Precision medicine in diffuse large B-cell lymphoma: Integrating molecular biomarkers, targeted therapies, and prognostic tools

Table 1 Comparison of diffuse large B-cell lymphoma molecular classification systems

Classification system	Year	Technology platform	Subtypes	Classification basis	Major advantages	Major limitations
COO classification	2000	GEP	2 (GCB/ABC)	Gene expression profiling	Biologically well-defined; established the foundation for molecular subtyping	GEP-dependent; difficult for routine clinical implementation
Hans et al[10], algorithm	2004	IHC	2 (GCB/non-GCB)	CD10/BCL6/MUM1	Simple operation; applicable in routine pathology	Accuracy approximately 80%; low sensitivity for ABC subtype identification
Schmitz et al[13], classification	2018	WES + translocations	4 (MCD/BN2/N1/EZB)	Mutations + chromosomal translocations	Mechanistically clear; provides explicit therapeutic guidance	WES-dependent; high cost
Chapuy et al[14], classification	2018	WES clustering	5 (C1-C5)	Whole-exome clustering	Independently validated; reveals additional subgroups	Clinical significance of some subtypes unclear
LymphGen	2020	Probabilistic classification	7	Naïve Bayes algorithm	Reproducible probabilistic classification; most widely applied	Relies on WES data quality
DLBClass	2025	Deep learning	Probabilistic	Neural network	Higher accuracy	High technical barrier; challenging for clinical implementation

COO: Cell-of-origin; GEP: Gene expression profiling; GCB: Germinal center B-cell-like; ABC: Activated B-cell-like; IHC: Immunohistochemistry; WES: Whole-exome sequencing; MCD: MYD88 (L265P)/CD79B-mutated subtype; BN2: BCL6 fusion/NOTCH2-mutated subtype; N1: NOTCH1-mutated subtype; EZB: EZH2-mutated/BCL2-translocated subtype; C1-C5: Clusters 1-5 in Chapuy classification; LymphGen: A probabilistic classification tool for genetic subtypes of DLBCL; DLBClass: A neural network-based molecular classifier for DLBCL.