Ding, Li

The bone marrow immune microenvironment (BMME) is a critical factor in multiple myeloma (MM) pathogenesis. Despite higher incidence of MM among self-identified African Americans, the relationship between African genetic similarity and the BMME remains unclear. We present the largest analysis to date assessing the impact of Ancestry on the BMME in myeloma (N=320, including 64 with ≥50% African genetic similarity, AFR-High) leveraging our recently generated Immune Atlas. AFR-High was associated with enrichment of late T-effector cells, driven by rare cytotoxic CD4⁺ T cells, suggesting a distinct adaptive BMME. AFR-High exhibited expansion of late-differentiated adaptive NK cells with high cytotoxic GNLY and GZMH expression. AFR-High depicted reduced CD14⁺ monocytes, increased CD16⁺ non-classical monocytes, and expansion of macrophage-like monocyte populations with enrichment of metabolic adaptation, efferocytosis and phagocytic clearance programs, and immunoregulatory pathways. These findings reveal a shift from an innate inflammatory immune landscape toward adaptive cytotoxic and immunoregulatory remodeling associated with African genetic similarity in MM.

Abstract Multiple myeloma is a malignancy of clonally expanded plasma cells shaped by complex interactions with the immune microenvironment (IME). To investigate immune correlates of treatment response and disease progression, we conducted multi-omics profiling including CD138neg single-cell RNA sequencing of 243 bone marrow samples from 102 patients (631,226 cells) and CD138pos bulk RNA and whole-genome sequencing from 209 samples. In longitudinal analyses, interferon-γ signaling associated with markers of impaired T-cell memory after autologous stem cell transplant, whereas naïve B-cell abundance and immunoglobulin diversity correlated with improved progression-free survival (HR = 0.48; P = 2.3e−4). At disease progression, multiple myeloma cells upregulated cancer–testis antigens (CTAg) and immune effector genes, with concurrent B-cell depletion, enrichment of myeloid-derived suppressor cell expression, and phenotypic T-cell exhaustion. These findings highlight dynamic immune–tumor interactions, identifying naïve B-cell reconstitution as a biomarker of durable response and CTAgs as potential targets for high-risk disease at progression. Significance: Longitudinal profiling of multiple myeloma and the IME revealed dynamic immune–tumor interactions across the disease course. Altered expression in CD8+ T cells limited memory phenotype after transplant, whereas naïve B-cell recovery associated with sustained treatment response. At progression, CTAg expression associated with immunosuppression, revealing novel mechanisms of immune dysregulation.

Abstract Introduction Understanding the tumor immune microenvironment (TIME) in multiple myeloma (MM) is critical for identifying prognostic biomarkers and therapeutic targets. We hypothesized that specific immune cell populations within the bone marrow TIME are associated with clinical outcomes and could enhance patient stratification. To this end, we conducted the largest CyTOF-based immune profiling study to date in MM, profiling >300 million bone marrow immune cells from patients enrolled in the CoMMpass study. Methods We analyzed >1,000 bone marrow samples from ~700 newly diagnosed MM patients enrolled in the MMRF CoMMpass study (NCT01454297), using a harmonized protocol and 41-antibody CyTOF panel at five academic centers. The majority were baseline samples (n=671), with additional samples spanning up to 3.5 years post-diagnosis (n>300). Each site contributed ~200 samples on average. The cohort included 40% females, with a median age of 63 years and BMI of 27.8. ISS stages were evenly distributed (I: 36%, II: 36%, III: 29%), and 84% had ECOG 0-1. Autologous stem cell transplant was performed in 58% of patients, and 22% received maintenance therapy. Results We profiled 311 million single cells across the cohort, identifying major immune compartments: Neutrophils (36.0%), CD4 T cells (19.7%), NK cells (9.9%), monocytes (7.6%), B cells (5.7%), and CD8 T cells (5.6%). Less abundant populations included dendritic cells, γδ T cells, plasmablasts, and pDCs. In our preliminary analysis with multivariate cox regression modeling, we identified clinical factors including stem cell transplant, ISS stage, and ECOG status, as independent predictors of overall survival (OS). After adjusting for age and sex, higher proportions of naïve B cells, pDCs, and γδ T cells were independently associated with improved OS, while CD4+ T cell abundance was positively associated with prolonged progression-free survival (PFS). In contrast, elevated neutrophil proportions were significantly linked to inferior OS and demonstrated a trend toward shorter PFS. Longitudinal immune profiling in more than 200 patients identified a gradual accumulation of naïve B cells and a pronounced post-treatment rise in CD45+ plasmablasts, highlighting persistent B cell activity as a candidate mechanism for sustained therapeutic efficacy. Conclusion This study presents the largest immune proteomic atlas in MM to date, linking TIME composition to clinical outcomes. A favorable immune landscape was marked by preserved humoral immunity (naïve B cells, plasmablast expansion), enrichment of pDCs and γδ T cells, and lower neutrophil abundance, collectively associated with improved survival outcomes. These findings lay the groundwork for immune-guided prognostication and highlight new opportunities for therapeutic targeting in MM. Ongoing analyses aim to further resolve baseline and longitudinal immune dynamics in relation to treatment response.

Abstract Background: Engaging diverse populations in cancer genomics research is of critical importance and is a fundamental goal of the NCI Participant Engagement and Cancer Genome Sequencing (PE-CGS) Network. Established as part of the Cancer Moonshot, PE-CGS is a consortium of stakeholders including clinicians, scientists, genetic counselors, and representatives of potential study participants and their communities. Participant engagement is an ongoing, bidirectional, and mutually beneficial interaction between study participants and researchers. PE-CGS sought to set priorities in participant engagement for conducting the network's research. Methods: PE-CGS deliberatively engaged its stakeholders in the following four-phase process to set the network's research priorities in participant engagement: (i) a brainstorming exercise to elicit potential priorities; (ii) a 2-day virtual meeting to discuss priorities; (iii) recommendations from the PE-CGS External Advisory Panel to refine priorities; and (iv) a virtual meeting to set priorities. Results: Nearly 150 PE-CGS stakeholders engaged in the process. Five priorities were set: (i) tailor education and communication materials for participants throughout the research process; (ii) identify measures of participant engagement; (iii) identify optimal participant engagement strategies; (iv) understand cancer disparities in the context of cancer genomics research; and (v) personalize the return of genomics findings to participants. Conclusions: PE-CGS is pursuing these priorities to meaningfully engage diverse and underrepresented patients with cancer and posttreatment cancer survivors as participants in cancer genomics research and, subsequently, generate new discoveries. Impact: Data from PE-CGS will be shared with the broader scientific community in a manner consistent with participant informed consent and community agreement.

As part of the Multiple Myeloma Research Foundation (MMRF) immune atlas pilot project, we compared immune cells of multiple myeloma bone marrow samples from 18 patients assessed by single-cell RNA sequencing (scRNA-seq), mass cytometry (CyTOF), and cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) to understand the concordance of measurements among single-cell techniques. Cell type abundances are relatively consistent across the three approaches, while variations are observed in T cells, macrophages, and monocytes. Concordance and correlation analysis of cell type marker gene expression across different modalities highlighted the importance of choosing cell type marker genes best suited to particular modalities. By integrating data from these three assays, we found International Staging System stage 3 patients exhibited decreased CD4+ T/CD8+ T cells ratio. Moreover, we observed upregulation of RAC2 and PSMB9, in natural killer cells of fast progressors compared with those of nonprogressors, as revealed by both scRNA-seq and CITE-seq RNA measurement. This detailed examination of the immune microenvironment in multiple myeloma using multiple single-cell technologies revealed markers associated with multiple myeloma rapid progression which will be further characterized by the full-scale immune atlas project. Significance: scRNA-seq, CyTOF, and CITE-seq are increasingly used for evaluating cellular heterogeneity. Understanding their concordances is of great interest. To date, this study is the most comprehensive examination of the measurement of the immune microenvironment in multiple myeloma using the three techniques. Moreover, we identified markers predicted to be significantly associated with multiple myeloma rapid progression.

Abstract Introduction: Multiple myeloma (MM) is a complex hematological malignancy with the heterogenous immune bone marrow (BM) environment contributing to tumor growth, drug resistance, and immune escape. T-Cells play a critical role in the clearance of malignant plasma cells from the tumor environment. However, T-Cells in multiple myeloma demonstrate impaired cytotoxicity, proliferation, and cytokine production due to the activation of immune inhibitory receptors from ligands produced by the myeloma cells. In this study, we investigate the behavior of T-Cells in MM patients by using single-cell RNA-Seq (scRNA-Seq) to compare the transcriptomic profiles of BM T-Cells of patients with rapid progressing (FP; PFS < 18mo) and non-progressing (NP; PFS > 4yrs) disease. Methods: Newly diagnosed MM patients (n=18) from the Multiple Myeloma Research Foundation (MMRF) CoMMpass study (NCT01454297) were identified as either rapid progressors or non-progressors based on their progression free survival since diagnosis. To capture transcriptomic data, scRNA-Seq was performed on 48 aliquots of frozen CD138-negative BM cells at three medical centers/universities (Beth Israel Deaconess Medical Center, Boston, Washington University in St. Louis, and Mount Sinai School of Medicine, NYC). Samples were collected at diagnosis prior to treatment. Surface marker expression for 29 proteins was captured for at least one sample per patient using CITE-Seq. After integration and batch correction, clustering was performed to identify cells of T or NK lineage. Uniform Manifold Approximation and Projection (UMAP) and differential expression were used to identify T-Lymphoid subtypes, and differences in NP and FP samples. Results: In this study, single cell transcriptomic profiles were identified for ~102,207 cells from 48 samples of 18 MM patients. 40,328 T (CD3+) and NK (CD3-, NKG7+) cells were isolated, and subclustered for further analysis (Fig 1A). Using differentially expressed markers for each cluster, the T-Lymphoid subset was refined into seven subtypes, consisting of various CD4+ T-Cells, CD8+ T-Cells, and NK cells (Fig 1B). The CD8+ cells were divided into three distinct phenotypes, namely a GZMK-, GZMB- CD8+ T-Cell cluster, a GZMK+ CD8+ Exhausted T-Cell cluster enriched in TIGIT and multiple chemokines (CCL3, CCL4, XCL2), and a GZMB+ NkT cluster enriched in cytolytic markers (PRF1, GNLY, NKG7) (Fig 1C). Differential expression between NP and FP samples in this CD8+ subset showed enrichment of the NkT cytotoxic markers in NP samples, while FP samples were enriched in the CD8+ Exhausted chemokine markers (Fig 1D). Furthermore, the proportion of CD8+ Exhausted T-Cells was enriched in FP samples (p.val < 0.05) (Fig 1E). Exhaustion markers were measured through both RNA and surface marker levels. In RNA, TIGIT was uniquely associated with the FP-enriched CD8+ Exhausted T-Cell cluster, and CD160 was uniquely expressed in FP samples (Fig 1F). CITE-Seq surface marker expression confirms enrichment of both TIGIT and PD1 in the CD8+ Exhausted T-Cell cluster, and along with more exhaustion in FP samples (p.val < 0.01). Conclusion: In this study, we have identified significant differences in T-Cell activity in patients with non-progressing and rapid-progressing multiple myeloma. T-Cells in rapid progressing patients appear to be in a suppressed state, with low cytolytic activity and enriched exhaustion markers. This GZMK+ T-Cell population shows strong similarities with an aging-associated subtype of effector memory T-Cells found to be enriched in older populations (Mogilenko et al, Immunity 54, 2021). These findings will be further validated in an expanded study, consisting both of a larger number of samples, and multiple samples at different timepoints from the same patient. Figure 1 Figure 1. Disclosures Jayasinghe: MMRF: Consultancy; WUGEN: Consultancy. Vij: BMS: Research Funding; Takeda: Honoraria, Research Funding; Sanofi: Honoraria, Research Funding; BMS: Honoraria; GSK: Honoraria; Oncopeptides: Honoraria; Karyopharm: Honoraria; CareDx: Honoraria; Legend: Honoraria; Biegene: Honoraria; Adaptive: Honoraria; Harpoon: Honoraria. Kumar: Carsgen: Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Beigene: Consultancy; Bluebird Bio: Consultancy; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Tenebio: Research Funding; Oncopeptides: Consultancy; Antengene: Consultancy, Honoraria; Roche-Genentech: Consultancy, Research Funding; Merck: Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; Amgen: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding. Avigan: Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Research Funding; Kite Pharma: Consultancy, Research Funding; Juno: Membership on an entity's Board of Directors or advisory committees; Partner Tx: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Aviv MedTech Ltd: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Legend Biotech: Membership on an entity's Board of Directors or advisory committees; Chugai: Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy; Parexcel: Consultancy; Takeda: Consultancy; Sanofi: Consultancy.

Abstract Multiple Myeloma (MM) is the second most common hematologic malignancy, marked by uncontrolled clonal expansion of plasma cells. MM genome is complex and heterogeneous, with a high frequency of structural variants (SVs) and copy-number abnormalities (CNAs). Single-cell sequencing technologies offer advantages over traditional bulk methods in cancer genomics research for evaluating cellular heterogeneity and investigating evolution of cellular subpopulations within the tumor. Thus, there is an impetus to translate the growing understanding of the genomic landscape of MM into dissecting the tumor heterogeneity using scRNA-seq. So far, there are no reported studies systematically comparing tumor and immune populations differences between MM NON-progressors (NPs) and RAPID-progressor (RPs) and investigating how clonal plasma cells contribute to progression in a large cohort. In addition, the bone marrow microenvironment plays an important role in the evolution of premalignant MM and MM progression. A previous study of single-cell transcriptomics analysis of Monoclonal gammopathy of undetermined significance (MGUS) and MM tumor microenvironment (TME) revealed that natural killer (NK) cell abundance is frequently increased in the early stages and associated with altered chemokine receptor expression. This study shed the light on the role of immune cells on disease progression from asymptomatic MGUS to symptomatic MM. However to date, how immune cells influence disease progression within symptomatic MM is still unclear. Here, we subjected 344 CD138-negative Bone Marrow Mononuclear cells (BMMC) samples to scRNA-seq. To control for the first line therapy, 272 patients were treated with RVD and Autologous stem cell transplant (ASCT) and were stratified into 3 groups based on progression: 200 NON-progressors (NPs) (PFS> 5 years), 7 Intermediate Progressors (IPs, PFS 2-4 years) and 38 RAPID-progressors (RPs) (PFS<=18 months). In addition, there are 72 subjects treated with RVD but without ASCT, including 34 NPs, 2 IPs and 36 RPs. From MMRF CoMMpass study (NCT01454297), we also have whole exome sequencing (WES) and bulk RNA-seq from CD138-positive fraction of BMMC samples. Our preliminary analysis of scRNA-seq of 6 samples treated by the same first-line therapies (RVD but without ASCT) revealed that plasma cells (PCs) from two patients (MMRF2550 and MMRF2562) with chromosome 13q deletion clustered together, whereas PCs from a patient (MMRF2187) with t(8;14) Myc translocation formed a distinct cluster. Interestingly, PCs from a patient (MMRF2271) with both chromosome 13q deletion and t(8;14) formed another independent cluster. These observations highlight the important role of genetic drivers in transcriptome profiles of plasma cells. In addition, we further investigated how progression features correlate with the immune microenvironment and identified differentially expressed genes between NK cells from RPs versus those from NPs, which could potentially serve as MM progression markers. Interestingly, several cytotoxicity genes are significantly upregulated in FPs, such as GZMB and KLRB1 (log Fold Change=1.44, and 8.41 respectively). Overall, our preliminary results provide a small glimpse of the interconnected nature of driver genetic alterations and progression features in MM tumor and TME. This study will provide a sufficiently broad, deep, and diverse vast dataset for accurately characterizing MM at single-cell resolution to help interrogate how genetic alterations and disease progression interplay MM tumor and TME. We hope this study could identify novel candidate targets for therapeutic approaches, and ultimately stratify patients by risk of progression for early intervention in the clinic. Disclosures Kumar: Roche-Genentech: Consultancy, Research Funding; Merck: Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Novartis: Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Beigene: Consultancy; Carsgen: Research Funding; Bluebird Bio: Consultancy; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Consultancy; Tenebio: Research Funding; BMS: Consultancy, Research Funding; Antengene: Consultancy, Honoraria; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding. Oh: Abbvie: Membership on an entity's Board of Directors or advisory committees; Blueprint Medicines: Membership on an entity's Board of Directors or advisory committees; Celgene Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Constellation: Membership on an entity's Board of Directors or advisory committees; CTI Biopharma: Membership on an entity's Board of Directors or advisory committees; Disc Medicine: Membership on an entity's Board of Directors or advisory committees; Geron: Membership on an entity's Board of Directors or advisory committees; Incyte: Membership on an entity's Board of Directors or advisory committees; Kartos Therapeutics: Membership on an entity's Board of Directors or advisory committees; PharamaEssentia: Membership on an entity's Board of Directors or advisory committees; Sierra Oncology: Membership on an entity's Board of Directors or advisory committees. Vij: BMS: Research Funding; Takeda: Honoraria, Research Funding; Sanofi: Honoraria, Research Funding; BMS: Honoraria; GSK: Honoraria; Oncopeptides: Honoraria; Karyopharm: Honoraria; CareDx: Honoraria; Legend: Honoraria; Biegene: Honoraria; Adaptive: Honoraria; Harpoon: Honoraria. Avigan: Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Research Funding; Kite Pharma: Consultancy, Research Funding; Juno: Membership on an entity's Board of Directors or advisory committees; Partner Tx: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Aviv MedTech Ltd: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Legend Biotech: Membership on an entity's Board of Directors or advisory committees; Chugai: Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy; Parexcel: Consultancy; Takeda: Consultancy; Sanofi: Consultancy.

The application of novel single-cell technologies provides personalized profiles on patient samples to improve standard of healthcare and evolve the field of precision medicine. In the field of Immuno-oncology, application of single cell RNA-seq (scRNA-seq) to evaluate gene expression profiles has provided new insights on the cancer subpopulation dynamic, network reconstruction, and cell trajectory inferences. Here we present a standardized and validated scRNA-seq analytical workflow applied to a series of common cryopreserved Multiple Myeloma (MM) bone marrow samples obtained from subjects enrolled in the Multiple Myeloma Research Foundation (MMRF) CoMMpass study (NCT01454297). scRNA-seq data was generated from common samples at three different academic research centers. While the high variability of scRNA-seq data raises computational challenges in data analysis, we have tested different quality control, alignment, batch correction, clustering and annotation methods to prove that the results are consistent for subsets of cells, independent of the center generating the data. To evaluate differences in cell type composition and cross-center differences, an in-depth analysis was performed on four CD138- sorted samples (>18,000 total cells) that were subject to scRNA-sequencing at 3 different centers. We tested three different batch correction methods including: Harmony, Seurat Merge and Seurat Anchor. Immune cell types (CD4 T, CD8 T, NK, Monocytes, Macrophages and pDCs), exhibited similarities in clustering structure and shared complementary differentially expressed genes while Plasma and B cells exhibited distinct center variation when using Seurat Merge. The evaluation of batch effects across samples generated at 3 centers identified subtle batch effects without significant impact of cellular clusterings. For data concordance analyses, Seurat Merge was used for batch effect correction and data normalization and a beta-variational autoencoder and random forest classifier was utilized to assign cell types. In comparing the proportion of each cell type identified across centers using dead cell removal bead depletion, the overall population of plasma and B cells were found to be dissimilar for a subset of samples which could be attributed to differences in bone marrow aliquot sampling. We further explored how the application of dead cell removal altered the distribution of tumor and immune populations for the same sample of interest. Samples that underwent dead cell removal have consistently higher NK and CD8+T cell proportion, while CD4+T is higher more often in non-treated samples. In summary, dead cell removal ultimately improved the overall data quality (more viable cells) without significantly altering the gene expression signatures or proportions of each cell type. Further comparative analysis of transcriptome signatures of various cell types (e.g., B cells, T cells, Plasma cells) across 3 centers depicted significant similarity in transcriptome profile. This suggests even if three centers captured different numbers of various cell types due to variation in protocols and aliquots, the cells still have significantly similar transcriptome profiles that might be helpful in producing the same biological results across three sites. Our in-depth cross-institutional assessment of tumor and immune cell types in MM will provide a valuable resource and thorough analysis strategy to the broader scientific community. Disclosures Dhodapkar: Celgene/BMS: Membership on an entity's Board of Directors or advisory committees, Other; Janssen: Membership on an entity's Board of Directors or advisory committees, Other; Kite: Membership on an entity's Board of Directors or advisory committees, Other; Lava Therapeutics: Membership on an entity's Board of Directors or advisory committees, Other; Roche/Genentech: Membership on an entity's Board of Directors or advisory committees, Other; Amgen: Membership on an entity's Board of Directors or advisory committees, Other.

e20626 Background: It is important to increase our understanding of recruitment barriers in the elderly population to improve their participation in clinical trials. The primary objective of this study was to describe the demographics, treatment, toxicity, and overall survival (OS) of all patients ≥ 65 years who presented to the Phase I clinical trials service at KCI between 1995–2005. Methods: Patient demographics, co-morbidities, tumor type, reason not enrolled on a Phase 1 clinical trial, toxicities, and OS were obtained by retrospective chart reviews. The patients were divided into 3 mutually exclusive groups: patients considered but not enrolled (PC), enrolled but not treated (PE), and treated (PT). OS was compared across these three groups. Results: 216 patients met the study criteria. Of these, there were 114 PC, 7 PE, and 95 PT. Lack of enrollment was due to ineligibility (30%), loss to follow-up (12%), refusal (8%), or no trial available (2%). Median age was 71 years, 63% were males and 87% were Caucasian. 59% had performance status (PS) 1; 16% had PS 2. 66% of patients had a history of cardiovascular disease but renal, liver, hematological diseases were found in less than 7% of patients at baseline. Colorectal (27%), lung (15%) and prostate (8%) were the 3 most common cancers. 54% received a cytotoxic agent, 62% a biologic agent, and 17% combination therapy. The 3 most common Grade 3–4 toxicities were: 15% leucopenia, 11% electrolyte abnormalities, and 4% anemia. The median OS for PC, PE, and PT was 3.9 mos, 2.2 mos, and 8.4 mos, respectively (p < 0.001 between any pair). However, PS 0–1 patients comprised 47%, 75%, and 73% of those 3 patient subgroups, respectively. Conclusions: More than half of the elderly patients initially considered for Phase 1 clinical trials at KCI were not enrolled due to ineligibility, refusal, or lost to follow up. Phase I chemotherapy appears feasible in patients age ≥ 65 years old. OS was significantly longer for patients treated on Phase I trials compared to those who were not, although that may be confounded by PS differences. More detailed OS comparisons will be presented. No significant financial relationships to disclose.

Abstract Pancreatic cancer is the fourth leading cause of cancer related deaths in the United States and continues to be hindered with limited treatment options. One of the current therapeutic regimens for pancreatic cancer often include the nucleoside analog gemcitabine (Gem), while it has been used for many years, the overall survival rate lingers around 6%. New therapies are needed to treat this disease. Fatty acid synthase (FASN) has received increased attention as a potential target for cancer therapy in the last decade. FASN is the key enzyme in the de novo synthesis of palmitate from malonyl-CoA. Palmitate is the precursor other long-chain fatty acids (FA). However, the precise mechanism by which pharmacological interference with endogenous FA biogenesis might facilitate apoptosis in tumor cells remains unresolved. We herein explored the molecular relationship between FASN activity, oxidative stress/redox balance, and the intrinsic apoptotic pathway in FASN overexpressing-dependent pancreatic cancer cells. About 68% of Pancreatic Ductal Carcinomas (PDA) expresses high levels of Fatty acid synthase (FASN), and its expression is correlated with disease free survival. A newly developed FASN inhibitor with excellent pharmaceutical properties was used for our studies TVB-3166 (in vitro) and TVB-3664 (in vivo) and ten different PDX derived cells were used for the studies. Pharmacological blockade of FASN activity significantly increased the NADPH/NADP+ ratio, promoted the production of reactive oxygen species (ROS). Accordingly, the inhibition of endogenous FA synthesis promoted mitochondrial membrane permeabilization, cytochrome c release, and apoptotic cell death. Each step of the FASN inhibition/BH3-only protein axis could be reversed with a ROS scavenger or the FASN end-product palmitate, thus confirming that the delineated pro-apoptotic cascade likely reflects on-target effects of FASN inhibition. Most importantly, the in vivo studies using PaCa-PDX reveal a significant decrease in tumor growth and furthermore a synergistic effect of between TVB-3664 and Gem. In conclusion, our findings uncover a novel FASN-dependent mitochondrial priming that links de novo FA biosynthesis with the intrinsic apoptotic threshold in cancer cells. The discovery that FASN-inhibited cancer cells exist in an apoptosis-prone state highly sensitive to BH3 mimetics warrants clinical exploration in FASN-overexpressing pancreatic cancer patients. Citation Format: Travis Vander Steen, Li Ding, Yu Zhang, George Kemble, Daniel Billadeau, Ruth Lupu. Fatty acid synthase: A new therapeutic target for pancreatic cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3723.