Second University Hospital, Sichuan University, Chengdu, China,⁶Fujian Medical University Union Hospital, Fuzhou, China,⁷Guangzhou Women and Children's Medical Center, Guangzhou, China,⁸Qilu Hospital of Shandong University, Jinan, China,⁹Shenzhen Children’s Hospital, Shenzhen, China,¹⁰Hunan Children's Hospital, Changsha, China,¹¹Union Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China,¹²Xiangya Hospital Central South University, Changsha, China,¹³Nanfang Hospital, Southern Medical University, Guangzhou, China,¹⁴Children's hospital of Fudan university, Shanghai, China,¹⁵Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China,¹⁶Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China,¹⁷Anhui Medical University Second Affiliated Hospital, Hefei, China,¹⁸Hong Kong Children’s Hospital, Hong Kong, Hong Kong,¹⁹Affiliated Hospital of Qingdao University, Qingdao, China,²⁰The Affiliated Hospital of Guizhou University, Guiyang, China,²¹Anhui Provincial Hosptical, Anhui, China,²²Jiangxi Provincial Children's Hospital, Nanchang, China,²³The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China,²⁴Xi 'an Northwest Women and Children Hospital, Xi 'an, China,²⁵Wuhan Children’s Hospital, Wuhan, China,²⁶Chair, Dept. of Oncology, St. Jude Children's Research Hospital, Memphis, TN,²⁷Department of Hematology & Oncology, Shanghai Children’s Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

摘要 Abstract

Acute lymphoblastic leukemia (ALL) is the most common childhood cancer. While genomic studies have identified key molecular subtypes and aberrations in ALL, it requires integrating multi-omics data to complete complex and time-consuming analyses in large retrospective cohorts. It is challenging to perform individualized clinical genomic analysis in real-world. We present a nationwide precision genomic study as part of the Chinese Children Cancer Group ALL 2020 clinical trial. Between 2020 and 2023, 6486 pediatric ALL patients were enrolled from 25 medical centers across 15 provinces in China. RNA-seq was performed for 5103 patients during diagnosis. We developed the National Children's Medical Center ALL Bio-Cloud (NCMC-ABC), an automated, cloud-based framework for real-time RNA-seq data process. NCMC-ABC is designed to analyze multiple clinically relevant genomic aberrations from single RNA-seq data, including molecular subtypes, coding and noncoding driver mutations, fusions and CNVs. The median turnaround time from sample collection to clinical reporting was 14 days across all hospitals, aligning with clinical treatment timelines. We established a molecular subtype classification framework for pediatric ALL, and successfully classified 94.94% of B-ALLs into 20 subtypes and 86.38% of T-ALLs into 11 subtypes. This framework significantly improved the traditional MICM approach, which classified only 48.58% of B-ALLs and did not account for T-ALL subtypes. The enhanced classification is due to the improved detection of key fusions ( DUX4 , PAX5 , ZNF384 , MEF2D rearrangements) and mutations ( PAX5 P80R and IKZF1 N159Y). Meanwhile, we achieved more precise subtyping of HYPO, HYPER and KMT2A BALLs. The refined subtypes unveiled a distinct profile of Chinese B-ALL patients, with higher frequencies of HYPER , ETV6, DUX4 and PH subtypes, and lower frequencies of Ph-like, iAMP21 and HYPO, compared to Western cohorts. Importantly, the refined framework directly improved the risk stratification of patients. We identified a median of 2.44 pathogenic SNPs/indels and 1.28 fusions per patient. The driver mutations were detected in 259 genes in B-ALL and 156 in T-ALL. We observed different driver mutation profiles in our cohort compared to the Western cohort. Mutations in RAS pathway ( NRAS , KRAS and PTPN11 ) were more frequent in Chinese patients, whereas the JAK-STAT ( JAK2 , IL7R , SH2B3 and CRLF2 ) pathway was more frequently mutated in Western cohort. We observed direct clinical relevance of these aberrations. For example, patients with TP53 and NR3C1 mutations showed inferior treatment response. The implementation of NCMC-ABC in a nationwide multicenter pediatric ALL clinical trial demonstrated its effectiveness and feasibility in real-world, improving risk stratification and therapeutic decision making in clinic.

利益披露 Disclosure

H. Wang, None.. J. Cai, None.. J. Yu, None.. Y. Fang, None.. J. Gao, None.. J. Li, None.. H. Jiang, None.. X. Ju, None.. S. Liu, None.. W. Kuang, None.. R. Jin, None.. L. Yang, None.. X. Wu, None.. X. Zhai, None.. Q. Hu, None.. H. Jiang, None.. N. Wang, None.. C. Li, None.. L. Sun, None.. J. Jin, None.. C. Li, None.. C. Liang, None.. Y. Dai, None.. K. Pan, None.. H. Xiong, None.. S. Shen, None.. Y. Liu, None.

在会议检索中打开