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Access to diagnosis using liquid biopsy (ADLiB): Identifying lymphoma in a tuberculosis-endemic setting
Katherine Antel1,2, Nina Xiong3, Mwanasha Merrill4
1Medical University of South Carolina Charleston South Carolina USA.
Insights
A novel liquid biopsy platform, ADLiB, uses plasma cell-free DNA (cfDNA) to accurately diagnose lymphoma in resource-limited settings. This approach aids in distinguishing lymphoma from tuberculosis (TB) and other conditions, improving diagnostic speed and accessibility.
Area of Science:
- Oncology
- Genomics
- Infectious Diseases
Background:
- Lymphoma diagnosis is challenging in HIV/TB-endemic regions due to overlapping symptoms and limited access to biopsies.
- Current diagnostic methods are often constrained in resource-limited settings, leading to delayed lymphoma detection.
Purpose of the Study:
- To develop and evaluate the Access to Diagnosis using Liquid Biopsy (ADLiB) platform for differentiating malignant from benign lymphadenopathy.
- To assess the utility of plasma cell-free DNA (cfDNA) molecular features in diagnosing lymphoma in resource-limited settings.
Main Methods:
- ADLiB platform utilizes ultra-low-pass whole genome and deep targeted sequencing of plasma cfDNA.
- Machine learning classifier integrates cfDNA features (tumor fraction, mutations, CNAs, SVs, TCR/BCR clonality, pathogen detection).
- Prospective cohort of 124 South African patients with lymphadenopathy.
Main Results:
- Lymphoma patients exhibited higher cfDNA concentrations, tumor fraction, and mutation burden compared to benign causes.
- Targeted structural variants showed 100% specificity for lymphoma.
- The ADLiB classifier achieved 95% sensitivity, 75% specificity, and 92% accuracy (AUC 0.89) in distinguishing malignant from benign lymphadenopathy.
Conclusions:
- ADLiB offers a minimally invasive and feasible approach for lymphoma diagnosis in resource-limited settings.
- This cfDNA-based method can accelerate diagnosis where traditional methods are constrained, especially in high-pathogen prevalence areas.
- Further validation of ADLiB could significantly improve lymphoma diagnostic capabilities globally.
Abstract:
In HIV- and tuberculosis (TB)-endemic regions, lymphoma diagnosis is often delayed because symptoms can overlap with TB, and access to biopsy and specialized pathology is limited. To address this, we developed and internally evaluated the Access to Diagnosis using Liquid Biopsy (ADLiB) platform-a plasma cell-free DNA (cfDNA)-based approach capturing molecular features of lymphoma and key pathogens. Plasma cfDNA undergoes ultra-low-pass whole genome sequencing and deep targeted sequencing (50,000× coverage). The combined output-including estimated tumor fraction, mutations, copy number alterations, structural variants, B- and T-cell receptor clonality, and pathogen detection-is integrated into a machine-learning classifier developed within this study to differentiate malignant from benign lymphadenopathy. In a prospective cohort of 124 South African patients with lymphadenopathy due to lymphoma (76.6%), benign causes (18.6%; TB adenitis [9.7%] and reactive [8.9%]), and disseminated solid tumors (4.8%), patients with lymphoma had higher cfDNA concentrations (mean 248.3 ng/mL), tumor fraction (mean 11.6%), and more mutations (mean 9.5), frequently in KMT2C, SOCS1, TP53, STAT3, and CREBBP. Targeted structural variants were 100% specific for lymphoma. The ADLiB machine-learning classifier distinguished malignant from benign lymphadenopathy within this cohort with 95% sensitivity, 75% specificity, and 92% accuracy (AUC 0.89). ADLiB provides a minimally invasive and feasible diagnostic approach to distinguish malignant from benign lymphadenopathy in resource-limited settings with high pathogen prevalence that can complicate lymphoma diagnosis. With further optimization and clinical validation, this cfDNA-based approach has the potential to accelerate and improve lymphoma diagnosis where traditional diagnostics are constrained.
