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Background: Follicular lymphoma (FL) shows marked variation in clinical course including spontaneous regression and histologic transformation (HT). Watchful waiting (W&W) is routinely applied to pts with newly diagnosed FL, but monitoring strategies are not standardized. Pts with progression within 1-2y of diagnosis have worse outcomes, but the biologic basis is unclear and biologic-based classifiers are not routinely applied at diagnosis. Circulating tumor DNA (ctDNA) is a highly tumor-specific biomarker that is prognostic in aggressive B-cell lymphomas, but its ability to serially monitor FL remains undefined. We applied a next-generation sequencing assay to identify tumor clonotypes for serial monitoring of peripheral blood in pts with untreated FL as part of an ongoing prospective clinical trial [NCT03190928]. Methods: Pts with grade I-II or 3A FL are eligible if evaluable disease on CT or FDG-PET, age ≥18, ECOG ≤2, no evidence of HT, and no prior systemic therapy. Pts undergo W&W until they meet uniform protocol-defined treatment criteria and remain on study until second-line therapy. Baseline testing includes labs, peripheral blood flow cytometry, BM biopsy/aspirate, CT and FDG-PET scans, and research biopsy. Pt have clinic visits every 4m for 2y, every 6m in years 3-5, then annually. CT scans are every 8m for 2y, then annually. FDG-PET scans are at baseline, at 2y, and any time of suspected progression. Peripheral blood samples including Streck tubes (plasma) and PBMCs are drawn at each clinic visit and stored. For ctDNA analysis, tumor DNA was amplified from FFPE using locus-specific primer sets for the Ig heavy-chain and light-chain loci along with BCL1/BCL2 translocations (Adaptive Biotechnologies). Amplified products were sequenced and tumor clonotypes were identified in plasma and PBMCs. Serial tracking of ctDNA was done in plasma and blinded to clinical outcomes. Results: 77 pts enrolled between July 2017 and July 2021. Median age was 57 (range 24-83) including 14 (18%) low-risk, 29 (38%) intermediate-risk, and 34 (44%) high-risk by FLIPI. Fourteen (18%) pts had stage I-II disease. Forty-three (56%) pts had monoclonal B-cells on peripheral blood flow cytometry. Twenty-nine (38%) pts progressed requiring frontline therapy including 7 (9%) pts with HT. Twenty-five (32%) pts were monitored ≥2y with no progression including 10 (13%) pts with evidence of at least some spontaneous regression by CT. Twenty (26%) pts were on study for Conclusions: ctDNA quantified from plasma in FL mirrors TMTV. Serial monitoring of ctDNA in patients without therapy demonstrated various patterns of fluctuation, including some patients in which ctDNA became undetectable coincident with spontaneous clinical regressions. ctDNA thus provides a non-invasive platform to monitor the natural history of FL, enabling future studies of tumor immune surveillance in this disease. Figure 1 Figure 1. Disclosures Jacob: Adaptive Biotechnologies: Current Employment, Current equity holder in publicly-traded company. Bagaev: BostonGene Corp.: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties: BostonGene. Meerson: BostonGene: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties: BostonGene. Postovalova: BostonGene Corp.: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties: BostonGene. Kudryashova: BostonGene: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties: BostonGene. Kotlov: BostonGene Corp: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties. Fowler: BostonGene: Current Employment, Current holder of stock options in a privately-held company.

While Epstein-Barr virus (EBV) and the Kaposi sarcoma herpesvirus (KSHV)/human herpesvirus (HHV) 8 have shown a definite association with lymphoproliferative disease, a role for the HHV-6 has been less clear. Similar to other herpesviruses, HHV-6 predominantly remains latent following initial infection, but can be reactivated during stress or immune suppression, and is the cause of roseola in young children. Existing as two distinct species, HHV-6B is more common, infecting ~90% of adults. HHV-6B, a T-lymphotropic virus, enters cells via CD134, a TNF receptor superfamily member, expressed on both naïve and CD4 +CD25 + T cells, leading to CD4 + lymphocyte depletion and impaired T cell activation. HHV-6 has been variably detected in classic Hodgkin (CHL) and T-cell lymphomas (TCL) by immunohistochemistry (IHC) and PCR with more recent data suggesting infection may be confined to tumor-associated lymphocytes. The specificity of these IHC antibodies is not well documented. The question remains whether HHV-6 in the tumor microenvironment of advanced disease is a consequence of immune dysfunction, or may play a more direct role in tumor initiation and progression by altering the tumor microenvironment. To address these questions, we evaluated HHV-6B viral gene expression patterns in lymphoma patient samples by RNA sequencing techniques. Following IRB approval, CHL, TCL, B-cell, and post-transplant lymphoproliferative disease (PTLD) cases were screened for potential HHV-6-association by IHC with an antibody against HHV-6 gp60/110 envelope glycoprotein (Millipore Sigma, MAB8537). Positive cases with available frozen tissue and adequate RNA (5) or sorted T-cell subsets from Hodgkin lymphoma (11) underwent bulk RNA-seq (rRNA depletion (Illumina), 50M reads/sample). Viral transcripts were identified by performing the Burrows-Wheeler Alignment by reference host alignment (to filter host and bad quality reads) followed by viral reference host alignment. Previous TCL databases with available RNAseq data were similarly evaluated. IHC analysis revealed 5/25 CHL, 34/52 TCL, 5/13 PTLD, 4/81 diffuse large B-cell lymphoma (DLBCL) and 2/28 follicular lymphoma (FL) with rare gp60/110-positive cells. This included 11 CHL cases with sorted T-cell subsets, of which one showed membranous and Golgi gp60/110 staining in background T-cells (25-year-old female, nodular sclerosis subtype, EBV-negative). Of these 11 CHL cases, RNAseq of T-cell subsets revealed a pattern of HHV-6B transcripts in only this case. Frozen tumor blocks were available from 5 additional cases with positive gp60/110 staining (2 CHL, 1 DLBCL, 1 FL and 1 PTLD), but RNAseq analysis did not identify any HHV-6B transcripts. Notably, these cases had dim cytoplasmic but not Golgi gp60/110 staining. RNA sequencing data derived from two independent TCL cohorts were analyzed for HHV-6B transcripts. Although no HHV-6B transcripts were detected via RNAseq in 20 angioimmunoblastic T-cell lymphoma samples from one TCL cohort, many had EBV-gene expression. HHV-6B transcripts were detected in two cases of anaplastic large cell lymphoma (ALCL) in a second TCL cohort (2/79 cases). High expression of the U67, U68, U79 and U90 genes was found, revealing overlap of the HHV-6B transcript expression between ALCL and CHL samples (Fig 1). Additionally, detection of two genes that could be driving tumor growth (U51, which encodes a G-protein receptor and U24, which inhibits proper T cell activation, reducing secretion of cytokines at infection site) demonstrates a specific viral gene expression pattern within the intratumor T-cell population. The potential presence of HHV-6B infection in the lymphoma microenvironment is controversial. To our knowledge, this is the first report conclusively demonstrating HHV-6B expression in CHL using RNAseq. Notably, the viral gene expression pattern seen in CHL overlaps with that found in two cases of ALCL, highlighting viral proteins of potential particular significance. These data may aid in development of a more reliable means of HHV-6B detection. For example, the immediate early gene U90, a transcriptional activator that may induce expression of both viral and cellular genes that affect the tumor microenvironment, was consistently expressed and may be a reliable marker of HHV-6B infection. Funding: HHV-6 Foundation Figure 1 Figure 1. Disclosures Tychinin: BostonGene Inc.: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties. Karelin: BostonGene Inc.: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties. Cherdintsev: BostonGene Inc.: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties. Kudryashova: BostonGene: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties: BostonGene. Egorov: BostonGene: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties. Degryse: BostonGene Inc.: Current Employment, Current holder of stock options in a privately-held company. Kotlov: BostonGene Corp: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties. Bagaev: BostonGene Corp.: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties: BostonGene. Roth: Merck: Consultancy; Janssen: Consultancy. Roshal: Celgene: Other: Provision of services; Physicians' Education Resource: Other: Provision of services; Auron Therapeutics: Other: Ownership / Equity interests; Provision of services. Rabadan: Genotwin: Other: Raul Rabadan is founder of Genotwin; AimedBio: Membership on an entity's Board of Directors or advisory committees. Elemento: Owkin: Consultancy, Other: Current equity holder; Freenome: Consultancy, Other: Current equity holder in a privately-held company; Volastra Therapeutics: Consultancy, Other: Current equity holder, Research Funding; One Three Biotech: Consultancy, Other: Current equity holder; Janssen: Research Funding; Eli Lilly: Research Funding; Champions Oncology: Consultancy; AstraZeneca: Research Funding; Johnson and Johnson: Research Funding.

Diagnosis of low-grade myelodysplastic syndromes (LG-MDS) is one of the most challenging in hematopathology as it relies predominantly on morphologic assessment of dysplasia. Prior studies have demonstrated poor interobserver agreement among pathologists. Histomorphological evaluation of bone marrow core biopsy samples remains the gold standard for diagnostic workup of LG-MDS, including myelodysplastic syndromes (MDS) and other myeloid neoplasms. However, this approach may be subjective, and cannot quantitatively assess subtle differences in marrow topography and the cellular microenvironment. Multiparametric in situ imaging (MISI) through various techniques enables multiple biomarker detection in a single tissue. BostonGene has developed an AI-based image analysis platform to reveal spatial information and subtle histomorphologic features in an objective, quantitative fashion. Here, we demonstrate the potential for automated AI-based imaging analysis of MISI to assist in the differentiation of LG-MDS samples from normal marrow tissues (NBM). Decalcified human bone marrow core biopsy tissues from LG-MDS (n=6) and uninvolved staging marrows (NBM, n=4) were first prepared by immunofluorescence-based MISI via staining with DAPI and CD34, CD38, CD117, CD71, CD15, and CD61 antibodies. BostonGene analyzed the resulting images (fig.1) using a proprietary AI-based imaging platform to identify cells by segmentation performed with the pre-trained instance segmentation neural network. Cell types were identified with mean marker expression values using an accelerated version of BostonGene's phenograph clustering algorithm. Pathologists manually masked fat and bone trabeculae. Using a combination of cell size/shape parameters and antigen expression levels, the following unique cell types were identified: myeloblasts, proerythroblasts, erythroid normoblasts, maturing granulocytes, megakaryocytes, mast cells, plasma cells, and B-cell precursors (hematogones). Data revealed differences in the cellular content of NBM and LG-MDS samples, and separation of LG-MDS samples with the del(5q) subtype (n=2). While linear slender islet-like small clusters of erythroid normoblasts were detected in NBM, we observed a chaotic arrangement of them in LG-MDS samples. In LG-MDS samples, we found an increase in the total number of erythroid normoblasts from 17% to 31%, LG-MDS-del(5q) had 14%. The ratio of maturing granulocytes to erythroid normoblasts (M:E ratio) was significantly lower in LG-MDS (0.63) than in NBM (1.95). The M:E ratio generated by MISI strongly correlated to the M:E ratio produced by manual differential count of bone marrow aspirate samples (R=0.83, p < 0.003). Additionally, fewer hematogones were identified in LG-MDS marrows compared to NBM samples, as reported by others using orthogonal methods. Topographic analysis showed the fat to cellular tissue area ratio was higher in NBM (0.73) than LG-MDS (0.41), but the ratio of trabecular area to total tissue area was higher in LG-MDS (1.67) than NMB (0.74). Spatially, myeloblasts and megakaryocytes were found closer to trabeculae in NBM than LG-MDS;12 different cell communities were identified;2 of them (cluster 3 - erythroid normoblasts enriched, cluster 5 - erythroid normoblasts contacting proerythroblasts) were distributed statistically significantly differently in NBM and LG-MDS samples, indicating the use of MISI with AI-based imaging to distinguish LG-MDS from NBM. Patients of MDS-del(5q) subtype differ significantly from other MDS samples and are more similar to NBM. AI-based image analysis applied to MISI of bone marrow tissue revealed multiple cell types in single tissue sections, along with histologically subtle differences in topography between NBM and LG-MDS samples. These results highlight the importance of integrating in situ tissue analysis with techniques that examine single cell characteristics for a more comprehensive picture of the differences between normal tissue and tumor samples. Coupling sophisticated imaging analytics with this imaging method may provide a more powerful tool for novel biomarker discovery of prognostic and therapeutic significance in the management of MDS and other marrow-based disorders. Figure 1 Figure 1. Disclosures Svekolkin: BostonGene Corp.: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties. Varlamova: BostonGene Corp.: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties. Galkin: BostonGene Corp.: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties. Akaeva: BostonGene Corp.: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties. Smirnova: BostonGene Corp.: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties. Ovcharov: BostonGene Corp.: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties. Polyakova: BostonGene Corp.: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties. Tabakov: BostonGene Corp.: Current Employment, Current holder of stock options in a privately-held company. Postovalova: BostonGene Corp.: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties: BostonGene. Koltakova: BostonGene Corp.: Current Employment, Current holder of stock options in a privately-held company. Gunn: BostonGene Corp.: Current Employment, Current holder of stock options in a privately-held company. Bagaev: BostonGene Corp.: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties: BostonGene.

Background: Diffuse large B-cell lymphoma (DLBCL) subtypes have differential response to BTK inhibitors (BTKi). Ibrutinib with R-CHOP improves survival in DLBCL subsets, but toxicity is limiting. Precise characterization of BTKi-responsive tumors enhances pt selection. Acalabrutinib (acala) is a BTKi with activity in DLBCL, but the molecular correlates of acala response are unknown. Circulating tumor DNA (ctDNA) is a prognostic biomarker in DLBCL including early changes during chemotherapy. PhasED-Seq is a novel ctDNA method that lowers the error profile of mutation detection by requiring the concordant detection of two separate mutations on an individual cell-free DNA molecule (Kurtz et al. Nat Biotechnol 2021). We employed a response-adapted study of acala for up to 14d prior to frontline therapy for aggressive B-cell lymphoma to determine the molecular profile of BTKi-responsive tumors. We report preliminary results including dynamic changes in ctDNA from this ongoing trial [NCT04002947]. Methods: Pts with untreated aggressive B-cell lymphoma and any HIV status are eligible if age ≥18, ≥stage 2, PS ≤2, and adequate organ function. Pts with PMBL, unmeasurable lesions, or active CNS disease are excluded. Screening includes labs, CT and FDG-PET, BM, and CSF with flow cytometry. Pts first receive acala 100mg twice daily x 14d. Pts with Results: 34 pts enrolled between August 2019 and July 2021 and completed the acala window. Median age was 64 (range 28-85) including 13 (38%) < 60y, 14 (41%) 61-69, and 7 (21%) ≥70y. Three (9%) pts had HIV and 17 (50%) were high-risk by IPI. The median diagnosis to treatment was 22.5d (4-53). IHC subtypes by Hans included 17 (50%) non-GCB, 16 (47%) GCB, and 1 (3%) T-cell/histiocyte-rich large B-cell lymphoma (TRLBCL). Four (12%) pts were high-grade B-cell lymphoma with MYC and BCL2 and/or BCL6 (HGBL-DH). Fifteen (44%) pts responded to acala during the window, while 19 (56%) pts had no response (Figure 1A). Acala responses were seen across DLBCL subtypes including 7 (47%) pts with non-GCB, 7 (47%) pts with GCB, and 1 (7%) pt with TRLBCL. Twenty pts had RNA sequencing to confirm cell-of-origin including 10 responders which included 7 (70%) GCB, 2 (20%) ABC, and 1 (10%) Unclassified. Notably, 13 (86%) BTKi-responsive tumors were CD10 negative and only 2 (18%) CD10+ tumors were BTKi-responsive. ctDNA dynamics strongly correlated with CT response as the log-fold change in ctDNA (hGE/mL) at the end of the window correlated with change on CT (r=0.75, p=0.0013). Remarkably, ctDNA dynamics after only 7d also correlated with change on CT (r=0.82, p=0.00006)(Figure 1B-C). Interestingly, one pt had improved symptoms and a 20-fold drop in ctDNA, but no corresponding CT changes suggesting that ctDNA changes may precede CT changes in some cases. Twenty-nine (85%) pts completed all planned cycles of therapy while 5 pts stopped chemotherapy early due to myelosuppression (n=3), CHF (n=1), and MI (n=1). Toxicity across 156 cycles was mostly hematologic. G3/G4 neutropenia occurred in 50% and 38% of cycles and febrile neutropenia in 10% of cycles. G3/G4 thrombocytopenia occurred in 22% and 12% of cycles. No increase in infections, atrial fibrillation, or bleeding were observed in pts treated with acala. All 27 pts who completed therapy achieved a CR. Two pts (1 acala responder) have relapsed from CR and 1 pt died of an MI. After a median follow-up of 9.2m the estimated 1-year PFS was 84.9% (95% CI: 58-95). Conclusions: Acalabrutinib prior to frontline therapy has activity in GCB, non-GCB, and HGBL-DH: confirmed by gene expression profiling. CD10+ GCB tumors are mostly acala-resistant. Toxicity is mainly hematologic and manageable across age groups including pts with HIV. ctDNA correlates with CT change and may predict response to targeted agents as early as 7 days. Updated clinical results within genetic subtypes will be presented at the meeting. Figure 1 Figure 1. Disclosures Chabon: Foresight Diagnostics: Current Employment, Current holder of individual stocks in a privately-held company, Current holder of stock options in a privately-held company. Lurain: CTI Biopharma: Research Funding; EMD-Serrono: Research Funding; Merck: Research Funding; BMS-Celgene: Research Funding; Janssen: Research Funding. Bagaev: BostonGene Corp.: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties: BostonGene. Postovalova: BostonGene Corp.: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties: BostonGene. Meerson: BostonGene: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties: BostonGene. Kudryashova: BostonGene: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties: BostonGene. Kotlov: BostonGene Corp: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties. Fowler: BostonGene: Current Employment, Current holder of stock options in a privately-held company. Kurtz: Roche: Consultancy; Foresight Diagnostics: Consultancy, Current holder of stock options in a privately-held company; Genentech: Consultancy. Alizadeh: CAPP Medical: Current holder of individual stocks in a privately-held company, Current holder of stock options in a privately-held company; Forty Seven: Current holder of individual stocks in a privately-held company, Current holder of stock options in a privately-held company; Foresight Diagnostics: Consultancy, Current holder of individual stocks in a privately-held company, Current holder of stock options in a privately-held company; Roche: Consultancy, Honoraria; Janssen Oncology: Honoraria; Celgene: Consultancy, Research Funding; Gilead: Consultancy; Bristol Myers Squibb: Research Funding; Cibermed: Consultancy, Current holder of individual stocks in a privately-held company, Current holder of stock options in a privately-held company.

Background - The tumor microenvironment (TME) plays a vital role in the growth and survival of mantle cell lymphoma (MCL) cells. However, characterization of the TME transcriptomic profile in MCL, its prognostic impact and response to Bruton's tyrosine kinase inhibitors (BTKi) is unknown. Unlike other lymphomas, the TME in MCL patients has not been fully characterized at the transcriptomic and genomic levels. To further understand the relevance of tumor-immune landscape in tissue microenvironments in the context of BTKi, we performed multi-omic profiling of the TME in tissues from MCL patients. Methods - Tissue biopsies were collected from MCL patients treated with BTKi. The study was conducted under an Institutional Review Board-approved protocol at The University of Texas MD Anderson Cancer Center. A total of 42 patients treated with BTKi were included. Among evaluable patients, DNA and RNA extraction was performed from fresh biopsies from lymph nodes and non-nodal tissues (including bone marrow). Whole exome (WES) and bulk RNA sequencing (RNA-seq) were performed to assess the somatic mutation profile, copy number abnormalities and gene expression profile to identify TME gene clusters. RNA sequencing data from an independent cohort of MCL patients from Scott et al (n = 122) was analyzed. Joint WES and RNA-seq, mutation calling, expression analysis, and cell type deconvolution from the transcriptome were performed using the BostonGene automated pipeline. Overall survival was calculated after starting BTKi therapy. Results - We obtained 42 MCL tissue samples (28 lymph nodes, 13 various tissues and one bone marrow) from patients treated with BTKi. Samples were obtained at/after starting treatment with BTKi at clinical progression. Unsupervised clustering based on the activities of the proposed transcriptomic signatures identified four distinct MCL subtypes based on tumor-immune cell gene signatures. We identified the four distinct MCL microenvironment signatures - normal lymph node like (N; n = 27), immune cell-enriched or "Hot" (IE; n = 46), mesenchymal (M; n = 44) and immune depleted/deserted or 'cold' (D; n = 51). The tumor proliferation rate signature and PI3K pathways were significantly overexpressed in immune-depleted (D) TME group. Evaluable patients were further classified based on response to BTKi as sensitive (n = 17), primary resistant (n = 11) or acquired resistant (n = 11). The TME was further dichotomized into immune cell rich and immune desert categories based on commonly involved immune cells and pathways. BTKi resistant MCL primarily exhibited immune depleted TME subtype. To explore the somatic mutation profile in relation to TME clusters, we performed a multiomic analysis combining WES data with RNA sequencing data and depicted according to the four TME clusters. Somatic mutations in TP53, NSD2, NOTCH1, KMT2D, SMARCA4, which were previously reported in ibrutinib-resistant MCL and/or in refractory high-risk MCL patients, were predominant in the immune-depleted TME cluster (D). Conclusions - Overall, we defined BTKi sensitivity and resistance by immune-hot and immune-cold TME portraits, respectively. The immune-depleted TME subtype (D) was characterized by dominant proliferation gene signature, overexpressed PI3K pathway, BTKi resistance and poor outcomes in MCL patients. Disclosures Jain: Lilly: Consultancy; kite: Consultancy. Nomie: BostonGene, Corp: Current Employment, Current holder of stock options in a privately-held company. Segodin: boston gene: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties. Egorov: BostonGene: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties. Kotlov: BostonGene Corp: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties. Vega: CRISPR Therapeutics and Geron: Research Funding; i3Health, Elsevier, America Registry of Pathology, Congressionally Directed Medical Research Program, and the Society of Hematology Oncology: Research Funding. Svekolkin: BostonGene Corp.: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties. Bagaev: BostonGene Corp.: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties: BostonGene. Frenkel: boston gene: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties. Attaulakhanov: boston gene: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties. Fowler: BostonGene, Corp: Current Employment, Current holder of stock options in a privately-held company; Bristol Myers Squibb, F. Hoffmann-La Roche Ltd, TG Therapeutics and Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding. Flowers: Sanofi: Research Funding; Amgen: Research Funding; EMD: Research Funding; Iovance: Research Funding; Janssen: Research Funding; Cancer Prevention and Research Institute of Texas: CPRIT Scholar in Cancer Research: Research Funding; Bayer: Consultancy, Research Funding; BeiGene: Consultancy; Pfizer: Research Funding; Celgene: Consultancy, Research Funding; Denovo: Consultancy; Novartis: Research Funding; Nektar: Research Funding; Epizyme, Inc.: Consultancy; Morphosys: Research Funding; Genmab: Consultancy; AbbVie: Consultancy, Research Funding; Takeda: Research Funding; TG Therapeutics: Research Funding; Xencor: Research Funding; Ziopharm: Research Funding; Burroughs Wellcome Fund: Research Funding; Eastern Cooperative Oncology Group: Research Funding; National Cancer Institute: Research Funding; Biopharma: Consultancy; Pharmacyclics/Janssen: Consultancy; Kite: Research Funding; Guardant: Research Funding; SeaGen: Consultancy; Cellectis: Research Funding; Karyopharm: Consultancy; Gilead: Consultancy, Research Funding; Genentech/Roche: Consultancy, Research Funding; Allogene: Research Funding; Adaptimmune: Research Funding; Spectrum: Consultancy; Acerta: Research Funding; 4D: Research Funding; Pharmacyclics: Research Funding. Wang: BGICS: Honoraria; Newbridge Pharmaceuticals: Honoraria; BioInvent: Research Funding; VelosBio: Consultancy, Research Funding; Juno: Consultancy, Research Funding; InnoCare: Consultancy, Research Funding; Hebei Cancer Prevention Federation: Honoraria; Janssen: Consultancy, Honoraria, Research Funding; Pharmacyclics: Consultancy, Research Funding; Mumbai Hematology Group: Honoraria; Scripps: Honoraria; The First Afflicted Hospital of Zhejiang University: Honoraria; Loxo Oncology: Consultancy, Research Funding; Moffit Cancer Center: Honoraria; Lilly: Research Funding; Bayer Healthcare: Consultancy; OMI: Honoraria; Imedex: Honoraria; Epizyme: Consultancy, Honoraria; Celgene: Research Funding; Physicians Education Resources (PER): Honoraria; Miltenyi Biomedicine GmbH: Consultancy, Honoraria; Kite Pharma: Consultancy, Honoraria, Research Funding; Chinese Medical Association: Honoraria; Clinical Care Options: Honoraria; Dava Oncology: Honoraria; CStone: Consultancy; DTRM Biopharma (Cayman) Limited: Consultancy; Genentech: Consultancy; Oncternal: Consultancy, Research Funding; Molecular Templates: Research Funding; CAHON: Honoraria; BeiGene: Consultancy, Honoraria, Research Funding; AstraZeneca: Consultancy, Honoraria, Research Funding; Anticancer Association: Honoraria; Acerta Pharma: Consultancy, Honoraria, Research Funding.

BACKGROUND: Follicular lymphoma (FL) is an indolent malignancy of germinal center B-cell origin. FL patients experience remarkable heterogeneity in their disease trajectory, with many patients slowly progressing over several years, and a subset of patients experiencing an aggressive clinical course. Uncovering the cell-intrinsic and -extrinsic factors that govern differential progression and outcome in FL patients is thus essential. Beyond the genetic and epigenetic aberrations that contribute to FL oncogenesis, the tumor microenvironment (TME) plays an integral role in supporting the proliferation and survival of malignant cells. In a process described as "re-education", FL tumor cells may actively subvert the normal functions of non-malignant cells present in the TME, including T cells, follicular dendritic cells (FDCs), macrophages, dendritic cells, and stromal cells to support their survival and growth. METHODS: To address the importance of the TME in FL, we molecularly profiled excisional lymph node biopies from untreated FL patients using multiple platforms: bulk RNA sequencing (RNAseq), single-cell RNA sequencing (scRS), and a unique high content imaging method, Iterative Bleaching Extends MultipleXity (IBEX), which utilizes chemical bleaching to image 40+ proteins in the same tissue section by antibody staining. In combination with advanced computational tools for the quantitative analysis of cell types and distribution in tissues, we have used this approach to evaluate the TME:FL interaction within 8 FL samples, with 4 normal lymph nodes as controls. RESULTS: Both FL and TME components reconstructed from bulk RNA-seq were similar to the cellular composition revealed by scRS and IBEX analyses. Moreover, the bulk RNAseq and scRS identified the expression of genes involved in tumorigenesis and oncogenic signaling, often unique to each case. However, RNAseq-based approaches often miss important cellular and acellular components not readily extracted from dense tissues. For example, IBEX imaging can trace the pattern of blood vessels within a section, which cannot be achieved with non-imaging methods. In one case, our multi-parameter imaging studies revealed the close spatial interaction between clonal FL B cells, expressing a B-cell receptor (BCR) possessing a de novo N-linked glycosylation site introduced by somatic hypermutation, and cells expressing dendritic cell-specific intercellular adhesion molecule-3-grabbing non-integrin (DC-SIGN). This contact may activate pro-survival signaling in the malignant B cells. CONCLUSIONS: Integration of bulk RNAseq, scRS, clonotype analysis, and IBEX reveals both shared and unique aspects of different FL tumors. These data highlight the importance of integrating direct tissue analysis by high-content imaging with methods examining aspects of isolated cells. This approach may provide a more complete understanding of tumor biology, which in turn will identify patients at risk for developing aggressive disease and rationally improve treatment strategies for FL. This research was supported in part by the Intramural Research Program of the NIH, NIAID and NCI Figure Disclosures Bagaev: BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Plotnikova:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Galkin:BostonGene: Current Employment, Patents & Royalties. Postovalova:BostonGene: Current Employment, Current equity holder in private company. Svekolkin:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Isaev:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Lozinsky:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Meerson:BostonGene: Current Employment. Varlamova:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Ovcharov:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Polyakova:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Nomie:BostonGene: Current Employment, Current equity holder in private company. Kotlov:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Tsiper:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Frenkel:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Attaulakhanov:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Fowler:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties.

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.

Research in DLBCL pathogenesis has largely focused on the lymphoma cells that defined molecular subtypes. To elucidate the role of the lymphoma microenvironment (LME) in this process, we developed and deconvoluted transcriptomics signatures of LME cells and pathways from 3,026 DLBCLs from 13 datasets including our new cohort of 127 pts. Mutations were available for 562 pts of the datasets and for 22 pts from our cohort (WES with matched normal). Applying density-based clustering we identified 4 LME signatures, independent of reported transcriptional and genetic classifications based on lymphoma cells: LME-1 DLBCLs (n=726, 24% - GCB/ABC: 35%/36%) were characterized by an "immunosuppressive" ME enriched for Tregs, myeloid-derived suppressor cells, CD8PD1high, natural killer and macrophages type 2 and prevalence of genetic mechanisms of immune escape in malignant cells such as mutations in B2M (33%) and CD70 (10%). Malignant cells in LME-1 DLBCL presented high activity of NF-kB and JAK/STAT signaling pathways, likely due to high frequency of co-occurring MYD88L265and CD79B mutations (40%) and the presence of a cytokine rich milieu including high expression of IL10, IL6 and TNFS13B. Good outcome genetic groups BN2 and EZB constituted 46% of pts LME-2 DLBCLs (n=484, 16% - GCB/ABC: 55%/30%) were characterized by an "anti-tumor immunity" ME enriched for T cells, follicular TH and follicular dendritic cells (FDC). Lymphoma cells presented the highest number of BCL2 translocations and EZH2 mutations (40%, p=0.001 vs. other LME groups) and activation of cell motility and chemotaxis pathways that likely account for the advance stage at presentation (70% were stages III/IV p=0.02 vs. 40-50% in the other LME groups). Lymphoma cells expressed higher levels of CCL20, CCR6 and CXCR5. Good outcome genetic groups BN2 and EZB constituted 74% of pts. Notably, ABC LME-2 DLBCLs had better prognosis that any other ABC DLBCL (p LME-3 DLBCLs (n=847, 28% - GCB/ABC: 56%/25%) were characterized by a "mesenchymal" ME enriched for cancer-associated fibroblasts (CAFs), reticular DC, FDC and endothelial cells. Lymphoma cells showed higher mutations in BCR/Pi3K signaling intermediates SGK1 and GNA13 (20 and 13%) and activation of the TGFB signaling and matrix remodeling pathways. LME-3 DLBCLs expressed higher levels of MMP9, MMP2, TIMP1 and TIMP2. Good outcome genetic groups BN2 and EZB constituted 75% of pts, and were no patients harboring NOTCH1 mutations. LME-3 DLBCL also had the higher proportion of non-cellular LME component represented by the extracellular matrix (ECM). Patients with DLBCL with higher ECM proportion had better progression free survival (p=0.0004). We specifically tested the ECM effect in a murine DLBCL model by analyzing changes in ECM proteins associated with disease progression by serial proteomics. DLBCL progression was accompanied by increase in lymphoma cells in detriment of CAFs and ECM proteins. Among them, we identified the small proteoglycan decorin (DCN). Accordingly, parental administration of recombinant DCN to DLBCL mice (n=10 vs vehicle n=10) decreased tumor volume (p LME-4 DLBCLs (n=969, 32% - GCB/ABC: 46%/38%) were characterized by a "depleted" ME with increased proportion of lymphoma cells with mutations in MYD88, PIM1 and HLA-C (36, 38 and 13%), higher genomic instability and epigenetic heterogeneity (by DNA methylation). Good outcome genetic groups BN2 and EZB constituted 68% of pts. Lymphoma cells showed activation of Pi3K signaling and hypermethylation and low expression of the TGFB mediator SMAD1 (p In sum, ME signatures in DLBCL associate with clinical outcomes independently of existing molecular subtypes, contribute to explain DLBCL biology and provide potential novel therapies Disclosures Rutherford: Karyopharm: Honoraria, Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Consultancy, Honoraria; Verastem: Consultancy, Honoraria; AstraZeneca: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Heron: Consultancy, Honoraria; Janssen Scientific Affairs: Consultancy, Honoraria; Juno Therapeutics Inc: Consultancy, Honoraria. Martin:Celgene: Consultancy; Teneobio: Consultancy; Sandoz: Consultancy; I-MAB: Consultancy; Karyopharm: Consultancy; Janssen: Consultancy. Leonard:Nordic Nanovector: Consultancy; Nordic Nanovector: Consultancy; Miltenyi: Consultancy; MorphoSys: Consultancy; Merck: Consultancy; Miltenyi: Consultancy; Sandoz: Consultancy; Epizyme, Inc: Consultancy; AstraZeneca: Consultancy; AstraZeneca: Consultancy; Bayer Corporation: Consultancy; Bayer Corporation: Consultancy; ADC Therapeutics: Consultancy; Gilead: Consultancy; Karyopharm Therapeutics: Consultancy; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy; Sutro Biopharma: Consultancy; Celgene: Consultancy; Gilead: Consultancy; BeiGene: Consultancy; Akcea Therapeutics: Consultancy; Sandoz: Consultancy; Akcea Therapeutics: Consultancy; BeiGene: Consultancy; MorphoSys: Consultancy; Sutro Biopharma: Consultancy; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy; ADC Therapeutics: Consultancy; Celgene: Consultancy; Epizyme, Inc: Consultancy; Karyopharm Therapeutics: Consultancy; Merck: Consultancy.

Introduction The tumor microenvironment of mantle cell lymphoma (MCL), an aggressive and incurable subtype of B-cell lymphoma, is dynamic and complex. The MCL microenvironment provides a niche for the tumor by promoting survival, therapeutic resistance, and immune evasion. Although the intrinsic mechanisms underlying MCL pathogenesis have been well-studied, as demonstrated by our understanding of the important roles that B-cell receptor signaling, the PI3K/AKT/mTOR axis, and OXPHOS play in MCL survival and the development of therapeutic resistance, the extrinsic mechanisms regulated by the lymphoma microenvironment are less well-known. Methods Whole exome sequencing (WES; n = 42) and RNA-seq (n = 76) were performed on fresh peripheral blood or apheresis patient primary samples with an extremely high MCL tumor percentage as determined by flow cytometry versus biopsies with a more diverse cellular mixture, including MCL tumor microenvironment components such as macrophages, T-cells, NK cells, and monocytes. Our previously published MCL cohort was also analyzed in this study (Zhang et al., Science Translational Medicine, 2019). Joint WES and RNAseq mutation calling, expression analysis, and cell type deconvolution from the transcriptome were performed using the BostonGene automated pipeline. Results To characterize the cellular composition and functional state of the MCL tumor microenvironment as well as tumor properties, we created 26 separate molecular signatures related to various functional processes such as anti-tumor immune infiltration, immune checkpoint inhibition, immunosuppression, and stromal compartment represented by angiogenesis and mesenchymal stromal cells. We also utilized these 26 immune and stromal signatures in conjunction with PROGENy (Pathway RespOnsive GENes) analysis to create a signature-based model associated with sensitivity or resistance to the Bruton's tyrosine kinase (BTK) inhibitor ibrutinib. In this model, increased T-cell, NK cell, and B-cell processes, in addition to p53 pathway activity, were associated with sensitivity to ibrutinib, demonstrating that the tumor microenvironment plays a critical role in the MCL response to this widely used FDA-approved agent. Moreover, initial analysis only identified one BTKC481S mutation in an ibrutinib-resistant MCL sample, again demonstrating that mutations in BTK are rare in ibrutinib-resistant MCL and that diverse mechanisms underlie the development of this resistance. For more in-depth analysis, we performed concurrent analysis of only the biopsy samples in conjunction with an additional previously published cohort (n = 123; Scott et al., JCO, 2017). Unsupervised clustering based on the activities of the proposed signatures produced 4 MCL types as follows: immune infiltration combined with increased stromal signatures (type MCL-A), high immune and checkpoint molecules expression with low stromal expression (type MCL-B), non-immune with increased stromal signature and tumor-promoting cytokines (type MCL-C), and lacking immune infiltration and stromal expression with highest content of malignant B cells (type MCL-D). Interestingly, the ibrutinib-resistant MCL samples primarily belonged to the MCL-C subtype (80%), whereas most of the ibrutinib-sensitive samples (70%) were assigned to subtype MCL-B (Chi-square test p-value = 0.01), which is prominent in anti-tumor immune infiltration without tumor-promoting stromal context, suggesting that ibrutinib may promote immune microenvironment effective action against MCL or work more effectively in an active immune environment. Conclusion The identified enriched immune cell signatures suggest that MCL cells may be sensitive to specific and novel immune checkpoint inhibitors and other immune activators. Ibrutinib sensitivity and resistance are clearly separated based on their tumor microenvironment portraits, suggesting that the tumor microenvironment has a prominent role in regulating ibrutinib activity and response. Furthermore, ibrutinib may alter the tumor microenvironment to promote anti-tumor activity, which requires further investigation. Disclosures Wang: MoreHealth: Consultancy, Equity Ownership; Celgene: Consultancy, Research Funding; Juno Therapeutics: Research Funding; Kite Pharma: Consultancy, Research Funding; Dava Oncology: Honoraria; Pharmacyclics: Consultancy, Honoraria, Research Funding; Acerta Pharma: Consultancy, Honoraria, Research Funding; Aviara: Research Funding; BeiGene: Research Funding; BioInvent: Consultancy, Research Funding; VelosBio: Research Funding; Pulse Biosciences: Consultancy; Loxo Oncology: Research Funding; Janssen: Consultancy, Honoraria, Research Funding, Speakers Bureau; AstraZeneca: Consultancy, Honoraria, Research Funding, Speakers Bureau.