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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.

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: 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.

SummaryIntroduction Detection of mutations in the myeloid differentiation primary response gene 88 (MYD88) has clinical implications on diagnosis and therapy, especially in patients with Waldenstrom's macroglobulinemia (WM) and IgM monoclonal gammopathy of unknown significance (IgM-MGUS). We describe a method that provides greatly increased sensitivity for detecting minority mutations in MYD88. Methods We used a locked nucleic acid oligonucleotide to block amplification of wild-type DNA during polymerase chain reaction (PCR). Sanger sequencing of amplified DNA was used for detecting mutations in MYD88 gene. This approach was used to test samples from patients with WM and IgM-MGUS. Results When compared to traditional PCR followed by Sanger sequencing, our methodology was significantly more sensitive (one mutant allele in a background of 200 wild-type alleles). Using sequencing allowed us to visualize the PCR product, giving advantages over other methodologies such as allele-specific PCR. Based on analyzing 36 randomly selected, MYD88 mutated, clinically tested samples, we demonstrate that traditional PCR failed to detect MYD88 mutations in 64% of the samples that were clearly positive by wild-type blocking PCR. Conclusion The new methodology is essential for attaining accurate results in clinical testing.

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.

Introduction: Isocitrate dehydrogenase 1 and 2 (IDH1/2) are homodimeric enzymes that play an important role in cellular metabolism, epigenetic regulation, and DNA repair. Early studies suggested that mutations in IDH1/2 were loss of function mutations associated with a tumor suppressor function. However, biallelic mutations are extremely rare, and studies demonstrate that mutant IDH1/2 enzymes are responsible for NADPH-dependent reduction of αKG to the oncometabolite d-2-hydroxyglutarate (D2HG), suggesting an oncoprotein. Cellular RNA levels are tightly regulated by very complex cellular processes, and the regulation of mutant mRNA in cancer cells is rarely studied. We explored the effects of IDH1/2 mutations on mRNA levels in patients with Acute myeloid leukemia (AML). 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 AML. Methods: RNA and DNA were extracted from 48 bone marrow and peripheral blood samples from patients with confirmed AML, including 12 patients with IDH1 mutations, 2 with IDH2 mutation and 34 samples from AML without IDH1/2 mutations. Samples were collected pretherapy as well as while on therapy. 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). Average coverage of DNA sequencing was >1000X. The RNA sequencing is based on hybrid capture and the number of reads ranged from 5 to 10 million. 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 176 mutations were detected using the DNA panel and 122 mutations using the RNA panel. Some mutations were called by RNA variant caller, but not by DNA variant caller and vice versa. All mutations detected in IDH1 and IDH2 were detected in both DNA and RNA. When the IDH1/2 mutations are considered (#14), the VAF in RNA (median: 41%, range: 13%-74%) was significant higher (P=0.006, Wilcoxon matched pairs test ) as compared with DNA (median:28%, range: 13%-74%). The VAF of the other 31 mutations that were detected in both DNA and RNA varied dependent on the gene. ASXL1, DNMT3A, RUNX1, PTPN11, SRSF2, STAG2 and U2AF1 mutations showed no significant difference between DNA and RNA in VAF (P=0.71). Although the number is small, mutations in NRAS and NPM1 showed significantly higher VAF in RNA as compared with with that of DNA (P=0.008). Conclusion: This data suggests that, in general, stability of mutant RNA varies between genes and between the mutations in the same gene. Mutant IDH1/2 RNA is significantly more stable in myeloid leukemic cells a compared with the wild-type mRNA. Most likely this reflects increased levels of mutant IDH1/2 as compared with wild-type IDH1/2, confirming that IDH1/2 is oncoprotein and may explain the efficacy of therapeutic inhibition of IDH1/2 in treating cancers. Furthermore this suggests that mRNA testing might be more sensitive in monitoring minimal residual disease in patients with IDH1/2 mutations. Figure Disclosures Albitar: Genomic Testing Ccoperative: Employment, Equity Ownership. Konopleva:Forty-Seven: Consultancy, Honoraria; Stemline Therapeutics: Consultancy, Honoraria, Research Funding; Calithera: Research Funding; Eli Lilly: Research Funding; AbbVie: Consultancy, Honoraria, Research Funding; Cellectis: Research Funding; Amgen: Consultancy, Honoraria; F. Hoffman La-Roche: Consultancy, Honoraria, Research Funding; Genentech: Honoraria, Research Funding; Ascentage: Research Funding; Kisoji: Consultancy, Honoraria; Reata Pharmaceuticals: Equity Ownership, Patents & Royalties; Ablynx: Research Funding; Astra Zeneca: Research Funding; Agios: Research Funding. Loghavi:GLG Consultants: Consultancy; AlphaSights: Consultancy; MDACC: Employment. Takahashi:Symbio Pharmaceuticals: Consultancy. Kantarjian:Jazz Pharma: Research Funding; Pfizer: Honoraria, Research Funding; Ariad: Research Funding; Cyclacel: Research Funding; Novartis: Research Funding; Astex: Research Funding; Takeda: Honoraria; Agios: Honoraria, Research Funding; BMS: Research Funding; Actinium: Honoraria, Membership on an entity's Board of Directors or advisory committees; AbbVie: Honoraria, Research Funding; Amgen: Honoraria, Research Funding; Daiichi-Sankyo: Research Funding; Immunogen: Research Funding. DiNardo:medimmune: Honoraria; agios: Consultancy, Honoraria; notable labs: Membership on an entity's Board of Directors or advisory committees; jazz: Honoraria; abbvie: Consultancy, Honoraria; celgene: Consultancy, Honoraria; daiichi sankyo: Honoraria; syros: Honoraria.