HIV and prior exposure to antiretroviral therapy alter tumour composition and tumour: T-cell associations in diffuse large B-cell lymphoma.

Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of lymphoma worldwide, accounting for up to 40% of new non-Hodgkin Lymphoma (NHL) globally. People living with HIV are up to 17 times more likely to develop NHL, and as such, DLBCL is the leading cause of cancer death in this high-risk population. While histologically indistinguishable, HIV-associated (HIV+) and HIV-negative (HIV-) DLBCL are molecularly distinct, and biological differences may have implications for the development of future therapeutic interventions. Further, the impact of immunologic differences in people with HIV, including preceding ART, remains largely unknown. Here, we investigate the impact of HIV infection and ART exposure on the clinical features of DLBCL and T-cell immune response by performing imaging mass cytometry on our unique patient cohort in Malawi. In this cohort, HIV infection is positively prognostic, and HIV+/ART-naïve patients have the best outcomes. No established biomarkers other than Ki67 are associated with HIV or ART status, and the only tumour-intrinsic biomarkers that remain prognostic are MYC and MYC/BCL2 protein co-expression. Finally, TCR clonality is associated with distinct tumour-T cell interactions by HIV/ART status, indicating differential anti-tumour immune responses. We demonstrate previously undescribed HIV and ART-related differences in the DLBCL tumour microenvironment.

HIV infection and ART exposure affect tumor TCR repertoire of diffuse large B cell lymphoma.

The most common subtype of lymphoma globally, diffuse large B cell lymphoma (DLBCL), is a leading cause of cancer death in people with HIV. The restructuring of the T cell compartment because of HIV infection and antiretroviral therapy (ART) may have implications for modern treatment selection, but current understanding of these dynamic interactions is limited. Here, we investigated the T cell response to DLBCL by sequencing the T cell receptor (TCR) repertoire in a cohort of HIV-negative (HIV-), HIV+/ART-experienced, and HIV+/ART-naive patients with DLBCL. HIV+/ART-naive tumor TCR repertoires were more clonal and more distinct from each other than HIV- and HIV+/ART-experienced ones. Further, increased overlap between tumor and blood TCR repertoires was associated with improved survival and HIV/ART status. Our study describes TCR repertoire characteristics for the first time to our knowledge in an African DLBCL cohort and demonstrates contributions of HIV infection and ART exposure to the DLBCL TCR repertoire.

Transcriptome profiling of pediatric extracranial solid tumors and lymphomas enables rapid low-cost diagnostic classification.

Approximately 80% of pediatric tumors occur in low- and middle-income countries (LMIC), where diagnostic tools essential for treatment decisions are often unavailable or incomplete. Development of cost-effective molecular diagnostics will help bridge the cancer diagnostic gap and ultimately improve pediatric cancer outcomes in LMIC settings. We investigated the feasibility of using nanopore whole transcriptome sequencing on formalin-fixed paraffin embedded (FFPE)-derived RNA and a composite machine learning model for pediatric solid tumor diagnosis. Transcriptome cDNA sequencing was performed on a heterogenous set of 221 FFPE and 32 fresh frozen pediatric solid tumor and lymphoma specimens on Oxford Nanopore Technologies' sequencing platforms. A composite machine learning model was then used to classify transcriptional profiles into clinically actionable tumor types and subtypes. In total, 95.6% and 89.7% of pediatric solid tumors and lymphoma specimens were correctly classified, respectively. 71.5% of pediatric solid tumors had prediction probabilities > 0.8 and were classified with 100% accuracy. Similarly, for lymphomas, 72.4% of samples that had prediction probabilities > 0.6 were classified with 97.6% accuracy. Additionally, FOXO1 fusion status was predicted accurately for 97.4% of rhabdomyosarcomas and MYCN amplification was predicted with 88% accuracy in neuroblastoma. Whole transcriptome sequencing from FFPE-derived pediatric solid tumor and lymphoma samples has the potential to provide clinical classification of both tissue lineage and core genomic classification. Further expansion, refinement, and validation of this approach is necessary to explore whether this technology could be part of the solution of addressing the diagnostic limitations in LMIC.