Tian, Shulan

Abstract Frontotemporal lobar degeneration with TDP-43 inclusions (FTLD-TDP) is a complex heterogeneous neurodegenerative disorder for which mechanisms are poorly understood. To explore transcriptional changes underlying FTLD-TDP, we performed RNA-sequencing on 66 genetically unexplained FTLD-TDP patients, 24 FTLD-TDP patients with GRN mutations and 24 control participants. Using principal component analysis, hierarchical clustering, differential expression and coexpression network analyses, we showed that GRN mutation carriers and FTLD-TDP-A patients without a known mutation shared a common transcriptional signature that is independent of GRN loss-of-function. After combining both groups, differential expression as compared to the control group and coexpression analyses revealed alteration of processes related to immune response, synaptic transmission, RNA metabolism, angiogenesis and vesicle-mediated transport. Deconvolution of the data highlighted strong cellular alterations that were similar in FTLD-TDP-A and GRN mutation carriers with NSF as a potentially important player in both groups. We propose several potentially druggable pathways such as the GABAergic, GDNF and sphingolipid pathways. Our findings underline new disease mechanisms and strongly suggest that affected pathways in GRN mutation carriers extend beyond GRN and contribute to genetically unexplained forms of FTLD-TDP-A.

Abstract Clinical practice guidelines recommend that individuals with HC MBL are evaluated annually for evidence of progression as it has been shown that they progress to develop CLL requiring therapy at a rate of 1-5%/year. Next-generation sequencing identified 59 genes recurrently mutated in CLL, and we previously showed that the tumor mutational load (TML), i.e., the total number of these genes mutated, is associated with a shorter time to first treatment (TTT) among 112 HC MBLs independent of the CLL international prognostic index (CLL-IPI), an established prognostic score. Herein, we continue to support our finding in additional HC MBLs and report the pooled results across the two cohorts. From the Mayo Clinic CLL Resource, we sequenced a total 167 HC MBL (112 from the initial round and 55 from additional sequencing). We extracted DNA from CD5+/CD19+ B-cells and sequenced the coding regions of 59 driver genes. Somatic mutations were called using MuTect2 in tumor-only mode. Germline variants were eliminated based on minor allele frequencies >0.01% and by those identified in public databases, unless present in known CLL mutation hotspots. After filtering, we computed the TML based on the number of genes with high impact (nonsense, frameshift and splicing) and hotspot mutations. Cox regression was used to estimate hazard ratios (HR) and 95% confidence intervals (CI), adjusted for sex and CLL-IPI. Stratified analyses were conducted by low/intermediate risk and high/very high CLL-IPI. Of the 167 HC MBL, low risk CLL-IPI comprised 60%, intermediate risk 28%, high risk 12%; and none were very-high risk. The most commonly mutated genes were SF3B1 (11%), NOTCH1 (9%), TP53 (7%), BIRC3 (7%), and MYD88 (6%); and 52 (31%) individuals had mutations in at least one gene. The median follow-up was 4.4 years, and 26 of the 167 individuals progressed to CLL requiring therapy. HC MBL with ≥2 mutated genes had a 28% 5-year risk of needing therapy compared to 6% among those with no mutated genes. Similar to our initial finding (HR=1.53, CI:1.12-2.07; P=0.007), we found a shorter TTT in the additional HC MBLs (HR=4.63, CI:1.57-13.7, P=0.006), and in the pooled cohort (HR=1.72; CI:1.29-2.28, P=0.0002). When we stratified by CLL-IPI risk, TML remained associated with TTT among individuals with low/intermediate CLL-IPI risk in the additional HC MBLs (HR=3.52, CI:1.11-11.2, P=0.03) and in the pooled cohort (HR=2.25, CI:1.34-3.77, P=0.002). A non-significant 2.8 fold association was observed with high risk CLL-IPI in the pooled cohort. In conclusion, the TML along with the CLL-IPI provide clinicians with a more accurate prognostic information regarding disease progression requiring therapy, especially for those MBLs with low to intermediate risk based on CLL-IPI. If validated, these findings may modify current practice guidelines for individuals with HC MBL. Citation Format: Geffen Kleinstern, Cecilia Bonolo de Campos, Nicholas J. Boddicker, Daniel R. O'Brien, Shulan Tian, Chantel Barney, Kari G. Rabe, Cristine Allmer, Laura Bruins, Aaron D. Norman, Timothy G. Call, Sameer A. Parikh, Jose F. Leis, Wei Ding, Huihuang Yan, James R. Cerhan, Neil E. Kay, Celine Vachon, Esteban Braggio, Susan L. Slager. Tumor mutational load derived from recurrently mutated genes in chronic lymphocytic leukemia (CLL) predicts time-to-first treatment in high-count monoclonal B-cell lymphocytosis (HC MBL) [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 884.

Abstract Chronic lymphocytic leukemia (CLL) is a neoplastic disease of mature B-cells with a highly heterogeneous clinical course. While European ancestry (EA) populations present an increased incidence of CLL, African ancestry (AA) populations have a younger median age of onset, higher frequency of adverse prognostic factors, and inferior clinical outcomes. Despite the considerable effort to characterize the genetic landscape of CLL, AA are overwhelmingly underrepresented. Our hypothesis is that the clinical differences observed between AA and EA populations are, in part, explained by underlying genetic features. To address this imbalance, we identified 90 AA patients diagnosed with CLL, 64% of which were untreated at sample collection. RNA and DNA were extracted from CD5+/CD19+ clonal B-cells. We performed mRNA-seq and targeted sequencing in 59 recurrently mutated somatic CLL driver genes. Differentially expressed genes were identified using edgeR. Data was compared to our previously analyzed EA CLL cohort (N=445). The median age at diagnosis was 59 years for AA and 66 for EA and 74% of AA and 50% of EA had unmutated IGHV (u-IGHV) status. When evaluating the entire AA and EA cohorts, there was a significant increased frequency of mutations in TP53, SF3B1, and NFKBIE, identified in 29%, 24%, and 20% of AA CLLs, compared to 5%, 9%, and 9%, respectively, in EA CLLs (p<0.01). When exclusively evaluating the untreated and u-IGHV cases, AA CLLs showed greater proportion of TP53 (30% vs 12%; p=0.0145), NFKBIE (30% vs 15%; p=0.0492), BIRC3 (21% vs 10%; p=0.0697), and KRAS (15% vs 7%; p=0.1683). Furthermore, there was an increase in mutations targeting relevant molecular pathways, such as NF-κB (42% vs 15%) and MAPK (18% vs 8%). Upregulation MAPK pathway was also confirmed by mRNA-seq analysis in the AA u-IGHV CLLs. Because of the high prevalence of TP53 mutations in the AA cohort, we further evaluated differential gene expression in the DNA Damage/Telomere Stress-Induced Senescence pathway. AA CLLs presented a significant downregulation of multiple genes associated with genome stability and cellular DNA damage response - DDR (including TP53 and ATM), double strand break repair (H2AFX and RAD50), telomere maintenance (POT1 and ACD), and cell cycle regulation (RB1, CCNA1, and CCNE2) (FDR<0.05). DDR is responsible for DNA repair or induction of apoptosis, with its deficiency resulting in the accumulation of chromosomal aberrations, negatively impacting clinical outcome in CLL. Disparities in cancer are influenced by numerous factors that affect disease risk, screening and diagnosis, access to treatment, and survival. We identified an increased number of genomic alterations in the AA CLL cohort, primarily inducing activation of NF-κB and MAPK pathways and DDR impairment, with the increased frequency of mutations, notably in TP53 and BIRC3, expected to negatively impact prognosis. Citation Format: Cecilia Bonolo de Campos, Daniel R. O'Brien, Chantal E. McCabe, Huihuang Yan, Geffen Kleinstern, Zhiquan Wang, Laura A. Bruins, Cristine Allmer, Nicholas J. Boddicker, Charla R. Secreto, Aaron D. Norman, Shulan Tian, Kari G. Rabe, Timothy G. Call, Sameer A. Parikh, Jose F. Leis, Wei Ding, Richard Furman, J Brice Weinberg, James R. Cerhan, Celine M. Vachon, Neil E. Kay, Susan L. Slager, Esteban Braggio. Characterization of underlying genomic features among African ancestry populations diagnosed with chronic lymphocytic leukemia [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 2209.

Abstract T-cell prolymphocytic leukemia (T-PLL) is a rare disease showing a rapid clinical course, with a median survival of <1 year. Whole-genome and whole-exome sequencing have identified key genetic lesions in T-PLL, including structural alterations such as inversions, translocations, and large copy number variants, as well as recurrent somatic mutations of genes encoding chromatin regulators and of those in the JAK-STAT signaling pathway. Epigenetic alteration is implicated in the susceptibility and progression of both cancer and non-cancerous diseases. However, genome-wide epigenetic data has not been generated for T-PLL, which prohibits the mechanistic studies of transcriptional misregulation in T-PLL. This study used micrococcal nuclease digestion and sequencing (MNase-seq) to profile the chromatin accessible regions, i.e., gene regulatory regions such as promoters, enhancers and insulators, in both T-PLL patients and healthy individuals. Our analysis revealed the prevalence of lost open chromatin regions in T-PLL. We also generated histone H3 lysine 4 monomethylation (H3K4me1) and lysine 27 acetylation (H3K27ac) chromatin immunoprecipitation and sequencing (ChIP-seq) data from the same samples. We identified 5,462 and 2,833 active enhancers (>=2.5 kb away from transcription start sites) that are lost and gained in T-PLL, respectively, compared to normal. The differential H3K27ac peaks are preferentially associated with the nearest genes differentially expressed between T-PLL and normal (chi-square test p <2.2e-16), indicating a role of epigenetic alteration in transcriptional misregulation. In addition, we identified alterations of active enhancers that target oncogenes with key roles in T-PLL, such as TCL1Aand MYC. Together, our analysis has provided insights into the epigenetic mechanisms that drive oncogenic activation in T-PLL. Citation Format: Shulan Tian, Henan Zhang, Eric Klee, Huihuang Yan, Wei Ding. Genome-wide epigenetic alterations in T-cell prolymphocytic leukemia [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 2119.

Abstract Background Antibody drug conjugates (ADCs) targeting the epidermal growth factor receptor (EGFR), such as depatuxizumab mafodotin (Depatux-M), is a promising therapeutic strategy for glioblastoma (GBM) but recent clinical trials did not demonstrate a survival benefit. Understanding the mechanisms of failure for this promising strategy is critically important. Methods PDX models were employed to study efficacy of systemic vs intracranial delivery of Depatux-M. Immunofluorescence and MALDI-MSI were performed to detect drug levels in the brain. EGFR levels and compensatory pathways were studied using quantitative flow cytometry, Western blots, RNAseq, FISH, and phosphoproteomics. Results Systemic delivery of Depatux-M was highly effective in nine of 10 EGFR-amplified heterotopic PDXs with survival extending beyond one year in eight PDXs. Acquired resistance in two PDXs (GBM12 and GBM46) was driven by suppression of EGFR expression or emergence of a novel short-variant of EGFR lacking the epitope for the Depatux-M antibody. In contrast to the profound benefit observed in heterotopic tumors, only two of seven intrinsically sensitive PDXs were responsive to Depatux-M as intracranial tumors. Poor efficacy in orthotopic PDXs was associated with limited and heterogeneous distribution of Depatux-M into tumor tissues, and artificial disruption of the BBB or bypass of the BBB by direct intracranial injection of Depatux-M into orthotopic tumors markedly enhanced the efficacy of drug treatment. Conclusions Despite profound intrinsic sensitivity to Depatux-M, limited drug delivery into brain tumor may have been a key contributor to lack of efficacy in recently failed clinical trials.

AbstractBackgroundCurrently, there is no biologically based rationale for drug selection in migraine prophylactic treatment.MethodsTo investigate the genetic variation underlying treatment response to verapamil prophylaxis, we selected 225 patients from a longitudinally established, deeply phenotyped migraine database (N = 5983), and collected uninterrupted quantitated verapamil treatment response data and DNA for these 225 cases. We recorded the number of headache days in the four weeks preceding treatment with verapamil and for four weeks, following completion of a treatment period with verapamil lasting at least five weeks. Whole‐exome sequencing (WES) was applied to a discovery cohort consisting of 21 definitive responders and 14 definitive non‐responders, and the identified single nucleotide polymorphisms (SNPs) showing significant association were genotyped in a separate confirmation cohort (185 verapamil treated patients). Statistical analysis of the WES data from the discovery cohort identified 524 SNPs associated with verapamil responsiveness (p < 0.01); among them, 39 SNPs were validated in the confirmatory cohort (n = 185) which included the full range of response to verapamil from highly responsive to not responsive.ResultsFourteen SNPs were confirmed by both percentage and arithmetic statistical approaches. Pathway and protein network analysis implicated myo‐inositol biosynthetic and phospholipase‐C second messenger pathways in verapamil responsiveness, emphasizing the earlier pathogenic understanding of migraine. No association was found between genetic variation in verapamil metabolic enzymes and treatment response.ConclusionOur findings demonstrate that genetic analysis in well‐characterized subpopulations can yield important pharmacogenetic information pertaining to the mechanism of anti‐migraine prophylactic medications.

Introduction: Richter's syndrome (RS) represents transformation of chronic lymphocytic leukemia (CLL) into a highly aggressive lymphoma with dismal prognosis. Transcriptomic alterations have been described in CLL but most studies focused on peripheral blood samples with minimal data on RS-involved tissue. Moreover, transcriptomic features of RS have not been well defined in the era of CLL novel therapies. In this study we investigated transcriptomic profiles of CLL/RS-involved nodal tissue using samples from a clinical trial cohort of refractory CLL and RS patients treated with Pembrolizumab (NCT02332980). Methods: Nodal samples from 9 RS and 4 CLL patients in MC1485 trial cohort were reviewed and classified as previously published (Ding et al, Blood 2017). All samples were collected prior to Pembrolizumab treatment. Targeted gene expression profiling of 789 immune-related genes were performed on FFPE nodal samples using Nanostring nCounter® Analysis System (NanoString Technologies, Seattle, WA). Differential expression analysis was performed using NanoStringDiff. Genes with 2 fold-change in expression with a false-discovery rate less than 5% were considered differentially expressed. Results: The details for the therapy history of this cohort were illustrated in Figure 1a. All patients exposed to prior ibrutinib before the tissue biopsy had developed clinical progression while receiving ibrutinib. Unsupervised hierarchical clustering using the 300 most variable genes in expression revealed two clusters: C1 and C2 (Figure 1b). C1 included 4 RS and 3 CLL treated with prior chemotherapy without prior ibrutinib, and 1 RS treated with prior ibrutinib. C2 included 1 CLL and 3 RS received prior ibrutinib, and 1 RS treated with chemotherapy. The segregation of gene expression profiles in samples was largely driven by recent exposure to ibrutinib. In C1 cluster (majority had no prior ibrutinb), RS and CLL samples were clearly separated into two subgroups (Figure 1b). In C2 cluster, CLL 8 treated with ibrutinib showed more similarity in gene expression to RS, than to other CLL samples treated with chemotherapy. In comparison of C2 to C1, we identified 71 differentially expressed genes, of which 34 genes were downregulated and 37 were upregulated in C2. Among the upregulated genes in C2 (majority had prior ibrutinib) are known immune modulating genes including LILRA6, FCGR3A, IL-10, CD163, CD14, IL-2RB (figure 1c). Downregulated genes in C2 are involved in B cell activation including CD40LG, CD22, CD79A, MS4A1 (CD20), and LTB, reflecting the expected biological effect of ibrutinib in reducing B cell activation. Among the 9 RS samples, we compared gene profiles between the two groups of RS with or without prior ibrutinib therapy. 38 downregulated genes and 10 upregulated genes were found in the 4 RS treated with ibrutinib in comparison with 5 RS treated with chemotherapy. The top upregulated genes in the ibrutinib-exposed group included PTHLH, S100A8, IGSF3, TERT, and PRKCB, while the downregulated genes in these samples included MS4A1, LTB and CD38 (figure 1d). In order to delineate the differences of RS vs CLL, we compared gene expression profiles between 5 RS samples and 3 CLL samples that were treated with only chemotherapy. RS samples showed significant upregulation of 129 genes and downregulation of 7 genes. Among the most significantly upregulated genes are multiple genes involved in monocyte and myeloid lineage regulation including TNFSF13, S100A9, FCN1, LGALS2, CD14, FCGR2A, SERPINA1, and LILRB3. Conclusion: Our study indicates that ibrutinib-resistant, RS-involved tissues are characterized by downregulation of genes in B cell activation, but with PRKCB and TERT upregulation. Furthermore, RS-involved nodal tissues display the increased expression of genes involved in myeloid/monocytic regulation in comparison with CLL-involved nodal tissues. These findings implicate that differential therapies for RS and CLL patients need to be adopted based on their prior therapy and gene expression signatures. Studies using large sample size will be needed to verify this hypothesis. Figure Disclosures Zhao: Merck: Current Employment. Blumenschein:Merck: Current Employment. Yearley:Merck: Current Employment. Wang:Novartis: Research Funding; Incyte: Research Funding; Innocare: Research Funding. Parikh:Verastem Oncology: Honoraria; GlaxoSmithKline: Honoraria; Pharmacyclics: Honoraria, Research Funding; MorphoSys: Research Funding; Ascentage Pharma: Research Funding; Genentech: Honoraria; AbbVie: Honoraria, Research Funding; Merck: Research Funding; TG Therapeutics: Research Funding; AstraZeneca: Honoraria, Research Funding; Janssen: Honoraria, Research Funding. Kenderian:Sunesis: Research Funding; MorphoSys: Research Funding; Humanigen: Consultancy, Patents & Royalties, Research Funding; Gilead: Research Funding; BMS: Research Funding; Tolero: Research Funding; Lentigen: Research Funding; Juno: Research Funding; Mettaforge: Patents & Royalties; Torque: Consultancy; Kite: Research Funding; Novartis: Patents & Royalties, Research Funding. Kay:Astra Zeneca: Membership on an entity's Board of Directors or advisory committees; Acerta Pharma: Research Funding; Juno Theraputics: Membership on an entity's Board of Directors or advisory committees; Dava Oncology: Membership on an entity's Board of Directors or advisory committees; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Sunesis: Research Funding; MEI Pharma: Research Funding; Agios Pharma: Membership on an entity's Board of Directors or advisory committees; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Research Funding; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Rigel: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Cytomx: Membership on an entity's Board of Directors or advisory committees. Braggio:DASA: Consultancy; Bayer: Other: Stock Owner; Acerta Pharma: Research Funding. Ding:DTRM: Research Funding; Astra Zeneca: Research Funding; Abbvie: Research Funding; Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding; Octapharma: Membership on an entity's Board of Directors or advisory committees; MEI Pharma: Membership on an entity's Board of Directors or advisory committees; alexion: Membership on an entity's Board of Directors or advisory committees; Beigene: Membership on an entity's Board of Directors or advisory committees.

Introduction: Novel targeted therapies have revolutionized treatments for both upfront and relapsed CLL. However, effective therapies have not been established for progressive CLL after developing resistance to both BTK inhibitor and BCL-2 inhibitors. Similarly, Richter's transformation (RS) remains an area of unmet clinical need. Hypothesizing that progressive CLL and RS possess significant but differential molecular abnormalities, we aimed to dissect the initial genomic profiles in a clinical cohort of patients seen in our routine clinical practice using targeted DNA sequencing and transcriptome analysis. Methods: Twelve CLL patients who had lymph node biopsies for evaluation of clinical progression at Mayo Clinic were included in this study. RNA sequencing and targeted DNA sequencing on a focused oncology panel of 1,711 genes were performed on Illumina HiSeq through Tempus Labs, Inc. (Chicago, IL). Differentially expressed (DE) genes were identified using edgeR at the cutoff of FDR &lt;=0.05 and fold-change &gt;=2. Somatic mutations and copy number alternations (CNAs) were identified through Mutect2 and PatternCNV, respectively. CN loss was defined to have log2(ratio) &lt;=-0.93 and CN gain to have log2(ratio) &gt;= 0.585. Pathway analysis was done through the Enrichr package. Results: Among the 12 patients, 7 had confirmed RS (large cell lymphoma histology) and 5 had CLL based on pathological findings. The clinical and therapeutic history for each patient is illustrated in Fig 1A. FISH results from 11 of the patients showed chromosomal abnormalities including del(13q), del(11q), del(17p) and tri(12) (Fig 1A). Hierarchical clustering of expression profiles from the top 10,000 most variable genes in expression revealed two major clusters (Fig 1A). Cluster C1 contains five RS cases, of which case S207 did not receive ibrutinib, S256 was on high-dose steroid and anti-CD20 post-ibrutinib therapy for &gt;1 year before biopsy, and case S025 was treated for &lt; 6 months with ibrutinib. These three form a subgroup separate from the other two RS in C1 who had 8 and 42 months of continuous ibrutinib therapy pre-biopsy, respectively. C2 contains two RS and five CLL cases. Within C2, CLL and RS cases showed similar transcriptomic profiles. Over 90% of the DE genes showed an overall increase of expression in C1, particularly within the 3-sample subgroup (Fig 1B). These genes are enriched in pathways known to play key roles in CLL, including PI3K-AKT signaling, NOTCH signaling and RAS/MAPK signaling, while genes up-regulated in C2 are enriched in WNT signaling. The results suggest marked differences in gene expression profiles and indicate molecular heterogeneity among RS and CLL patients. In addition, targeted DNA sequencing data were analyzed to identify somatic mutations and CNAs. The analysis confirmed the FISH results of del(13q), del(11q), del(17p) or tri(12) in 8 of the cases (Fig 1A). The mutation profiles are similar between C1 and C2 for the recurrently mutated genes published in CLL. However, we identified recurrent CNAs that occurred preferentially in C1 (covering 9 genes) or C2 (48 genes) (Fig 1C). Manual inspections of the reads coverage revealed that 5 of the 9 genes are from chr1q21.1 to chr1q22 that showed clear CN gains largely in C1. Of the 48 genes, 6 are from chrXp11 that showed CN losses, and 27 are from chr17q12 to chr17q25 that showed CN gains. Therefore, the majority of the 57 genes are from large-scale CNAs. Notably, for the CNAs mostly occurring in C2, the affected genes are enriched in WNT signaling, Cell cycle/protein kinase activity and chromatin modifying enzymes. Collectively, the results support the variability of transcriptomic signatures and genetic lesions between RS and CLL. Conclusion: This study revealed a noticeable heterogeneity in gene expression and genetic variation between RS and CLL, albeit in a small cohort. The vast majority of the DE genes showed elevated expression in C1 containing only RS cases, and these genes are enriched in PI3K-AKT signaling, NOTCH signaling, and RAS/MAPK signaling. The pattern is more pronounced for a subgroup in C1 who were overall less exposed to ibrutinib. For somatic variation, there is a strong tendency of occurrence of recurrent CNAs towards C2 cohort, mainly involving gains of chr17q and deletions of chrXp11. Given these advances we are encouraged to further pursue the genetic abnormalities with defined roles in aggressive and transformed CLL. Disclosures Liu: Eisal: Research Funding; Genentech: Research Funding; GRAIL: Research Funding; Menarini Silicon Biosystems: Research Funding; Merck: Research Funding; Seattle Genetics: Research Funding; Tesaro: Research Funding. Parikh:Genentech: Honoraria; Ascentage Pharma: Research Funding; AbbVie: Honoraria, Research Funding; Verastem Oncology: Honoraria; Pharmacyclics: Honoraria, Research Funding; GlaxoSmithKline: Honoraria; Merck: Research Funding; MorphoSys: Research Funding; TG Therapeutics: Research Funding; AstraZeneca: Honoraria, Research Funding; Janssen: Honoraria, Research Funding. Wang:Incyte: Research Funding; Innocare: Research Funding; Novartis: Research Funding. Kenderian:Gilead: Research Funding; Humanigen: Consultancy, Patents & Royalties, Research Funding; MorphoSys: Research Funding; Lentigen: Research Funding; Novartis: Patents & Royalties, Research Funding; Torque: Consultancy; Mettaforge: Patents & Royalties; BMS: Research Funding; Tolero: Research Funding; Sunesis: Research Funding; Juno: Research Funding; Kite: Research Funding. Kay:Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Juno Theraputics: Membership on an entity's Board of Directors or advisory committees; Cytomx: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Agios Pharma: Membership on an entity's Board of Directors or advisory committees; Astra Zeneca: Membership on an entity's Board of Directors or advisory committees; Sunesis: Research Funding; MEI Pharma: Research Funding; Acerta Pharma: Research Funding; Rigel: Membership on an entity's Board of Directors or advisory committees; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Abbvie: Research Funding; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding; Dava Oncology: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding. Braggio:DASA: Consultancy; Bayer: Other: Stock Owner; Acerta Pharma: Research Funding. Ding:Astra Zeneca: Research Funding; DTRM: Research Funding; Beigene: Membership on an entity's Board of Directors or advisory committees; alexion: Membership on an entity's Board of Directors or advisory committees; MEI Pharma: Membership on an entity's Board of Directors or advisory committees; Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding; Octapharma: Membership on an entity's Board of Directors or advisory committees; Abbvie: Research Funding.

Introduction: PD-1 inhibits the cytotoxic T-cell functions via the interaction with its ligands (PDL1 and PDL2). Recently, our group showed a selective response (~40%) with PD-1 blocking antibody pembrolizumab in patients with Richter transformation (RT), particularly after prior exposure to ibrutinib (Wei Ding et al, Blood, 2017). Additionally, PD-1 showed an increased expression in tumor B-cells of patients with RT (Rong He et al, AJSP, 2018). Here we investigated the functional implications of the PD-1 signaling axis in lymphoma B-cell pathobiology. Methods: 26 CLL-involved lymph node (LN) and 20 RT-involved LN samples were tested for PD-1 expression by immunohistochemistry (mouse clone NAT105, Abcam). Then, we checked PD-1 expression in 10 lymphoma cell lines and 1 CLL cell line (MEC-1) by both, flow cytometry and Western blot (WB) analysis. Over-expression of PD-1 using pLEX-lentiviral system (Thermo Scientific) was evaluated for in vitro cell cycle regulation and in vivo tumor growth. Overexpression of PD-1 was further examined on the regulation of cell signaling pathways using human phospho kinase array kit (R&D) and WB analysis. Gene expression signatures in CLL and RT patients were also identified by Illumina-based RNA sequencing using Formalin-Fixed Paraffin-embedded (FFPE)-nodal tissue obtained by clinical biopsy (Tempus Labs; Chicago, IL). Results: The expression of PD-1 was significantly increased in RT-LN compared to CLL-LN (mean ± SEM in RT vs. CLL, 30.6% ± 4.7% vs. 11.5% ± 2.8%, p &lt; 0.001) [Fig 1A]. PD-1 expression was highest in patients with RT where the last prior CLL therapy was ibrutinib. To test the role of PD-1 in lymphoma B-cells, its expression was assessed in lymphoma and CLL cell lines. The expression of PD-1 was found to be variable, but at very low-levels in lymphoma lines OCI-LY7 and OCI-LY19 (data not shown). Constitutive lentiviral (pLEX-PD-1)-mediated overexpression of PD-1 in OCI-LY7 and OCI-LY19 cells led to increased cell growth (1.4 and 1.9 fold compared to original lines on day 4, respectively, Fig 1BI-II), which was further confirmed by cell cycle analysis which showed an increase in S phase by 20.4% and 24.53% in OCI-LY7 and OCI-LY19 cells (Fig 1B III), respectively. When the luciferase + PD-1 expressing OCI-LY7 cells were injected intravenously (1X106 cells) into NSG mice, increased tumor growth was observed at 22 days of follow up by bioluminescence imaging (p&lt;0.01, Fig 1C). Using phospho-kinase array, an overall decrease of phosphorylation was detected on multiple sites of p53 (S392, S15, S46) and CHK2 (T68) (data not shown). This finding was further confirmed by WB analyses (Fig1DI). In addition, decreased of total p53 and increased phosphorylation on both SHP-1 and SHP-2 were found in PD-1 overexpressing OCI-LY19 and OCI-LY17 cell lines (Fig 1DI). Immunoprecipitation experiments with anti-PD1 antibody showed that PD-1 was in the same complex with SHP-1 and SHP-2 in OCI-LY7 cells but only with SHP-2 in OCI-LY19 cells (Fig 1DII). In parallel, mRNA sequencing was performed on 11 nodal tissues from either RT (n=8) or progressive CLL (n=3) after they developed clinical progression. The expression of PD-1 (PDCD1) was positively correlated with the expression of SHP-1 (PTPN6, r=0.56) and Cyclin E1 (CCNE1, r=0.62), implicating a direct role of PD-1 in regulating cellular proliferation and the induction in phosphatase expression in RT and CLL. Conclusion: Our data support the notion that PD-1 overexpression in lymphoma cells modifies cell proliferation in vitro and in vivo and regulates the function of phosphatase SHP and p53- pathways. These findings also indicate that aggressive lymphoma or CLL leukemic cells have the ability to hijack the PD-1 pathway and may result in downregulation of the p53 mediated DNA repair. This novel information provide for new strategies to further evaluate the interaction of checkpoint signals with DNA repair pathway, and possibly provide novel targets for Richter's transformation. Disclosures Ding: alexion: Membership on an entity's Board of Directors or advisory committees; Beigene: Membership on an entity's Board of Directors or advisory committees; Abbvie: Research Funding; Astra Zeneca: Research Funding; Octapharma: Membership on an entity's Board of Directors or advisory committees; Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding; DTRM: Research Funding; MEI Pharma: Membership on an entity's Board of Directors or advisory committees. Sakemura:Humanigen: Patents & Royalties. Parikh:Janssen: Honoraria, Research Funding; Merck: Research Funding; Pharmacyclics: Honoraria, Research Funding; Ascentage Pharma: Research Funding; GlaxoSmithKline: Honoraria; MorphoSys: Research Funding; AstraZeneca: Honoraria, Research Funding; Verastem Oncology: Honoraria; Genentech: Honoraria; AbbVie: Honoraria, Research Funding; TG Therapeutics: Research Funding. Wang:Novartis: Research Funding; Incyte: Research Funding; Innocare: Research Funding. Liu:Eisal: Research Funding; Genentech: Research Funding; GRAIL: Research Funding; Menarini Silicon Biosystems: Research Funding; Merck: Research Funding; Seattle Genetics: Research Funding; Tesaro: Research Funding. Braggio:DASA: Consultancy; Bayer: Other: Stock Owner; Acerta Pharma: Research Funding. Ansell:Seattle Genetics: Research Funding; Takeda: Research Funding; AI Therapeutics: Research Funding; ADC Therapeutics: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Bristol Myers Squibb: Research Funding. Kenderian:Sunesis: Research Funding; Tolero: Research Funding; Torque: Consultancy; BMS: Research Funding; Gilead: Research Funding; Kite: Research Funding; Humanigen: Consultancy, Patents & Royalties, Research Funding; Mettaforge: Patents & Royalties; Novartis: Patents & Royalties, Research Funding; MorphoSys: Research Funding; Lentigen: Research Funding; Juno: Research Funding. Kay:Acerta Pharma: Research Funding; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Juno Theraputics: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Research Funding; MEI Pharma: Research Funding; Sunesis: Research Funding; Astra Zeneca: Membership on an entity's Board of Directors or advisory committees; Agios Pharma: Membership on an entity's Board of Directors or advisory committees; Cytomx: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Dava Oncology: Membership on an entity's Board of Directors or advisory committees; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding; Rigel: Membership on an entity's Board of Directors or advisory committees.