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Diffuse large B-cell lymphoma (DLBCL) is a biologically and clinically heterogeneous disease. Transcriptomic and genetic characterization of DLBCL has increased the understanding of its intrinsic pathogenesis and provided potential therapeutic targets. However, the role of the microenvironment in DLBCL biology remains less understood. Here, we performed a transcriptomic analysis of the microenvironment of 4,655 DLBCLs from multiple independent cohorts and described four major lymphoma microenvironment categories that associate with distinct biological aberrations and clinical behavior. We also found evidence of genetic and epigenetic mechanisms deployed by cancer cells to evade microenvironmental constraints of lymphoma growth, supporting the rationale for implementing DNA hypomethylating agents in selected patients with DLBCL. In addition, our work uncovered new therapeutic vulnerabilities in the biochemical composition of the extracellular matrix that were exploited to decrease DLBCL proliferation in preclinical models. This novel classification provides a road map for the biological characterization and therapeutic exploitation of the DLBCL microenvironment.Significance:In a translational relevant transcriptomic-based classification, we characterized the microenvironment as a critical component of the B-cell lymphoma biology and associated it with the DLBCL clinical behavior establishing a novel opportunity for targeting therapies.This article is highlighted in the In This Issue feature, p. 1307

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Severe and prolonged lymphopenia frequently occurs in patients with glioblastoma after standard chemoradiotherapy and has been associated with worse survival, but its underlying biological mechanism is not well understood. To address this, we performed a correlative study in which we collected and analyzed peripheral blood of patients with glioblastoma ( n = 20) receiving chemoradiotherapy using genomic and immune monitoring technologies. RNA sequencing analysis of the peripheral blood mononuclear cells (PBMC) showed an elevated concentration of myeloid-derived suppressor cell (MDSC) regulatory genes in patients with lymphopenia when compared with patients without lymphopenia after chemoradiotherapy. Additional analysis including flow cytometry and single-cell RNA sequencing further confirmed increased numbers of circulating MDSC in patients with lymphopenia when compared with patients without lymphopenia after chemoradiotherapy. Preclinical murine models were also established and demonstrated a causal relationship between radiation-induced MDSC and systemic lymphopenia using transfusion and depletion experiments. Pharmacological inhibition of MDSC using an arginase-1 inhibitor (CB1158) or phosphodiesterase-5 inhibitor (tadalafil) during radiation therapy (RT) successfully abrogated radiation-induced lymphopenia and improved survival in the preclinical models. CB1158 and tadalafil are promising drugs in reducing radiation-induced lymphopenia in patients with glioblastoma. These results demonstrate the promise of using these classes of drugs to reduce treatment-related lymphopenia and immunosuppression.

Human leukocyte antigen class I (HLA-I) molecules bind and present peptides at the cell surface to facilitate the induction of appropriate CD8+ T cell-mediated immune responses to pathogen- and self-derived proteins. The HLA-I peptide-binding cleft contains dominant anchor sites in the B and F pockets that interact primarily with amino acids at peptide position 2 and the C-terminus, respectively. Nonpocket peptide–HLA interactions also contribute to peptide binding and stability, but these secondary interactions are thought to be unique to individual HLA allotypes or to specific peptide antigens. Here, we show that two positively charged residues located near the top of peptide-binding cleft facilitate interactions with negatively charged residues at position 4 of presented peptides, which occur at elevated frequencies across most HLA-I allotypes. Loss of these interactions was shown to impair HLA-I/peptide binding and complex stability, as demonstrated by both in vitro and in silico experiments. Furthermore, mutation of these Arginine-65 (R65) and/or Lysine-66 (K66) residues in HLA-A*02:01 and A*24:02 significantly reduced HLA-I cell surface expression while also reducing the diversity of the presented peptide repertoire by up to 5-fold. The impact of the R65 mutation demonstrates that nonpocket HLA-I/peptide interactions can constitute anchor motifs that exert an unexpectedly broad influence on HLA-I-mediated antigen presentation. These findings provide fundamental insights into peptide antigen binding that could broadly inform epitope discovery in the context of viral vaccine development and cancer immunotherapy.

Personalized cancer vaccines designed to target neoantigens represent a promising new treatment paradigm in oncology. In contrast to classical idiotype vaccines, we hypothesized that ‘polyvalent’ vaccines could be engineered for the personalized treatment of follicular lymphoma (FL) using neoantigen discovery by combined whole exome sequencing (WES) and RNA sequencing (RNA-Seq). Fifty-eight tumor samples from 57 patients with FL underwent WES and RNA-Seq. Somatic and B-cell clonotype neoantigens were predicted and filtered to identify high-quality neoantigens. B-cell clonality was determined by alignment of B-cell receptor (BCR) CDR3 regions from RNA-Seq data, grouping at the protein level, and comparison to the BCR repertoire of RNA-Seq data from healthy individuals. An average of 52 somatic mutations per patient (range: 2-172) were identified, and two or more (median: 15) high-quality neoantigens were predicted for 56 of 58 samples. The predicted neoantigen peptides were composed of missense mutations (76%), indels (9%), gene fusions (3%), and BCR sequences (11%). Building off of these preclinical analyses, we initiated a pilot clinical trial using personalized neoantigen vaccination combined with PD-1 blockade in patients with relapsed or refractory FL (#NCT03121677). Synthetic long peptide (SLP) vaccines were successfully synthesized for and administered to all four patients enrolled to date. Initial results demonstrate feasibility, safety, and potential immunologic and clinical responses. Our study suggests that a genomics-driven personalized cancer vaccine strategy is feasible for patients with FL, and this may overcome prior challenges in the field.

BackgroundNeoantigen (NeoAg) peptides displayed at the tumor cell surface by human leukocyte antigen molecules show exquisite tumor specificity and can elicit T cell mediated tumor rejection. However, few NeoAgs are predicted to be shared between patients, and none to date have demonstrated therapeutic value in the context of vaccination.MethodsWe report here a phase I trial of personalized NeoAg peptide vaccination (PPV) of 24 stage III/IV non-small cell lung cancer (NSCLC) patients who had previously progressed following multiple conventional therapies, including surgery, radiation, chemotherapy, and tyrosine kinase inhibitors (TKIs). Primary endpoints of the trial evaluated feasibility, tolerability, and safety of the personalized vaccination approach, and secondary trial endpoints assessed tumor-specific immune reactivity and clinical responses. Of the 16 patients with epidermal growth factor receptor (EGFR) mutations, nine continued TKI therapy concurrent with PPV and seven patients received PPV alone.ResultsOut of 29 patients enrolled in the trial, 24 were immunized with personalized NeoAg peptides. Aside from transient rash, fatigue and/or fever observed in three patients, no other treatment-related adverse events were observed. Median progression-free survival and overall survival of the 24 vaccinated patients were 6.0 and 8.9 months, respectively. Within 3–4 months following initiation of PPV, seven RECIST-based objective clinical responses including one complete response were observed. Notably, all seven clinical responders had EGFR-mutated tumors, including four patients that had continued TKI therapy concurrently with PPV. Immune monitoring showed that five of the seven responding patients demonstrated vaccine-induced T cell responses against EGFR NeoAg peptides. Furthermore, two highly shared EGFR mutations (L858R and T790M) were shown to be immunogenic in four of the responding patients, all of whom demonstrated increases in peripheral blood neoantigen-specific CD8+ T cell frequencies during the course of PPV.ConclusionsThese results show that personalized NeoAg vaccination is feasible and safe for advanced-stage NSCLC patients. The clinical and immune responses observed following PPV suggest that EGFR mutations constitute shared, immunogenic neoantigens with promising immunotherapeutic potential for large subsets of NSCLC patients. Furthermore, PPV with concurrent EGFR inhibitor therapy was well tolerated and may have contributed to the induction of PPV-induced T cell responses.

4591 Background: UC is associated with high recurrence rates, progression, and resistance to platinum-based therapy. Checkpoint inhibitors (CPIs) are often used for treating UC, but predictive biomarkers that characterize response are lacking in the majority of patients. Defining the TME is essential to understanding patient response to CPIs. Employing a transcriptome-based classification platform, we sought to identify predictive and prognostic subtypes of UC using malignant cell and TME features. Methods: We collected a metacohort of 2,418 UC samples from 14 publicly available datasets, one of which had atezolizumab response data (IMvigor210). Using the methodology described in Bagaev et al. 2021, we selected 28 signatures composed of specific gene sets reflecting distinct cellular processes. Analysis of signature expression and unsupervised clustering was performed. Results: We identified 7 recurring novel UC subtypes (Table 1) with unique genomic and molecular characteristics. The subtypes and key findings include: an immune desert (D) subtype characterized by genomic instability high HER2 expression; an immune desert, FGFR-altered (D-FGFR) subtype with FGFR alterations; an immune enriched (IE) subtype with an enriched TME and high CPI response; a fibrotic (F) subtype with a mesenchymal TME, and strong TGFβ signaling; an immune enriched, fibrotic (IE-F) subtype with a mixed TME and a high CPI response rate; a fibrotic, basal (F-B) subtype with a mesenchymal TME and minimal genomic targets, and a neuroendocrine-like (NE-L) subtype with a high CPI response rate. Conclusions: UC can be classified into 7 subtypes with distinct prognoses, CPI response rates, and druggable targets using malignant cell and TME profiling. Patients with IE, IE-F, and NE-L UCs may be good candidates for CPIs. D UCs may benefit from HER2-i and PARP-i, while FGFR-i might be more suitable for D-FGFR UCs. TGFβi and PARPi may be effective for F UCs, but F-B UCs have no targetable findings. These findings warrant additional investigation for clinical translation. [Table: see text]

Purpose:We examined cabazitaxel, a novel next-generation taxoid, in patients with metastatic gastric cancer in a multicenter phase II study.Patients and Methods:Patients who have progressed on one or more prior therapies for locally advanced, unresectable, or metastatic disease were eligible, and prior taxane therapy was allowed. Taxane-naïve and pretreated cohorts were analyzed independently for efficacy. The primary endpoint for both cohorts was progression-free survival (PFS) using RECIST 1.1, using a Simon's two-stage design (10% significance and 80% power) for both cohorts. Comprehensive molecular annotation included whole exome and bulk RNA sequencing.Results:Fifty-three patients enrolled in the taxane-naïve cohort (Arm A) and 23 patients in the prior-taxane cohort (Arm B), from January 8, 2013, to April 8, 2015: median age 61.7 years (range, 35.5–91.8 years), 66% male, 66% Caucasian. The most common adverse events included neutropenia (17% Arm A and 39% Arm B), fatigue/muscle weakness (13%), and hematuria (12%). In Arm A, the 3-month PFS rate was 28% [95% confidence interval (CI), 17%–42%] and did not meet the prespecified efficacy target. The 3-month PFS rate in Arm B was 35% (95% CI, 16%–57%) and surpassed its efficacy target. HER2 amplification or overexpression was associated with improved disease control (P = 0.003), PFS (P = 0.04), and overall survival (P = 0.002). An M2 macrophage signature was also associated with improved survival (P = 0.031).Conclusions:Cabazitaxel has modest activity in advanced gastric cancer, including in patients previously treated with taxanes. Her2 amplification/overexpression and M2 high macrophage signature are potential biomarkers for taxane efficacy that warrant further evaluation.

Diffuse large B-cell lymphoma (DLBCL) is a biologically and clinically heterogeneous disease. Transcriptomic and genetic characterization of DLBCL has increased the understanding of its intrinsic pathogenesis and provided potential therapeutic targets. However, the role of the microenvironment in DLBCL biology remains less understood. Here, we performed a transcriptomic analysis of the microenvironment of 4,655 DLBCLs from multiple independent cohorts and described four major lymphoma microenvironment categories that associate with distinct biological aberrations and clinical behavior. We also found evidence of genetic and epigenetic mechanisms deployed by cancer cells to evade microenvironmental constraints of lymphoma growth, supporting the rationale for implementing DNA hypomethylating agents in selected patients with DLBCL. In addition, our work uncovered new therapeutic vulnerabilities in the biochemical composition of the extracellular matrix that were exploited to decrease DLBCL proliferation in preclinical models. This novel classification provides a road map for the biological characterization and therapeutic exploitation of the DLBCL microenvironment.Significance:In a translational relevant transcriptomic-based classification, we characterized the microenvironment as a critical component of the B-cell lymphoma biology and associated it with the DLBCL clinical behavior establishing a novel opportunity for targeting therapies.This article is highlighted in the In This Issue feature, p. 1307

The clinical use of molecular targeted therapy is rapidly evolving but has primarily focused on genomic alterations. Transcriptomic analysis offers an opportunity to dissect the complexity of tumors, including the tumor microenvironment (TME), a crucial mediator of cancer progression and therapeutic outcome. TME classification by transcriptomic analysis of >10,000 cancer patients identifies four distinct TME subtypes conserved across 20 different cancers. The TME subtypes correlate with patient response to immunotherapy in multiple cancers, with patients possessing immune-favorable TME subtypes benefiting the most from immunotherapy. Thus, the TME subtypes act as a generalized immunotherapy biomarker across many cancer types due to the inclusion of malignant and microenvironment components. A visual tool integrating transcriptomic and genomic data provides a global tumor portrait, describing the tumor framework, mutational load, immune composition, anti-tumor immunity, and immunosuppressive escape mechanisms. Integrative analyses plus visualization may aid in biomarker discovery and the personalization of therapeutic regimens.

589 Background: Spatiotemporal heterogeneity, paucity of actionable targets, and complexity of the tumor microenvironment (TME) are major barriers to therapeutic advances in pancreatic ductal adenocarcinoma (PDAC). We reconstructed the transcriptomic data from a heterogeneous cohort of PDAC patients (pts) to examine the TME and identify putative therapeutic strategies. Methods: Transcriptomic profiling and targeted gene sequencing data (Tempus) on primary or metastatic specimens from PDAC pts treated at the Medical College of Wisconsin (MCW) between 2015-2020 were analyzed. Mutation calling, expression analysis, cell type deconvolution from the transcriptome, and TME reconstruction were performed using BostonGene’s automated pipelines. Mann-Whitney U test and Fisher's exact test were used to assess statistical significance. Results: The cohort (N = 79) comprised of resectable (19%), borderline resectable (37%), locally advanced (24%) and metastatic (20%) PDAC pts. The most frequently used tumor sites for transcriptomic profiling were pancreas primary (59%), liver (16%), lung (10%) and peritoneum (10%). Four distinct subtypes were identified based on the BostonGene classification of the transcriptomic TME– Immune Enriched (IE; 14%), Fibrotic (F; 28%), Immune Enriched & Fibrotic (IEF; 36%), and Immune Depleted (ID; 22%). Analyses of the cellular composition of the TME subtypes with RNA-seq-based deconvolution showed that T-cell fractions (CD4, CD8) were higher in the IE/IEF subtypes compared to the F/ID subtypes (CD8 means: 6.4% vs 2.9%, p < 0.001; CD4 means: 15.1% vs. 7.6%, p < 0.001), while fibroblast content was higher in the F/IEF subtypes compared to the IE/ID subtypes (37.4% vs 18.4%; p < 0.001). KRAS wild-type (WT) tumors were enriched in the IEF subtype (58%), while KRAS mutated tumors comprised all four transcriptomic subtypes. Primary PDACs that underwent radiotherapy were significantly more enriched in fibroblasts compared to samples from the TCGA cohort that did not undergo radiotherapy (means: 30%(MCW) vs. 20% (TCGA), p < 0.001). Primary PDACs were enriched in the IEF subtype (46%), while liver and lung metastases were enriched in the ID (74%) and IE subtypes (70%), respectively. When pts were dichotomized to short (< 400 days) versus long (> 800 days) survivors, tumors from pts with longer survival demonstrated a trend towards enrichment in CD4/CD8 T cells and IE subtype that did not meet statistical significance. Conclusions: Lung metastases and KRAS WT PDACs harbor an immunogenic TME while liver metastases harbor an immune-cold TME, highlighting the biologic heterogeneity of PDAC. The efficacy of immunotherapeutic strategies in PDAC pts who demonstrate an IE/IEF transcriptomic subtype merits prospective evaluation. The four distinct subtypes identified by TME transcriptomic classification highlight the possibility of personalized immunotherapeutic strategies in PDAC.

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.

The emergence of immune checkpoint inhibitors (ICI) have revolutionized treatment of many cancer types, including renal cell carcinoma (RCC). Although there are currently three ICI combination regimens approved as standard of care for the first-line treatment of advanced RCC, the utility of PD-L1 detected by immunohistochemistry (IHC) as a predictive biomarker remains somewhat controversial. In this study, we sought to determine the ability to detect PD-L1 expression accurately and sensitively in comparison with IHC as well as whether RNA-seq-based cellular deconvolution potentially correlates with PD-L1 IHC for the development of a deconvolution-based biomarker. Tumors from 103 clear cell RCC (ccRCC) patients were collected to compare sensitivity and clinical utility of RNA-seq and IHC in PD-L1 detection. Additionally, we used gene expression signatures to comprehensively characterize the ccRCC tumor microenvironments (TMEs) from RNA-seq to examine PD-L1 expression levels across varied TMEs. This classification system revealed that the ccRCC samples clustered into an immune-enriched microenvironment with high lymphocyte infiltration and an immune desert microenvironment predominantly composed of malignant cells. For RNA-seq-based cellular deconvolution, a unique and robust machine learning algorithm, named Kassandra, was developed using over 8,000 RNA profiles of sorted cells that outperformed all current cellular deconvolution algorithms. The RNA-seq of ccRCC clinical tumors (n =89) were analyzed by Kassandra. Our RNA-seq analysis identified ccRCC tumors with high (maximum), intermediate and low (minimum) PD-L1 expression, which were then validated with IHC. Among these, 5/5, 10/10 and 87/88 ccRCC tumors with high, medium and low expression, respectively, detected via RNA-seq matched with the PD-L1 levels detected with IHC, suggesting a high degree of concordance (p < 0.01) between the two methods. Notably, the immune-enriched ccRCC clusters had a higher percentage of PD-L1-positive malignant and immune cells. PD1+ CD8 T cells, identified via RNAseq-based cellular deconvolution (Kassandra), most strongly correlated with PD-L1 IHC (p < 0.05). PD-L1 status was predicted with an 0.82 AUC performance score using a regression model. In conclusion, our results indicated that PD-L1 expression detected by RNA-seq as well as the presence of PD1+ CD8 T cells determined by applying Kassandra strongly correlated with the PD-L1 values obtained via IHC, potentially providing a less costly and more robust alternative to the conventional methods. In addition, an increased number of PD-L1-positive tumor and immune cells within the immune-enriched TME may suggest the significance of the TME composition as a predictive factor in response to ICIs. Citation Format: Natalia Miheecheva, Maria Sorokina, Akshaya Ramachandran, Yang Lyu, Danil Stupichev, Alexander Bagaev, Ekaterina Postovalova, Krystle Nomie, Felix Frenkel, Maria Tsiper, Nathan Fowler, Ravshan Ataullakhanov, James J. Hsieh. Evaluating the clinical utility of RNA-seq-based PD-L1 expression and cellular deconvolution as alternatives to conventional immunohistochemistry in clear cell renal cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 161.

Gastric Cancer (GC) is the second leading cause of cancer-related mortality (9.7% of the total) and most patients with advanced disease will die within one year of diagnosis. GC is histologically classified into intestinal, diffuse and the mixed types, and into four molecular subtypes based on genetic profiling (i.e. microsatellite instable (MSI), EBV positive, chromosomal instable, and genomically stable). Although the molecular annotation is meaningful, the tumor immune microenvironment (TIME) has largely not been evaluated. Using an integrated collection of 11 public cohorts (n=2,270), we identified 5 unique GC TIME phenotypes by unsupervised clustering of gene expression signatures of RNA that describe the composition and biology of TIME and properties of malignant cells: two stromal-enriched subtypes - 1) Mesenchymal-wnt activated, high stroma activation and WNT signaling and 2) Fibrotic, only high stroma activation; two immune enriched subtypes 3) Inflamed non-fibrotic, high immune infiltration and low stromal compartment and 4) B-cell inflamed, high B cell activation; and 5) Immune Depleted, lowest immune and the highest malignant cell properties. The clusters were robust, being identified across all 11 datasets and across all stages of disease. Intestinal, diffuse, and mixed histologies were identified in each cluster. These clusters will be orthogonally validated using immunohistochemistry. The current molecular subtypes were represented in each of our TIME clusters, with some enrichment. Specifically, we found enrichment of MSI molecular subtype, characterized by hypermutation and microsatellite instability, in “Inflamed, non-fibrotic” cluster whereas the MSS/EMT subtype, associated with poor overall survival, was enriched in “Mesenchymal, wnt activated” cluster. We also saw specific enrichment of EBV positive tumors, known to have good prognosis, in both immune-enriched clusters. These results show a high concordance with the current TCGA/ACRG molecular subtypes of GC. These TIME clusters are prognostic in GC. The “Inflamed, non-fibrotic” cluster demonstrated a better overall and relapse free survival whereas stromal enriched clusters exhibited the worst (p We have defined and characterized the TIME for GC. The GC microenvironment is both prognostic for patient outcome and predictive of response to cytotoxic therapy. Citation Format: Prashant V. Thakkar, Olga Kudryashova, Daria Melikhova, Naira Samarina, Sandrine Degryse, Alexander Bagaev, Felix Frenkel, Svetlana Podsvirova, Dmitry Tychinin, Sandipto Sarkar, Rhonda K. Yantiss, Nathan Fowler, Manish A. Shah. Tumor immune microenvironment based molecular functional clustering reveals a prognostic signature that predicts overall survival in patients with gastric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3182.

Systemic lymphopenia is common among glioblastoma (GBM) patients after chemoradiotherapy and is observed up to six months to 1 year after irradiation. Systemic lymphopenia is associated with a worse survival outcome in GBM. To delineate the underlying mechanisms of lymphopenia in GBM, we prospectively evaluated peripheral blood of GBM patients treated with standard radiation therapy (RT) and concurrent temozolomide as well as conducted in vivo experiments using a syngeneic murine GBM model. Among 20 patients prospectively evaluated, 10 patients (50%) developed lymphopenia and the other 10 did not. The RNA sequencing analysis of the peripheral blood mononuclear cell (PBMC) showed an elevated level of myeloid-derived suppressor cells (MDSCs) regulatory genes including Arg-1 in lymphopenic patients compared to non-lymphopenic patients after chemoradiotherapy. The single-cell RNA sequencing (scRNA-Seq) analysis of two selected phenotypical patients also demonstrated that a severe lymphopenic patient had elevated levels of two types of monocytic MDSCs (M-MDSCs) after RT compared to a non-lymphopenic patient. Immunophenotyping analysis of the PBMCs using flow cytometry confirmed reductions of CD4 and CD8 T cells as well as increased polymorphonuclear MDSCs (PMN-MDSCs) and M-MDSCs in lymphopenic patients compared to non-lymphopenic patients. The in vitro T cell proliferation and functional assays showed that MDSCs from lymphopenic patients exhibited were more suppressive compared to MDSCs from non-lymphopenic patients. Using a syngeneic orthotopic mouse (C57BL/6) GBM (GL261) model, we demonstrated that fractionated RT (2 Gy/day x 5) reduced CD4/CD8 cells and increased MDSCs compared to sham irradiated mice. The MDSCs from the irradiated mice also had increased Arg-1 expression. The addition of Arg-1 inhibitor (Arg1i) or phosphodiesterase type 5 inhibitor (PDE5i) to RT blocked the radiation-induced increase of MDSCs and reduction of CD4/CD8 cells. The combination of either Arg1i or PDE5i also enhanced the survival of mice with orthotopic tumors when compared to mice treated with RT alone or drug alone. In conclusion, we found that RT can induce an increase in circulating MDSCs in both patients and mice and the resulting circulating MDSCs can suppress T-cell proliferation and function. Inhibition of MDSCs using either Arg1i or PDE5i during RT may prevent systemic lymphopenia and affect disease control, which should warrant further investigation in clinical trials. Citation Format: Subhajit Ghosh, Matthew Inkman, Jin Zhang, Sukrutha Thotala, Ekaterina Tikhonova, Natalia Miheecheva, Felix Frenkel, Ravshan Ataullakhanov, Xiaowei Wang, Dennis Hallahan, Jiayi Huang, Dinesh Thotala. Association of circulating myeloid derived suppressor cells with systemic lymphopenia following chemoradiotherapy in glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 507.

Although genomic analyses of clear cell renal cell carcinoma (ccRCC) patients have revealed targetable pathways that have led to FDA-approved therapies, the responses to these therapies remain limited. The significant success of immune checkpoint inhibitors and anti-angiogenic agents suggest that ccRCC has a unique tumor microenvironment composition and tumor behavior that influences therapeutic response. Here, we describe an integrated proteogenomic method to study intratumoral heterogeneity (ITH), microenvironment composition, tumor spatial behavior, and cellular communities in ccRCC. A unique AI-based segmentation platform for multiplex immunofluorescence (MxIF) was developed to analyze an entire tissue slide at single-cell resolution, including 70 regions of interest per slide, providing significant information regarding spatial architecture. Primary ccRCC tumors collected from patients were biopsied at multiple locations and subjected to MxIF (20 markers, n = 10 sites, 4 pts, ~1,000,000 cells), RNA-seq (n = 8 sites, 3 pts) and CyTOF (n = 21 sites, 6 pts), allowing integrated multi-omics analysis at the single-cell level. Integrated analysis showed that genomic intratumor heterogeneity (ITH) was remarkably similar across all regions biopsied from the same patient, and the cellular populations present within each region of the same patient were alike. However, some cell types such as TCM CD4 T cells and Tem CD38 and Tem PD1+CD69+CD38- CD8 T cells, and CD163+PDL1+LAMP+ showed great inter-patient differences in the proportion of these cell populations. Notably, 14 CD4 T cell, 13 CD8 T cell, and 10 macrophage subpopulations were identified across the ccRCC tumors. Moreover, 14 microenvironment proximity communities based on MxIF imaging analysis of ~1 million cells were identified. The presence of B cell- and T cell-enriched communities (e.g., CD8 T cell enrichment and T-cell enrichment at the tumor border) correlated with the expression of interferon-gamma, PD1, IL-6, IL-10, PD-L1, CXCL13, and others. Macrophage-enriched communities correlated with the expression of CXCL12 and PDGFRB. Finally, tertiary lymphoid structures within the “B cell-enriched” communities correlating with the expression of CXCL13 were found in two tumors collected from one patient, with subsequent validation via H&E staining. Further, B cell repertoire (BCR) analysis of RNA-seq of the tumors from this patient showed the presence of a large B cell clone in the tumors. In conclusion, via MxIF, 14 distinct spatial microenvironment communities with unique cytokine expression patterns were identified in ccRCC. Uncovering the spatial behavior of tumors can lead to the development of effective therapies personalized for each patient based on microenvironment composition and architecture. Citation Format: Natalia Miheecheva, Akshaya Ramachandran, Yang Lyu, Ekaterina Postovalova, Viktor Svekolkin, Ilia Galkin, Pavel Ovcharov, Diana Shamsutdinova, Vladimir Zyrin, Alexander Bagaev, Krystle Nomie, Felix Frenkel, Ravshan Ataullakhanov, James J. Hsieh. Integrated multiregional transcriptomic and multi-parameter single-cell imaging analysis of clear cell renal cell carcinoma elucidates diverse cellular communities present within the tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2742.

Somatic recombination and accumulation of mutations in V-D-J segments result in vast heterogeneity of T-cell receptor (TCR) and immunoglobulin repertoires1,2. High-throughput profiling of immune receptors has become an important tool for studies of adaptive immunity and for the development of diagnostics, vaccines, and immunotherapies3,4,5,6,7. There are efficient molecular and software tools for the targeted sequencing of TCR and immunoglobulin repertoires6,8, including MiXCR, developed by our team9. However, sufficient amount and quality of tissue or extracted RNA or DNA are not always available for analysis.

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.

e15085 Background: Analysis of the genetic and transcriptomic profile of solid tumors via next-generation sequencing (NGS) assays is fundamental to propel precision medicine into clinical practice. NGS technology applied to tumor analysis allows for the characterization of somatic alterations, clonality, altered gene expression, and other parameters using a small amount of tissue. Therefore, to uncover cancer-promoting and suppressing activity of the tumor and the tumor microenvironment (TME), we developed the BostonGene TUMOR PORTRAIT assay, integrating whole-exome sequencing (WES) and mRNA sequencing (RNA-seq). Here, we demonstrate the analytical and clinical validity of the assay. Methods: The accuracy, reproducibility, and robustness of the BostonGene assay were evaluated using reference genomic DNA, reference RNA, well-characterized cell lines, and fresh frozen (FF) tumor and normal tissue containing known single nucleotide variants (SNVs), indels, copy number alterations (CNAs), gene fusions and a reference RNA Spike-In mix containing known levels of specific transcripts. The analysis was performed using the BostonGene automated pipeline. Additionally, we demonstrated high concordance of gene expression measured using two orthogonal techniques, RNA-seq and RT-PCR. Results: The BostonGene TUMOR PORTRAIT assay demonstrated high specificity (>98.1%, >99.8%, >96.9%) and sensitivity (>98.3%, >99.2%, >97.1%) for the detection of SNVs, indels, and CNAs, respectively, with low false-positive and false-negative rates. The assay demonstrated a 100% concordance in the mutation (SNV/indel) call rate across all replicates, and a 100% concordance in the mutation (SNV/indel) and copy number variation (CNV) call rate across all runs. The measurement of gene expression from RNA-seq was achieved with high accuracy (>0.96%) and low variation across genes (95.8%) and specificity (>99%) in a reproducible manner. Using the integrated pipeline that utilizes both WES and RNA-seq, we were able to accurately compute all disease-relevant molecular parameters including tumor genomics, tumor transcriptome phenotype, expression of clinically actionable therapeutic targets, tumor microenvironment composition, and tumor clonality within the single BostonGene TUMOR PORTRAIT report. Conclusions: Analytical and clinical validation results show that the BostonGene TUMOR PORTRAIT assay, based on the sequencing of DNA and RNA, provides a comprehensive and accurate view of the tumor molecular profile, identifying all clinically actionable genetic, transcriptomic, and TME targets. Validation of the BostonGene TUMOR PORTRAIT assay provides a solid foundation for the future development of precision oncology.

Follicular lymphoma (FL) is the most common indolent non-Hodgkin's lymphoma; however, it remains incurable with conventional therapies and is poorly responsive to checkpoint blockade. Additionally, because FL develops so slowly (and often asymptomatically), a major research focus has been to avoid chemotherapy treatments to limit the potential development of treatment-related side effects and the risk of therapy-related second cancers. The mutational processes that lead to lymphomagenesis and progression also produce tumor-specific mutant antigens (TSMAs) that can be targeted by the immune system to control malignancies. Personalized cancer vaccines designed for these TSMAs represent a promising new strategy for treatment of FL. However, the feasibility of this approach and precisely how to optimize effective vaccine design, informed by next-generation sequencing data, are not fully understood. We hypothesize that (tumor/normal) whole-exome sequencing (WES) and (tumor) RNA sequencing (RNA-Seq) can be used to predict patient HLA typing and neoepitopes to engineer personalized cancer vaccines for FL. DNA and RNA from 58 patients' FL biopsies underwent WES and RNA-Seq. pVACtools and MiXCR predicted potential somatic and B-cell clonotype neoantigens, which were filtered to identify high-quality TSMAs. B-cell oligoclonality was determined by comparison to B-cell receptor (BCR) repertoire profiling of healthy individual lymph nodes. RNA-seq data allowed us to identify expressed TSMAs. Complementary in silico analysis based on mRNA-based peptide reconstruction and custom HLA affinity binding predictions were performed. An average of 52 somatic mutations per patient (range: 2-172) were identified. At least one high-quality TSMA was predicted for 57 of 58 patients. Five or more TSMA candidates were identified for 52 (90%) patients with a mean of 17 predicted peptides per patient (range: 0-45). 81% (813/1,004) of the total predicted TSMA peptides arose from missense mutations, 9% (94/1,004) from indels, and 10% (97/1,004) from BCR. 78% (45/58) of patients have both somatic and BCR vaccine candidates, while 21% (12/58) of patients had only somatic vaccine candidates. No fusion genes were identified within the cohort that could have been a source of neoepitope candidates. Predicted TSMAs were identified in multiple genes recurrently mutated in lymphoma (e.g., BCL2). There was a high prediction concordance with the orthogonal BostonGene Vaccine Module V1 pipeline. These preclinical results led to a first-in-human pilot trial of personalized TSMA vaccine combined with anti-PD-1 mAb for rel/ref FL patients (NCT03121677), with one response observed within 4 patients evaluable for response to date. TSMA peptides suitable for cancer vaccines were identified for most FL patients via next-generation sequencing, MiXCR and pVACtools. This preclinical study suggests that FL patients will be candidates for TSMA vaccine clinical trials, and pilot clinical results provide proof of concept for this approach. Citation Format: Cody A. Ramirez, Felix Frenkel, Olga Plotnikova, Vladislav Belousov, Alexander Bagaev, Elena Ocheredko, Susanna Kiwala, Jasreet Hundal, Zachary L. Skidmore, Marcus Watkins, Michelle Becker-Hapak, Thomas B. Mooney, Jason Walker, Catrina Fronick, Robert Fulton, Robert Schreiber, Nancy L. Bartlett, Brad Kahl, Ravshan Ataullakhanov, Malachi Griffith, Obi Griffith, Todd A. Fehniger. Identification of predicted neoantigen vaccine candidates in follicular lymphoma patients [abstract]. In: Proceedings of the AACR Virtual Meeting: Advances in Malignant Lymphoma; 2020 Aug 17-19. Philadelphia (PA): AACR; Blood Cancer Discov 2020;1(3_Suppl):Abstract nr PO-56.

Identifying the unique molecular signature of tumors is essential for rational design of personalized treatments tailored to specific genomic alterations. It is now well accepted that the non-malignant microenvironment plays a critical role in tumor behavior and response to therapy. However, to date, multi-compartmental comprehensive genomic and transcriptomic analyses of malignant and nonmalignant compartments remain rare, are highly unstructured, and difficult to translate for practical treatment decisions. Here, we present a new analytic platform that integrates whole exome sequence (WES) and whole transcriptome (RNASeq) data to provide comprehensive tumor portraits. The portrait is based on the analysis of 28 newly curated gene sets capturing selective and specific molecular and functional properties of nonmalignant components of tumor microenvironment (such as fibroblasts, macrophages, dendritic cells, lymphocytes, extracellular matrices and blood and lymphatic networks), as well as intrinsic properties and genomic profile of malignant cells, including mutational and neoantigenic load, oncogenic and clinically actionable alterations. Analysis of tumor RNASeq data from over 8500 patients using unsupervised louvain dense clustering analysis based on detection of tightly connected networks revealed four unique and consistent microenvironment types characterized by distinct immune, stromal and cancer cell composition. Type A (immune “inflamed” cancers) were characterized by increased fibrosis and low mutational burden; Type B was characterized by increased infiltrating cytotoxic T lymphocytes and high mutations. Type C and D were characterized by negligible immune infiltration and minimal non-malignant cellular populations, while type C had increased vascularization and fibrosis. Remarkably, the four microenvironment types have high prognostic significance and are differently associated with immunotherapy response. In over 400 patients with skin and bladder cancers, type B was associated with a response rate (CR/PR) of >38% following immunotherapy (anti-PD1 /CTLA4) vs By identifying the unique combination of driver genomic alterations, immune processes and stromal components present in each tumor, this novel multicompartmental analysis provides a robust tool for better prediction of response to immunotherapy and for future tailoring of personalized therapeutic combinations. This comprehensive model, summarized and visualized as a Molecular-Functional portrait of the tumor (MF-portrait), could ultimately lead to better clinical outcomes. Citation Format: Alexander Bagaev, Nikita Kotlov, Felix Frenkel, Viktor Svekolkin, Nava Almog, Ravshan Ataullakhanov, Nathan Fowler. Integrated whole exome and transcriptome analyses of the tumor and microenvironment provide new opportunities for rational design of cancer therapy [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4418.

e17106 Background: Clear cell renal cell carcinoma (ccRCC) exhibits conspicuous intratumor heterogeneity (ITH) - a driver of tumor evolution and metastasis. ITH in RCC has been studied extensively with bulk tumor DNA sequencing, which lacks the ability to integrate single cell resolution data, spatial architecture, and microenvironment composition. Therefore, we analyzed primary ccRCC tumors at multiple biopsy sites with CyTOF, multiplex immunofluorescence (MxIF), whole exome sequencing (WES), RNA sequencing (RNA-seq), single nuclei RNA-seq (snRNA-seq), and whole genome bisulfite sequencing (WGBS). Methods: Primary ccRCC tumors collected from 6 patients (pts) were biopsied at multiple locations and subjected to CyTOF (n = 21 sites, 6 pts), MxIF (20 markers, n = 8 sites, 3 pts), WES (n = 8 sites, 3 pts), RNA-seq (n = 8 sites, 3 pts), snRNA-seq (n = 8 sites, 3 pts), and WGBS (n = 8 sites, 3 pts), enabling integrated multi-omics analysis. MxIF, CyTOF, and genomic/transcriptomic analyses were performed by BostonGene. Results: Genomic intratumor (IT) evolution of ccRCC cells was tracked with WES, and subclonal distribution of SETD2, STAG2, TSC2 and PBRM1 mutations was observed in different IT regions. Different regions of the same tumor were similar, whereas individual patient tumors were distinct according to tumor microenvironment cellular composition measured by CyTOF or deconvoluted from RNA-seq. The cellular deconvolution of the ccRCC tumors reconstructed from RNA-seq correlated with CyTOF, snRNA-seq and WGBS, showing high concordance among the methods. The promoter CpG island methylation levels, averaged across all genes, positively correlated with ccRCC grade. MxIF revealed spatial IT heterogeneity in the distribution of immune infiltrate components. Macrophages and T cells dispersed among malignant cells; whereas, T cells formed clusters at unique tumor margins. Conclusions: The utilization of multi-omics methods produced a high-resolution portrait of the ccRCC tumor composition and identified differential ITH among regions within the primary tumors or among individual primary tumors. This study demonstrated strong concordance among the different technologies, suggesting that tumor deconvolution by bulk RNA-seq might be clinically applicable for ccRCC tumors. MxIF analysis enabled a fine elucidation of the spatial relationships among the tumor and the immune and stromal cells, missed by common omic platforms. Integrated single cell multi-omics could render specific pathobiological and therapeutic insights that impact treatment decisions.

e20065 Background: PI3K dysregulation has been linked to ibrutinib resistance in mantle cell lymphoma (MCL); however, the investigation of PI3K signaling as an important ibrutinib resistance mechanism has been rarely assessed in clinical samples. Therefore, we sought to more fully characterize PI3K dysregulation in ibrutinib-sensitive and -resistant MCL clinical samples at the genomic and transcriptomic levels. Methods: Whole exome sequencing (WES; n = 41) and RNA-seq (n = 93) were performed on fresh peripheral blood, apheresis, or biopsy patient primary samples. All analysis was performed using the BostonGene automated pipeline. Results: To evaluate ibrutinib resistance in MCL, we compared the activity of 11 cellular pathways calculated by PROGENy between ibrutinib-sensitive and -resistant MCL. The PI3K pathway was the most differentially expressed between the two groups (q < 0.005; > 1 median absolute deviation in ibrutinib-resistant MCL). Increased PI3K pathway expression in the ibrutinib-resistant MCL tumors strongly correlated with hyperproliferation (r = 0.49; p = 6e-07). Both hyperproliferation and enriched PI3K expression associated with more frequent NOTCH2 somatic mutations (~22% ibrutinib-resistant MCL with high PI3K expression (p = 0.004) and hyperproliferation (p = 0.003)) that generate a premature stop codon in the PEST domain, likely resulting in hyperactive NOTCH2. Frequent TP53 mutations were identified in ibrutinib-resistant MCL tumors (58%; 3-fold greater vs ibrutinib-sensitive tumors; p = 0.02) based on our cohort and the Agarwal et al., 2019, Nature Medicine cohort. PI3K signaling is a known p53 activator, which may lead to a compensatory tumor suppressive mechanism, indicating that PI3K activation may induce mutational pressure on TP53 to promote MCL survival. VH gene usage variability of the B-cell receptor (BCR), the inducer of PI3K signaling, was not different between ibrutinib-sensitive and -resistant MCL, suggesting that diverse BCR expression does not underlie enriched ibrutinib-resistant MCL PI3K expression. Conclusions: PI3K inhibitors have resulted in underwhelming MCL clinical outcomes; yet, the advent of next-generation PI3K inhibitors such as copanlisib with potentially greater efficacy and less toxicity warrants the continued investigation of PI3K signaling in ibrutinib-resistant MCL. We identified a strong correlation between gain-of-function NOTCH2 mutations and enriched PI3K signaling, suggesting that dual inhibition of NOTCH and PI3K may overcome ibrutinib resistance in MCL.

8054 Background: Follicular lymphoma (FL) is incurable with conventional therapies and poorly responsive to immune checkpoint blockade. There is a need for new therapies without long-term complications of chemotherapy and with curative potential. We hypothesize that FL contains tumor-specific mutant antigens (TSMAs) that can be targeted by the immune system by vaccination. Recent reports have highlighted the potential for unique immunoglobulin peptides to elicit immune response in lymphomas. We utilized whole exome sequencing (WES) and RNA sequencing (RNA-Seq) of FL patient samples to infer HLA genotype, and predict TSMAs with the goal of designing a personalized cancer vaccine, supported by recent reports of this approach in solid cancers. Methods: DNA and RNA from 58 patients’ FL biopsies underwent WES and RNA-Seq. pVACtools and MiXCR predicted potential somatic and B-cell clonotype neoantigens, which were filtered to identify high quality TSMAs. B-cell oligoclonality was determined by comparison to B-cell receptor (BCR) repertoire profiling of healthy individual lymph nodes. RNA-seq data allowed us to identify expressed TSMAs. Complementary in silico analysis based on mRNA-based peptide reconstruction and custom HLA affinity binding predictions were performed. Results: An average of 52 somatic mutations per patient (range: 2-172) were identified. At least one high quality TSMA was predicted for 57 of 58 patients. Five or more TSMA candidates were identified for 52 (90%) patients with a mean of 17 predicted peptides per patient (range: 0-45). 81% (813/1,004) of the total predicted TSMA peptides arose from missense mutations, 9% (94/1,004) from indels, and 10% (97/1,004) from BCR. 78% (45/58) of patients have both somatic and BCR vaccine candidates, while 21% (12/58) of patients had only somatic vaccine candidates. Predicted TSMAs were identified in multiple genes recurrently mutated in lymphoma (e.g., BCL2). There was a high prediction concordance with the orthogonal BostonGene Vaccine Module V1 pipeline. These pre-clinical results led to a first-in-human pilot trial of personalized TSMA vaccine combined with anti-PD-1 mAb for rel/ref FL patients (NCT03121677), with one response observed within 4 patients evaluable for response to date. Conclusions: TSMA peptides suitable for cancer vaccines were identified for most FL patients via next-generation sequencing, MiXCR and pVACtools. This pre-clinical study suggests that FL patients will be candidates for TSMA vaccine clinical trials and pilot clinical results provide proof of concept for this approach.

6561 Background: The addition of biomarkers as companion diagnostics and Next Generation Sequencing (NGS) have dramatically increased therapeutic efficacy and have aided precision medicine development. The unique genomic profile and tumor microenvironment (TME) composition of each patient can be ascertained through NGS. Using TCGA and Geo datasets, we characterized head and neck cancers (HNC) according to the cellular and functional state of their TME and conducted a pilot validation study using prospectively collected HNC tumors. Methods: To stratify the TME of HNC tumors into molecular functional portraits, we analyzed the sequencing data of 1,486 HNC tumor samples and 143 controls (normal, oral leukoplakia) from TCGA and GEO data sets. For the prospective pilot study, resected tissue from oropharyngeal carcinomas independent of HPV status were processed for whole exome (WES) and RNA-seq (n = 6; HPV-positive = 1). Results: To characterize the cellular composition and functional state of HNC tumors and their TMEs, we created 26 separate molecular signatures related to functional processes such as immune checkpoint inhibition, immune infiltration, immunosuppression, and stromal activities represented by angiogenesis and mesenchymal stromal cells. Unsupervised clustering of these signatures delineated tumors into 4 types: immune infiltration with increased stromal signatures (type A), immune infiltration with decreased stromal signatures (type B), no immune infiltration with increased stromal signature (type C), and no immune infiltration and decreased stromal signatures (type D). Most HPV-positive tumors were type B (p = 1e-27) and associated with increased survival compared to the HPV-negative tumors (types C and D; p = 3e-05). Type B HPV-positive tumors had reduced FAT1 and TP53 mutations, whereas type B HPV-negative tumors had increased caspase 8 mutations/loss. In the validation cohort, actionable mutations were found in PI3KCA and TSC2 in types A and B HPV-negative tumors. Moreover, while the HPV-positive tumor was classified as type C, we identified a caspase 8 homozygous deletion and absence of FAT1 and TP53 mutations, supporting the TCGA and GEO analysis. Conclusions: Exome and transcriptome analyses with cellular deconvolution from bulk RNA-seq enrich tumor characterization by including major TME components, providing a comprehensive biomarker profile for precision therapy and clinical decision making. Our prospective analysis identified TME parameters comparable with the large datasets and revealed targetable genomic alterations.

e21026 Background: Comprehensive molecular profiling and the use of biomarkers as companion diagnostics have transformed precision medicine for cancer patients. To identify patient-specific tumor microenvironment and biomarker profiles, we assessed the accuracy of our deconvolution algorithm in identifying cellular compositions from whole exome (WES) and whole transcriptome (RNA-seq) sequencing of solid tumors compared with cell populations identified by Mass Cytometry by Time of Flight (CyTOF) in surgically resected tissue from non-small cell lung cancer (NSCLC) patients. Methods: Resected NSCLC tissue was divided for RNA-seq and WES of whole tissue (n = 9) and for generating tissue single cell suspensions through mechanical dissociation and enzymatic digestion (n = 11). Bulk RNA-seq and CyTOF were performed on all cell suspensions. Cellular phenotypes were identified using clustering algorithms in CyTOF and predicted from bulk RNA-seq using our proprietary computational method. Results: Cellular composition reconstructed from RNA-seq correlated with the composition detected by CyTOF (R2= 0.922, n = 7) from cell suspensions. To recover the cell percentage from bulk RNA-seq, a machine learning framework was trained on the cell compendia comprising 7,117 unique cell type RNA-seq profiles. A two-stage hierarchical learning procedure generated a gradient boosting Light GBM model that included training on artificial RNA-seq mixtures of different cell types. With this method, we found that stromal and malignant cells were depleted during single cell suspension preparation, resulting in statistically significant differences in the tumor cell composition reconstructed from solid tissue and single cell suspensions. Immune cell types namely T cells and macrophages were similarly represented in both the bulk tumor tissue and matched single cell suspensions. Transcriptomics revealed a subgroup of patients whose tumors were B-cell-enriched, which was validated in other NSCLC cohorts and was associated with greater CD4+ and CD8+ T cell infiltration and improved clinical outcomes. Conclusions: Since preparation of single cell suspensions leads to the loss of several cellular components, RNA-seq of tumor bulk tissue better describes the molecular and cellular properties of the tumor microenvironment. The combination of RNA-seq and WES of tumor tissue provides a comprehensive profile of cellular composition, suggesting that this combination is ideal for precision medicine applications.

8055 Background: Mantle cell lymphoma (MCL) is an aggressive and incurable B-cell lymphoma. Although the role of the MCL tumor microenvironment (TME) in survival and therapeutic response has been studied, greater knowledge regarding the tumor-immune interaction is needed to develop MCL immunotherapeutic strategies. Methods: Whole exome sequencing (WES; n = 41) and RNA-seq (n = 93) were performed on fresh peripheral blood, apheresis, or biopsy MCL patient primary samples. Joint WES and RNA-seq mutation calling, expression analysis were performed by BostonGene. Results: Both tumor and TME molecular signatures were characterized based on ibrutinib response. Concurrent analysis of MCL biopsy samples with an additional previously published cohort (n = 122; Scott et al., JCO, 2017) identified 4 MCL microenvironment signatures (Nomie et al., Blood, 2019) in which the ibrutinib-resistant MCL samples primarily belonged to the “stroma-enriched” subtype (29%; 6/8 resistant, non-immune with increased stromal signature and tumor-promoting cytokines), whereas most of the ibrutinib-sensitive samples were assigned to the “immune-hot” subtype (53%, 9/9 sensitive; anti-tumor infiltration, high immune and checkpoint molecule expression with low stromal expression, Chi-square test p-value = 0.001). NOTCH1 gain-of-function mutations (25%, 3/12 resistant) in the PEST domain were found exclusively in the ibrutinib-resistant cohort associated with the microenvironment-depleted subtype. Frequent recurring inactivating mutations in the epigenetic modifier KMT2D (30%) were identified in MCL cells associated with the ‘immune-suppressed” subtype (p < 0.05). Loss-of-function mutations in epigenetic modifiers have been tied to immune evasion. PD-L1 was significantly downregulated in the ibrutinib-resistant MCL tumors (p = 0.03), indicating that targeting the PD-L1 and PD-1 immune checkpoint axis may not be beneficial. Conclusions: Ibrutinib sensitivity and resistance were defined by immune-hot and immune-cold TME portraits, respectively, suggesting that the TME has a prominent role in mediating ibrutinib response. Ibrutinib has been suggested to activate the immune TME through its off target inhibition of interleukin 2–inducible T-cell kinase (ITK). The immune activation by ibrutinib suggests that anti-tumor activity of ibrutinib may be better harnessed by combining ibrutinib with immunotherapy.

e17506 Background: The inclusion of multiparametric MRI (mpMRI) to prostate cancer (PCa) diagnostics has increased the detection rate and has improved the prediction rate of clinically significant PCa. Yet, mpMRI has a false negative rate of 12.6%, missing 6-13% of clinically significant PCa. The mechanisms underlying MRI visibility are poorly understood; therefore, to probe the molecular and cellular underpinnings of PCa MRI visibility, we profiled tissue from Gleason score and clinically matched patients with MRI-visible and MRI-invisible PCa who underwent radical prostatectomy. Methods: MRI-invisible (n = 7) and MRI-visible (n = 8) PCa tumors were evaluated with multiplex immunofluorescence (MxIF; 14 markers) and were subjected to gene expression profiling using the HTG EdgeSeq Oncology Biomarker Panel (2,549 genes). Gene expression analysis was also performed using The Cancer Genome Atlas (TCGA), including normal prostate (n = 52) and PCa (n = 387) tissue. Analyses were performed using the BostonGene automated pipeline. Results: MpMRI-visible PCa tumors (62.5%) displayed compact epithelial tumor architecture compared with mpMRI-invisible PCa tumors (28.5%). mpMRI-visible PCa had higher malignant cell density (p = 0.04) and increased neighboring malignant cells (p = 0.07), correlating with MRI-visible PCa complex tumor architecture (r = 0.49 for neighboring malignant cells vs tumor cell density). Tumor stromal organization differences were determined by measuring Wasserstein distances between distributions, and mpMRI-invisible PCa stroma appeared more similar to normal tissue. The visible group exhibited lower expression of stroma-enriched genes such as filamin C (FLNC) (FDR < 0.1) and cellular adhesion-related genes (FDR < 0.4), with gene expression signatures markedly different compared to normal prostate tissue. Higher malignant cell density, neighboring malignant cells, and Wasserstein distances, and low FLNC expression – all mpMRI visibility characteristics – were associated with patient relapse (p = 0.02). Low stroma signature expression in the TCGA cohort correlated with inferior PCa PFS (p = 0.005). Conclusions: This is the first integrated multi-omics analysis of clinically matched mpMRI-visible and -invisible PCa. mpMRI-invisible tumors exhibited molecular, cellular, and structural characteristics more akin to normal prostate tissue, which may render them undetectable by imaging. A stroma-associated gene signature, a mpMRI-invisible tumor feature, correlated with better PCa clinical outcomes.

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.