Biological mechanisms underlying clinical heterogeneity in central nervous system lymphoma (CNSL) are largely unknown. While previous studies suggest the chemokine CLL19 as a crucial factor for the formation of CNSL in murine models, its role in human disease remains elusive. Here, we performed in-depth genetic and transcriptomic profiling of 82 CNSL specimens and identified distinct genetic aberrations and tumor cell compositions in lymphomas with high CCL19 expression, both of which were associated with immunosuppressive and anti-apoptotic signatures. CCL19 levels varied widely across CNSL patients. High CCL19 expression was significantly and independently associated with inferior progression-free and overall survival. Spatial and single-nucleus analyses as well as immunohistochemistry revealed pericytes within vessel-rich areas as the predominant source of CCL19, accompanied by significant co-localization of CCL19 with its primary receptor CCR7 that was enriched in plasmablast-like malignant B cells, as well as dendritic cells, NK cells, and CD4+ T cells. Collectively, our study identified pericyte-derived CCL19 as a novel prognostic marker in CNSL that is associated with unfavorable genetic aberrations and modifications of the immune landscape towards a resting tumor microenvironment. Spatial CCR7 co-localization suggests avenues for future therapeutic strategies targeting the CCL19-CCR7 axis.

Background: HIV infection increases lymphoma risk, with many cases linked to co-infection with latent Epstein-Barr Virus (EBV). Immunosuppression and oncogenic viruses contribute to aggressive cancers that often present at advanced stages and progress rapidly. Prior studies of HIV-associated lymphomas have generally been restricted to relatively small cohorts and pretreatment tumor specimens, and there is a lack of integrated methods for tracking viral dynamics alongside host tumor and immune responses. We characterized the genomic landscapes from a large HIV-positive (HIV+) lymphoma cohort as contrasted with HIV– counterparts, revealing how viral reactivation and immune function influence cancer development. Methods: We comprehensively profiled 305 blood samples from 122 patients, including 51 HIV+diffuse large B cell lymphoma (HIV+/DLBCL), 10 HIV+ Burkitt lymphoma (HIV+/BL), and 61 HIV–/DLBCL patients. HIV+ patients were treated with SC-EPOCH-RR (NCT000019253). Tumor EBV status was determined by Epstein-Barr-encoded small RNA (EBER) ISH. Samples were profiled by VirCAPP-Seq targeting 180 viral species (Garofalo, Blood 2019), CAPP-Seq targeting 186 B-cell lymphoma genes (Newman, Nat Med 2014), and a custom panel targeting HLA genes. Results: To capture virome dynamics, we applied unsupervised clustering to serial samples of HIV+ lymphomas using viral family abundances. We identified 3 distinct Viral abundance clusters (V1-3), each reflecting a unique spectrum of active species, with gradually decreasing viral abundances from V1 to V3. V1 reflected high abundance across anelloviruses (AV), polyomaviruses, EBV, and other herpesviruses. V2 had high abundance of AV, but low EBV abundance. V3 had low abundance across all viral families. Consistent with the degree of immunosuppression, pretreatment samples were enriched in V1. Patients achieving remission had a gradual clearance of diverse viruses during and after therapy, transitioning towards V2/V3 clusters over time. In contrast, patients experiencing progression (PD) or associated death remained enriched in V1 at time of PD. We genotyped EBV subtypes with adequate genomic coverage of HIV+ lymphoma alongside EBV+ cell lines, classical Hodgkin lymphomas, and post-transplant lymphoproliferative disorders. HIV+ lymphomas had higher prevalences of EBV type 2 and type 1/2 co-infections (p<0.05) compared to immunocompetent lymphomas. Hosts with evidence of HLA-A/B supertype homozygosity had a higher risk of death from PD (p<0.01). We also found that while antiretroviral treatment (ART) suppressed circulating HIV RNA as expected, HIV cfDNA was not affected by ART, likely measuring latent HIV infected reservoirs. Pretreatment mutant ctDNA levels correlated with cell-free EBV (cfEBV) levels in EBER+HIV+ lymphomas (p<0.01). EBER– cases exhibited more frequent and diverse somatic alterations, including evidence of genomic hypermutation from microsatellite instability. HIV+cfEBV-/DLBCL showed higher mutant frequencies of TP53 and epigenetic modifier genes, while HIV+cfEBV+/DLBCL had fewer distinguishing recurrent mutations. Compared to HIV–/DLBCL, HIV+/DLBCL showed more frequent and diverse TP53 alterations but fewer MYD88, CD79B, and PIM1 alterations. Classifying by LymphGen (Wright, Cancer Cell 2020), we identified a higher prevalence of unclassified cases (“Other”, p<0.05) and a lower prevalence of MCD than HIV-/DLBCL, with EBER+ cases more often unclassified than EBER– cases. We applied PhasED-Seq (Kurtz, Nat Biotechnol 2021) to monitor ctDNA minimal residual disease (MRD) during chemoimmunotherapy in serial HIV+ cases. The presence of MRD after 3 cycles of therapy strongly predicted disease-free survival (DFS) (p<0.05). Considering diverse clinical, genomic and viromic features, cfEBV level, IPI, and non-GCB histology each remained independently prognostic of DFS outcomes (p<0.05) in HIV+ lymphomas. Conclusions: Noninvasive genomic profiling is readily feasible and identifies distinct viral, immune, and somatic alteration signatures that characterize HIV-associated lymphomas and distinguish them from immunocompetent counterparts. Broad viral activity at diagnosis predicts poorer outcomes in HIV+ lymphomas, underscoring the need for controlling viral reactivation. The diversity of host and EBV subtypes and host MRD response dynamics holds promise not only for monitoring treatment efficacy but could also inform early lymphoma detection in people living with HIV.

Introduction Primary central nervous system lymphomas (PCNSL) are aggressive extranodal non-Hodgkin lymphomas confined to the CNS, accounting for 5–7% of primary brain tumors. PCNSL are biologically unique among brain malignancies due to their extracerebral origin and their specific migration and proliferation in the CNS compartment. Somatic mutations in genes involved in the B-cell receptor and Toll-like receptor signaling pathways are frequently found in PCNSL, suggesting a common genetic mechanism underlying CNS tropism. However, the developmental stage at which these genetic aberrations arise and their phylogenetic hierarchies remain unknown. Here, we applied ultrasensitive targeted capture sequencing to directly characterize tumor-specific mutational profiles in circulating and bone marrow- (BM-) resident cells to map the cellular hierarchies of lymphoma-specific genetic alterations and elucidate potential processes involved in PCNSL tropism. Methods We collected peripheral blood (PB) and BM mononuclear cell (PBMC/BMMC) samples at baseline from nine patients with PCNSL, all of whom showed no evidence of systemic lymphoma involvement by conventional imaging and PB/BM assessment. PBMCs and BMMCs were FACS-sorted into purified B-cell and T-cell populations. We then performed targeted capture next-generation sequencing (CAPP-Seq), covering immunoglobulin (Ig) regions and 100 additional genes recurrently mutated in PCNSL, to profile the mutational landscape in all nine tumor samples. These somatic alterations were tracked in bulk PBMC and BMMC samples (n=18) as well as the corresponding sorted B-cell and T-cell subsets (n=36). To control for sequencing errors and to evaluate the specificity of the monitoring approach, the same sorting and sequencing strategy was applied to PBMC/BMMC samples from four patients in complete remission one year after allogeneic stem cell transplantation for myeloid neoplasia. Results We detected a median of 256 somatic mutations in PCNSL tumor samples (range: 24-327), with PIM1 (78%), MYD88 (67%), and CD79B (67%)being the most frequently mutated genes. At a predefined specificity threshold of 95%, we identified tumor-specific alterations in 7 of 9 PCNSL patients (78%) by ultrasensitive tracking of mutations in PB or BM samples. While tumor mutations were detectable in 2 of 9 bulk PBMC and 4 of 9 bulk BMMC samples (median allele frequency [AF]: 0.003%), detection rates and allelic fractions were substantially higher in purified B-cell subsets, with 7 of 9 PB B-cell (78%) and 6 of 9 BM B-cell samples (67%) yielding positive monitoring results with a median AF of 0.03%. Notably, no PCNSL-specific somatic variants were found in any T-cell subset from either compartment. Furthermore, tumor-specific clonal VDJ rearrangements were observed in most PB B-cell samples (5/9, 56%), but only in 2 of 9 BM B-cell samples and in none of the bulk specimens or T-cell populations. Next, we focused on the PB B-cell compartment to delineate PCNSL-specific genetic patterns in circulating cells. The majority of detected variants were located in immunoglobulin regions (median of 81%) and corresponded to subclonal events in matched tumor biopsies. However, we also identified various pathogenic somatic mutations in key driver genes, including MYD88 (n=2), CD79B (n=1), and TBL1XR1 (n=1), all of which represented truncal variants in corresponding tumors. Of note, the most frequently observed aberrations involved the 5′UTR region of DTX1 (5/9 cases, 56%), a gene implicated in extranodal dissemination of germinal center (GC) B-cells. Conclusion Our findings reveal the presence of circulating and BM-resident malignant or premalignant cells in the majority of PCNSL patients, and the detection of mutations in driver genes commonly associated with PCNS lymphomagenesis. Tumor-specific mutations were exclusively identified in the B-cell compartment, while T-cell subsets remained unaffected. This absence of somatic alterations in T-cell populations together with an accumulation of Ig mutations and the presence of clonal VDJ rearrangements in B cells suggests that (pre-)malignant clones originate outside the CNS at the GC-experienced B-cell stage, without involvement of earlier hematopoietic precursors.

Background: Mature lymphoid neoplasms (MLN) are currently diagnosed by integrating histopathological, immunophenotypic, cytogenetic, and molecular profiles of tumor tissue biopsies (Campo, Blood 2022; Alaggio, Leuk 2022). While genetic subtyping of DLBCL, based on mutational profiles and copy number alterations, has further advanced this classification, many cases remain unclassified (Wright, Cancer Cell 2020; Chapuy, Blood 2025). This highlights the need for additional diagnostic approaches, such as gene expression profiling (GEP) and cytogenetic methods, to capture significant biological heterogeneity. GEP has proven particularly valuable in identifying not only cell-of-origin (COO), but also high-grade B-cell lymphomas (HGBCL), offering enhanced sensitivity and the ability to detect occult rearrangements using a “dark-zone signature” (DZsig, Ennishi, JCO 2018; Alduaij, Blood 2023). However, the required tissue biopsies for GEP are invasive, carry procedural risks, and may delay diagnosis. While multi-cancer early detection (MCED) liquid biopsy tests based on methylation can allow detection of diverse solid tumors, most cancers detected by MCED when screening otherwise healthy adults are MLNs (Schrag, Lancet 2023) that cannot currently be further resolved using these tests. To overcome these limitations, we explored plasma cell-free DNA (cfDNA) as a non-invasive alternative for classifying diverse MLNs, determining COO and genetic subtypes in DLBCL, identifying genetic subtypes in HL, and detecting HGBCL. Methods: We applied EPIC-Seq (Esfahani, Nat Biotech 2022) to infer tumor gene expression from cfDNA fragmentomic signals in blood plasma before therapy. We designed a targeted capture panel informed by prior lymphoid GEP studies (n=56 studies, >10,000 tumors), including B- and T-cell differentiation markers, canonical immunohistochemistry markers, and recurrent genomic mutations and fusions for tumor burden assessment via variant allele frequency (VAF). The final panel encompassed 1,986 transcription start sites from 1,676 genes and covered 2.6 MB of genomic space. We then profiled plasma cfDNA from 748 subjects, including 666 patients across 10 lymphoid neoplasms (BL, CLL, DLBCL, FL, HL, MCL, MM, PBMCL, PTCL, WM), along with 58 healthy controls and 24 subjects with Other non-lymphoid solid tumors (carcinomas of the lung, liver, and pancreas). Results: Recognizing the influence of tumor burden on inferred tumor gene expression from plasma cfDNA, we developed a machine learning model to predict tumor mutant variant allele fraction (VAF) from EPIC-Seq data and validated it against mutation-based tumor burden measurements (Pearson R=0.96). This established a low VAF background threshold distinguishing from healthy controls (1%), below which samples were excluded for classification purposes. We then trained a multi-histology classifier on 396 patients across 10 groups (BL, CLL, DLBCL, FL, HL, MCL, MM, PTCL, Healthy, Other). Independent validation on 169 held-out samples demonstrated high individual classification accuracies and an overall top 2 accuracy of 94%. Beyond histological classification, EPIC-Seq showed high discriminatory potential for molecular HL subtypes (H1/H2, Alig, Nature 2024; AUC>0.8, p<0.0001) and accurately identified DLBCL GCB vs ABC COO subtypes (AUC>0.8, p<0.001). For genetic subtyping of DLBCL (e.g. Wright, Cancer Cell 2020; Chapuy, Blood 2025), we trained a classifier on publicly available tumor RNA-Seq data and successfully applied it to our plasma EPIC-Seq data from DLBCL patients, observing a strong concordance between EPIC-Seq inferred molecular subtypes and LymphGen mutational classifications. Finally, we evaluated cell-free DZsig in 49 LBCL patients (28% harboring MYC+BCL2 double-hit by FISH), demonstrating significant discrimination between HGBCL-DH-BCL2 and DLBCL NOS (AUC=0.75, p=0.005). As expected, DZsig scores in BL, known to originate from the dark zone, did not significantly differ from HGBCL-DH-BCL2.Conclusion: Our study demonstrates clinical feasibility and high accuracy of EPIC-Seq-based cfDNA analysis for non-invasive classification and molecular subtyping of diverse mature lymphoid neoplasms. This approach effectively addresses clinically relevant diagnostic challenges, including COO determination, genetic subtyping in DLBCL and HL, identification of HGBCL-DH-BCL2, and other high risk LBCLs harboring dark-zone signature.

Background: Variability in tumor proliferation is a key prognostic factor in Mantle Cell Lymphoma (MCL). However, its measurement by gene expression profiling of frozen tumor tissue specimens (Rosenwald et al. Cancer Cell) or Ki-67 index of fixed tumor tissues (Katzenberger et al.Blood) have each faced limitations. The MCL35 gene expression signature is a validated independent prognostic biomarker for risk stratification in MCL, relying on tumor RNA profiling to accurately quantify variability in tumor proliferation on routinely fixed tissues (Scott et al. JCO). However, its clinical use can be limited by the requirement for invasive lymph node or tumor tissue biopsies, their attendant risks and associated costs, as well as specific technical challenges when profiling bone marrow specimens and leukemic MCL samples. While liquid biopsies might offer a promising alternative, no approach has yet bridged the molecular gap between blood- versus tissue-derived prognostic signatures for faithfully measuring MCL-proliferation associated risk noninvasively. Here, we tackle this challenge. Methods: We profiled 161 total samples from 117 patients from the Phase 3 randomized LYMA trial (Le Gouill et al. NEJM) relying on R-DHAP/ASCT for first remission induction in treatment-naïve MCL. Cases were selected based on availability of baseline paired blood and/or tissue specimens at diagnosis. We profiled 88 matched-tumor plasma specimens from 44 patients; for 73 patients, either isolated tumor specimens (n=56) or plasma specimens (n=17) were available. Tumor specimens (n=100; 68% FFPE, 32% Frozen) were expression profiled by RNA-Seq. Plasma specimens were profiled by EPIC-Seq (Esfahani et al. Nat Biotech) using a customized lymphoma-specific panel including key proliferation associated MCL genes (Mutter et al. Blood). We developed a novel deep generative ML model to better infer transcriptomic expression from cfDNA fragmentomic features. Trained on both paired and unpaired tumor and plasma samples from MCL patients (60% training, 40% test), this new generative ML model (termed iEPIC) learns a domain transfer function aiming to reconstruct RNA-like profiles from raw blood plasma EPIC-Seq Promoter Fragmentation Entropy (PFE) measurements. We computed MCL35 scores from these predicted profiles using optimized coefficients. Model performance was benchmarked against ground-truth tumor-derived MCL35 scores and evaluated for clinical validity in survival stratification and prediction of early progression (POD24). Results: When considering tumor gene expression profiles by RNA-Seq (n=100), higher MCL35 scores were significantly associated with blastoid morphology (p<0.001) and tumor Ki67% index (p<0.001), as expected. Higher tumor MCL35 scores were also significantly associated with both inferior PFS and OS, whether as a continuous (p=0.007 [PFS], p=0.004 [OS]) or categorical (p=0.01 [PFS], p=0.01 [OS]) variable. Multivariate Cox models confirmed the superior prognostic value of MCL35 after adjusting for other key prognostic variables including MIPI and Ki67. When considering inferred expression profiles from plasma cfDNA using EPIC-Seq, the new iEPIC GEP model significantly improved single-gene level correlations between noninvasive plasma measurements from cfDNA and invasive tumor tissue measurements from RNA. This improvement included correlation gains in key mitotic proliferation genes, such as MKI67 (p<0.01), TOP2A (p<0.01), and FOXM1 (p<0.01). When extended to the full MCL35 proliferation signature, iEPIC achieved ~0.7 Pearson correlation against tumor RNA-Seq, allowing improvements in precision, recall, and F1 score of noninvasive measurements by ~23%. Our noninvasive iEPIC GEP model also significantly stratified patients across Ki67 groups (p < 0.02), similar to tumor RNA-Seq (p<0.002). For prediction of POD24 status, this noninvasive iEPIC GEP model achieved reasonably high performance (AUC=0.73), as compared with invasive MCL35 from tumor RNA-Seq (AUC=0.83). Conclusions: We describe a novel non-invasive approach for computing MCL tumor proliferation score directly from blood-based cfDNA, toward enabling risk stratification in MCL without tissue biopsies. This model maintains gene-level biological fidelity and could be deployed in settings where tissue is unavailable or serial monitoring is required. We expect that this innovation could yield a significant advance toward liquid biopsy-driven precision medicine for patients with MCL.

Background Axicabtagene ciloleucel(axi-cel) has improved outcomes in relapsed or refractory large B-cell lymphoma(r/rLBCL); however, more than half of patients(pts) experience disease progression or death especially among pts with elevated lactate dehydrogenase(LDH). Axi-Cel-2, a second axi-cel infusion administered shortly after standard-of-care(SOC) axi-cel, aims to improve target to effector ratio and increase exposure to less-exhausted CAR T-cells(CAR-T). We report interim Phase 1b results examining the safety and efficacy of Axi-Cel-2(NCT05794958). Method Adults with r/rLBCL, elevated LDH, and second-line(2L) axi-cel eligibility are included. Bridging therapy(BT) with steroids and/or radiation is permitted. After lymphodepleting chemotherapy(LDC), all pts receive SOC axi-cel. Pts without grade(gr) ≥3 cytokine release syndrome(CRS) or immune effector cell–associated neurotoxicity(ICANS) are eligible for Axi-Cel-2, administered 7–14 days after the SOC infusion, at 0.5×10⁶ CAR+ cells/kg(safety run-in) or 2×10⁶ CAR+ cells/kg(phase 1b). Single LDC is used for both infusions. Target enrollment is 20 pts receiving Axi-Cel-2. The primary endpoint is incidence of dose-limiting toxicity(DLT) with key secondary endpoint being 12-month(mo) progression-free survival(PFS). Results are referenced to SOC axi-cel infusion(day 0). Blood samples are collected over 28 days post SOC axi-cel to assess CAR-T kinetics and phenotype(Hamilton, Blood Adv 2024), as well as gene expression and clonal dynamics on days 7 and 17 in fold change(FC) using single-cell multi-omics(Good, Nat Med 2022). Molecular responses are assessed using PhasED-Seq(Kurtz, Nat Biotechnol 2021) to quantify pretreatment circulating tumor DNA(ctDNA) and minimal residual disease(MRD) while EPIC-Seq(Esfahani, Nat Biotechnol 2022) is used to evaluate CD19 antigen loss. Results As of July 15, 2025, 16 pts have been enrolled(15 evaluable). The median age was 61.5 years(range/r, 19–83), with 38% aged ≥65 years. Sixty-nine % of pts had refractory disease, and 56% had an IPI score ≥3. BT was administered in 81% of pts, and all received prophylactic steroids. Four pts were ineligible for Axi-Cel-2 due to gr ≥3 ICANS or active infection. Eleven pts received Axi-Cel-2(3 in the safety lead-in and 8 in the Phase 1b), and the analysis focuses on these pts. The median day of Axi-Cel-2 infusion was 10(r, 7-12). No DLT or serious adverse event were observed within 28 days of Axi-Cel-2. Following Axi-Cel-2 infusion, 3 patients experienced gr 1 CRS or ICANS, all with onset within 1 day and resolution within 1 day. All 11 pts were discharged within a median of 2 days(r, 2–4) after Axi-Cel-2, with 55% discharged at the minimum 2-day stay. One pt died due to progressive disease. At a median follow-up of 5.8 mos(IQR: 3.1–14.9), the overall response rate(95% CI) was 90.9%(58.7–99.8), including complete response rate of 72.7%(39.0–94.0). The 6-mo rates(95% CI) were 79.5%(39.3–94.5) for duration of response, 78.7%(38.1–94.3) for PFS, and 100% for overall survival. A second CAR-T expansion was observed in 88% of pts receiving the phase 1b dose. Median area-under-the-curve over 28 days was 259 cells/µL×day(n=11; IQR: 63–1094) in pts receiving Axi-cel-2 and 134 cells/µL×day(n=30; IQR: 26–557) in the 2L SOC historical cohort. Spectral flow cytometry showed higher GZMB+ CD8+ and lower PD-1+ proportions in CAR+ cells after Axi-Cel-2 vs SOC axi-cel(n=4; p<0.02). Single-cell RNA-seq revealed increased CX3CR1(2.7 log2FC) and TBX21(T-bet; 1.5 log2FC) and decreased NFKBIA(−1.7 log2FC) (all adj. p<0.001) in circulating CAR-T cells. These findings indicate reduced CAR-T exhaustion with enhanced cytotoxicity, activation, and migration after Axi-Cel-2. Using PhasED-Seq, ctDNA was measured in 7 pts receiving Axi-Cel-2(5 responders and 2 progressors), and median pretreatment ctDNA was 227 hGE/mL(r, 36–891). Among the responders, MRD negativity was 60% at 1 mo and 80% at 3 mos; the remaining pt had decline in ctDNA level to <1 hGE/mL by 3 mos. Both progressors had early ctDNA rise with CD19 antigen escape, confirmed by expression profiling and EPIC-Seq. Conclusion Interim Phase 1b results(n=11) show Axi-Cel-2 was well-tolerated without DLTs and induced a second CAR-T expansion with more cytotoxic, less-exhausted phenotypes and deep molecular responses. These findings support the potential of Axi-Cel-2 to improve outcomes in this high-risk population. Updated results will be presented at the meeting.

Background Central nervous system lymphomas (CNSL) display remarkable clinical heterogeneity, yet accurate prediction of outcomes remains challenging. The IPCG criteria are widely used in routine practice for the assessment of treatment response. However, the value of the IPCG criteria for ultimate outcome prediction is largely unclear, mainly due to the uncertainty in delineating complete from partial responses during and after treatment. Methods We explored various MRI features including semi-automated 3D tumor volume measurements at different disease milestones and their association with survival in 93 CNSL patients undergoing curative-intent treatment. Results At diagnosis, patients with more than 3 lymphoma lesions, periventricular involvement, and high 3D tumor volumes showed significantly unfavorable PFS and OS. At first interim MRI during treatment, the IPCG criteria failed to discriminate outcomes in responding patients. Therefore, we randomized these patients into training and validation cohorts to investigate whether 3D tumor volumetry could improve outcome prediction. We identified a 3D tumor volume reduction of ≥97% as the optimal threshold for risk stratification (=3D early response, 3D_ER). Applied to the validation cohort, patients achieving 3D_ER had significantly superior outcomes. In multivariate analyses, 3D_ER was independently prognostic of PFS and OS. Finally, we leveraged prognostic information from 3D MRI features and circulating biomarkers to build a composite metric that further improved outcome prediction in CNSL. Conclusions We developed semi-automated 3D tumor volume measurements as strong and independent early predictors of clinical outcomes in CNSL patients. These radiologic features could help improve risk stratification and help guide future treatment approaches.

Introduction: The Bruton's tyrosine kinase inhibitor (BTKi) ibrutinib has shown clinical efficacy as monotherapy and in combination with immunochemotherapy in patients with central nervous system lymphoma (CNSL). In addition to its direct impact on lymphoma cells, there is growing evidence that ibrutinib regulates the tumor microenvironment (TME) and T-cell immunity in systemic B-cell lymphomas such as chronic lymphocytic leukemia. However, the effect of ibrutinib on the unique TME of the brain in CNSL patients has not been explored yet. Here, we used human slice cultures from a CNSL patient undergoing complete brain tumor resection due to a radiologically suspected glioblastoma to investigate the influence of ibrutinib treatment on the myeloid compartment of the brain TME. Methods: Slices of human CNSL tissue were cultured and treated over five consecutive days either with ibrutinib in physiological concentrations observed in the cerebrospinal fluid (CSF) of lymphoma patients (ibrutinib-treated) or with DMSO (DMSO-treated). Untreated slice cultures served as additional controls. Single nuclei suspensions from the slice cultures of all three conditions were analyzed using 10x single nucleus RNA sequencing (snRNA-Seq), with the primary focus of investigating the cellular composition and transcriptional profiles of tumor cells and the TME. In addition, we performed shallow whole genome sequencing and targeted capture NGS (CAPP-Seq) of the bulk tumor as well as the slice cultures to characterize their genetic profiles. To validate our results, we performed snRNA-Seq of cells isolated from CSF of an additional patient undergoing ibrutinib therapy for progressive CNSL. Results: Genetic profiling of the slice cultures revealed the presence of characteristic mutations in MYD88, CD79B, PIM1, and TBL1XR1 genes as well as copy number alterations that are associated with immune evasion, including losses of 6p21 (HLA-D) and 1p13 (CD58). These genetic alterations were identical to those identified in the tumor bulk and their allelic representation mirrored the fraction of B-cells in the slice cultures, confirming the presence of the same malignant B-cell clone. Next, we successfully delineated nine cellular components of the brain from slice cultures by snRNA-Seq, including B-cells, T-cells, myeloid cells, endothelial cells, stromal cells, oligodendrocytes, astrocytes, and neurons. By integrating the myeloid cell compartment from all three conditions, we identified five distinct subclusters based on their transcriptional signatures. In ibrutinib-treated slices, the proportion of myeloid cells with an antigen-presenting expression pattern, defined by high expression of CIITA, CD74, and other HLAs, increased compared to those in DMSO-treated or untreated slice cultures. Conversely, the cluster of SPP1-expressing myeloid cells substantially decreased after ibrutinib treatment compared to the controls. Pseudotime analysis indicated that these two expression patterns are the result of two distinct polarization states of myeloid cells within the TME of CNSL. Finally, snRNA-Seq of CSF cells from an additional CNSL patient receiving ibrutinib identified the same upregulated antigen-presenting expression pattern in the myeloid cell compartment, validating these findings. Conclusion: Based on a unique human slice culture model from primary CNSL tissue, our data suggest a modulating effect of ibrutinib on the myeloid compartment in human CNSL by shifting myeloid cells from an immunosuppressive phenotype expressing SPP1 to an antigen-presenting phenotype. Despite being inherently limited by this single-case analysis, these results indicate an unknown immune-activating effect of ibrutinib on the brain TME in CNSL patients.

Background: Mature T-cell lymphoma (TCL) outcomes remain inferior to aggressive B-cell counterparts, and thus represent unmet need. Given high relapse rates after remissions induced by current regimens, more sensitive MRD methods are needed to personalize treatment. We tackled these questions by integrating distinct host and viral molecular features for noninvasive detection, risk profiling, and monitoring of diverse mature TCLs. Methods: We studied 132 patients diagnosed with diverse TCLs (PTCL-NOS n=21, ALCL n=13, TFH lymphoma n=21, ATLL n=54, ENKTL n=6, CTCL n=15, others n=2). 37% of pts had newly diagnosed TCL primarily treated with CHOP-based regimens, while 62% had relapsed/refractory disease treated with salvage chemotherapies (26%), nivolumab (5%), and/or allogeneic HSCT (31%). We molecularly profiled 530 specimens, including serial blood plasma samples drawn before, during, and at the end-of-therapy (EOT) (n=274), baseline tumor tissues (n=128), and matched normal cells (n=128). Plasma cfDNA for all cases were profiled to detect somatic mutations in 259 recurrently mutated TCL genes by CAPP-Seq, genome-wide CNVs by CANARy (Chabon 2020 Nature), clonotypic VDJ at all TCR/BCR loci by SABER, T/B-cell abundance by QUARTZ (Sugio ASH 2024 abstract #193441), and inferred expression of 381 TCL/immune-related genes by EPIC-Seq (Esfahani Nature biotechnology 2022). Viral load and genotyping for EBV and HTLV-1 were assessed by VirCAPP-Seq (Garofalo ASH2022). Plasma cfRNA expression was profiled using RARE-Seq, by targeting 6.8k coding genes (Nesselbush 2024 Cancer Research). Results: We found that mutation-based disease burden measurements in cfDNA by CAPP-Seq were significantly correlated with clonotypic cell-free TCR levels (cfTCR; R=0.95, p<0.001) across all patients and plasma HTLV-1 load in ATLL patients (R=0.84, p<0.001). When considering outcomes after CHOP-based regimens, detection of ctDNA-MRD at EOT predicted significantly higher relapse risk (p= 0.003), inferior PFS (p=0.002) and OS (p=0.012). Among 30 patients with CR by PET, there were 4 patients with negative and all of them had PFS with 1.1 year of median observation time. All nonCR patients were MRD positive at EOT. Moreover, higher baseline ctDNA levels predicted inferior outcomes in non-ALCL/ATLL PTCLs (relapse=0.003, PFS=0.003) and ATLL (relapse p=0.022, PFS p=0.037). Of interest, we confirmed that 5' deletions of HTLV-1 genome were associated with significantly higher relapse rate after allo-HSCT (p=0.006). Separately, when comparing plasma cfDNA vs cfRNA, mutation-based ctDNA burden was significantly correlated with cfRNA tumor burden in PTCL-NOS (R=0.58, n=18), ALCL (R=0.77, n=11), AITL (R=0.50, n=24), and ATLL (R=0.95, n=13) using corresponding gene expression signatures for each entity. However, for cfTCR defined from tumor biopsies, we found significantly higher cfTCR in cfDNA than cfRNA (p<0.001), where 43.2% had MRD exclusively detected in cfDNA, while 4.5% had MRD exclusively detected in cfRNA. By comparing paired baseline cfDNA & cfRNA samples obtained at time of tumor biopsies, cfDNA had significantly higher overlap in TCR/BCR repertoires with the tumor tissue than cfRNA (TCR p<0.01, BCR p=0.02) or PBMC DNA (TCR p<0.001, BCR p=0.01). Furthermore, expression levels of TCL TME genes (30 marker genes for B cells, and M1&M2 macrophage, and eosinophil in LM22 [Newman, Nat. Methods 2015]) inferred from cfDNA by EPIC-Seq were significantly correlated with RNA expression of tumor tissues in 15 cases (mean R=0.56, PTCL-NOS n=5, TFH n=4, ENKTL n=3, ALCL n=3). Collectively, these results suggest that cfDNA may reflects not only TCL tumor cells, but also TME. When assessing baseline plasma samples by EPIC-Seq, we found significant clustering of cases based on inferred expression of TME constituents. In patients treated with chemotherapies (n=53), a ‘Cold TME’ phenotype (low expression of B cells, and myeloid cells, but not T/NK cells) was significantly associated with poor outcome. Conclusions: Integrated noninvasive profiling of diverse TCLs is feasible and can predict outcomes by capturing clinically relevant tumor burden before therapy, as well as post-treatment MRD. Additionally, cfRNA and viral genome analysis complement cfDNA monitoring results. Importantly, our results suggest that when compared with cfRNA, cfDNA better reflects both tumor burden and tumor immune microenvironments.

Introduction: Central nervous system lymphomas (CNSL) are aggressive extranodal Non-Hodgkin lymphomas confined to the CNS compartment, mostly classified as diffuse large B-cell lymphomas. The gold standard for CNSL diagnosis is histopathological assessment of tumor tissue obtained by stereotactic brain biopsies. Yet, in high-risk situations or when patients obtain concurrent corticosteroids or antiplatelet therapy, biopsies are often delayed or a final diagnosis remains unconfirmed. In these scenarios, minimal-invasive CNSL identification from cerebrospinal fluid (CSF) could have transformative impact on the clinical management of these patients. Here, we developed a sensitive and highly specific digital droplet PCR (ddPCR) assay for the detection of MYD88 L265P in circulating tumor DNA (ctDNA) from CSF for its use in clinical routine. Methods: We underwent a certification process by the ‘National accreditation body of the Federal Republic of Germany’ (Deutsche Akkreditierungsstelle, DAkkS) in our accredited clinical laboratory at the University Medical Center Freiburg (Germany) to develop a ddPCR-based laboratory developed test (LDT) for the detection of MYD88 L265P in body fluids. The assay performance was independently validated in CSF and plasma samples collected from patients with contrast-enhancing CNS lesions and confirmed histopathological diagnosis. Following clinical implementation, our technology was applied to CSF specimens submitted from hospitalized patients between January 2022 and June 2024 for routine testing of MYD88 L265P. A subset of patients participated in our observational study (DRKS00034686), facilitating the evaluation of clinical, pathological, and radiological information as well as the assessment of clinical implications following CSF-ctDNA analyses. Results: The MYD88 L265P ddPCR assay development process revealed a limit of detection (LOD) of 0.05% mutant allele frequency (AF) and a limit of blank (LOB) of 0.5 detected copies per mL sample volume, leading to certification of the LDT by the DAkkS for its routine use in our clinical laboratory environment. Following this process, we comprehensively validated the technology's performance in 77 CSF specimens and 91 plasma samples from an independent cohort of 128 patients with contrast-enhancing brain lesions and verified CNSL (n=87) or other primary brain tumors and infectious/inflammatory brain diseases (‘Non-CNSL’, n=41). We identified the MYD88 L265P mutation with a sensitivity of 67% from CSF and 37% from plasma samples of patients with CNSL, while the hotspot variant was never detected in Non-CNSL patients, revealing a specificity and positive predictive value of 100% for both analytes. Levels of ctDNA were 24-fold higher in MYD88 L265P-positive CSF samples than in blood plasma (5.5% vs. 0.23%, p=0.0001). After certification and independent validation, we applied the ddPCR assay to 205 CSF samples collected from 182 hospitalized patients and submitted for routine testing of MYD88 L265P from 21 distinct centers. CSF volumes ranged from 0.5 to 10 mL, with a median of 2.9 mL. We detected the hotspot variant in 33% of CSF samples and reported the results to treating physicians with a median turnaround time of 5 days. 127 patients, providing 143 CSF samples, participated in our observational study. Reasons for CSF submission in the observational study included the presence of an unclear brain lesion with CNSL as differential diagnosis and surgical high-risk situation (n=49), suspected secondary CNSL involvement/relapse or PCNSL relapse (n=64), or ctDNA monitoring in patients with known CNSL (n=30). In 60% of CSF-ctDNA positive cases (31/52), our results guided or helped guiding further treatment and surgical management of patients. Specifically, minimal-invasive identification of MYD88 L265P obviated the need for neurosurgical biopsies or accelerated the diagnosis of CNSL in 19 patients (37%) and led to the initiation of CNSL-specific treatment in 25 cases (48%). 95% of patients receiving CNS-directed therapies based on CSF-ctDNA results showed radiographic and/or clinical response of their brain lesions. Conclusion: Our results demonstrate that minimal-invasive identification of CNSL by ctDNA profiling can effectively guide treatment and surgical management in clinical routine and has practice-changing impact for a subset of patients with unknown CNS lesions.

Background: People living with human immunodeficiency virus (PLWH) have a higher risk of lymphoproliferative disease and latent virus reactivation due to immune dysregulation including dysfunctional surveillance of Epstein-Barr virus (EBV). The EBV latency patterns differ in variant lymphoma histologies in PLWH, and lytic gene expression is associated with EBV replication and lymphoma development. Despite significant progress in HIV and lymphoma treatments, patients with HIV-associated lymphomas continue to have inferior overall survival compared to immunocompetent individuals. Methods for tracking viral infection and the immune state of PLWH are lacking, and the genomic landscape in HIV infection is less well characterized. Comprehensive viral and host genomic profiling using cell-free DNA (cfDNA) and tumor tissues allows the evaluation of viral abundance, viral and host mutations, and immune response in PLWH. Methods: We studied 246 serial plasma and 20 tumor samples from 51 HIV-associated diffuse large B cell lymphoma (HIV/DLBCL) and 10 HIV-associated Burkitt lymphoma (HIV/BL) patients. Patients were treated with SC-EPOCH-RR (NCT000019253). Tumor EBV status was determined by Epstein-Barr-encoded small RNA (EBER) ISH. Tumor, cfDNA, and PBMC were profiled by VirCAPP-Seq targeting 180 viral species (Garofalo, Blood 2019), CAPP-Seq targeting 186 B-cell lymphoma genes (Newman, Nature Medicine 2014; Alig, Nature 2024), and TCR repertoires by SABER (Shukla, Blood 2020). Results: Clinical characteristics: 73% of HIV/DLBCL patients were stage IV (Ann Arbor), 49% were germinal center B cell subtype, 24% were tumor EBER+, and 45% were on antiretroviral therapy (ART) pretreatment (preTX). Viral profiling: In HIV/DLBCL samples, all preTX plasma had EBV load (cfEBV) above healthy controls (mean=0.1, SD=0.3 copies/ml) and 47% had cfEBV > 100copies/mL (cfEBV+) by VirCAPP-Seq. Tumor EBER+ cases had ~100x higher cfEBV than EBER- (p<0.001). The cfEBV was similar between HIV/DLBCL and HIV/BL. Patients with preTX ART had lower HIV and Anellovirus loads (all p<0.05). EBV and Anellovirus loads decreased on therapy (post-C2, all p<0.01) but slightly increased at the last follow-up compared to post-C2. We observed 3 patterns of EBV mutation in preTX plasma: 1) Lytic mutational subgroup: lytic phase gene mutations in all EBER+ and some EBER- cases. 2) Latent phase subgroup: fewer altered lytic genes in some EBER- cases. 3) Undetected mutation or low EBV genome depth (<10x) subgroup: all are EBER- cases. EBER+ cases had more coding variants in EBNA-1, EBNA-3, and select lytic genes than EBER-. Immune & mutational profiling: Baseline CD4 count was higher in ART-treated patients (mean 306 vs 145 cells/l, p<0.05), and slightly but not significantly higher in EBER- cases. TCR clonotypes and Gini coefficient were higher in patients with baseline CD4 >100 cells/l (all p<0.05), indicating T cell clonal expansion. TCR Gini coefficient slightly increased and Chao1 index slightly decreased at the end of C2, suggesting lower TCR diversity after chemotherapy. Recurrent mutations in HIV/DLBCL plasma samples were TP53, CREBBP, KMT2D, STAT3, HIST1H1E, and SGK1. Mutations of MYC, TP53, and CCND3 were enriched in HIV/BL plasma samples. Clinical response: 82% of HIV/DLBCL achieved interim complete response/unconfirmed complete response (CR/CRu) after 2 or 3 cycles, and 18% had progression or death (PD) < 1 year. All HIV/Burkitt patients achieved CR/CRu after 3 or 4 cycles. Lymphoma and cfEBV mean allele frequency decreased (all p<0.01) with fewer detected mutations post-C2. PreTX cfEBV were ~100x higher in HIV/DLBCL patients experiencing PD (p<0.01), with 90% in the lytic mutational subgroup. Both EBER+ and cfEBV+ were associated with inferior PFS (all p<0.05). Patients with baseline CD4 > 100 cells/l had superior PFS (p<0.05). Patients with a lower preTX TCR diversity had a higher risk of PD. Conclusions: Integrated non-invasive profiling of a large cohort of PLWH revealed distinct virome and immunogenomic features across different lymphoma histologies and clinical responses. Elevated cfEBV, tumor EBER+, EBV lytic gene mutations, lower CD4 count and lower preTX TCR diversity indicate worse clinical response. These findings may help personalize therapy for improved long-term outcomes, and our approach provides potential monitoring for global immune states in immunosuppressed individuals.

PURPOSE Clinical outcomes of patients with CNS lymphomas (CNSLs) are remarkably heterogeneous, yet identification of patients at high risk for treatment failure is challenging. Furthermore, CNSL diagnosis often remains unconfirmed because of contraindications for invasive stereotactic biopsies. Therefore, improved biomarkers are needed to better stratify patients into risk groups, predict treatment response, and noninvasively identify CNSL. PATIENTS AND METHODS We explored the value of circulating tumor DNA (ctDNA) for early outcome prediction, measurable residual disease monitoring, and surgery-free CNSL identification by applying ultrasensitive targeted next-generation sequencing to a total of 306 tumor, plasma, and CSF specimens from 136 patients with brain cancers, including 92 patients with CNSL. RESULTS Before therapy, ctDNA was detectable in 78% of plasma and 100% of CSF samples. Patients with positive ctDNA in pretreatment plasma had significantly shorter progression-free survival (PFS, P < .0001, log-rank test) and overall survival (OS, P = .0001, log-rank test). In multivariate analyses including established clinical and radiographic risk factors, pretreatment plasma ctDNA concentrations were independently prognostic of clinical outcomes (PFS HR, 1.4; 95% CI, 1.0 to 1.9; P = .03; OS HR, 1.6; 95% CI, 1.1 to 2.2; P = .006). Moreover, measurable residual disease detection by plasma ctDNA monitoring during treatment identified patients with particularly poor prognosis following curative-intent immunochemotherapy (PFS, P = .0002; OS, P = .004, log-rank test). Finally, we developed a proof-of-principle machine learning approach for biopsy-free CNSL identification from ctDNA, showing sensitivities of 59% (CSF) and 25% (plasma) with high positive predictive value. CONCLUSION We demonstrate robust and ultrasensitive detection of ctDNA at various disease milestones in CNSL. Our findings highlight the role of ctDNA as a noninvasive biomarker and its potential value for personalized risk stratification and treatment guidance in patients with CNSL. [Media: see text]

The scarcity of malignant Hodgkin and Reed-Sternberg cells hampers tissue-based comprehensive genomic profiling of classic Hodgkin lymphoma (cHL). By contrast, liquid biopsies show promise for molecular profiling of cHL due to relatively high circulating tumour DNA (ctDNA) levels1-4. Here we show that the plasma representation of mutations exceeds the bulk tumour representation in most cases, making cHL particularly amenable to noninvasive profiling. Leveraging single-cell transcriptional profiles of cHL tumours, we demonstrate Hodgkin and Reed-Sternberg ctDNA shedding to be shaped by DNASE1L3, whose increased tumour microenvironment-derived expression drives high ctDNA concentrations. Using this insight, we comprehensively profile 366 patients, revealing two distinct cHL genomic subtypes with characteristic clinical and prognostic correlates, as well as distinct transcriptional and immunological profiles. Furthermore, we identify a novel class of truncating IL4R mutations that are dependent on IL-13 signalling and therapeutically targetable with IL-4Rα-blocking antibodies. Finally, using PhasED-seq5, we demonstrate the clinical value of pretreatment and on-treatment ctDNA levels for longitudinally refining cHL risk prediction and for detection of radiographically occult minimal residual disease. Collectively, these results support the utility of noninvasive strategies for genotyping and dynamic monitoring of cHL, as well as capturing molecularly distinct subtypes with diagnostic, prognostic and therapeutic potential.

Introduction While cell-free DNA (cfDNA) plays an increasingly defined role in aggressive lymphomas, its characteristics in indolent lymphomas are less established. Many follicular lymphoma (FL) patients experience durable remissions and late relapses ( Fig A). We therefore explored circulating tumor DNA (ctDNA) kinetics during long-term blood-based surveillance in patients with FL. In addition to noninvasive detection using somatic mutations, we also evaluated gene expression inference from cfDNA utilizing a novel fragmentomic method to detect histological transformation (tFL). Methods We studied 121 serial samples in a cohort of 17 FL patients (median 7 per patient) with long term clinical and blood-based MRD monitoring (median 5.6 years, max 10.8). We considered serial ctDNA status at 4 milestones: baseline/pre-treatment, post-treatment, in radiographic remission (MRD), and at relapse. We also profiled pre-treatment cfDNA for 9 tFL cases. Cases were genotyped to identify somatic mutations from diagnostic tumor or baseline plasma, with matched PBMCs used to censor germline variants and clonal hematopoiesis. ctDNA status in subsequent samples was evaluated via CAPP-Seq (SNVs) and PhasED-Seq (PVs). The cfDNA fragmentation profiles of pre-treatment FL and control samples were evaluated for inferred gene expression via EPIC-seq (Esfahani Nat Biotech 2022) using a novel 1676-gene panel focused on lymphoid neoplasms, including classic and transformed FL. Results Treatment & Relapse Kinetics Most patients had advanced disease (88% stage III-IV) and Low to Intermediate risk FLIPI (71% 0-2). At first cfDNA evaluation, 9/17 (53%) were previously untreated. Median pre-treatment ctDNA levels were 73.4 hGE/mL in FL, as compared to 236 hGE/mL in DLBCL (Kurtz JCO 2018). Post-treatment, median ctDNA levels dropped to 1.2 hGE/mL. Depth of response was similar between rituximab monotherapy and cytotoxic regimens, with median -2.2 log10 fold change from baseline. MRD Detection PVs were observed in all tumors, consistent with expected aberrant somatic hypermutation in FL. To evaluate MRD detection via PhasED-Seq, we considered plasma timepoints with undetectable ctDNA by CAPP-Seq (n=26) from patients (n=9) with subsequent clinical relapse. MRD was detected in 11 additional specimens (42%) by PhasED-Seq. Among FL patients tested for MRD while in radiographic remission, low-level ctDNA was detectable in 62% of plasma specimens (8/13) obtained within 1 month of negative PET/CT, suggesting potential additional utility for surveillance. Long-term Genomic Stability Sequential biopsies of FL tumors have shown genomic heterogeneity between diagnosis and relapse. We therefore evaluated ctDNA concordance across variant classes & regions. Relative AF was calculated pre-treatment and at relapse (median 2 years between samples, range 1-8 years), normalized by total ctDNA levels. The most stable lesions included missense mutations in CREBBP and TP53, nonsense KMT2D mutations, and BCL2 5'-UTR variants. BCL6 intronic mutations and chr2/chr14 SHM varied between diagnosis/relapse. Plasma versus Cellular Concentrations In contrast to DLBCL, circulating FL cells are frequently detectable at low levels in blood. To compare relative FL concentrations in the cellular versus plasma compartments, we profiled 10 paired specimens from patients with radiographic disease. While tumor-confirmed variants were detected in 7 of 10 cellular specimens, mean allelic frequency (AF) was 5.3x higher in plasma. Fragmentomic Assessment of Transformation We compared gene expression profiles inferred from cfDNA from pre-treatment lymphoma (FL=15, tFL=9) versus control (n=20) samples ( Fig B). Considering genes associated with FL (Huet Lancet Onc 2018), FL patients had significantly higher inferred expression than healthy adults (p=0.036). Likewise, patients with tFL showed higher inferred expression of genes associated with histologic transformation (Gentles Blood 2009, p=0.025). Conclusions Sensitive methods are required for effective MRD assessment in FL given lower ctDNA shedding as compared with DLBCL. Frequent detectable MRD in radiographic remission suggests that integrating molecular surveillance may augment serial imaging in the long-term management of FL. Simultaneous inferred gene expression from FL cfDNA appears to hold promise, including for noninvasive detection of transformation.

Background : Current diagnostic criteria for mature B-cell tumors require a combination of histopathological, immunophenotypic, cytogenetic, and genetic evaluations on tissue biopsy specimens (Campo, Blood 2022; Alaggio, Leuk 2022). Despite this battery of techniques, substantial heterogeneity remains in specific subgroups that can be further resolved using additional molecular diagnostic methods, including gene expression profiling (GEP). For example, among aggressive B-cell tumors, a subset recognized as high-grade B-cell lymphomas can be identified as harboring MYC+BCL2 lesions by FISH (HGBCL-DH-BCL2). However, a significant remaining high-risk subset also have a characteristic tumor gene expression “double-hit signature” or “dark zone signature” (DZsig) (Ennishi, JCO 2018; Alduaij, Blood 2023). Yet, inadequate tissue specimens can limit the application of these techniques for some patients. Separately, the invasive nature of tissue biopsies and their attendant risks can preclude adequate diagnostic evaluations in some patients, and logistical barriers for obtaining surgical tissue specimens can result in diagnostic delays. To address these unmet needs, we tested the performance of a novel noninvasive strategy to detect and classify mature B-cell tumors using plasma cfDNA, with special attention to HGBCL-DH-BCL2 and DZsig. Methods : We used EPIC-Seq to infer expression levels for genes of interest, using cfDNA fragmentomic signals at their transcription start sites (Esfahani, Nat Biotech 2022). We first curated and catalogued genes from prior GEP studies (n=56) of >10k diverse lymphoid tumors profiled by various RNA techniques. We systematically prioritized genes recurrently identified in multiple studies of tumor-specific expression profiles as compared with other tumor types and normal tissues. We included canonical markers used for immunodiagnosis by IHC and FACS, as well as key lineage and differentiation markers of normal B and T cells, and other leukocyte subsets. Finally, we also included recurrently mutated genomic regions and fusion hotspots for quantifying ctDNA levels using variant allelic fractions (VAF). To validate performance of DZsig in FFPE for HGBCL-DH-BCL2, we analyzed 95 FFPE biopsies by FISH (20% HGBCL-DH-BCL2), IHC, and RNA-seq. We then analyzed 36 corresponding pretreatment cfDNA samples (28% HGBCL-DH-BCL2) and used EPIC-Seq to infer gene expression. Results: Starting from our curated catalogue, we designed a targeted lymphoid EPIC-Seq panel (1676 genes; 2.6 MB) to resolve 11 major mature lymphoid malignancies and their clinically relevant molecular subtypes (cHL, DLBCL, HGBCL, FL, MCL, BL, MZL, PMBCL, SLL/CLL, PTCL, and MM). We next applied this EPIC-Seq panel to profile cfDNA samples from 207 lymphoma patients, including cHL (n=113), DLBCL (n=66), FL (n=15), and MCL (n=13) as well as healthy adults to assess specificity. We tested histology specific signatures derived from several existing RNA GEP datasets for cHL from sorted HRS cells (~85k by scRNA-Seq) and for DLBCL (n=959), FL (n=635), and MCL (n=100) from bulk RNA-Seq. Using this approach, we found high correlations between EPIC-Seq signature scores from cfDNA and corresponding circulating tumor VAFs in each histology [cHL (R p=0.83), DLBCL (R p=0.83), FL (R p=0.87), MCL (R p=0.81); Fig A]. In addition, when measuring these tumor derived signatures in cfDNA, each histology was significantly distinguishable from healthy controls (AUCs: cHL 0.94, DLBCL 0.91, FL 0.77, MCL 0.96). We next evaluated the DZsig when measured in LBCL patients and comparing paired tumor and cfDNA samples. We first confirmed the performance of the DZsig for detection of HGBCL-DH-BCL2 in 95 FFPE tumors by RNA-Seq (p<0.0001, AUC 0.85). We then noninvasively measured the DZsig in plasma cfDNA by EPIC-Seq (cfDZsig) and found significant discrimination of HGBCL-DH-BCL2 and DLBCL (p=0.0088, AUC 0.78, Fig B). Conclusions: These results confirm that inferred gene expression by cfDNA profiling allows noninvasive diagnosis of multiple lymphoma subtypes, including classification of challenging diagnostic entities such as HGBCL and DZsig+ tumors. Our ability to design and validate a pan-lymphoid gene panel for detection and classification of several lymphoma subtypes suggests the promise of this approach for other diverse tumors as well as the refinement of cfDZsig using a larger cohort.

Introduction: The scarcity of malignant Hodgkin and Reed-Sternberg (HRS) cells has hampered comprehensive genomic profiling of classic Hodgkin lymphoma (cHL) from tumor tissue. Multiple recent studies have demonstrated that plasma cfDNA profiling facilitates molecular characterization of cHL. Leveraging noninvasive genotypes and Latent Dirichlet Allocation, we recently defined 2 genetic cHL subtypes in a large international cohort comprising 366 patients including pediatric and adult patients of all ages (Alig et al., ASH 2022 and ICML 2023). Cluster H1 comprised ~2/3 of cases and was dominated by a high somatic mutational burden, and non-silent mutations in genes canonically involved in NF-κB, JAK/STAT and PI3K signaling as well as in B2M. Conversely, cluster H2 (~1/3 of cases) was primarily characterized by recurrent somatic copy number aberrations as well as mutations in TP53 and KMT2D. Herein, we validate these previously identified genetic subtypes in external datasets as well as through orthogonal methods including tissue-based and noninvasive transcriptional and immune profiling. Methods: To validate genetic cHL subtypes, we first leveraged public cHL genotypes from 61 patients obtained through whole genome/exome sequencing (WGS/WES) of flow-sorted HRS cells (Maura et al, Blood Cancer Disc. 2023). Using our previously locked down probabilistic classifier, we assigned the H1/H2 subtype, and then correlated cluster assignments with clinical variables. To explore transcriptional differences between genetic subtypes, we profiled baseline plasma samples (n=113) from the larger plasma genotyping cohort (n=293) using EPIC-Seq (Esfahani et al, Nat Biotechnol 2022), which allows for noninvasive gene expression profiling from cfDNA fragmentation patterns at transcription start sites. Further, we used SABER (Sworder, Cancer Cell 2023) to enumerate T-cell receptor (TCR) rearrangements in cfDNA (cfTCR, n=292). Finally, to assess subtype-specific immune infiltrate patterns, we profiled cHL tumor specimens using RNA-Seq (n=64), and applied CIBERSORTx. Results: Similar to observations in our cfDNA discovery cohort, H1 was found to be the more prevalent subtype in the external validation set comprising 56% of tumors, while 44% were classified as H2. Recurrence frequencies of genetic features were comparable to and significantly correlated with those from our plasma discovery cohort ( R S=0.59 [H1] and R S=0.63 [H2], P<0.001 each). Of note, when considering the whole genome space, the higher mutational burden of H1 tumors could be confirmed ( P<0.01), and this association was independent of the tumor EBV status. In agreement with the discovery cohort, the bimodal age distribution and increased EBV positivity of the H2 subtype could also be recapitulated (31% vs 6% EBV+, P<0.01). To explore transcriptional differences between H1/H2 subtypes, we took advantage of the plasma enrichment of HRS cell ctDNA and utilized EPIC-Seq to noninvasively infer expression of 1,676 targeted genes. Tracking signatures derived from scRNA-Sequencing in plasma samples, we found that both HRS cells and the cHL tumor microenvironment can be successfully profiled by EPIC-Seq. Strikingly, we found substantial enrichment of a cytokine response signature in H1 tumors, while T-cell activation was among the top upregulated signatures in H2 tumors ( Fig. A). Importantly, the T-cell signature found in H2 was accompanied by a higher abundance of T-cell clones as quantified by SABER in baseline plasma samples ( P<0.001, Fig. B). Notably, cfTCR fragment length profiles resembled the mutant ctDNA profiles, strongly suggesting a tumor origin of the TCR rearrangements detected in plasma. Lastly, immune cell deconvolution of bulk RNA-Seq specimens indicated a higher abundance of CD8+ T-cells in H2 tumors ( P<0.01), further confirming our prior observations. Conclusions: Collectively, these results serve to validate H1 and H2 as distinct cHL subtypes, to confirm the characteristic genotypes defining them, and recapitulate their distinctive associations with key clinical and pathological variables including age and EBV status. We further validate the subtypes using orthogonal methods revealing dominant cytokine driven signaling in H1. Conversely, H2 tumors, which largely lack B2M mutations, despite their lower mutational burden, are rather immunogenic triggering T-cell responses.

Background: Early noninvasive identification of therapeutic responses could help accelerate the development of more effective therapies for T-cell lymphoma (TCL), and to improve patient outcomes. However, standardized noninvasive disease-monitoring strategies remain elusive for the most common mature T-cell tumors. Therefore, the development of highly sensitive and accurate disease monitoring methods for TCL represents a strong unmet clinical need. We tackled this challenge using an integrated liquid biopsy strategy leveraging both cell-free DNA (cfDNA) and cell-free RNA (cfRNA), by simultaneously interrogating tumor-specific aberrations, including somatic mutations, clonotypic TCR rearrangements, and TCL gene expression signatures. Methods: We studied 83 serial blood samples from 36 patients diagnosed with TCL and treated in the US or Japan, including 63 longitudinal samples from 30 cases from Stanford University receiving standard TCL therapies (NCT00398177), and 20 blood samples from 6 subjects with relapsed/refractory TCL treated in Kyushu University hospital. Cell-free DNA samples for all cases were profiled to simultaneously detect somatic mutations by CAPP-Seq (Newman et al 2016 Nature Biotech) as well as clonotypic VDJ rearrangements by SABER (Shukla et al 2020 ASH, Sworder et al 2022 Cancer Cell). Therefore, an integrated CAPP-Seq targeted panel was designed for hybrid capture to target recurrently mutated genes in diverse T-cell neoplasms along with TCR rearrangements at 4 loci. Captured libraries were sequenced to a median deduplicated depth of ~3000x. For CAPP-Seq, single nucleotide variants (SNVs) were identified by the analysis of tumor tissue or pre-treatment cfDNA; constitutional germline variants and clonal hematopoiesis (CHIP) variants were censored using matched DNA from either CD4+ T-cell depleted PBMCs or buccal swabs. For SABER, tumor clonotypic VDJ sequences were defined as most prevalent clone in tumor tissue or pretreatment plasma cfDNA. Plasma cfRNA was profiled for a subset of patients (20 serial samples from 13 patients) using RARE-Seq (Nesselbush et al, in preparation). We then measured correlations between radiographic disease status and circulating disease levels, as measured using mutant ctDNA and clonotypic TCR molecules, and cfRNA gene expression signatures. Results: Among the 36 TCL subjects, all cases (100%) could be noninvasively detected in plasma cfDNA using either somatic mutations (mutant ctDNA only: 86%) or using clonotypic cell-free TCR rearrangements (cfTCR only: 88%). When considering the concentrations of these distinct tumor reporters, mutant ctDNA levels (hGE/mL plasma) and cfTCR levels (# of clones/mL plasma) were significantly correlated (Pearson R = 0.65, p<0.01, Fig. 1A). When considering longitudinal measurements during and after therapy, circulating levels of both ctDNA and clonotypic cfTCR were significantly lower in patients experiencing objective responses (CR/PR) than in patients experiencing SD/PD (p<0.01). Moreover, when comparing acellular plasma versus cellular (PBMC) blood fractions, circulating disease levels were significantly higher in plasma and often became undetectable in PBMCs (Fig. 1B). When considering TCR repertoires, CDR3 diversity was significantly lower at timepoints of measurable disease (pre-treatment, SD, PD). Furthermore, clonotypic tumor cell-free TCR (tumor cfTCR) fragments were significantly shorter than non-tumor cfTCR molecules (p<0.01). When considering cfRNA, a gene expression signature of activated CD4 memory T-cells was strongly correlated with both mutant ctDNA and tumor cfTCR levels (R=0.85, 0.64), and was higher at timepoints of active TCL disease. Conclusions: Noninvasive TCL detection and monitoring appears feasible through liquid biopsies interrogating cfDNA and cfRNA, with advantages over PBMCs. The complementarity for disease detection using tumor-specific somatic mutations, clonotypic TCR rearrangements, and gene expression levels, strongly suggests synergies to enable an integrated multimodal liquid biopsy framework for TCL. This novel platform should allow more accurate assessment of disease status and MRD, and to inform risk-adapted treatment strategies in TCL.