Salles, Gilles

Abstract Purpose: Tafasitamab, an Fc-modified, humanized, anti-CD19 monoclonal antibody, in combination with lenalidomide, demonstrated efficacy in transplant-ineligible patients with relapsed/refractory (R/R) diffuse large B-cell lymphoma (DLBCL), in the single-arm, phase II L-MIND study (NCT02399085). RE-MIND, a retrospective observational study, generated a historic control for L-MIND to delineate the contribution of tafasitamab to the efficacy of the combination. Patients and Methods: Data were retrospectively collected from patients with R/R DLBCL treated with lenalidomide monotherapy for comparison with tafasitamab + lenalidomide–treated patients (L-MIND). Key eligibility criteria were aligned with L-MIND. Estimated propensity score–based Nearest Neighbor 1:1 Matching methodology balanced the cohorts for nine prespecified prognostic baseline covariates. The primary endpoint was investigator-assessed best overall response rate (ORR). Secondary endpoints included complete response (CR) rate, progression-free survival (PFS), and overall survival (OS). Results: Data from 490 patients going through lenalidomide monotherapy were collected; 140 qualified for matching with the L-MIND cohort. The primary analysis included 76 patients from each cohort who received a lenalidomide starting dose of 25 mg/day. Cohort baseline covariates were comparable. A significantly better ORR of 67.1% (95% confidence interval, 55.4–77.5) was observed for the combination therapy versus 34.2% (23.7–46.0) for lenalidomide monotherapy [odds ratio, 3.89 (1.90–8.14); P < 0.0001]. Higher CR rates were achieved with combination therapy compared with lenalidomide monotherapy [39.5% (28.4–51.4) vs. 13.2% (6.5–22.9)]. Survival endpoints favored combination therapy. Lenalidomide monotherapy outcomes were similar to previously published data. Conclusions: RE-MIND enabled the estimation of the additional treatment effect achieved by combining tafasitamab with lenalidomide in patients with R/R DLBCL.

Abstract Limited data exist to describe the clinical features and outcomes for elderly patients with follicular lymphoma (FL). The Follicular Lymphoma Analysis of Surrogacy Hypothesis (FLASH) group performed a prospectively planned pooled analysis of individual patient data from first-line randomized controlled trials (RCTs) and examined associations between age (≤70 vs >70 years), clinical characteristics, and FL outcomes. We identified 18 multicenter clinical RCTs in the FLASH database that enrolled elderly patients (>70 years). Primary end points were early disease outcomes, CR24 and CR30, and progression-free survival (PFS) at 24 months (PFS24). Secondary end points were PFS and overall survival (OS). We identified 5922 previously untreated FL patients from 18 RCTs. Patients age >70 years (vs ≤70 years) more commonly had elevated lactate dehydrogenase, hemoglobin <12 g/dL, ECOG PS ≥2, and elevated β2-microglobulin. Median follow-up was 5.6 years. Patients >70 years did not differ from patients ≤70 years in rates of CR24, CR30, or PFS24. With a median OS of 14.6 years for all patients, median OS was 7.4 and 15.7 years for patients >70 and ≤70 years of age, respectively (hazard ratio = 2.35; 95% confidence interval = 2.03-2.73; P < .001). Age >70 years was a significant predictor of OS and PFS due to higher rates of death without progression, but not PFS24, CR24, or CR30. FL patients >70 years treated on trials have similar early disease outcomes to younger patients. There is no disease-specific outcome difference between age groups. Age alone should not disqualify patients from standard treatments or RCTs.

Abstract Disease progression after frontline therapy for Diffuse large B‐cell lymphoma (DLBCL) is a clinically significant event. Patients who experience early progression or have refractory disease have especially poor outcomes. Simple, clinically applicable prognostic tools are needed for selecting patients for consideration for novel therapies and prognostication in the relapsed/refractory (R/R) setting. Model building was performed in patients from the Surrogate endpoints in aggressive lymphoma (SEAL) consortium with disease progression after frontline immunochemotherapy. The primary endpoint was overall survival (OS) measured from date of progression. Validation was performed in the University of Iowa/Mayo Clinic SPORE Molecular Epidemiology Resource (MER) and Danish National Lymphoma Register (LYFO) cohorts. Model performance was assessed using time‐dependent concordance indices (c‐statistic) and calibration with metrics evaluated at 2 years from progression. Note, 1234 of 5112 patients treated with frontline immunochemotherapy in the SEAL consortium developed progressive disease. Time to progression on immunochemotherapy and age at progression were strongly associated with post‐progression OS (both p < 0.001). A prognostic model was developed incorporating spline fit for both variables. The model had good concordance in the discovery (0.67) and validation sets (LYFO c = 0.64, MER c = 0.68) with generally good calibration. Time to progression on frontline therapy is strongly associated with post‐progression OS in DLBCL. We developed and validated a simple to apply clinical prognostic tool in the R/R setting. The useful prediction of expected outcomes in R/R DLBCL and can inform treatment decisions such as considerations for CAR‐T therapy as well as trial designs. The model is available in smartphone‐based point of care applications.

Background: Diffuse large B-cell lymphoma (DLBCL) is frequently curable after frontline therapy, however, relapses can occur after achievement of a PET-negative complete remission (CR). Early detection of relapse may be associated with improved outcomes with second-line curative intent therapy or novel therapies. Controversy exists regarding the utility of post-therapy surveillance imaging which is associated with patient (pt) anxiety, radiation exposure, false-positive results, and cost. A retrospective study found that serial minimal residual disease (MRD) monitoring during DLBCL surveillance could identify pts at risk of relapse before clinical evidence of disease (Roschewski, Lancet Oncol, 2015). In this multicenter prospective study, we assessed whether a next-generation sequencing (NGS)-based MRD assay could be used for early detection of molecular relapse in DLBCL. Methods: Eligible pts with DLBCL or high-grade B-cell lymphoma (HGBCL) who received anthracycline-containing chemotherapy were enrolled across five cancer centers. In pts who achieved a PET-negative CR, serial peripheral blood samples were obtained every 3 months (mos) and CT scans every 6 mos for 2 years (yrs) post-treatment. The clonoSEQ® next generation sequencing (NGS) MRD assay (Adaptive Biotechnologies, Seattle, WA) that leverages multiplex PCR followed by NGS to identify and track rearrangements of IgH, V-J, D-J and IgK/L loci and translocations in Bcl1/2-IgH was used. MRD positive was defined as any detectable rearrangement and MRD negative as no evidence of rearrangement. We also reported MRD positivity above the limit of detection (LOD) (observations required to have 95% reproducibility). Interim futility analysis was performed after approximately 50% of anticipated relapses occurred to assess the preliminary sensitivity of the assay. Progression-free survival (PFS) was defined as time from treatment start date to relapse or death. Results: 501 pts were enrolled and 401 were evaluable (pre-treatment tumor pathology available, completed frontline treatment, and achieved PET-negative CR at end-of-treatment). Baseline characteristics were median age of 62 yrs (range 19-95), male sex 58%, advanced stage 61%, and poor-risk by R-IPI 40% (Table 1). Histologies included DLBCL, NOS (86%), primary mediastinal B-cell lymphoma (6%), HGBCL (5%), T-cell rich B-cell lymphoma (1%), and other (1%). Pts received regimens including RCHOP x 6 cycles (36%), REPOCH x 6 cycles (20%), clinical trial (15%), combined modality therapy (RCHOP+RT) (10%), and other RCHOP variations (19%). Among the 401 evaluable pts, 44 relapses have occurred as of June 1, 2020. In 47% of pts (18/38), clinical relapse was detected using surveillance imaging alone (typically CT CAP with IV contrast) in an otherwise asymptomatic pt with a normal physical exam and laboratory evaluation. Clinical relapse was detected by clinical symptoms alone in 11% (4/28), by the evaluation of an oncologist alone in 0%, and by imaging and clinical symptoms and/or evaluation by an oncologist in 42% (16/38) (missing data in 6 pts). With a median follow-up of 2.2 yrs, the 2-yr PFS was 88.1% (84.8, 91.4) (Figure 1). Advanced age, advanced stage, and poor-risk R-IPI were associated with inferior PFS. Of the 44 relapses, 43 pts were included in the interim analysis and tumor-specific clonotypes were identified in 39 pts (91%). In 56% (22/39), the MRD assay was positive at or before clinical relapse. In 38% (15/39), the MRD assay was positive above the limit of detection at or prior to clinical relapse, with only 10 relapses detected more than 3 mos prior to relapse, reflecting the poor clinical sensitivity of the assay. The NGS-MRD assay was more sensitive in the acellular (plasma cell-free DNA) than in the cellular (circulating leukocytes) compartment. Results: Overall outcomes are excellent for DLBCL and HGBCL pts who achieve PET-negative complete remission. In interim analysis, surveillance MRD assessment using the clonoSEQ® assay fails to consistently identify pts at risk of recurrence before clinical evidence of relapse in DLBCL. In a prospective analysis, a substantial proportion (47%) of clinical relapses are detected by imaging in asymptomatic pts, supporting the value of CT surveillance imaging in DLBCL, particularly for pts with advanced stage or high-risk disease. Additional analyses are forthcoming for MRD assessment in all pts, including those in remission. Disclosures Kumar: Celgene: Research Funding; Pharmacyclics: Research Funding; Adaptive Biotechnologies,: Research Funding; AbbVie: Research Funding; Seattle Genetics: Research Funding; Celgene: Honoraria, Other: Honoraria for Advisory Board; Astra Zeneca: Honoraria, Other: Honoraria for Advisory Board; Kite Pharmaceuticals: Honoraria, Other: Honoraria for Advisory Board. Westin:Kite: Consultancy, Research Funding; Curis: Consultancy, Research Funding; Genentech: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Amgen: Consultancy; Astra Zeneca: Consultancy, Research Funding; Morphosys: Consultancy, Research Funding; 47: Research Funding; Janssen: Consultancy, Research Funding; BMS: Consultancy, Research Funding. Nowakowski:Curis: Consultancy; Seattle Genetics: Consultancy; Nanostrings: Research Funding; MorphoSys: Consultancy, Research Funding; Roche: Consultancy, Research Funding; Celgene/BMS: Consultancy, Research Funding; Kite: Consultancy; Denovo: Consultancy; Kymera: Consultancy; Ryvu: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other. Lossos:Verastem: Consultancy, Honoraria; Seattle Genetics: Consultancy, Other; NCI: Research Funding; Stanford University: Patents & Royalties; Janssen Biotech: Honoraria; Janssen Scientific: Consultancy, Other. Batlevi:Life Sci, GLG, Juno/Celgene, Seattle Genetics, Kite: Consultancy; Janssen, Novartis, Epizyme, Xynomics, Bayer, Autolus, Roche/Genentech: Research Funding. Drullinsky:Roche: Research Funding; Novartis: Research Funding. Gerecitano:Janssen: Current Employment. Hamlin:Celgene: Consultancy; Karyopharm: Consultancy; Juno Therapeutics: Consultancy; Portola: Research Funding; Molecular Templates: Research Funding; Incyte: Research Funding; J&J Pharmaceuticals: Research Funding; Portola Pharmaceutics: Consultancy. Horwitz:ASTEX: Consultancy; Affirmed: Consultancy; Vividion Therapeutics: Consultancy; Verastem: Consultancy, Research Funding; Myeloid Therapeutics: Consultancy; Miragen: Consultancy; Kura Oncology: Consultancy; Janssen: Consultancy; GlaxoSmithKline: Consultancy; Daiichi Sankyo: Research Funding; C4 Therapeutics: Consultancy; Beigene: Consultancy; Portola: Consultancy, Research Funding; Mundipharma: Consultancy; Innate Pharma: Consultancy; Corvus: Consultancy; Trillium: Consultancy, Research Funding; Seattle Genetics: Consultancy, Research Funding; Millenium/Takeda: Consultancy, Research Funding; Kyowa Hakka Kirin: Consultancy, Research Funding; Infinity/Verastem: Research Funding; Forty Seven: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Aileron: Consultancy, Research Funding; ADCT Therapeutics: Consultancy, Research Funding. Jacob:Adaptive Biotechnologies: Current Employment, Current equity holder in publicly-traded company. Joffe:AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Epizyme: Membership on an entity's Board of Directors or advisory committees. von Keudell:Bayer: Research Funding; Pharmacyclics: Research Funding; Genentech: Research Funding. Lahoud:MorphoSys: Other: Advisory Board. Matasar:Bayer: Consultancy, Honoraria, Research Funding; Seattle Genetics: Consultancy, Honoraria, Research Funding; Rocket Medical: Consultancy, Research Funding; Teva: Consultancy; F. Hoffmann-La Roche Ltd: Consultancy, Honoraria, Research Funding; GlaxoSmithKline: Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Daiichi Sankyo: Consultancy; IGM Biosciences: Research Funding; Janssen: Honoraria, Research Funding; Pharmacyclics: Honoraria, Research Funding; Immunovaccine Technologies: Honoraria, Research Funding; Merck: Consultancy; Juno Therapeutics: Consultancy; Genentech, Inc.: Consultancy, Honoraria, Research Funding. Mullins:Adaptive Biotechnologies: Current Employment, Other: shareholder. Noy:Pharmacyclics: Research Funding; NIH: Research Funding; Rafael Pharma: Research Funding; Janssen: Consultancy; Pharmacyclics: Consultancy; Medscape: Consultancy; Targeted Oncology: Consultancy; Morphosys: Consultancy. Straus:Seattle Genetics: Consultancy, Membership on an entity's Board of Directors or advisory committees; ASH: Other: Conference in December 2019 on HL to other physicians during ASH; Elsevier: Membership on an entity's Board of Directors or advisory committees, Other: CME writer; OncLive: Speakers Bureau; Takeda Pharmaceuticals: Research Funding, Speakers Bureau; NY Lymphoma Rounds: Consultancy; Targeted Oncology: Consultancy, Speakers Bureau; Imedex, Inc.: Speakers Bureau; Karyopharm Therapeutics: Membership on an entity's Board of Directors or advisory committees. Younes:Takeda: Consultancy; HCM: Consultancy; AstraZeneca: Current Employment; Novartis: Consultancy; Daiichi Sankyo: Consultancy; Janssen: Consultancy; Epizyme: Consultancy; BMS: Consultancy; BioPath: Consultancy; Curis: Consultancy. Zelenetz:MEI Pharma: Research Funding; MorphoSys: Research Funding; Sandoz: Research Funding; Celgene: Research Funding; Roche: Research Funding; Gilead: Research Funding; Genentech/Roche: Consultancy; Gilead: Consultancy; Celgene: Consultancy; Janssen: Consultancy; Amgen: Consultancy; Novartis: Consultancy; Adaptive Biotechnology: Consultancy; BeiGene: Membership on an entity's Board of Directors or advisory committees.

Background: With the incorporation of positron emission tomography (PET) imaging as part of the standard staging evaluation of follicular lymphoma (FL), it is generally recommended to obtain the diagnostic biopsy from a lesion with the highest standardized uptake value (SUV) to rule out de novo histologic transformation (HT). In some cases such an approach might be impractical, while in other cases a biopsy from a diseased area with a high SUV may still demonstrate an indolent histology. To date, there is no data to guide treatment choices in patients (pts) with FL with high SUVmax but with no documented evidence of HT. Specifically, it remains unclear whether anthracycline-containing regimens, such as R-CHOP, provide a better outcome than R-Bendamustine (BR). Furthermore, it is unknown whether rituximab (R) maintenance is beneficial in this setting. Therefore, we aimed to compare the efficacy of R-CHOP vs BR in newly diagnosed FL pts with high SUVmax at baseline PET and to clarify the role of maintenance. Patients and Methods: We retrospectively identified 261 consecutive pts with newly diagnosed biopsy-proven FL1-3A and a SUVmax≥13 on baseline PET, who were treated with frontline R-CHOP (n=183) or BR (n=78) at 7 US cancer centers based on the physician's choice. Progression-free survival (PFS) was calculated from starting treatment until progression, relapse or death. Cut-offs of SUVmax ≥13≤18 and SUVmax&gt;18 were used for sub-analysis (Haematologica;2020;105:1907-1913). Results: Median age was 59 years. The baseline characteristics of the two groups differed significantly for a younger age (58 vs 62 years, p=0.009), a higher rate of B-symptoms (26% vs 10%, p=0.005) and baseline SUVmax&gt;18 (49% vs 36%, p=0.04) in the R-CHOP group. A diagnostic biopsy was obtained from the site at the highest SUV in 129 (71%) and 43 (55%) pts, respectively (p=0.01). These suggest a potential selection of R-CHOP in pts with adverse features. At the end of therapy (EoT), R-CHOP treated pts achieved higher PET-CT complete response rates (CR 82% vs 69%, p=0.02). This superiority held in pts with diagnostic biopsy at the highest SUV site (CR 83% vs 67%, p=0.03), but not in the others (CR 80% vs 71%, p=0.37). Progression occurred at EoT in 11 and 14 pts receiving R-CHOP and BR (6% vs 18%, p=0.02), with a higher rate of HT in the BR group (13% vs 4%, p=0.006). After a median follow-up of 76 months (range, 4-208 months), there was no significant difference in PFS between R-CHOP and BR treated pts (hazard ratio [HR] 1.22, p=0.34). PFS was strongly associated with FLIPI 2 (HR 2.32, p=0.01) and 3-5 (HR 2.69, p=0.003) in the univariate as well as in the multivariate analysis (FLIPI 2 HR 2.19, p=0.02; FLIPI 3-5 HR 2.51, p=0.01). There was a trend toward overall survival (OS) benefit in the R-CHOP group (Fig.1A). In the univariate analysis for OS, BR regimen (HR 2.12, p=0.03), age &gt;60 (HR 2.54, p=0.006), FLIPI 3-5 (HR 5.13, p=0.03) and biopsy at the highest SUV site (HR 0.44, p=0.01) were prognostic, however none of those remained significant in the multivariate analysis (BR regimen HR 1.84, p=0.09; age &gt;60 HR 1.92, p=0.08; FLIPI 3-5 HR 3.11, p=0.14; biopsy at the highest SUV site HR 0.60, p=0.13; SUVmax&gt;18 HR 1.87, p=0.055). Among pts with EoT response after R-CHOP or BR (n=170 and n=64, respectively), one third in each group (36% vs 41%, respectively) received R-maintenance for a median of 8 administrations (range, 2-12). A significant PFS advantage (landmark analysis) was observed with R-maintenance (Fig.1B; HR 0.53, p=0.02); however, this did not translate in survival benefit (HR 0.67, p=0.49). In a multivariable model, R-maintenance (HR 0.53, p=0.02), FLIPI 2 (HR 2.47, p=0.03) and 3-5 (HR 2.79, p=0.01) remained independent prognosticators of PFS, while no parameters significantly affected OS. The 2-year HT rate (10% vs 17%) and cumulative incidence did not statistically differ between R-CHOP and BR groups (Fig.1C, p=0.159). Early progression (POD24) occurred in 43 and 22 pts who received R-CHOP and BR, respectively (24% vs 28%) and resulted in a significantly higher risk of death (HR 4.12, p=0.006). Conclusion: In newly diagnosed FL pts with SUVmax≥13 at baseline PET, R-CHOP was more effective in achieving CR and reducing the risk of early HT compared with BR, and it was associated with a trend toward survival advantage. Rituximab maintenance significantly prolonged PFS. A prospective evaluation with a larger population will be needed to confirm our findings. Disclosures Joffe: Epizyme: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Membership on an entity's Board of Directors or advisory committees. Ruella:Abclon, BMS, NanoString: Consultancy; Abclon: Consultancy, Research Funding; UPenn/Novartis: Patents & Royalties. Svoboda:Adaptive: Consultancy; Astra-Zeneca: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Imbrium: Consultancy; Incyte: Research Funding; Pharmacyclics: Consultancy, Research Funding; Merck: Research Funding; Seattle Genetics: Consultancy, Research Funding; TG: Research Funding; Genmab: Consultancy; Atara: Consultancy. Witzig:Immune Design: Research Funding; Spectrum: Consultancy; Karyopharm Therapeutics: Research Funding; Acerta: Research Funding; Celgene: Consultancy, Research Funding; AbbVie: Consultancy; MorphSys: Consultancy; Incyte: Consultancy. Smith:TG Therapeutics: Consultancy, Research Funding; Janssen: Consultancy; BMS: Consultancy; Celgene: Consultancy, Research Funding; Pharmacyclics: Research Funding; Karyopharm: Consultancy, Research Funding; FortySeven: Research Funding; Acerta: Research Funding; Genentech/Roche: Consultancy, Other: Support of parent study and funding of editorial support, Research Funding. Armand:Affimed: Consultancy, Research Funding; Otsuka: Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria, Research Funding; IGM: Research Funding; Infinity: Consultancy; Genentech: Research Funding; Sigma Tau: Research Funding; Celgene: Consultancy; Adaptive: Consultancy, Research Funding; Tensha: Research Funding; Roche: Research Funding; Merck & Co., Inc.: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy; ADC Therapeutics: Consultancy. Nastoupil:Janssen: Honoraria, Research Funding; LAM Therapeutics: Research Funding; Gilead/KITE: Honoraria; Gamida Cell: Honoraria; TG Therapeutics: Honoraria, Research Funding; Merck: Research Funding; Novartis: Honoraria, Research Funding; Genentech, Inc.: Honoraria, Research Funding; Karus Therapeutics: Research Funding; Bayer: Honoraria; Celgene: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding. Ansell:Takeda: Research Funding; Regeneron: Research Funding; Bristol Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Affimed: Research Funding; AI Therapeutics: Research Funding; ADC Therapeutics: Research Funding. Zelenetz:Novartis: Consultancy; Adaptive Biotechnology: Consultancy; Genentech/Roche: Consultancy; Gilead: Consultancy; Gilead: Research Funding; Celgene: Consultancy; Roche: Research Funding; Amgen: Consultancy; BeiGene: Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy; Sandoz: Research Funding; Celgene: Research Funding; MorphoSys: Research Funding; MEI Pharma: Research Funding. Younes:BioPath, Xynomic, Epizyme, and F. Hoffmann-La Roche: Consultancy; Janssen, Curis, Merck, Bristol-Myers Squibb, Syndax Pharmaceuticals, F. Hoffmann-La Roche, Curis (Inst), Johnson & Johnson (Inst), Novartis (Inst): Research Funding; Janssen, AbbVie, Merck, Curis, Epizyme, F. Hoffmann-La Roche, Takeda, Bristol-Myers Squibb, Bayer HealthCare Pharmaceuticals, Celgene, Incyte, Janssen Pharmaceuticals, Merck, Sanofi, Seattle Genetics, Takeda Millennium: Honoraria; AstraZeneca: Current Employment; MSKCC: Ended employment in the past 24 months.

Background L-MIND (NCT02399085) is an ongoing, open-label, single-arm, Phase II study of tafasitamab + lenalidomide (LEN) in patients with relapsed/refractory (R/R) diffuse large B-cell lymphoma (DLBCL) who are ineligible for autologous stem cell transplant. Progression-free survival (PFS) estimates the treatment effect of the study drug only, while overall survival (OS) also includes effects of potential subsequent cancer treatments or palliative care. Given that not all OS and PFS events have occurred after a long follow-up duration, traditional survival analyses may underestimate survival benefit for tafasitamab + LEN by assuming the same mortality rate for the whole population. We used mixture cure models to estimate the proportion of long-term survivors (LTS) and survival benefit associated with tafasitamab + LEN treatment. Methods In the L-MIND study (data cut-off: Nov 30, 2019), 80/81 enrolled patients with R/R DLBCL received tafasitamab + LEN, with a median and maximum follow-up duration of 31.8 months (95% CI: 27.2-35.9) and 43.5 months, respectively. The Kaplan-Meier (KM) plots of PFS and OS were observed to plateau after 30 months, with the majority of events reported up to that time-point and very few events beyond this time-point. Patients without a PFS event or patients who did not decease after the observed plateau exhibit a high survival benefit from tafasitamab + LEN treatment and are considered LTS. Thus, the patient population can be considered to consist of two sub-populations - LTS and non-LTS. A "mixture cure model" was fitted on the PFS and OS data to estimate the proportion of LTS and associated mean survival for the two sub-populations (Lambert PC, et al. 2007). To incorporate background mortality (Bansal, et al. 2019), age- and sex-specific US mortality rates (US CDC 2017) were factored in for the entire study population (enrolled in Europe and US). The weighted average of the mean survival for the two sub-populations provided an estimate of the mean survival for the whole population. For comparative purposes, standard parametric models without considering an LTS proportion were fitted on PFS and OS data additionally. Sensitivity analyses were performed considering different survival distributions (exponential distribution, Weibull distribution and log-logistic distribution). Results By using a mixture cure model fitted to PFS and OS data separately, the estimated proportion of LTS for the tafasitamab + LEN combination was 42% (95% CI: 30-55) and 47% (95% CI: 29-66), respectively. The predicted mean survival when mixture cure model was fitted on PFS data was 16.7 years for LTS patients vs 0.5 years for non-LTS patients, yielding 7.3 years for the overall population. Under this mixture cure model, the survival rates of patients with tafasitamab + LEN treatment were 40.2% and 36.0% at 2 and 5 years. Similar results were obtained when the mixture cure model was fitted on OS data. Using a standard parametric model (Weibull distribution), the predicted mean survival in the overall population was 2.8 years fitting PFS data (Figure 1) and 4.5 years fitting OS data (Figure 2). Conclusions PFS and OS KM curves for the L-MIND study reaching a long plateau and not following a Weibull distribution suggest the presence of an LTS subgroup. Standard parametric models may lead to a biased estimation of the OS benefit in such situations. The mixture cure model suggests that the treatment of R/R DLBCL patients with tafasitamab + LEN is associated with a high LTS proportion. The survival rates of patients with tafasitamab + LEN treatment were 40.2% and 36.0% at 2 and 5 years. Disclosures Salles: Takeda:Honoraria;BMS/Celgene:Honoraria, Other: consultancy or advisory role;Debiopharm:Consultancy, Honoraria, Other: consultancy or advisory role;Genmab:Honoraria, Other;Karyopharm:Honoraria;Kite, a Gilead Company:Honoraria, Other: consultancy or advisory role ;Epizyme:Honoraria, Other: consultancy or advisory role;Janssen:Honoraria, Other: consultancy or advisory role;Abbvie:Other: consultancy or advisory role;Roche:Honoraria, Other: consultancy or advisory role;Novartis:Honoraria, Other: consultancy or advisory role;MorphoSys:Honoraria, Other: consultancy or advisory role;Autolos:Other: consultancy or advisory role.Goswami:MorphoSys AG:Ended employment in the past 24 months.Bagnardi:MorphoSys AG:Consultancy.Dey:MorphoSys AG:Current Employment.Winderlich:MorphoSys AG:Current Employment.Ambarkhane:MorphoSys AG:Current Employment.Huang:MorphoSys AG:Current Employment.Nowakowski:Curis:Consultancy;Kymera:Consultancy;Kite:Consultancy;Ryvu:Consultancy, Membership on an entity's Board of Directors or advisory committees;Celgene/BMS:Consultancy, Research Funding;Seattle Genetics:Consultancy;MorphoSys:Consultancy, Research Funding;NanoString: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 &lt; 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 &lt; 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.

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.