Academy of Finland
Funding decision
Applicant / Contact person Häkkinen, Antti
Organisation University of Helsinki
Project title Efficient computational methods to analyze big data in cancer at single-cell resolution
Decision No. 322927
Decision date 07.05.2019
Funding period 01.09.2019 - 31.08.2022
Funding 277 880
WebFOCUS Report
Project description
Modern cancer research relies on next-generation sequencing technologies, which allow genome-wide profiling of patient tissue at various molecular levels. The emergence of single-cell technologies allow quantification at even a deeper level, revealing the tumor composition and the behavior of the constituent individual cells. However, interpreting these data and translating them to biological insights remains as a limiting factor, as previous methods handle the novel characteristics and the massive volume poorly, rendering much information unexploited. We propose to develop novel computational methods for analyzing the emerging single-cell data. We apply the methods on ovarian cancer patient data to identify which mechanisms cause the patients to develop anticancer drug resistance, resulting in ineffective treatment and patient death. Novel computational methods in this domain are desperately needed in order to translate the vast data more effectively into patient care and survival.