Mapping Molecular Networks Using Proteomics: A Vision for Patient-Tailored Combination Therapy

Emanuel F Petricoin; Verena E Bichsel; Valerie S Calvert; Virginia Espina; Mary Winters; Lynn Young; Claudio Belluco; Bruce J Trock; Marc Lippman; David A Fishman; Dennis C Sgroi; Peter J Munson; Laura J Esserman; Lance A Liotta

doi:10.1200/JCO.2005.02.509

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Mapping Molecular Networks Using Proteomics: A Vision for Patient-Tailored Combination Therapy

Journal article

Peer reviewed

Mapping Molecular Networks Using Proteomics: A Vision for Patient-Tailored Combination Therapy

Emanuel F Petricoin, Verena E Bichsel, Valerie S Calvert, Virginia Espina, Mary Winters, Lynn Young, Claudio Belluco, Bruce J Trock, Marc Lippman, David A Fishman, …

Journal of clinical oncology, Vol.23(15), pp.3614-3621

2005-05-20

DOI: https://doi.org/10.1200/JCO.2005.02.509

PMID: 15908672

Abstract

Mapping tumor cell protein networks in vivo will be critical for realizing the promise of patient-tailored molecular therapy. Cancer can be defined as a dysregulation or hyperactivity in the network of intracellular and extracellular signaling cascades. These protein signaling circuits are the ultimate targets of molecular therapy. Each patient's tumor may be driven by a distinct series of molecular pathogenic defects. Thus, for any single molecular targeted therapy, only a subset of cancer patients may respond. Individualization of therapy, which tailors a therapeutic regimen to a tumor molecular portrait, may be the solution to this dilemma. Until recently, the field lacked the technology for molecular profiling at the genomic and proteomic level. Emerging proteomic technology, used concomitantly with genomic analysis, promises to meet this need and bring to reality the clinical adoption of molecular stratification. The activation state of kinase-driven signal networks contains important information relative to cancer pathogenesis and therapeutic target selection. Proteomic technology offers a means to quantify the state of kinase pathways, and provides post-translational phosphorylation data not obtainable by gene arrays. Case studies using clinical research specimens are provided to show the feasibility of generating the critical information needed to individualize therapy. Such technology can reveal potential new pathway interconnections, including differences between primary and metastatic lesions. We provide a vision for individualized combinatorial therapy based on proteomic mapping of phosphorylation end points in clinical tissue material.

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Collaboration types: Domestic collaboration; International collaboration
Citation topics: 2 Chemistry; 2.211 Mass Spectrometry; 2.211.497 Proteomics
Web Of Science research areas: Oncology
ESI research areas: Clinical Medicine

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Title: Mapping Molecular Networks Using Proteomics: A Vision for Patient-Tailored Combination Therapy
Creators: Emanuel F Petricoin - From the US Food and Drug Administration (FDA) –National Cancer Institute (NCI) Clinical Proteomics Program, Office of Cellular and Gene Therapy, Center for Biologics Evaluation and Research (CBER), FDA; FDA-NCI Clinical Proteomics Program, Laboratory of Pathology, NCI, Center for Cancer Research (CCR)/National Institutes of Health (NIH); Biostatistics Section, Mathematical and Statistical Computing Laboratory, Center for Information Technology, NIH, Bethesda; Johns Hopkins University, Department of
Verena E Bichsel - From the US Food and Drug Administration (FDA) –National Cancer Institute (NCI) Clinical Proteomics Program, Office of Cellular and Gene Therapy, Center for Biologics Evaluation and Research (CBER), FDA; FDA-NCI Clinical Proteomics Program, Laboratory of Pathology, NCI, Center for Cancer Research (CCR)/National Institutes of Health (NIH); Biostatistics Section, Mathematical and Statistical Computing Laboratory, Center for Information Technology, NIH, Bethesda; Johns Hopkins University, Department of
Valerie S Calvert - From the US Food and Drug Administration (FDA) –National Cancer Institute (NCI) Clinical Proteomics Program, Office of Cellular and Gene Therapy, Center for Biologics Evaluation and Research (CBER), FDA; FDA-NCI Clinical Proteomics Program, Laboratory of Pathology, NCI, Center for Cancer Research (CCR)/National Institutes of Health (NIH); Biostatistics Section, Mathematical and Statistical Computing Laboratory, Center for Information Technology, NIH, Bethesda; Johns Hopkins University, Department of
Virginia Espina - From the US Food and Drug Administration (FDA) –National Cancer Institute (NCI) Clinical Proteomics Program, Office of Cellular and Gene Therapy, Center for Biologics Evaluation and Research (CBER), FDA; FDA-NCI Clinical Proteomics Program, Laboratory of Pathology, NCI, Center for Cancer Research (CCR)/National Institutes of Health (NIH); Biostatistics Section, Mathematical and Statistical Computing Laboratory, Center for Information Technology, NIH, Bethesda; Johns Hopkins University, Department of
Mary Winters - From the US Food and Drug Administration (FDA) –National Cancer Institute (NCI) Clinical Proteomics Program, Office of Cellular and Gene Therapy, Center for Biologics Evaluation and Research (CBER), FDA; FDA-NCI Clinical Proteomics Program, Laboratory of Pathology, NCI, Center for Cancer Research (CCR)/National Institutes of Health (NIH); Biostatistics Section, Mathematical and Statistical Computing Laboratory, Center for Information Technology, NIH, Bethesda; Johns Hopkins University, Department of
Lynn Young - From the US Food and Drug Administration (FDA) –National Cancer Institute (NCI) Clinical Proteomics Program, Office of Cellular and Gene Therapy, Center for Biologics Evaluation and Research (CBER), FDA; FDA-NCI Clinical Proteomics Program, Laboratory of Pathology, NCI, Center for Cancer Research (CCR)/National Institutes of Health (NIH); Biostatistics Section, Mathematical and Statistical Computing Laboratory, Center for Information Technology, NIH, Bethesda; Johns Hopkins University, Department of
Claudio Belluco - From the US Food and Drug Administration (FDA) –National Cancer Institute (NCI) Clinical Proteomics Program, Office of Cellular and Gene Therapy, Center for Biologics Evaluation and Research (CBER), FDA; FDA-NCI Clinical Proteomics Program, Laboratory of Pathology, NCI, Center for Cancer Research (CCR)/National Institutes of Health (NIH); Biostatistics Section, Mathematical and Statistical Computing Laboratory, Center for Information Technology, NIH, Bethesda; Johns Hopkins University, Department of
Bruce J Trock - From the US Food and Drug Administration (FDA) –National Cancer Institute (NCI) Clinical Proteomics Program, Office of Cellular and Gene Therapy, Center for Biologics Evaluation and Research (CBER), FDA; FDA-NCI Clinical Proteomics Program, Laboratory of Pathology, NCI, Center for Cancer Research (CCR)/National Institutes of Health (NIH); Biostatistics Section, Mathematical and Statistical Computing Laboratory, Center for Information Technology, NIH, Bethesda; Johns Hopkins University, Department of
Marc Lippman - From the US Food and Drug Administration (FDA) –National Cancer Institute (NCI) Clinical Proteomics Program, Office of Cellular and Gene Therapy, Center for Biologics Evaluation and Research (CBER), FDA; FDA-NCI Clinical Proteomics Program, Laboratory of Pathology, NCI, Center for Cancer Research (CCR)/National Institutes of Health (NIH); Biostatistics Section, Mathematical and Statistical Computing Laboratory, Center for Information Technology, NIH, Bethesda; Johns Hopkins University, Department of
David A Fishman - From the US Food and Drug Administration (FDA) –National Cancer Institute (NCI) Clinical Proteomics Program, Office of Cellular and Gene Therapy, Center for Biologics Evaluation and Research (CBER), FDA; FDA-NCI Clinical Proteomics Program, Laboratory of Pathology, NCI, Center for Cancer Research (CCR)/National Institutes of Health (NIH); Biostatistics Section, Mathematical and Statistical Computing Laboratory, Center for Information Technology, NIH, Bethesda; Johns Hopkins University, Department of
Dennis C Sgroi - From the US Food and Drug Administration (FDA) –National Cancer Institute (NCI) Clinical Proteomics Program, Office of Cellular and Gene Therapy, Center for Biologics Evaluation and Research (CBER), FDA; FDA-NCI Clinical Proteomics Program, Laboratory of Pathology, NCI, Center for Cancer Research (CCR)/National Institutes of Health (NIH); Biostatistics Section, Mathematical and Statistical Computing Laboratory, Center for Information Technology, NIH, Bethesda; Johns Hopkins University, Department of
Peter J Munson - From the US Food and Drug Administration (FDA) –National Cancer Institute (NCI) Clinical Proteomics Program, Office of Cellular and Gene Therapy, Center for Biologics Evaluation and Research (CBER), FDA; FDA-NCI Clinical Proteomics Program, Laboratory of Pathology, NCI, Center for Cancer Research (CCR)/National Institutes of Health (NIH); Biostatistics Section, Mathematical and Statistical Computing Laboratory, Center for Information Technology, NIH, Bethesda; Johns Hopkins University, Department of
Laura J Esserman - From the US Food and Drug Administration (FDA) –National Cancer Institute (NCI) Clinical Proteomics Program, Office of Cellular and Gene Therapy, Center for Biologics Evaluation and Research (CBER), FDA; FDA-NCI Clinical Proteomics Program, Laboratory of Pathology, NCI, Center for Cancer Research (CCR)/National Institutes of Health (NIH); Biostatistics Section, Mathematical and Statistical Computing Laboratory, Center for Information Technology, NIH, Bethesda; Johns Hopkins University, Department of
Lance A Liotta - From the US Food and Drug Administration (FDA) –National Cancer Institute (NCI) Clinical Proteomics Program, Office of Cellular and Gene Therapy, Center for Biologics Evaluation and Research (CBER), FDA; FDA-NCI Clinical Proteomics Program, Laboratory of Pathology, NCI, Center for Cancer Research (CCR)/National Institutes of Health (NIH); Biostatistics Section, Mathematical and Statistical Computing Laboratory, Center for Information Technology, NIH, Bethesda; Johns Hopkins University, Department of
Publication Details: Journal of clinical oncology, Vol.23(15), pp.3614-3621
Academic Unit: Miller School of Medicine; UMMG Dept of Medicine - Hematology/Oncology
Language: English
Resource Type: Journal article
PMID: 15908672
Record Identifier: 991031600651102976