Διιδρυματικό ΠΜΣ Τεχνολογίες Πληροφορικής στην Ιατρική και τη Βιολογία
Βιβλιοθήκη Σχολής Θετικών Επιστημών

2015-02-06

2015

Αηδονόπουλος Ορφέας

Σταύρος Περαντώνης (PhD - Διευθυντής Ερευνών: ΕΚΕΦΕ Δημόκριτος), Λεωνίδας Αλεξόπουλος (Επίκουρος Καθηγητής: ΕΜΠ), Γεώργιος Βερνίκος (PhD - Ερευνητής)

A method for parsing clinical outcomes and combining them with phosphoproteomic and genomic data for predicting drug efficacy: application in hepatocellular carcinoma

Αγγλικά

Aνάπτυξη μεθόδου για πρόβλεψη κλινικής αποδοτικότητας φαρμάκων μέσω ενσωμάτωσης γενομικών, κλινικών και φωσφοπρωτεομικών δεδομένων: Eφαρμογή στον ηπατικό καρκίνο

Systems biology has become an essential component of drug discovery, attempting
to combine experimental data with computational modeling to capture different
levels of cellular function (such as signaling, transcription, regulation) and
integrate them in predictive models. These models are then used to best
understand the drug mode of action (MOA), identify new targets and predict
clinical drug efficacy and toxicity. In this study, we tried to identify
signaling pathways related to drug efficacy in one of the most lethal
malignancies worldwide, hepatocellular carcinoma (HCC). Particularly, gene
expression data were collected for various HCC cell lines treated with
anticancer compounds of known clinical efficacy. Each compound had been
categorized with a ‘pass’ or ‘fail’ label according to their success or failure
in human clinical trials. For labeling each drug we constructed a graphical
user interface that parses clinical trials databases for clinical outcomes
containing the respective compound. Thus, having available a dataset consisting
of labeled drugs as observations and genes as features a supervised learning
method was applied (feature selection) to identify genes predictive of the
drugs’ clinical efficacy. Finally, using the extracted data as an input to a
pathway construction algorithm, we were able to infer signaling networks on the
proteomic level that best fit the measured gene expression signatures. We
identified reactions and pathways playing an important role as accurate
predictors for the efficacy of nine drugs in HCC.

Μηχανική μάθηση, Σηματοδοτικά μονοπάτια, Βιολογικά δίκτυα, Γονιδιακή έκφραση, Ανάλυση δεδομένων

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https://pergamos.lib.uoa.gr/uoa/dl/object/1320816