Citations

Abstract Abstract 1024 Background: The MMRC is a non-profit, disease-focused consortium founded in 2004. Sixteen North American member institutions with expertise in multiple myeloma (MM) work collaboratively with the MMRC Inc. (Norwalk, CT) and numerous pharmaceutical partners to speed development of new treatment options to MM patients. In December 2007, MMRC Inc. implemented business solutions to address barriers to rapid activation of phase I-II trials and established benchmarks for initiating and conducting these studies. In December 2010, we reported significantly faster trial start up and accrual data from previous years1,2. Today, we update and expand on MMRC performance data and analysis of progress. Methods: Twenty-five (25) trials conducted within the Consortium from May 2006 to July 2011 had sufficient start up trial data for review. FPFD was defined as the time from the member institutions' receipt of the final protocol (FP) from the trial sponsor, to the time the first patient was dosed on the trial at any participating MMRC member institution. With respect to enrollment, pre-study enrollment commitment (EC) established between MMRC and the study sponsor was defined as the total number of subjects committed to receive at least one dose of study drug across all participating MMRC centers on a trial; baseline enrollment timeline (BET) was prospectively defined as the target time period to attain EC. Results: Mean time to FPFD in the recent group of trials (RG; n=18; Sept 08-Jul 11) held steady at 131 calendar days from receipt of FP as compared to 181 days for the early group of trials (EG; n=7; Jun06-Sept08) representing a 28% reduction in time to FPFD. More importantly, there was a 20% decrease in time to FPFD by all participating MMRC centers on any MMRC trial from 189 days in the RG compared to 236 days in the EG representing an important achievement especially in the Phase I/II arena. MMRC trial accrual data was available for 17/25 trials (2 EG trials were missing data and 6 RG trials continue enrolling). The pre-study mean MMRC EC was 44 subjects per trial (n=19 trials; 849 patients); the mean actual MMRC enrollment was 49 subjects per trial (n=19; 935 patients through July 11) representing a 10% over enrollment versus committed enrollment. A total of 17/19 evaluable trials (89%) met their EC; 12/19 trials met EC within BET (71%) of which 8/12 trials (67%) reached EC 34% faster than their BET (representing a mean reduction of 4.5 months). The overall pre-study mean BET for 19 trials was 13.6 months. MMRC's actual mean enrollment timeline was 12.8 months for the group of 17 evaluable trials representing improvement over the original BET by a mean of 10%. Discussion: MMRC's acceleration of clinical trials provides physicians and patients with rapid access to novel compounds; industry with data for important drug development decisions; and academic institutions with more trials of high scientific interest. Even so, our data uncovered opportunities for improvement. The submission time from receipt of the final protocol from the trial sponsor to Scientific Review Committee (SRC) took an average 30 calendar days across all trials. A reduction to half this time could make a difference to patients and therefore warrants further exploration. Conclusion: Today, drug development in multiple myeloma is fast-paced and highly competitive. MMRC's frequent review of trial metrics provides valuable insight to continually speed answers to physicians, patients and industry. Disclosures: Richardson: Multiple Myeloma Research Consortium: Annual grant in support Clinical Trial Project Management. Vij:Multiple Myeloma Research Consortium: Annual grant in support of clinical trial Project Management. Lonial:Multiple Myeloma Research Consortium: Annual grant in support of clinical trial Project Management. Siegel:Multiple Myeloma Research Consortium: Annual grant in support of clinical trial Project Management. Jakubowiak:Multiple Myeloma Research Consortium: Annual grant in support of clinical trial Project Management. Reece:Multiple Myeloma Research Consortium: Annual grant in support of clinical trial Project Management. Jagannath:Multiple Myeloma Research Consortium: Annual grant in support of clinical trial Project Management. Hofmeister:Multiple Myeloma Research Consortium: Annual grant in support of clinical trial Project Management. Stewart:Multiple Myeloma Research Consortium: Annual Grant in support of clinical trial Project Management. Wolf:Multiple Myeloma Research Consortium: Annual grant in support of clinical trial Project Management. Krishnan:Multiple Myeloma Research Consortium: Annual grant in support of clinical trial Project Management. Zimmerman:Multiple Myeloma Research Consortium: Annual grant in support of clinical trial Project Management. Kumar:Multiple Myeloma Research Consortium: Annual grant in support of clinical trial Project Management. Roy:Multiple Myeloma Research Consortium: Annual grant in support of clinical trial Project Management. Fay:Multiple Myeloma Research Consortium: Annual grant in support of clinical trial Project Management. Anderson:Multiple Myeloma Research Consortium: Annual grant in support of clinical trial Project Management.

Abstract Abstract 3803 Background: The MMRC is a non-profit, disease-focused consortium founded in 2004 comprised of 13 North American centers with expertise in multiple myeloma (MM). The MMRC Inc. (Norwalk, CT), MMRC's member institutions, and several pharmaceutical partners work closely to speed early development of new treatment options for MM patients. In December 2007, MMRC headquarters staff implemented multiple project management (PM) business solutions to address trial barriers that delay activation of our phase I-II clinical trials and established trial metric benchmarks considered attainable at our member institutions. In November 2009, we reported initial data1 on trial activation: MMRC trials initiated between Sep08-Jul09 (n=5) demonstrated a 38% decrease in mean time to first patient dosed (FPFD), compared with the Early Group trials (EG) initiated before PM solutions (Jun06-Sept08; n=7 trials). These results also confirmed improvement over published metrics from Dilts et al 2,3. We present additional data on time to FPFD and now enrollment, to assess impact of the MMRC PM resources and processes on trial efficiency. Methods: Twenty-one (21) trials conducted within the MMRC from May 2006 to June 2010 had sufficient trial data for review. Data were collected by MMRC-funded project managers at the clinical centers using a web-based clinical trial management system (CTMS). FPFD was defined as the time from the member institutions' receipt of the final protocol (FP) from the trial sponsor, to the time the first patient was dosed on the trial at any participating MMRC member institution. With respect to enrollment, pre-study enrollment commitment (EC) established between MMRC and the study sponsor was defined as the total number of subjects committed to receive at least one dose of study drug across all participating MMRC centers on a trial; baseline enrollment timeline (BET) was prospectively defined as the target time period to attain EC. Results: Mean time to FPFD was 181 calendar days for the early group trials (EG, n=7) and 122 days for the recent group (RG) trials (n=14, Sep08 – Jun10), representing a 32% reduction of time to FPFD in the RG. Additionally, time from final protocol to first patient dosed at all MMRC centers on a trial decreased 18% from a mean of 7.7 months (EG) to 6.3 months (RG). With respect to MMRC trial enrollment, data was available for 16/21 trials (5 EG and 11 RG). 2 EG trials were missing data and 3 RG trials were still enrolling as of June 2010. The mean MMRC pre-study EC was 39 subjects per trial (n=16 trials; 626 enrollment target); the mean actual MMRC enrollment was 49 subjects per trial (n=16; 783 enrolled through Jun 29, 2010) representing a 25% increase in actual versus committed enrollment. Two trials did not meet MMRC EC: MMRC investigators discontinued their involvement in these trials at approximately 30% target EC due to trial complexity or low patient enrollment. 14/16 evaluable trials (88%) met their EC; 11/16 trials met EC within BET (69%) of which 8/16 trials (50%) reached EC 34% faster than their BET (representing a mean reduction of 4.5 months). The overall pre-study mean BET for 16 trials was 13.1 months. MMRC's actual mean enrollment timeline was 11.3 months for the group of 14 evaluable trials representing improvement over the original BET by a mean of 1.9 months (14%). We believe FP to FPFD is a more meaningful metric beyond that of first patient consented. Moreover, we believe that if all participating trial centers focus their efforts on dosing the first patient and within a targeted timeframe, it may improve our research efforts overall. Conclusion: Development of drugs in the clinical setting has become time and resource intensive. Activating and enrolling trials promptly is a priority for drug development. The MMRC's standardization of processes and support for site-based PM resources results in improved trial metrics. MMRC member centers met or exceeded pre-specified enrollment targets in 88% of the trials analyzed to date. Ongoing monitoring of trial conduct continues to reveal areas where increased focus is needed to realize further trial efficiencies. These trial metrics and measures to improve efficiency may be applied with similar expected benefit in all oncology disciplines. Disclosures: Wear: Multiple Myeloma Research Consortium (MMRC): Employment. Richardson:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC; Millennium: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Johnson & Johnson: Membership on an entity's Board of Directors or advisory committees. Revta:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Vij:Multiple Myeloma Research Consortium (MMRC): Research Funding; Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Fiala:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Lonial:Bristo-Myers Squibb: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene Corporation: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Millenium Pharmaceuticals Inc: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Onyx: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Francis:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Siegel:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC; Celgene: Speakers Bureau; Millennium: Speakers Bureau. Schramm:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Jakubowiak:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC, Research Funding; Millennium: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Honoraria, Speakers Bureau; Centocor Ortho-Biotech: Honoraria, Speakers Bureau; Exelixis: Honoraria, Speakers Bureau; Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Harvey:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Reece:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC; Celgene: Honoraria, Research Funding; Ortho Biotech: Honoraria, Research Funding; Merck: Honoraria, Research Funding; Amgen: Honoraria, Research Funding; Facet: Research Funding; Otsuka: Honoraria, Research Funding. Gul:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Jagannath:Celgene: Honoraria; Millenium: Honoraria; Orthobiotec: Honoraria; Onyx Pharma: Honoraria; Merck: Honoraria; Proteolix: Honoraria; Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. La:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Hofmeister:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Jansak:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Stewart:Millennium: Consultancy; Celgene: Honoraria; Multiple Myeloma Research Consortium: Member Institution of the MMRC. Hagerty:Mayo Clinic: Employment; Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Wolf:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC; Celgene: Speakers Bureau; Millennium: Speakers Bureau; Novartis: Speakers Bureau; Orthobiotech: Speakers Bureau. Davis:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Krishnan:Celgene: Speakers Bureau; Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Duarte:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Zimmerman:Millennium, Celgene: Speakers Bureau. Cisneros:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Kumar:Celgene: Consultancy, Research Funding; Millennium: Research Funding; Merck: Consultancy, Research Funding; Novartis: Research Funding; Genzyme: Consultancy, Research Funding; Cephalon: Research Funding; Bayer: Research Funding; Multiple Myeloma Research Consortium: Member Institution of the MMRC. Birgin:Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC. Ott:Multiple Myeloma Research Consortium (MMRC): Employment. Tasca:Multiple Myeloma Research Consortium (MMRC): Employment. Kelley:Multiple Myeloma Research Consortium (MMRC): Employment. Anderson:Millennium: Consultancy; Multiple Myeloma Research Consortium (MMRC): Member Institution of the MMRC; Celgene: Consultancy; Novartis: Consultancy; Onyx: Consultancy; Merck: Consultancy; Bristol-Myers Squibb: Consultancy; Acetylon: Equity Ownership, Membership on an entity's Board of Directors or advisory committees. Giusti:Multiple Myeloma Research Consortium (MMRC): Employment.

Abstract Diffuse large B-cell lymphoma (DLBCL) is a genetically heterogeneous disease characterized by multiple low-frequency alterations including somatic mutations, copy number alterations (CNAs) and chromosomal rearrangements. We sought to identify previously unrecognized low-frequency genetic events, integrate recurrent alterations into comprehensive signatures and associate these signatures with clinical parameters. For these reasons, our multi-institutional international group assembled a cohort of 304 primary DLBCLs from newly diagnosed patients, 87% of whom were uniformly treated with state-of-the-art therapy (rituximab-containing CHOP regimen) and had long term followup. Tumors were subjected to whole exome sequencing with an extended bait set that included custom probes designed to capture recurrent chromosomal rearrangements. In this cohort, 47% of samples had available transcriptional profiling and assignment to associated disease subtypes. Analytical pipelines developed at the Broad Institute were used to detect mutations (MuTect), CNAs (Recapseq+Allelic Capseq) and chromosomal rearrangements (dRanger+Breakpointer) and assess clonality (Absolute). To analyze formalin-fixed paraffin-embedded tumors without paired normals we developed a method which utilized 8334 unrelated normal samples to stringently filter recurrent germline events and artifacts. Significant mutational drivers were identified using the MutSig2CV algorithm and recurrent CNAs were assessed with GISTIC2.0. In addition, we utilized a recently developed algorithm, CLUMPS2, to prioritize somatic mutations which cluster in 3-dimensional protein structure. With this approach, we identified > 90 recurrently mutated genes, 34 focal amplifications and 41 focal deletions, 20 arm-level events and > 200 chromosomal rearrangements in the DLBCL series. Of note, 33% of the mutational drivers were also perturbed by chromosomal rearrangements or CNAs, underscoring the importance of a comprehensive genetic analysis. In the large DLBCL series, we identified several previously unrecognized but potentially targetable alterations including mutations in NOTCH2 (8%) and TET2 (5%). The majority of identified chromosomal rearrangements involved translocations of potent regulatory regions to intact gene coding sequences. The most frequently rearrangements involved Ig regulatory elements which were translocated to BCL2, MYC, BCL6 and several additional genes with known roles in germinal center B-cell biology. After identifying recurrent somatic mutations, CNAs and chromosomal rearrangements, we performed hierarchical clustering and identified subsets of DLBCLs with comprehensive signatures comprised of specific alterations. A large subset of tumors shared recurrent alterations previously associated with follicular lymphoma including mutations of chromatin modifiers such as CREBBP, MLL2, and EZH2 in association with alterations of TNFRSF14 and GNA13 and translocations of BCL2. This cluster was enriched in GCB-type DLBCLs and contained a subset with select genetic alterations associated with an unfavorable outcome. An additional cohort of tumors was characterized by alterations perturbing B-cell differentiation including recurrent BCL6 translocations or alterations of PRDM1. A subset of these DLBCLs had alterations of NOTCH2 and additional pathway components or mutations of MYD88 in association with TNFAIP3, CD70 and EBF1, a master regulator of B-cell differentiation. An additional group of DLBCLs exhibited frequent MYD88 mutations in association with alterations of CD79B, PIM1, TBL1XR1 and ETV6 and BCL2 copy gain; these tumors were highly enriched for ABC-type DLBCLs. This coordinate signature and additional alterations of p53 pathway components were associated with outcome. We explored bases for the identified genetic alterations in DLBCL by performing an in silico mutational signature analysis. The most frequent mutational signatures were those of spontaneous deamination (aging) and AID with rare cases of microsatellite instability. We also assessed the clonality of identified genetic features to define cancer cell fraction and establish the timing of specific genetic events. The comprehensive genetic signatures of clinically annotated DLBCLs provide new insights regarding approaches to targeted therapy. Disclosures Link: Kite Pharma: Research Funding; Genentech: Consultancy, Research Funding. Rodig:Perkin Elmer: Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding. Pfreundschuh:Boehringer Ingelheim, Celegene, Roche, Spectrum: Other: Advisory board; Roche: Honoraria; Amgen, Roche, Spectrum: Research Funding. Shipp:Gilead: Consultancy; Sanofi: Research Funding; BMS: Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Membership on an entity's Board of Directors or advisory committees; Bayer: Membership on an entity's Board of Directors or advisory committees, Research Funding.

Abstract Background: Follicular lymphoma (FL) is the second most common nodal lymphoma worldwide and remains incurable for most patients (pts). FL is recognized to be a highly heterogeneous disease and a subset of pts experience remarkable poor outcome. In a recent study, 19-26% of pts receiving first-line R-CHOP experienced progression of disease (POD) within 24 months after diagnosis and had a 5-year (yr) overall survival (OS) of only 34-50%, as compared to a 5-yr OS of 90-94% for pts without POD within 24 months (Casulo et al., J Clin Oncol 2015). Consequently, event-free survival at 12 (EFS12) and 24 months (EFS24) have been suggested as novel surrogate endpoints for poor overall survival in clinical trials and useful for patient counseling (Maurer et al., ASH 2014). We have previously shown that a clinicogenetic risk model (m7-FLIPI) that includes the mutation status of seven genes (EZH2, ARID1A, MEF2B, EP300, FOXO1, CREBBP, and CARD11), the FL International Prognostic Index (FLIPI), and the Eastern Cooperative Oncology Group (ECOG) performance status, improves risk stratification for failure-free survival (FFS) and OS in pts with FL receiving first-line immunochemotherapy (Pastore et al., Lancet Oncology 2015). An online tool is available at: http://www.glsg.de/m7-flipi. It would be advantageous to determine prognosis prior to front-line therapy rather than after POD. Thus, we aimed to investigate the predictive utility of the m7-FLIPI for early POD. Patients and Methods: Clinical and mutation data were available from two independent cohorts: 151 pts who received R-CHOP and IFN maintenance as part of a randomized trial of the German Low-Grade Lymphoma Study Group (GLSG), and 107 pts from a population-based registry of the British Columbia Cancer Agency (BCCA) who received R-CVP. Among the latter, 93 (87%) received R-maintenance by intention to treat. All pts had symptomatic, advanced stage or bulky disease considered ineligible for curative radiotherapy, and a biopsy specimen obtained </=12 months prior to the initiation of first-line therapy. POD was defined as progression, relapse, or death due to any cause. POD12 and POD24 were defined based on POD status at 12 and 24 months after therapy initiation, respectively. Logistic regression analysis was performed to assess if m7-FLIPI was predictive of early POD using the statistical software R (version 3.1.2). Results: In the GLSG cohort, median age was 57 yrs (range 27-77), 51% had high-risk FLIPI, and 5-yr failure-free survival (FFS) and OS rates were 66% and 83%, respectively (median follow-up for OS 7.7 yrs). In the BCCA cohort, median age was higher (62 yrs, 37-83), but high-risk FLIPI was similarly frequent (50%); 5-yr FFS and OS rates were 58% and 74%, respectively (median follow-up for OS 6.7 yrs). A total of 43 GLSG (28%) and 24 BCCA pts (22%) were classified as high-risk by m7-FLIPI, with a 5-yr OS of 65% and 42%, respectively. In the GLSG cohort, 8 (5%) and 29 pts (19%) had POD within 12 and 24 months, respectively, 4 and 10 pts were censored before POD12 and POD24 (table). High-risk m7-FLIPI pts were significantly more likely to experience early POD with an Odds Ratio (OR) of 20.80 (95% confidence interval (CI) 3.53-395.96; p=0.0052) for POD12 and 6.33 (95% CI 2.67-15.69; p<0.0001) for POD24. Likewise, in the BCCA cohort, 16 (15%) and 28 pts (26%) experienced POD12 and POD24, respectively (table). The OR for high-risk m7-FLIPI pts was 4.69 (95% CI 1.52-14.65; p=0.0068) for POD12, and 5.36 (95% CI 2.03-14.60; p=0.0008) for POD24. In the GLSG and BCCA cohorts, the m7-FLIPI had a sensitivity of 88% and 50%, a specificity of 75% and 82%, a positive predictive value (PPV) of 16 and 33%, and a negative predictive value (NPV) of 96% and 90% to predict POD12. For POD24, sensitivity was 62% and 46%, specificity 79% and 86%, PPV 42% and 54%, and NPV 82% and 82%, respectively. Conclusion: We conclude that patients classified as high-risk m7-FLIPI are highly enriched among those with early progression of disease. However, approximately one-half of patients with progression by 24 months after therapy initiation are classified as low-risk m7-FLIPI. Thus, further efforts are needed to refine the m7-FLIPI and thereby capture additional cases that may benefit from innovative first-line approaches. Table. Population m7-FLIPI N POD12 POD24 GLSG Total 151 8/147* 29/141* High-risk 43 7 18 Low-risk 108 1 11 BCCA Total 107 16/107 28/107 High-risk 24 8 13 Low-risk 83 8 15 *Pts censored were removed Disclosures Ansell: Bristol-Myers Squibb: Research Funding; Celldex: Research Funding.

One-third of peripheral T-cell lymphomas are “not otherwise specified” (PTCL-NOS), but they have been subdivided into 2 subgroups based on gene expression profiling. Amador and colleagues generated an immunohistochemical algorithm that parallels the molecular separation of PTCL-NOS and provides useful prognostic information.

Abstract Primary mediastinal large B-cell lymphoma (PMBCL) is recognized as a distinct entity in the World Health Organization classification. Currently, the diagnosis relies on consensus of histopathology, clinical variables, and presentation, giving rise to diagnostic inaccuracy in routine practice. Previous studies have demonstrated that PMBCL can be distinguished from subtypes of diffuse large B-cell lymphoma (DLBCL) based on gene expression signatures. However, requirement of fresh-frozen biopsy material has precluded the transfer of gene expression–based assays to the clinic. Here, we developed a robust and accurate molecular classification assay (Lymph3Cx) for the distinction of PMBCL from DLBCL subtypes based on gene expression measurements in formalin-fixed, paraffin-embedded tissue. A probabilistic model accounting for classification error, comprising 58 gene features, was trained on 68 cases of PMBCL and DLBCL. Performance of the model was subsequently evaluated in an independent validation cohort of 158 cases and showed high agreement of the Lymph3Cx molecular classification with the clinicopathological diagnosis of an expert panel (frank misclassification rate, 3.8%). Furthermore, we demonstrate reproducibility of the assay with 100% concordance of subtype assignments at 2 independent laboratories. Future studies will determine Lymph3Cx’s utility for routine diagnostic purposes and therapeutic decision making.

SummaryMantle cell lymphoma (MCL) is still considered incurable and the course of the disease is highly variable. Established risk factors include the Mantle Cell Lymphoma International Prognostic Index (MIPI) and the quantification of the proliferation rate of the tumour cells, e.g. by Ki‐67 immunohistochemistry. In this study, we aimed to validate the prognostic value of the gene expression‐based MCL35 proliferation assay in patient cohorts from randomized trials of the European Mantle Cell Lymphoma Network. Using this assay, we analysed the gene expression proliferation signature in routine diagnostic lymph node specimens from MCL Younger and MCL Elderly trial patients, and the calculated MCL35 score was used to assign MCL patients to low (61%), standard (27%) or high (12%) risk groups with significantly different outcomes. We confirm here in our prospective clinical trial cohort of MCL patients, that the MCL35 assay is strongly prognostic, providing additional information to the Ki‐67 index and the MIPI. Thus, this robust assay may assist in making treatment decisions or in devising risk‐adapted prospective clinical trials in the future.

Key PointsA new molecular assay identifies conventional and leukemic nonnodal MCL with differing clinicobiological features. The integration of the novel assay with genetic alterations identifies subsets of MCL patients with different management and outcome.

Key Points HGBL-DH/TH makes up 8% of de novo DLBCL, with HGBL-DH/TH with BCL2 rearrangement being a GCB phenomenon. Restricting FISH testing to tumors with dual protein expression and GCB subtype results in testing <15% of tumors, but missing ∼35% of HGBL-DH/TH.