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Patients with advanced head and neck squamous cell carcinoma (HNSCC) associated with human papillomavirus (HPV) demonstrate favorable clinical outcomes compared to patients bearing HPV-negative HNSCC. We sought to characterize the association between HPV status and mutational profiles among patients served by the Veterans Health Administration (VHA).We performed a retrospective analysis of all Veterans with primary HNSCC tumors who underwent next-generation sequencing (NGS) through the VHA's National Precision Oncology Program between July 2016 and February 2019. HPV status was determined by clinical pathology reports of p16 immunohistochemical staining; gene variant pathogenicity was classified using OncoKB, an online precision oncology knowledge database, and mutation frequencies were compared using Fisher's exact test.A total of 79 patients met inclusion criteria, of which 48 (60.8%) had p16-positive tumors. Patients with p16-negative HNSCC were more likely to have mutations in TP53 (p0.0001), and a trend towards increased mutation frequency was observed within NOTCH1 (p = 0.032) and within the composite CDK/Rb pathway (p = 0.065). Mutations in KRAS, NRAS, HRAS, and FBXW7 were exclusively identified within p16-positive tumors, and a trend towards increased frequency was observed within the PI3K pathway (p = 0.051). No difference in overall mutational burden was observed between the two groups.In accordance with the previous studies, no clear molecular basis for improved prognosis among patients harboring HPV-positive disease has been elucidated. Though no targeted therapies are approved based upon HPV-status, current efforts to trial PI3K inhibitors and mTOR inhibitors across patients with HPV-positive disease bear genomic rationale based upon the current findings.

Introduction Cognitive computing point‐of‐care decision support tools which ingest patient attributes from electronic health records and display treatment options based on expert training and medical literature, supplemented by real world evidence (RWE), might prove useful to expert and novice oncologists. The concordance of augmented intelligence systems with best medical practices and potential influences on physician behavior remain unknown. Methods Electronic health records from 88 breast cancer patients evaluated at a USA tertiary care center were presented to subspecialist experts and oncologists focusing on other disease states with and without reviewing the IBM Watson for Oncology with Cota RWE platform. Results The cognitive computing “recommended” option was concordant with selection by breast cancer experts in 78.5% and “for consideration” option was selected in 9.4%, yielding agreements in 87.9%. Fifty‐nine percent of non‐concordant responses were generated from 8% of cases. In the Cota observational database 69.3% of matched controls were treated with “recommended,” 11.4% “for consideration”, and 19.3% “not recommended.” Without guidance from Watson for Oncology (WfO)/Cota RWE, novice oncologists chose 75.5% recommended/for consideration treatments which improved to 95.3% with WfO/Cota RWE. The novices were more likely than experts to choose a non‐recommended option (P, Cognitive computing point‐of‐care decision support tools which ingest patient attributes from electronic health records and display treatment options based on expert training and medical literature, supplemented by real world evidence, might prove useful to expert and novice oncologists. We compared recommendations from one platform and noted concordance with disease expert opinions. The platform was also able to influence novice oncologists in test cases. Since nearly a fifth of similar real world cases received non‐recommended therapies. Our study demonstrates a need for decision support tools.

Breast cancer has a high annual incidence among females in India, with an age adjusted rate of 25.8 cases per 100,000 women and a mortality rate of 12.7 per 100,000 women1. Approximately 75% of breast cancers are estrogen receptor positive (ER+), and unfortunately, a significant number will develop resistance to first line hormonal therapy. Although several mechanisms have been linked to hormonal therapy resistance, no biomarker in clinical use currently exists to predict hormonal therapy resistance in a reliable manner. Comprehensive genomic profiling (CGP) is not routinely performed for metastatic breast carcinoma (MBC) in a relapsed setting in India. This research highlights utility of CGP on a MBC sample combined with interpretation by Watson™ for Genomics (WfG), an artificial-intelligence (AI)-based decision-support system (DSS), when a patient in a middle-income country developed resistance to endocrine therapy. A 33-year-old Indian female was diagnosed with ER+, progesterone-receptor-negative (PR-), human-epidermal-receptor-2-negative (HER2-) breast cancer. Two years later, she noted an axillary mass which was biopsied and revealed an ER+, PR-, HER2- MBC metastasis. CGP was performed with a panel from Illumina designed to detect genomic alterations in 170 genes, including 148 genes for substitutions and indels, 55 genes for fusion and splice variants, and 59 genes for copy number variation detection. Variant annotation and interpretation performed using WfG revealed co-existing ESR1 D538G mutation and ESR1-CCDC170 fusion. In addition, alterations in CCND1 resulting in activation of the CDK signaling pathway, an oncogenic mutation in MTOR resulting in activation of the MTOR/PI3K signaling pathway, and amplification of FGFR1, a poor prognostic factor in MBC, were also identified. While ESR1 D538 and ESR1-CCDC170 fusion individually have been reported to confer resistance to endocrine therapy, to the best of our knowledge, our report is the first HR+ MBC case with these two oncogenic alterations co-occurring in a metastatic tumor suggesting a synergistic role. CGP in this patient revealed a possible mechanism of resistance to endocrine therapy informing the physician to change treatment to a generic mTOR inhibitor based on the oncogenic MTOR mutation. In conclusion, assessment of DNA/RNA tumor variants in one assay along with a robust AI-based DSS can provide relevant information for consideration by the clinician when developing a treatment plan, may create efficiencies in tumor sample usage, time, and cost of treatment. Further research is needed to explore the functional synergy of the oncogenic ESR1 mutation and fusion detected in this patient's tumor and the role of identifying these pathogenic variants through circulating tumor cell or cell-free DNA assays to detect minimal residual disease. Citation Format: Ravindra Kolhe, Ashis Mondal, Nikhil Sahajpal, Meenakshi Ahluwalia, Yull Arriaga, Allan Njau, David Brotman, Jane Snowdon, Gretchen Jackson, Dilhan Weeraratne. Co-existing ESR1 D538G mutation and ESR1-CCDC170 fusion in a patient with metastatic hormone receptor positive breast cancer with progression after hormonal therapy [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4105.

Next-generation sequencing (NGS) is increasingly used to inform diagnostic, therapeutic, and prognostic decisions in AML at the time of first presentation. We highlight the utility of NGS combined with Watson™ for Genomics (WfG), an artificial-intelligence-based decision-support system, in identifying new clinically actionable alterations as a result of clonal evolution in the relapsed disease setting. In less than 3 minutes, WfG identified an IDH1 R132H pathogenic mutation in the relapsed sample sequenced with the Illumina TruSight Tumor 170-gene panel leading to the compassionate use of ivosidenib. In addition, mutations in two genes resulting in increased sensitivity to PARP inhibitors and mutations in PTEN resulting in activation of the MTOR/PI3K signaling pathway were detected by WfG. In February 2018, a previously healthy 23-year old Caucasian female presented with AML consisting of 80% blasts with positive FLT3 mutation. She received induction cytarabine plus daunorubicin (7+3) followed by multikinase inhibitor therapy with midostaurin on days 8 to 21. A 28-day bone marrow biopsy showed persistent disease with 40% blasts. In March 2018, the patient underwent re-induction chemotherapy with mitoxantrone, etoposide, and cytarabine (MEC) followed by midostaurin on days 8 to 21. A bone marrow biopsy after completion of re-induction therapy showed complete remission with < 5% blasts (CR1). In May 2018, a matched donor was not found on a bone marrow registry, and the patient underwent a post-remission dual unrelated umbilical cord blood (UCB) hematopoietic stem cell transplantation (HSCT). Engraftment was not achieved. In July 2018, the patient had disease relapse with the presence of circulating blasts and 7% of blasts in the bone marrow. While NGS is typically not performed on relapsed samples, WfG identified IDH1 R132H and PTEN C78T pathogenic mutations using the 170-gene panel. Based on these results, the patient initiated azacytidine plus the IDH1 inhibitor ivosidenib. In August 2018, a bone marrow biopsy showed less than 5% blasts positive for a FLT3-ITD mutation. In September 2018, the patient underwent reduced-intensity conditioning with fludarabine, cyclophosphamide, and total body irradiation followed by haploidentical allogeneic HSCT from her mother. Maintenance therapy with azacytidine plus ivosidenib was continued until the present time. Currently, the patient is in remission for over 15 months without evidence of AML minimal residual disease. She has developed mild skin chronic graft-versus-host disease that is controlled with standard treatment. She works on a full-time basis and has excellent functional status. The combination of CGP, artificial intelligence, and expert care has resulted in an excellent outcome in a patient with relapsed AML. In conclusion, our experience suggests that CGP testing should be considered at different time points, at least in the relapsed setting, to help treating physicians alter or help improve clinical outcome. CGP testing in a relapsed setting is precluded because it is not covered by payers. In support of optimal care, we have initiated a new program for compassionate use of genomic testing, where such testing is medically necessary, but not covered by insurance or payer supported. Citation Format: Ravindra Kolhe, Ashis Mondal, Vamsi Kota, Nkhil Sahajpal, Meenakshi Ahluwalia, Allan Njau, Dilhan Weeraratne, Yull Arriaga, David Brotman, Gretchen Jackson, Jane Snowdon. Clinical utility of comprehensive genomic testing with artificial-intelligence-based analysis to identify targetable sub-clonal events in relapsed acute myeloid leukemia (AML) [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6579.

e14051 Background: Adoption of next-generation sequencing (NGS) technology in routine clinical practice has enabled the detection of genetic aberrations such as single nucleotide variants, copy number alterations, and gene fusions. Pathway and network analyses (PNA) are key components for evaluation of NGS data in a clinical setting to explain findings involving thousands of altered genes and proteins with a smaller and more interpretable set of altered processes. Though PNA have been applied to identify driver genes and pathways in cohort-based analyses, its application in precision oncology remains unexplored. We investigate the potential utility of the Watson for Genomics (WfG) pathway analyses tool in interpreting complex and multiple genomic alterations in individual cancers. Methods: DNA and RNA isolated from 70 patient tumors across 30 different cancer types were processed with Illumina’s TST170 NGS platform. WfG’s feature of pathway analyses was used to identify gene variants, signaling pathways, networks, and the drugs targeting these alterations based on evidence in the clinical literature and FDA drug databases. Results: Analyses defined 5 different pathway/network models: 1) downstream therapeutic targets, 2) synthetic lethality, 3) combinatorial downstream targets + synthetic lethality, 4) two or more pathways converging to downstream targets, and 5) complex profile analyses. The five PNA models are illustrated by the following unique cases. 1) A thyroid cancer case with HRAS variant and activated RAF1 downstream pathway showed MAPK1/3 were suggestive of relevant targets. 2) An acute myeloid leukemia case with BRCA1, BRCA2 and PTEN variants, targeting a common synthetic lethal partner PARP1 was ideal for therapy. 3) A penile carcinoma case with BRAF, CDKN2A and TP53 variants, targeting the BRAF downstream pathway in combination with either CDKN2A or TP53 were the likely choice for therapy. 4) A glioma case with activated PI3K and MEK downstream pathway, targeting a common downstream marker would block both pathways. 5) A breast carcinoma case with a complex pathogenic variant profile provided relevant clinical information and levels of evidence for multiple drug targets. Conclusions: We discovered that the integrated WfG pathway analyses tool is ideal for visualization of the variants with levels of evidence from clinical literature and FDA drug databases that can help inform treatment options and provides a holistic understanding of a specific tumor profile allowing the treating clinician to select personalized targeted therapy.

2005 Background: Next-Generation Sequencing (NGS) gene panels are often completed to guide therapeutic decisions for patients with advanced stage non-small cell lung cancer (NSCLC). Patients with highly-actionable gene variants may experience improved therapeutic treatments and reduced toxicities with use of targeted agents. Ensuring appropriate prescription of targeted therapies is therefore of high importance. We sought to identify barriers to targeted agent use within the Veterans Health Affairs’ (VHA) National Precision Oncology Program (NPOP). Methods: A retrospective evaluation examined the cohort of NSCLC patients who underwent NGS multi-gene panels through NPOP between July 2015 and February 2019. A level of evidence for drug actionability was assigned to each observed oncogenic gene variant using an artificial intelligence offering (IBM Watson for Genomics: WfG). WfG level 1 and 2A evidence was reviewed by NPOP staff to exclude gene variants that did not conform to NPOP level 1 and 2A definitions. Anti-neoplastic drug prescriptions and oncology provider notes were obtained for all included patients from the VHA Corporate Data Warehouse. Review of clinical notes of patients who did not receive targeted agents was performed to categorize the reason(s). Results: Of 1764 NSCLC patients who successfully underwent NGS gene panel testing, 156 (8.9%) received therapeutic level 1 (7.3%) or 2A (1.6%) options for targeted agents based on WfG evidence analysis. In total, 117 (6.6%) patients had NPOP level 1 and 2A gene variants, all within ALK, BRAF, EGFR, ERBB2, MET, and RET. Of these, 49 (41.2%) patients were not prescribed available targeted agents. The three most common reasons were: (1) treating provider did not comment on NGS results (30.7%), (2) patient did not carry a diagnosis of advanced stage disease (18.4%), and (3) patient had begun an alternative systemic therapy prior to completion of sequencing (16.3%). No patient was denied access to a level 1 or 2A targeted drug due to utilization-management review. Conclusions: A substantial minority of patients with advanced NSCLC bearing highly-actionable gene variants are not prescribed available targeted agents. Further provider- and pathologist-directed educational effort are needed, as well as implementation of health informatics systems to provide near real-time decision support for test ordering and interpretation.

e13023 Background: Next generation sequencing (NGS) in hematological tumors is increasingly shaping clinical treatment decisions at the point of care. While the impact of NGS panels in solid tumors is largely therapeutic, targeted sequencing in hematological tumors can additionally provide diagnostic and prognostic insights. Additional data generated in hematological tumor sequencing makes manual interpretation and annotation of variants tedious and non-scalable. In this study we compared hematological tumor variant interpretation using an artificial intelligence decision-support system, Watsonä for Genomics (WfG), with expert guided manual curation. Methods: Patients with hematological tumors at Hallym University, College of Medicine between December 2017 and December 2018, were sequenced using the 54 gene Illumina TruSight Myeloid Panel. WfG interpreted and annotated all patients’ sequencing results, a subset of which were assessed manually to ascertain concordance. Results: 54 South Korean patients with hematological malignancies were analyzed (23 Acute Myeloid Leukemia, 12 myeloproliferative neoplasm, 5 myelodysplastic syndrome, 5 multiple myeloma and 9 others). Comparison of manual and WfG interpretation of 10 randomly selected cases yielded 90% (9/10) concordance and identification of 9 clinically actionable variants (33%) not found in manual interpretation. In total, WfG identified that 71% (38/54) of all cases had at least one clinically actionable therapeutic alteration (a variant targeted by a US FDA approved drug, off-label drug, or clinical trial). 33% (18/54) of cases had genes that were targeted by a US FDA approved therapy including JAK2, IDH1, IDH2, and FLT3. In cases without therapeutic alterations, WfG identified diagnostic or prognostic insights in an additional 20% (11/54) of patients. 9% (5/54) had no clinically actionable information. Conclusions: WfG variant interpretation correlated well with manually curated expert opinion and identified clinically actionable insights missed by manual interpretation. WfG has obviated the need for labor-intensive manual curation of clinical trials and therapy, enabling our center to exponentially scale our NGS operations.

e14300 Background: Acral and mucosal melanoma are rare subtypes accounting for about 3% of all melanoma cases. The cutaneous melanoma genomic landscape is well defined; however, little is known about the acral and mucosal melanoma mutational spectrum. In this pilot study, we evaluated the genomic and neo-antigen profiles and tumor mutational burden (TMB) from acral and mucosal melanoma patients with the aim of designing personalized vaccines and longitudinally tracking patients’ clinical courses. Methods: Tumor whole exome sequencing and neo-antigen profiling of 5 acral and 3 mucosal melanoma patients at Shanghai Tenth Peoples Hospital, Tongji University, China between April 2018 and January 2019 was performed using YuceBio’s proprietary analytics platform. Watsonä for Genomics, an artificial intelligence decision-support system, was used for variant interpretation and annotation. A comparative analysis was performed on Chinese acral melanoma data with the published Caucasian acral cohort from the Translational Genomics Research Institute (TGen) and The Cancer Genome Atlas (TCGA) predominantly Caucasian cutaneous melanoma data set. Results: TMB in our acral/mucosal melanoma cohort was 2.26/Megabase (Mb) compared to over 20/Mb in published cutaneous melanoma studies. Tumor neo-antigen burden (TNAB) in our group was 1.03 neo-epitopes/Mb. Low TNAB levels were associated with low TMB levels in all tumors. Incidence of BRAF and NRAS mutant cases in our cohort was 0% (0/8) and 13% (1/8) respectively compared to 19% (5/27) and 7% (2/27) of the Caucasian acral population in the TGen dataset. Incidence of BRAF and NRAS mutations in the TCGA cutaneous melanoma dataset was 54% (237/440) and 28% (125/440), respectively. Conclusions: TMB was significantly lower in acral/mucosal than in cutaneous melanoma and may be a surrogate for TNAB. Detection of BRAF and NRAS mutations, the two most prevalent driver mutations in cutaneous melanoma, were significantly lower frequencies in both Chinese and Caucasian acral melanoma patients in this study, suggesting alternate cancer drivers may exist in this subtype. Strategies to address challenges of low TNAB in vaccine development are being explored.

e13025 Background: Next generation sequencing (NGS) studies on various cancer types have revealed clinical insights on the molecular etiology of tumors and defined new treatment paradigms. However, the genomic landscape of a tumor type may be predicated on race. Application of broad-brush findings from one population to another may lead to erroneous treatment decisions. While the majority of published NGS cancer studies focus on Caucasian populations, few studies evaluate the molecular profiles of a tumor type between different demographies. In this study, acute myeloid leukemia (AML) patients of East Asian descent were sequenced at a South Korean hospital, interpreted, and compared with the Caucasian population from The Cancer Genome Atlas (TCGA). Methods: 23 South Korean AML patients were sequenced in 2018 using the 54 gene Illumina TruSight Myeloid Panel at Hallym University, College of Medicine. Orthogonal testing for FLT3-internal tandem duplication (ITD) was done by Sanger sequencing. Watsonä for Genomics (WfG), an artificial intelligence decision-support system, was used for variant interpretation and annotation. Additionally, 181 AML patients of Caucasian descent from the TCGA dataset were analyzed for comparison. Results: WfG identified at least 1 clinically actionable therapeutic alteration in 70% (16/23) of all Asian cohort cases. FLT3-ITD or tyrosine kinase domain (TKD) mutations were reported in 27% (49/181) of cases in the TCGA cohort but only 9% (2/23) of the Asian cohort. DNMT3A mutations were detected in 25% (45/181) and 74% (17/23) of the TCGA and Asian cohorts, respectively. Other oncogenic mutations in AML including NRAS, IDH1, IDH2, CEBPA, TET2, and NPM1 were represented at comparable frequencies between the two populations. Conclusions: Tumor biomarkers that affect prognosis can be informative in the clinical setting. In our study, FLT3 and DNMT3A, predictors of poor prognosis in AML, demonstrated decreased and increased respective frequencies in South Koreans compared to Caucasians, suggesting that some mutational signatures that predict cancer outcome may vary by race.

e19533 Background: Therapeutic, prognostic, and diagnostic insights gained from next generation sequencing (NGS) are a key premise of genomics-informed cancer care in hematological diseases. Particularly in BCR-ABL negative myeloproliferative neoplasms (MPN), insights gained from NGS is integral for risk stratification and prognostication. In this study, MPN patients of South Korean descent were sequenced, interpreted, and compared with a published validation cohort to identify variations in mutational profiles specific to demographics. Methods: 31 South Korean MPN patients including 12 essential thrombocythemia, 6 polycythemia vera, 6 primary myelofibrosis, and 7 chronic myelogenous leukemia were sequenced in 2018 and 2019 using the 54 gene Illumina TruSight Myeloid Panel at Hallym University College of Medicine. Orthogonal testing for CALR mutations was done by Sanger sequencing. Watson for Genomics (WfG), an artificial intelligence offering was used for variant interpretation and annotation. A cohort of 151 MPN patients previously published in the New England Journal of Medicine (NEJM) was used for comparison (PMID:24325359). Results: The table shows identified actionable mutations. Conclusions: Two novel pathogenic mutations in CALR (c.1162delG and c.1100_1145del)) were identified in Korean MPN patients. NOTCH1 pathogenic mutations were exclusive while TP53 mutations were significantly enriched in the Korean cohort suggesting that these pathways may play a role in MPN. TP53 mutations in MPN are clinically significant as they have been associated with increased risk for leukemic transformation. Of note, MPL mutations were not detected in the Korean cohort. In conclusion, race and ethnicity may contribute to some mutational signatures in cancer. [Table: see text]

e13547 Background: Improved scalability and affordability of next generation sequencing (NGS) has pivoted cancer care toward genomics-driven treatment decisions. Particularly in advanced-stage or refractory cancer, clinical insights gleaned from NGS have become an integral option as these patients have typically exhausted all lines of available therapy. As precision oncology evolves, NGS is expected to have a differential impact based on the cancer type. In this study, a comprehensive NGS panel was used to determine the strength of clinical evidence in various advanced stage tumor samples from The Cancer Genome Atlas (TCGA). Methods: A hybrid capture panel, Oncofoco, was developed to evaluate SNVs, INDELs, CNVs and TMB in 366 genes. The panel’s utility was validated by interrogating a broader cohort of 2847 TCGA samples (advanced tumors with T3 or T4; or N > = 1; or M > = 1). Watsonä for Genomics, an artificial intelligence offering, was used for variant interpretation and annotation of the 366 genes. A clinical evidence classification system that evaluated the strength of biomarker/drug response associations was used for annotation with level 1/R1 strongest and level 4 weakest from clinical literature, FDA drug labels and guidelines (PMID:28890946). Results: The highest level of evidence for the top nine frequently occurring advanced stage cancers in TCGA is shown in Table. Conclusions: Thyroid cancer and cutaneous melanoma have emerged as the cancer types with the most level 1 evidence (FDA approved drugs) owing to BRAF V600E mutations. Kidney and prostate cancers show no cases with level 1 evidence and also had the largest fraction of unactionable tumors. Over half of colorectal cancer cases had level R1 resistance evidence attributed to KRAS and NRAS mutations. The clinical utility of NGS in late-stage refractory cancer varies widely by tumor type. The presence of level 3 and level 4 evidence in all cancer types bodes well for the development of new targeted drugs. [Table: see text]