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Developing highly active catalysts for hydrogen evolution reaction based on earth-abundant materials is challenging. Nitrogen doping has recently been reported to improve catalytic properties by modifying the electrochemical properties of titanium carbide MXene. However, systematic doping engineering, such as optimization of doping concentration, doping site, and thermodynamic phase stabilization have not been systematically controlled, which retards the reliable production of high-activity MXene catalysts. In this study, the optimum doping concentration of nitrogen and doping process conditions on O-functionalized Ti2C MXene for hydrogen evolution reaction were investigated using density functional theory with thermodynamics. To confirm the optimum nitrogen concentration, the catalytic properties are examined considering the Gibbs free energy of hydrogen adsorption and conductivity for 2.2-11.0 at % nitrogen concentration. It was confirmed that 8.8 at % nitrogen-doped Ti2CO2 had optimum catalytic properties under standard conditions. Moreover, when the doping concentration was higher, the decrease in the adsorption energies of hydrogen and the transition in the energy dispersion of the conduction band led to deterioration of the catalytic properties. Through theoretical results, the feasible process conditions for optimum nitrogen concentration while maintaining the structure of MXene are presented using a thermodynamics model taking into account chemical reactions with various nitrogen sources. This study provides further understanding of the nitrogen-doping mechanism of Ti2CO2 for hydrogen evolution reactions.

Background: Copy Number Variations (CNVs) are prominent features of cancer cells. From a clinical standpoint, their accurate detection at a low cost is a priority. With regular increases in the number of markers to be tested, the cost effectiveness and practicality of gold standard techniques like Fluorescence In Situ Hybridization (FISH) are slowly decreasing. Cost-efficient Next Generation Sequencing (NGS) targeted gene panels can be scaled up but accurately detecting CNVs from the resulting data remains challenging. We demonstrate large amounts of data and machine learning can help bridge the gap between the two techniques. Methods: We collected the sequencing data of 6,277 patients tested using a custom amplicon based NGS assay designed to detect somatic alterations in 297 hematological cancer relevant genes such that at least one concurrent FISH test was also performed. FISH results were used to infer the gain, loss, or normality information for each corresponding gene. The annotated genes were then used to curate a training set by extracting 20 features per gene from the alignment results. A 3-class random forest classifier was trained using this dataset. The selected model was evaluated on a distinct set of 2,738 patients. Results: Evaluation results are provided in Table 1 for 8 genes for which the FISH probe used to infer the gain, loss, or normality information directly spanned the gene region. The predicted CNVs are almost a perfect match with FISH for 6 of these genes with a limit of detection at 20% abnormal cells. In most cases, the model reduces discordant calls by over 50% compared to using existing CNV detection software only. Conclusion: We show the CNV detection capabilities of a targeted NGS assay can closely match the gold-standard FISH technique by analytically correcting the biases introduced by the targeting procedures. The model presented here is used to detect CNVs in ALL patients after a successful formal validation in our laboratory. Table 1. Evaluation results. Gene FISH Positive Cases (Gain/Loss) FISH Negative Cases (Normal) All FISH Cases Total Concordant Sensitivity Total Concordant Specificity Total Concordant Accuracy ATM 72 72 100.00% 641 629 98.13% 713 701 98.32% CBFB 23 22 95.65% 517 506 97.87% 540 528 97.78% EGR1 171 169 98.83% 1,541 1,538 99.81% 1,712 1,707 99.71% KMT2A 27 25 92.59% 474 472 99.58% 501 497 99.20% MET 113 113 100.00% 1,593 1,582 99.31% 1,706 1,695 99.36% NF1 15 15 100.00% 908 908 100.00% 923 923 100.00% TERT 10 10 100.00% 1,723 1,709 99.19% 1,733 1,719 99.19% TP53 76 73 96.05% 1,567 1,556 99.30% 1,643 1,629 99.15% Citation Format: Christophe N. Magnan, Hyunjun Nam, Shashikant Kulkarni, Segun C. Jung. Bridging the gap between targeted NGS and FISH gene-level CNV detection capabilities in hematologic malignancies. [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 4294.

Background: WHO recognizes 23 genomic rearrangements or fusions which define subclasses of AML, MDS/MPN and related neoplasms, and their detection is essential for patient management. Discerning true fusions from artificial calls in RNAseq-based tests is challenging due to biological and technical noise. We previously developed a method to identify fusion transcripts by a single-tube NGS assay capable of concurrent analysis of DNA and RNA alterations in ALL patients. We expanded the assay with an improved fusion calling algorithm and used it to study the landscape of myeloid RNA fusions in the clinical setting. Methods: Total nucleic acid (TNA) from bone marrow or peripheral blood was analyzed in our clinical laboratory by a CLIA grade custom amplicon-based multimodal NGS assay, targeting 302 genes by DNA-seq and 185 genes by RNA-seq. Libraries were sequenced on a NovaSeq6000 instrument, and fusions were called from RNA: de-duplicated and error-corrected UMI reads were processed by an in-house developed BI pipeline leveraging machine learning, to assign a final confidence score (F1). Deidentified patient data was used according to an approved IRB. Results: Distribution of F1 scores was used to improve the discrimination between technical noise and real fusion calls. Analytical validation of RNA fusion calling against FISH and Sanger-seq in 74 hematologic disorder samples demonstrated 98.2% specificity and 97.0% sensitivity. Data from 789 patients was used to study the distribution of myeloid fusion events in community cases. 17% of patients had fusions involving genes from WHO/NCCN recommendations. Frequencies for most common fusions were 2.3% (18/789) for KMT2A, 2% for PML::RARA, 1.9% for BCR::ABL1, 0.8% for RUNX1::RUNX1T1 and CBFB::MYH11 and 0.6% for NUP98. Fusions of PDGFRA, ETV6, ZNF384, FGFR1 and other genes were also observed and BCR::ABL1 fusions were seen not only in CML patients but also in a patient with AML. For KMT2A, 25% (2/8) fusions detected by NGS were confirmed by Sanger-seq but missed by FISH, which correlates with higher sensitivity of the NGS assay. Novel fusions were called in ~8% of patients. This included an AML patient with a CCND2::MGP fusion, resulting in cyclin D2 (CCND2), frequently activated by DNA mutations in AML, fused to matrix Gla protein, a highly expressed gene in hematopoietic progenitor cells. The fusion was confirmed by Sanger-seq, and shown to lack ex5 of CCND2, which contains Thr280, a residue required for ccnd2 degradation. This fusion is thus predicted to generate high cellular levels of oncogenic ccnd2-mgp. Conclusions: Frequencies of well-known fusions in real world data obtained by a robust low-noise RNA fusion assay were similar to other studies done in academic setting. Reliable detection of bona-fide RNA fusions with this clinical test is invaluable for patient care and novel fusion identification. Citation Format: Michal Krawczyk, Chaugiang Duong, Lina Zelinger, Fei Ye, Hyunjun Nam, Brad Thomas, Vincent Funari, Shashikant Kulkarni, Fernando Lopez-Diaz. Landscape of known and novel myeloid neoplasia fusions identified by a multimodal comprehensive genomic profiling test in 789 patients [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 1401.

Background: Aneuploidy and large-scale Copy Number Variations (CNVs) are prominent features of cancer cells. While Fluorescence in situ hybridization (FISH) and conventional cytogenetics (CC) are the gold standard for detecting aneuploidy and CNVs, NGS-based assays are currently used for high-resolution detection of copy number alterations assessing the whole genome. However, although an increasing number of NGS-based tools have been developed for detecting aneuploidy or CNVs from whole genome or exome sequencing data, only a limited number of options are available for targeted gene panels. Despite mechanisms provided to establish normal profiles for a specific panel, the accuracy of these tools at the chromosome level suffer when only a small number of regions are targeted on each chromosome. Here we leveraged on a custom amplicon based NGS assay designed to detect somatic alterations (SNVs and indels) in 297 hematological cancer relevant genes, previously validated in our clinical laboratory. We introduce a simple approach to accurately predict chromosome-level CNVs such as monosomy and trisomy for a targeted gene panel, commonly used in a clinical setting. Methods: Mutation profiles, including SNVs, INDELs, and structural changes, were interrogated with an in-house bioinformatics pipeline that utilized PureCN and CNVkit algorithms to detect structural changes. The first step consists of finding optimal panel-specific decision thresholds for gains and losses at the gene level. This step was performed using an independent set of 1,314 clinical samples sequenced with the NeoType® Heme assay developed by NeoGenomics Laboratories, Inc. for which at least one FISH test was performed in addition to the sequencing. Three genes (ATM, TP53, and NF1) were used to find optimal decision thresholds based on the FISH result for these markers. These thresholds are used afterward to predict a gain or a loss for any other gene in the panel. The second step consists of predicting the chromosome-level gain or loss based on the individual predictions at the gene level by simply observing the frequency of targeted genes on the corresponding chromosome predicted as either gained or lost by the first step approach. The 19, 7, and 18 targeted genes in the NGS panel (Table 1) were respectively used to predict monosomy 7, trisomy 8, and trisomy 12 in a second set of over 7,000 clinical samples with known ploidy for chromosomes with clinically relevant ploidy abnormalities in hematological malignancies. Results: Evaluation of the first stage gene-level CNV prediction on 1,314 clinical samples shows a concordance rate of 97.95% between NGS and FISH results on ATM, TP53, and NF1. When we evaluated the second stage chromosome-level CNV prediction in clinical samples sequenced using the same targeted panel and assessed by FISH for chromosome-level variation on chromosomes 7, 8 and 12 (Table 1), a heatmap of the predicted Log 2 ratios for each sample and targeted gene from the first step shows a clear distinctive signal between aneuploidy and diploid samples (Figure 1). At the chromosome level, the concordance rate between the final prediction and the FISH results is consistently observed above 93% (Table 2). Roughly 50% of the 12, 78, and 40 discordant calls for monosomy 7, trisomy 8, and trisomy 12, respectively captured by FISH but not by NGS can be explained by low tumor content (less than 20%) in the tested samples. The concordance rate between NGS and FISH is consistently observed above 96% when leaving these samples aside. Note that results in Table 2 are obtained using all samples to decide the optimal decision threshold for the chromosome-level prediction, but are found identical when using a leave-one-out evaluation procedure, and nearly identical when using a repeated cross-validation procedure. Conclusion: This study demonstrates that chromosome-level CNVs can be accurately predicted in hematologic malignancies even when the number of targeted genes on a given chromosome is low. Despite the simplicity of the approach, the two stages bioinformatics pipeline based on an ensemble method allowed us to gain between 8% and 46% accuracy compared to relying only on the prediction of a single tool like PureCN. Samples with low tumor content remain, however, a difficult case to tackle with bulk NGS as it is difficult to distinguish a CNV from the natural variability of the sequencing coverage. Figure 1 Figure 1. Disclosures Nam: NeoGenomics Laboratories, Inc.: Current Employment. Magnan: NeoGenomics Laboratories, Inc.: Current Employment. Lopez-Diaz: NeoGenomics Laboratories, Inc.: Current Employment. Bender: NeoGenomics Laboratories, Inc.: Current Employment. Agersborg: NeoGenomics Laboratories, Inc.: Current Employment. Jung: NeoGenomics Laboratories, Inc.: Current Employment. Funari: NeoGenomics Laboratories, Inc.: Current Employment.

Background: Paired-box Pax gene family protein 5 (PAX5)/B-cell specific activator protein (BSAP) is a transcription factor encoded by the PAX5 gene and has an essential role in B-cell differentiation and maturation. High PAX5 expression is detected ensures commitment to B-cell lineage. PAX5 is normally downregulated at the plasma cell stage of B-cell development. Complete or partial deletion of the PAX5 gene has been found as secondary event associated with BCR-ABL1 or TCF3-PBX1 fusions in Acute Lymphocytic Leukemia (ALL) cases. PAX5 expression is a diagnostic marker for B-cell lineage and may help quantify minimal residual disease in B-ALL. Lineage determination of leukemic blasts is most often performed by flow cytometry, but also by immunohistochemistry (IHC). Evaluation of PAX5 is most commonly available by IHC and is not widely performed by flow cytometry. In cases with limited specimen for evaluation or aberrant loss of some B-cell markers, determining quantitative levels of RNA from lineage-specific genes, such as PAX5, could be a valuable clinical diagnostic tool for ALL patients. Our existing single tube NGS based assay for simultaneous detection of DNA alterations and RNA fusions in heme malignancies from Total Nucleic Acid (TNA), can also be used to detect PAX5 gene expression through select exons enrichment along with a total of 213 genes. However, one of the current challenges for NGS-based gene expression profiling is to setup a threshold for overexpression. Here we developed a cutoff criterion for PAX5 overexpression and evaluated the performance of PAX5 gene expression analysis using the in-use heme assay and its potential use in clinical laboratory for cell lineage detection. Methods: RNA sequencing was performed on TNA extracted from ALL samples and from 32 healthy donors using partial anchored amplicon based (Qiagen, inc) heme NGS assay. PAX5 RNA expression was calculated by TPM (transcript per million) counts normalized to TPM of the house-keeping gene GUSB. A commercially available qRT-PCR assay was used as orthogonal method to confirm the gene expression. The expression call by NGS based on the normalized value was confirmed by a commercial qRT-PCR assay in house validated through serial dilutions of template for six log scale. The analytical cutoff was determined from normalized TPM calculation from 32 healthy volunteers following CLSI guideline (CLSI_EP17-A2) and evaluated the outcome with IHC positive /negative clinical samples (a CLIA validated assay). Further, we used the established cutoff to evaluate the sensitivity or specificity in cohort of ALL samples. Results: In this study we established the cutoff for PAX5 gene over-expression using the currently in-use heme NGS assay. First, a cutoff was established following the method in the CLSI guidelines and tested for sensitivity and specificity in the ALL sample cohort. PAX5 TPM normalization to GUSB or to the geometric mean of four house keeping genes (GUSB, PGD, RPL5 and RPL19) showed a strong correlation (R2>0.95), and GUSB was selected for further normalization since GUSB TPM values were most conserved across all the samples. Independent in-house evaluation for commercial qRT-PCR assay showed efficiency at 94.3 and 96% for GUSB and PAX5, respectively (with linearity R2>0.95), and been used to compare the NGS and IHC data as independent orthogonal assay. When a cohort of samples for Pax5 by IHC (positive and negative), a sensitivity at 67% and specificity at 100% were observed for the NGS based Pax5 detection. NGS results on the discordant samples were confirmed by qRT-PCR to have low RNA expression. Notably the discordant, IHC positive samples contained very low numbers of B cells. Alongside with other possible mechanisms of increased protein levels such as increased protein translation/increased protein stability could explain the discordance between RNA expression and the protein detection by IHC. Conclusions: In this study we demonstrate that NeoGenomics's (heme) NGS assay can be used for PAX5 gene over-expression analysis on ALL. The heme NGS is inexpensive and is already integrated in the benchwork workflow without adding extra burden and can be used as an objective quantification of PAX5 levels overcoming the challenges associated with the relative signal intensity biases in IHC testing. This type of RNA testing can be useful especially with specimens having limited material. Disclosures Ghosal: NeoGenomics Laboratories: Current Employment. Alarcon: NeoGenomics Laboratories: Current Employment. Koo: Neo Genomics Laboratories: Current Employment. Kang: Neo Genomics Laboratories: Current Employment. Ramesh: Neo Genomics Laboratories: Current Employment. Gyuris: Neo Genomics Laboratories: Current Employment. Jung: NeoGenomics Laboratories, Inc.: Current Employment. Thomas: NeoGenomics Laboratories, Inc.: Current Employment. Fabunan: NeoGenomics Laboratories, Inc.: Current Employment. Magnan: NeoGenomics Laboratories, Inc.: Current Employment. Nam: NeoGenomics Laboratories, Inc.: Current Employment. Petersen: Neo Genomics Laboratories: Current Employment. Lopez-Diaz: NeoGenomics Laboratories, Inc.: Current Employment. Bender: NeoGenomics Laboratories, Inc.: Current Employment. Agersborg: NeoGenomics Laboratories, Inc.: Current Employment. Ye: Neo Genomics Laboratories: Current Employment. Funari: NeoGenomics Laboratories, Inc.: Current Employment.

Background: Acute Lymphocytic Leukemia (ALL) is the most common childhood cancer and accounts for about a quarter of adult acute leukemias. Current NCCN recommendations for clinical testing for risk stratification and treatment guidance include karyotyping, FISH testing for translocations, and RT-PCR for gene fusions and sequencing for DNA mutations detection. Most NGS based approaches test DNA mutations and RNA fusions separately, thereby requiring higher input material and multiple workflows adding to the cost and turn-around-time. An NGS based assay for the detection of DNA variants (NeoGenomics Heme NGS assay) in heme malignancies using Total Nucleic Acid (TNA) is already available in our clinical laboratory and complements FISH based fusion detection and karyotyping but an integral assay to detect both DNA and RNA alterations with a simple workflow for ALL is needed. Methods: We used TNA or RNA spiked-in with DNA to simulate TNA samples, extracted from 93 bone marrow and peripheral blood samples from patients and healthy donors, along with commercial fusion reference myeloid samples Seraseq Myeloid Fusion RNA Mix (SeraCare Inc.) controls. DNA/RNA libraries were prepared using a custom amplicon based Multimodal NGS panel (Qiagen Inc.) targeting 297 genes and 213 genes (select exons) for DNA and RNA fusion detection, respectively. The enriched dual indexed amplicon libraries were sequenced on an Illumina NovaSeq 6000. The sequence data was processed with a customized bioinformatic pipeline for DNA variant as well as a novel machine learning algorithm for RNA fusion detection. We analyzed sensitivity, specificity, accuracy, reproducibility, and repeatability for clinical use. The DNA variants were orthogonally confirmed using other NGS assays, and the RNA fusions were confirmed on an RNA-seq Archer assay or RT-Sanger confirmation assays. Results: Here, we developed and validated a single tube comprehensive NGS panel using a custom multimodal chemistry that uses TNA as input for simultaneous dual detection of DNA and RNA abnormalities in ALL patients' samples. We performed the analytical validation of our Heme NGS assay for the RNA panel to detect fusions in ALL, using TNA input for comprehensive DNA and RNA mutation detection. The fusion concordance was 95% for the RNA fusion panel. The assay detected BCR-ABL1 (7/7), ETV6-RUNX1 (1/1), KMT2A fusions (4/5), TCF3-PBX1 (1/1), and PCM1-JAK2(1/1). The specificity was determined at 100% using a set of 42 fusion negative samples. The limit of detection (LOD) was analyzed using serial dilutions to up to 3 log reduction (LR) using a the Seraseq Myeloid Fusion sample. The fusions were detected down to 1 LR. The reproducibility was tested using a positive fusion and Seraseq samples across three runs and was reported at 100%. Next, a small cohort of ALL samples (n=8) was included as part of this study to simultaneously evaluate DNA and RNA mutations. We detected pathogenic DNA variants in genes previously reported in ALL that included NOTCH1, PTEN, FLT3, IKZF1, JAK1, JAK2, KRAS, NF1, PAX5, U2AF1, TP53, and also RNA fusion BCR-ABL1, and the results were confirmed by an orthogonal NGS assay (NexCourse and RNA-Seqv1 for fusions). One sample carrying a BCR-ABL1 fusion (detected by RNA panel) also harbored mutations in IKZF1 in DNA (detected by DNA panel) that is reported as unfavorable prognostic biomarker for Ph-Like ALL demonstrating comprehensive panel could identify multiple variants within the same sample, demonstrating the advantage DNA+RNA testing has over the classical single gene FISH/RT-PCR testing for the efficient risk stratification and treatment in ALL patients. Conclusions: In this study, we demonstrated that the single tube TNA based NeoGenomics NGS assay can simultaneously detect the DNA and RNA biomarkers associated with ALL for improved diagnostic and prognostic recommendations. The single-tube assay for detection of both RNA fusions and DNA variants using the same sample could offer comprehensive and cost-effective solution for clinical laboratory test for ALL patient care. This is a promising approach that might be used as a dual DNA/RNA alterations detection on other hematological neoplasia. Disclosures Ramesh: Neo Genomics Laboratories: Current Employment. Koo: Neo Genomics Laboratories: Current Employment. Kang: Neo Genomics Laboratories: Current Employment. Ghosal: NeoGenomics Laboratories: Current Employment. Alarcon: NeoGenomics Laboratories: Current Employment. Gyuris: Neo Genomics Laboratories: Current Employment. Jung: NeoGenomics Laboratories, Inc.: Current Employment. Magnan: NeoGenomics Laboratories, Inc.: Current Employment. Nam: NeoGenomics Laboratories, Inc.: Current Employment. Thomas: NeoGenomics Laboratories, Inc.: Current Employment. Fabunan: NeoGenomics Laboratories, Inc.: Current Employment. Petersen: Neo Genomics Laboratories: Current Employment. Lopez-Diaz: NeoGenomics Laboratories, Inc.: Current Employment. Bender: NeoGenomics Laboratories, Inc.: Current Employment. Agersborg: NeoGenomics Laboratories, Inc.: Current Employment. Ye: Neo Genomics Laboratories: Current Employment. Funari: NeoGenomics Laboratories, Inc.: Current Employment.

Background: Multiple myeloma (MM) is a blood cancer type affecting plasma cell in bone marrow. MM is heterogenous in nature but t(11;14)(q13;q32) translocation is a common prognostic marker among MM patients. One of the most frequent oncogenic drivers involved in this chromosomal rearrangement is CCND1 (Cyclin D1) gene translocation downstream to the immunoglobulin heavy chain (IGH), which results on overexpression of CCND1, thus promoting abnormal cell proliferation. Oncogenic CCND1 RNA levels can result from translocations such as t(11;14), gene amplifications, increased transcription rates and/or RNA stability. Indeed, CCND1 RNA overexpression has a favorable prognostic value for patients treated with high doses of chemotherapies but important challenges remain in accurate detection of CCND1 RNA levels. Currently, FISH is the gold standard method for detecting t(11:14) translocations at the DNA level. However, it cannot detect CCND1 overexpression. Therefore, a method that can detect CCND1 overexpression levels, as well as in frame transcripts has clinical implications. In the current study we leveraged in-use NeoGenomics Heme TNA single tube NGS assay to enable the detection of CCND1 RNA overexpression as a complementary test to FISH testing. Methods: We performed RNA sequencing from 32 healthy donors and on fixed cell pellets from 94 CD138-enriched BM samples from MM patients and from using the amplicon based (Qiagen, inc) NGS assay. We developed pipeline for gene expression by TPM count (transcript per million) for CCND1, and further normalized to the "housekeeping" gene GUSB. We validated the normalization to GUSB by comparing to normalization using the geometric mean of four housekeeping genes (GUSB, PGD, RPL5 and RPL19) showing a high correlation (R 2>0.95). A commercially available qRT-PCR assay was used as orthogonal method to further confirm the linearity of the quantitative gene expression signal in NGS. The analytical cutoff was determined from normalized TPM calculation from 32 healthy volunteers following CLSI guideline (CLSI_EP17-A2) and further updated from MM-PCE samples with t(11:14) translocations from a CLIS-validated FISH assay . Results: From 94 CD138-enriched BM samples, 26 had t(11:14) translocations, or CCND1 gains as detected by FISH, 15 samples were confirmed negative by FISH and 32 normal volunteers with no suspected disease. Also, we determined the analytical cutoff for CCND1 overexpression based on the CLSI guidelines to be 2.37 times the expression level of GUSB ("housekeeping" gene) using normal volunteers (n=32) (sensitivity 86% and specificity 77%). We found specificity to be low, so further evaluated the threshold using a ROC curve analysis with multiple tests. Using Fischer's exact test, we found CCND1 expression 3.27 times the GUSB expression to yield higher specificity of 86.5 % and sensitivity for 78.9%. Further, we used 26 FISH positive and 32 normal samples to build a new model and determined the cutoff for CCND1 overexpression to be 4.15 times GUSB expression, which resulted lower sensitivity but higher specificity (75% sensitivity and 100% specificity). When we evaluated 15 FISH negative samples with this cutoff we observed CCND1 was not overexpressed in six samples, but 9 samples did have some degree of overexpression. Overexpression was confirmed by qRT-PCR. Two CCND1 high- and low- expressing normal samples (MM-PCE 27 and 48) were further evaluated using alternative extraction methods to test the dependencies on extractions and the data showed concordant to each other for overexpression. Interestingly, 1 sample (MM-PCE-27) showed very high overexpression without t(11:14) translocation event (~100 fold over expressed). Cytogenetic studies were discordant with FISH as well for this sample, showing abnormalities related to chr7q,13q,12p but no indication of any chr11 related event. Conclusions: In this study we evaluated our existing NGS assay for CCND1 overexpression using TNA as a surrogate for traditional FISH, while demonstrating the accuracy of the RNA quantitation by NGS using qRT-PCR. We developed an RNA-seq based CCND1 expression assay that could be used to complement traditional FISH testing especially if there is limited specimen. The confirmation of overexpression in FISH negative samples may suggest new ways to improve MM patients risk stratification and treatment. Disclosures Ghosal: NeoGenomics Laboratories: Current Employment. Alarcon: NeoGenomics Laboratories: Current Employment. Koo: Neo Genomics Laboratories: Current Employment. Kang: Neo Genomics Laboratories: Current Employment. Ramesh: Neo Genomics Laboratories: Current Employment. Gyuris: Neo Genomics Laboratories: Current Employment. Jung: NeoGenomics Laboratories, Inc.: Current Employment. Thomas: NeoGenomics Laboratories, Inc.: Current Employment. Fabunan: NeoGenomics Laboratories, Inc.: Current Employment. Magnan: NeoGenomics Laboratories, Inc.: Current Employment. Nam: NeoGenomics Laboratories, Inc.: Current Employment. Petersen: Neo Genomics Laboratories: Current Employment. Lopez-Diaz: NeoGenomics Laboratories, Inc.: Current Employment. Yamahata: Neo Genomics Laboratories: Current Employment. Bender: NeoGenomics Laboratories, Inc.: Current Employment. Agersborg: NeoGenomics Laboratories, Inc.: Current Employment. Ye: Neo Genomics Laboratories: Current Employment. Funari: NeoGenomics Laboratories, Inc.: Current Employment.

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.

Background: The use of sequencing technologies to detect gene fusions (GFs) from RNA shows promising results for the future of cancer diagnosis and treatment. Major obstacles for this approach include target design and lack of well-curated databases of RNA breakpoints. Currently, off-the-shelf designs include full transcript targeting that results in massive and costly amounts of data. Directly targeting the known GFs from RNA by designing probes targeting the fusion junction sequence is studied here as an alternative to whole-exome sequencing (WES). We present notably a novel algorithm capable of designing the probes to accurately target the desired fusions from RNA. Methods: For a given GF detected either from DNA or from RNA, the algorithm is as follows: (1) Collect gene and isoform information for both partners from seven public databases; (2) For each candidate pair of isoforms, locate where the breakpoints will be observed and assign a score based on various criteria such as sequence completion, coding information, transcript support level, % identity with and % visible on hg38; (3) Select the top scoring pair of transcripts and extract the chimeric probe sequence. Two sets of probes extracted with this protocol targeting 524 and 1632 known GFs were synthetized and tested on several samples (Table 1). The Agilent SureSelect Human All Exon V6 capture kit was used to compare targeting efficiency against WES. Results: Targeted enrichment of a SeraSeq control showed a 5 to 20 fold increase in supporting evidence over WES. On 10 clinical samples, we observed 10-30x increase in supporting reads. A higher sensitivity is observed in both cases. Conclusion: We developed a novel algorithm capable of accurately identifying the most likely location of an RNA fusion junction and generating the probe sequences for oligo synthesis. This method not only enriches for more supporting data but also reduces the associated costs. Average number of supporting reads per fusion per million reads for WES & direct targetingSampleKnown FusionWES (A)WES (B)524 TF (A)524 TF (B)1632 TF (A)1632 TF (B)SeraSeq 0710-0496CCDC6→RET8102703558178CD74→ROS13581592832246250EGFR→SEPTIN1418172343159180FGFR3→BAIAP2L114542832612572FGFR3→TACC3239861879270203LMNA→NTRK123112152807167PAX8→PPARG29191932466662SLC34A2→ROS1102217642589142SLC45A3→BRAF1115433420141105TFG→NTRK13540275377128132TMPRSS2→ERG0055934817079TPM3→NTRK11523246359106117Avg. SeraSeq12 Fusions1821373430132116Clinical S1EML4→ALK2515344NA81NAClinical S2EWSR1→FLI15738514NA111NAClinical S3TES→MET2030115NA75NAClinical S4EZR→ROS14311,059NA266NAClinical S5SDC4→ROS1553,354NA1,082NAClinical S6SH3BP5→PPARG004NA1NAClinical S7H2BC21→NTRK11118NA4NAClinical S8COL1A1→PDGFB2092505,530NA2,289NAClinical S9KIF5B→RET2424437NA174NAClinical S10POC1B→GLI1158416NA161NAAvg. Clinical10 Fusions36401,179NA424NA Citation Format: Christophe N. Magnan, Steven P. Rivera, Fernando J. Lopez-Diaz, Chen-Yin Ou, Kenneth B. Thomas, Hyunjun Nam, Lawrence M. Weiss, Segun C. Jung, Vincent A. Funari. An efficient probe design algorithm for direct fusion targeting from RNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 241.

Background: Next generation sequencing (NGS) is an integral component in the characterization of hematologic malignancies, including chronic lymphocytic leukemia (CLL). Fluorescence in situ hybridization (FISH) and conventional cytogenetics (CC) are cost effective and are currently the gold standard for detecting copy number abnormalities (CNAs) in hematologic malignancies. NGS is emerging as a comprehensive assay that can detect CNAs while surveying the whole genome for single nucleotide variants and loss of heterozygosity (CN-LOH). Identifying CNA events in addition to mutations and RNA fusions may help identify and characterize the highly complex genetic landscape of hematologic malignancies. Methods: A custom total nucleic acid (TNA) NGS panel was designed which consists of mutation profiles of 297 genes, transcriptome profile of 213 genes, and genomic backbones of 14 chromosomes to identify unbalanced abnormalities. Two-hundred seventy CLL patients were included in the study (abnormalities detected in 236 cases in total: 61 cases by CC; 230 cases by FISH; and 53 cases by both CC and FISH, and no abnormalities detected in 34 cases by both FISH and CC). Mutation profiles including SNVs, indels, and structural changes were interrogated with a custom bioinformatic pipeline which utilized PureCN and CNVkit algorithms to identify structural changes. NGS results were compared to results of CC and FISH. CNA detection of sex chromosome and balanced rearrangement including translocation and inversion was excluded from the analysis Results: CNAs were detected by NGS in 56 of 61 cases (91%) reported by CC and in 178 of 230 cases (77%) detected by FISH. Seventy-seven CNAs detected by CC and 202 CNAs detected by FISH were identified by NGS. NGS failed to detect 13q deletion, detected by FISH in 48 cases. Abnormalities not detected by neither cytogenetics nor FISH were detected by NGS in 108 (gain) and 32 (loss) cases. In addition, we observed abnormalities in 9 of 34 cases by NGS reported as normal by both FISH and cytogenetics. CN-LOH was detected in 9% of cases predominantly on 13q, 17p and 22q. In addition to trisomy 12, gains of 20p and 20q were observed in each 72 (30%) and 43 (18%) cases. CN gains of 7p, 8q, and 17q were also observed in 12%, 12%, and 7% of cases, respectively. Oncogenic driver mutations in KRAS (p.G12D) and (p.G13D) were observed in four and five cases with CN gains, respectively. IKZF3, a recurrent hotspot pathogenic mutation in CLL and a potential prognostic marker that may positively regulate MYC, was detected in five patients with CN gains. CN loss of 11q, 2q, 13q, 3p, 17p, 21q, and 6q were among the most common chromosomes with CN loss (Figure 1). Notably, LOH of RB1, DLEU7, COG3, and FOX1 genes on 13q, of TP53, WRAP53, SLC52A1, CTC1, and ABR genes on 17p and of PRDM1, EPHA7, and CASP8AP2 genes on 6q were observed. Identifying cases with 13q14 deletions that include RB1 could change the CLL patient management due to the aggressive clinical course. Recurrent loss of function mutations in KMT2C (p.E2798Gfs*11), NOTCH1 (p.P2514Rfs*4), and TP53 (p.H179R) in 7q, 9q, and 17p were observed. Identifying both CN loss combined with loss of function mutations in tumor suppressors could help improve patient care. Conclusions: Abnormalities detected by cytogenetics were mostly detected by NGS, but NGS offers a higher resolution including CN events of various length, LOH events, and single gene mutations. CNAs detected at higher resolution is useful in identifying patients with 13q14 loss that include/exclude RB1 which may affect patient management. However, an accurate detection of the CNA could be affected in part by a baseline established by a panel of normal and the depth of coverage. Differences in sensitivity of methodologies can also be attributed to in vitro proliferation and tissue culture conditions utilized for CC analysis. CC and FISH can identify clones with multiple abnormalities as well as clonal evolution. Comprehensive genomic profile including high resolution copy number changes and mutational profiles, detectable by NGS, may provide better profiling for a patient for clinical management. Disclosures Jung: NeoGenomics: Current Employment. Thangavelu:NeoGenomics: Current Employment. Nam:NeoGenomics: Current Employment. Bender:NeoGenomics: Current Employment. Agersborg:NeoGenomics: Current Employment. Weiss:Bayer: Other: speaker; Genentech: Other: Speaker; Merck: Other: Speaker; NeoGenomics: Current Employment. Funari:NeoGenomics: Current Employment.

Background: Many new guidelines require a comprehensive genomic profiling approach for diagnosis, risk stratification and therapy decisions. Limitations in sample quantity and throughput may limit the number of single biomarker tests (FISH, karyotyping, sequencing, qRT-PCR, etc.) that can be performed for the patient. There are currently multiple commercial NGS assay options for total nucleic acid, however they involve independent parallel workflows and twice the amount of sample and effort. Here we developed a novel consolidated DNA/RNA workflow in a single-tube assay utilizing custom QIAseq multimodal chemistry. This simplified workflow enables a discovery approach of all critical DNA/RNA abnormalities in hematologic malignancies, extending our NGS capabilities to large structural changes, RNA fusions and expression. Methods: 297 Heme-focused genes and 14 chromosomes were targeted in the genome, along with 213 RNA genes targeting 712 exons involved in known fusions in the transcriptome using a custom QIAseq workflow. Captured DNA/RNA targets from 135 patients were sequenced with unique dual indices on an Illumina's NovaSeq 6000. Coverage and variant allele frequency from all gene and chromosomal targets in 25 disease free patients was compared to the same genomic targets in 76 patients that were referred for a suspected hematological malignancy (e.g. MDS, CML, AML, ALL, etc.). We compared results from our custom algorithm to karyotyping and FISH. In addition, we assessed the relationship between structural changes and the average mutation load for each indication. Positives gene fusions were confirmed by qRT-PCR or Sanger sequencing. Results: Cytogenetic abnormalities in 30/32 patients were confirmed by karyotyping and FISH; two cases with abnormalities were missed by NGS. NGS detected additional abnormalities not detected by cytogenetics, including a case of loss of chr17 including deletion of driver genes, NF1 and SUZ12. No significant relationship between chromosome abnormalities and tumor mutation burden was observed. However, patients referred for myeloid disorders with structural abnormalities had a significantly higher mutational burden (p Conclusions: This study confirms the validity and utility of simple but efficient comprehensive genomic profiling for use in hematologic malignancies. Coupled with FISH and cytogenetics tests, NGS can offer a better diagnostic and prognostic testing service for patients with hematologic disease to assist in treatment selection as well as precise patient care. Citation Format: Segun C. Jung, YongXin Yu, Yanglong Mou, Hyunjun Nam, Cynthie Wong, Samuel Koo, Brad Thomas, Forrest Blocker, Derek Lyle, Ryan Bender, Sally Agersborg, Lawrence M. Weiss, Vincent A. Funari. A comprehensive genomic profiling approach to interrogate hematologic malignancies using a novel multimodal next generation sequencing assay in a single-tube [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 182.