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Personalized Therapy of NSCLC Based on KIF5B/RET Fusion Oncogene
Although several studies have reported genomic driver mutations in NSCLC over
the past decade, the molecular pathogenesis of more than 40 % of NSCLC is still
unknown. To identify new molecular targets in NSCLC, the combined analysis of
massively parallel whole-genome and transcriptome sequencing for cancer was per-
formed on an adenocarcinoma patient, who is a nonsmoker and has no family his-
tory of cancer (Ju et al. 2012 ). The cancer showed no known driver mutation in
EGFR or KRAS and no EML4-ALK fusion. However, a novel fusion gene between
KIF5B and RET proto-oncogene was found, which is caused by a pericentric inver-
sion of 10p11.22-q11.21. This fusion gene overexpresses chimeric RET receptor
tyrosine kinase, which could spontaneously induce cellular transformation. KIF5B-
RET fusion has been identifi ed in a few more cases indicating that a subset of
NSCLC could be caused by a fusion of KIF5B and RET, and suggest the chimeric
oncogene as a promising molecular target for the personalized diagnosis and treat-
ment of lung cancer.
Predicting Response of NSCLC to Platinum-Based Therapy
Platinum-based chemotherapy is a primary treatment for patients with advanced
NSCLC. There is need for a convenient method is to identify the sensitivity of indi-
vidual patient to platinum-based regimen. Genetic variants in DNA repair genes
represent important determinants of drug effi cacy. Xeroderma pigmentosum group
A (XPA) codon23 and xeroderma pigmentosum group D (XPD) codon751 SNPs
are involved in clinical response to platinum-based chemotherapy in advanced
NSCLC patients. A study has confi rmed that XPA A23G, a SNP in blood cells
detected by 3D polyacrylamide gel-based DNA microarray method, might be a
promising biomarker in predicting favorable prognosis of NSCLC patients and
designing individualized treatments (Cheng et al. 2013 ).
Proteomics for Discovery of Metabolic Biomarkers of Lung Cancer
Human primary lung adenocarcinoma tumors have been analyzed using global MS
to elucidate the biological mechanisms behind relapse after surgery (Pernemalm
et al. 2013 ). In total, >3,000 proteins were identifi ed with high confi dence and
supervised multivariate analysis was used to select 132 proteins separating the prog-
nostic groups. Based on in-depth bioinformatics analysis, the authors hypothesized
that the tumors with poor prognosis had a higher glycolytic activity and HIF activa-
tion. By measuring the bioenergetic cellular index of the tumors, they could detect
a higher dependency of glycolysis among the tumors with poor prognosis. Further,
they could also detect an up-regulation of HIF1α mRNA expression in tumors with
early relapse. Finally, they selected three proteins that were upregulated in the poor
10 Personalized Therapy of Cancer