ALBERT

Description: 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.

Description: 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, Kallistopizzly, 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.