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Pathology as Integrative Research Biology

Pathology as Integrative Research Biology. GEMS = Genetically Engineered Mice PERLS = Computer Languages. Robert D. Cardiff, M.D., Ph.D. And Jose J. Galvez, M.D. Center for Comparative Medicine University of California, Davis. PATHOLOGY. INTEGRATION of STRUCTURE AND FUNCTION.

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Pathology as Integrative Research Biology

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  1. Pathology as Integrative Research Biology GEMS = Genetically Engineered Mice PERLS = Computer Languages Robert D. Cardiff, M.D., Ph.D. And Jose J. Galvez, M.D. Center for Comparative Medicine University of California, Davis

  2. PATHOLOGY INTEGRATION of STRUCTURE AND FUNCTION INTEGRATION CONTEXT

  3. PATHOLOGY Interpretation of morphologic alterations requires knowledge of and integration of structure, function, natural history, etiology and clinical context. Armed with this information, pathology provides integrative biology. Without this information, histopathology is useless.

  4. VALIDATION: Pathology’s New Challenge VERIFICATION: Yes, that is a tumor of the mammary gland. VALIDATION: …a malignant neoplasm of the mammary gland (adenocarcinoma, mammary gland) initiated by the Her2/neu gene with a solid, lobular pattern and cells with oval, uniform nuclei and relatively abundant cytoplasm. The tumor resembles lobular carcinoma of the human breast because it….

  5. PERLSof the Information Age Validation is a process that accurately matches words (terminology) and/or pictures (images). Validation requires comparison words or images that define the characteristics of mouse and human tumors. Model Validation is the process of delineating the attributes (characteristics) of an experimental system that accurately match the attributes (characteristics) of human disease.

  6. PERLS The characteristicsare documented by images obtained using technical protocolsand described using terminology. The MMHCC Steering Committee has asked the MMHCC Pathologists to provide all four.

  7. PERLS • Controlled vocabulary. What do YOUmean? (Diagnostic Terminology) • Description Logic: How doYOUdescribe it? (Characteristics) • Objects: How will YOUrepresent it? (Images) • Machine Languages: WillMYcomputer understand and integrate it? Will THEIRcomputers retrieve and use it? (Protocols) • Integration:Pathology will integratestructure and function. BUT,Pathologists will need to learn and use computer languages.

  8. MOUSE MODELS OFHUMAN BREAST CANCER GEMS • Genetically Engineered Mice (GEM) have unique tumor phenotypes • GEM integrate Structure and Function. • The in-vivo biological test for oncogenicity of potential oncogenes (surrogate for human disease)

  9. GEM MAMMARY TUMOR MORPHOLOGY • Resemble “SPONTANEOUS” mammary tumors: fgf-3, notch-3, wnt-1,wnt-10b • Mimic HUMAN BREAST CANCER: c-erbB2, src, myc, SV40 Tag, IGFr-2, others • Unique GENE-SPECIFIC “SIGNATURE” PHENOTYPE: c-erbB2, myc, ras, IGF-2, SV40 Tag, ret-1, others • Unique PATHWAY PATHOLOGY: wnt-1 vs erbB GEM mammary tumors are unique.

  10. PATTERNS OF HUMAN BREAST CANCER Lobular Cribriform NST Tubular The genes and structure of breast cancers are repeated.

  11. HUMAN CANCER • Lobular Carcinoma ---E-Cadherin • Medullary Carcinoma-BrCa1 • Comedo Carcinoma--- Her-2 (erbB2) • Others The genes and structure of breast cancers are repeated.

  12. Breast Cancer Associated with c-erbB2 GEMS MOUSE HUMAN HUMAN MOUSE

  13. PERLS • Controlled vocabulary. What do YOUmean? (Diagnostic Terminology) • Description Logic: How do YOU describe it? (Characteristics) • Objects: How will YOU represent it? (Images) • Machine Languages: Will MY computer understand and integrate it? Will THEIR computers retrieve and use it? (Protocols) • Integration: Pathology will integrate structure and function. BUT, Pathologists will need to learn and use computer languages.

  14. GEM MAMMARY TUMORS that • Mimic HUMAN BREAST CANCER: c-erbB2, src, myc, SV40 Tag, IGFr-2, others Lobular Carcinoma B A IS A OR B HUMAN?

  15. GEMS • Controlled vocabulary. What do YOU mean? (Diagnostic Terminology) • Description Logic: How do YOU describe it? (Characteristics) • Objects: How will YOU represent it? (Images) • Machine Languages: Will MY computer understand and integrate it? Will THEIR computers retrieve and use it? (Protocols) • Integration: Pathology will integrate structure and function. BUT, Pathologists will need to learn and use computer languages. The whole slide?

  16. GEM MAMMARY TUMOR MORPHOLOGY • Resemble “SPONTANEOUS” mammary tumors: fgf-3, notch-3, wnt-1,wnt-10b • Mimic HUMAN BREAST CANCER: c-erbB2, src, myc, SV40 Tag, IGFr-2, others • Unique GENE-SPECIFIC “SIGNATURE” PHENOTYPE: c-erbB2, myc, ras, IGF-2, SV40 Tag, ret-1, others • Unique PATHWAY PATHOLOGY: wnt-1 vs erbB GEM mammary tumors are unique.

  17. GEM MAMMARY TUMORS Unique GENE-SPECIFIC “SIGNATURE” PHENOTYPE: c-erbB2, myc, ras, IGF-2, SV40 Tag, ret-1, others RAS MYC NEU STRUCTURE and FUNCTION

  18. PERLS • Controlled vocabulary. What do YOU mean? (Diagnostic Terminology) • Description Logic: How doYOUdescribe it? (Characteristics) • Objects: How will YOU represent it? (Images) • Machine Languages: Will MY computer understand and integrate it? Will THEIR computers retrieve and use it? (Protocols) • Integration: Pathology will integrate structure and function. BUT, Pathologists will need to learn and use computer languages.

  19. PATHWAY PATHOLOGY WNT PATHWAY ERBB PATHWAY ANTI-SMOOTH MUSCLE ACTIN ERBB2 WNT1 • Myoepithelium • Branching Ductules • Acinar or Solid • Keratinization • Stroma • Expansile

  20. PERLS • Controlled vocabulary. What do YOU mean? (Diagnostic Terminology) • Description Logic: How do YOU describe it? (Characteristics) • Objects: How will YOU represent it? (Images) • Machine Languages: Will MY computer understand and integrate it? Will THEIR computers retrieve and use it? (Protocols) • Integration: Pathology will integrate structure and function. BUT, Pathologists will need to learn and use computer languages.

  21. PATHWAY PATHOLOGY WNT PATHWAY Developmental H and E Anti-CK8 • Myoepithelium • Branching Ductules • Acinar or Solid • Keratinization • Stroma • Expansile

  22. PATHWAY PATHOLOGY WNT PATHWAY Developmental AE-13 “Hard Keratin” (Hair Keratin) • Myoepithelium • Branching Ductules • Acinar or Solid • Keratinization • Stroma • Expansile De novo Hair Morphogenesis

  23. PERLS • Controlled vocabulary. What do YOU mean? (Diagnostic Terminology) • Description Logic: How do YOU describe it? (Characteristics) • Objects: How will YOU represent it? (Images) • Machine Languages: Will MY computer understand and integrate it? Will THEIR computers retrieve and use it? (Protocols) • Integration: Pathology will integrate structure and function. BUT, Pathologists will need to learn and use computer languages.

  24. Molecular HierarchyExpression MicroArrays PERLS MYC/p53-Rb ERBB/RAS Desai KV, Xiao N, Wang W, Gangi L, Greene J, Powell JI, Dickson R, Furth P, Hunter K, Kucherlapati R, Simon R, Liu ET, Green JE. Initiating oncogenic event determines gene-expression patterns of human breast cancer models. Proc Natl Acad Sci U S A 2002 May 14;99(10):6967-72

  25. GEMS and PERLS INTEGRATION of STRUCTURE AND FUNCTION MYC/p53-Rb CONTEXT ERBB/RAS Desai KV, Xiao N, Wang W, Gangi L, Greene J, Powell JI, Dickson R, Furth P, Hunter K, Kucherlapati R, Simon R, Liu ET, Green JE. Initiating oncogenic event determines gene-expression patterns of human breast cancer models. Proc Natl Acad Sci U S A 2002 May 14;99(10):6967-72

  26. CONCLUSIONS: • Structure and Function: • Genotype (genes) predicted by the structure of the tumor (phenotype). • Pathway Pathologyidentifies the “target genes” • Integration requires controlled vocabulary and description logic. • Validation requires the detailed characterization by the pathologists and their colleagues.

  27. COLLABORATORS Cory Abate-Shen, Birgit Anderegg, Andrew Arnold, Allan Balmain, Peter Barry, Mina Bissell, Alexander Borowsky, Chris Bowlus, Debbie Cabral, Chen, Chester, Lewis Chodosh, Steven Chua, Clemensia Colmenares, Denise Connolly, Corley, Jerry Cunha, Jim DeGregori, Gerald Denis, Chuxia Deng, Micheal DiGiovanna, Dube, David Eberhard, Ecsedy, Ellis, Ari Elson, Adrain Erlebacher, Linda Foote, Gerth, Laurie Glimcher, Jeff Gregg, Alain Guimond, Paul Gumerlock, Tom Hamilton, Michelle Harrington, John Hassell, Jim Hechler, Claudia Hofmann, Kathleen Hruska, Jeff Hsu, Kent Hunter, John Hutchinson, Yulia Kaluzhny, M. Kavanaugh, Michelle Kelliher, James Kim, Dani Kitzberg, Jeanine Kleeman, John Klingensmith, Backesh Kumar, Kurihara, Esther Landesmen, Allan Lau, TeriLaufer, Ben Leader, Michel Lebel, Aya Leder, Phil Leder, Eva Lee, Fred Lee, Lin, Kent Lloyd, J. Lund, Carol MacLeod, Phil Mack, Jeannie Maglione, Albert Man, Mani, Shyamala-Harris, Jennifer Michaelson, Kieko Miyoshi, Mills, Misa, Moran, Amy Moser, Bill Muller, A. Nissim, O’Neil, Chris Ormandy, Bob Oshima, JH Park, Quadri, Glenn Radice, Ann Ranger, Katya Ravid, Andrea Rosner, Tom Rothstein, Pradip Roy-Burman, Cornelius Rosse, Robert Russell, Enriqu Saez, Saquib, Earl Sawai, Charles Sawyers,Emmitt Schmidt, Schneider, Nicole Schreiber-Agus, Peter Seigel, Dave Seldin, Stu Sell, Michael Shen, Trevor Shepherd, Rachel Sheppard, David Sherr, Stuart Schnitt, Toshi Shioda, Jonathan Shillingford,Shyamala Mani, Ranu Nandi, Katherine Siminovitch, Radek Skoda, G. Sonenshein, MichaelSong, Z. Song, Lisa Stubbs, Amy Sung, R. Sung, Amy Lanping, R. Taneja, Ann Thor, George Thomas, Tilghman, N. Tulchin, Terry Van Dyke, Barbara Vanderhyden, P. Vogt, BenVollrath, Judy Walls, Walter Witke, Kay-Uwe Wagner, Y.Z. Wang, L Wang, M. Weinstein, M. Weiss, Chris Westphal, Don White, Jolene Windle, Hong Wu, Larry Young, Youd, Cindy Zahnow, Lianxing Zheng, BenRich, DaGong Wang, Lothar Henninghausen, NCI BioInformatics, Apelon.

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