CS Forum: Anil Korkut, UT MD Anderson Cancer Center

2017-07-28 10:15:00 2017-07-28 11:00:00 Europe/Helsinki CS Forum: Anil Korkut, UT MD Anderson Cancer Center CS department's public guest lecture on 'Overcoming resistance to targeted cancer therapy with network pharmacology'. The lecture is open to everyone free-of-charge. http://cs.aalto.fi/en/midcom-permalink-1e7656b31cbefe0656b11e7984027a5dc4a89c289c2 Konemiehentie 2, 02150, Espoo

CS department's public guest lecture on 'Overcoming resistance to targeted cancer therapy with network pharmacology'. The lecture is open to everyone free-of-charge.

28.07.2017 / 10:15 - 11:00
seminar room T5, Konemiehentie 2, 02150, Espoo, FI

Dr. Anil Korkut
Dept. Bioinformatics & Comp Bio
UT MD Anderson Cancer Center

Host: Prof Samuel Kaski
Time: 10:15 (coffee at 10:00)
Venue: T5, CS building

Overcoming resistance to targeted cancer therapy with network pharmacology

Abstract

Resistance to targeted therapies, either intrinsic (pre-existing at the time of treatment) or acquired (emerging as the tumor adapts to therapy), is a major challenge in modern oncology. Blocking multiple escape routes using drug combinations is the best solution to the drug resistance problem. However, discovery of effective combinations remains a challenging task due to complexity of the underlying biological processes (e.g., multiple feedback loops in signaling pathways, tumor heterogeneity and combinatorial genomic alterations). Our laboratory develops and uses a combination of computational and experimental tools to study this important problem and nominate mitigation strategies such as anti-resistance drug combinations.  We are developing algorithms to build predictive models of signaling pathways and drug response using drug response data (phosphoprotein, mRNA transcript) as constraints. We use such models to predict responses to untested combinatorial drug perturbations and nominate effective combination therapy candidates through in vitro validation of our predictions. We are building discovery platforms to develop better precision therapies as our methods capture tumor-type specific events and are generalizable to diverse cancer types or drug resistance problems. In this presentation, I will describe our recent algorithmic advances and applications in specific cancer types to identify adaptive resistance pathways and novel drug combinations.