Current Oncology

The treatment landscape for endometrial carcinoma (EC) is undergoing a paradigm shift from traditional histopathological dualism to precision medicine grounded in the Cancer Genome Atlas (TCGA) molecular classification. The “No Specific Molecular Profile” (NSMP) subgroup, the largest molecular cohort, has emerged as a particularly promising target for endocrine-based strategies. While endocrine therapy (ET) has been a mainstay for over 60 years due to its favorable safety profile, its efficacy as monotherapy remains modest. This review provides a comprehensive overview of current endocrine strategies, including traditional agents like progestins and aromatase inhibitors, and focuses on novel combination therapies designed to overcome resistance. Recent clinical trials have demonstrated that integrating molecularly targeted agents, such as CDK4/6 and mTOR inhibitors, significantly improves clinical outcomes. Specifically, patients with TP53 wild-type status and CTNNB1 mutations exhibit exceptional responses to these combinations. Furthermore, we discuss the potential of next-generation selective estrogen receptor degraders (SERDs) and the importance of refining patient selection through robust predictive biomarkers. Driven by molecular insights, endocrine therapy is transitioning from a secondary palliative option into a definitive cornerstone of precision oncology, offering a personalized and effective treatment for patients with advanced or recurrent endometrial carcinoma.

Background: Gemcitabine–cisplatin (GC) combined with a programmed death-ligand 1 (PD-L1) inhibitor has become an important first-line regimen for advanced intrahepatic cholangiocarcinoma (ICC). However, overall efficacy remains modest, and inter-patient heterogeneity in outcomes is substantial, highlighting the need for simple biomarkers for pretreatment risk stratification. The systemic immune-inflammation index (SII), derived from peripheral neutrophil, lymphocyte, and platelet counts, has been associated with prognosis in various malignancies, but its clinical relevance in advanced ICC treated with first-line GC plus PD-L1 inhibitor remains unclear. Aims: To evaluate the association of baseline SII with objective response and survival outcomes in patients with advanced ICC receiving first-line GC plus PD-L1 inhibitor. Methods: We retrospectively analyzed 193 consecutive patients with advanced ICC who received first-line GC plus a PD-L1 inhibitor at our center. Baseline clinicopathologic characteristics and laboratory parameters were collected, and SII was calculated as platelet count (×10⁹/L) × neutrophil count (×10⁹/L)/lymphocyte count (×10⁹/L). Receiver operating characteristic (ROC) analysis was performed to assess the discriminative ability of baseline SII for objective response and to determine an internally derived cut-off value. Patients were categorized into low- and high-SII groups accordingly. Logistic regression was used to identify factors associated with objective response rate (ORR). Progression-free survival (PFS) and overall survival (OS) were estimated by the Kaplan–Meier method and compared using the log-rank test. Multivariable Cox proportional hazards models were constructed to evaluate the independent prognostic significance of SII for PFS and OS. Results: Among the 193 patients included, 55 achieved complete or partial response and 138 had stable or progressive disease, yielding an ORR of 28.5%. Baseline SII showed good discrimination for objective response (AUC = 0.91), and the optimal cut-off value was 495.75. Patients in the low-SII group had a significantly higher ORR than those in the high-SII group (p < 0.001). Kaplan–Meier analysis demonstrated that both PFS and OS were longer in the low-SII group than in the high-SII group (median OS: 13.0 vs. 8.0 months, log-rank p < 0.001; median PFS: 8.5 vs. 6.0 months, p = 0.025). In multivariable Cox models adjusting for differentiation, CA19-9, tumor multiplicity, and distant metastasis, SII grouping remained independently associated with PFS and OS, and distant metastasis was consistently associated with increased risks of progression and death. Conclusions: Baseline SII is a readily available prognostic biomarker associated with objective response and survival in patients with advanced ICC treated with first-line GC plus PD-L1 inhibitor. Given the retrospective single-center design, the absence of a non-immunotherapy comparator cohort, and internal cut-off derivation, these findings should be interpreted as hypothesis-generating and warrant external validation.

ER-low breast cancer (1–9% ER expression) represents a biologically and clinically distinct entity at the interface between ER-positive and ER-negative disease. Although traditionally managed as hormone receptor-positive, mounting evidence indicates that ER-low tumors share molecular signatures, aggressive behavior, and chemotherapeutic responsiveness with triple-negative breast cancer. Accurate ER assessment is hindered by methodological variability and interpretative challenges, leading to potential misclassification and suboptimal treatment choices. While the benefit of endocrine therapy remains uncertain, ER-low tumors consistently show sensitivity to chemotherapy and promising responses to neoadjuvant chemo-immunotherapy, paralleling outcomes observed in triple-negative breast cancer cohorts. Emerging artificial intelligence tools, including digital pathology and multimodal deep learning, may enhance ER quantification, reduce observer variability, and enable more precise patient stratification. This review synthesizes current pathological and clinical insights into ER-low breast cancer and highlights evolving therapeutic strategies, with a forward-looking perspective on AI-driven approaches to optimize personalized treatment for this challenging subtype.