ALBERT

Description: Background:Patients with clonal hematopoiesis (CH) in the absence of WHO-classified myeloid disease are of special interest given their increased prevalence with age, predisposition to morbid cardiovascular complications, and amplified risk of overt hematologic malignancy. Pts are often stratified by normal peripheral blood counts into clonal hematopoiesis of indeterminate potential (CHIP), or those with unexplained cytopenias as clonal cytopenias of undetermined significance (CCUS). However, less is known about pts with elevated counts and clonal hematopoiesis who do not fulfill WHO criteria for any myeloproliferative neoplasia (MPN). We leveraged Vanderbilt University Medical Center's unique biobank, BioVU, to identify the prevalence of JAK2V617Facross 48,000 pts to evaluate the clinical changes in progression from CH to overt myeloid disease. Methods:To develop a reference JAKV617Ftraining set, next generation sequencing via Illumina Trusight Myeloid Panel (NGS) was performed on BioVU samples (N=133) from pts with confirmed myeloproliferative malignancy. Of those pts, 78 harbored JAK2V617Fwith a range of variant allele frequencies (VAF). Matched samples in this training set (N=133) were also analyzed via Infinium® Expanded Multi-Ethnic Genotyping Array (MEGAEX). SNP array JAK2V617Fvariant intensity was extracted (rs77375493; NM_004972.3(JAK2): c.1849G〉T (p.Val617Phe). A regression model was built using NGS VAF as a dependent variable and MEGAEX intensity data as independent variable (r2=0.9931).Based on this model, we imputed JAK2V617FVAF for all 48,000 pts in our cohort. Pts with JAK2V617Fwere subdivided into: clinically confirmed myeloid disease, or JAK2V617Fwithout a diagnosis of MPN. Upon review of the EMR, the latter group was further dived into: 1) probable undiagnosedMPN, 2) CHIP, 3) CCUS, or 4) CH with associated elevated peripheral blood counts (CHAPbc). Only lab values after the date of JAK2V617Fdetection were included. Confirmed malignancy was defined by WHO classification of disease. Pts with evidence of possible WHO classified PV or ET with Hgb 〉18.5g/dl in men, 〉16.5g/dl in women, or PLT count 〉450k/mcl regardless of gender were classified as probable undiagnosedMPN. CHIP was defined as JAK2V617Fwithout abnormal counts across a patient's EMR lifetime, except when confounding events, e.g. trauma surgery or overt iron deficiency anemia, incorrectly skewed values. CCUS was defined as JAK2V617Fin the presence of unexplained cytopenias; hemoglobin (Hgb)

Description: Mast cells play a critical role in innate immunity, allergy, and autoimmune diseases. The receptor/ligand interactions that mediate mast cell activation are poorly defined. The α2β1 integrin, a receptor for collagens, laminins, decorin, E-cadherin, matrix metalloproteinase-1 (MMP-1), endorepellin, and several viruses, has been implicated in normal developmental, inflammatory, and oncogenic processes. We recently reported that α2 integrin subunit–deficient mice exhibited markedly diminished neutrophil and IL-6 responses during Listeria monocytogenes–and zymosan-induced peritonitis. Peritoneal mast cells require α2β1 integrin expression for activation in response to pathogens, yet the ligand and molecular mechanisms by which the α2β1 integrin induces activation and cytokine secretion remain unknown. We now report that the α2β1 integrin is a novel receptor for multiple collectins and the C1q complement protein. We demonstrate that the α2β1 integrin provides a costimulatory function required for mast cell activation and cytokine secretion. This finding suggests that the α2β1 integrin is not only important for innate immunity but may serve as a critical target for the regulation of autoimmune/allergic disorders.

Description: Myelodysplastic syndromes (MDS) are heterogeneous bone marrow failure neoplasms marked by cytopenias, reduced quality of life and predilection to transform into AML. While several treatments for AML have recently been approved, the available treatments for MDS are lacking, and adaptation of AML therapy to MDS is complicated. This is due, in part, to the heterogeneity of MDS. Despite this heterogeneity, most clonal cells in MDS have an imbalance of mitochondrial-controlled BCL2 family proteins resulting in dysregulated apoptosis. These anti- (or pro-) apoptotic proteins compete for ligand to block (or promote) apoptosis, providing an opportunity to selectively target anti-apoptotic proteins and advance therapy for MDS. Venetoclax (VEN), a newly FDA-approved therapy that specifically inhibits the anti-apoptotic protein BCL2, has yielded response rates of up to 50-70% in elderly AML including impressive responses in transformed MDS which previously failed DNMTi (DiNardo et al, 2019, Wei et al, 2019). Upregulation of the anti-apoptotic protein, induced myeloid cell leukemia-1 (MCL1), is a known resistance mechanism in AML resistant or refractory to BCL2 inhibition (Pan et al, 2014), and MCL1 increases when some MDS samples are treated with BCL2 inhibitors (Jilg et al, 2016). Therefore, strategic inhibition of BCL2 and/or MCL1 is a logical therapeutic approach in MDS. We have shown efficacy of MCL1 inhibitors in the laboratory against AML patient samples that are dependent on MCL1 protein or resistant to BCL2 inhibition, including AML cells that arose from MDS (Ramsey et al, 2018). Here, our goal was to determine the sensitivity of MDS cells to inhibition of specific anti-apoptotic proteins, elucidate the characteristic determinants of response, and investigate synergy with combined BCL2 and MCL1 inhibition. We cultured MDS patient samples and determined the in vitro sensitivity of 35 MDS patient samples to selective BCL2, BCL-XL and MCL1 inhibitors using CellTiter-Glo to determine the relative cell viability concentrations (GI50) for each selective inhibitor after 48 hours of exposure. While there was little sensitivity to BCL-XL inhibition across all samples, we detected a gradient of low to high response to the BCL2 inhibitor with low to higher blast count MDS subtypes; higher blast count MDS (EB1 and EB2) were more sensitive than low blast count subtypes (RS-SLD/MLD and MLD). Interestingly, nearly all MDS subtypes were sensitive to the selective MCL1 inhibitor, S63845. To determine if there were any correlations between sensitivity to specific inhibitors and mutational status, a targeted NGS panel of 37 commonly mutated genes in myeloid disease was conducted on all samples. As expected, we observed an increased number of SF3B1 mutations in the lower blast count MDS-RS patient samples. Likewise, though there were only two samples in this cohort containing mutations in PTPN11, one was completely resistant to BCL2 inhibition.(Stevens et al, ASH 2018) Otherwise, we did not observe any correlation between specific mutations and BH3 dependence. Since upregulation of MCL1 is seen in VEN treated MDS cells and is a known resistance mechanism for VEN treatment in AML, we treated these same patient samples with VEN+S63845 to determine any synergistic benefit of combining these drugs. While there were differential responses to VEN monotherapy between subtypes, all MDS subtypes exhibited response benefit to the addition S63845 to VEN. Drug synergy was confirmed with the Zero Interaction Potency model. These results were further corroborated by increased annexin-V staining and reduced colony formation in methylcellulose. Toxicity experiments in the MISTRG immunocompromised animal model indicate that the combination of selective inhibitors of MCL1 and BCL2 are tolerated, and treatment of MDS within these xenografts is underway and will be presented. Overall, our data suggests that higher blast count MDS subtypes (EB1 and EB2) are more likely to respond to VEN monotherapy than low blast count subtypes, while all MDS subtypes may respond to MCL1 inhibition. Moreover, drug synergy can be obtained across all subtypes of MDS by combining BCL2 and MCL1 inhibitors. BCL2 inhibition is changing the standard of care in AML, thus, refining the design of clinical trials testing BCL2 and MCL1 inhibitors in MDS and the precision of patient selection for therapy is a great priority. Disclosures Savona: Boehringer Ingelheim: Patents & Royalties; Celgene Corporation: Membership on an entity's Board of Directors or advisory committees; AbbVie: Membership on an entity's Board of Directors or advisory committees; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sunesis: Research Funding; Incyte Corporation: Membership on an entity's Board of Directors or advisory committees, Research Funding; Karyopharm Therapeutics: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Selvita: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding.

Description: Key Points CUX1 is a transcription factor encoded on a region of chromosome 7 that is frequently deleted in high-risk acute myeloid leukemia. Haploinsufficiency of CUX1/cut promotes hematopoietic overgrowth in both Drosophila melanogaster and human xenograft mouse models in vivo.

Description: Circulating platelets exhibit rapid signaling and adhesive responses to collagen that facilitate hemostasis at sites of vessel injury. Because platelets are anuclear, their collagen receptors must be expressed by megakaryocytes, platelet precursors that arise in the collagen-rich environment of the bone marrow. Whether and how megakaryocytes regulate collagen adhesion during their development in the bone marrow are unknown. We find that surface expression of activated, but not wild-type, α2 integrins in hematopoietic cells in vivo results in the generation of platelets that lack surface α2 receptors. Culture of hematopoietic progenitor cells ex vivo reveals that surface levels of activated, but not wild-type, α2 integrin receptors are rapidly down-regulated during cell growth on collagen but reach wild-type levels when cells are grown in the absence of collagen. Progenitor cells that express activated α2 integrins are normally distributed in the bone marrow in vivo and exhibit normal migration across a collagen-coated membrane ex vivo. This migration is accompanied by rapid down-regulation of activated surface integrins. These studies identify ligand-dependent removal of activated α2 receptors from the cell surface as a mechanism by which integrin function can be negatively regulated in hematopoietic cells during migration between the adhesive environment of the bone marrow and the nonadhesive environment of the circulating blood.

Description: Annotation and interpretation of full scan electrospray mass spectra of metabolites is complicated by the presence of a wide variety of ions. Not only protonated, deprotonated, and neutral loss ions but also sodium, potassium, and ammonium adducts as well as oligomers are frequently observed. This diversity challenges automatic annotation and is often poorly addressed by current annotation tools. In many cases, annotation is integrated in metabolomics workflows and is based on specific chromatographic peak-picking tools. We introduce mzAdan, a nonchromatography-based multipurpose standalone application that was developed for the annotation and exploration of convolved high-resolution ESI-MS spectra. The tool annotates single or multiple accurate mass spectra using a customizable adduct annotation list and outputs a list of [M+H]+ candidates. MzAdan was first tested with a collection of 408 analytes acquired with flow injection analysis. This resulted in 402 correct [M+H]+ identifications and, with combinations of sodium, ammonium, and potassium adducts and water and ammonia losses within a tolerance of 10 mmu, explained close to 50% of the total ion current. False positives were monitored with mass accuracy and bias as well as chromatographic behavior which led to the identification of adducts with calcium instead of the expected potassium. MzAdan was then integrated in a workflow with XCMS for the untargeted LC-MS data analysis of a 52 metabolite standard mix and a human urine sample. The results were benchmarked against three other annotation tools, CAMERA, findMAIN, and CliqueMS: findMAIN and mzAdan consistently produced higher numbers of [M+H]+ candidates compared with CliqueMS and CAMERA, especially with co-eluting metabolites. Detection of low-intensity ions and correct grouping were found to be essential for annotation performance.

Description: The 17q23 amplicon is associated with poor outcome in ER+ breast cancers, but the causal genes to endocrine resistance in this amplicon are unclear. Here, we interrogate transcriptome data from primary breast tumors and find that among genes in 17q23, PRR11 is a key gene associated with a poor response to therapeutic estrogen suppression. PRR11 promotes estrogen-independent proliferation and confers endocrine resistance in ER+ breast cancers. Mechanistically, the proline-rich motif-mediated interaction of PRR11 with the p85α regulatory subunit of PI3K suppresses p85 homodimerization, thus enhancing insulin-stimulated binding of p110-p85α heterodimers to IRS1 and activation of PI3K. PRR11-amplified breast cancer cells rely on PIK3CA and are highly sensitive to PI3K inhibitors, suggesting that PRR11 amplification confers PI3K dependence. Finally, genetic and pharmacological inhibition of PI3K suppresses PRR11-mediated, estrogen-independent growth. These data suggest ER+/PRR11-amplified breast cancers as a novel subgroup of tumors that may benefit from treatment with PI3K inhibitors and antiestrogens.

Description: Background: Myelofibrosis (MF) is a devastating myeloproliferative neoplasm that is hallmarked by marrow fibrosis, symptomatic extramedullary hematopoiesis, and risk of leukemic transformation, most commonly driven by janus kinase 2 (JAK2) pathway mutations. MF risk classification systems guide prognosis, decisions regarding allogeneic stem cell transplantation, and disease modifying agents. Key systems include the Dynamic International Prognostic Scoring System (DIPSS) 2009, DIPSS plus 2010, Genetics-Based Prognostic Scoring System (GPSS) 2014, and Mutation-Enhanced International Prognostic Scoring System (MIPSS) 2014. System contributions include dynamic scoring (DIPSS), cytogenetics (DIPSS Plus), and high risk molecular mutations (GPSS and MIPSS). To power the next generation of MF risk prognostication, and ascertain new prognostic factors, large scale electronic health record (EHR) and genomic data will need integration. As a proof of concept, we leveraged our de-identified research EHR (2.9 million records) and linked genomic biobank (288,000 patients) to develop an all-inclusive phenotype-genotype-prognostic system for MF and recapitulate DIPSS, DIPSS Plus, GPSS and MIPSS. Methods: Our previously described methods (Bejan et al. AACR 2018) utilized natural language processing to algorithmically identify 306 MF patients. A subset (N=125) had available DNA for genotyping. We automatically extracted: age greater than 65, leukocyte count (WBC) greater than 25x109/L, hemoglobin (Hgb) less than 10g/dL, platelets (PLT) less than 100 x 109/L, circulating myeloid blasts ≥ 1%, and 10% weight loss compared to baseline as a proxy for constitutional symptoms. Transfusion data was not included. Karyotype data was manually reviewed. Next generation sequencing (NGS) was performed on biobanked peripheral blood DNA with the Trusight Myeloid Panel (Illumina®). Genotyped samples were restricted to dates after MF diagnosis. Multivariate Cox proportional hazard analysis was performed on all clinical and genomic variables. DIPSS plus was calculated without adjustment but lacked transfusion data. DIPSS, GPSS and MIPSS scores were calculated by published methods. Results: Multivariate Cox proportional hazard regression identified Hgb (HR=6.4; P=0.006), myeloid blasts (HR=3.8; P=0.03), and ASXL1 (HR=5.2; P=0.02) as significant in our cohort with regard to overall survival (OS). We noted a strong trend for high risk karyotype (HR=5.6; P=0.07). Our DIPSS model median survival (N=120) for each subgroup; low risk (median survival not met), intermediate-1 (108 months), intermediate-2 (47 months) and high risk (6 months) P=0.0002 (Figure 1a). DIPSS Plus (N=122) integrated karyotype data and PLT count with similar survival with the exception of high risk (4 months) P=0.00003 (Figure 1b). The percentage of patients with driver mutations in JAK2V617F (57%), CALR (3%) and MPLW515 (7.2%); JAK2WT, CALRWT and MPLWT triple negative (34%); high molecular risk ASXL1 (15%), EZH2 (6%), IDH1/2 (7%), SRFS2 (17%); other variants of interest TET2 (9.6%), TP53 (29%) and DNMT3A (16.8%). MIPSS (N=125; 48 months follow up) noted low risk, intermediate-1, and intermediate-2 (median survival not met) and high risk (32 months) P=0.0001 (Figure 1c). GPSS (N=125; 48 months follow up) did not demonstrate statistical separation among groups (Figure 1d). Discussion: This proof of concept transformed raw EHR records into clinical risk scores for MF. The addition of retrospective DNA analysis via NGS opens the possibility of multi-institutional EHR-biobank studies to most accurately create a system to define MF risk. Our sample size limited the significance of age, PLTs, poor risk mutations and other variables previously shown to impact OS. Likewise, we lacked the capacity to track transfusion dependence, previously shown to have prognostic relevance. Still, prognostication via the EHR mimics common scoring systems in MF and supports correct MF case selection, accurate laboratory extraction and reproducible genotyping of biobanked samples. Similar to the original GPSS report, our low risk cohort was small (N=2) and will benefit from expansion of genotyping underway. Finally, this phenotype-genotype-prognostic paradigm represents a technical advance and a unique opportunity to deploy patient specific comorbidities from lifetime EHR records to further refine risk across all myeloid disease. Disclosures Savona: Boehringer Ingelheim: Consultancy; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Incyte: Membership on an entity's Board of Directors or advisory committees, Research Funding.