ALBERT

Description: Genomic aberrations in a series of paired biopsy samples from patients who presented initially with follicle center lymphoma (FCL) and subsequently transformed to diffuse large B-cell lymphoma (DLBCL) were measured by array comparative genomic hybridization (CGH). The consequences of these aberrations on gene expression were determined by comparison with expression analysis on these specimens using cDNA microarrays. A heterogeneous pattern of acquired genomic abnormalities was observed upon transformation, some of which were recurrent in small subsets of patients. Some of the genomic aberration acquired upon transformation, such as gain/amplification of 1q21-q24, 2p16 (REL/BCL11A gene loci), 3q27-q29 (including theBCL6 locus), 7q11.2-q22.1, 12pter-q12, 18q21 (including theBCL2 locus) and Xq, and deletion of 6q22-q24, 13q14-q21 and 17p13 (P53 locus) have been previously implicated in the FCL/DLBCL pathogenesis. In addition, novel genomic imbalances not previously reported in association with FCL transformation, such as overrepresentation of 4p12-pter, 5p12-p15, 6p12.3-p21, 9p23, 9q13-q31, 16q, 17q21, and loss of 1p36.3, 4q21-q23, 5q21-q23, 9q31-qter, 11q24-q25, and 15q23, were identified. We observed a differential expression profile of many genes within regions of gain and deletion upon transformation, including novel target genes associated with FCL transformation. However, other genes did not show deregulated expression despite their location within these areas. In summary, the combination of array CGH and expression analysis provides a more comprehensive picture of the transformation of FCL to DLBCL. This process is associated with the acquisition of a variable spectrum of genomic imbalances affecting recurrent chromosomal areas that harbor overexpressed or underexpressed genes targeted upon transformation.

Description: 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.

Description: 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

Description: Abstract 2390 Background Hematological malignancies are among leading causes of cancer-related deaths in the United States. We sought to identify novel immunological signatures of survival in blood cancers, with a particular focus on tumor-infiltrating cells (TICs), known to have prognostic significance in several tumor types [1–3]. We employed a computational genomics approach to retrospectively analyze thousands of gene expression profiles (GEPs) from individual tumors. By incorporating prior knowledge of genes specifically enriched in purified immune cell phenotypes, we estimated the relative contributions of distinct TICs in blood cancer GEP admixtures, and derived cancer-specific TIC abundance signatures with strong prognostic significance. Method We assembled a GEP atlas from publicly available human microarray samples spanning diverse immune cell phenotypes and activation states, termed Immunome+. We separately generated GEPs of nearly 200 follicular lymphoma (FL) tumors obtained from a recently completed phase III clinical trial, and collected publicly available transcriptome profiles from 〉2000 patients with diverse hematological malignancies. To deconvolve GEP admixtures, we modeled each mRNA mixture by a system of linear equations, and given Immunome+, we optimally solved the system using a previously described method. We also estimated p-values using a novel approach, which yielded “goodness-of-fit” estimates for GEP deconvolution. Inferred TIC fractions were related to clinical outcomes using Cox proportional hazards regression. Results To benchmark our deconvolution strategy, we applied it to a variety of positive control expression profiles, and obtained estimates of cell-type frequencies that robustly correlated with known cell type proportions (GSE19380, GSE20300). We also assessed the cell type specificity of genes within Immunome+, and found that our method could robustly classify purified immune cell GEPs in external data sets with a mean accuracy of 94%. Next, we analyzed a variety of blood cancer data sets, including our own, to infer TIC relative proportions and associations with overall survival. Strikingly, we found that TIC abundance patterns and prognostic associations are highly correlated within the same tumor types profiled by different laboratories, whereas different tumor types have distinct immune cell infiltration and prognostic signatures. Among multiple TIC prognostic associations, we found that a higher proportion of estimated macrophage infiltrates is significantly associated with increased overall survival in independent cohorts of DLBCL, irrespective of rituximab treatment (P = 1.8×10−5). This is consistent with the enrichment of macrophage genes in the Stromal-1 prognostic signature identified by Lenz et al. [2]. We also found a significant association between CD4+ T-cells and favorable overall survival in FL (P= 0.01), including our cohort (n = 196) and one previously described [1]. Accordingly, we suggest that deconvolution is a promising strategy to disentangle known cell populations from heterogeneous blood cancer samples. Conclusions Our approach represents a novel way to explore the landscape of immunological signatures relating to cancer clinical outcomes, and offers a new resource for making experimentally testable predictions, and for discovery of new immunotherapeutic targets. Disclosures: No relevant conflicts of interest to declare.

Description: Abstract 1015 Poster Board I-37 Background: Early mortality, mostly from hemorrhagic complications, occurs in less than 10% of patients currently treated in clinical trials for acute promyelocytic leukemia (APL). However, data about the proportion of patients developing such complications prior to clinical trial enrollment are scarce in the literature (Sanz M, et al Blood 2009). Approximately 5% of newly diagnosed patients with APL have been reported not to be eligible for participation in clinical trials due to very poor clinical condition, and their outcome has never been reported. However, enrollment on clinical trials may be difficult in specific clinical situations, such as after hours/weekend admissions and/or emergent requirement for therapy. This study reports the incidence, time of occurrence and clinical features of APL patients with a focus on early mortality. Methods: 150 consecutive APL patients treated at Stanford University between 8/1986 and 7/2009 were identified. Thirteen patients were excluded for lack of appropriate clinical information. Clinical features of patients with APL were analyzed for factors that might be relate to prognosis, including age, gender, white blood cell count, platelet count, fibrinogen, PTT, and INR. Continuous variables were compared with the t-test and categorical variables by Fisher's exact test or X-square statistic. The Kaplan–Meier method was applied to assess overall survival time. Results: Of the 137 patients included in this analysis, the median age at diagnosis was 45 (1-93) years and 78 (57%) were females. Using the PETHEMA criteria, there were 37, 46 and 20 patients with high-, intermediate- and low-risk disease (34 patients could not be classified based on partial/missing data). With a median follow-up time of 748 (0-6,235) days for the entire cohort, 52 (38%) have died. 19 (14%) and 11 (8%) of these patients died within 7 and 3 days of presentation, respectively. Patients with high-risk features had a 13% and 24% chance of dying with 3 and 7 days of presentation, respectively, with significantly inferior outcomes (p=0.045) when compared to those with intermediate-risk patients (6% and 13%) and low-risk disease (5% and 5%). Patients with unknown risk category faired similarly to low-risk patients. The most common cause of early mortality in these 19 patients was intracranial hemorrhage (n=11). Patients with early death (ED) (either

Description: Several gene-expression signatures predict survival in diffuse large B-cell lymphoma (DLBCL), but the lack of practical methods for genome-scale analysis has limited translation to clinical practice. We built and validated a simple model using one gene expressed by tumor cells and another expressed by host immune cells, assessing added prognostic value to the clinical International Prognostic Index (IPI). LIM domain only 2 (LMO2) was validated as an independent predictor of survival and the “germinal center B cell–like” subtype. Expression of tumor necrosis factor receptor superfamily member 9 (TNFRSF9) from the DLBCL microenvironment was the best gene in bivariate combination with LMO2. Study of TNFRSF9 tissue expression in 95 patients with DLBCL showed expression limited to infiltrating T cells. A model integrating these 2 genes was independent of “cell-of-origin” classification, “stromal signatures,” IPI, and added to the predictive power of the IPI. A composite score integrating these genes with IPI performed well in 3 independent cohorts of 545 DLBCL patients, as well as in a simple assay of routine formalin-fixed specimens from a new validation cohort of 147 patients with DLBCL. We conclude that the measurement of a single gene expressed by tumor cells (LMO2) and a single gene expressed by the immune microenvironment (TNFRSF9) powerfully predicts overall survival in patients with DLBCL.

Description: Gene expression profiling studies sub-classified diffuse large B-cell lymphomas (DLBCL) into two clinically distinct types: germinal center B cell (GCB)-like and activated B-cell (ABC)-like tumors, characterized by long and short survival, respectively. At least two markers of the GCB-phenotype - BCL6 and HGAL - are IL-4 target genes whose high expression independently predicts better overall survival. Gene expression analysis of DLBCL also demonstrated higher levels of mRNA expression of components of the IL-4 signaling pathway (IL-4Rα, IRS, p110 subunit of PI-3 kinase, and PKC delta) in the GCB-like DLBCL (Alizadeh et al Nature. 2000;403:503). Identification of IL-4 inducible genes in normal B-lymphocytes revealed additional IL-4 target genes that are expressed at higher levels in GCB-like DLBCL compared to ABC-like DLBCL. Together, these observations support the distinct activity of the IL-4 signaling pathway in DLBCL subtypes. Accordingly, IL-4 stimulation of GCB-like (SUDHL6, SUDHL4 and OCILY19) and ABC-like (OCILY10 and OCILY3) DLBCL cell lines increased expression of its known target genes only in GCB-like, but not in ABC-like DLBCL. Further, IL-4 stimulation led to AKT phosphorylation in the ABC-like but not in the GCB-like cells. Conversely, IL-4 induced STAT6 phosphorylation (pSTAT6) in all the tested GCB-like and in the OCILY10 cell lines but not in the OCILY3 ABC-like cell-line. IL-4 induced progressive accumulation of large quantities of pSTAT6 in both the cytoplasm and in the nucleus in the GCB-like DLBCL. In contrast, in IL-4 treated ABC-like OCILY10 cells, pSTAT6 did not accumulate in either cytoplasm or nucleus, and much smaller amounts of pSTAT6 were detected in the nuclear extracts from stimulated cells. The latter observation was at least partially attributed to different extent of pSTAT6 nuclear deposphorylation and proteasomal degradation in the GCB-like and ABC-like DLBCL, as determined by exposure of these cell lines to STAT6 nuclear export inhibitor (leptomycin B) and phosphatase and proteasome inhibitors. Nuclear protein tyrosine phosphatase assays revealed significantly higher phosphatase activity in the ABC-like compared to the GCB-like DLBCL cell-lines. Evaluation of mRNA expression of 51 known tyrosine phosphatases in the GCB-like and ABC-like DLBCL tumors based on array data revealed that mRNA expression of 13 protein tyrosine phosphatases was significantly higher (p

Description: Abstract 4742 Introduction While the distance patients travel to a treatment center (DTC) adversely impacts survival of patients with trauma, cardiac, or neurological disorders, as well as certain solid tumors, less is known of its influence in acute myeloid leukemia (AML). Care for patients with AML involves frequent emergent and urgent management, often complicating primary therapy provided in distant tertiary referral centers. We therefore hypothesized that increased DTC has a negative impact on outcome. We tested this hypothesis by assessing the effect of DTC on survival of patients with AML receiving care at a single institution. Patients and Methods Within the Stanford Leukemia Database, we identified 884 consecutive adult patients between 1993 and 2009 meeting the following criteria: age 〉=18, newly diagnosed AML (excluding APL), clinical management at Stanford University Medical Center (SUMC), and verified residence location available for DTC determination. Of these, 571 were deemed fit by the admitting physician to receive myelosuppressive induction chemotherapy. DTC was calculated by straight-line journey distance between home address at the time of diagnosis and treatment center. Results The median age for the entire cohort is 55 years and 322 patients (36%) are older than 60 years of age. Median survival for the entire cohort was 14.0 months. DTC was not univariately associated with outcome as a continuous variable. When testing for a critical DTC threshold impacting outcomes across the entire cohort, we found a significant correlation between longer DTC and adverse outcomes, shorter DTC was associated with lower OS. Patients living within 20 miles of SUMC had a worse median overall survival (10.4 months versus 15.0 months, HR 1.23, corrected p-value 0.02). However, when adjusted for administration of induction chemotherapy (p

Description: Background: Anti-CD19 chimeric antigen receptor (CAR19) T-cells have significant activity in patients with relapsed/refractory DLBCL (rrDLBCL). While the majority of rrDLBCL patients receiving axicabtagene ciloleucel (Axi-cel)achieve complete responses, a significant subset of patients experience disease progression (Locke FL, et al. Lancet Oncol. 2019). Circulating tumor DNA (ctDNA) analysis has demonstrated utility for predicting therapeutic benefit in DLBCL, as well as for detecting emergent resistance mechanisms to targeted therapies. Here we apply cell-free DNA (cfDNA) analysis to patients receiving Axi-cel, to characterize molecular responses, resistance mechanisms, and to track CAR19 cells. Methods: We performed Cancer Personalized Profiling by Deep Sequencing (CAPP-Seq) on DNA from germline and plasma samples collected prior to CAR T-cell infusion, multiple time-points post infusion, and, where available, at the time of relapse from 30 patients receiving Axi-cel for rrDLBCL at Stanford University. We designed a novel hybrid-capture panel and analysis pipeline designed to detect both tumor variants, as well as Axi-cel specific recombinant retroviral sequences to quantify CAR19 levels in cfDNA. Tumor variants were identified prior to and following Axi-cel therapy to assess for emergent variants, and Axi-cel specific sequences were quantified. Results: The median follow-up for the 30 patients after Axi-cel infusion was 10 months, with 47% (14/30) of patients experiencing disease progression after Axi-cel therapy. We identified an average of 164.3 SNVs per case (range:1-685) before Axi-cel therapy; the most common coding variants identified at baseline were in MLL2 (29.2%), BCL2 (22.5%), and TP53 (19.3%). When treated as a continuous variable, pretreatment ctDNA levels were prognostic of PFS (HR 2.16, 95% CI 1.11-4.21, P=0.02). Using a previously established ctDNA threshold to stratify disease burden (2.5 log10(hGE/mL); Kurtz et al. JCO 2018), we observed significantly superior PFS in patients with low pretreatment ctDNA levels treated with Axi-cel (Fig. 1A). In the majority of Axi-cel treated patients (62.9%), ctDNA was detectable at day 28, and PFS was significantly longer in patients with undetectable ctDNA at this time-point (Fig. 1B). Multiple putative resistance mechanisms were identified at relapse after Axi-cel, including emergent variants in CD19, HVEM, and TP53, as well as copy number gains in PD-L1 (Fig. 1C). For example, in one patient, a CD19 stop-gain mutation, which was not detected prior to treatment or at the time of the first interim PET scan, emerged at the time of relapse (Fig. 1D). Finally, we found cfDNA evidence for Axi-cel DNA in 74% of patients 28 days after therapy, including in patients without evidence of circulating CAR T-cells in PBMCs. Axi-cel levels in cfDNA as measured by CAPP-Seq were significantly correlated with CAR19 flow cytometry (Pearson r=0.55, P=.015; Fig. 1E). Conclusions: Baseline and interim ctDNA measurements have prognostic significance in DLBCL patients being treated with CAR19 T-cells, and potential emergent resistance mutations, including in CD19, can be identified in patients via cfDNA analysis. Quantification of CAR19 T-cells using cfDNA is significantly correlated with flow cytometric quantification, indicating that these cells can be quantified via cfDNA. Taken together, these data indicate that cfDNA analysis is a powerful tool for predicting response to CAR19 therapy, identifying genomic determinants of resistance and quantifying CAR19 cells, which may in turn inform the next therapeutic steps. Figure 1: A) Kaplan Meier analysis of PFS, with patients stratified based on pre-Axi-cel therapy ctDNA level, above and below a previously established threshold (2.5 log10[haploid Genome Equivalents/mL]). B) A Kaplan Meier plot depicting PFS stratification for patients with detectable versus undetectable ctDNA at day 28 after Axi-cel infusion. C) Oncoprint depicting selected emergent and baseline tumor variants in progressors and non-progressors after Axi-cel therapy. D) Change in mean ctDNA variant allele frequency (VAF) and emergence of a CD19 stop-gain mutation (CD19 pTrpX) at the time of relapse in a patient who initially achieved a CR at day 28 after CAR19 infusion. E) Relationship between CAR19 T-cell quantification by cfDNA and flow cytometry. (ND: Not detected) Disclosures Kurtz: Roche: Consultancy. Chabon:Lexent Bio Inc: Consultancy. Khodadoust:Corvus Pharmaceuticals: Research Funding. Majzner:Xyphos Inc.: Consultancy; Lyell Immunopharma: Consultancy. Mackall:Obsidian: Research Funding; Lyell: Consultancy, Equity Ownership, Other: Founder, Research Funding; Nektar: Other: Scientific Advisory Board; PACT: Other: Scientific Advisory Board; Bryologyx: Other: Scientific Advisory Board; Vor: Other: Scientific Advisory Board; Roche: Other: Scientific Advisory Board; Adaptimmune LLC: Other: Scientific Advisory Board; Glaxo-Smith-Kline: Other: Scientific Advisory Board; Allogene: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Apricity Health: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Unum Therapeutics: Equity Ownership, Membership on an entity's Board of Directors or advisory committees. Diehn:Roche: Consultancy; Quanticell: Consultancy; Novartis: Consultancy; AstraZeneca: Consultancy; BioNTech: Consultancy. Miklos:Miltenyi Biotech: Membership on an entity's Board of Directors or advisory committees; Becton Dickinson: Research Funding; AlloGene: Membership on an entity's Board of Directors or advisory committees; Kite-Gilead: Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Juno: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees; Precision Bioscience: Membership on an entity's Board of Directors or advisory committees. Alizadeh:Genentech: Consultancy; Janssen: Consultancy; Pharmacyclics: Consultancy; Gilead: Consultancy; Celgene: Consultancy; Chugai: Consultancy; Roche: Consultancy; Pfizer: Research Funding.