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Background: Hematopoietic stem and progenitor cells (HSPCs) are capable of generating all human blood cells. Although these cells have been extensively evaluated using both sorted (Corces et al. Nat. Genet. 2016) and single cell assays (Pellin et al. Nat. Commun. 2019), there remains significant uncertainty as to the degree of heterogeneity within HSPC subpopulations and their relevance to disease. The phenotypic diversity within HSPCs needs to be better characterized in order to understand the pathogenesis of numerous blood disorders including hematologic malignancies. To address this need, we extensively characterized healthy bone marrow mononuclear cells (BMMCs) with both whole transcriptome analysis (WTA) and surface marker evaluation. We hypothesized that by utilizing concurrent RNA and multiplexed (n=35) surface marker analysis, we could not only improve HSPC clustering but also characterize specific phenotypic states along unique hematopoietic differentiation trajectories. This analysis allowed us to identify new subpopulations within currently defined hematopoietic stem cells (HSCs), multipotent progenitors (MPPs) and lymphoid primed multipotent progenitors (LMPPs). Methods: Three healthy adult BMMC samples were stained with antibody conjugated oligonucleotides (BD Abseq) and analyzed using a nanowell cell capture system (BD Rhapsody). We filtered 8,070 high quality cells for 2,508 HSPCs, myeloid cells, and lymphocyte precursors. These cells were first clustered using matrix factorization in order to identify unique HSPC states. Novel clusters within the HSC, MPP, and LMPP subpopulations were purified using flow cytometry, and functionally evaluated with both in vitro and in vivo assays. Results: The antibody-derived tags (ADTs) obtained from BD Abseq comprised 33 of the most informative features (n=2000) and resulted in more stable clustering as determined by within sum of squares (WSS = 898 versus 934 for mRNA alone for 20 clusters). Additionally, we were able to design a targeted HSPC panel (n=500 genes) with Abseq which identified similar cell clusters compared to the WTA alone and WTA plus ADT data (Rand index = 0.88). HSPC clustering identified the putative hematopoietic stem cell (HSC), a multipotent progenitor (MPP), and a new lymphoid primed multipotent progenitor (LMPP). A new computational sorting strategy was derived to purify these primitive HSPCs, and subsequently validated with flow cytometry. The functional evaluation of these sorted populations revealed that HSC and MPP cells were capable of increased serial replating ability in vitro suggesting enhanced self-renewal capabilities compared to LMPP cells. All three HSPC subpopulations produced gradients of erythroid and myeloid colonies in methylcellulose, and T-cells in T-cell expansion assays in vitro. The HSCs were the only cells that were able to produce long term engraftment in immunodeficient mice after serial transplantations. Additionally, common myeloid progenitor (CMP), granulocyte-monocyte progenitor (GMP), and megakaryocyte-erythroid progenitor (MEP) clusters that expressed canonical surface markers were identified. The primitive HSPC clusters were converted into a signature matrix using CIBERSORTx (Newman et al. Nat. Biotechnol. 2019), and bulk acute myeloid leukemia (AML) and healthy samples were deconvolved into respective healthy cell clusters. Using multivariate Cox proportional hazard analysis, we found that high levels of MPPs at diagnosis was the strongest feature associated with worse overall survival (HR = 78.46, 95% confidence interval 7.32-828). Interestingly, after performing paired analysis of diagnostic and relapse cases, the HSC and LMPP expanded significantly in relapsed AML after chemotherapy (p < 0.05) whereas the MPP expanded considerably after stem cell transplant but did not reach significance (p = 0.09). Conclusions: In summary, we identified new cell type clusters within previously defined HSC, MPP, and LMPP subpopulations, and unique surface marker associations using combined single cell WTA and surface marker analysis. We were able to purify these primitive HSPCs using these new markers, in addition to classical markers like CD34, CD90, and CD45RA. Importantly, deconvolution analysis provided preliminary insights into their clinical relevance in AML. Disclosures Ediriwickrema: Nanosive SAS: Patents & Royalties. Majeti: BeyondSpring Inc: Membership on an entity's Board of Directors or advisory committees; Circbio inc: Membership on an entity's Board of Directors or advisory committees; Kodikaz Therapeutics Solutions Inc: Membership on an entity's Board of Directors or advisory committees; Coherus Biosciences: Membership on an entity's Board of Directors or advisory committees; Acuta Capital Partners: Consultancy; Gilead Sciences, Inc: Patents & Royalties.

Background: Diffuse large B cell lymphoma (DLBCL) exhibits significant clinical and biological heterogeneity, in part due to cell-of-origin subtypes, somatic alterations, and diverse stromal constituents within the tumor microenvironment (TME). Several immunologically-active lymphoma therapies are known to rely on innate and adaptive anti-tumor responses occurring within this dynamic TME, including agents that are approved (e.g., rituximab, lenalidomide, CART19, ibrutinib) or emerging (e.g., anti-CD47, checkpoint inhibitors). We hypothesized that a large-scale characterization of the cellular heterogeneity in DLBCL might reveal previously unknown biological variation in the TME linked to tumor subtypes and genotypes, therapeutic responses and clinical outcomes, with implications for future personalization of immunotherapy. Methods: Using a combination of lymphoma single-cell RNA sequencing (scRNA-seq) and bulk tumor transcriptome deconvolution (CIBERSORTx; Newman et al., Nat Biotech, 2019), we developed a new machine learning framework for identifying cellular states and ecosystems that reflect fundamental TME subtypes and distinctions in tumor biology (Fig. 1). Specifically, using CIBERSORTx, we purified the transcriptomes of B cells and 12 different TME cell types, including immune and stromal subsets, from 1,279 DLBCL tumor biopsies profiled in 3 prior studies (Reddy et al., Cell 2017; Schmitz et al., NEJM 2018; Chapuy et al., Nat Med 2018). Then, we defined distinct transcriptional states for each of the 13 cell types, which we validated at single-cell resolution, using a combination of two scRNA-seq techniques (Smart-Seq2 and 10x Chromium 5' GEP, BCR and TCR) to profile primary DLBCL, FL, and human tonsils, as well as leveraging multiple scRNA-seq datasets from previous studies. We identified robust co-associations between cell states that form tumor cellular ecosystems, which we validated in independent datasets of bulk DLBCL tumor gene expression profiles. Finally, we related TME ecosystems to defined tumor subtypes, including genotype classes, and to clinical outcomes. Results: By systematically characterizing the landscape of cellular heterogeneity in nearly 1,300 DLBCL tumors, we defined an atlas of 49 distinct transcriptional states across 13 major cell types. These novel cell states spanned diverse innate and adaptive immune effector cells of the lymphoid and myeloid lineages, as well as tumor-associated fibroblasts. Remarkably, 94% of these states (46 of 49) could be validated in a compendium of ~200,000 single-cell transcriptomes derived from lymphomas, healthy control tonsils, and other tissue types. Moreover, single cells from DLBCL, FL and tonsils best mirrored these newly discovered cell states. We next characterized the biology and potential clinical utility of each cell state. We observed clear distinctions in the transcriptional programs of immune and stromal elements between germinal center and activated B cell DLBCL, as well as between known mutational subtypes. Importantly, many cell states reflected novel phenotypic groupings, and the majority were significantly associated with overall survival (P Conclusion: We describe a novel computational framework to digitally dissect the DLBCL TME and an atlas of novel states for diverse cell types in these tumors. We show how cellular states form cohesive tumor ecosystems, which exhibit distinct clinical outcomes and novel somatic alterations. These results expand our understanding of cellular heterogeneity in DLBCL, with implications for the development of individualized immunotherapies. Disclosures Kurtz: Roche: Consultancy. Advani:Kura: Research Funding; Merck: Research Funding; Millennium: Research Funding; Pharmacyclics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Regeneron: Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Cell Medica, Ltd: Consultancy; Kyowa Kirin Pharmaceutical Developments, Inc.: Consultancy; Stanford University: Employment, Equity Ownership; Janssen: Research Funding; AstraZeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Consultancy, Research Funding; Infinity Pharma: Research Funding; Bayer: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Research Funding; Celmed: Consultancy, Membership on an entity's Board of Directors or advisory committees; Forty-Seven: Research Funding; Roche/Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead Sciences, Inc./Kite Pharma, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Autolus: Consultancy, Membership on an entity's Board of Directors or advisory committees; Agensys: Research Funding. Diehn:Roche: Consultancy; AstraZeneca: Consultancy; Novartis: Consultancy; BioNTech: Consultancy; Quanticell: Consultancy. Alizadeh:Janssen: Consultancy; Genentech: Consultancy; Pharmacyclics: Consultancy; Chugai: Consultancy; Celgene: Consultancy; Gilead: Consultancy; Roche: Consultancy; Pfizer: Research Funding.

Introduction Both blinatumomab and lenalidomide have proven, but limited, efficacy in relapsed/refractory (R/R) non-Hodgkin's lymphoma (NHL). Failure of blinatumomab to mediate durable responses is due to its inability to recruit competent cytotoxic T cells which leads to eventual T cell exhaustion. Lenalidomide has been shown to improve efficacy of rituximab through T and NK cell activation even in patients who have previously failed rituximab containing regimens. Based on this, we hypothesized that lenalidomide, when combined with blinatumomab, will enhance its efficacy. We report safety, efficacy and correlative analysis of blinatumomab and lenalidomide in R/R NHL. Methods We conducted a phase I, open-label trial involving patients 18 years and older with R/R CD19+ NHL who have received at least two prior chemotherapeutic or biologic regimens and were not eligible for standard curative options at time of enrollment. Previous CD19-targeted therapy was allowed. Study consisted of a dose escalation followed by a dose expansion phase once the maximum tolerated dose (MTD)/ recommended phase II dose (RP2D) was established using a Phase I Queue modified 3+3 design. The escalation phase has been completed and consisted of blinatumomab continuous infusion (level 1 and 2: 9 mcg/day to 112 mcg/day) from days 1-56 and lenalidomide (level 1: 10 mg and level 2: 20 mg daily) days 29-49 of a 56-day induction cycle. Patients who responded underwent consolidation with blinatumomab continuous infusion days 1-7 and lenalidomide days 1-21 of a 28-day cycle for a maximum of 6 cycles followed by lenalidomide maintenance for 2 years or until unacceptable toxicity or disease progression. Dose limiting toxicities (DLT) included any grade 3/4 drug related adverse events (AE) observed during and up to 7 days after blinatumomab/lenalidomide simultaneous administration. Additional patients were accrued to replace patients who had grade 3/4 AE or progressed before receiving lenalidomide for dose-finding purposes. Primary endpoints included toxicity and determination of the MTD/RP2D during the first 8 weeks of blinatumomab/ lenalidomide induction. Secondary endpoints included overall response rate (ORR), complete response (CR) rate, progression free survival (PFS) which was censored at time of transplant and immune response biomarkers. Results As of July 17, 2019, 18 patients initiated therapy; 7 with diffuse large B cell lymphoma, 3 with mantle cell lymphoma, 3 with follicular lymphoma, 2 with nonspecified B cell lymphoma and 1 each with marginal zone lymphoma, Burkitt's lymphoma and small lymphocytic lymphoma. Median age was 58 (range 30-84) years and the median number of prior regimens was 2.5 (range 2-5); 5 patients had previous stem cell transplant (SCT). 6 patients had disease progression prior to starting lenalidomide. 3 patients received blinatumomab/lenalidomide at dose level 1 with no DLT noted. 9 patients received blinatumomab/lenalidomide at dose level 2 with 4 requiring blinatumomab dose reduction prior to starting lenalidomide. Due to favorable safety profiles with the combination using the MTD/RP2D of lenalidomide 20 daily, upfront doublet therapy was initiated as part of the planned expansion phase. Most common grade 3/4 adverse events were lymphopenia (39%), hypophosphatemia (22%) and hyponatremia (11%). 1 patient (5.5%) experienced grade 3 neurotoxicity. No grade 3/4 cytokine release syndrome or treatment related deaths were seen. At time of data cutoff, three patients remain on active treatment. At a median follow-up time of 14.3 months, ORR was 56% for all patients and 83% (50% CR) for those who received blinatumomab/lenalidomide combination therapy. Median PFS was 3.8 months (95% CI, 1.1 to NR) for all patients and 8.3 months (95% CI, 2.2 to NR) for those who received blinatumomab/lenalidomide combination therapy. 3 patients who achieved response underwent allogeneic SCT and remained in remission for 14.2 to 22.3 months thereafter. Correlative studies are pending and will be reported at time of presentation. Conclusions The combination of blinatumomab and lenalidomide, given at the RP2D dose of 20 mg daily, is safe and well tolerated. This regimen demonstrates encouraging efficacy in this heavily pretreated patient population and is a promising alternative treatment option for R/R NHL patients who are not candidates for aggressive cytotoxic chemotherapy or as a bridge to allogeneic SCT. Disclosures Abedi: Abbie: Speakers Bureau; Takeda: Speakers Bureau; BMS: Speakers Bureau; Celgene: Speakers Bureau; Gilead: Speakers Bureau. Costello:Takeda: Honoraria, Research Funding; Janssen: Research Funding; Celgene: Consultancy, Honoraria, Research Funding. Zain:Seattle Genetics: Consultancy; Spectrum: Consultancy. Budde:F. Hoffmann-La Roche Ltd: Consultancy. William:Defined Health: Consultancy; Techspert: Consultancy; Celgene Corporation: Consultancy; Kyowa Kirin, Inc.: Consultancy; Guidepoint Global: Consultancy. Foss:Spectrum: Other: fees for non-CME/CE services ; Acrotech: Consultancy; miRagen: Consultancy; Eisai: Consultancy; Mallinckrodt: Consultancy; Seattle Genetics: Consultancy, Other: fees for non-CME/CE services . Jonas:AbbVie, Accelerated Medical Diagnostics, AROG, Celgene, Daiichi Sankyo, Esanex, Forma, Genentech/Roche, GlycoMimetics, Incyte, LP Therapeutics, Pharmacyclics: Research Funding; AbbVie, Amgen, Celgene, GlycoMimetics, Jazz, Pharmacyclics, Tolero: Consultancy, Membership on an entity's Board of Directors or advisory committees; AbbVie, Amgen, GlycoMimetics: Other: Travel expenses. Rosenberg:Amgen: Consultancy, Research Funding. Tuscano:Seattle Genetics: Honoraria; Amgen: Honoraria; Celgene: Honoraria, Research Funding; Novartis: Research Funding; Spectrum: Research Funding; Takada: Research Funding; Abbvie: Research Funding; Genentech: Research Funding; Pharmacyclics: Research Funding. OffLabel Disclosure: The use of blinatumomab and lenalidomide in patients with aggressive lymphoma

Background: Outcomes for patients with diffuse large B-cell lymphoma (DLBCL) remain heterogeneous. Several clinical and molecular risk factors have been described, both at time of diagnosis (i.e., International Prognostic Index and cell of origin) and during therapy (i.e., interim PET scans). However, these risk factors fail to consistently identify individuals destined for treatment failure. We recently described circulating tumor DNA (ctDNA) as a novel biomarker in DLBCL, demonstrating pretreatment ctDNA levels are prognostic of outcomes (Scherer F, Sci Transl Med 2016). A major advantage of ctDNA assessment is the ability to collect serial assessments over time. However, the best approach for integrating multiple ctDNA measurements with established prognostic factors remains unknown. Using the dynamics of ctDNA, we developed a method to quantify personalized disease risk as it changes during therapy. Methods: We profiled 468 samples from 125 subjects collected during their first three cycles of immunochemotherapy using cancer personalized profiling by deep sequencing (CAPP-Seq). Early ctDNA dynamics were correlated with outcomes in a training cohort to define a response threshold that was tested in a validation cohort. Finally, we integrated ctDNA and clinical factors to dynamically assess disease risk over time in a continuous model. Results: Prior to therapy, ctDNA was detectable in 98% of subjects. Pretreatment levels of ctDNA were prognostic of event-free survival in patients receiving either frontline anthracycline-based (n=92) or salvage regimens (n=33) (EFS: HR 2.7, 95% CI 1.5-4.7, P=0.0005), confirming prior results. In the training cohort, ctDNA levels changed rapidly, with a 2-log or 100-fold decrease after one cycle (early molecular response, EMR) stratifying outcomes (Fig 1A). In the validation cohort, patients achieving EMR had superior outcomes at 24-months to those who did not (Fig 1B) (EFS: HR 24, 95% CI 6.6-89, P Next, we integrated serial ctDNA measurements with established risk-factors to develop a model to predict an individual's disease risk. This model - the Continuous Individualized Risk Index (CIRI) - provides a personalized estimate of disease risk over time. As more information becomes available during a patient's course of disease, CIRI updates the disease risk, integrating the new information (Fig 1C). In patients receiving frontline therapy, CIRI outperformed the IPI for identification of 24-month EFS and OS (Fig 1D-E) (EFS24: AUC 0.64 vs 0.79; net reclassification improvement 0.47, P=0.02; OS: AUC 0.56 vs 0.84; net reclassification improvement 0.74, P=0.004). Conclusions: Baseline and interim ctDNA measurements have prognostic significance in aggressive lymphomas. Integration of serial ctDNA measurements through a continuous, dynamic risk model can identify personalized outcome probabilities, yielding superior risk estimates. Dynamic risk assessment is potentially widely applicable and could guide future personalized therapeutic approaches. Figure 1: A) A spider-plot depicts the dynamics of ctDNA during the first two cycles of therapy in 14 subjects. B) A Kaplan-Meier estimate depicts EFS for patients achieving or not achieving EMR. C) A schema for CIRI is shown. When a patient is diagnosed with DLBCL, the IPI is calculated, giving an initial estimate of risk. Additional pretreatment risk factors (i.e., cell of origin and pretreatment ctDNA) can be added, thereby updating the estimate of risk. As a patient undergoes therapy, further predictors of risk are obtained, such as ctDNA at cycle 2 and cycle 3, and interim imaging. These predictors can be used to update the patient's estimated risk. Below is a CIRI risk model for two exemplar patients with the same initial pretreatment risk factors. Patient P214 (red) died of disease at day 210, while patient P224 (blue) is in a continued complete response. D) AUC for prediction of EFS and OS by IPI, EMR, interim PET scans, and CIRI. E) A Kaplan-Meier estimate demonstrates risk stratification of EFS by CIRI. Disclosures Advani: Sutro: Consultancy; Cell Medica: Research Funding; Janssen: Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Celgene: Research Funding; Nanostring: Consultancy; Bayer Healthcare Pharmaceuticals: Research Funding; Juno Therapeutics: Consultancy; FortySeven: Research Funding; Gilead: Consultancy; Spectrum: Consultancy; Pharmacyclics: Research Funding; Agensys: Research Funding; Regeneron: Research Funding; Millennium: Research Funding; Infinity: Research Funding; Merck: Research Funding; Kura: Research Funding; Pharmacyclics: Consultancy; Seattle Genetics: Research Funding; Genentech: Research Funding. Newman: Roche: Consultancy. Westin: Celgene: Membership on an entity's Board of Directors or advisory committees; Apotex: Membership on an entity's Board of Directors or advisory committees; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Novartis Pharmaceuticals Corporation: Membership on an entity's Board of Directors or advisory committees. Gaidano: Janssen: Consultancy, Honoraria; Roche: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Gilead: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria. Diehn: Varian Medical Systems: Research Funding; Roche: Consultancy; Novartis: Consultancy; Quanticel Pharmaceuticals: Consultancy. Alizadeh: Genentech: Consultancy; Celgene: Consultancy; CiberMed: Consultancy; Roche: Consultancy; Gilead: Consultancy.

Immune heparin-induced thrombocytopenia (HIT) is associated with antibodies directed against a complex of platelet factor 4 (PF4) and heparin. We were able to affinity purify anti-PF4–heparin IgG (HIT IgG) from the plasma of 2 patients with HIT. Under conditions that were more physiological and sensitive than those in previous studies, we observed that this HIT IgG caused platelet aggregation on the addition of heparin. Platelets activated with HIT IgG increased their release and surface expression of PF4. We quantitated, for the first time, the binding of affinity-purified HIT iodine 125–IgG to platelets as they activated in a plasma milieu. Binding of the HIT IgG was dependent on heparin and required some degree of platelet activation. Blocking the platelet FcγRII with the monoclonal antibody IV.3 did not prevent HIT IgG binding to activated platelets. We concluded that anti-PF4–heparin IgG is the component in these HIT plasmas that induces platelet aggregation. The Fab region of HIT IgG binds to PF4–heparin on the surface of activated platelets. We propose that only then does the Fc portion of the bound IgG further activate the same or adjacent platelets through the Fc receptor. Our data support a dynamic model of platelet activation in which released PF4 enhances further antibody binding and more release.

Background: Molecular characterization of tumors currently relies in large part on invasive tissue biopsies, which can often be unavailable or limiting. For example, while biological heterogeneity between diffuse large B-cell lymphoma (DLBCL) patients can be stratified using classification of cell-of-origin (COO), current methods relying on tumor gene expression profiles (GEP) or multi-parametric immunohistochemistry (IHC) require adequate tissue, and can be hampered by modest accuracy and reproducibility. Circulating tumor DNA (ctDNA) is an emerging noninvasive biomarker for tumor detection with potential for DLBCL disease monitoring. Here, we evaluated the utility ofctDNA for COO classification of patients with DLBCL. Methods: We previously built and trained a novel DLBCL COO Naive Bayesian classifier, using somatic alterations in 32 genes and associated with COO subtypes that were previously classified by Affymetrix mRNA GEP of frozen tumors (n=142 Training; Scherer et al., ASH Annual Meeting 2015 / ASCO Annual Meeting 2016). Here, we applied CAPP-Seq, a capture-based targeted high-throughput sequencing (HTS) method (Newman et al., Nat Med 2014), to genetically profile and classify 146 DLBCL patients in independent cohorts. We employed a test/validation framework (n=76 Test, n=70 Validation) to classify patients diagnosed and treated at Stanford University, MD Anderson Cancer Center, or at the University of Eastern Piedmont, Italy. We compared COO classification from pre-treatment plasma samples (n=119), tumor biopsy tissues (n=82, of which 71% were from FFPE), or both (n=41) against the current clinical standard immunohistochemistry method (Hans algorithm, n=117). Kaplan Meier analyses and Cox proportional-hazard models were used to assess clinical utility of biopsy-free and tumor genotyping for prediction of clinical outcomes from time of DLBCL diagnosis. Results: Genotyping of both test and validation cohorts using either tumor or plasma samples allowed COO classification in 100% of patients (GCB=77 / non-GCB=69) with 77% overall concordance to blinded and centralized Hans IHC classification. As expected, histologically transformed (tFL/DLBCL) and double-hit DLBCLs (MYC and BCL2/BCL6 translocated) were strongly biased toward GCB DNA subtype (26/29, 90%), while primary central nervous system lymphomas were almost invariably classified as ABC/non-GCB by ctDNA (6/7, 87%). The concordance between DNA-based COO classification from tumor vs. plasma was 88% (36/41). In the test cohort, COO classification by genotyping from either tissue or plasma was significantly associated with PFS (P=0.003 and 0.02, respectively, Cox proportional-hazard), while classification by Hans criteria failed to show a significant survival difference (P=0.25). The superior outcome of patients with GCB DNA COO subtype was validated when extended to the full cohort (P=0.02, Cox proportional-hazard; P=0.03, log-rank (Figure 1)). We observed a similar, albeit not significant, trend for overall survival. Conclusions: DLBCL COO subtypes can be accurately classified using somatic alterations detectable as circulating tumor DNA. We envision that this approach might help overcome some of the limitations of mRNA GEP and protein/IHC-based methods, including the requirement for invasive biopsies, tissue availability, and suboptimal assay performance. Furthermore, this strategy could support future clinical trials in helping to identify poor-risk groups at diagnosis, to guide future therapy selection, and to improve treatment decisions for patients with DLBCL. Figure 1. Figure 1. Disclosures Newman: Roche: Consultancy. Lovejoy:Roche: Employment. Levy:Kite Pharma: Consultancy; Five Prime Therapeutics: Consultancy; Innate Pharma: Consultancy; Beigene: Consultancy; Corvus: Consultancy; Dynavax: Research Funding; Pharmacyclics: Research Funding. Gaidano:Janssen: Consultancy, Honoraria, Speakers Bureau; Novartis: Consultancy, Honoraria, Speakers Bureau; Gilead: Consultancy, Honoraria, Speakers Bureau; Morphosys: Consultancy, Honoraria; Karyopharm: Consultancy, Honoraria; Roche: Consultancy, Honoraria, Speakers Bureau. Diehn:Roche: Consultancy; Novartis: Consultancy; Quanticel Pharmaceuticals: Consultancy; Varian Medical Systems: Research Funding. Alizadeh:Gilead: Consultancy; Celgene: Consultancy; Genentech: Consultancy; Roche: Consultancy.

Background: Ibrutinib, a covalent inhibitor of Bruton tyrosine kinase (BTK), plays an emerging role in targeted therapy of several lymphoid malignancies. Due to its activity in B-cell receptor (BCR) dependent human B-cell cancers, Ibrutinib is currently approved for the treatment of patients with chronic lymphocytic leukemia (CLL) and mantle cell lymphoma (MCL). In addition, Ibrutinib responses can be seen in patients with follicular lymphoma (FL) and in a subset of patients with ABC- (activated B-cell-) like diffuse large B-cell lymphoma (DLBCL) (Wilson et al., Nat Med 2015; Fowler et al., ASH Annual Meeting, 2015). However, resistance mutations in BTK and other genes (e.g., PLCγ2)are a major cause of treatment failure and early identification of those aberrations remains challenging. Circulating tumor DNA (ctDNA) is an emerging biomarker with potential for disease monitoring as well as biopsy-free detection of evolving somatic aberrations. In this case study, we show that noninvasive genotyping of blood plasma allows early detection of resistance mutations duringIbrutinib therapy in a patient with relapsed/refractory follicular lymphoma associated with histological transformation. Methods: We profiled two tumor lymph node (LN) biopsy specimens and 8 plasma samples before, during, and after Ibrutinib therapy of a 67-year-old man with relapsed/refractory FL with evidence of transformation. Preceding Ibrutinib, this patient received 6 lines of lymphoma treatment over a ~3 year disease course, including Rituximab monotherapy, Benadmustine/Rituximab, and R-CHOP. Despite achieving stable disease after 12 weeks, the patient experienced significant disease progression after additional 4 months on Ibrutinib (Figure 1). We profiled these 10 serial samples using CAPP-Seq, a capture-based targeted high-throughput sequencing (HTS) method (Newman et al., Nat Med, 2014), which allows the sensitive identification of emerging somatic aberrations in circulating tumor DNA. Results: We detected the emergence of two independent BTK C481S resistance mutations in the blood plasma during Ibrutinib therapy, which were undetectable immediately before treatment. The major resistance clone harboring the BTK C481S A>T mutation (Figure 1, orange) was identified 2 months after the start of Ibrutinib, with allele frequencies as low as 0.03%. The second resistant subclone emerged 63 days later, carrying a BTK C481S somatic mutation with a substitution of C to G (Figure 1, green). Importantly, despite encoding an identical amino acid mutation, these two adjacent variants were never observed within the samectDNA molecule, demonstrating convergent evolution of independent resistantsubclones. We confirmed the existence of the two resistance mutations by tumor genotyping of a LN biopsy after clinical diagnosis of disease progression. Notably, while the minorsubclone completely disappeared under subsequentimmunochemotherapy with Rituximab/Bendamustine/Lenalidomide (RBL), the major resistance clone reappeared at the time of disease progression and the patient ultimately succumbed to his disease. Conclusions: Resistance mutations in BTK C481S have been described in tumor cells of Ibrutinib-refractory patients with CLL and MCL, with in vitro evidence of responsiveness to BH3-mimetics and SYK inhibitors. This study, to our knowledge, is the first report of its noninvasive detection in the plasma of a patient with Non-Hodgkin lymphoma, with the subsequent partial eradication of a resistant subclone. Therefore, we envision that biopsy-free genotyping will allow early detection of lymphoma patients developing Ibrutinib resistance and will be incorporated into future clinical trials involving targeted therapies. Figure 1 Figure 1. Disclosures Newman: Roche: Consultancy. Levy:Kite Pharma: Consultancy; Five Prime Therapeutics: Consultancy; Innate Pharma: Consultancy; Beigene: Consultancy; Corvus: Consultancy; Dynavax: Research Funding; Pharmacyclics: Research Funding. Diehn:Varian Medical Systems: Research Funding; Novartis: Consultancy; Roche: Consultancy; Quanticel Pharmaceuticals: Consultancy.

Background: Classical Hodgkin lymphoma (HL) expresses surface CD30. Brentuximab vedotin (BV) is an antibody-drug conjugate that selectively delivers a potent cytotoxic agent, monomethyl auristatin E (MMAE), specifically to cells expressing surface CD30. Although BV elicits a high response rate (75%) in HL, patients who do not achieve complete response (CR) will eventually develop resistance to BV. We have developed 2 BV-resistant HL cell models and shown that resistance to BV is due to MDR1 upregulation rather than downregulation of surface CD30. MDR1 upregulation leads to increased drug exporter pump and decreased intracellular MMAE, the cytotoxic agent in BV. Our hypothesis is that by inhibiting MDR1, we can overcome resistance to BV and also achieve synergy with BV in HL cell lines and mouse xenograft model. Methods: HL cell lines (L428 and KMH2) were used for in vitro experiments. The selection of BV resistant cell model (L428R and KMH2) was done using pulsatile treatment with BV. Both BV resistant cell models were confirmed with MTS assay and cell growth assays. MDR1 upreguation was confirmed by qRT-PCR and western blot. Intracellular MMAE concentration was assessed by mass spectrometry. MDR1 inhibitors verapamil (VrP, 10 ug/ml) and cyclosporine (CsA, 5 uM) were chosen. NSG mice were used for mouse xenograft experiments. Results: MTS assay showed that the IC50 to BV increased 12 folds in L428R (32 ug/ml in L428, 391 ug/ml in L428R), and that the IC50 to BV has increased 17 folds in KMH2R (13 ug/ml in KMH2, 219 ug/ml in KMH2R). QRT-PCR shows that L428R and KMH2R had upregulation of MDR1 mRNA, and western blot shows L428R and KMH2R had overexpression of PgP (MDR1) as compared to L428 and KMH2. Mass spectrometry shows that the intracellular MMAE concentration was 7.6 folds lower in L428R as compared to L428 after treatment with BV. We then examined the effect of MDR1 inhibitors (VrP and CsA) on the IC50 of BV in L428R and KMH2R. The addition of VrP to L428R was able to decrease the IC50 from 344 ug/ml to 22.8 ug/ml (15 fold). The addition of VrP to KMH2R was able to decrease the IC50 from 170 ug/ml to 22 ug/ml (7.7 fold). The addition of CsA to L428R was able to decrease the IC50 from 275 ug/ml to 0.0068 ug/ml (40441 fold). The addition of CsA to KMH2 was able to decrease the IC50 from 129 ug/ml to 0.11 ug/ml (1098 fold). Also, the addition of VrP to L428R was able to increase the intracellular MMAE levels of L428R by 6.4 folds. We then examined the effect of MDR1 inhibitors plus BV in BV naïve parental cell lines. The addition of VrP to L428 was able to decrease the IC50 of BV from 69 ug/ml to 0.03 ug/ml (2300 folds). The addition of VrP to KMH2 was able to decrease the IC50 of BV from 6.3 ug/ml to 0.0013 ug/ml (4846 folds). The addition of CsA to L428 was able to decrease the IC50 of BV from 36 ug/ml to 0.0085 ug.ml (4235 folds). The addition of CsA to KMH2 was able to decrease the IC50 of BV from 7.1 ug/ml to 0.0002 ug/ml (32645 folds). Neither the VrP or CsA had individual effects on HL parental or resistant cell lines. Because CsA seems to have the strongest effect in KMH2 cell lines, we examined the effect of CsA plus BV in KMH2 mouse xenograft model. KMH2 was injected into the right flank of the mice to form subcutaneous lymphomas and mice were separated into 3 groups of 5. Group 1 is control without addition of BV or CsA. Group 2 is control plus BV at dose of 0.3 mg/kg intravenously every 4 days. Group 3 is treated with BV at doses of 0.3 mg/kg intravenously every 4 days and CsA intraperitoneally at 15 mg BID. We show that group 2 had a 37% reduction in tumor volume as compared to group 1 and that group 3 had a 70% reduction in tumor volume as compared to group 1 (Figure 1). Conclusion: MDR1 upregulation increases efflux pump activity and decreases intracellular accumulation of MMAE, and leads to resistance to BV in HL. This mechanism of resistance can be overcome with MDR1 inhibitors VrP and CsA. The addition of MDR1 inhibitors to BV is able to increase the intracellular accumulation of MMAE, and restore the sensitive to BV. We are also able to show synergery between MDR1 inhibitors and BV in the parental non-resistant HL cell lines and mouse xenograft model. This is the first study showing synergy between MDR1 inhibition and antibody drug conjugates. A clinical trial has been initiated to examine the safety/efficacy of the combination of MDR1 inhibitors plus BV in patients with relapsed/refractory HL. Figure 1. Figure 1. Disclosures Chen: Seattle Genetics: Consultancy, Honoraria, Research Funding, Speakers Bureau; Merck: Consultancy, Research Funding; Millenium: Consultancy, Research Funding, Speakers Bureau; Genentech: Consultancy, Speakers Bureau. Kwak:Antigenics: Equity Ownership; Celltrion: Consultancy; XEME BioPharma: Consultancy, Equity Ownership; Sella Life Sciences: Consultancy.

Neoantigens arising through somatic mutations are increasingly recognized as key tumor antigens driving clinical immune responses. We sought to identify human lymphoma neoantigens through a genomic and proteomic characterization of peptide ligands of major histocompatibility complex class I (MHC-I) and class II (MHC-II) of a cohort of patients with untreated mantle cell lymphoma. Peptide identification was performed through analysis of MHC-I and MHC-II peptide ligands using liquid chromatography and tandem mass spectrometry (LC/MS-MS), and augmented by generating patient-specific proteome databases through tumor:normal whole exome sequencing and targeted immunoglobulin gene sequencing in 17 patients. A total of 66 neoantigen peptides from 9 patients were found to be presented by MHC, and surprisingly all neoantigenic peptides were derived from either the lymphoma immunoglobulin heavy or light chain genes. There was no detectable MHC presentation of hundreds of candidate somatic neoantigenic peptides from other genes despite widespread antigen presentation of the proteome including proteins encoded by germline heterozygous single nucleotide polymorphisms. Clonotypic lymphoma immunoglobulin sequences were among the most commonly presented proteins by both MHC-I and MHC-II, yet MHC-I presentation of variable regions was nearly absent and included only a single neoantigen derived from IGVH. In contrast, the variable region was frequently presented by MHC-II and included neoantigen peptides created through somatic hypermutation or VDJ recombination. Autologous circulating T-cells specific for immunoglobulin-derived neoantigens could be detected using MHC-II tetramers and their frequency dynamically changed with vaccination and therapy. These findings suggest that immunoglobulin derived neoantigens are frequently presented by lymphoma, and represent potential targets for CD4, but not CD8, T-cell mediated therapies. Figure. MHC-I and MHC-II presentation of lymphoma immunoglobulin. (A) MHC-I (left) and MHC-II (right) presented peptides derived from patients' immunoglobulin were mapped to the immunoglobulin domains. Peptides recovered from all seventeen patients are mapped to a schematic of the IgM molecule with a heat map indicating the number of recovered peptides covering each position. (B) Expanded view of the immunoglobulin heavy chain variable region. The heatmap represents the total number of MHC-presented peptides that span that position, including non-neoantigens for MHC-I (above) and MHC-II (below). Neoantigen peptides created by either somatic hypermutation or VDJ rearrangement are mapped above or below the heat map. Red, positions within the recovered peptides which are altered from germline variable genes thus creating neoantigens. Boxes, peptides derived from a single patient. Figure. MHC-I and MHC-II presentation of lymphoma immunoglobulin. (A) MHC-I (left) and MHC-II (right) presented peptides derived from patients' immunoglobulin were mapped to the immunoglobulin domains. Peptides recovered from all seventeen patients are mapped to a schematic of the IgM molecule with a heat map indicating the number of recovered peptides covering each position. (B) Expanded view of the immunoglobulin heavy chain variable region. The heatmap represents the total number of MHC-presented peptides that span that position, including non-neoantigens for MHC-I (above) and MHC-II (below). Neoantigen peptides created by either somatic hypermutation or VDJ rearrangement are mapped above or below the heat map. Red, positions within the recovered peptides which are altered from germline variable genes thus creating neoantigens. Boxes, peptides derived from a single patient. Disclosures Newman: Roche: Consultancy. Carlton:Adaptive Biotechnologies: Employment, Equity Ownership. Moorhead:Adaptive Biotechnologies: Employment. Faham:Adaptive Biotechnologies Corp: Employment.

Background: Diffuse large B cell lymphoma (DLBCL) is a clinically heterogeneous disease, where almost half of patients fail to fully respond to therapy and have poor outcomes. Prognostic molecular markers, including translocations in BCL2, BCL6, and MYC, can help identify patients at high risk for treatment failure. However, these markers rely on diagnostic tumor samples which must be obtained invasively and are not universally available. Circulating tumor-specific DNA (ctDNA) could provide a source of material for detection of these translocations directly from the blood. We sought to examine the performance for detection of these translocations from ctDNA, as well as their association with eventual clinical outcomes. Methods: We profiled tumor and plasma samples from patients with DLBCL receiving combination immunochemotherapy at Stanford University and MD Anderson Cancer Center. As the clinical gold standard, tumor samples were assessed with break-apart fluorescence in situ hybridization (FISH) to detect breaks in BCL2, BCL6, and MYC. DNA from tumor and plasma samples was then sequenced using CAPP-Seq, a targeted next-generation sequencing based method for detection of ctDNA (Newman AM et al, Nature Medicine 2014). Translocations were identified from paired-end reads using FACTERA (Newman AM et al, Bioinformatics 2014). We compared the technical performance of our sequencing-based approach to FISH from tumor samples. Finally, we assessed the prognostic value of our method in relation to clinical outcomes. Results: We prospectively enrolled 86 patients with DLBCL as two cohorts, either at Stanford University (n=40) or at MD Anderson Cancer Center (n=46). We first profiled tumor samples from 40 patients (Stanford cohort) for translocations using FISH as part of the clinical standard of care, identifying 24 translocations in total (12 in BCL2, 7 in BCL6, and 5 in MYC). All patients had tumor and plasma available for sequencing. By analyzing DNA from tumor samples with CAPP-Seq, we identified 92% of all translocations previously identified by FISH (11/12 BCL2, 7/7 BCL6, 4/5 MYC). Furthermore, without prior knowledge of tumor genotypes, we were able to identify 79% (19/24) translocations directly from the plasma (10/12 BCL2, 4/7 BCL6, 5/5 MYC). The performance of genotyping translocations from ctDNA improved with increasing tumor burden, such that 95% (19/20) of translocations were detected in samples with >16pg/mL (e.g. 5 genome equivalents/mL) of ctDNA. We next validated the performance of noninvasive genotyping for translocations in an independent cohort of 46 pretreatment plasma samples (MD Anderson Cancer Center cohort). In patients with detectable ctDNA, we successfully identified 78% (18/23) of translocations previously seen by FISH. Furthermore, in two patients, we detected translocations previously missed by FISH from FFPE tissue specimens (BCL6-IGH and MYC-IGH). In addition to known translocation partners, we observed several novel partner genes for MYC (e.g. PTMA and 7p15.2) and BCL6 (e.g. IRF1). Importantly, across both cohorts, we identified 82% (9/11) of patients with both MYC and BCL2/BCL6 translocations (double-hit lymphoma, DHL) directly from the plasma without knowledge of the tumor. Moreover, DHL detected noninvasively significantly predicted both progression-free and overall survival (p Conclusions: Identification of translocations from plasma without tissue samples is feasible with high sensitivity for all clinically relevant genes. Detection of translocations from plasma is prognostic of eventual outcomes, including overall survival. This technique has potential clinical utility in patients without easily accessible tissue, as well as for monitoring evolution of tumor genotypes over time. Disclosures Newman: Roche: Consultancy. Levy:Kite Pharma: Consultancy; Five Prime Therapeutics: Consultancy; Innate Pharma: Consultancy; Beigene: Consultancy; Corvus: Consultancy; Dynavax: Research Funding; Pharmacyclics: Research Funding. Westin:Celgene: Membership on an entity's Board of Directors or advisory committees; Chugai: Membership on an entity's Board of Directors or advisory committees; Spectrum: Membership on an entity's Board of Directors or advisory committees; ProNAi: Membership on an entity's Board of Directors or advisory committees. Diehn:Roche: Consultancy; Novartis: Consultancy; Quanticel Pharmaceuticals: Consultancy; Varian Medical Systems: Research Funding.

We have previously shown that normal-density human peripheral blood eosinophils transcribe and translate mRNA for granulocyte-macrophage colony-stimulating factor (GM-CSF) and that the intracellular distribution was granular as assessed by light microscopy immunocytochemistry. The present study was conducted to confirm this apparent association between GM-CSF and the crystalloid granule using a subcellular fractionation method for human eosinophils and immunogold electron microscopy (EM). Highly purified (> 99%, by negative selection using anti-CD16 immunomagnetic microbeads) human peripheral blood eosinophils were obtained from four asthmatic subjects (not taking systemic medication), homogenized and density fractionated (5 x 10(7) cells/subject) on linear Nycodenz gradients. Twenty-four fractions were collected from each cell preparation and analyzed for marker enzyme activities as well as total protein. Dot blot analysis with specific monoclonal antibodies (MoAbs) was used to detect the eosinophil granule proteins major basic protein (MBP) and eosinophil cationic protein (ECP). An anti-CD9 MoAb was used as an eosinophil plasma membrane marker. Lactate dehydrogenase (LDH) was used as a cytosolic marker. Immunoreactivity for GM-CSF was detected by a specific enzyme-linked immunosorbent assay using a polyclonal antihuman GM-CSF antibody and confirmed by dot blot. GM-CSF coeluted with the cellular fractions containing granule markers (MBP, ECP, eosinophil peroxidase, hexosaminidase, and arylsulphatase), but not those containing cytoplasm (LDH+) or membrane (CD9+) markers. EM examination of pooled fractions associated with the peak of GM-CSF immunoreactivity confirmed that they contained crystalloid and small granules, but not plasma membrane. In addition, quantification, using immunogold labeling with an anti/GM-CSF MoAb, indicated preferential localization of gold particles over the eosinophil granule cores of intact cells. Thus, our results indicate that GM-CSF resides as a granule-associated, stored mediator in unstimulated human eosinophils.

Background: As a single agent, rituximab produces an overall response rate (ORR) of 20-50% in patients with relapsed/refractory (r/r) B-cell non-Hodgkin lymphomas (NHL). Acquired resistance occurs, however, partially due to defective host immune effector mechanisms. Efforts to augment immune function have included combining rituximab with immune modulators such as IL-2, IL-12, interferonand lenalidomide. These efforts have resulted in improved efficacy of rituximab. Ipilimumab is a human IgG1κ monoclonal antibody (mAb) specific for the cytotoxic T lymphocyte-associated antigen-4 (CTLA-4, CD152) expressed on activated T cells. CTLA-4 is a negative regulator of T-cell responses. Ipilimumab augments the immune response by disrupting the interaction of CTLA-4 with its ligands, B7-1 (CD80) and B7-2 (CD86). We therefore hypothesize that ipilimumab will augment the efficacy of rituximab. This phase I with an expansion cohort was designed to assess the safety of this combined therapy. Methods: Patients ≥ 18 years old with r/r CD20+ NHL, measurable disease, KPS ≥ 70%, neutrophils ≥ 1000, platelets ≥ 50,000 and adequate kidney and liver function were eligible. In this phase I with an expansion cohort, after establishing the recommended Phase II dose (RP2D), study patients would be randomized to differing treatment schedules of rituximab and ipilimumab. For induction in phase I, rituximab was given every week for four weeks and ipilimumab was administered every 3 weeks for 4 doses. Primary endpoints included toxicity and determination of the maximum tolerated dose (MTD)/RP2D during the first 7 weeks of induction of ipilimumab (2 doses) when given in combination with rituximab (4 doses, 375mg/m2 per dose). Secondary end points included immune response, ORR and progression free survival (PFS). In the phase I component, a standard 3+3 design was used to determine the MTD/RP2D. After the RP2D was established, 20 more patients would be randomized to one of two schedules: Arm A - ipilimumab on Day 1 together with the first dose of rituximab, or Arm B - ipilimumab on Day 15 together with the third rituximab dose. After induction in both the Phase I and the expansion cohorts, ipilimumab and rituximab were given every 12 weeks (+/- 1 week) on the same day for 1 year. Results: As of August 1 2015, 21 patients have been enrolled (1SLL, 9FL, 6DLCL, 1MCL, 4 indeterminate): 15 are evaluable for toxicity assessment at 7 weeks. The median age was 62 (32-71) and the median number of prior regimens was 2 (1-6). The RP2D of ipilimumab when given with rituximab (375 mg/m2) was 3 mg/kg, which was the highest dose tested. The most common grade 1-2 AEs (>20%) were:anemia (33%); diarrhea (20%); constipation (27%); nausea/vomiting (33%); fatigue (40%); anorexia (33%); low albumin (53%); hypocalcemia (33%); hyponatremia (27%). The most common grade 3-4 AEs (≥ 5%) were: anemia (20%); diarrhea (13%); leukopenia (13%); and hyponatremia (27%). There were no study related deaths. Twelve patients remain on treatment, 6 came off due to progression, 2 due to toxicity, and there was one death (unrelated). Conclusion: The RP2D of ipilimumab when given in combination with rituximab is 3 mg/kg. This dose is safe and moderately well tolerated. Enrollment continues in the expansion cohort to assess efficacy and the ability of ipilimumab to augment the rituximab-mediated immune response. Disclosures Off Label Use: Ipilimumab to augment rituxan in treating lymphoma. Chen:Merck: Consultancy, Research Funding; genentech: Consultancy, Speakers Bureau; Millennium: Consultancy, Research Funding, Speakers Bureau; Seattle Genetics: Consultancy, Research Funding, Speakers Bureau.

Background MLN8237 is an oral inhibitor of aurora kinase A (AURKA) that causes mitotic spindle defects, mitotic delay, and apoptosis in lymphoma cell lines and mouse models. Human studies have shown promising responses in hematologic malignancies. Vorinostat is an oral HDAC inhibitor that is FDA-approved for cutaneous T-cell lymphoma, and is under study in other lymphomas. AURKA inhibitors in combination with vorinostat show synergistic pro-apoptotic effects in vitro in Hodgkin lymphoma (HL) and non-Hodgkin lymphoma (NHL) cell lines (Kretzner 2011, Cancer Res 71:3912). Our phase I multicenter study assessed the safety and tolerability of MLN8237 combined with vorinostat in patients with lymphoid malignancies [[NCT01567709][1]] and determined the maximum tolerated dose (MTD) to be 20 mg twice daily (BID) of MLN8237 and 200 mg BID of vorinostat orally in an interrupted dosing schedule (Schedule II). We have recently completed accrual to an expanded cohort at MTD and report preliminary data on our secondary endpoints among patients treated on Schedule II of this study. Methods Eligible patients were ≥18 years old with relapsed or refractory (r/r) lymphoid malignancies (HL, B-NHL, T-NHL), measureable disease, ECOG performance status 0-2, neutrophils ≥1500/µL, platelets ≥100,000/µL, and adequate kidney and liver function. Secondary endpoints were toxicities, clinical response, pharmacokinetic (PK) analysis, and correlative studies. A 3+2 modified rolling-6 design was employed to determine the MTD. Enrollment was initiated on a continuous dosing schedule (Schedule I) that was poorly tolerated, with adverse events (AEs) on dose levels 1 and 2 leading to many dose delays primarily due to gastrointestinal intolerance and myelosuppression. The protocol was amended to the interrupted dosing Schedule II: MLN8237 escalated from 20 to 50 mg BID on days 1-3 and 8-10, and vorinostat given at 200 mg BID on days 1-5 and 8-12 of a 21-day cycle. Results We treated 25 patients (11 DLBCL, 7 HL, 3 FL, 2 MCL, 1 PTCL, 1 NK/T cell) on the interrupted dosing Schedule II. Median age was 59 years (range 26-78). Mediannumber of prior therapies was 4 (range 1-10); 9 patients (36%) underwent prior stem cell transplantation. See Table for treatment summary. MTD of the combination is 20 mg BID for MLN8237 and 200 mg BID for vorinostat on the interrupted schedule. The commonest (>5%) ≥ grade 3 drug-related AEs were neutropenia (52%), thrombocytopenia (44%), leukopenia (44%), anemia (28%), lymphopenia (24%), febrile neutropenia (12%), oral mucositis (8%), diarrhea (8%), and lung infection (8%). There were no study-related deaths. 4 patients stopped treatment due to AEs and 13 due to progressive disease (PD). 2 patients achieved complete remission (CR); both had DLBCL, and both halted therapy after completing 2 further cycles of treatment post-CR. They both remain in CR (18 months and 1 month at data lock). 1 patient had a partial response (PR), and 8 patients maintained stable disease (SD). PKs demonstrated a clearance of 230 L/h (sd=495) and 2.94 L/h (sd=1.57) for vorinostat and MLN8237, respectively. Archived baseline biopsies are being analyzed to determine AURKA expression. Six fresh paired tumor biopsies were obtained before and on-treatment in the expanded cohort at MTD for correlative studies. Conclusions MLN8237 when given in combination with vorinostat is safe and tolerable in an interrupted dosing schedule among heavily pre-treated patients with r/r lymphoid malignancies. The MTD for MLN8237 is 20 mg BID on days 1-3 and 8-10, combined with vorinostat at 200 mg BID on days 1-5 and 8-12, of 21 day cycles. The commonest AEs were hematologic and gastrointestinal. Promising responses were seen in several patients, especially those with DLBCL, which support phase 2 exploration of this therapy in patients with intermediate-high grade NHL. PK analysis suggests that combination therapy exposures are similar to single agent exposure. Correlative studies done in a 12-patient expanded cohort will be presented. [Trial supported in part by UM1CA186717] | Schedule II: MLN8237 (mg)/ Vorinostat (mg) | # of patients treated | # of cycles completed Median (range) | # of dose limiting toxicities (DLT) | DLT Description | Best response | | ------------------------------------------------- | --------------------- | -------------------------------------------- | ----------------------------------- | --------------------------------------------------------------------------------------------- | --------------------------------------------- | | Dose level 1 (30/200) | 7 | 3 (0-18) | 2 | 1 pt had grade 3 febrile neutropenia; 1 pt had grade 3 thrombocytopenia requiring transfusion | 1 CR, 3 SD, 2 PD, 1 N/A | | Dose level -1 (20/200) | 18 | 2 (0-14) | | | 1 CR, 1 PR, 5 SD, 8 PD, 3 too early to assess | Table 1. Disclosures Siddiqi: Kite pharma: Other: attended advisory board meeting; Seattle Genetics: Speakers Bureau; Pharmacyclics/Jannsen: Speakers Bureau. Off Label Use: Vorinostat is only FDA-approved for CTCL but in this study it is being used in conjunction with MLN8237 (not FDA-approved) for all lymphomas.. Beumer: Millenium: Other: Research support. Forman: Mustang Therapeutics: Research Funding. [1]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT01567709&atom=%2Fbloodjournal%2F126%2F23%2F2731.atom

Background: The prognosis for diffuse large B-cell lymphoma (DLBCL) patients who fail initial therapy remains poor. Current prognostic methods to identify patients destined for failure employ baseline molecular profiles or imaging data at fixed milestones, thus sub-optimally capturing functional response dynamics. Noninvasive detection of tumor-specific DNA sequences in the plasma, or circulating tumor DNA (ctDNA), provides a window of opportunity to observe these changes early during therapy. We sought to relate early ctDNA kinetics during therapy to tumor volume, therapeutic responses, and ultimate clinical outcomes. Methods: Using CAPP-Seq, a next-generation sequencing platform for detection of ctDNA (Newman Nature Medicine 2014), we prospectively profiled patients with DLBCL receiving combination immunochemotherapy at Stanford University. Tumor samples were used to define tumor specific somatic alterations, which were then monitored in plasma. We examined two methods of assessing ctDNA change over time: a simple heuristic model (assessing the change in ctDNA concentration from cycle 1 to cycle 2), and a biologically based mathematical model of ctDNA dynamics to predict tumor volume and patient outcomes. Results: We sequenced tumor and plasma samples (n=135) from ten patients receiving Rituximab-containing regimens. Plasma samples were collected prior to, during, and immediately after chemotherapy, with a median of 7 samples per patient during the first therapy cycle. Across patients, ctDNA concentrations varied over a 6-log range (Figure 1). The change in ctDNA concentration between cycle 1 and cycle 2 generally tracked with FDG PET/CT response - patients achieving a PR or CR had an average decrease of 2.9±0.8 logs in ctDNA concentration, compared to an increase of 0.3±0.8 logs for those with SD or PD (p Conclusions: Given its high specificity and large dynamic range, ctDNA provides an opportunity to monitor the dynamics of therapeutic response in patients with DLBCL. Methods capturing these dynamics correlate with radiographic response. Given the complexity of tumor dynamics, heuristic models of ctDNA may less faithfully capture ultimate clinical outcomes. Personalized mathematical models of ctDNA can potentially reflect tumor dynamics and predict clinical outcomes for individual patients. Figure 1. Personalized tumor modeling from ctDNA tumor dynamics. a) An example of a model of ctDNA fit to observed data for a single patient (DLBCL010). b) The corresponding tumor volume prediction over time for patient DLBCL010. c) Summary of the mathematical model across ten patients, demonstrating the fit between measured data and the model. Figure 1. Personalized tumor modeling from ctDNA tumor dynamics. a) An example of a model of ctDNA fit to observed data for a single patient (DLBCL010). b) The corresponding tumor volume prediction over time for patient DLBCL010. c) Summary of the mathematical model across ten patients, demonstrating the fit between measured data and the model. Disclosures Newman: Roche: Consultancy. Klass:Roche: Employment. Gambhir:CellSight: Consultancy. Diehn:Roche: Consultancy. Alizadeh:Genentech: Consultancy; Roche: Consultancy; Celgene: Consultancy.

Background and Methods DLBCL patients exhibit striking heterogeneity in therapeutic responses, yet current methods to assess residual disease have shown suboptimal performance. Circulating tumor DNA (ctDNA) is a promising noninvasive biomarker for disease burden. While High-Throughput Sequencing of immunoglobulin genes (Ig-HTS) has shown utility for ctDNA detection in DLBCL, only a small number of tumor markers are profiled, limiting its sensitivity and broad applicability (Kurtz DM, Blood 2015 and Roschewski M, Lancet Oncology 2015). Here, we studied DLBCL patients using CAPP-Seq, a recently developed approach for ultrasensitive assessment of ctDNA that profiles multiple genomic regions across hundreds of kilobases in a single HTS assay (Newman AM, Nat. Med. 2014). Results We designed a 250kb DLBCL CAPP-Seq panel targeting 1053 regions from 268 genes, incorporating recurrent single nucleotide variants (SNVs), insertions/deletions (indels) and rearrangements, and evaluated its genotyping performance on diverse tumors from 63 patients (Table 1). We detected somatic mutations in 98% of patients (median: 143 variants/pt), including AID-related events in well-known hotspots. Moreover, we detected 100% of BCL2/BCL6 rearrangements and most MYC translocations previously identified by FISH (n = 30). Biopsy-confirmed tumor mutations were detectable with 99.3% specificity in 97% of pretreatment plasma samples (n = 37) with a range of 0.007% to 32% mean fractional abundance. Median recovery rates for both SNVs and fusions in plasma exceeded 85%, indicating robust tumor genotyping performance. Furthermore, in most patients, paired allelic fractions between tumor biopsies and pretreatment plasma samples were significantly correlated (P < 0.05), demonstrating that ctDNA can accurately mirror the mutational profiles found in tumor tissues. We next evaluated the utility of direct biopsy-free genotyping of pretreatment plasma using a novel ctDNA genotyping approach. We noninvasively identified variants in 97% of cases (n=37), and confirmed a median of 95% of SNVs per cfDNA sample in paired tumor biopsies, indicating that plasma DNA is an effective surrogate for direct tumor genotyping. Having established the technical performance in pretreatment cfDNA, we next assessed utility of CAPP-Seq for minimal residual disease (MRD) detection (Figure 1). Plasma samples at time points of complete response (n = 22) were collected from 10 patients, all of whom ultimately relapsed. Each plasma sample was then analyzed for evidence of biopsy-confirmed mutations from pretreated tumors. Strikingly, ctDNA was detected in 9 of 10 patients (90%), including 3 of 3 isolated CNS relapses, with fractional abundances as low as 0.004%. Among patients with CAPP-Seq detectable MRD, the median time between first detection and relapse was 162 days, and all patients whose MRD levels were uniformly below the detection limit had blood collections >8 months prior to relapse. Conclusions To summarize, we validated robust technical performance of a novel approach for noninvasive tumor genotyping in DLBCL using pretreatment plasma, and for MRD surveillance. Given the demonstrated advantages of CAPP-Seq in the setting of radiographic remission, we envision its utility for improved relapse prediction. Moreover, we anticipate that this biopsy-free genotyping approach will have applications for monitoring subclonal dynamics and emergent mutations in response to therapy. FS, DMK and AMN contributed equally. Disclosures Newman: Roche: Consultancy. Klass:Roche: Employment. Diehn:Roche: Consultancy. Alizadeh:Genentech: Consultancy; Celgene: Consultancy; Roche: Consultancy.

Background: Both Hodgkin lymphoma (HL) and anaplastic large cell lymphoma (ALCL) express surface CD30. Brentuximab vedotin (BV) is an antibody-drug conjugate that delivers a potent cytotoxic agent, monomethyl auristatin E (MMAE), specifically to cells expressing surface CD30. Although BV elicits a high response rate (75% in HL and 86% in ALCL), the majority of patients who do not attain complete response (CR) will eventually develop resistance to BV. It is not known whether resistance to BV is through a) CD 30 alterations b) resistance to cytotoxic agent MMAE or c) overexpression of drug exporters. We developed 2 BV-resistant cell models and obtained primary lymphoma samples from patients with relapsed/progressive disease post BV therapy. We examined CD30 expression, MMAE resistance, drug exporter expression, and gene expression profiles in vitro and in vivo to determine mechanisms of resistance to BV. Methods: HL cell line(L428) and ALCL cell line (KARPAS 299) were used for in vitro experiments. The selection of BV resistant cell model (L428R and KARPAS 299R) used two different approaches (pulsatile or constant exposure). Both BV resistance and MMAE resistance were confirmed by MTS assays. CD30 expression was measured by flow cytometry,qRT-PCR, and Western blotting. Drug exporter expression was measured using qRT-PCR to MDR1, MRP1, and MRP3. In vivo experiments utilized primary tumor samples from 15 HL and 4 ALCL patients who had developed relapsed/progressive disease post BV treatment. CD30 expression was assessed by immunohistocytochemistry (IHC). Gene expression profiling was performed in both parental and resistant HL and ALCL cells, and in 4 ALCL primary tumor samples using Affymetrix whole genome GeneChip® Human Genome U133 2.0 Plus. Results: MTS assay showed the IC50 of KARPAS 299R to BV shifted from 24 +/- 10 ng/ml to 28 +/- 9 ug/ml, an 1183-fold increase. MTS assay also showed the IC50 of KARPAS 299R to MMAE only increased 2-fold when compared to KARPAS 299. Flow cytometry showed downregulation of surface CD30 expression in KARPAS 299R as compared to KARPAS 299 parental (59% vs. 96%, median intensity 78 +/- 17 vs. 591 +/- 51). This downregulation was confirmed by qRT-PCR and Western blotting for CD30. As KARPAS 299R is a mixed cell population, we sorted them into CD30+ and CD30- subpopulations. We then analyzed for BV sensitivity based on CD 30 expression status in KARPAS 299R. MTS assay showed that KARPAS 299R CD30+ cells were equally as resistant to BV as KARPAS 299R CD30- cells (figure 1A). IHC performed in 4 ALCL primary tumor samples showed persistent CD 30 expression in relapsed/progressive tumor specimens post BV treatment. Gene expression profiling on KARPAS 299R showed downregulation of CD30 as compared to KARPAS 299. Gene expression profiling on pre- and post-treatment ALCL samples (8) did not show significant differences in CD30 expression. The top four upregulated genes in relapsed/progressive samples as compared to pretreatment samples were LCE3D, WNT3, TNNT, CITED2. The top four downregulated genes in relapsed/progressive samples as compared to pretreatment samples were CXCL13, C4orf7, MS4A1, and IGJ. MTS assay showed that the IC50 of L428R to BV has shifted from 32 +/- 11 ug/ml to 391 +/- 92 ug/ml, a 12-fold increase. MTS assays showed the IC50 of L428R to MMAE has increased 99-folds when compared to L428 (figure 1B). No difference was seen in CD 30 expression by flow cytometry, qRT-PCR, or western blotting between L428R vs. L428. IHC performed in 15 HL primary tumors show persistent CD30 expression in relapsed/progressive tumor specimen post-BV treatment. qRT-PCR showed upregulation of MDR1mRNA in L428R as compared to L428. Gene expression profiling on L428R showed upregulation of MDR1 as compared to L428. Conclusion: Downregulation of CD30 is seen in BV-resistant ALCL cell model. However, sensitivity to BV did not depend solely on the level of CD30 expression as CD30+ cell subpopulations still exhibited resistance to BV in vitro. Upregulation of MDR-1 and resistance to MMAE were seen in BV-resistant HL cells, rather than downregluation of CD30. Downregulation of CD30 was not seen in HL or ALCL primary tumors. Further work is ongoing to explore/validate potential targets derived from gene expression profiling in ALCL primary tumors. Figure 1A Sensitivity to BV is not related to CD30 expression Figure 1A. Sensitivity to BV is not related to CD30 expression Figure 1B. Figure 1B. Viability of L-428 parental versus BV-resistant cells Disclosures Chen: Seattle Genetics, Inc.: Consultancy, Research Funding, Speakers Bureau, Travel expenses Other.

Background: Amplification or overexpression of AURKA, a key mitotic regulator, is seen in various tumors and is associated with poor outcome in relapsed/refractory (r/r) lymphoma patients. MLN8237 is an oral inhibitor of AURKA, leading to mitotic spindle defects, mitotic delay, and apoptosis in lymphoma cell lines and mouse models. Human studies have shown promising responses in hematologic malignancies. Vorinostat is an oral HDAC inhibitor FDA-approved for cutaneous T cell lymphoma, and is under study in other lymphomas. AURKA inhibitors, MK0457 and MK5108, when combined with vorinostat and applied to Hodgkin lymphoma (HL) and non-Hodgkin lymphoma (NHL) cell lines, convincingly showed synergistic pro-apoptotic effects compared with the AURKA inhibitor alone (Kretzner 2011, Cancer Res 71:3912). Our phase I multicenter study assessed the safety and tolerability of MLN8237 combined with vorinostat in patients with lymphoid malignancies [NCT01567709]. Methods: Patients ³18 years old with r/r mature lymphoid malignancies (HL, B-NHL, T-NHL), measureable disease, ECOG performance status 0-2, neutrophils ³1500/µL, platelets ³100,000/µL, and adequate kidney and liver functions were eligible. The primary endpoints were maximum tolerated dose (MTD) of MLN8237 when combined with vorinostat and description of adverse events (AEs). Secondary endpoints were toxicity, clinical response, pharmacokinetic (PK) analysis and correlative studies. A 3+2 modified rolling design was employed. Enrollment was initiated on Schedule I: MLN8237 escalated from 30-50 mg twice daily (BID) on days 1-7 and vorinostat given at 200 mg BID on days 1-14 of a 21-day cycle*. AEs in patients treated on Schedule I dose levels 1 and 2 led to many dose delays, primarily due to gastrointestinal intolerance and myelosuppression. The protocol was amended to the interrupted dosing Schedule II: MLN8237 escalated from 20-50 mg BID on days 1-3 and 8-10, and vorinostat given at 200 mg BID on days 1-5 and 8-12 of a 21-day cycle. Results: As of August 1 2014, 23 patients have been treated (3 HL, 6 DLBCL, 7 FL, 1 MCL, 1 MZL, 2 B-CLL/SLL, 2 ALCL, 1 NK/T cell). Median age was 61 years (range 28-82). Median number of prior therapies was 4 (range 1-8); 9 patients (39%) underwent prior stem cell transplantation. See Table for treatment summary. MTD of the combination is 20mg BID for MLN8237 and 200mg BID for vorinostat in an interrupted schedule. The most common grade 1-2 AEs (>/=30%) were fatigue (78%), nausea (65%), diarrhea (65%), vomiting (56%), hypokalemia (48%), hypertension (47%), anemia (43%) high alkaline phosphatase (39%), hyponatremia (39%), thrombocytopenia (35%), anorexia (31%), alopecia (31%), constipation (30%), high ALT (30%), high AST (30%), high creatinine (30%), leukopenia (30%). Commonest (>5%) >/= grade 3 drug-related AEs were thrombocytopenia (66%), neutropenia (60%), leukopenia (52%), lymphopenia (43%), anemia (34%), febrile neutropenia (13%), and oral mucositis (8%). There were no study-related deaths. 6 patients stopped protocol treatment due to AEs/intolerance and 7 due to progressive disease (PD). Most patients (8) had stable disease (SD) and 1 patient achieved a complete remission (CR). Conclusions: MLN8237 when given in combination with vorinostat is safe and tolerable in an interrupted schedule among heavily pre-treated patients with r/r lymphoid malignancies. The MTD for MLN8237 is 20mg BID on days 1-3 and 8-10, combined with vorinostat at 200mg BID on days 1-5 and 8-12, of 21 day cycles. The commonest AEs are hematologic and gastrointestinal. PK analysis, correlative studies and response assessments are ongoing in a 12-patient expansion cohort. [Trial supported in part by UM1CA186717] Abstract 4483. TableMLN8237 (mg)/ Vorinostat (mg)# treated# completed cyclesMedian (range)# DLTsDLT DescriptionBest responsesSCHEDULE I (continuous)Dose level 1 (30/400*)62 (1-3)1Grade 3 thrombocytopenia requiring a plt transfusion2 SD, 2 PD, 2 N/ADose level 2 (20/200)32 (2-13)2Both pts had grade 4 thrombocytopenia2 SD, 1 PDSCHEDULE II (interrupted)Dose level 1 (30/200)73 (0-18)21 pt had grade 3 febrile neutropenia; 1 pt had grade 3 thrombocytopenia requiring a plt transfusion1 CR, 3 SD, 2 PD, 1 N/ADose level -1 (20/200)71 (1-3)01 SD, 2 PD, 4 too early *Initial vorinostat dosing was 400mg daily but subsequent cohorts were switched to 200mg BID for better tolerance. Disclosures Off Label Use: MLN8237 and vorinostat have not been FDA-approved for all lymphoid malignancies..

Immune heparin-induced thrombocytopenia (HIT) is associated with antibodies directed against a complex of platelet factor 4 (PF4) and heparin. We were able to affinity purify anti-PF4-heparin IgG (HIT IgG) from the plasma of 2 patients with HIT. Under conditions that were more physiological and sensitive than those in previous studies, we observed that this HIT IgG caused platelet aggregation on the addition of heparin. Platelets activated with HIT IgG increased their release and surface expression of PF4. We quantitated, for the first time, the binding of affinity-purified HIT iodine 125-IgG to platelets as they activated in a plasma milieu. Binding of the HIT IgG was dependent on heparin and required some degree of platelet activation. Blocking the platelet FcgammaRII with the monoclonal antibody IV.3 did not prevent HIT IgG binding to activated platelets. We concluded that anti-PF4-heparin IgG is the component in these HIT plasmas that induces platelet aggregation. The Fab region of HIT IgG binds to PF4-heparin on the surface of activated platelets. We propose that only then does the Fc portion of the bound IgG further activate the same or adjacent platelets through the Fc receptor. Our data support a dynamic model of platelet activation in which released PF4 enhances further antibody binding and more release.

Background Brentuximab vedotin (BV) is an antibody-drug conjugate that delivers a potent cytotoxic agent, monomethyl auristatin E (MMAE), specifically to cells expressing surface CD30. In pivotal phase II trials BV demonstrated a complete response rate (CR) of 34% in patients with relapsed/refractory Hodgkin lymphoma (HL) and CR of 50% in patients with relapsed/refractory anaplastic large cell lymphoma (ALCL). However, patients who do not attain CR will eventually develop progressive disease despite active treatment with BV. We aim to select for BV resistant lymphoma cell lines and examine for alterations in surface CD30 expression in cell lines. Methods In vitro experiments used two HL cell lines (L428, KM-H2) and one ALCL cell line (Karpas 299). The selection of BV resistant cell lines was done in two different approaches (pulsatile and constant exposure). Confirmation of BV resistance was done by MTS assays and cell counting assays. Measurement of surface CD30 was performed by flow cytometry using monoclonal mouse anti-human CD 30, clone Ber-H2. Results MTS assay showed the IC50 of parental cell lines to be: L428 (24 ug/ml), KM-H2 (9 ug/ml), and Karpas (16 ng/ml). The cell lines were incubated at supra-IC50 dosage until no proliferation was seen and then rescued with BV free media in the pulsatile approach. Only L428 was able to be rescued and kept in BV free media until consistent proliferation was seen (defined as incremental growth for 3 consecutive weeks). L428 was then incubated again in 50 mg/ml of BV until consistent proliferation was seen. For the constant exposure approach, the cell lines were incubated at sub-IC50 dosages and dosages of BV were adjusted up depending on cell proliferation rates. Only KM-H2 and Karpas 299 showed consistent proliferation in the constant exposure approach. We were able to select for 1 resistant line per cell type. Cell counting assay showed that all three resistant cell lines were able to proliferate in the presence of BV at dosages twice their respective IC50s (Figure 1, only L428 is shown). MTS assays showed that all resistant cell lines had increased IC50s (Table 1). Flow cytometry showed downregulation of surface CD30 expression in Karpas 299 and KM-H2 but not L428 (Table1). For Karpas 299, both the percentage of CD30+ cells and the median CD30 intensity decreased in the resistant line. For KM-H2, only the median CD30 intensity decreased in the resistant line. Conclusion Prolonged exposure of HL and ALCL cell lines to BV led to resistant cell lines. When compared to their parental cell lines, two out of three resistant lines showed downregulation in surface CD30 expression. Disclosures: Chen: Seattle Genetics, Inc.: Consultancy, Research Funding, Speakers Bureau, Trave expenses Other.

Abstract 1231 Background: Current methods for defining and isolating human hematopoietic stem and progenitor cells using surface markers enrich for unique functional properties of these populations. However, significant functional heterogeneity in these compartments remains with important implications for understanding normal and altered hematopoiesis. Using flow sorting to enrich >10,000 cells as progenitor subpopulations, we previously characterized the gene expression signature of normal human HSC (Majetiet al 2009 PNAS 106(9):3396–3401). We hypothesized that interrogation of the transcriptomes of single cells from this compartment could resolve remaining heterogeneity and help identify and better define features of progenitor cells and hematopoietic stem cells (HSCs). Methods: Using normal human bone marrow aspirates and a FACS Aria II instrument equipped with a specialized single-cell sorting apparatus, we sorted cells enriched for HSCs based on expression of Lin-CD34+CD38-CD90+CD45RA− into 1-cell, 10-cell, 100-cell, and 40000-cell (bulk) representations. We used at least 5 replicates per group and verified single cell deposition by direct visualization. We amplified cDNA from these corresponding inputs using an exponential whole transcriptome amplification (WTA) scheme (Miltenyi SuperAmp), and evaluated gene expression profiles by two microarray platforms (Agilent/GE Healthcare 60K, and Affymetrix U133 plus 2.0), and by RNA-Seq (Illumina). We used gene expression correlation between replicates within and between microarrays as means of assessing methodological reproducibility and estimating population heterogeneity. Results: Whole transcriptome amplification yielded cDNA ranging from 0.2–1 kb for 10 and 100 cells, with significantly lower size distribution of amplified cDNA observed for single cells. Gene expression profiles had significantly better replicate reproducibility and array coverage with the Agilent microarray platform when compared with the Affymetrix U133 Plus 2.0 platform (gene coverage of 84 % for 100 cells, 73 % for 10 cells and 50% for 1 cell for Agilent vs 24 % for 100 cells, 11 % for 10 cells and 5.7% for 1 cell for Affymetrix). RNA-Seq profiling of the same populations is ongoing with major technical optimizations focused on reducing amplification of non-human templates while maintaining library complexity and representation. Using biological replicates for each input size, we observed high inter-replicate correlation levels for expression profiles obtained for bulk sorted HSCs from 8 healthy donors (∼40000-cells, average r=0.97) and for 100-cell and 10-cell inputs from a single donor (r=0.96–0.99, respectively). While intra-array concordance of replicate measurements (n=14642) was high (r>0.91) within each of 5 single cells from a single donor, comparison of 5-single cells from the same donor identified significant heterogeneity, when compared to the 10-cell and 100-cell sub-clusters (Figure 1). Individual genes characteristically expressed by these heterogeneous single cell populations are currently being investigated by FACS and Fluidigm arrays. A larger experiment characterizing 192 single progenitor cells, employing Agilent microarrays and RNA-Seq is currently in progress. Conclusions: Single cell transcriptome profiling is feasible, with best performance on 60-mer microarrays. Single cell transcriptomes exhibit lower, but reasonable levels of reproducibility (r>0.7) and precision as compared with higher cell numbers. Gene expression profiles of single cells capture gene expression heterogeneity in HSCs. Disclosures: No relevant conflicts of interest to declare.

Abstract 2390 Background Hematological malignancies are among leading causes of cancer-related deaths in the United States. We sought to identify novel immunological signatures of survival in blood cancers, with a particular focus on tumor-infiltrating cells (TICs), known to have prognostic significance in several tumor types [1–3]. We employed a computational genomics approach to retrospectively analyze thousands of gene expression profiles (GEPs) from individual tumors. By incorporating prior knowledge of genes specifically enriched in purified immune cell phenotypes, we estimated the relative contributions of distinct TICs in blood cancer GEP admixtures, and derived cancer-specific TIC abundance signatures with strong prognostic significance. Method We assembled a GEP atlas from publicly available human microarray samples spanning diverse immune cell phenotypes and activation states, termed Immunome+. We separately generated GEPs of nearly 200 follicular lymphoma (FL) tumors obtained from a recently completed phase III clinical trial, and collected publicly available transcriptome profiles from >2000 patients with diverse hematological malignancies. To deconvolve GEP admixtures, we modeled each mRNA mixture by a system of linear equations, and given Immunome+, we optimally solved the system using a previously described method. We also estimated p-values using a novel approach, which yielded “goodness-of-fit” estimates for GEP deconvolution. Inferred TIC fractions were related to clinical outcomes using Cox proportional hazards regression. Results To benchmark our deconvolution strategy, we applied it to a variety of positive control expression profiles, and obtained estimates of cell-type frequencies that robustly correlated with known cell type proportions (GSE19380, GSE20300). We also assessed the cell type specificity of genes within Immunome+, and found that our method could robustly classify purified immune cell GEPs in external data sets with a mean accuracy of 94%. Next, we analyzed a variety of blood cancer data sets, including our own, to infer TIC relative proportions and associations with overall survival. Strikingly, we found that TIC abundance patterns and prognostic associations are highly correlated within the same tumor types profiled by different laboratories, whereas different tumor types have distinct immune cell infiltration and prognostic signatures. Among multiple TIC prognostic associations, we found that a higher proportion of estimated macrophage infiltrates is significantly associated with increased overall survival in independent cohorts of DLBCL, irrespective of rituximab treatment (P = 1.8×10−5). This is consistent with the enrichment of macrophage genes in the Stromal-1 prognostic signature identified by Lenz et al. [2]. We also found a significant association between CD4+ T-cells and favorable overall survival in FL (P= 0.01), including our cohort (n = 196) and one previously described [1]. Accordingly, we suggest that deconvolution is a promising strategy to disentangle known cell populations from heterogeneous blood cancer samples. Conclusions Our approach represents a novel way to explore the landscape of immunological signatures relating to cancer clinical outcomes, and offers a new resource for making experimentally testable predictions, and for discovery of new immunotherapeutic targets. Disclosures: No relevant conflicts of interest to declare.

Abstract 790 Several investigators have defined gene expression profiling (GEP) signatures in Diffuse Large B-cell Lymphoma (DLBCL) enriched for macrophage and stromal genes, suggesting an active innate immune response against lymphoma. We hypothesize that the malignant B-cells drive tumor-associated macrophage (TAM) dysfunction in a subset of patients with DLBCL which is relevant for their biology and prognosis. We performed GEP on TAM from diagnostic DLBCL in order to recognise key genes and pathways open to functional validation. Single cell suspensions from 8 DLBCL and 8 reactive lymph nodes were used in this study. TAMs were flow sorted using CD36 expression. cDNA synthesis and amplification was performed using the Nugen Ovation Pico WTA system and the Affymetrix GeneChip human gene 1.0 ST platform was used. We used bioinformatics analysis of GEP data from DLBCL whole tumors, DLBCL purified B-cells, in vitro manipulated macrophages and other immune cells in order to define macrophage-enriched genes as well as specific M1/M2 signatures. We identified a 221 gene signature that significantly distinguished DLBCL TAMs from control macrophages, with 165 genes upregulated and 56 genes downregulated. Moreover, a comparative transcriptome analysis of 22 diverse immune cell phenotypes/activation states (IRIS: GSE22886) revealed that 26% of these genes are highly macrophage-enriched. Gene Ontology analysis revealed an over-representation for transcripts involved in inflammatory response (p 6.8×10−23), wound healing (p 1.9×10−20), chemotaxis (p 3.2 ×10−9), and cell motility (p 1.7×10−7). Upregulated genes in TAMs included well known M1 (complement components, CXCL9 or CXCL10) as well as M2 genes (MSR, CD163 or MARCO) (Table 1). In our signature, there was enrichment for M1 compared to M2 genes as defined by bioinformatics analysis. TAMs showed overexpression of the CSF1R gene as well as the chemokines CCL2 and CCL5, suggesting an autocrine feed-back loop of macrophage chemotaxis and survival in DLBCL. Moreover, TAMs showed upregulation of the lymphocyte attractants CCL20, CXCL9 and CXCL10, together with T-cell immunosupressants indoleamine 2,3-dioxygenase 1 and PD-L1, which would support a role for macrophages in T-cell recruitment and dysfunction in DLBCL. We also saw strong upregulation of 7 metallothionein isoforms in TAMs. These are proteins known to be expressed in macrophages and linked to response to oxidative damage, modulation of inflammation and cell proliferation. However their role in cancer microenvironment is unclear. We describe for the first time the GEP from DLBCL TAMs. The TAM transcriptome has partial overlapping genes with both M1 and M2 gene signatures, but also has a characteristic GEP potentially driven by their presence in the DLBCL microenvironment. Although further molecular and functional validation is required, this data provides a platform of genes which serve as excellent candidates for future exploration to understand DLBCL pathogenesis and to define new therapeutic targets. Table 1: Genes of particular interest represented in our TAM signature. Probe collapse was done using the highest expression. The Benjamini-Hochberg multiple hypothesis test was used to determine significance (FDR 0.05). Gene IDs Gene description Log2 Fold Change Adjusted p value MT1E-H, L, M, X, MT2A metallothionein 1E-H, 1M, 1X, 2A 2.3 – 4.4 0.00145 - 0.00017 C1QA, B and C complement component 1, q subcomponent, A, B and C chains 3.1 – 3.6 0.00136 - 0.00255 C2 complement component 2 3.2 0.00136 CCL2, 4, 5, 8, 20 chemokine (C-C motif) ligand 2, 4, 5, 8 and 20 2.4 - 3.7 0.046 - 0.00130 CD163 CD163 molecule 4.6 0.00025 CD14 CD14 molecule 3.4 0.00091 CD274 CD274 molecule 3.7 0.00447 CSF1R colony stimulating factor 1 receptor 2.1 0.00886 CXCL9-11 chemokine (C-X-C motif) ligand 9, 10 and 11 4.2 – 4.4 0.012 - 0.00158 FCGR1B Fc fragment of IgG, high affinity Ib, receptor (CD64) 4.9 0.00091 FCGR2A Fc fragment of IgG, low affinity IIa, receptor (CD32) 3.3 0.00139 FCGR3A Fc fragment of IgG, low affinity IIIa, receptor (CD16a) 5.2 0.00011 IDO1 indoleamine 2,3-dioxygenase 1 4.8 0.00232 MARCO macrophage receptor with collagenous structure 2.6 0.02029 MSR1 macrophage scavenger receptor 1 3.7 0.01467 Disclosures: Gribben: Celgene: Honoraria; Roche: Honoraria; Pharmacyclics: Honoraria; GSK: Honoraria; Mundipharma: Honoraria; Gilead: Honoraria.

Background: The indolent (follicular, marginal zone and mantle cell) lymphomas tend to recur with decreasing intervals of remission after standard chemotherapy. New modalities of treatment are necessary. Vorinostat (SAHA) is an orally administered hydroxamic acid histone deacetylase inhibitor with activity against class I and II deacetylases with preclinical and clinical activity against various forms of lymphoma. Methods: We report the results of a phase II study of oral vorinostat in patients with relapsed or refractory (to chemotherapy and/or rituximab) follicular, marginal zone, or mantle cell lymphoma. Vorinostat is given at 200 mg PO twice daily for 14 consecutive days on a 21 day cycle. CT scanning and/or FDG-PET are performed after every three cycles, as is marrow biopsy for those with marrow involvement at time of entry into study. Patients may have received up to four prior chemotherapy regimens including tositumomab or ibritumomab; previous transplant is allowed. Results: 37 patients (14 female, 23 male) were accrued (2 ineligible patient due to wrong histology are excluded from the analysis). The median age for evaluable patients at treatment was 65 (32–79) years. Histologies represented: follicular lymphoma (FL)-20, mantle cell (MC)-8, marginal zone (MZL)- 7. Treatment was well tolerated. Grade 3–4 toxicities possibly attributable to study drug were thrombocytopenia, neutropenia, anemia, diarrhea, anorexia, myalgia, hypokalemia, hypophosphatemia, thrombus (1 patient) and fatigue. 18 patients were taken off study due to progression. Three pts came off study due to toxicities (fatigue and diarrhea after 10 cycles, diarrhea and dizziness after 2 cycles, DVT after 5 cycles), 1 stopped therapy due to intercurrent illness, 1 came off for alternate therapy. By the current Cheson criteria, 6 patients achieved complete remission (CR), and 4 patients achieved partial remission (PR), for an overall response rate (CR+PR) of 29%. By histology, 10 formal responses were seen in patients with follicular (8) or marginal zone lymphoma (2), thus for patients with FL and MZL the response rate is 37%, whereas no responses were seen in mantle cell lymphoma. Two patients with PR as best response subsequently progressed (one at 6 months, one after 16 months), the other two remain on therapy. One CR was achieved after 2 years of stable disease on therapy. At a median follow up of 12 months, median progression-free survival for the 35 eligible patients is 7 months; 5 patients are progression-free for more than 18 months. Eleven patients remain on therapy. Conclusions: The histone deacetylase inhibitor vorinostat is well tolerated over long durations of therapy, and shows promising activity (10 CR+PR out of 27 patients) against relapsed/refractory follicular and marginal zone lymphoma.