213outcomes. Combining signature-based predictions across several pathways identifi es
coordinated patterns of pathway deregulation that distinguish between specifi c can-
cers and tumor subtypes. The majority of adenocarcinomas of the lung are found to
be deregulated for the oncogene Ras, while only a tiny minority of squamous cell
carcinomas exhibited Ras deregulation. Hence, deregulation of the Ras pathway is
an important signature of adenocarcinomas but not of squamous cell carcinoma.
Clustering tumors based on pathway signatures further defi nes prognosis in
respective patient subsets, demonstrating that patterns of oncogenic pathway dereg-
ulation underlie the development of the oncogenic phenotype and refl ect the biology
and outcome of specifi c cancers. Predictions of pathway deregulation in cancer cell
lines are also shown to predict the sensitivity to therapeutic agents that target com-
ponents of the pathway. Linking pathway deregulation with sensitivity to therapeu-
tics that target components of the pathway provides an opportunity to make use of
these oncogenic pathway signatures to guide the use of personalized cancer thera-
pies. If the Ras and Myc pathways are activated in a tumor, physicians could choose
drugs that target only Myc and Ras. If the SRC and E2F3 pathways are highly
active, then drugs can be selected that target these pathways. Because tumors arise
from multiple defective genes and their malfunctioning proteins, treatments must
target multiple genes and their pathways. The likelihood that someone will be cured
by a single drug is low, and the new approach can guide physicians as to which
combination of drugs will most likely produce the best outcome.
The next step in the research is to validate the new method in samples from can-
cer patients who have been treated with one of the pathway-specifi c drugs to deter-
mine if the pathway predictors are able to select those patients most likely to respond
to the drug. A positive result would then form the basis for a clinical study that
would evaluate the effectiveness of the pathway prediction to guide the most effec-
tive use of therapeutics.
Quantum Dot-Based Test for DNA Methylation
DNA methylation contributes to carcinogenesis by silencing key tumor suppressor
genes. An ultrasensitive and reliable quantum dot (QD)-based assay, MS-qFRET
(fl uorescence resonance energy transfer), can detect and quantify DNA methylation
( Bailey et al. 2009 ). The direct application of MS-qFRET on clinical samples offers
great promise for its translational use in early cancer diagnosis, and prognostic
assessment of tumor behavior, as well as monitoring response to therapeutic agents.
Gene DNA methylation indicates a higher risk of developing cancer and is also seen
as a warning sign of genetic mutations that lead to development of cancer. Moreover,
since different cancer types possess different genetic biomarkers, e.g. lung cancer
biomarkers differ from leukemia biomarkers, the test should identify the cancer a
patient is at risk of developing. This test could be used for frequent screening for
cancer and replacing traditionally invasive methods with a simple blood test. It
could also help determine whether a cancer treatment is effective and thus enable
personalized chemotherapy.
Impact of Molecular Diagnostics on the Management of Cancer