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Diagnosis and classification of tumors is increasingly dependent on biomarkers. RNA expression profiling using next-generation sequencing provides reliable and reproducible information on the biology of cancer. This study investigated targeted transcriptome and artificial intelligence for differential diagnosis of hematologic and solid tumors. RNA samples from hematologic neoplasms (N = 2606), solid tumors (N = 2038), normal bone marrow (N = 782), and lymph node control (N = 24) were sequenced using next-generation sequencing using a targeted 1408-gene panel. Twenty subtypes of hematologic neoplasms and 24 subtypes of solid tumors were identified. Machine learning was used for diagnosis between two classes. Geometric mean naïve Bayesian classifier was used for differential diagnosis across 45 diagnostic entities with assigned rankings. Machine learning showed high accuracy in distinguishing between two diagnoses, with area under the curve varying between 1 and 0.841. Geometric mean naïve Bayesian algorithm was trained using 3045 samples and tested on 1415 samples, and showed correct first-choice diagnosis in 100%, 88%, 85%, 82%, 88%, 72%, and 72% of acute lymphoblastic leukemia, acute myeloid leukemia, diffuse large B-cell lymphoma, colorectal cancer, lung cancer, chronic lymphocytic leukemia, and follicular lymphoma cases, respectively. The data indicate that targeted transcriptome combined with artificial intelligence are highly useful for diagnosis and classification of various cancers. Mutation profiles and clinical information can improve these algorithms and minimize errors in diagnoses.

Purpose: Cytogenetic abnormalities are currently the most important predictors of response and clinical outcome for patients with acute myeloid leukemia (AML) or advanced-stage myelodysplastic syndrome (MDS). Because clinical outcomes vary markedly within cytogenetic subgroups, additional biological markers are needed for risk stratification.Experimental Design: We assessed the utility of measuring pretreatment proteasome chymotrypsin-like, caspase-like, and trypsin-like activities in plasma to predict response and survival of patients with AML (n = 174) or advanced-stage MDS (n = 52).Results: All three enzymatic activities were significantly (P < 0.001) increased in the plasma of patients with AML and MDS compared with normal controls. Both chymotrypsin-like and caspase-like activities, but not trypsin-like activity, correlated with outcome. Chymotrypsin-like and caspase-like activities, but not trypsin-like activity, predicted response in univariate analysis (P = 0.002). However, only chymotrypsin-like activity was independent predictor of response from age grouping (P < 0.0001), but only chymotrypsin-like activity was independent of cytogenetics, age, performance status, blood urea nitrogen, and β2-microglobulin in multivariate Cox regression models. Chymotrypsin-like activity was also a strong independent predictor of survival in patients with intermediate karyotype (n = 124).Conclusions: Measuring plasma chymotrypsin-like activity may provide a powerful biomarker for risk stratification in patients with AML and advanced-stage MDS, including those with normal karyotype.

Breast cancer in premenopausal women (preM) is frequently associated with worse prognosis compared to that in postmenopausal women (postM). There is, however, a paucity of studies characterizing molecular alterations in premenopausal patients compared to postmenopausal ones in patients with breast cancer, but without any evidence of predisposition inherited mutations. We used molecular profiling of DNA and RNA using next generation sequencing (NGS) and evaluated the molecular differences between PreM and post-M breast cancers in patient without risk of predisposition to cancer. Case descriptions were collected by COTA, inc. This included 52 patients with breast cancer evaluated clinically at a referral cancer center (John Theurer Cancer Center, Hackensack, NJ, USA) and the DNA and RNA of their tissue samples were evaluated by Genomic Testing Cooperative using next generation sequencing (NGS) of a targeted panel of 1,408 RNA genes and 434 DNA genes. All patients were treated with standard of care therapy by subspecialized oncologists. Machine learning algorithm and Geometric Mean Naïve Bayesian (GMNB) were used to select RNA genes and distinguish between preM and postM cases. PreM patients showed lower cumulative survival proportion (P=0.03). TP53, PIK3CA and GATA3 mutations were the most common mutations in preM and postM patients. However, PTEN mutation was more common in preM cases. The machine learning system chose the expression level of 20 RNA genes (p=0.001) that distinguishes between preM and postM cases. The 20 genes are TCF7L2, C11orf30 (EMSY), ARIH2, MLH1, IRF2BP2, FGFR1OP, NFE2L2, EPC1, SOS1, MLLT4, IL13RA2, GIT2, MAP3K1, SF3B1, ERCC4, ADD3, CHUK, FRK, REEP3 and MAP2K6. The 20 genes were able to distinguish between preM and postM with area under the curve of (AUC) of 0.987 (95% Confidence interval of 0.947 to 1.00) (Figure), sensitivity of 93.8 and specificity of 97.1. Leave-one-out curve showed AUC of 0.893 (95% confidence interval of 0.783 to 1.00). TCF7L2, C11orf30 (EMSY), IRF2BP2, NFE2L2, EPC1 and FRK were highly expressed in preM cases with p values of 0.004, 0.008, 0.007, 0.019, 0.01 and 0.03, respectively. Together this data suggests that breast cancer in patients with no germline predisposition to cancer differ biologically in preM patients as compared to postM tumors and these differences should be considered in treatment and management plans of these patients. Citation Format: Ahmad Charifa, Andre Goy, Andrew Pecora, Deena Graham, Donna McNamara, Andrew Ip, Wanlong Ma, Maher Albitar. The molecular landscape of premenopausal versus postmenopausal breast cancer in patients without inherited predisposition mutations [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 929.

PD-L1 immunohistochemistry (IHC) is routinely used to predict the clinical response to immune checkpoint inhibitors (ICIs); however, multiple assays and antibodies have been used. This study aimed to evaluate the potential of targeted transcriptome and artificial intelligence (AI) to determine PD-L1 RNA expression levels and predict the ICI response compared to traditional IHC. RNA from 396 solid tumors samples was sequenced using next-generation sequencing (NGS) with a targeted 1408-gene panel. RNA expression and PD-L1 IHC were assessed across a broad range of PD-L1 expression levels. The geometric mean naïve Bayesian (GMNB) classifier was used to predict the PD-L1 status. PD-L1 RNA levels assessed by NGS demonstrated robust linearity across high and low expression ranges, and those assessed using NGS and IHC (Tumor cells (TC), Tumor Proportion Score (TPS) and tumor-infiltrating immune cells (ICs) were highly correlated (Tables 1). RNA sequencing provided in-depth information on the tumor microenvironment and immune response, including CD19, CD22, CD8A, CTLA4, and PD-L2 expression status. Sub-analyses showed a sustained correlation of mRNA expression with IHC (TPS and ICs) across different solid tumor types. Machine learning showed high accuracy in predicting PD-L1 status, with the area under the curve varying between 0.988 and 0.920. Targeted transcriptome sequencing combined with AI is highly useful for predicting PD-L1 status. Measuring PD-L1 mRNA expression by NGS is comparable to measuring PD-L1 expression by IHC for predicting ICI response. RNA expression has the added advantages of being amenable to standardization and avoidance of interpretation bias, along with an in-depth evaluation of the tumor microenvironment. Correlation between PD-L1 expression levels and PD-L1 IHC results IHC test results Variable Cases (N) Mean Median Range Lower Quartile Upper Quartile 10th Percentile 90th Percentile Std. Dev. TC1% CD274 90.00 14.87 10.11 0.62 - 77.53 4.60 18.62 2.95 32.35 15.75 TC10% CD274 46.00 20.81 14.03 2.90 - 77.53 8.67 26.32 4.21 51.02 17.33 IC1% CD274 129.00 9.38 5.43 0.29 - 77.53 3.21 10.31 1.74 19.05 12.13 IC10% CD274 36.00 12.50 8.52 0.29 - 77.53 4.72 13.80 4.18 31.83 13.95 TPS1% CD274 207.00 10.19 5.63 0.29 - 133.81 3.03 11.65 1.74 22.25 14.74 TPS10% CD274 98.00 15.50 9.67 0.29 - 133.81 4.91 18.74 2.90 31.87 18.57 TPS30% CD274 31.00 28.50 18.62 2.90 - 133.81 11.47 35.94 6.67 52.69 27.31 Citation Format: Ahmad Charifa, Alfonso Lam, Hong Zhang, Andrew Ip, Andrew Pecora, Stanley Waintraub, Deena Graham, Donna McNamara, Martin Gutierrez, Andrew Jennis, Ipsa Sharma, Jeffrey Estella, Wanlong Ma, Andre Goy, Maher Albitar. Predicting PD-L1 status in solid tumors using transcriptomic data and artificial intelligence algorithms. [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 4337.

Background: Human epidermal growth factor receptor-2 (HER2) and hormone receptors are typically used as binary biomarkers for selecting breast cancer therapy. There is a need to explore the clinical relevance of these biomarkers as continuous variables. This is particularly relevant for the new class of antibody-drug conjugates (ADC), in which a relatively low HER2 expression level is adequate for targeting tumor cells. We explored the potential of RNA profiling, determined by next generation sequencing (NGS), to provide more flexible clinical biomarkers as compared with immunohistochemistry (IHC) or fluorescent in situ hybridization (FISH). Methods: Clinical and laboratory data from 57 breast cancers collected by the COTA real-world data company were used to study biomarker levels as detected by routine clinical transcriptomic tests. HER2 (ERBB2), estrogen receptor alpha (ESR1), and androgen receptor (AR) mRNA levels were compared with reported HER2 and estrogen receptor (ER) IHC and FISH results. Results: RNA levels accurately reflected and predicted HER2 amplification (see table below). Importantly, RNA data showed significant variation and overlap in the levels of ERBB2 mRNA between cases scored by IHC as zero, 1+, and 2+. This variation correlated with progression-free survival (PFS). Similarly, the ESR1 RNA levels accurately reflected ER status and demonstrated significant variation between positive cases. RNA data also showed significant variations in the AR levels. Patients wssith high AR mRNA levels had significantly better PFS (P=0.05). Patients expressing high ER and AR levels had significantly better PFS than those expressing low ESR1 and AR levels (P=0.03). Conclusions: These findings suggest that RNA analysis using NGS is an alternative to IHC and FISH. RNA provides continuous data that can determine cut-off points predicting response to therapy and should be explored in predicting ADC response. ERBB2 mRNA levels (FPKM) in various HER2 IHC groupss IHC Score Valid N Mean Median Minimum Maximum Quartile Range Std.Dev. Zero 19 231 243 63 537 170 110 Zero vs one 3 151 155 45 253 208 104 Zero to one vs one 18 397 302 172 845 365 222 One Vs two 13 495 423 159 1094 273 284 Two vs three 4 7523 7649 937 13859 7183 5310 Citation Format: Maher Albitar, Andre Goy, Andrew Pecora, Deena Graham, Donna Donna McNamara, Ahmad Ahmad Charifa, Andrew IP, Wanlong Ma, Stanley Waintraub. Real-world transcriptomic biomarkers as replacement for immunohistochemistry and FISH studies in breast cancer [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 967.

PURPOSE Randomized trials established the superiority of ibrutinib-based therapy over chemoimmunotherapy in chronic lymphocytic leukemia. Durability of progression-free survival (PFS) with ibrutinib can vary by patient subgroup. Clinical tools for prognostication and risk-stratification are needed. PATIENTS AND METHODS Patients treated with ibrutinib in phase II and III trials provided the discovery data set and were subdivided into discovery and internal validation cohorts. An external validation cohort included 84 patients enrolled in our investigator-initiated phase II trial. Univariable analysis of 18 pretreatment parameters was performed using PFS and overall survival (OS) end-points. Multivariable analysis and machine-learning algorithms identified four factors for a prognostic model that was validated in internal and external cohorts. RESULTS Factors independently associated with inferior PFS and OS were as follows: TP53 aberration, prior treatment, β-2 microglobulin ≥ 5 mg/L, and lactate dehydrogenase > 250 U/L. Each of these four factors contributed one point to a prognostic model that stratified patients into three risk groups: three to four points, high risk; two points, intermediate risk; zero to one point, low risk. The 3-year PFS rates for all 804 patients combined were 47%, 74%, and 87% for the high-, the intermediate-, and the low-risk group, respectively ( P < .0001). The 3-year OS rates were 63%, 83%, and 93%, respectively ( P < .0001). The model remained significant when applied to treatment-naïve and relapsed/refractory cohorts individually. For 84 patients in the external cohort, BTK and PLCG2 mutations were tested cross-sectionally and at progression. The cumulative incidences of mutations were strongly correlated with the model. In the external cohort, Richter’s transformation occurred in 17% of the high-risk group, and in no patient in the low-risk group. CONCLUSION Patients at increased risk of ibrutinib failure can be identified at treatment initiation and considered for clinical trials.

Background & Aim: Many references have reported the feasibility of various artificial liver support systems in the treatment of acute-on-chronic liver failure caused by Hepatitis B. However, there is still controversy over their efficacy, safety, and impact on survival rates. Meta-analysis was conducted to assess the efficacy, safety, adverse reactions, and survival rate of hepatitis B-caused ACLF treated with artificial liver support systems based on the double plasma molecular absorption system (DPMAS). Methods: The keywords or abstractions “liver failure, severe hepatitis, acute-on-chronic liver failure” were searched on the databases such as CBM, PubMed, Cochrane library, etc. and the Revman5.3 software was used to analyze the literature data. Results: After analysis, DPMAS improved total bilirubin and 90-day survival rate higher than plasma exchange (PE). However, there was no statistical significance in international standardized ratio (INR). the results of international normalized ratio (INR) were [MD=0.06, 95% CI (-0.38, 0.49), P>0.05], that of 90-day survival rate were [OR=3.07, 95% CI (1.87, 5.02), P

3047 Background: Expressed RNA can capture mutations, changes in expression levels due to methylation, and provide information on cell of origin, growth, and proliferation status. We developed an approach to isolate fragmented RNA from peripheral blood plasma and explored its potential to be used in liquid biopsy. Methods: Peripheral blood cfRNA was extracted from patients with neoplasms in B-cell (#105), T-cell (#16), Myeloid (#73), and from solid tumors (#44), Normal individuals (#51), and reactive post-transplant (#137). RNA was sequenced using a 1459-gene panel. Expression profile was generated using Cufflinks. Results: cfRNA levels of various solid tumor biomarkers (CA-125, CA-15-3, CEA 8, Keratin19, Keratin6A...) were significantly higher (P < 0.0001) in samples from solid tumors as compared with normal control. Similarly, cfRNA lymphoid markers (CD19, CD22, CD79A, and CD79B...) and cfRNA myeloid markers (CD33, CD14, CD117, CD56...) were all higher in B-cell lymphoid neoplasms and myeloid neoplasms, respectively (P < 0.0001), as compared with control. In evaluating the host immune system, cfRNA CD4:CD8B and CD3D:CD19 ratios in normal controls were as expected (median: 5.92 and 6.87, respectively) and were significantly lower in solid tumors (median 3.40 and 2.23, respectively, P < 0.0002). Solid tumor cfRNA showed CTLA4:CD8B ratio significantly higher in tumors than in normal (median 0.74 vs 0.19, P = 0.0001), while there was no difference in cfRNA PD-L1:CD8B ratio (median 1.45 vs 1.77, P = 0.96). Similar distinct patterns are noted for various cytokine and chemokines. cfRNA was highly predictive of diagnosis (AUC > 0.98) of solid tumors, B-cell lymphoid neoplasms, T-cell lymphoid neoplasms, and myeloid neoplasms as compared with normal control. When a specific neoplastic disease was considered against all cases including control and other neoplasms, the AUC varied between 0.77 and 0.949. Conclusions: This data shows that liquid biopsy using targeted sequencing of cfRNA in patients with various types of cancer provides comprehensive and reliable information on the neoplastic disease as well as the host. [Table: see text]

3048 Background: Expressed RNA can capture mutations, gene fusions, and biomarker profiles. In principle, each abnormal cell has one copy of mutated gene, but numerous copies of mutated RNA. Cell-free RNA (cfRNA) is not used due to the assumption that it is degraded. Next Generation Sequencing (NGS) by design is particularly adaptable for fragmented DNA and RNA. We developed an approach to isolate cell-free total nucleic acid (cfTNA) and cell-free RNA (cfRNA) from peripheral blood. Using targeted sequencing, we explored the potential of this approach to detect mutations, fusion mRNA, and copy number variation (CNV) in solid tumors and hematologic neoplasms. Methods: Peripheral blood cfTNA and cfRNA were extracted from B-cell lymphoid neoplasms (#105), T-cell neoplasms (#16), Myeloid neoplasms (#73), solid tumors (#44), and Normal individuals (#51), and sequenced using a targeted panel of 1459 genes. Results: Numbers of mutations detected in solid tumors and hematologic neoplasms were significantly (P > 0.0001) higher in cfRNA (No. = 1229) than in cfTNA (No. = 1004). Overall variant allele frequency (VAF) was significantly higher in cfRNA than in cfTNA (P < 0.0001). However, numerous mutations detected by RNA were not detected by cfTNA and vice versa. In general, nonsense mutations were more likely to be detected by cfTNA than by cfRNA and at higher VAF. Low-level mutations (VAF < 10%) were more likely to be detected by cfRNA than by cfTNA. For example, 136 mutations in TP53 gene were detected using cfRNA and only 70 mutations were detected in cfTNA. KRAS mutations were also higher in cfRNA (#33) as compared with cfTNA (#21). In contrast, when most of the mutations were nonsense, as in ASXL1 gene, more mutations were detected by cfTNA (24 vs 23). When mutations were detected in both cfRNA and cfTNA, mutation load (level of mutant copies) was overall slightly higher in cfTNA (P = 0.06), likely due to higher degradation of RNA, but varied significantly dependent on the type of mutated gene and type of mutation. cfRNA was reliable in detecting fusion transcripts in solid tumors and in hematologic neoplasms (SLC34A2-ROS1, DDX5-BCL6, ETV6-RUNX1, RUNX1T1-RUNX1, PML-RARA, RUNX1-ZFPM2, DEK-NUP214, EP300-ZNF384) irrespective of the breakpoint or partner gene. The cfTNA detected various CNVs expected by cytogenetic analysis when tumor fraction was adequate (VAF > 10%). Conclusions: This data demonstrates that using cfRNA and cfTNA provides complementary comprehensive information for evaluating mutations, fusion genes, and CNV. This approach increased sensitivity and reliability of liquid biopsy. Furthermore, the cfRNA provides critical information on relative expression of various genes that can be used as biomarkers in characterizing the neoplastic process (see ASCO abstract, Liquid Biopsy Based on Cell-Free RNA and Biomarkers profiling of hematologic and solid tumors).

3018 Background: Diagnosis and classification of tumors is becoming increasingly dependent on biological and molecular biomarkers. RNA expression profiling using next generation sequencing (NGS) provides information on various biological and molecular changes in the cancer and in the microenvironment. We explored the potential of using targeted transcriptome and artificial intelligence (AI) in the differential diagnosis and classification of various hematologic and solid tumors. Methods: RNA from hematologic neoplasms (N = 2606) and solid tumors (N = 2038) as well as normal bone marrow and lymph node control (N = 806) were sequenced by NGS using a targeted 1408-gene panel. The hematologic neoplasms included 20 different subtypes. Solid tumors included 24 different subtypes. Machine learning is used for comparing two classes at a time. Geometric Mean Naïve Bayesian (GMNB) classifier is used to provide differential diagnosis across 45 diagnostic entities with assigned ranking. Results: Machine learning showed high accuracy in distinguishing between two diagnoses with AUC varied between 1 (Sarcoma vs GIST) and 0.841 (MDS vs normal control) (examples in Table). For differential diagnosis between all 45 different diagnoses, we used 3045 samples for training the GMNB algorithm and 1415 samples for testing. Correct first choice diagnosis was obtained in 100% of ALL, 88% of AML, 85% of DLBCL, 82% of colorectal cancer, 88% of lung cancer, 72% of CLL, and 72% of follicular lymphoma. The algorithm had difficulty in typically overlapping diagnoses and diagnosed as first choice 19% of MDS, 46% of normal, and 12% of MPN. Diagnosis improved significantly when second choice was considered. Conclusions: Targeted RNA profiling with proper AI can provide highly useful tools for the pathologic diagnosis and classification of various cancers. Additional information such as mutation profile and clinical information can improve these algorithms, reduce subjectivity, and minimize errors in pathologic diagnoses. [Table: see text]

Circulating cell-free DNA (ccfDNA) allows for noninvasive peripheral blood sampling of cancer-associated mutations and has established clinical utility in several solid tumors. We performed targeted next-generation sequencing of ccfDNA and bone marrow at the time of diagnosis and after achieving remission in 22 patients with acute myeloid leukemia (AML). Among 28 genes sequenced by both platforms, a total of 39 unique somatic mutations were detected. Five mutations (13%) were detected only in ccfDNA, and 15 (38%) were detected only in bone marrow. Among the 19 mutations detected in both sources, the concordance of variant allelic frequency (VAF) assessment by both methods was high (R2 = 0.849). Mutations detected in only 1 source generally had lower VAF than those detected in both sources, suggesting that either method may miss small subclonal populations. In 3 patients, sequencing of ccfDNA detected new or persistent leukemia-associated mutations during remission that appeared to herald overt relapse. Overall, this study demonstrates that sequencing of ccfDNA in patients with AML can identify clinically relevant mutations not detected in the bone marrow and may play a role in the assessment of measurable residual disease. However, mutations were missed by both ccfDNA and bone marrow analyses, particularly when the VAF was

// Maher Albitar 1 , Sucha Sudarsanam 1 , Wanlong Ma 1 , Shiping Jiang 1 , Wayne Chen 1 , Vincent Funari 1 , Forrest Blocker 1 and Sally Agersborg 1 1 NeoGenomics Laboratories, Aliso Viejo, CA, USA Correspondence to: Maher Albitar, email: maher.albitar@neogenomics.com Keywords: NSCLC; PD-L1; EGFR; MET; TP53 Received: October 06, 2017 Accepted: January 09, 2018 Published: February 08, 2018 ABSTRACT Background: The role of MET amplification in lung cancer, particularly in relation to checkpoint inhibition and EGFR WT, has not been fully explored. In this study, we correlated PD-L1 expression with MET amplification and EGFR , KRAS , or TP53 mutation in primary lung cancer. Methods: In this retrospective study, tissue collected from 471 various tumors, including 397 lung cancers, was tested for MET amplification by FISH with a MET /centromere probe. PD-L1 expression was evaluated using clone SP142 and standard immunohistochemistry, and TP53 , KRAS , and EGFR mutations were tested using next generation sequencing. Results: Our results revealed that PD-L1 expression in non-small cell lung cancer is inversely correlated with EGFR mutation (P=0.0003), and positively correlated with TP53 mutation (P=0.0001) and MET amplification (P=0.004). Patients with TP53 mutations had significantly higher MET amplification (P=0.007), and were more likely (P=0.0002) to be EGFR wild type. There was no correlation between KRAS mutation and overall PD-L1 expression, but significant positive correlation between PD-L1 expression and KRAS with TP53 co-mutation (P=0.0002). A cut-off for the ratio of MET : centromere signal was determined as 1.5%, and 4% of lung cancer patients were identified as MET amplified. Conclusions: This data suggests that in lung cancer both MET and TP53 play direct roles in regulating PD-L1 opposing EGFR . Moreover, KRAS and TP53 co-mutation may cooperate to drive PD-L1 expression in lung cancer. Adding MET or TP53 inhibitors to checkpoint inhibitors may be an attractive combination therapy in patients with lung cancer and MET amplification.

BACKGROUND: Distinguishing between low- and high-grade prostate cancers (PCa) is important, but biopsy may underestimate the actual grade of cancer. We have previously shown that urine/plasma-based prostate-specific biomarkers can predict high grade PCa. Our objective was to determine the accuracy of a test using cell-free RNA levels of biomarkers in predicting prostatectomy results.METHODS: This multicenter community-based prospective study was conducted using urine/blood samples collected from 306 patients. All recruited patients were treatment-naïve, without metastases, and had been biopsied, designated a Gleason Score (GS) based on biopsy, and assigned to prostatectomy prior to participation in the study. The primary outcome measure was the urine/plasma test accuracy in predicting high grade PCa on prostatectomy compared with biopsy findings. Sensitivity and specificity were calculated using standard formulas, while comparisons between groups were performed using the Wilcoxon Rank Sum, Kruskal-Wallis, Chi-Square, and Fisher's exact test.RESULTS: GS as assigned by standard 10-12 core biopsies was 3 + 3 in 90 (29.4%), 3 + 4 in 122 (39.8%), 4 + 3 in 50 (16.3%), and > 4 + 3 in 44 (14.4%) patients. The urine/plasma assay confirmed a previous validation and was highly accurate in predicting the presence of high-grade PCa (Gleason ≥3 + 4) with sensitivity between 88% and 95% as verified by prostatectomy findings. GS was upgraded after prostatectomy in 27% of patients and downgraded in 12% of patients.CONCLUSIONS: This plasma/urine biomarker test accurately predicts high grade cancer as determined by prostatectomy with a sensitivity at 92-97%, while the sensitivity of core biopsies was 78%.

Introduction: Liquid biopsy has been reported to be useful in predicting residual disease in patients with diffuse large B-cell lymphoma (DLBCL). Most of the studies focused on quantifying the level of circulating lymphoma-specific DNA. We explored the clinical relevance of the specific mutated genes in predicting progression in patients with DLBCL. Method: Peripheral blood samples were collected from patients with DLBCL based on their visit to clinic without other specific selection. Median age of patients is 69 (range 28-91), with 51% of the patients being male. These patients were treated on multiple protocols including R-CHOP, R-EPOCH, Magrath, HCVAD, CAR-T (#2 patients), and others. cfDNA was extracted and sequenced by next generation sequencing using 177 gene panel. The panel uses single primer extension (SPE) approach with UMI. Sequencing depth is increased to more than 2000X after removing duplicates. Low level mutations are confirmed by inspecting BAM file. Results: A total of 86 sample from 61 patients were collected post clinical remission at different time points (median 28 weeks, range: 1-994 weeks). Of these samples, 56 (65%) from 46 patients (75%) were positive. However, 6 of these samples from 4 patients had germline mutations or mutations in TET2, ASXL1, or DNMT3A that are consistent with CHIP (clonal hematopoiesis of indeterminate potential). The remaining 50 positive samples from 42 patients had 8 repeats on the same patients collected at different time points. Comparing the 19 negative patients with the 42 positive patients post-remission, patients with residual molecular disease were significantly older than patients without residual disease (P=0.01). However, there was no significant difference between the two groups in gender, ethnic background, LDH, cell of origin classification, or TP53 positivity by IHC. Patients with residual disease showed tendency for short progression-free survival (P=0.08). Focusing on patients with specific mutations detected in the cfDNA showed that 14 (23%) patients had mutations either in TP53 or MYD88. There was no significant difference in age between these two groups or any of the other clinical variables. However, patients with TP53/MYD88 mutations had significantly shorter survival (P=0.04). Conclusion: Post-remission residual disease as measured by circulating cfDNA is an independent predictor of potential relapse in patients with DLBCL. However, presence of it is important to determine the aggressiveness of the residual circulating clone. Residual circulating lymphoma DNA with TP53 or MYD88 mutations is a strong predictor of earlier relapse. Figure 1 Figure 1. Disclosures Pecora: Genetic testing cooperative: Other: equity investor; Genetic testing cooperative: Membership on an entity's Board of Directors or advisory committees. Feldman: Alexion, AstraZeneca Rare Disease: Honoraria, Other: Study investigator. Goy: Bristol Meyers Squibb: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; MorphoSys: Honoraria, Other; AbbVie/Pharmacyclics: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; AstraZeneca: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Consultancy, Honoraria; Acerta: Consultancy, Research Funding; Elsevier's Practice Update Oncology, Intellisphere, LLC(Targeted Oncology): Consultancy; Celgene: Consultancy, Honoraria, Research Funding; Michael J Hennessey Associates INC: Consultancy; Elsevier PracticeUpdate: Oncology: Consultancy, Honoraria; Janssen: Membership on an entity's Board of Directors or advisory committees; Bristol Meyers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Kite, a Gilead Company: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Medscape: Consultancy; Gilead: Membership on an entity's Board of Directors or advisory committees; Genentech/Hoffman la Roche: Research Funding; AbbVie/Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; OncLive Peer Exchange: Honoraria; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Xcenda: Consultancy, Honoraria; Vincerx pharma: Membership on an entity's Board of Directors or advisory committees; Rosewell Park: Consultancy; LLC(Targeted Oncology): Consultancy; Genomic Testing Cooperative: Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees, Other: Leadership role; Xcenda: Consultancy; Hoffman la Roche: Consultancy; Incyte: Honoraria; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Infinity/Verastem: Research Funding; Janssen: Research Funding; Karyopharm: Research Funding; Vincerx: Honoraria, Membership on an entity's Board of Directors or advisory committees; Physicians' Education Resource: Consultancy, Other: Meeting/travel support; COTA (Cancer Outcome Tracking Analysis): Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees, Other: Leadership role; Phamacyclics: Research Funding; Constellation: Research Funding; Hackensack Meridian Health, Regional Cancer Care Associates/OMI: Current Employment.

Introduction: Acute graft-vs.-host disease (aGVHD) remains a major diagnostic and clinical problem in patients after allogenic hematopoietic stem cell transplant (HSCT). Finding biomarkers that play a role in aGVHD not only helps in predicting and diagnosing aGVHD, but might help in developing prophylaxis and therapeutic approaches. Using Next Generation Sequencing (NGS) and targeted RNA sequencing along with a machine learning approach to predict, we investigated the potential of discovering new biomarkers that can predict aGVHD. Methods: RNA extracted from bone marrow aspiration samples collected around day 90 post HSCT from 46 patients were sequenced using 1408 targeted genes. cDNA was first generated, then adapters were ligated. The coding regions of the expressed genes were captured from this library using sequence-specific probes to create the final library. Sequencing was performed using an Illumina NextSeq 550 platform. Ten million reads per sample in a single run were required. Read length was 2 × 150 bp. Expression profile was generated using Cufflinks. A machine learning system is developed to predict the GVHD cases and to discover the relevant genes. A subset of genes relevant to GVHD is automatically selected for the classification system, based on a k-fold cross-validation procedure (with k=10). For an individual gene, a Naïve Bayesian classifier was constructed on the training of k-1 subsets and tested on the other testing subset. To eliminate the underflow problem commonly associated with the standard Naïve Bayesian classifiers, we applied Geometric Mean Naïve Bayesian (GMNB) as the classifier to predict GVHD. The processes of gene selection and GVHD classification are applied iteratively to obtain an optimal classification system and a subset of genes relevant to GVHD. Results: The analyzed bone marrow samples included patients transplanted for aplastic anemia (#1), acute lymphoblastic leukemia (#9), acute myeloid leukemia (#16), mixed phenotype acute leukemia (#1), myelodysplastic syndrome (#10), chronic myelomonocytic leukemia (#5), and myeloproliferative neoplasm (#4). Of the 46 patients, 30 (65%) had a diagnosis of aGVHD (grade 2-4). The GMNB modified Bayesian model selected 7 genes as top classifiers. These top classifier genes included Class II Major Histocompatibility gene (CIITA), B-cell markers genes (CD19 and CD22), early T-cell related gene (TCL1A), hematopoietic-specific transcription factor (IKZF3), a gene involved in protein-protein interaction, and a gene involved in DNA helicase nucleotide excision repair (ERCC3). When these 7 genes were used in GMNB-modified classifier with 10-fold cross validation to predict aGVHD, the model classified 28 of the 30 positive cases accurately and 14 of the 16 negative cases accurately. The sensitivity was 93% (95% CI, 76%-99%). The specificity was 87.5% (95% CI: 60%-97%). The positive predictive value (PPV) was 93% (95% CI: 76%-99%) and the negative predictive value (NPV) was 87.5% (95% CI: 60%-98%). Conclusion: While most biomarker discovery has been focused on inflammatory cytokines, chemokines, and their receptors, our data suggest that hematopoietic proliferation and transcription regulators in bone marrow might provide important information for the diagnosis and prediction of aGVHD. This data suggests that biomarkers related to B-cell, T-cell, and MHC play a role in aGVHD at the bone marrow level. These findings also suggest that targeting these biomarkers in the bone marrow might be a realistic approach for prophylaxis and treatment that needs to be explored. Although further validation is needed, this study suggests that targeted RNA sequencing by NGS combined with machine learning algorithm can be a practical and cost-effective approach for the diagnosis and prediction of aGVHD. Figure 1 Figure 1. Disclosures Pecora: Genetic testing cooperative: Other: equity investor; Genetic testing cooperative: Membership on an entity's Board of Directors or advisory committees. Goy: Rosewell Park: Consultancy; Elsevier's Practice Update Oncology, Intellisphere, LLC(Targeted Oncology): Consultancy; Acerta: Consultancy, Research Funding; Genentech/Hoffman la Roche: Research Funding; Vincerx pharma: Membership on an entity's Board of Directors or advisory committees; Physicians' Education Resource: Consultancy, Other: Meeting/travel support; Vincerx: Honoraria, Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Xcenda: Consultancy; Janssen: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Kite, a Gilead Company: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; OncLive Peer Exchange: Honoraria; Xcenda: Consultancy, Honoraria; AbbVie/Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; COTA (Cancer Outcome Tracking Analysis): Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees, Other: Leadership role; Elsevier PracticeUpdate: Oncology: Consultancy, Honoraria; Infinity/Verastem: Research Funding; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Bristol Meyers Squibb: Membership on an entity's Board of Directors or advisory committees; MorphoSys: Honoraria, Other; Genomic Testing Cooperative: Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees, Other: Leadership role; Celgene: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria; Hoffman la Roche: Consultancy; Michael J Hennessey Associates INC: Consultancy; LLC(Targeted Oncology): Consultancy; Medscape: Consultancy; Bristol Meyers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; AbbVie/Pharmacyclics: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Incyte: Honoraria; Constellation: Research Funding; Janssen: Research Funding; Karyopharm: Research Funding; Phamacyclics: Research Funding; Hackensack Meridian Health, Regional Cancer Care Associates/OMI: Current Employment. Rowley: ReAlta Life Sciences: Consultancy.

Introduction: Using next generation sequencing (NGS) in monitoring residual disease in patients with myeloid neoplasms is complicated by the significant heterogeneity in these diseases and the frequent presence of CHIP (clonal hematopoiesis of indeterminate potential) in patients with hematologic neoplasms on which these neoplasms arise. This is particularly relevant post hematopoietic stem cell transplant (HSCT). We explored the ability of using plasma cell-free DNA (cfDNA) in monitoring patients after HSCT and evaluated the potential of using liquid biopsy as a replacement for bone marrow biopsy. Method: cfDNA was isolated from 204 peripheral blood samples obtained from 75 patients, collected at various time points ranging from 27 days to 650 days (median 178 days) post-transplant. DNA from 102 bone marrow (BM) samples was extracted and sequenced using the same panel and approach as cfDNA. Diagnoses included 30 acute myeloid leukemia (AML), 2 chronic myelogenous leukemia (CML), 5 chronic myelomonocytic leukemia (CMML), 4 lymphoma, 10 myelodysplastic syndrome (MDS), 2 multiple myeloma (MM), 9 myeloproliferative neoplasm (MPN), 1 aplastic anemia, and 11 acute lymphoblastic leukemia. cfDNA was sequenced by NGS using 177 gene panel on Illumina platform. Single primer extension (SPE) approach with UMI was used. Sequencing depth was increased to more than 2000X after removing duplicates. Low-level mutations were confirmed by inspecting BAM file. Results: 156 cfDNA samples (76%) tested negative and 48 samples from 30 different patients were positive. The negative samples were collected from 28 days to 650 days post-transplant (median 277 days). The positive samples were collected from 27 days to 650 days post-transplant (median 188 days). One of these positive patients was in full clinical relapse at the time of testing. No negative patient who remained negative had clinical relapse. Five patients converted from negative to positive and 12 from positive to negative with subsequent testing. Three from the converted to positive patients developed clinical relapse. Patients who were positive without clinical relapse had median variant allele frequency (VAF) of 0.85% (range: 0.01-13.25) and typically one mutated gene. The mutated genes in this group were: JAK2, IDH2, ASXL1, TET2, DNMT3A, ASXL1, PTPN11, SF3B1, MPL, CEBPA1. Patients who had clinical relapse (#4) had median VAF of 16.33% (0.4%-57.63%) with multiple mutated genes. The mutated genes in this group were: TP53, FLT3, ASXL1, CEBPA, EZH1, NRAS, SETBP1, TET2. To evaluate relevance to BM testing, we compared BM samples with cfDNA samples collected within 120 days of each other. This showed 17 pairs with concordant negative results, 10 with concordant positive results, 5 pairs with positive by cfDNA but negative by BM cells, and one pair with positive by BM but negative by cfDNA. This BM positive sample was performed at 78 days after the cfDNA sample and showed mutation in DNMT3A gene at VAF of 0.63%. Four of the 5 pairs with positive cfDNA but negative BM were collected approximately 3 months after bone marrow and the 5th case was one month prior to BM sample. Conclusion: These data suggest that monitoring residual disease after HSCT using cfDNA and NGS is a reliable approach and may replace the need of bone marrow biopsy. However, low-level mutations should not be used as the sole criterion for determining relapse. Variant allele frequency and the mutated gene should be considered in evaluating actionable findings. Disclosures Pecora: Genetic testing cooperative: Membership on an entity's Board of Directors or advisory committees; Genetic testing cooperative: Other: equity investor. Rowley: ReAlta Life Sciences: Consultancy.

Introduction: Cytogenetic analysis is important for stratifying patients with various myeloid neoplasms. It has been reported that whole-genome sequencing can be used as an alternative to cytogenetic analysis in acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). With the increasing use of liquid biopsy in the diagnosis and monitoring of patients with various types of neoplasms, we explored the potential of using liquid biopsy and next generation sequencing (NGS) in detecting chromosomal structural abnormalities or copy number variation (CNV) in patients with myeloid neoplasms. For practical approach and for capturing single nucleotide variants (SNV) and to achieve enough depth in sequencing, we used targeted sequencing for determining the chromosomal structural abnormalities in cell-free DNA (cfDNA) in patients with myeloid neoplasms. Methods: Peripheral blood plasma samples from 144 patients with myeloid neoplasms were used to extract cfDNA for NGS testing. This included 49 patients with MDS, 31 with AML, and 64 patients with myeloproliferative neoplasms (MPN). The median age was 68.5 (range: 24-96); 56 (39%) were female. cfDNA was sequenced using 275 gene panel. The panel uses single primer extension (SPE) approach with UMI. Sequencing depth was increased to more than 1000X (after removing duplicates). CNVkit software was used for analyzing and visualizing copy number variations. All samples were confirmed to be diagnostic by showing mutations in diagnostic genes with variant allele frequency >20% or by showing diagnostic chromosomal structural abnormalities (e.g., 5q deletion in MDS, 5q- syndrome). Cytogenetic data on 35 corresponding bone marrow samples (18 AML and 17 MDS) were available for comparison. Results: Of the 144 samples, 47 (33%) showed chromosomal structural abnormalities. In the AML group, 20 of 31 (65%) showed cytogenetic abnormalities by cfDNA testing. Of these positive AML patients, 18 (90%) (58% of total AML) had poor-risk cytogenetics. Therefore, the AML patients with normal cytogenetics or cytogenetic abnormalities other than high-risk constituted 42% of total AML patients. Of the MDS group, 11 of 49 MDS patients (22%) showed cytogenetic abnormalities by cfDNA testing, 6 of whom (54.5%) had high-risk cytogenetics. Overall, 12% of all MDS had poor-risk cytogenetics by cfDNA testing. In the MPN group, 16 of 64 (25%) showed cytogenetic abnormalities, 2 of which (12.5%) had 7q deletion (3% of all MPN); the rest (87.5%) of cytogenetic-positive MPN (22% of total MPN) had other abnormalities including 20q-, +8, 12q, 17p-, 11q-, trisomy 9, trisomy 21 and others. To compare chromosomal abnormalities as detected by cfDNA NGS testing with conventional cytogenetic analysis of corresponding bone marrow samples, we classified cytogenetic findings based on risk stratification into either intermediate-risk or poor-risk. Of the 36 cases, there was 100% concordance between cfDNA data and cytogenetic data when findings were grouped based on risk classification. Two of the conventional cytogenetic samples showed no metaphases while one showed intermediate-risk abnormalities by cfDNA NGS analysis and the second showed poor-risk cytogenetic abnormalities by cfDNA NGS analysis. These 36 cases included 16 cases with normal cytogenetics. Simple abnormalities such as 5q-, 7q-, +8 were called in identical fashion, but some other abnormalities such as derivative chromosome and marker chromosome were resolved or interpreted differently by the cfDNA NGS analysis. The NGS panel design used in this study does not cover fusion genes or chromosomal translocation, and chromosomal translocations were missed at this time. Conclusions: This data shows that liquid biopsy using and targeted NGS is reliable in detecting chromosomal structural abnormalities in myeloid neoplasms and potentially can replace the need for conventional cytogenetic testing. While the current study was not designed to detect chromosomal translocations, a small, targeted panel of 275 genes is adequate for standard risk classification of myeloid neoplasms into intermediate or high-risk. Considering that in the same test complete mutation profiling can also be achieved along with chromosomal structural analysis, liquid biopsy in myeloid neoplasms might be considered as an efficient replacement to bone marrow biopsy in patients with myeloid neoplasms when fusion genes are not expected. Figure 1 Figure 1. Disclosures Goy: Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Infinity/Verastem: Research Funding; COTA (Cancer Outcome Tracking Analysis): Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees, Other: Leadership role; OncLive Peer Exchange: Honoraria; Bristol Meyers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Vincerx: Honoraria, Membership on an entity's Board of Directors or advisory committees; Elsevier PracticeUpdate: Oncology: Consultancy, Honoraria; AbbVie/Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Vincerx pharma: Membership on an entity's Board of Directors or advisory committees; LLC(Targeted Oncology): Consultancy; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Xcenda: Consultancy, Honoraria; Gilead: Membership on an entity's Board of Directors or advisory committees; Acerta: Consultancy, Research Funding; Rosewell Park: Consultancy; Janssen: Membership on an entity's Board of Directors or advisory committees; Elsevier's Practice Update Oncology, Intellisphere, LLC(Targeted Oncology): Consultancy; AbbVie/Pharmacyclics: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; MorphoSys: Honoraria, Other; Incyte: Honoraria; Novartis: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol Meyers Squibb: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Genomic Testing Cooperative: Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees, Other: Leadership role; Celgene: Consultancy, Honoraria, Research Funding; Kite, a Gilead Company: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech/Hoffman la Roche: Research Funding; Janssen: Research Funding; Karyopharm: Research Funding; Michael J Hennessey Associates INC: Consultancy; Hoffman la Roche: Consultancy; Physicians' Education Resource: Consultancy, Other: Meeting/travel support; Medscape: Consultancy; Phamacyclics: Research Funding; Constellation: Research Funding; Xcenda: Consultancy; Hackensack Meridian Health, Regional Cancer Care Associates/OMI: Current Employment. Pecora: Genetic testing cooperative: Other: equity investor; Genetic testing cooperative: Membership on an entity's Board of Directors or advisory committees. Koprivnikar: Bristol Myers Squibb: Speakers Bureau. McCloskey: BMS: Honoraria, Speakers Bureau; COTA: Other: Equity Ownership; Takeda: Consultancy, Speakers Bureau; Pfizer: Consultancy; Novartis: Consultancy; Jazz: Consultancy, Speakers Bureau; Incyte: Speakers Bureau; Amgen: Speakers Bureau.

// Adam Albitar 1 , Wanlong Ma 1 , Ivan DeDios 1 , Jeffrey Estella 1 , Inhye Ahn 2 , Mohammed Farooqui 3 , Adrian Wiestner 3 , Maher Albitar 1 1 NeoGenomics Laboratories, Irvine, CA, USA 2 Medical Oncology Service, National Cancer Institute, Bethesda, MD, USA 3 Hematology Branch, National Heart, Lung, and Blood Institute, Bethesda, MD, USA Correspondence to: Maher Albitar, email: malbitar@neogenomics.com Keywords: ibrutinib, CLL, resistance, PLCγ2, BTK Received: August 23, 2016 Accepted: January 27, 2017 Published: February 13, 2017 ABSTRACT Patients with chronic lymphocytic leukemia (CLL) that develop resistance to Bruton tyrosine kinase (BTK) inhibitors are typically positive for mutations in BTK or phospholipase c gamma 2 (PLCγ2). We developed a high sensitivity (HS) assay utilizing wild-type blocking polymerase chain reaction achieved via bridged and locked nucleic acids. We used this high sensitivity assay in combination with Sanger sequencing and next generation sequencing (NGS) and tested cellular DNA and cell-free DNA (cfDNA) from patients with CLL treated with the BTK inhibitor, ibrutinib. We also tested ibrutinib-naive patients with CLL. HS testing achieved 100x greater sensitivity than Sanger. HS Sanger sequencing was capable of detecting < 1 mutant allele in background of 1000 wild-type alleles (1:1000). Similar sensitivity was achieved with HS NGS. No BTK or PLCγ2 mutations were detected in any of the 44 ibrutinib-naive CLL patients. We demonstrate that without the HS testing 56% of positive samples would have been missed for BTK and 85% of PLCγ2 would have been missed. With the use of HS, we were able to detect multiple mutant clones in the same sample in 37.5% of patients; most would have been missed without HS testing. We also demonstrate that with HS sequencing, plasma cfDNA is more reliable than cellular DNA in detecting mutations. Our studies indicate that wild-type blocking and HS sequencing is necessary for proper and early detection of BTK or PLCγ2 mutations in monitoring patients treated with BTK inhibitors. Furthermore, cfDNA from plasma is very reliable sample-type for testing.

Background: Unnecessary biopsies and overdiagnosis of prostate cancer (PCa) remain a serious healthcare problem. We have previously shown that urine- and plasma-based prostate-specific biomarkers when combined can predict high grade prostate cancer (PCa). To further validate this test, we performed a prospective multicenter study recruiting patients from community-based practices. Patients and Methods: Urine and plasma samples from 2528 men were tested prospectively. Results were correlated with biopsy findings, if a biopsy was performed as deemed necessary by the practicing urologist. Of the 2528 patients, biopsy was performed on only 524 (21%) patients. Results: Of the 524 patients, Gleason≥3+4 PCa was found in 161 (31%) and Gleason ≥4+3 was found in 62 (12%) of the patients. The urine/plasma biomarkers algorithm showed sensitivity and specificity of 75% and 69% for predicting Gleason ≥3+4. However, upon incorporating prostate size and prior history of biopsy in the algorithm, we achieved a sensitivity between 97% and 86% and a specificity between 36% and 57%, dependent on the used cut-off point. Sensitivity for predicting PCa Gleason ≥4+3 was between 96% and 99% and specificity between 59% and 37%, dependent on the cut-off point. Diagnosis of Gleason ≥3+4 was missed in 1% to 3% of tested patients and of Gleason ≥4+3 in 0.2% to 1%. Conclusion: This test when integrated with prostate volume and the prior prostate biopsy enhance the sensitivity and specificity for predicting the presence of high grade prostate cancer with negative predictive value (NPV) of 90% to 97% for Gleason ≥3+4 and between 98% to 99% for Gleason ≥4+3.

Purpose In patients with chronic myelogenous leukemia (CML), point mutations in the BCR-ABL1 kinase domain are the most common cause of treatment failure with a tyrosine kinase inhibitor (TKI). It is not clear whether the splice variant BCR-ABL135INS is also associated with treatment failure. Patients and methods We reviewed all CML patients who had BCR-ABL1 kinase mutation analysis performed between August 1, 2007, and January 15, 2014. Patients who had BCR-ABL135INS detected had their medical records reviewed to determine response to TKI therapy. Results Two hundred and eighty four patients had kinase mutation testing performed; of these, 64 patients (23%) had BCR-ABL135INS detected. Forty-five patients were in chronic phase (70%), 10 were in accelerated phase (16%), 6 were in blastic phase (9%), and 3 were in other settings (5%). Of the 34 chronic phase patients who began therapy with imatinib, 23 patients (68%) failed therapy: 8 patients (24%) had primary refractory disease, 11 patients (32%) progressed, and 4 patients (12%) had disease progression after dose interruption. In contrast to the patients with disease progression or lack of response, none of 23 patients who were responding to imatinib had BCR-ABL135INS detected. DNA sequencing of commonly mutated spliceosomal genes SF3B1, U2AF1, SRSF2, ZRSR2, SFA31, PRPF408, U2A565, and SF1 did not reveal mutations in seven BCR-ABL135INS –positive patients tested. Conclusions The splice variant BCR-ABL135INS is frequently found in patients who are resistant to imatinib. Mutations in the commonly mutated spliceosomal proteins do not contribute to this association.

Demonstrating the presence of myelodysplastic syndrome (MDS)-specific molecular abnormalities can aid in diagnosis and patient management. We explored the potential of using peripheral blood (PB) cell-free DNA (cf-DNA) and next-generation sequencing (NGS).We performed NGS on a panel of 14 target genes using total nucleic acid extracted from the plasma of 16 patients, all of whom had confirmed diagnoses for early MDS with blasts5%. PB cellular DNA from the same patients was sequenced using conventional Sanger sequencing and NGS.Deep sequencing of the cf-DNA identified one or more mutated gene(s), confirming the diagnosis of MDS in all cases. Five samples (31%) showed abnormalities in cf-DNA by NGS that were not detected by Sanger sequencing on cellular PB DNA. NGS of PB cell DNA showed the same findings as those of cf-DNA in four of five patients, but failed to show a mutation in the RUNX1 gene that was detected in one patient's cf-DNA. Mutant allele frequency was significantly higher in cf-DNA compared with cellular DNA (p = 0.008).These data suggest that cf-DNA when analyzed using NGS is a reliable approach for detecting molecular abnormalities in MDS and should be used to determine if bone marrow aspiration and biopsy are necessary.

Prevalence of somatic mutations in patients with aplastic anemia using peripheral blood cfDNA as compared with BM

e16061 Background: Fibroblast Growth Factor Receptors (FGFR1-4) abnormalities (fusion, amplification and mutations) are common in urothelial, breast and endometrial cancers. However, FGFR1-4 have been shown to play a major role in cell proliferation, differentiation, and apoptosis in other types of cancers including colorectal (CRC) and lung cancers. We explored the value of using DNA and RNA next generation sequencing (NGS) in determining the presence of abnormalities in FGFR1-4 in various types of cancer. Methods: Using targeted panel and next generation sequencing (NGS), we analyzed DNA sequencing data (434 genes) in 438 Solid tumors and RNA data (1408 genes) in 160 lung cancers and 53 colorectal cancers (CRC). The expression levels of the CRC and lung cancer were also compared with expression levels of 32 cases of endometrial, urothelial and breast cancers as a group of cancers known to have high incidence. Results: The DNA data showed mutations in 85 samples and CNV in 12 samples. The detected mutations were 18% in FGFR1, 25% in FGFR2, 45% in FGFR3, and 12% in FGFR4. Only 20% of the detected mutations by NGS testing can be detected if the PCR-based FDA-approved kit was used. Analysis of the expression levels of FGFR1-4 mRNA in CRC and lung cancer showed highest expression in FGFR2, followed by FGFR1 then FGFR3. Expression of FGFR4 was the lowest (P < 0.0001). There was no difference between CRC and lung cancer in FGFR1 and FGFR2 mRNA, but FGFR3 was slightly higher in lung cancer as compared with CRC (P = 0.01). FGFR4 was significantly higher in CRC as compared with lung cancer (P < 0.0001). No fusion involving FGFR1-4 was detected in any of the tested CRC or lung cancers. Upon comparing overall expression between CRC/lung cancer with the group of cancers that are known to have high incidence of FGFR1-4 abnormalities (urothelial, breast, and endometrial), FGFR1 and FGFR2 mRNA were significantly lower in CRC/lung cancers (P < 0.0001 and P = 0.0002, respectively), but there was no significant difference in FGFR3. However, significant overlap is noted. In contrast, FGFR4 was significantly higher in CRC (P < 0.0001). Conclusions: This data suggests that while FGFR1-3 genes are overall expressed in CRC and lung, some cases may have significantly high expression of FGFR1-3 and perhaps these cases should be singled out for treatment with FGFR inhibitors. Furthermore, NGS testing for mutations significantly more efficient and can detect significant number of mutations that can be missed if PCR-based testing is used. NGS testing of DNA and RNA is the most appropriate testing for abnormalities in FGFR1-4.

e21535 Background: N TRK (NTRK1, NTRK2 or NTRK3) fusions are oncogenic drivers of various types of cancer and lead to activation of the PI3K/AKT pathway. Treatment of patients with NTRK fusion using TRK inhibitors, such as larotrectinib or entrectinib, is associated with high response rates ( > 75%). However these fusions are very rare in most common cancers, such as lung and colorectal cancers. We examined the expression levels of NTRK1, NTRK2, and NTRk3 in non-small cell lung cancer using targeted RNA sequencing. We also examined the effects of NTRK overexpression on PI3K/AKT pathway in non-small cell lung cancer. Methods: RNA was extracted from 160 FFPE samples of non-small cell lung cancers and sequenced using targeted next generation sequencing (NGS). The RNA sequencing was based on hybrid capture using a targeted panel of 1408 genes. The number of reads ranged from 5 to 10 million. The RNA levels were measured using Cufflinks as FPKM. Results: NTRK2 fusion was detected in one sample (0.04%). However, overexpression of NTRK1-3 mRNA was more common and showed bimodal expression pattern. Most of the samples (75%) had low levels (median 0.6, 6,79, and 3.17 FPKM for NTR2, NTRK2, and NTRK3, respectively) while the upper 25% of samples had significantly (P < 0.0001) higher levels (Median 2.7, 280, and 17.7 KPFM for NTRK1, NTRK2, and NTRK3, respectively). There was significant difference in expression between the three genes (P < 0.0001). The highest levels were detected in NTRK2 and lowest in NTRK1. In addition, significant variation in alternative splicing was noted in NTRK2. All cases with high expression levels (above the upper quartile) showed significant variation in alternative splicing involving almost all exons. To explore, if high expression correlates with activation of the PI3K/AKT1 pathway, we examined the correlation between high levels (above upper quartile) of NTRK2 with various genes in PI3K/AKT pathway. High levels of NTRK2 mRNA (above the upper quartile) correlated with significantly higher levels of PIK3CA (P < 0.0001) and MDM2 (P = 0.0001). There was no correlation between high expression of NTRK1 or NTRK3 and PIK3CA or MDM2 mRNA. Conclusions: Although fusions involving NTRK1-3 genes in lung cancer are rare, significantly high expression levels can be seen almost 25% of lung cancers. Furthermore, high expression in NTRK2 correlates with activation in the PI3K/AKT pathway. This data suggests that therapy targeting NTRK2 overexpression and/or activation of PI3K/AKT pathway in patients with high NTRK2 mRNA should be explored.

e21534 Background: Expression level of PD-L1 mRNA as determined by next generation sequencing (NGS) is becoming more routinely available in cancer profiling. We explored the relevance of PD-L1 mRNA expression as detected by RNA sequencing in providing information relevant to clinical practice and compared lung cancer with colorectal cancer in activating interferon pathway. Methods: RNA was extracted from 160 non-small cell lung cancers and 53 colorectal cancers (CRC) as well as from 103 samples of various types cancers that were also analyzed for PD-L1 expression by IHC. The RNA sequencing was performed using a targeted panel of 1408 genes. IHC for PD-L1 staining was performed with the FDA-cleared diagnostic assay using the 22C3 antibody. Results: Based on comparing the number of PD-L1 IHC positive tumor cells and CD274 mRNA, significant correlation between the two methodology can be demonstrated as continuous variables (P < 0.0001) as well as when the IHC positivity is grouped as 0 to 1, 2 to 9, 10 to 49, and > = 50 (P < 0.0001). However, cancers with PD-L1 by IHC > 50% had distinctly higher levels of CD274 mRNA, while all other levels of IHC positivity showed significant overlap. Overall lung cancer showed significantly higher levels in STAT1 (P = 0.002), STAT3 (P < 0.0001), STAT4 (P = 0.005), PTPN2 (0.002) as compared with CRC. However, when only CRC cases that show PD-L1 overexpression were considered, there was no significant difference in the expression in any of these genes as compared lung cancer cases that also show PD-L1 overexpression. The only difference is that lung cancer cases with PD-L1 overexpression showed higher levels of IKBKE mRNA as compared with CRC cases with PD-L1 overexpression (P = 0.004). In addition, lung cancer overall showed higher levels of PD-L2 (P = 0.0002), PD-1 (P = 0.0002), CD8A (P < 0.0001), and CD19 (P = 0.0008), but CRC with PD-L1 overexpression showed no difference in the expression of any of these genes when compared to lung cancer cases with PD-L1 overexpression. Conclusions: While CRC shows overall significantly lower RNA levels of PD-L1, PD-L2, PD-1, CD19, CD8A and genes involved in interferon pathway, CRC cases with high expression of PD-L1 mRNA are similar to lung cancer high expressors of PD-L1. Furthermore, PD-L1 quantification using NGS testing can be used to select patient for immunotherapy and might provide valuable information on co-markers that may provide better insight to selecting patients for immunotherapy.

Digital image correlation method has higher requirements for search speed and accuracy. Single algorithm is difficult to achieve high speed and high precision. A combinatorial digital image correlation algorithm is proposed in this paper, it quickly obtains the maximum correlation coefficient point of the sub-pixel level based on the genetic algorithm, and uses this point as the fitting window to obtain the sub-pixel displacement value through spline interpolation and quadratic surface fitting. Taking the simulated speckle pattern as the research object, the accuracy and speed of the combined algorithm are compared and analyzed. The results show that the combinatorial digital image correlation algorithm has obvious advantages over a simple algorithm in the search speed and measurement accuracy, and meets the requirements of the speed and precision of the digital image correlation in the sub-pixel micro-displacement measurement.

With its unique scale pattern and color pattern, herbaceous fish skin is one of the popular high-grade leather. Its physical and mechanical properties, such as tensile strength, are important indicators to measure the inner quality of leather. This paper adopts a set of horizontal tensile testing machine for different parts of the grass fish skin stretch experiment, and the error source is analyzed, the results showed that the experimental scheme can realize the mechanical properties of the flexible materials such as grass fish skin tests, and the accuracy and reliability. The results showed that the strength of the fish skin was different in the head, abdomen and tail, which was related to the function of the fish.

Digital image correlation method (DIC) has higher requirements in displacement measurement. For this reason, the application of inverse compositional algorithm in sub pixel displacement measurement is studied, and compared with the traditional algorithms. The grass carp skin high precision stretch test was taken as the research object, and the inverse compositional algorithm was tested experimentally in the whole pixel and sub pixel level displacement measurement. The results show that the inverse compositional algorithm has obvious advantages in measurement accuracy and meets the requirements of DIC for measurement accuracy in displacement measurement. It provides a good theoretical and experimental basis for full field strain measurement.

Background The role of MET amplification in lung cancer, particularly in relation to checkpoint inhibition and EGFR WT, has not been fully explored. In this study, we correlated PD-L1 expression with MET amplification and EGFR, KRAS, or TP53 mutation in primary lung cancer. Methods In this retrospective study, tissue collected from 471 various tumors, including 397 lung cancers, was tested for MET amplification by FISH with a MET/centromere probe. PD-L1 expression was evaluated using clone SP142 and standard immunohistochemistry, and TP53, KRAS, and EGFR mutations were tested using next generation sequencing. Results Our results revealed that PD-L1 expression in non-small cell lung cancer is inversely correlated with EGFR mutation (P=0.0003), and positively correlated with TP53 mutation (P=0.0001) and MET amplification (P=0.004). Patients with TP53 mutations had significantly higher MET amplification (P=0.007), and were more likely (P=0.0002) to be EGFR wild type. There was no correlation between KRAS mutation and overall PD-L1 expression, but significant positive correlation between PD-L1 expression and KRAS with TP53 co-mutation (P=0.0002). A cut-off for the ratio of MET: centromere signal was determined as 1.5%, and 4% of lung cancer patients were identified as MET amplified. Conclusions This data suggests that in lung cancer both MET and TP53 play direct roles in regulating PD-L1 opposing EGFR. Moreover, KRAS and TP53 co-mutation may cooperate to drive PD-L1 expression in lung cancer. Adding MET or TP53 inhibitors to checkpoint inhibitors may be an attractive combination therapy in patients with lung cancer and MET amplification.

Introduction We compared mutations detected in EGFR, KRAS, and BRAF genes using next-generation sequencing (NGS) and confirmed by Sanger sequencing with mutations that could be detected by FDA-cleared testing kits. Methods Paraffin-embedded tissue from 822 patients was tested for mutations in EGFR, KRAS, and BRAF by NGS. Sanger sequencing of hot spots was used with locked nucleic acid to increase sensitivity for specific hot-spot mutations. This included 442 (54%) lung cancers, 168 (20%) colorectal cancers, 29 (4%) brain tumors, 33 (4%) melanomas, 14 (2%) thyroid cancers, and 16% others (pancreas, head and neck, and cancer of unknown origin). Results were compared with the approved list of detectable mutations in FDA kits for EGFR, KRAS, and BRAF. Results Of the 101 patients with EGFR abnormalities as detected by NGS, only 58 (57%) were detectable by cobas v2 and only 35 (35%) by therascreen. Therefore, 42 and 65%, respectively, more mutations were detected by NGS, including two patients with EGFR amplification. Of the 117 patients with BRAF mutation detected by NGS, 62 (53%) mutations were within codon 600, detectable by commercial kits, but 55 (47%) of the mutations were outside codon V600, detected by NGS only. Of the 321 patients with mutations in KRAS detected by NGS, 284 (88.5%) had mutations detectable by therascreen and 300 (93.5%) had mutations detectable by cobas. Therefore, 11.5 and 6.5% additional KRAS mutations were detected by NGS, respectively. Conclusion NGS provides significantly more comprehensive testing for mutations as compared with FDA-cleared kits currently available commercially.

Accurate detection and identification of low frequency mutations can be problematic when assessing residual disease after therapy, screening for emerging resistance mutations during therapy, or when patients have few circulating tumor cells. Wild-type blocking PCR followed by sequencing analysis offers high sensitivity, flexibility, and simplicity as a methodology for detecting these low frequency mutations. By adding a custom designed locked nucleic acid oligonucleotide to a new or previously established conventional PCR based sequencing assay, sensitivities of approximately 1 mutant allele in a background of 1,000 WT alleles can be achieved (1:1,000). Sequencing artifacts associated with deamination events commonly found in formalin fixed paraffin embedded tissues can be partially remedied by the use of uracil DNA glycosylase during extraction steps. The optimized protocol here is specific for detecting MYD88 mutation, but can serve as a template to design any WTB-PCR assay. Advantages of the WTB-PCR assay over other commonly utilized assays for the detection of low frequency mutations including allele specific PCR and real-time quantitative PCR include fewer occurrences of false positives, greater flexibility and ease of implementation, and the ability to detect both known and unknown mutations.

To avoid relying solely on serum prostate-specific antigen (sPSA) in screening for prostate cancer (PCa), we developed a scoring system for detecting PCa and the prediction of aggressiveness. We analyzed urine and plasma specimens from 121 patients with PCa or benign prostatic hyperplasia (BPH) for the levels of UAP1, PDLIM5, IMPDH2, HSPD1, PCA3, PSA, TMPRSS2, ERG, GAPDH, and B2M genes. Patient age, sPSA level, and polymerase chain reaction data were entered through multiple algorithms to determine models most useful for the detection of cancer and predicting aggressiveness.In the first algorithm, we distinguished PCa from BPH (area under the receiver operating characteristic curve [AUROC] of 0.78). Another algorithm distinguished patients with the Gleason score (GS) of ≥7 from GS of7 cancer or BPH (AUROC of 0.88). By incorporating the two algorithms into a scoring system, 75% of the analyzed samples showed concordance between the two models (99% specificity and 68% sensitivity for predicting GS ≥7 in this group).A scoring system incorporating two algorithms using urine and plasma biomarkers highly predicts the presence of GS ≥7 PCa in 75% of patients. Our algorithms may assist with both biopsy indication and patient prognosis.

Introduction: Targeted RNA sequencing using Next Generation Sequencing (NGS) has significant advantages over transcriptome sequencing. In addition to information on mutations, fusion and alternative splicing, RNA quantification using targeted RNA sequencing is sensitive, reproducible and provides better dynamic range. We used targeted RNA sequencing for RNA profiling of diffuse large B-cell lymphoma (DLBCL) and explored its utility in the sub-classification of DLBC to ABC and GCB. Method: RNA extracted from 441 FFPE lymphnode samples with DLBC lymphoma and sequenced targeting 1408 genes. These cases were previously subclassified as ABC vs GCB using expression profiling and immunohistochemistry. We first normalized RNA expression data to PAX5 expression, then we tried to narrow down important markers using univariate significance tests. Setting the cutoff for false discovery rate at 0.0001, 48 variables remained significant, including 46 RNA levels and two genes (MYD88 and EZH2) mutation status. Using 60% of samples as training set, we used multiple statistical approaches for classification. Deep learning emerged as the best approach. We used autoencoder with 5 hidden layers and developed a model for classification of ABC vs GCB. To further improve on classification, we divided patients in each subgroup based on survival using simple tree model. In this tree model, expression level of CD58 emerged as a powerful prognostic marker for the ABC group and RLTPR expression in the GCB group. Results: Using probability of scoring developed based on deep learning and logestic regression, approximately 30% of the cases had a score between 0.5 and 0.75. For the remaining 70% of patients, the ABC vs GCB classification showed sensitivity and specificity of 96% and 97% for the testing set. We also applied the same approach to 60 independent cases classified using NanoString (Lymph2Cx). Our model showed sensitivity and specificity of 96% and 97% in the NanoString independent cases. Using the tree model for further classification of the ABC and GCB classes, CD58 mRNA levels separated the ABC group into two subgroups (ABC1 and ABC2) and RLTPR mRNA separated the GCB into two subgroups (GCB1 and GCB2) with significant difference in overall survival (P=0.0010) and progression-free survival (PFS) (P=0.0027). Conclusion: Targeted RNA sequencing is very reliable and practical for the subclassification of DLBCL and can provide clinical-grade reproducible test for prognostically subclassification of DLBCL. Figure Disclosures Albitar: Genomic Testing Ccoperative: Employment, Equity Ownership. De Dios:Genomic Testing Ccoperative: Employment. Tam:Takeda: Consultancy; Paragon Genomics: Consultancy. Hsi:Abbvie: Research Funding; Eli Lilly: Research Funding; Cleveland Clinic&Abbvie Biotherapeutics Inc: Patents & Royalties: US8,603,477 B2; Jazz: Consultancy. Ferreri:Roche: Research Funding; Celgene: Consultancy, Research Funding; Novartis: Consultancy; Kite: Consultancy. Piris:Millenium/Takeda: Membership on an entity's Board of Directors or advisory committees, Other: Lecture Fees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees, Research Funding; Jansen: Membership on an entity's Board of Directors or advisory committees, Other: Lecture Fees; Nanostring: Membership on an entity's Board of Directors or advisory committees; Kyowa Kirin: Membership on an entity's Board of Directors or advisory committees; Kura: Research Funding. Kantarjian:Ariad: Research Funding; Agios: Honoraria, Research Funding; Daiichi-Sankyo: Research Funding; Novartis: Research Funding; BMS: Research Funding; Takeda: Honoraria; Actinium: Honoraria, Membership on an entity's Board of Directors or advisory committees; AbbVie: Honoraria, Research Funding; Jazz Pharma: Research Funding; Cyclacel: Research Funding; Immunogen: Research Funding; Amgen: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding; Astex: Research Funding.

Introduction: The principle of precision cancer medicine is to customize therapy based on the genomic profiles of the cancer and the host constitution/response to the cancer. Since RNA expression is influence by many genetic mechanisms, RNA profiling may provide broader coverage of genomic changes and might be a better predictor of response to therapy. However, incorporating the many biological changes of the host and the cancer in the decision of selecting therapeutic approach is not practical without using computer-aided algorithms. This is particularly relevant when combination therapy is used. We explored the potential of developing algorithms for the prediction of complete response (CR) to novel combination therapy in patients with acute myeloid leukemia (AML) using targeted RNA expression profiling. Methods: RNA was extracted from the peripheral blood (PB) and bone marrow (BM) samples from patients with AML being treated on two different protocols: FLAG-IDA+venetoclax (F-I-V)( Abou Dalle I et al, ASH 2019; the NCT # is NCT03214562) and ivosidenib+venetoclax (I-V). In the initial study, 22 samples (9 PB and 13 BM) were used as training set. Subsequently 16 PB samples from the F-I-V arm and 4 from the I-V arm were collected and tested blindly as testing set after the development and locking of the algorithm. RNA was sequenced using NGS panel composed on compared of 1408 genes. The RNA sequencing is based on hybrid capture and the number of reads ranged from 5 to 10 million. RNA quantification was performed using Cufflinks. The RNA levels were normalized to ABL1 mRNA levels. Each gene is normalized by the mean and standard deviation of the gene. To develop a model for predicting CR, we used the training set in each arm and first evaluated the performance of each of the 1408 genes using receiver operating characteristic (ROC) curve. Then used the following mathematical methods for developing algorithms for predicting CR: Support Vector machine (SVM), Bayesian modeling with Gaussian Processes (GP), and Naïve Bayesian (NB). Results: In univariate analysis, multiple genes showed very high AUC. In the F-I-V arm, top genes in predicting CR were GLI3, SETBP1, SH3D19, ARHGAP20, ETS1, IKZF2, GNG4 and MAGEE1 with AUC ranged from 0.74 to 0.85. In the I-V arm, the top genes in predicting CR were STL, TNFRSF10D, PTGS2, RET, TFRC, NAV3, WSB1, and GAS1 with AUC varied from 0.91 to 0.96. Using the training samples we developed algorithms for predicting CR by SVM, NB and Bayesian GP. Upon testing these models using leave-one-out (LOO), the three algorithms performed similarly with AUC around 0.97 for the I-V arm and around 0.96 for the F-I-V arm. There was no difference between BM and PB in predicting CR. Therefore, we collected and sequenced only peripheral blood for blind testing. The three algorithms were tested using 16 PB samples from the F-I-V arm and 4 samples from the I-V arm. The SVM and NB algorithms predicted CR correctly in 15 of the 16 samples (94%) while Bayesian GP missed 4 of the 16 samples. As for the I-V arm, the NB predicted CR correctly in the 4 samples, while both SVM and Bayesian GP missed 3 of 4. Conclusions: While the data is limited and further validation is need, algorithms using RNA expression profiling of peripheral blood using targeted RNA NGS may provide an excellent tool for customizing therapeutic approach, especially in the age of combination therapy when number of cases for training can be limited. Furthermore, this study suggests that modeling using Nave Bayesian is reliable approach in developing prediction algorithms. Disclosures Albitar: Genomic Testing Ccoperative: Employment, Equity Ownership. Konopleva:Reata Pharmaceuticals: Equity Ownership, Patents & Royalties; Ascentage: Research Funding; Kisoji: Consultancy, Honoraria; Ablynx: Research Funding; Agios: Research Funding; Amgen: Consultancy, Honoraria; F. Hoffman La-Roche: Consultancy, Honoraria, Research Funding; Genentech: Honoraria, Research Funding; Astra Zeneca: Research Funding; Calithera: Research Funding; Stemline Therapeutics: Consultancy, Honoraria, Research Funding; Forty-Seven: Consultancy, Honoraria; Eli Lilly: Research Funding; AbbVie: Consultancy, Honoraria, Research Funding; Cellectis: Research Funding. Loghavi:MDACC: Employment; AlphaSights: Consultancy; GLG Consultants: Consultancy. Takahashi:Symbio Pharmaceuticals: Consultancy. Kantarjian:Daiichi-Sankyo: Research Funding; Ariad: Research Funding; Cyclacel: Research Funding; AbbVie: Honoraria, Research Funding; Agios: Honoraria, Research Funding; Amgen: Honoraria, Research Funding; BMS: Research Funding; Jazz Pharma: Research Funding; Novartis: Research Funding; Takeda: Honoraria; Pfizer: Honoraria, Research Funding; Immunogen: Research Funding; Actinium: Honoraria, Membership on an entity's Board of Directors or advisory committees; Astex: Research Funding. DiNardo:syros: Honoraria; daiichi sankyo: Honoraria; celgene: Consultancy, Honoraria; jazz: Honoraria; abbvie: Consultancy, Honoraria; agios: Consultancy, Honoraria; medimmune: Honoraria; notable labs: Membership on an entity's Board of Directors or advisory committees.

Cellular RNA levels are tightly regulated by very complex nuclear and cytoplasmic processes. The regulation of mutant mRNA in cancer cells is rarely studied. We explored the effects of mutations on mRNA levels in patients with diffuse large B-cell lymphoma (DLBCL). Using next generation sequencing (NGS) and variant allele frequency (VAF) of mutant RNA, we compared relative mutant mRNA or variant allele frequency (RNA-VAF) with variant allele frequency of mutant DNA (DNA-VAF) in the same samples from patients with DLBCL. Methods: RNA and DNA were extracted from 427 FFPE samples from patients with DLBCL. We sequenced the DNA using 177 gene panel and the RNA using 1408 gene panel. The DNA sequencing is based on Single Primer Extension (SPE) library preparation with unique molecular identifier (UMI) (Qiagen, Germantown, MD). The RNA sequencing is based on hybrid capture. Sequencing data of DNA is analyzed using the DRAGEN Platform. Sequence duplicates were removed before calculating VAF. The RNA sequencing data is analyzed using Illumina basespace. RNA VAF is calculated also after removing duplicates using Isaac variant caller. Only mutations detected by both DNA and RNA variant callers are compared. Results: A total of 1770 mutations were detected using the DNA panel and 2207 mutations were detected using the larger RNA sequencing panel. We focused on the most commonly mutated genes that included in both DNA and RNA panels and compared the VAF of the same mutations between DNA and RNA. The selected genes are: KMT2D, NOTCH2, CARD11, MYC, MYD88, EZH2, TP53, CD79B, BCL2, and TET2. The overall VAF in the RNA was significantly higher (P Figure Disclosures Albitar: Genomic Testing Ccoperative: Employment, Equity Ownership. Tam:Takeda: Consultancy; Paragon Genomics: Consultancy. Hsi:Abbvie: Research Funding; Jazz: Consultancy; Eli Lilly: Research Funding; Cleveland Clinic&Abbvie Biotherapeutics Inc: Patents & Royalties: US8,603,477 B2. Piris:Nanostring: Membership on an entity's Board of Directors or advisory committees; Kyowa Kirin: Membership on an entity's Board of Directors or advisory committees; Kura: Research Funding; Millenium/Takeda: Membership on an entity's Board of Directors or advisory committees, Other: Lecture Fees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees, Research Funding; Jansen: Membership on an entity's Board of Directors or advisory committees, Other: Lecture Fees. Kantarjian:Agios: Honoraria, Research Funding; Daiichi-Sankyo: Research Funding; Pfizer: Honoraria, Research Funding; AbbVie: Honoraria, Research Funding; Immunogen: Research Funding; Novartis: Research Funding; Jazz Pharma: Research Funding; Actinium: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Research Funding; Astex: Research Funding; Takeda: Honoraria; BMS: Research Funding; Cyclacel: Research Funding; Ariad: Research Funding.

Background: Circulating cell-free DNA (ccfDNA) is highly fragmented DNA in plasma that is released by normal or tumor cells when they undergo apoptosis or necrosis. ccfDNA allows for non-invasive sampling of somatic genomic alterations and is informative in various solid tumors, including as a marker of measurable residual disease (MRD). We sought to assess the utility of baseline assessment and tracking of leukemia-associated mutations through peripheral blood sampling of ccfDNA in patients (pts) with acute leukemias. Methods: Plasma ccfDNA was isolated and analyzed using a next-generation sequencing (NGS) assay of 275 genes. This NGS analysis is based on Single Primer Extension library preparation with unique molecular identifier (Qiagen, Germantown, MD); a sequence coverage ≥ 100X (after removing duplicates) was required. Amplicon-based NGS was also performed on DNA extracted from the bone marrow (BM) in a CLIA-certified molecular diagnostics laboratory. This BM panel detects mutations in the coding sequence of 28 leukemia-associated genes, with an analytic sensitivity of 5-10%. The ccfDNA panel included all 28 genes evaluated on the BM NGS panel (ABL1, ASXL1, BRAF, DNMT3A, EGFR, EZH2, FLT3, GATA1, GATA2, HRAS, IDH1, IDH2, IKZF1, JAK2, KIT, KRAS, MDM2, MLL, MPL, MYD88, NOTCH1, NPM1, NRAS, PTPN11, RUNX1, TET2, TP53, WT1). Established bioinformatics pipelines were used to identify somatic variants. Results: Twenty-four pts (AML, n=22; ALL, n=2) underwent paired ccfDNA and BM sequencing at diagnosis prior to receiving frontline intensive chemotherapy. For baseline samples, ccfDNA was collected a median of 6 days after BM collection (range, 0-27 days) and a median of 0.5 days after start of induction chemotherapy (range, -7 to 7 days). Eleven pts (46%) also had ccfDNA collected at ≥1 time point during remission. Among the 28 genes of interest, the median number of mutations per pt detected in BM and in ccfDNA was 1 (range, 0-4) for both assays (P=0.39). A total of 40 mutations were detected: 18 mutations (45%) were detected by both methods, 7 (18%) were detected only in ccfDNA, and 15 (38%) were detected only in BM. Time from start of chemotherapy until ccfDNA collection did not appear to impact the concordance of ccfDNA and BM mutation analysis (P=0.87). Among mutations detected by ccfDNA in baseline samples, the median variant allelic frequency (VAF) was 33.7% (range, 2.7-90.8%). Among the 18 overlapping mutations, the concordance of VAF assessment by both methods was high (R2 = 0.849). Mutations detected by only one of the two methods were generally of lower VAF than those detected by both methods, suggesting that either method may miss small subclonal populations. The median VAF of mutations (as measured in ccfDNA) that were detected by both methods was higher than those detected only in ccfDNA (39.8% vs 25.2%, respectively; P=0.04); similarly, the median VAF of mutations (as measured in BM) that were detected by both methods was higher than those detected only in BM (40.2% vs 6.6%; P=0.001). Among the 7 mutations detected only by ccfDNA, ASXL1 was detected in 2 pts, WT1 in 1 pt, IDH1 in 1 pt, and BRAF and two EGFR mutations in 1 pt. Among the 5 pts in whom mutations were detected in ccfDNA but not BM, 2 eventually relapsed. In both pts, the discordant mutation (IDH1 and ASXL1) was detected in the relapse BM, suggesting that these were true mutations that were missed by NGS of the baseline BM. ccfDNA detected leukemia-associated mutations during remission that appeared to herald overt relapse (Figure 1). Two pts with t(8;21) AML developed new RUNX1 mutations detected by ccfDNA while in remission and subsequently relapsed 3 months and 14 months later. In both of these pts, the new RUNX1 mutation was confirmed in the BM at the time of morphological relapse. Another pt with AML had persistent TP53 and TET2 mutations detected by ccfDNA 1 month after allogeneic stem cell transplant and subsequently relapsed 1 month later. Conclusions: This study demonstrates that sequencing of ccfDNA can identify prognostic or targetable mutations not detected by BM NGS. However, true mutations were missed by both ccfDNA and BM analysis, suggesting that these methodologies may be complementary in the assessment and monitoring of pts with leukemia. The use of ccfDNA as a non-invasive method to detect mutations and track MRD in AML and other leukemias should be evaluated in larger, prospective cohorts. Disclosures Short: Takeda Oncology: Consultancy, Research Funding; AstraZeneca: Consultancy; Amgen: Honoraria. Jabbour:Amgen: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Cyclacel LTD: Research Funding; AbbVie: Consultancy, Research Funding; Adaptive: Consultancy, Research Funding; Takeda: Consultancy, Research Funding. Garcia-Manero:Amphivena: Consultancy, Research Funding; Helsinn: Research Funding; Novartis: Research Funding; AbbVie: Research Funding; Celgene: Consultancy, Research Funding; Astex: Consultancy, Research Funding; Onconova: Research Funding; H3 Biomedicine: Research Funding; Merck: Research Funding. Kantarjian:BMS: Research Funding; Amgen: Honoraria, Research Funding; Agios: Honoraria, Research Funding; Immunogen: Research Funding; Takeda: Honoraria; Novartis: Research Funding; Ariad: Research Funding; Astex: Research Funding; Pfizer: Honoraria, Research Funding; Actinium: Honoraria, Membership on an entity's Board of Directors or advisory committees; Daiichi-Sankyo: Research Funding; Jazz Pharma: Research Funding; Cyclacel: Research Funding; AbbVie: Honoraria, Research Funding. Ravandi:Macrogenix: Consultancy, Research Funding; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Xencor: Consultancy, Research Funding; Menarini Ricerche: Research Funding; Cyclacel LTD: Research Funding; Selvita: Research Funding.