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NanoFlares for Detection of CTCs
NanoFlares are nanoconstructs that enable live-cell detection of intracellular
mRNA. NanoFlares, when coupled with fl ow cytometry, can be used to fl uores-
cently detect genetic markers of CTCs in the context of whole blood enabling detec-
tion of as few as 100 live cancer cells per mL of blood and subsequent culture of
those cells (Halo et al. 2014 ). This technique can also be used to detect CTCs in a
murine model of metastatic breast cancer. As such, NanoFlares are the fi rst genetic-
based approach for detecting, isolating, and characterizing live cancer cells from
blood and may provide new opportunities for cancer diagnosis, prognosis, and
personalized therapy.
Pathway-Based Analysis of Cancer
Conversion of Gene-Level Information into Pathway-Level Information
Gene-level information obtained by gene expression studies needs to be converted
into pathway-level level information to generate biologically relevant representa-
tion of each tumor sample. An algorithm, Pathifi er, infers pathway deregulation
scores for each tumor sample on the basis of expression data in a context-specifi c
manner for every particular dataset and type of cancer that is being investigated
(Drier et al. 2013 ). Multiple pathway-based representation of algorithm on three
colorectal cancer (CRC) datasets as well as two glioblastoma multiforme (GBM)
datasets was shown to be reproducible, preserved much of the original informa-
tion, and enabled inference of complex biologically signifi cant information. They
discovered several pathways that were signifi cantly associated with survival of
GBM patients and two whose scores are predictive of survival in CRC: CXCR3-
mediated signaling and oxidative phosphorylation. They also identifi ed a subclass
of proneural and neural GBM with signifi cantly better survival, and an EGF
receptor- deregulated subclass of CRC. Pathifi er is useful for personalized manage-
ment of cancer.
Personalized Therapies Based on Oncogenic Pathways Signatures
The ability to defi ne cancer subtypes, recurrence of disease and response to specifi c
therapies using DNA microarray-based gene expression signatures has been dem-
onstrated in several studies. By introducing a series of oncogenes into otherwise
normal cells and comparing gene expression patterns in normal cells versus cells
harboring oncogenes, it can be shown that each cellular signaling pathway is associ-
ated with a unique gene expression signature. When evaluated in several large col-
lections of human cancers, these gene expression signatures identify patterns of
pathway deregulation in tumors and clinically relevant associations with disease
10 Personalized Therapy of Cancer