Mondello, Patrizia

Abstract Background Several therapies are recommended by NCCN/ESMO guidelines for autologous stem cell transplant (ASCT)-ineligible patients with relapsed/refractory (R/R) diffuse large B-cell lymphoma (DLBCL). In the single-arm, Phase II L-MIND study (NCT02399085), the chemotherapy-free regimen tafasitamab + lenalidomide (LEN) demonstrated efficacy for this patient population. In the absence of randomized clinical trial data, RE-MIND2 (NCT04697160), an observational, retrospective cohort study, compared patient outcomes from L-MIND with matched patient populations treated with NCCN/ESMO recommended therapies for ASCT-ineligible patients with R/R DLBCL. Methods Data were retrospectively collected between 1 April and 13 November 2020 from academic and public hospitals, as well as private practices in North America, Europe and Asia Pacific. To ensure consistency with L-MIND I/E criteria, patients aged ≥18 years with histologically confirmed DLBCL and who had received ≥2 systemic therapies for DLBCL (including ≥1 anti-CD20 therapy) were enrolled. For the main analysis, patients from the L-MIND tafasitamab + LEN cohort were matched with patients from the RE-MIND2 observational cohort using estimated propensity score-based 1:1 nearest neighbor matching, balanced for six baseline characteristics: age (<70 vs ≥70 years), refractoriness to last line of therapy (yes vs no), number of prior lines of therapy (1 vs 2/3), history of primary refractoriness (yes vs no), prior ASCT (yes vs no), and Eastern Cooperative Oncology Group performance status (0-1 vs ≥2). A sensitivity analysis was performed using an inverse probability of treatment weighting method calculating the average effect of the treatment on the treated, to generate balanced cohorts based on nine baseline characteristics: age, refractoriness to last line of therapy, number of previous lines of therapy, history of primary refractoriness, prior ASCT, Ann Arbor Stage (I/II vs III/IV), elevated LDH (yes vs no), neutropenia (yes vs no), and anemia (yes vs no). Additional sensitivity analyses accounting for missing baseline characteristics using multiple imputation technique were performed. Data are presented for tafasitamab + LEN versus polatuzumab vedotin + bendamustine + rituximab (pola-BR), rituximab + LEN (R2), and CD19 CAR-T therapies (CAR-T). The primary endpoint was overall survival (OS). Secondary endpoints included objective response rate (ORR), complete response rate, progression-free survival and duration of response. Results Of 3,454 patients enrolled from 200 sites, 106, 106, and 149 patients were treated with pola-BR, R2, and CAR-T, respectively. For the comparative analysis, matched patient pairs were created using 1:1 nearest neighbor matching with a caliper. Matched pairs consisted of: tafasitamab + LEN vs pola-BR, n=24 pairs; vs R2, n=33 pairs; and vs CAR-T, n=37 pairs. A significant OS benefit was associated with tafasitamab + LEN compared to pola-BR (HR: 0.44, 95% confidence interval [CI]: 0.20-0.96; p=0.038) and R2 (HR=0.44, 95% CI: 0.22-0.84; p=0.014) (Figure 1A-B). There was no significant difference in OS benefit between tafasitamab + LEN and CAR-T (HR=0.95, 95% CI: 0.47-1.91; p=0.891) (Figure 1C). ORR was 62.5% (15/24) for tafasitamab + LEN vs 58.3% (14/24) for pola-BR (p=1.000), 63.6% (21/33) vs 30.3% (10/33) for R2 (p=0.013), and 59.5% (22/37) vs 75.7% (28/37) for CAR-T (p=0.214). Improved outcomes were also observed with tafasitamab + LEN for other secondary endpoints (Table 1). Results are consistent with those obtained in the sensitivity analyses. Conclusions The results of this retrospective cohort analysis suggest that the novel tafasitamab + LEN combination may significantly improve health outcomes, with a prolonged survival benefit for ASCT-ineligible R/R DLBCL patients, relative to NCCN/ESMO recommended therapies. Tafasitamab + LEN improved survival outcomes compared with pola-BR and R2 in closely matched patient populations. Comparable outcomes were observed for tafasitamab + LEN vs CAR-T. Although based on limited patient numbers, these data may be clinically relevant in the context of emerging therapies for R/R DLBCL. While this study design does not replace randomized data, it remains more rigorous than inter-trial comparison. The limitations of comparing clinical trial and matched retrospective real-world data will be discussed. Funding: MorphoSys AG. Figure 1 Figure 1. Disclosures Nowakowski: Celgene, MorphoSys, Genentech, Selvita, Debiopharm Group, Kite/Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene, NanoString Technologies, MorphoSys: Research Funding. Joffe: AstraZeneca. Epizyme: Consultancy. Fleury: Abbvie, Astrazeneca, BMS, Celgene, Janssen, Kite-Gilead, Merck, Novartis, Roche, Seattle Genetics: Other: conference and advisory role. Peters: AbbVie, Incyte: Consultancy, Honoraria. Greil: Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses, Research Funding; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses, Research Funding; Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses, Research Funding; BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses, Research Funding; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses, Research Funding; AstraZeneca: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses; MSD: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses, Research Funding; Gilead: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses, Research Funding; Daiichi: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses, Research Funding; Sankyo: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Accommodations, Expenses, Research Funding; Sanofi: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Merck: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Honoraria, Other: Travel, Accommodations, Expenses, Research Funding; Sandoz: Honoraria, Research Funding. Ku: Roche: Consultancy; Antegene: Consultancy; Genor Biopharma: Consultancy. Marks: Kite/Gilead: Membership on an entity's Board of Directors or advisory committees; AbbVie: Other: Meeting attendance; Kite/Gilead: Honoraria; Merck: Consultancy. Kim: AstraZeneca: Research Funding. Zinzani: BeiGene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Bristol Myers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; EUSA Pharma: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; ADC Therapeutics: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Merck Sharp & Dohme: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celltrion: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Kyowa Kirin: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Verastem: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sandoz: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Incyte: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; TG Therapeutics Inc: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Servier: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen-Cilag: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; ImmuneDesign: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Portola: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Sanofi: Consultancy, Membership on an entity's Board of Directors or advisory committees; Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Trotman: Beigene: Research Funding; Celgene: Research Funding; Bristol Myers Squibb: Research Funding; Roche: Research Funding; Janssen: Research Funding; PCYC Pharmacyclics: Research Funding. Sabatelli: Incyte: Current Employment, Current equity holder in publicly-traded company. Huang: MorphoSys: Current Employment. Waltl: MorphoSys: Current Employment. Winderlich: MorphoSys: Current Employment, Current equity holder in publicly-traded company. Ambarkhane: MorphoSys: Current Employment, Other: Support for attending meetings/travel. Kurukulasuriya: MorphoSys: Current Employment, Current equity holder in publicly-traded company, Other: Support for attending meetings/travel. Cordoba: Pfizer: Research Funding; Kyowa-Kirin: Membership on an entity's Board of Directors or advisory committees; Roche: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; AbbVie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; ADCTherapeutics: Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Incyte: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Kite: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Hess: Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Incyte: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead/Kite: Research Funding; AstraZeneca: Honoraria, Membership on an entity's Board of Directors or advisory committees; Genmab: Honoraria, Membership on an entity's Board of Directors or advisory committees; Gilead: Honoraria, Membership on an entity's Board of Directors or advisory committees; EUSA: Honoraria, Membership on an entity's Board of Directors or advisory committees. Salles: Miltneiy: Consultancy; Novartis: Consultancy; Morphosys: Consultancy, Honoraria; Janssen: Consultancy; Genentech/Roche: Consultancy; Kite/Gilead: Consultancy; Regeneron: Consultancy, Honoraria; Velosbio: Consultancy; Takeda: Consultancy; Allogene: Consultancy; Ipsen: Consultancy; Loxo: Consultancy; Rapt: Consultancy; Genmab: Consultancy; Incyte: Consultancy; Epizyme: Consultancy, Honoraria; Debiopharm: Consultancy; BMS/Celgene: Consultancy; Beigene: Consultancy; Abbvie: Consultancy, Honoraria; Bayer: Honoraria.

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>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>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 >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 >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>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 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.

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.

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.