Mutation Burden in Multiple Myeloma Is Captured By Gene Expression Profiles

Abstract In the current study we sought to determine whether mutation burden (MB) is reflected in gene expression patterns. We generated 389 gene expression profiles and 311 mutation profiles from 182 cases, split into a training set of 97 and test set of 84. Copy number variants (CNV), rearrangements (TX) and short variant mutations (SV) were identified with FoundationOne Heme (F1) and U133Plus2.0 (u133) gene expression data, GEP70 and GEP80 risk scores, subgroup and CNV calls provided by Signal Genetics. 145 Kegg pathways, transcription factor binding sites (TFbs) for 195 TF mapped to u133 genes and 1161 F1 features and u133 GEP signatures/scores were evaluated for enrichment of defined genes. u133 data for normal plasma cells (n=22), MGUS (n=44), smoldering MM (n=12), relapsed MM (n=76), and MM cell lines (n=42) were derived from GSE31162. Newly diagnosed MM samples came from GSE31162 (n=584), GSE19784 (n=321), GSE15695 (n=247) and E-MTAB-317 (n=233). Of 593 genes assayed by F1, 293 were mutated at least twice. There were a total of 3454 mutations (average = 11, minimum = 3, and maximum = 37). KRAS was mutated in 40 tumors, while TP53 was mutated 45 times in 31 tumors. TP53 and 62 other genes had 2 or more unique mutations in a single tumor. A linear curve of MB exhibited a sharp upward inflection at 19 mutations. We sought to determine if GEP could identify characteristic features of MM flanking the inflection point. A training set of 97 (86 <= 15 MB and 11 >= 21 MB) and a test set of 84 (49 <= 15 MB, 28 >15 but < 21 MB and 7 >= 21MB) was produced. A mean ratio identified 576 genes exhibiting 2-fold higher expression in MM with high MB (hiMB) and 1617 genes from low MB (loMB). Notably, forty-four of the 293 mutated genes were in this list of genes. A geometric mean ratio of the two gene sets was then calculated for all samples. The mean of the resulting score (MB.2) was higher in MM with hiMB (1.66) than MM with loMB (-0.235) in the training set. MM with hiMB (0.329) had a higher MB.2 score than the group with intermediate MB.2 (0.158) and both higher than MM with loMB (-0.122). MB.2 was lowest in normal PC (-0.518) and progressively increased with disease progression: MGUS (-0.341), SMM (-0.308), MM (0.199), relapsed MM (0.334), MM cell lines (1.168).[SP1] 57% of the 182 cases harbored only SV mutations, 32% had SV and CNV, 32% SV and TX and 7% had SV, CNV and TX mutations. SV only mutations were present in 76% of MB.2 quartile 1 (MBq1) and 30% of MBq4. SV, CNV and TX mutations were present in 4% of MBq1 and 17% of MBq4. MB.2 was positively correlated with GEP70, GEP80, proliferation index, and TP53 target genes in MM and genes modulated by thalidomide and dexamethasone in PGx studies, in at least 6 of the 7 cohorts studied. The CD2 subtype, a myeloid classification and GEP70 low risk were significantly overrepresented in both MBq1 and GEP70q1 in all 7 cohorts. Conversely, the MF, MS, and PR subtypes, GEP70 and GEP80 high risk, as well as +1q, amp1q21, and del13q were significantly overrepresented in MBq4 and GEP70q4 in all 7 cohorts. MB.2 [SP2] genes derived from MM with loMB where enriched in 45 of 148 Kegg pathways. Notable were Hedgehog, Prostaglandin, Tx factors in cancer, HOX, MYB signaling, ephrin-B reverse signaling and embryonic stem cells. Five pathways related to B-cell biology were enriched. Mitotic cell cycle, integrin signaling, chromatin acetylation, ubiquitin ligation, and G1 to G1/S were underrepresented. MB.2 genes from MM with hiMB were enriched in TP53, lipid lysis, complement cascade, adherens junctions, Wnt regulation of CYR61, cyclins, prostaglandins, cell cycle, and MYC target pathways. Interferon signaling, TNF-NF-kB, EGFR, NOD, endoplasmic reticulum, ubiquitin ligation, Wnt-Hedgehog-NOTCH and BMP-SMAD modules were underrepresented. An enrichment of Rel and NF-kB TFbs was observed for genes negatively correlated with MB.2. and genes positively correlated with MB.2 and GEP70 were enriched for E2F and TP53 binding site. In conclusion, we show that MB can be captured by GEP in MM, that MB increases with disease progression, and pathways enriched by hiMB and loMB are different and may imply differences in pathogenesis as well as treatment. [SP1]This is an important finding - suggest emphasizing more [SP2]Starting with this para suggest referring to groups are low vs high mutation burden for improved readability. Disclosures Shaughnessy: Signal Genetics: Consultancy, Patents & Royalties. Cho:Genentech Roche: Membership on an entity's Board of Directors or advisory committees, Research Funding; Agenus, Inc.: Research Funding; Janssen: Consultancy, Research Funding; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees, Research Funding; Ludwig Institute for Cancer Research: Membership on an entity's Board of Directors or advisory committees. Chari:Novartis: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Array Biopharma: Consultancy, Research Funding; Amgen Inc.: Honoraria, Research Funding; Pharmacyclics: Research Funding. van Laar:Signal Genetics, Inc.: Employment. Jagannath:Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bristol Myer Squibb: 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. Barlogie:Signal Genetics: Patents & Royalties.

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Gene Expression and Cytokine Analyses Identify Markers of Progression from CLL-like Monoclonal B-Cell Lymphocytosis to Chronic Lymphocytic Leukemia

Blood ◽

10.1182/blood-2019-122038 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 3027-3027

Author(s):

Gonzalo Blanco ◽

Anna Puiggros ◽

Barbara Sherry ◽

Lara Nonell ◽

Eulalia Puigdecanet ◽

...

Keyword(s):

Gene Expression ◽

B Cells ◽

B Cell ◽

Board Of Directors ◽

Research Funding ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Advisory Committees ◽

Cytokine Levels

INTRODUCTION: Chronic lymphocytic leukemia (CLL)-like monoclonal B cell lymphocytosis (MBL) is considered a precursor of CLL. It is found in 5-10% of elderly healthy individuals and shows a progression rate to CLL requiring therapy of 1.1% per year. A balance between microenvironmental factors and intrinsic properties of the emerging B cell clone may be decisive for the transition from MBL to CLL, although biomarkers of progression remain unknown. The objective is to describe biological markers (B cell gene expression profiles and serum cytokine levels) that predict progression from MBL to CLL. METHODS: Gene expression profiles of clonal B cells from 14 MBL subjects (median age: 76 years, clonal B cells: 0.5-4.3 x109/L) were evaluated. With a median follow-up from analysis of 59 months (range: 10-77), 3 cases (21.4%) had progressed to CLL Binet stage A at last follow-up (clonal lymphocytosis >5x109/L, range: 6.2-7.9). Clonal B cells (CD19+CD5+) were isolated from peripheral blood by immunomagnetic methods (Miltenyi Biotec). Extracted RNA (RIN>7) was hybridized to GeneChip Human Gene 2.0 ST arrays (Affymetrix). Gene expression profiles were compared between MBL cases that progressed to CLL (P-MBL, n=3) and non-progressive MBL cases (NP-MBL, n=11). Differential gene expression was evaluated employing linear models for microarrays in R, and genes with P<0.05 and Fold Change >1.5 or <-1.5 were considered differentially expressed. To obtain insight into the functional significance of the differential genetic signatures, the Ingenuity Pathway Analysis tool (IPA, QIAGEN) was employed. On the other hand, serum levels of IL1β, IL2, IL4, IL5, IL6, IL8, IL10, IL12, IL15, IL17, IFNα, IFNγ, TNFα, GM-CSF, CCL3, CCL4, CCL19, CXCL9, CXCL10 and CXCL11 were quantified using the U-PLEX Platform (Meso Scale Discovery) and Human CXCL9/MIG Quantikine ELISA Kit (R&D Systems) in 41 MBL subjects (median age: 67 years, clonal B cells: 0.5-4.8 x109/L). With a median follow-up from analysis of 47 months (range: 0-117), 5 of them (12.2%) had progressed to CLL Binet stage A at last follow-up (clonal lymphocytosis >5x109/L, range: 6.4-17.3). Clonal B cells and cytokine levels were compared between P-MBL (n=5) and NP-MBL (N=36). For cytokine levels, the optimal cut-off values to stratify MBL cases according to their progression risk were assessed using the maxstat R package, whereas for clonal B cells a cut-off value of 3.9 x109/L was considered according to the results obtained by Kostopoulos et al (Blood Cancer J, 2017). The effect of different covariates on progression-free survival was evaluated using log-rank test. Cox proportional hazards regression models were performed to assess their independent prognostic value. P<0.05 was considered significant. RESULTS: A total of 455 genes were differentially expressed (250 upregulated and 205 downregulated in P-MBL). IPA predicted an inhibition of apoptosis as well as proteins with tumor suppressor activity (SMARCA4) in P-MBL, besides enhanced bioenergetic processes (transmembrane potential of mitochondria) and anti-inflammatory features (activation of IL13 pathway and decreased chemotaxis of phagocytes and granulocytes) (Table 1). P-MBL displayed increased clonal B cells (4.2 vs. 1.7 x109/L, P=0.003) and levels of IL10 (1.15 vs. 0.9 pg/mL, P=0.087) as well as diminished levels of IL6 (2.04 vs. 3.75 pg/mL, P=0.041). MBL cases with ≥3.9 x109/L clonal B cells, ≥1.08 pg/mL of IL10 and ≤2.04 pg/mL of IL6 had an increased risk of progression to CLL (P<0.001, P=0.006 and P=0.034, respectively) (Figure 1, Table 2). Multivariate analysis for clonal B cells and levels of IL10 maintained significance for both factors (HR=12.8, P=0.013 and HR=10.2, P=0.047, respectively) (Table 2). CONCLUSIONS: 1. P-MBL cases showed an inhibition of the apoptotic pathway and an activation of bioenergetic processes, which may account for the increased clonal B cells observed in this group. 2. P-MBL exhibited enhanced anti-inflammatory features, including augmented levels of the anti-inflammatory cytokine IL10. 3. Increased clonal B cells and IL10 levels predicted a higher risk of progression to CLL, suggesting that an augmented proliferative rate of clonal B cells together with a supporting tumor microenvironment are required for progression from MBL to CLL. ACKNOWLEDGEMENTS. PI11/01621, PI15/00437, 2017/SGR437, Fundació La Caixa, Fundación Española de Hematología y Hemoterapia (FEHH). Disclosures Gimeno: JANSSEN: Consultancy, Speakers Bureau; Abbvie: Speakers Bureau. Rai:Cellectis: Membership on an entity's Board of Directors or advisory committees; Genentech/Roche: Membership on an entity's Board of Directors or advisory committees; Astra Zeneca: Membership on an entity's Board of Directors or advisory committees; Pharmacyctics: Membership on an entity's Board of Directors or advisory committees. Abrisqueta:Roche: Consultancy, Honoraria, Other: Travel, Accommodations, expenses, Speakers Bureau; Abbvie: Consultancy, Honoraria, Other: Travel, Accommodations, expenses, Speakers Bureau; Janssen: Consultancy, Honoraria, Other: Travel, Accommodations, expenses, Speakers Bureau; Celgene: Consultancy, Honoraria. Bosch:AbbVie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Kyte: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; F. Hoffmann-La Roche Ltd/Genentech, Inc.: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Honoraria, Research Funding; Acerta: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; AstraZeneca: Honoraria, Research Funding; Takeda: Honoraria, Research Funding.

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Distinct Gene Expression Signatures in Patients with Richter's Syndrome and Chronic Lymphocytic Leukemia with Prior Exposure to Ibrutinib

Blood ◽

10.1182/blood-2020-136172 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 30-31

Author(s):

Hanyin Wang ◽

Shulan Tian ◽

Qing Zhao ◽

Wendy Blumenschein ◽

Jennifer H. Yearley ◽

...

Keyword(s):

Gene Expression ◽

B Cell ◽

Board Of Directors ◽

Research Funding ◽

Cell Activation ◽

Expression Profiles ◽

Lymphocytic Leukemia ◽

B Cell Activation ◽

Advisory Committees ◽

Upregulated Genes

Introduction: Richter's syndrome (RS) represents transformation of chronic lymphocytic leukemia (CLL) into a highly aggressive lymphoma with dismal prognosis. Transcriptomic alterations have been described in CLL but most studies focused on peripheral blood samples with minimal data on RS-involved tissue. Moreover, transcriptomic features of RS have not been well defined in the era of CLL novel therapies. In this study we investigated transcriptomic profiles of CLL/RS-involved nodal tissue using samples from a clinical trial cohort of refractory CLL and RS patients treated with Pembrolizumab (NCT02332980). Methods: Nodal samples from 9 RS and 4 CLL patients in MC1485 trial cohort were reviewed and classified as previously published (Ding et al, Blood 2017). All samples were collected prior to Pembrolizumab treatment. Targeted gene expression profiling of 789 immune-related genes were performed on FFPE nodal samples using Nanostring nCounter® Analysis System (NanoString Technologies, Seattle, WA). Differential expression analysis was performed using NanoStringDiff. Genes with 2 fold-change in expression with a false-discovery rate less than 5% were considered differentially expressed. Results: The details for the therapy history of this cohort were illustrated in Figure 1a. All patients exposed to prior ibrutinib before the tissue biopsy had developed clinical progression while receiving ibrutinib. Unsupervised hierarchical clustering using the 300 most variable genes in expression revealed two clusters: C1 and C2 (Figure 1b). C1 included 4 RS and 3 CLL treated with prior chemotherapy without prior ibrutinib, and 1 RS treated with prior ibrutinib. C2 included 1 CLL and 3 RS received prior ibrutinib, and 1 RS treated with chemotherapy. The segregation of gene expression profiles in samples was largely driven by recent exposure to ibrutinib. In C1 cluster (majority had no prior ibrutinb), RS and CLL samples were clearly separated into two subgroups (Figure 1b). In C2 cluster, CLL 8 treated with ibrutinib showed more similarity in gene expression to RS, than to other CLL samples treated with chemotherapy. In comparison of C2 to C1, we identified 71 differentially expressed genes, of which 34 genes were downregulated and 37 were upregulated in C2. Among the upregulated genes in C2 (majority had prior ibrutinib) are known immune modulating genes including LILRA6, FCGR3A, IL-10, CD163, CD14, IL-2RB (figure 1c). Downregulated genes in C2 are involved in B cell activation including CD40LG, CD22, CD79A, MS4A1 (CD20), and LTB, reflecting the expected biological effect of ibrutinib in reducing B cell activation. Among the 9 RS samples, we compared gene profiles between the two groups of RS with or without prior ibrutinib therapy. 38 downregulated genes and 10 upregulated genes were found in the 4 RS treated with ibrutinib in comparison with 5 RS treated with chemotherapy. The top upregulated genes in the ibrutinib-exposed group included PTHLH, S100A8, IGSF3, TERT, and PRKCB, while the downregulated genes in these samples included MS4A1, LTB and CD38 (figure 1d). In order to delineate the differences of RS vs CLL, we compared gene expression profiles between 5 RS samples and 3 CLL samples that were treated with only chemotherapy. RS samples showed significant upregulation of 129 genes and downregulation of 7 genes. Among the most significantly upregulated genes are multiple genes involved in monocyte and myeloid lineage regulation including TNFSF13, S100A9, FCN1, LGALS2, CD14, FCGR2A, SERPINA1, and LILRB3. Conclusion: Our study indicates that ibrutinib-resistant, RS-involved tissues are characterized by downregulation of genes in B cell activation, but with PRKCB and TERT upregulation. Furthermore, RS-involved nodal tissues display the increased expression of genes involved in myeloid/monocytic regulation in comparison with CLL-involved nodal tissues. These findings implicate that differential therapies for RS and CLL patients need to be adopted based on their prior therapy and gene expression signatures. Studies using large sample size will be needed to verify this hypothesis. Figure Disclosures Zhao: Merck: Current Employment. Blumenschein:Merck: Current Employment. Yearley:Merck: Current Employment. Wang:Novartis: Research Funding; Incyte: Research Funding; Innocare: Research Funding. Parikh:Verastem Oncology: Honoraria; GlaxoSmithKline: Honoraria; Pharmacyclics: Honoraria, Research Funding; MorphoSys: Research Funding; Ascentage Pharma: Research Funding; Genentech: Honoraria; AbbVie: Honoraria, Research Funding; Merck: Research Funding; TG Therapeutics: Research Funding; AstraZeneca: Honoraria, Research Funding; Janssen: Honoraria, Research Funding. Kenderian:Sunesis: Research Funding; MorphoSys: Research Funding; Humanigen: Consultancy, Patents & Royalties, Research Funding; Gilead: Research Funding; BMS: Research Funding; Tolero: Research Funding; Lentigen: Research Funding; Juno: Research Funding; Mettaforge: Patents & Royalties; Torque: Consultancy; Kite: Research Funding; Novartis: Patents & Royalties, Research Funding. Kay:Astra Zeneca: Membership on an entity's Board of Directors or advisory committees; Acerta Pharma: Research Funding; Juno Theraputics: Membership on an entity's Board of Directors or advisory committees; Dava Oncology: Membership on an entity's Board of Directors or advisory committees; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Sunesis: Research Funding; MEI Pharma: Research Funding; Agios Pharma: Membership on an entity's Board of Directors or advisory committees; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Research Funding; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Rigel: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Cytomx: Membership on an entity's Board of Directors or advisory committees. Braggio:DASA: Consultancy; Bayer: Other: Stock Owner; Acerta Pharma: Research Funding. Ding:DTRM: Research Funding; Astra Zeneca: Research Funding; Abbvie: Research Funding; Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding; Octapharma: Membership on an entity's Board of Directors or advisory committees; MEI Pharma: Membership on an entity's Board of Directors or advisory committees; alexion: Membership on an entity's Board of Directors or advisory committees; Beigene: Membership on an entity's Board of Directors or advisory committees.

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Baseline and on-Treatment Bone Marrow Microenvironments Predict Myeloma Patient Outcomes and Inform Potential Intervention Strategies

Blood ◽

10.1182/blood-2018-99-113169 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 1882-1882 ◽

Cited By ~ 1

Author(s):

Samuel A Danziger ◽

Mark McConnell ◽

Jake Gockley ◽

Mary Young ◽

Adam Rosenthal ◽

...

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

Bone Marrow ◽

Board Of Directors ◽

Research Funding ◽

Expression Profiles ◽

Cell Types ◽

Employment Equity ◽

Advisory Committees ◽

Equity Ownership

Abstract Introduction The multiple myeloma (MM) tumor microenvironment (TME) strongly influences patient outcomes as evidenced by the success of immunomodulatory therapies. To develop precision immunotherapeutic approaches, it is essential to identify and enumerate TME cell types and understand their dynamics. Methods We estimated the population of immune and other non-tumor cell types during the course of MM treatment at a single institution using gene expression of paired CD138-selected bone marrow aspirates and whole bone marrow (WBM) core biopsies from 867 samples of 436 newly diagnosed MM patients collected at 5 time points: pre-treatment (N=354), post-induction (N=245), post-transplant (N=83), post-consolidation (N=51), and post-maintenance (N=134). Expression profiles from the aspirates were used to infer the transcriptome contribution of immune and stromal cells in the WBM array data. Unsupervised clustering of these non-tumor gene expression profiles across all time points was performed using the R package ConsensusClusterPlus with Bayesian Information Criterion (BIC) to select the number of clusters. Individual cell types in these TMEs were estimated using the DCQ algorithm and a gene expression signature matrix based on the published LM22 leukocyte matrix (Newman et al., 2015) augmented with 5 bone marrow- and myeloma-specific cell types. Results Our deconvolution approach accurately estimated percent tumor cells in the paired samples compared to estimates from microscopy and flow cytometry (PCC = 0.63, RMSE = 9.99%). TME clusters built on gene expression data from all 867 samples resulted in 5 unsupervised clusters covering 91% of samples. While the fraction of patients in each cluster changed during treatment, no new TME clusters emerged as treatment progressed. These clusters were associated with progression free survival (PFS) (p-Val = 0.020) and overall survival (OS) (p-Val = 0.067) when measured in pre-transplant samples. The most striking outcomes were represented by Cluster 5 (N = 106) characterized by a low innate to adaptive cell ratio and shortened patient survival (Figure 1, 2). This cluster had worse outcomes than others (estimated mean PFS = 58 months compared to 71+ months for other clusters, p-Val = 0.002; estimate mean OS = 105 months compared with 113+ months for other clusters, p-Val = 0.040). Compared to other immune clusters, the adaptive-skewed TME of Cluster 5 is characterized by low granulocyte populations and high antigen-presenting, CD8 T, and B cell populations. As might be expected, this cluster was also significantly enriched for ISS3 and GEP70 high risk patients, as well as Del1p, Del1q, t12;14, and t14:16. Importantly, this TME persisted even when the induction therapy significantly reduced the tumor load (Table 1). At post-induction, outcomes for the 69 / 245 patients in Cluster 5 remain significantly worse (estimate mean PFS = 56 months compared to 71+ months for other clusters, p-Val = 0.004; estimate mean OS = 100 months compared to 121+ months for other clusters, p-Val = 0.002). The analysis of on-treatment samples showed that the number of patients in Cluster 5 decreases from 30% before treatment to 12% after transplant, and of the 63 patients for whom we have both pre-treatment and post-transplant samples, 18/20 of the Cluster 5 patients moved into other immune clusters; 13 into Cluster 4. The non-5 clusters (with better PFS and OS overall) had higher amounts of granulocytes and lower amounts of CD8 T cells. Some clusters (1 and 4) had increased natural killer (NK) cells and decreased dendritic cells, while other clusters (2 and 3) had increased adipocytes and increases in M2 macrophages (Cluster 2) or NK cells (Cluster 3). Taken together, the gain of granulocytes and adipocytes was associated with improved outcome, while increases in the adaptive immune compartment was associated with poorer outcome. Conclusions We identified distinct clusters of patient TMEs from bulk transcriptome profiles by computationally estimating the CD138- fraction of TMEs. Our findings identified differential immune and stromal compositions in patient clusters with opposing clinical outcomes and tracked membership in those clusters during treatment. Adding this layer of TME to the analysis of myeloma patient baseline and on-treatment samples enables us to formulate biological hypotheses and may eventually guide therapeutic interventions to improve outcomes for patients. Disclosures Danziger: Celgene Corporation: Employment, Equity Ownership. McConnell:Celgene Corporation: Employment. Gockley:Celgene Corporation: Employment. Young:Celgene Corporation: Employment, Equity Ownership. Schmitz:Celgene Corporation: Employment, Equity Ownership. Reiss:Celgene Corporation: Employment, Equity Ownership. Davies:MMRF: Honoraria; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; TRM Oncology: Honoraria; Abbvie: Consultancy; ASH: Honoraria; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria. Copeland:Celgene Corporation: Employment, Equity Ownership. Fox:Celgene Corporation: Employment, Equity Ownership. Fitch:Celgene Corporation: Employment, Equity Ownership. Newhall:Celgene Corporation: Employment, Equity Ownership. Barlogie:Celgene: Consultancy, Research Funding; Dana Farber Cancer Institute: Other: travel stipend; Multiple Myeloma Research Foundation: Other: travel stipend; International Workshop on Waldenström's Macroglobulinemia: Other: travel stipend; Millenium: Consultancy, Research Funding; European School of Haematology- International Conference on Multiple Myeloma: Other: travel stipend; ComtecMed- World Congress on Controversies in Hematology: Other: travel stipend; Myeloma Health, LLC: Patents & Royalties: : Co-inventor of patents and patent applications related to use of GEP in cancer medicine licensed to Myeloma Health, LLC. Trotter:Celgene Research SL (Spain), part of Celgene Corporation: Employment, Equity Ownership. Hershberg:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Dervan:Celgene Corporation: Employment, Equity Ownership. Ratushny:Celgene Corporation: Employment, Equity Ownership. Morgan:Takeda: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Research Funding.

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Transcriptional Regulation of Hematopoietic Stem Cells in Aging and Myelodysplastic Syndrome Reveals DDIT3 As a Potential Driver of Transformation

Blood ◽

10.1182/blood-2019-122686 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 3764-3764

Author(s):

Teresa Ezponda ◽

Juan P. Romero ◽

Marina Ainciburu ◽

Ana Alfonso ◽

Nerea Berastegui ◽

...

Keyword(s):

Gene Expression ◽

Board Of Directors ◽

Research Funding ◽

Expression Profiles ◽

Genetic Alterations ◽

Transcriptional Profile ◽

Gene Set Enrichment Analysis ◽

Advisory Committees ◽

Hematopoietic Stem ◽

Transcriptional Changes

Myelodysplastic syndromes (MDS) are clonal hematopoietic stem cell (HSC) malignancies characterized by ineffective hematopoiesis. Genetic alterations do not fully explain the molecular pathogenesis of the disease, indicating that other types of lesions, such as transcriptional aberrations, may play a role in its development. Moreover, MDS prevalence is almost exclusive to older patients, suggesting that elderly-related alterations may predispose to the development of this clinical entity. Thus, study of the transcriptional lesions occurring in the aging-MDS axis could shed some light of the molecular bases of the disease. To characterize the transcriptional profile of HSCs in aging and MDS, we isolated CD34+, CD38-, CD90+, CD45RA- cells from 11 untreated MDS patients with unilineage and multilineage dysplasia (median of 75 y/o), as well as from 16 young and 8 elderly healthy donors (median of 21 and 70 y/o, respectively), and their expression profile was analyzed using MARS-seq. Unsupervised principal component analysis demonstrated that the three groups of HSCs clustered separately, indicating that different expression profiles characterize healthy young and elderly, and MDS-associated HSCs. To better understand the gene expression deregulation of HSCs, we analyzed the transcriptional dynamisms along the aging-MDS axis, detecting groups of genes following different patterns of expression. Some gene clusters showed exclusive alteration either in aging or in the progression from elderly HSCs to MDS-HSCs, other groups of genes presented a continuous alteration along the axis, and some displayed opposite regulation in aging and in the transition to MDS (Figure 1). Genes showing specific downregulation in aging were involved in DNA damage sensing and repair, and in cell cycle regulation, whereas genes overexpressed in this process were enriched in apoptosis regulators and in cancer-associated genes, including AML-related factors. These findings indicate that transcriptional changes in aging may predispose for MDS and AML, and potentially other malignancies. Interestingly, we detected a group of genes in which the age-mediated upregulation of gene expression was reversed to that of young HSCs in MDS, indicating a "rejuvenation" profile of malignant HSCs. These genes were involved in response to inflammation, to different types of stress conditions such as hypoxia or radiation, and to cytokines. Elderly HSCs may upregulate such genes in response to the known inflammatory microenvironment of elderly bone marrow. Intriguingly, the decrease in expression detected in MDS suggests that malignant HSCs lose the ability of reacting to such stimuli, possibly favoring their survival in a hostile microenvironment. Finally, the analyses performed allowed for the identification of genes showing MDS-specific deregulation. Genes specifically overexpressed in MDS compared to normal (both young and elderly) HSCs, we enriched in transcriptional and epigenetic regulators, and among them, we detected the presence of DDIT3/CHOP, a member of the CCAAT/enhancer-binding protein (C/EBP) family of transcription factors. To determine its potential effects on hematopoietic deregulation, DDIT3 was exogenously overexpressed in healthy HSCs. Notably, its upregulation produced an erythroid bias in an ex-vivo differentiation system, with an increase in the percentage of erythroblasts and a decrease in granulocytes and monocytes compared to HSCs transduced with the empty vector. Transcriptomic analysis of transduced HSCs not subjected to differentiation demonstrated how DDIT3 overexpression produced an erythroid-prone state of HSCs, suggesting it may act as a pioneer factor in MDS-HSCs. Furthermore, gene set enrichment analysis showed that DDIT3 overexpression produced an MDS-like transcriptional profile, suggesting this factor may be key in the acquisition of the disease. Altogether, our results demonstrate that HSCs undergo transcriptional changes in the aging-MDS axis that may alter their intrinsic functions as well as their response to the microenvironment, ultimately contributing to the acquisition of the disease. In particular, our data show that DDIT3 may be a potential driver of MDS transformation. Disclosures Paiva: Amgen, Bristol-Myers Squibb, Celgene, Janssen, Merck, Novartis, Roche, and Sanofi; unrestricted grants from Celgene, EngMab, Sanofi, and Takeda; and consultancy for Celgene, Janssen, and Sanofi: Consultancy, Honoraria, Research Funding, Speakers Bureau. Díez-Campelo:Celgene Corporation: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding.

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Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer

BMC Cancer ◽

10.1186/s12885-021-08412-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Baojie Wu ◽

Shuyi Xi

Keyword(s):

Gene Expression ◽

Cervical Cancer ◽

Differentially Expressed Genes ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Molecular Targets ◽

Enrichment Analysis ◽

Ppi Network ◽

Hub Genes ◽

Kegg Pathways

Abstract Background This study aimed to explore and identify key genes and signaling pathways that contribute to the progression of cervical cancer to improve prognosis. Methods Three gene expression profiles (GSE63514, GSE64217 and GSE138080) were screened and downloaded from the Gene Expression Omnibus database (GEO). Differentially expressed genes (DEGs) were screened using the GEO2R and Venn diagram tools. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Gene set enrichment analysis (GSEA) was performed to analyze the three gene expression profiles. Moreover, a protein–protein interaction (PPI) network of the DEGs was constructed, and functional enrichment analysis was performed. On this basis, hub genes from critical PPI subnetworks were explored with Cytoscape software. The expression of these genes in tumors was verified, and survival analysis of potential prognostic genes from critical subnetworks was conducted. Functional annotation, multiple gene comparison and dimensionality reduction in candidate genes indicated the clinical significance of potential targets. Results A total of 476 DEGs were screened: 253 upregulated genes and 223 downregulated genes. DEGs were enriched in 22 biological processes, 16 cellular components and 9 molecular functions in precancerous lesions and cervical cancer. DEGs were mainly enriched in 10 KEGG pathways. Through intersection analysis and data mining, 3 key KEGG pathways and related core genes were revealed by GSEA. Moreover, a PPI network of 476 DEGs was constructed, hub genes from 12 critical subnetworks were explored, and a total of 14 potential molecular targets were obtained. Conclusions These findings promote the understanding of the molecular mechanism of and clinically related molecular targets for cervical cancer.

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DYRK1A Is Regulated By Oncogenic KMT2A and Required for Survival of KMT2A-Rearranged Acute Lymphoblastic Leukemia

Blood ◽

10.1182/blood-2019-123475 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 2742-2742

Author(s):

Christian Hurtz ◽

Gerald Wertheim ◽

Rahul S. Bhansali ◽

Anne Lehman ◽

Grace Jeschke ◽

...

Keyword(s):

Gene Expression ◽

B Cells ◽

Board Of Directors ◽

Research Funding ◽

Lymphoblastic Leukemia ◽

Leukemia Cell ◽

Negative Regulator ◽

Advisory Committees ◽

Normal Tissues ◽

Pharmacologic Inhibition

Background: Research efforts have focused upon uncovering critical leukemia-associated genetic alterations that may be amenable to therapeutic targeting with new drugs. Targeting the oncogenic BCR-ABL1 fusion protein in Philadelphia chromosome-positive B-cell acute lymphoblastic leukemia (B-ALL) with tyrosine kinase inhibitors to shut down constitutive signaling activation and induce leukemia cell cytotoxicity has remarkably improved patients' survival and has established a precision medicine paradigm for kinase-driven leukemias. However, multiple subtypes of B-ALL are driven through non-tyrosine fusion proteins, including the high-risk KMT2A-rearranged (KMT2A-R) subtype common in infants with B-ALL, leaving many patients with insufficient treatment options. Objectives: KMT2A-R B-ALL is associated with chemoresistance, relapse, and poor survival with a frequency of 75% in infants and 10% in older children/adults with B-ALL. Current intensive multiagent chemotherapy regimens induce significant side effects yet fail to cure the majority of patients, demonstrating continued need for novel therapeutic approaches. The goals of our study were to i) identify signaling molecules required for KMT2A-R B-ALL cell survival, ii) select ALL-associated targets that are not essential in normal tissues, and iii) develop new treatment strategies that may benefit patients with KMT2A-R ALL. Results: We performed a genome-wide kinome CRISPR screen using the pediatric KMT2A-R cell line SEM and identified DYRK1A among other signaling molecules as required for leukemia cell survival. DYRK1A is a member of the dual-specificity tyrosine phosphorylation-regulated kinase family and has been reported as a critical oncogene in a murine Down syndrome (DS) model of megakaryoblastic leukemia. In normal hematopoiesis, DYRK1A controls the transition from proliferation to quiescence during lymphoid development. Deletion of DYRK1A results in increased numbers of B cells in S-G2-M phase, yet also significantly reduces cell proliferation. Meta-analysis of ChIP-Seq data from two KMT2A-AFF1 cell lines (SEM and RS4;11) and a human KMT2A-Aff1-FLAG-transduced ALL model demonstrates that both N-terminal (KMT2AN) and C-terminal (AFF1C) and the FLAG-tagged KMT2A-Aff1 fusion directly bind to the DYRK1A promoter. Gene expression and RT-PCR analyses of SEM cells treated with inhibitors against two important KMT2A fusion complex proteins, DOT1L (histone methyltransferase) and menin (tumor suppressor), demonstrate that only menin inhibition induced DYRK1A downregulation. Interestingly, deletion of germline KMT2A in murine B-cells did not decrease DYRK1A expression. Taken together, these results suggest direct transcriptional regulation through the KMT2A fusion complex. Surprisingly, RNA and protein expression of DYRK1A was reduced in KMT2A-R ALL compared to other B-ALL subtypes. We then identified MYC as a potential negative regulator of DYRK1A that could explain the lower RNA and protein expression levels observed. A gain-of-function experiment showed marked downregulation of DYRK1A when MYC was ectopically expressed in murine B-cells, while loss of MYC resulted in DYRK1A upregulation. Parallel analysis of publicly available gene expression data from children with high-risk B-ALL (NCI TARGET database) showed significantly higher MYC RNA expression levels in KMT2A-R ALL as compared to other ALL subtypes, further validating our findings that MYC acts as a negative regulator of DYRK1A. Finally, to assess pharmacologic inhibition, we treated multiple KMT2A-rearranged ALL cell lines with the novel DYRK1A inhibitor EHT 1610 and identified sensitivity to DYRK1A inhibition. We then queried the Achilles database and identified that DYRK1A is not a common essential gene in normal tissues, suggesting minimal potential for on-target/off-tumor effects of DYRK1A inhibition. Conclusions: We identified a novel mechanism in KMT2A-R ALL in which DYRK1A is positively regulated by the KMT2A fusion protein and negatively regulated by MYC. Genetic deletion and pharmacologic inhibition of DYRK1A resulted in significant growth disadvantage of KMT2A-R ALL cells. While further studies are needed, we predict that combining DYRK1A inhibitors with chemotherapy could decrease relapse risk and improve long-term survival of patients with KMT2A-R B-ALL. Disclosures Crispino: MPN Research Foundation: Membership on an entity's Board of Directors or advisory committees; Sierra Oncology: Consultancy; Scholar Rock: Research Funding; Forma Therapeutics: Research Funding. Tasian:Incyte Corportation: Research Funding; Gilead Sciences: Research Funding; Aleta Biotherapeutics: Membership on an entity's Board of Directors or advisory committees. Carroll:Astellas Pharmaceuticals: Research Funding; Incyte: Research Funding; Janssen Pharmaceuticals: Consultancy.

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Dynamic Epigenetic Landscapes Define Multiple Myeloma Progression and Drug Resistance

Blood ◽

10.1182/blood-2020-142872 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 32-33

Author(s):

Rafael Renatino-Canevarolo ◽

Mark B. Meads ◽

Maria Silva ◽

Praneeth Reddy Sudalagunta ◽

Christopher Cubitt ◽

...

Keyword(s):

Gene Expression ◽

Drug Resistance ◽

Disease Progression ◽

Board Of Directors ◽

Research Funding ◽

Chromatin Accessibility ◽

Refractory Disease ◽

Advisory Committees ◽

Cytogenetic Abnormalities ◽

Genome Wide

Multiple myeloma (MM) is an incurable cancer of bone marrow-resident plasma cells, which evolves from a premalignant state, MGUS, to a form of active disease characterized by an initial response to therapy, followed by cycles of therapeutic successes and failures, culminating in a fatal multi-drug resistant cancer. The molecular mechanisms leading to disease progression and refractory disease in MM remain poorly understood. To address this question, we have generated a new database, consisting of 1,123 MM biopsies from patients treated at the H. Lee Moffitt Cancer Center. These samples ranged from MGUS to late relapsed/refractory (LR) disease, and were comprehensively characterized genetically (844 RNAseq, 870 WES, 7 scRNAseq), epigenetically (10 single-cell chromatin accessibility, scATAC-seq) and phenotypically (537 samples assessed for ex vivo drug resistance). Mutational analysis identified putative driver genes (e.g. NRAS, KRAS) among the highest frequent mutations, as well as a steady increase in mutational load across progression from MGUS to LR samples. However, with the exception of KRAS, these genes did not reach statistical significance according to FISHER's exact test between different disease stages, suggesting that no single mutation is necessary or sufficient to drive MM progression or refractory disease, but rather a common "driver" biology is critical. Pathway analysis of differentially expressed genes identified cell adhesion, inflammatory cytokines and hematopoietic cell identify as under-expressed in active MM vs. MGUS, while cell cycle, metabolism, DNA repair, protein/RNA synthesis and degradation were over-expressed in LR. Using an unsupervised systems biology approach, we reconstructed a gene expression map to identify transcriptomic reprogramming events associated with disease progression and evolution of drug resistance. At an epigenetic regulatory level, these genes were enriched for histone modifications (e.g. H3k27me3 and H3k27ac). Furthermore, scATAC-seq confirmed genome-wide alterations in chromatin accessibility across MM progression, involving shifts in chromatin accessibility of the binding motifs of epigenetic regulator complexes, known to mediate formation of 3D structures (CTCF/YY1) of super enhancers (SE) and cell identity reprograming (POU5F1/SOX2). Additionally, we have identified SE-regulated genes under- (EBF1, RB1, SPI1, KLF6) and over-expressed (PRDM1, IRF4) in MM progression, as well as over-expressed in LR (RFX5, YY1, NBN, CTCF, BCOR). We have found a correlation between cytogenetic abnormalities and mutations with differential gene expression observed in MM progression, suggesting groups of genetic events with equivalent transcriptomic effect: e.g. NRAS, KRAS, DIS3 and del13q are associated with transcriptomic changes observed during MGUS/SMOL=>active MM transition (Figure 1). Taken together, our preliminary data suggests that multiple independent combinations of genetic and epigenetic events (e.g. mutations, cytogenetics, SE dysregulation) alter the balance of master epigenetic regulatory circuitry, leading to genome-wide transcriptional reprogramming, facilitating disease progression and emergence of drug resistance. Figure 1: Topology of transcriptional regulation in MM depicts 16,738 genes whose expression is increased (red) or decreased (green) in presence of genetic abnormality. Differential expression associated with (A) hotspot mutations and (B) cytogenetic abnormalities confirms equivalence of expected pairs (e.g. NRAS and KRAS, BRAF and RAF1), but also proposes novel transcriptomic dysregulation effect of clinically relevant cytogenetic abnormalities, with yet uncharacterized molecular role in MM. Figure 1 Disclosures Kulkarni: M2GEN: Current Employment. Zhang:M2GEN: Current Employment. Hampton:M2GEN: Current Employment. Shain:GlaxoSmithKline: Speakers Bureau; Amgen: Speakers Bureau; Karyopharm: Research Funding, Speakers Bureau; AbbVie: Research Funding; Takeda: Honoraria, Speakers Bureau; Sanofi/Genzyme: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Honoraria, Speakers Bureau; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Adaptive: Consultancy, Honoraria; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Siqueira Silva:AbbVie: Research Funding; Karyopharm: Research Funding; NIH/NCI: Research Funding.

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Proposal of a Novel Latin American International Prognostic Index (LATAM-IPI) for Diffuse Large B-Cell Lymphoma (DLBCL) By the Grupo Para El Estudio De Linfoproliferativos En Latino-America (GELL)

Blood ◽

10.1182/blood-2020-142836 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 25-27

Author(s):

Luis Villela Villela ◽

Ana Ramirez-Ibarguen ◽

Brady E Beltran ◽

Camila Peña ◽

Denisse A. Castro ◽

...

Keyword(s):

High Risk ◽

Board Of Directors ◽

Research Funding ◽

Scoring Systems ◽

Risk Groups ◽

Training Set ◽

Extranodal Involvement ◽

Advisory Committees ◽

Internal Validation ◽

Biological Variables

Introduction. There are different scoring systems to differentiate risk groups in patients with DLBCL treated with chemoimmunotherapy. Those systems have used the same 5 variables (age, performance status, LDH, stage, extranodal involvement) for 27 years. However, LATAM data have not been included in the development of previous scoring systems. It is important to mention that novel biological variables, such as albumin, beta-2-microglobulin (B2M) and platelet/lymphocyte ratio (PLR), have been reported and could improve discrimination (Villela et al. Blood 2019; 134Suppl_1: 1613). Therefore, we carried out a large, multinational study to develop and validate a LATAM-IPI score. Methods. This is a retrospective cohort of 1030 patients with a diagnosis of DLBCL treated with standard chemoimmunotherapy with curative intent between 2010 and 2018. Data were obtained from 8 LATAM countries: Argentina, Colombia, Chile, Guatemala, Mexico, Paraguay, Peru, and Venezuela. The five classic IPI variables (age, ECOG, extranodal involvement, LDH, stage) were analyzed and albumin and PLR were added (Villela et al. Blood 2019; 134Suppl_1: 1613). B2M was not included because it was not requested regularly in all countries. Development of LATAM-IPI: The training set consisted of 85% of the sample, randomly selected, and the remaining 15% was reserved for internal validation. Using the training set, the univariate and multivariate association between clinical prognostic factors and OS was analyzed fitting Cox proportional-hazard models. Outcomes. Clinical characteristics of the training (n=878) and internal validation (n=151) cohorts are shown in Table 1. There were no statistical differences in baseline characteristics between the cohorts. The median follow-up for the whole cohort was 36 months (IQR: 11-57). When exploring the classic IPI variables on the training set, all variables were associated with high risk of mortality [age 65-74, Hazard Ratio (HR) 1.24, 95% CI 0.96 to 1.58, p=0.08; age ≥75, HR 1.71, 95% CI 1.28 to 2.28, p=0.0003), ECOG (≥ 2, HR=2, 95% CI 1.61 to 2.53; p<0.0001), EN (≥2, HR=1.53, 95% CI 1.18 to 1.97; p=0.0012), stage (III/IV, HR=2.1, 95% CI 1.64 to 2.69; p<0.0001) and LDH (ratio 1.1-2.9, HR=1.55, 95% CI 1.22 to 1.97; p=0.0003; ratio ≥3, HR= 2.68, 95% CI 1.93 to 3.7, p<0.0001). Similarly, the biological variables Albumin (≤3.5 mg/dL, HR 2.37, 95% CI 1.9 to 2.95, p<0.0001) and PLR (≥273, HR= 1.52, 95% CI 1.23 to 1.87; p=0.0001) were associated with high risk of death. Next, these variables were evaluated by multivariate analysis. The independent variables were albumin (<3.5 mg/dL, HR 1.84, 95% CI 1.45 to 2.3, p<0.0001, 1 point), LDH (ratio 1.1 to 2.9, HR 1.30, 95% CI 1.02 to 1.67, p=0.03, 1 point; ratio ≥3, HR=1.84, 95% CI 1.31 to 2.5, p=0.0004, 2 points), advanced stage (HR 1.65, 95% CI 1.27 to 2.13, p=0.0001, 1 point), age (≥75, HR= 1.51, 95% CI 1.15 to 1.98, p=0.003, 1 point), and ECOG (≥2, HR 1.40, 95% CI 1.10 to 1.77, p=0.005). Now, for the development of LATAM-IPI, the groups were distributed as follows: 0 points, low; 1-3 points, intermediate; 4-6 points, high risk. There were no differences in the distribution of the risk groups between training and validation sets (Table 2). In the learning cohort, the 5-year OS rates for low, intermediate and high risk were 81%, 63% and 33%, respectively (p<0.0001). In the validation cohort, the 5-year OS rates for low, intermediate and high risk were 81%, 63% and 44%, respectively (p=0.02) (Figure 1). Conclusions: Using multinational learning and validation cohorts including over 1,000 DLBCL patients treated with standard chemoimmunotherapy in LATAM, we developed a novel LATAM-IPI score using age ≥75 years, ECOG ≥2, advanced stage, LDH ratio (1.1-29 and ≥3) and albumin <3.5 mg/dl. Next steps are to disseminate our results with other involved researchers in LATAM to prospectively assess and reproduce our results. We expect this score will help to further define the prognosis of DLBCL patients in LATAM. Disclosures Villela: amgen: Speakers Bureau; Roche: Other: advisory board, Speakers Bureau. Idrobo:Janssen: Honoraria, Speakers Bureau; Amgen: Honoraria, Speakers Bureau; Abbvie: Honoraria, Speakers Bureau; Tecnofarma: Honoraria, Speakers Bureau; Takeda: Honoraria, Speakers Bureau. Gomez-Almaguer:Amgen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; AbbVie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene/BMS: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; AstraZeneca: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Pfizer: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Roche: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Castillo:Janssen: Consultancy, Research Funding; TG Therapeutics: Research Funding; Kymera: Consultancy; Abbvie: Research Funding; Beigene: Consultancy, Research Funding; Pharmacyclics: Consultancy, Research Funding.

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