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Multiple myeloma (MM) is a highly refractory hematologic cancer. Targeted immunotherapy has shown promise in MM but remains hindered by the challenge of identifying specific yet broadly representative tumor markers. We analyzed 53 bone marrow (BM) aspirates from 41 MM patients using an unbiased, high-throughput pipeline for therapeutic target discovery via single-cell transcriptomic profiling, yielding 38 MM marker genes encoding cell-surface proteins and 15 encoding intracellular proteins. Of these, 20 candidate genes were highlighted that are not yet under clinical study, 11 of which were previously uncharacterized as therapeutic targets. The findings were cross-validated using bulk RNA sequencing, flow cytometry, and proteomic mass spectrometry of MM cell lines and patient BM, demonstrating high overall concordance across data types. Independent discovery using bulk RNA sequencing reiterated top candidates, further affirming the ability of single-cell transcriptomics to accurately capture marker expression despite limitations in sample size or sequencing depth. Target dynamics and heterogeneity were further examined using both transcriptomic and immuno-imaging methods. In summary, this study presents a robust and broadly applicable strategy for identifying tumor markers to better inform the development of targeted cancer therapy. Significance: Single-cell transcriptomic profiling and multiomic cross-validation to uncover therapeutic targets identifies 38 myeloma marker genes, including 11 transcribing surface proteins with previously uncharacterized potential for targeted antitumor therapy.

Multiple myeloma (MM) is a highly refractory hematologic cancer. Targeted immunotherapy has shown promise in MM but remains hindered by the challenge of identifying specific yet broadly representative tumor markers. We analyzed 53 bone marrow (BM) aspirates from 41 MM patients using an unbiased, high-throughput pipeline for therapeutic target discovery via single-cell transcriptomic profiling, yielding 38 MM marker genes encoding cell-surface proteins and 15 encoding intracellular proteins. Of these, 20 candidate genes were highlighted that are not yet under clinical study, 11 of which were previously uncharacterized as therapeutic targets. The findings were cross-validated using bulk RNA sequencing, flow cytometry, and proteomic mass spectrometry of MM cell lines and patient BM, demonstrating high overall concordance across data types. Independent discovery using bulk RNA sequencing reiterated top candidates, further affirming the ability of single-cell transcriptomics to accurately capture marker expression despite limitations in sample size or sequencing depth. Target dynamics and heterogeneity were further examined using both transcriptomic and immuno-imaging methods. In summary, this study presents a robust and broadly applicable strategy for identifying tumor markers to better inform the development of targeted cancer therapy.Significance:Single-cell transcriptomic profiling and multiomic cross-validation to uncover therapeutic targets identifies 38 myeloma marker genes, including 11 transcribing surface proteins with previously uncharacterized potential for targeted antitumor therapy.

Pearson R correlation values between bulk RNA expression and scRNA expression in PCs for target genes.

Multiple myeloma (MM) is characterized by the uncontrolled proliferation of plasma cells. Despite recent treatment advances, it is still incurable as disease progression is not fully understood. To investigate MM and its immune environment, we apply single cell RNA and linked-read whole genome sequencing to profile 29 longitudinal samples at different disease stages from 14 patients. Here, we collect 17,267 plasma cells and 57,719 immune cells, discovering patient-specific plasma cell profiles and immune cell expression changes. Patients with the same genetic alterations tend to have both plasma cells and immune cells clustered together. By integrating bulk genomics and single cell mapping, we track plasma cell subpopulations across disease stages and find three patterns: stability (from precancer to diagnosis), and gain or loss (from diagnosis to relapse). In multiple patients, we detect “B cell-featured” plasma cell subpopulations that cluster closely with B cells, implicating their cell of origin. We validate AP-1 complex differential expression (JUN and FOS) in plasma cell subpopulations using CyTOF-based protein assays, and integrated analysis of single-cell RNA and CyTOF data reveals AP-1 downstream targets (IL6 and IL1B) potentially leading to inflammation regulation. Our work represents a longitudinal investigation for tumor and microenvironment during MM progression and paves the way for expanding treatment options., Clonal evolution in multiple myeloma (MM) needs to be understood in both the tumor and its microenvironment. Here the authors perform single-cell multi-omics profiling of samples from MM patients at different stages, finding transitions in the immune cell composition throughout progression.

Multiple myeloma is a plasma cell blood cancer with frequent chromosomal translocations leading to gene fusions. To determine the clinical relevance of fusion events, we detect gene fusions from a cohort of 742 patients from the Multiple Myeloma Research Foundation CoMMpass Study. Patients with multiple clinic visits enable us to track tumor and fusion evolution, and cases with matching peripheral blood and bone marrow samples allow us to evaluate the concordance of fusion calls in patients with high tumor burden. We examine the joint upregulation of WHSC1 and FGFR3 in samples with t(4;14)-related fusions, and we illustrate a method for detecting fusions from single cell RNA-seq. We report fusions at MYC and a neighboring gene, PVT1, which are related to MYC translocations and associated with divergent progression-free survival patterns. Finally, we find that 4% of patients may be eligible for targeted fusion therapies, including three with an NTRK1 fusion., Multiple myeloma is characterised by frequent gene fusions. Here, the authors use data from the Multiple Myeloma Research Foundation CoMMpass Study to further investigate fusion genes in this disease and their clinical relevance.

Multiple myeloma (MM) is a hematological cancer of the antibody-secreting plasma cells. Despite therapeutic advancements, MM remains incurable due to high incidence of drug-resistant relapse. In recent years, targeted immunotherapies, which take advantage of the immune system's cytotoxic defenses to specifically eliminate tumor cells expressing certain cell surface and intracellular proteins have shown promise in combating this and other B cell hematologic malignancies. A major limitation in the development of these therapies lies in the discovery of optimal candidate targets, which require both high expression in tumor cells as well as stringent tissue specificity. In an effort to identify potential myeloma-specific target antigens, we performed an unbiased search for genes with specific expression in plasma and/or B cells using single-cell RNA-sequencing (scRNAseq) of 53 bone marrow samples taken from 42 patients. By comparing >40K plasma cells to >97K immune cells across our cohort, we were able to identify a total of 181 plasma cell-associated genes, including 65 that encode cell-surface proteins and 116 encoding intracellular proteins. Of particular interest is that the plasma cells from each patient were shown to be transcriptionally distinct with unique sets of genes expressed defining each patient's malignant plasma cells. Using pathway enrichment analysis, we found significant overrepresentation of cellular processes related to B-Cell receptor (BCR) signaling, protein transport, and endoplasmic reticulum (ER) stress, involving genes such as DERL3, HERPUD1, PDIA4, PDIA6, RRBP1, SSR3, SSR4, TXNDC5, and UBE2J1. To note, our strategy successfully captured several of the most promising MM therapeutic targets currently under pre-clinical and clinical trials, including TNFRSF17(BCMA), SLAMF7, and SDC1 (CD138). Among these, TNFRSF17 showed very high plasma cell expression, with concomitant sharp exclusion of other immune cell types. To ascertain tissue specificity of candidate genes outside of the bone marrow, we analyzed gene and protein expression data from the Genotype-Tissue Expression (GTEx) portal and Human Protein Atlas (HPA). We found further support for several candidates (incl. TNFRSF17,SLAMF7, TNFRSF13B (TACI), and TNFRSF13C) as being both exclusively and highly expressed in lymphoid tissues. While several surface candidates were not found to be lymphocyte-restricted at the protein level, they remain relevant considerations as secondary targets for bi-specific immunotherapy approaches currently under development. To further investigate potential combinatorial targeting, we examine sample-level patterns of candidate co-expression and mutually-exclusive expression using correlation analysis. As the majority of our detected plasma cell-specific genes encode intracellular proteins, we investigated the potential utility of these epitopes as therapeutic targets via MHC presentation. Highly expressed candidates include MZB1, SEC11C, HLA-DOB, POU2AF1, and EAF2. We analyzed protein sequences using NetMHC and NETMHCII to predict high-affinity peptides for common class-I and class-II HLA alleles. To correlate MHC allelic preference with candidate expression in our cohort, we performed HLA-typing for 29 samples using Optitype. To support our scRNAseq-driven findings, we cross-referenced gene expression data with 907 bulk RNA-sequencing samples, including 15 from internal studies and 892 from the Multiple Myeloma Research Foundation (MMRF), as well as bulk global proteomics data from 4 MM cell lines (TIB.U266, RPMI8226, OPM2, MM1ST) and 4 patients. We see consistent trends across both cohorts, with high positive correlation (Pearson R ranging between 0.60 and 0.99) for a majority of genes when comparing scRNA and bulk RNA expression in the same samples. Our experimental design and analysis strategies enabled the efficient discovery of myeloma-associated therapeutic target candidates. In conclusion, this study identified a set of promising myeloma CAR-T targets, providing novel treatment options for myeloma patients. Disclosures Goldsmith: Wugen Inc.: Consultancy. DiPersio:Magenta Therapeutics: Membership on an entity's Board of Directors or advisory committees.

SUMMARY In the absence of a dominant driving mutation other than uniformly present TP53 mutations, deeper understanding of the biology driving ovarian high-grade serous cancer (HGSC) requires analysis at a functional level, including post-translational modifications. Comprehensive proteogenomic and phosphoproteomic characterization of 83 prospectively collected ovarian HGSC and appropriate normal precursor tissue samples (fallopian tube) under strict control of ischemia time reveals pathways that significantly differentiate between HGSC and relevant normal tissues in the context of homologous repair deficiency (HRD) status. In addition to confirming key features of HGSC from previous studies, including a potential survival-associated signature and histone acetylation as a marker of HRD, deep phosphoproteomics provides insights regarding the potential role of proliferation-induced replication stress in promoting the characteristic chromosomal instability of HGSC and suggests potential therapeutic targets for use in precision medicine trials., Graphical Abstract, In Brief McDermott et al. present the proteogenomic analysis of prospectively collected ovarian high-grade serous cancer samples and appropriate normal precursor samples under tight ischemic control. They identify tumor-associated signaling pathways and mitotic and cyclin-dependent kinases as key oncogenic drivers potentially related to chromosomal instability.

SUMMARY To elucidate the deregulated functional modules that drive clear cell renal cell carcinoma (ccRCC), we performed comprehensive genomic, epigenomic, transcriptomic, proteomic, and phosphoproteomic characterization of treatment-naive ccRCC and paired normal adjacent tissue samples. Genomic analyses identified a distinct molecular subgroup associated with genomic instability. Integration of proteogenomic measurements uniquely identified protein dysregulation of cellular mechanisms impacted by genomic alterations, including oxidative phosphorylation-related metabolism, protein translation processes, and phospho-signaling modules. To assess the degree of immune infiltration in individual tumors, we identified microenvironment cell signatures that delineated four immune-based ccRCC subtypes characterized by distinct cellular pathways. This study reports a large-scale proteogenomic analysis of ccRCC to discern the functional impact of genomic alterations and provides evidence for rational treatment selection stemming from ccRCC pathobiology., Graphical Abstract, In Brief Comprehensive proteogenomic characterization in 103 treatment-naive clear cell renal cell carcinoma patient samples highlights tumor-specific alterations at the proteomic level that are unrevealed by transcriptomic profiling and proposes a revised subtyping scheme based on integrated omics analysis.

SUMMARY Gene fusions represent an important class of somatic alterations in cancer. We systematically investigated fusions in 9,624 tumors across 33 cancer types using multiple fusion calling tools. We identified a total of 25,664 fusions, with a 63% validation rate. Integration of gene expression, copy number, and fusion annotation data revealed that fusions involving oncogenes tend to exhibit increased expression, whereas fusions involving tumor suppressors have the opposite effect. For fusions involving kinases, we found 1,275 with an intact kinase domain, the proportion of which varied significantly across cancer types. Our study suggests that fusions drive the development of 16.5% of cancer cases and function as the sole driver in more than 1% of them. Finally, we identified druggable fusions involving genes such as TMPRSS2, RET, FGFR3, ALK, and ESR1 in 6.0% of cases, and we predicted immunogenic peptides, suggesting that fusions may provide leads for targeted drug and immune therapy., In Brief Gao et al. analyze a 9,624 sample TCGA cohort with 33 cancer types to detect gene fusion events. They provide a landscape of fusion events detected, relate fusions to gene expression, focus on kinase fusion structures, examine mutually exclusive mutation and fusion patterns, and highlight fusion druggability.

The common nonsynonymous variant rs16969968 in the α5 nicotinic receptor subunit gene (CHRNA5) is the strongest genetic risk factor for nicotine dependence in European Americans and contributes to risk in African Americans. To comprehensively examine whether other CHRNA5 coding variation influences nicotine dependence risk, we performed targeted sequencing on 1582 nicotine-dependent cases (Fagerstrom Test for Nicotine Dependence score⩾4) and 1238 non-dependent controls, with independent replication of common and low frequency variants using 12 studies with exome chip data. Nicotine dependence was examined using logistic regression with individual common variants (minor allele frequency (MAF)⩾0.05), aggregate low frequency variants (0.05>MAF⩾0.005) and aggregate rare variants (MAF

Summary MIRMMR predicts microsatellite instability status in cancer samples using methylation and mutation information, in contrast to existing methods that rely on observed microsatellites. Additionally, MIRMMR highlights those genetic alterations contributing to microsatellite instability. Availability and implementation Source code is freely available at https://github.com/ding-lab/MIRMMR under the MIT license, implemented in R and supported on Unix/OS X operating systems. Supplementary information Supplementary data are available at Bioinformatics online.

Multiple myeloma (MM) is a disease defined by clonal proliferation of abnormal plasma cells from B-cells. Improved treatments for MM have led to improving overall lifespan, but still remains incurable due to acquired resistance to therapy and tumor heterogeneity. Single-cell RNA sequencing studies (scRNA-seq) of MM patients have highlighted the significant inter-individual heterogeneity and subclonal architecture of the malignant plasma cell populations, emphasizing the importance of developing personalized therapies specific to a patients molecular pathogenesis. In this study, we have integrated scRNA-seq with single-cell proteomics (sc-Prot) for 10 plasma cells and CD4+ T cells to validate and prioritize driver events in malignant cells and evaluate the tumor microenvironment. This effort will be expanded to another 10 cases to further integrate scRNA-seq, snATAC-seq, whole exome sequencing and bulk RNA-sequencing on a fraction of the cells isolated from bone marrow. The remaining cells will be sorted using FACS to select for specific malignant and immune cells including 40 plasma cells, 15 CD4+ T and 15 CD8+ T cells. These sorted cells will be profiled with a scProt technology (BASIL nanoPOTS) to illuminate their cell-to-cell heterogeneity. In our pilot study comparing bulk and single-cell proteomic data of a single patient's plasma cells (CD138+) for 400 representative proteins, while a majority of expression signatures are concurrent between the two methods, some signaling pathways including translation and apoptotic cleavage are discordant. Our findings stress the importance of interrogating subpopulations of immune and malignant cells at the single-cell level to further refine the transcriptomic and proteomic heterogeneity of MM in a cell type specific manner. With the aid of single-cell technology, we have assessed the heterogeneity of malignant and immune cell types to evaluate transcriptomic and proteomic changes contributing to altering the interplay between the immune environment and tumor cells. Disclosures Fiala: Incyte: Research Funding. Rettig:WashU: Patents & Royalties: Patent Application 16/401,950. O'Neal:Wugen: Patents & Royalties: Patent Pending; WashU: Patents & Royalties: Patent Pending. DiPersio:WUGEN: Equity Ownership, Patents & Royalties, Research Funding; Macrogenics: Research Funding, Speakers Bureau; Cellworks Group, Inc.: Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy; Magenta Therapeutics: Equity Ownership; RiverVest Venture Partners Arch Oncology: Consultancy, Membership on an entity's Board of Directors or advisory committees; NeoImmune Tech: Research Funding; Karyopharm Therapeutics: Consultancy; Incyte: Consultancy, Research Funding; Amphivena Therapeutics: Consultancy, Research Funding; Bioline Rx: Research Funding, Speakers Bureau. Vij:Bristol-Myers Squibb: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Genentech: Honoraria; Janssen: Honoraria; Karyopharm: Honoraria; Sanofi: Honoraria; Takeda: Honoraria, Research Funding.

Complex insertions and deletions (indels) are formed by simultaneously deleting and inserting DNA fragments of different sizes at a common genomic location. Here we present a systematic analysis of somatic complex indels in the coding sequences of samples from over 8,000 cancer cases using Pindel-C. We discovered 285 complex indels in cancer-associated genes (such as PIK3R1, TP53, ARID1A, GATA3 and KMT2D) in approximately 3.5% of cases analyzed; nearly all instances of complex indels were overlooked (81.1%) or misannotated (17.6%) in previous reports of 2,199 samples. In-frame complex indels are enriched in PIK3R1 and EGFR, whereas frameshifts are prevalent in VHL, GATA3, TP53, ARID1A, PTEN and ATRX. Furthermore, complex indels display strong tissue specificity (such as VHL in kidney cancer samples and GATA3 in breast cancer samples). Finally, structural analyses support findings of previously missed, but potentially druggable, mutations in the EGFR, MET and KIT oncogenes. This study indicates the critical importance of improving complex indel discovery and interpretation in medical research.

Multiple myeloma (MM) is an incurable hematological malignancy characterized by the clonal proliferation of malignant plasma cells in the bone marrow. Like other cancers, MM is a genetically complex and heterogeneous disease. One of its distinctive characteristics is that it is preceded by a pre-malignant condition known as monoclonal gammopathy of undetermined significance (MGUS), which then progresses to asymptomatic (smoldering) multiple myeloma (SMM) and, ultimately, to late-stage MM. Its progression through these stages is determined by a sequence of genomic aberrations, starting with germline events that predispose to the disease, followed by early initiating events and the later acquisition of mutations that contribute to disease progression. Although considerable progress has been made in the past 6 years in cataloguing somatic events underlying MM development and progression, little is known about its genetic predisposition. Therefore, large-scale germline genomic variant studies are urgently needed. Recently, our group has published the largest-scale pan-cancer study of >10K adult and >1K pediatric cases that revealed new insights on germline predisposition variants across 33 cancer types (853 pathogenic or likely pathogenic variants) (Huang et al., 2018). Here, we aim to apply a similar strategy to MM cases. The CoMMpass study, promoted by MMRF (Multiple Myeloma Research Foundation) is a longitudinal, prospective observational study involving the collection and analysis of sequencing and clinical data from >1K MM patients at diagnosis and relapse. We performed germline variant calling on 808 normal samples from this dataset using GenomeVIP (https://github.com/ding-lab/GenomeVIP), which integrates multiple tools: VarScan2 and Genome Analysis ToolKit (GATK) for the identification of single nucleotide variants (SNVs) and indels; and Pindel for indel prediction. Variants were limited to coding regions of full length transcripts obtained from Ensembl release 70 plus the additional two base pairs flanking each exon that cover splice donor/acceptor sites. SNVs were based on the union of raw GATK and VarScan calls. Indels were required to be called by at least two out of the three callers (GATK, Pindel, VarScan). Variant calls from all tools were merged, filtered (allelic depth ≥ 5 for the alternative allele; rare variants with allele frequency ≤ 0.01 in 1000 Genomes and ExAC), and annotated using Variant Effect Predictor (VEP), resulting in an average of 1,653 variants per sample. Further, we applied CharGer (Characterization of Germline Variants, https://github.com/ding-lab/CharGer) to classify the identified germline variants as pathogenic, likely pathogenic, and prioritized variants of unknown significance (VUS). CharGer is an automatic variant classification pipeline developed by our group which adopts ACMG-AMP guidelines specifically for rare variants in cancer. Here, we were able to classify a total of 635 germline variants as pathogenic and 150 as likely pathogenic, affecting 90% of samples. Among pathogenic variants, 28 were found in known cancer predisposition genes including BRCA1 and BRCA2 - which have been previously associated with MM risk - BRIP1, CHEK2, TP53, TERT, and PMS2. Ongoing analyses include: functional characterization of these variants, identifying genes with enriched pathogenic or likely pathogenic variants in our dataset; investigation of LOH and two-hit (biallelic) events; gene and protein expression analyses in carriers of pathogenic germline variants of the respective gene; scanning for rare, germline copy number variations (CNVs); and identification of variants in post-translational modification sites that may affect protein signaling. Additionally, we are currently working on improving our CharGer tool by integrating new tumor associated data, such as DNA-Seq, RNA-Seq, Methyl-Seq and MS proteomics data, to improve variant classification. The preliminary results and analysis strategies described here will allow for efficient and cost-effective discovery of genetic changes relevant to MM etiology. Ultimately, we hope this work will impact our overall understanding of the genetics underlying MM predisposition, allowing for the development of better prevention and early detection strategies. Disclosures Vij: Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Karyopharma: Honoraria, Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Honoraria, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Jansson: Honoraria, Membership on an entity's Board of Directors or advisory committees.

Introduction: Gene fusions are the result of genomic rearrangements that create hybrid protein products or bring the regulatory elements of one gene into close proximity of another. Fusions often dysregulate gene function or expression through oncogene overexpression or tumor suppressor underexpression (Gao, Liang, Foltz, et al. Cell Rep 2018). Some fusions such as EML4--ALK in lung adenocarcinoma are known druggable targets. Fusion detection algorithms utilize discordantly mapped RNA-seq reads. Careful consideration of detection and filtering procedures is vital for large-scale fusion detection because current methods are prone to reporting false positives and show poor concordance. Multiple myeloma (MM) is a blood cancer in which rapidly expanding clones of plasma cells spread in the bone marrow. Translocations that juxtapose the highly-expressed IGH enhancer with potential oncogenes are associated with overexpression of partner genes, although they may not lead to a detectable gene fusion in RNA-seq data. Previous studies have explored the fusion landscape of multiple myeloma cohorts (Cleynen, et al. Nat Comm 2017; Nasser, et al. Blood 2017). In this study, we developed a novel gene fusion detection pipeline and post-processing strategy to analyze 742 patient samples at the primary time point and 64 samples at follow-up time points (806 total samples) from the Multiple Myeloma Research Foundation (MMRF) CoMMpass Study using RNA-seq, WGS, and clinical data. Methods and Results: We overlapped five fusion detection algorithms (EricScript, FusionCatcher, INTEGRATE, PRADA, and STAR-Fusion) to report fusion events. Our filtered call set consisted of 2,817 fusions with a median of 3 fusions per sample (mean 3.8), similar to glioblastoma, breast, ovarian, and prostate cancers in TCGA. Major recurrent fusions involving immunoglobulin genes included IGH--WHSC1 (88 primary samples), IGL--BMI1 (29), and the upstream neighbor of MYC, PVT1, paired with IGH (6), IGK (3), and IGL (11). For each event, we used WGS data when available to determine if there was genomic support of the gene fusion (based on discordant WGS reads, SV event detection, and MMRF CoMMpass Seq-FISH WGS results) (Miller, et al. Blood 2016). WGS validation rates varied by the level of RNA-seq evidence supporting each fusion, with an overall rate of 24.1%, which is comparable to previously observed pan-cancer validation rates using low-pass WGS. We calculated the association between fusion status and gene expression and identified genes such as BCL2L11, CCND1/2, LTBR, and TXNDC5 that showed significant overexpression (t-test). We explored the clinical connections of fusion events through survival analysis and clinical data correlations, and by mining potentially druggable targets from our Database of Evidence for Precision Oncology (dinglab.wustl.edu/depo) (Sun, Mashl, Sengupta, et al. Bioinformatics 2018). Major examples of upregulated fusion kinases that could potentially be targeted with off-label drug use include FGFR3 and NTRK1. We examined the evolution of fusion events over multiple time points. In one MMRF patient with a t(8;14) translocation joining the IGH locus and transcription factor MAFA, we observed IGH fusions with TOP1MT (neighbor of MAFA) at all four time points with corresponding high expression of TOP1MT and MAFA. Using non-MMRF single-cell RNA data from different patients, we were able to track cell-type composition over time as well as detect subpopulations of cells harboring fusions at different time points with potential treatment implications. Discussion: Gene fusions offer potential targets for alternative MM therapies. Careful implementation of gene fusion detection algorithms and post-processing are essential in large cohort studies to reduce false positives and enrich results for clinically relevant information. Clinical fusion detection from untargeted RNA-seq remains a challenge due to poor sensitivity, specificity, and usability. By combining MMRF CoMMpass data from multiple platforms, we have produced a comprehensive fusion profile of 742 MM patients. We have shown novel gene fusion associations with gene expression and clinical data, and we identified candidates for druggability studies. Disclosures Vij: Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Jazz Pharmaceuticals: Honoraria, Membership on an entity's Board of Directors or advisory committees; Jansson: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Karyopharma: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding.

Two large cancer genomic consortia recently published the largest and highest-quality consensus mutations calls for both whole-exome sequencing (WES) and whole-genome sequencing (WGS) in cancer: The Cancer Genome Atlas (TCGA), and the International Cancer Genetics Consortia (ICGC), respectively. Together these datasets encompass more than 60M mutations from ~13,000 samples (~10,000 WES and ~3,000 WGS). An intersecting set of 742 samples, from 22 cancer types, was sequenced using both platforms and mutations were identified using a combined 13 variant calling tools (7 WES and 5 WGS). These samples represent an ideal dataset to compare and contrast WES with WGS performance, reliability, and reproducibility of mutation calling in exons, and provide the community with key regions flanking exons that play a role in carcinogenesis. MAF files were collected using strict filtering criteria for initial file release, including the elimination of germline contaminants, 8-oxo-guanine artifacts, depth filtering and repeat masking. Additional filtering included minimum coverage requirements and restriction of both WES and WGS to variants detected within targeted exons. Finally, we restricted our data to known cancer genes. This final step suggests that these 742 samples have anywhere between 11.5K to 12.3K mutations from covered exons in potential cancer driver genes—WES and WGS, respectively. Preliminary results found that ~70% of samples had had >80% congruent mutations between both platforms; ~25% of samples had had >80% congruent mutations calls in one or the other platform; and the remaining samples had poor performance in replicating identical mutations. We observed that a majority of the variants unique to a sequencing platform were primarily from mutations with low VAF. We also sought to explore regions of the genome that are captured by both technologies despite the knowledge that WES did not target these regions. This is made possible by obtaining access to the primary data resources, and relaxing filtering criteria to include other regions such as 3' and 5' UTR, exon flanking regions, and intronic regions. We identified many recurrent mutations from non-exonic regions that were corroborated using both platforms that have not been previously reported in pan-cancer efforts. At this historic junction in time, as preliminary results from whole-genome sequencing efforts emerge and large exome sequencing efforts taper, 742 samples spanning both efforts can provide insights into the lessons learned from exome sequencing, and provide a solid foundation stepping forward into whole-genome analysis. We will continue to glean insights into the etiology of human disease by using both technologies; however, these mutation calls highlight the challenges that still exist in somatic variant calling, and provide grounds for more critical evaluation of genomic findings in cancer. Citation Format: Matthew H. Bailey, Liang-Bo Wang, Wen-Wei Liang, Steven Foltz, Guanlan Dong, Michael C. Wendl, Michael McLellan, Angela C. Hirbe, Jared Simpson, Mark Gerstein, Li Ding. Reproducibility assessment of mutations calls in exome- and whole-genome sequencing using consensus calling from TCGA and ICGC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 419.

Author(s): Yao, Nengliang; Foltz, Steven M; Odisho, Anobel Y; Wheeler, David C | Abstract: ContextFinancial and demographic pressures in US require an understanding of the most efficient distribution of physicians to maximize population-level health benefits. Prior work has assumed a constant negative relationship between physician supply and mortality outcomes throughout the US and has not addressed regional variation.MethodsIn this ecological analysis, geographically weighted regression was used to identify spatially varying relationships between local urologist density and prostate cancer mortality at the county level. Data from 1,492 counties in 30 eastern and southern states from 2006-2010 were analyzed.FindingsThe ordinary least squares (OLS) regression found that, on average, increasing urologist density by 1 urologist per 100,000 people resulted in an expected decrease in prostate cancer mortality of -0.499 deaths per 100,000 men (95% CI -0.709 to -0.289, p-value l 0.001), or a 1.5% decrease. Geographic weighted regression demonstrated that the addition of one urologist per 100,000 people in counties in the southern Mississippi River states of Arkansas, Mississippi, and Louisiana, as well as parts of Illinois, Indiana, and Wisconsin is associated with decrease of 0.411 to 0.916 in prostate cancer mortality per 100,000 men (1.6-3.6%). In contrast, the urologist density was not significantly associated with the prostate state mortality in the new England region.ConclusionsThe strength of association between urologist density and prostate cancer mortality varied regionally. Those areas with the highest potential for effects could be targeted for increasing the supply of urologists, as it associated with the largest predicted improvement in prostate cancer mortality.