Integrating genomic and ex vivo drug sensitivity profiling to predict treatment outcomes in Korean patients with non-Hodgkin lymphoma

May 26, 2026

Abstract

Non-Hodgkin lymphoma (NHL) exhibits marked interpatient heterogeneity in treatment response, yet links between tumor genotype and pharmacologic phenotype in primary samples remain incompletely defined. We integrate whole-genome sequencing of patient-derived tumor tissue with ex vivo drug sensitivity profiling across standard chemotherapeutics in a cohort of 32 patients encompassing aggressive and indolent NHL, and relate these data to clinical outcomes. Ex vivo sensitivities span more than 1,000-fold IC₅₀ ranges across drugs. HLA-DQB1 is the most frequently mutated with recurrent co-mutations in immune-regulatory genes. Mutation-wise association analyses reveal gene-specific drug effects, including increased actinomycin sensitivity with EGFR mutations, resistance to dexamethasone, prednisone and mechlorethamine with ITK mutations, and multi-drug resistance patterns linked to epigenetic regulators (KMT2D, SETD2). EP300 mutations are associated with enhanced prednisone sensitivity across subtypes. Clinically, FAT4 and CD22 mutations correlate with inferior progression-free survival. FAT4-mutated samples show reduced ex vivo sensitivity to cyclophosphamide while CD22-mutated samples demonstrate enhanced sensitivity to vinblastine, suggesting opportunities for regimen optimization. These data define an integrative functional-genomic framework that uncovers mutation–drug interactions with outcome correlates and motivate prospective studies testing genotype-informed therapy selection in NHL.

Scientific Publications

Quantitative ex vivo synergy profiling uncovers heterogeneous combination responses in acute myeloid leukemia

Biotechnology and Bioprocess Engineering
Learn More →

Quantitative ex vivo assessment of chemotherapy synergy using patient-derived non-Hodgkin lymphoma samples

Biotechnology and Bioprocess Engineering
Learn More →

Predicting Chemotherapy Response in Patients With Advanced or Metastatic Pancreatic Cancer Using Machine Learning

JCO Clinical Cancer Informatics
Learn More →

Identification of novel genetic mutations for the treatment prognostication of canine lymphoma

npj Precision Oncology
Learn More →

A study on the relationship between MDR1 mutation and ex vivo drug sensitivities of canine lymphomas

Biotechnology and Bioprocess Engineering
Learn More →

Recent advances in and applications of ex vivo drug sensitivity analysis for blood cancers

Blood Research
Learn More →

Prognostic Utility of the Flow Cytometry and Clonality Analysis Results for Feline Lymphomas

Veterinary Sciences
Learn More →

Multimodal machine learning models identify chemotherapy drugs with prospective clinical efficacy in dogs with relapsed B-cell lymphoma

Frontiers in Oncology
Learn More →

Prognostic value of European LeukemiaNet 2022 criteria and genomic clusters using machine learning in older adults with acute myeloid leukemia

Haematologica
Learn More →

ML-based sequential analysis to assist selection between VMP and RD for newly diagnosed multiple myeloma

npj Precision Oncology
Learn More →

Predicting Dynamic Clinical Outcomes of the Chemotherapy for Canine Lymphoma Patients Using a Machine Learning Model

Veterinary Sciences
Learn More →

Predicting likelihood of in vivo chemotherapy response in canine lymphoma using ex vivo drug sensitivity and immunophenotyping data in a machine learning model

Veterinary and Comparative Oncology
Learn More →