Citations

9057 Background: The incidence of CAM use among patients with cancer is higher when compared to the general population. However, there are few studies examining CAM use in NHL survivors, and limited data are available regarding beliefs in CAM. This study was conducted to examine the prevalence of CAM use in NHL, define CAM beliefs among NHL survivors, and explore differences between patients with indolent and aggressive lymphoma. Methods: Newly diagnosed lymphoma patients were prospectively enrolled within 9 months of diagnosis in the University of Iowa/Mayo Clinic SPORE Molecular Epidemiology Resource from 2002-2008. NHL patients who completed the 3-year post diagnosis questionnaire, which includes questions regarding CAM use and beliefs, were included in this study. Chi-squared tests and Wilcoxon rank-sum tests were used to assess the association of CAM use with prognostic and demographic factors. Results: 719 patients were included with a median age of 63 years (range 22-92). 53% were male. Overall, 636 (89%) reported ever using CAM. 78% of patients used vitamins and 54% alternative therapies (chiropractic (36%) and massage therapy (24%)). Among CAM users, 141 (22%) believe CAM can assist the body to heal, 123 (19%) believe CAM can relieve cancer symptoms, 115 (18%) believe CAM use gives a feeling of control, 106 (17%) believe CAM can boost immunity, 24 (4%) believe CAM can cure cancer, and 35 (6%) believe CAM can prevent the spread of cancer. Female gender was associated with increased overall CAM use (p<0.0001) as well as use of vitamins (p<0.0001), herbal supplements (p=0.006) and alternative therapy (p=0.0002) specifically for cancer. Older age was also associated with increased vitamin use (p=0.005) and decreased herbal supplements use (p=0.008). There was no significant difference in overall CAM use between those with follicular lymphoma grades I-II (n=195, 91%) and non-relapsed diffuse large B-cell lymphoma (n=151, 87%), although massage therapy was utilized more often by FL survivors (29% versus 18%, p=0.005). Conclusions: CAM modalities are used by the majority of NHL survivors (89%). The assessment of CAM use and education regarding potential harms is imperative for the NHL survivor.

8053 Background: Lenalidomide (LEN) is an immunomodulatory agent that has shown significant single-agent anti-lymphoma activity in patients with relapsed disease and in combination may enhance the efficacy of rituximab by various mechanisms. There is limited data on the role of LEN in combination regimens for treatment-naïve patients. This phase II single arm trial was designed to evaluate tumor response, toxicity (AE) and survival with a combination of LEN, rituximab, cyclophosphamide (CTX) and dexamethasone (DEX) (LR-CD) in symptomatic untreated patients with low grade B-cell non-Hodgkin lymphoma. Methods: Eligibility required age ≥18, ECOG PS ≤2, confirmed diagnosis of low-grade B-cell lymphoma (FL1-2, SLL, MZL or LPL/WM), measurable nodes ≥2cm or IgM ≥400mg/dL(LPL/WM), ANC ≥1400/mm3, platelets ≥100,000/mm3, creatinine ≤2mg/dL, signed informed consent and in need of therapy. Treatment consisted of IV rituximab 375mg/m2 D1, oral LEN 20mg D1-21, CTX 250mg/m2 D1, 8, 15, DEX 40mg D1, 8, 15, 22 and ASA 325 mg daily in a 28 day cycle. Treatment continued 2 cycles beyond best response (max 12). Toxicity was assessed by NCI CTCAE v3.0. Results: 28 patients have enrolled at Mayo Clinic with 25 evaluable for toxicity and 21 for response: Median age 65(43-83), 72% male, FL 24%, MZL 28%, LPL/WM 38%, and SLL 4%. Median number of cycles given was 5. Overall response in 21 patients with response data is 95% (95% CI 76-100%) with 19% CR (95% CI 5-42%), and 76% PR (confirmed and unconfirmed, 95% CI 53-92%). The ORR in 8 patients with LPL/WM with response data was 88%, all PR (95% CI 47-100%). At a median f/u of 14.7 months 96% are alive without progression. 1 death (unrelated) occurred 7.7 months after completing 12 cycles of treatment. The most common grade ≥3 AEs were neutropenia (37.5%), leukopenia (16.7%), anemia (12.5%) and fatigue (12.5%). Conclusions: The combination LR-CD with the novel agent LEN is feasible, well tolerated and produces high response rates in symptomatic patients with low grade B-cell NHL. Toxicities are manageable and similar to reported data of single agent LEN.

e19055 Background: IRAK4 is a signaling modulator of Toll-like receptors (TLRs) and members of the IL-1R family. TLR/IL-1R activity is frequently dysregulated in NHL, including DLBCL and Waldenström macroglobulinemia (WM). CA-4948, an oral inhibitor of IRAK4, has demonstrated anti-tumor activity in preclinical models. Methods: Dose finding cohorts (3+3 design) will test QD or BID dosing schedule in pts with R/R NHL. Dose expansion will occur in 2 cohorts of R/R NHL with or without MYD88 mutation at the RP2D or MTD. Key eligibility criteria: age ≥ 18 years, ECOG ≤1, and adequate organ function. Objectives: safety, MTD and RP2D (primary); PK and anti-tumor activity (secondary); exploratory biomarkers and PD effects. Plasma cytokine levels are being evaluated post ex-vivo TLR stimulation in whole blood. Results: 13 pts have been treated (6 at 50 mg QD; 3 at 100 mg QD; 4 at 50 mg BID). Tumor types included DLBCL (7), FL (5), WM (1). The most frequent treatment-emergent AEs (≥10% of pts) were fatigue (23%), conjunctivitis, constipation, neutrophil count decrease, white blood cell count decrease (each 15%). One pt treated at 100 mg QD experienced Gr 3 rash which resolved following oral steroid treatment; dose reduction to 50 mg QD was tolerated without flare up. One SAE of disease progression occurred. 9 pts completed the DLT evaluation period thru Cycle 2 including 2 ongoing in Cycle 4 and 6. Preliminary PK showed that CA-4948 is rapidly absorbed with maximum plasma concentration observed 1.0 to 4.0 hrs post administration. T1/2 in plasma was about 6 hrs. Minimal to no accumulation was observed following multiple doses. Plasma exposures were dose proportional. Preliminary PD data showed, similar to murine studies, on target reduction of NF-kB-associated factors such as IL-6 post-treatment from pts with complete sample sets (N = 8). Conclusions: Current dose/schedule is well tolerated with preliminary PK and PD effects at the initial dose level. MTD has not been reached. Clinical trial information: NCT03328078.

Background: DLBCL has traditionally been classified by cell of origin (COO) subcategories based on tumor gene expression profiles which include Activated B-Cell (ABC) and Germinal Center B-Cell (GCB). Recently, using tumor samples from patients treated with RCHOP, new classification models have focused on DNA alterations. However, a comprehensive integrative approach using a large transcriptomic data set across both newly diagnosed (nd) and relapsed/refractory (r/r) DLBCL is yet to be accomplished. A robust clustering of this type will allow for identification of biologically driven DLBCL patient subgroups and may predict patient outcome and inform treatment approaches. Methods: RNAseq was performed on a total of 882 DLBCL tumor FFPE biopsies from 2 ndDLBCL cohorts (cohort 1 and 2) and 2 r/r DLBCL cohorts (cohorts 3 and 4). Cohort 1 (N=267) was commercially sourced and served as the discovery cohort. Cohort 2 (N=340) was from the Mayo/Iowa Lymphoma SPORE Molecular Epidemiology Resource (MER) and served as the replication cohort. Cohort 3 (N=189) was from the CC-122-ST-001 and CC-122-DLBCL-001 clinical trials (NCT01421524 and NCT02031419), and cohort 4 (N=86) was from r/r patients from the MER. Clustering input consisted of gene expression data, gene set variation analysis (GSVA) scores computed from the hallmark gene sets of MSigDB gene sets, as well as immune cell abundance estimates from a DLBCL-specific deconvolution method. An integrative clustering method iClusterPlus was applied to the input data to identify patient subgroups. A multinomial generalized linear model classifier was trained on the discovery dataset and applied to cohorts 2, 3, and 4 to assess patterns of gene expression and clinical features among the subgroups. Results: Integrative clustering identified 8 subgroups of ndDLBCL patients (DLBCL1-8; D1-D8) in cohort 1. Classifiers trained on cohort 1 were applied to cohort 2 and the same 8 clusters were identified. Among RCHOP treated patients in cohort 2, subgroups D4 (p&lt;0.01) and D8 (p&lt;0.0001) had significantly worse survival outcomes than the rest of the population. D4 comprised 21% of the MER ndDLBCL replication cohort (cohort 2) with a median event-free survival (mEFS) of 38.2 months and a median overall survival (mOS) of 80.3 months. D8 comprised 5% of the cohort with a mEFS of 7.5 months and a mOS of 12.1 months. The remaining 6 subgroups were standard risk, with mEFS ranging from 82.1 months to not reached, and none reaching mOS. The subgroups were not uniquely defined by previously known molecular classification methods such as COO or double hit signature (DHITsig), nor by clinical risk factors such as age or international prognostic index (IPI). Within D4, 92% of patients were ABC, representing a high risk subset of ABC patients. The mEFS in D4 ABCs was 38.2 months, while mEFS of non-D4 ABCs was not reached (p&lt;0.005). Transcriptomic analysis revealed a lower abundance of immune infiltration. D4 was associated with high IPI, with 49% of D4 having IPI&gt;2, compared to 33% of non-D4 with IPI&gt;2 (p&lt;0.05). D8 represented a high-risk subset which was 73% GCB. The mEFS of D8 GCBs was 5.4 months, while mEFS of non-D8 GCBs was not reached (p&lt;0.0001). Transcriptomic analysis revealed low expression of immune response and cytokine signaling pathways, consistent with the low abundance of immune cells in D8. This subgroup consisted of 63% DHITsig positive patients. Although only 20% of all DHITsig positive patients were in D8, these D8 DHITsig patients showed significantly worse survival than non-D8 DHITsig patients (mEFS 11.3 months vs. not reached, p&lt;0.0001). In the r/r DLBCL setting, D1-D8 were all present, with an increased prevalence of D4 and D8 in Cohort 3 (30% and 17%, respectively) and Cohort 4 (30% and 14%) compared to the newly diagnosed setting. Mutational data for these cohorts has been collected and is being interpreted in the context of the discovered subgroups. Conclusion: A novel integrative clustering of transformed gene expression data revealed 8 biologically homogeneous groups, two of which had inferior outcomes when treated with RCHOP therapy. Furthermore, these two subgroups were more prevalent in r/r DLBCL. This classification allows for the transcriptomic identification of high-risk patients underserved by RCHOP therapy. *Ortiz, Wenzl and Stokes contributed equally **Gandhi and Novak contributed equally Disclosures Ortiz: Celgene Corporation: Employment, Equity Ownership. Stokes:Celgene Corporation: Employment, Equity Ownership. Huang:Celgene Corporation: Employment, Equity Ownership. Maurer:Celgene: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding. Towfic:Celgene Corporation: Employment, Equity Ownership. Hagner:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Ratushny:Celgene Corporation: Employment, Equity Ownership. McConnell:Celgene Corporation: Employment, Equity Ownership. Danziger:Celgene Corporation: Employment, Equity Ownership. Stong:Celgene Corporation: Employment, Equity Ownership. Lata:Celgene Corporation: Employment, Equity Ownership. Kamalakaran:Celgene Corporation: Employment, Equity Ownership. Mavrommatis:Celgene Corporation: Employment, Equity Ownership. Trotter:Celgene Corporation: Employment, Equity Ownership. Czuczman:Celgene Corporation: Employment, Equity Ownership. Ansell:Seattle Genetics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding. Cerhan:NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding. Nowakowski:Genentech, Inc.: Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Curis: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding; MorphoSys: Consultancy, Research Funding. Gandhi:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Novak:Celgene Coorperation: Research Funding.

Background With lymphoma survival rates increasing and a growing population of long-term survivors, the development of cardiovascular disease (CVD) in this patient population is of increasing importance. Anthracyclines are critical in the management of many lymphoma subtypes. However, there is a risk of developing anthracycline-induced CVD. Here, we estimate the cumulative incidence of CVD in adult lymphoma survivors and investigate risk factors associated with post diagnosis CVD. Methods Participants were from the Mayo component of the Molecular Epidemiology Resource (MER) of the University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence (SPORE). From 2002-2015, the MER offered enrollment to all patients with newly diagnosed lymphoma who are US residents and age &gt;18 years. Participants completed a risk factor questionnaire, and clinical and treatment data were abstracted from medical records. Patients were contacted every 6 months for the first 3 years after diagnosis and annually thereafter to assess disease status, re-treatment and new onset morbidity including CVD. CVD events, including congestive heart failure (CHF), coronary artery disease (CAD), valvular heart disease (VHD), and arrhythmia were identified and validated against medical records. CHF was validated with the Cardiovascular Health Study Criteria and/or the Framingham Criteria. CAD, arrhythmia, and VHD were validated using clinical definitions. We calculated the cumulative incidence of CVD, with death modeled as a competing risk. The association of risk factors and treatments with risk of CVD was estimated using hazard ratios (HR) and 95% confidence intervals (CI) from Cox regression with a competing risk of death. Risk factors included age, sex, diabetes, smoking, body mass index (BMI), and treatment with anthracyclines or radiation therapy. Results The study consisted of 3,063 lymphoma patients after excluding those with chronic lymphocytic leukemia and CVD prior to lymphoma diagnosis. The median age at diagnosis was 59 years (range 18-95), and 56% were males. At a median follow-up was 6.9 years (range 0.8-17.1), 640 patients (21%) had died without CVD and 485 patients self-reported CVD post lymphoma diagnosis, of which 280 (57.7%) were validated. Cardiovascular events included 86 CHF, 78 CAD, 40 VHD, and 164 arrhythmias. The cumulative incidence of CVD (Figure 1) at 5 and 10 years was 6.0% (95% 5.2%-7.0%) and 10.7% (95% CI 9.5%-12.1%), respectively. In multivariable analysis, increasing age (HR=3.93 per 5 years, p&lt;0.001), male sex (HR=1.33, p=0.03), former smoker (HR=1.04, p=0.77), current smoker (HR=1.96, p&lt;0.001), BMI&gt;30 kg/m2 (HR=1.50, p=0.01), and anthracycline treatment (HR=1.49. p&lt;0.001) were all significantly associated with risk of overall CVD, while there was no association with diabetes (HR=0.92, p=0.70) or radiation therapy (HR=1.05, p=0.78) in the multivariable model. Anthracycline use was significantly associated with increased risk of CHF (HR=2.64, p&lt;0.001) and arrhythmia (HR=1.51, p&lt;0.01), but not VHD (HR=0.83, p=0.56) or CAD (HR=1.23, p=0.31) after adjustment for the cardiac risk factors. The number of anthracycline cycles ranged from 0 to 12. 63.2% of individuals that received anthracyclines received 6 cycles. The 5-year cumulative incidence of CVD for 0, 1-5, 6, and &gt;6 anthracycline cycles was 5%, 6.9%, 7.4%, and 7.7%, respectively. Adjusting for cardiac risk factors, the number of anthracycline cycles was significantly associated with increased risk of CVD (1-5 cycles HR=1.34, p=0.11; 6 cycles HR=1.51, p&lt;0.01; &gt;6 cycles HR=2.04, p=0.03). Furthermore, the number of anthracycline cycles was associated with CHF (1-5 cycles HR=2.82, p&lt;0.001; 6 cycles HR=2.46, p&lt;0.001; &gt;6 cycles HR=5.33, p&lt;0.001) and arrhythmia (1-5 cycles HR=1.31, p=0.27; 6 cycles HR=1.60, p&lt;0.01; &gt;6 cycles HR=1.65, p=0.27). Conclusions In the modern treatment era, the risk of new onset CVD in patients with lymphoma without a history of CVD is approximately 1% per year after diagnosis. Arrhythmia and CHF were the most commonly occurring CVD events in this cohort. Both traditional CVD risk factors and treatment with anthracyclines was associated with an increased risk of developing CVD, and anthracyclines were a risk factor for arrhythmia and CHF in particular. Prevention of CVD in lymphoma patients will need to address both treatment and traditional lifestyle factors. Figure 1 Disclosures Maurer: Morphosys: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding; Celgene: Research Funding. Nowakowski:Selvita: Membership on an entity's Board of Directors or advisory committees; Genentech, Inc.: Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Curis: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; NanoString: Research Funding; MorphoSys: Consultancy, Research Funding. Cerhan:NanoString: Research Funding; Celgene: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees.

Background: Disease progression (PD) after frontline IC therapy for DLBCL is a clinically significant event and only 20-40% of patients achieve durable remissions with salvage chemotherapy. Patients who experience early PD or have disease refractory to IC have especially poor outcomes. Chimeric antigen receptor T-cell therapy has emerged as a novel effective therapy for selected cases of relapsed or refractory (r/r) DLBCL, although its availability is limited by cost and logistic challenges. Simple, clinically applicable prognostic tools would be useful for selecting patients for consideration for novel therapies vs those who are more likely to be successfully managed by conventional therapies, but few have been developed for r/r DLBCL. Methods: Model building was performed in patients with PD after IC from 13 frontline, multicenter, randomized DLBCL clinical trials from the SEAL Consortium. All patients received rituximab and an anthracycline-based combination IC and were followed systematically; however therapy received, clinical, and laboratory data at progression were not available. OS was defined as time from first progression until death from any cause. Associations between variables and OS were evaluated using Cox models; splines were used to model functional forms. Validation was performed in the University of Iowa/Mayo Clinic SPORE Molecular Epidemiology Resource (MER) and Danish LYFO cohorts. Model performance was assessed using time-dependent concordance indices (c-stat), Brier scores, and calibration with metrics evaluated at two years from progression. Results: Model Development (SEAL): 1125 of 5112 patients had PD after IC. Median time to PD (TTP) was 11.8 months (IQR 6.6-23.5); median age at PD was 68 years (IQR 60-73). At a median follow-up of 21 months from PD (IQR 7-43), 732 patients (65%) had died and 24 month overall survival after PD (OS24) was 35% (95% CI: 32-38). TTP was strongly associated with post PD OS (spline p-value&lt;0.0001) with OS24 ranging from 18% in patients with TTP &lt;6 months compared to 69% in patients with TTP &gt; 36 months (Figure A). Additionally, age at PD (spline p-value&lt;0.0001) was associated with post PD OS. A model consisting of age at PD including patients 40-80 years and TTP ranging 0-60 months was created (n=1011, Figure B) due to sparse data outside these intervals. C-stat was 0.67, and the model was well calibrated (predicted OS24 = 34% vs. actual OS24 = 34%). External Validation (MER): The model was validated in 290 patients with DLBCL who initiated 2nd line therapy after PD to IC. Median age at PD was younger than the SEAL cohort at 62 years (IQR 57-70). Patients with biopsy proven indolent histology at relapse were excluded. Median TTP was 8.2 months (IQR 5.1-17.0). At a median follow-up of 87 months from PD, 201 patients (69%) had died. OS24 was 47% (95% CI: 41-53). The model showed similar concordance (c-stat=0.65), but underestimated OS24 (predicted =33% vs. 47% actual, Figure C). External Validation (LYFO): The model was validated in 599 patients with DLBCL and PD after frontline IC. Median age at PD was 67 years (IQR 60-73). Median TTP was 10.1 months (IQR 6.0-19.4). At a median follow-up of 82 months from PD, 435 patients (73%) had died. OS24 was 38% (95% CI: 34-42). The model showed similar concordance (c-stat=0.67) when applied to the LYFO cohort, but again underestimated OS24 (predicted =32% vs. 38% actual, Figure D). Case vignettes: A 74 year old patient who progressed 9 months after diagnosis would have a predicted OS24 of 20%. A 58 year old patient who progressed 32 months after diagnosis would have a predicted OS24 of 65% Conclusions: TTP to following IC is strongly associated with post-progression survival in DLBCL. We developed a model from the largest frontline clinical trial dataset in DLBCL and validated a simple to apply clinical prognostic tool in the r/r setting. The model allows better understanding of expected outcomes in r/r DLBCL and can aid design and interpretation of trial results in this setting. The model underestimated the actual survival probability when applied to non-trial validation cohorts. Recalibration of the model for transplant eligible patients and development of smartphone based point-of-care application of the model is ongoing. Figure Disclosures Maurer: Celgene: Research Funding; Nanostring: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees. Schmitz:Riemser: Consultancy, Honoraria; Celgene: Equity Ownership; Gilead: Honoraria; Novartis: Honoraria. Farooq:Kite Pharma: Research Funding; Celgene: Honoraria. Flowers:Optimum Rx: Consultancy; Burroughs Wellcome Fund: Research Funding; BeiGene: Consultancy, Research Funding; Karyopharm: Consultancy; AstraZeneca: Consultancy; Gilead: Consultancy, Research Funding; Eastern Cooperative Oncology Group: Research Funding; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy, Research Funding; Bayer: Consultancy; TG Therapeutics: Research Funding; Acerta: Research Funding; Celgene: Consultancy, Research Funding; National Cancer Institute: Research Funding; Denovo Biopharma: Consultancy; Pharmacyclics/Janssen: Consultancy, Research Funding; Spectrum: Consultancy; Millenium/Takeda: Research Funding; AbbVie: Consultancy, Research Funding; V Foundation: Research Funding. Frederiksen:Alexion: Research Funding; Gilead: Research Funding; Abbvie: Research Funding; Janssen Pharmaceuticals: Research Funding; Novartis: Research Funding. Cunningham:Eli Lilly: Research Funding; 4SC: Research Funding; Bayer: Research Funding; MedImmune: Research Funding; Celgene: Research Funding; AstraZeneca: Research Funding; Merrimack: Research Funding; Sanofi: Research Funding; Amgen: Research Funding; Janssen: Research Funding; Merck: Research Funding; Clovis: Research Funding. Jørgensen:Gilead: Membership on an entity's Board of Directors or advisory committees; Roche: Membership on an entity's Board of Directors or advisory committees. Poeschel:Amgen: Other: Travel, accommodations, expenses; Abbvie: Other: Travel, accomodations, expenses; Hexal: Speakers Bureau; Roche: Other: Travel, accomodations, expenses. Nowakowski:F. Hoffmann-La Roche Ltd: Research Funding; Curis: Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding; MorphoSys: Consultancy, Research Funding; Genentech, Inc.: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding. Seymour:Takeda: Consultancy; Celgene: Consultancy, Research Funding, Speakers Bureau; AbbVie: Consultancy, Honoraria, Research Funding, Speakers Bureau; Acerta: Consultancy; Janssen: Consultancy, Research Funding; Roche: Consultancy, Research Funding, Speakers Bureau. Merli:Takeda: Honoraria, Other: Travel Expenses; Gilead: Honoraria; Mundipharma: Honoraria; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel expenses, Research Funding; Sandoz: Membership on an entity's Board of Directors or advisory committees; Teva: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Other: Travel Expenses; Janssen: Honoraria. Haioun:novartis: Honoraria; celgene: Honoraria; roche: Consultancy; celgene: Consultancy; gilead: Consultancy; takeda: Consultancy; janssen cilag: Consultancy; amgen: Honoraria; servier: Honoraria. Tilly:roche: Membership on an entity's Board of Directors or advisory committees; servier: Honoraria; merck: Honoraria; Gilead: Honoraria; Janssen: Honoraria; BMS: Honoraria; Karyopharm: Consultancy; Astra-Zeneca: Consultancy; Celgene: Consultancy, Research Funding; Roche: Consultancy. Salles:Amgen: Honoraria, Other: Educational events; BMS: Honoraria; Merck: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis, Servier, AbbVie, Karyopharm, Kite, MorphoSys: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events; Autolus: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events; Epizyme: Consultancy, Honoraria; Roche, Janssen, Gilead, Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events. El-Galaly:Roche: Employment, Other: Travel support; Takeda: Other: Travel support.

Background: The combination of lenalidomide+rituximab (R2) recently showed superior efficacy vs R-placebo in patients (pts) with R/R iNHL (Leonard et al. J Clin Oncol 2019). Based on these AUGMENT study results, R2 was approved by the US FDA for treatment of adult pts with previously treated follicular lymphoma (FL) or marginal zone lymphoma (MZL). Advanced age at diagnosis is a risk factor in pts with iNHL. We performed post-hoc subgroup analyses by age from AUGMENT and data here focus on pts age ≥ 70 y. Methods: AUGMENT (NCT01938001) is a multicenter, double-blind, randomized phase III study of R2 vs R-placebo in pts with FL grade 1-3a or MZL previously treated with ≥ 1 systemic therapy with R/R disease but not refractory to rituximab. Pts were randomized 1:1 to R2 or R-placebo. R2 was oral lenalidomide 20 mg/d, d1-21/28 for 12c plus rituximab IV 375 mg/m2 weekly in c1 and d1 of c2-5. R-placebo was rituximab+placebo on the same schedule. The primary endpoint was progression-free survival (PFS) per 2007 IWG response criteria (without PET) as assessed by IRC (central review). Secondary endpoints included overall response rate (ORR), complete response (CR), time to next anti-lymphoma treatment (TTNLT) and safety. Post-hoc analyses were performed by dividing pts into age &lt; 70 y and ≥ 70 y subgroups, the latter group considered unfit for chemotherapy. Results: Of 358 pts randomized (R2, n = 178; R-placebo, n = 180), 267 pts were age &lt; 70 y (R2, n = 131; R-placebo, n = 136), and 91 pts were age ≥ 70 y (R2, n = 47; R-placebo, n = 44). Baseline characteristics including histology, disease status, and prior treatments are shown in the table and were similar across treatment arms in pts ≥ 70 y. At a median follow-up of 28.3 mo, the study met its primary endpoint of PFS, with a hazard ratio (HR) of 0.46 (95% CI, 0.34-0.62; P &lt; 0.0001) in the overall population. R2 had superior PFS vs R-placebo in both &lt; 70 and ≥ 70 y subgroups, with HR of 0.41 (95% CI, 0.29-0.59) and HR of 0.66 (95% CI, 0.37-1.18), respectively. In pts ≥ 70 y, median PFS with R2 vs R-placebo was 24.9 vs 14.3 mo; ORR/CR was 81%/26% vs 59%/16%; and TTNLT was not reached in either arm. Efficacy results for all pts and those &lt; 70 y are reported in the table; notably in pts receiving R2, mPFS was longer in pts &lt; 70 y vs ≥ 70 y (39.4 mo [95% CI, 22.9-NE] vs 24.9 mo [95% CI, 16.4-NE]). In pts ≥ 70 y, any-grade adverse events (AEs) with a ≥ 10 % difference between R2 vs R-placebo included neutropenia (63% vs 11%), constipation (33% vs 16%), cough (33% vs 16%), leukopenia (26% vs 2%), anemia (24% vs 9%), pyrexia (24% vs 9%), pruritus/pruritus generalized (24% vs 2%), muscle spasms (22% vs 11%), rash/rash maculopapular (22% vs 5%), headache (20% vs 9%), thrombocytopenia (17% vs 2%), dyspepsia (13% vs 2%), influenza (13% vs 2%), back pain (7% vs 18%), and nasopharyngitis (4% vs 16%). Also, tumor flare was reported in 9% vs 0% of pts, respectively. In pts ≥ 70 y, 75% of R2 pts vs 36% of R-placebo pts had ≥ 1 grade 3/4 AE, mainly due to neutropenia (50% vs 7%). All other grade 3/4 AEs occurred in &lt; 10% of pts ≥ 70 y in both treatment arms. One grade 5 AE occurred in pts ≥ 70 y (R-placebo arm). In the R2 arm, the median number of treatment cycles was 12 for both the &lt; 70 y vs ≥ 70 y subgroups; however, fewer older pts completed 12 cycles of lenalidomide (76% vs 57%), and more started lenalidomide at the lower dose of 10 mg (6% vs 35%) because of low creatinine clearance, respectively. In the R2 &lt; 70 y and ≥ 70 y subgroups, the average daily dose of lenalidomide was 17.9 mg/d (range, 5.6-20.0) and 14.4 mg/d (range 4.2-20.0), and median relative dose intensity was 95% and 86%, respectively. Conclusions: Similar to the results in the original population, R2 showed superior efficacy vs rituximab monotherapy (plus placebo) as measured by the primary end point of PFS and secondary end points of ORR and CR in pts with R/R FL grade 1-3a and MZL irrespective of age. The efficacy and safety profiles of R2 and R-placebo in pts ≥ 70 y were similar to those reported in the overall population. Older pts treated with R2 vs R-placebo had superior mPFS (24.9 vs 14.3 mo). They were more likely to start lenalidomide at a lower dose and had lower median dose intensity which may have contributed to their shorter mPFS vs younger pts receiving R2. These data show that R2 maintained efficacy improvements vs R-placebo in pts ≥ 70 y, despite higher unfit status and lower overall lenalidomide treatment/exposure. Thus, R2 is an effective and available treatment option for pts with iNHL, including those with advanced age. Disclosures Trněný: Amgen: Consultancy, Honoraria; F. Hoffmann-La Roche: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Celgene: Consultancy; MorphoSys: Consultancy, Honoraria; Incyte: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Gilead Sciences: Consultancy, Honoraria. Leonard:Nordic Nanovector: Consultancy; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy; AstraZeneca: Consultancy; Celgene: Consultancy; Bayer Corporation: Consultancy; Epizyme, Inc: Consultancy; Karyopharm Therapeutics: Consultancy; Sutro Biopharma: Consultancy; Merck: Consultancy; Gilead: Consultancy; BeiGene: Consultancy; ADC Therapeutics: Consultancy; Sandoz: Consultancy; Akcea Therapeutics: Consultancy; Miltenyi: Consultancy; MorphoSys: Consultancy. Nowakowski:F. Hoffmann-La Roche Ltd: Research Funding; NanoString: Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees; Curis: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Genentech, Inc.: Research Funding; MorphoSys: Consultancy, Research Funding. Izutsu:Chugai, Celgene, Daiichi Sankyo, Astra Zeneca, Eisai, Symbio, Ono, Bayer, Solasia, Zenyaku, Incyte, Novartis, Sanofi, HUYA Bioscience, MSD, Astellas Amgen, Abbvie, ARIAD, Takeda, Pfizer: Research Funding; Kyowa Kirin, Eisai, Takeda, MSD, Chugai, Nihon Medi-physics, Janssen, Ono, Abbvie, Dainihon Sumitomo, Bayer, Astra Zeneca, HUYA Japan, Novartis, Bristol-Byers Squibb, Mundi, Otsuka, Daiichi Sankyo, Astellas, Asahi Kasei: Honoraria; Celgene: Consultancy; Eisai, Symbio, Chugai, Zenyaku: Research Funding; Eisai, Chugai, Zenyaku: Honoraria. Fowler:TG Therapeutics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding. Thieblemont:Roche: Honoraria, Research Funding; Gilead: Honoraria; Novartis: Honoraria; Kyte: Honoraria; Janssen: Honoraria; Celgene: Honoraria; Cellectis: Membership on an entity's Board of Directors or advisory committees. Zinzani:Verastem: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; MSD: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Eusapharma: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sanofi: Consultancy; Celltrion: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Gilead: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen-Cilag: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Servier: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sandoz: Membership on an entity's Board of Directors or advisory committees; Immune Design: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Portola: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Roche: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Kyowa Kirin: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; TG Therapeutics: Honoraria, Speakers Bureau. Kalambakas:Celgene Corporation: Employment, Equity Ownership. Czuczman:Celgene Corporation: Employment, Equity Ownership. Fustier:Celgene Corporation: Employment, Equity Ownership. Wu:Celgene Corporation: Employment, Equity Ownership. Gribben:Janssen: Consultancy, Honoraria, Research Funding; Acerta/Astra Zeneca: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding.

Background: Diffuse large B cell lymphoma (DLBCL) is the most common aggressive lymphoid malignancy in adults. Though standard immunochemotherapy regimens can result in clinical remission and cure in a majority of patients, approximately 30% of patients are primary refractory or eventually relapse, and their prognosis is very poor. Recent progress by large scale genomic and transcriptomic profiling of DLBCL patients has resulted in a deep understanding of disease drivers in newly diagnosed DLBCL (ndDLBCL). However, previous genomic studies of relapsed and/or refractory DLBCL (rrDLBCL) are limited by small sample sizes and much less is known about the genomic landscape or the changes in clonal populations that occur within a patient undergoing R-CHOP therapy. Analysis of a large cohort of rrDLBCL is needed to uncover the importance of known drivers, define clonal shifts that occur in relapsing patients, and discover novel mutations that impact resistance to therapy. Methods: DNA sequencing (whole exome or genome sequencing) was performed on tumor and matching germline samples from two rrDLBCL patient cohorts (N=143). The first rrDLBCL cohort consisted of 68 formalin fixed paraffin embedded (FFPE) tumor samples with matched germline obtained from the Mayo/Iowa Lymphoma SPORE Molecular Epidemiology Resource (MER); including 18 diagnostic/relapse paired samples. The second cohort consisted of 75 FFPE samples from 2 rrDLBCL clinical trials CC-122-ST-001 and CC-122-DLBCL-001 (NCT01421524 and NCT02031419). In addition, a ndDLBCL cohort (n=364) consisting of MER front line samples were also analyzed. CNV and SV calling was performed with Sclust and Manta, respectively. Mutational drivers were identified with cDriver. Cosmic mutational signatures were calculated for all samples. Results: For comparison of overall mutation rates between the ndDLBCL and rrDLBCL we analyzed the frequency of mutations in known driver genes [n=211, Chapuy et al, 2018; Schmitz et al, 2018; Reddy et al 2017]. Of these, the top 5 most mutated genes found in the relapse cohort were KMT2D (34.3%), IGLL5 (25.9%), CREBBP (22.4%), TP53 (21.0%), and HIST1H1E (19.6%) all of which were previously reported as mutated genes in rrDLBCL [Morin et al. 2016; Park et al. 2016;Greenawalt et al. 2017]. None of these frequencies changed significantly from the MER ndDLBCL cohort. However, frequencies of 2 genes NCOR1 and ACTB were increased significantly from 3.3% to 13.3% (p= 5.85 x 10-05) and 0.5% to 6.3% (p=2.56 x 10-04), respectively, in the rrDLBCL cohorts. Analysis of the previously reported driver mutations in 18 paired samples revealed a significant increase in the mean variant allele frequency (VAF) from diagnosis to relapse of mutations in BCL2 and BIRC6 from 18.5% to 28.8%(p=0.016) and 4.3% to 13.7%(p=0.011), respectively. In our analysis of novel drivers in rrDLBCL cohorts, 59 significant driver mutations (FDR 0.1) were identified, 23 were novel driver mutations while the remaining 36 have been previously reported as drivers of newly diagnosed DLBCL. These include novel mutations in genes such as IL4R (8.8%), CDK5R1 (6.7%), LAPTM5 (3.7%) and others whose function in lymphoma are unknown. In a secondary analysis, we performed mutational signature analysis for rrDLBCL and identified groups of patients with COSMIC mutational signatures that are associated with aging, defects in homologous end joining resulting in large insertions/deletions, and with defective mismatch repair often found in microsatellite unstable tumors. Together with the high prevalence of TP53 mutations, these results indicate genetic instability as a hallmark of relapse/refractory DLBCL. Analysis of CNV and SV will also be reported. Conclusion: Here we report to our knowledge the largest genomic dataset in relapsed and /or refractory DLBCL using tumor biopsies. Previously identified mutated genes in rrDLBCL were confirmed and novel drivers identified. Paired samples analysis reveals clonal shifts which occur during treatment and at time of relapse. Whether the novel driver mutations appeared as a function of selection of a rare clone over the course of therapy or a therapy induced mutation will be the focus of ongoing work. Novel variants including SNV, CNV and SV can potentially lead to new insights and deepen our understanding of the mechanisms of resistance in DLBCL. * Wenzl, Ortiz and Stong contributed equally **Novak and Gandhi contributed equally Disclosures Ortiz: Celgene Corporation: Employment, Equity Ownership. Stong:Celgene Corporation: Employment, Equity Ownership. Huang:Celgene Corporation: Employment, Equity Ownership. Maurer:Morphosys: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; Nanostring: Research Funding. Towfic:Celgene Corporation: Employment, Equity Ownership. Hagner:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Nikbakht:Celgene Corporation: Employment, Equity Ownership. Stokes:Celgene Corporation: Employment, Equity Ownership. Lata:Celgene Corporation: Employment, Equity Ownership. Kamalakaran:Celgene Corporation: Employment, Equity Ownership. Mavrommatis:Celgene Corporation: Employment, Equity Ownership. Trotter:Celgene Corporation: Employment, Equity Ownership. Czuczman:Celgene Corporation: Employment, Equity Ownership. Ansell:Regeneron: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding. Cerhan:Celgene: Research Funding; NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees. Nowakowski:Bayer: Consultancy, Research Funding; Curis: Research Funding; Celgene: Consultancy, Research Funding; MorphoSys: Consultancy, Research Funding; Genentech, Inc.: Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding. Novak:Celgene Coorperation: Research Funding. Gandhi:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties.

Introduction: Peripheral T-cell lymphomas (PTCL) are a heterogeneous group of mature aggressive T-cell non-Hodgkin lymphomas. They carry a worse prognosis for most subtypes compared with their B-cell counterparts. Despite the recent approval of newer therapies, the response rate and duration of clinical benefit is short and the outlook for patients with relapsed/refractory (RR) PTCL remains poor. There is therefore a need for novel effective therapies in PTCL. Targeting the profoundly immunosuppressive tumor microenvironment (TME) in PTCL is one such approach. Preclinical data show that malignant cells in PTCL overexpress programmed death ligand 1 (PD-L1), which signals via programmed death-1 (PD-1) receptor, and provides an inhibitory signal further suppressing antitumor immunity. PD-1/PD-L1/2 interactions in PTCL are particularly complicated as both the receptor and ligands can be expressed on the malignant T-cell. While the use of anti-PD-1 blocking antibodies has shown remarkable efficacy particularly in relapsed Hodgkin lymphoma, only a small number of patients with PTCL have been treated with checkpoint blockade. We conducted an investigator-initiated phase 2 prospective study of single-agent nivolumab for RR PTCL. We report here the results of the prespecified interim analysis. Study design and patient selection: Nivolumab was given at a flat dose of 240 mg intravenously (IV) every 2 weeks for 8 cycles then 480 mg IV every 4 weeks starting cycle 9. The primary objective was to assess the overall response rate (ORR) defined as proportion of subjects achieving either a partial response (PR) or complete response (CR) within 12 cycles of treatment. Secondary objectives were to assess safety and tolerability of nivolumab in PTCL and to assess progression-free survival (PFS), duration of response (DOR) and overall survival (OS). Pre-planned sample size was 39, assuming that an ORR of 10% or less would be considered negative and an ORR of 30% or greater would warrant further study. We used a one-stage design with an interim analysis (upon enrolling 12 subjects) based on a Simon optimal design to assess efficacy. This design had a 90% power with a 1-sided 10% level test. The Duffy and Santner method was utilized to determine confidence intervals for the ORR. Kaplan-Meier methods were used to assess PFS, OS, and DOR. This study was sponsored by Bristol-Myers Squibb (NCT03075553). Results: Twelve patients who received at least one cycle of nivolumab were included in this interim analysis. Patient characteristics are illustrated in table 1. Half (6/12) of the patients had angioimmunoblastic T-cell lymphoma (AITL), 3/12 had PTCL, not otherwise specified (NOS), one had ALK negative anaplastic large cell lymphoma (ALK- ALCL). Most (11/12) were advanced stage (stage 4), had extranodal disease and half of the patients had received a prior autologous transplant. The ORR was 33% (4/12) (95% CI: 12.3 - 63.7%): 1 CR seen in ALK-ALCL; 2 PR, 1 in PTCL, NOS and 1 in enteropathy associated T-cell lymphoma; 1 CR in AITL. The median DOR was 3.6 months (95% CI: 1.9-6.9). The median PFS for all 12 patients was short at 1.9 months (95% CI: 1.5-8.7); median OS was 7.9 months (95% CI: 3.4-10.8) (Panel A). Hyperprogressive disease (defined as dramatic progression within 1 cycle of treatment) occurred in 4 patients. Observed grade 3 and higher adverse events (AEs) were as follows: non-hematologic AEs in 5/12 (41.7%), while hematologic AEs were seen in 3/12 (25%) patients. Conclusions: Nivolumab had modest clinical activity in patients with R/R PTCL and the study met the criteria at interim analysis to continue accrual. However, due to the high number of patients with hyperprogressive disease, the moderate activity of the drug, and short DOR, a decision was made to halt the study. These findings likely reflect the distinct biology of PTCL and should be considered when designing future studies using checkpoint inhibitors in these diseases. Further studies are indicated using nivolumab in combination (rather than a single-agent) and use of biomarkers to better predict the responders. Disclosures Bennani: Purdue Pharma: Other: Advisory board; Adicet Bio: Other: Advisory board; Seattle Genetics: Other: Advisory board; Adicet Bio: Other: Advisory board; Purdue Pharma: Other: Advisory board; Adicet Bio: Other: Advisory board; Seattle Genetics: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Kite Pharma: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Kite Pharma: Other: Advisory board; Purdue Pharma: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Kite Pharma: Other: Advisory board; Seattle Genetics: Other: Advisory board. Nowakowski:Selvita: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding; Celgene: Consultancy, Research Funding; Bayer: Consultancy, Research Funding; Curis: Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Genentech, Inc.: Research Funding; MorphoSys: Consultancy, Research Funding. Ansell:Merck: Other: research funding for clinical trials; Seattle Genetics: Other: research funding for clinical trials; Takeda: Other: research funding for clinical trials; Bristol Myers Squibb: Other: research funding for clinical trials; Regeneron: Other: research funding for clinical trials; Affimed: Other: research funding for clinical trials; Pfizer: Other: research funding for clinical trials; AI Therapeutics: Other: research funding for clinical trials.

Background: Follicular lymphoma (FL) is the most frequent indolent lymphoma. While immunochemotherapy (IC) treated FL patients who achieve event-free status at 24 months after diagnosis (EFS24) have the subsequent life expectancy of the general population, those who fail to achieve EFS24 have aggressive disease with poor outcomes. Similarly, in FL patients initially observed or treated with rituximab monotherapy, EFS at 12 months (EFS12) is a strong predictor of subsequent outcome. Thus, an unmet patient need is to predict at diagnosis those at greatest risk of early failure in order to identify better treatments. The lymphoma microenvironment may be a key determinant of early failure. Therefore, we aimed to improve risk stratification of newly diagnosed FL patients by using a discovery and validation study design to identify microenvironment determinants of early failure and then integrate them into the Follicular Lymphoma International Prognostic Index (FLIPI). Patients and Methods: We evaluated 496 patients with FL grade 1-3A who were prospectively enrolled into the Molecular Epidemiology Resource (MER) cohort from 2002-2012. In the discovery set of patients (N=166), we stained tissue microarrays for CD4, CD8, FOXP3, CD32b, CD14, CD68, CD70, SIRPa, TIM3, PD-1 and PDL1. Immunohistochemical staining was determined both within and between malignant follicles. Stains were scored to the nearest decile as the percentages of cells positive. Scores for each stain were dichotomized as 0% vs &gt;0% except for CD32b (&lt;20% vs ≥20%), CD70 (&lt;10% vs ≥10%), and SIRPa (&lt;30% vs ≥30%). EFS was defined as time from diagnosis to progression, relapse, retreatment, or death. Early failure was defined as failing to achieve EFS24 for patients treated with IC at diagnosis and failing to achieve EFS12 for patients who were observed or received other treatments. Risk of early failure was estimated using odds ratios (ORs) and 95% confidence intervals (CI) from logistic regression models. Markers at P≤0.15 were brought forward for validation in a separate set of patients from the MER cohort (N=330), with P&lt;0.005 being declared statistically significant after a Bonferroni correction (0.05/10 tests). In the combined dataset, we also used Cox regression to assess associations with continuous EFS and overall survival (OS). Mass cytometry (CyTOF) was then performed on a subset of patients (N=51) to comprehensively characterize the phenotype of intratumoral immune cells associated with outcome. Results: In the discovery set, CD4, CD8, FOXP3, PD-1, and SIRPα were associated with risk of early failure at P≤0.15 and were subsequently evaluated in the validation set. Only CD4 expression inside the follicle validated. Specifically, lack of CD4 expression inside the follicle was associated with higher risk of early failure in the discovery (OR=1.77, P=0.15) and validation (OR=2.59, P=0.001) sets, with a pooled OR=2.29 (95% CI 1.47-3.58; P&lt;0.001). CyTOF analysis characterized the favorable CD4+ T-cell population associated with achieving EFS12/24 as activated effector memory T cells lacking exhaustion markers (Figure 1A). Adjustment of CD4 expression for FLIPI in the combined dataset did not weaken this association (OR=2.38, 95% CI 1.51-3.79), and lack of CD4 expression was prognostic within each FLIPI group 0-1 (OR=2.65; p=0.036), 2 (OR=2.07; P=0.064) and 3-5 (OR=2.51; P=0.015). We next established a bio-clinical risk model (termed BioFLIPI) that combined CD4 intrafollicular expression and FLIPI into a 1-4 scale, where any expression of intrafollicular CD4 moved a patient up one FLIPI risk group, adding a fourth (high) risk group for FLIPI 3-5 and any CD4 inside expression. The BioFLIPI score was strongly associated with risk of early failure: compared to a BioFLIPI score of 1 (24% of patients), patients with a score of 2 (35% of patients; OR=2.17, 95% CI 1.08-4.69), 3 (31% of patients; OR=3.53, 95% CI 1.78-7.54), and 4 (10% of patients; OR=8.92, 95% CI 4.00-21.1) had increasing risk of early failure. The c-statistic for BioFLIPI was 0.665, which was higher than that for FLIPI alone (0.636). BioFLIPI was also strongly predictive of continuous EFS [Figure 1B], OS [Figure 1C], and for EFS and EFS24 when restricted to IC-treated patients [Figure 1D]. Conclusion: Integration of CD4 expression inside the follicle at diagnosis with FLIPI improves identification of FL patients at risk for early failure. Disclosures Novak: Celgene Coorperation: Research Funding. Nowakowski:Curis: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding; Genentech, Inc.: Research Funding; MorphoSys: Consultancy, Research Funding. Cerhan:NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding. Ansell:LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment.