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Purpose: T cells infiltrating follicular lymphoma (FL) tumors are considered dysfunctional, yet the optimal target for immune checkpoint blockade is unknown. Characterizing coinhibitory receptor expression patterns and signaling responses in FL T-cell subsets might reveal new therapeutic targets.Experimental Design: Surface expression of 9 coinhibitory receptors governing T-cell function was characterized in T-cell subsets from FL lymph node tumors and from healthy donor tonsils and peripheral blood samples, using high-dimensional flow cytometry. The results were integrated with T-cell receptor (TCR)-induced signaling and cytokine production. Expression of T-cell immunoglobulin and ITIM domain (TIGIT) ligands was detected by immunohistochemistry.Results: TIGIT was a frequently expressed coinhibitory receptor in FL, expressed by the majority of CD8 T effector memory cells, which commonly coexpressed exhaustion markers such as PD-1 and CD244. CD8 FL T cells demonstrated highly reduced TCR-induced phosphorylation (p) of ERK and reduced production of IFNγ, while TCR proximal signaling (p-CD3ζ, p-SLP76) was not affected. The TIGIT ligands CD112 and CD155 were expressed by follicular dendritic cells in the tumor microenvironment. Dysfunctional TCR signaling correlated with TIGIT expression in FL CD8 T cells and could be fully restored upon in vitro culture. The costimulatory receptor CD226 was downregulated in TIGIT+ compared with TIGIT− CD8 FL T cells, further skewing the balance toward immunosuppression.Conclusions: TIGIT blockade is a relevant strategy for improved immunotherapy in FL. A deeper understanding of the interplay between coinhibitory receptors and key T-cell signaling events can further assist in engineering immunotherapeutic regimens to improve clinical outcomes of cancer patients. Clin Cancer Res; 24(4); 870–81. ©2017 AACR.

Table S2. Expression of TIGIT ligands in B cells and T cells.

Supplementary Figure 1. IL-21 produc􀆟on is reduced in CD4 FL T cells; Supplementary Figure 2. TCR-induced signaling effectors have different kine􀆟c; Supplementary Figure 3. Iden􀆟fica􀆟on of T-cell subsets; Supplementary Figure 4. Distribu􀆟on of T-cell subsets in FL LN; Supplementary Figure 5. Expression pa􀆩erns of co-inhibitory receptors in CD8 and CD4 T-cell subsets; Supplementary Figure 6. Contribu􀆟on of Tregs among CD4+TIGIT+ T cells; Supplementary Figure 7. TIGIT is a highly expressed co-inhibitory receptor in FL; Supplementary Figure 8. TIGIT expression is stable over 􀆟me; Supplementary Figure 9. Recovery of TCR-induced signaling in TIGIT+ CD8+ FL T cells is robust.

Supplementary Figure 1. IL-21 produc􀆟on is reduced in CD4 FL T cells; Supplementary Figure 2. TCR-induced signaling effectors have different kine􀆟c; Supplementary Figure 3. Iden􀆟fica􀆟on of T-cell subsets; Supplementary Figure 4. Distribu􀆟on of T-cell subsets in FL LN; Supplementary Figure 5. Expression pa􀆩erns of co-inhibitory receptors in CD8 and CD4 T-cell subsets; Supplementary Figure 6. Contribu􀆟on of Tregs among CD4+TIGIT+ T cells; Supplementary Figure 7. TIGIT is a highly expressed co-inhibitory receptor in FL; Supplementary Figure 8. TIGIT expression is stable over 􀆟me; Supplementary Figure 9. Recovery of TCR-induced signaling in TIGIT+ CD8+ FL T cells is robust.

Recent studies have emphasized the importance of single-cell spatial biology, yet available assays for spatial transcriptomics have limited gene recovery or low spatial resolution. Here we introduce CytoSPACE, an optimization method for mapping individual cells from a single-cell RNA sequencing atlas to spatial expression profiles. Across diverse platforms and tissue types, we show that CytoSPACE outperforms previous methods with respect to noise tolerance and accuracy, enabling tissue cartography at single-cell resolution.

Metastasis is responsible for most breast cancer–related deaths; however, identifying the cellular determinants of metastasis has remained challenging. Here, we identified a minority population of immatureTHY1+/VEGFA+tumor epithelial cells in human breast tumor biopsies that display angiogenic features and are marked by the expression of the oncogene,LMO2. Higher abundance ofLMO2+basal cells correlated with tumor endothelial content and predicted poor distant recurrence–free survival in patients. UsingMMTV-PyMT/Lmo2CreERT2mice, we demonstrated thatLmo2lineage–traced cells integrate into the vasculature and have a higher propensity to metastasize. LMO2 knockdown in human breast tumors reduced lung metastasis by impairing intravasation, leading to a reduced frequency of circulating tumor cells. Mechanistically, we find that LMO2 binds to STAT3 and is required for STAT3 activation by tumor necrosis factor–α and interleukin-6. Collectively, our study identifies a population of metastasis-initiating cells with angiogenic features and establishes the LMO2-STAT3 signaling axis as a therapeutic target in breast cancer metastasis.

Recent studies have emphasized the importance of single-cell spatial biology, yet available assays for spatial transcriptomics have limited gene recovery or low spatial resolution. Here we introduce CytoSPACE, a method for aligning single-cell and spatial transcriptomes via convex linear optimization. Across diverse platforms and tissue types, we show that CytoSPACE outperforms previous methods with respect to noise-tolerance, accuracy, and efficiency, enabling improved analysis of spatial transcriptomics data at single-cell resolution.

Profiling of circulating tumor DNA (ctDNA) in the bloodstream shows promise for noninvasive cancer detection. Chromatin fragmentation features have previously been explored to infer gene expression profiles from cell-free DNA (cfDNA), but current fragmentomic methods require high concentrations of tumor-derived DNA and provide limited resolution. Here we describe promoter fragmentation entropy as an epigenomic cfDNA feature that predicts RNA expression levels at individual genes. We developed 'epigenetic expression inference from cell-free DNA-sequencing' (EPIC-seq), a method that uses targeted sequencing of promoters of genes of interest. Profiling 329 blood samples from 201 patients with cancer and 87 healthy adults, we demonstrate classification of subtypes of lung carcinoma and diffuse large B cell lymphoma. Applying EPIC-seq to serial blood samples from patients treated with PD-(L)1 immune-checkpoint inhibitors, we show that gene expression profiles inferred by EPIC-seq are correlated with clinical response. Our results indicate that EPIC-seq could enable noninvasive, high-throughput tissue-of-origin characterization with diagnostic, prognostic and therapeutic potential.

Severe immune-related adverse events (irAEs) occur in up to 60% of patients with melanoma treated with immune checkpoint inhibitors (ICIs). However, it is unknown whether a common baseline immunological state precedes irAE development. Here we applied mass cytometry by time of flight, single-cell RNA sequencing, single-cell V(D)J sequencing, bulk RNA sequencing and bulk T cell receptor (TCR) sequencing to study peripheral blood samples from patients with melanoma treated with anti-PD-1 monotherapy or anti-PD-1 and anti-CTLA-4 combination ICIs. By analyzing 93 pre- and early on-ICI blood samples and 3 patient cohorts (n = 27, 26 and 18), we found that 2 pretreatment factors in circulation-activated CD4 memory T cell abundance and TCR diversity-are associated with severe irAE development regardless of organ system involvement. We also explored on-treatment changes in TCR clonality among patients receiving combination therapy and linked our findings to the severity and timing of irAE onset. These results demonstrate circulating T cell characteristics associated with ICI-induced toxicity, with implications for improved diagnostics and clinical management.

Recent advances in high‐throughput molecular profiling technologies and multiplexed imaging platforms have revolutionized our ability to characterize the tumor immune microenvironment. As a result, studies of tumor‐associated immune cells increasingly involve complex data sets that require sophisticated methods of computational analysis. In this review, we present an overview of key assays and related bioinformatics tools for analyzing the tumor‐associated immune system in bulk tissues and at the single‐cell level. In parallel, we describe how data science strategies and novel technologies have advanced tumor immunology and opened the door for new opportunities to exploit host immunity to improve cancer clinical outcomes.

Single-cell RNA-sequencing has emerged as a powerful technique for characterizing cellular heterogeneity, but it is currently impractical on large sample cohorts and cannot be applied to fixed specimens collected as part of routine clinical care. We previously developed an approach for digital cytometry, called CIBERSORT, that enables estimation of cell type abundances from bulk tissue transcriptomes. We now introduce CIBERSORTx, a machine learning method that extends this framework to infer cell-type-specific gene expression profiles without physical cell isolation. By minimizing platform-specific variation, CIBERSORTx also allows the use of single-cell RNA-sequencing data for large-scale tissue dissection. We evaluated the utility of CIBERSORTx in multiple tumor types, including melanoma, where single-cell reference profiles were used to dissect bulk clinical specimens, revealing cell-type-specific phenotypic states linked to distinct driver mutations and response to immune checkpoint blockade. We anticipate that digital cytometry will augment single-cell profiling efforts, enabling cost-effective, high-throughput tissue characterization without the need for antibodies, disaggregation or viable cells. CIBERSORTx, a suite of computational tools, enables inference of cell type abundance and cell-type-specific gene expression profiles from bulk RNA profiles.

SUMMARYMetastasis is responsible for the majority of breast cancer-related deaths, however identifying the cellular determinants of metastasis has remained challenging. Here, we identified a minority population of immature THY1+/VEGFA+ tumor epithelial cells in human breast tumor biopsies that display angiogenic features and are marked by the expression of the oncogene, LMO2. Higher abundance of LMO2+ basal cells correlated with tumor endothelial content and predicted poor distant recurrence-free survival in patients. Using MMTV-PyMT/Lmo2CreERT2 mice, we demonstrated that Lmo2 lineage- traced cells have a higher propensity to metastasize. LMO2 knockdown in human breast tumors reduced lung metastasis by impairing intravasation, leading to a reduced frequency of circulating tumor cells. Mechanistically, we find that LMO2 binds to STAT3 and is required for STAT3 activation by TNFα and IL6. Collectively, our study identifies a population of metastasis-initiating cells with angiogenic features and establishes the LMO2-STAT3 signaling axis as a therapeutic target in breast cancer metastasis.One sentence summaryLMO2 modulates STAT3 signaling in breast cancer metastasis.

Background Although chemo-immunotherapy has led to an improved overall survival for most B-cell lymphoma types, relapsed and refractory disease remains a challenge. The malaria drug artesunate has previously been identified as a growth suppressor in some cancer types and was tested as a new treatment option in B-cell lymphoma. Methods We included artesunate in a cancer sensitivity drug screen in B lymphoma cell lines. The preclinical properties of artesunate was tested as single agent in vitro in 18 B-cell lymphoma cell lines representing different histologies and in vivo in an aggressive B-cell lymphoma xenograft model, using NSG mice. Artesunate-treated B lymphoma cell lines were analyzed by functional assays, gene expression profiling, and protein expression to identify the mechanism of action. Results Drug screening identified artesunate as a highly potent anti-lymphoma drug. Artesunate induced potent growth suppression in most B lymphoma cells with an IC50 comparable to concentrations measured in serum from artesunate-treated malaria patients, while leaving normal B-cells unaffected. Artesunate markedly inhibited highly aggressive tumor growth in a xenograft model. Gene expression analysis identified endoplasmic reticulum (ER) stress and the unfolded protein response as the most affected pathways and artesunate-induced expression of the ER stress markers ATF-4 and DDIT3 was specifically upregulated in malignant B-cells, but not in normal B-cells. In addition, artesunate significantly suppressed the overall cell metabolism, affecting both respiration and glycolysis. Conclusions Artesunate demonstrated potent apoptosis-inducing effects across a broad range of B-cell lymphoma cell lines in vitro, and a prominent anti-lymphoma activity in vivo, suggesting it to be a relevant drug for treatment of B-cell lymphoma.

Background Diffuse large B-cell lymphoma (DLBCL) is a genetically and clinically heterogeneous disease. The cell-of-origin (COO) classification subdivides DLBCL into the transcriptionally defined activated B-cell (ABC) and germinal center B-cell (GCB) subtypes. While RNA based methods are considered the gold standard to determine COO, they are rarely used in clinical routine due to logistical and methodological challenges. Alternatives include the immunohistochemistry-based Hans algorithm and classifiers to infer the COO subtype from DNA sequencing [Esfahani et al., Blood 2019]. Despite the undisputed value of these methods, the concordance with the gold standard RNA and their prognostic implication are limited. We have recently shown that expression of individual genes can be inferred from cfDNA fragmentation patterns using a method called EPIC-Seq (EPigenetic expression Inference from Cell-free DNA Sequencing) [Esfahani et al., Cancer Res. 2020]. We therefore reasoned that EPIC-Seq may improve COO classification compared to other non-RNA methods. Methods A gene expression model, using cfDNA fragmentation patterns, was trained using leukocyte RNA-sequencing and deep whole genome profiling of the plasma cell-free DNA of an individual with no evidence of circulating disease. The trained model takes two features into account to infer gene expression: 1. promoter fragmentation entropy (PFE), and 2. normalized coverage at the nucleosome-depleted region of a given transcription start site (TSS). We then used a capture panel targeting TSS specifically designed for EPIC-Seq. We selected genes based on their power to discriminate COO subtypes in tumor RNA sequencing [Schmitz et al., NEJM 2018]. We first developed a classifier to distinguish DLBCL cases from healthy plasma using the inferred gene expression. Moreover, we defined GCB and ABC signature scores as the average inferred expression of a set of 'GCB genes' (n=34) and 'ABC genes' (n=34), respectively. Finally, we defined the COO score as the difference between the GCB and ABC scores. To validate our assay and method, we profiled 71 plasma samples from 68 healthy individuals and 90 pretreatment plasma samples from patients with large B-cell lymphomas using EPIC-Seq. Results We first evaluated the performance of the EPIC-Seq classifier in distinguishing DLBCL cases from controls and achieved an AUC of 0.92 in a cross-validation setting (Fig 1a). We then compared the result of EPIC-Seq COO classifier with the genotype-based method previously developed in our group. We observed epigenetic scores to be significantly correlated with previously described mutation-based GCB scores (r=0.75, P=1E-5, Fig. 1b). When comparing to the Hans classification algorithm, we observed significantly higher GCB scores in cases classified as GCB by Hans as compared with non-GCB cases (Wilcox P=0.001, Fig. 1c). Comparing the prognostic power of epigenetic and mutation-based COO labels in previously untreated patients using univariate Cox regressions, the EPIC-Seq classifier better stratified event-free survival (EFS) with higher GCB scores being associated with favorable outcomes (n=70, EPIC-Seq: HR=0.13, P=0.033 vs CAPP-Seq: HR=0.95, P=0.62). Importantly, when binarizing patients into GCB and non-GCB cases by the median, patients with tumors classified as GCB had significantly longer EFS than non-GCB counterparts (log-rank P=0.013, Fig. 1d). The Hans algorithm, in contrast, failed to stratify patients for EFS, among patients analyzed by both immunohistochemistry and DNA genotyping (Fig. 1e). Finally, we profiled n=12 additional DLBCL cases by both RNA-sequencing and EPIC-Seq. Strikingly, we found EPIC-COO scores to be significantly correlated with RNA based GCB scores (r=0.84, P=6E-4, Fig. 1f) underscoring the concordance of EPIC-Seq based COO classification with a gold standard scoring system. Conclusions We here apply EPIC-Seq, a method to infer expression of individual genes from cfDNA fragmentation patterns, to classify DLBCL patients into COO subtypes. COO classification by EPIC-Seq outperformed both Hans and mutation-based methods with regards to outcome stratification and correlated well with RNA-based methods. Overall, these results suggest that EPIC-Seq has utility for noninvasive classification of DLBCL cell-of-origin subtypes and may help to overcome logistical and methodological challenges of RNA-based methods. Figure 1 Figure 1. Disclosures Shahrokh Esfahani: Foresight Diagnostics: Current holder of stock options in a privately-held company. Kurtz: Genentech: Consultancy; Foresight Diagnostics: Consultancy, Current holder of stock options in a privately-held company; Roche: Consultancy. Diehn: Foresight Diagnostics: Current holder of individual stocks in a privately-held company, Current holder of stock options in a privately-held company; CiberMed: Current holder of stock options in a privately-held company, Patents & Royalties; Illumina: Research Funding; Varian Medical Systems: Research Funding; BioNTech: Consultancy; RefleXion: Consultancy; AstraZeneca: Consultancy; Roche: Consultancy. Alizadeh: Gilead: Consultancy; Bristol Myers Squibb: Research Funding; Janssen Oncology: Honoraria; Celgene: Consultancy, Research Funding; Forty Seven: Current holder of individual stocks in a privately-held company, Current holder of stock options in a privately-held company; CAPP Medical: Current holder of individual stocks in a privately-held company, Current holder of stock options in a privately-held company; Roche: Consultancy, Honoraria; Foresight Diagnostics: Consultancy, Current holder of individual stocks in a privately-held company, Current holder of stock options in a privately-held company; Cibermed: Consultancy, Current holder of individual stocks in a privately-held company, Current holder of stock options in a privately-held company.

Introduction: Regulatory T cells (Tregs), a highly immunosuppressive subset of CD4 + T cells, represent a key challenge in the tumor microenvironment by limiting potent antitumor immune responses. While high densities of tumor-infiltrating Tregs are associated with poor prognosis in patients with various types of solid cancers, their prognostic impact in B-cell non-Hodgkin lymphoma (NHL) remains unclear. Emerging studies suggest substantial heterogeneity in the phenotype and suppressive capacities of Tregs, emphasizing the importance of understanding Treg diversity and the need for additional markers to identify highly suppressive Tregs. Our in-depth characterization of Tregs in NHL tumors could open new paths for rational drug design, facilitating selective therapeutic manipulation of Tregs to reduce immunosuppression and improve anti-tumor immunity. Methods: Single-cell suspensions from NHL patients (diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), mantle cell lymphoma (MCL) and healthy donors (tonsils and peripheral blood)) were analyzed by fluorescent flow- and mass cytometry to characterize Tregs, focusing on their expression of co-stimulatory and co-inhibitory checkpoint receptors. The immunosuppressive capacity of Tregs was measured by in vitro co-culture of FACS-sorted subsets of Tregs together with autologous CellTrace Violet-labelled T effector cells as responder cells, using samples from FL and tonsils. Live CD4 + T cells were obtained by FACS sorting from DLBCL (n = 3), FL (n = 3) and healthy donor tonsils (n = 3) and subjected to single-cell RNA sequencing (scRNA-seq), Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) and scTCR-seq by the 10X Genomics platform. The computational framework of CIBERSORTx was used to generate unique signature matrices for the three Treg subsets identified by scRNA-seq, to facilitate validation in separate scRNA-seq cohorts (King, Sci Immunol 2021; Roider, Nat Cell Biol 2020), and to impute frequencies of the Treg subsets in cohorts with bulk RNA-seq data (Chapuy, Nat Med 2018; Schmitz, NEJM 2018; Pastore, Lancet Oncol 2015). Results: Immunophenotyping by mass cytometry revealed a subset of activated Tregs identified by co-expression of TIGIT, CTLA-4, PD-1, ICOS and OX40, and higher expression of FOXP3, CD25 and CD45RO, that was present in DLBCL and tonsils, but lacking in peripheral blood. This was validated by fluorescent flow cytometry, demonstrating significantly higher frequencies of activated Tregs in NHL tumors compared to PBMCs and tonsils from healthy donors. The phenotypic heterogeneity of intratumoral Tregs reflected different suppressive capacities as activated Tregs more potently suppressed the proliferation of autologous effector CD4 + and CD8 + T cells than naïve Tregs. For global transcriptomic profiling of CD4 + T cells from FL, DLBCL and tonsillar samples, we integrating scRNA-seq and CITE-seq data from 17,774 cells, revealing 13 distinct cellular states including three states of Tregs: naïve, activated and non-conventional LAG3 +FOXP3 - Tregs. Activated Tregs had higher expression of checkpoint receptors (TNFRSF4, TNFRSF18, ICOS), phosphatases (DUSP2, DUSP4), NF-κB pathway (NFKBIA, TNFAIP3, NFKBIZ, REL), chemokine receptors (CXCR4) and transcription factors (JUNB, IRF1, STAT3) as compared to naïve Tregs. We next used a computational approach to develop unique signature matrices for each Treg subset. This approach demonstrated strong concordance between CIBERSORTx estimated cell abundances of the three Treg subsets and the ground truth, and was validated in two external scRNA-seq cohorts. The development of unique signature matrices for Treg subsets facilitated imputation of their frequencies in bulk RNA-seq datasets. These analyses revealed that higher frequency of activated Tregs was enriched in the germinal B cell subtype of DLBCL and was associated with adverse outcome in FL. Conclusion: This study demonstrates that Tregs infiltrating NHL tumors are transcriptionally and functionally diverse and include highly immunosuppressive activated Tregs co-expressing several checkpoint receptors, which distinguish them from peripheral blood Tregs. Activated intratumoral Tregs could hamper clinical responses to checkpoint blockade, and identifying and targeting their vulnerabilities has the potential to improve anti-tumor immune responses. Disclosures Holte: Gilead: Membership on an entity's Board of Directors or advisory committees; Roche: Membership on an entity's Board of Directors or advisory committees; Nordic: Membership on an entity's Board of Directors or advisory committees; Nanovector: Membership on an entity's Board of Directors or advisory committees, Other: lectures honorarias; Novartis: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees. Alizadeh: Cibermed: Consultancy, Current holder of individual stocks in a privately-held company, Current holder of stock options in a privately-held company; 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.

Biological heterogeneity in diffuse large B cell lymphoma (DLBCL) is partly driven by cell-of-origin subtypes and associated genomic lesions, but also by diverse cell types and cell states in the tumor microenvironment (TME). However, dissecting these cell states and their clinical relevance at scale remains challenging. Here, we implemented EcoTyper, a machine learning framework integrating transcriptome deconvolution and single-cell RNA sequencing, to characterize clinically relevant DLBCL cell states and ecosystems. Using this approach, we identified five cell states of malignant B cells that vary in prognostic associations and differentiation status. We also identified striking variation in cell states for 12 other lineages comprising the TME and forming cell-state interactions in stereotyped ecosystems. While cell-of-origin subtypes have distinct TME composition, DLBCL ecosystems capture clinical heterogeneity within existing subtypes and extend beyond cell-of-origin and genotypic classes. These results resolve the DLBCL microenvironment at systems-level resolution and identify opportunities for therapeutic targeting (https://ecotyper.stanford.edu/lymphoma).

Determining how cells vary with their local signaling environment and organize into distinct cellular communities is critical for understanding processes as diverse as development, aging, and cancer. Here we introduce EcoTyper, a machine learning framework for large-scale identification and validation of cell states and multicellular communities from bulk, single-cell, and spatially resolved gene expression data. When applied to 12 major cell lineages across 16 types of human carcinoma, EcoTyper identified 69 transcriptionally defined cell states. Most states were specific to neoplastic tissue, ubiquitous across tumor types, and significantly prognostic. By analyzing cell-state co-occurrence patterns, we discovered ten clinically distinct multicellular communities with unexpectedly strong conservation, including three with myeloid and stromal elements linked to adverse survival, one enriched in normal tissue, and two associated with early cancer development. This study elucidates fundamental units of cellular organization in human carcinoma and provides a framework for large-scale profiling of cellular ecosystems in any tissue.

Introduction: Regulatory T cells (Tregs), a highly immunosuppressive subset of CD4 T cells, are enriched in B-cell non-Hodgkin lymphoma (NHL) and constitute a barrier to potent antitumor immune responses. Despite extensive studies, the significance of tumor-infiltrating Tregs on disease outcome is unclear and while Tregs may express co-inhibitory and co-stimulatory receptors, the role of intratumoral Tregs in the context of immune checkpoint therapy remains elusive. Emerging evidence suggests heterogeneity among Tregs and their suppressive capacities in cancer, emphasizing the need for additional markers to identify highly suppressive Tregs. Therefore, an in-depth characterization of Treg heterogeneity in NHL could provide important insight into the disease pathogenesis and have implications for rational drug design. Methods: Expression of checkpoint receptors in Tregs was characterized by fluorescence flow cytometry and mass cytometry analysis of single-cell suspensions from diffuse large B-cell lymphoma (DLBCL; n = 16), follicular lymphoma (FL; n = 8), mantle cell lymphoma (MCL; n = 10), marginal zone lymphoma (MZL; n = 2), chronic lymphocytic lymphoma (CLL; n = 7), as well as tonsils (n = 8) and peripheral blood (n = 4) from healthy donors. Functional characterization of intratumoral Tregs was performed by a proliferation assay using FACS-sorted Tregs as suppressor cells and autologous CellTrace Violet-labelled T effector cells as responder cells. Single-cell RNA sequencing (scRNA-seq) was performed on FACS-sorted CD4 T cells from 3 DLBCL, 3 FL and 3 healthy donor tonsils using the 10X Genomics single cell 5' based library construction and VDJ libraries for TCR-sequencing. Additionally, for simultaneous profiling of phenotypic features with the mRNA expression in single cells, Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITEseq) was applied. The Treg compartment was characterized by clustering into distinct transcriptional Treg states and differential expression of marker genes. Results: TIGIT and CTLA-4 were identified as common markers of intratumoral Tregs, in addition to FOXP3 and CD25. Unsupervised computational analysis revealed two distinct Treg subsets, based on contrasting expression of PD-1, OX40, CD226 and ICOS (Figure 1A). One subset displayed a checkpoint receptorlow phenotype that corresponded to peripheral blood Tregs. The second subset had a checkpoint receptorhigh phenotype with elevated levels of PD-1, OX40, ICOS, TIGIT, CTLA-4 and increased levels of activation markers CD28, CD69 and CD95/Fas. The frequency of checkpoint receptorhigh Tregs was significantly increased in NHL tumors, compared to PBMCs and tonsils from healthy donors. FL tumors had the highest frequency of Tregs with receptorhigh phenotype among the NHL entities (median frequency of 86%, range 71-92%) and DLBCL had the highest donor-to-donor variation (median frequency of 77%, range 35-98%) (Figure 1B). This phenotypic heterogeneity of the Treg compartment reflected different suppressive capacities of the two subsets. Checkpoint receptorhigh Tregs were more potent mediators of immunosuppression in terms of suppressing the proliferation of autologous effector CD4 and CD8 T cells (Figure 1C). Furthermore, transcriptomic analysis of CD4 T cells by scRNA-seq and CITEseq revealed distinct transcriptomic signatures of the checkpoint receptorhigh and -receptorlow subsets. In addition, a third subset of Tregs, characterized by increased expression of LAG3 and immunosuppression-associated genes (CTLA-4, IL10, CD38, KLRB1) but lack of FOXP3, was identified (Figure 1D-E). Analysis of scTCR-sequences to compare TCR repertoires and to identify developmental trajectories will further add to our knowledge of intratumoral Tregs. Conclusions: These results reveal heterogeneity within the Treg compartment in NHL based on expression of checkpoint receptors, transcriptional profiles and suppressive capacities. As intratumoral Treg phenotypes differ from peripheral blood Tregs, this presents new therapeutic opportunities. Specific targeting of intratumoral Tregs would lead to stronger antitumor effects while limiting immune-related adverse events. A deeper understanding of Treg heterogeneity within the tumor microenvironment could therefore open new paths for rational design of immune checkpoint therapy. Disclosures Kolstad: Merck: Research Funding; Nordic Nanovector: Membership on an entity's Board of Directors or advisory committees, Research Funding. Alizadeh:Janssen: Consultancy; Genentech: Consultancy; Pharmacyclics: Consultancy; Chugai: Consultancy; Celgene: Consultancy; Gilead: Consultancy; Roche: Consultancy; Pfizer: Research Funding.

Key Points Diagnostic and relapse diffuse large B-cell lymphoma (DLBCL) biopsies reveal increased mutational burden/loss of heterozygosity in HLA-A. Serially sampled tumor biopsies provide insight into therapeutic targets and evolutionary divergence in relapsed/refractory DLBCL.

Objective The study sought to develop a comprehensive and current description of what Clinical Informatics Subspecialty (CIS) physician diplomates do and what they need to know. Materials and Methods Three independent subject matter expert panels drawn from and representative of the 1695 CIS diplomates certified by the American Board of Preventive Medicine contributed to the development of a draft CIS delineation of practice (DoP). An online survey was distributed to all CIS diplomates in July 2018 to validate the draft DoP. A total of 316 (18.8%) diplomates completed the survey. Survey respondents provided domain, task, and knowledge and skill (KS) ratings; qualitative feedback on the completeness of the DoP; and detailed professional background and demographic information. Results This practice analysis resulted in a validated, comprehensive, and contemporary DoP comprising 5 domains, 42 tasks, and 139 KS statements. Discussion The DoP that emerged from this study differs from the 2009 CIS Core Content in 2 respects. First, the DoP reflects the growth in amount, types, and utilization of health data through the addition of a practice domain, tasks, and KS statements focused on data analytics and governance. Second, the final DoP describes CIS practice in terms of tasks in addition to identifying knowledge required for competent practice. Conclusions This study (1) articulates CIS diplomate tasks and knowledge used in practice, (2) provides data that will enable the American Board of Preventive Medicine CIS examination to align with current practice, (3) informs clinical informatics fellowship program requirements, and (4) provides insight into maintenance of certification requirements.

Summary Although treatment of non-small cell lung cancer (NSCLC) with immune checkpoint inhibitors (ICIs) can produce remarkably durable responses, most patients develop early disease progression. Furthermore, initial response assessment by conventional imaging is often unable to identify which patients will achieve durable clinical benefit (DCB). Here, we demonstrate that pre-treatment circulating tumor DNA (ctDNA) and peripheral CD8 T cell levels are independently associated with DCB. We further show that ctDNA dynamics after a single infusion can aid in identification of patients who will achieve DCB. Integrating these determinants, we developed and validated an entirely noninvasive multiparameter assay (DIREct-On, Durable Immunotherapy Response Estimation by immune profiling and ctDNA-On-treatment) that robustly predicts which patients will achieve DCB with higher accuracy than any individual feature. Taken together, these results demonstrate that integrated ctDNA and circulating immune cell profiling can provide accurate, noninvasive, and early forecasting of ultimate outcomes for NSCLC patients receiving ICIs.

Although treatment of non-small cell lung cancer (NSCLC) with immune checkpoint inhibitors (ICI) can produce remarkably durable responses, most patients develop early disease progression. Furthermore, initial response assessment by conventional imaging is often unable to identify which patients will achieve durable clinical benefit (DCB). Here, we analyze 211 samples from 99 patients and demonstrate that pre-treatment circulating tumor DNA (ctDNA) and circulating immune profiles are independently associated with DCB. We further show that ctDNA dynamics after a single ICI infusion can identify the majority of patients who will achieve DCB. Integrating these determinants, we describe an entirely noninvasive multi-analyte assay (DIREct-On, Durable Immunotherapy Response Estimation by immune profiling and ctDNA- On-treatment) that robustly predicted DCB, and that was validated in two independent cohorts (AUC = 0.89-0.93, PPV = 92-100%, HR = 0.04-0.11). Taken together, these results demonstrate that integrated ctDNA and circulating immune cell profiling can provide accurate, noninvasive, and early forecasting of ultimate outcomes for NSCLC patients receiving ICI. Citation Format: Barzin Y. Nabet, Mohammad S. Esfahani, Emily G. Hamilton, Jacob J. Chabon, Everett J. Moding, Hira Rizvi, Chloe B. Steen, Aadel A. Chaudhuri, Chih Long Liu, Angela B. Hui, Henning Stehr, Linda Goljenola, Michael C. Jin, Young-Jun Jeon, Diane Tseng, Taha Merghoub, Joel W. Neal, Heather A. Wakelee, Sukhmani K. Padda, Kavitha J. Ramchandran, Millie Das, Rene F. Bonilla, Christopher Yoo, Emily L. Chen, Ryan B. Ko, Aaron M. Newman, Matthew D. Hellmann, Ash A. Alizadeh, Maximilian Diehn. A noninvasive approach for early prediction of therapeutic benefit from immune checkpoint inhibition for lung cancer [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 5666.

Tumors are complex ecosystems consisting of malignant, immune, and stromal elements whose dynamic interactions drive patient survival and response to therapy. A comprehensive understanding of the diversity of cellular states within the tumor microenvironment (TME), and their patterns of co-occurrence, could provide new diagnostic tools for improved disease management and novel targets for therapeutic intervention. To address this challenge, we developed EcoTyper, a novel machine learning framework for large-scale identification of TME cell states and their co-association patterns from bulk, single-cell, and spatially resolved tumor expression data. EcoTyper starts by “purifying” cell type-specific gene expression profiles of epithelial cells, immune, and stromal cell types from bulk tissue transcriptomes using CIBERSORTx (Newman et al., Nat Biotechnol 2019). It then identifies transcriptional states for each cell type, validates them in scRNA-seq data, and uncovers co-occurrence patterns between cell states in order to define tumor cellular ecosystems. Applied to 6,475 tumor and adjacent normal samples from solid tumor types profiled by The Cancer Genome Atlas (TCGA), EcoTyper identified robust transcriptional states across 12 major cell types, including epithelial, fibroblast, endothelial, and 9 immune subsets. These states included both known and novel cellular phenotypes, nearly all of which could be validated in a compendium of scRNA-seq tumor atlases spanning ~140,000 cells. Most cell states were specific to neoplastic tissue, ubiquitous across tumor types, and significantly associated with overall survival, both in TCGA and in 9,062 held-out tumor specimens (Gentles/Newman et al., Nat Medicine 2015). We found that specific cell states co-occur in distinct cellular communities with characteristic patterns of ligand-receptor interactions, genomic features, clinical outcomes, and spatial organization. One such ecosystem defined a normal-like state that was strongly enriched in non-malignant samples. Others delineated novel pro- and anti-tumor inflammatory environments involving specific fibroblast, endothelial, and immune cell transcriptional programs. In summary, large-scale deconvolution of cell type-specific transcriptomes across thousands of solid tumors revealed a comprehensive atlas of TME cell states and cellular ecosystems. Our results provide a high-resolution portrait of cellular heterogeneity in the TME across multiple solid tumor types, with implications for novel diagnostics and immunotherapeutic targets. References: 1. Newman, A.M., et al., Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat Biotechnol, 2019. 37(7): p. 773-782. 2. Gentles, A.J., et al., The prognostic landscape of genes and infiltrating immune cells across human cancers. Nature medicine, 2015. 21(8): p. 938. Citation Format: Bogdan A. Luca, Chloé B. Steen, Armon Azizi, Magdalena Matusiak, Joanna Przybyl, Nastaran Neishaboori, Almudena Espín Pérez, Maximilian Diehn, Ash A. Alizadeh, Matt van de Rijn, Andrew J. Gentles, Aaron M. Newman. Atlas of clinically-distinct cell states and cellular ecosystems across human solid tumors [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 3443.

The tumor microenvironment (TME) plays critical roles in cancer development, tumor progression, and susceptibility to therapy. However, the phenotypic states and interaction patterns of its underlying cell types remain poorly understood. To address this challenge, we developed a new computational framework, EcoTyper, for large-scale dissection of cell states and cellular communities (i.e., ecosystems) from tumor genomic profiles. EcoTyper integrates single-cell RNA sequencing (scRNA-seq) with CIBERSORTx, an algorithm for bulk RNA-seq deconvolution (Newman et al., Nat Biotechnol, 2019), to identify and validate cellular states and ecosystems that reflect fundamental distinctions in TME biology. Here we applied EcoTyper to diffuse large B cell lymphoma (DLBCL), an aggressive B cell malignancy with clinically distinct molecular subtypes and several immunologically-active therapies. We sought to determine whether EcoTyper could reveal novel biological variation in the DLBCL TME linked to tumor subtypes and genotypes, therapeutic responses, and clinical outcomes. We applied our approach to define transcriptional states from 13 cell types, including malignant, immune, and stromal cells, in over 1,500 DLBCL tumors. Remarkably, nearly all cell states identified by EcoTyper were validated in independent scRNA-seq and bulk RNA-seq datasets. Moreover, many cells states reflected novel phenotypic groupings, and the majority showed strong associations with overall survival, specific mutational profiles, and tumor molecular subtypes. Additionally, by identifying DLBCL tumors with similar communities of cellular states, we defined novel cellular ecosystems, or “ecotypes”, with distinct biological characteristics and clinical outcomes. Several ecotypes showed significant enrichments in canonical or novel tumor genotypes, suggesting an evolutionary interplay between the tumor and host TME. In summary, we developed a novel computational framework to dissect the TME at scale and present the most comprehensive atlas to date of cell states and cellular communities in DLBCL. Our approach is extensible to nearly any cancer type and may lead to the development of novel diagnostics and individualized immunotherapies. Citation Format: Chloe B. Steen, Bogdan Luca, Mohammad S. Esfahani, Barzin Y. Nabet, Brian Sworder, Farshad Farshidfar, David Kurtz, Chih Long Liu, Ranjana H. Advani, Yasodha Natkunam, June H. Myklebust, Maximilian Diehn, Andrew Gentles, Ash Alizadeh, Aaron M. Newman. Landscape of tumor cell states and cellular ecosystems in diffuse large B cell lymphoma [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 1557.

Purpose For indolent lymphoma, the optimal timing, sequence, and choice of therapeutic regimens remain a matter of debate. In two Nordic Lymphoma Group randomized trials, symptomatic or clearly progressing patients were treated first line with a rituximab-containing regimen without chemotherapy. The purpose of this study was to assess long-term survival, risk of transformation, and need of new therapies. Methods Data were collected at cross-sectional follow-up for 321 patients with indolent lymphoma (84% with follicular lymphomas [FL]) included in one of two Nordic Lymphoma Group trials (accrual 1998 to 1999 and 2002 to 2008). All patients received first-line therapy with one or two cycles of four weekly infusions of rituximab 375 mg/m2, and 148 were randomly allocated to the addition of interferon alfa-2a. Follow-up data were retrieved from initial trial databases and medical records on repeated clinical evaluations. Results At the end of follow-up, 73% of patients were alive, with a median follow-up after random assignment of 10.6 years. Among all, 36% (38% with FL) had never needed chemotherapy. For patients with FL who required new therapy within 24 months because of early disease progression, the 10-year survival rate was 59% versus 81% for those with longer remission. Interferon was not shown to improve long-term outcome. Transformation was diagnosed in 20% of all patients (2.4% per person-year) and in 18% with FL. An additional malignancy was found in 12%. Conclusion Approximately one third of patients with symptomatic indolent lymphoma (30% with FL, 23% without FL) did not need new therapy in the long term after first-line rituximab without chemotherapy. In the entire cohort, 10-year survival was excellent with no major safety issues, which suggests that chemotherapy can be delayed safely in the majority of patients.

The aim of the present study was to assess indications for induction and describe the characteristics and delivery outcome in medical compared to non-medical/elective inductions. During a three-month period, 1663 term inductions were registered in 24 delivery units in Norway. Inclusion criteria were singleton pregnancies with cephalic presentation at gestational age 37+0 and beyond. Indications, pre-induction Bishop scores, mode of delivery and adverse maternal and fetal outcomes were registered, and compared between the medically indicated and elective induction groups. Ten percent of the inductions were elective, and the four most common indications were maternal request (35%), a previous negative delivery experience or difficult obstetric history (19%), maternal fatigue/tiredness (17%) and anxiety (15%). Nearly half of these inductions were performed at 39+0–40+6 weeks. There were fewer nulliparous women in the elective compared to the medically indicated induction group, 16% vs. 52% (p

Single-cell RNA-sequencing has emerged as a powerful technique for characterizing cellular heterogeneity, but it is currently impractical on large sample cohorts and cannot be applied to fixed specimens collected as part of routine clinical care. We previously developed an approach for digital cytometry, called CIBERSORT, that enables estimation of cell type abundances from bulk tissue transcriptomes. We now introduce CIBERSORTx, a machine learning method that extends this framework to infer cell-type-specific gene expression profiles without physical cell isolation. By minimizing platform-specific variation, CIBERSORTx also allows the use of single-cell RNA-sequencing data for large-scale tissue dissection. We evaluated the utility of CIBERSORTx in multiple tumor types, including melanoma, where single-cell reference profiles were used to dissect bulk clinical specimens, revealing cell-type-specific phenotypic states linked to distinct driver mutations and response to immune checkpoint blockade. We anticipate that digital cytometry will augment single-cell profiling efforts, enabling cost-effective, high-throughput tissue characterization without the need for antibodies, disaggregation or viable cells.

Background Diffuse large B-cell lymphoma (DLBCL) is a genetically and clinically heterogeneous disease. The cell-of-origin (COO) classification subdivides DLBCL into the transcriptionally defined activated B-cell (ABC) and germinal center B-cell (GCB) subtypes. Recently, 2 novel classifiers based on genetic features were independently proposed further unraveling the diversity of DLBCL [Schmitz et al, NEJM2018; Chapuy et al, Nat Med2018]. The concordance between the 2 novel classification systems has not yet been systematically studied. However, both classifiers are largely complementary to COO subtypes, and describe overlapping genotypes. We previously demonstrated the feasibility of COO classification by noninvasive plasma genotyping in a limited gene panel using Cancer Personalized Profiling by Deep Sequencing (CAPP-Seq) [Scherer et al, STM2016], and this approach has now been replicated by others. In this study, we take first steps toward a comprehensive non-invasive classification of novel DLBCL genetic subtypes using a limited gene panel. Methods We analyzed genetic profiling of 476 DLBCL patients reported by Schmitz et al (NEJM 2018) as a training set to build 2 classifiers in a limited gene panel applicable to plasma genotyping from CAPP-Seq: (1) A COO classifier (i.e. ABC, GCB and Unclassified); (2) A comprehensive genetic classifier (i.e. EZB, BN2, MCD, N1 and Other as defined in Schmitz et al, NEJM 2018). Features were limited to genetic alterations captured by our plasma genotyping panel, and those with population frequency of at least 10% in at least one genetic subtype. Our final model comprised 100 features: 64 recurrently mutated genes, 26 amplifications, 7 deletions and 3 translocations (BCL2, BCL6 and MYC). After cross-validation in the training set, we applied the 2 classifiers to the dataset from Chapuy et al (Nat Med 2018) as well as pretreatment plasma genotyping data from patients previously reported by our group [Kurtz et al, JCO 2018]. Results We first evaluated our 2 classifiers in a 10-fold cross-validation (CV) framework in the NEJM 2018 dataset of Schmitz et al. Despite modest performance of our GCB/ABC classification, COO labels had the expected significant prognostic associations (Fig. 1A). Overall accuracy of our second classifier to determine novel genetic subtypes was 82% (Fig. 1B). Consistent with the original study, inferred MCD and N1 subtypes had adverse prognosis compared to EZB and BN2 (Fig. 1C). We next applied our classifiers to the Chapuy et al (Nat Med 2018) dataset. Again, consistent with findings by Schmitz et al (NEJM 2018), the EZB subset of GCB cases had inferior outcome compared to non-EZB cases (Fig. 1D). We next examined the cross-correlation between the two classifiers and observed the expected enrichment patterns of ABCs in the MCD subset and enrichment of GCBs in the EZB subset (Fig. 1E). Finally, we applied our classifiers to plasma genotyping data previously reported by our group [Kurtz et al., JCO 2018]. We restricted the analysis to cases with a mean variant allele fraction ≥0.5% (n=68). Similar to the original study, 59% of cases (40/68) were labeled unclassifiable (i.e. Other). We compared the distribution of COO subtypes within the Schmitz genetic clusters. Representation of ABC and GCB within the clusters inferred from Plasma genotyping (Fig. 1F) was similar to the distribution from Tumor genotyping (Fig. 1E). Conclusions We describe 2 new classifiers applicable to noninvasive plasma genotyping data that recapitulate transcriptionally and genetically defined DLBCL subtypes. Using independent datasets, we show the feasibility of classification with a limited feature set with good prediction accuracy and prognostic stratification of defined subtypes. Genotyping of pretreatment plasma samples suggest that comprehensive non-invasive classification of genetic subtypes of DLBCL is achievable. Disclosures Kurtz: Roche: Consultancy. Diehn:BioNTech: Consultancy; Quanticell: Consultancy; Roche: Consultancy; AstraZeneca: Consultancy; Novartis: Consultancy. Alizadeh:Roche: Consultancy; Genentech: Consultancy; Janssen: Consultancy; Pharmacyclics: Consultancy; Gilead: Consultancy; Celgene: Consultancy; Chugai: Consultancy; Pfizer: Research Funding.

Introduction Relapses of diffuse large B-cell lymphoma typically occur within 2-3 years and only 10% of these patients reach a 3-year progression-free survival compared to 65% at diagnosis. Our ability to distinguish patients at risk for relapse remains based on clinical staging. We hypothesized that identifying genetic alterations in serial tumour biopsies at diagnosis and relapse would improve our ability to identify high-risk patients, make therapeutic selections and reveal molecular markers for chemo-immunotherapy resistant tumours. However, relatively few relapsed/refractory biopsies have been sequenced. A unique, clinically annotated, Nordic DLBCL cohort was used to identify significantly mutated genes, assess potential driver genes, comprehensively examine clonal evolution, and gauge the importance of clinical relapsed sampling. Methods To address the lack of information on the molecular foundations of relapsed/refractory DLBCL, we performed whole exome sequencing (WES) on 42 DLBCL cases, with 34% representing relapsed/refractory biopsies and 13 serially sample cases. Enriched with relapsed/refractory diffuse large B-cell lymphoma cases, we performed multiple computational analyses to identify significantly mutated genes (MutSig2CV), mutational signatures (NMF and DeConstructsSig), driver genes (IntOgen and CADD), clonal evolution architecture (SciClone and ClonEvol), druggable gene analysis (DGIdb), and HLA-inference and mutation calling (Polysolver). Results Clonal evolution analysis of 13 paired diagnostic and relapsed biopsies revealed that relapsed/refractory biopsies have remarkable similarities to diagnostic biopsies and often present with late divergent clonal evolution of the tumor. Mutational analysis of 18 serially sampled tumors determined that in the majority of cases druggable oncogenic variants do arise at relapse. In addition, time to relapse correlated with divergence of mutations from the diagnostic biopsy. In addition to being identified as a significantly mutated gene, mutations in HLA-A had an increased incidence in cases that ultimately relapsed. This result led to an in-depth investigation into the mutational prevalence, timing, impact on prognosis, and loss of heterozygosity in the human leukocyte antigen (HLA) haplotypes of relapsed/refractory DLBCL. HLA-A mutagenesis and loss of heterozygosity was discovered as mechanisms of immune evasion in cases that go on to relapse from R-CHOP like therapies (Figure 1). Conclusions Our results yield insight into the development of chemo-immunotherapy resistant diffuse large B-cell lymphoma, and highlight the clinical importance of sampling relapsed biopsies. Analysis of immune evasion through MHC Class I/II, specifically HLA-A, may provide better characterization of patients for relapse prediction. In the age of personalized medicine it will be instrumental to determine if relapsed biopsies offer additional insight for salvage therapy treatment. Divergence of biopsies, as characterized by shared genomic mutations, increase with time and the majority of cases present with new alterations in druggable genes post-therapy. Disclosures Leppa: Roche: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Celgene: Consultancy; Bayer: Research Funding. Holte:Novartis: Honoraria, Other: Advisory board.

Background: Diffuse large B cell lymphoma (DLBCL) exhibits significant clinical and biological heterogeneity, in part due to cell-of-origin subtypes, somatic alterations, and diverse stromal constituents within the tumor microenvironment (TME). Several immunologically-active lymphoma therapies are known to rely on innate and adaptive anti-tumor responses occurring within this dynamic TME, including agents that are approved (e.g., rituximab, lenalidomide, CART19, ibrutinib) or emerging (e.g., anti-CD47, checkpoint inhibitors). We hypothesized that a large-scale characterization of the cellular heterogeneity in DLBCL might reveal previously unknown biological variation in the TME linked to tumor subtypes and genotypes, therapeutic responses and clinical outcomes, with implications for future personalization of immunotherapy. Methods: Using a combination of lymphoma single-cell RNA sequencing (scRNA-seq) and bulk tumor transcriptome deconvolution (CIBERSORTx; Newman et al., Nat Biotech, 2019), we developed a new machine learning framework for identifying cellular states and ecosystems that reflect fundamental TME subtypes and distinctions in tumor biology (Fig. 1). Specifically, using CIBERSORTx, we purified the transcriptomes of B cells and 12 different TME cell types, including immune and stromal subsets, from 1,279 DLBCL tumor biopsies profiled in 3 prior studies (Reddy et al., Cell 2017; Schmitz et al., NEJM 2018; Chapuy et al., Nat Med 2018). Then, we defined distinct transcriptional states for each of the 13 cell types, which we validated at single-cell resolution, using a combination of two scRNA-seq techniques (Smart-Seq2 and 10x Chromium 5' GEP, BCR and TCR) to profile primary DLBCL, FL, and human tonsils, as well as leveraging multiple scRNA-seq datasets from previous studies. We identified robust co-associations between cell states that form tumor cellular ecosystems, which we validated in independent datasets of bulk DLBCL tumor gene expression profiles. Finally, we related TME ecosystems to defined tumor subtypes, including genotype classes, and to clinical outcomes. Results: By systematically characterizing the landscape of cellular heterogeneity in nearly 1,300 DLBCL tumors, we defined an atlas of 49 distinct transcriptional states across 13 major cell types. These novel cell states spanned diverse innate and adaptive immune effector cells of the lymphoid and myeloid lineages, as well as tumor-associated fibroblasts. Remarkably, 94% of these states (46 of 49) could be validated in a compendium of ~200,000 single-cell transcriptomes derived from lymphomas, healthy control tonsils, and other tissue types. Moreover, single cells from DLBCL, FL and tonsils best mirrored these newly discovered cell states. We next characterized the biology and potential clinical utility of each cell state. We observed clear distinctions in the transcriptional programs of immune and stromal elements between germinal center and activated B cell DLBCL, as well as between known mutational subtypes. Importantly, many cell states reflected novel phenotypic groupings, and the majority were significantly associated with overall survival (P Conclusion: We describe a novel computational framework to digitally dissect the DLBCL TME and an atlas of novel states for diverse cell types in these tumors. We show how cellular states form cohesive tumor ecosystems, which exhibit distinct clinical outcomes and novel somatic alterations. These results expand our understanding of cellular heterogeneity in DLBCL, with implications for the development of individualized immunotherapies. Disclosures Kurtz: Roche: Consultancy. Advani:Kura: Research Funding; Merck: Research Funding; Millennium: Research Funding; Pharmacyclics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Regeneron: Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Cell Medica, Ltd: Consultancy; Kyowa Kirin Pharmaceutical Developments, Inc.: Consultancy; Stanford University: Employment, Equity Ownership; Janssen: Research Funding; AstraZeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Consultancy, Research Funding; Infinity Pharma: Research Funding; Bayer: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Research Funding; Celmed: Consultancy, Membership on an entity's Board of Directors or advisory committees; Forty-Seven: Research Funding; Roche/Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead Sciences, Inc./Kite Pharma, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Autolus: Consultancy, Membership on an entity's Board of Directors or advisory committees; Agensys: Research Funding. Diehn:Roche: Consultancy; AstraZeneca: Consultancy; Novartis: Consultancy; BioNTech: Consultancy; Quanticell: Consultancy. Alizadeh:Janssen: Consultancy; Genentech: Consultancy; Pharmacyclics: Consultancy; Chugai: Consultancy; Celgene: Consultancy; Gilead: Consultancy; Roche: Consultancy; Pfizer: Research Funding.

To investigate the association between adherence to health recommendations and detection of advanced colorectal neoplasia (ACN) in colorectal cancer (CRC) screening.A total of 14832 women and men were invited to CRC screening, 6959 in the fecal immunochemical test arm and 7873 in the flexible sigmoidoscopy arm. These were also sent a self-reported lifestyle questionnaire to be completed prior to their first CRC screening. A lifestyle score was created to reflect current adherence to healthy behaviors in regard to smoking, body mass index, physical activity, alcohol consumption and food consumption, and ranged from zero (poorest) to six (best). Odds ratios (ORs) and 95%CIs were calculated using multivariable logistic regression to evaluate the association between the single lifestyle variables and the lifestyle score and the probability of detecting ACN.In all 6315 women and men completed the lifestyle questionnaire, 3323 (53%) in the FIT arm and 2992 (47%) in the FS arm. This was 89% of those who participated in screening. ACN was diagnosed in 311 (5%) participants of which 25 (8%) were diagnosed with CRC. For individuals with a lifestyle score of two, three, four, and five-six, the ORs (95%CI) for the probability of ACN detection were 0.82 (0.45-1.16), 0.43 (0.28-0.73), 0.41 (0.23-0.64), and 0.41 (0.22-0.73), respectively compared to individuals with a lifestyle score of zero-one. Of the single lifestyle factors, adherence to non-smoking and moderate alcohol intake were associated with a decreased probability of ACN detection compared to being a smoker or having a high alcohol intake 0.53 (0.42-0.68) and 0.63 (0.43-0.93) respectively.Adopted healthy behaviors were inversely associated with the probability of ACN detection. Lifestyle assessment might be useful for risk stratification in CRC screening.