海报库PosterKeep
PO.BCS01.10 · 生物信息与计算

一种整合AI/ML与COSMIC特征谱分析的新方法：将急变期CML的异质性解析为单患者水平的泛癌可干预程序，为复发难治及转移性癌症提供可重定位疗法

A novel integrated AI ML and COSMIC signature profiling approach resolving blast crisis CML heterogeneity into single patient level pan cancer actionable programs enabling repurposable therapies for relapsed refractory and metastasized cancers

海报缩略图：一种整合AI/ML与COSMIC特征谱分析的新方法：将急变期CML的异质性解析为单患者水平的泛癌可干预程序，为复发难治及转移性癌症提供可重定位疗法
编号 4186 展板 13 时间 4/21 09:00–12:00 区域 Section 4 主讲 Zafar Iqbal, PhD
分会场 Integrative Computational Approaches 2
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作者与单位 Authors & Affiliations

Zafar Iqbal1, Abdulkareem AlGarni2, Lubna Alnuaim3, Sahrish Khan4, Sohail Rao5, Yaqob Taleb6, Nasser Mohammed AlQahtani7, Muhammad Alshuaibi7, Essa Al Mansour8, Mashael AlShuker8, Azfar Athar Ishaqui9, Giuseppe Saglio10, Kaleem Ahmed11, Rizwan Naeem12, Masood A. Shammas13, Muhammad Farooq Sabar4

1Department of Zoology & SBB, Univ. of the Punjab, Lahore, Pakistan,2King Abdulaziz Hospital; Genomic & Experimental Medicine Group (GEM) & QAAA; CLSP, College of Applied Medical Sciences (CoAMS-A), King Saud Bin Abdulaziz University for Health Sciences (KSAU-HS); KAIMRC-ER, NGHA, Al-Ahsa, Saudi Arabia,3CLNS, Genomic & Experimental Medicine Group (GEM), College of Applied Medical Sciences (CoAMS-A), King Saud Bin Abdulaziz University for Health Sciences (KSAU-HS); KAIMRC-ER, NGHA, Al-Ahsa, Saudi Arabia,4CAMB & SBB, Univ. of the Punjab, Lahore, Pakistan,5Department of Zoology & SBB, INNOVACORETM Center for Research & Biotechnology, San Antonio, TX,6Department of Basic Medical Sciences, College of Applied Medical Sciences (CoAMS-A), King Saud Bin Abdulaziz University for Health Sciences (KSAU-HS); King Abdullah International Medical Research Centre (KAIMRC-ER), Al-Ahsa, Saudi Arabia,7Adult Oncology, Department of Medicine, King Abdulaziz Hospital / KAIMRC-ER, Al-Ahsa, Saudi Arabia,8Medical Oncology, Department of Medicine, King Abdulaziz Hospital, Al-Ahsa, Saudi Arabia,9Department of Clinical Pharmacy, College of Pharmacy, King Khalid University, Abha, Saudi Arabia,10Department of Hematology & Internal Medicine, Orbassano University Hospital,, Department of Clinical and Biological Sciences, University of Turin, Turin, Italy,11International Islamic University Islamabad (IIUI), Islamabad, Pakistan,12Montefiore Medical Centre & Alberyt Einstein College of Medicine, New York, NY,13DFCI, Department of Oncology, Harvard Medical School, Dana-Farber Cancer Institute, University of Harvard, Boston USA., Boston, MA

摘要 Abstract

中文摘要
急变期CML（BC-CML）是一个高度难治且异质的阶段。我们应用了一套整合全外显子组测序（WES）、COSMIC突变特征与机器学习（ML）的流程，并结合AI引导的药物匹配，以在单患者分辨率上对BC-CML进行分层。在157例患者中的19例BC-CML病例中，WES揭示了显著升高的突变负荷、广泛的染色体损伤以及独特的分子结构。ML将BC-CML解析为三种亚型，各自富集独特的COSMIC特征与可靶向通路。治疗匹配将每种亚型与可重定位的FDA批准药物相关联，支持一种与复发、难治及转移性癌症相关的可扩展精准肿瘤学策略。 Introduction（引言） BC-CML代表着终末阶段，其中继发突变与基因组应激破坏了TKI应答[1]。髓系与实体瘤之间共享的致癌程序支持泛癌治疗重定位[2]。WES-ML-COSMIC策略可增强超越常规分期的分子分层[3]。我们应用该框架来定义可干预的BC-CML亚型。 Methods（方法） 在IRB批准下共分析了157例患者（123例CP、15例AP、19例BC）[4]。DNA在Illumina NovaSeq上测序，使用BWA-MEM比对至GRCh38[5]，通过GATK及COSMIC/VEP处理[6]。PCA+scikit-learn实现ML聚类[7]。COSMIC特征使用SigProfilerExtractor生成[8]。PanDrugs指导药物重定位[9]。 Results（结果） BC-CML表现出>2,500个体细胞突变，突变负荷较CP/AP高约54%，热点位于1、7、17和19号染色体。ML定义了三种亚型： 簇1（BRCA2/TP53）：HR缺陷；COSMIC S3/S5。 簇2（IDH1/2、TET2）：表观遗传/代谢失调；S1/S2。 簇3（JAK2、CSF3R）：细胞因子/氧化应激信号；S13/S18。 药物匹配分别将它们与PARP/MDM2抑制剂、IDH抑制剂/HMAs及JAK抑制剂相关联。 Discussion（讨论） 这一整合的WES-ML-COSMIC框架将BC-CML异质性转化为离散、可靶向的生物学程序[2]。三个簇反映了不同的进化压力——HR失败、表观遗传漂移及细胞因子驱动的氧化应激——各自与可重定位的FDA批准疗法相关联[3]。这种单患者水平的匹配支持一种可扩展的精准肿瘤学模型，适用于BC-CML之外的复发、难治及转移性癌症[4,9]。 References（参考文献） Kwon HJ Mol Cancer 2025;24:114. Cruz-Rodriguez N Blood 2025;145:931. Herraiz-Gil S Appl Sci 2025;15:2798. Awada H Cancers 2023;15:2248. Li H Bioinformatics 2009;25:1754. DePristo MA Nat Genet 2011;43:491. Pedregosa F JMLR 2011;12:2825. Sondka Z NAR 2023;52:D1210. Mao Y Mol Cancer 2025;24:123.
查看英文原文 English abstract
Blast crisis CML (BC-CML) is a highly refractory and heterogeneous phase. We applied an integrated whole-exome sequencing (WES), COSMIC mutational signature, and machine-learning (ML) pipeline with AI-guided drug mapping to stratify BC-CML at single-patient resolution. In 19 BC-CML cases within 157 patients, WES revealed markedly elevated mutational burden, widespread chromosomal damage, and distinct molecular architectures. ML resolved three BC-CML subtypes enriched for unique COSMIC signatures and targetable pathways. Therapeutic mapping linked each subtype to repurposable FDA-approved agents, supporting a scalable precision-oncology strategy relevant to relapsed, refractory, and metastatic cancers. Introduction BC-CML represents a terminal stage where secondary mutations and genomic stress disrupt TKI response [1]. Shared oncogenic programs across myeloid and solid tumors support pan-cancer therapeutic repurposing [2]. A WES-ML-COSMIC strategy enhances molecular stratification beyond conventional staging [3]. We applied this framework to define actionable BC-CML subtypes. Methods A total of 157 patients (123 CP, 15 AP, 19 BC) were analyzed under IRB approval [4]. DNA was sequenced on Illumina NovaSeq, aligned to GRCh38 using BWA-MEM [5], processed through GATK and COSMIC/VEP [6]. PCA+scikit-learn enabled ML clustering [7]. COSMIC signatures were generated using SigProfilerExtractor [8]. PanDrugs guided drug repurposing [9]. Results BC-CML displayed >2,500 somatic mutations and ~54% higher mutational burden than CP/AP with hotspots on chromosomes 1, 7, 17, and 19. ML defined three subtypes: Cluster 1 (BRCA2/TP53): HR-deficiency; COSMIC S3/S5. Cluster 2 (IDH1/2, TET2): epigenetic/metabolic dysregulation; S1/S2. Cluster 3 (JAK2, CSF3R): cytokine/oxidative-stress signaling; S13/S18. Drug mapping linked them to PARP/MDM2 inhibitors, IDH inhibitors/HMAs, and JAK inhibitors respectively. Discussion This integrated WES-ML-COSMIC framework translates BC-CML heterogeneity into discrete, targetable biological programs [2]. The three clusters reflect distinct evolutionary pressures-HR failure, epigenetic drift, and cytokine-driven oxidative stress-each linked to repurposable FDA-approved therapies [3]. This single-patient-level mapping supports a scalable precision-oncology model for relapsed, refractory, and metastatic cancers beyond BC-CML [4,9]. References Kwon HJ Mol Cancer 2025;24:114. Cruz-Rodriguez N Blood 2025;145:931. Herraiz-Gil S Appl Sci 2025;15:2798. Awada H Cancers 2023;15:2248. Li H Bioinformatics 2009;25:1754. DePristo MA Nat Genet 2011;43:491. Pedregosa F JMLR 2011;12:2825. Sondka Z NAR 2023;52:D1210. Mao Y Mol Cancer 2025;24:123.
利益披露 Disclosure
Z. Iqbal, None.. A. AlGarni, None.. L. Alnuaim, None.. S. Khan, None.. S. Rao, None.. Y. Taleb, None.. N. M. AlQahtani, None.. M. Alshuaibi, None.. E. Al Mansour, None.. M. AlShuker, None.. A. A. Ishaqui, None.. G. Saglio, None.. K. Ahmed, None.. R. Naeem, None.. M. A. Shammas, None.. M. F. Sabar, None.

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