PIK3CA

AI revolutionizes ovarian cancer (OC) diagnosis and prognosis, enhancing precision medicine through radiomics, pathomics, genomics, and other omics. It aids in differentiating benign from malignant tumors, predicting treatment responses, and assessing gene mutations, offering a non-invasive, economical approach to improve patient outcomes.

Targeted therapies and immunotherapies have improved cancer treatment outcomes, but challenges like drug resistance and the need for accurate biomarkers persist. Patient-derived xenograft (PDX) models are highlighted as effective preclinical tools for studying cancer biology, drug response, and resistance, offering insights into personalized medicine and novel therapeutic strategies.

Bladder cancer's complexity arises from genetic mutations, oncogene activation, and tumor suppressor gene inactivation. Its heterogeneity complicates treatment, with surgery and chemotherapy being primary options. Recent genomic research aims to improve diagnosis, treatment strategies, and understand chemosensitivity differences among patients.

Hormones like estrogen and progesterone, produced by ovaries and other tissues, influence cell actions and can promote hormone-sensitive breast cancers. These cancers, containing hormone receptors, are treated by blocking hormone production or effects using drugs like SERMs, aromatase inhibitors, and GnRH agonists. Treatments vary by cancer stage and patient specifics, aiming to reduce recurrence and improve survival.

Precision cancer medicine evolves with biotechnological advances, utilizing tumor and cell-free DNA profiling, immune markers, and RNA analysis for personalized therapy. Clinical trials now focus on gene-directed, histology-agnostic treatments, showing matched therapy improves outcomes. Challenges include tumor complexity and access to therapies, with solutions like early disease profiling and combination therapies. Real-world data mining and advanced trial designs aim to accelerate precision oncology implementation.

Tumor mutational burden (TMB) is a potential biomarker for immune checkpoint inhibitors (ICIs) efficacy, with high TMB linked to better ICI response. Despite FDA approval, TMB's clinical application faces challenges, including lack of standardized cut-off values and variability in testing methods. Efforts like the FOCR TMB Harmonization Project aim to improve TMB testing reliability. This study compares TMB values from three commercial panels to the reference F1CDx assay, proposing specific cut-offs for clinical use, enhancing patient selection for immunotherapy.