Molecular Classification of Endometrial Cancer and Its Impact on Therapy Selection.
Natalia GalantPaweł KrawczykMarta MonistAdrian ObaraŁukasz GajekAnna GrendaMarcin NicośEwa Anna KalinkaJanusz MilanowskiPublished in: International journal of molecular sciences (2024)
Endometrial cancer (EC) accounts for 90% of uterine cancer cases. It is considered not only one of the most common gynecological malignancies but also one of the most frequent cancers among women overall. Nowadays, the differentiation of EC subtypes is based on immunohistochemistry and molecular techniques. It is considered that patients' prognosis and the implementation of the appropriate treatment depend on the cancer subtype. Patients with pathogenic variants in POLE have the most favorable outcome, while those with abnormal p53 protein have the poorest. Therefore, in patients with POLE mutation, the de-escalation of postoperative treatment may be considered, and patients with abnormal p53 protein should be subjected to intensive adjuvant therapy. Patients with a DNA mismatch repair (dMMR) deficiency are classified in the intermediate prognosis group as EC patients without a specific molecular profile. Immunotherapy has been recognized as an effective treatment method in patients with advanced or recurrent EC with a mismatch deficiency. Thus, different adjuvant therapy approaches, including targeted therapy and immunotherapy, are being proposed depending on the EC subtype, and international guidelines, such as those published by ESMO and ESGO/ESTRO/ESP, include recommendations for performing the molecular classification of all EC cases. The decision about adjuvant therapy selection has to be based not only on clinical data and histological type and stage of cancer, but, following international recommendations, has to include EC molecular subtyping. This review describes how molecular classification could support more optimal therapeutic management in endometrial cancer patients.
Keyphrases
- endometrial cancer
- end stage renal disease
- machine learning
- papillary thyroid
- chronic kidney disease
- deep learning
- ejection fraction
- healthcare
- newly diagnosed
- single molecule
- squamous cell carcinoma
- prognostic factors
- stem cells
- patients undergoing
- polycystic ovary syndrome
- childhood cancer
- patient reported outcomes
- open label
- bone marrow
- protein protein
- adipose tissue
- artificial intelligence
- cell therapy
- skeletal muscle
- insulin resistance
- study protocol