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Introduction The tumor ecosystem in multiple myeloma (MM) involves complex interactions among various cell types, including clonal plasma cells (PCs) and immune cells. Previous research has indicated that the bone marrow (BM) microenvironment's metabolite and lipid profiles differ between MM patients and those with precursor conditions. This study aims to explore if differences in cell composition, metabolic phenotype of clonal PCs, and the spatial organization of various cell types are linked to the early versus late progression of high-risk smoldering MM (SMM) to MM. Methods We identified high-risk SMM patients using the IMWG 20-2-20 criteria, focusing on early progressors (EP), who progressed to MM within one year, and later progressors (LP), who progressed after more than four years. Using a high-dimensional single-cell spatial imaging platform, CODEX (CO-Detection by indEXing), we identified clonal PCs and immune cell types as well as their metabolic phenotypes at the single-cell level, in paired FFPE BM samples at SMM diagnosis and at the time of progression to MM. Our validated panel included markers such as CD3e, CD4, CD8, CD14, CD20, CD31, CD38, CD45, CD66a, CD68, CD138, HLA-DR, ASCT2, pNRF2, GLUT1, LDHA, ATPA5, CPT1A, and Citrate synthase. We employed single-cell segmentation, artifact cleanup, signal thresholding, and spatial analysis. Repeated measure models accommodated multiple regions of interest (ROIs) per patient and accounted for potential intrapatient correlation, while Poisson models estimated incidence rate ratios (RR) of cell types. Spatial statistical models evaluated cell type segregation and integration at the time of SMM diagnosis. Results Single cell events were acquired from 6 unique SMM patients who had paired FFPE BM samples from the time of their SMM diagnosis and at the time of their progression to MM. There were 3 EP SMM patients (Time to Progression (TTP):8, 8, 10 mos) and 3 LP SMM patients (TTP: 51, 58, 117 mos). The median number of single cell events identified per patient slide sample was 104,390 (Range: 24,357 - 314,877). We identified 8 main cell types: PCs, CD4+ T-cells, CD8+ T-cells, B-cells, granulocytes, Monocyte/Macrophages and two groups of other immune cells. The PCs and the remainder of the non-PCs were further classified based on the expression of the following metabolic markers: ASCT2 (Glutamine importer), CPT1A (Fatty acid metabolism), GLUT1 (Glucose importer), LDHA (Glycolysis), Citrate synthase and ATPA5 (Oxidative Phosphorylation). There was a higher RR of granulocytes (RR 2.1, CI 1.6-2.8, p<0.001) in EP compared to LP samples at the time of SMM diagnosis, but a lower RR of CD4 T cells (RR 0.40, CI 0.3-0.6, p< 0.001). Focusing on metabolic phenotypes of the PCs, we observed higher incidence RR of most metabolic markers: ASCT2 (RR=1.77; p=0.032), ATPA5 (RR=1.25; p<0.001), CPT1a (RR=2.42; p=0.015), and LDHA (RR 2.13; p<0.001) in EP compared to LP samples at the time of SMM diagnosis. However, GLUT1 had a lower incidence RR in EP patients (RR=0.79; p<0.001). The same modeling for non-PCs at the time of SMM diagnosis yielded higher expression of LDHA (RR=1.98; p<0.001) and ASCT2 (RR=3.3; p=0.002). Using the x and y coordinates and the corresponding phenotype classifications, we performed spatial statistical modeling based on spatial point processes to generate aggregated measures of how different types of cells of interest interacted. CD4 T cells were more isolated in LPs compared to EPs (p =0.046). Segregation indices for PCs and CD4 T cells indicated that they were more integrated/collocated in EPs. Granulocytes were more isolated in EPs although the difference was not as pronounced (p= 0.07). PCs were more integrated with granulocytes (p=0.06) and B cells (p=0.07) in LP patients. Further analyses of paired samples at the time of progression to MM will be reported at the time of the presentation. Conclusion This study is the first to provide whole-slide, single-cell, spatial proteomic characterization of clonal PCs and immune cells in the FFPE BMs of high-risk SMM patients with varying progression times to MM. Distinct metabolic pathway markers in clonal PCs and their spatial relationships with immune cell subsets are closely associated with the risk of progression to MM. These findings illuminate the complex interactions between the metabolic states of clonal PCs and their spatial orientation relative to immune cells in the pathogenesis of MM.

Introduction: In myeloma, accurate risk stratification remains a challenge due to substantial biological heterogeneity and highly variable survival outcomes. Cross-sectional imaging is commonly obtained at the time of diagnosis, but imaging findings are not currently incorporated into risk stratification efforts in a standardized manner, despite their prognostic significance. Here, we investigate the use of novel image-based prognostic biomarkers obtained by automated image segmentation and radiomics feature extraction using machine learning methods. Methods: We obtained the pre-treatment 18F-FDG PET/CT scans of 443 patients with newly diagnosed myeloma and complete demographic and cytogenetic data (seen at Mayo Clinic between 2005 and 2019). The native DICOM images were pre-processed and subjected to automated image segmentation using nnU-Net (v1.0, inference using a pre-trained hematologic malignancies model). Limitations of this automated segmentation approach include heterogeneity introduced by varying image quality, technical differences related to PET reconstruction, and potential inclusion of non-neoplastic FDG-avid lesions in segmented regions. The quality of the automated image segmentation was evaluated by comparing the radiomics features obtained from ground-truth segmentation (manual lesion segmentation) and model segmentation in a separate cohort of patients with hematologic malignancies (n=72). We selected 42 radiomics features that moderately (r≥0.30, p<0.005, n=23) or strongly (r≥0.50, p<0.001, n=19) correlated between ground-truth (manual) and model (automated) segmentation for further evaluation in myeloma patients. PyRadiomics was used to extract discrete radiomics features for clinical prediction modeling. We evaluated the prognostic significance of tumor size-, shape-, and texture-related radiomics features and compared their predictive power to established prognostic factors (International Staging System [ISS], Revised ISS [R-ISS] stage and high-risk FISH cytogenetics). The outcome of interest was overall survival and the predictive power of competing proportional hazards regression models was assessed using Harrell's C-statistic. For modeling purposes, patients without measurable tumor volume were categorized as having undetectable radiomics features (0 for all features). All radiomics features were median dichotomized for downstream modeling. Forward feature selection and testing for multicollinearity were used to remove redundant features. Select radiomics features were divided into quintiles to generate an additive radiomics risk score and associated risk strata. Results: Median age at diagnosis of the 443 myeloma patients was 63 years (32-91) and 270 patients (62%) were men. After a median follow-up of 2.4 years (95% CI 0.1-7.4), median overall survival was 6.5 years (95% CI 5.5-NR). Most patients had detectable metabolic tumor volume (n=309, 70%) on their pre-treatment 18F-FDG PET/CT scan. Several of the 42 radiomics features were intercorrelated and two features were selected for further investigation based on their independent prognostic significance (forward feature selection). Higher maximum axial tumor diameter (HR 1.90, 95% CI 1.31-2.76, p=0.001) and a lower concentration of low gray-level values in the tumor (HR 1.73, 95% CI 1.20-2.49, p=0.004) were independently associated with overall survival in newly diagnosed myeloma. These associations were independent of age at diagnosis, sex, and R-ISS stage. We devised a simple additive risk score representing the sum of quintiles of the two radiomics features and divided the patients into three risk categories akin to the R-ISS. The predictive power of this radiomics risk score (Harrell's C=0.60) was comparable to the ISS (C=0.59) and the R-ISS (C=0.57). Conclusions: Automated image segmentation and radiomics feature extraction from 18F-FDG PET/CT scans is feasible. This approach yields image-based biomarkers that correlate with results obtained by manual imaging segmentation. Several radiomics features such as tumor size and heterogeneity have independent prognostic significance in newly diagnosed myeloma. These novel biomarkers rival the predictive power of the currently established risk stratification methods and may be used to improve risk stratification in myeloma.

CD19-targeted chimeric antigen receptor T (CART19) cell therapy has shown outstanding clinical success. However, patients can develop CART-induced toxicities, including cytokine release syndrome (CRS) and neurotoxicity. Studies have found that activation of myeloid cells by granulocyte monocyte-colony stimulating factor (GM-CSF) can contribute to toxicity development. GM-CSF depletion leads to reduced myeloid activation, prevention of CART-induced toxicities, and improved CART efficacy. CART cells upregulate cytokine receptors upon activation, and our results indicate that CART19 cells significantly upregulate GMCSFR upon antigen-specific stimulation (<10% vs >30%, p<0.01). This led us to hypothesize that GMCSFR signaling contributes to CART cell activity. To understand the role of GM-CSFR upregulation on CART cell functions, we compared the impacts of neutralizing GM-CSF or blocking GM-CSF receptor alpha (GM-CSFRα) on CART19. First, 4-1BB-costimulated CART19 cells generated from healthy donors were stimulated with CD3/CD28 beads at a 1:3 ratio for 6 days. CART cells were maintained at 1 x 106/mL throughout the culture and supplemented with IgG isotype control, GM-CSF neutralizing antibody, or GM-CSFRα blockade at 10 μg/mL, 20 μg/mL, or 100 μg/mL. On day 6, cells were debeaded and rested for 2 days. Then, CART cells were cocultured with NALM6, a CD19+ acute lymphoblastic leukemia cell line, at a ratio of 1:1 for 3 days. The cocultures were supplemented with the same doses of antibodies listed above. Antigen-specific proliferation of CART cells was measured by flow cytometry. Our data suggested that CART19 proliferated is not impacted by GM-CSF neutralization but is inhibited by GM-CSFRα blockade (IgG isotype vs 10 μg/mL GM-CSFRα blockade, p<0.0001; IgG isotype vs 20 μg/mL GM-CSFRα blockade, p<0.0001; IgG isotype vs 100 μg/mL GM-CSFRα blockade, p=0.009). CART cells were assessed in a cytotoxicity assay with NALM6 luciferase+ cells as well, which did not show significant impacts from GM-CSF neutralization or GM-CSFRα blockade. To evaluate the impacts of GM-CSF neutralization and GM-CSFRα blockade in a mouse model, we engrafted NSG mice with 1 million NALM6 luciferase+ cells intravenously. Mice were then treated with 1.4 million CART19 plus 10 mg/kg IgG isotype control, GM-CSF neutralizing antibody, or GM-CSFRα blockade intraperitoneally. GM-CSFRα blockade drastically suppressed antitumor activities of CART (CART19+GM-CSFRα blockade vs CART19+IgG isotype control, p<0.0001; CART19+GM-CSFRα blockade vs CART19+GM-CSF neutralizing antibody, p<0.0001; CART19+ GM-CSFRα blockade vs untransduced T cell, n.s.). Serum cytokines were analyzed 2 days after CART treatment, showing that mice with GM-CSFRα blockade had significantly lower levels of human IFNγ (p=0.0189) and IL-2 (p=0.0121) compared to IgG isotype control group, while GM-CSF neutralization did not show significant impacts. Mice treated with CART plus IgG isotype control or GM-CSF neutralizing antibody showed prolonged survival compared to mice treated with GM-CSFRα blockade or untransduced T cells (p=0.0285). To unravel the mechanisms behind functional differences between GM-CSFRα blockade and GM-CSF neutralization, CART cells (n = 3 biological replicates) were cocultured with NALM6 at a 1:1 ratio supplemented with 10 μg/mL GM-CSF neutralizing antibody, GM-CSFRα blockade, or IgG isotype control overnight, followed by T cell isolation, bulk RNA isolation, and RNA sequencing (RNAseq) analysis. Our RNAseq data indicated significant upregulation (Log2FC>0.5, padj. <0.05) of T cell exhaustion- and dysfunction-associated genes (RGS16, PDCD1, CTLA4, NR4A3, CD101, LAG3) as well as negative regulators of T cell functions (G0S2, PIK3IP1, LGALS3) in the GM-CSFRα blockade group compared to GM-CSF neutralizing antibody or IgG isotype control groups. T cell activation-associated pathways were enriched in the GM-CSFRα blockade group. Overall, our data show that GM-CSFRα blockade impacts CART functions differently from GM-CSF neutralization. Specifically, GM-CSFRα blockade inhibits CART proliferation in vitro and antitumor activity in vivo. Interestingly, GM-CSFRα blockade leads to enrichment of T cell exhaustion- and dysfunction-associated genes in CART19, which is worth further investigation. These findings may be applicable to other cytokine receptors upregulated on activated CART cells.

BACKGROUND: Packed red blood cell (pRBC) transfusions in patients undergoing surgery for cancer are given to treat anemia or acute hemorrhage. Evidence indicates that pRBC transfusions are associated with poor perioperative and oncological outcomes. The ARCA-1 (Perioperative Care in the Cancer Patient-1) study was designed to test the association between perioperative pRBC transfusions and postoperative morbidity and mortality in patients undergoing cancer surgery. The primary hypothesis of our study was that perioperative pRBC transfusions have a negative impact on postoperative morbidity and 1-year mortality. METHODS: ARCA-1 was an international multicenter prospective observational cohort study. Participating centers enrolled a minimum of 30 consecutive adult patients with cancer who underwent surgery with curative intent. The primary end point was all-cause mortality 1 year after major cancer surgery. Secondary end points were rates of perioperative blood product use, 1-year cancer-specific mortality, overall survival, and 30-day morbidity and mortality. We performed a propensity score matching analysis to adjust for selection bias. A multivariable logistic regression model was fitted to estimate the effects of significant covariates on 1-year mortality, cancer-related mortality, and overall survival. RESULTS: A total of 1079 patients were included in the study. The rate of perioperative pRBC transfusions was 21.1%. Preoperative comorbidities, including anemia, American Society of Anesthesiologists (ASA) score of III to IV, a history of coronavirus disease 2019 (COVID-19), myocardial infarction, stroke, need for dialysis, history of blood transfusions, and metastatic disease were statistically significantly more frequent in transfused patients compared to nontransfused patients. The 1-year mortality rate was higher in transfused patients before (19.7% vs 6.5%; P < .0001) and after (17.4% vs 13.2%; P = .29) propensity score matching. 1-year mortality was 1.97 times higher in transfused than in no-transfused patients (odd ratio [OR], 1.97; 95% confidence interval [CI], 1.13–3.41). The odds of 1-year cancer mortality for patients who had perioperative pRBCs was 1.82 times higher (OR, 1.82; 95% CI, 0.97–3.43) compared to those who did not receive perioperative pRBC transfusion. The effect of perioperative pRBC transfusion on overall survival was also significant (hazard ratio [HR], 1.85; 95% CI, 1.15–2.99). Transfused patients also had a higher rate of 30-day postoperative mortality before (3.5% vs 0.7%; P = .0009) and after propensity score matching (4.2% vs 1.8%; P = .34). CONCLUSIONS: This international, multicenter observational study showed that perioperative pRBC transfusion was associated with an increased mortality risk.

Chronic pain affects over 50 million people in the United States, particularly older adults, making effective assessment and treatment essential in primary care. Actigraphy, which monitors and records limb movement to estimate wakefulness and sleep, has emerged as a valuable tool for assessing pain by providing insights into activity patterns. This review highlights the non-invasive, cost-effective nature of actigraphy in pain monitoring, along with its ability to offer continuous, detailed data on patient movement. However, actigraphy’s reliance on physical activity as a proxy for pain, and its inability to directly measure pain intensity, limit its applicability to certain pain types, such as neuropathic pain. Further research is needed to overcome these limitations and to improve the effectiveness of actigraphy in diverse clinical settings.

Abstract Background Breast cancer is one of the most common types of cancer, with around 2.3 million cases diagnosed in 2020. One in five cancer patients develops chronic lymphedema caused by multifactorial triggers and treatment-related factors. This can lead to swelling, skin infections, and limb dysfunction, negatively affecting the patient's quality of life. This retrospective cohort study aimed to determine the associations between demographic and breast cancer characteristics and postoperative cellulitis in breast cancer survivors who underwent lymphovenous bypass surgery (LVB) at Mayo Clinic, Florida. Methods We performed a retrospective chart review. Data were collected retrospectively from 2016 to 2022. Sixty adult breast cancer survivors who underwent LVB were included in the final analysis based on specific inclusion and exclusion criteria. Patients were excluded if they did not meet the inclusion criteria or had incomplete follow-up data. Demographic and surgical data were extracted, including body mass index (BMI), type of anastomosis, number of anastomoses, and preoperative cellulitis status. Lymphedema measurements were performed using tape measurements. Fisher's exact test was used to determine statistically significant associations between variables and postoperative cellulitis. Results Postoperative cellulitis was more common in patients aged 60 to 69 years (43.2%), whites (75.0%), overweight or obese (90.9%), with one to four anastomoses (81.8%), and nonsmokers (79.5%). The mean International Society of Lymphology (ISL) criteria for both postoperative cellulitis and no postoperative cellulitis was 1.93. Statistically significant associations with postoperative cellulitis were found for the number of anastomoses (p = 0.021), smoking status (p = 0.049), preoperative cellulitis (p = 0.04), and the length of years with lymphedema diagnosis variable (p = 0.004). Conclusion Our results suggest that a greater number of anastomoses, smoking, preoperative cellulitis, and years with lymphedema are significantly associated with an increased risk of postoperative cellulitis. Awareness of these risk factors is crucial for monitoring and early treatment of infections following surgery.

2531 Background: The immune co-stimulatory receptor OX40 is expressed on CD4+ T cells, including regulatory T cells (Tregs). First generation OX40 agonists faced limitations from interference with endogenous OX40-OX40L binding, leading to decreased surface OX40 levels on T cells. Distinguished by its unique epitope on OX40, the novel next generation IgG1 monoclonal antibody (mAb) HFB301001 facilitates agonistic activity without impeding endogenous OX40 ligand binding. This allows for effective co-stimulation of T cells without diminishing OX40 levels. Additionally, HFB301001 exhibited superior in vivoanti-tumor activity and ability to enhance effector CD8+ T cell function compared to a benchmark first generation OX40 agonist in preclinical studies. Here, we report initial data of an ongoing dose-escalation study (NCT05229601) of HFB301001 in patients (pts) with advanced solid tumors. Methods: A Phase I trial designed to evaluate safety, pharmacokinetics (PK), pharmacodynamics (PD), and preliminary efficacy of HFB301001. Eligible pts ≥18 years old (ECOG PS 0-1) with advanced solid tumors are enrolled in 4 different dose cohorts in monotherapy (Q4W) dose escalation. Solid tumor types were selected using Drug Intelligent Science (DIS®), employing integrative bulk and single cell RNAseq (scRNAseq) analyses to confirm and prioritize prospective targets in specific immune cell types. Sequential analysis of peripheral blood and tumor tissue biomarkers is performed. Radiographic response is assessed every 8 weeks. Results: As of January 06, 2024, 26 pts were enrolled, all having received prior systemic therapy (median 2, range 1-4); 81% received prior checkpoint inhibitor treatment. The majority of pts (77%) were male, median age was 62.5 years (range 41-78). Treatment-related adverse events (TRAEs) occurred in 34.6% of pts, none ≥ grade 3. Common TRAEs included rash (19%), pruritis (12%) and arthralgia (8%). No dose-limiting toxicities were observed. Preliminary results demonstrate dose-proportional increases in maximum concentration, typical mAb volume of distribution, and nonlinear clearance. On treatment biopsies revealed expansion of both natural killer and cytotoxic CD8+ T cells, with no notable expansion of Tregs. Analysis of peripheral blood by scRNAseq and immunophenotyping revealed expansion of cytotoxic CD8+T cells relative to CD4+ T cells post treatment. Preliminary efficacy analysis indicates a disease-control rate (DCR) of > 60% with greatest effect seen in hepato-cellular and renal cell carcinoma. Median time on treatment was 1.9 months (range 0.9-17 months). Conclusions: HFB301001 demonstrates a favorable safety profile, dose-dependent PK and PD immunomodulation, and DCR in heavily pretreated solid tumors. HFB301001 warrants further evaluation and expansion in specific tumor types is planned. Clinical trial information: NCT05229601 .