Myeloablative allogeneic hematopoietic stem cell transplantation (allo-HSCT) is increasingly used in patients with lymphoma who experience disease relapse after autologous hematopoietic stem cell ...transplantation (auto-HSCT) because the allograft is tumor free and may induce a graft-versus-tumor effect. We analyzed 114 patients treated with this approach from 1990 to 1999 to assess disease progression, progression-free survival (PFS), and overall survival (OS). Cumulative incidence of disease progression at 3 years was 52%, whereas treatment-related mortality was 22%, lower than previously reported. Three-year probabilities of OS and PFS were 33% and 25%, respectively. With prolonged follow-up, however, nearly all patients experienced disease progression, and 5-year probabilities were 24% and 5%, respectively. Complete remission at the time of allo-HSCT and use of total body irradiation (TBI) in patients with non-Hodgkin lymphoma (NHL) were associated with lower rates of disease progression and higher rates of OS. In summary, allo-HSCT is feasible for patients with lymphoma who have relapses after auto-HSCT and can result in prolonged survival for some, but it is usually not curative. Most likely to benefit are patients who have HLA-matched sibling donors, are in remission, and have good performance status.
1033 Background: Multiple studies have demonstrated that ERBB2 (HER2) exon 16 skipping is associated with resistance to antibody-based therapy. However, ERBB2 gene contains 30 exons with potential ...for alternative splicing involving all exons. With the increasing utilization of antibody-drug conjugates (ADC) in treating patients, it is important to evaluate the various ERBB2 isoforms that are expressed in various tumors. Toward this goal we evaluated the various splicing forms and exons skipping in domains II and III that are involved in dimerization and binding to pertuzumab, and in domain IV, which is a juxtamembrane domain and involved in trastuzumab binding. Methods: RNA was extracted from 3170 FFPE tissue from various types of tumors including breast, ovary, endometrium, lung, colon, stomach, esophagus, and others. RNA was sequenced using a hybrid capture-targeted panel. Data analysis is focused on ERBB2 gene. Results: Of the 3170 tested solid tumor samples, 824 (26%) showed high expression meeting the criteria for gene amplification and “low-HER2”. Of these cases, 199 (24%) samples showed exon skipping in domains II and III ranging from exons 6 to 14. Skipping in domain IV was detected in 236 cases (29%) involving exons from 13 to 29. However, 107 samples of the 824 (13%) showed skipping of exons in both domains II/III and domain IV. The most common exon skipping were in exons 6 to 7 and in exon 16. Most of the cases with exon 16 skipping (1.5% of total) also showed skipping in exons 6 and 7. Conclusions: Alternative splicing and exon skipping in HER2 transcripts is common and involves critical functional domains of the protein. The percentage of skipped transcripts is low. However, since these isoforms have deficiency in critical domains for membrane anchoring and for dimerization, the biological effects might be more significant than that expected from percentage of abnormal protein. Further studies are needed to correlate exon skipping with efficacy and outcome of antibody-based therapy. Table: see text
e13044 Background: The recent success of treatment with HER2-directed antibody-drug conjugate T-DXd (trastuzumab-deruxtecan) in treating low-HER2 expression breast cancer raises the possibility of ...similar success in other tumors that express similarly low levels of HER2. HER2 amplification has been reported in various types of tumors at relatively low frequency. However, “low-HER2” expression in various types of tumors remains undefined. In this study, we utilized next generation sequencing to explore the levels of ERBB2 (HER2), ERBB3, ERBB4, PIK3CA, and ERS1 mRNA in various types of cancers and attempted to set limits reflecting distinct biology of tumors based of level of ERBB2 expression. Methods: RNA was extracted from FFPE samples from patients with cancers of breast (N=203), ovary (N=95), endometrium (N=75), and esophagus/stomach (N=46). RNA was sequenced via a 1408 gene panel using a standard hybrid capture approach. Results: Based on previous data correlating gene amplification with gene expression (DOI: 10.1002/imed.1051), we defined ERBB2 expression as high, low, and very-low when ERBB2 expression measured >1500 TPM (transcripts per million), 800-1500 TPM, and <800 TPM, respectively. ERBB2 high, low, and very-low were detected in 20%, 32%, and 48% of breast cancer; 5%, 29%, and 65% of endometrial cancer; 9%, 26%, and 65% of ovarian cancer; 12%, 16%, and 72% of esophageal/stomach cancer; and 8%, 19%, and 73% of lung cancer. With minor exceptions, cases classified as ERBB2 high and low from the five tumor types showed no correlation with levels of PIK3CA, ESR1, or ERBB4 mRNA. In contrast, cases with relatively very-low ERBB2 showed significant correlation with PIK3CA, ESR1, and ERBB4 mRNA. Endometrial cancer was an exception and showed no correlation with PIK3CA mRNA in very-low ERBB2 cases. Activation of PIK3CA has been reported to be associated with resistance to anti-HER2 therapy. These findings suggest that cases showing high or low ERBB2 are independent of PIK3CA and likely less influenced by the negative effects of PIK3CA on response to HER2 inhibitors. Cases classified as ERBB2 high or low showed significantly higher (P<0.0001) PIK3CA mutations as compared with ERBB2 very-low cases (27% vs 14%). ERBB2 mutations were relatively rare, detected in only 7%, 8%, 14%, 1%, and 4% of breast, endometrial, esophageal/stomach, ovarian, and lung cancers, respectively. However, significantly (P=0.001) higher mutation rate is detected in ERBB2-high and -low as compared with very-low ERBB2 cases (8% vs 6%). Conclusions: ERBB2/HER2 RNA quantification in cancers can define a biologically distinct group of patients. Patients defined as very-low ERBB2 as classified in this approach appear to be biologically distinct and more likely to be resistant to anti-HER2 therapy. Clinical trials using this approach in selecting patients for treatment with antibody-drug conjugates are justified and needed.
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Background: Nivolumab/ipilimumab (Nivo/Ipi) is a standard therapy in patients with unresectable stage III/IV melanoma, demonstrating an objective response rate (ORR) of 58% and 12-month ...progression free survival (PFS) rate of 49% Wolchok J, NEJM 2017. c-MET/VEGFR2 inhibition with cabozantinib (Cabo) has modest clinical activity in melanoma and can augment antitumor immunity by increasing CD8+ Tcell and macrophage tumor infiltration, decreasing intratumoral regulatory Tcell levels, and increasing tumor cell MHC antigen presentation. Nivo/Ipi plus Cabo showed clinical activity and acceptable toxicity profile in GU malignancies Apolo AB, JCO 2020. We performed a study of Nivo/Ipi plus Cabo in advanced melanoma patients aimed at improving efficacy. Methods: We conducted a multicenter phase II study of induction Nivo 3mg/kg IV and Ipi 1mg/kg IV and Cabo 40mg PO daily every 3 weeks for 4 cycles, followed by Nivo 480mg IV plus Cabo 40mg PO daily every 4 weeks for up to 2 years, until disease progression or unacceptable toxicity/patient withdrawal. Patients with unresectable stage III/IV melanoma and no prior anti-PD-1 or CTLA-4 exposure (unless in adjuvant setting > 6 months since last dose) were included. Uveal melanoma excluded. Primary endpoint was 12-month PFS rate. Secondary endpoints included ORR, overall survival (OS), and adverse events (AEs). Interim analysis was preplanned after first 14 subjects were evaluated in a Simon 2 stage design (goal >8 subjects 12-month progression free). Results: As of 1/2023, 14 subjects were enrolled across three cancer centers within the Georgetown-Lombardi Comprehensive Cancer Center Consortium. Median age 66.5 years, all stage IV disease, 10 with cutaneous primary site, 5 with elevated LDH, and 6 with BRAF V600 mutant tumor. Median follow up was 10.6 mos. 12-month PFS rate was 30% (3/10 evaluable subjects); 3 subjects pending PFS evaluation; median PFS 10.3 mos. ORR was 46% (6/13 evaluable subjects) with 2 CR, 4 PR, and 1 SD. Responders had cutaneous (5) or unknown (1) primary sites. 9 patients were alive at last follow up. 12-month OS rate was 67% (6/9 evaluable subjects). Reasons for treatment discontinuation included AEs (4), patient withdrawal (1), and disease progression (6). 3 subjects remain on study drug(s). Treatment related AEs (TRAEs) were observed in 13 subjects; 9 experienced a grade 3-4 TRAE (most frequent were elevated AST/ALT 29% and hypokalemia 14%). Most frequent grade 2 TRAEs were diarrhea (35%) and palmar-planter-erythrodysesthesia (29%). No treatment related deaths occurred. Conclusions: Clinical activity was observed with Nivo/Ipi plus Cabo in advanced melanoma but did not meet the predefined threshold to advance to the second stage of enrollment. TRAE profile was similar to prior reports. Biomarker analyses are planned to identify patient profiles associated with particular benefit from this regimen. Clinical trial information: NCT04091750 .
Introduction: Human leukocyte antigen (HLA) plays a major role in the interaction between the immune system and oncogenic process in various types of tumors. Emerging data suggests that the HLA ...system may have profound effects on response to immunotherapy and other types of therapy. Structural defects in HLA genes and variation in methylation have been reported to play a role in the expression of various HLA-I and HLA-II genes. Furthermore, an association between the expression of specific HLA-I or HLA-II genes and prognosis and outcome has been reported in some types of leukemia. Since HLA-I is expressed on all cells while HLA-II is expressed mainly in antigen presenting cells, we hypothesized that profiling the expression levels of both HLA-I and HLA-II in the bone marrow of patients with various myeloid neoplasms may provide information on the interaction between the microenvironment and the leukemic cell in these diseases. We used next generation sequencing to evaluate the expression level of HLA-I and HLA-II genes in myeloid neoplasms and compared these levels to normal bone marrow control. We used targeted next generation sequencing (NGS) for quantification of the RNA expression of 15 HLA-I and HLA-II genes. Methods: RNA was extracted from fresh bone marrow aspiration samples from 115 acute myeloid leukemia (AML), 196 clonal hematopoiesis of indeterminate potential (CHIP), 84 myeloproliferative neoplasm (MPN), 177 myelodysplastic syndrome (MDS), 40 acute lymphoblastic leukemia (ALL), and 279 normal bone marrow cases. RNA sequencing was performed using a targeted hybrid capture panel. The sequenced HLA-I genes were HLA-A, HLA-B, HLA-C, HLA-E, HLA-F, and HLA-H. The sequenced HLA-II genes were: HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQB1, HLA-DQB2, HLA-DRA, HLA-DRB1, HLA-DRB5, and HLA-DRB6. Salmon v1.4.0 software was used for expression quantification (TPM). To evaluate the depth of the significance in variation of the expression profiles between diseases we tested machine learning algorithm (random forest) incorporating the expression of all 15 genes in blindly classifying diseases. Two thirds of samples were used for training the random forest algorithm and one third was used for testing. Results: Bone marrow samples with CHIP showed no significant difference from normal bone marrow in level of expression of HLA-I genes but showed significantly (P <0.001) lower level of most HLA-II markers (DMB, DQA1, DQB2, DRB1, and DRB5). The same pattern was observed in MDS samples. In contrast, AML showed significant (P < 0.001) increase in the expression of both HLA-1 genes (A, C, and F) and HLA-II genes (DQA1, DPA1, DQB1, DRB6, and DPB1). Bone marrow samples from ALL patients showed significantly (P< 0.001) higher expression levels in DRB1 and DQA1 as compared with normal bone marrow. MPN samples showed no statistically significant difference from normal BM in any of the HLA-I or HLA-II genes, after adjusting for multiple testing. Machine leaning algorithm poorly classified MDS and AML and suggests complex overlap between the two diseases in HLA response. In contrast, the machine learning algorithm showed significant difference between MPN, MDS, and AML, and ability to distinguish all MPN cases with AUC of 0.738 (95% CI : 0.546-0.930) in the testing set (see figure). Conclusions: These findings show that the bone marrow microenvironment immune profile in MDS and CHIP likely plays a major role in the disease. Furthermore, this data suggests that this immune microenvironment is critical in CHIP and perhaps that therapeutic approaches supporting this immune microenvironment may help in preventing CHIP from progressing to MDS. The data also supports that AML is a disease that completely overwhelms the HLA immune system. In contrast, MPN appears to evade the HLA system and perhaps activating this system may help in the therapy of MPN.
Introduction: Flow cytometry performs multi-parameter analysis of cells and analyzes surface and intracellular markers for accurate phenotypic characterization of a cell population. Flow cytometry is ...used extensively in the diagnosis and classification of various hematologic neoplasms. However, analysis of the generated data is time consuming and remains subjective, requiring special skill and experience. Furthermore, some diagnostic classes, such as myeloproliferative neoplasms (MPN) and myelodysplastic syndrome (MDS), are difficult to diagnose using flow cytometry. The RNA levels of the CD markers used in flow cytometry can be reliably quantified using next generation sequencing (NGS). However, when all cells are jointly sequenced, studying subpopulation of cells is lost, which hinders accurate diagnosis. However, machine learning algorithms are capable of multi-marker normalizing and compensate for the loss of subclonal analysis. To validate this assumption, we explored the potential of using the RNA levels of 30 CD markers along with a machine learning algorithm in the differential diagnosis between various types of hematologic neoplasms. Methods: RNA was extracted from fresh bone marrow and peripheral blood samples from 172 acute myeloid leukemia (AML), 369 normal control, 68 MPN, 218 MDS, 93 acute lymphoblastic leukemia (ALL), 74 chronic lymphocytic leukemia (CLL), 38 mantle cell lymphoma, and 83 multiple myeloma cases. The samples were consecutive and collected without selection. RNA sequencing was performed using a targeted hybrid capture panel that included CD1A, CD2, CD3D, CD3E, CD3G, CD4, CD5, CD7, CD8A, CD8B, CD10, CD14, CD19, CD20, CD22, CD33, CD34, CD38, CD40, CD44, CD47, CD68, CD70, CD74, CD79A, CD79B, CD81, CD138, CD200, and CD274 genes. Salmon v1.4.0 software was used for expression quantification (TPM). Machine learning algorithm (random forest) was used for classifying diseases. Two thirds of samples were used for training the random forest algorithm and one third was used for testing. Results: While frequently a diagnosis can be made by simply inspecting the RNA levels of various CD markers, machine learning is needed when the fraction of the neoplastic cells is low. Using machine learning (random forest), diagnosis of most hematologic neoplasms was achieved with high sensitivity and specificity in the testing set. Area under the curve (AUC) was at 0.972 (95% CI: 0.950-0.994) for AML vs. normal, 0.936 (95% CI: 0.898-0.974) for normal vs. MM, 0.965 (95% CI: 0.909-1.00) for mantle vs. CLL, 0.962 (95% CI: 0.907-1.00) for CLL vs. ALL, 0.935 (95% CI: 0.866-1.00) for CLL vs. normal, and 0.964 (95% CI: 0.927-1.00) for AML vs. ALL. Diseases that are difficult to diagnose by routine flow cytometry were diagnosed by RNA expression and machine learning at acceptable accuracy. For example, AUC was at 0.761 (95% CI: 0.689-0.834) for MDS vs. normal, 0.831 (95% CI: 0.762-0.901) for MDS vs. AML, 0.888 (95% CI: 0.822-0.954) for MDS vs. MPN, and 0.785 (95% CI: 0.698-0.872) for MPN vs. normal. Conclusions: This data demonstrates that NGS quantification of RNA from 30 CD markers when combined with machine learning is adequate for reliable diagnosis of various types of hematologic neoplasms. This approach can provide valuable information to distinguish between MPN, MDS, and normal bone marrow that flow cytometry cannot provide. Furthermore, this technology can be automated and less susceptible to human error and practically can be used as a replacement to routine flow cytometry analysis.
Left ventricular dyssynchrony is an adverse consequence of ST-elevation myocardial infarction (STEMI) and bears an unfavorable prognosis. Mechanical dyssynchrony as measured by phase analysis from ...gated single photon emission computed tomography (GSPECT) correlates well with other imaging methods of assessing dyssynchrony but has not been studied in STEMI. We hypothesized that systolic dyssynchrony as measured by GSPECT would correlate with adverse remodeling after STEMI.
In 28 subjects suffering STEMI, GSPECT with technetium-99m sestamibi was performed immediately after presentation (day 5) and remotely (6 months). Parameters of left ventricular dyssynchrony (QRS width, histogram bandwidth (HBW) and phase standard deviation (PSD)) were measured from GSPECT using the Emory Cardiac Toolbox. Left ventricular volumes, ejection fraction (LVEF) and infarct size were also assessed.
After successful primary percutaneous coronary intervention to the infarct-related artery, subjects had an LVEF of 46.4% ± 11% and a resting perfusion defect of 27.4% ± 16% at baseline. Baseline QRS width was normal (91.5 ± 17.5 ms). Subjects with STEMI had dyssynchrony compared with a cohort of 22 normal subjects (age 57.2 ± 10.6 years, <5% perfusion defect) by both HBW (100.3° ± 70.7° vs 26.5° ± 5.3°, P < .0001) and PSD (35.3° ± 16.9° vs 7.9° ± 2.1°, P < .0001). Baseline HBW correlated with resting perfusion defect size (r = 0.67, P < .001), end-systolic volume (r = 0.72, P < .001), end-diastolic volume (r = 0.63, P = .001), and inversely with LVEF (r = −0.74, P < .001). HBW and PSD improved over the follow-up period (−24.1 ± 35.9 degrees, P = .003 and −8.7° ± 14.6°, P = .006, respectively), and improvement in HBW correlated with reduction in LV end-systolic volumes (r = 0.43, P = .034). Baseline HBW and PSD, however, did not independently predict LVEF at 6 months follow-up.
After STEMI, subjects exhibit mechanical dyssynchrony as measured by GSPECT phase analysis without evidence of electrical dyssynchrony. Improvement in mechanical dyssynchrony correlates with beneficial ventricular remodeling. The full predictive value of this measure in post-infarct patients warrants further study.
Introduction: Cytogenetic analysis is important for stratifying patients with various myeloid neoplasms. It has been reported that whole-genome sequencing can be used as an alternative to cytogenetic ...analysis in acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). With the increasing use of liquid biopsy in the diagnosis and monitoring of patients with various types of neoplasms, we explored the potential of using liquid biopsy and next generation sequencing (NGS) in detecting chromosomal structural abnormalities or copy number variation (CNV) in patients with myeloid neoplasms. For practical approach and for capturing single nucleotide variants (SNV) and to achieve enough depth in sequencing, we used targeted sequencing for determining the chromosomal structural abnormalities in cell-free DNA (cfDNA) in patients with myeloid neoplasms.
Methods: Peripheral blood plasma samples from 144 patients with myeloid neoplasms were used to extract cfDNA for NGS testing. This included 49 patients with MDS, 31 with AML, and 64 patients with myeloproliferative neoplasms (MPN). The median age was 68.5 (range: 24-96); 56 (39%) were female. cfDNA was sequenced using 275 gene panel. The panel uses single primer extension (SPE) approach with UMI. Sequencing depth was increased to more than 1000X (after removing duplicates). CNVkit software was used for analyzing and visualizing copy number variations. All samples were confirmed to be diagnostic by showing mutations in diagnostic genes with variant allele frequency >20% or by showing diagnostic chromosomal structural abnormalities (e.g., 5q deletion in MDS, 5q- syndrome). Cytogenetic data on 35 corresponding bone marrow samples (18 AML and 17 MDS) were available for comparison.
Results: Of the 144 samples, 47 (33%) showed chromosomal structural abnormalities. In the AML group, 20 of 31 (65%) showed cytogenetic abnormalities by cfDNA testing. Of these positive AML patients, 18 (90%) (58% of total AML) had poor-risk cytogenetics. Therefore, the AML patients with normal cytogenetics or cytogenetic abnormalities other than high-risk constituted 42% of total AML patients. Of the MDS group, 11 of 49 MDS patients (22%) showed cytogenetic abnormalities by cfDNA testing, 6 of whom (54.5%) had high-risk cytogenetics. Overall, 12% of all MDS had poor-risk cytogenetics by cfDNA testing. In the MPN group, 16 of 64 (25%) showed cytogenetic abnormalities, 2 of which (12.5%) had 7q deletion (3% of all MPN); the rest (87.5%) of cytogenetic-positive MPN (22% of total MPN) had other abnormalities including 20q-, +8, 12q, 17p-, 11q-, trisomy 9, trisomy 21 and others. To compare chromosomal abnormalities as detected by cfDNA NGS testing with conventional cytogenetic analysis of corresponding bone marrow samples, we classified cytogenetic findings based on risk stratification into either intermediate-risk or poor-risk. Of the 36 cases, there was 100% concordance between cfDNA data and cytogenetic data when findings were grouped based on risk classification. Two of the conventional cytogenetic samples showed no metaphases while one showed intermediate-risk abnormalities by cfDNA NGS analysis and the second showed poor-risk cytogenetic abnormalities by cfDNA NGS analysis. These 36 cases included 16 cases with normal cytogenetics. Simple abnormalities such as 5q-, 7q-, +8 were called in identical fashion, but some other abnormalities such as derivative chromosome and marker chromosome were resolved or interpreted differently by the cfDNA NGS analysis. The NGS panel design used in this study does not cover fusion genes or chromosomal translocation, and chromosomal translocations were missed at this time.
Conclusions: This data shows that liquid biopsy using and targeted NGS is reliable in detecting chromosomal structural abnormalities in myeloid neoplasms and potentially can replace the need for conventional cytogenetic testing. While the current study was not designed to detect chromosomal translocations, a small, targeted panel of 275 genes is adequate for standard risk classification of myeloid neoplasms into intermediate or high-risk. Considering that in the same test complete mutation profiling can also be achieved along with chromosomal structural analysis, liquid biopsy in myeloid neoplasms might be considered as an efficient replacement to bone marrow biopsy in patients with myeloid neoplasms when fusion genes are not expected.
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Goy: Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Infinity/Verastem: Research Funding; COTA (Cancer Outcome Tracking Analysis): Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees, Other: Leadership role; OncLive Peer Exchange: Honoraria; Bristol Meyers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Vincerx: Honoraria, Membership on an entity's Board of Directors or advisory committees; Elsevier PracticeUpdate: Oncology: Consultancy, Honoraria; AbbVie/Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Vincerx pharma: Membership on an entity's Board of Directors or advisory committees; LLC(Targeted Oncology): Consultancy; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Xcenda: Consultancy, Honoraria; Gilead: Membership on an entity's Board of Directors or advisory committees; Acerta: Consultancy, Research Funding; Rosewell Park: Consultancy; Janssen: Membership on an entity's Board of Directors or advisory committees; Elsevier's Practice Update Oncology, Intellisphere, LLC(Targeted Oncology): Consultancy; AbbVie/Pharmacyclics: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; MorphoSys: Honoraria, Other; Incyte: Honoraria; Novartis: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol Meyers Squibb: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Genomic Testing Cooperative: Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees, Other: Leadership role; Celgene: Consultancy, Honoraria, Research Funding; Kite, a Gilead Company: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech/Hoffman la Roche: Research Funding; Janssen: Research Funding; Karyopharm: Research Funding; Michael J Hennessey Associates INC: Consultancy; Hoffman la Roche: Consultancy; Physicians' Education Resource: Consultancy, Other: Meeting/travel support; Medscape: Consultancy; Phamacyclics: Research Funding; Constellation: Research Funding; Xcenda: Consultancy; Hackensack Meridian Health, Regional Cancer Care Associates/OMI: Current Employment. Pecora: Genetic testing cooperative: Other: equity investor; Genetic testing cooperative: Membership on an entity's Board of Directors or advisory committees. Koprivnikar: Bristol Myers Squibb: Speakers Bureau. McCloskey: BMS: Honoraria, Speakers Bureau; COTA: Other: Equity Ownership; Takeda: Consultancy, Speakers Bureau; Pfizer: Consultancy; Novartis: Consultancy; Jazz: Consultancy, Speakers Bureau; Incyte: Speakers Bureau; Amgen: Speakers Bureau.
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