Tallforest: Multi-omic classifier for T-lineage acute lymphoblastic leukemia

作者：Petri Pölönen, Yiping Fan, Shaohua Lei, Qingsong Gao, Yiming Wu, Ti‐Cheng Chang, Haley Newman, Lahari Uppuluri, Niroshan Nadarajah, Torsten Haferlach, Markéta Žaliová, Jan Trka, Lu Wang, Hiroto Inaba, Stanley Pounds, Gang Wu, David T. Teachey, Charles G. Mullighan · 发表于：Blood · 年份：2025 · DOI：10.1182/blood-2025-336 · 被引用次数：4 · 研究领域：Acute Lymphoblastic Leukemia research、Genomics and Rare Diseases、Acute Myeloid Leukemia Research

Abstract We previously demonstrated that T-lineage acute lymphoblastic leukemia (T-ALL) can be classified into 15 molecular subtypes based on whole transcriptome, exome, and genome sequencing (WTS/WGS)1. These subtypes are defined by distinct drivers and co-occurring alterations, correspond to specific T-cell developmental stages and show differences in clinical outcomes. Using integrated multi-omics, we identified putative coding and non-coding driver alterations in over 95% of cases, highlighting the value of genomics-based classification for mechanistic investigation and clinical risk stratification. However, implementing such a WGS/WTS classification system is challenging. Accurate interpretation of WGS and WTS data requires advanced computational and genomics expertise, particularly for detecting non-coding or cryptic alterations. Technical issues, such as low tumor purity or tumor-in-normal (TIN) contamination poses challenges for variant detection. Furthermore, reliance on a single omics modality may result in ambiguous subtype calls, especially in complex or borderline cases. To address these challenges and facilitate clinical translation, we developed TALLForest an automated WTS/WGS-based classifier that integrates gene expression, structural variants (SVs), copy number variants (CNVs), and small variants (SNVs/indels) to assign consensus molecular subtypes in T-ALL. TALLForest classifies samples using a gene expression-based random forest (RF) model trained on 1,145...