Tan, Melinda

Abstract Background: Patients (pts) with myeloma (MM) receiving CAR-T therapy have usually been exposed to many prior therapies. Melphalan (HDM) and other chemotherapies can induce cellular senescence. It has recently been shown that MM cells can also exhibit features of therapy-induced senescence (TIS). TIS tumor cells are associated with resistance to therapy and relapse. To investigate the presence of TIS MM cells prior to CAR-T and association with response, we examined the transcriptome and phenotype of MM prior to CAR-T. Method: We used scRNA-seq data from bone marrow (BM) MM cells: Boiarsky, et al. (GSE193531, dbGaP: phs001323.v3.p1) newly diagnosed MM (NDMM) pts; Dhodapkar, et al. (GSE210079), pre- and post CAR-T; pts who received CAR-T at Mayo Clinic, pre- (N=10) and post- (N=2). To evaluate TIS, single-cell gene set enrichment analysis (scGSEA) for several gene sets (PMID: 40164720): SenUp (Senescence Upregulated), SenGA (Senescence Growth Arrest), and SCAPs (Senescent Cell Anti-apoptosis Pathways); and a Plasma Cell Senescence (PCSen) gene set (GSE5900, GSE47552). To assess TIS and myeloid phenotype, BM MM cells (CD138+) were stained for CD14, CD16, KLRG1 and GPNMB and analyzed by CytoFlex and Kaluza. Results: In the integrated dataset, scGSEA showed significantly higher enrichment of PCSen, SenUp, SenGA, and SCAPs in late-stage MM cells (both pre- and post-CAR-T) compared to NDMM and healthy donors (p<0.05, Kruskal-Wallis with Dunn’s posttest). Conversely, the Normal gene set was significantly enriched in healthy plasma cells relative to MM (p<0.05). Expression of CDKN2A, which encodes the senescence marker p16, was also increased in late-stage MM relative to NDMM or healthy plasma cells. Notably, in our newly generated scRNA-seq samples collected within 6 months before CAR-T, a significant inverse correlation between senescence marker expression and progression free survival (PFS) post-CAR-T was seen, suggesting that MM cells exhibiting TIS may be more resistant to T cell mediated cytotoxicity. We found that 62.5% (25/40) of pts had >1% MM cells that were CD14+. Among them, the median CD14+ MM cells were 5.5% (1-71%); and median CD14+CD16+ MM cells were 2.14% (0.4-67%). Among 7 pts with available samples for KLRG1 and GPNMB assessment, 100% (7/7) had KLRG+ MM cells (median: 59% (24-99%)) and 57% (4/7) had GPNMB+ MM cells (median: 15% (3.9-30%)), and KLRG1+GPNMB+ MM cells (median: 9.6% (3.8-17%)). Conclusion: Our study shows that late-line MM cells exhibit transcriptional and phenotype profiles consistent with TIS. This senescent phenotype may contribute to resistance to immunotherapies, including CAR-T. Additional phenotype data is collected and will be presented. A phase II clinical trial is ongoing (NCT06940297) to assess for the combined treatment with senolytic drugs dasatinib and quercetin peri-CART to deepen clinical response. Citation Format: Sofia Arbelaez, Andre De Menezes Silva Corraes, Gabriel Alvares Borges, Chen Wu, Christoph Schaefers, Malvika Gupta, Zuoyi Shao, Kevin Regan, Rayaan Kamal, Angelo Jose Guilatco, Neal I. Sannuli, Ying Li, Taxiarchis Kourelis, Wilson Gonsalves, Moritz Binder, Melinda Tan, Nadine Abdallah, Megan Weivoda, Yi Lin. Myeloma cells with therapy-induced senescence-like phenotype are associated with resistance to CAR-T therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 6467.

Abstract Background: Therapy-induced senescence (TIS), a tumor cell state associated with distinct gene expression patterns and implicated in therapy resistance, may be activated in Multiple Myeloma (MM) patients (pt) by genotoxic chemotherapies, such as high-dose melphalan (HDM). We investigated whether TIS is activated in MM cells following chemotherapy and linked to resistance to T-cell directed therapies. Method: We integrated scRNA-seq data from healthy donors (CNTRL) and MM pts (newly-diagnosed (NDMM), pre/post-CAR-T) bone marrow plasma cells (PCs) using two public datasets (Boiarsky, et al.; Dhodapkar, et al.) and a newly generated pt dataset. Senescence was evaluated using scGSEA with published gene sets (SenUp, SenGA, and SCAPs). For in vitro MM cytotoxicity assay, human H929 MM cells were treated with vehicle (Veh) or HDM (3µM, IC90) for 6h and examined for TIS features. H929VEH or H929HDM were co-cultured with CNTRL T cells with or without teclistamab (Tec, 10 nM) or with day 14 PBMCs from MM pts post-CAR-T (E:T 5:1). CAR+ T cells were high among the day 14 PBMC (median±SD: 93.4±7.2% of CD3+ cells, n=6). Results: scGSEA revealed significantly higher enrichment of SenUp, SenGA, and SCAPs in late-stage MM cells compared to NDMM and CNTRL (p<0.05). Expression of CDKN2A, encoding the senescence marker p16, was also increased in late-stage MM relative to NDMM/CNTRL PCs. Of interest, cells enriched for senescence also exhibited enrichment for myeloid markers (TET2, CEBP family). Notably, in pre-CAR-T, increased baseline senescence or myeloid marker expression showed an inverse association with CAR-T progression-free survival (PFS), suggesting TIS confers resistance to T cell mediated cytotoxicity. This may be due to myeloid plasticity. TIS MM cells generated in vitro, H929HDM, exhibited a distinct morphology characterized by increased cell size and enhanced cytoplasmic granularity. Increased CD14 expression was seen in 4 of the 6 experiments (median, range % increase: 11.2%, 2.5%-21%), consistent with myeloid plasticity; Increased KLRG1 expression was seen in 5 of the 7 experiments (median MFI ratio, range: 3.7, 1.3-471). No significant change was seen in GPNMB expression. Compared to H929VEH, H929HDM had a reduction in killing by CAR-T cells (median±SD: 22.8±32.94% n=6, p=0.016, Wilcoxon paired test). Similarly, H929HDM had a 10.69% decrease in Tec-mediated T-cell killing compared to H929VEH (n=8, p=0.02, Wilcoxon paired test). Conclusions: Late-stage MM cells exhibit transcriptional signatures consistent with TIS, which can be associated with decreased PFS to CAR-T. We report an in vitro model of TIS MM cells for testing of resistance to T cell killing. Based on these findings, a phase II trial (NCT06940297) is underway to test the hypothesis that peri-CAR-T senolytic therapy with dasatinib and quercetin will deepen responses by targeting this resistant TIS MM population. Citation Format: Christoph Schaefers, Angelo Jose Guilatco, Sofia Arbelaez, Gabriel Alvares Borges, Chen Wu, Andre de Menezes Silva Corraes, Malvika Gupta, Zuoyi Shao, Kevin Regan, Rayaan Kamal, Neal I. Sannuli, Ying Li, Taxiarchis Kourelis, Wilson Gonsalves, Melinda Tan, Nadine Abdallah, Yi Lin, Megan Weivoda. Myeloma cells with therapy-induced senescence-like phenotype have increased resistance to killing by T cell directed therapies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 6475.

Abstract Large Language Models (LLM) are being widely adopted into the medical field for their impressive ability to analyze and summarize large amounts of text data. These models enable clinicians and researchers to extract meaningful insights from complex datasets and may assist with decision making. Here we present our workflows and application of LLM for the interpretation and summarization of clinical data related to CAR-T cell therapy. Using an LLM (Gemini 2.5 pro) in the Google Cloud Computing (GCP) environment, two applications were developed to analyze CAR-T cell therapy clinical data: 1) extracting and summarizing CRS and ICANS event-related data to streamline the compliance team workflow, and 2) identifying features available at time of CAR-T infusion able to classify patients into high- or low-monitoring needs 14 days post CAR-T. Patient data (vitals, labs, hematology notes, and EKGs) were extracted from the electronic medical record (EMR) using Google BigQuery into SQL tables in GCP. For each application, relevant data fields were retrieved, formatted into JSON objects, and embedded in the LLM prompt for context-aware processing. Both applications have undergone iterative prompt engineering after analyzing the LLM output against ground truth data in the EMR. For the application extracting and summarizing CRS and ICANS events, the LLM was optimized on Mayo Clinic Rochester data. When compared to the IEC compliance database, the LLM achieved 100% accuracy and F1 score for CRS events, and 96% accuracy and 82% F1 score for ICANS events. The match rate for CRS and ICANS grades were 83% and 89% respectively. We applied the same LLM to Mayo Clinic Arizona (MCA) and Mayo Clinic Florida (MCF) cases, who document clinical notes and toxicity flowsheet differently, and achieved an accuracy (MCA: 93%, MCF:93%) and F1 score (MCA: 96%, MCF: 96%) for CRS and accuracy (MCA: 81%, MCF:82%) and F1 score (MCA: 76%, MCF: 75%) for ICANS. LLM was able to capture events missed by manual reviews. For most of the discrepancies where compliance team final adjudication is needed, LLM will be updated to flag discrepancies for review by the compliance team. For the application related to the monitoring needs 14 days post CAR-T, the LLM identified 5 categories predictive of high or low monitoring needs post CAR-T infusion (disease status, inflammatory and tumor burden markers, hematologic status, renal function and performance status). Our model achieved a sensitivity of 85.7%, specificity of 23.8%, and F1 score of 65.5% for our first cohort, compared with data extracted by the LLM from the EMR. For our second cohort, with demographics statistically similar to cohort 1 and using the same 5 categories, the LLM achieved a sensitivity of 83.3%, a specificity of 28.6%, and F1 score of 65.4%. Our workflow and applications of LLM’s provide examples and guidance to others interested in applying LLM’s for clinical and research applications. Citation Format: Emmanuel Contreras Guzman, Matthew Jankowski, Andre De Menezes Silva Corraes, Malvika Gupta, Monica L. Shaw, Madiha Iqbal, Talal Hilal, Saurabh Chhabra, Ricardo Daniel Parrondo, Jody K. Mclean, Kim R. Riester, Kayla Joseph, Melinda Tan, Holly Ross, Cleyonia Barnett, Sylvia Carter, Semy Girmay, Rachel Wolan, Milana Ramsey, Christian Downhour, Kristy Morgan, Shae Sibley, Erica Rushing, Lucy Holmes, Allison Burgstahler, Stephen M. Ansell, Hassan Alkhateeb, Matthew Hathcock, Ramona Bruno, Allison C. Rosenthal, Hemant Murthy, Patrick B. Johnston, Jonas Paludo, Yi Lin. Applications of large language models to CAR-T cell therapy clinical data using Google cloud computing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2740.

Abstract Introduction: Therapeutic outcomes in B-cell non-Hodgkin’s lymphoma (B-NHL) have substantially improved with the introduction of chimeric antigen receptor T-cell (CAR-T) therapy. Yet, persistent immune dysfunction and underlying metabolic dysregulation remain major obstacles to achieving durable clinical responses in B-NHL. We hypothesize that mitochondrial pathways play a critical role in mediating anti-tumor immunity during CAR-T therapy. Methods: Peripheral blood mononuclear cells were collected from healthy controls (CNTRL, n=5) and patients with advanced stage lymphoma (LYM) who received FDA approved CAR-T (n=32). Samples were collected before lymphodepletion (BL), at peak CAR-T expansion (PK) and one-month post-infusion (M1). Single-cell RNA sequencing was used to interrogate differential mitochondrial gene expression. Samples were obtained from LYM patients with complete remission for >6 months (CR), primary refractory (PD1), or relapsed (PD2) disease. Results: Compared to CNTRLs, LYM patients exhibited reduced expression of cytochrome c oxidase. At BL, CR patients demonstrated broader mitochondrial differential gene expression across all T-cell subsets relative to PD1 and PD2 and showed higher expression of MT-ATP-6, MT-ATP-8, and MT-CO3. Following CAR-T infusion, global mitochondrial gene expression increased across patients corresponding with expansion of CD8 effector T cells (Tem tumor circulating) and activated peripheral memory T cells (Tpm). Certain mitochondrial genes, such as MT-CO1, were durably expressed across LYM patients at BL, PK, and M1 in peripheral and central memory T cells (Tpm, Tcm). At PK, cytochrome c oxidase and ATP synthase genes were upregulated in CR Tpm and Tcm. At M1, MT-ATP-6, MT-ATP-8, MT-CO3 were significantly upregulated in CD8 memory precursor effector T-cells (MPECs) of CR patients compared to both PD1 and PD2. While CR and PD2 patients appeared clinically responsive at M1, CR samples maintained significantly higher MT-ATP-8 expression across T cell subsets (CD8 MPECS, Tpm, Tcm) compared to PD2 (p values 0.008; <0.0001; <0.0001). Conclusions: We report a temporal shift in immunometabolism with CAR-T therapy in LYM. Clinical responders exhibited sustained mitochondrial activity characterized by persistent expression of ATP synthase in CD8 MPECs and cytochrome c oxidase in Tpm and Tcm. In sum, mitochondrial programming may distinguish effective versus dysfunctional T cells and identify targets for therapeutic development. Citation Format: Jacqueline Turner, Panwen Wang, Patrizia Mondello, Melinda Tan, Christoph Schaefers, Chen Wu, Andre de Menezes Silva Corraes, Kevin Regan, Zuoyi Shao, Ma Audrey, Arushi Khurana, Nora N. Benanni, Yucai Wang, Paul Hampel, Saad J. Kenderian, Jonas Paludo, Urshila Durani, Patrick B. Johnston, Jose Caetano Villasboas, Stephen M. Ansell, Haidong Dong, Ying Li, Zeng Hu, Yi Lin. Mitochondrial transcriptome profiles associated with clinical responses to CAR-T therapy in aggressive lymphoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1213.