TRAF3 loss-of-function reveals the noncanonical NF-κB pathway as a therapeutic target in diffuse large B cell lymphoma.

Here, we report recurrent focal deletions of the chr14q32.31-32 locus, including TRAF3, a negative regulator of NF-κB signaling, in de novo diffuse large B cell lymphoma (DLBCL) (24/324 cases). Integrative analysis revealed an association between TRAF3 copy number loss with accumulation of NIK, the central noncanonical (NC) NF-κB kinase, and increased NC NF-κB pathway activity. Accordingly, TRAF3 genetic ablation in isogenic DLBCL model systems caused upregulation of NIK and enhanced NC NF-κB downstream signaling. Knockdown or pharmacological inhibition of NIK in TRAF3-deficient cells differentially impaired their proliferation and survival, suggesting an acquired onco-addiction to NC NF-κB. TRAF3 ablation also led to exacerbated secretion of the immunosuppressive cytokine IL-10. Coculturing of TRAF3-deficient DLBCL cells with CD8+ T cells impaired the induction of Granzyme B and interferon (IFN) γ, which were restored following neutralization of IL-10. Our findings corroborate a direct relationship between TRAF3 genetic alterations and NC NF-κB activation, and highlight NIK as a potential therapeutic target in a defined subset of DLBCL.

Loss of CREBBP and KMT2D cooperate to accelerate lymphomagenesis and shape the lymphoma immune microenvironment.

Despite regulating overlapping gene enhancers and pathways, CREBBP and KMT2D mutations recurrently co-occur in germinal center (GC) B cell-derived lymphomas, suggesting potential oncogenic cooperation. Herein, we report that combined haploinsufficiency of Crebbp and Kmt2d induces a more severe mouse lymphoma phenotype (vs either allele alone) and unexpectedly confers an immune evasive microenvironment manifesting as CD8+ T-cell exhaustion and reduced infiltration. This is linked to profound repression of immune synapse genes that mediate crosstalk with T-cells, resulting in aberrant GC B cell fate decisions. From the epigenetic perspective, we observe interaction and mutually dependent binding and function of CREBBP and KMT2D on chromatin. Their combined deficiency preferentially impairs activation of immune synapse-responsive super-enhancers, pointing to a particular dependency for both co-activators at these specialized regulatory elements. Together, our data provide an example where chromatin modifier mutations cooperatively shape and induce an immune-evasive microenvironment to facilitate lymphomagenesis.

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.

ARID1A orchestrates SWI/SNF-mediated sequential binding of transcription factors with ARID1A loss driving pre-memory B cell fate and lymphomagenesis.

ARID1A, a subunit of the canonical BAF nucleosome remodeling complex, is commonly mutated in lymphomas. We show that ARID1A orchestrates B cell fate during the germinal center (GC) response, facilitating cooperative and sequential binding of PU.1 and NF-kB at crucial genes for cytokine and CD40 signaling. The absence of ARID1A tilts GC cell fate toward immature IgM+CD80-PD-L2- memory B cells, known for their potential to re-enter new GCs. When combined with BCL2 oncogene, ARID1A haploinsufficiency hastens the progression of aggressive follicular lymphomas (FLs) in mice. Patients with FL with ARID1A-inactivating mutations preferentially display an immature memory B cell-like state with increased transformation risk to aggressive disease. These observations offer mechanistic understanding into the emergence of both indolent and aggressive ARID1A-mutant lymphomas through the formation of immature memory-like clonal precursors. Lastly, we demonstrate that ARID1A mutation induces synthetic lethality to SMARCA2/4 inhibition, paving the way for potential precision therapy for high-risk patients.

Chronic Lymphocytic Leukemia IGHV Somatic Hypermutation Detection by Targeted Capture Next-Generation Sequencing.

BACKGROUND: Somatic hypermutation (SHM) status of the immunoglobulin heavy variable (IGHV) gene plays a crucial role in determining the prognosis and treatment of patients with chronic lymphocytic leukemia (CLL). A common approach for determining SHM status is multiplex polymerase chain reaction and Sanger sequencing of the immunoglobin heavy locus; however, this technique is low throughput, is vulnerable to failure, and does not allow multiplexing with other diagnostic assays. METHODS: Here we designed and validated a DNA targeted capture approach to detect immunoglobulin heavy variable somatic hypermutation (IGHV SHM) status as a submodule of a larger next-generation sequencing (NGS) panel that also includes probes for ATM, BIRC3, CHD2, KLHL6, MYD88, NOTCH1, NOTCH2, POT1, SF3B1, TP53, and XPO1. The assay takes as input FASTQ files and outputs a report containing IGHV SHM status and V allele usage following European Research Initiative on CLL guidelines. RESULTS: We validated the approach on 35 CLL patient samples, 34 of which were characterized using Sanger sequencing. The NGS panel identified the IGHV SHM status of 34 of 35 CLL patients. We showed 100% sensitivity and specificity among the 33 CLL samples with both NGS and Sanger sequencing calls. Furthermore, we demonstrated that this panel can be combined with additional targeted capture panels to detect prognostically important CLL single nucleotide variants, insertions/deletions, and copy number variants (TP53 copy number loss). CONCLUSIONS: A targeted capture approach to IGHV SHM detection can be integrated into broader sequencing panels, allowing broad CLL prognostication in a single molecular assay.

Multisite clinical cross-validation and variant interpretation of a next generation sequencing panel for lymphoid cancer prognostication.

AIMS: Genomic sequencing of lymphomas is under-represented in routine clinical testing despite having prognostic and predictive value. Clinical implementation is challenging due to a lack of consensus on reportable targets and a paucity of reference samples. We organised a cross-validation study of a lymphoma-tailored next-generation sequencing panel between two College of American Pathologists (CAP)-accredited clinical laboratories to mitigate these challenges. METHODS: A consensus for the genomic targets was discussed between the two institutes based on recurrence in diffuse large B-cell lymphoma, follicular lymphoma, mantle cell lymphoma, chronic lymphocytic leukaemia and T-cell lymphomas. Using the same genomic targets, each laboratory ordered libraries independently and a cross-validation study was designed to exchange samples (8 cell lines and 22 clinical samples) and their FASTQ files. RESULTS: The sensitivity of the panel when comparing different library preparation and bioinformatic workflows was between 97% and 99% and specificity was 100% when a 5% limit of detection cut-off was applied. To evaluate how the current standards for variant classification of tumours apply to lymphomas, the Association for Molecular Pathology/American Society of Clinical Oncology/CAP and OncoKB classification systems were applied to the panel. The majority of variants were assigned a possibly actionable class or likely pathogenic due to more limited evidence in the literature. CONCLUSIONS: The cross-validation study highlights the benefits of sample and data exchange for clinical validation and provided a framework for reporting the findings in lymphoid malignancies.

CD5 Gene Signature Identifies Diffuse Large B-Cell Lymphomas Sensitive to Bruton's Tyrosine Kinase Inhibition.

PURPOSE: A genetic classifier termed LymphGen accurately identifies diffuse large B-cell lymphoma (DLBCL) subtypes vulnerable to Bruton's tyrosine kinase inhibitors (BTKis), but is challenging to implement in the clinic and fails to capture all DLBCLs that benefit from BTKi-based therapy. Here, we developed a novel CD5 gene expression signature as a biomarker of response to BTKi-based therapy in DLBCL. METHODS: CD5 immunohistochemistry (IHC) was performed on 404 DLBCLs to identify CD5 IHC+ and CD5 IHC- cases, which were subsequently characterized at the molecular level through mutational and transcriptional analyses. A 60-gene CD5 gene expression signature (CD5sig) was constructed using genes differentially expressed between CD5 IHC+ and CD5 IHC- non-germinal center B-cell-like (non-GCB DLBCL) DLBCLs. This CD5sig was applied to external DLBCL data sets, including pretreatment biopsies from patients enrolled in the PHOENIX study (n = 584) to define the extent to which the CD5sig could identify non-GCB DLBCLs that benefited from the addition of ibrutinib to rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP). RESULTS: CD5 expression was observed in 12% of non-GCB DLBCLs. CD5+ DLBCLs displayed transcriptional features of B-cell receptor (BCR) activation and were enriched for BCR-activating mutations known to correlate with BTKi sensitivity. However, most CD5+ DLBCLs lacked canonical BCR-activating mutations or were LymphGen-unclassifiable (LymphGen-Other). The CD5sig recapitulated these findings in multiple independent data sets, indicating its utility in identifying DLBCLs with genetic and nongenetic bases for BCR dependence. Supporting this notion, CD5sig+ DLBCLs derived a selective survival advantage from the addition of ibrutinib to R-CHOP in the PHOENIX study, independent of LymphGen classification. CONCLUSION: CD5sig is a useful biomarker to identify DLBCLs vulnerable to BTKi-based therapies and complements current biomarker approaches by identifying DLBCLs with genetic and nongenetic bases for BTKi sensitivity.

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.

ESQmodel: biologically informed evaluation of 2-D cell segmentation quality in multiplexed tissue images.

MOTIVATION: Single cell segmentation is critical in the processing of spatial omics data to accurately perform cell type identification and analyze spatial expression patterns. Segmentation methods often rely on semi-supervised annotation or labeled training data which are highly dependent on user expertise. To ensure the quality of segmentation, current evaluation strategies quantify accuracy by assessing cellular masks or through iterative inspection by pathologists. While these strategies each address either the statistical or biological aspects of segmentation, there lacks a unified approach to evaluating segmentation accuracy. RESULTS: In this article, we present ESQmodel, a Bayesian probabilistic method to evaluate single cell segmentation using expression data. By using the extracted cellular data from segmentation and a prior belief of cellular composition as input, ESQmodel computes per cell entropy to assess segmentation quality by how consistent cellular expression profiles match with cell type expectations. AVAILABILITY AND IMPLEMENTATION: Source code is available on Github at: https://github.com/Roth-Lab/ESQmodel.

Biological heterogeneity in diffuse large B-cell lymphoma.

Diffuse large B-cell lymphoma (DLBCL) is heterogeneous both in clinical outcomes and the underlying disease biology. Over the last 2 decades, several different approaches for dissecting biological heterogeneity have emerged. Gene expression profiling (GEP) stratifies DLBCL into 3 broad groups (ABC, GCB, and DZsig/MHG), each with parallels to different normal mature B cell developmental states and prognostic implications. More recently, several different genomic approaches have been developed to categorize DLBCL based on the co-occurrence of tumor somatic mutations, identifying more granular biologically unified subgroups that complement GEP-based approaches. We review the molecular approaches and clinical evidence supporting the stratification of DLBCL patients based on tumor biology. By offering a platform for subtype-guided therapy, these divisions remain a promising avenue for improving patient outcomes, especially in subgroups with inferior outcomes with current standard-of-care therapy.

Denoising Non-Stationary Signals via Dynamic Multivariate Complex Wavelet Thresholding.

Over the past few years, we have seen an increased need to analyze the dynamically changing behaviors of economic and financial time series. These needs have led to significant demand for methods that denoise non-stationary time series across time and for specific investment horizons (scales) and localized windows (blocks) of time. Wavelets have long been known to decompose non-stationary time series into their different components or scale pieces. Recent methods satisfying this demand first decompose the non-stationary time series using wavelet techniques and then apply a thresholding method to separate and capture the signal and noise components of the series. Traditionally, wavelet thresholding methods rely on the discrete wavelet transform (DWT), which is a static thresholding technique that may not capture the time series of the estimated variance in the additive noise process. We introduce a novel continuous wavelet transform (CWT) dynamically optimized multivariate thresholding method (WaveL2E). Applying this method, we are simultaneously able to separate and capture the signal and noise components while estimating the dynamic noise variance. Our method shows improved results when compared to well-known methods, especially for high-frequency signal-rich time series, typically observed in finance.

ETS1 phosphorylation at threonine 38 is associated with the cell of origin of diffuse large B cell lymphoma and sustains the growth of tumour cells.

The transcriptional factor ETS1 is upregulated in 25% of diffuse large B cell lymphoma (DLBCL). Here, we studied the role of ETS1 phosphorylation at threonine 38, a marker for ETS1 activation, in DLBCL cellular models and clinical specimens. p-ETS1 was detected in activated B cell-like DLBCL (ABC), not in germinal centre B-cell-like DLBCL (GCB) cell lines and, accordingly, it was more common in ABC than GCB DLBCL diagnostic biopsies. MEK inhibition decreased both baseline and IgM stimulation-induced p-ETS1 levels. Genetic inhibition of phosphorylation of ETS1 at threonine 38 affected the growth and the BCR-mediated transcriptome program in DLBCL cell lines. Our data demonstrate that ETS1 phosphorylation at threonine 38 is important for the growth of DLBCL cells and its pharmacological inhibition could benefit lymphoma patients.

Distribution and clinical impact of molecular subtypes with dark zone signature of DLBCL in a Japanese real-world study.

The distribution and clinical impact of cell-of-origin (COO) subtypes of diffuse large B-cell lymphoma (DLBCL) outside Western countries remain unknown. Recent literature also suggests that there is an additional COO subtype associated with the germinal center dark zone (DZ) that warrants wider validation to generalize clinical relevance. Here, we assembled a cohort of Japanese patients with untreated DLBCL and determined the refined COO subtypes, which include the DZ signature (DZsig), using the NanoString DLBCL90 assay. To compare the distribution and clinical characteristics of the molecular subtypes, we used a data set from the cohort of British Columbia Cancer (BCC) (n = 804). Through the 1050 patient samples on which DLBCL90 assay was successfully performed in our cohort, 35%, 45%, and 6% of patients were identified to have germinal center B-cell-like (GCB) DLBCL, activated B-cell-like (ABC) DLBCL, and DZsig-positive (DZsigpos) DLBCL, respectively, with the highest prevalence of ABC-DLBCL, differing significantly from the BCC result (P < .001). GCB-DLBCL, ABC-DLBCL, and DZsigpos-DLBCL were associated with 2-year overall survival rates of 88%, 75%, and 66%, respectively (P < .0001), with patients with DZsigpos-DLBCL having the poorest prognosis. In contrast, GCB-DLBCL without DZsig showed excellent outcomes after rituximab-containing immunochemotherapy. DZsigpos-DLBCL was associated with the significant enrichment of tumors with CD10 expression, concurrent MYC/BCL2 expression, and depletion of microenvironmental components (all, P < .05). These results provide evidence of the distinct distribution of clinically relevant molecular subtypes in Japanese DLBCL and that refined COO, as measured by the DLBCL90 assay, is a robust prognostic biomarker that is consistent across geographical areas.

Novel precision medicine approaches and treatment strategies in hematological malignancies.

Genetic testing has been applied for decades in clinical routine diagnostics of hematological malignancies to improve disease (sub)classification, prognostication, patient management, and survival. In recent classifications of hematological malignancies, disease subtypes are defined by key recurrent genetic alterations detected by conventional methods (i.e., cytogenetics, fluorescence in situ hybridization, and targeted sequencing). Hematological malignancies were also one of the first disease areas in which targeted therapies were introduced, the prime example being BCR::ABL1 inhibitors, followed by an increasing number of targeted inhibitors hitting the Achilles' heel of each disease, resulting in a clear patient benefit. Owing to the technical advances in high-throughput sequencing, we can now apply broad genomic tests, including comprehensive gene panels or whole-genome and whole-transcriptome sequencing, to identify clinically important diagnostic, prognostic, and predictive markers. In this review, we give examples of how precision diagnostics has been implemented to guide treatment selection and improve survival in myeloid (myelodysplastic syndromes and acute myeloid leukemia) and lymphoid malignancies (acute lymphoblastic leukemia, diffuse large B-cell lymphoma, and chronic lymphocytic leukemia). We discuss the relevance and potential of monitoring measurable residual disease using ultra-sensitive techniques to assess therapy response and detect early relapses. Finally, we bring up the promising avenue of functional precision medicine, combining ex vivo drug screening with various omics technologies, to provide novel treatment options for patients with advanced disease. Although we are only in the beginning of the field of precision hematology, we foresee rapid development with new types of diagnostics and treatment strategies becoming available to the benefit of our patients.

Time to progression of disease and outcomes with second-line BTK inhibitors in relapsed/refractory mantle cell lymphoma.

Time to progression of disease (POD) after first-line (1L) therapy is prognostic in mantle cell lymphoma (MCL), although studies have included a broad range of 1L, second-line (2L), and subsequent lines of therapy. The purpose of this study was to evaluate the factors predicting outcomes in patients with relapsed/refractory (R/R) MCL exclusively initiating 2L Bruton's tyrosine kinase inhibitors (BTKis) after 1L rituximab-containing therapy. Patients were accrued from 8 international centers (7 main, 1 validation cohort). Multivariable models evaluating the association between time to POD and clinical/pathologic factors were constructed and converted into nomograms and prognostic indexes predicting outcomes in this population. A total of 360 patients were included, including 160 in the main cohort and 200 in the validation cohort. Time to POD, Ki67 ≥ 30%, and MCL International Prognostic Index (MIPI) were associated with progression-free survival (PFS2) and overall survival (OS2) from the start of 2L BTKis. C-indexes were consistently ≥0.68 in both cohorts. Web/application-based calculators based on nomograms and prognostic indexes to estimate PFS2 and OS2 were constructed. The 2L BTKi MIPI identifies 3 groups with distinct 2-year PFS2, including high risk (14%), intermediate risk (50%), and low risk (64%). Time to POD, Ki67, and MIPI are associated with survival outcomes in patients with R/R MCL receiving 2L BTKis. Simple clinical models incorporating these variables may assist in planning for alternative therapies such as chimeric antigen receptor T-cell therapy, allogeneic stem cell transplantation, or novel agents with alternative mechanisms of action.

Relapse Timing Is Associated With Distinct Evolutionary Dynamics in Diffuse Large B-Cell Lymphoma.

PURPOSE: Diffuse large B-cell lymphoma (DLBCL) is cured in more than 60% of patients, but outcomes remain poor for patients experiencing disease progression or relapse (refractory or relapsed DLBCL [rrDLBCL]), particularly if these events occur early. Although previous studies examining cohorts of rrDLBCL have identified features that are enriched at relapse, few have directly compared serial biopsies to uncover biological and evolutionary dynamics driving rrDLBCL. Here, we sought to confirm the relationship between relapse timing and outcomes after second-line (immuno)chemotherapy and determine the evolutionary dynamics that underpin that relationship. PATIENTS AND METHODS: Outcomes were examined in a population-based cohort of 221 patients with DLBCL who experienced progression/relapse after frontline treatment and were treated with second-line (immuno)chemotherapy with an intention-to-treat with autologous stem-cell transplantation (ASCT). Serial DLBCL biopsies from a partially overlapping cohort of 129 patients underwent molecular characterization, including whole-genome or whole-exome sequencing in 73 patients. RESULTS: Outcomes to second-line therapy and ASCT are superior for late relapse (>2 years postdiagnosis) versus primary refractory (<9 months) or early relapse (9-24 months). Diagnostic and relapse biopsies were mostly concordant for cell-of-origin classification and genetics-based subgroup. Despite this concordance, the number of mutations exclusive to each biopsy increased with time since diagnosis, and late relapses shared few mutations with their diagnostic counterpart, demonstrating a branching evolution pattern. In patients with highly divergent tumors, many of the same genes acquired new mutations independently in each tumor, suggesting that the earliest mutations in a shared precursor cell constrain tumor evolution toward the same genetics-based subgroups at both diagnosis and relapse. CONCLUSION: These results suggest that late relapses commonly represent genetically distinct and chemotherapy-naïve disease and have implications for optimal patient management.

Exploring new prognostic biomarkers in Mantle Cell Lymphoma: a comparison of the circSCORE and the MCL35 score.

Mantle cell lymphoma (MCL) is a biologically and clinically heterogeneous disease, emphasizing the need for prognostic biomarkers. In this study we aimed at comparing the prognostic value of two RNA-based risk scores, circSCORE and MCL35, in 149 patients from the MCL2 (ISRCTN87866680) and MCL3 (NCT00514475) patient cohorts. Both risk scores provided significant stratification of high versus low risk for progression free survival (PFS) and overall survival (OS). The circSCORE retained significant prognostic value in adjusted multivariable Cox regressions for PFS, but not for OS. Furthermore, circSCORE added significant prognostic value to MIPI in the pooled cohort (MCL2 and MCL3) for PFS and OS, and for PFS in MCL3 alone, outperforming Ki67 and MCL35. We suggest a new, combined MIPI-circSCORE with improved prognostic value, and with potential for future clinical implementation, if validated in a larger, independent cohort.