Combination of Circulating Cell-Free DNA and Metabolic Tumor Burden to Distinguish Non-Small Cell Lung Cancer from Tuberculosis

Background: Non-small cell lung (NSCLC) holds high mortality owing to the difficulty to early detection from other lung mass, such as tuberculosis. This study evaluates the clinical value of the combination of circulating cell-free DNA (cfDNA) quantification and metabolic tumor burden to distinguish NSCLC from tuberculosis. Methods: A total of 149 NSCLC patients, 151 tuberculosis patients and 150 healthy controls were included. Quantifying serum cfDNA fragments from ALU (115 bp) gene by RT-PCR. Metabolic tumor burden (SUV-Maxa) values were detected by preoperative the 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET/CT). A549 cell, NCI–H460 cell, NSCLC and tuberculosis mice model were used to elucidate the specific mechanism. Results: Serum cfDNA levels and SUV-Maxa were higher in NSCLC patients than those in healthy controls and those in tuberculosis. Meanwhile, mice models showed the similar discovery. In addition, obvious correlations of cfDNA and metabolic tumor burden were only existed in NSCLC patients and mice model, rather than tuberculosis and control. Moreover, the combination of cfDNA and metabolic tumor burden displayed better effect to distinguish NSCLC from tuberculosis than alone use. Mechanistically, upregulated Glucose transporter 1 (GLU1) increased necroptosis-induce cfDNA rise by FasL/caspase 8/caspase 3 pathway and promoted metabolic tumor burden in NSCLC. Conclusions: The combination of cfDNA and metabolic tumor burden displayed better effect to distinguish NSCLC from tuberculosis, owing to upregulated GLU1 increased cfDNA levels by FasL/caspase 8/caspase 3 pathways and promoted metabolic tumor burden in NSCLC.

Prognostic Impact of Metabolic Tumor Burden in Large B-Cell Lymphoma Patients Receiving CAR T-Cell Therapy

Introduction Chimeric antigen receptor (CAR) T-cell therapy provides long-term remissions in a substantial proportion of patients with large B-cell lymphoma (LBCL) who have relapsed or are refractory (R/R) to chemoimmunotherapy. The identification of prognostic factors to identify which patients will benefit most from this therapy is crucial to improve patient selection. Even though metabolic tumor burden assessed by 18F-fluorodeoxyglucose Positron Emission Tomography (PET) has a confirmed prognostic value in the setting of chemoimmunotherapy, its predictive role after CAR T-cell therapy is not established. Methods We conducted a single-center study including all patients with R/R LBCL who received a single infusion of CD19-targeted second-generation CAR T-cells carrying a 4-1BB costimulatory domain from July 2018 to January 2020. Adverse events were graded according to the American Society for Transplantation and Cellular Therapy (ASTCT) consensus. All patients underwent a baseline PET scan and metabolic disease evaluation after infusion. Disease response assessment was conducted according to Lugano criteria. Metabolic tumor volume (MTV) and maximum standardized uptake value (SUVmax) were measured at baseline and at 1-month after CAR T-cell infusion, and correlated with disease response and development of adverse events. To identify the optimal cut-offs for metabolic parameters we used the maximally selected log-rank statistics in the PFS analysis. Results Thirty-five consecutive patients with R/R LBCL who received CAR T-cell therapy were included in the study. Patients' baseline characteristics are summarized in Table 1. Median age at treatment was 58 years, and 74% were males. At the time of diagnosis most of them had an advanced stage of disease (86%) and were refractory to the last therapy (n=31, 88%). Best response after CAR T-cell therapy included 9 (26%) patients in complete remission (CR) and 16 (46%) in partial remission (PR). Ten (28%) patients were in progressive disease (PD) at the 1-month disease evaluation. At a median follow-up of 7.6 months, median PFS and OS were 3.4 months and 8.2 months, respectively. Regarding toxicity, eleven (31%) patients developed clinically significant CAR T-cell related toxicity, defined as grade 2 or higher CRS (n=7, 20%) and grade 2 or higher ICANS (n=6, 17%). Median baseline MTV and MTV41% were 270 cm3(IQR 87-875) and 119 cm3 (IQR 32-300), respectively. Median SUVmax was 24 (IQR 17-32). Patients who responded (CR and PR) had lower baseline MTV values compared with non-responders (median of 228 cm3 vs 645 cm3, p=0.04) (Figure 1a). No association was found between MTV41% or SUVmax and disease response. In terms of PFS, a high baseline MTV (>82 cm3) was associated with a lower PFS compared to patients with lower MTV values (median PFS, 2.1 months vs. 6 months; HR 3.15, p= 0.02) (Table 2 and Figure 2). Patients with high baseline MTV41% values (>25 cm3) also had an inferior PFS (HR 3.44, p= 0.02); no association was found between baseline SUVmax and PFS (Table 2). As per OS, there was no significant association with baseline MTV, MTV41% and SUVmax (Table 2). Regarding toxicity, there was no significant association between baseline MTV, MTV 41% and SUVmax values with grade 2 or higher CRS and ICANS events (Figure 1b). All patients underwent a 1-month post-infusion PET evaluation. Disease response at this timepoint was: CR in 8 patients (23%), PR in 17 patients (49%) and PD in 10 patients (28%). For patients in CR and PR at 1-month the probability of PFS at 6 months was 62.5% and 12.7%, respectively (HR=3.89, p=0.02). For patients in PR at the 1-month evaluation, MTV values at that timepoint were predictive for PFS; patients in PR with low (<35 cm3) and high 1-month MTV values had a 6m-PFS of 33% and 0%, respectively (Figure 3)(HR=4.6, p = 0.01). In these patients, SUVmax values also predicted PFS and a trend towards significance was observed for MTV41% (HR=3, p=0.07). Conclusion Metabolic tumor burden parameters measured by 18FDG-PET before and 1-month after CAR-T cell infusion identify LBCL patients who benefit most from these therapies and could aid patient selection. Disclosures Iacoboni: Novartis, Gilead, Celgene, Roche: Honoraria. Villacampa:AstraZeneca: Other: advisory role; Merck Sharp & Dohme: Honoraria. Abrisqueta:AbbVie: Consultancy, Honoraria, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau; Celgene: Consultancy, Honoraria; Roche: Consultancy, Honoraria, Speakers Bureau. Bosch:Hoffmann-La Roche: Research Funding. Barba:Novartis, Celgene, Gilead, Pfizer, Amgen, Roche: Honoraria.

Combination of Circulating Cell-Free DNA and Metabolic Tumor Burden to Distinguish Non-Small Cell Lung Cancer from Tuberculosis

Background: Non-small cell lung (NSCLC) holds high mortality owing to the difficulty to early detection from other lung mass, such as tuberculosis. This study evaluates the clinical value of the combination of circulating cell-free DNA (cfDNA) quantification and metabolic tumor burden to distinguish NSCLC from tuberculosis. Methods: A total of 149 NSCLC patients, 151 tuberculosis patients and 150 healthy controls were included. Quantifying serum cfDNA fragments from ALU (115 bp) gene by RT-PCR. Metabolic tumor burden (SUV-Maxa) values were detected by preoperative the 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET/CT). A549 cell, NCI–H460 cell, NSCLC and tuberculosis mice model were used to elucidate the specific mechanism. Results: Serum cfDNA levels and SUV-Maxa were higher in NSCLC patients than those in healthy controls and those in tuberculosis. Meanwhile, mice models showed the similar discovery. In addition, obvious correlations of cfDNA and metabolic tumor burden were only existed in NSCLC patients and mice model, rather than tuberculosis and control. Moreover, the combination of cfDNA and metabolic tumor burden displayed better effect to distinguish NSCLC from tuberculosis than alone use. Mechanistically, upregulated Glucose transporter 1 (GLU1) increased necroptosis-induce cfDNA rise by FasL/caspase 8/caspase 3 pathway and promoted metabolic tumor burden in NSCLC. Conclusions: The combination of cfDNA and metabolic tumor burden displayed better effect to distinguish NSCLC from tuberculosis, owing to upregulated GLU1 increased cfDNA levels by FasL/caspase 8/caspase 3 pathways and promoted metabolic tumor burden in NSCLC.

A Fully Automated Measurement of Total Metabolic Tumor Burden in Diffuse Large B-Cell Lymphoma and Follicular Lymphoma

Introduction: Baseline total metabolic tumor volume (TMTV) from FDG-PET/CT scans has been shown to be prognostic for progression-free survival (PFS) in diffuse large B-cell lymphoma (DLBCL; Kostakoglu et al. Blood 2017) and follicular lymphoma (FL; Meignan et al. J Clin Oncol 2016). Fully automated TMTV measurements could increase reproducibility and enable results in real-time after a PET/CT scan. Although numerous methods for tumor segmentation on FDG PET images are published, they typically involve a manual step to identify a point within each tumor, performed by a trained reader, followed by semi-automatic identification of the tumor margins. To allow for rapid segmentation of whole body metabolic tumor burden, we developed a fully automated approach based on deep learning algorithms. Methods: An image processing pipeline was developed using FDG-PET/CT images from two large Phase III, multicenter trials, in first-line (1L) DLBCL (GOYA, NCT01287741, n=1418) and FL (GALLIUM, NCT01332968, n=1401). FDG-PET/CT scans were acquired according to a standardized imaging charter using a range of scanner models. Images were automatically preprocessed and used as inputs to cascaded 2D and region-specific 3D convolutional neural networks. The resulting tumor masks were then used for feature extraction. For simplicity, our prognostic analysis is limited to three variables: TMTV, number of identified lesions, and bulky disease (longest tumor diameter >7.5cm). For tumor segmentation, neural networks were trained on 2,266 scans from 1,133 patients in GOYA, and tested (out-of-sample) on 1,064 scans from 532 patients with evaluable baseline and end-of-treatment scans in GALLIUM. Manually directed, semi-automated tumor masks reviewed by board certified radiologists were used as ground truth for both training and testing. Based on the extracted tumor information, prognostic analyses for PFS were conducted on 1,139 evaluable pretreatment PET/CT scans from GOYA, and 541 patients from GALLIUM. Kaplan-Meier methodology was used for survival analysis, and a Cox proportional hazards (CPH) model was used for multivariate analysis. Results: From the out-of-sample validation step, the Dice Similarity Coefficient for the segmented tumor burden was 0.886, while the voxelwise sensitivity was 0.926. The lesion-level correlation between extracted and measured TMTV was 0.987. For PFS in the 1L DLBCL trial (GOYA), our calculated patient-level TMTV quartiles closely replicate the prognostic results of the semi-automated analysis reported by Kostakoglu et al. (Fig 1A, Table 1). A high lesion count above Q3 (>12 lesions [Fig 1B]) and bulky disease were also prognostic for PFS. To evaluate the prognostic value of the derived metrics, a simple risk score (RS) was constructed by considering the quantity: RS-DLBCL = 𝟙(TMTV >330ml) + 𝟙(nr. lesions ≥12) + 𝟙(bulky disease >1), where 𝟙(.) denotes the indicator function and 330ml is the median TMTV in GOYA. Multivariate CPH analysis verified the unique contribution of RS-DLBCL (p<0.0005) when added to the International Prognostic Index (IPI) score (p<0.01); derived from the multivariate model, the estimated HRs for RS-DLBCL are given in Table 2. In the 1L FL trial (GALLIUM), baseline TMTV >510mL was prognostic for PFS (HR, 1.59; p<0.013; Fig 1C). A high lesion count above Q3 (>18 lesions) and bulky disease (Fig 1D) were also prognostic. Three-year PFS for patients with TMTV <510mL was 85.1% (81.3-89.1%), while for TMTV >510mL, it was 77.3% (71.3-83.7%). A RS for 1L FL was defined similarly as for DLBCL: RS-FL = 𝟙(TMTV >510ml) + 𝟙(nr. lesions >18) + 𝟙(bulky disease). RS-FL (p<0.034) was significant when added to a CPH model with FLIPI (p<0.024). Estimated HRs for RS-FL after adjusting for FLIPI are given in Table 2. Conclusion: We present a novel approach for a fully automated whole body metabolic tumor burden segmentation on FDG-PET/CT scans for non-Hodgkin lymphoma patients. This method allows for the extraction of a range of tumor burden features from FDG-PET/CT. For example, TMTV, number of lesions, and bulky disease-features shown to be prognostic for PFS-in addition to known factors such as IPI/FLIPI. Our method is fast and produces a complete pt-level assessment in <5mins. Further development including clinical and biomarker covariates, and considering organ involvement, may yield better prognostic performance to identify pts who are likely to progress within 1-2 years. Disclosures Jemaa: Genentech, Inc./F. Hoffmann-La Roche Ltd: Employment. Fredrickson:Genentech, Inc.: Employment; F. Hoffmann-La Roche Ltd: Equity Ownership. Coimbra:Genentech, Inc.: Employment. Carano:Genentech, Inc.: Employment; F. Hoffmann-La Roche Ltd: Equity Ownership. El-Galaly:Takeda: Other: Travel support; Roche: Employment, Other: Travel support. Knapp:F. Hoffmann-La Roche Ltd: Employment. Nielsen:F. Hoffmann-La Roche Ltd: Employment, Equity Ownership. Sahin:F. Hoffmann-La Roche Ltd: Employment, Equity Ownership. Bengtsson:Genentech, Inc.: Employment; F. Hoffmann-La Roche Ltd: Equity Ownership. de Crespigny:Genentech, Inc.: Employment; F. Hoffmann-La Roche Ltd: Equity Ownership.