Skip to main content
Grant Details

Grant Number: 5R01CA183793-05 Interpret this number
Primary Investigator: Wang, Wenyi
Organization: University Of Tx Md Anderson Can Ctr
Project Title: Statistical Methods for Genomic Analysis of Heterogeneous Tumors
Fiscal Year: 2018
Back to top


DESCRIPTION (provided by applicant): Solid tissue samples frequently consist of two distinct compartments, an epithelium-derived tumor and its surrounding stroma. Current analysis of tissue samples composed of both tumor cells and stromal cells may under-detect gene expression signatures associated with cancer prognosis or response to treatment. Modeling the separate tissue compartments is necessary for a better understanding of the biological mechanisms underlying cancer. However, compartmental modeling is difficult from a methodological perspective, and adequate statistical methods have not yet been developed for this purpose. Current methods for in silico separation of expression levels from different compartments of a tissue sample have limited utility as they require previous knowledge of either the various mixing proportions of the patient samples, or the actual expression levels in a few genes (i.e., reference genes) across all tissue compartments. This challenge significantly limits our ability to identify molecular subtypes in both tumor and stroma that are predictive of personalized therapeutic targets. This proposal is to develop novel methods and analytic tools to address these important challenges for the in silico dissection of tumor samples and to demonstrate the utility of these tools by investigating the effect of individual tumor sample components and their interactions with drug treatments for lung cancer. Our Aim 1 will provide a Bayesian hierarchical model and related software tools that will have the ability to computationally "dissect" signals within patient samples. This model will take advantage of all existing data and multiple data types, which consequently reduces the need for the prior knowledge that would otherwise be difficult to obtain. This will enable researchers to investigate the expression profiles of individual tumor tissue and surrounding stromal tissues for a much larger set of samples than was previously feasible. It will also provide new ways to increase the accuracy of the genomic analysis of any mixed samples. Our Aim 2 will re-analyze, by deconvolution, what is to our knowledge the largest set of genomic data for the molecular profiling of lung tumors, all of which were collected at MD Anderson Cancer Center. Lung cancer leads amongst all cancers in causing death anywhere in the world. A thorough understanding of tumor biology is critical to the design of effective treatment modalities. Our analyses will include genomic data from more than 500 patients, generated from two innovative biomarker-based clinical trials: the Biomarker-integrated Approaches of Targeted Therapy for Lung Cancer Elimination (BATTLE) trials, and the Profiling of Resistance Patterns & Oncogenic Signaling Pathways in Evaluation of Cancers of the Thorax and Therapeutic Target Identification (PROSPECT) trials. We focus on the study of one prototype example, lung cancer, because of the public impact of the disease and also the likely role of the tumor-stroma interaction in determining clinical outcomes. Our proof-of-principle investigation of the lung cancer data would be the first of its kind, and has the potential to identify new biomarkers predictive of the effects of drug treatments on the survival time of individuals with lung cancer.

Back to top


A Pan-Cancer Analysis Reveals High-Frequency Genetic Alterations in Mediators of Signaling by the TGF-? Superfamily.
Authors: Korkut A. , Zaidi S. , Kanchi R.S. , Rao S. , Gough N.R. , Schultz A. , Li X. , Lorenzi P.L. , Berger A.C. , Robertson G. , et al. .
Source: Cell Systems, 2018-09-14 00:00:00.0; , .
EPub date: 2018-09-14 00:00:00.0.
PMID: 30268436
Related Citations

Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines.
Authors: Ellrott K. , Bailey M.H. , Saksena G. , Covington K.R. , Kandoth C. , Stewart C. , Hess J. , Ma S. , Chiotti K.E. , McLellan M. , et al. .
Source: Cell Systems, 2018-03-28 00:00:00.0; 6(3), p. 271-281.e7.
PMID: 29596782
Related Citations

Accurate RNA Sequencing From Formalin-Fixed Cancer Tissue To Represent High-Quality Transcriptome From Frozen Tissue.
Authors: Li J. , Fu C. , Speed T.P. , Wang W. , Symmans W.F. .
Source: Jco Precision Oncology, 2018; 2018, .
EPub date: 2018-01-26 00:00:00.0.
PMID: 29862382
Related Citations

RNA-seq mixology: designing realistic control experiments to compare protocols and analysis methods.
Authors: Holik A.Z. , Law C.W. , Liu R. , Wang Z. , Wang W. , Ahn J. , Asselin-Labat M.L. , Smyth G.K. , Ritchie M.E. .
Source: Nucleic Acids Research, 2017-03-17 00:00:00.0; 45(5), p. e30.
PMID: 27899618
Related Citations

Estimating Tp53 Mutation Carrier Probability In Families With Li-fraumeni Syndrome Using Lfspro
Authors: Peng G. , Bojadzieva J. , Ballinger M.L. , Li J. , Blackford A.L. , Mai P.L. , Savage S.A. , Thomas D.M. , Strong L.C. , Wang W. .
Source: Cancer Epidemiology, Biomarkers & Prevention : A Publication Of The American Association For Cancer Research, Cosponsored By The American Society Of Preventive Oncology, 2017-01-30 00:00:00.0; , .
PMID: 28137790
Related Citations

Bayesian analysis of longitudinal dyadic data with informative missing data using a dyadic shared-parameter model.
Authors: Ahn J. , Morita S. , Wang W. , Yuan Y. .
Source: Statistical Methods In Medical Research, 2017-01-01 00:00:00.0; , p. 962280217715051.
EPub date: 2017-01-01 00:00:00.0.
PMID: 28629259
Related Citations

MuSE: accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling from sequencing data.
Authors: Fan Y. , Xi L. , Hughes D.S. , Zhang J. , Zhang J. , Futreal P.A. , Wheeler D.A. , Wang W. .
Source: Genome Biology, 2016-08-24 00:00:00.0; 17(1), p. 178.
EPub date: 2016-08-24 00:00:00.0.
PMID: 27557938
Related Citations

Identification of germline DICER1 mutations and loss of heterozygosity in familial Wilms tumour.
Authors: Palculict T.B. , Ruteshouser E.C. , Fan Y. , Wang W. , Strong L. , Huff V. .
Source: Journal Of Medical Genetics, 2016 Jun; 53(6), p. 385-8.
PMID: 26566882
Related Citations

Bayesian variable selection for binary outcomes in high-dimensional genomic studies using non-local priors.
Authors: Nikooienejad A. , Wang W. , Johnson V.E. .
Source: Bioinformatics (oxford, England), 2016-05-01 00:00:00.0; 32(9), p. 1338-45.
EPub date: 2016-05-01 00:00:00.0.
PMID: 26740524
Related Citations

Next-Generation Molecular Testing of Newborn Dried Blood Spots for Cystic Fibrosis.
Authors: Lefterova M.I. , Shen P. , Odegaard J.I. , Fung E. , Chiang T. , Peng G. , Davis R.W. , Wang W. , Kharrazi M. , Schrijver I. , et al. .
Source: The Journal Of Molecular Diagnostics : Jmd, 2016 Mar; 18(2), p. 267-82.
PMID: 26847993
Related Citations

The Molecular Taxonomy of Primary Prostate Cancer.
Authors: Cancer Genome Atlas Research Network .
Source: Cell, 2015-11-05 00:00:00.0; 163(4), p. 1011-25.
PMID: 26544944
Related Citations

An ensemble approach to accurately detect somatic mutations using SomaticSeq.
Authors: Fang L.T. , Afshar P.T. , Chhibber A. , Mohiyuddin M. , Fan Y. , Mu J.C. , Gibeling G. , Barr S. , Asadi N.B. , Gerstein M.B. , et al. .
Source: Genome Biology, 2015-09-17 00:00:00.0; 16, p. 197.
EPub date: 2015-09-17 00:00:00.0.
PMID: 26381235
Related Citations

Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection.
Authors: Ewing A.D. , Houlahan K.E. , Hu Y. , Ellrott K. , Caloian C. , Yamaguchi T.N. , Bare J.C. , P'ng C. , Waggott D. , Sabelnykova V.Y. , et al. .
Source: Nature Methods, 2015 Jul; 12(7), p. 623-30.
PMID: 25984700
Related Citations

FamSeq: a variant calling program for family-based sequencing data using graphics processing units.
Authors: Peng G. , Fan Y. , Wang W. .
Source: Plos Computational Biology, 2014 Oct; 10(10), p. e1003880.
PMID: 25357123
Related Citations

The somatic genomic landscape of chromophobe renal cell carcinoma.
Authors: Davis C.F. , Ricketts C.J. , Wang M. , Yang L. , Cherniack A.D. , Shen H. , Buhay C. , Kang H. , Kim S.C. , Fahey C.C. , et al. .
Source: Cancer Cell, 2014-09-08 00:00:00.0; 26(3), p. 319-30.
EPub date: 2014-09-08 00:00:00.0.
PMID: 25155756
Related Citations

Back to Top