Unbalanced MYC break-apart FISH patterns indicate the presence of a MYC rearrangement in HGBCL-DH-BCL2.

Fluorescence in situ hybridization (FISH) using break-apart probes is recommended for identifying high-grade B-cell lymphoma with MYC and BCL2 rearrangements (HGBCL-DH-BCL2). Unbalanced MYC break-apart patterns, where the red or green signal is lost, are commonly reported as an equivocal result by clinical laboratories. In a cohort of 297 HGBCL-DH-BCL2, 13% of tumors had unbalanced MYC break-apart patterns with loss of red (LR: 2%) or green (LG: 11%) signal. To determine the significance of these patterns, MYC rearrangements were characterized by sequencing in 130 HGBCL-DH-BCL2, including 3 LR and 14 LG tumors. A MYC rearrangement was identified for 71% of tumors with LR or LG patterns, with the majority involving immunoglobulin loci or other recurrent MYC rearrangement partners. The architecture of these rearrangements consistently preserved the rearranged MYC allele, with the MYC gene predicted to be on the derivative chromosome containing the signal that is still present in nearly all cases. MYC protein expression, MYC mRNA expression, and the proportion of tumors expressing the dark zone signature was not significantly different between balanced and unbalanced groups. These results support a recommendation that unbalanced MYC break-apart FISH patterns be reported as positive for MYC rearrangement in the context of diagnosing HGBCL-DH-BCL2.

Motive and opportunity: MYC rearrangements in high-grade B-cell lymphoma with MYC and BCL2 rearrangements (an LLMPP study).

ABSTRACT: Rearrangements that place the oncogenes MYC, BCL2, or BCL6 adjacent to superenhancers are common in mature B-cell lymphomas. Lymphomas with diffuse large B-cell lymphoma (DLBCL) or high-grade morphology with both MYC and BCL2 rearrangements are classified as high-grade B-cell lymphoma with MYC and BCL2 rearrangements ("double hit"; HGBCL-DH-BCL2) and are associated with aggressive disease and poor outcomes. Although it is established that MYC rearrangements involving immunoglobulin (IG) loci are associated with inferior outcomes relative to those involving other non-IG superenhancers, the frequency of and mechanisms driving IG vs non-IG MYC rearrangements have not been elucidated. Here, we used custom targeted capture and/or whole-genome sequencing to characterize oncogene rearrangements across 883 mature B-cell lymphomas including Burkitt lymphoma, follicular lymphoma, DLBCL, and HGBCL-DH-BCL2 tumors. We demonstrate that, although BCL2 rearrangement topology is consistent across entities, HGBCL-DH-BCL2 have distinct MYC rearrangement architecture relative to tumors with single MYC rearrangements or with both MYC and BCL6 rearrangements (HGBCL-DH-BCL6), including both a higher frequency of non-IG rearrangements and different architecture of MYC::IGH rearrangements. The distinct MYC rearrangement patterns in HGBCL-DH-BCL2 occur on the background of high levels of somatic hypermutation across MYC partner loci in HGBCL-DH-BCL2, creating more opportunity to form these rearrangements. Furthermore, because 1 IGH allele is already disrupted by the existing BCL2 rearrangement, the MYC rearrangement architecture in HGBCL-DH-BCL2 likely reflects selective pressure to preserve both BCL2 and B-cell receptor expression. These data provide new mechanistic explanations for the distinct patterns of MYC rearrangements observed across different lymphoma entities.

TMTV-Net: fully automated total metabolic tumor volume segmentation in lymphoma PET/CT images - a multi-center generalizability analysis.

PURPOSE: Total metabolic tumor volume (TMTV) segmentation has significant value enabling quantitative imaging biomarkers for lymphoma management. In this work, we tackle the challenging task of automated tumor delineation in lymphoma from PET/CT scans using a cascaded approach. METHODS: Our study included 1418 2-[18F]FDG PET/CT scans from four different centers. The dataset was divided into 900 scans for development/validation/testing phases and 518 for multi-center external testing. The former consisted of 450 lymphoma, lung cancer, and melanoma scans, along with 450 negative scans, while the latter consisted of lymphoma patients from different centers with diffuse large B cell, primary mediastinal large B cell, and classic Hodgkin lymphoma cases. Our approach involves resampling PET/CT images into different voxel sizes in the first step, followed by training multi-resolution 3D U-Nets on each resampled dataset using a fivefold cross-validation scheme. The models trained on different data splits were ensemble. After applying soft voting to the predicted masks, in the second step, we input the probability-averaged predictions, along with the input imaging data, into another 3D U-Net. Models were trained with semi-supervised loss. We additionally considered the effectiveness of using test time augmentation (TTA) to improve the segmentation performance after training. In addition to quantitative analysis including Dice score (DSC) and TMTV comparisons, the qualitative evaluation was also conducted by nuclear medicine physicians. RESULTS: Our cascaded soft-voting guided approach resulted in performance with an average DSC of 0.68 ± 0.12 for the internal test data from developmental dataset, and an average DSC of 0.66 ± 0.18 on the multi-site external data (n = 518), significantly outperforming (p < 0.001) state-of-the-art (SOTA) approaches including nnU-Net and SWIN UNETR. While TTA yielded enhanced performance gains for some of the comparator methods, its impact on our cascaded approach was found to be negligible (DSC: 0.66 ± 0.16). Our approach reliably quantified TMTV, with a correlation of 0.89 with the ground truth (p < 0.001). Furthermore, in terms of visual assessment, concordance between quantitative evaluations and clinician feedback was observed in the majority of cases. The average relative error (ARE) and the absolute error (AE) in TMTV prediction on external multi-centric dataset were ARE = 0.43 ± 0.54 and AE = 157.32 ± 378.12 (mL) for all the external test data (n = 518), and ARE = 0.30 ± 0.22 and AE = 82.05 ± 99.78 (mL) when the 10% outliers (n = 53) were excluded. CONCLUSION: TMTV-Net demonstrates strong performance and generalizability in TMTV segmentation across multi-site external datasets, encompassing various lymphoma subtypes. A negligible reduction of 2% in overall performance during testing on external data highlights robust model generalizability across different centers and cancer types, likely attributable to its training with resampled inputs. Our model is publicly available, allowing easy multi-site evaluation and generalizability analysis on datasets from different institutions.

Evaluating Outcome Prediction via Baseline, End-of-Treatment, and Delta Radiomics on PET-CT Images of Primary Mediastinal Large B-Cell Lymphoma.

OBJECTIVES: Accurate outcome prediction is important for making informed clinical decisions in cancer treatment. In this study, we assessed the feasibility of using changes in radiomic features over time (Delta radiomics: absolute and relative) following chemotherapy, to predict relapse/progression and time to progression (TTP) of primary mediastinal large B-cell lymphoma (PMBCL) patients. MATERIAL AND METHODS: Given the lack of standard staging PET scans until 2011, only 31 out of 103 PMBCL patients in our retrospective study had both pre-treatment and end-of-treatment (EoT) scans. Consequently, our radiomics analysis focused on these 31 patients who underwent [18F]FDG PET-CT scans before and after R-CHOP chemotherapy. Expert manual lesion segmentation was conducted on their scans for delta radiomics analysis, along with an additional 19 EoT scans, totaling 50 segmented scans for single time point analysis. Radiomics features (on PET and CT), along with maximum and mean standardized uptake values (SUVmax and SUVmean), total metabolic tumor volume (TMTV), tumor dissemination (Dmax), total lesion glycolysis (TLG), and the area under the curve of cumulative standardized uptake value-volume histogram (AUC-CSH) were calculated. We additionally applied longitudinal analysis using radial mean intensity (RIM) changes. For prediction of relapse/progression, we utilized the individual coefficient approximation for risk estimation (ICARE) and machine learning (ML) techniques (K-Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA), and Random Forest (RF)) including sequential feature selection (SFS) following correlation analysis for feature selection. For TTP, ICARE and CoxNet approaches were utilized. In all models, we used nested cross-validation (CV) (with 10 outer folds and 5 repetitions, along with 5 inner folds and 20 repetitions) after balancing the dataset using Synthetic Minority Oversampling TEchnique (SMOTE). RESULTS: To predict relapse/progression using Delta radiomics between the baseline (staging) and EoT scans, the best performances in terms of accuracy and F1 score (F1 score is the harmonic mean of precision and recall, where precision is the ratio of true positives to the sum of true positives and false positives, and recall is the ratio of true positives to the sum of true positives and false negatives) were achieved with ICARE (accuracy = 0.81 ± 0.15, F1 = 0.77 ± 0.18), RF (accuracy = 0.89 ± 0.04, F1 = 0.87 ± 0.04), and LDA (accuracy = 0.89 ± 0.03, F1 = 0.89 ± 0.03), that are higher compared to the predictive power achieved by using only EoT radiomics features. For the second category of our analysis, TTP prediction, the best performer was CoxNet (LASSO feature selection) with c-index = 0.67 ± 0.06 when using baseline + Delta features (inclusion of both baseline and Delta features). The TTP results via Delta radiomics were comparable to the use of radiomics features extracted from EoT scans for TTP analysis (c-index = 0.68 ± 0.09) using CoxNet (with SFS). The performance of Deauville Score (DS) for TTP was c-index = 0.66 ± 0.09 for n = 50 and 0.67 ± 03 for n = 31 cases when using EoT scans with no significant differences compared to the radiomics signature from either EoT scans or baseline + Delta features (p-value> 0.05). CONCLUSION: This work demonstrates the potential of Delta radiomics and the importance of using EoT scans to predict progression and TTP from PMBCL [18F]FDG PET-CT scans.

Semi-supervised learning towards automated segmentation of PET images with limited annotations: application to lymphoma patients.

Manual segmentation poses a time-consuming challenge for disease quantification, therapy evaluation, treatment planning, and outcome prediction. Convolutional neural networks (CNNs) hold promise in accurately identifying tumor locations and boundaries in PET scans. However, a major hurdle is the extensive amount of supervised and annotated data necessary for training. To overcome this limitation, this study explores semi-supervised approaches utilizing unlabeled data, specifically focusing on PET images of diffuse large B-cell lymphoma (DLBCL) and primary mediastinal large B-cell lymphoma (PMBCL) obtained from two centers. We considered 2-[18F]FDG PET images of 292 patients PMBCL (n = 104) and DLBCL (n = 188) (n = 232 for training and validation, and n = 60 for external testing). We harnessed classical wisdom embedded in traditional segmentation methods, such as the fuzzy clustering loss function (FCM), to tailor the training strategy for a 3D U-Net model, incorporating both supervised and unsupervised learning approaches. Various supervision levels were explored, including fully supervised methods with labeled FCM and unified focal/Dice loss, unsupervised methods with robust FCM (RFCM) and Mumford-Shah (MS) loss, and semi-supervised methods combining FCM with supervised Dice loss (MS + Dice) or labeled FCM (RFCM + FCM). The unified loss function yielded higher Dice scores (0.73 ± 0.11; 95% CI 0.67-0.8) than Dice loss (p value < 0.01). Among the semi-supervised approaches, RFCM + αFCM (α = 0.3) showed the best performance, with Dice score of 0.68 ± 0.10 (95% CI 0.45-0.77), outperforming MS + αDice for any supervision level (any α) (p < 0.01). Another semi-supervised approach with MS + αDice (α = 0.2) achieved Dice score of 0.59 ± 0.09 (95% CI 0.44-0.76) surpassing other supervision levels (p < 0.01). Given the time-consuming nature of manual delineations and the inconsistencies they may introduce, semi-supervised approaches hold promise for automating medical imaging segmentation workflows.

Exploration of Germline Correlates and Risk of Immune-Related Adverse Events in Advanced Cancer Patients Treated with Immune Checkpoint Inhibitors.

Immune checkpoint inhibitors (ICIs) are increasingly used in the treatment of many tumor types, and durable responses can be observed in select populations. However, patients may exhibit significant immune-related adverse events (irAEs) that may lead to morbidity. There is limited information on whether the presence of specific germline mutations may highlight those at elevated risk of irAEs. We evaluated 117 patients with metastatic solid tumors or hematologic malignancies who underwent genomic analysis through the ongoing Personalized OncoGenomics (POG) program at BC Cancer and received an ICI during their treatment history. Charts were reviewed for irAEs. Whole genome sequencing of a fresh biopsy and matched normal specimens (blood) was performed at the time of POG enrollment. Notably, we found that MHC class I alleles in the HLA-B27 family, which have been previously associated with autoimmune conditions, were associated with grade 3 hepatitis and pneumonitis (q = 0.007) in patients treated with combination PD-1/PD-L1 and CTLA-4 inhibitors, and PD-1 inhibitors in combination with IDO-1 inhibitors. These data highlight that some patients may have a genetic predisposition to developing irAEs.

Brentuximab vedotin plus cyclophosphamide, doxorubicin, etoposide, and prednisone followed by brentuximab vedotin consolidation in CD30-positive peripheral T-cell lymphomas: a multicentre, single-arm, phase 2 study.

BACKGROUND: CD30 expression is universal in anaplastic large-cell lymphoma and is expressed in some other peripheral T-cell lymphoma subtypes. Incorporation of brentuximab vedotin into initial therapy for people with CD30-positive peripheral T-cell lymphomas prolonged progression-free survival, but there is room for improvement, especially for people with non-anaplastic large-cell lymphoma subtypes. METHODS: We conducted a multicentre, international, single-arm, phase 2 trial to evaluate the safety and activity of CHEP-BV (cyclophosphamide, doxorubicin, prednisone, brentuximab vedotin, and etoposide) followed by brentuximab vedotin consolidation in patients with CD30-expressing peripheral T-cell lymphomas across five academic centres in the USA and Canada. Adults aged 18 years or older with newly diagnosed, untreated CD30-positive peripheral T-cell lymphomas, Eastern Cooperative Oncology Group score of 0-2, and adequate organ function were eligible to receive six planned cycles of CHEP-BV (ie, 1·8 mg/kg brentuximab vedotin intravenously on day 1, cyclophosphamide 750 mg/m2 intravenously on day 1, doxorubicin 50 mg/m2 intravenously on day 1, etoposide 100 mg/m2 daily intravenously on days 1-3, and prednisone 100 mg daily orally on days 1-5) with prophylactic G-CSF. Patients who responded to the treatment could receive brentuximab vedotin consolidation for up to ten additional cycles either after autologous haematopoietic stem-cell transplantation (HSCT) or directly after CHEP-BV. The primary endpoints were unacceptable toxicity during a 3-plus-3 safety lead-in in participants who received study treatment and completed the safety evaluation period (to confirm the recommended phase 2 dose of brentuximab vedotin in CHEP-BV) and the complete response rate after CHEP-BV induction therapy in participants who received study treatment and had response evaluation. The study was registered at ClinicalTrials.gov (NCT03113500), and this cohort completed the trial. The trial is ongoing with the enrolment of a new cohort. FINDINGS: 54 patients were screened for eligibility and 48 were eligible for the study. The participants (18 [38%] women and 30 [63%] men; 34 [71%] White, four [8%] Black, five [10%] Asian, ten [21%] Hispanic, and 37 [77%] non-Hispanic people) were recruited and enrolled between Dec 4, 2017, and June 14, 2021, and followed up until Aug 25, 2023, when the database was locked for analysis. 48 participants were evaluable for toxicity, and 47 were evaluable for response (one participant died from COVID-19 before response assessment). During the safety lead-in, one of six participants had an unacceptable toxicity (ie, platelet count <10 000 per mm3 in a participant with extensive bone marrow involvement), and the proposed phase 2 dose of 1·8 mg/kg brentuximab vedotin in CHEP-BV was confirmed. At completion of CHEP-BV, 37 of 47 participants had complete response, yielding a complete response rate of 79% (95% CI 64-89). The most common CHEP-BV-related toxicities of grade 3 or higher were neutropenia (14 [29%] of 48), leukopenia (11 [23%]), anaemia (ten [21%]), febrile neutropenia (ten [21%]), lymphopenia (nine [19%]), and thrombocytopenia (nine [19%]). There were no treatment-related deaths. INTERPRETATION: In patients with mostly CD30-expressing peripheral T-cell lymphomas other than non-anaplastic large-cell lymphoma, CHEP-BV (with or without autologous HSCT) followed by brentuximab vedotin consolidation was safe and active. FUNDING: SeaGen, Leukemia and Lymphoma Society, Lymphoma Research Foundation, and the National Cancer Institute of the National Institutes of Health.

Spatially Resolved Tumor Microenvironment Predicts Treatment Outcomes in Relapsed/Refractory Hodgkin Lymphoma.

PURPOSE: About a third of patients with relapsed or refractory classic Hodgkin lymphoma (r/r CHL) succumb to their disease after high-dose chemotherapy followed by autologous stem-cell transplantation (HDC/ASCT). Here, we aimed to describe spatially resolved tumor microenvironment (TME) ecosystems to establish novel biomarkers associated with treatment failure in r/r CHL. PATIENTS AND METHODS: We performed imaging mass cytometry (IMC) on 71 paired primary diagnostic and relapse biopsies using a marker panel specific to CHL biology. For each cell type in the TME, we calculated a spatial score measuring the distance of nearest neighbor cells to the malignant Hodgkin Reed Sternberg cells within the close interaction range. Spatial scores were used as features in prognostic model development for post-ASCT outcomes. RESULTS: Highly multiplexed IMC data revealed shared TME patterns in paired diagnostic and early r/r CHL samples, whereas TME patterns were more divergent in pairs of diagnostic and late relapse samples. Integrated analysis of IMC and single-cell RNA sequencing data identified unique architecture defined by CXCR5+ Hodgkin and Reed Sternberg (HRS) cells and their strong spatial relationship with CXCL13+ macrophages in the TME. We developed a prognostic assay (RHL4S) using four spatially resolved parameters, CXCR5+ HRS cells, PD1+CD4+ T cells, CD68+ tumor-associated macrophages, and CXCR5+ B cells, which effectively separated patients into high-risk versus low-risk groups with significantly different post-ASCT outcomes. The RHL4S assay was validated in an independent r/r CHL cohort using a multicolor immunofluorescence assay. CONCLUSION: We identified the interaction of CXCR5+ HRS cells with ligand-expressing CXCL13+ macrophages as a prominent crosstalk axis in relapsed CHL. Harnessing this TME biology, we developed a novel prognostic model applicable to r/r CHL biopsies, RHL4S, opening new avenues for spatial biomarker development.

International Prognostic Score for Nodular Lymphocyte-Predominant Hodgkin Lymphoma.

PURPOSE: Nodular lymphocyte-predominant Hodgkin lymphoma (NLPHL) is a rare cancer, and large international cooperative efforts are needed to evaluate the significance of clinical risk factors and immunoarchitectural patterns (IAPs) for all stages of pediatric and adult patients with NLPHL. METHODS: Thirty-eight institutions participated in the Global nLPHL One Working Group retrospective study of NLPHL cases from 1992 to 2021. We measured progression-free survival (PFS), overall survival (OS), transformation rate, and lymphoma-specific death rate. We performed uni- and multivariable (MVA) Cox regression stratified by management to select factors for the lymphocyte-predominant international prognostic score (LP-IPS) validated by five-fold cross-validation. RESULTS: We identified 2,243 patients with a median age of 37 years (IQR, 23-51). The median follow-up was 6.3 years (IQR, 3.4-10.8). Most had stage I to II (72.9%) and few B symptoms (9.9%) or splenic involvement (5.4%). IAP was scored for 916 (40.8%). Frontline management included chemotherapy alone (32.4%), combined modality therapy (30.5%), radiotherapy alone (24.0%), observation after excision (4.6%), rituximab alone (4.0%), active surveillance (3.4%), and rituximab and radiotherapy (1.1%). The PFS, OS, transformation, and lymphoma-specific death rates at 10 years were 70.8%, 91.6%, 4.8%, and 3.3%, respectively. On MVA, IAPs were not associated with PFS or OS, but IAP E had higher risk of transformation (hazard ratio [HR], 1.81; P < .05). We developed the LP-IPS with 1 point each for age ≥45 years, stage III-IV, hemoglobin <10.5 g/dL, and splenic involvement. Increasing LP-IPS was significantly associated with worse PFS (HR, 1.52) and OS (HR, 2.31) and increased risk of lymphoma-specific death (HR, 2.63) and transformation (HR, 1.41). CONCLUSION: In this comprehensive study of all ages of patients with NLPHL, we develop the LP-IPS to identify high-risk patients and inform upcoming prospective clinical trials evaluating de-escalation of therapy for patients with low LP-IPS scores (<2).