Prediction of prognosis and chemosensitivity in gastrointestinal cancers
Author
Demirkol, Seçil
Advisor
Güre, Ali Osmay
Date
2018-01Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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Abstract
Colon and gastric cancers are the third and fifth cancer types with the poorest survival.
Surgery is considered the primary treatment option, which can be curative. However the
decision as to whether chemotherapy administration after surgery is needed, is critical for
especially stage 2 colon cancer; since a group of patients who receive chemotherapy do not
have significantly improved clinical outcome. For this purpose, clinical risk factors are currently
evaluated to determine patients with a high risk of progression. However this is not standardized
by guidelines yet. Therefore, in this thesis my first aim was to ex vivo validate two novel
independent mRNA based biomarkers, ULBP2 and SEMA5A, which have been previously
identified in our lab, and to generate a prognostic signature which could stratify colon cancer
patients with differential prognostic profiles using the best stratification method. I showed that a
3-group prognostic signature, SU-GIB, based on expression of these two genes is associated
with cancer-specific, disease-free, and overall survival independent of clinical confounding
factors in colon cancer. I performed in silico analysis in order to understand the biological details
of the prognostic distinction, and revealed that patients with poorer prognosis show higher
expression of pro-inflammatory cytokines and a more mesenchymal profile. Patients with better
clinical outcome exhibit a more epithelial profile with higher levels of phosphorylated EGFR and Shc proteins. Analysis of high-throughput drug cytotoxicity databases also showed that colon
cancer cell lines with „Bad‟ SU-GIB signature are more sensitive to a dual PI3K-MTOR inhibitor,
BEZ235.
In this thesis, my second goal was to define prognostic and molecular sub-groups for
gastric cancer and characterize the specific biology of each sub-group. Utilizing an
unsupervised approach on publicly available microarray data, I identified 3 biological groups, which harbor differential characteristics related to ECM involvement, EMT, proliferation and cell
cycle. Moreover I identified distinct prognostic groups which are related to this molecular classification which can further stratify patients with known pathologic subtypes (diffuse and intestinal).
I found that EMT was an important parameter for molecular and prognostic classifications for both types of cancers. I therefore, studied high-throughput drug cytotoxicity databases in order to identify selective compounds with selective growth inhibition on epithelial or mesenchymal cancer cells. I identified EGFR inhibitors as being significantly more effective on epithelial cancer cells regardless of the tissue type. My future plans include the large scale validation of SU-GIB, ex vivo validation of the gastric prognostic signature, and in vitro studies that would demonstrate effectivity of EGFR inhibitors on epithelial cancer cells and combination of EGFR inhibitors with MET inducers.