Diffuse Large B-cell Lymphoma (DLBCL) exhibits significant genetic heterogeneity that impacts patient responses to R-CHOP chemotherapy. While some patients respond well, others face poor outcomes due to distinct genetic subtypes affecting drug efficacy. Recognizing the necessity for personalized treatment, this in silico study aimed to investigate how genetic profiling and multi-omic approaches could enhance the identification of prognostic biomarkers and help stratify DLBCL patients for improved therapeutic responses.
A narrative review assessed existing biomarkers in DLBCL and their impact on treatment stratification. We searched the IntOGen database to find genes that are altered and linked to DLBCL, and we succeeded in examining mutation frequencies utilizing GnomAD. The Cancer Genome Interpreter was then used to identify drugs linked to DLBCL-altered genes, contextualizing pharmacological guidance based on subtype-specific vulnerabilities.
Our results demonstrate the practical application of genetic profiling of DLBCL subtypes in significantly improving treatment outcomes. Clinical trials have shown that patients who are subtyped and receive tailored pharmacological therapies have notably higher complete recovery rates and reduced relapse rates compared to non-subtyped patients within the same cohort. This evidence supports the effectiveness of treatment strategies, especially for high-risk subtypes that exhibit chemoresistance.
Our analysis revealed statistically significant variations in allele frequency for specific gene alterations across different ethnic groups, underscoring the importance of ancestry-based stratification. However, we also identified a pressing need for research that includes diverse populations, as standard therapies developed primarily from Caucasian cohorts may not be effective for non-Caucasian patients. This aspect of our research has the potential to address healthcare disparities and improve patient outcomes.