Transcriptomic Comparison of Non-Hodgkin Lymphomas in Relapsed/Refractory Versus Newly Diagnosed Patients Using Single FFPE Slides

Background Patients newly diagnosed with non-Hodgkin Lymphomas (NHLs) have been historically treated with B-cell depleting antibodies and chemotherapy. Standard of care (SoC) for patients with refractory or relapsed (R/R) disease has rapidly evolved over the past decade to incorporate chimeric antigen receptor T-cell therapy (CAR T), lenalidomide, antibody-directed chemotherapies and stem cell transplantation. This diverse treatment landscape can transform the tumor microenvironment and drive heterogenous tumor escape mechanisms. Understanding this varied tumor contexture will help inform efficacy results from NHL therapeutic studies. Core needle biopsies are used to sample tumor from R/R NHL patients; the bulk of tissue may be consumed for histopathology testing, leaving insufficient sample for transcriptomic profiling. We have shown that HTG EdgeSeq is useful for single-slide analysis of formalin fixed, paraffin embedded (FFPE) tissues (Loya et. al., JCOASCO Suppl. 2022). Here, we use this technique to compare tumors of R/R and newly diagnosed patients with Diffuse Large B-cell Lymphoma (DLBCL) and Follicular Lymphoma (FL). MethodsSamples and Transcriptomics: NHL tumor samples from newly diagnosed, treatment naïve patients (DLBCL n = 99, FL n = 19) and lymph node controls (n = 18) were sourced commercially. Tumor samples from a Phase 1/2a trial of epcoritamab in R/R patients (NCT03625037) were collected prior to treatment (DLBCL n = 38, FL n = 5). Single FFPE slides were processed using the EdgeSeq platform (19,000 gene HT panel, HTG Molecular). Tumors originated from lymphoid organs, gastrointestinal tract, testes, and the pleural cavity. Immunohistochemistry imaging for CD3, PAX5, CD20 was also performed (CellCarta, Belgium). Bioinformatics: Differential expression analysis was performed using linear models in R. Gene set enrichment analysis (GSEA) was performed with GSVA in R (Hänzelmann et. al., BMC Bioinformatics. 2013) using published signature sets (mSigDB: Liberzon et al., Cell Syst. 2015, XCell: Aran, et. al., Genome Biol. 2017). Results Single 5μm slides were analyzed with an average tissue size of 8mm2 for core needle biopsies and 40mm2 for resections. The number of detected genes was comparable to RNAseq, suggesting the HTG platform provides comprehensive data using core needle biopsies obtained during routine histopathology. We compared DLBCL tumor to normal lymph nodes and derived a gene set that was highly correlated to pathologist derived tumor purity. This quantitative proxy allows automated identification of samples that may contain source organ tissue. We applied differential expression analysis to identify gene, pathway and immune cell signatures contrasting newly diagnosed and R/R DLBCL. Top genes upregulated in R/R DLBCL tumors included B-cell markers as expected, T-cell function (CD25/IL2RA), and an intriguing epigenetic mechanism (YTHDC1; a N6-methyladenosine reader with a role in lymphoma proliferation [Liu et al., Front Onco 2022]). MYC pathway signaling was enriched in R/R tumors; consistent with known mutational risk factors for progression (Bisso et. al., Immunol Rev. 2019). Th2 polarized CD4+ T-cell signature was higher in treatment naïve DLBCL compared to R/R and normal lymph nodes. Fibroblast gene signatures were higher in R/R DLBCL, potentially a result of microenvironment remodeling due to chemotherapy or tumor driven adaptation (Dumontet et. al., Front Immunol. 2021). Comparison of DLBCL to FL was also performed in both R/R and untreated patient cohorts. Immune cell signatures for Tregs and CD4+ naïve T-cells were enriched in FL, whereas M1 macrophage signatures were enriched in DLBCL (findings were similar in both R/R and treatment naïve groups). Conclusions The EdgeSeq platform enabled whole-transcriptome profiling from very limited tissue samples in lymphoma clinical trials. This study highlights the importance of characterizing the transcriptional heterogeneity of NHL in relation to genetic alterations and T-cell immunity. Ongoing analysis with large patient cohorts in epcoritamab clinical trials will add statistical power to uncover associations with International Prognostic Score, Cell of Origin, effects of different treatment combinations, and genomic alterations on clinical outcomes.