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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:Diffuse large B-cell lymphoma (DLBCL) is molecularly and clinically heterogeneous, and can be subtyped according to genetic alterations, cell-of-origin, or microenvironmental signatures using high-throughput genomic data at the DNA or RNA level. Although high-throughput proteomic profiling has not been available for DLBCL subtyping, MYC/BCL2 protein double expression (DE) is an established prognostic biomarker in DLBCL. The purpose of this study is to reveal the relative prognostic roles of DLBCL genetic, phenotypic, and microenvironmental biomarkers.Experimental Design:We performed targeted next-generation sequencing; IHC for MYC, BCL2, and FN1; and fluorescent multiplex IHC for microenvironmental markers in a large cohort of DLBCL. We performed correlative and prognostic analyses within and across DLBCL genetic subtypes and MYC/BCL2 double expressors.Results:We found that MYC/BCL2 double-high-expression (DhE) had significant adverse prognostic impact within the EZB genetic subtype and LymphGen-unclassified DLBCL cases but not within MCD and ST2 genetic subtypes. Conversely, KMT2D mutations significantly stratified DhE but not non-DhE DLBCL. T-cell infiltration showed favorable prognostic effects within BN2, MCD, and DhE but unfavorable effects within ST2 and LymphGen-unclassified cases. FN1 and PD-1–high expression had significant adverse prognostic effects within multiple DLBCL genetic/phenotypic subgroups. The prognostic effects of DhE and immune biomarkers within DLBCL genetic subtypes were independent although DhE and high Ki-67 were significantly associated with lower T-cell infiltration in LymphGen-unclassified cases.Conclusions:Together, these results demonstrated independent and additive prognostic effects of phenotypic MYC/BCL2 and microenvironment biomarkers and genetic subtyping in DLBCL prognostication, important for improving DLBCL classification and identifying prognostic determinants and therapeutic targets.

Supplementary Figure from Genetic Subtyping and Phenotypic Characterization of the Immune Microenvironment and MYC/BCL2 Double Expression Reveal Heterogeneity in Diffuse Large B-cell Lymphoma

Supplementary Data from Genetic Subtyping and Phenotypic Characterization of the Immune Microenvironment and MYC/BCL2 Double Expression Reveal Heterogeneity in Diffuse Large B-cell Lymphoma

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.

Purpose: Definitive diagnosis of prostate cancer is based on biopsies, a procedure associated with side-effects. The use of biomarkers in blood and urine could potentially help clinicians select patients for whom biopsies are needed. The aim of the study was to test a new urine and plasma biomarker test in detecting medium and high grade prostate cancer. Materials and methods: Blood and urine samples were prospectively collected from 41 patients prior to prostate biopsy or TUR-P and again after 3 months. The cohort included patients with suspicion of prostate cancer and patients with prior prostate cancer diagnosis. The mRNA expression of ten selected genes measured by PCR were used together with clinical data in multiple algorithms for prediction of medium-high grade prostate cancer in prostate biopsies. The testing was originally developed and validated in the USA. The method was transferred to a local Danish laboratory. Medium and high grade cancer was defined as Gleason score ≥ 3 + 4. Results: Using the biomarker test, prior to any prostate procedures, the sensitivity for detecting medium-high grade prostate cancer was 100% and the specificity was 56% and 63%, depending on the cut-off point used. When using the biomarker test, following biopsy or TUR-P, the sensitivity and specificity were reduced to 89% and 28–34% respectively. When comparing results, there was a significant difference (p < 0.05), favoring the test performed prior to the procedures. Conclusions: We were able to predict the presence of medium-high grade prostate cancer, thereby confirming earlier findings of the biomarker test.

There is no standard or widely effective treatment of patients with moderate aplastic anemia (MAA) or hypo-productive uni-lineage cytopenias (UC). Eltrombopag (EPAG), a small molecule thrombopoietin mimetic, has previously been shown to result in durable multi-lineage hematologic responses with low toxicity in patients with refractory severe aplastic anemia (SAA). Its safety and efficacy in MAA are unknown. This prospective phase 2 study enrolled previously untreated and treated MAA and UC patients with clinically relevant cytopenias. EPAG was administered at doses escalating from 50 to 300 mg/d. Hematologic responses were assessed at 16 to 20 weeks. Responding patients were continued on EPAG until reaching defined robust or stable blood counts. EPAG was reinstituted for relapse. Thirty-four patients were enrolled between 2012 and 2017, including 31 with MAA and 3 with UC. Seventeen patients responded in at least 1 eligible lineage by the primary end point. A striking improvement in anemia was observed in a patient with Diamond-Blackfan anemia. EPAG was well tolerated, and it was discontinued for robust or stable blood counts in 12 of 17 patients after a median of 8 months. A majority required re-initiation of EPAG for declining counts, and all regained response. Two of 34 patients developed non–chromosome 7 bone marrow cytogenetic abnormalities while taking EPAG, without dysplasia or increased blasts. Somatic mutation allele frequencies in cancer genes did not increase overall on EPAG. EPAG is a well-tolerated oral treatment of cytopenias in patients with MAA/UC. This trial was registered at www.clinicaltrials.gov as #NCT01328587.

Purpose: Diffuse large B-cell lymphoma (DLBCL) is molecularly and clinically heterogeneous, and can be subtyped according to genetic alterations, cell-of-origin, or microenvironmental signatures using high-throughput genomic data at the DNA or RNA level. Although high-throughput proteomic profiling has not been available for DLBCL subtyping, MYC/BCL2 protein double expression (DE) is an established prognostic biomarker in DLBCL. The purpose of this study is to reveal the relative prognostic roles of DLBCL genetic, phenotypic, and microenvironmental biomarkers. Experimental Design: We performed targeted next-generation sequencing; IHC for MYC, BCL2, and FN1; and fluorescent multiplex IHC for microenvironmental markers in a large cohort of DLBCL. We performed correlative and prognostic analyses within and across DLBCL genetic subtypes and MYC/BCL2 double expressors. Results: We found that MYC/BCL2 double-high-expression (DhE) had significant adverse prognostic impact within the EZB genetic subtype and LymphGen-unclassified DLBCL cases but not within MCD and ST2 genetic subtypes. Conversely, KMT2D mutations significantly stratified DhE but not non-DhE DLBCL. T-cell infiltration showed favorable prognostic effects within BN2, MCD, and DhE but unfavorable effects within ST2 and LymphGen-unclassified cases. FN1 and PD-1–high expression had significant adverse prognostic effects within multiple DLBCL genetic/phenotypic subgroups. The prognostic effects of DhE and immune biomarkers within DLBCL genetic subtypes were independent although DhE and high Ki-67 were significantly associated with lower T-cell infiltration in LymphGen-unclassified cases. Conclusions: Together, these results demonstrated independent and additive prognostic effects of phenotypic MYC/BCL2 and microenvironment biomarkers and genetic subtyping in DLBCL prognostication, important for improving DLBCL classification and identifying prognostic determinants and therapeutic targets.

Diffuse large B-cell lymphoma (DLBCL) is the most common type of lymphoma with high mutation burdens but a low response rate to immune checkpoint inhibitors. In this study, we performed targeted next-generation sequencing and fluorescent multiplex immunohistochemistry, and investigated the clinical significance and immunological effect of mutation numbers in 424 DLBCL patients treated with standard immunochemotherapy. We found that KMT2D and TP53 nonsynonymous mutations (MUT) were significantly associated with increased nonsynonymous mutation numbers, and that high mutation numbers (MUThigh) were associated with significantly poorer clinical outcome in germinal center B-cell-like DLBCL with wild-type TP53. To understand the underlying mechanisms, we identified a gene-expression profiling signature and the association of MUThigh with decreased T cells in DLBCL patients with wild-type TP53. On the other hand, in overall cohort, MUThigh was associated with lower PD-1 expression in T cells and PD-L1 expression in macrophages, suggesting a positive role of MUThigh in immune responses. Analysis in a whole-exome sequencing dataset of 304 patients deposited by Chapuy et al. validated the correlation of MUT-KMT2D with genomic complexity and the significantly poorer survival associated with higher numbers of genomic single nucleotide variants in activated B-cell–like DLBCL with wild-type TP53. Together, these results suggest that KMT2D inactivation or epigenetic dysregulation has a role in driving DLBCL genomic instability, and that genomic complexity has adverse impact on clinical outcome in DLBCL patients with wild-type TP53 treated with standard immunochemotherapy. The oncoimmune data in this study have important implications for biomarker and therapeutic studies in DLBCL.

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]

Myelodysplastic syndromes (MDS) are a group of clonal myeloid disorders characterized by low blood counts and a propensity to develop acute myeloid leukemia. The management of lowerrisk (LR) MDS with persistent cytopenias remains suboptimal. Eltrombopag, a thrombopoietin-receptor agonist, can improve platelet counts in LR-MDS and trilineage hematopoiesis in aplastic anemia. We conducted a phase II dose modification study to investigate the safety and efficacy of eltrombopag in LR-MDS. The eltrombopag dose was escalated from 50 mg/day to a maximum of 150 mg/day over a period of 16 weeks. The primary efficacy endpoint was hematologic response at 16-20 weeks. Eleven of 25 (44%) patients responded; five and six patients had uni- or bi-lineage hematologic responses, respectively. The predictors of response were presence of a paroxysmal nocturnal hemoglobinuria clone, marrow hypocellularity, thrombocytopenia, and elevated plasma thrombopoietin levels at study entry. The safety profile was consistent with that found in previous eltrombopag studies in aplastic anemia; no patients discontinued the drug due to adverse events. Three patients developed reversible grade 3 liver toxicity and one patient had increased reticulin fibrosis. Ten patients discontinued eltrombopag after achieving a robust response (median time 16 months); four of them reinitiated eltrombopag because of declining blood counts, and all attained a second robust response. Six patients had disease progression not associated with expansion of mutated clones and no patient progressed to develop acute myeloid leukemia on study. In conclusion, eltrombopag was well-tolerated and effective in restoring hematopoiesis in some patients with low or intermediate-1 risk MDS. This study was registered at clinicaltrials.gov as #NCT00932156.

Purpose Primary central nervous system lymphoma (PCNSL) is a rare type of non-Hodgkin lymphoma. Previous studies have identified MYD88, CD79b and PIM1 as the most common genetic mutations in PCNSL. The extent to which mutations vary by ethnicity is unknown. The purpose of this study was to describe differences in genetic mutations and survival by Hispanic ethnicity in PCNSL. Methods 30 patients with PCNSL were examined for mutations in 275 genes by DNA analysis and 1408 genes by RNA analysis utilizing next generation sequencing. Results 60% of patients were Hispanic. 125 different mutated genes were detected. The most commonly affected genes were: MYD88 (44%), CARD11 (21%), CD79b (17%), PIM1 (17%) and KMT2D (17%) . MYD88 mutation was less frequent in Hispanic patients (27% vs 66%, P=.02). More Hispanic patients had >3 mutated genes (89% vs 55 %. P=.03). Two-year progression-free survival (PFS) and overall survival (OS) in Hispanic vs. non-Hispanic patients (PFS 60% vs 27%, P=.09), (OS 60% vs 36%, P=.23). MYD88, CARD11, PIM1, and KMT2D were not associated with significant differences in OS or PFS. CD79b mutation correlated with superior 2-yr PFS (P=.04). Conclusions We identified highly recurrent genetic alterations in PCNSL. Our data suggest that heterogeneity in some mutations may be related to ethnicity. There was no statistically significant difference in 2-yr PFS and OS in our Hispanic patients. Studies on larger population may further help to describe differences in tumor biology, and outcomes in Hispanic patients.

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

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous entity of B-cell lymphoma. Cell-of-origin (COO) classification of DLBCL is required in routine practice by the World Health Organization classification for biological and therapeutic insights. Genetic subtypes uncovered recently are based on distinct genetic alterations in DLBCL, which are different from the COO subtypes defined by gene expression signatures of normal B cells retained in DLBCL. We hypothesize that classifiers incorporating both genome-wide gene-expression and pathogenetic variables can improve the therapeutic significance of DLBCL classification. To develop such refined classifiers, we performed targeted RNA sequencing (RNA-Seq) with a commercially available next-generation sequencing (NGS) platform in a large cohort of 418 DLBCLs. Genetic and transcriptional data obtained by RNA-Seq in a single run were explored by state-of-the-art artificial intelligence (AI) to develop a NGS-COO classifier for COO assignment and NGS survival models for clinical outcome prediction. The NGS-COO model built through applying AI in the training set was robust, showing high concordance with COO classification by either Affymetrix GeneChip microarray or the NanoString Lymph2Cx assay in 2 validation sets. Although the NGS-COO model was not trained for clinical outcome, the activated B-cell–like compared with the germinal-center B-cell–like subtype had significantly poorer survival. The NGS survival models stratified 30% high-risk patients in the validation set with poor survival as in the training set. These results demonstrate that targeted RNA-Seq coupled with AI deep learning techniques provides reproducible, efficient, and affordable assays for clinical application. The clinical grade assays and NGS models integrating both genetic and transcriptional factors developed in this study may eventually support precision medicine in DLBCL.

GATA2 deficiency is an inherited or sporadic genetic disorder characterized by distinct cellular deficiency, bone marrow failure, various infections, lymphedema, pulmonary alveolar proteinosis, and predisposition to myeloid malignancies resulting from heterozygous loss-of-function mutations in the GATA2 gene. How heterozygous GATA2 mutations affect human hematopoietic development or cause characteristic cellular deficiency and eventual hypoplastic myelodysplastic syndrome or leukemia is not fully understood. We used induced pluripotent stem cells (iPSCs) to study hematopoietic development in the setting of GATA2 deficiency. We performed hematopoietic differentiation using iPSC derived from patients with GATA2 deficiency and examined their ability to commit to mesoderm, hemogenic endothelial precursors (HEPs), hematopoietic stem progenitor cells, and natural killer (NK) cells. Patient-derived iPSC, either derived from fibroblasts/marrow stromal cells or peripheral blood mononuclear cells, did not show significant defects in committing to mesoderm, HEP, hematopoietic stem progenitor, or NK cells. However, HEP derived from GATA2-mutant iPSC showed impaired maturation toward hematopoietic lineages. Hematopoietic differentiation was nearly abolished from homozygous GATA2 knockout (KO) iPSC lines and markedly reduced in heterozygous KO lines compared with isogenic controls. On the other hand, correction of the mutated GATA2 allele in patient-specific iPSC did not alter hematopoietic development consistently in our model. GATA2 deficiency usually manifests within the first decade of life. Newborn and infant hematopoiesis appears to be grossly intact; therefore, our iPSC model indeed may resemble the disease phenotype, suggesting that other genetic, epigenetic, or environmental factors may contribute to bone marrow failure in these patients following birth. However, heterogeneity of PSC-based models and limitations of in vitro differentiation protocol may limit the possibility to detect subtle cellular phenotypes.

AimsAt the time of analysis, two widely used, drug-specific, tumour-cell programmed death ligand 1 (PD-L1) assays were approved by the US Food and Drug Administration for anti-PD-1 therapies: the Dako PD-L1 immunohistochemistry (IHC) 28-8 pharmDx assay and the Dako PD-L1 IHC 22C3 pharmDx assay. Given that the majority of current PD-L1 testing in US clinical practice is performed at commercial reference laboratories, we aimed to evaluate the concordance of the 28-8 and 22C3 assays in a real-world setting.MethodsMatched PD-L1 IHC 28-8 and 22C3 results from routine assessment were obtained from 1930 patients, including 412 confirmed to have lung cancer, submitted from hospitals in over 38 US states/territories. Biopsies were stained, reviewed and scored by trained/certified pathologists at a single cancer reference laboratory between 2015 and 2017. Rate of concordance between assay findings was assessed by Bland-Altman analysis; overall per cent agreement (OPA), positive per cent agreement and negative per cent agreement; and Cohen’s kappa.ResultsPD-L1 IHC 28-8 and 22C3 displayed strong correlation across all samples and in samples with a confirmed lung cancer diagnosis irrespective of biopsy site. The OPA was 97%–98% for all samples, depending on the expression level defining PD-L1 positivity. In the Bland-Altman analysis, the mean difference in percentage of tumour cells positively stained for PD-L1 between the paired assay findings was –0.80% for all samples and –0.93% in samples with a confirmed lung cancer diagnosis.ConclusionsThese data, in conjunction with recent findings, support the analytical concordance of the PD-L1 IHC 28-8 and 22C3 assays for assessing per cent tumour-cell membrane PD-L1 expression.

Purpose While HER2 testing is well established in directing appropriate treatment for breast cancer, a small percentage of cases show equivocal results by immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH). Alternative probes may be used in equivocal cases. We present a single community-based institution’s experience in further evaluating these cases. Patients and methods Between 2014 and 2016, 4255 samples were submitted for HER2 amplification testing by alternative probes, TP53, RAI1, and RARA. Of the patients tested by FISH, 505/3908 (12.9%) also had IHC data. Results Most (73.9%) FISH equivocal cases remained equivocal after IHC testing. However, 50.5% of equivocal cases were classified as HER2 amplified by alternative probes. Most cases were positive by more than one probe: 78% of positive cases by RAI1 and 73.9% by TP53. There was a significant difference between IHC and FISH alternative testing (p

// 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.

BACKGROUND Acquired copy-neutral loss of heterozygosity has been described in myeloid malignant progression with an otherwise normal karyotype. CASE REPORT A 65-year-old woman with MPL-mutated essential thrombocythemia and progression to myelofibrosis was noted upon routine pretransplant testing to have mixed field reactivity with anti-D and an historic discrepancy in RhD type. The patient had never received transfusions or transplantation. RESULTS Gel immunoagglutination revealed group A red blood cells and a mixed-field reaction for the D phenotype, with a predominant D-negative population and a small subset of circulating red blood cells carrying the D antigen. Subsequent genomic microarray single nucleotide polymorphism profiling revealed copy-neutral loss of heterozygosity of chromosome 1 p36.33-p34.2, a known molecular mechanism underlying fibrotic progression of MPL-mutated essential thrombocythemia. The chromosomal region affected by this copy-neutral loss of heterozygosity encompassed the RHD, RHCE, and MPL genes. We propose a model of chronological molecular events that is supported by RHD zygosity assays in peripheral lymphoid and myeloid-derived cells. CONCLUSION Copy-neutral loss of heterozygosity events that lead to clonal selection and myeloid malignant progression may also affect the expression of adjacent unrelated genes, including those encoding for blood group antigens. Detection of mixed-field reactions and investigation of discrepant blood typing results are important for proper transfusion support of these patients and can provide useful surrogate markers of myeloproliferative disease progression.

// 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.