Successful integration of nanotechnology into the current paradigm of cancer therapy requires proper understanding of the interface between nanoparticles (NPs) and cancer cells, as well as other key components within the tumor microenvironment (TME), such as normal fibroblasts (FBs) and cancer-associated FBs (CAFs). So far, much focus has been on cancer cells, but FBs and CAFs also play a critical role: FBs suppress the tumor growth while CAFs promote it. It is not yet known how NPs interact with FBs and CAFs compared to cancer cells. Hence, our goal was to elucidate the extent of NP uptake, retention, and toxicity in cancer cells, FBs, and CAFs to further understand the fate of NPs in a real tumor-like environment. The outcome of this would guide designing of NP-based delivery systems to fully exploit the TME for a better therapeutic outcome. We used gold nanoparticles as our model NP system due to their numerous applications in cancer therapy, including radiotherapy and chemotherapy. A cervical cancer cell line, HeLa, and a triple-negative breast cancer cell line, MDA-MB-231 were chosen as cancer cell lines. For this study, a clinically feasible 0.2 nM concentration of GNPs was employed. According to our results, the cancer cells and CAFs had over 25-and 10-fold higher NP uptake per unit cell volume compared to FBs, respectively. Further, the cancer cells and CAFs had over 30% higher NP retention compared to FBs. There was no observed significant toxicity due to GNPs in all the cell lines studied. Higher uptake and retention of NPs in cancer cells and CAFs vs FBs is very important in promoting NP-based applications in cancer therapy. Our results show potential in modulating uptake and retention of GNPs among key components of TME, in an effort to develop NP-based strategies to suppress the tumor growth. An ideal NP-based platform would eradicate tumor cells, protect FBs, and deactivate CAFs. Therefore, this study lays a road map to exploit the TME for the advancement of "smart" nanomedicines that would constitute the next generation of cancer therapeutics.
In particle therapy treatment planning, dose calculation is conducted using patient-specific maps of tissue ion stopping power ratio (SPR) to predict beam ranges. Improving patient-specific SPR prediction is therefore essential for accurate dose calculation. In this study, we investigated the use of the Spectral CT 7500, a second-generation dual-layer spectral computed tomography (DLCT) system, as an alternative to conventional single-energy CT (SECT) for patient-specific SPR prediction. This dual-energy CT (DECT)-based method allows for the direct prediction of SPR from quantitative measurements of relative electron density and effective atomic number using the Bethe equation, whereas the conventional SECT-based method consists of indirect image data-based prediction through the conversion of calibrated CT numbers to SPR. The performance of the Spectral CT 7500 in particle therapy treatment planning was characterized by conducting a thorough analysis of its SPR prediction accuracy for both tissue-equivalent materials and common non-tissue implant materials. In both instances, DLCT was found to reduce uncertainty in SPR predictions compared to SECT. Mean deviations of 0.7% and 1.6% from measured SPR values were found for DLCT- and SECT-based predictions, respectively, in tissue-equivalent materials. Furthermore, end-to-end analyses of DLCT-based treatment planning were performed for proton, helium, and carbon ion therapies with anthropomorphic head and pelvic phantoms. 3D gamma analysis was performed with ionization chamber array measurements as the reference. DLCT-predicted dose distributions revealed higher passing rates compared to SECT-predicted dose distributions. In the DLCT-based treatment plans, measured distal-edge evaluation layers were within 1 mm of their predicted positions, demonstrating the accuracy of DLCT-based particle range prediction. This study demonstrated that the use of the Spectral CT 7500 in particle therapy treatment planning may lead to better agreement between planned and delivered dose compared to current clinical SECT systems.
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