Streptococcus agalactiae, also known as Group B Streptococcus (GBS), is a Gram-positive bacterium isolated from the vaginal tract of approximately 25% of women. GBS colonization of the female reproductive tract is of particular concern during pregnancy as the bacteria can invade gestational tissues or be transmitted to the newborn during passage through the birth canal. Infection of the neonate can result in life-threatening pneumonia, sepsis and meningitis. Thus, surveillance of GBS strains and corresponding virulence potential during colonization is warranted. Here we describe a panel of GBS isolates from the vaginal tracts of a cohort of pregnant women in Michigan, USA. We determined that capsular serotypes III and V were the most abundant across the strain panel, with only one isolate belonging to serotype IV. Further, 12.8% of strains belonged to the hyper-virulent serotype III, sequence type 17 (ST-17) and 15.4% expressed the serine rich repeat glycoprotein-encoding gene srr2. Functional assessment of the colonizing isolates revealed that almost all strains exhibited some level of β-hemolytic activity and that ST-17 strains, which express Srr2, exhibited increased bacterial adherence to vaginal epithelium. Finally, analysis of strain antibiotic susceptibility revealed the presence of antibiotic resistance to penicillin (15.4%), clindamycin (30.8%), erythromycin (43.6%), vancomycin (30.8%), and tetracycline (94.9%), which has significant implications for treatment options. Collectively, these data provide important information on vaginal GBS carriage isolate virulence potential and highlight the value of continued surveillance.
Key Points Leukemia-associated mutations can be detected many years before the onset of secondary leukemias in myeloma patients. Stem and progenitor cells can act as reservoirs of mutations before the onset of secondary MDS and AML after treatment of myeloma.
Background: Gene Fusion events are common occurrences in malignancies, and are frequently drivers of malignancy. FISH and qPCR are two methods often used for identifying highly prevalent gene fusions/translocations. However, these are single target assays, requiring a lot of effort and sample if multiple assays are needed for multiple targets like sarcoma. High-throughput parallel (NextGen) DNA and RNA sequencing are also in current use to detect and characterize gene fusions. RNA sequencing (RNAseq) has the advantage that multiple markers can be targeted at one time and RNA fusions are readily identified from their product transcripts. While many fusion calling algorithms exist for use on RNAseq data, sensitive fusion callers, needed for samples of low tumor content, often present high false positive rates. Further, there currently is no single variable or element in NGS data that can be used to filter out false positive calls by extant callers. Individual sensitive fusion callers may be considered weak predictors of gene fusions. Combining their results into a single fusion call involves evaluating many elements, which can be a time consuming and difficult manual task. In order to achieve higher accuracy in fusion calls than can be achieved using individual fusion callers, we have combined the results of multiple fusion callers by use of an ensemble learning approach based on random forest models. Our method selects the best group of callers from among several callers, and provides an algorithmic means of combining their results, presenting a metric that can be immediately interpreted as the probability that a called fusion is a true fusion call. Methods: Random forest models were generated with the randomForest package in R, and then tuned using the R caret package. Training data sets consisted of fusion calls deemed true by review and by orthogonal methods including PCR/Sanger sequencing and the commercial Archer™ fusion calling system. We present the results of training on calls made by five fusion callers Arriba, STAR-Fusion, FusionCatcher, deFuse, and Kallisto/pizzly. Logistic training variables (seen vs not seen by the fusion caller) were used for the five callers. Variables also included metrics for the magnitude and balance of coverage on either side of candidate fusion breakpoints reported by Arriba and STAR Fusion ("coverage balance") and a single metric consisting of the number of sequencing reads that cross the candidate breakpoint. The model was validated by 10-fold cross-validation on 598 fusion calls by the five callers. Results: The resulting model is superior to the simple strategy of requiring agreement by n of five callers, particularly with regard to specificity (Table 1). Also, "importance of variables," reported by randomForest, gauges the relative contribution of variables in the model. Here it shows that one caller, Kallisto\pizzly, does not contribute to the model (Table 2). Conclusion: Random Forest modeling provides a viable means of combining gene fusion call data from multiple callers into a single fusion calling tool with improved performance over simple combinations of fusion calls. An additional benefit is seen in that building and evaluating such models can guide the selection of fusion callers, thereby eliminating non-contributory calling methods and ensuring optimal utilization of computational resources. Disclosures Thomas: NeoGenomics,Inc.: Current Employment. Mou:NeoGenomics: Current Employment. Keeler:NeoGenomics: Current Employment. Magnan:NeoGenomics: Current Employment. Funari:NeoGenomics: Current Employment. Weiss:Merck: Other: Speaker; Bayer: Other: speaker; Genentech: Other: Speaker; NeoGenomics: Current Employment. Brown:NeoGenomics,Inc.: Current Employment. Agersborg:NeoGenomics: Current Employment.
Introduction: Tumor mutational burden (TMB) is the number of somatic mutations per megabase in a tumor's genome and has shown promise as a predictive biomarker of response to immune checkpoint inhibitors across several cancers. TMB is typically measured by whole exome sequencing (WES TMB) or by targeted next-generation sequencing gene panels (panel TMB). As more assays are developed to estimate TMB, harmonization is emerging as an unmet need and is a key goal of the Friends of Cancer Research (Friends) TMB Harmonization Project. Phase I of the Harmonization Project demonstrated correlation between panel TMB and WES TMB using TCGA data and defined theoretical sources of variability across panels. In phase IIA, sustainable TMB reference standard materials generated from human derived cell lines were used to characterize variability in TMB measurements across panels and assessed for utility in TMB alignment. Phase IIB aims to characterize variability in TMB measurements in clinical samples and to establish best practices for estimating and aligning TMB in order to improve consistency across panels. Methods: Fifteen laboratories (16 targeted gene panels) at different stages of development participated in phase IIB. Thirty formalin-fixed paraffin-embedded (FFPE) samples with >30% tumor content were acquired; tumor DNA was isolated by a single reference lab. TMB values were calculated for DNA extracted from lung (N=10), bladder (N=10), and gastric tumors (N=10) using WES and a uniform bioinformatics pipeline agreed upon by all Consortium members. DNA samples were also sent to all laboratories, and each used their own sequencing and bioinformatics pipelines to estimate TMB from the genes represented in their respective panels. For each tumor sample, a median across panel TMB estimates was calculated; individual panel TMB estimates were translated to fold-changes relative to the sample median to quantify variability. Association between WES TMB (reference) and panel TMB will be assessed by regression analysis; dependence of association on cancer type was investigated. Results: A subset of tumor samples (9 bladder, 7 lung, and 5 gastric) was analyzed using 11 panels at the time of abstract submission. Median panel TMB values ranged 0.60 - 40.26 across samples, with median of median values of 5.35. Fold-change from sample-level medians ranged 0x - 6.67x. Assessment of these clinical samples by WES and all 16 gene panels, as well as regression analysis results, are forthcoming. Conclusions: The Friends TMB Harmonization Project has made substantial progress in characterization of TMB measurement variability and association between WES TMB and panel TMB. These are important steps toward alignment of TMB estimates generated by different gene panels which may improve the interpretation of findings within clinical development programs and ultimately enhance the usefulness of this predictive biomarker in clinical decision making. Citation Format: Diana M. MERINO, Laura M. Yee, Lisa M. McShane, P. Mickey Williams, Tomas Vilimas, Rajesh Patidar, J. Carl Barrett, Shu-Jen Chen, Jen-Hao Cheng, Jeffrey M. Conroy, Dinesh Cyanam, Kenneth R. Eyring, David A. Fabrizio, Vincent Funari, Elizabeth P. Garcia, Sean T. Glenn, Christopher D. Gocke, Vikas Gupta, Lisa M. Haley, Matthew D. Hellmann, Laurel Keefer, Lauryn R. Keeler, Brett Kennedy, Alexander J. Lazar, Laura E. MacConaill, Kristen L. Meier, Arnaud Papin, Naiyer A. Rizvi, Ethan Sokol, Phillip Stafford, John F. Thompson, Warren Tom, Victor J. Weigman, Mingchao Xie, Chen Zhao, Mark D. Stewart, Jeff Allen. Alignment of TMB measured on clinical samples: Phase IIB of the Friends of Cancer Research TMB Harmonization Project [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5671.
Background: A cost effective and comprehensive genomic profiling (CGP) approach for diagnosis, risk stratification and therapy would be useful for the evaluation of oncologic specimens. Available approaches involving additive testing for DNA and RNA abnormalities through traditional methods (e.g. Sanger, FISH, cytogenetics, qRT-PCR) are not comprehensive, require multiple different workflows and are sample consuming, often resulting in incomplete testing. While there are next generation sequencing (NGS) assays designed for detecting DNA and RNA abnormalities, they have separate workflows that require twice the amount of sample and effort. To address this, we developed a novel total nucleic acid (TNA) extraction method and single tube workflow utilizing TNA and a custom multimodal chemistry designed for hematologic malignancies. This consolidated workflow enables an efficient discovery based approach for both DNA/RNA abnormalities including single nucleotide variants (SNVs), InDels, copy number variants (CNVs), large structural changes from DNA and gene fusions and gene expression levels from RNA. This method maximizes data derived from valuable samples while delivering a comprehensive profile of the patient's tumor which can help guide therapeutic and clinical decisions. Methods: Total nucleic acid (TNA) was extracted from bone marrow and peripheral blood of 95 patients (CML, CMML, CLL, AML and myeloid disorders). 297 genes that have DNA mutations specific to hematological cancers were targeted, along with 213 genes that were targeted for clinically significant RNA abnormalities. Enriched genomic and transcriptomic regions of interest from 85 patients were successfully sequenced with unique dual indices on an Illumina NovaSeq 6000. DNA variant detection as well as fusion detection from RNA were compared to traditional orthogonal NGS assays that use DNA input or compared to qRT-PCR and Sanger sequencing assays that use RNA as input. Results: In this study, we developed an efficient and high-quality TNA extraction method that can purify enough total nucleic acid from bone marrow, peripheral blood, cytogenetic pellets, flow suspension, and FFPE samples for the downstream NGS assay. The average OD 260/280 value was 1.9 and the OD 260/230 was 2.18. After sequencing, 256/262 (97.7% accuracy) SNV and Indel variants that were candidate pathogenic mutations were concordant from 38 patients. Meanwhile, 100% (7/7) of all BCR/ABL1 gene fusions which had an international scale (IS) value above 6.4% were concordant. In addition, 69 fusion positive samples containing 20 unique gene fusions which had been previously reported by an independent ArcherDX assay designed specifically for gene fusions were also evaluated with this chemistry. Analysis revealed a 92.5% (64/69) concordance. More importantly, the QIAseq multimodal TNA NGS assay detected both DNA and RNA abnormalities in a single tube. For example, in one myeloid leukemia patient, we not only identified pathogenic variants of ASXL1 and JAK2 which had been previously detected by a DNA NGS assay, but also detected a concurrent BCR-FGFR1 fusion which had been previously reported by a FISH assay. Moreover, we were able to provide more comprehensive genomic profiling by investigating many DNA and RNA abnormalities simultaneously. In our study, for 5 patients that previously been tested for BCR-ABL1 fusion only, we are able to assess BCR-ABL1 fusion status from RNA as well as identify pathogenic DNA variants at the same time, including JAK2 p.V617F, U2AF1 p.S34F, ASXL1 p.E635Rfs*15, BRCA p.S1982Rfs*22, and DNMT3A p.S708Vfs*71, which provides valuable information to assist diagnosis and treatment in a cost effective and efficient way. Conclusions: We developed a single tube TNA based workflow with a custom multimodal chemistry that simultaneously detects many DNA and RNA abnormalities in a cost effective and efficient way while reducing sample requirements. This unique TNA NGS assay provides comprehensive genomic profiling for hematologic malignancies and improves the diagnostic testing options for precise patient care. Disclosures Yu: NeoGenomics: Current Employment. Alarcon:NeoGenomics: Current Employment. Mou:NeoGenomics: Current Employment. Jung:NeoGenomics: Current Employment. Nam:NeoGenomics: Current Employment. Thomas:NeoGenomics: Current Employment. Keeler:NeoGenomics: Current Employment. Shinbrot:NeoGenomics: Current Employment. Magnan:NeoGenomics: Current Employment. Bender:NeoGenomics: Current Employment. Jiang:NeoGenomics: Current Employment. Agersborg:NeoGenomics: Current Employment. Weiss:Bayer: Other: speaker; Genentech: Other: Speaker; Merck: Other: Speaker; NeoGenomics: Current Employment. Ye:NeoGenomics: Current Employment. Funari:NeoGenomics: Current Employment.
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