Access to Diagnosis through Liquid Biopsy (ADLiB): A Cell-Free DNA Assay to Diagnose Lymphoma and Differentiate from Other Causes of Enlarged Lymph Nodes

Background: People with immune suppression due to HIV, congenital conditions, or immunosuppressive therapy are at higher risk for aggressive B-cell lymphomas. In many low-resource areas, such as sub-Saharan Africa, this cancer risk is compounded by delayed diagnoses due to several key factors. Firstly, diagnostic confounding or misdiagnosis of lymphoma as extra-pulmonary tuberculosis (EPTB), which can present with similar symptoms such as enlarged lymph nodes, fever, weight loss, cytopenias, and pleural effusion. Secondly, limited resources and access to specialized biopsy and pathology services in areas also affected by TB and HIV epidemics. Finally, the propensity of immune suppression-associated lymphomas to involve the mediastinal and internal organs makes biopsies both more resource-intensive and hazardous.

To enhance lymphoma diagnosis in this setting, we propose that these diagnostic challenges can be addressed using cell-free DNA (cfDNA) from the plasma with a panel designed to target common genetic mutations in lymphoma, including key structural variants, identify clonal B cell populations, and identify confounding pathogens or pathogens associated with cancer in the setting of immunodeficiency. We report the performance of the panel on patients with enlarged lymph nodes in an HIV- and TB-endemic setting.

Methods: We developed an integrated cfDNA panel to detect lymphoma or infectious disease and tested it on plasma from patients presenting with enlarged lymph nodes to a tertiary hospital in Cape Town, South Africa (n=129, 43 (33%) HIV+). The panel comprises three modules:1.) Recurrent Alterations targets recurrent lymphoma-associated genetic abnormalities, including single nucleotide variants (SNV), structural variants (SV), and copy number alterations (CNA); 2.) Clonality targets the variable, diversity, and joining genes of the immunoglobulin heavy chain and light chain B-cell receptor (BCR) and T-cell receptor (TCR) gene loci to identify clonal lymphocyte populations; 3.) Pathogens targets genetic features of infectious diseases that may mimic lymphoma (EPTB) or cause cancer amid immune suppression (EBV, HHV-8, HPV16, HPV18).

We sequenced cfDNA extracted from the plasma using ultra-low-pass whole genome sequencing (ULP-WGS) with iChorCNA to detect tumor burden, followed by panel-based sequencing at 50,000X on a subset of cases (n=14). We developed a cloud-based analysis pipeline incorporating Mutect2, LoFreq, SvABA and LUMPY for genetic variants and MiXCR to identify clonal populations and provide a measure of the diversity of BCR/TCR. To detect pathogens, reads were aligned against pathogen reference genomes or baited regions of genomes.

Results: Using ULP-WGS all B-cell lymphoma types and “other cancers” (disseminated solid tumor involving the lymph node) had a median tumor burden above the threshold of 3%, whereas benign lymphadenopathy, EPTB and PTCL were below the threshold. On panel-based sequencing we were able to detect SVs t(8;14) in 2/2 cases of Burkitt lymphoma and one case of plasmablastic lymphoma; and t(14;18) in 2/2 of FL and in one case of DLBCL. We detected SNVs with 80% sensitivity and 70% precision down to a VAF of 1% and were able to identify copy number aberrations commonly associated with lymphoma. Our clonality module demonstrated statistically significant differences in BCR diversity as measured using 1-Pielou’s index (p=0.002), confirming diversity in the setting of lymphoma. Finally, we detected EBV in cases with EBV-associated lymphoma. We detected genetic features of all other targeted infectious pathogens in benchmarked cell lines and commercial reagents; orthogonal benchmarking of primary patient plasma is ongoing.

Conclusion: We demonstrate the technical feasibility to detect genomic aberrations associated with lymphoma including translocations, SNVs, and CNA, as well as the presence of infectious pathogens and quantitative B-cell clonality on a single assay. Our data support the potential for an integrated cfDNA assay to resolve lymphoma from other causes of lymphadenopathy in high-need, resource-limited settings. Ongoing efforts include sequencing the full cohort, including cases with disseminated TB, and developing a machine learning classifier using the outputs from the panel to distinguish lymphoma from other causes of enlarged lymph nodes.