Central Nervous System Involvement By Mantle Cell Lymphoma

Background: While extranodal involvement by mantle cell lymphoma (MCL) is relatively common, involvement of the central nervous system (CNS) is rare (<5% of cases), with limited treatment options. We report the outcomes of 36 patients (pts) with CNS involvement compared to 72 matched control MCL pts without CNS involvement. Methods: MCL pts with CNS involvement seen at Mayo Clinic between 1/1995-9/2020 were identified using the Mayo Data Explorer tool. CNS involvement was defined by tissue biopsy confirmed CNS MCL, CSF analysis demonstrating lymphoma cells, and/or neuroimaging findings compatible with CNS involvement. A 2:1 control group of MCL pts without CNS involvement, matched by age (+/- 2 years) and year of diagnosis (+/- 1 year), was selected among all MCL cases. Medical records were reviewed for baseline characteristics, treatment modalities, and outcomes. Kaplan-Meier method was used for time to event analysis. Wilcoxon test was used to compare continuous variables and Chi square test was used for categorical variables. Results: Out of 1,753 pts with MCL, 36 (2%) had evidence of CNS involvement, including 4 pts with CNS involvement at initial MCL diagnosis. Baseline characteristics of pts with CNS involvement (CNS MCL group) and those without CNS involvement (control group) are shown in Table 1. At MCL diagnosis, non-CNS extranodal involvement was seen in 30 (83%) pts in the CNS MCL group (24 pts with 1 site and 6 pts with ≥ 2 sites), with bone marrow being the most common extranodal site of involvement (n=24, 67%). For the control group, 54 (75%) pts had extranodal involvement (44 pts with 1 site and 10 pts with ≥ 2 sites), and bone marrow was also the most common extranodal site of involvement (n=50, 69%). Notably, advanced stage disease (stage 3-4) was more commonly seen in the CNS MCL group (n=32, 97%) than in the control group (n=59, 83%) (p=0.04) at MCL diagnosis. Blastoid variant was present in a higher proportion of pts in the CNS MCL group (n=11, 31%) compared to the control group (n=8, 11%) (p=0.02). The CNS MCL group also presented with a higher median serum LDH at diagnosis (239 U/L [range 153-1901] vs. 187 U/L [range 124-588], p=0.02), and higher Ki-67 (40% [range 15-100] vs. 30% [range 10-90], p=0.04) compared to the control group. The most common frontline treatment regimen was anthracycline-based therapies (i.e. R-CHOP, Nordic regimen, R-hyperCVAD) for both groups (58% in CNS MCL group and 56% in control group). 14 (39%) pts in the CNS MCL group underwent autologous stem cell transplant in CR1 vs. 31 pts (43%) in the control group. Similar use of rituximab maintenance was seen in both groups (31% in CNS MCL group and 25% in control group). Median total lines of therapy from initial MCL diagnosis was 3 (range 1-9) in CNS MCL group and 2 (range 1-9) in the control group. The median follow-up from MCL diagnosis was 134 months (95% CI:119-163) for the entire cohort. Median OS from MCL diagnosis was 50.3 months (95% CI: 20.9-71.1) for the CNS MCL group compared to 97.1 months (95% CI: 82.6-192.7; p=<0.001) for the control group (Figure 1). Median time from MCL diagnosis to CNS involvement was 25 months (range 0-167). Median OS from CNS involvement was 4.7 months (95% CI: 2.3-6.7). At last follow up, 31 (86%) pts were deceased from the CNS MCL group, compared to 38 (52%) pts in the control group. For the CNS MCL group, the causes of death were CNS lymphoma in 10 (32%) pts, systemic lymphoma in 9 (29%) pts, treatment-related complication in 7 (23%) pts, and other/unknown in 5 (16%) pts. For the control group, the causes of death were systemic lymphoma in 15 (39%) pts, treatment-related in 2 (5%) pts, and other/unknown in 21 (55%) pts. Conclusion: In pts with MCL, CNS involvement is associated with worse outcomes as evident by a shorter median OS from initial MCL diagnosis (50 months vs. 97 months). Involvement of the CNS by lymphoma is an important contributor for the shorter OS as suggested by the median OS of only 5 months from CNS involvement. Advanced stage, blastoid variant, elevated LDH, and elevated Ki67 at MCL diagnosis were features more commonly seen in the CNS MCL cohort. Validation of risk factors at initial MCL diagnosis associated with CNS involvement and exploring the role of CNS prophylaxis are important topics for further investigation. Figure 1 Figure 1. Disclosures Paludo: Karyopharm: Research Funding. Wang: Novartis: Research Funding; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees; MorphoSys: Research Funding; InnoCare: Research Funding; Eli Lilly: Membership on an entity's Board of Directors or advisory committees; LOXO Oncology: Membership on an entity's Board of Directors or advisory committees, Research Funding; Incyte: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Research Funding. Bennani: Purdue Pharma: Other: Advisory Board; Daichii Sankyo Inc: Other: Advisory Board; Kyowa Kirin: Other: Advisory Board; Vividion: Other: Advisory Board; Kymera: Other: Advisory Board; Verastem: Other: Advisory Board. Nowakowski: Celgene, MorphoSys, Genentech, Selvita, Debiopharm Group, Kite/Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene, NanoString Technologies, MorphoSys: Research Funding. Witzig: Karyopharm Therapeutics, Celgene/BMS, Incyte, Epizyme: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene/BMS, Acerta Pharma, Kura Oncology, Acrotech Biopharma, Karyopharm Therapeutics: Research Funding. Habermann: Seagen: Other: Data Monitoring Committee; Tess Therapeutics: Other: Data Monitoring Committee; Incyte: Other: Scientific Advisory Board; Morphosys: Other: Scientific Advisory Board; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Ansell: Bristol Myers Squibb, ADC Therapeutics, Seattle Genetics, Regeneron, Affimed, AI Therapeutics, Pfizer, Trillium and Takeda: Research Funding.

Myeloablative Fractionated Busulfan Conditioning Regimen with Sorafenib in Patients with AML: Results of Phase I Clinical Trial

Background: Myeloablative conditioning can be given safely to older patients by administering busulfan over a longer period (fractionated busulfan regimen) than the standard four-day regimen. (Popat, et al Lancet Haematology 2018). This longer conditioning regimen duration allows the addition of oral targeted agents like sorafenib, which may be synergistic with conditioning chemotherapy and thus further improve disease control. Therefore, we added sorafenib to fludarabine and fractionated busulfan regimen (f-bu) in a phase 1 dose-finding trial studying 4 different doses of sorafenib with f-bu (NCT03247088). Here we report the results of this trial. Methods: Between 3/2018 and 6/2021, 24 patients with AML aged 18 to 70 years with adequate organ function and 8/8-HLA matched related or unrelated donors were enrolled prospectively. The dose of sorafenib was varied among the four values 200, 400, 600, and 800 mg administered from day -24 to -5. Dose-limiting toxicity (DLT) was defined as grade 3 or higher regimen-related non-hematologic, non-infectious, non-GVHD toxicity occurring between day -24 and day 3. The Bayesian Model Averaging Continual Reassessment Method (BMA-CRM) with target DLT probability 0.30 was used to choose doses for successive cohorts of 3 patients. The first cohort was treated at the lowest sorafenib dose 200, with all successive cohorts' doses chosen adaptively by the BMA-CRM. The doses and schedules of busulfan and fludarabine were fixed, with f-Bu dose targeting an area under the concentration vs time curve (AUC) of 20,000 ± 12% μmol.min given over 3 weeks. The first two doses of busulfan (80 mg/m2 IV each) were administered on days -20 and -13 on an outpatient basis. The last four Bu doses were calculated to give a total course AUC of 20,000 ± 12% μmol.min and were given as inpatient following each dose of Flu 40 mg/m2 on days -6 through -3. GVHD prophylaxis was post-transplant cyclophosphamide (PTCy) 50mg/kg on days 3 and 4 and tacrolimus. Recipients of unrelated donor grafts also received MMF. All patients were eligible to receive post-transplant maintenance sorafenib after engraftment. Results: The median age was 52 years (range, 30-70). Disease status was CR in 16 (66.6%) patients, CRi in 5 (20.8%), and advanced in 3 (12.5%). Adverse risk karyotype was present in 10 (41.7%) patients. MRD was present in 13 (54.2%). 9 (38%) had mutated flt3. The donor was unrelated in 14 (58%), and peripheral blood stem cells were the graft source in 21(87.5%). Due to the absence of DLTs, the BMA-CRM assigned 200mg, 400mg, 600mg, and 800mg of sorafenib, respectively, to the first 4 cohorts, and the next 4 cohorts were given 800mg. Only 2 dose-limiting skin toxicities were seen, one in cohort 3 with 600mg of sorafenib and the second in cohort 6 with 800mg of sorafenib. 800mg was the final recommended phase 2 dose. The median follow-up in 20 surviving patients was 7.6 months and 1-year progression free survival was 89% (95% CI 75-100%). Other outcomes are summarized in Table 1. Conclusion: Sorafenib can be safely added to the fractionated busulfan regimen. Early data on efficacy appear promising, with an 89% PFS at 1 year of follow up. Figure 1 Figure 1. Disclosures Popat: Bayer: Research Funding; Abbvie: Research Funding; Novartis: Research Funding; Incyte: Research Funding. Hosing: Nkarta Therapeutics: Membership on an entity's Board of Directors or advisory committees. Rezvani: Bayer: Other: Scientific Advisory Board ; AvengeBio: Other: Scientific Advisory Board ; Navan Technologies: Other: Scientific Advisory Board; GSK: Other: Scientific Advisory Board ; Virogin: Other: Scientific Advisory Board ; Affimed: Other: License agreement and research agreement; education grant, Patents & Royalties, Research Funding; Pharmacyclics: Other: Educational grant, Research Funding; Caribou: Other: Scientific Advisory Board; GemoAb: Other: Scientific Advisory Board ; Takeda: Other: License agreement and research agreement, Patents & Royalties. Qazilbash: Bristol-Myers Squibb: Other: Advisory Board; Biolline: Research Funding; Amgen: Research Funding; Oncopeptides: Other: Advisory Board; NexImmune: Research Funding; Angiocrine: Research Funding; Janssen: Research Funding. Daver: Daiichi Sankyo: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; ImmunoGen: Consultancy, Research Funding; Astellas: Consultancy, Research Funding; Genentech: Consultancy, Research Funding; Gilead Sciences, Inc.: Consultancy, Research Funding; Trillium: Consultancy, Research Funding; Glycomimetics: Research Funding; Abbvie: Consultancy, Research Funding; Hanmi: Research Funding; Bristol Myers Squibb: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; FATE Therapeutics: Research Funding; Sevier: Consultancy, Research Funding; Novimmune: Research Funding; Trovagene: Consultancy, Research Funding; Novartis: Consultancy; Jazz Pharmaceuticals: Consultancy, Other: Data Monitoring Committee member; Dava Oncology (Arog): Consultancy; Celgene: Consultancy; Syndax: Consultancy; Shattuck Labs: Consultancy; Agios: Consultancy; Kite Pharmaceuticals: Consultancy; SOBI: Consultancy; STAR Therapeutics: Consultancy; Karyopharm: Research Funding; Newave: Research Funding. Ravandi: Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Jazz: Honoraria, Research Funding; Amgen: Honoraria, Research Funding; AstraZeneca: Honoraria; Novartis: Honoraria; Xencor: Honoraria, Research Funding; Taiho: Honoraria, Research Funding; Astex: Honoraria, Research Funding; AbbVie: Honoraria, Research Funding; Agios: Honoraria, Research Funding; Prelude: Research Funding; Syros Pharmaceuticals: Consultancy, Honoraria, Research Funding. Shpall: Magenta: Consultancy; Bayer HealthCare Pharmaceuticals: Honoraria; Magenta: Honoraria; Adaptimmune: Consultancy; Novartis: Consultancy; Navan: Consultancy; Novartis: Honoraria; Takeda: Patents & Royalties; Affimed: Patents & Royalties; Axio: Consultancy. Mehta: CSLBehring: Research Funding; Kadmon: Research Funding; Syndax: Research Funding; Incyte: Research Funding.

Follicular Lymphoma Tumor-Cell Transcriptional Programs Associate with Distinct Somatic Alterations and Tumor-Immune Microenvironments

Background: Follicular lymphoma (FL) exhibits significant clinical, cellular, molecular, and genetic heterogeneity. Our understanding of FL biology and molecular classifications of FL, to date, has largely been driven by pathologic classification (Grade 1-3b), genetic classification (m7-FLIPI), or gene expression profiling (IR-1/2; Huet-23), along with limited studies on small cohorts or targeted panels. In order to further understand the biological underpinnings and complexity of FL, large-scale and integrated whole exome sequencing (WES) and RNA sequencing (RNAseq) studies are needed. Using a highly-annotated cohort of 93 FL tumors with matched RNAseq, WES, and CyTOF data, we have explored the transcriptomic signature of purified FL B cells and identified unique molecular subsets that are defined by distinct pathway activation, immune content, and genomic signatures. Methods: Frozen cell suspensions from 93 untreated FL (Grade 1-3b) patients' tumor biopsies, enrolled in the University of Iowa/Mayo Clinic Lymphoma SPORE, were used for the study. DNA was isolated from whole tumor cell suspensions, and RNA was isolated from both whole tumor and B cell-enriched cell suspensions. RNAseq and WES were performed in the Mayo Clinic Genome Analysis Core. RNAseq and WES data were processed using a standard pipeline and novel driver genes were identified using Chasm+ driver analysis. Copy number variants were identified from WES data using GISTIC 2.0. NMF clustering and single sample gene set testing for B cell lineage and tumor microenvironment (TME) signatures were performed in R using the NMF and singscore packages. Results: Unsupervised clustering of RNAseq data identified three distinct expression programs which separated patient B cell samples into 3 groups: Group 1 (G1, n=20), Group 2 (G2, n=24), Group 3 (G3, n=43). While no clinical attributes were defined by any single group, G1 consisted of cases that had less aggressive characteristics (63% Stage I-II, 79% FLIPI 0-1). To identify unique transcriptional pathways driving the three expression programs, we scored gene level contributions to NMF expression programs and employed gene set enrichment analysis. This revealed significant pathway association with type-I IFN signaling (G1), DNA repair and stress response (G2), and epigenetic modulation (G3) as differentiating programs between the 3 groups (FDR q<0.001). VIPER master regulator activity inferencing revealed that these pathways were likely being controlled by differential activity in NF-kB, IRFs, STAT1, BCL6, and FOXO1. Each program significantly enriched for, but were not defined by, portions of specific germinal center programs, such as pre-memory (G1), light-zone-to-dark-zone transition (G2), and a pre-light-zone intermediate (G3). We next assessed the connection between B cell programs and the tumor microenvironment (TME) using available paired CyTOF data on 67 cases, which revealed an active TME in G1, with an abundance of CD8 T cell and NK cell populations, a wide variety of immune content in G2 that consisted mostly of Tfh and myeloid cells, and a poorly populated immune compartment in G3 compared to G1 and G2. Finally, somatic driver mutations and copy number alterations from WES were identified and associated with the three clusters. The three groups distinguished themselves by significant enrichment of copy number alterations (TNFAIP3-loss , 1q23-gain, 1q32-gain) in G2, while 10q-loss and mutations in BCL2 and chromatin modifiers (KMT2D and CREBBP) enriched in G3. G1, overall, had lower alteration burden and had weak associations with any specific alterations, suggesting an alternative mechanism for driving the G1 program. Conclusion: In this study, we have identified three unique FL tumor B cell groups, defined by specific transcriptional programs. Pathways such as inflammation, DNA damage response, and chromatin modification contribute most to the differences between B cell samples and group membership. Additionally, each program associated with specific genetic events as well as TME composition, highlighting potential drivers of these B cell states. This study improves the understanding of the mechanisms driving FL tumors and motivates further investigation into transcriptional consequences of genetic events as well as potential tumor intrinsic factors that may influence the TME. Figure 1 Figure 1. Disclosures Maurer: BMS: Research Funding; Genentech: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding. Rimsza: NanoString Technologies: Other: Fee-for-service contract. Link: MEI: Consultancy; Genentech/Roche: Consultancy, Research Funding; Novartis, Jannsen: Research Funding. Habermann: Tess Therapeutics: Other: Data Monitoring Committee; Seagen: Other: Data Monitoring Committee; Incyte: Other: Scientific Advisory Board; Morphosys: Other: Scientific Advisory Board; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Ansell: Bristol Myers Squibb, ADC Therapeutics, Seattle Genetics, Regeneron, Affimed, AI Therapeutics, Pfizer, Trillium and Takeda: Research Funding. King: Celgene/BMS: Research Funding. Cerhan: Genentech: Research Funding; Regeneron Genetics Center: Other: Research Collaboration; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; NanoString: Research Funding. Novak: Celgene/BMS: Research Funding.

Dissecting Richter's Syndrome in a Multiplexed CRISPR-Based Mouse Model Reveals Key Changes in MYC, Interferon and BCR Signaling Underlying Transformation

Richter's syndrome (RS) represents one of the foremost challenges in CLL management, and its pathogenesis remains largely undefined. We recently leveraged CRISPR-Cas9 in vivo gene editing to develop mouse models of RS by engineering multiplexed loss-of-function lesions typical of CLL (Atm, Trp53, Chd2, Birc3, Mga, Samhd1) in early stem and progenitor cells [Lineage - Sca-1 + c-kit + (LSK)] from MDR-Cd19Cas9 donor mice. These animals express Cas9-GFP in a B-cell restricted fashion and the leukemogenic MDR lesion, which mimics del(13q) when the sgRNA-transduced LSK cells are transplanted in CD45.1 immunocompetent recipients. Through these methods, we observed not only development of CLL, but also transformation into RS, and even captured a stage where CLL and RS were co-existing in the same animal (CLL/RS). We hypothesized that the molecular events underlying RS development would be markedly distinct from those of CLL and performed transcriptome analysis of FACS-sorted CLL and/or RS cells (5 CLL, 4 CLL/RS, 10 RS) and normal B cell controls from 4 age-matched wild type MDR-Cd19Cas9 mice. We identified a unique transcriptional profile of RS (ANOVA, FDR<0.1), characterized by upregulation of pathways involved in cell survival and proliferation (E2F/MYC targets, G2-M checkpoint, mitotic spindle). In contrast, genes involved in interferon gamma response, JAK-STAT and BCR signaling were predominantly downregulated. We asked whether these oncogenic circuitries would be recapitulated in human RS. By correlating the differentially expressed genes in murine RS with those of 7 human RS cases (compared to matched CLL), we identified similar pathway dysregulations with >100 commonly altered genes including upregulated cell cycle regulators (CDK1, CCNA2) and downregulated signaling adapters (ITPKB, MAP3K9). To further dissect gene regulatory networks driving transformation in the mouse, we profiled one CLL and one RS case by single cell ATAC sequencing (scATAC-seq). Consistent with the RNA-seq profiles, we detected increased chromatin accessibility of MYC-family associated transcription factor motifs (MAX, MYCN), and reduced accessibility of the pro-inflammatory STAT2 motif in RS (-log10adjP>50). Functionally, decreased interferon gamma responses were confirmed by the reduced ability of RS cells to phosphorylate STAT1 and STAT3 at 5' and 15' after IFN-gamma stimulation, compared to CLL and normal B cells (Western Blot). To define the genetic landscape underlying these changes, we performed whole genome sequencing analysis, and identified loss of chr12 and chr16 as recurrent events in RS (6/8 cases) and CLL/RS (2/2), but not in CLL cases (0/5). Among the genes encoded by these chromosomes, we identified several epigenetic drivers (Dnmt3a, Crebbp, Setd3/4), MAP kinase family members (Map4k5, Mapk1), cytoskeletal regulators (Hcls1, Rhoj), and interferon family receptors (Ifnar1/2, Ifngr2), suggesting that broad epigenetic modifications together with loss of BCR and interferon signaling molecules represent key events of transforming disease. RS cases were also characterized by a significantly higher number of full chromosome amplifications or deletions (median=6; range: 2-9), as compared to CLL or CLL/RS (1; 0-5, P=0.0008), consistent with the high degree of genomic instability observed in human disease. Finally, we asked whether the observed changes would impact RS therapeutic vulnerabilities, and exposed 15 primary murine RS splenocyte samples to 20 drugs in vitro for 24 hours, followed by CellTiter-Glo assessment of cellular viability. We observed strong sensitivity to the BRD4 inhibitor JQ1 and the mTOR inhibitor everolimus (both reported to interfere with MYC signaling, P<0.0001), and to CDK inhibitors (e.g. the CDK4/6 inhibitor palbociclib, P=0.0007), modest activity of the JAK1/2 inhibitor ruxolitinib (P=0.05), and minimal, if any, response to ibrutinib, venetoclax and fludarabine. In conclusion, we define the evolutionary trajectories and therapeutic vulnerabilities of RS in a mouse model, with unique transcriptional, genetic, and epigenetic features, indicative of broad changes in MYC, IFN and BCR signaling pathways and remarkable similarities with human disease. In-depth analyses of BCR signaling and in vivo treatment studies are underway and will refine mechanistic insights into the biology of RS. Disclosures Davids: Surface Oncology: Research Funding; Eli Lilly and Company: Consultancy; Genentech: Consultancy, Research Funding; Takeda: Consultancy; MEI Pharma: Consultancy; Janssen: Consultancy; Verastem: Consultancy, Research Funding; Ascentage Pharma: Consultancy, Research Funding; Pharmacyclics: Consultancy, Research Funding; TG Therapeutics: Consultancy, Research Funding; Astra-Zeneca: Consultancy, Research Funding; Merck: Consultancy; Adaptive Biotechnologies: Consultancy; Research to Practice: Consultancy; AbbVie: Consultancy; MEI Pharma: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Celgene: Consultancy; BeiGene: Consultancy. Letai: Dialectic Therapeutics: Other: equity holding member of the scientific advisory board; Flash Therapeutics: Other: equity holding member of the scientific advisory board; Zentalis Pharmaceuticals: Other: equity holding member of the scientific advisory board. Neuberg: Madrigal Pharmaceuticals: Other: Stock ownership; Pharmacyclics: Research Funding. Wu: Pharmacyclics: Research Funding; BioNTech: Current equity holder in publicly-traded company.

Influence of Treatment Facility Type and Annual Patient Volume on Overall Survival in Patients with Mantle Cell Lymphoma: A National Cancer Database Analysis

Introduction: Mantle cell lymphoma (MCL) is an uncommon hematological malignancy with an estimated incidence of 1 per 100,000 persons per year in the United States and represents only about 5% of all non-Hodgkin lymphomas. Several studies have shown that treatment at academic centers and a higher hospital case volume are associated with improved outcomes for uncommon hematological malignancies, probably due to increased provider expertise and access to novel therapies. Treatment of MCL can be complex given the heterogenous nature of the disease and a frequent need for autologous stem cell transplantation in eligible patients. However, the impact of treatment at an academic center and facility patient volume on the survival of patients with MCL has not been well studied in large cohorts. In this study, we utilized the National Cancer Database (NCDB) to investigate the impact of treatment at an academic center and treatment facility volume on the overall survival (OS) of patients with MCL. Methods: The NCDB was used to identify adult patients (≥ 18 years) with newly diagnosed MCL from 2004 through 2017. For facility patient volume analysis, patients were divided into groups based on the average number of new MCL patients seen annually: Tercile 1 [T1] (1-3 patients/year), Tercile 2 [T2] (4-5 patients/year) and Tercile 3 [T3] (≥6 patients/year). Treating centers were divided into Academic and Non-academic using the NCDB definitions. Academic centers were defined as centers that accessions more than 500 newly diagnosed cancer cases per year, participate in postgraduate medical education in at least four program areas including internal medicine and surgery and participates in cancer-related clinical trials. The primary endpoint was overall survival (OS). Survival analysis was performed using the Kaplan-Meier method and Cox hazards proportional model. Statistical analysis was performed using SPSS version 25. Results: We identified 22,752 patients with MCL during the study period. 9,484 (42%) patients were treated at academic centers and 13,070 (57%) were treated at non-academic centers. In terms of facility patient volume 10,948 patients (48%) were in the T1 group, 4,637 (20%) were in the T2 group and 7,166 (31%) were in the T3 group. No significant differences were found in baseline demographics (age, gender, race/ethnicity, comorbidity scores), socioeconomical variables (insurance type, median income, area of residence) and disease-related factors (B-symptoms, Ann Arbor stage) between patients treated academic vs nonacademic centers, or between patients in T1 vs T2 vs T3 groups. Notably, compared to lower volume facilities, T3 facilities were more likely to be academic centers (T3: 81% vs T2: 42% vs T1: 16%, p<0.001) . After a median follow-up of 3.4 years, the median overall survival (OS) was 5.6 years for the entire cohort. The median OS was inferior for patients treated at lower volume facilities (4.1 years for T1, 5.1 years for T2 and 9.0 years for T3, p<0.001) (Figure 1A). Similarly, the median OS was shorter for patients treated at non-academic centers vs academic centers (4.3 years vs 7.5 years respectively, p<0.001) (Figure 1B). In a multivariate analysis, treatment at a lower patient volume facility (Hazard ratio [HR] Q1= 1.26 [95%CI = 1.18-1.34]) and treatment at a non-academic center (HR = 1.1, 95%CI = 1.01-1.12) were both independent prognostic factors of inferior OS, after adjusting for demographics (age, gender, ethnicity, area of residence) and socioeconomic variables (income and insurance status). Conclusion: Patients with MCL treated at academic and higher volume facilities had a higher OS compared to patients treated at non-academic and lower volume facilities.. Additional research is needed to fully understand the mechanisms behind these differences. Patients with MCL may benefit from an early referral to academic and high-volume centers. Figure 1 Figure 1. Disclosures Munoz: Merck: Research Funding; Portola: Research Funding; Genentech: Research Funding; Incyte: Research Funding; Janssen: Research Funding; Seattle Genetics: Research Funding; Pharmacyclics/Abbvie, Bayer, Gilead/Kite Pharma, Pfizer, Janssen, Juno/Celgene, BMS, Kyowa, Alexion, Beigene, Fosunkite, Innovent, Seattle Genetics, Debiopharm, Karyopharm, Genmab, ADC Therapeutics, Epizyme, Beigene, Servier: Consultancy; Gilead/Kite Pharma, Kyowa, Bayer, Pharmacyclics/Janssen, Seattle Genetics, Acrotech/Aurobindo, Beigene, Verastem, AstraZeneca, Celgene/BMS, Genentech/Roche.: Speakers Bureau; Millennium: Research Funding; Pharmacyclics: Research Funding; Celgene: Research Funding; Physicians' Education Resource: Honoraria; Gilead/Kite Pharma: Research Funding; Kyowa: Honoraria; Bayer: Research Funding; Seattle Genetics: Honoraria; OncView: Honoraria; Targeted Oncology: Honoraria. Paludo: Karyopharm: Research Funding. Habermann: Seagen: Other: Data Monitoring Committee; Incyte: Other: Scientific Advisory Board; Tess Therapeutics: Other: Data Monitoring Committee; Morphosys: Other: Scientific Advisory Board; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Nowakowski: Daiichi Sankyo: Consultancy; Zai Labolatory: Consultancy; TG Therapeutics: Consultancy; Blueprint Medicines: Consultancy; Nanostrings: Research Funding; MorphoSys: Consultancy; Kymera Therapeutics: Consultancy; Incyte: Consultancy; Ryvu Therapeutics: Consultancy; Kyte Pharma: Consultancy; Genentech: Consultancy, Research Funding; Roche: Consultancy, Research Funding; Celgene/Bristol Myers Squibb: Consultancy, Research Funding; Selvita: Consultancy; Curis: Consultancy; Karyopharm Therapeutics: Consultancy; Bantham Pharmaceutical: Consultancy. Wang: Novartis: Research Funding; LOXO Oncology: Membership on an entity's Board of Directors or advisory committees, Research Funding; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees; Genentech: Research Funding; Eli Lilly: Membership on an entity's Board of Directors or advisory committees; MorphoSys: Research Funding; InnoCare: Research Funding; Incyte: Membership on an entity's Board of Directors or advisory committees, Research Funding.

Characteristics, Management and Outcomes of Patients with Intravascular Lymphoma: A Mayo Clinic Experience

Introduction: Intravascular lymphoma (IVL) is an extra nodal non-Hodgkin lymphoma with tropism for vascular endothelium. It is characterized by growth of large cells within the lumen of small to medium sized blood vessels. Central nervous system (CNS) and skin are predominantly involved. This report represents a retrospective single-institution review of IVL. Methods: We identified patients (pts) with IVL evaluated at Mayo Clinic Cancer Center between January 2003 and December 2018. Demographic, clinical, radiologic, pathologic, and therapeutic data were extracted. Statistical analysis of overall survival (OS) and progression free survival (PFS)] was performed using Kaplan-Meier method. Results: Total number of pts was 55; 22% (12/55) had CNS-only IVL, 14.5% (8/55) had CNS and non-CNS IVL, and 63.6% (35/55) had non-CNS IVL. Eighty seven percent (47/54) pts were B cell type, 11% (6/54) were T cell type, one pt had NK cell type IVL and another was unknown. Four pts were diagnosed by autopsy. Median age at diagnosis was 68 years (range, 40-85). Sixty-four percent were males. ECOG performance status was <2 in 66%. The median follow-up time from diagnosis was 63 months [CI 95%, 9-NR], and 47% (26/55) were alive. The most common diagnostic biopsy sites were bone marrow (BM) 45% (25/55), skin 25% (14/55), and brain 29% (16/55). Twenty-nine patients had a PET scan. Seventy nine percent (23/29) had abnormal PET findings, with mean SUV of 8.6 (range 2.5-19.1). Of the 35 pts with non-CNS IVL, 76% (16/21) had abnormal PET; furthermore, the diagnosis was made with biopsies of the following sites: bone marrow 54% (19/35), skin 40% (14/35), lung 14% (5/35), liver 5.7% (2/35), spleen 2.8% (1/35), and omentum 2.8% (1/35). Forty-six percent (13/28) received CNS prophylaxis and ten percent (3/55) had relapse in CNS. Two out of the three pts who had CNS relapse had received CNS prophylaxis. The median time to CNS relapse in non-CNS IVL was 9 months. The most common first-line regimen was high-dose methotrexate+ rituximab containing regimen 62% (10/16) in IVL with CNS involvement and RCHOP (60%) (17/28) in non-CNS IVL. Seventeen percent of (8/48) pts received autologous stem cell transplant (ASCT) and 63% (5/8) pts were transplanted in first complete remission (CR1), and 3 pts after the first relapse. Median OS (mOS) for the whole cohort was 57 months, [CI 95%, 9-NR], and median PFS was 7 months [CI 95%, 2-NR]. There was no significant difference in mOS between groups; CNS-only IVL- 9 months (CI 95%, 1-NR), non-CNS IVL -62 months (CI 95%, 20-NR) vs combined CNS and non-CNS IVL- 4 months (CI 95%, 3-NR). mOS for those who received ASCT in CR1 was not reached (CI 95%, 10-NR) vs 51 months in non-transplant group (CI 95%, 3-NR) p=0.24. In pts with non-CNS IVL, there was no significant difference in mOS between CNS prophylaxis subgroup (NR: CI 95%, 57-NR) vs no-CNS prophylaxis subgroup (20 months: CI 95%, 0-NR), p=0.12. In those with CNS IVL mOS for early diagnosis (0-30 days from symptom onset to diagnosis was NR (CI 95%, 3-NR) vs mOS for late diagnosis (>30 days {31-14,440})-5months (CI 95% 1-NR), p=0.29]. Conclusion: 1. BM was most frequently involved in our patients. We suggest that BM biopsy should be part of diagnostic testing when IVL is suspected. 2. Most cases are of B-cell linage, consistent with reported literature. All non-B cell cases were in non-CNS locations. 3. PET scans were abnormal in more than 70% of cases indicating that this imaging modality is vital in the diagnosis due to odd location and small size of lesions. 4.Overall prognosis in the literature was poor with most patients surviving <1 year. Our cohort has mOS of 57 months. The reason(s) for better survival in our cohort could not be definitively determined. 5. CNS involvement had an overall trend towards poor prognosis; however, those diagnosed early had better outcomes; this did not reach statistical significance due to small sample size. 6. mOS was not reached for those transplanted CR1. There was a trend towards a better survival associated with CNS prophylaxis versus no prophylaxis in non-CNS IVL. 7. We suggest that CNS-centric therapeutic approach and intensive consolidation with ASCT should be considered in managing IVL. Figure 1 Figure 1. Disclosures Nowakowski: Celgene, NanoString Technologies, MorphoSys: Research Funding; Celgene, MorphoSys, Genentech, Selvita, Debiopharm Group, Kite/Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees. Habermann: Incyte: Other: Scientific Advisory Board; Seagen: Other: Data Monitoring Committee; Tess Therapeutics: Other: Data Monitoring Committee; Morphosys: Other: Scientific Advisory Board; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Witzig: Karyopharm Therapeutics, Celgene/BMS, Incyte, Epizyme: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene/BMS, Acerta Pharma, Kura Oncology, Acrotech Biopharma, Karyopharm Therapeutics: Research Funding. Ansell: Bristol Myers Squibb, ADC Therapeutics, Seattle Genetics, Regeneron, Affimed, AI Therapeutics, Pfizer, Trillium and Takeda: Research Funding. Tun: Mundipharma, Celgene, BMS, Acrotech, TG therapeutics, Curis, DTRM: Research Funding; Gossamer Bio, Acrotech: Consultancy.

Integration of Tumor Transcriptomic, Genomic, and Immune Profiles Reveals Distinct Populations of Low-Grade B-Cell Lymphomas with Poor Outcome

Introduction: Low-grade B-cell lymphomas (LGBCL), aside from follicular lymphoma and chronic lymphocytic leukemia/small lymphocytic lymphoma, account for approximately 10% of B-cell non-Hodgkin lymphomas and consist of several subtypes. While a majority of LGBCL cases have an overall favorable prognosis, we have previously shown that cases who have an event (relapse or progression, transformation, or re-treatment) within 24 months of diagnosis (EFS24) have an inferior overall survival (OS) compared to those achieving EFS24 (Tracy et al., AJH 2019;94:658-66). However, the underlying biological characteristics associated with early failure and aggressive disease across LGBCL subtypes are unknown. In this study, we used matched transcriptomic, genomic, and immune profiling data from LGBCL cases, the largest cohort to date, and asked whether there were unique biological phenotypes across different LGBCL subtypes and whether we could identify signatures associated with aggressive LGBCL. Validation of the prognostic utility of this signature was performed on a previously published, independent cohort of 63 pre-treatment LGBCL cases. Methods: Tumors from 64 newly diagnosed LGBCL patients from the Molecular Epidemiology Resource of the University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence were included in this study (SMZL (n = 48), NMZL (n = 6), LPL (n = 5), B-NOS (n = 3), EMZL (n = 2)). RNA sequencing (RNAseq) data from 61 LGBCL tumors and 5 benign CD19+CD27+ memory B samples was subjected to NMF clustering to define groups. Differential expression and pathway analysis were used to identify biological characteristics of each cluster. CIBERSORT was used to identify immune cells in the tumor microenvironment. Whole exome sequencing (WES) was performed on 61 tumor-normal pairs. Singscore was used to assign a single score per patient representing gene expression of the survival-associated transcriptomic signature identified in this study. Results: NMF analysis of RNAseq data identified 5 clusters of patients, denoted LGBCL1-5 (Fig 1A). Patients from the same diagnostic subtype did not exclusively cluster together, with all LGBCL clusters comprised of patients from multiple subtypes (Fig 1B). Exploring the association between patient cluster and outcome, we observed significantly inferior event-free survival (EFS) (HR 2.24; 95% CI 1.01-4.98) and overall survival (OS) (HR 5.59; 95% CI 2.00-15.63) in LGBCL5 patients compared to LGBCL1-4 (Fig 1C). In addition, 80% of the transformation cases in our cohort were classified as LGBCL5 (Fig 1D). Differential expression and pathway analysis showed distinct processes significantly upregulated in each cluster (FDR < 0.05), with LGBCL5 demonstrating enrichment of cell cycle and mitosis pathways. CIBERSORT identified increased immune cell content in LGBCL3 and LGBCL5 compared to other clusters, with high frequencies of mast cells in both (p = 0.0002), increased CD8 T cells in LGBCL3 (p < 0.0001), and increased T follicular helper cells in LGBCL5 (p = 0.004). WES identified previously reported alterations in NOTCH, NFkB, and chromatin remodeling pathways and novel variants in LGBCL, including mutations in HNRNPK, CLTC, HLA-A, HLA-B and HLA-C. Assessment of alterations by cluster showed significant enrichment of TNFAIP3 (OR 5.54; 95% CI 1.20-28.14) and BCL2 alterations (OR 5.49; 95% CI 1.07-32.02) in LGBCL5 cluster. Finally, we identified a cell cycle-related transcriptomic signature of 108 genes upregulated in LGBCL5 and EFS24 failure cases. Cases with high expression of this signature showed significantly inferior EFS (HR 14.25; 95% CI 4.90-41.38) and OS (HR 7.82; 95% CI 2.40-25.44) compared to cases with low expression in our discovery cohort. This observation was reproduced in an independent validation cohort, where patients with high expression of this signature demonstrated significantly inferior EFS (HR 5.70; 95% CI 1.49-21.79) and OS (HR 10.07; 95% CI 2.00-50.61). Conclusions: In this study, we are the first to define mechanisms of pathogenesis in LGBCL with shared transcriptomic, genomic, and immune profiles present across LGBCL subtypes. We then further defined a gene expression signature associated with inferior patient outcome, with application of this signature to an independent validation cohort demonstrating proof of concept and utility of this signature as a prognostic marker in LGBCL patients. Figure 1 Figure 1. Disclosures Maurer: Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Research Funding; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding; Nanostring: Research Funding. Paludo: Karyopharm: Research Funding. Habermann: Tess Therapeutics: Other: Data Monitoring Committee; Morphosys: Other: Scientific Advisory Board; Incyte: Other: Scientific Advisory Board; Seagen: Other: Data Monitoring Committee; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Link: MEI: Consultancy; Genentech/Roche: Consultancy, Research Funding; Novartis, Jannsen: Research Funding. Rimsza: NanoString Technologies: Other: Fee-for-service contract. Ansell: Bristol Myers Squibb, ADC Therapeutics, Seattle Genetics, Regeneron, Affimed, AI Therapeutics, Pfizer, Trillium and Takeda: Research Funding. Cerhan: Genentech: Research Funding; Regeneron Genetics Center: Other: Research Collaboration; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; NanoString: Research Funding. Novak: Celgene/BMS: Research Funding.

Maturity State and MCL-1 Dependence Predetermines Response to NOTCH1 Inhibition in T-ALL

Introduction: Acute T cell lymphoblastic leukemia (T-ALL) is an aggressive hematopoietic malignancy in children and young adults that frequently becomes treatment-refractory and relapses. The Notch1 pathway is a key oncogenic driver in T-ALL and is aberrantly activated in more than 50% of the cases. Despite promising pre-clinical data using gamma secretase inhibitors such as DBZ to target NOTCH1, resistance is rapidly occurring in vivo. As molecular heterogeneity has been linked to treatment escape, we focused our study on defining transcriptional cell states driving resistance to NOTCH inhibition and understanding their relation to mitochondrial priming. Methods: 5 primary T-ALLs harboring NOTCH activating mutations were engrafted in NSG (NOD-scidIL2Rgnull) mice. Upon reaching ~ 10% of human CD45+ positive leukemic blasts in the peripheral blood, randomized groups of 8 mice per primary T-ALL were treated with DBZ (Dipenzazepine; 10 μM/kg every other day through tail vein) or vehicle (VEH). 3 mice per group were sacrificed after one week of treatment to assess short-term effect of DBZ, while the remaining 5 mice were weekly monitored for disease progression, leukemic blasts were collected from lymphoid organs and overall survival was determined. Full-length transcriptome analysis of 3188 blasts present in the blood of 20 sensitive and 22 refractory mice was performed by Smart-Seq2. Based on scRNA features, 'scVelo' and 'CytoTRACE' were used to identify developmental potential and differentiation trajectories. Cell fate and transcriptional regulatory networks were defined and reconstructed using 'SCENIC'. Assessment of mitochondrial priming as measured by BH3 profiling was used to identify anti-apoptotic vulnerabilities present in these PDX models. Results: Upon DBZ, short or long-term disease control was observed in two strains, while rapid resistance occurred in three strains, thus establishing two sensitive and three refractories to NOTCH inhibition PDX models. Immunohistochemical analysis showed decreased expression of active NOTCH1 in spleen biopsies of all strains, validating the efficacy of DBZ and suggesting a mechanism of resistance independent of ICN1. Single cell transcriptional profiling showed enrichment of immature hematopoietic signatures and co-expression of lymphoid and myeloid progenitor programs in refractory models. Interestingly, pre-existing cells harboring refractory-like transcriptional circuits within the untreated sensitive population were identified. Upon treatment, despite increased differentiation in all models, lineage promiscuity was maintained in refractory strains, suggesting that cellular plasticity mediates treatment escape. Next, we characterized cell states driving treatment refraction. RNA velocity projections identified two distinct immature states differing in cell cycle and oncogenic signaling. Clustering of untreated, sensitive leukemic cells in immature state imply that aberrant lineage commitment can predict response to NOTCH inhibition in vivo. These observations were further confirmed by differentiation state analysis, where prior to treatment, high developmental potential was correlated to treatment escape. Surprisingly, in addition to early lineage differentiation drivers such as BCL11A, state-specific regulons analysis associated immature states with BCLAF1 a transcriptional regulator of apoptosis. We postulated that these transcriptional circuits lead to differential apoptotic priming, therefore the dependence on individual anti-apoptotic proteins was evaluated. Mitochondrial priming at baseline revealed BCL-2 dependence in sensitive strains whereas MCL1-dependence was observed in refractory ones. Upon DBZ treatment, while dependency profiles in refractory strains remained unchanged, a functional switch from BCL-2 to MCL1-dependency occurred in sensitive models. Conclusion: Our results suggest that response to NOTCH inhibition is predetermined by cell maturity states and their associated transcriptional circuits responsible for differential sensitivity to apoptotic priming via BCL2 and MCL1. These data suggest that combining BH3 and lineage commitment profiling may predict drug responses in vivo. Moreover, our findings highlight the importance of targeting co-existing cell states to overcome transcriptional heterogeneity as a driver of treatment escape. Disclosures Letai: Zentalis Pharmaceuticals: Other: equity holding member of the scientific advisory board; Dialectic Therapeutics: Other: equity holding member of the scientific advisory board; Flash Therapeutics: Other: equity holding member of the scientific advisory board. Weinstock: Daiichi Sankyo: Consultancy, Research Funding; Verastem: Research Funding; Abcuro: Research Funding; Bantam: Consultancy; ASELL: Consultancy; SecuraBio: Consultancy; AstraZeneca: Consultancy; Travera: Other: Founder/Equity; Ajax: Other: Founder/Equity.