Tumor heterogeneity attributes substantial challenges in determining the treatment regimen. Along with the conventional treatment, such as chemotherapy and radiotherapy, targeted therapy has greater impact in cancer management. Owing to the recent advancements in proteomics, we aimed to mine and re-interrogate the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data sets which contain deep scale, mass spectrometry (MS)-based proteomic and phosphoproteomic data sets conducted on human tumor samples. Quantitative proteomic and phosphoproteomic data sets of tumor samples were explored and downloaded from the CPTAC database for six different cancers types (breast cancer, clear cell renal cell carcinoma (CCRCC), colon cancer, lung adenocarcinoma (LUAD), ovarian cancer, and uterine corpus endometrial carcinoma (UCEC)). We identified 880 phosphopeptide signatures for differentially regulated phosphorylation sites across five cancer types (breast cancer, colon cancer, LUAD, ovarian cancer, and UCEC). We identified the cell cycle to be aberrantly activated across these cancers. The correlation of proteomic and phosphoproteomic data sets identified changes in the phosphorylation of 12 kinases with unchanged expression levels. We further investigated phosphopeptide signature across five cancer types which led to the prediction of aurora kinase A (AURKA) and kinases-serine/threonine-protein kinase Nek2 (NEK2) as the most activated kinases targets. The drug designed for these kinases could be repurposed for treatment across cancer types.