Wang, Panwen

Abstract Chimeric antigen receptor (CAR) T-cell therapy has demonstrated remarkable response rates in relapsed/refractory aggressive B-cell non-Hodgkin's lymphoma. However, durable remissions occur in only 40% of treated patients, highlighting mechanisms of therapeutic resistance and immune escape. While cytotoxicity is principally executed by CAR T-cells, monocytes shape CAR-T cell efficacy and orchestrate cytokine-driven immunity. In this study, we defined distinct metabolic profiles of T-cell and monocyte subsets based on clinical response. Single-cell RNA sequencing (scRNA-seq) and T-cell receptor sequencing (scTCR-seq) were performed on peripheral blood mononuclear cells at three timepoints: before lymphodepletion (BL), at peak CAR-T expansion (PK), and one-month post-infusion (M1). Data were processed with CellRanger v7.0.1 and analyzed with the immunopipe pipeline (Wang et al., 2025). Seurat v4.3.0 was utilized for unsupervised hierarchical clustering analysis; subsets identified included classical monocytes (CM), intermediate monocytes (IM), non-classical monocytes (NM), and monocytic myeloid derived suppressor cells (mMDSC). Metabolic profiles were compared across patients achieving ≥6-month complete remission (CR, n=16), primary refractory disease (PD1, n=4), or relapsed after a prior response (PD2, n=12). A total of 107035 T cells and 129981 monocytes from 77 serial samples in 32 patients were analyzed. CR patients had a higher T-cell:monocyte ratio than PD1/PD2 at all time points, especially at BL and PK (BL 1.2 vs 0.4, p=0.011; PK 1.3 vs 0.3, p=0.027). Clinical responses were compared with metabolic pathway analysis and identified oxidative phosphorylation (OXPHOS) as the dominant pathway. OXPHOS was consistently enriched in PD cohorts across T-cell subsets, particularly in PD1 vs CR at BL (mucosal-associated invariant T-cell (MAIT) and T peripheral memory (Tpm)), and at PK (CD4/CD8 memory precursor effector cells (MPEC) and activated MPECs (MPECa), CD4 T-helper 1 proliferating (Th1p), CD4 Th2 activated (Th2a), regulatory T-cell (Treg), CD8 naive senescent (CD8NS), CD8 T effector memory (Tem) tumor recirculating (TR), CD8 Tpm, CD8 T resident memory (Trm) TR and MAIT Tpm). By M1, OXPHOS enrichment in CD4/CD8MPECa, CD4Th2a, CD8NS, CD8TemTR reversed, now predominant in CR although CD8Tpm and CD8TrmTR remained enriched in PD1. In the monocyte subsets at BL, PD2 showed OXPHOS enrichment in CD163+ monocytes (mono), IM IL1, mMDSC SIRPA, mMDSC TGFβ, and CM IL6 subsets compared to PD1 and CR, while PD1 showed OXPHOS enrichment in IM CD38, CM TGFβ, and mMDSC HIF1A vs CR and PD2. This persisted at PK. By M1, the pattern reversed and mirrored that of T-cells, with CR patients showing OXPHOS enrichment in CD163+mono, CM IL6, CM TGFβ, IM CD38, mMDSC HIF1A, mMDSC SIRPA and mMDSC TGFβ, and additionally in CM EMT, CM p53, NM, NM CD36 vs PD1/PD2. Glycolysis/gluconeogenesis (GLY) was also enriched across subsets. In T-cells, PD1 vs CR showed GLY enrichment in CD4Th2a, CD8Tpm, CD8Tpm proliferating and MAIT Tpm at PK. In monocytes, GLY was enriched in PD1/2 vs CR at BL (CD163+mono, CM IL6) and PK (CD163+mono, IM CD18, mMDSC SIRPA). By M1, GLY metabolism was enriched in CR vs PD1/PD2 in CD163+mono, CM IL6, mMDSC SIRPA. Additional GLY enriched subsets at CR vs PD1 included CM, CM p53, EMT, IM CD38, IM IL1, mMDSC HIF1A, NM, NM CD36. Inositol phosphate metabolism (IP) was predominantly enriched in PD2 vs CR at BL (CD8NS, CD8Tcm, CD8Tpm activated), PK (CD4Th2, CD8NS, CD8Tem TR, CD8Tpm proliferating, γδT Tem) and M1 (CD4/CD8 MPECa, CD4 Th1/Th2, CD8NS, CD8Tpm, CD8 Tpm proliferating, Treg and MAIT Tpm). Notably, Treg was the only T-cell subset enriched for IP at PK in CR vs PD1/PD2. Additionally, the TCA cycle was enriched in PD1 Tregs and Th2 cells at BL and PK vs CR/PD2. A higher T-cell:monocyte ratio in CR vs PD1/PD2 supports prior evidence that elevated pre-lymphodepletion lymphocyte:monocyte ratios predict better outcomes. We identified distinct metabolic signatures in T-cells and monocytes between CR and PD1/PD2. OXPHOS enrichment in T-cells and monocytes at M1 in CR patients may reflect oxidative programming as a hallmark of sustained anti-tumor immunity. Conversely, GLY enrichment in PD1 T-cells at PK may reflect short-lived effector states lacking persistence. Overall, these findings highlight metabolic profiling as a promising predictive biomarker and therapeutic programming target to enhance CAR T-cell outcomes.

Abstract Introduction: A subset of patients with chronic lymphocytic leukemia (CLL) experiences disease progression from CLL to Richter transformation (RT), characterized by the occurrence of a high-grade lymphoma. The outcomes of patients with RT are typically dismal and identifying better therapeutic strategies remains critically important for these patients. To discern the biologic underpinnings driving this transformation, we studied immune changes and genetic evolution in samples from CLL patients who developed RT. Methods: We identified 18 patients with CLL who developed RT; and performed single cell (sc) RNA-sequencing(seq) on sorted B and T cells from peripheral blood mononuclear cells (PBMCs) at the time of RT (n=18) and at the earlier time of CLL diagnosis (n=11). The median time between the CLL sample and the one closest to RT was 4.3 years (range, 0.2-7.3); and patients received a median of 1 CLL directed treatment (range, 0 – 7). A total of 749,906 cells and 36,601 genes were captured during scRNAseq and 720,580 cells and 21,892 genes were retained for downstream analysis, respectively. Analysis was performed using the immunopipe (https://github.com/pwwang/immunopipe) that wraps around Seurat (v5.1.0) and identified enriched pathways using Enrichr service. The sc-T cell receptor (TCR)-repertoire(R)-seq data was analyzed and visualized using scplotter (). The whole exome sequencing (WES) analysis with tumor B-cells from same patients of CLL & RT stages were done using the Agilent sureselect Human all exon v5 + UTR capture kit. Reads from paired end sequencing were processed with the sarek pipeline. Results: Analysis of 720,580 cells from 11 CLL and 18 RT samples showed 16 distinct cell types- 3 B and 13 T cell subtypes. We observed an increasing trend of CD8 TEM [mean 0.548 vs. 0.384, p = 0.12 (Wilcoxon test)] but a decreasing trend of CD4 TCM (mean 0.236 vs. 0.337, p = 0.08) and B-memory (mean 0.0085 vs. 0.0195, p = 0.51) subsets in RT compared to their CLL stage. When comparing transcriptional changes, we found that among the top 18 highly variable genes, the expression of FP236383.3 (lncRNA) and TCL1A was significantly higher (p<0.0001) in the B cell subsets of patients with RT vs. CLL stage. Additionally, in T cell subsets—primarily dnT, γδT, and CD8/CD4 naïve cells—the expression of FP236383.3, NKG7, GNLY, and CCL5 was significantly higher (p<0.0001), while the expression of LDHB, GZMK, and IL32 was significantly lower (p<0.0001) in RT vs. CLL stage samples. Comparison of specific gene set enrichment analysis showed significantly increased expression of GZMB in gdT (p<0.001) and decreased expression of GZMA, GZMK, TIGIT and TCF7 in dnT and CD8 Naïve cells (p<0.001) for RT vs. CLL stage. The top 10 differentially expressed pathways in RT included oxidative phosphorylation, allograft rejection, IFN-α/-γ responses, TNF-α signaling via NF-κβ, complement, Myc targets V1, mTORC1, hypoxia, and p53 signaling across various B and T cell subsets compared to the CLL stage. Initial analysis of TCR-R data showed that patients with RT stage have lower number of unique clones (p<0.5) and reduced clonal diversity (p<0.5), and a higher number of large and hyperexpanded clones compared to their CLL stage. Additionally, WES data indicated that the most frequently mutated gene is TP53 in tumor B cells of both CLL and RT stages. However, a higher percentage of RT-stage samples carry mutations in MGA, ATM, and IRF2BP2 genes compared to their CLL-stage. WES data also showed a trend toward differing frequencies of copy number amplification at 19q12 (p=0.10) and 4q13.1 (p=0.52) between RT and CLL stages. Conclusions: The main findings of our study are as follows:The transcriptional landscape showed significant changes in multiple genes and pathway enrichment related to cell growth, differentiation and immune response in RT vs. CLL stage.A decreased fraction of CD4 TCM cells and reduced TCR clonal diversity were observed in samples at the RT stage and may indicate a poor prognosis for the patients at this stage. WES data of tumor B cells also indicated possible genetic alterations in the RT vs. CLL stage. Data from in-depth analyses on understanding of the dynamics of T cell response and additional experiments on these patients are expected to provide valuable insights into tumor genetic heterogeneity, transcriptional and immune regulation in blood-based immune subsets.

Abstract Aberrant DNA methylation and copy-number alterations (CNA) drive Barrett’s esophagus progression to esophageal adenocarcinoma; however, their combined utility for early detection is unclear. We aimed to identify and validate methylated DNA markers (MDM) and CNAs to distinguish esophageal adenocarcinoma/high-grade dysplasia (HGD) from nondysplastic Barrett’s esophagus (NDBE). In this multiphase, multicenter study, we discovered and validated MDMs and quantified CNAs utilizing whole-genome methylation sequencing of esophageal brushings. DNA biomarkers identified from discovery were further validated in independent patients with paired esophageal brushing and swallowed capsule sponge samples. MDMs were filtered against a reduced representation bisulfite sequencing dataset obtained from independent tissue samples to advance only concordant candidates. CNA burden was quantified using ichorCNA-derived aneuploidy scores (AS). Two hundred MDMs discovered in HGD (N = 18) and esophageal adenocarcinoma (N = 18) versus NDBE brushing samples (N = 18) were tested in independent samples (N = 146). A 52-MDM panel achieved a cross-validated AUC of 0.88 [95% confidence interval (CI), 0.82–0.95]; the addition of AS improved discrimination of HGD/esophageal adenocarcinoma from NDBE to 0.91 (95% CI, 0.86–0.97) AUC. At 80% specificity, the combined model detected 93% of esophageal adenocarcinoma and 88% of HGD cases. In paired capsule sponge samples, a 58-MDM panel achieved a cross-validated AUC of 0.77 (95% CI, 0.66–0.88); a combined 58-MDM and AS model achieved AUC 0.80 (95% CI, 0.7–0.9). MDMs and AS discerned HGD/esophageal adenocarcinoma from normal esophagus/NDBE in endoscopic brushing and capsule sponge samples. This approach may improve Barrett’s esophagus surveillance. Prevention Relevance: This study demonstrates that combining epigenetic and genomic biomarkers across minimally invasive sampling methods can accurately distinguish HGD/esophageal adenocarcinoma from nondysplastic Barrett’s esophagus, offering promising, less invasive strategies to improve BE surveillance and enable endoscopic therapy for esophageal adenocarcinoma prevention and treatment.

ABSTRACT Intratumor heterogeneity arises from ongoing somatic evolution complicating cancer diagnosis, prognosis, and treatment. Here we present TEATIME (es t imating e volution a ry events t hrough s i ngle-ti m epoint s e quencing), a novel computational framework that models tumors as mixtures of two competing cell populations: an ancestral clone with baseline fitness and a derived subclone with elevated fitness. Using cross-sectional bulk sequencing data, TEATIME estimates mutation rates, timing of subclone emergence, relative fitness, and number of generations of growth. To quantify intratumor fitness asymmetries, we introduce a novel metric—fitness diversity—which captures the imbalance between competing cell populations and serves as a measure of functional intratumor heterogeneity. Applying TEATIME to 33 tumor types from The Cancer Genome Atlas, we revealed divergent as well as convergent evolutionary patterns. Notably, we found that immune-hot microenvironments constraint subclonal expansion and limit fitness diversity. Moreover, we detected temporal dependencies in mutation acquisition, where early driver mutations in ancestral clones epistatically shape the fitness landscape, predisposing specific subclones to selective advantages. These findings underscore the importance of intratumor competition and tumor-microenvironment interactions in shaping evolutionary trajectories, driving intratumor heterogeneity. Lastly, we demonstrate that TEATIME-derived evolutionary parameters and fitness diversity offer novel prognostic insights across multiple cancer types.

Abstract Single-cell sequencing technologies provide us with information at the level of individual cells. Combining single-cell RNA-seq and single-cell TCR-seq profiling enables the exploration of cell heterogeneity and T-cell receptor repertoires simultaneously. Integrating both types of data can play a crucial role in enhancing our understanding of T-cell-mediated immunity and, in turn, facilitate the advancement of immunotherapy. Here, we present immunopipe, a comprehensive and flexible pipeline to perform integrated analysis of scRNA-seq and scTCR-seq data. In addition to the command line tool, we provide a user-friendly web interface for pipeline configuration and execution monitoring, benefiting researchers without extensive programming experience. With its comprehensive functionality and ease of use, immunopipe empowers researchers to uncover valuable insights from scRNA-seq and scTCR-seq data, ultimately advancing the understanding of immune responses and immunotherapy development.

Abstract Background Hypomagnesemia has been correlated with inferior outcomes in patients with large B cell lymphoma (LBCL) undergoing stem cell transplants. As T-cell and myeloid cell dysfunction have been associated with low magnesium conditions, we investigated whether serum magnesium (Mg) levels could predict clinical outcomes in LBCL patients who received chimeric antigen receptor T-cell therapy. Methods Patients with LBCL who received axi-cel under the ZUMA-1 trial or as FDA approved therapy at Mayo Clinic were examined. Serum samples were obtained at specified time points and cytokine analysis was performed. Single cell RNA sequencing was performed on peripheral blood mononuclear cells. The Student T-test, Kruskal Wallis, or Fisher's Exact Tests were used to compare differences in demographics across Mg levels. Survival curves were plotted using the Kaplan-Meier methodology and compared using the Wilcoxon test. Results We found that hypomagnesemia before lymphodepletion chemotherapy predicted for inferior progression-free and overall survival in the pivotal study ZUMA-1 (NCT02348216). These results were validated in an independent cohort of LBCL patients receiving axicabtagene ciloleucel (axi-cel) at Mayo Clinic. Hypomagnesemia correlated with increased inflammatory serum markers and cytokine levels including ferritin, IL-6, IL1Ra, IL-8, and MIP1a. scRNAseq analysis unveiled altered immune interactions between monocytes and T cells with a concordant immune suppressive transcriptome. Conclusions Hypomagnesemia at the time of CAR-T infusion associates with an unfavorable inflammatory profile and decreased response and survival in LBCL patients receiving axi-cel. These findings suggest a potentially actionable prognostic factor for patients with large cell lymphoma undergoing CAR-T.

Abstract Introduction: Barrett’s esophagus (BE) confers increased cancer risk. Endoscopic surveillance is recommended to distinguish non-dysplastic BE (NDBE), low grade dysplasia (LGD), high grade dysplasia (HGD), or adenocarcinoma (EAC) for endoscopic intervention. However, this paradigm is limited by sampling error, leading to missed/interval EAC. Herein, we aimed to classify these samples more accurately by performing whole genome methylation sequencing (WGMS) and utilizing multiple genomic features on endoscopic brush samples. Materials and methods: Endoscopic brush samples from patients with NDBE (n=18), HGD (18), EAC (18), and normal controls (18) were collected and underwent WGMS (discovery dataset). Sequence reads were processed through internal pipeline to extract CpG methylation and read level methylation ratio (alpha value). Copy number aberration (CNA) detection was performed using both CNVkit and ichorCNA. Differential methylated regions (DMRs) were identified through comparing EAC/HGD with NDBE and normal samples. Targeted methylation sequencing for selected DMRs and shallow WGMS were performed on EAC (41), HGD (26), LGD (23), NDBE (42), and normal controls (37) as validation. Ratio of the reads with alpha value>0.6 for each DMR was used for further analysis. Detected CNA segments were summarized into chromosome arm loss or gain (aneuploidy score or AS representing the number of chromosome arms gained or lost in a sample) and trimmed median absolute deviation (tMAD). Classification models were developed using the discovery set and the best performing one was applied to the validation dataset. Results: Genome-wide DNA methylation largely segregated different groups of samples as diagnosed; however, some unexpected group crossings were observed. Top 200 DMRs were identified between EAC/HGD and NDBE and normal samples. CNA changes were widespread in EAC, very common in HGD but rare in NDBE (17/18 EACs,14/18 HGDs, 5/18 NDBEs with AS>0). With tMAD > 0.015, 16/18 EACs, 8/18 HGDs but none of 18 NDBEs passed the threshold. EAC or HGD samples without detectable CNAs had very low “tumor” fraction as determined by methylation-based cell type deconvolution or ichorCNA. Similar results were found in the validation dataset. A multi-omics (AS, tMAD, estimated tumor fraction, and DMR methylation) model could predict EAC, HGD, or NDBE with 0.81 accuracy through cross validation (10X) and 0.73 in the independent dataset. This increased to 0.98 and 0.90 in separating high risk EAC/HGD from NDBE, respectively. Conclusion: Multi-omics data obtained from single WGMS allow for a comprehensive analysis of BE samples with varying dysplasia levels. Using classification modeling, these characteristics can be effectively used to accurately identify and distinguish between EAC and HGD from NDBE, suggesting their utility as adjuncts to endoscopic surveillance biopsies. Citation Format: Zhifu Sun, Caroline L. Matchett, Seth W. Slettedahl, William R. Taylor, Panwen Wang, Calise Berger, Caryn E. Anderson, Melissa A. Passe, Ramona Lansing, Collin E. Chalmers, Patrick H. Foote, Jeanette E. Eckel Passow, Doug W. Mahoney, John B. Kisiel, Prasad G. Iyer. Multi-omics cancer risk assessment of Barrett’s esophagus with DNA methylation sequencing from endoscopic brush samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB249.

Abstract Background Although CAR T-cell therapy has achieved remarkable response in patients (pts) with B-cell non-Hodgkin’s lymphoma (NHL), 20-30% of pts still have primary refractory disease (PD1), and another 30-40% relapse (PD2). We and others have shown that monocytes can modulate T-cell functions and impact clinical response to chemoimmunotherapy in NHL. Given that monocytes nadir and recovery kinetics differ from lymphocytes post lymphodepletion chemotherapy, we hypothesize that monocyte phenotype at peak CAR-T expansion and in the early month post CAR-T can modulate immune responses. Methods scRNAseq were performed on peripheral blood mononuclear cells of NHL pts prior to lymphodepletion chemotherapy (pre), at peak CAR-T expansion (peak), and one month following CAR-T (M1). Seurat v4.3.0 was used for unsupervised hierarchical clustering analysis. Lugano criteria was used to determine clinical responses which were categorized as CR (complete remission), PD1, or PD2. Results We analyzed 129,981 monocytes from 77 samples of 32 pts (16 CR, 4 PD1, 12 PD2) and 5 healthy controls. Unsupervised hierarchical clustering analysis identified 13 monocyte clusters: 4 classical monocyte (CM) clusters with transcriptome profiles for enriched signaling in IL-6, TGFβ, EMT, and p53; 2 intermediate monocyte (IM) clusters enriched for IL-1 or CD38 signaling; 2 non-classical monocytes (NM), one with increased CD36 expression; 3 monocytic MDSC clusters with enrichment of TGFβ, SIRPα, and HIF1α signaling; and 2 lymphoma-associated monocytes, expressing CD163 or CD169. At Pre and Peak, the distribution of the monocyte clusters was similar in pts with CR, PD2, or PD1, with only a slight decrease in the percent of NM in the PD2 group at Pre and slight increase of CD36 NM in PD1 at Peak. Compared to Pre, all pts had increased percent of CD36 NM and IL-1 IM at Peak. Interestingly, while the distribution of monocyte clusters was similar at Pre and Peak across clinical responses, GSEA showed that monocytes in CR pts, compared to PD1 and PD2, had gene expression enrichment of MYC target V2 pathway at Pre and TGFβ pathway at Peak. At M1, pts in PD1 had increased percent of p53 and TGFβ CM and decreased percent of NM. In contrast, pts with CR had decreased percent of IL-6 CM, p53 CM, TGFβ CM, IL-1 IM and increased percent of NM. Notably, as pts with CR and PD2 may appear similar clinically at M1, pts with PD2, compared to CR, have increased IL-6 CM, IL-1 IM, and decreased NM. In addition, GSEA showed that PD2 pts had increased IFNα response. Our results demonstrate that monocyte phenotype differences at Pre and Peak could be associated with CAR-T clinical response, and that persistent inflammatory monocyte signaling at M1 in pts with clinical response is associated with relapse. These data suggest a role for development of monocyte biomarkers to predict response and therapeutic targeting. Citation Format: Andre de Menezes Silva Corraes, Panwen Wang, Radhika Bansal, Henan Zhang, Zuoyi Shao, Kevin Regan, Eider Moreno Cortes, Arushi Khurana, Nora Bennani, Yucai Wang, Paul Hampel, Jonas Paludo, Patrick Johnston, Hemant Murthy, Madiha Iqbal, Stephen Ansell, Javier Munoz, Allison Rosenthal, Mohamed Kharfan-Dabaja, Januario Castro, Saad Kenderian, Haidong Dong, Ying Li, Yi Lin. Monocyte transcriptome profiles associated with clinical response in patients with B-cell non-Hodgkin’s lymphoma receiving CD19-directed CAR-T therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4001.