Guzman, Emmanuel Contreras

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.