Citations

Background To develop a preclinical thromboembolic occlusion model for studying revascularization strategies. Methods Clot analog with barium sulfate was injected into the distal aorta in 9 New Zealand white rabbits. The situation of aorta occlusion was compared among fibrin-rich (n=4), red blood cell (RBC)-rich (n=3), and whole blood clot analogs (n=2) using digital subtraction angiography. Arterial geometries, histologic features and circumferential stretch of the distal aorta in rabbits were compared with the common carotid artery in swine and the distal internal carotid artery (ICA) in humans. Aspiration thrombectomy and mechanical thrombectomy using a stent retriever were performed in two rabbits. Results The aortic bifurcation was occluded after a single delivery of clot in 4 cases. It was occluded after the second clot injection in the 5 remaining rabbits. Fragmentation of RBC-rich clots occurred during clot injection in 2 cases. The mean diameters of the distal aorta and right common iliac artery in rabbits were 3.7±0.4 and 2.8±0.3 mm, respectively; the mean diameters of human ICA, and first and second segments of the middle cerebral artery (M1, M2) were 3.6±0.4, 3.1±0.4, and 2.4±0.4 mm, respectively. Arterial revascularization was achieved in both rabbits. Geometric, mechanical and histological factors of the distal aorta in rabbit were more close to human distal ICA than swine carotid artery. Conclusion Arterial occlusion can be achieved at the aortic bifurcation in rabbits, which is comparable to human ICA bifurcation. This thrombectomy model has the potential to be used for testing of thrombectomy devices.

Tumor necrosis factor-α-induced gene/protein (TSG)-6 regulates the immunomodulatory properties of MSCs, but its ability to protect the ischemic kidney is unknown. In pigs with renal artery stenosis, we show that MSC delivery increased renal vein TSG-6, decreased kidney inflammatory macrophages, and improved renal function. In vitro, TSG-6 decreased inflammatory macrophages and tubular cell injury. Therefore, TSG-6 released from MSCs may decrease renal tubular cell injury, which is associated with regulating macrophage function and phenotype.

Background: Multiple myeloma (MM) is typically diagnosed in patients (pts) aged 65-74 y (SEER: Myeloma. 2019); therefore, many pts present with concomitant metabolic disorders such as diabetes (18%-22% prevalence of type 2 diabetes [T2D; BMC Cancer. 2020;20:489]). Obesity (body mass index [BMI], ≥ 30 kg/m2) is associated with increased prevalence of T2D (Int J Clin Pract. 2007;61:737), and both are suggested risk factors for MM (Blood. 2012;119:4845; Bone Marrow Transpl. 2014;49:1009). Results from single-center, retrospective studies have shown poorer clinical features and survival outcomes in diabetic pts with MM vs their nondiabetic counterparts (Eur J Haematol. 2012;89:320, Br J Cancer. 2014;111:628). The Connect® MM Registry (NCT01081028) is a large, US, multicenter, prospective observational cohort study of largely community-based pts with NDMM. Reported here is a descriptive analysis assessing differences in baseline (BL) characteristics, treatment patterns, and survival outcomes in diabetic vs nondiabetic pts enrolled in the Connect MM Registry. Methods: Pts with NDMM (N = 3011) were enrolled at 250 community, academic, and government sites. Eligible pts were aged ≥ 18 y with symptomatic MM diagnosed ≤ 2 months (mo) pre-enrollment per International Myeloma Working Group criteria (Leukemia. 2009;23:3). Diabetic pts were defined as those with a history of diabetes and also receiving antidiabetic treatment at BL; those without a history of diabetes and not receiving antidiabetic treatment at BL were defined as nondiabetic pts. Descriptive statistics were used for BL characteristics and treatment regimens. Survival outcomes (progression-free survival [PFS] and overall survival [OS]) were analyzed using Cox regression and adjusted for covariates. Results: Of the 2845 pts included in this analysis, 467 (16%) were diabetic, and 2378 (84%) were nondiabetic. The median age was 69 y for diabetic pts and 66 y for nondiabetic pts, with fewer Black pts (12.2% vs 19.5%) in the nondiabetic group. The proportion of men was higher in the diabetic vs the nondiabetic group (61.9% vs 56.6%). Similar percentages of pts had ECOG PS of 0-1 in both groups (53.3% vs 55.3%). Obesity was more prevalent in diabetic vs nondiabetic pts (55.9% vs 30.4%). More diabetic pts had a history of hypertension requiring treatment vs nondiabetic pts (84.2% vs 52.5%). The prevalence of chromosomal abnormalities was comparable between the groups (within the limitation that data were missing in a substantial number of pts). Among MM-defining criteria, diabetic pts had a higher incidence of hypercalcemia (11.8% vs 8.5%), anemia (50.3% vs 46.5%), and increased creatinine (23.8% vs 19%), whereas measured kidney function was similar. A lower proportion of diabetic than nondiabetic pts received stem cell transplantation (SCT; 28.9% vs 41.5%) and triplet regimens during first induction (48.2% vs 52.8%%). The use of ≥ 2 novel agents during first line (1L) was lower in diabetic than nondiabetic pts (27.8% vs 34.6%), but use of a single novel agent (63.4% vs 60%) and alkylator in 1L (20.6% vs 22.4%) was comparable. The top five 1L regimens were similar for both groups, with lenalidomide + bortezomib + dexamethasone being the most common 1L regimen, and lenalidomide the most common maintenance therapy. Diabetic pts had significantly shorter adjusted median PFS (24.4 vs 31.5 mo; hazard ratio [HR], 1.24; P = 0.001; Fig. 1A) and median OS (60 vs 72.6 mo; HR, 1.29; P = 0.001; Fig. 1B) than nondiabetic pts. In a subgroup analysis by BMI, diabetic pts had significantly shorter OS than nondiabetic pts (for BMI < 30 [50.8 vs 73.1 mo; HR, 1.51; P = 0.0001] and BMI ≥ 30 [56.4 vs 78.6 mo; HR, 1.51; P < 0.0001]). Conclusions: Diabetic pts with NDMM had poorer BL features and were less likely to receive SCT and triplet regimens and/or aggressive upfront treatment. Regardless of BMI, diabetic pts had shorter median OS than nondiabetic pts, suggesting that diabetes might be a stronger factor for survival than obesity. These results support a previous analysis of the Connect MM Registry that identified history of diabetes as a predictive prognostic factor for poor OS (Br J Haematol. 2019;187:602). Limitations of this analysis include limited detail on the care/management of diabetes and steroid use/dosage (which can be challenging for glycemic control). These findings highlight the need to provide better supportive care for diabetes management in MM pts to improve survival. Disclosures La: Celgene BMS: Consultancy. Jagannath:BMS, Janssen, Karyopharm, Legend Biotech, Sanofi, Takeda: Consultancy. Ailawadhi:Amgen: Research Funding; Celgene: Honoraria; Takeda: Honoraria; Janssen: Research Funding; Pharmacyclics: Research Funding; Cellectar: Research Funding; BMS: Research Funding; Medimmune: Research Funding; Phosplatin: Research Funding. Durie:Amgen, Celgene, Johnson & Johnson, and Takeda: Consultancy. Gasparetto:Adaptive Biotechnologies: Consultancy, Honoraria; Sanofi: Consultancy, Honoraria, Other: TRAVEL, ACCOMMODATIONS, EXPENSES (paid by any for-profit health care company), Speakers Bureau; Karyopharm: Consultancy, Honoraria, Other: TRAVEL, ACCOMODATIONS, EXPENSES (paid by any for-profit health care company), Speakers Bureau; Janssen: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; GSK: Consultancy, Honoraria, Other: TRAVEL, ACCOMMODATIONS, EXPENSES (paid by any for-profit health care company); BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; AbbVie: Consultancy, Honoraria. Hardin:Celgene Pharmaceutical Company: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: TRAVEL, ACCOMMODATIONS, EXPENSES. Lee:Sanofi: Consultancy; Janssen: Consultancy, Research Funding; GlaxoSmithKline: Consultancy, Research Funding; Genentech: Consultancy; Takeda: Consultancy, Research Funding; Regeneron: Research Funding; Genentech: Consultancy; Amgen: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Daiichi Sankyo: Research Funding. Narang:Maryland Oncology Hematology: Current Employment; Beigene: Consultancy, Honoraria; BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; J&J: Consultancy, Honoraria. Omel:Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: TRAVEL, ACCOMMODATIONS, EXPENSES; BMS/Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees. Rifkin:McKesson: Current equity holder in publicly-traded company, Ended employment in the past 24 months, Other: Stock ownership; Takeda, Amgen, Celgene, BMS, Mylan, Coherus BioSciences, Fresenius: Consultancy; AbbVie: Other: Investigator in AbbVie sponsored clinical trials; Takeda, Amgen, BMS (Celgene): Membership on an entity's Board of Directors or advisory committees. Terebelo:Newland Medical Associates: Current Employment; BMS: Membership on an entity's Board of Directors or advisory committees. Toomey:Celgene/BMS: Membership on an entity's Board of Directors or advisory committees. Wagner:Celgene Inc.: Membership on an entity's Board of Directors or advisory committees; Connect Multiple Myeloma Registry: Membership on an entity's Board of Directors or advisory committees. Dhalla:BMS: Current Employment, Current equity holder in publicly-traded company. Liu:TechData Inc: Consultancy. Yang:BMS: Current Employment, Current equity holder in publicly-traded company. Joshi:Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company; Eisai Inc.: Ended employment in the past 24 months. Abonour:Takeda: Consultancy; Janssen: Honoraria, Research Funding; Celgene: Consultancy; BMS: Consultancy, Research Funding.

Background: Multiple myeloma (MM) is a clonal plasma cell malignancy characterized by a complex genomic landscape with molecular and clinical heterogeneity. Venetoclax (Ven) is a selective, potent, oral BCL-2 inhibitor currently under investigation as a potential targeted therapy for the treatment of relapsed/refractory (RR) MM. Using a next-generation sequencing (NGS)-based approach, the mutational landscape and gene expression profile (GEP) of patients (pts) with RRMM in BELLINI was evaluated for potential impact on observed clinical outcomes in key biomarker subgroups. Methods: BELLINI (NCT02755597) is a randomized, double-blind, multicenter Phase 3 study of bortezomib + dexamethasone (Bd) + either Ven or placebo (Pbo) in pts with RRMM who received 1-3 prior therapies and were sensitive or naïve to proteasome inhibitors. Pts were randomized 2:1 to receive Ven 800 mg/d or Pbo + Bd. The following biomarker analyses were performed by central laboratory assessments of CD138-enriched bone marrow mononuclear cells collected at baseline: BCL2 gene expression by quantitative PCR (qPCR), cytogenetic abnormalities by interphase fluorescence in situ hybridization (FISH), identification of somatic mutations by whole-exome sequencing (WES), and transcriptomic analysis by RNA sequencing (RNAseq). Correlation between mutational status, BCL2high status (Harrison et al. Blood. 2019:134 suppl:142), cytogenetics, GEP, and survival outcomes were examined by pairwise t-test and by hazard ratio (HR) from Cox proportional hazard model. Results: As of the data cutoff (13 Sep 2019), 291 pts were randomized (194 to Ven and 97 to Pbo); 262 pts (90.0%) were evaluable by FISH, 257 pts (88.3%) by qPCR, 197 pts (67.7%) by WES, and 184 pts (63.2%) by RNAseq. The mutational landscape of evaluable pts was highly heterogeneous (Figure 1A). MAPK pathway mutations were the most prevalent (62/197 pts; 31.5%); however, recurrent mutations in the NF-kB pathway (TRAF3 or CYLD) were also observed (12/197 pts; 6.1%). Within the MAPK pathway, the most frequently mutated genes were KRAS (39/197 pts; 19.8%), NRAS (8/197 pts; 4.1%), BRAF (8/197 pts; 4.1%), and NF1 (12/197 pts; 6.1%). Although a higher rate of pts with a MAPK mutation was observed in the t(11;14) and BCL2high subgroup compared to pts without these biomarkers (39/101 pts [38.6%] vs 23/95 pts [24.2%]; P=.04), improvement in progression-free survival (PFS) with Ven+Bd was observed in pts with (HR=0.08; 95% CI, 0.02-0.31; P<.001) or without (HR, 0.31 [95% CI, 0.16-0.63]; P=.001) a MAPK pathway mutation. High somatic mutation loads are associated with increased genomic instability, resistance to therapy, and decreased overall survival (OS) in MM. High tumor mutation burden (TMB), defined as ≥20 mutations/megabase of sequenced DNA, was observed in 12/197 pts (6.1%). Although there were a limited number of high TMB pts, reduced PFS (HR, 4.55 [95% CI, 2.33-8.88]; P<.001) and OS (HR, 3.89 [95% CI, 1.76-8.56]; P<.001) compared to low TMB pts was observed regardless of treatment arm. WES detected a recurrent BCL2 mutation (Ala43Thr) in 15/197 pts (7.6%). This BCL2 mutation has not previously been identified in MM and did not appear to affect survival outcomes among pts in either arm. Supervised clustering of RNAseq data grouped pts into respective MM GEP (Zhan et al. Blood. 2006;108:2020) at the following frequencies: CD-1 (2.7%), CD-2 (8.2%), HY (40.2%), MF (1.6%), MS (10.9%), LB (5.4%), and PR (31.0%). As shown in Figure 1B, 16/20 pts (80%) allocated to the CD-1/CD-2 cluster had a t(11;14) mutation, but 17/20 pts (85%) and 2/3 pts (67%) in MS and MF GEP groups had t(4;14) or t(14;16) mutations, respectively. The HY GEP group was primarily (56/74 pts; 76%) hyperdiploid by FISH. Though a higher rate of pts with a HY GEP (50/98 pts [51.0%] vs 24/86 pts [27.9%]; P<.001) and a lower rate of PR GEP (11/98 pts [11.2%] vs 46/86 pts [53.5%]; P<.001) were observed in the t(11;14) and BCL2high subgroups compared with non-t(11;14) and BCL2low subgroups, respectively, improvement in PFS with Ven+Bd was independent of GEP classification. Conclusion: Using an integrative approach of genomic and transcriptomic profiling, response to Ven+Bd in key biomarker subgroups was shown to be independent of concurrent mutations in key oncogenic signaling pathways and GEPs known to be prognostic in MM; however, high mutation load appears to be a clinically relevant risk factor that negatively affects survival in MM. Disclosures O'Dwyer: Celgene: Other: TRAVEL, ACCOMMODATIONS, EXPENSES (paid by any for-profit health care company); BMS: Research Funding; Carrick Therapeutics: Current equity holder in publicly-traded company, Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Consultancy, Other: TRAVEL, ACCOMMODATIONS, EXPENSES (paid by any for-profit health care company); Janssen: Consultancy, Other: TRAVEL, ACCOMMODATIONS, EXPENSES (paid by any for-profit health care company); AbbVie: Consultancy; ONK Therapeutics: Consultancy, Current Employment, Current equity holder in publicly-traded company, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding. Spencer:Roche: Honoraria; Sanofi: Consultancy, Honoraria; Pharmamar: Research Funding; AbbVie: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Honoraria; BMS: Consultancy, Honoraria, Research Funding, Speakers Bureau; Takeda: Honoraria, Research Funding, Speakers Bureau; Amgen: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Secura Bio: Consultancy, Honoraria; Servier: Consultancy, Honoraria, Research Funding; HaemaLogiX: Consultancy, Honoraria, Research Funding. Kumar:Janssen Oncology: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Celgene/BMS: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Takeda: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; BMS: Consultancy, Research Funding; Tenebio: Other, Research Funding; Oncopeptides: Consultancy, Other: Independent Review Committee; IRC member; Kite Pharma: Consultancy, Research Funding; AbbVie: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Amgen: Consultancy, Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments, Research Funding; Genecentrix: Consultancy; Cellectar: Other; MedImmune: Research Funding; Karyopharm: Consultancy; Sanofi: Research Funding; Merck: Consultancy, Research Funding; Adaptive Biotechnologies: Consultancy; Carsgen: Other, Research Funding; Dr. Reddy's Laboratories: Honoraria; Genentech/Roche: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Novartis: Research Funding. Popat:AbbVie: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Other: Travel support, Research Funding; Janssen: Consultancy, Honoraria, Other: TRAVEL, ACCOMMODATIONS, EXPENSES (paid by any for-profit health care company); Bristol Myers Squibb: Consultancy, Honoraria; GSK: Consultancy, Honoraria, Other: TRAVEL, ACCOMMODATIONS, EXPENSES (paid by any for-profit health care company); Celgene: Consultancy, Honoraria. Moreau:Abbvie: Consultancy, Honoraria; Sanofi: Consultancy, Honoraria; Celgene/Bristol-Myers Squibb: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Takeda: Honoraria; Novartis: Honoraria. Dail:Genentech: Current Employment, Current equity holder in publicly-traded company. Jalaluddin:AbbVie: Current Employment, Other: may hold stock or stock options. Yang:Abbvie: Current Employment, Current equity holder in publicly-traded company. Lu:AbbVie: Current Employment, Current equity holder in publicly-traded company. Zha:Abbvie: Current Employment, Current equity holder in publicly-traded company. Dunbar:Abbvie: Current Employment, Current equity holder in publicly-traded company. Vishwamitra:AbbVie, Inc.: Current Employment, Other: may hold stock or other options. Mantis:Abbvie: Current Employment, Current equity holder in publicly-traded company. Melhem-Bertrandt:AbbVie: Current Employment; Astellas: Ended employment in the past 24 months. Ross:AbbVie: Current Employment, Current equity holder in publicly-traded company. Harrison:F. Hoffmann-La Roche: Consultancy, Honoraria; CRISPR Therapeutics: Consultancy, Honoraria; GSK: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen-Cilag: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Consultancy, Honoraria, Patents & Royalties: wrt panobinostat; Janssen: Honoraria; Haemalogix: Consultancy; BMS: Consultancy, Honoraria.

MYC gene amplification and somatic mutations are frequent in both adult and pediatric AML although how MYC drives and contributes to the development and maintenance of AML has not been resolved. Transcription factor EB (TFEB) is a master regulator of genes that control autophagy and lysosome biogenesis, a central catabolic recycling pathway that regulates cell survival. Given the oncogenic effects of MYC in AML and that the induction of autophagy compromises AML cell growth and survival, we tested if the oncogenic effect of MYC depends on its suppression of TFEB transcription programs in AML. In support of this hypothesis, inducible MYC expression in K562 and THP-1 leukemia cells was sufficient to suppress expression of TFEB and its target genes. Further and conversely, MYC knockdown in NB4 AML cells provoked increased expression of TFEB mRNA and protein, as well as increased expression of TFEB target genes. Notably, dose response studies demonstrated that expression of TFEBS211A, constitutively nuclear form of TFEB that is refractory to control by mTORC1 signaling, dramatically impairs proliferation of HL60, OCI-AML2 and OCI-AML3 AML cells. In addition, induction of TFEBS211A provoked rampant apoptosis. Of important, overexpression of TFEBS211A in HL-60 and OCI-AML3 cells was also sufficient to promote monocytic and granulocytic differentiation, as judged by morphological changes and the acquisition of mature monocytic and granulocytic markers including CD11b, Gr1, and CD15. To identify TFEB targets that might contribute to myeloid/granulocytic differentiation, we performed RNA-seq analysis of HL60 leukemia cells engineered to inducibly express the TFEBS211A transgene. Using a cut-off of fold change>4 with q<0.01, a total of 1152 genes were differentially regulated following the induction of TFEBS211A in HL60 cells. As expected, this included the robust induction of nearly all TFEB target genes associated with the autophagy-lysosome pathway, but also of STAT1, KLF4, KLF6, CEBPB, CSF1 and GATA2 genes that are necessary and/or sufficient to provoke terminal monocytic and granulocytic differentiation of AML cells. Surprisingly, among genes induced by TFEB in HL60 cells is IDH1, which catalyzes the production of α-ketoglutarate (α-KG), a required substrate of the TET family of dioxygenases that convert 5-methylcytosine (5mC) to 5-hydroxymethylcytosine (5hmC). In particular, TFEBS211A expression provoked increases in levels of α-KG and significant increases in global levels of 5hmC in genomic DNA of HL60 leukemia cells both ex vivo and in vivo. Furthermore, TFEB-mediated induction of IDH1/2 mRNA and protein, and of IDH1 promoter activity, was antagonized by inducible expression of MYC. To assess the global effects of TFEB on 5mC/5hmC landscapes, we performed paired reduced representation bisulfite (BS)- and oxidation bisulfite (oxBS)-sequencing. As predicted, TFEBS211A induced both loss and gains of 5mC, but there were more losses (n = 722) than gains (n = 459) across all 22 chromosomes. Quite remarkably, and consistent with TFEB-provoked increases of 5hmC signals, TFEBS211A exclusively induced 5hmC gains (in a total of 863 genes), and 37% and 36% of these 5hmC gains occurred in promoter regions and CpG islands, and across all 22 chromosomes. Comparison of BS- and oxBS-seq versus RNA-seq analyses of HL60 cells expressing TFEB revealed significant changes in mRNA levels and concomitant differential changes in 5mC and 5hmC marks in KLF4, KLF6, STAT3, TP73, andFOXO1 that have pivotal roles in controlling myeloid cell differentiation and death. Collectively, these findings demonstrate that MYC suppresses TFEB expression and function in AML cells, and that TFEB functions as a tumor suppressor that provokes AML cell differentiation and death. Strikingly, these responses rely on epigenetic control, where TFEB directly induces the transcription of IDH1 and IDH2 to provoke global hydroxylation of 5-methylcytosine and the expression of genes that drive terminal differentiation and apoptosis. Thus, a MYC-TFEB-IDH1/2-TET2 circuit controls AML cell fate. Disclosures Murphy: Merck: Research Funding; Puma Biotech: Research Funding. Ballabio:CASMA Therapeutics: Other: Co-Founder. Kaufmann:Takeda Pharmaceuticals: Research Funding.

Introduction: Elevated pro-inflammatory cytokines are a hallmark feature of myeloproliferative neoplasms (MPNs). The pro-inflammatory cytokine interleukin-8 (IL8) is increased in patients with myelofibrosis (MF) and correlates with adverse outcome, including overall survival. Previously, the Levine/Fang labs identified increased IL8 secretion from individual CD34+ stem cells in a subset of MF patients. The role of IL8 and its cognate receptors CXCR1/2 in MF pathogenesis has not been delineated. Methods: Single-cell cytokine assays were performed on isolated CD34+ cells from 60 clinically annotated MPN patients (20 MF, 20 PV, 20 ET) using a previously described micro-chip platform (Kleppe et al, Can Disc 2013). 10 healthy donors (CD34+ cells from hip replacements) were used as controls. Integrated RNA-Seq and Assay for Transposase-Accessible Chromatin followed by next-generation sequencing (ATAC-Seq) was performed on CD34+ cells from MPN patients with and without expanded IL8 secreting clones for gene expression/chromatin accessibility analysis. To model the role of IL8-CXCR2 on fibrosis in vivo, the human MPLW515L transplant model (hMPLW515L) of MF was used. Specifically, wild-type (WT) murine bone marrow (Creneg-Cxcr2f/f; Cxcr2WT) or marrow lacking the CXCR2 receptor (VavCre-Cxcr2f/f; Cxcr2KO)were retrovirally infected with MSCV-hMPLW515L-IRES-GFP and transplanted into lethally irradiated WT recipient mice and monitored for disease. Blood counts, chimerism, and flow cytometry were assayed. Moribund mice were sacrificed and assayed for grade reticulin fibrosis and overall survival. Results: Single-cell cytokine assays confirmed an increased proportion of IL8-secreting CD34+ cells in MF patients (40%) in comparison to other MPN sub-types (10% PV/0% ET) (Figure 1A). MF patients with expanded IL8 secreting clones (defined as >50% of total CD34+ cells) had increased leukocytosis (p<0.0001), larger spleen sizes (p=0.0004), greater prevalence of constitutional symptoms (p=0.0084), and higher-grade reticulin fibrosis in marrow (Figure 1B) in comparison to MF patients without prevalent IL8 clones. IHC confirmed increased IL8 expression in marrow biopsies from 8/15 MF patients in comparison to 0/4 normal controls (Figure 1C), and high IL8 expression was also observed in MF splenic megakaryocytes (MKs) as well as in splenic stromal/endothelial cells not seen in normal spleen (Figure 1D). Integrated RNA-Seq/ATAC-Seq analysis of IL8-high MF patients confirmed up-regulation of IL8-CXCR2 signaling and enrichment in pro-inflammatory pathways (i.e TNFa, NFkB, etc) by GSEA, as well as increased expression/accessibility of pro-inflammatory genes S100A8 and S100A9-previously implicated in fibrosis development. Flow analysis of IL8-high MF CD34+ cells revealed enhanced surface expression of CXCR2 and its analog CXCR1, such that MF was characterized by increased IL8 ligand and receptor expression (Figure 1E) and coincided with enhanced NFkB pathway activity (Figure 1F). Consistent with this, colony forming assays of cultured MF CD34+ cells revealed enhanced colony output when cultured with IL8 compared to WT CD34+ cells-an effect ameliorated by co-treatment with the CXCR1/2 antagonist Reparixin (Figure 1G). In vivo, hMPLW515L adoptive transplant with Cxcr2KO hematopoietic donor cells demonstrated improved leukocytosis, thrombocytosis (Figure 2A) and splenomegaly in comparison to Cxcr2WT hMPLW515L recipient mice. Pathologic analysis revealed a reduction in reticulin fibrosis in bone marrow (Figure 2B) and spleen, translating into an improvement in overall survival (Figure 2C). Notably, a significant reduction in dysplastic MKs-a hallmark feature of MF-was also observed in Cxcr2KO hMPLW515L mice (Figure 2D) supporting a role for CXCR2 signaling in MK proliferation. Conclusion: IL8 secreting clones are associated with increased symptom severity and fibrosis grade in MF. Gene expression of MF CD34+ IL8 secreting clones shows up-regulation of inflammatory genes S100A8/A9, implicated in myofibroblast proliferation. Cxcr2 KO abrogates fibrosis formation and prolongs survival in the hMPLW515L model, and CXCR1/2 inhibition impairs colony forming capacity of MF CD34+ cells. These data suggest pharmacologic inhibition of this pathway should be investigated as potential therapy in MF and in PV/ET patients at high risk of fibrotic transformation. Disclosures Fan: IsoPlexis: Current Employment, Current equity holder in private company; Singleron Biotechnologies: Current Employment, Current equity holder in private company. Levine:Morphosys: Consultancy; Prelude Therapeutics: Research Funding; Qiagen: Current equity holder in publicly-traded company, Membership on an entity's Board of Directors or advisory committees; Gilead: Honoraria; Loxo: Current equity holder in private company, Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy; Amgen: Honoraria; Astellas: Consultancy; Imago: Current equity holder in private company, Membership on an entity's Board of Directors or advisory committees; C4 Therapeutics: Current equity holder in private company, Membership on an entity's Board of Directors or advisory committees; Isoplexis: Current equity holder in private company, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Honoraria, Research Funding; Roche: Consultancy, Honoraria, Research Funding; Lilly: Consultancy, Honoraria; Janssen: Consultancy. Hoffman:Protagonist: Consultancy; Abbvie: Membership on an entity's Board of Directors or advisory committees; Dompe: Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees; Forbius: Consultancy.

BACKGROUND: Angioimmunoblastic T cell lymphoma (AITL) has a unique histological profile comprised of a relatively small number of malignant CD4+ T-cells of TFH phenotype inter-mixed with an extensive infiltrate of multi-lineage immune cells. In our study, we have utilized mass cytometry, high-dimensional analysis, and single-cell transcriptome analysis to provide novel insights into the unique phenotypes that comprise this intra-tumoral microenvironment. We then extended this work to explore clinical associations including peripheral serum analysis of AITL patients and normal controls. To our knowledge, this represents the first such analysis of its kind in AITL. METHODS: We designed two novel CyTOF antibody (Ab) panels to identify and characterize cells of T, B, NK, monocyte and fDC lineages. Samples analyzed included a cohort of 25 biopsy specimens from 8 histologically confirmed AITL patients (5 lymph node (LN), 3 spleen (SP)) and 17 normal controls across key comparator immune tissue types (7 LN, 6 SP, 4 tonsil (TL)). Extensive high-dimensional analysis of CyTOF data was then performed to provide novel insights into key phenotypes and trends of malignant and non-malignant populations in AITL. We then performed CITE-Seq on control and AITL samples to gain further insight into the RNA transcriptome of key T cell populations at the cellular level. Finally, peripheral serum analysis of cytokines, soluble immune receptors, and ligands were then measured by multiplex ELISA from a separate cohort of 22 samples (5 AITL, 17 control) distinct from the individuals analyzed in the original high-dimensional study cohort. RESULTS: While the presence of "reactive" CD8+ populations is a known histologic hallmark of AITL, we describe the gross expansion of novel CD8+ populations with distinctive immunophenotypic features which have not previously been detailed in this malignancy. Using single-cell protein expression data from CyTOF, these expanded CD8+ populations can be broadly categorized as "effector memory" (CCR7-, CD45RO+, CD45RA-) and further characterized phenotypically by markers of progressive exhaustion, checkpoint inhibition, and terminal differentiation (PD1++, TIGIT++, ICOS+, TIM3+). Further analysis of the single-cell transcriptome from these expanded CD8+ populations via CITE-Seq revealed an expression signature consistent with dysfunction and limited cytotoxic activity (including significant down-regulation of granzyme, perforin, and IFN-g) when compared to benign and malignant controls. Interestingly, when compared to CD8+ populations of identical phenotype found in control tissues, these cells also featured marked upregulation of XCL2 and XCL1 in AITL. Additionally, global shifts in infiltrating CD19+ B cell phenotypes were seen in AITL, marked specifically by diminished expression of both CXCR5 and CD73. Finally, soluble PD-1 and other key immune molecules implicated in the expanded tumor microenvironment were found to be significantly increased in the peripheral serum of AITL patients compared to controls (1567.9 pg/mL (1109.3 S.E.) in AITL vs 29.79 (8.84 S.E.) in controls; P<0.0001). CONCLUSIONS: High-dimensional and single-cell transcriptome analysis of the AITL microenvironment yielded several novel insights which have not been previously described in this malignancy. Highlights include the gross expansion of distinct CD8+ populations - the majority of which are of an exhausted, dysfunctional phenotype featuring marked upregulation of XCL2 and XCL1 - and the global loss of CXCR5 and CD73 expression among AITL CD19+ B cell populations. Taken together, this suggests the presence of aberrant non-malignant immune subsets within the AITL microenvironment which may contribute to novel mechanisms of immune escape. Disclosures Cerhan: NanoString: Research Funding; BMS/Celgene: Research Funding. Ansell:Bristol Myers Squibb: Research Funding; ADC Therapeutics: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; AI Therapeutics: Research Funding; Trillium: Research Funding; Takeda: Research Funding.

AbstractPurposeSevere adverse events (AEs), such as Guillain‐Barré syndrome (GBS) occur rarely after influenza vaccination. We identify highly associated AEs with GBS and develop prediction models for GBS using the US Vaccine Adverse Event Reporting System (VAERS) reports following trivalent influenza vaccination (FLU3).MethodsThis study analyzed 80 059 reports from the US VAERS between 1990 and 2017. Several AEs were identified as highly associated with GBS and were used to develop the prediction model. Some common and mild AEs that were suspected to be underreported when GBS occurred simultaneously were removed from the final model. The analyses were validated using European influenza vaccine AEs data from EudraVigilance.ResultsOf the 80 059 reports, 1185 (1.5%) were annotated as GBS related. Twenty‐four AEs were identified as having strong association with GBS. The full prediction model, using age, sex, and all 24 AEs achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 85.4% (90% CI: [83.8%, 86.9%]). After excluding the nine (e.g., pruritus, rash, injection site pain) likely underreported AEs, the final AUC became 77.5% (90% CI: [75.5%, 79.6%]). Two hundred and one (0.25%) reports were predicted as of high risk of GBS (predicted probability >25%) and 84 actually developed GBS.ConclusionThe prediction performance demonstrated the potential of developing risk‐prediction models utilizing the VAERS cohort. Excluding the likely underreported AEs sacrificed some prediction power but made the model more interpretable and feasible. The high absolute risk of even a small number of AE combinations suggests the promise of GBS prediction within the VAERS dataset.

Eosinophils protect against liver injury during orthotopic liver transplantation and other conditions of hepatic ischemia-reperfusion injury in mice.

Giving birth too soon accounts for half of all newborn deaths worldwide. Clinical symptoms alone are not sufficient to identify women at risk of giving birth too early, as such a pragmatic approach to reducing the incidence of preterm birth entails developing early strategies for intervention before it materializes.