200
(Gerlinger et al. 2012 ). Multiple samples from each patient’s primary and metastatic
tumor sites were obtained in a study of renal-cell cancer before and after treatment.
About two thirds of the mutations that were found in single biopsies were not uni-
formly detectable throughout all the sampled regions of the same patient’s tumor. A
“favorable prognosis” gene profi le and an “unfavorable prognosis” gene profi le
were expressed in different regions of the same tumor. Therefore, a single tumor
biopsy cannot be considered representative of the landscape of genomic abnormali-
ties in a tumor. Another fi nding of this is that different regions of the tumor have
different mutations in the very same genes (so-called convergent evolution), includ-
ing in SETD2, PTEN, and KDM5C, which underscores the importance of changing
particular tumor-cell functions as the tumor expands and evolves. From the function
of the genes that were targeted for different mutations, it would appear that altera-
tions in epigenetic mechanisms and signal transduction as the tumor evolves are
keys to the tumor’s survival. Genes that are affected by convergent evolution may be
suitable targets for functional inhibition or restoration.
Systems Biology of Cancer
Cancer systems biology addresses the increasing challenge of cancer as a complex,
multifactorial disease by using model-based approaches that range from genome-
wide regulatory and signaling networks to kinetic models of key pathways. It aims
at a holistic view of cancer by use of “omics” technologies and integrates several
aspects of cancer including genetics, epigenetics, histology, clinical manifestations
and epidemiology. Use of patient-specifi c computational and mathematical models
of cancer will signifi cantly improve the specifi city and effi cacy of targeted therapy,
and will facilitate the development of personalized management of cancer (Du and
Elemento 2014 ). Models of systems biology of cancer and tools for checking them
Table 10.1 Factors that drive the development of personalized therapy in cancerAdvances in application of proteomic technologies in cancer
Advances in cancer vaccine technologies
Cancer biomarkers can be used for diagnosis as well as drug targets
Examples of personalized treatment of cancer are already in practice
Incentive to development from motivated physicians, patients and third party payers
Increasing cancer burden with aging US population is a driving force for development. At current
incidence rates, the total number of cancer cases is expected to double by 2050 (1.3–2.6
million)
Molecular diagnosis of cancer is advancing rapidly
Progress in pathophysiology of cancer
Search for better treatments due to limited effi cacy and toxicity of chemotherapy
Sequencing is increasingly applied to understanding cancer and molecular diagnosis
Transcriptional profi ling in cancer
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10 Personalized Therapy of Cancer