Jing Wang1, Dmitri Mouradov2, Xiaojing Wang1, Robert N Jorissen2, Matthew C Chambers3, Lisa J Zimmerman3, Suhas Vasaikar1, Christopher G Love2, Shan Li4, Kym Lowes4, Karl-Johan Leuchowius4, Helene Jousset4, Janet Weinstock5, Christopher Yau6, John Mariadason7, Zhiao Shi8, Yuguang Ban9, Xi Chen10, Robert J C Coffey11, Robbert J C Slebos12, Antony W Burgess13, Daniel C Liebler3, Bing Zhang14, Oliver M Sieber15. 1. Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas. 2. Systems Biology and Personalised Medicine Division, The Walter and Eliza Hall Institute of Medial Research, Parkville, VIC, Australia; Department of Medical Biology, The University of Melbourne, Parkville, VIC, Australia. 3. Department of Biochemistry, Vanderbilt University, Nashville, Tennessee. 4. Systems Biology and Personalised Medicine Division, The Walter and Eliza Hall Institute of Medial Research, Parkville, VIC, Australia. 5. Structural Biology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia. 6. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom; Department of Statistics, University of Oxford, Oxford, United Kingdom. 7. Olivia Newton-John Cancer Research Institute, Heidelberg, VIC, Australia; La Trobe University School of Cancer Medicine, Melbourne, VIC, Australia. 8. Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas. 9. Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, Florida. 10. Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, Florida; Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, Florida. 11. Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee; Veterans Affairs Medical Center, Nashville, Tennessee. 12. Clinical Science Lab, Moffitt Cancer Center, Tampa, Florida. 13. Department of Medical Biology, The University of Melbourne, Parkville, VIC, Australia; Structural Biology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia; Department of Surgery, The University of Melbourne, Parkville, VIC, Australia. 14. Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas. Electronic address: bing.zhang@bcm.edu. 15. Systems Biology and Personalised Medicine Division, The Walter and Eliza Hall Institute of Medial Research, Parkville, VIC, Australia; Department of Medical Biology, The University of Melbourne, Parkville, VIC, Australia; Department of Surgery, The University of Melbourne, Parkville, VIC, Australia; School of Biomedical Sciences, Monash University, Clayton, VIC, Australia. Electronic address: sieber.o@wehi.edu.au.
Abstract
BACKGROUND AND AIMS: Proteomics holds promise for individualizing cancer treatment. We analyzed to what extent the proteomic landscape of human colorectal cancer (CRC) is maintained in established CRC cell lines and the utility of proteomics for predicting therapeutic responses. METHODS: Proteomic and transcriptomic analyses were performed on 44 CRC cell lines, compared against primary CRCs (n=95) and normal tissues (n=60), and integrated with genomic and drug sensitivity data. RESULTS: Cell lines mirrored the proteomic aberrations of primary tumors, in particular for intrinsic programs. Tumor relationships of protein expression with DNA copy number aberrations and signatures of post-transcriptional regulation were recapitulated in cell lines. The 5 proteomic subtypes previously identified in tumors were represented among cell lines. Nonetheless, systematic differences between cell line and tumor proteomes were apparent, attributable to stroma, extrinsic signaling, and growth conditions. Contribution of tumor stroma obscured signatures of DNA mismatch repair identified in cell lines with a hypermutation phenotype. Global proteomic data showed improved utility for predicting both known drug-target relationships and overall drug sensitivity as compared with genomic or transcriptomic measurements. Inhibition of targetable proteins associated with drug responses further identified corresponding synergistic or antagonistic drug combinations. Our data provide evidence for CRC proteomic subtype-specific drug responses. CONCLUSIONS: Proteomes of established CRC cell line are representative of primary tumors. Proteomic data tend to exhibit improved prediction of drug sensitivity as compared with genomic and transcriptomic profiles. Our integrative proteogenomic analysis highlights the potential of proteome profiling to inform personalized cancer medicine.
BACKGROUND AND AIMS: Proteomics holds promise for individualizing cancer treatment. We analyzed to what extent the proteomic landscape of humancolorectal cancer (CRC) is maintained in established CRC cell lines and the utility of proteomics for predicting therapeutic responses. METHODS: Proteomic and transcriptomic analyses were performed on 44 CRC cell lines, compared against primary CRCs (n=95) and normal tissues (n=60), and integrated with genomic and drug sensitivity data. RESULTS: Cell lines mirrored the proteomic aberrations of primary tumors, in particular for intrinsic programs. Tumor relationships of protein expression with DNA copy number aberrations and signatures of post-transcriptional regulation were recapitulated in cell lines. The 5 proteomic subtypes previously identified in tumors were represented among cell lines. Nonetheless, systematic differences between cell line and tumor proteomes were apparent, attributable to stroma, extrinsic signaling, and growth conditions. Contribution of tumor stroma obscured signatures of DNA mismatch repair identified in cell lines with a hypermutation phenotype. Global proteomic data showed improved utility for predicting both known drug-target relationships and overall drug sensitivity as compared with genomic or transcriptomic measurements. Inhibition of targetable proteins associated with drug responses further identified corresponding synergistic or antagonistic drug combinations. Our data provide evidence for CRC proteomic subtype-specific drug responses. CONCLUSIONS: Proteomes of established CRC cell line are representative of primary tumors. Proteomic data tend to exhibit improved prediction of drug sensitivity as compared with genomic and transcriptomic profiles. Our integrative proteogenomic analysis highlights the potential of proteome profiling to inform personalized cancer medicine.
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