339et al. 2009 ). The model included a cancer-negative sigmoidoscopy/colonoscopy in
the last 10 years, polyp history in the last 10 years, history of CRC in fi rst-degree
relatives, aspirin and NSAID use, hormone use, cigarette smoking, body mass
index, current leisure-time vigorous activity, and vegetable consumption ( http://www.can-
cer.gov/colorectalcancerrisk ). The absolute risk model for CRC was well calibrated
in a large prospective cohort study (Park et al. 2009a ). This prediction model, which
estimates an individual’s risk of CRC given age and risk factors, may be a useful
tool for physicians, researchers, and policy makers.
The success of chemotherapy depends on various factors such as gender, age and
histological subtype of tumor. The difference in drug effects between different gen-
otypes can be signifi cant. Promising candidates have been identifi ed with predictive
value for response and toxicity to chemotherapy in CRC. These candidates need to
be incorporated into large, prospective clinical trials to confi rm their impact for
response and survival to chemotherapy that has been reported in retrospective anal-
yses. Confi rmed predictive markers, together with additional yet to be identifi ed
pharmacogenomic key players, will provide the basis for tailoring chemotherapy in
the future. The rationale for this approach is based on the identifi cation of the in vivo
interactions among patient’s characteristics, disease physiopathology, and drug
pharmacodynamics and pharmacokinetics. Despite the recent encouraging data, the
clinical use of targeted therapy is hampered by several questions that need to be
answered such as optimal biologic dose and schedule, lack of predictive surrogate
biomarkers, and modalities of combination with chemotherapy/radiotherapy. To
improve this situation, high throughput methods have been used to discover prog-
nostic and predictive biomarkers for CRC. There is still a need for multiple bio-
marker testing and to identify panels of predictive biomarkers in order to improve
response rates and decrease toxicity with the ultimate aim of tailoring treatment
according to an individual patient and tumor profi le. Three major genetic and epi-
genetic alterations that drive CRC tumorigenesis have been identifi ed: microsatel-
lite instability (MSI), chromosomal instability (CIN) and CpG island methylator
phenotype (CIMP). These alterations have mainly been used as biomarkers for
defi ning CRC prognosis, but recent data have demonstrated their correlation with
treatment response. Utilization of KRAS gene status as a therapeutic biomarker for
the administration of EGFR inhibitors is a notable example of how molecular profi l-
ing can provide unique advantages for the identifi cation of subpopulations of
patients with a high response rate to standard of care.
Most of the targeted inhibitors in development or in clinical use are molecules
with high affi nity for growth factor receptors, such as FGFR, VEGFR, PDGFR,
mast/stem cell growth factor receptor (KITR) and EGFR. Introduction of MAbs that
bind to growth factors into the combination chemotherapy regimens currently used
in metastatic CRC has been shown to be effective, and has further widened the treat-
ment options. The present scientifi c consensus is that the large individual differ-
ences in treatment response among CRC patients is due to the fact that each patient’s
tumor is different at the molecular level as a result of the unique genetic and envi-
ronmental background of that patient (Silvestri et al. 2013 ). Therefore, an under-
standing of these molecular differences is essential for optimizing treatment
Personalized Management of Cancers of Various Organs