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Background: Chimeric Antigen Receptor (CAR) T cell therapy is a promising cancer immunotherapy that is growing exponentially. The doubling time of medical knowledge in 2010 was 3.5 years, and the projection for 2020 is just 73 days. In the last five years, the number of PubMed publications on cancer applications of CAR T cells has tripled. Therefore, to remain updated in the field represents a challenge for patients, care providers and researchers. In this review we provide a focused summary of the currently ongoing clinical trials, with a comprehensive overview of advances in CAR T cell therapy, beyond CD19, emphasizing on antigenic targets, development phases, and leading sponsor pharmaceutical companies. Methods: We retrieved the available data from the national registry of clinical trials (clinicaltrials.gov) using the following keywords: "CAR T cell", "CAR T cell and cancer", "chimeric antigen receptor", "CAR T AND tumor antigen", 'CAR T cell antigens", "Tumor antigens targeted by CAR T cells", "engineered T cells", "modified T cell", "CAR T cells in Cancer", "CAR T cell therapy", "CAR T cell therapy AND Cancer" until December 31, 2018 and manually excluded the trials unrelated to CAR T-cell therapies on cancer, by reviewing the detailed information provided on the website as well as preliminary data published. Results: The analysis included 271 clinical trials posted on the clinicaltrials.gov website from the United States by the cut-off date. For efficacy analysis, we retrieved information from 52 trials, by NCT number on a PubMed search. The majority of CAR T clinical research is focused on hematological cancer (57%), followed by CNS 8%, GI 6%, Skin 5%, Genitourinary 4%, Breast 4%, Gynecologic 4%, Respiratory 3%, Sarcoma 2%, Mesothelioma 2% and others 5%. The most used target in CAR T cell therapy and the leaders in phase 3 trials are CD19 (42%) and BCMA (12%), followed by CD20, NY-ESO-1, Mesothelin, HER2, GD2, MAGE-A3 and CD30. An essential step in CAR T cell therapy development is the selection of the right antigen/target. Here, we provide an overview of the clinically relevant targets that are actively being using by clinical trials in the United States. For example, CD19 appears to be a leading target regarding CAR T cell therapy on cancer with 116 trials (42% of total CAR T cells trials) on going just in the United States with a significant increment in the previous years. Similarly, with BCMA is one of the targets with more phase 3 trials (Figure 1) with promising results on patients with Multiple Myeloma with and the objective response of 85%, CR 45%, and PFS of 11.8 months. Second-generation CARs with either CD28 or 4-1BB as costimulatory signaling domain are preferred, with 4-1BB being the most commonly chosen. Conclusions: Our findings show growing trends in the development of CAR T cell-based therapies, combination and possible retargeting therapies in the future for solid tumor and hematologic malignances; taking into account the amount of important information and the complexity of the database, we have developed this analysis to understand how to generate in the future a friendly platform for researchers and patients to have an detailed overview of the clinical trials in cellular therapies Disclosures No relevant conflicts of interest to declare.

Abstract Introduction: Primary refractory and early relapsed acute myeloid leukemia (RR-AML) continue to pose a therapeutic challenge. Salvage therapy treatment regimens for refractory and relapsed disease are not standardized but include the commonly-used mitoxantrone, etoposide, and cytarabine (MEC) and cladribine, cytarabine, filgrastim, and mitoxantrone (CLAG-M). Previous studies have suggested a potential benefit of CLAG-M, and therefore we performed an analysis of consecutive patients selected to receive either CLAG-M or MEC as salvage therapy for RR-AML. Methods: The study included 150 consecutive patients with RR-AML who received either CLAG-M or MEC between 09/01/2009 and 12/31/2017. Patients were identified through a pharmacy database based on use of mitoxantrone. Baseline disease and patient-related characteristics and clinical outcomes including allogeneic transplantation (ASCT) were obtained by systematic review of EMR. Overall survival (OS) and progression free survival (PFS) were estimated according to the Kaplan Meier method and log-rank test was used to compare outcomes between patients who received either of the two regimens. Results: There were 150 consecutive treated patients with RR-AML included in this analysis, 34 patients received CLAG-M and 116 patients received MEC. The median age was 57 years (18-76), and, and 52% were male. Forty-seven (31%) were treated for early relapsed disease (<6 months after successful induction), 17% (>6 months after induction), and the remainder had primary refractory disease (mean number of induction regimens was 1.8, range 1-6). AML risk was defined by cytogenetic criteria. There were a predominance of intermediate-risk subtypes at 47% (71/150) and followed by high-risk at 37% (55/150). Overall CR/CRi rates for CLAG-M and MEC were 74% (23/31) and 58% (63/108) in patients eligible for analysis (p=0.18). The median follow-up from relapse was 23.5 months (1-76). The median OS was 9.5 months for CLAG-M and 10.0 months for MEC (HR=0.88, 95%CI=0.54-1.41, p=0.59). In total, 76 patients (51%) were able to proceed to ASCT following salvage therapy with either CLAG-M or MEC. Among transplant recipients 56% (19/34) and 49% (57/116) were treated with CLAG-M and MEC, respectively. Median OS after ASCT was 13.0 months for CLAG-M and 31.0 months for MEC (HR=1.76, 95%CI=0.87-3.56, p=0.12). The median OS for refractory AML was 13.0 and 66.0 months for CLAG-M and MEC recipients, respectively (HR=0.92, 95%CI=0.46-1.84, p=0.24). Median OS for those with early relapse was 12.0 months for CLAG-M and 10.0 months for MEC (HR=0.69, 95%CI=0.22-2.11, p=0.51). Lastly, among those who had late relapse, median OS was 9.0 and 48.0 months for CLAG-M and MEC, respectively (HR=17.6, 95%CI=1.57-198, p<0.001). A univariate analysis was performed to investigate the impact of factors associated with OS, including treatment, age, gender, whether they proceeded to ASCT, cytogenetic risk type, and donor source. ASCT was associated with significant improvement in both PFS (HR=0.24, 95%CI=0.16- 0.37, p<0.001) and OS (HR=0.26, 95%CI=0.17-0.40, p<0.001) in this cohort. Age greater than 65 was associated with worse PFS (HR=1.02, 95%CI=1.00-1.03, p=0.04) but not OS (HR=1.01, 95%CI=1.00-1.03, p=0.06) after controlling for other variables including the impact of ASCT. Discussion: Despite a lower 30-day induction mortality we did not identify significant differences in outcomes among patients selected to receive CLAG-M vs. MEC. There was a statistically significant improvement in survival in patients who had late relapses and were treated with MEC, and a trend towards improvement in OS of those treated with MEC who proceeded to ASCT. As expected, ASCT was associated with significant improvement in OS in this cohort, and our results strongly support the use of ASCT as consolidation of remission after relapse for AML. Our data suggests that MEC may be a superior regimen, in particular including those with late relapse. Figure. Figure. Disclosures Foran: Agios: Research Funding; Xencor, Inc.: Research Funding. Al-Kali:Novartis: Research Funding. Palmer:Novartis: Research Funding.

Abstract Introduction Over the last 15 years gene expression profiling (GEP) has been used to define myeloma molecular subgroups and to determine clinical prognosis. Two major molecular subgroup classifications have been used: the UAMS which determines 7 subgroups and the TC classification based on the presence of IgH translocations and expression of D group cyclins. For prognosis, although a number of different GEP signatures have been defined, the widely used GEP70 identifies 15% of patients with high risk (HR) disease who have a median PFS and OS of 1.75 and 2.83 years. An ideal classification system would identify clinically relevant subgroups with distinct etiology and biology using standardized techniques. We have examined a large group of patients characterized at multiple genetic levels to optimize the diagnostic approach of newly diagnosed patients going forward. Materials and methods Study subjects included 1349 cases enrolled in Total Therapy trials (median follow up 7.5 years). Gene expression profiling was used to determine GEP70 risk status, molecular subgroup by UAMS and TC classifications, and to devise a new and extended TC classification (TC10). Interphase FISH associated with IgH translocations and 1q+ and 17p- were used to build GEP proxies. Data from mutational analysis generated by the FoundationOne targeted sequence panel was also incorporated. Results were validated on the UK MRC MyelomaIX and Hovon65/GMMG-4 studies. Results An initial agnostic analysis of GEP data using sparse k-means clustering verified the existence of TC based groups. Six groups were identified that corresponded overwhelmingly with known TC subgroups; CCND1-t(11;14), D1-HRD, D2-HRD, MMSET, MAF/CCND2, and CCND3. Further comparisons between the molecular subgroup and TC classifier revealed that the UAMS 7 subgroups clustered strongly within one predominant TC group: CD-1 and CD-2 to t(11;14), HY to D1, LB and PR to D2, MF to t(14;16) or t(14;20), and MS to t(4;14). As the UAMS molecular subgroups are largely contained within the TC framework, we aimed to extend the TC by developing the TC10. To extend the known TC subgroups, unsupervised clustering was applied to the 3 largest subgroups [t(11;14), D1, and D2] to determine the strongest single divisor within each respective subgroup. The dominant feature within the t(11;14) cases was CD20 expression, while the D1 and D2 subgroups both split according to RRAS2. CD20 is associated with PAX5 and VPREB3 expression, and RRAS2 is associated with decreased PTP4A3 and increased TNFAIP3 and BIRC3 expression. RRAS2 activation within D1 subgroup and CD20 activation within t(11;14) cases corresponds to an increased time to response to induction therapy suggesting they constitute important biological subgroups. The TC10 combines the known etiologic subgroups of the TC with functionally relevant subdivisions to create 10 novel subgroups: t(11;14) CD20+/-, D1: RRAS2+/-, D2: RRAS2+/-, t(4;14), t(14;16), t(14;20), and t(6;14). Analysis of mutational data revealed that RRAS2 and CD20 activation within the D1, D2, and t(11;14) subgroups reduced the number of mutations in the MAPK pathway. Further mutational analysis revealed that median mutational load was highest in t(14;16) and lowest in D2: RRAS2+ subgroups. The GEP70 score identifies 15% of patients with HR disease and is specific for this purpose. In an analysis of risk assessment methods, we compared GEP detected adverse lesions [t(4;14), t(14;16), t(14;20), 17p- and 1q+] with the GEP70 and revealed that GEP70 HR identified samples have lower OS rates than cases with more than one adverse lesion (validated in external sets). GEP70 HR segregates non-uniformly across molecular subgroups as over 40% of all HR cases are found in the TC10 t(4;14), t(14;16), and t(14;20) subgroups. GEP70 HR cases also have a higher mutational load than low risk cases. Furthermore, GEP70 HR is uniquely associated with 1q+ and 17p- as cases with at least one of these adverse lesions are 4.9 times as likely to be GEP70 HR as cases with neither. Conclusion GEP profiling has a central role in simplifying and standardizing the molecular subgroup designation and risk stratifying of MM patients. The GEP70 risk score reliably identifies HR cases and outperforms FISH in risk assessment, even in validation data sets. The TC10 provides a classification system that improves upon previous methods by defining both etiological and functionally meaningful subgroups. Disclosures Stein: University of Arkansas for Medical Sciences: Employment. Davies:University of Arkansas for Medical Sciences: Employment; Celgene: Consultancy; Janssen: Consultancy; Millenium: Consultancy; Onyx: Consultancy. Heuck:University of Arkansas for Medical Sciences: Employment; Celgene: Consultancy; Janssen: Other: Advisory Board; Millenium: Other: Advisory Board; Foundation Medicine: Honoraria. Weinhold:University of Arkansas for Medical Sciences: Employment; Janssen Cilag: Other: Advisory Board. Chavan:University of Arkansas for Medical Sciences: Employment. Thanendrarajan:University of Arkansas for Medical Sciences: Employment. Epstein:University of Arkansas for Medical Sciences: Employment. Yaccoby:University of Arkansas for Medical Sciences: Employment. Zangari:University of Arkansas for Medical Sciences: Employment; Novartis: Research Funding; Onyx: Research Funding; Millennium: Research Funding. van Rhee:University of Arkansa for Medical Sciences: Employment. Kaiser:Janssen: Honoraria; Amgen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; BristolMyerSquibb: Consultancy; Chugai: Consultancy. Sonneveld:Janssen-Cilag, Celgene, Onyx, Karyopharm: Honoraria, Research Funding; novartis: Honoraria. Goldschmidt:Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Onyx: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Bristol-Myers Squibb: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen-Cilag: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Millenium: Honoraria, Research Funding, Speakers Bureau; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Chugai: Honoraria, Research Funding, Speakers Bureau; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Barlogie:University of Arkansas for Medical Sciences: Employment. Morgan:Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; MMRF: Honoraria; CancerNet: Honoraria; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; University of Arkansas for Medical Sciences: Employment; Weismann Institute: Honoraria; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees.

Abstract Introduction The spectrum and frequency of mutations in newly diagnosed and relapsed MM has been reported and exhibits a pattern which is distinct from other peripheral lymphoid disorders. A number of data sources have suggested that MM is not a single disease, but rather a collection of molecularly diverse entities which present as malignancies of plasma cells with different clinical courses and response to therapy. To test this hypothesis we examined the spectrum of common mutations and their associations with gene expression in etiological subgroups defined by a simplified TC classification based on translocation subgroup and the deregulation of a D group cyclin. Materials and methods We examined a set of gene expression data from 907 newly diagnosed MM patients and classified them according to an updated TC model. An additional set of 482 cases underwent both sequencing by the FoundationOne Heme targeted panel and gene expression profiling. Mutational data were analyzed for significant associations with gene expression data within five primary TC subgroups: D1, D2, CCND (translocated 11q13 or 6p21), MMSET, and MAF. Results Gene expression signatures of GEP70 risk status, chromosomal aberrations [1q+, 1p-, 13q-, 17p-, HRD], proliferation index, NF-kB activation, and BCL2/MCL1 ratio were not distributed evenly across the TC subgroups, consistent with distinct biological differences amongst groups. Having noted differential expression patterns between subgroups, we hypothesized that distinct patterns of mutation may also exist across TC subgroups. Mutations in RB1 and CDKN2C, seen primarily in the D2 and MMSET subgroups, were mutually exclusive yet both associated with increased proliferation and HR status. Thus two paths to extreme proliferation emerge through either ahomozygous deletion of CDKN2C (with low expression of CDKN2C) or an RB1 alteration (with high expression of CDKN2C). Mutations in KRAS, NRAS, and BRAF were not evenly distributed across subgroups and were significantly inversely associated with an NF-kB signature. RRAS2 was also significantly inversely associated with MAPK mutations in the D1 and D2 subgroups, while genes encoding sprouty-related proteins, SPRED1 and SPRED2, were positively associated with MAPK mutations in the CCND-11q13 subgroup. Overall, MAPK mutations were most significantly associated with elevated expression of DKK1, a known Wnt antagonist, and DUSP6, a known inhibitor of the pathway. These gene expression patterns were primarily localized in the D1, D2, and CCND-11q13 subgroups. In contrast, the MMSET and MAF subgroups had a unique patterns of expression not seen in the D1, D2, or CCND subgroups. A gene set enrichment analysis showed that the DNA replication and cell cycle pathways were significantly enriched in the MMSET and MAF subgroups in the presence of MAPK pathway mutations. Within the MMSET subgroup, MAPK mutations were positively associated with GEP70 HR, proliferation index, and membership of the UAMS-PR subtype, while being negatively associated with FGFR3 expression. This result indicates that the reliance on FGFR3 signaling as an oncogenic driver is lost in the presence of a MAPK activating mutation. Conclusions In MM proliferative signals are delivered via the RAS and NF-kB pathways and activation of these two pathways appears to offer mutually exclusive pathways to disease progression since the majority of cases exhibit a reciprocal relationship between these two signaling pathways. The MMSET and MAF subgroups lack a strong association between NF-kB and MAPK signaling pathways, which may indicate that their initiating translocation event and subsequent genetic patterns provide a unique background in which MAPK alterations accelerate progression. As mutational interactions are differential across TC subgroups, we propose a comprehensive approach to MM classification that includes the etiologic designation by simplified TC subgroups and the subsequent use of genetic and biological markers characteristic of acquired features associated with disease progression, such as MAPK or NF-kB activation. Distinct patterns of RNA expression are associated with DNA mutations in myeloma when contextualized by etiologic subgroups. This indicates that the cellular background in which a mutation occurs has a distinct impact on downstream expression patterns-this observation is particularly relevant to the MMSET and MAF subgroups. Disclosures Pawlyn: Celgene: Consultancy, Honoraria, Other: Travel Support; Takeda Oncology: Consultancy. Morgan:Univ of AR for Medical Sciences: Employment; Takeda: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Bristol Meyers: Consultancy, Honoraria; Janssen: Research Funding.

Abstract Introduction High expression of the H3K27 histone methyltransferase EZH2 mRNA in myeloma (MM) patient samples is associated with molecular features of high risk disease, including increased proliferation, and adverse outcomes (1). Mutations or deletions in the H3K27 demethylase KDM6A are associated with similar findings (2) and would be expected to have the same epigenetic effect, increasing H3K27me3 levels, a mark associated with repression of gene expression. We, therefore, sought to identify the role EZH2 plays in controlling myeloma cell proliferation. Methods A panel of MM cell lines and primary patient samples (CD138 selected from bone marrow with consent) representing a variety of different MM molecular subgroups were used. Cell viability (WST-1), cell cycle (PI) and apoptosis (AnnexinV/PI, Caspase-Glo 3/7) assays were performed. Affymetrix gene expression arrays followed by validation with RT-PCR were used to identify patterns of gene expression change with EZH2i. Western blotting confirmed changes at the protein level and Chip-PCR was performed using a validated antibody and isotype control to identify H3K27me3 changes at the relevant gene promotors. Affymetrix gene expression data for 1213 patients enrolled in the Total Therapy studies were used to investigate the relevance of our findings in myeloma patient samples. Results We confirmed a reduction in viability following EZH2i using two chemically distinct, specific small molecule inhibitors (EPZ005687 and UNC1999) and the negative control compound UNC2400. There was a reduction in viability in 6/8 cell lines and 5/6 patient samples. Response to inhibition was not related to molecular subgroup or the presence of high-risk molecular features including del17p. Global levels of H3K27me3 measured by Western blot were reduced in all cell lines regardless of response to EZH2i. In responding cell lines EZH2i induced cell cycle arrest at G1/S followed by induction of apoptosis. Gene expression arrays performed using mRNA from KMS11 and KMM1 cell lines highlighted a change in expression of cell cycle control genes associated with EZH2i. This finding was validated using qRT-PCR, which demonstrated upregulation of the cyclin dependent kinase inhibitors CDKN2B, CDKN1A or both. These findings were confirmed at the protein level by Western blotting. Chip-PCR experiment using cell lysates from KMS11 cells following incubation with EZH2i over 6 days identified changes in H3K27me3 at the promoter and transcriptional start site (PROM/TSS) regions of the CDKN2B and CDKN1A genes. The most specific changes occurred at the CDKN1A PROM/TSS, which were more heavily marked with H3K27me3 at baseline compared to a region approx. 5KB upstream. Given these results, which suggest that CDKN1A expression may be controlled by changes in H3K27me3, we explored the effect of CDKN1A mRNA expression in our patient datasets. We found the expression of EZH2 and CDKN1A to be inversely correlated (R=-0.170, p<0.0001) and that low expression of CDKN1A was associated with a significantly shorter progression free and overall survival (p<0.001). In order to confirm whether these gene expression changes could be used as a potential biomarker of response we looked at our panel of cell lines with variable responses to EZH2i. We identified a consistent increase in expression of CDKN1A only in responding cell lines suggesting it could be used as a biomarker of efficacy in the clinic. Conclusions These data support the hypothesis that CDKN1A expression is suppressed by increased H3K27me3, due to high expression of EZH2 and that this can be reversed with pharmacological EZH2 inhibition leading to a reduction in proliferation of myeloma cells. We provide data which supports the investigation of EZH2i in clinical trials of myeloma patients, which has the potential to be an effective therapeutic strategy even for those with high-risk disease, for whom current treatment approaches are ineffective.Pawlyn et al, EZH2 Overexpression in Myeloma Patients Shortens Survival and in-vitro Data Supports a Potential New Targeted Treatment Strategy. AACR and IMW abstracts, 2015Pawlyn et al, The Spectrum and Clinical Impact of Epigenetic Modifier Mutations in Myeloma. Clinical Cancer Research, 2016 Disclosures Pawlyn: Celgene: Consultancy, Honoraria, Other: Travel Support; Takeda Oncology: Consultancy. Kaiser:Celgene: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; BMS: Consultancy, Other: Travel Support; Takeda: Consultancy, Other: Travel Support; Chugai: Consultancy. Jones:Celgene: Honoraria, Research Funding. Jackson:Amgen: Consultancy, Honoraria, Speakers Bureau; Roche: Consultancy, Honoraria, Speakers Bureau; MSD: Consultancy, Honoraria, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau; Celgene: Consultancy, Honoraria, Other: Travel support, Research Funding, Speakers Bureau; Takeda: Consultancy, Honoraria, Other: Travel support, Research Funding, Speakers Bureau. Bergsagel:Novartis: Research Funding; Amgen, BMS, Novartis, Incyte: Consultancy. Morgan:Univ of AR for Medical Sciences: Employment; Janssen: Research Funding; Bristol Meyers: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding. Davies:Celgene: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Janssen: Consultancy, Honoraria.

Abstract Multiple myeloma (MM) is a predominantly incurable form of plasma cell cancer. Although immunomodulatory imide drugs (IMiDs) such as thalidomide, lenalidomide, and pomalidomide constitute the backbone of most MM therapies, little is understood about how these drugs function to alter the tumor/microenvironment in vivo. Recently, it was demonstrated that IMiDs bind to the substrate receptor protein cereblon (CRBN) leading to the ubiquitination and degradation of multiple neoantigen protein targets, often DNA-binding zinc finger regulatory proteins such as ZFP91, IKZF1 and IKZF3. Interestingly, CRBN differs in amino acid sequence between mice and humans rendering mice non-responsive to IMiDs’ effects. This difference has made it difficult to study the complex pleotropic actions of IMiDs in vivo. Consequently, our lab has generated an immunocompetent transgenic mouse that expresses full-length human CRBN under the control of its endogenous regulatory elements (hCRBN+). We crossed this mouse with our well-established Vk*MYC mouse to generate a novel humanized Vk*MYChCRBN+ strain that develops human-like multiple myeloma disease, is sensitive to IMiD therapy, and maintains a fully competent immune system. The purpose of generating our Vk*MYChCRBN+mouse is to determine the mechanism of action of IMiDs on the tumor only, the host only, and to understand the cross-talk between tumor and microenvironment in a clinically relevant in vivo model of MM. We find that hCRBN+ mice are sensitive to IMiD treatment in vivo by displaying degradation of known target proteins such as Ikzf1/Ikzf3 in splenocytes. We also show that non-tumor immune cells such as T and NK cells respond to IMiD treatment by increased proliferation and expression of effector molecules such as IL-2 and IFN-γ. Furthermore, by engrafting IMiD-sensitive Vk*MYChCRBN+ MM cells into an IMiD-insensitive WT host we establish that IMiDs have tumor-intrinsic effects in vivo that are independent of the host’s immune system. These tumor-intrinsic IMiD effects synergize not only with dexamethasone, which has been shown clinically, but also with enhancer-targeting BET or p300/CBP inhibitors. Conversely, when engrafting IMiD-insensitive Vk*MYC MM cells into an IMiD-sensitive hCRBN+ host we were unable to demonstrate significant IMiD-induced host immune activation or tumor suppression. We conclude, based on our preliminary data, that IMiD activity is predominantly tumor-intrinsic. Moving forward, we are currently utilizing single-cell RNA sequencing to map the cellular and transcriptional changes across time in both tumor cells and the immune microenvironment during IMiD treatment. Ultimately, our goal is to gain mechanistic understanding in order to optimize clinical practice. Citation Format: Seth J. Welsh, Meaghen E. Sharik, Victoria M. Garbitt, Link R. Taylor, Daniel L. Riggs, Caleb Stein, Grady Day, Courtney J. Hillukka, Zach J. Hammond, P Leif Bergsagel, Marta Chesi. A clinically relevant mouse model to understand how IMiDs modulate the host-tumor immunolandscape in multiple myeloma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4628.

Background: Seventy percentage of patients who receive paclitaxel have diffuse, refractory myalgias, and arthralgias. Based on anecdotal reports, this study explored whether loratadine, an antihistamine, palliates these symptoms. Methods: The medical records of postoperative ovarian and patients with endometrial cancer were studied, as these patients are routinely prescribed paclitaxel. Records were screened for patients who received paclitaxel and loratadine concurrently. Results: Forty patients are the focus of this report. Eight had paclitaxel-induced myalgias and arthralgias and then took loratadine; of these, 6 (75%; 95% confidence interval: 35%, 97%) manifested evidence of symptom improvement: “She did experience some migrating generalized body aches and pains…but this has resolved.” Of those already receiving loratadine but with no myalgias and arthralgias, only 11 of 32, or 34% (95% confidence interval: 19%, 53%), developed myalgias and arthralgias (in contrast to the previously reported symptom rate of 70%). No adverse events were clearly attributed to loratadine. Conclusion: These preliminary data support further study of loratadine for paclitaxel-induced myalgias and arthralgias.

Abstract Increasing evidence recognizes Alzheimer's disease (AD) as a multifactorial and heterogeneous disease with multiple contributors to its pathophysiology, including vascular dysfunction. The recently updated AD Research Framework put forth by the National Institute on Aging–Alzheimer's Association describes a biomarker‐based pathologic definition of AD focused on amyloid, tau, and neuronal injury. In response to this article, here we first discussed evidence that vascular dysfunction is an important early event in AD pathophysiology. Next, we examined various imaging sequences that could be easily implemented to evaluate different types of vascular dysfunction associated with, and/or contributing to, AD pathophysiology, including changes in blood‐brain barrier integrity and cerebral blood flow. Vascular imaging biomarkers of small vessel disease of the brain, which is responsible for >50% of dementia worldwide, including AD, are already established, well characterized, and easy to recognize. We suggest that these vascular biomarkers should be incorporated into the AD Research Framework to gain a better understanding of AD pathophysiology and aid in treatment efforts.