Hypoxia in tumors is closely related to drug resistance. It has not been verified whether hypoxia-inducible factor-1α (HIF-1α) or ABCG2 is related to hypoxia-induced resistance. Ursolic acid (UA), when used in combination with cisplatin can significantly increase the sensitivity of ovarian cancer stem cells (CSCs) to cisplatin, but the exact mechanism is unknown. The cell growth inhibitory rate of cisplatin under different conditions was evaluated using Cell Counting Kit-8 (CCK-8) in adherence and sphere cells (SKOV3, A2780, and HEY). The expression of HIF-1α and ABCG2 was tested using quantitative PCR, western blotting, and immuno-fluorescence under different culture conditions and treated with UA. Knockdown of HIF-1α by shRNA and LY294002 was used to inhibit the activity of PI3K/Akt pathway. Ovarian CSCs express stemness-related genes and drug resistance significantly higher than normal adherent cells. Under hypoxic conditions, the ovarian CSCs grew faster and were more drug resistant than under normoxia. UA could inhibit proliferation and reverse the drug resistance of ovarian CSC by suppressing ABCG2 and HIF-1α under different culture conditions. HIF-1α inhibitor YC-1 combined with UA suppressed the stemness genes and ABCG2 under hypoxic condition. The PI3K/Akt signaling pathway activation plays an important functional role in UA-induced downregulation of HIF-1α and reduction of ABCG2. UA inhibits the proliferation and reversal of drug resistance in ovarian CSCs by suppressing the expression of downregulation of HIF-1α and ABCG2.

Radiomic feature analysis has been shown to be effective at analyzing diagnostic images to model cancer outcomes. It has not yet been established how to best combine radiomic features in cancer patients with multifocal tumors. As the number of patients with multifocal metastatic cancer continues to rise, there is a need for improving personalized patient-level prognosis to better inform treatment. We compared six mathematical methods of combining radiomic features of 3,596 tumors in 831 patients with multiple brain metastases and evaluated the performance of these aggregation methods using three survival models: a standard Cox proportional hazards model, a Cox proportional hazards model with LASSO regression, and a random survival forest. Across all three survival models, the weighted average of the largest three metastases had the highest concordance index (95% confidence interval) of 0.627 (0.595–0.661) for the Cox proportional hazards model, 0.628 (0.591–0.666) for the Cox proportional hazards model with LASSO regression, and 0.652 (0.565–0.727) for the random survival forest model. This finding was consistent when evaluating patients with different numbers of brain metastases and different tumor volumes. Radiomic features can be effectively combined to estimate patient-level outcomes in patients with multifocal brain metastases. Future studies are needed to confirm that the volume-weighted average of the largest three tumors is an effective method for combining radiomic features across other imaging modalities and tumor types.

Background: Radiomic feature analysis has been shown to be effective at modeling cancer outcomes. It has not yet been established how to best combine these radiomic features in patients with multifocal disease. As the number of patients with multifocal metastatic cancer continues to rise, there is a need for improving personalized patient-level prognostication to better inform treatment. Methods: We compared six mathematical methods of combining radiomic features of 3596 tumors in 831 patients with multiple brain metastases and evaluated the performance of these aggregation methods using three survival models: a standard Cox proportional hazards model, a Cox proportional hazards model with LASSO regression, and a random survival forest. Results: Across all three survival models, the weighted average of the largest three metastases had the highest concordance index (95% confidence interval) of 0.627 (0.595-0.661) for the Cox proportional hazards model, 0.628 (0.591-0.666) for the Cox proportional hazards model with LASSO regression, and 0.652 (0.565-0.727) for the random survival forest model. Conclusions: Radiomic features can be effectively combined to establish patient-level outcomes in patients with multifocal brain metastases. Future studies are needed to confirm that the volume-weighted average of the largest three tumors is an effective method for combining radiomic features across other imaging modalities and disease sites.

No abstract