Towards accurate indel calling for oncopanel sequencing through an international pipeline competition at precisionFDA.

Accurately calling indels with next-generation sequencing (NGS) data is critical for clinical application. The precisionFDA team collaborated with the U.S. Food and Drug Administration's (FDA's) National Center for Toxicological Research (NCTR) and successfully completed the NCTR Indel Calling from Oncopanel Sequencing Data Challenge, to evaluate the performance of indel calling pipelines. Top performers were selected based on precision, recall, and F1-score. The performance of many other pipelines was close to the top performers, which produced a top cluster of performers. The performance was significantly higher in high confidence regions and coding regions, and significantly lower in low complexity regions. Oncopanel capture and other issues may have occurred that affected the recall rate. Indels with higher variant allele frequency (VAF) may generally be called with higher confidence. Many of the indel calling pipelines had good performance. Some of them performed generally well across all three oncopanels, while others were better for a specific oncopanel. The performance of indel calling can further be improved by restricting the calls within high confidence intervals (HCIs) and coding regions, and by excluding low complexity regions (LCR) regions. Certain VAF cut-offs could be applied according to the applications.

Extend the benchmarking indel set by manual review using the individual cell line sequencing data from the Sequencing Quality Control 2 (SEQC2) project.

Accurate indel calling plays an important role in precision medicine. A benchmarking indel set is essential for thoroughly evaluating the indel calling performance of bioinformatics pipelines. A reference sample with a set of known-positive variants was developed in the FDA-led Sequencing Quality Control Phase 2 (SEQC2) project, but the known indels in the known-positive set were limited. This project sought to provide an enriched set of known indels that would be more translationally relevant by focusing on additional cancer related regions. A thorough manual review process completed by 42 reviewers, two advisors, and a judging panel of three researchers significantly enriched the known indel set by an additional 516 indels. The extended benchmarking indel set has a large range of variant allele frequencies (VAFs), with 87% of them having a VAF below 20% in reference Sample A. The reference Sample A and the indel set can be used for comprehensive benchmarking of indel calling across a wider range of VAF values in the lower range. Indel length was also variable, but the majority were under 10 base pairs (bps). Most of the indels were within coding regions, with the remainder in the gene regulatory regions. Although high confidence can be derived from the robust study design and meticulous human review, this extensive indel set has not undergone orthogonal validation. The extended benchmarking indel set, along with the indels in the previously published known-positive set, was the truth set used to benchmark indel calling pipelines in a community challenge hosted on the precisionFDA platform. This benchmarking indel set and reference samples can be utilized for a comprehensive evaluation of indel calling pipelines. Additionally, the insights and solutions obtained during the manual review process can aid in improving the performance of these pipelines.

The impact of misclassification errors on the performance of biomarkers based on next-generation sequencing, a simulation study.

The development of next-generation sequencing (NGS) opens opportunities for new applications such as liquid biopsy, in which tumor mutation genotypes can be determined by sequencing circulating tumor DNA after blood draws. However, with highly diluted samples like those obtained with liquid biopsy, NGS invariably introduces a certain level of misclassification, even with improved technology. Recently, there has been a high demand to use mutation genotypes as biomarkers for predicting prognosis and treatment selection. Many methods have also been proposed to build classifiers based on multiple loci with machine learning algorithms as biomarkers. How the higher misclassification rate introduced by liquid biopsy will affect the performance of these biomarkers has not been thoroughly investigated. In this paper, we report the results from a simulation study focused on the clinical utility of biomarkers when misclassification is present due to the current technological limit of NGS in the liquid biopsy setting. The simulation covers a range of performance profiles for current NGS platforms with different machine learning algorithms and uses actual patient genotypes. Our results show that, at the high end of the performance spectrum, the misclassification introduced by NGS had very little effect on the clinical utility of the biomarker. However, in more challenging applications with lower accuracy, misclassification could have a notable effect on clinical utility. The pattern of this effect can be complex, especially for machine learning-based classifiers. Our results show that simulation can be an effective tool for assessing different scenarios of misclassification.

US FDA public meeting: identification of concepts and terminology for multicomponent biomarkers.

The US FDA convened a virtual public workshop with the goals of obtaining feedback on the terminology needed for effective communication of multicomponent biomarkers and discussing the diverse use of biomarkers observed across the FDA and identifying common issues. The workshop included keynote and background presentations addressing the stated goals, followed by a series of case studies highlighting FDA-wide and external experience regarding the use of multicomponent biomarkers, which provided context for panel discussions focused on common themes, challenges and preferred terminology. The final panel discussion integrated the main concepts from the keynote, background presentations and case studies, laying a preliminary foundation to build consensus around the use and terminology of multicomponent biomarkers.

Assessing reproducibility of inherited variants detected with short-read whole genome sequencing.

BACKGROUND: Reproducible detection of inherited variants with whole genome sequencing (WGS) is vital for the implementation of precision medicine and is a complicated process in which each step affects variant call quality. Systematically assessing reproducibility of inherited variants with WGS and impact of each step in the process is needed for understanding and improving quality of inherited variants from WGS. RESULTS: To dissect the impact of factors involved in detection of inherited variants with WGS, we sequence triplicates of eight DNA samples representing two populations on three short-read sequencing platforms using three library kits in six labs and call variants with 56 combinations of aligners and callers. We find that bioinformatics pipelines (callers and aligners) have a larger impact on variant reproducibility than WGS platform or library preparation. Single-nucleotide variants (SNVs), particularly outside difficult-to-map regions, are more reproducible than small insertions and deletions (indels), which are least reproducible when > 5 bp. Increasing sequencing coverage improves indel reproducibility but has limited impact on SNVs above 30×. CONCLUSIONS: Our findings highlight sources of variability in variant detection and the need for improvement of bioinformatics pipelines in the era of precision medicine with WGS.

A targeted simulation-extrapolation method for evaluating biomarkers based on new technologies in precision medicine.

New technologies for novel biomarkers have transformed the field of precision medicine. However, in applications such as liquid biopsy for early tumor detection, the misclassification rates of next generation sequencing and other technologies have become an unavoidable feature of biomarker development. Because initial experiments are usually confined to specific technology choices and application settings, a statistical method that can project the performance metrics of other scenarios with different misclassification rates would be very helpful for planning further biomarker development and future trials. In this article, we describe an approach based on an extended version of simulation extrapolation (SIMEX) to project the performance of biomarkers measured with varying misclassification rates due to different technological or application settings when experimental results are only available from one specific setting. Through simulation studies for logistic regression and proportional hazards models, we show that our proposed method can be used to project the biomarker performance with good precision when switching from one to anther technology or application setting. Similar to the original SIMEX model, the proposed method can be implemented with existing software in a straightforward manner. A data analysis example is also presented using a lung cancer data set and performance metrics for two gene panel based biomarkers. Results demonstrate that it is feasible to infer the potential implications of using a range of technologies or application scenarios for biomarkers with limited human trial data.

Best practices for benchmarking germline small-variant calls in human genomes.

Standardized benchmarking approaches are required to assess the accuracy of variants called from sequence data. Although variant-calling tools and the metrics used to assess their performance continue to improve, important challenges remain. Here, as part of the Global Alliance for Genomics and Health (GA4GH), we present a benchmarking framework for variant calling. We provide guidance on how to match variant calls with different representations, define standard performance metrics, and stratify performance by variant type and genome context. We describe limitations of high-confidence calls and regions that can be used as truth sets (for example, single-nucleotide variant concordance of two methods is 99.7% inside versus 76.5% outside high-confidence regions). Our web-based app enables comparison of variant calls against truth sets to obtain a standardized performance report. Our approach has been piloted in the PrecisionFDA variant-calling challenges to identify the best-in-class variant-calling methods within high-confidence regions. Finally, we recommend a set of best practices for using our tools and evaluating the results.

Author Correction: Best practices for benchmarking germline small-variant calls in human genomes.

In the version of this article initially published online, two pairs of headings were switched with each other in Table 4: "Recall (PCR free)" was switched with "Recall (with PCR)," and "Precision (PCR free)" was switched with "Precision (with PCR)." The error has been corrected in the print, PDF and HTML versions of this article.

Identification, characterization, and targeting of IL-4 receptor by IL-4-Pseudomonas exotoxin in mouse models of anaplastic thyroid cancer.

Thyroid cancer is a rapidly increasing endocrine cancer. Since interleukin-4 receptor (IL-4R) is overexpressed in human solid cancer, we examined expression of IL-4R in 50 cases of anaplastic thyroid cancer (ATC), 37 well-differentiated papillary cancer (WDPC), 35 well-differentiated follicular cancer of thyroid (WDFC), and 37 normal thyroid specimens by immunohistochemistry (IHC) and in-situ hybridization (ISH) techniques. We demonstrated that IL-4Rα was overexpressed in 36/50 (72%) ATC, 20/35 (57%) WDFC, and 11/37 (30%) WDPC tumors. Other two subunits of IL-4R, interleukin-13 receptor α1 (IL-13Rα1) and interleukin-2 receptor gamma (IL-2RγC), were either weakly expressed or absent. As ATC is a highly aggressive cancer with higher incidence of IL-4Rα expression, we characterized IL-4R in 3 ATC cell lines. RT-qPCR and IFA results showed that IL-4Rα is overexpressed while IL-13Rα1 is weakly expressed. Control human umbilical vein endothelial cell line (HUVEC) showed weak expression of IL-4Rα. Binding and competition studies with 125I-IL-4 in ATC cell lines demonstrated that IL-4 specifically bound to IL-4Rα on cell surface. ATC cell lines were highly sensitive to a chimeric fusion cytotoxin consisting of circularly permuted IL-4 and truncated Pseudomonas exotoxin (IL-4-PE), which killed them in a concentration dependent manner. IL-4-PE also blocked colony formation of ATC cell lines in clonogenic assays. IL-4-PE mediated a significant antitumor activity in mouse models of ATC. Intratumoral administration of IL-4-PE caused significant regression of established tumors in a dose dependent manner and increased the overall survival without any visible toxicity. Thus, IL-4Rα in ATC may represent a novel therapeutic target and IL-4-PE may serve as an investigational therapeutic option for ATC.

Overall conceptual framework for studying the genetics of autoimmune diseases following vaccination: a regulatory perspective.

The US Vaccine Adverse Event Reporting System contains case reports of autoimmune diseases (ADs) occurring following vaccinations. ADs are rare and occur in unvaccinated people, making the potential association between vaccines and ADs challenging to evaluate. Developing mechanistic pathways that link genes, immune mediators, vaccine components and ADs would be helpful for hypothesis generation, enhancing theories of biologic plausibility and grouping rare autoimmune adverse events to increase the ability to detect and evaluate safety signals. Here, we propose a conceptual framework for investigating the genetics of ADs as safety signals following vaccination, potentially contributing to the identification of relevant biomarkers. We also discuss a study design that incorporates genetic information into postmarket clinical evaluation of autoimmune adverse events following vaccination.

Comparison of RNA-seq and microarray-based models for clinical endpoint prediction.

BACKGROUND: Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model. RESULTS: We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models. CONCLUSIONS: We demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.

In silico analysis of autoimmune diseases and genetic relationships to vaccination against infectious diseases.

BACKGROUND: Near universal administration of vaccines mandates intense pharmacovigilance for vaccine safety and a stringently low tolerance for adverse events. Reports of autoimmune diseases (AID) following vaccination have been challenging to evaluate given the high rates of vaccination, background incidence of autoimmunity, and low incidence and variable times for onset of AID after vaccinations. In order to identify biologically plausible pathways to adverse autoimmune events of vaccine-related AID, we used a systems biology approach to create a matrix of innate and adaptive immune mechanisms active in specific diseases, responses to vaccine antigens, adjuvants, preservatives and stabilizers, for the most common vaccine-associated AID found in the Vaccine Adverse Event Reporting System. RESULTS: This report focuses on Guillain-Barre Syndrome (GBS), Rheumatoid Arthritis (RA), Systemic Lupus Erythematosus (SLE), and Idiopathic (or immune) Thrombocytopenic Purpura (ITP). Multiple curated databases and automated text mining of PubMed literature identified 667 genes associated with RA, 448 with SLE, 49 with ITP and 73 with GBS. While all data sources provided valuable and unique gene associations, text mining using natural language processing (NLP) algorithms provided the most information but required curation to remove incorrect associations. Six genes were associated with all four AIDs. Thirty-three pathways were shared by the four AIDs. Classification of genes into twelve immune system related categories identified more "Th17 T-cell subtype" genes in RA than the other AIDs, and more "Chemokine plus Receptors" genes associated with RA than SLE. Gene networks were visualized and clustered into interconnected modules with specific gene clusters for each AID, including one in RA with ten C-X-C motif chemokines. The intersection of genes associated with GBS, GBS peptide auto-antigens, influenza A infection, and influenza vaccination created a subnetwork of genes that inferred a possible role for the MAPK signaling pathway in influenza vaccine related GBS. CONCLUSIONS: Results showing unique and common gene sets, pathways, immune system categories and functional clusters of genes in four autoimmune diseases suggest it is possible to develop molecular classifications of autoimmune and inflammatory events. Combining this information with cellular and other disease responses should greatly aid in the assessment of potential immune-mediated adverse events following vaccination.

Interleukin-4 receptor alpha overexpression in human bladder cancer correlates with the pathological grade and stage of the disease.

Previously, we have demonstrated that interleukin-4 receptor α (IL-4Rα) is overexpressed on a variety of human cancers and can serve as target for IL-4 immunotoxin comprised of IL-4 and a mutated Pseudomonas exotoxin. However, its expression and association with grade and clinical stage of bladder cancer has not been studied. IL-4Rα expression was examined in human bladder cancer cell lines, mouse xenografts, and biopsy specimens at mRNA and protein levels by real-time RT-PCR and IHC/ISH techniques. We also examined the effect of IL-4 on proliferation and invasion of bladder carcinoma cell lines. For tissue microarray (TMA) results, we analyzed the precision data using exact binomial proportion with exact two-sided P-values. We used Cochran-Armitage Statistics with exact two-sided P-values to examine the trend analysis of IL-4Rα over grade or stage of the bladder cancer specimens. The influence of age and gender covariates was also analyzed using multiple logistic regression models. IL-4Rα is overexpressed in five bladder cancer cell lines, while normal bladder and human umbilical vein cell lines (HUVEC) expressed at low levels. Two other chains of IL-4 receptor complex, IL-2RγC and IL-13Rα1, were absent or weakly expressed. IL-4 modestly inhibited the cell proliferation, but enhanced cell invasion of bladder cancer cell lines in a concentration-dependent manner. Bladder cancer xenografts in immunodeficient mice also maintained IL-4Rα overexpression in vivo. Analysis of tumor biopsy specimens in TMAs revealed significantly higher IL-4Rα immunostaining (≥ 2+) in Grade 2 (85%) and Grade 3 (97%) compared to Grade 1 tumors (0%) (P ≤ 0.0001). Similarly, 9% stage I tumors were positive for IL-4Rα (≥ 2+) compared to 84% stage II (P ≤ 0.0001) and 100% stages III-IV tumors (P ≤ 0.0001). IL-13Rα1 was also expressed in tumor tissues but at low levels and it did not show any correlation with the grade and stage of disease. However, the IL-2RγC was not expressed. Ten normal bladder specimens demonstrated ≤ 1+ staining for IL-4Rα and IL-13Rα1 and no staining for IL-2RγC. These results demonstrate that IL-4Rα is overexpressed in human bladder cancer, which correlates with advanced grade and stage of the disease. Thus, IL-4Rα may be a bladder tumor-associated protein and a prognostic biomarker.

Gene markers of cellular aging in human multipotent stromal cells in culture.

INTRODUCTION: Human multipotent stromal cells (MSCs) isolated from bone marrow or other tissue sources have great potential to treat a wide range of injuries and disorders in the field of regenerative medicine and tissue engineering. In particular, MSCs have inherent characteristics to suppress the immune system and are being studied in clinical studies to prevent graft-versus-host disease. MSCs can be expanded in vitro and have potential for differentiation into multiple cell lineages. However, the impact of cell passaging on gene expression and function of the cells has not been determined. METHODS: Commercially available human MSCs derived from bone marrow from six different donors, grown under identical culture conditions and harvested at cell passages 3, 5, and 7, were analyzed with gene-expression profiling by using microarray technology. RESULTS: The phenotype of these cells did not change as reported previously; however, a statistical analysis revealed a set of 78 significant genes that were distinguishable in expression between passages 3 and 7. None of these significant genes corresponded to the markers established by the International Society for Cellular Therapy (ISCT) for MSC identification. When the significant gene lists were analyzed through pathway analysis, these genes were involved in the top-scoring networks of cellular growth and proliferation and cellular development. A meta-analysis of the literature for significant genes revealed that the MSCs seem to be undergoing differentiation into a senescent cell type when cultured extensively. Consistent with the differences in gene expression at passage 3 and 7, MSCs exhibited a significantly greater potential for cell division at passage 3 in comparison to passage 7. CONCLUSIONS: Our results identified specific gene markers that distinguish aging MSCs grown in cell culture. Confirmatory studies are needed to correlate these molecular markers with biologic attributes that may facilitate the development of assays to test the quality of MSCs before clinical use.

Targeting angiogenesis via a c-Myc/hypoxia-inducible factor-1alpha-dependent pathway in multiple myeloma.

Bone marrow angiogenesis is associated with multiple myeloma (MM) progression. Here, we report high constitutive hypoxia-inducible factor-1alpha (Hif-1alpha) expression in MM cells, which is associated with oncogenic c-Myc. A drug screen for anti-MM agents that decrease Hif-1alpha and c-Myc levels identified a variety of compounds, including bortezomib, lenalidomide, enzastaurin, and adaphostin. Functionally, based on transient knockdowns and overexpression, our data delineate a c-Myc/Hif-1alpha-dependent pathway mediating vascular endothelial growth factor production and secretion. The antiangiogenic activity of our tool compound, adaphostin, was subsequently shown in a zebrafish model and translated into a preclinical in vitro and in vivo model of MM in the bone marrow milieu. Our data, therefore, identify Hif-1alpha as a novel molecular target in MM and add another facet to anti-MM drug activity.

Chromosomal instability is associated with higher expression of genes implicated in epithelial-mesenchymal transition, cancer invasiveness, and metastasis and with lower expression of genes involved in cell cycle checkpoints, DNA repair, and chromatin maintenance.

Chromosomal instability-a hallmark of epithelial cancers-is an ongoing process that results in aneuploidy and karyotypic heterogeneity of a cancer cell population. Previously, we stratified cancer cell lines in the NCI-60 drug discovery panel based on their karyotypic complexity and heterogeneity. Using this stratification in conjunction with drug response data for the cell lines allowed us to identify classes of chemical compounds whose growth-inhibitory activity correlates with karyotypic complexity and chromosomal instability. In this article, we asked the question: What are the biological processes, pathways, or genes associated with chromosomal instability of cancer cells? We found that increased instability of the chromosomal content in a cancer cell population, particularly, persistent gains and losses of chromosomes, is associated with elevated expression of genes involved with aggressive cellular behavior, including invasion- and metastasis-associated changes in cell communication, adhesion, motility, and migration. These same karyotypic features are negatively correlated with the expression of genes involved in cell cycle checkpoints, DNA repair, and chromatin maintenance.

Challenges in DNA microarray studies from the regulatory perspective.

Genomic classifiers using DNA microarrays are becoming powerful tools in the medical community with the potential to revolutionize the diagnosis and treatment of disease. However, despite the tremendous interest in using these classifiers in diagnosis and the management of disease, few genomic classifiers have made it into clinical practice. Some of the major challenges for the development and validation of genomic classifiers will be discussed in this article together with some of their difficulties.

Detailed DNA methylation profiles of the E-cadherin promoter in the NCI-60 cancer cells.

E-cadherin (E-cad) is a transmembrane adhesion glycoprotein, the expression of which is often reduced in invasive or metastatic tumors. To assess E-cad's distribution among different types of cancer cells, we used bisulfite-sequencing for detailed, base-by-base measurement of CpG methylation in E-cad's promoter region in the NCI-60 cell lines. The mean methylation levels of the cell lines were distributed bimodally, with values pushed toward either the high or low end of the methylation scale. The 38 epithelial cell lines showed substantially lower (28%) mean methylation levels compared with the nonepithelial cell lines (58%). The CpG site at -143 with respect to the transcriptional start was commonly methylated at intermediate levels, even in cell lines with low overall DNA methylation. We also profiled the NCI-60 cell lines using Affymetrix U133 microarrays and found E-cad expression to be correlated with E-cad methylation at highly statistically significant levels. Above a threshold of approximately 20% to 30% mean methylation, the expression of E-cad was effectively silenced. Overall, this study provides a type of detailed analysis of methylation that can also be applied to other cancer-related genes. As has been shown in recent years, DNA methylation status can serve as a biomarker for use in choosing therapy.

Women participating in a dietary intervention trial maintain dietary changes without much effect on household members.

This study examined whether subjects who participated in a 12-mo intervention would maintain their diets 1 yr after the study ended and whether the diets of household members were affected. Premenopausal women, who had at least one first-degree relative with breast cancer (n = 122), were randomized to one of four diets: control, low fat (15% of energy), high fruit and vegetable (FV, nine servings per day), and combination low fat, high FV. Study subjects and one household member were asked to complete the Block '95 food-frequency questionnaire (FFQ) at baseline, 1 yr, and 2 yr. Study subjects also completed 24-h recalls and 4-day food records at baseline and Year 1. Fat and FV intakes by all three assessment methods compared reasonably well except that fat intakes by FFQ were somewhat higher. FV intakes by FFQ in the high-FV and combination arms increased significantly from 4 servings per day to about 10 servings per day at Year 1 and 7 servings per day at Year 2. FV intakes increased much more modestly in the low-fat and control arms. Fat intakes in the low-fat and combination arms were lower at Year 1 than Year 2, but mean Year 2 fat intakes of 26-28% were still significantly lower than those at baseline. In household members, the only significant change was a small decrease in energy from fat at Year 1 in the household members of subjects who were in the combination arm. These results indicate that study subjects were making large dietary changes independently of their household members and that fat and FV intakes in study subjects 1 yr after intervention stopped were still substantially different from intakes at baseline.

Integrating data on DNA copy number with gene expression levels and drug sensitivities in the NCI-60 cell line panel.

Chromosome rearrangement, a hallmark of cancer, has profound effects on carcinogenesis and tumor phenotype. We used a panel of 60 human cancer cell lines (the NCI-60) as a model system to identify relationships among DNA copy number, mRNA expression level, and drug sensitivity. For each of 64 cancer-relevant genes, we calculated all 4,096 possible Pearson's correlation coefficients relating DNA copy number (assessed by comparative genomic hybridization using bacterial artificial chromosome microarrays) and mRNA expression level (determined using both cDNA and Affymetrix oligonucleotide microarrays). The analysis identified an association of ERBB2 overexpression with 3p copy number, a finding supported by data from human tumors and a mouse model of ERBB2-induced carcinogenesis. When we examined the correlation between DNA copy number for all 353 unique loci on the bacterial artificial chromosome microarray and drug sensitivity for 118 drugs with putatively known mechanisms of action, we found a striking negative correlation (-0.983; 95% bootstrap confidence interval, -0.999 to -0.899) between activity of the enzyme drug L-asparaginase and DNA copy number of genes near asparagine synthetase in the ovarian cancer cells. Previous analysis of drug sensitivity and mRNA expression had suggested an inverse relationship between mRNA levels of asparagine synthetase and L-asparaginase sensitivity in the NCI-60. The concordance of pharmacogenomic findings at the DNA and mRNA levels strongly suggests further study of L-asparaginase for possible treatment of a low-synthetase subset of clinical ovarian cancers. The DNA copy number database presented here will enable other investigators to explore DNA transcript-drug relationships in their own domains of research focus.