Genomic Profiling Data Research Articles

Genomic Ancestry in B Cell Lymphoid Malignancies

Genomic Ancestry in B Cell Lymphoid Malignancies

Variation in Breast Cancer Subtype Incidence and Distribution by Race/Ethnicity in the United States From 2010 to 2015

Breast cancer is the most commonly diagnosed cancer and the leading cause of death in women worldwide. Yet the racial/ethnic disparity in incidences and distributions of breast cancer remains largely unknown. To examine the racial/ethnic patterns associated with the incidence of the subtypes of breast cancer and distribution of patients across clinicopathological variables. This population-based cohort study analyzed data from the Surveillance, Epidemiology, and End Results (SEER) database, which collected data from 18 SEER cancer registries that identified patients with breast cancer in the US diagnosed between January 1, 2010, and December 31, 2015. The inclusion criteria were (1) female patients with primary unilateral breast cancer who underwent surgical treatment; (2) record of estrogen receptor, progesterone receptor, and ERBB2 status; (3) record of medical history and histological subtype of the specified tumor location; and (4) data on patient race/ethnicity, lateral tumor position, tumor size, tumor TNM stage, and number of tumors. Incidence and distribution rates were identified and compared for different molecular subtypes, histological grades, pathological patterns, T stages, TNM stages, and tumor sites of breast cancers for each racial/ethnic group. Patient race/ethnicity was classified as follows: non-Hispanic White, Hispanic White, Black, Asian/Pacific Islander, American Indian/Alaskan Native, and unknown. Data were analyzed from January 1, 2010, to December 31, 2015. Incidence rates of 4 molecular subtypes: hormone receptor (HR)-positive and ERBB2-negative, HR-positive and ERBB2-positive, HR-negative and ERBB2-positive, and triple-negative breast cancer (TNBC). Data on distribution by histological grades (grades 1-4 and unknown), pathological patterns, T stages, TNM stages, and tumor sites was also extracted. Annual age-standardized incidence rates and incidence rate ratios (IRRs) were calculated with 95% CIs. Race/ethnicity case-to-case odds ratios were estimated using polytomous regression. A total of 239 211 women with breast cancer were analyzed (median [interquartile range]) age, 60 [50-69] years). The annual incidence rate of all breast cancers was 31.3 (95% CI, 31.2- 31.5) per 100 000 people in non-Hispanic White women (the reference group), which was higher compared with the incidence among Black women (IRR, 1.04; 95% CI, 1.02-1.05; P < .001). The incidence rates were also lower in Asian/Pacific Islander (IRR, 0.90; 95% CI, 0.89-0.92; P < .001), American Indian/Alaskan native (IRR, 0.82; 95% CI, 0.81-0.83; P < .001), and Hispanic White women (IRR, 0.79; 95% CI, 0.75-0.83; P < .001). In Black patients, the incidences of the HR-positive and ERBB2-positive (IRR, 1.12; 95% CI, 1.08-1.16; P < .001), HR-negative and ERBB2-positive (IRR, 1.46; 95% CI, 1.38-1.54; P < .001), and TNBC (IRR, 2.07; 95% CI, 2.01-2.14; P < .001) subtypes were higher than those in non-Hispanic White patients, but the incidence of the HR-positive and ERBB2-negative subtype in Black women was lower (IRR, 0.86; 95% CI, 0.84-0.87; P < .001). The incidences of histological grade 1 (IRR, 0.75; 95% CI, 0.73-0.78; P < .001) and grade 2 (IRR, 0.91; 95% CI, 0.89-0.93; P < .001) were lower in Asian/Pacific Islander vs non-Hispanic White patients. Non-Hispanic White patients had a higher proportion of lobular carcinoma (9.7% [n = 15 718]) and tubular adenocarcinoma (0.6% [n = 997]) than Black (7.2% [n = 1933]; 0.3% [n = 81]), Asian/Pacific Islander (5.7% [n = 1202]; 0.3% [n = 55]), Hispanic White (7.2% [n = 1985]; 0.3% [n = 88]), and American Indian/Alaskan Native patients (7.2% [n = 101]; 0.4% [n = 5]). This cohort study found notable disparities in incidences and proportions of different molecular subtypes, histological grades, pathological patterns, T stages, TNM stages, and tumor sites associated with race/ethnicity. The findings suggest that combining epidemiologic with genomic and molecular profiling data warrants further research.

Longitudinal Cytokine Profiling Identifies GRO-α and EGF as Potential Biomarkers of Disease Progression in Essential Thrombocythemia.

Myeloproliferative neoplasms (MPNs) are characterized by deregulation of mature blood cell production and increased risk of myelofibrosis (MF) and leukemic transformation. Numerous driver mutations have been identified but substantial disease heterogeneity remains unexplained, implying the involvement of additional as yet unidentified factors. The inflammatory microenvironment has recently attracted attention as a crucial factor in MPN biology, in particular whether inflammatory cytokines and chemokines contribute to disease establishment or progression. Here we present a large-scale study of serum cytokine profiles in more than 400 MPN patients and identify an essential thrombocythemia (ET)-specific inflammatory cytokine signature consisting of Eotaxin, GRO-α, and EGF. Levels of 2 of these markers (GRO-α and EGF) in ET patients were associated with disease transformation in initial sample collection (GRO-α) or longitudinal sampling (EGF). In ET patients with extensive genomic profiling data (n = 183) cytokine levels added significant prognostic value for predicting transformation from ET to MF. Furthermore, CD56+CD14+ pro-inflammatory monocytes were identified as a novel source of increased GRO-α levels. These data implicate the immune cell microenvironment as a significant player in ET disease evolution and illustrate the utility of cytokines as potential biomarkers for reaching beyond genomic classification for disease stratification and monitoring.

Characteristics and outcomes of real-world (RW) patients (pts) with microsatellite instability-high (MSI-H) solid tumors treated with pembrolizumab monotherapy (P) after FDA approval.

3060 Background: The first tumor-agnostic, biomarker-based FDA approval in oncology was P in May 2017 for pts with MSI-H or mismatch repair (MMR) deficient tumors. 1 yr overall survival (OS) was &gt;70% for colorectal cancer (CRC) and &gt;60% for non-CRC in P-treated MSI-H clinical trial pts (Le 2019; Marabelle 2019). As tumor-agnostic therapies are a new paradigm, it is important to assess their use and effectiveness in routine clinical practice. We examined characteristics and outcomes of RW pts with MSI-H solid tumors who received P after May 2017. Methods: Pts with MSI-H solid tumors who received P after May 2017 were selected from the Flatiron Health-Foundation Medicine (FH-FMI) clinico-genomic database, a nationwide de-identified EHR-derived database linked to comprehensive genomic profiling (CGP) data. Pts with 2+ visits in the FH network from 01/2011-09/2019 with CGP prior to P use were included. Clinical characteristics were assessed at first P use. Time to treatment discontinuation (TTD) and OS from first P use were estimated with Kaplan-Meier analyses of all pts and the largest tumor types. Results: 33,395 pts had a solid tumor tested for MSI by CGP, of which 557 (1.7%) were MSI-H (median age 68 yrs; 34% male). 129 MSI-H pts across 33 tumor types received first P after May 2017. CRC (N=36) and Endometrial cancer (N=39) were most common. 52 pts (40%) had a concurrent MMR alteration (MLH1, MSH2, MSH6 or PMS2); median TMB was 32.2 mut/mb (IQR 20.9-47.5). Median number of therapies prior to P was 1; median time from CGP to first P use was 3 mos. Table shows OS and TTD. Conclusions: In this RW study, P use was observed across 33 MSI-H tumor types. Median OS exceeded 1 yr across all pts and in CRC, Endometrial, and Other cohorts. 1 yr OS rate was consistent with P trial outcomes. Further study should evaluate whether effectiveness differs across diseases, MSI testing method, or other genomic attributes to improve treatment selection. [Table: see text]

MP08-11 RENAL VEIN OSTIUM WALL INVASION OF RENAL CELL CARCINOMA WITH AN INFERIOR VENA CAVA TUMOR THROMBUS: COMPREHENSIVE MOLECULAR CHARACTERIZATION AND ITS IMPLICATIONS FOR CLINICAL PRACTICE

You have accessJournal of UrologyKidney Cancer: Basic Research & Pathophysiology I (MP08)1 Apr 2020MP08-11 RENAL VEIN OSTIUM WALL INVASION OF RENAL CELL CARCINOMA WITH AN INFERIOR VENA CAVA TUMOR THROMBUS: COMPREHENSIVE MOLECULAR CHARACTERIZATION AND ITS IMPLICATIONS FOR CLINICAL PRACTICE Cheng Liu*, Jianjun Yu, Mengyao Tan, Shidong Jia, and Lulin Ma Cheng Liu*Cheng Liu* More articles by this author , Jianjun YuJianjun Yu More articles by this author , Mengyao TanMengyao Tan More articles by this author , Shidong JiaShidong Jia More articles by this author , and Lulin MaLulin Ma More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000828.011AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Renal cell carcinoma (RCC) is a common cancer worldwide with up to 10% of patients accompanying intravascular tumor thrombus (TT). Patients with RCC may develop phenotypically distinct TT, some of which adhere densely to the venous wall (DATT, densely adherent TT) while some others do not adhere to the endothelium (NATT, non-adherent/minimal adherence TT). To explore whether patients with NATT or DATT can benefit more from the surgical resection and characterize molecular difference between the two groups for patient stratification and prognosis. METHODS: Clinical information and overall survival of 77 non-metastatic patients who went caval thrombectomy was retrospectively analyzed. Whole exome sequencing (WES) and whole transcriptome sequencing (WTS) were performed to profile genomic and transcriptomic landscape of matched primary and TT samples. Genomic alterations and transcriptomic profiling data were processed using public and in-house bioinformatics tools and analyzed for enriched mutations and pathways. Differentially mutated and expressed genes between DATT and NATT groups were assessed by Fisher exact test and DESeq2 respectively. TGCA expression dataset was analyzed through unsupervised hierarchical clustering. RESULTS: Patients with DATT showed worse survival after the surgical operation (Hazard Ratio: 5.1, P = 0.03). WES of tumor samples from prospectively enrolled patients revealed that VHL, PBRM1, BAP1, ARID1A and SETD2 are the most frequent mutated genes, consistent with public TCGA RCC data. Notably, SETD2, PTEN and TP53 mutations are significantly enriched in NATT patients relative to DATT. Analogously, transcriptomic profiling revealed P53 signaling pathway, DNA damage repair pathways and genes involved in tumor metastasis are upregulated in NATT. By contrast, genes related to cell-cell adhesion, blood coagulation, hypoxia, and VEGF signaling pathway are enriched in DATT samples. Interestingly, expression data from public primary RCC samples uncovered a subgroup of samples that are NATT-like harbor enriched TP53, PTEN and SETD2 mutations and have poorer progression-free survival and overall survival, suggesting that NATT may intrinsically represent a more aggressive subgroup of patients. CONCLUSIONS: Patients with non-adherent/minimal adherence TT (NATT) benefit more from surgery than that with densely adherent TT. Interestingly, molecular characterization by whole-exome and whole-transcriptomic sequencing delineated that patients with NATT harbor significantly enriched mutations in SETD2, PTEN and TP53 and may represent a more aggressive subtype. Such genes may serve as predictive biomarkers to stratify patients who can benefit most from the intervention of surgery. Source of Funding: The study was funded by Peking University Medicine Fund of Fostering Young Scholars’ Scientific & Technological Innovation BMU2018PY003; the National Natural Science Foundation of China (Nos.: 81711530048, 81572515, 81672522); Peking University Third Hospital Clinical Research Fund BYSY2018062, BYSY2018012. © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 203Issue Supplement 4April 2020Page: e110-e110 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information Cheng Liu* More articles by this author Jianjun Yu More articles by this author Mengyao Tan More articles by this author Shidong Jia More articles by this author Lulin Ma More articles by this author Expand All Advertisement PDF downloadLoading ...

Enabling population assignment from cancer genomes with SNP2pop

In many cancers, incidence, treatment efficacy and overall prognosis vary between geographic populations. Studies disentangling the contributing factors may help in both understanding cancer biology and tailoring therapeutic interventions. Ancestry estimation in such studies should preferably be driven by genomic data, due to frequently missing or erroneous self-reported or inferred metadata. While respective algorithms have been demonstrated for baseline genomes, such a strategy has not been shown for cancer genomes carrying a substantial somatic mutation load. We have developed a bioinformatics tool for the assignment of population groups from genome profiling data for both unaltered and cancer genomes. Despite extensive somatic mutations in the cancer genomes, consistency between germline and cancer data reached of 97% and 92% for assignment into 5 and 26 ancestral groups, respectively. Comparison with self-reported meta-data estimated a matching rate between 88–92%, mostly limited by interpretation of self-reported ethnicity labels compared to the standardized mapping output. Our SNP2pop application allows to assess population information from SNP arrays as well as sequencing platforms and to estimate the population structure in cancer genomics projects, to facilitate research into the interplay between ethnicity-related genetic background, environmental factors and somatic mutation patterns in cancer biology.

Response to systemic therapy and survival outcomes in de-novo (D1) metastatic castration-sensitive prostate cancer (mCSPC) versus mCSPC with prior local therapy (D0).

46 Background: A real-world study (n= 436, PMID: 29707790) suggest that D1 mCSPC has worse outcomes than D0 mCSPC. The objective of our study is to validate these findings in a real-world setting, and compare clinical and tumor genomic characteristics associated with D1 vs. D0 mCSPC. Methods: In this retrospective study, eligible patients had mCSPC, received androgen deprivation therapy (ADT) +/- intensification with docetaxel or novel hormonal therapy. D1 mCSPC was diagnosed as those with distant metastasis at the first diagnosis of prostate cancer. Progression-free survival (PFS) was per PCWG2 criteria or clinical progression, and overall survival (OS) was date of death or last follow up from start of ADT for mCSPC. Baseline clinical characteristics were compared using the t test. Survival was compared using the log-rank test with Kaplan-Meier methods. Results: Of 396 eligible patients, 242 had D1 mCSPC. Men with D1 mCSPC were younger, had significantly higher median PSA, Gleason score, and alkaline phosphatase at diagnosis of mCSPC, and had significantly shorter PFS and OS with standard ADT, as well as intensified ADT, compared to men with D0 mCSPC (Table). Conclusions: These findings validate data from a previous real-world study that D1 CSPC is associated with inferior prognosis and outcomes on systemic therapy compared to D0 mCSPC. These data may aid with counseling and treatment selection, as well as patient stratification in future trials in mCSPC. Comparison of comprehensive genomic profiling data, obtained through CLIA certified labs, will be presented at the meeting.[Table: see text]

Genomic correlates of response to PD-1 axis inhibitors (IO) in metastatic urothelial cancer (mUC) using explainable artificial intelligence (AI).

570 Background: AI is increasingly being used in clinical cancer genomics research. Probabilistic Graphical Models (PGMs) are AI algorithms that capture multivariate, mutli-level dependencies in complex patterns in large datasets while retaining human interpretability. We hypothesize that PGMs can identify clinical and genomic features that correlate with IO response in patients (pts) with mUC. Methods: In this retrospective study eligibility criteria were: diagnosis of mUC, receipt of IO for mUC, comprehensive genomic profiling data available from CLIA certified labs. The Bayesian Network (BN, PGM based AI) was used to discover clinical characteristics and selected genomic alterations relevant to IO response by RECIST 1.1 (investigator assessed). Results: Overall, 95 pts (73 men) with mUC were evaluated. 45 (47%) were ever smokers.The presented BN correctly captured the clinical landscape of mUC explaining significant relationship between included variables (p&lt;0.0001). Ever smokers and pts with de novo metastasis had higher TMB and better response to IO. Inactivating MLL2 alterations were more prevalent in non-smokers, and negatively correlated with response to IO. FGFR3 alterations did not predict response to IO. Significant associations are presented in Table. Conclusions: These hypothesis-generating data (by a novel approach, i.e. PGM based AI) showed that smoking and high-TMB were associated with improved response to IO; in contrast, inactivating MLL2 alternations and visceral metastasis predicted inferior response. FGFR3 alterations did not correlate with response. This model validated previous findings and found new hypothesis-generating relationship, such as altered MLL2 gene; external validation is needed.[Table: see text]

Conformational Landscapes of HER2 Exon 20 Insertions Explain Their Sensitivity to Kinase Inhibitors in Lung Adenocarcinoma.

HER2 exon 20 insertion (ex20ins) is one of the most intractable problems in lung cancer. Most ex20ins are resistant to available EGFR or pan-HER tyrosine kinase inhibitors (TKIs), with the exception of a few mutants. However, the mechanism for TKI response and resistance of HER2 ex20ins remains poorly understood. Next-generation sequencing-based genomic profiling data of 4139 patients with lung cancer were interrogated for HER2 ex20ins. Structural modeling and molecular dynamics simulations of common HER2 ex20ins were carried out to provide insights into the mechanism of activation and response heterogeneity of ex20ins. Molecular docking was performed to predict affinity to TKIs. Therapeutic decisions for patients were made on the basis of the results of genomic profiling. From 155 HER2-mutant lung cancer cases, Y772_A775dup and G778_P780dup were identified in 74 (47.7%) and 18 (11.6%) cases, respectively. Molecular dynamics simulations revealed that HER2 ex20ins led to ligand-independent kinase activation by changing the conformational landscape of HER2 kinase and restricting kinase conformation in the active state. G778_P780dup had a three-amino acid extension in the αC-β4 loop and retained the HER2-characteristic G776 and G778. Compared with Y772_A775dup, it had less restriction on kinase conformational sampling and higher affinity to afatinib, dacomitinib, pyrotinib, and poziotinib. Treating lung adenocarcinomas carrying G778_P780dup with these inhibitors led to sustained tumor responses in six of the 10 patients. The kinase conformational landscape dictated by the length of the αC-β4 loop and residues at HER2 776 and 778 position explains TKI sensitivity in ex20ins. This finding could guide therapeutic decisions with currently available therapies and future drug development strategies.

Precision Medicine in Soft Tissue Sarcoma Treatment.

Soft tissue sarcoma (STS) is a rare component of malignant diseases. STS includes various histological subtypes, and there are some important differences among the different histological subtypes regarding the mutation profile and sensitivity to antitumor agents. Many clinical trials of STS incorporating many different histological subtypes in various populations have been conducted; it is difficult to compare the findings and make conclusions about clinical efficacy. Targeted therapies focusing on specific histological subtypes and precision therapy focusing on the specific genetic mutation(s) of each STS patient are being investigated. Since STS patients are a small population, new clinical trial designs are required to evaluate and establish new targeted therapies for each histological subtype that has a limited number of patients, and preclinical investigations are needed to detect targetable mutations. Now that cancer genome profiling is used in clinical practice, it is urgently necessary to connect the genome profiling data obtained in clinical settings to the optimal clinical treatment strategies. Herein we review the development and challenges of precision therapy in the management of STS patients.

Complex Polyclonal Resistance Mechanisms to Ivosidenib Monotherapy in IDH1-Mutant Relapsed or Refractory Acute Myeloid Leukemia Revealed By Single Cell Sequencing Analyses

Complex Polyclonal Resistance Mechanisms to Ivosidenib Monotherapy in IDH1-Mutant Relapsed or Refractory Acute Myeloid Leukemia Revealed By Single Cell Sequencing Analyses

Simulation of Chemotherapy Effects in Older Breast Cancer Patients With High Recurrence Scores.

Tumor genomic expression profile data are used to guide chemotherapy choice, but there are gaps in evidence for women aged 65 years and older. We estimate chemotherapy effects by age and comorbidity level among women with early-stage, hormone receptor-positive, human epidermal growth factor receptor 2(HER2)-negative breast cancers and Oncotype DX scores of 26 or higher. A discrete-time stochastic state transition simulation model synthesized data from population studies and clinical trials to estimate outcomes over a 25-year horizon for subgroups based on age (65-69, 70-74, 75-79, and 80-89 years) and comorbidity levels (no or low, moderate, severe). Outcomes were discounted at 3%, and included quality-adjusted life-years (QALYs), life-years, and breast cancer and other-cause mortality with chemoendocrine vs endocrine therapy. Sensitivity analysis tested the effect of varying uncertain parameters. Women aged 65-69 years with no or low comorbidity gained 0.16 QALYs with chemo-endocrine and reduced breast cancer mortality from 34.8% to 29.7%, for an absolute difference of 5.1%; this benefit was associated with a 12.8% rate of grade 3-4 toxicity. Women aged 65-69 years with no or low or moderate comorbidity levels, and women aged 70-74 years with no or low comorbidity had small chemotherapy benefits. All women aged 75 years and older experienced net losses in QALYs with chemo-endocrine therapy. The results were robust in sensitivity analyses. Chemotherapy had greater benefits as treatment effectiveness increased, but toxicity reduced the QALYs gained. Among women aged 65-89 years whose tumors indicate a high recurrence risk, only those aged 65-74 years with no or low or moderate comorbidity have small benefits from adding chemotherapy to endocrine therapy. Genomic expression profile testing (and chemotherapy use) should be reserved for women aged younger than 75 years without severe comorbidity.

P1.14-24 Characterization of Acquired Receptor Tyrosine Kinase Fusions as Mechanisms of Resistance to EGFR Tyrosine Kinase Inhibitors

Responses to EGFR targeted therapy are generally temporary due to evitable drug resistance. The prevalence and characteristics of receptor tyrosine kinase (RTK) fusions as acquired resistance to EGFR-TKIs are rarely investigated. We retrospectively reviewed genomic profiling data of 3873 EGFR (exon 18-21)-mutant lung cancer patients with more than once NGS detection. A total of 16 patients who acquired RTK fusions during EGFR-TKI treatment with paired pre- and post- EGFR-TKI samples were identified. Their treatment history was collected. The newly acquired RTK fusions during EGFR-TKI treatment included RET (n = 6, 37.5%), ALK (n = 5, 31.3%), NTRK1 (n = 4, 25.0%), ROS1 (n = 1, 6.3%) and FGFR3 (n = 1, 6.3%). All the RET and EML4-ALK fusions were uncommon variants non-KIF5B-RET and E2:A20 (V5), respectively. Interestingly, RET fusion occurred only after osimertinib treatment, and contributed to drug resistance in 50.0% (6/12) of patients treated with osimertinib, indicating that fusions had different prevalence when functioned as resistance mechanisms to EGFR-TKI. Moreover, we found that in all patients developing drug resistance to EGFR-TKIs due to fusion emergence (n=16), those had treatment history of third-generation EGFR-TKI accounted for 75.0% (n=12). We extended the current knowledge of resistance mechanisms to EGFR-TKIs in non-small cell lung cancer. Detection of RTK fusions should be included in genomic profiling panels to uncover potential resistance mechanism of EGFR-TKI which might inform therapeutic strategies such as combination therapy approaches to circumvent tumorigenesis.

Pan-Cancer Analysis of CDK12 Loss-of-Function Alterations and Their Association with the Focal Tandem-Duplicator Phenotype.

CDK12 loss-of-function (LOF) genomic alterations are associated with focal tandem duplications (FTDs) in ovarian and prostate cancers. Because these FTDs may produce fusion-induced neoantigens (FINAs), CDK12 alteration is a candidate biomarker for immune checkpoint inhibitor sensitivity. Here we determine the prevalence of CDK12-LOF alterations and their association with FTDs across diverse tumor types. A total of 142,133 tumor samples comprising 379 cancer types were sequenced (August 2014 to April 2018) by hybrid capture-based comprehensive genomic profiling (Foundation Medicine, Cambridge, MA) as part of routine clinical care. Results were analyzed for base substitutions, short insertions/deletions, rearrangements, and copy number alterations. CDK12-LOF genomic alterations were assessed for zygosity status and association with FTDs/focal copy number gain. CDK12 genomic alterations were detected in 1.1% of all cases, most frequently in prostate cancer (5.6%), but were also observed at >1% frequency in 11 cancer types. Across multiple cancer types, including prostate, gastric/esophageal, ovarian, breast, and endometrial cancer, the number of FTDs was significantly increased in CDK12-LOF versus CDK12 wild-type cases. Notably, CDK12-LOF was not consistently associated with a homologous recombination deficiency genomic signature. Quantitative assessment of CDK12-associated FTDs by measurement of single copy number gains identified novel likely deleterious CDK12 kinase-domain mutations in prostate and ovarian cancers. Detection of CDK12-LOF genomic alterations and their association with FTDs in a diverse spectrum of malignancies suggests that immunotherapy approaches targeting FINAs derived from CDK12-associated FTDs may be a broadly applicable strategy that could be explored across cancer types in a tumor-agnostic manner. CDK12 inactivation in ovarian and prostate cancer results in the generation of focal tandem duplications, which can cause fusion-induced neoantigens. In prostate cancer, CDK12 alterations have demonstrated promise as a potential predictive biomarker for response to immune checkpoint blockade. This study evaluated genomic profiling data from >142,000 tumors to determine the prevalence of CDK12 loss-of-function genomic alterations across tumor types and demonstrated that CDK12 alterations are associated with the tandem-duplicator phenotype in cancer types other than ovarian and prostate cancer. The association of CDK12 alterations with focal tandem duplications across broad cancer types suggests that CDK12 inactivation warrants further investigation as a pan-cancer biomarker for immunotherapy benefit.

Abstract 671: Evaluation of machine learning tools for cancer drug response prediction with genomic profile data and drug response data from PDX model studies

Abstract Cancer are diseases caused by genomic aberrations which lead to abnormal cell proliferation and metastasis. Cancer treatment based on cancer mutation profiles are the focus of research and development of cancer precision medicine. Currently only a small percentage of cancer patients’ genomic information are associated with known drug responses, while others lack known cancer drivers or have cancer drivers that are not druggable. Machine learning (ML) tools have been developed recently for cancer biomarker discovery and cancer drug response prediction. However, due to a lack of sufficient amount of high quality drug response data, especially data for new drugs, the application of ML tools in this area is still in its early stage. In this study, we compared the performance of about a dozen ML methods, such as random forest classifiers, logistic regression classifiers, and support vector machines (SVM), trained on data of about 2300 treatment responses to average 12 different cancer drugs in 98 NSCLC, 55 gastric and 38 HCC PDX models with WES, CNV and RNA-seq information. Data preparation including genomic profile data selection, dimensionality feature reduction and processing was performed with considerations of known cancer gene functions, signal pathways, and pharmacodynamics information. Each dataset was randomly divided into training dataset and test dataset for ML algorithm training and evaluation. Performance of ML algorithms in testing was measured by their drug response prediction accuracy. Different regularization methods, such as LASSO and Elastic Net, were also evaluated in order to improve classification accuracy and identify relevant biomarker features. From comparing results from different algorithms or from the same algorithms but with differently prepared datasets, it seems that selection of functionally unknown gene aberrations for data preparation plays a significant role in prediction accuracy. Increasing dataset number or different data selection in different cancer types can improve prediction accuracy. Citation Format: Henry Gu, Jingjing Jiang. Evaluation of machine learning tools for cancer drug response prediction with genomic profile data and drug response data from PDX model studies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 671.

Abstract 1124: S100A14 suppresses metastasis of nasopharyngeal carcinoma via inhibition of NF-kB signaling

Abstract Nasopharyngeal carcinoma (NPC) has a high incidence rate in southern China and Southeast Asia. Distant metastasis is the main reason for treatment failure. By analyzing our previously published whole genomic expression profiling data comparing low- versus high-metastasis NPC cells, we found that S100A14 expression was dramatically suppressed in high-metastasis cells. The reduced expression of S100A14 protein in the primary NPC collated with poorer overall survival of the NPC patients. Our preliminary experiments showed that S100A14 could inhibit the migratory and invasive abilities, as well as the epithelial-mesenchymal translation of NPC cells. RNA sequencing analyses found that NF-kB signaling pathway was the most dramatically influenced pathway by S100A14 with a negative relationship found between S100A14 and NF-kB signaling. We therefore propose a hypothesis that S100A14 inhibits NPC metastasis through inhibition of NF-kB signaling. In the proposed study, we will use a series of in vitro and in vivo approaches to clarify the inhibitory effects of S100A14 in terms of nuclear localization of P50 and P65, expression of IL-1beta, phosphorylation activation of the key molecules in NF-kB signaling pathway, as well as the in vivo metastasis of NPC cells. The accomplishment of the proposed study will reveal a novel mechanism of inhibiting NPC metastasis via inhibitory effects of S100A14 upon NF-kB signaling, which should be composed of drugable targets for metastasis prevention and treatment. Citation Format: Dong-Fang Meng, Chao-Nan Qian. S100A14 suppresses metastasis of nasopharyngeal carcinoma via inhibition of NF-kB signaling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1124.

Abstract 5105: A plug-and-play infrastructure for scalable bioinformatics operations

Abstract Genome profiling represents a critical pillar for clinical, translational, and basic research studies. Hospitals, core facilities, and research enterprises invest significant resources to generate genomic data sets. Yet, data management and analysis is frequently manual, which demands significant operator time and often results in siloed resources rendering them as single-use assets. Centralization of the genomic capital in a framework that enables automated processing, metadata integration and continuous interrogation maximizes return for investment and serves as the critical catalyst for research innovation, clinical translation and reproducible research. We developed Isabl, a plug-and-play infrastructure for scalable bioinformatics operations. Isabl provides solutions for databasing, assets management, tracking, automated and reproducible data processing. Dynamic reporting and meta-analysis across data assets is enabled. Isabl is built on four main components. First, an individual-centric and extensible relational database with tracking support for samples (temporal, spatial, aliquot), experimental data (assays, platforms, sequencing runs), cohorts (clinical trials, research projects) and versioned bioinformatics applications (assembly aware, tools, results). Second, the database is exposed through a fully featured RESTful API that enables horizontal integration with information systems such as sequencing cores LIMS, variant visualization platforms like cBioPortal, and where applicable, clinical and biospecimen institutional databases. Third, a Software Development Kit (SDK) built for Next Generation Sequencing assets management. The SDK enables automated execution of data import and language-agnostic bioinformatics applications (alignment, variant calling, post-processing) with support for cohort and individual level reporting features. Furthermore, the SDK facilitates dynamic retrieval of results using vertical and horizontal queries (individual and cohort level, respectively). Lastly, Isabl comes with a Single Page Web Application that fosters user interaction with multidisciplinary teams (i.e. researchers, project coordinators, engineers, clinicians) facilitating tracking of analyses, results visualization, and dynamic query processing. Isabl is currently supporting the Memorial Sloan Kettering Genome Pediatrics Precision Medicine Initiative, a prototype platform that delivers integrated, real-time automated reporting of clinical targeted gene re-sequencing, research whole genome and transcriptome profiling data; as well as linked data from pre-clinical models (i.e. PDX) and single cells studies. As an open-source tool, Isabl democratizes access to a purpose built, automated, scalable and fully integrable bioinformatics architecture. Isabl will be available at https://github.com/isabl-io. Citation Format: Juan S. Medina-Martínez, Juan E. Arango-Ossa, Gunes Gundem, Max F. Levine, Minal Patel, Noushin R. Farnoud, Venkata D. Yellapantula, Gao Teng, Joseph G. Mccarter, Elsa Bernard, Franck Rapaport, Dominik Glodzik, Ross L. Levine, Andrew Kung, Elli Papaemmanuil. A plug-and-play infrastructure for scalable bioinformatics operations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 5105.

&lt;p&gt;Characterization of acquired receptor tyrosine–kinase fusions as mechanisms of resistance to EGFR tyrosine–kinase inhibitors&lt;/p&gt;

Purpose: Responses to EGFR-targeted therapy are generally temporary, due to inevitable drug resistance. The prevalence and characteristics of receptor tyrosine–kinase (RTK) fusion as acquired resistance to EGFR tyrosine–kinase inhibitors (TKIs) are rarely investigated.Methods: We retrospectively reviewed genomic profiling data of 3873 EGFR (exons 18–21)-mutant lung cancer patients with more than once next-generation sequencing detection. A total of 16 patients who acquired RTK fusions during EGFR-TKI treatment with paired pre- and post-EGFR-TKI samples were identified. Their treatment history was collected.Results: Newly acquired RTK fusions during EGFR-TKI treatment included RET (n=6, 37.5%), ALK (n=5, 31.3%), NTRK1 (n=4, 25.0%), ROS1 (n=1, 6.3%), and FGFR3 (n=1, 6.3%). All RET and EML4–ALK fusions were uncommon variants of KIF5B-RET and E2:A20 (V5), respectively. Interestingly, RET fusion occurred only after osimertinib treatment, and contributed to drug resistance in 50% (6 of 12) of patients treated with osimertinib, indicating that fusions had different prevalence when functioning as resistance mechanisms to EGFR TKIs. Moreover, we found that in all patients developing drug resistance to EGFR TKIs due to fusion emergence (n=16), those that had a treatment history of third-generation EGFR TKIs accounted for 75% (n=12).Conclusion: We have extended the current knowledge of resistance mechanisms to EGFR TKIs in non-small-cell lung cancer. Detection of RTK fusions should be included in genomic profiling panels to uncover potential resistance mechanisms of EGFR TKIs, which might inform therapeutic strategies, such as combination-therapy approaches, to circumvent tumorigenesis.

Integrative Analysis of Genomic and Clinical Data Reveals Intrinsic Characteristics of Bladder Urothelial Carcinoma Progression.

The progression of bladder cancer is generally a complex and dynamic process, involving a variety of biological factors. Here, we aimed to identify a set of survival-related genes that play an important role in the progression of bladder cancer and uncover their synergistic patterns. Based on the large-scale genomic profiling data and clinical information of 404 bladder cancer patients derived from The Cancer Genome Atlas (TCGA) database, we first discovered 1078 survival-related genes related to their survival states using univariate and multivariate Cox proportional hazardous regression. We then investigated the dynamic changes of the cooperative behaviors of these 1078 genes by analyzing their respective genomic features, including copy number variations, DNA methylations, somatic mutations, and microRNA regulatory networks. Our analyses showed that during the progression of bladder cancer, the biological disorder involving the identified survival-related genes can be reflected by multiple levels of abnormal gene regulation, ranging from genomic alteration to post-transcriptional dysregulation. In particular, the stage-specific co-expression networks of these genes undergo a series of structural variations. Our findings provide useful hints on understanding the underlying complex molecular mechanisms related to the evolution of bladder cancer and offer a new perspective on clinical diagnosis and treatment of bladder cancer.

On-target Resistance to the Mutant-Selective EGFR Inhibitor Osimertinib Can Develop in an Allele-Specific Manner Dependent on the Original EGFR-Activating Mutation.

The third-generation EGFR inhibitor, osimertinib, is the first mutant-selective inhibitor that has received regulatory approval for the treatment of patients with EGFR-mutant lung cancer. Despite the development of highly selective third-generation inhibitors, acquired resistance remains a significant clinical challenge. Recently, we and others have identified a novel osimertinib resistance mutation, G724S, which was not predicted in in vitro screens. Here, we investigate how G724S confers resistance to osimertinib.Experimental Design: We combine structure-based predictive modeling of G724S in combination with the 2 most common EGFR-activating mutations, exon 19 deletion (Ex19Del) and L858R, with in vitro drug-response models and patient genomic profiling. Our simulations suggest that the G724S mutation selectively reduces osimertinib-binding affinity in the context of Ex19Del. Consistent with our simulations, cell lines transduced with Ex19Del/G724S demonstrate resistance to osimertinib, whereas cells transduced with L858R/G724S are sensitive to osimertinib. Subsequent clinical genomic profiling data further suggest G724S occurs with Ex19Del but not L858R. Furthermore, we demonstrate that Ex19Del/G724S retains sensitivity to afatinib, but not to erlotinib, suggesting a possible therapy for patients at the time of disease relapse. Altogether, these data suggest that G724S is an allele-specific resistance mutation emerging in the context of Ex19Del but not L858R. Our results fundamentally reframe the problem of targeted therapy resistance from one focused on the "drug-resistance mutation" pair to one focused on the "activating mutation-drug-resistance mutation" trio. This has broad implications across clinical oncology.