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. 2018 Oct 15;2:CCI.18.00012. doi: 10.1200/CCI.18.00012

PANOPLY: Omics-Guided Drug Prioritization Method Tailored to an Individual Patient

Krishna R Kalari 1,✉, Jason P Sinnwell 1, Kevin J Thompson 1, Xiaojia Tang 1, Erin E Carlson 1, Jia Yu 1, Peter T Vedell 1, James N Ingle 1, Richard M Weinshilboum 1, Judy C Boughey 1, Liewei Wang 1, Matthew P Goetz 1, Vera Suman 1
PMCID: PMC7010444  PMID: 30652605

Abstract

Purpose

The majority of patients with cancer receive treatments that are minimally informed by omics data. We propose a precision medicine computational framework, PANOPLY (Precision Cancer Genomic Report: Single Sample Inventory), to identify and prioritize drug targets and cancer therapy regimens.

Materials and Methods

The PANOPLY approach integrates clinical data with germline and somatic features obtained from multiomics platforms and applies machine learning and network analysis approaches in the context of the individual patient and matched controls. The PANOPLY workflow uses the following four steps: selection of matched controls to the patient of interest; identification of patient-specific genomic events; identification of suitable drugs using the driver-gene network and random forest analyses; and provision of an integrated multiomics case report of the patient with prioritization of anticancer drugs.

Results

The PANOPLY workflow can be executed on a stand-alone virtual machine and is also available for download as an R package. We applied the method to an institutional breast cancer neoadjuvant chemotherapy study that collected clinical and genomic data as well as patient-derived xenografts to investigate the prioritization offered by PANOPLY. In a chemotherapy-resistant patient-derived xenograft model, we found that that the prioritized drug, olaparib, was more effective than placebo in treating the tumor (P < .05). We also applied PANOPLY to in-house and publicly accessible multiomics tumor data sets with therapeutic response or survival data available.

Conclusion

PANOPLY shows promise as a means to prioritize drugs on the basis of clinical and multiomics data for an individual patient with cancer. Additional studies are needed to confirm this approach.

INTRODUCTION

There has been substantial progress in the fight against cancer; however, cancer remains the second leading cause of death in the United States.1 A major focus of cancer research has been the identification of oncogenic drivers and the development of drugs that selectively target those driver events. This approach has led to the development of agents that have been shown to successfully target the driver mutational events, such as trastuzumab, which targets HER2-positive breast cancer2,3; imatinib, which inhibits the BCR-ABL1 tyrosine kinase produced by the Philadelphia translocation in chronic myelogenous leukemia4; vemurafenib, which is used to treat BRAF V600E–mutant malignant melanoma5; agents targeting EGFR mutations in non–small-cell lung carcinoma6; and crizotinib, which is used to treat non–small-cell lung cancer with ALK rearrangements.7

The mapping of the human genome has opened the door to the exploration of the tumor and environmental features to uncover the drivers of cancer and its resistance to treatment. A number of commercial gene sequencing platforms (eg, FoundationOne [Foundation Medicine, Cambridge, MA], Ambry Genetics [Aliso Viejo, CA]) have been developed to identify tumor mutations used in clinical decision making. Most of these platforms are focused on detecting a limited number of gene abnormalities in specific genes and do not include comprehensive multiomics data analysis. For many tumor types, this leads to the inability to link mutational drivers with druggable targets. A pressing need exists for better approaches to identify right drugs for an individual patient using multiomics data. Although most of the studies to date use single omics data type to predict drug response, there are algorithms that use two or more genomic features to predict drug response in cancer cell lines8-10 and in The Cancer Genome Atlas (TCGA) subsets.11 There are databases such as The University of Texas MD Anderson Cancer Center’s Personalized Cancer Therapy,12 Vanderbilt’s My Cancer Genome,13 the Broad Institute’s Tumor Alterations Relevant for Genomics-Driven Therapy (TARGET),14 TCGA,15 and the Catalogue of Somatic Mutations in Cancer (COSMIC),16 which contain information on the frequency of alterations in thousands of patients with cancer. Programs such as DriverNet17 and IntOGen18 analyze a single type of omics data, such as somatic mutations, to identify potential driver genes. Other programs, such as XSeq,19 OncoRep,20 OncoIMPACT,21 and iCAGES,22 integrate information on somatic mutations, copy number alterations (CNAs), and/or gene expression. Although integrating these data types represents substantial progress toward the full molecular characterization needed for precision cancer care, no comprehensive method for integrating clinical and multiomics data has yet been developed and validated for selecting the most compatible agents for a given patient’s omics profile. Thus, we built a workflow called PANOPLY (Precision Cancer Genomic Report: Single Sample Inventory) that identifies molecular alterations that are unique to a patient with cancer compared with matched controls with similar disease characteristics who had a favorable clinical course and then performs a comprehensive, integrated multiomics analysis to identify druggable genomic events for an individual’s tumor. The results are summarized in a report that the patient’s medical oncology team can use to choose the particular agent to be administered.

In brief, PANOPLY uses machine learning and knowledge-driven network analysis to analyze patient-specific alterations (CNAs, germline and somatic alterations from DNA, and RNA gene expression and expressed mutations) driving oncogenesis and prioritize drugs that target the networks and pathways associated with these cancer-driving alterations. We describe the workflow and provide examples using both institutional and publicly available data sets where PANOPLY was used to identify drugs for individual patients and subgroups of patients whose disease was resistant to standard chemotherapy. We confirmed PANOPLY predictions in a patient with chemotherapy-resistant triple-negative breast cancer (TNBC) using patient-derived xenografts (PDXs) by testing the top drugs for that patient.

MATERIALS AND METHODS

Data Sources

Data sets used in the article are described in the Data Supplement.

PANOPLY Workflow

The PANOPLY workflow is available for download as an R package from Kalari Lab (http://kalarikrlab.org/Software/Panoply.html) and GitHub. A high-level overview of the workflow is shown in Figure 1A. Steps 1 to 4 are provided in the Data Supplement.

Fig 1.

Fig 1.

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(A) A high-level overview of PANOPLY (Precision Cancer Genomic Report: Single Sample Inventory). Step 1: A patient of interest is compared with matched controls. Step 2: For each patient, gene expression data, copy number alteration (CNA), single nucleotide variant (SNV), and expressed single nucleotide variant (eSNV) data will be provided to identify patient-specific driver alterations. Step 3: Multiomics data will be provided to the random forest and network analysis methods to identify the top prioritized drugs to target genes that are driving oncogenesis in the patient. Step 4: A genomics case report listing the drugs to prioritize, on the basis of their ability to target driver mutations and their dysregulated gene networks, is generated for researchers and clinicians. (B) An in-depth description of steps using a patient example. Application of PANOPLY to a patient from the Breast Cancer Genome Guided Therapy Study (BEAUTY) with triple-negative breast cancer (TNBC). Step 1 is a comparison of a patient with TNBC who did not respond to standard chemotherapy to a set of matched control patients who have TNBC and responded to standard chemotherapy. Step 2 is when all of the molecular data comparisons are performed to identify patient-specific events compared with matched controls. In Step 3, random forest (RF) analysis, drug network test (DNT), and drug meta test (DMT) are conducted using the features obtained from step 2. In step 4, the P.score (combination of network tests) and RF.score for anticancer drugs are calculated and prioritized on the basis of high P.score and low RF.score. CNN, Curated Cancer Network; OG, oncogenes; TS, tumor suppressors.

False-Positive Rate and True-Positive Rate Simulations

We evaluated the false-positive rate (FPR) under three scenarios of varying levels of correlation between the drug-gene networks and the gene-gene networks: scenario 1 (Sc1) included all gene-gene and drug-gene networks; scenario 2 (Sc2) included reduced gene-gene networks and complete drug-gene networks; and scenario (Sc3) included reduced gene-gene and reduced drug-gene networks. We describe the justification for these scenarios in the Data Supplement, where the binary clustering of the gene-gene and drug-gene networks is provided.

We evaluated the true-positive rate (TPR) of the PANOPLY workflow using just the simulated multivariate normal data by spiking in increased amounts of expression for a subset of genes in the olaparib gene-drug network. Using the same framework as the FPR simulations, we changed the mean expression level for a subset of genes for the patients but not for the matched controls (Data Supplement).

Confirmation of PANOPLY’s Drug Predictions With PDXs

To validate PANOPLY’s drug predictions, we tested PDX models23 obtained from the Breast Cancer Genome Guided Therapy Study (BEAUTY)24 for a patient (BC_051_1_1) with TNBC whose tumor did not respond to neoadjuvant paclitaxel and anthracycline plus cyclophosphamide treatment (Data Supplement).

RESULTS

Statistical Performance of PANOPLY Using Simulated Data

FPR.

We observed that the FPRs for the drug network test (DNT), drug meta test (DMT), and P.score are controlled near the nominal α = .05 and P = .01 levels under a typical analysis with PANOPLY (Data Supplement). We evaluated three scenarios (Sc1, Sc2, and Sc3) with varying correlation of the gene-gene and drug-gene networks and with varying matched control set sizes (2, 4, 8, and 16 controls). Table 1 lists the results for a set size of eight controls for DNT and DMT for all scenarios with the two data sets. The FPRs are slightly higher in the TCGA breast cancer normal adjacent samples than the simulated multivariate normal set. The DNT error rates are adequate for all three simulation scenarios (Sc1-Sc3) but are perhaps too conservative in Sc2 and Sc3. The DMT was observed to be higher than the nominal levels in all scenarios (Sc1-Sc3) in both data sets but is much closer to the nominal level when the correlation cancer gene networks are reduced in both Sc2 and Sc3. In summary, the suggested setting for running the workflow is Sc2, where only the patient-specific events are considered for testing all 374 drugs.

Table 1.

FPR and TPR for DNT and DMT P Values at α = .05 Using Simulated MVN Distribution (n = 500 sets) and TCGA Normal Adjacent Data (n = 94 sets) for Matched Control Size of Eight Controls and Sc1 to Sc3

graphic file with name CCI.18.00012t1.jpg

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TPR.

We quantify TPR as the proportion of simulated data sets for which the DNT and DMT P values for olaparib are significant in the following two ways: the P value is less than α = .05, and the P value is one of the 10 lowest of all drugs. As shown in Table 1 for a set size of eight controls and α = .05, the TPRs for DNT and DMT are comparable with gene set A, whereas the DNT TPR dramatically decreases with more realistic network-specific gene-gene networks that are activated (gene sets B and C), as expected. Complete results for DNT, DMT, and P.score are discussed in the Data Supplement. The Data Supplement also demonstrates that the TPR is robust to the size of the reference sample population (matched controls sets of n = 2, n = 4, n = 8, and n = 16), that the top 10 metric verifies the α = .05 TPR rates, and that P.score is useful in ranking drugs that perform well across DNT or DMT. Furthermore, the Data Supplement shows the TPR gains in DMT and P.score for incremental increases in overexpression of genes targeted by a drug; we also observed higher TPR values in the model when DNA aberration events are spiked in addition to RNA expression (Data Supplement).

Patient Case Study

We applied the PANOPLY workflow to prioritize drugs for a patient from the BEAUTY trial (patient BC_051_1_1) who did not respond to neoadjuvant chemotherapy and for whom a set of matched controls (n = 9) were found among the BEAUTY patients with TNBC who had a pathologic complete response to neoadjuvant chemotherapy. The PANOPLY report for this patient is available in the Data Supplement. Here, we provide the experimental validation of a PANOPLY-prioritized drug using PDX models and the comparison of PANOPLY analysis for patient BC_051_1 with other methods.

Experiment validation.

Somatic and germline mutation, CNA, gene expression, and expressed mutation data for the patient were compared and contrasted with her matched controls using the PANOPLY workflow (Data Supplement). A high-level, step-by-step analysis of the PANOPLY algorithm for this patient is shown in Figure 1B. The PANOPLY results indicated that olaparib was the most promising drug for this patient (Table 2). Figure 2A shows histologic images of the patient’s tumor and a corresponding PDX, both from the pre–neoadjuvant chemotherapy (NAC) and post-NAC time points. Pre- and post-NAC patient tumor and its corresponding PDX had similar morphologic features and a triple-negative staining pattern (Fig 2A). For both the pre-NAC and post-NAC PDXs, tumor volume at day 12 was significantly lower in the olaparib group than in the vehicle group (Wilcoxon rank sum test, P = .04 and P < .001, respectively; Fig 2B). The PDX results show promise that this approach may be successful in identifying an effective therapy for patients.

Table 2.

Top Drugs Recommended by PANOPLY for BEAUTY Patient (BC_051_1_1) With Chemotherapy-Resistant Triple-Negative Breast Cancer and PDX Model

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Fig 2.

Fig 2.

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Patient-derived xenografts (PDXs) confirm the prediction of PANOPLY (Precision Cancer Genomic Report: Single Sample Inventory) that olaparib is an effective treatment of a patient with chemotherapy-resistant triple-negative breast cancer (TNBC; patient BC_051_1_1). (A) The top panel shows histologic stains of the patient’s tumor and PDXs (before and after treatment). (B) Cytotoxicity data show the PDX response to the top predicted drug olaparib compared with no treatment. (The left plot shows the olaparib drug response data from pretreatment mice, whereas the right plot shows the data from post-treatment PDX models. Both of the data sets were generated using the vehicle as controls.) ER, estrogen receptor; H&E, hematoxylin and eosin; HER2, human epidermal growth factor receptor 2; Ki67, marker to measure proliferative index; PostNAC, post neoadjuvant chemotherapy; PR, progesterone receptor; PreNA, pre neoadjuvant chemotherapy

Comparison of PANOPLY with existing methods.

Recent bioinformatics software, such as iCAGES and OncoRep, attempts to incorporate a tabulation of anticancer drug options targeting observed driver genes. These software programs were developed independently and with their own design assumptions and intent. Table 3 provides a summary of these two software implementations, in comparison with PANOPLY. We were able to generate a similar iCAGES report with default settings for patient BC_051_1_1 by providing required somatic mutation (variant call format [VCF] ) and the CNA data (browser extensible data [BED] format). We were not able to configure the current architecture of the Amazon Web services required for the omics_pipe workflow (which precedes the OncoRep analysis module).

Table 3.

Comparison of OncoRep,20 iCAGES,22 and PANOPLY Methods

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Extensibility of the Workflow to Cohort Studies and Public Domain Data Sets

PANOPLY drug predictions for patients with chemotherapy-resistant TNBC.

PANOPLY provides a prioritized drug list for each patient in the cohort. This list corresponds to a unique set of gene targets for each patient, which can be compared and contrasted with similar chemotherapy-resistant patients using existing clustering methods. The genomic characteristics of these clusters can be reverse engineered to find qualifying genomic events that would qualify future patients for drug bucket trials. An illustration of this application of PANOPLY is provided using the 17 patients from the BEAUTY trial with chemotherapy-resistant TNBC. Non-negative matrix factorization clustering25 was performed with the drug priority scores of these 17 patients. On the basis of the cophenetic and average silhouette scores, two clusters were selected to be optimal. The percentile ranking of the top 10% of drugs (35 of 344 drugs) was aggregated per sample cluster using the median score and presented as a heatmap (Fig 3A). The target genes of the drug clusters were collated, and a word cloud was generated with the targets (Fig 3B). As shown in Figure 3A, cluster 1 consists of nine samples; the patients in that group primarily had kinase inhibitors as their top prioritized drugs (n = 16 drugs) and the drugs in that cluster predominantly target the PIK3CA-mTOR-AKT signaling pathways. The other cluster from Figure 3A consists of a set of prioritized drugs (n = 19) for eight patients; as shown in Figure 3B, these drugs primarily target genes associated with cell cycle control, specifically targeting the histone deacetylases (HDAC1) and the Aurora kinases A and B.

Fig 3.

Fig 3.

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Clustering and word cloud plots of 17 patients with chemotherapy-resistant triple-negative breast cancer (TNBC). (A) Two-way hierarchical clustering of the top 10% of the drugs predicted by PANOPLY (Precision Cancer Genomic Report: Single Sample Inventory) for 17 patients with basal TNBC. The heatmap shows that there are two sample and drug clusters implicated in the non-negative matrix factorization (NMF) clustering analysis. (B) Word cloud of the target genes from the two drug clusters predicted by the NMF analysis.

PANOPLY drug predictions for a public data set such as TCGA with molecular and clinical data.

Here, we present the capability of the PANOPLY workflow to be executed with publicly available data sets. An example COAD (colon adenocarcinoma) patient’s (TCGA-AA-3488) report is discussed briefly here and can be obtained from the Data Supplement. The patient displayed 645 driver genes: 226 CNAs were found in the patient, and 419 genes were overexpressed in the patient’s tumor sample relative to the matched control samples. Of the 226 CNAs, 113 were amplifications, and 113 were deletions. Of those, the BRAF, CDKN2A, CDKN2B, FGF10, IL7R, INHBA, JAK2, KEL, MAFA, NTRK1, NTRK2, PIK3CA, PRSS1, RBL1, SKIL, SMO, SOX2, and SPTA1 cancer-related genes were both amplified and overexpressed. Of the patient’s 645 events, 53 genes were differentially expressed between the patient and matched controls and can be targeted by antineoplastic drugs. On the basis of the network and random forest analysis of driver genes and gene expression data, PANOPLY ranked the following drugs as significant for patient TCGA-AA-3488 with significant P.score and random forest score: lestaurtinib (JAK2, NTRK1, and NTRK2), LY2784544 (JAK2), GDC-0032 (PIK3CA), NVP-BGT226 (MTOR and PIK3CA), regorafenib (MAPK11, RAF1, BRAF, KRAS, KIT, FGFR1/2, PDGFRA/B, ARAF, KDR, EPHA2, ABL1, NTRK1, CYP3A4, CYP2C8/9, CYP2B6, CYP2C19, ABCB1, ABCG2, UGT1A1/9, FLT1/4, RET, TEK, and DDR2), and ARQ736 (BRAF; Data Supplement).

Similarly, we also applied PANOPLY to TCGA breast cancer data (BRCA). The exemplary report was prepared for a TCGA tumor sample (TCGA-AR-A1AR) and is presented in the Data Supplement.

DISCUSSION

In creating PANOPLY, our goal was to develop a flexible workflow capable of analyzing multiple forms of omics and clinical data to identify driver genes, their effects on gene networks, and the drugs capable of targeting altered gene networks in patients with cancer. Using gene expression data, CNA, and DNA variants from publicly available and in-house data sets, we demonstrate that PANOPLY holds promise in identifying agents capable of targeting driver gene–induced changes, both for individual patients and for subgroups of patients who share a cancer subtype and response pattern. This is evident in the example where PANOPLY prioritized olaparib as a promising treatment of a patient with chemotherapy-resistant TNBC and that agent was found to reduce tumor size when applied to that patient’s xenografts. When the same patient’s data were run through iCAGES, the drug with the highest iCAGES drug score (0.52) was doxorubicin, the drug administered as part of the patient’s NAC regimen that failed to produce a pathologic complete response, whereas olaparib scored much lower (1.3 × 10−4) among potential agents. Doxorubicin intercalates DNA and thereby indirectly targets TP53 (TOP2A), which is observed in a substantial proportion of patients with cancer. Coconsidering associated genes involved with DNA damage repair and/or active cellular uptake would presumably provide a more accurate prediction of drug efficacy, as implemented by PANOPLY.

The PANOPLY workflow currently analyzes the patient’s molecular and clinical data together along with the knowledge databases, such as Reactome, Drug-Gene Interaction Database, and others, for drug-gene network analysis. This represents a substantial advancement relative to existing programs, such as XSeq,19 OncoRep,20 and OncoIMPACT,21 which integrate only molecular data such as somatic mutations or CNAs and gene expression. Currently, medical oncologists have access to genomics reports that were generated using a limited target panel for decision making. Working closely with clinicians, basic scientists, and pharmacologists, we have developed PANOPLY to integrate molecular, clinical, and drug data to prioritize targets and facilitate individualized treatment of patients. Using clinical and molecular profiles of the patient’s disease, PANOPLY provides a personalized list of prioritized drugs along with links to literature concerning drug efficacy. The oncologist will still have to go through the list and refine drug selection on the basis of the inherent clinical knowledge, ancillary clinical trials, and insurance coverage availability.

Obtaining the whole genome and transcriptome data is challenging for implementation of PANOPLY in a routine setting. PANOPLY’s framework can be customized to analyze focused gene panels with expression and mutation data by modifying the gene adjacency matrix (Reactome.adj module), as described in the user manual of the PANOPLY package. However, we do not recommend this practice because it compromises our algorithm and its capabilities. Several studies24,26-32 have both molecular and clinical data publicly available; however, most of these analyses use a cohort-based comparison that may not be specific to an individual. As individual interventions become a reality on a routine basis in the precision medicine era, PANOPLY’s framework to harness the current knowledge in genomics and guide patient treatment on the basis of clinical and multiomics data will become more valuable.

Presently, generating multiomics data for a patient is still cost prohibitive, but as the cost of clinical sequencing continues to decline,33-36 cost may become less of a concern, making this process more feasible in the near future. Obtaining clinical data and designing a matched control set are laborious tasks. However, extensive application of the electronic medical records in clinics and the recent development of functions such as optmatch in R provide ways to obtain optimally matched cohorts. For now, if no matched controls are available for a study, we recommend using TCGA multiomics and clinical data to design a matched control set. Although the performance might be compromised as a result of the batch effects between data sets, appropriate adjustments of data should minimize the effect. As an option, we provide a module in PANOPLY to perform adjustment to counter for the batch effect. Another limitation of PANOPLY is that the method cannot delineate the clinical effectiveness of a similar class of drugs. We plan to accomplish this in the future by bringing in drug knowledge such as chemical structure, molecular size, and drug dosage. Our method is dependent on drug-gene target annotations that are heavily biased by product literature and databases. Moreover, clinical translation of PANOPLY results is constrained by the cost effectiveness of developing PDXs for drug validation.

In summary, PANOPLY is a flexible framework that can integrate many other types of omics data, including protein expression, methylation expression, structural variants, circular RNAs, long noncoding RNAs, and fusion data with modifications to its code. PANOPLY’s framework can also be extended to the metastatic setting; additional clinical data such as prior exposure to drugs and features of primary and recurrent disease would be required. Additional genomics data sets are needed to modify the existing approach for metastatic tumors. As more is learned about the molecular underpinnings of cancer using various resources, we plan to expand our knowledge base to improve PANOPLY’s predictions using PDX and cell lines. We have validated PANOPLY’s predictions in a single patient at present using PDXs; in the future, we plan to validate PANOPLY-predicted drugs in PDXs derived from additional patients. As we and several other groups have shown, PDX models faithfully represent tumor biology,23,37 so these results should provide insight into PANOPLY’s reliability. In conclusion, our results indicate that combining multiple sources of omics and clinical data to predict promising agents for a patient or groups of patients with cancer is feasible. With additional validations, PANOPLY can be a tool to help clinicians in their decision-making process.

Footnotes

Supported by the Mayo Clinic Center for Individualized Medicine (all authors), Mayo Clinic Breast Specialized Program of Research Excellence Grant No. P50CA116201 (K.R.K., J.N.I., R.M.W., L.W., M.P.G., and V.S.), Grant No. U54 GM114838 (K.R.K., R.M.W., J.C.B., L.W., and M.P.G.), Mayo Comprehensive Cancer Center Grant No. P30CA 15083-43 (M.P.G. and V.S.), and the Division of Biostatistics and Informatics at the Mayo Clinic (K.R.K., J.P.S., K.J.T., X.T., E.E.C., and P.T.V.).

AUTHOR CONTRIBUTIONS

Conception and design: Krishna R. Kalari, Jason P. Sinnwell, Kevin J. Thompson, James N. Ingle, Richard M. Weinshilboum, Liewei Wang, Vera Suman

Financial support: Krishna R. Kalari

Provision of study material or patients: Krishna R. Kalari, Judy C. Boughey

Collection and assembly of data: Krishna R. Kalari, Jason P. Sinnwell, Xiaojia Tang, Jia Yu, Peter T. Vedell, Judy C. Boughey, Liewei Wang

Data analysis and interpretation: Krishna R. Kalari, Jason P. Sinnwell, Kevin J. Thompson, Xiaojia Tang, Erin E. Carlson, Jia Yu, Peter T. Vedell, Matthew P. Goetz, Vera Suman

Manuscript writing: All authors

Final approval of manuscript: All authors

Accountable for all aspects of the work: All authors

AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST

The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO's conflict of interest policy, please refer to www.asco.org/rwc or ascopubs.org/jco/site/ifc.

Krishna R. Kalari

No relationship to disclose

Jason P. Sinnwell

No relationship to disclose

Kevin J. Thompson

No relationship to disclose

Xiaojia Tang

No relationship to disclose

Erin E. Carlson

No relationship to disclose

Jia Yu

No relationship to disclose

Peter T. Vedell

No relationship to disclose

James N. Ingle

No relationship to disclose

Richard M. Weinshilboum

Stock and Other Ownership Interests: OneOme

Judy C. Boughey

Research Funding: Myriad Genetics (Inst)

Patents, Royalties, Other Intellectual Property: Patent pending: Methods and Materials for Assessing Chemotherapy Responsiveness and Treating Cancer (Inst)

Liewei Wang

No relationship to disclose

Matthew P. Goetz

Consulting or Advisory Role: Eli Lilly, bioTheranostics, Genomic Health, Novartis, Eisai

Research Funding: Eli Lilly, Pfizer

Patents, Royalties, Other Intellectual Property: Methods and Materials for Assessing Chemotherapy Responsiveness and Treating Cancer; Methods and Materials for Using Butyrylcholinesterases to Treat Cancer

Vera Suman

No relationship to disclose

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