Lamba Jatinder, Marchi Francisco, Landwehr Marieke, Schade Ann-Kathrin, Shastri Vivek, Ghavami Matin, Sckaff Fernando, Marrero Richard, Nguyen Nam, Mansinghka Vikash, Cao Xueyuan, Slayton William, Starostik Petr, Ribeiro Raul, Rubnitz Jeffrey, Klco Jeffery, Gamis Alan, Triche Timothy, Ries Rhonda, Kolb Edwards Anders, Aplenc Richard, Alonzo Todd, Pounds Stanley, Meshinchi Soheil, Cogle Christopher, Elsayed Abdelrahman
University of Florida.
Massachusetts Institute of Technology.
Res Sq. 2024 Dec 12:rs.3.rs-5450972. doi: 10.21203/rs.3.rs-5450972/v1.
Acute Myeloid Leukemia (AML) is an aggressive cancer with dismal outcomes, vast subtype heterogeneity, and suboptimal risk stratification. In this study, we harmonized DNA methylation data from 3,314 patients across 11 cohorts to develop the Acute Leukemia Methylome Atlas (ALMA) of diagnostic relevance that predicted 27 WHO 2022 acute leukemia subtypes with an overall accuracy of 96.3% in discovery and 90.1% in validation cohorts. Specifically, for AML, we also developed , a prognostic classifier of overall survival (OS) (HR=4.40; 95% CI=3.45-5.61; P<0.0001), and a targeted using a stepwise EWAS-CoxPH-LASSO model predictive of OS (HR=3.84; 95% CI=3.01-4.91; P<0.0001). Finally, we developed a specimen-to-result protocol for simultaneous whole-genome and epigenome sequencing that accurately predicted diagnoses and prognoses from twelve prospectively collected patient samples using long-read sequencing. Our study unveils a new paradigm in acute leukemia management by leveraging DNA methylation for diagnostic and prognostic applications.
急性髓系白血病（AML）是一种侵袭性癌症，预后不佳，亚型异质性大，风险分层不理想。在本研究中，我们整合了来自11个队列的3314名患者的DNA甲基化数据，以开发具有诊断相关性的急性白血病甲基化图谱（ALMA），该图谱可预测27种世界卫生组织2022年急性白血病亚型，在发现队列中的总体准确率为96.3%，在验证队列中的准确率为90.1%。具体而言，对于AML，我们还开发了一种总生存期（OS）的预后分类器（HR=4.40；95%CI=3.45-5.61；P<0.0001），以及一种使用逐步EWAS-CoxPH-LASSO模型预测OS的靶向分类器（HR=3.84；95%CI=3.01-4.91；P<0.0001）。最后，我们开发了一种用于同时进行全基因组和表观基因组测序的样本到结果方案，该方案使用长读测序从12个前瞻性收集的患者样本中准确预测诊断和预后。我们的研究通过利用DNA甲基化进行诊断和预后应用，揭示了急性白血病管理的新范式。
