Novak, Anne J.

Background: Indolent B-cell lymphomas are a group of neoplastic disorders that account for approximately one-third of non-Hodgkin lymphomas in the Western world. They are clinically indolent but pathologically diverse and generally composed of follicular lymphoma (FL), extranodal marginal zone lymphoma (EMZL), lymphoplasmacytic lymphoma (LPL), nodal marginal zone lymphoma (NMZL), splenic marginal zone lymphoma (SMZL), and small lymphocytic lymphoma. The overall prognosis is favorable in most patients, but others can have a more aggressive disease with associated progression, relapse, or histologic transformation. We have previously reported that the most common cause of death in FL was lymphoma. Here we report on the cause of death (COD), in other non-follicular indolent B-cell lymphomas (NFIBL) during the first decade of the rituximab era. Methods: Participants were from the Molecular Epidemiology Resource (MER) of the University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence (SPORE). From 2002 -2015, MER offered enrollment to all patients with newly diagnosed lymphoma who were US residents and age >18 years. Patients were treated per physician choice and followed prospectively. An event was defined as progression or relapse, initiation of 2nd line therapy, or death from any cause. EFS24 was defined using event-free survival at 24 months from diagnosis. Overall survival was defined as the time from diagnosis until death due to any cause. Overall survival from EFS24 was defined as time from event defining date (achieving EFS24 or early event). For decendents, copies of the death certificate or medical records associated with death were reviewed. Cumulative incidence estimates of the cause of death in NFIBL were calculated as competing risks utilizing the cuminc function from the cmprsk package in R version 3.6.2. COD was categorized as a result of lymphoma (progression or therapy-related) vs. other causes vs. unknown/missing. Results: 820 patients with newly diagnosed NFIBL were enrolled in this study. The subtypes included extranodal marginal zone (EMZL, N = 362), unclassifiable low-grade B-cell lymphoma (LGBCL-U, N = 202), lymphoplasmacytic lymphoma (LPL, N = 92), nodal marginal zone lymphoma (NMZL, N = 80), and splenic marginal zone lymphoma (SMZL, N = 84). The median age at diagnosis was 63 years (range 18-92), and 50.5% were male. Baseline clinicopathologic characteristics showed that the LDH was abnormal in 19.3%, hemoglobin was abnormal in 33.8%, and 62.8% had stage III-IV disease. The IPI score was 0-1 in 51%, 2 in 36.6%, 3 in 10.6%, and 4-5 in 1.8%. At a median follow-up of 83 months (range 0-193), 172 (21%) patients had died, and the primary cause of death was non-lymphoma related in 87 (50.6%), lymphoma related 48 (27.9%), and unknown in 37 (21.5%). When examining all subtypes combined, the 10-year estimate for lymphoma related death was 7% (95% CI: 5.2-9.5) which was lower than the rate of non-lymphoma related death (10 years estimate 14%, 95% CI: 11.3-17.4), Figure 1A, Table 1. The 10-year estimate of lymphoma related death varied by subtype, ranging from 2.7% in EMZL to 16.3% in LPL (table 1), while non-lymphoma related death at 10 years ranged from 10.2% in EMZL to 22.5% in SMZL; only in LPL was the lymphoma related rate higher than the non-lymphoma. Patients who failed to achieve EFS24 from diagnosis had significantly higher rates of lymphoma related death (18% at 10 years from failure, 95% CI: 11.7-29.1, Figure 1B) compared to patients who achieved EFS24 (3.9% at 10 years from achieving EFS24, 95% CI: 2.3-6.8, Figure 1C). Rates were similar by EFS24 for non-lymphoma related death (14.5%, 95% CI: 11.0-19.0 vs. 11.2, 95% CI: 6.3-19.7). Figure 1B-C. Conclusion: The most common cause of death in NFIBL at 10 years was unrelated to lymphoma, in contrast to our previous data in follicular lymphoma which showed lymphoma as the most common cause of death at 10 years. However, similar to follicular lymphoma, patients with NFIBL who failed to achieve EFS24 to frontline therapy had significantly increased risk of lymphoma related death. Further research into the cause of death in NFIBL is warranted. Disclosures Maurer: Celgene / BMS: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees; Kite: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding. Witzig:Celgene: Consultancy, Research Funding; MorphSys: Consultancy; AbbVie: Consultancy; Incyte: Consultancy; Acerta: Research Funding; Karyopharm Therapeutics: Research Funding; Immune Design: Research Funding; Spectrum: Consultancy. Novak:Celgene/BMS: Research Funding. Farooq:Kite, a Gilead Company: Honoraria. Cerhan:BMS/Celgene: Research Funding; NanoString: Research Funding.

Background: Follicular Lymphoma (FL) is the second most common non-Hodgkin lymphoma and presents with significant clinical, cellular, molecular, and genetic heterogeneity. Despite the name and defining microanatomic location, the genetic and molecular identity and pathogenesis of the FL tumor cell is largely uncharacterized. Prior clinical and molecular classifications of FL have been primarily driven by pathologic classification (Grade 1-3b), genetic classification (M7-FLIPI), or gene expression profiling (IR-1/2). Using a unique cohort of 93 FL tumors, we have explored the transcriptomic signature of purified FL B cells, along with their matched whole tumor, and identified unique molecular subsets which are defined by distinct pathway activation, immune content, and genomic signatures identified through whole exome sequencing (WES). Methods: Frozen tumor biopsies from 93 untreated FL (Grade 1-3b) patients enrolled in the University of Iowa/Mayo Clinic Lymphoma SPORE were used for the study. DNA was isolated from whole tumor cell suspensions and RNA was isolated from both whole tumor and B cell enriched cell suspensions. RNA sequencing (RNAseq) and WES was performed in the Mayo Clinic Genome Analysis Core. RNAseq and WES data were processed using the Mayo Clinic standard pipeline and novel driver genes were identified using 20/20+ driver analysis. Copy number variants were identified using GISTIC 2.0. NMF clustering and single sample gene set testing, for B cell lineage and tumor microenvironment (TME) signatures, was performed in R using the NMF and SingScore packages. Results: NMF consensus clustering of FL B cell RNAseq data identified two distinct subsets, C1 (n=32) and C2 (n=57). Clinically, C1 was associated with being FL grade 3 (p<0.001) and more aggressive therapy (e.g. immunochemotherapy) at diagnosis while other clinical factors were similar between the groups. To determine the biologic underpinnings that drove the NMF analysis, the top NMF metagenes were incorporated (n=389 for C1 and n=293 for C2) for GO analysis. Among those genes were AICDA, IRF4, and EZH2 in C1, and BCL2 in C2. Additionally, we identified significant gene set enrichment with NMF metagenes and calculated differential gene set expression comparing C1, C2, and normal B cells (n=5). Together, these analyses suggest C1 displays significantly (p<0.05) enhanced DNA replication and metabolic activity, whereas C2 displays significantly enhanced cytoskeleton rearrangement, repressed inflammatory cytokine signaling, and NF-kB activity. Based on recent scRNAseq data suggesting that FL derives from distinct germinal center B cells, the B lineage difference between C1 and C2 was explored. Both subgroups were heterogeneous for dark (DZ), intermediate (INT), light (LZ) zones, and plasmablast (PBL) gene signatures. When compared to benign B cell samples, C1 was enriched for a pre-PBL (p<0.01), and C2 for an INT phenotype related to a LZ signature (p<0.01). To define if a genomic signature was associated with C1 and C2, mutations and copy number variants (>5% frequency) were examined. TNFAIP3, TP53, and BCL6 alterations were enriched in C1 samples, whereas C2 associated with alterations in BCL2, KMT2D, CREBBP, REL, and MYC. Finally, B cell clusters were analyzed for TME signatures. C1 samples displayed significant enrichment of macrophage, cytotoxic cell, gamma-delta-Tcell, and endothelial cell TME elements (p<0.05) consistent with an inflamed/hot tumor. In contrast, C2 samples exhibited significantly lower signatures (p<0.05) in all entities except for the fibroblast signature, compared to C1 and normal, consistent with an immune desert phenotype. Conclusion: Our results suggest that B cells from FL patients display two distinct transcriptomic signatures. C1 identifies an immunologically active tumor, driven by TNFAIP3 alterations, with pre-PBL characteristics, DNA replication and repair, inflammatory cytokine secretion/signaling, and hyper-metabolic characteristics. C2 identifies an immunologically quiet tumor, driven by alterations in BCL2 and chromatin modifiers, with an intermediate GC phenotype, repressed cytokine signaling, and active cell cycle progression and cytoskeleton rearrangement. This study improves our understanding of the mechanisms driving FL tumors and motivates further investigation into the relationship between tumor intrinsic factors that may influence the TME. Disclosures Maurer: Morphosys: Membership on an entity's Board of Directors or advisory committees; Kite: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding; Celgene / BMS: Research Funding. Ansell:ADC Therapeutics: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; AI Therapeutics: Research Funding; Takeda: Research Funding; Seattle Genetics: Research Funding; Bristol Myers Squibb: Research Funding. Cerhan:NanoString: Research Funding; BMS/Celgene: Research Funding. Novak:Celgene/BMS: Research Funding.

Background The importance of the immune system in modulating the trajectory of lymphoma outcomes has been increasingly recognized. We recently showed that CD4+ cells are associated with clinical outcomes in a prospective cohort of almost 500 patients with follicular lymphoma (FL). Specifically, we showed that the absence of CD4+ cells inside follicles was independently associated with increased risk of early clinical failure. These data suggest that the composition, as well as the spatial distribution of immune cells within the tumor microenvironment (TME), play an important role in FL. To further define the architecture of the TME in FL we analyzed a FL tumor section using the Co-Detection by Indexing (CODEX) multiplex immunofluorescence system. Methods An 8-micron section from a formalin-fixed paraffin-embedded block containing a lymph node specimen from a patient with FL was stained with a cocktail of 15 CODEX antibodies. Five regions of interest (ROIs) were imaged using a 20X air objective. Images underwent single-cell segmentation using a Unet neural network, trained on manually segmented cells (Fig 1A). Cell type assignment was done after scaling marker expression and clustering using Phenograph. Each ROI was manually masked to indicate areas inside follicles (IF) and outside follicles (OF). Relative and absolute frequencies of cell types were calculated for each region. Cellular contacts were measured as number and types of cell-cell contacts within two cellular diameters. To identify proximity communities, we clustered cells based on number and type of neighboring masks using Phenograph. The number of cell types and cellular communities were calculated inside and outside follicles after adjustment for total IF and OF areas. The significance of cell contact was measured using a random permutation test. Results We identified 13 unique cell subsets (11 immune, 1 endothelial, 1 unclassified) in the TME of our FL section (Fig. 1A). The unique phenotype of each subset was confirmed using a dimensionality reduction tool (t-SNE). The global composition of the TME varied minimally across ROIs and consisted primarily of B cells, T cells, and macrophages subsets - in decreasing order of frequency. Higher spatial heterogeneity across ROIs was observed in the frequency of T cell subsets in comparison to B cells subsets. Inspecting the spatial distribution of T cell subsets (Fig. 1B), we observed that cytotoxic T cells were primarily located in OF areas, whereas CD4+ T cells were found in both IF and OF areas. Notably, the majority of CD4+ T cells inside the follicles expressed CD45RO (memory phenotype), while most of the CD4+ T cells outside the follicles did not. Statistical analysis of the spatial distribution of CD4+ memory T cell subsets confirmed a significant increase in their frequency inside follicles compared to outside (20.4% vs 11.2%, p < 0.001; Fig. 1D). Cell-cell contact analysis (Fig 1C) showed increased homotypic contact for all cell types. We also found a higher frequency of heterotypic contact between Ki-67+CD4+ memory T cells and Ki-67+ B cells. Pairwise analysis showed these findings were statistically significant, indicating these cells are organized in niches rather than randomly distributed across image. Analysis of cellular communities (Fig. 1C) identified 13 niches, named according to the most frequent type of cell-cell contact. All CD4+ memory T cell subsets were found to belong to the same neighborhood (CD4 Memory community). Analysis of the spatial distribution of this community confirmed that these niches were more frequently located inside follicles rather than outside (26.3±4% vs 0.004%, p < 0.001, Fig. 1D). Conclusions Analysis of the TME using CODEX provides insights on the complex composition and unique architecture of this FL case. Cells were organized in a pattern characterized by (1) high degree of homotypic contact and (2) increased heterotypic interaction between activated B cells and activated CD4+ memory T cells. Spatial analysis of both individual cell subsets and cellular neighborhoods demonstrate a statistically significant increase in CD4+ memory T cells inside malignant follicles. This emerging knowledge about the specific immune-architecture of FL adds mechanistic details to our initial observation around the prognostic value of the TME in this disease. These data support future studies using modulation of the TME as a therapeutic target in FL. Figure 1 Disclosures Galkin: BostonGene: Current Employment, Patents & Royalties. Svekolkin:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Postovalova:BostonGene: Current Employment, Current equity holder in private company. Bagaev:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Ovcharov:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Varlamova:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Novak:Celgene/BMS: Research Funding. Witzig:AbbVie: Consultancy; MorphSys: Consultancy; Incyte: Consultancy; Acerta: Research Funding; Karyopharm Therapeutics: Research Funding; Immune Design: Research Funding; Spectrum: Consultancy; Celgene: Consultancy, Research Funding. Nowakowski:Nanostrings: Research Funding; Seattle Genetics: Consultancy; Curis: Consultancy; Ryvu: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other; Kymera: Consultancy; Denovo: Consultancy; Kite: Consultancy; Celgene/BMS: Consultancy, Research Funding; Roche: Consultancy, Research Funding; MorphoSys: Consultancy, Research Funding. Cerhan:BMS/Celgene: Research Funding; NanoString: Research Funding. Ansell:Trillium: Research Funding; Takeda: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Bristol Myers Squibb: Research Funding; AI Therapeutics: Research Funding; ADC Therapeutics: Research Funding.

Abstract Constitutive activation of B-cell receptor (BCR) signaling is a major driving mechanism for the proliferation and survival of various B-cell lineage non-Hodgkin lymphomas (NHL). Blocking BCR signaling using the first-in-class Bruton's tyrosine kinase (BTK) inhibitor, ibrutinib, has proved effective and is currently FDA approved for the treatment of several B-cell lymphomas. Unfortunately, significant subsets of patients possess either primary or acquired resistance to ibrutinib, often resulting in worse prognosis and poorer responses to subsequent therapies. Therefore, overcoming ibrutinib resistance is an urgent clinical need requiring new tools to both delineate the molecular mechanisms of resistance and develop viable therapeutics. To that end, we developed a mantle cell lymphoma (MCL) cell line, MCIR1, from a patient with clinically acquired ibrutinib resistance. MCIR1 is a bona fide MCL cell line featuring CD19+/CD5+/CD10-/CD23-/t(11;14)+/TP53+/-; and lacking the previously described mutations in BTK (C481S) and phospholipase C-gamma-2 (R665W, L845F). Importantly, MCIR1 cells possess hallmarks of ibrutinib resistance, including the insensitivity of ERK1/2 phosphorylation to ibrutinib in vitro, and the irresponsiveness of MCIR1 xenograft tumors to ibrutinib treatment in mice as compared with ibrutinib-sensitive Jeko-1 tumors. To delineate the molecular mechanism(s) of ibrutinib resistance, we identified by RNA-Seq analysis that MCIR1 cells possess robust activity of the non-canonical NFkB pathway and elevated expression of NFkB target genes, suggesting that this pathway likely drives their resistance to ibrutinib. Finally, we demonstrated the utility of MCIR1 as a tool for testing new drugs to combat ibrutinib resistance in vitro by showing its sensitivity to the anti-apoptotic B-cell lymphoma-2 (Bcl-2) protein inhibitor, venetoclax. To our knowledge, MCIR1 is the first patient-derived ibrutinib-resistant cell line to be established. Hence, MCIR1 is especially useful because uniquely possesses the relevant clinical and biological features of patients with ibrutinib-resistant MCL. We propose MCIR1 as a tool to further the understanding of molecular mechanisms conferring ibrutinib resistance by providing a clinically relevant system with the capacity for molecular and genetic manipulations. Further, these features make MCIR1 an ideal model to screen drugs for combating ibrutinib resistance through both in vivo and in vitro approaches. Citation Format: Kevin E. Nowakowski, Jithma P. Abeykoon, Mary J. Stenson, Michael M. Timm, Curtis A. Hanson, Daniel L. Van Dyke, Anne J. Novak, Xiaosheng Wu, Thomas E. Witzig. MCIR1: A patient-derived ibrutinib-resistant mantle cell lymphoma line for the study of ibrutinib resistance and drug discovery [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 650.

Abstract Purpose: TNF-related apoptosis inducing ligand (TRAIL) expression by immune cells contributes to antitumor immunity. A naturally occurring splice variant of TRAIL, called TRAILshort, antagonizes TRAIL-dependent cell killing. It is unknown whether tumor cells express TRAILshort and if it impacts antitumor immunity. Experimental Design: We used an unbiased informatics approach to identify TRAILshort expression in primary human cancers, and validated those results with IHC and ISH. TRAILshort-specific mAbs were used to determine the effect of TRAILshort on tumor cell sensitivity to TRAIL, and to immune effector cell dependent killing of autologous primary tumors. Results: As many as 40% of primary human tumors express TRAILshort by both RNA sequencing and IHC analysis. By ISH, TRAILshort expression is present in tumor cells and not bystander cells. TRAILshort inhibition enhances cancer cell lines sensitivity to TRAIL-dependent killing both in vitro and in immunodeficient xenograft mouse models. Immune effector cells isolated from patients with B-cell malignancies killed more autologous tumor cells in the presence compared with the absence of TRAILshort antibody (P < 0.05). Conclusions: These results identify TRAILshort in primary human malignancies, and suggest that TRAILshort blockade can augment the effector function of autologous immune effector cells. See related commentary by de Miguel and Pardo, p. 5546

Abstract Purpose: T-cell immunoglobulin and ITIM domain (TIGIT), a member of the immune checkpoint family, is important in normal T-cell biology. However, the phenotypical profile and clinical relevance of TIGIT in follicular lymphoma is largely unknown. Experimental Design: Biopsy specimens from a cohort of 82 patients with follicular lymphoma were analyzed using mass cytometry to explore the phenotype and biological and clinical significance of TIGIT+ T cells. Results: TIGIT is highly expressed on intratumoral T cells and its expression alters T-cell phenotype in follicular lymphoma. TIGIT is abundantly expressed on Treg cells, resulting in an enhanced suppressive property. TIGIT expression on non-Treg/TFH T cells defines a population that exhibits an exhausted phenotype. Clinically, increased numbers of TIGIT+ T cells are associated with inferior patient outcomes and poor survival. We observe that anti–PD-1 therapy with pembrolizumab alters the phenotype of TIGIT+ T subsets and identifies a role for CD28 expression on TIGIT+ T cells in treatment response. Conclusions: The current study provides a comprehensive analysis of the phenotypic profile of intratumoral TIGIT+ T subsets and their prognostic relevance in follicular lymphoma. Inhibition of TIGIT signaling may be an additional mechanism to prevent T-cell suppression and exhaustion in B-cell lymphoma.

8052 Background: A significant proportion of patients with FL experience an early relapse and a subsequent poor outcome. While several prognostic indices have been developed, none were designed to predict early failure. Recently, we established that lack of intrafollicular CD4+ T-cell expression predicted risk of early failure, and integrating this microenvironment biomarker with the Follicular Lymphoma International Prognostic Index, termed BioFLIPI, further improved identification of FL patients at risk of early failure ( Blood 2019;134(suppl1):121). However, the microenvironment may be influenced by the genetic composition of tumor. We investigated whether the CD4 biomarker and BioFLIPI were impacted by genetic features of the tumor as assessed by a 23-gene expression prognostic score ( Lancet Oncol 2018;19:549-61). Methods: Of the 186 cases with FL grade 1-3A treated with immunochemotherapy (IC) in our prior study, 152 had digital expression quantification of 23 selected genes (23-GEP score), which used RNA from formalin-fixed, paraffin-embedded samples. Event-free survival (EFS) was defined as time from diagnosis to progression, relapse, retreatment, or death. Early failure was defined as failing to achieve EFS at 24 months. Risk of early failure was estimated using odds ratios (ORs) and 95% confidence intervals from logistic regression models. We also used Cox regression to assess associations with continuous EFS and overall survival (OS). Results: 28% of patients failed to achieve EFS24. Lack of CD4+ intrafollicular expression (38% of patients, OR = 2.33, p = 0.024) and high risk 23-GEP score (26% of patients, OR = 3.52, p = 0.001) each predicted early failure, and in a multivariable model that included FLIPI, both CD4+ (OR = 2.26, p = 0.046) and 23-GEP score (OR = 2.26, p = 0.0.057) remained predictors. Similarly, BioFLIPI modeled as a continuous score (1-4, OR per one point increase = 2.31, p < 0.001) predicted early failure, and the association remained (OR = 2.14, p < 0.001) when the high risk 23-GEP score (OR = 2.79, p = 0.013) was included in the model. When stratified on 23-GEP score, BioFLIPI was a stronger predictor of early failure in low risk (74%, OR = 2.51, p = 0.002) relative to high risk (26%, OR = 1.55, p = 0.27) patients. Similar patterns were observed for EFS and OS. Conclusions: CD4+ T-cell infiltrate and tumor gene expression appear to be independently predictive of early failure in newly diagnosed FL patients treated with IC. Future studies should integrate and validate these measures.

Abstract [Background]: MCL is an aggressive non-Hodgkin lymphoma (NHL) with a median survival of 3-4 years. New agents with mechanisms of action different than standard chemotherapy are needed to improve the outcome of MCL. Recent studies have documented single agent activity of bortezomib and rituximab in MCL. Analogs of rapamycin (temsirolimus) have also shown single agent activity in MCL (J Clin Oncol, June 27, 2005 epub). In order to provide rationale for a clinical trial combining rapamycin analogs with bortezomib or rituximab, we performed in vitro studies of the effect of combinations of these drugs on human MCL cell lines. [Methods]: Two human lymphoma cell lines - Granta 519 and M0258 - were incubated with various concentrations of rapamycin, bortezomib, and rituximab to determine the LD50 of each drug alone. For the experiments with rituximab a secondary goat anti-human (GAH) IgG Fc antibody was used to crosslink cell bound rituximab and induce cell killing. Percent viability was assessed after 48 hours using annexin/propidium iodide staining and flow cytometry. Tests for drug synergy used combinations of rapamycin and bortezomib and the Granta 519 cell line. Fixed ratios of the two drugs were tested and the results were evaluated for synergism (combination index &lt;1 in median effect analysis) in inducing cell death. The drug concentrations used spanned the LD50 of each drug determined from the single-agent studies. In the studies combining rituximab with bortezomib or rapamycin, a single fixed dose of rituximab and GAH Fc was tested with various concentrations of the other drugs to detect additive anti-tumor effects. Studies were performed at least in triplicate. [Results]: All three drugs exhibited single-agent activity in vitro against the two cell lines. The LD50 for rapamycin ranged from 17.5 – 25 micromolar for the Granta 519 cell line and 7.5–10 micromolar for M0258. The LD50 for bortezomib ranged from 2–5 ng/ml in both cell lines. Incubation of the cells with various concentrations of rituximab and GAH Fc had mild cytotoxic activity (reduced viability to 50–70% compared to GAH Fc control). This activity was not concentration dependent indicating saturation of CD20 by rituximab; therefore, a fixed concentration of rituximab was used for the combination experiments. When rituximab was added to bortezemib the % viability decreased by up to 37% (compared to bortezomib alone), and the bortezomib LD50 typically decreased to &lt;1–2 ng/ml (LD50 was 2–5 ng/ml with bortezomib alone). The addition of rapamycin to rituximab increased the cytotoxicity by 23% compared to either drug alone and decreased the rapamycin LD50. When rapamycin and bortezomib were combined as treatment of Granta 519 cells, rapamycin was synergistic with bortezomib with a combination index &lt;1. Sequencing the rapamycin either before or after the bortezomib treatment did not effect synergy in either direction. [Conclusion]: In vitro, rapamycin synergizes with bortezomib to enhance its cytotoxicity in MCL lines. Rituximab enhances the cytotoxicity of both of rapamycin and bortezomib. These in vitro studies provide the rationale to test combinations of these active agents in patients with MCL.

Abstract The proposed immune-mediated mechanisms of action of Rituximab (ADCC or “vaccinal” effect) suggest that the efficacy of rituximab therapy depends on the immunocompetency of the host. Therapies, such as IL-12, that stimulate the immune system may increase the efficacy of rituximab. Recently, we reported that absolute lymphocyte count (ALC), as a surrogate maker of immune recovery, is a survival prognostic factor in autologous stem cell transplantation for non-Hodgkin’s lymphoma (NHL). We hypothesize host immune competence (i.e, normal ALC counts) would predict a superior survival in NHL patients treated with rituximab-based regimens. The primary end-point of the study was to assess whether the ALC count prior to rituximab therapy predicted progression-free survival (PFS) and the second end-point was to identify which lymphocyte subset was responsible for affecting PFS. We studied 41 NHL patients that participated in our Phase I interleukin-12 plus rituximab study. These patients were selected because the Phase I study required baseline ALC count and flow cytometry analysis providing lymphocyte subset data prior to treatment. There were 20 females and 21 males in the study. The median age of the cohort was 53 years (range: 34–84). PFS was assessed from the initial date of treatment. The median follow-up was 42 months (1–84 months). Patients with an ALC ≥ 0.9 × 109/l experienced a superior PFS versus an ALC &lt; 0.9 × 109/l prior to rituximab therapy (31 months vs 4 months, p &lt; 0.0001). The relapse rate in the ALC ≥ 0.9 × 109/l group was 55% (11/20) versus 95 % (20/21) in the ALC &lt; 0.9 × 109/l (p &lt; 0.0016). Patients with a CD4 count ≥ 500 cells/μl also experienced a superior PFS versus a CD4 count &lt; 500 cells/μl (21 months vs 9 months, p &lt; 0.0062). The relapse rate in the CD4 count ≥ 500 cells/μl group was 56% (9/16) versus 88 % (22/25) in the CD4 count &lt; 500 cells/μl (p &lt; 0.0216). No association with PFS was identified for CD3, CD8, CD19, CD16/56, the use of IL-12, or lymphoma type by WHO classification. CD4 count was found to be an independent prognostic factor for PFS. In the subgroup of patients with follicular lymphoma, patients with an ALC ≥ 0.9 × 109/l experienced a superior PFS versus an ALC &lt; 0.9 × 109/l prior to rituximab therapy (35 months vs 9 months, p &lt; 0.0076). The relapse rate in the ALC ≥ 0.9 × 109/l group was 45% (5/11) versus 90 % (9/10) in the ALC &lt; 0.9 × 109/l (p &lt; 0.0243). Patients with a CD4 count ≥ 500 cells/μl also experienced superior PFS versus a CD4 count &lt; 500 cells/μl (not reached vs 9 months, p &lt; 0.0098). The relapse rate in the CD4 count ≥ 500 cells/μl group was 29% (2/7) versus 86 % (12/14) in the group with a CD4 count &lt; 500 cells/μl (p &lt; 0.0216). CD4 count was found to be an independent prognostic factor for PFS when compared to the Follicular Lymphoma International Prognostic Index at the time of treatment. Our data suggest the significance of an immunocompetent host in predicting the efficacy of rituximab therapy. These data support our previous report that patients with higher number of activated CD4+ cells at the lymphoma site have a better prognosis (Ansell et al, JCO2001; 19: 720–6). These findings warrant further studies to understand the role of ALC (i.e, CD4 count) in the mechanism of action of rituximab. Supported in part by CA69912