Targeting Akt in cancer for precision therapy.
Hui HuaHongying ZhangJingzhu ChenJiao WangJieya LiuYangfu JiangPublished in: Journal of hematology & oncology (2021)
Biomarkers-guided precision therapeutics has revolutionized the clinical development and administration of molecular-targeted anticancer agents. Tailored precision cancer therapy exhibits better response rate compared to unselective treatment. Protein kinases have critical roles in cell signaling, metabolism, proliferation, survival and migration. Aberrant activation of protein kinases is critical for tumor growth and progression. Hence, protein kinases are key targets for molecular targeted cancer therapy. The serine/threonine kinase Akt is frequently activated in various types of cancer. Activation of Akt promotes tumor progression and drug resistance. Since the first Akt inhibitor was reported in 2000, many Akt inhibitors have been developed and evaluated in either early or late stage of clinical trials, which take advantage of liquid biopsy and genomic or molecular profiling to realize personalized cancer therapy. Two inhibitors, capivasertib and ipatasertib, are being tested in phase III clinical trials for cancer therapy. Here, we highlight recent progress of Akt signaling pathway, review the up-to-date data from clinical studies of Akt inhibitors and discuss the potential biomarkers that may help personalized treatment of cancer with Akt inhibitors. In addition, we also discuss how Akt may confer the vulnerability of cancer cells to some kinds of anticancer agents.
Keyphrases
- cancer therapy
- signaling pathway
- cell proliferation
- drug delivery
- clinical trial
- pi k akt
- papillary thyroid
- induced apoptosis
- epithelial mesenchymal transition
- phase iii
- stem cells
- gene expression
- climate change
- squamous cell
- protein protein
- machine learning
- randomized controlled trial
- long non coding rna
- protein kinase
- squamous cell carcinoma
- oxidative stress
- big data
- lymph node metastasis
- double blind
- phase ii
- young adults
- copy number
- deep learning