Bock, Allison M.

Background Diffuse large B cell lymphoma (DLBCL) is the most common aggressive lymphoid malignancy in adults. Though standard immunochemotherapy regimens can result in clinical remission and cure in a majority of patients, approximately 30% of patients are primary treatment resistant or eventually relapse (rrDLBCL), and their prognosis is very poor. Recent molecular classification by large scale genomic and transcriptomic profiling of newly diagnosed DLBCL tumors has resulted in a deeper understanding of disease drivers, however, the molecular heterogeneity of rrDLBCL is yet to be fully explored. Therefore, in this study, we used an unsupervised approach to better define transcriptional and genetic programs in rrDLBCL samples hypothesizing that molecular stratification within this high-risk group may lead to a better understanding of this disease, which remains an unmet need, and a more personalized therapeutic approach. Methods Data used in this study included RNA (n=143) and whole exome (n=126) sequencing data from available FFPE tumor samples at the time of a relapse (any line of treatment, r1-r10 relapse timepoints included in analysis, one per patient), consented to the Molecular Epidemiology Resource (n=61), banked in the Mayo Lymphoma Biobank (n=50), or consented to the CC-122-ST-001 clinical trial (n=32, NCT01421524). Unsupervised clustering was performed on protein coding gene expression values using the non-negative matrix factorization (NMF) approach. NMF was carried out by performing 200 runs on cluster sizes from 2 to 6. Cell of origin (COO) was determined using the method described by Reddy et al. The tumor microenvironment was analyzed using CIBERSORTx and Lymphoma Microenvironment Classification (LME). Genetic classification was done using LymphGen. Results In order to identify unique transcriptional programs in rrDLBCL, we implemented NMF and consensus clustering to define an ideal quantity of stable patient clusters. We identified 4 rrDLBCL patient clusters (rrC1-rrC4) as the most stable solution, based on consensus clustering selection criteria of iterative NMF runs. While relapse timepoint was not associated with individual clusters, we did observe significant differences in tumor characteristics between the clusters. COO classification found that rrC1 and rrC4 were enriched for GCB-DLBCL while rrC3 was enriched for ABC-DLBCL classification (P < .001). To explore the biologic programs in each cluster, we used pathFindR, which leverages protein-protein interaction networks to identify disease related pathways, rrC1 and rrC4 were defined by pathways involved in cell cycle and mismatch repair; oxidative phosphorylation in rrC2; and MAPK and NF-kB in rrC3. Next, LME classification was done to define patterns in the tumor microenvironment and we found a significant difference between groups (P < .001), with rrC2 and rrC4 tumors enriched for a depleted TME, while rrC1 and rrC3 had a more immune rich or inflammatory TME. This was supported by CIBERSORTx analysis identifying significant differences (P > .001) in abundance of CD8 T cells, memory CD4 T cells, and M1 macrophages between clusters, with rrC4 having the lowest abundance. We then explored genetic patterns in each cluster. Gene level mutation enrichment analysis showed BCL7A mutation exclusively in C4 (6/6, P = .03). The clusters were not associated with LymphGen classification. Lastly, our recently published high risk signature classification that is associated with early clinical failure and is characterized by metabolic dysregulation, a depleted TME, T53 alterations, and poor outcome, was significantly different between the groups (P = .03) with enrichment for high-risk cases in rrC4. In summary, we show for the first time that rrDLBCL patients can be classified into four gene expression clusters that are associated with distinct pathway, TME, and genetic programs. These clusters should now be tested to learn if they can help select patients for newer therapies for rrDLBCL such as CAR-T and bispecific antibodies.

Background: Relapsed DLBCL occurs in up to 40% of patients and has a poor prognosis. Current prognostic tools such as the international prognostic index (IPI) and newer forms (NCCN-IPI) only include baseline clinical features. PET/CT features have not yet been incorporated into prognostic risk models despite being essential for staging and response assessment. Deep learning computer algorithms have consistently demonstrated the ability to analyze imaging data and identify features that are not readily apparent to the human eye. Using a deep learning computer vision model that automatically extracted radiomic features from PET/CTs, we examined the association of changes between baseline PET and interim PET (PET2) radiomic features on survival outcomes and compared the prognostic performance with NCCN-IPI. Methods: Adult patients with newly diagnosed diffuse large B-cell lymphoma (DLBCL) between 2005 and 2015 and enrolled in the prospective MER cohort were included. Native DICOM images from FDG PET/CT scans at baseline and interim PET (PET2, after 2-3 cycles of chemotherapy) were preprocessed for regularity and then segmented using the nnU-Net architecture with an external lymphoma-specific model. Feature extraction and standard uptake value (SUV) characterization were accomplished with PyRadiomics and NiBabel. Our computational method included no manual segmentation correction. Individual radiomics features (n=115) and the % change above (Δ High) and below (Δ low) 50 th quartile from baseline to PET2 were evaluated for association with event free survival (EFS) and overall survival (OS) using a univariate Cox regression model and by Kaplan Meier analysis. EFS was defined as time from PET2 to first systemic relapse or death from any cause. OS was defined as time from PET2 to death from any cause. Additionally, Spearman and Pearson correlation matrices were used to guide feature selection while minimizing collinearity. We then assessed the association of the highest scoring radiomics features with EFS in a multivariate cox regression model. Results: A total of 506 patients had baseline and PET2 scans available for radiomic feature extraction. Median age at diagnosis was 61 years (range 18-90), 43% were females. NCCN-IPI composition and outcomes of our cohort was comparable to the previously published NCCN cohort, with patient distribution and 5-year OS as follows: Low (9%, 5-y OS 83%), Low-intermediate (38%, 5-y OS 49%), High-intermediate (41%, 5-y OS 31%), High (11%, 5-y OS 12%). At a median follow up of 81 months,there were 308 events, and272 patients had died.Several radiomics features were associated with EFS including change in tumor surface area (SA Δ) (HR: 1.75, 95% CI 1.36-2.24, p<0.005, median of 26,927 mm 2, Figure 1A) and change in total metabolic tumor volume (TMTV Δ) (HR: 1.64, 95% CI 1.27-2.10, p<0.005, median of 120,473 mm 3, Figure 1B). As a reference, NCCN-IPI univariate Cox regression analysis of EFS produced a Harrell's C-statistic (c) of 0.624. Single radiomics features of SA Δ and TMTV Δ individually produced unadjusted c-statistics of 0.616 and 0.614 respectively. Combinations of radiomics features (SA Δ, TMTV Δ and NCCN-IPI classification) demonstrated cumulative benefit (c=0.652). Conclusion: Automatic PET-CT radiomic feature extraction using deep learning models is feasible among patients with newly diagnosed DLBCL. The change in tumor SA and TMTV appear to be promising radiomic biomarkers. A low decline in either tumor SA or TMTV by PET2 is associated with higher risk of relapse and poor survival outcomes. As individual characteristics, these features are similar prognostic indicators of EFS as the NCCN-IPI. Further investigation into risk stratification when combining radiomics with clinical and genomic variables is warranted. Figure 1. Event free survival for DLBCL patients with A) a change in tumor surface area by PET2 less than 50% percentile (SA_Δ Low) and greater than 50 th percentile (SA_Δ High) and B) a change in TMTV by PET2 less than 50% percentile (TMTV_Δ Low) and greater than 50 th percentile (TMTV_Δ High).

7543 Background: Total metabolic tumor volume (TMTV) and maximum standardized uptake value (SUVmax) may improve upon standard 18F-fluorodeoxyglucose positron emission tomography/computed tomography (PET-CT)-based assessment of advanced DLBCL during treatment. We report the association of TMTV and SUVmax changes at interim PET-CT (PET2) on survival outcomes in patients with primary refractory DLBCL (prDLBCL) and early relapsing DLBCL (erDLBCL). Methods: Adult patients with prDLBCL (less than a complete response (CR) or progression by the end of treatment (EOT)) or erDLBCL (CR at EOT with relapse within 12 months) between 2005 and 2019 at Mayo Clinic Rochester were included. The % change (Δ) from baseline to PET2 (after 2 cycles of 1L therapy) of TMTV and SUVmax were measured. A PET segmentation threshold of 1.5 liver mean SUV + two standard deviations with a minimum volume constraint of 0.5 mL was utilized (MIM Software, Inc., Cleveland OH, USA) with manual input as needed. Functional splines analysis determined SUVmax and TMTV threshold for analysis. Results: 131 patients had complete PET-CT data (N=79 prDLBCL, N=52 erDLBCL). Baseline median TMTV and SUVmax were 767.4 cm3 (range 0.9-6840.4) and 20.5 (2.0-52.6), respectively. At a median follow up of 77.6 months, 81 patients had died. 2-year OS rate was 53% (95% CI: 42-67) for patients with a PET2 ΔSUVmax decline ≥65% compared to 15% (95% CI: 7-29) for patients with a PET2 ΔSUVmax decline <65%(p<0.001). PrDLBCL had a 2-year OS rate of 48% (95% CI: 31-74) for a ΔSUVmax ≥65% compared to 7% (95% CI: 2-21) for a ΔSUVmax <65%(p<0.001), which captured 58% of prDLBCL patients (N=46). Two-year OS rate was 43% (95% CI: 34-54) for patients with a ΔTMTV ≥75% compared to 5% (95% CI: 1-35) for patients with ΔTMTV <75% (p<0.001). All patients with a ΔTMTV <75% had prDLBCL. PrDLBCL had a 2-year OS rate of 28% (95% CI: 18-44) for a ΔTMTV ≥75%. The outcomes for ΔSUVmax <65% and ΔTMTV <75% at PET2 remained significant in a cox regression model adjusted for IPI and bulky disease (>10cm)(Table). Conclusions: A ΔTMTV decline <75% or a ΔSUVmax decline <65% at PET2 identified an ultra-high-risk subgroup of DLBCL with particularly poor outcomes, that captured a high percentage of prDLBCL patients, and was significant after adjusting for IPI and bulky baseline disease. These patients may benefit from clinical trials evaluating alternative PET-adapted treatment strategies, but further evaluation among all patients with DLBCL is needed. [Table: see text]

Abstract In this multi-institutional retrospective study, we examined the characteristics and outcomes of 160 patients with high-grade B-cell lymphoma, not otherwise specified (HGBL-NOS)—a rare category defined by high-grade morphologic features and lack of MYC rearrangements with BCL2 and/or BCL6 rearrangements ("double hit"). Our results show that HGBL-NOS tumors are heterogeneous: 83% of patients had a germinal center B-cell immunophenotype, 37% a dual-expressor immunophenotype (MYC and BCL2 expression), 28% MYC rearrangement, 13% BCL2 rearrangement, and 11% BCL6 rearrangement. Most patients presented with stage IV disease, a high serum lactate dehydrogenase, and other high-risk clinical factors. Most frequent first-line regimens included dose-adjusted cyclophosphamide, doxorubicin, vincristine, and etoposide, with rituximab and prednisone (DA-EPOCH-R; 43%); rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP; 33%); or other intensive chemotherapy programs. We found no significant differences in the rates of complete response (CR), progression-free survival (PFS), or overall survival (OS) between these chemotherapy regimens. CR was attained by 69% of patients. PFS at 2 years was 55.2% and OS was 68.1%. In a multivariable model, the main prognostic factors for PFS and OS were poor performance status, lactate dehydrogenase &gt;3 × upper limit of normal, and a dual-expressor immunophenotype. Age &gt;60 years or presence of MYC rearrangement were not prognostic, but patients with TP53 alterations had a dismal PFS. Presence of MYC rearrangement was not predictive of better PFS in patients treated with DA-EPOCH-R vs R-CHOP. Improvements in the diagnostic criteria and therapeutic approaches beyond dose-intense chemotherapy are needed to overcome the unfavorable prognosis of patients with HGBL-NOS.