Precision Oncology

Background/Objectives: Gestational choriocarcinoma is an uncommon neoplasia related to pregnancy. Signs and symptoms remain unspecific, while abnormal uterine bleeding is the main symptom. Methods: We report a case of a 40-year-old woman with gestational choriocarcinoma presenting as cervical ectopic pregnancy and a review of the literature. Results: After emergency evaluation, acute blood loss was controlled surgically. Diagnosis was set by histological examination of the uterus. Histological confirmation typically is difficult, due to high vascularization of the lesions. Conclusions: Diagnosis requires a high level of clinical suspicion, correlation with laboratory findings and specialized histopathological confirmation in cases which biopsy is offered. Treatment with chemotherapy is the main way of management, achieving treatment and fertility preservation. Surgical intervention is required for cases complicated by hemorrhage leading to hemodynamic instability.

Immune checkpoint inhibitors (ICIs) have translated the scope of cancer therapy, but their immune-restorative mechanism can lead to immune-related adverse events (irAEs), which can affect several organs with varying severity. Early identification of patients at risk of irAEs is prudent to inform clinical decisions in precision oncology and remains a challenge as current clinical and biomarker methods still lack predictive accuracy. Artificial Intelligence (AI) presents a promising strategy for improving early detection, risk stratification as well as monitoring of irAEs. This narrative review summarizes the current AI modalities for detecting and predicting irAEs risk occurring with ICI therapy, including clinical Machine Learning models, radiomics-based approaches, natural language processing (NLP) systems and the integration of modalities with multimodal AI frameworks. Clinical Machine Learning models demonstrate moderate predictive performance whereas radiomics-derived modeling appears promising for pneumonitis. NLP and language models have achieved higher accuracy for retrospective irAEs detection. Multimodal AI applications offer theoretical potential through diverse data integration that captures the complex biology of irAEs; however, current evidence is limited. All these modalities face limitations of inadequate sample sizes, retrospective design, heterogeneous outcome definitions, class imbalance, and insufficient external validation. AI-based models have significant potential for personalized immunotherapy monitoring but require prospective multicenter validation, standardized datasets and clinically interpretable frameworks prior to implementation. Future advances in multimodal modeling can also enable precise prediction and early detection of irAEs, ultimately improving the safety and effectiveness of cancer immunotherapy.

Macroautophagy is a key adaptive process that enables tumour cells to withstand the cytotoxic stress induced by standard antitumour treatments, thereby contributing to therapeutic resistance. In this context, identifying regulators capable of modulating autophagy is crucial for restoring sensitivity to these treatments. The actin cytoskeleton has emerged as a regulatory axis of autophagy, governing both its dynamics and its functional control. Drugs that target actin, and in particular actin-binding proteins (ABPs), can modulate the autophagy triggered by antitumour treatments and thereby sensitise cancer cells. This review summarises recent advances in ABP-targeted strategies for modulating autophagy across different tumour contexts and examines their potential in combination therapies.