Tumor-repopulating cells evade ferroptosis via PCK2-dependent phospholipid remodeling.

Whether stem-cell-like cancer cells avert ferroptosis to mediate therapy resistance remains unclear. In this study, using a soft fibrin gel culture system, we found that tumor-repopulating cells (TRCs) with stem-cell-like cancer cell characteristics resist chemotherapy and radiotherapy by decreasing ferroptosis sensitivity. Mechanistically, through quantitative mass spectrometry and lipidomic analysis, we determined that mitochondria metabolic kinase PCK2 phosphorylates and activates ACSL4 to drive ferroptosis-associated phospholipid remodeling. TRCs downregulate the PCK2 expression to confer themselves on a structural ferroptosis-resistant state. Notably, in addition to confirming the role of PCK2-pACSL4(T679) in multiple preclinical models, we discovered that higher PCK2 and pACSL4(T679) levels are correlated with better response to chemotherapy and radiotherapy as well as lower distant metastasis in nasopharyngeal carcinoma cohorts.

Adjuvant Apatinib in Nasopharyngeal Carcinoma With Residual Epstein-Barr Virus DNA After Radiation Therapy: A Biomarker-Driven, Phase 2 Trial.

PURPOSE: We previously demonstrated that real-time monitoring of plasma Epstein-Barr virus DNA (EBV DNA) during chemoradiation therapy defined 4 distinct phenotypic clusters of nasopharyngeal carcinoma. In particular, the treatment-resistant group, defined as detectable EBV DNA at the end of radiation therapy, had the worst prognosis and is thought to have minimal residual disease. METHODS AND MATERIALS: This is the first phase 2 trial to use a targeted agent, apatinib (an inhibitor of vascular endothelial growth factor receptor 2 tyrosine kinase), in the treatment-resistant group. Eligible patients had plasma EBV DNA > 0 copies/mL at the end of radiation therapy (±1 week). Patients received apatinib (500 mg, once daily) until disease progression, unacceptable toxicity, or for a maximum of 2 years. The primary endpoint was disease-free survival (DFS). RESULTS: Twenty-five patients were enrolled and 23 patients who received apatinib were included in the analyses. Three-year DFS was 47.8% and overall survival was 73.9%. Patients with plasma vascular endothelial growth factor-A ≤150 pg/mL at 28 days after the initiation of treatment had significantly better 3-year DFS (66.7% vs 14.3%; P = .041) and overall survival (88.9% vs 42.9%; P = .033). The most common adverse event of grade ≥3 was nasopharyngeal necrosis (26%), oral/pharyngeal pain (22%), and hand-foot syndrome (22%). Nineteen patients had serial EBV DNA data. Fourteen patients had plasma EBV DNA clearance (turn to 0), and 5 (36%) of these 14 patients had disease recurrence or death, whereas all 5 patients without EBV DNA clearance had disease recurrence or death (3-year DFS: 64.3% vs 0%; P = .001). CONCLUSIONS: The use of antiangiogenic agents shortly after radiation therapy might increase the risk of necrosis. This approach needs to be avoided until translational and preclinical studies reveal the underlying mechanism of interaction between radiation therapy and antiangiogenic agents.

Machine Learning-Based Ultrasomics Improves the Diagnostic Performance in Differentiating Focal Nodular Hyperplasia and Atypical Hepatocellular Carcinoma.

BACKGROUND: The typical enhancement patterns of hepatocellular carcinoma (HCC) on contrast-enhanced ultrasound (CEUS) are hyper-enhanced in the arterial phase and washed out during the portal venous and late phases. However, atypical variations make a differential diagnosis both challenging and crucial. We aimed to investigate whether machine learning-based ultrasonic signatures derived from CEUS images could improve the diagnostic performance in differentiating focal nodular hyperplasia (FNH) and atypical hepatocellular carcinoma (aHCC). PATIENTS AND METHODS: A total of 226 focal liver lesions, including 107 aHCC and 119 FNH lesions, examined by CEUS were reviewed retrospectively. For machine learning-based ultrasomics, 3,132 features were extracted from the images of the baseline, arterial, and portal phases. An ultrasomics signature was generated by a machine learning model. The predictive model was constructed using the support vector machine method trained with the following groups: ultrasomics features, radiologist's score, and combination of ultrasomics features and radiologist's score. The diagnostic performance was explored using the area under the receiver operating characteristic curve (AUC). RESULTS: A total of 14 ultrasomics features were chosen to build an ultrasomics model, and they presented good performance in differentiating FNH and aHCC with an AUC of 0.86 (95% confidence interval [CI]: 0.80, 0.89), a sensitivity of 76.6% (95% CI: 67.5%, 84.3%), and a specificity of 80.5% (95% CI: 70.6%, 85.9%). The model trained with a combination of ultrasomics features and the radiologist's score achieved a significantly higher AUC (0.93, 95% CI: 0.89, 0.96) than that trained with the radiologist's score (AUC: 0.84, 95% CI: 0.79, 0.89, P < 0.001). For the sub-group of HCC with normal AFP value, the model trained with a combination of ultrasomics features, and the radiologist's score remain achieved the highest AUC of 0.92 (95% CI: 0.87, 0.96) compared to that with the ultrasomics features (AUC: 0.86, 95% CI: 0.74, 0.89, P < 0.001) and radiologist's score (AUC: 0.86, 95% CI: 0.79, 0.91, P < 0.001). CONCLUSIONS: Machine learning-based ultrasomics performs as well as the staff radiologist in predicting the differential diagnosis of FNH and aHCC. Incorporating an ultrasomics signature into the radiologist's score improves the diagnostic performance in differentiating FNH and aHCC.

Preoperative Pathological Grading of Hepatocellular Carcinoma Using Ultrasomics of Contrast-Enhanced Ultrasound.

RATIONALE AND OBJECTIVES: To develop an ultrasomics model for preoperative pathological grading of hepatocellular carcinoma (HCC) using contrast-enhanced ultrasound (CEUS). MATERIAL AND METHODS: A total of 235 HCCs were retrospectively enrolled, including 65 high-grade and 170 low-grade HCCs. Representative images of four-phase CEUS were selected from the baseline sonography, arterial, portal venous, and delayed phase images. Tumor ultrasomics features were automatically extracted using Ultrasomics-Platform software. Models were built via the classifier support vector machine, including an ultrasomics model using the ultrasomics features, a clinical model using the clinical factors, and a combined model using them both. Model performances were tested in the independent validation cohort considering efficiency and clinical usefulness. RESULTS: A total of 1502 features were extracted from each image. After the reproducibility test and dimensionality reduction, 25 ultrasomics features and 3 clinical factors were selected to build the models. In the validation cohort, the combined model showed the best predictive power, with an area under the curve value of 0.785 (95% confidence interval [CI] 0.662-0.909), compared to the ultrasomics model of 0.720 (95% CI 0.576-0.864) and the clinical model of 0.665 (95% CI 0.537-0.793). Decision curve analysis suggested that the combined model was clinically useful, with a corresponding net benefit of 0.760 compared to the other two models. CONCLUSION: We presented an ultrasomics-clinical model based on multiphase CEUS imaging and clinical factors, which showed potential value for the preoperative discrimination of HCC pathological grades.

Multiparametric ultrasomics of significant liver fibrosis: A machine learning-based analysis.

OBJECTIVE: To assess significant liver fibrosis by multiparametric ultrasomics data using machine learning. MATERIALS AND METHODS: This prospective study consisted of 144 patients with chronic hepatitis B. Ultrasomics-high-throughput quantitative data from ultrasound imaging of liver fibrosis-were generated using conventional radiomics, original radiofrequency (ORF) and contrast-enhanced micro-flow (CEMF) features. Three categories of features were explored using pairwise correlation and hierarchical clustering. Features were selected using diagnostic tests for fibrosis, activity and steatosis stage, with the histopathological results as the reference. The fibrosis staging performance of ultrasomics models with combinations of the selected features was evaluated with machine-learning algorithms by calculating the area under the receiver-operator characteristic curve (AUC). RESULTS: ORF and CEMF features had better predictive power than conventional radiomics for liver fibrosis stage (both p < 0.01). CEMF features exhibited the highest diagnostic value for activity stage (both p < 0.05), and ORF had the best diagnostic value for steatosis stage (both p < 0.01). The machine-learning classifiers of adaptive boosting, random forest and support vector machine were found to be optimal algorithms with better (all mean AUCs = 0.85) and more stable performance (coefficient of variation = 0.01-0.02) for fibrosis staging than decision tree, logistic regression and neural network (mean AUC = 0.61-0.72, CV = 0.07-0.08). The multiparametric ultrasomics model achieved much better performance (mean AUC values of 0.78-0.85) than the features from a single modality in discriminating significant fibrosis (≥ F2). CONCLUSION: Machine-learning-based analysis of multiparametric ultrasomics can help improve the discrimination of significant fibrosis compared with mono or dual modalities. KEY POINTS: • Multiparametric ultrasomics has achieved much better performance in the discrimination of significant fibrosis (≥ F2) than the single modality of conventional radiomics, original radiofrequency and contrast-enhanced micro-flow. • Adaptive boosting, random forest and support vector machine are the optimal algorithms for machine learning.