Sarangi, Vivekananda

Abstract The human angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2) proteins play key roles in the cellular internalization of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the coronavirus responsible for the coronavirus disease of 2019 (COVID-19) pandemic. We set out to functionally characterize the ACE2 and TMPRSS2 protein abundance for variant alleles encoding these proteins that contained non-synonymous single-nucleotide polymorphisms (nsSNPs) in their open reading frames (ORFs). Specifically, a high-throughput assay, deep mutational scanning (DMS), was employed to test the functional implications of nsSNPs, which are variants of uncertain significance in these two genes. Specifically, we used a ‘landing pad’ system designed to quantify the protein expression for 433 nsSNPs that have been observed in the ACE2 and TMPRSS2 ORFs and found that 8 of 127 ACE2, 19 of 157 TMPRSS2 isoform 1 and 13 of 149 TMPRSS2 isoform 2 variant proteins displayed less than ~25% of the wild-type protein expression, whereas 4 ACE2 variants displayed 25% or greater increases in protein expression. As a result, we concluded that nsSNPs in genes encoding ACE2 and TMPRSS2 might potentially influence SARS-CoV-2 infectivity. These results can now be applied to DNA sequence data for patients infected with SARS-CoV-2 to determine the possible impact of patient-based DNA sequence variation on the clinical course of SARS-CoV-2 infection.

Abstract Introduction: Low-grade B-cell lymphomas (LGBCL), aside from follicular lymphoma and chronic lymphocytic leukemia/small lymphocytic lymphoma, account for approximately 10% of B-cell non-Hodgkin lymphomas and consist of several subtypes. While a majority of LGBCL cases have an overall favorable prognosis, we have previously shown that cases who have an event (relapse or progression, transformation, or re-treatment) within 24 months of diagnosis (EFS24) have an inferior overall survival (OS) compared to those achieving EFS24 (Tracy et al., AJH 2019;94:658-66). However, the underlying biological characteristics associated with early failure and aggressive disease across LGBCL subtypes are unknown. In this study, we used matched transcriptomic, genomic, and immune profiling data from LGBCL cases, the largest cohort to date, and asked whether there were unique biological phenotypes across different LGBCL subtypes and whether we could identify signatures associated with aggressive LGBCL. Validation of the prognostic utility of this signature was performed on a previously published, independent cohort of 63 pre-treatment LGBCL cases. Methods: Tumors from 64 newly diagnosed LGBCL patients from the Molecular Epidemiology Resource of the University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence were included in this study (SMZL (n = 48), NMZL (n = 6), LPL (n = 5), B-NOS (n = 3), EMZL (n = 2)). RNA sequencing (RNAseq) data from 61 LGBCL tumors and 5 benign CD19+CD27+ memory B samples was subjected to NMF clustering to define groups. Differential expression and pathway analysis were used to identify biological characteristics of each cluster. CIBERSORT was used to identify immune cells in the tumor microenvironment. Whole exome sequencing (WES) was performed on 61 tumor-normal pairs. Singscore was used to assign a single score per patient representing gene expression of the survival-associated transcriptomic signature identified in this study. Results: NMF analysis of RNAseq data identified 5 clusters of patients, denoted LGBCL1-5 (Fig 1A). Patients from the same diagnostic subtype did not exclusively cluster together, with all LGBCL clusters comprised of patients from multiple subtypes (Fig 1B). Exploring the association between patient cluster and outcome, we observed significantly inferior event-free survival (EFS) (HR 2.24; 95% CI 1.01-4.98) and overall survival (OS) (HR 5.59; 95% CI 2.00-15.63) in LGBCL5 patients compared to LGBCL1-4 (Fig 1C). In addition, 80% of the transformation cases in our cohort were classified as LGBCL5 (Fig 1D). Differential expression and pathway analysis showed distinct processes significantly upregulated in each cluster (FDR < 0.05), with LGBCL5 demonstrating enrichment of cell cycle and mitosis pathways. CIBERSORT identified increased immune cell content in LGBCL3 and LGBCL5 compared to other clusters, with high frequencies of mast cells in both (p = 0.0002), increased CD8 T cells in LGBCL3 (p < 0.0001), and increased T follicular helper cells in LGBCL5 (p = 0.004). WES identified previously reported alterations in NOTCH, NFkB, and chromatin remodeling pathways and novel variants in LGBCL, including mutations in HNRNPK, CLTC, HLA-A, HLA-B and HLA-C. Assessment of alterations by cluster showed significant enrichment of TNFAIP3 (OR 5.54; 95% CI 1.20-28.14) and BCL2 alterations (OR 5.49; 95% CI 1.07-32.02) in LGBCL5 cluster. Finally, we identified a cell cycle-related transcriptomic signature of 108 genes upregulated in LGBCL5 and EFS24 failure cases. Cases with high expression of this signature showed significantly inferior EFS (HR 14.25; 95% CI 4.90-41.38) and OS (HR 7.82; 95% CI 2.40-25.44) compared to cases with low expression in our discovery cohort. This observation was reproduced in an independent validation cohort, where patients with high expression of this signature demonstrated significantly inferior EFS (HR 5.70; 95% CI 1.49-21.79) and OS (HR 10.07; 95% CI 2.00-50.61). Conclusions: In this study, we are the first to define mechanisms of pathogenesis in LGBCL with shared transcriptomic, genomic, and immune profiles present across LGBCL subtypes. We then further defined a gene expression signature associated with inferior patient outcome, with application of this signature to an independent validation cohort demonstrating proof of concept and utility of this signature as a prognostic marker in LGBCL patients. Figure 1 Figure 1. Disclosures Maurer: Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Research Funding; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding; Nanostring: Research Funding. Paludo: Karyopharm: Research Funding. Habermann: Tess Therapeutics: Other: Data Monitoring Committee; Morphosys: Other: Scientific Advisory Board; Incyte: Other: Scientific Advisory Board; Seagen: Other: Data Monitoring Committee; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Link: MEI: Consultancy; Genentech/Roche: Consultancy, Research Funding; Novartis, Jannsen: Research Funding. Rimsza: NanoString Technologies: Other: Fee-for-service contract. Ansell: Bristol Myers Squibb, ADC Therapeutics, Seattle Genetics, Regeneron, Affimed, AI Therapeutics, Pfizer, Trillium and Takeda: Research Funding. Cerhan: Genentech: Research Funding; Regeneron Genetics Center: Other: Research Collaboration; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; NanoString: Research Funding. Novak: Celgene/BMS: Research Funding.

Abstract Background: Follicular lymphoma (FL) exhibits significant clinical, cellular, molecular, and genetic heterogeneity. Our understanding of FL biology and molecular classifications of FL, to date, has largely been driven by pathologic classification (Grade 1-3b), genetic classification (m7-FLIPI), or gene expression profiling (IR-1/2; Huet-23), along with limited studies on small cohorts or targeted panels. In order to further understand the biological underpinnings and complexity of FL, large-scale and integrated whole exome sequencing (WES) and RNA sequencing (RNAseq) studies are needed. Using a highly-annotated cohort of 93 FL tumors with matched RNAseq, WES, and CyTOF data, we have explored the transcriptomic signature of purified FL B cells and identified unique molecular subsets that are defined by distinct pathway activation, immune content, and genomic signatures. Methods: Frozen cell suspensions from 93 untreated FL (Grade 1-3b) patients' tumor biopsies, enrolled in the University of Iowa/Mayo Clinic Lymphoma SPORE, were used for the study. DNA was isolated from whole tumor cell suspensions, and RNA was isolated from both whole tumor and B cell-enriched cell suspensions. RNAseq and WES were performed in the Mayo Clinic Genome Analysis Core. RNAseq and WES data were processed using a standard pipeline and novel driver genes were identified using Chasm+ driver analysis. Copy number variants were identified from WES data using GISTIC 2.0. NMF clustering and single sample gene set testing for B cell lineage and tumor microenvironment (TME) signatures were performed in R using the NMF and singscore packages. Results: Unsupervised clustering of RNAseq data identified three distinct expression programs which separated patient B cell samples into 3 groups: Group 1 (G1, n=20), Group 2 (G2, n=24), Group 3 (G3, n=43). While no clinical attributes were defined by any single group, G1 consisted of cases that had less aggressive characteristics (63% Stage I-II, 79% FLIPI 0-1). To identify unique transcriptional pathways driving the three expression programs, we scored gene level contributions to NMF expression programs and employed gene set enrichment analysis. This revealed significant pathway association with type-I IFN signaling (G1), DNA repair and stress response (G2), and epigenetic modulation (G3) as differentiating programs between the 3 groups (FDR q<0.001). VIPER master regulator activity inferencing revealed that these pathways were likely being controlled by differential activity in NF-kB, IRFs, STAT1, BCL6, and FOXO1. Each program significantly enriched for, but were not defined by, portions of specific germinal center programs, such as pre-memory (G1), light-zone-to-dark-zone transition (G2), and a pre-light-zone intermediate (G3). We next assessed the connection between B cell programs and the tumor microenvironment (TME) using available paired CyTOF data on 67 cases, which revealed an active TME in G1, with an abundance of CD8 T cell and NK cell populations, a wide variety of immune content in G2 that consisted mostly of Tfh and myeloid cells, and a poorly populated immune compartment in G3 compared to G1 and G2. Finally, somatic driver mutations and copy number alterations from WES were identified and associated with the three clusters. The three groups distinguished themselves by significant enrichment of copy number alterations (TNFAIP3-loss , 1q23-gain, 1q32-gain) in G2, while 10q-loss and mutations in BCL2 and chromatin modifiers (KMT2D and CREBBP) enriched in G3. G1, overall, had lower alteration burden and had weak associations with any specific alterations, suggesting an alternative mechanism for driving the G1 program. Conclusion: In this study, we have identified three unique FL tumor B cell groups, defined by specific transcriptional programs. Pathways such as inflammation, DNA damage response, and chromatin modification contribute most to the differences between B cell samples and group membership. Additionally, each program associated with specific genetic events as well as TME composition, highlighting potential drivers of these B cell states. This study improves the understanding of the mechanisms driving FL tumors and motivates further investigation into transcriptional consequences of genetic events as well as potential tumor intrinsic factors that may influence the TME. Figure 1 Figure 1. Disclosures Maurer: BMS: Research Funding; Genentech: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding. Rimsza: NanoString Technologies: Other: Fee-for-service contract. Link: MEI: Consultancy; Genentech/Roche: Consultancy, Research Funding; Novartis, Jannsen: Research Funding. Habermann: Tess Therapeutics: Other: Data Monitoring Committee; Seagen: Other: Data Monitoring Committee; Incyte: Other: Scientific Advisory Board; Morphosys: Other: Scientific Advisory Board; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Ansell: Bristol Myers Squibb, ADC Therapeutics, Seattle Genetics, Regeneron, Affimed, AI Therapeutics, Pfizer, Trillium and Takeda: Research Funding. King: Celgene/BMS: Research Funding. Cerhan: Genentech: Research Funding; Regeneron Genetics Center: Other: Research Collaboration; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; NanoString: Research Funding. Novak: Celgene/BMS: Research Funding.

Single nucleotide variants in the open reading frames (ORFs) of pharmacogenes are important causes of interindividual variability in drug response. Because of the very large number of missense variants in ORFs, it is difficult to link the genotypes of these variants to their phenotypes using “one at a time” expression systems. Therefore, the functional characterization of variants of unknown significance in clinically important pharmacogenes remains a major challenge in pharmacogenomics. Deep mutational scanning (DMS) is a high‐throughput technique that make it possible to analyze hundreds of different missense variants in a parallel and scalable fashion. Engineered “landing pad” HEK 293T cells were used as a platform to integrate pooled variant libraries, resulting in one variant per cell for further assessment. We adapted this DMS system to analyze the function of missense variants in the ORFs of CYP2C9 and CYP2C19 . The ORFs of CYP2C9 and CYP2C19 were fused to green fluorescent protein, and individual variants with different fluorescence intensities were used to indicate different levels of protein expression. Pooled variant libraries were generated by nicking mutagenesis, and we then used recombinase to integrate the libraries into landing pad cells, one per cell. Multiplexed functional selection performed with Fluorescence Activated Cell Sorting separated cells into different bins by fluorescence readout at the single–cell level. Amplicon sequencing of DNA collected in each bin, followed by computational analysis of the frequency of variants appearing in each bin, was used to determine levels of protein expression. To identify potentially severely damaging variants for these two important pharmacogenes, we studied 243 missense variants in the CYP2C9 and CYP2C19 ORFs that had been identified by the Exome Aggregation Consortium Project and by the Mayo Clinic RIGHT 1K Study. We found that 13 of 115 CYP2C9 and 25 of 128 CYP2C19 missense variants displayed less than approximately 25% of the wildtype protein expression, a level that may have clinical relevance for drug metabolism. Variant calling by the DMS method for the severely damaging variants was compared with the results of individual variant assay by western blot and with the predictions of computational algorithms such as Polyphen and SIFT. Our results suggest that DMS is an efficient method for the high‐throughput identification of damaging ORF variants that might have potential clinical application in pharmacogenomics. Support or Funding Information Funded by NIH grant U19 GM61388 (The Pharmacogenomics Research Network), R01 GM28157, R01 GM125633, T32 GM08685 and by the Mayo Clinic Center for Individualized Medicine This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

Genomic sleuthing spots early divergence After fertilization, the human zygote divides into two cells. Fasching et al. used genomic analysis from cellular samples taken much later in development to back-calculate the cell division trees that went before. Although the first cell division in human development looks symmetrical from the outside, the fates followed by daughter cells from each of those first two blastomeres are anything but the same. As much as 90% of blood cells are derived from just one of the first two blastomeres. Science , this issue p. 1245

Background Diffuse large B-cell lymphoma (DLBCL) is the most common aggressive non-Hodgkin lymphoma subtype with a 5-year survival of ~64%. While DLBCL is treated using immunochemotherapy (IC) with curative intent, 20%-40% of patients do not reach remission or relapse post IC. Clonal somatic mutations have been associated with aging, hematologic malignancies (predominately myeloid), and reduced OS in the general population. The objective of this study was to evaluate the association of clonal somatic mutations with event-free survival (EFS) and overall survival (OS). Methods We studied newly diagnosed DLBCL cases treated with IC who were enrolled in the Molecular Epidemiology Resource (MER) of the University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence (SPORE). Clinical and treatment data were abstracted from medical records and all patients were systematically followed for disease outcomes. Pre-treatment DNA was extracted from matched peripheral blood and paraffin-embedded tumor tissue, and whole exome sequencing was conducted at 100x coverage. From the peripheral blood, allele counts from GVCF files produced by HaplotypeCaller in GATK were extracted for 42 genes commonly associated with clonal hematopoiesis (e.g. DNMT3A, TET2, and ASXL1). Mutations were deemed clonal somatic if the population minor allele frequency was <0.5% in ExAC and had a variant allele fraction (VAF) that deviated from 0.5 using a binomial distribution test at p<0.001. Furthermore, mutations had to have a minimum alternate read count of at least 5 reads, a VAF greater than or equal to 10%, and met at least one of the following criteria: CAVA impact of "HIGH"; pathogenic/likely pathogenic per ClinVar; or reported in COSMIC at least 10 times. EFS was defined as time from diagnosis to disease progression, relapse, retreatment, or death due to any cause. The association of clonal somatic mutations with prognosis was estimated using Kaplan-Meier curves, and hazard ratios (HR) and 95% confidence intervals (CI) from Cox regression. Associations were adjusted for age at diagnosis, sex, and the International Prognostic Index (IPI). Results The study consisted of 261 DLBCL patients treated with IC. The median age at diagnosis was 65 years (range 20-90) and 56% were male. With a median follow-up time of 5.1 years (range 0.1-15.0), there were 100 events and 80 deaths. A total of 17 (6.5%) patients had clonal somatic mutations, and16 patients were over the age of 60. A total of 8 (of 42) genes had clonal somatic mutations, with SF3B1, ASXL1, and TET2, being the most frequent (4 individuals per gene). VAFs ranged from 0.10 to 0.28 and none of the patients had multiple mutations. Additionally, the clonal somatic variants found in the peripheral blood were abscent in the tumor sample. Of the 17 patients with clonal somatic mutations, 12 had an event while 88 patients without a mutation had an event. In a univariate analysis, clonal somatic mutations were associated with inferior EFS (HR=2.55, 95% CI 1.39-4.68, p=0.002; Figure 1A). After adjusting for age, sex, and IPI, clonal somatic mutations remained associated with inferior EFS (HR=2.02, 95% CI 1.09-3.74, p=0.026). Clonal somatic mutations were also associated with inferior OS in the univariate analysis (HR=2.06, 95% CI 0.99-4.29, p=0.053), which attenuated after multivariate adjustment (HR=1.59, 95% CI: 0.76-3.34, p=0.22, Figure 1B). Although based on small numbers, mutations in SF3B1 were associated with inferior EFS (HR=3.25, 95% CI 1.16-9.12, p=0.025), but did not reach significance for OS (HR=2.56, 95% CI 0.78-8.38, p=0.120). Conclusions In this novel study of newly diagnosed DLBCL patients, clonal somatic mutations were identified in 6.5% of patients and were associated with inferior outcomes. Additional research is required at deeper sequencing to validate these findings and integration with tumor genomics is required to understand the prognosis of DLBCL patients with smaller clonal populations. Disclosures Shah: Dren Bio: Consultancy. Maurer:Celgene / BMS: Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees; Morphosys: Membership on an entity's Board of Directors or advisory committees; Kite: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding. Martin:Celgene: Consultancy; Karyopharm: Consultancy, Research Funding; Morphosys: Consultancy; Regeneron: Consultancy; Incyte: Consultancy; Kite: Consultancy; Cellectar: Consultancy; Bayer: Consultancy; Beigene: Consultancy; Sandoz: Consultancy; I-MAB: Consultancy; Janssen: Consultancy; Teneobio: Consultancy. Witzig:AbbVie: Consultancy; Incyte: Consultancy; Acerta: Research Funding; Karyopharm Therapeutics: Research Funding; Immune Design: Research Funding; Spectrum: Consultancy; Celgene: Consultancy, Research Funding; MorphSys: Consultancy. Nowakowski:NanoString: Research Funding; Seattle Genetics: Consultancy; Curis: Consultancy; Kymera: Consultancy; Kite: Consultancy; Ryvu: Consultancy, Membership on an entity's Board of Directors or advisory committees; MorphoSys: Consultancy, Research Funding; Celgene/BMS: Consultancy, Research Funding. Novak:Celgene/BMS: Research Funding. Cerhan:NanoString: Research Funding; BMS/Celgene: Research Funding.

Abstract Background: DNA polymerase epsilon (POLE) is encoded by the POLE gene, and POLE-driven tumors are characterized by high mutational rates. POLE-driven tumors are relatively common in endometrial and colorectal cancer, and their presence is increasingly recognized in ovarian cancer (OC) of endometrioid type. POLE-driven cases possess an abundance of TCT>TAT and TCG>TTG somatic mutations characterized by mutational signature 10 from the Catalog of Somatic Mutations in Cancer (COSMIC). By quantifying the contribution of COSMIC mutational signature 10 in RNA sequencing (RNA-seq) we set out to identify POLE-driven tumors in a set of unselected Mayo Clinic OC. Methods: Mutational profiles were calculated using expressed single-nucleotide variants (eSNV) in the Mayo Clinic OC tumors (n=195), The Cancer Genome Atlas (TCGA) OC tumors (n=419), and the Genotype-Tissue Expression (GTEx) normal ovarian tissues (n=84). Non-negative Matrix Factorization (NMF) of the mutational profiles inferred the contribution per sample of four distinct mutational signatures, one of which corresponds to COSMIC mutational signature 10. Results: In the Mayo Clinic OC cohort we identified six tumors with a predicted contribution from COSMIC mutational signature 10 of over five mutations per megabase. These six cases harbored known POLE hotspot mutations (P286R, S297F, V411L, and A456P) and were of endometrioid histotype (P=5e-04). These six tumors were hypermutated with a higher tumor mutation load (mean, 54.02 mutations per megabase) compared to non-POLE endometrioid OC cases (mean, 7.69 mutations per megabase; P=5e-04), and had an early onset (average age of patients at onset, 48.33 years) when compared to non-POLE endometrioid OC cohort (average age at onset, 60.13 years; P=.008). Samples from TCGA and GTEx had a low COSMIC signature 10 contribution (median 0.16 mutations per megabase; maximum 1.78 mutations per megabase) and carried no POLE hotspot mutations.Conclusions: From the largest cohort of RNA-seq from endometrioid OC to date (n=53), we identified six hypermutated samples likely driven by POLE (frequency, 11%). Our result suggests the clinical need to screen for POLE driver mutations in endometrioid OC, which can guide enrollment in immunotherapy clinical trials.