Cellular senescence imaging and senolysis monitoring in cancer therapy based on a ß-galactosidase-activated aggregation-induced emission luminogen.

Cellular senescence is a permanent state of cell cycle arrest characterized by increased activity of senescence associated ß-galactosidase (SA-ß-gal). Notably, cancer cells have been also observed to exhibit the senescence response and are being considered for sequential treatment with pro-senescence therapy followed by senolytic therapy. However, there is currently no effective agent targeting ß-galactosidase (ß-Gal) for imaging cellular senescence and monitoring senolysis in cancer therapy. Aggregation-induced emission luminogen (AIEgen) demonstrates strong fluorescence, good photostability, and biocompatibility, making it a potential candidate for imaging cellular senescence and monitoring senolysis in cancer therapy when endowed with ß-Gal-responsive capabilities. In this study, we introduced a ß-Gal-activated AIEgen named QM-ß-gal for cellular senescence imaging and senolysis monitoring in cancer therapy. QM-ß-gal exhibited good amphiphilic properties and formed aggregates that emitted a fluorescence signal upon ß-Gal activation. It showed high specificity towards the activity of ß-Gal in lysosomes and successfully visualized DOX-induced senescent cancer cells with intense fluorescence both in vitro and in vivo. Encouragingly, QM-ß-gal could image senescent cancer cells in vivo for over 14 days with excellent biocompatibility. Moreover, it allowed for the monitoring of senescent cancer cell clearance during senolytic therapy with ABT263. This investigation indicated the potential of the ß-Gal-activated AIEgen, QM-ß-gal, as an in vivo approach for imaging cellular senescence and monitoring senolysis in cancer therapy via highly specific and long-term fluorescence imaging. STATEMENT OF SIGNIFICANCE: This work reported a ß-galactosidase-activated AIEgen called QM-ß-gal, which effectively imaged DOX-induced senescent cancer cells both in vitro and in vivo. QM-ß-gal specifically targeted the increased expression and activity of ß-galactosidase in senescent cancer cells, localized within lysosomes. It was cleared rapidly before activation but maintained stability after activation in the DOX-induced senescent tumor. The AIEgen exhibited a remarkable long-term imaging capability for senescent cancer cells, lasting over 14 days and enabled monitoring of senescent cancer cell clearance through ABT263-induced apoptosis. This approach held promise for researchers seeking to achieve prolonged imaging of senescent cells in vivo.

Stacking Ensemble Learning-Based [18F]FDG PET Radiomics for Outcome Prediction in Diffuse Large B-Cell Lymphoma.

This study aimed to develop an analytic approach based on [18F]FDG PET radiomics using stacking ensemble learning to improve the outcome prediction in diffuse large B-cell lymphoma (DLBCL). Methods: In total, 240 DLBCL patients from 2 medical centers were divided into the training set (n = 141), internal testing set (n = 61), and external testing set (n = 38). Radiomics features were extracted from pretreatment [18F]FDG PET scans at the patient level using 4 semiautomatic segmentation methods (SUV threshold of 2.5, SUV threshold of 4.0 [SUV4.0], 41% of SUVmax, and SUV threshold of mean liver uptake [PERCIST]). All extracted features were harmonized with the ComBat method. The intraclass correlation coefficient was used to evaluate the reliability of radiomics features extracted by different segmentation methods. Features from the most reliable segmentation method were selected by Pearson correlation coefficient analysis and the LASSO (least absolute shrinkage and selection operator) algorithm. A stacking ensemble learning approach was applied to build radiomics-only and combined clinical-radiomics models for prediction of 2-y progression-free survival and overall survival based on 4 machine learning classifiers (support vector machine, random forests, gradient boosting decision tree, and adaptive boosting). Confusion matrix, receiver-operating-characteristic curve analysis, and survival analysis were used to evaluate the model performance. Results: Among 4 semiautomatic segmentation methods, SUV4.0 segmentation yielded the highest interobserver reliability, with 830 (66.7%) selected radiomics features. The combined model constructed by the stacking method achieved the best discrimination performance. For progression-free survival prediction in the external testing set, the areas under the receiver-operating-characteristic curve and accuracy of the stacking-based combined model were 0.771 and 0.789, respectively. For overall survival prediction, the stacking-based combined model achieved an area under the curve of 0.725 and an accuracy of 0.763 in the external testing set. The combined model also demonstrated a more distinct risk stratification than the International Prognostic Index in all sets (log-rank test, all P < 0.05). Conclusion: The combined model that incorporates [18F]FDG PET radiomics and clinical characteristics based on stacking ensemble learning could enable improved risk stratification in DLBCL.

Therapeutic potential of tumor treating fields for malignant brain tumors.

BACKGROUND: Malignant brain tumors are among the most threatening diseases of the central nervous system, and despite increasingly updated treatments, the prognosis has not been improved. Tumor treating fields (TTFields) are an emerging approach in cancer treatment using intermediate-frequency and low-intensity electric field and can lead to the development of novel therapeutic options. RECENT FINDINGS: A series of biological processes induced by TTFields to exert anti-cancer effects have been identified. Recent studies have shown that TTFields can alter the bioelectrical state of macromolecules and organelles involved in cancer biology. Massive alterations in cancer cell proteomics and transcriptomics caused by TTFields were related to cell biological processes as well as multiple organelle structures and activities. This review addresses the mechanisms of TTFields and recent advances in the application of TTFields therapy in malignant brain tumors, especially in glioblastoma (GBM). CONCLUSIONS: As a novel therapeutic strategy, TTFields have shown promising results in many clinical trials, especially in GBM, and continue to evolve. A growing number of patients with malignant brain tumors are being enrolled in ongoing clinical studies demonstrating that TTFields-based combination therapies can improve treatment outcomes.

Unusual Electron Donor-Acceptor Sequenced NIR AIEgen for Highly Efficient Mitochondria-Targeted Cancer Cell Photodynamic Therapy.

Photodynamic therapy (PDT) is recognized to be a promising strategy for anticancer treatment. Considering the progressive application of PDT in clinical trials, highly efficient and photostable photosensitizers (PSs) are in strong demand. Aggregation-induced emission (AIE) based PSs are promising phototheranostic materials for tumor imaging and PDT due to their high fluorescence efficiency and photostability. Herein, a mitochondria-targeted PS, TPA-2TCP with AIE characteristics is developed by adopting an acceptor-π-donor-π-acceptor (A-π-D-π-A) structure. The untypical sequence of the electron donors and electron acceptors endows the derived AIE PS with evident redshift of the absorption and emission, and efficient generation of reactive oxygen species. With the positively charged pyridinium groups, nanoparticulated AIE PS (TPA-2TCP NPs) exhibits high cell binding efficiency towards 4T1 breast cancer cells, leading to the massive cell death via the apoptotic pathway under white light irradiation, demonstrating its potential application in cancer imaging and PDT.

Positron emission tomography molecular imaging-based cancer phenotyping.

During the past several decades, numerous studies have provided insights into biological characteristics of cancer cells and identified various hallmarks of cancer acquired in the tumorigenic processes. However, it is still challenging to image these distinctive traits of cancer to facilitate the management of patients in clinical settings. The rapidly evolving field of positron emission tomography (PET) imaging has provided opportunities to investigate cancer's biological characteristics in vivo. This article reviews the current status of PET imaging on characterizing hallmarks of cancer and discusses the future directions of PET imaging strategies facilitating in vivo cancer phenotyping.

Quantitative proteomics revealed extensive microenvironmental changes after stem cell transplantation in ischemic stroke.

The local microenvironment is essential to stem cell-based therapy for ischemic stroke, and spatiotemporal changes of the microenvironment in the pathological process provide vital clues for understanding the therapeutic mechanisms. However, relevant studies on microenvironmental changes were mainly confined in the acute phase of stroke, and long-term changes remain unclear. This study aimed to investigate the microenvironmental changes in the subacute and chronic phases of ischemic stroke after stem cell transplantation. Herein, induced pluripotent stem cells (iPSCs) and neural stem cells (NSCs) were transplanted into the ischemic brain established by middle cerebral artery occlusion surgery. Positron emission tomography imaging and neurological tests were applied to evaluate the metabolic and neurofunctional alterations of rats transplanted with stem cells. Quantitative proteomics was employed to investigate the protein expression profiles in iPSCs-transplanted brain in the subacute and chronic phases of stroke. Compared with NSCs-transplanted rats, significantly increased glucose metabolism and neurofunctional scores were observed in iPSCs-transplanted rats. Subsequent proteomic data of iPSCs-transplanted rats identified a total of 39 differentially expressed proteins in the subacute and chronic phases, which are involved in various ischemic stroke-related biological processes, including neuronal survival, axonal remodeling, antioxidative stress, and mitochondrial function restoration. Taken together, our study indicated that iPSCs have a positive therapeutic effect in ischemic stroke and emphasized the wide-ranging microenvironmental changes in the subacute and chronic phases.

A novel analytic approach for outcome prediction in diffuse large B-cell lymphoma by [18F]FDG PET/CT.

PURPOSE: This study aimed to develop a novel analytic approach based on 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography/computed tomography ([18F]FDG PET/CT) radiomic signature (RS) and International Prognostic Index (IPI) to predict the progression-free survival (PFS) and overall survival (OS) of patients with diffuse large B-cell lymphoma (DLBCL). METHODS: We retrospectively enrolled 152 DLBCL patients and divided them into a training cohort (n = 100) and a validation cohort (n = 52). A total of 1245 radiomic features were extracted from the total metabolic tumor volume (TMTV) and the metabolic bulk volume (MBV) of pre-treatment PET/CT images. The least absolute shrinkage and selection operator (LASSO) algorithm was applied to develop the RS. Cox regression analysis was used to construct hybrid nomograms based on different RS and clinical variables. The performances of hybrid nomograms were evaluated using the time-dependent receiver operator characteristic (ROC) curve and the Hosmer-Lemeshow test. The clinical utilities of prediction nomograms were determined via decision curve analysis. The predictive efficiency of different RS, clinical variables, and hybrid nomograms was compared. RESULTS: The RS and IPI were identified as independent predictors of PFS and OS, and were selected to construct hybrid nomograms. Both TMTV- and MBV-based hybrid nomograms had significantly higher values of area under the curve (AUC) than IPI in training and validation cohorts (all P < 0.05), while no significant difference was found between TMTV- and MBV-based hybrid nomograms (P > 0.05). The Hosmer-Lemeshow test showed that both TMTV- and MBV-based hybrid nomograms calibrated well in the training and validation cohorts (all P > 0.05). Decision curve analysis indicated that hybrid nomograms had higher net benefits than IPI. CONCLUSION: The hybrid nomograms combining RS with IPI could significantly improve survival prediction in DLBCL. Radiomic analysis on MBV may serve as a potential approach for prognosis assessment in DLBCL. TRIAL REGISTRATION: NCT04317313. Registered March 16, 2020. Public site: https://clinicaltrials.gov/ct2/show/NCT04317313.

CRISPR/Cas9-edited triple-fusion reporter gene imaging of dynamics and function of transplanted human urinary-induced pluripotent stem cell-derived cardiomyocytes.

PURPOSE: To investigate the post-transplantation behaviour and therapeutic efficacy of human urinary-induced pluripotent stem cell-derived cardiomyocytes (hUiCMs) in infarcted heart. METHODS: We used clustered regularly interspaced short palindromic repeats/CRISPR-associated nuclease 9 (CRISPR/Cas9) technology to integrate a triple-fusion (TF) reporter gene into the AAVS1 locus in human urine-derived hiPSCs (hUiPSCs) to generate TF-hUiPSCs that stably expressed monomeric red fluorescent protein for fluorescence imaging, firefly luciferase for bioluminescence imaging (BLI) and herpes simplex virus thymidine kinase for positron emission tomography (PET) imaging. RESULTS: Transplanted cardiomyocytes derived from TF-hUiPSCs (TF-hUiCMs) engrafted and proliferated in the infarcted heart as monitored by both BLI and PET imaging and significantly improved cardiac function. Under ischaemic conditions, TF-hUiCMs enhanced cardiomyocyte (CM) glucose metabolism and promoted angiogenic activity. CONCLUSION: This study established a CRISPR/Cas9-mediated multimodality reporter gene imaging system that can determine the dynamics and function of TF-hUiCMs in myocardial infarction, which is helpful for investigating the application of stem cell therapy.