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

3. PATHOLOGYPATHOLOGY CONTEXT INTEGRATION INTEGRATIONof STRUCTURE AND FUNCTION

4. 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.

5. 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….

6. Model Validation is the process of delineating the attributes (characteristics) of an experimental system that accurately match the attributes (characteristics) of human disease. 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. PERLS of the Information Age

7. The characteristics are documented by images obtained using technical protocols and described using terminology. The MMHCC Steering Committee has asked the MMHCC Pathologists to provide all four. PERLS

8. 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.

9. MOUSE MODELS OF HUMAN BREAST CANCER 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) GEMS

10. GEM MAMMARY TUMOR MORPHOLOGY GEM mammary tumors are unique. 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

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

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

13. Breast Cancer Associated with c-erbB2 HUMAN MOUSE HUMANMOUSE GEMS

14. 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.

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

16. The whole slide? 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.

17. GEM MAMMARY TUMOR MORPHOLOGY GEM mammary tumors are unique. 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

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

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

22. 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.

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

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

25. 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.

26. Molecular Hierarchy Expression MicroArrays 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 ERBB/RASMYC/p53-Rb PERLS

27. 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 MYC/p53-Rb ERBB/RAS INTEGRATIONof STRUCTURE AND FUNCTION CONTEXT GEMS and PERLS

28. CONCLUSIONS: 1.Structure and Function: Genotype (genes) predicted by the structure of the tumor (phenotype). Pathway Pathology identifies the “target genes” 2.Integration requires controlled vocabulary and description logic. 3.Validation requires the detailed characterization by the pathologists and their colleagues.

29. 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, Michael Song, 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. COLLABORATORSCOLLABORATORS