Predictive bioinformatics identifies novel regulators of proliferation in a cancer stem cell model.

Abstract:

:The cancer stem cell model postulates that tumors are hierarchically organized with a minor population, the cancer stem cells, exhibiting unlimited proliferative potential. These cells give rise to the bulk of tumor cells, which retain a limited ability to divide. Without successful targeting of cancer stem cells, tumor reemergence after therapy is likely. However, identifying target pathways essential for cancer stem cell proliferation has been challenging. Here, using a transcriptional network analysis termed GAMMA, we identified 50 genes whose correlation patterns suggested involvement in cancer stem cell division. Using RNAi depletion, we found that 21 of these target genes showed preferential growth inhibition in a breast cancer stem cell model. More detailed initial analysis of 6 of these genes revealed 4 with clear roles in the fidelity of chromosome segregation. This study reveals the strong predictive potential of transcriptional network analysis in increasing the efficiency of successful identification of novel proliferation dependencies for cancer stem cells.

journal_name

Stem Cell Res

journal_title

Stem cell research

authors

Fields E,Wren JD,Georgescu C,Daum JR,Gorbsky GJ

doi

10.1016/j.scr.2017.11.009

subject

Has Abstract

pub_date

2018-01-01 00:00:00

pages

1-7

eissn

1873-5061

issn

1876-7753

pii

S1873-5061(17)30239-8

journal_volume

26

pub_type

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