TY - JOUR
T1 - Data integration and mechanistic modelling for breast cancer biology: current state and future directions
AU - Mo, Hanyi
AU - Breitling, Rainer
AU - Francavilla, Chiara
AU - Schwartz, Jean-marc
N1 - Funding Information:
We thank Joseph Parsons and all members of our teams for useful discussions and for reading the manuscript. Research in CF lab is supported by Wellcome Trust (WT Sir Henry Dale fellowship 107636/Z/15/Z and 107636/Z/15/A ) and CR-UK Non-Clinical Training Award ( A27445 ).
Funding Information:
We thank Joseph Parsons and all members of our teams for useful discussions and for reading the manuscript. Research in CF lab is supported by Wellcome Trust (WT Sir Henry Dale fellowship 107636/Z/15/Z and 107636/Z/15/A) and CR-UK Non-Clinical Training Award (A27445).
Publisher Copyright:
© 2022 The Author(s)
PY - 2022/6
Y1 - 2022/6
N2 - Breast cancer is one of the most common cancers threatening women worldwide. A limited number of available treatment options, frequent recurrence and drug resistance exacerbate the prognosis of breast cancer patients. Thus, there is an urgent need for methods to investigate novel treatment options, while taking into account the vast molecular heterogeneity of breast cancer. Recent advances in molecular profiling technologies, including genomics, epigenomics, transcriptomics, proteomics and metabolomics data, enable approaching breast cancer biology at multiple levels of omics interaction networks. Systems biology approaches, including computational inference of “big data” and mechanistic modelling of specific pathways, are emerging to identify potential novel combinations of breast cancer subtype signatures and more diverse targeted therapies.
AB - Breast cancer is one of the most common cancers threatening women worldwide. A limited number of available treatment options, frequent recurrence and drug resistance exacerbate the prognosis of breast cancer patients. Thus, there is an urgent need for methods to investigate novel treatment options, while taking into account the vast molecular heterogeneity of breast cancer. Recent advances in molecular profiling technologies, including genomics, epigenomics, transcriptomics, proteomics and metabolomics data, enable approaching breast cancer biology at multiple levels of omics interaction networks. Systems biology approaches, including computational inference of “big data” and mechanistic modelling of specific pathways, are emerging to identify potential novel combinations of breast cancer subtype signatures and more diverse targeted therapies.
KW - Breast cancer
KW - Deep learning
KW - Multi-omics modelling
KW - Network biology
KW - Precision oncology
UR - http://www.scopus.com/inward/record.url?scp=85129912396&partnerID=8YFLogxK
U2 - 10.1016/j.coemr.2022.100350
DO - 10.1016/j.coemr.2022.100350
M3 - Review article
C2 - 36034741
SN - 2451-9650
VL - 24
SP - None
JO - Current Opinion in Endocrine and Metabolic Research
JF - Current Opinion in Endocrine and Metabolic Research
M1 - 100350
ER -
