Jessen, Erik

Abstract The pathogenesis of primary sclerosing cholangitis (PSC) is unclear, although studies implicate IL-17A as an inflammatory mediator in this disease. However, a direct assessment of IL-17 signaling in PSC cholangiocytes is lacking. In this study we aimed to investigate the response of PSC extrahepatic cholangiocyte organoids (ECO) to IL-17A stimulation. Cholangiocytes obtained from PSC and non-PSC patients by endoscopic retrograde cholangiography (ERC) were cultured as ECO. The ECO were treated with vehicle or IL-17A and assessed by transcriptomics, secretome analysis, and genome sequencing (GS). Unsupervised clustering of all integrated scRNA-seq data identified 8 cholangiocyte clusters which did not differ between PSC and non-PSC ECO. However, PSC ECO cells demonstrated a robust response to IL-17 treatment, noted by an increased number of differentially expressed genes (DEG) by transcriptomics, and more abundant chemokine and cytokine expression and secretion. After rigorous filtering, GS identified candidate somatic variants shared among PSC ECO from unrelated individuals. However, no candidate rare variants in genes regulating the IL-17 pathway were identified, but rare variants regulating the MAPK signaling pathway were present in all PSC ECO. In conclusion, PSC and non-PSC patient derived ECO respond differently to IL-17 stimulation implicating this pathway in the pathogenesis of PSC.

Abstract Objective: Endometrial cancer (EC) is the most common gynecologic malignancy and serous histology is the second most common subtype. It is associated with poorer outcomes as recurrent disease tends to be resistant to chemotherapy. Given the dearth of proven targeted therapies in this setting and the tendency for serous ECs to have homologous recombination deficiency (HRD), this study aimed to test the synergy between poly (ADP-ribose) polymerase inhibitors (PARPis) and SN38, an active metabolite of DNA topoisomerase I, irinotecan. PARPis are currently FDA approved in ovarian cancer and have their greatest efficacy in tumors with HRD while topoisomerase inhibitors are a standard chemotherapy option for recurrent cancer. Methods: A genomic instability score (GIS) was derived from low-pass whole genome sequencing-based bioinformatics analysis according to three previously reported HRD metrics: telomeric allelic imbalance, large state transitions, and loss of heterozygosity. GIS was calculated for 34 patient derived xenograft (PDX) tumors, of which eight had serous histology. PARPi, rucaparib, and SN38 were used to treat EC PDX tumors in ex vivo 3D cell culture experiments using RealTime Glo for viability after 4-5 days of drug exposure. Synergy was assessed by calculating the combination index (CI) using Chou-Talalay method. Results: GIS ≥ 42 was noted in 62.5% (5/8) of serous EC PDX tumors and only 19.2% (5/26) of non-serous EC PDX tumors. Synergy (CI < 1.0) between rucaparib and SN38 was demonstrated in 85.7% (6/7) of serous EC PDX ex vivo 3D cell culture experiments, but only 62.5% (10/16) of non-serous EC PDX tumors. Conclusions: Most serous EC PDX tumors had a high GIS, consistent with HRD, when compared to a minority of non-serous histologies. Furthermore, combination therapy of rucaparib and SN38 had a synergistic effect in almost all serous EC PDX models, while still being effective in a majority of non-serous models. Additional PDX tumors need to be assessed to establish the predictive value of a GIS on synergy between rucaparib and SN38. Further in vivo studies are also needed to confirm these ex vivo findings of synergy. Ultimately, the hope is to generate preclinical data which justifies the use of PARPis in serous ECs as monotherapy or in combination with other anticancer therapies. Citation Format: Conway Xu, Xiaonan Hou, Erik Jessen, Chen Wang, S. John Weroha. Combination therapy with poly (ADP-ribose) polymerase inhibitor in endometrial cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5495.

Abstract Objectives Cirrhosis-like hepatocellular carcinoma (CL-HCC) is a rare hepatocellular malignancy characterized by multiple tumor nodules that clinically, radiologically, macroscopically, and microscopically mimic cirrhosis. We aimed to elucidate the molecular biology of CL-HCC and determine tumor nodule clonality. Methods We performed RNA sequencing on formalin-fixed, paraffin-embedded tissue from confirmed CL-HCC cases (n = 6), along with corresponding nonneoplastic hepatic tissue (n = 4) when available. Transcriptomes from our previous work on steatohepatitic hepatocellular carcinoma and The Cancer Genome Atlas (TCGA) were used for comparison purposes. Results Histologically, CL-HCC displayed innumerable nodules and extensive vascular invasion. Intratumoral nodule comparison indicated that the multiple nodules were all clonally related, not independent primaries. The unique histomorphologic appearance corresponded with a distinct transcriptome compared with other HCCs, including fibrolamellar HCC (n = 6), steatohepatitic HCC (n = 8), and conventional HCC in TCGA (n = 424). Tumor-normal gene expression analysis revealed consistent overexpression of several genes involved in degradation of tissue matrix. No recurrent translocations or point mutations were identified. CL-HCC showed a gene expression profile indicative of zone 2 hepatocytes. Conclusions CL-HCC’s distinctive clinicopathologic features correspond to a unique gene expression profile, increased expression of invasive markers, features of zone 2 hepatocytes, and features suggestive of intratumoral nodule monoclonality.

Abstract Background: Cholangiocarcinoma is an aggressive malignancy with limited systemic therapeutics. We have recently identified LCK, a non-receptor tyrosine kinase, as a potential therapeutic target in select subtypes of cholangiocarcinoma. Proteomic profiling in other cancer types has proven beneficial in predicting response to systemic therapies. Herein, we present proteomic profiling of cholangiocarcinoma can predict sensitivity to a novel LCK inhibitor in patient derived xenograft models. Methods: Proteins from 28 unique cholangiocarcinoma patient derived xenograft models (PDX) were isolated, labeled with tandem mass tags (TMT) and then analyzed by liquid chromatography and mass spectroscopy (LC/MS). Phosphopeptides were separately evaluated both by Immobilized Metal Affinity Chromatography and pY-enrichment techniques. Following normalization, the global proteome, serine/threonine phosphoproteome, and tyrosine phosphoproteome were analyzed and a proteomic signature was created for each tumor. These signatures were compared to PDX models with known response profiles to NTRC0652-0, an LCK inhibitor (1 resistant and 1 sensitive). The top 200 differentially abundant proteins (log2FC 2.6) were then utilized to develop a sensitive and a resistant signature. Correlation indices were then calculated across the 28 PDX model library. An additional predicted resistant and predicted sensitive model was expanded. Tumor bearing mice were randomized based on tumor volume to either treatment with NTRC (30mg/kg PO daily), n=5, or vehicle, n=4, (10% Kolliphor, 10% DMSO, 80% Water, 2 mol equivalents HCl). Tumors were measured weekly with calipers and then weighed at the end of the study. Results: In the top 200 differentially abundant proteins that represented the proteomic signature there was over representation of proteins with decreased abundance. There were only 12 proteins with higher abundance in the sensitive model as compared to the resistant. Utilizing the signature developed from the global proteomics data, nine tumors were predicted to be sensitive while 17 were predicted to be resistant to NTRC. PDX115 is a an intrahepatic CCA PDX known to contain an IDH1 mutation and was predicted to be resistant by modeling. The model was resistant to single agent NTRC with no demonstrable effect on tumor growth in vivo. PDX175 is a distal CCA PDX and was predicted to be sensitive by modeling. In vivo growth was inhibited by NTRC as compared to vehicle treated mice following two weeks of treatment (983% vs 435%, p=0.03). Additionally, final tumor weight was significantly lower in NTRC treated mice (0.651 g vs 0.317 g, p=0.04). Conclusion: Cholangiocarcinoma proteomic signatures can be used to predict therapeutic response to a novel LCK inhibitor in preclinical models. Whether or not this could be applied to other treatment modalities in cholangiocarcinoma remains to be determined. Citation Format: Ryan Watkins, Roberto Alva-Ruiz, Caitlin Conboy, Dong-Gi Mun, Erik Jessen, Diep Vu, Jos de Man, Rogier Buijsman, Akhilesh Pandey, Mitesh Borad, Gregory Gores, Rory Smoot. Proteomic profiling of cholangiocarcinoma predicts response to a novel small molecule inhibitor in preclinical models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3094.

Abstract Chronic lymphocytic leukemia (CLL) is characterized by multiple copy number abnormalities (CNVs) with prognostic value. Identifying these structural variations is central to defining CLL pathogenesis, risk stratification, and therapeutic approaches. Fluorescence in situ hybridization (FISH) is the clinical gold standard in detecting prognostic CNVs in CLL. However, next-generation sequencing (NGS) techniques have become more readily available for clinical genomic applications and can also be used to identify CNVs. Here we present bioinformatic methods to accurately identify CNVs in CLL using NGS data. We used the CNV-calling algorithm PatternCNV to detect clinically relevant CNVs: deletion 17p13 [del(17p)], deletion 11q23 [del(11q)], deletion 13q14 [del(13q)], and trisomy 12. PatternCNV was run on 2274 samples (1500 somatic and 774 germline samples) from six different sequencing batches, screened using a targeted sequencing panel that covers all exons of 59 recurrently CLL mutated genes and additional amplicons covering the minimal affected regions of relevant CNVs. To correct for potential batch effects, PatternCNV was initially run to quantify exon coverage behavior without the chromosomes containing recurrent CNV events, 11, 12, 13, and 17. Principal component analyses and correlation matrices were analyzed, grouping the samples into four distinct clusters that contain similar exon coverage patterns. Samples in each of the four clusters were then independently re-run through PatternCNV using all chromosomes. Visual analysis of CNV plots revealed a bias in normalization. To correct this, the log2ratios were corrected to center the log ratio on the median coverage. Sample noisiness was calculated from the difference in the median absolute deviation (DiffMAD) and samples with a DiffMAD score greater than 0.3 were excluded. All CNV analyses were blinded to clinical FISH results. The effectiveness of our CNV calling was evaluated in 522 CLL patients who had FISH conducted within three months of the sample date. We excluded samples with low tumor metrics identified by FISH (less than 20% of cells with either del(17p), del(11q), trisomy 12 or del(13q)). When we compared our CNV analyses with the FISH data, we found high concordance 99.6% for del(17p), 97.5% for del(11p), 99.1% for trisomy 12, and 93.7% for del(13q). N=46 total discordant pairs were identified, with the highest discordance for del(13q), N=28, followed by del(11q), N=12. These novel bioinformatic methods allow for accurate detection of CNVs across NGS sequencing batches. The high concordance in detecting CNVs between targeted NGS and the gold standard of FISH, suggest NGS is an accurate tool for calling CNVs in CLL. Further, NGS can infer clinically relevant CNVs in genomic locations not targeted by FISH. Citation Format: Chantal E. McCabe, Erik Jessen, Daniel R. O'Brien, Julia E. Wiedmeier-Nutor, Susan L. Slager, Esteban Braggio. Identifying copy number variations in chronic lymphocytic leukemia using targeted next generation sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3352.

AbstractAltered energy metabolism and changes in glycolytic and oxidative phosphorylation pathways are hallmarks of all cancer cells. The expression of select genes associated with the production of various enzymes and proteins involved in glycolysis and oxidative phosphorylation were assessed in the clonal plasma cells derived from patients with newly diagnosed multiple myeloma (NDMM) enrolled in the Multiple Myeloma Research Foundation (MMRF) CoMMpass data set. A scoring system consisting of assigning a point for every gene where their fragments per kilobase of transcript per million (FPKM) was above the median yielded a minimum of 0 and a maximum of 12 for the set of genes in the glycolytic and oxidative phosphorylation pathways to create a total energy metabolism molecular signature (EMMS) score. This EMMS score was independently associated with worse progression free survival (PFS) and overall survival (OS) outcomes of patients with NDMM. A higher EMMS score was more likely to be present in clonal plasma cells derived from Multiple myeloma (MM) patients than those from patients with monoclonal gammopathy of undetermined significance (MGUS). This was functionally confirmed by the clonal plasma cells from MM patients having a higher rate of mitochondrial and glycolysis‐derived ATP formation than clonal plasma cells from MGUS patients. Thus, this study provides evidence for the effect of energy metabolism within clonal plasma cells on pathogenesis and outcomes of patients with MM. Exploiting the energy‐producing metabolic pathways within clonal plasma cells for diagnostic and therapeutic purposes in MM should be explored in the future.

Abstract Introduction: Multiple gene expression profile assays have successfully prognosticated the overall survival (OS) outcomes of patients with Newly Diagnosed Multiple Myeloma (NDMM) and are now available for use in clinical practice. The genes included in these profiles encompass several aspects of cancer biology broadly such as cell-cycle biology, energy metabolism, migration, adhesion, immune evasion etc. Since altered energy metabolism is one of the hallmarks of all cancer cells, our goal was to take a deep-dive into specific genes associated with intracellular central carbon energy metabolism (CCEM) pathways such as oxidative phosphorylation (OxPhos) as well as glycolysis and lactate fermentation (Glyco-Lac) in MM cells to determine their impact on the pathogenesis and prognosis of MM patients. Methods: Gene expression level data via RNA sequencing from the COMMPASS dataset (made available by the Multiple Myeloma Research Foundation (MMRF)) was utilized. The expression levels of all genes associated with the OxPhos (N = 118) and Glyco-Lac (N = 34) pathways were evaluated in CD138+ cells derived from patients with NDMM. Each gene was dichotomized into low and high expression groups -based on their expression level above and below the median fragments per kilobase of transcript per million (FPKM), respectively. The prognostic significance of dichotomizing each of the 152 genes in this NDMM cohort was evaluated by levels of their hazard ratios (HRs) for OS determined by the log-rank method. Genes from the OxPhos and Glyco-Lac pathways were selected based on their significant negative impact on OS, wide representation of their biologic activity throughout the pathway and high independence from each other. This resulted in the selection of 12 distinct genes of interest. A scoring system consisting of assigning a point for every gene where their FPKM was above the median yielded a minimum of 0 and a maximum of 12 for the set of genes in the OxPhos and Glyco-Lac pathways to create an energy metabolism score (EMS). Patients were then categorized into "low" (0-8) or "high" (9-12) EMS groups. In addition, patients were designated as having high-risk cytogenetics if they had any of the following: t(4;14), t(14;16), t(14;20), del17p, and Amp 1q. Survival analysis was performed using the Kaplan-Meier survival analysis and compared via the log-rank method. Finally, the distribution of the EMS score among patients within a spectrum of plasma cell disorders such as, monoclonal gammopathy of undetermined significance (MGUS), smoldering multiple myeloma and MM as well as normal plasma cells was assessed using a previously published transcriptome dataset (GSE6477). Results: A total of 766 patients were included in this study. The selected genes included in this scoring system and their biological relevance in the OxPhos and Glyco-Lac pathways are depicted in Figure A. The distribution of low and high EMS groups across ISS stage and cytogenetic risk are depicted in Figure B and C respectively. The median OS (mOS) and PFS for the high (N = 248) vs. low (N = 518) EMS groups was 55 vs. 94 months (HR: 2.41; (95%CI: 1.85-3.14); P <0.0001) (Figure D) and 24 vs. 42 months (HR: 1.74; (95%CI: 1.42-2.13); P <0.0001). Patients with ISS 2 stage disease categorized as having a high or low EMS score had a similar mOS to patients with ISS 3 and ISS 1 stage disease respectively (Figure E). Similarly, the mOS was similar among patients with standard risk and high-risk cytogenetics when categorized by low and high EMS (Figure F). Finally, the relative expression of most of the 12 select CCEM genes (Figure G) and the probability of a high EMS score increased among patients along the spectrum of plasma cell disorders (Normal plasma cell -> MGUS -> SMM -> MM) (Figure H). Conclusions: The expression levels of specific genes associated with the activity of intracellular CCEM pathways such as glycolysis, lactate fermentation and oxidative phosphorylation in plasma cells provides evidence for the impact of cellular energy metabolism on the pathogenesis and survival outcomes of MM. This provides an opportunity for developing future studies aimed at understanding the energy metabolism pathways of plasma cell malignancies in greater detail so as to exploit them for diagnostic and therapeutic purposes in MM. Figure 1 Figure 1. Disclosures Kumar: Novartis: Research Funding; Amgen: Consultancy, Research Funding; Beigene: Consultancy; Carsgen: Research Funding; Merck: Research Funding; Antengene: Consultancy, Honoraria; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Consultancy; Roche-Genentech: Consultancy, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Tenebio: Research Funding; Bluebird Bio: Consultancy; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding. Dispenzieri: Alnylam: Research Funding; Sorrento Therapeutics: Consultancy; Oncopeptides: Consultancy; Pfizer: Research Funding; Takeda: Research Funding; Janssen: Consultancy, Research Funding. Kapoor: AbbVie: Research Funding; Takeda: Research Funding; Karyopharm: Consultancy; Cellectar: Consultancy; BeiGene: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; Amgen: Research Funding; Ichnos Sciences: Research Funding; Regeneron Pharmaceuticals: Research Funding; Glaxo SmithKline: Research Funding; Karyopharm: Research Funding; Sanofi: Research Funding. Dingli: Apellis: Consultancy; Sanofi: Consultancy; Janssen: Consultancy; GSK: Consultancy; Novartis: Research Funding; Alexion: Consultancy. Lin: Gamida Cell: Consultancy; Takeda: Research Funding; Kite, a Gilead Company: Consultancy, Research Funding; Juno: Consultancy; Novartis: Consultancy; Legend: Consultancy; Janssen: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Bluebird Bio: Consultancy, Research Funding; Sorrento: Consultancy; Merck: Research Funding; Vineti: Consultancy. Gertz: Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria; Aurora Biopharma: Other: Stock option; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring; Ionis Pharmaceuticals: Other: Advisory Board; Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy. Fonseca: Aduro: Consultancy; Kite: Consultancy; Juno: Consultancy; Merck: Consultancy; Takeda: Consultancy; Caris Life Sciences: Membership on an entity's Board of Directors or advisory committees; Patent: Prognosticaton of myeloma via FISH: Patents & Royalties; AbbVie: Consultancy; Scientific Advisory Board: Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees; OncoTracker: Consultancy, Membership on an entity's Board of Directors or advisory committees; Mayo Clinic in Arizona: Current Employment; Amgen: Consultancy; BMS: Consultancy; Celgene: Consultancy; Bayer: Consultancy; Janssen: Consultancy; Novartis: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; GSK: Consultancy.

Abstract Background: Traditionally, mutational burden and mutational signatures have been assessed by tumor-normal pair DNA sequencing. The requirement of having both normal and tumor samples is not always feasible from a clinical perspective, and led us to investigate the efficacy of using RNA sequencing of only the tumor sample to determine the mutational burden and signatures, and subsequently molecular cause of the cancer. The potential advantages include reducing the cost of testing, and simultaneously providing information on the gene expression profile and gene fusions present in the tumor. Results: In this study, we devised supervised and unsupervised learning methods to determine mutational signatures from tumor RNA-seq data. As applications, we applied the methods to a training set of 587 TCGA uterine cancer RNA-seq samples, and examined in an independent testing set of 521 TCGA colorectal cancer RNA-seq samples. Both diseases are known associated with microsatellite instable high (MSI-H) and driver defects in DNA polymerase ɛ (POLɛ). From RNA-seq called variants, we found majority (>95%) are likely germline variants, leading to C>T enriched germline variants (dbSNP) widely applicable in tumor and normal RNA-seq samples. We found significant associations between RNA-derived mutational burdens and MSI/POLɛ status, and insignificant relationship between RNA-seq total coverage and derived mutational burdens. Additionally we found that over 80% of variants could be explained by using the COSMIC mutational signature-5, -6 and -10, which are implicated in natural aging, MSI-H, and POLɛ, respectively. For classifying tumor type, within UCEC we achieved a recall of 0.56 and 0.78, and specificity of 0.66 and 0.99 for MSI and POLɛ respectively. By applying learnt RNA signatures from UCEC to COAD, we were able to improve our classification of both MSI and POLɛ. Conclusions: Taken together, our work provides a novel method to detect RNA-seq derived mutational signatures with effective procedures to remove likely germline variants. It can leads to accurate classification of underlying driving mechanisms of DNA damage deficiency.

Background: Multiple myeloma (MM) remains an incurable plasma cell (PC) malignancy. Despite improvements in clinical outcomes, variability in treatment response exists. Some patients experience innate resistance, while others develop resistance over the course of treatment. Identifying signatures of treatment response and resistance is essential to predicting effective treatment strategies. Given the phenotypic clonal heterogeneity observed in MM, we hypothesize that resistant and sensitive subpopulations have unique phenotypic signatures. We used mass cytometry (CyTOF) to characterize primary MM samples and correlated their phenotypic signatures with treatment response. Methods: We characterized 102 primary MM samples by CyTOF using 37 antibodies (see figure). Frozen whole BM specimens were thawed, stained with antibodies, barcoded, and analyzed on a Helios mass cytometer. Data was processed using Cytobank or Pathsetter and analyzed using t-distributed stochastic neighbor embedding (t-SNE) algorithm. T-SNE plots for CD45+ cells were created using lineage defining markers. T-SNE plots of neoplastic PCs (CD138+,CD3-,CD16-,CD19-,cytoplasmic kappa or lambda+) were created using all remaining markers. A differential analysis of each sample's clusters was performed against all other events. Z-score values, representing marker changes from the differential analysis, for each cluster were used to group similar clusters across all samples, allowing us to compare sub populations of PCs between patients (see figure). From these groups of clusters with similar changes in markers, we calculated the proportion of cells with increases or decreases in markers for each sample. Results were compared to IMWG 90-day response criteria using a random forest model. Results: We performed mass cytometry on 102 samples from 68 unique MM patients. Median age was 66 years (range 34-95) with a 1.5:1 male:female ratio. 37 (36%) had NDMM, 55 (54%) had RRMM, 2 (2%) had SMM and 8 cases (8%) had unknown status. FISH studies revealed 47 (46%) had hyperdiploidy without a recurrent primary IgH rearrangement, 31 (30%) had t(11;14), 13 (13%) had t(4;14), 5 (5%) had 14;16 or 14;20, 3 (3%) had hyperhaploidy and 3 (3%) had an undefined primary. 54 (53%) had 1q gain/amp, 22 (22%) had TP53 deletion, 22 (22%) had a MYC rearrangement and 71% had high mSMART risk. Nearly all patients received an immunomodulatory compound, a proteasome inhibitor and dexamethasone. 2 (2%) had complete response (CR), 10 (9.8%) very good partial response (VGPR), 36 (35%) had partial response (PR), 14 (14%) had minimal response (MR), 13 (13%) had stable disease (SD) and 17 (17%) had progressive disease (PD). Characterization of CD45+ cells identified a relatively conserved and reproducible map from each patient. Notable differences included an ~2-fold reduction in the percentage of total B cells (CD45+,CD3-,CD19+)/total CD45+ cells in cases with high risk mSMART, RRMM and TP53 deletion compared to cases with standard risk, NDMM or normal TP53 status. Similar B-cell reductions were found in RRMM vs. NDMM samples from matched-patients. In contrast to the conserved t-SNE structure of the CD45+ population, the neoplastic PC population displayed dramatically reduced t-SNE structural consistency. Heterogeneity between patient samples and between the same patient samples over time was observed. We focused our analysis on 22 patients with serial samples. The proportion of each t-SNE group present in each patient sample was calculated and then correlated to the 90-day response metric. We identified a phenotypic group whose proportion increased in relation to disease progression (R2=0.85). This group had elevated BCL-2, pSTAT3, CD56 levels and reduced CD45 and CD81 levels. Evaluating individual markers, we found an increase in the proportion of cells with elevated BCL-2, pS6, Ki67, and IKZF1 levels in relation to disease progression. We also observed an increase in the proportion of cells with reduced pCREB levels and a reduction in the proportion of cells with reduced CD147 levels in relation to disease progression. Conclusions: We have utilized CyTOF to characterize phenotypic and functional markers in MM cells on a single cell level and can identify unique subgroups that change in relation to disease progression. Future analysis will correlate phenotypic signatures with ISS stage, mSMART risk, cytogenetic abnormality and type of treatment. Figure 1 Disclosures Kumar: Kite Pharma: Consultancy, Research Funding; Oncopeptides: Consultancy, Other: Independent Review Committee; IRC member; Celgene/BMS: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Takeda: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Sanofi: Research Funding; MedImmune: Research Funding; Karyopharm: Consultancy; Merck: Consultancy, Research Funding; Carsgen: Other, Research Funding; Cellectar: Other; Novartis: Research Funding; Adaptive Biotechnologies: Consultancy; AbbVie: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Genecentrix: Consultancy; Genentech/Roche: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Janssen Oncology: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Amgen: Consultancy, Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments, Research Funding; Tenebio: Other, Research Funding; BMS: Consultancy, Research Funding; Dr. Reddy's Laboratories: Honoraria.

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 <=0.05 and fold-change >=2. Somatic mutations and copy number alternations (CNAs) were identified through Mutect2 and PatternCNV, respectively. CN loss was defined to have log2(ratio) <=-0.93 and CN gain to have log2(ratio) >= 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 >1 year before biopsy, and case S025 was treated for < 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.