Multi-modal meta-analysis of cancer cell line omics profiles identifies ECHDC1 as a novel breast tumor suppressor
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Multi-modal meta-analysis of cancer cell line omics profiles identifies ECHDC1 as a novel breast tumor suppressor. / Jaiswal, Alok; Gautam, Prson; Pietilä, Elina A.; Timonen, Sanna; Nordström, Nora; Akimov, Yevhen; Sipari, Nina; Tanoli, Ziaurrehman; Fleischer, Thomas; Lehti, Kaisa; Wennerberg, Krister; Aittokallio, Tero.
In: Molecular Systems Biology, Vol. 17, No. 3, e9526, 2021, p. 1-25.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Multi-modal meta-analysis of cancer cell line omics profiles identifies ECHDC1 as a novel breast tumor suppressor
AU - Jaiswal, Alok
AU - Gautam, Prson
AU - Pietilä, Elina A.
AU - Timonen, Sanna
AU - Nordström, Nora
AU - Akimov, Yevhen
AU - Sipari, Nina
AU - Tanoli, Ziaurrehman
AU - Fleischer, Thomas
AU - Lehti, Kaisa
AU - Wennerberg, Krister
AU - Aittokallio, Tero
PY - 2021
Y1 - 2021
N2 - Molecular and functional profiling of cancer cell lines is subject to laboratory-specific experimental practices and data analysis protocols. The current challenge therefore is how to make an integrated use of the omics profiles of cancer cell lines for reliable biological discoveries. Here, we carried out a systematic analysis of nine types of data modalities using meta-analysis of 53 omics studies across 12 research laboratories for 2,018 cell lines. To account for a relatively low consistency observed for certain data modalities, we developed a robust data integration approach that identifies reproducible signals shared among multiple data modalities and studies. We demonstrated the power of the integrative analyses by identifying a novel driver gene, ECHDC1, with tumor suppressive role validated both in breast cancer cells and patient tumors. The multi-modal meta-analysis approach also identified synthetic lethal partners of cancer drivers, including a co-dependency of PTEN deficient endometrial cancer cells on RNA helicases.
AB - Molecular and functional profiling of cancer cell lines is subject to laboratory-specific experimental practices and data analysis protocols. The current challenge therefore is how to make an integrated use of the omics profiles of cancer cell lines for reliable biological discoveries. Here, we carried out a systematic analysis of nine types of data modalities using meta-analysis of 53 omics studies across 12 research laboratories for 2,018 cell lines. To account for a relatively low consistency observed for certain data modalities, we developed a robust data integration approach that identifies reproducible signals shared among multiple data modalities and studies. We demonstrated the power of the integrative analyses by identifying a novel driver gene, ECHDC1, with tumor suppressive role validated both in breast cancer cells and patient tumors. The multi-modal meta-analysis approach also identified synthetic lethal partners of cancer drivers, including a co-dependency of PTEN deficient endometrial cancer cells on RNA helicases.
KW - cancer driver
KW - data integration
KW - multi-omics data
KW - reproducibility
KW - synthetic lethality
UR - http://www.scopus.com/inward/record.url?scp=85103386933&partnerID=8YFLogxK
U2 - 10.15252/msb.20209526
DO - 10.15252/msb.20209526
M3 - Journal article
C2 - 33750001
AN - SCOPUS:85103386933
VL - 17
SP - 1
EP - 25
JO - Molecular Systems Biology
JF - Molecular Systems Biology
SN - 1744-4292
IS - 3
M1 - e9526
ER -
ID: 261611114