Table 2.

The comparisons between CPTAC study and our work.

	CPTAC study	Our study
Subtyping with phosphoproteome data	No	Yes, the subtypes were significantly associated with different overall survival based on kinase activity(P = .0013).
Subtyping with proteome data	Yes, but the subtypes were not associated with clinical outcome(P > .5).	Yes, the subtypes were tend to be associated with different overall survival(P = .058).
Method for determing kinase activity	They did not predict kinase activity.	Two methods: Mean values and Kinase substrate enrichment analysis (KSEA).
Nomination potential druggable kinases	No	Yes, we identified 35 potential druggable kinases.
Identifying aberrantly activated signalling pathways	Yes, they used proteins and phosphoproteins whose abundance were associated with survival (a two-sided t-test) to identify pathways: The RhoA-regulatory, PDGFRB, and integrinlike kinase pathways.	Yes, we used kinases whose increased activities in tumours are associated with poor survival (log-rank test) to paint the altered signalling, which were centered on the PI3K/AkT/mTOR pathway, cell cycle and MAP kinase signalling pathways.
Development of patient-specific kinase inhibitors	No	Yes, we developed a patient-specific hierarchy of clinically actionable kinases and selected kinase inhibitors by considering kinase activation and kinase inhibitor selectivity.
Integrating proteomic data with the genomic data	Yes	No
The main significance of the study	Layering proteomic and genomic data from ovarian tumours provides insights into how signalling pathways correspond to specific genome rearrangements.	This work detailed the processes of how to subtype cancer with phosphorylation data to be associated with clinical outcome, and nominate actionable kinase targets for clinical intervention.