Biomedical Informatics for Cancer Research

  • Michael F. Ochs
  • John T. Casagrande
  • Ramana V. Davuluri

Table of contents

  1. Front Matter
    Pages i-xviii
  2. Concepts, Issues, and Approaches

    1. Front Matter
      Pages 1-1
    2. Michael F. Ochs, John T. Casagrande, Ramana V. Davuluri
      Pages 3-15
    3. Joyce C. Niland, Layla Rouse
      Pages 17-37
    4. Waqas Amin, Hyunseok Peter Kang, Michael J. Becich
      Pages 39-71
    5. Tahsin Kurc, Ashish Sharma, Scott Oster, Tony Pan, Shannon Hastings, Stephen Langella et al.
      Pages 73-90
    6. Frank J. Manion, William Weems, James McNamee
      Pages 91-115
    7. Michael F. Ochs
      Pages 117-137
    8. Robert A. Gatenby
      Pages 139-147
    9. Vincent J. Carey, Victoria Stodden
      Pages 149-175
  3. Tools and Applications

    1. Front Matter
      Pages 202-202
    2. John Speakman
      Pages 203-213
    3. Paul Fearn, Frank Sculli
      Pages 215-225
    4. Richard Evans, Mark DeTomaso, Reed Comire, Vaibhav Bora, Jeet Poonater, Aarti Vaishnav et al.
      Pages 227-239
    5. Matt Stine, Vicki Beal, Nilesh Dosooye, Yingliang Du, Rama Gundapaneni, Andrew Pappas et al.
      Pages 241-251
    6. Juli Klemm, Anand Basu, Ian Fore, Aris Floratos, George Komatsoulis
      Pages 253-266
    7. Eleanor Howe, Kristina Holton, Sarita Nair, Daniel Schlauch, Raktim Sinha, John Quackenbush
      Pages 267-277
    8. Stephen Langella, Shannon Hastings, Scott Oster, Philip Payne, Frank Siebenlist
      Pages 279-290
    9. Roger D. Peng, Duncan Temple Lang
      Pages 291-300
    10. Amanda Blackford, Giovanni Parmigiani
      Pages 301-314
    11. Deborah Nolan, Roger D. Peng, Duncan Temple Lang
      Pages 335-345
  4. Back Matter
    Pages 347-354

About this book


In the past two decades, the large investment in cancer research led to identification of the complementary roles of genetic mutation and epigenetic change as the fundamental drivers of cancer. With these discoveries, we now recognize the deep heterogeneity in cancer, in which phenotypically similar behaviors in tumors arise from different molecular aberrations. Although most tumors contain many mutations, only a few mutated genes drive carcinogenesis. For cancer treatment, we must identify and target only the deleterious subset of aberrant proteins from these mutated genes to maximize efficacy while minimizing harmful side effects.

Together, these observations dictate that next-generation treatments for cancer will become highly individualized, focusing on the specific set of aberrant driver proteins identified in a tumor. This drives a need for informatics in cancer research and treatment far beyond the need in other diseases. For each individual cancer, we must find the molecular aberrations, identify those that are deleterious in the specific tumor, design and computationally model treatments that target the set of aberrant proteins, track the effectiveness of these treatments, and monitor the overall health of the individual. This must be done efficiently in order to generate appropriate treatment plans in a cost effective manner. State-of-the-art techniques to address many of these needs are being developed in biomedical informatics and are the focus of this volume.


Annotation Biomedicine LIMS algorithms bioinformatics databases genome medical informatics

Editors and affiliations

  • Michael F. Ochs
    • 1
  • John T. Casagrande
    • 2
  • Ramana V. Davuluri
    • 3
  1. 1.Sydney Kimmel Comprehensive, Cancer CenterJohn Hopkins UniversityBaltimoreUSA
  2. 2.USC / Norris Comprehensive Cancer Ctr.Los AngelesUSA
  3. 3.Wistar InstitutePhiladelphiaUSA

Bibliographic information

  • DOI
  • Copyright Information Springer Science+Business Media, LLC 2010
  • Publisher Name Springer, Boston, MA
  • eBook Packages Medicine Medicine (R0)
  • Print ISBN 978-1-4419-5712-2
  • Online ISBN 978-1-4419-5714-6
  • Buy this book on publisher's site