Comprehensive comparison of molecular portraits between cell lines and tumors in breast cancer

BackgroundProper cell models for breast cancer primary tumors have long been the focal point in the cancer’s research. The genomic comparison between cell lines and tumors can investigate the similarity and dissimilarity and help to select right cell model to mimic tumor tissues to properly evaluate the drug reaction in vitro. In this paper, a comprehensive comparison in copy number variation (CNV), mutation, mRNA expression and protein expression between 68 breast cancer cell lines and 1375 primary breast tumors is conducted and presented.ResultsUsing whole genome expression arrays, strong correlations were observed between cells and tumors. PAM50 gene expression differentiated them into four major breast cancer subtypes: Luminal A and B, HER2amp, and Basal-like in both cells and tumors partially. Genomic CNVs patterns were observed between tumors and cells across chromosomes in general. High C > T and C > G trans-version rates were observed in both cells and tumors, while the cells had slightly higher somatic mutation rates than tumors. Clustering analysis on protein expression data can reasonably recover the breast cancer subtypes in cell lines and tumors. Although the drug-targeted proteins ER/PR and interesting mTOR/GSK3/TS2/PDK1/ER_P118 cluster had shown the consistent patterns between cells and tumor, low protein-based correlations were observed between cells and tumors. The expression consistency of mRNA verse protein between cell line and tumors reaches 0.7076. These important drug targets in breast cancer, ESR1, PGR, HER2, EGFR and AR have a high similarity in mRNA and protein variation in both tumors and cell lines. GATA3 and RP56KB1 are two promising drug targets for breast cancer. A total score developed from the four correlations among four molecular profiles suggests that cell lines, BT483, T47D and MDAMB453 have the highest similarity with tumors.ConclusionsThe integrated data from across these multiple platforms demonstrates the existence of the similarity and dissimilarity of molecular features between breast cancer tumors and cell lines. The cell lines only mirror some but not all of the molecular properties of primary tumors. The study results add more evidence in selecting cell line models for breast cancer research.

[1]  Jean YH Yang,et al.  Bioconductor: open software development for computational biology and bioinformatics , 2004, Genome Biology.

[2]  Aleix Prat Aparicio.  Comprehensive molecular portraits of human breast tumours , 2012 .

[3]  Ryan D. Morin,et al.  Mutational evolution in a lobular breast tumour profiled at single nucleotide resolution , 2009, Nature.

[4]  Sean Ferree,et al.  Analytical validation of the PAM50-based Prosigna Breast Cancer Prognostic Gene Signature Assay and nCounter Analysis System using formalin-fixed paraffin-embedded breast tumor specimens , 2014, BMC Cancer.

[5]  R. Tibshirani,et al.  Diagnosis of multiple cancer types by shrunken centroids of gene expression , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[6]  L. Waldron,et al.  mRNA transcript quantification in archival samples using multiplexed, color-coded probes , 2011, BMC biotechnology.

[7]  K. Coombes,et al.  A Technical Assessment of the Utility of Reverse Phase Protein Arrays for the Study of the Functional Proteome in Non-microdissected Human Breast Cancers , 2010, Clinical Proteomics.

[8]  Irmtraud M. Meyer,et al.  The clonal and mutational evolution spectrum of primary triple-negative breast cancers , 2012, Nature.

[9]  M. Lacroix,et al.  Relevance of Breast Cancer Cell Lines as Models for Breast Tumours: An Update , 2004, Breast Cancer Research and Treatment.

[10]  P. Sorger,et al.  Profiling phospho-signaling networks in breast cancer using reverse-phase protein arrays , 2013, Oncogene.

[11]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[12]  M. Gerstein,et al.  Comparing protein abundance and mRNA expression levels on a genomic scale , 2003, Genome Biology.

[13]  Marc Hafner,et al.  Analysis of growth factor signaling in genetically diverse breast cancer lines , 2014, BMC Biology.

[14]  A. Ellis.  Breast , 2002, BMJ : British Medical Journal.

[15]  Laura M. Heiser,et al.  Modeling precision treatment of breast cancer , 2013, Genome Biology.

[16]  Steven J. M. Jones,et al.  Comprehensive molecular portraits of human breast tumors , 2012, Nature.

[17]  Pei Wang,et al.  Demonstrating the feasibility of large-scale development of standardized assays to quantify human proteins , 2013, Nature Methods.

[18]  Adam A. Margolin,et al.  The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity , 2012, Nature.

[19]  V. Bazan,et al.  Breast cancer genome-wide association studies: there is strength in numbers , 2012, Oncogene.

[20]  Wen-Lin Kuo,et al.  A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes. , 2006, Cancer cell.

[21]  E. Conant,et al.  Breast cancer screening in an era of personalized regimens: A conceptual model and National Cancer Institute initiative for risk‐based and preference‐based approaches at a population level , 2014, Cancer.

[22]  Helga Thorvaldsdóttir,et al.  Integrative Genomics Viewer , 2011, Nature Biotechnology.

[23]  C. Sander,et al.  Evaluating cell lines as tumour models by comparison of genomic profiles , 2013, Nature Communications.

[24]  A. Børresen-Dale,et al.  Mutational Processes Molding the Genomes of 21 Breast Cancers , 2012, Cell.

[25]  F. Markowetz,et al.  The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups , 2012, Nature.

[26]  W. Bodmer,et al.  Cancer cell lines for drug discovery and development. , 2014, Cancer research.

[27]  Valerie Speirs,et al.  Choosing the right cell line for breast cancer research , 2011, Breast Cancer Research.

[28]  Lester L. Peters,et al.  Genome-wide association study identifies novel breast cancer susceptibility loci , 2007, Nature.

[29]  L. Pusztai,et al.  Biomarker Analysis of Neoadjuvant Doxorubicin/Cyclophosphamide Followed by Ixabepilone or Paclitaxel in Early-Stage Breast Cancer , 2013, Clinical Cancer Research.

[30]  W. Jones,et al.  Microarray-Based Gene Expression Profiling for Molecular Classification of Breast Cancer and Identification of New Targets for Therapy , 2010 .

[31]  Charles M. Perou,et al.  A Comparison of Gene Expression Signatures from Breast Tumors and Breast Tissue Derived Cell Lines , 2002, Disease markers.

[32]  Benjamin J. Raphael,et al.  Mutational landscape and significance across 12 major cancer types , 2013, Nature.

[33]  M. Westerfield,et al.  Characterization of paired tumor and non‐tumor cell lines established from patients with breast cancer , 1998, International journal of cancer.

[34]  A. Arance,et al.  Clinical implications of the intrinsic molecular subtypes of breast cancer. , 2015, Breast.

[35]  S. Le,et al.  Sequence signatures and mRNA concentration can explain two-thirds of protein abundance variation in a human cell line , 2010, Molecular systems biology.

[36]  X. Chen,et al.  Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. , 2011, The Journal of clinical investigation.

[37]  K. Tomczak,et al.  The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge , 2015, Contemporary oncology.

[38]  A. Jemal,et al.  International variation in prostate cancer incidence and mortality rates. , 2012, European urology.

[39]  Mark T. W. Ebbert,et al.  Intrinsic Subtypes from PAM50 Gene Expression Assay in a Population-Based Breast Cancer Cohort: Differences by Age, Race, and Tumor Characteristics , 2014, Cancer Epidemiology, Biomarkers & Prevention.

[40]  A. Schneeweiss,et al.  Multigene Assays for Classification, Prognosis, and Prediction in Breast Cancer: a Critical Review on the Background and Clinical Utility , 2013, Senologie.

[41]  Joshua M. Stuart,et al.  Subtype and pathway specific responses to anticancer compounds in breast cancer , 2011, Proceedings of the National Academy of Sciences.

[42]  A. Børresen-Dale,et al.  The landscape of cancer genes and mutational processes in breast cancer , 2012, Nature.

[43]  H. Hakonarson,et al.  ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data , 2010, Nucleic acids research.

[44]  John D. Minna,et al.  Molecular Profiling of Breast Cancer Cell Lines Defines Relevant Tumor Models and Provides a Resource for Cancer Gene Discovery , 2009, PloS one.

[45]  Z. Hall.  Cancer , 1906, The Hospital.