Overexpression of ABCC1 and ABCG2 confers resistance to talazoparib, a poly (ADP-Ribose) polymerase inhibitor.

AIMS: The overexpression of ABC transporters on cancer cell membranes is one of the most common causes of multidrug resistance (MDR). This study investigates the impact of ABCC1 and ABCG2 on the resistance to talazoparib (BMN-673), a potent poly (ADP-ribose) polymerase (PARP) inhibitor, in ovarian cancer treatment. METHODS: The cell viability test was used to indicate the effect of talazoparib in different cell lines. Computational molecular docking analysis was conducted to simulate the interaction between talazoparib and ABCC1 or ABCG2. The mechanism of talazoparib resistance was investigated by constructing talazoparib-resistant subline A2780/T4 from A2780 through drug selection with gradually increasing talazoparib concentration. RESULTS: Talazoparib cytotoxicity decreased in drug-selected or gene-transfected cell lines overexpressing ABCC1 or ABCG2 but can be restored by ABCC1 or ABCG2 inhibitors. Talazoparib competitively inhibited substrate drug efflux activity of ABCC1 or ABCG2. Upregulated ABCC1 and ABCG2 protein expression on the plasma membrane of A2780/T4 cells enhances resistance to other substrate drugs, which could be overcome by the knockout of either gene. In vivo experiments confirmed the retention of drug-resistant characteristics in tumor xenograft mouse models. CONCLUSIONS: The therapeutic efficacy of talazoparib in cancer may be compromised by its susceptibility to MDR, which is attributed to its interactions with the ABCC1 or ABCG2 transporters. The overexpression of these transporters can potentially diminish the therapeutic impact of talazoparib in cancer treatment.

Gd(III)-Catalyzed Regio-, Diastereo-, and Enantioselective [4 + 2] Photocycloaddition of Naphthalene Derivatives.

Catalytic asymmetric dearomatization (CADA) reactions have evolved into an efficient strategy for accessing chiral polycyclic and spirocyclic scaffolds from readily available planar aromatics. Despite the significant developments, the CADA reaction of naphthalenes remains underdeveloped. Herein, we report a Gd(III)-catalyzed asymmetric dearomatization reaction of naphthalene with a chiral PyBox ligand via visible-light-enabled [4 + 2] cycloaddition. This reaction features application of a chiral Gd/PyBox complex, which regulates the reactivity and selectivity simultaneously, in excited-state catalysis. A wide range of functional groups is compatible with this protocol, giving the highly enantioenriched bridged polycycles in excellent yields (up to 96%) and selectivity (up to >20:1 chemoselectivity, >20:1 dr, >99% ee). The synthetic utility is demonstrated by a 2 mmol scale reaction, removal of directing group, and diversifications of products. Preliminary mechanistic experiments are performed to elucidate the reaction mechanism.

MRI-based automated machine learning model for preoperative identification of variant histology in muscle-invasive bladder carcinoma.

OBJECTIVES: It is essential yet highly challenging to preoperatively diagnose variant histologies such as urothelial carcinoma with squamous differentiation (UC w/SD) from pure UC in patients with muscle-invasive bladder carcinoma (MIBC), as their treatment strategy varies significantly. We developed a non-invasive automated machine learning (AutoML) model to preoperatively differentiate UC w/SD from pure UC in patients with MIBC. METHODS: A total of 119 MIBC patients who underwent baseline bladder MRI were enrolled in this study, including 38 patients with UC w/SD and 81 patients with pure UC. These patients were randomly assigned to a training set or a test set (3:1). An AutoML model was built from the training set, using 13 selected radiomic features from T2-weighted imaging, semantic features (ADC values), and clinical features (tumor length, tumor stage, lymph node metastasis status), and subsequent ten-fold cross-validation was performed. A test set was used to validate the proposed model. The AUC of the ROC curve was then calculated for the model. RESULTS: This AutoML model enabled robust differentiation of UC w/SD and pure UC in patients with MIBC in both training set (ten-fold cross-validation AUC = 0.955, 95% confidence interval [CI]: 0.944-0.965) and test set (AUC = 0.932, 95% CI: 0.812-1.000). CONCLUSION: The presented AutoML model, that incorporates the radiomic, semantic, and clinical features from baseline MRI, could be useful for preoperative differentiation of UC w/SD and pure UC. CLINICAL RELEVANCE STATEMENT: This MRI-based automated machine learning (AutoML) study provides a non-invasive and low-cost preoperative prediction tool to identify the muscle-invasive bladder cancer patients with variant histology, which may serve as a useful tool for clinical decision-making. KEY POINTS: • It is important to preoperatively diagnose variant histology from urothelial carcinoma in patients with muscle-invasive bladder carcinoma (MIBC), as their treatment strategy varies significantly. • An automated machine learning (AutoML) model based on baseline bladder MRI can identify the variant histology (squamous differentiation) from urothelial carcinoma preoperatively in patients with MIBC. • The developed AutoML model is a non-invasive and low-cost preoperative prediction tool, which may be useful for clinical decision-making.

MRI-based radiomic models to predict surgical margin status and infer tumor immune microenvironment in breast cancer patients with breast-conserving surgery: a multicenter validation study.

OBJECTIVES: Accurate preoperative estimation of the risk of breast-conserving surgery (BCS) resection margin positivity would be beneficial to surgical planning. In this multicenter validation study, we developed an MRI-based radiomic model to predict the surgical margin status. METHODS: We retrospectively collected preoperative breast MRI of patients undergoing BCS from three hospitals (SYMH, n = 296; SYSUCC, n = 131; TSPH, n = 143). Radiomic-based model for risk prediction of the margin positivity was trained on the SYMH patients (7:3 ratio split for the training and testing cohorts), and externally validated in the SYSUCC and TSPH cohorts. The model was able to stratify patients into different subgroups with varied risk of margin positivity. Moreover, we used the immune-radiomic models and epithelial-mesenchymal transition (EMT) signature to infer the distribution patterns of immune cells and tumor cell EMT status under different marginal status. RESULTS: The AUCs of the radiomic-based model were 0.78 (0.66-0.90), 0.88 (0.79-0.96), and 0.76 (0.68-0.84) in the testing cohort and two external validation cohorts, respectively. The actual margin positivity rates ranged between 0-10% and 27.3-87.2% in low-risk and high-risk subgroups, respectively. Positive surgical margin was associated with higher levels of EMT and B cell infiltration in the tumor area, as well as the enrichment of B cells, immature dendritic cells, and neutrophil infiltration in the peritumoral area. CONCLUSIONS: This MRI-based predictive model can be used as a reliable tool to predict the risk of margin positivity of BCS. Tumor immune-microenvironment alteration was associated with surgical margin status. CLINICAL RELEVANCE STATEMENT: This study can assist the pre-operative planning of BCS. Further research on the tumor immune microenvironment of different resection margin states is expected to develop new margin evaluation indicators and decipher the internal mechanism. KEY POINTS: • The MRI-based radiomic prediction model (CSS model) incorporating features extracted from multiple sequences and segments could estimate the margin positivity risk of breast-conserving surgery. • The radiomic score of the CSS model allows risk stratification of patients undergoing breast-conserving surgery, which could assist in surgical planning. • With the help of MRI-based radiomics to estimate the components of the immune microenvironment, for the first time, it is found that the margin status of breast-conserving surgery is associated with the infiltration of immune cells in the microenvironment and the EMT status of breast tumor cells.

Identifying interactions among air pollutant emissions on diabetes prevalence in Northeast China using a complex network.

BACKGROUND: Low air quality related to ambient air pollution is the largest environmental risk to health worldwide. Interactions between air pollution emissions may affect associations between air pollution exposure and chronic diseases. Therefore, this study aimed to quantify interactions among air pollution emissions and assess their effects on the association between air pollution and diabetes. METHODS: After constructing long-term emission networks for six air pollutants based on data collected from routine monitoring stations in Northeast China, a mutual information network was used to quantify interactions among air pollution emissions. Multiple linear regression analysis was then used to explore the influence of emission interactions on the association between air pollution exposure and the prevalence of diabetes based on data reported from the Northeast Natural Cohort Study in China. RESULTS: Complex network analysis detected three major emission sources in Northeast China located in Shenyang and Changchun. The effects of particulate matter (PM2.5 and PM10) and ground-level ozone (O3) emissions were limited to certain communities but could spread to other communities through emissions in Inner Mongolia. Emissions of sulfur dioxide (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO) significantly influenced other communities. These results indicated that air pollutants in different geographic areas can interact directly or indirectly. Adjusting for interactions between emissions changed associations between air pollution emissions and diabetes prevalence, especially for PM2.5, NO2, and CO. CONCLUSIONS: Complex network analysis is suitable for quantifying interactions among air pollution emissions and suggests that the effects of PM2.5 and NO2 emissions on health outcomes may have been overestimated in previous population studies while those of CO may have been underestimated. Further studies examining associations between air pollution and chronic diseases should consider controlling for the effects of interactions among pollution emissions.

Machine learning radiomics of magnetic resonance imaging predicts recurrence-free survival after surgery and correlation of LncRNAs in patients with breast cancer: a multicenter cohort study.

BACKGROUND: Several studies have indicated that magnetic resonance imaging radiomics can predict survival in patients with breast cancer, but the potential biological underpinning remains indistinct. Herein, we aim to develop an interpretable deep-learning-based network for classifying recurrence risk and revealing the potential biological mechanisms. METHODS: In this multicenter study, 1113 nonmetastatic invasive breast cancer patients were included, and were divided into the training cohort (n = 698), the validation cohort (n = 171), and the testing cohort (n = 244). The Radiomic DeepSurv Net (RDeepNet) model was constructed using the Cox proportional hazards deep neural network DeepSurv for predicting individual recurrence risk. RNA-sequencing was performed to explore the association between radiomics and tumor microenvironment. Correlation and variance analyses were conducted to examine changes of radiomics among patients with different therapeutic responses and after neoadjuvant chemotherapy. The association and quantitative relation of radiomics and epigenetic molecular characteristics were further analyzed to reveal the mechanisms of radiomics. RESULTS: The RDeepNet model showed a significant association with recurrence-free survival (RFS) (HR 0.03, 95% CI 0.02-0.06, P < 0.001) and achieved AUCs of 0.98, 0.94, and 0.92 for 1-, 2-, and 3-year RFS, respectively. In the validation and testing cohorts, the RDeepNet model could also clarify patients into high- and low-risk groups, and demonstrated AUCs of 0.91 and 0.94 for 3-year RFS, respectively. Radiomic features displayed differential expression between the two risk groups. Furthermore, the generalizability of RDeepNet model was confirmed across different molecular subtypes and patient populations with different therapy regimens (All P < 0.001). The study also identified variations in radiomic features among patients with diverse therapeutic responses and after neoadjuvant chemotherapy. Importantly, a significant correlation between radiomics and long non-coding RNAs (lncRNAs) was discovered. A key lncRNA was found to be noninvasively quantified by a deep learning-based radiomics prediction model with AUCs of 0.79 in the training cohort and 0.77 in the testing cohort. CONCLUSIONS: This study demonstrates that machine learning radiomics of MRI can effectively predict RFS after surgery in patients with breast cancer, and highlights the feasibility of non-invasive quantification of lncRNAs using radiomics, which indicates the potential of radiomics in guiding treatment decisions.

Association of genetic risk and lifestyle with pancreatic cancer and their age dependency: a large prospective cohort study in the UK Biobank.

BACKGROUND: Pancreatic cancer (PC) is influenced by both genetic and lifestyle factors. However, further research is still needed to comprehensively clarify the relationships among lifestyle, genetic factors, their combined effect on PC, and how these associations might be age-dependent. METHODS: We included 340,631 participants from the UK Biobank. Three polygenic risk score (PRS) models for PC were applied, which were derived from the previous study and were categorized as low, intermediate, and high. Two healthy lifestyle scores (HLSs) were constructed using 9 lifestyle factors based on the World Cancer Research Fund/American Institute of Cancer Research (WCRF/AICR) lifestyle score and the American Cancer Society (ACS) guidelines and were categorized as unfavorable, intermediate, and favorable. Data were analyzed using Cox proportional hazards models. RESULTS: There were 1,129 cases of incident PC during a median follow-up of 13.05 years. Higher PRS was significantly associated with an increased risk of PC (hazard ratio [HR], 1.58; 95% confidence intervals [CI], 1.47-1.71). Adhering to a favorable lifestyle was associated with a lower risk (HR, 0.48; 95% CI, 0.41-0.56). Participants with an unfavorable lifestyle and a high PRS had the highest risk of PC (HR, 2.84; 95% CI, 2.22-3.62). Additionally, when stratified by age, a favorable lifestyle was most pronounced associated with a lower risk of PC among participants aged ≤ 60 years (HR, 0.35; 95% CI, 0.23-0.54). However, the absolute risk reduction was more pronounced among those aged > 70 years (ARR, 0.19%, 95% CI, 0.13%-0.26%). A high PRS was more strongly associated with PC among participants aged ≤ 60 years (HR, 1.89; 95% CI, 1.30-2.73). Furthermore, we observed a significant multiplicative interaction and several significant additive interactions. CONCLUSIONS: A healthy lifestyle was associated with a lower risk of PC, regardless of the participants' age, sex, or genetic risk. Importantly, our findings indicated the age-dependent association of lifestyle and genetic factors with PC, emphasizing the importance of early adoption for effective prevention and potentially providing invaluable guidance for setting the optimal age to start preventive measures.

Multiple brain regions are involved in reaction to acute restraint stress in CYLD-knockout mice.

The lysine 63 deubiquitinase cylindromatosis (CYLD) is expressed at high levels in the brain and is considered to be involved in anxious and depressive behavior, cognitive inflexibility, and autism disorders. Previous research was limited in some brain regions, including the hippocampus, striatum, and amygdala. To better understand whether CYLD plays a role in adaptation to stress and which brain regions are involved, we analyzed the behavior of CYLD-knockout mice in the elevated plus maze (EPM) and light-dark box test (LDT) after acute restraint stress (ARS) and mapped their c-Fos immunoreactivity in brain sections. Here we report that CYLD deficiency leads to an unexpected reaction to ARS in mice, and is accompanied by significant neuronal activation of brain regions including the medial prefrontal cortex (mPFC), dorsal striatum (DS), nucleus accumbens (NAc), and basal lateral amygdala (BLA), but not ventral hippocampus (vHPC). Our findings show that CYLD participates in ARS-induced anxious behavior and that this involves multiple brain regions.

Machine learning on MRI radiomic features: identification of molecular subtype alteration in breast cancer after neoadjuvant therapy.

OBJECTIVES: Recent studies have revealed the change of molecular subtypes in breast cancer (BC) after neoadjuvant therapy (NAT). This study aims to construct a non-invasive model for predicting molecular subtype alteration in breast cancer after NAT. METHODS: Eighty-two estrogen receptor (ER)-negative/ human epidermal growth factor receptor 2 (HER2)-negative or ER-low-positive/HER2-negative breast cancer patients who underwent NAT and completed baseline MRI were retrospectively recruited between July 2010 and November 2020. Subtype alteration was observed in 21 cases after NAT. A 2D-DenseUNet machine-learning model was built to perform automatic segmentation of breast cancer. 851 radiomic features were extracted from each MRI sequence (T2-weighted imaging, ADC, DCE, and contrast-enhanced T1-weighted imaging), both in the manual and auto-segmentation masks. All samples were divided into a training set (n = 66) and a test set (n = 16). XGBoost model with 5-fold cross-validation was performed to predict molecular subtype alterations in breast cancer patients after NAT. The predictive ability of these models was subsequently evaluated by the AUC of the ROC curve, sensitivity, and specificity. RESULTS: A model consisting of three radiomics features from the manual segmentation of multi-sequence MRI achieved favorable predictive efficacy in identifying molecular subtype alteration in BC after NAT (cross-validation set: AUC = 0.908, independent test set: AUC = 0.864); whereas an automatic segmentation approach of BC lesions on the DCE sequence produced good segmentation results (Dice similarity coefficient = 0.720). CONCLUSIONS: A machine learning model based on baseline MRI is proven useful for predicting molecular subtype alterations in breast cancer after NAT. KEY POINTS: • Machine learning models using MRI-based radiomics signature have the ability to predict molecular subtype alterations in breast cancer after neoadjuvant therapy, which subsequently affect treatment protocols. • The application of deep learning in the automatic segmentation of breast cancer lesions from MRI images shows the potential to replace manual segmentation..

Functional Guilds, Community Assembly, and Co-occurrence Patterns of Fungi in Metalliferous Mine Tailings Ponds in Mainland China.

Metalliferous mine tailings ponds are generally characterized by low levels of nutrient elements, sustained acidic conditions, and high contents of toxic metals. They represent one kind of extreme environments that are believed to resemble the Earth's early environmental conditions. There is increasing evidence that the diversity of fungi inhabiting mine tailings ponds is much higher than previously thought. However, little is known about functional guilds, community assembly, and co-occurrence patterns of fungi in such habitats. As a first attempt to address this critical knowledge gap, we employed high-throughput sequencing to characterize fungal communities in 33 mine tailings ponds distributed across 18 provinces of mainland China. A total of 5842 fungal phylotypes were identified, with saprotrophic fungi being the major functional guild. The predictors of fungal diversity in whole community and sub-communities differed considerably. Community assembly of the whole fungal community and individual functional guilds were primarily governed by stochastic processes. Total soil nitrogen and total phosphorus mediated the balance between stochastic and deterministic processes of the fungal community assembly. Co-occurrence network analysis uncovered a high modularity of the whole fungal community. The observed main modules largely consisted of saprotrophic fungi as well as various phylotypes that could not be assigned to known functional guilds. The richness of core fungal phylotypes, occupying vital positions in co-occurrence network, was positively correlated with edaphic properties such as soil enzyme activity. This indicates the important roles of core fungal phylotypes in soil organic matter decomposition and nutrient cycling. These findings improve our understanding of fungal ecology of extreme environments.

Design, synthesis, and biological evaluation of phenylurea indole derivatives as ABCG2 inhibitors.

Three series of phenylurea indole derivatives were synthesized with potent inhibitory activities on ABCG2 with simple and efficient synthetic routes. Among these compounds, four phenylurea indole derivatives 3c-3f with extended π system were discovered as the most potent ABCG2 inhibitors, while these compounds showed no inhibition on ABCB1. Compounds 3c and 3f were selected for further investigation to explore the mechanisms of action on reversing ABCG2-mediated multidrug resistance (MDR). The results revealed that compounds 3c and 3f increased the accumulation of mitoxantrone (MX) in ABCG2-overexpressing cells, but they did not alter the expression level or localization of ABCG2 in cells. In addition, both 3c and 3f significantly stimulated the ATP hydrolysis of ABCG2 transporter indicating that they can be competitive substrates of ABCG2 transporter, and thereby increase the accumulation of mitoxantrone in ABCG2-overexpressing H460/MX20 cells. Both 3c and 3f was docked into the drug-binding site of the human ABCG2 transporter protein (PDB 6FFC) with high affinities. This study showed that extending the π system of phenylurea indole derivatives enhanced their inhibitory activities on ABCG2, which may provide a clue for the further research to discover more potent ABCG2 inhibitors.

The survival and outcome of older patients with primary aneurysmal subarachnoid haemorrhage: a 2-year follow-up, multi-centre, observational study.

BACKGROUND AND PURPOSE: The management of older aneurysmal subarachnoid haemorrhage (aSAH) cases is a clinical challenge. This study aimed to analyse the survival and functional outcomes in older aSAH patients (age ≥ 70 years) to provide evidence for making treatment decisions for such patients. METHODS: We performed a 2-year follow-up analysis of the Chinese Multi-Centre Cerebral Aneurysm Database for older patients suffering from aSAH from 2017 to 2020. A survival analysis was used to investigate the mean survival and hazard ratios for death. Binary logarithmic regression was performed to investigate the odds ratio for independent survival and dependent survival. RESULTS: A total of 1,136 consecutive older patients with aSAH were assessed in this study, and 944 patients (83.1%) were followed up. The overall mean survival was 37.79 ± 1.04 months. A total of 380 (40.25%) patients died within 2 years after aSAH. In survival analysis, the predictors of mortality were older age, intracerebral haemorrhage (ICH) history, Hunt-Hess (H-H) grade, World Federation of Neurosurgical Societies (WFNS) grade and operative treatment decreased the risk of mortality compared to conservative treatment. In binary logarithmic regression, the predictors of dependent survival were hypertension, diabetes, WFNS grade. CONCLUSIONS: The risk for 2-year mortality after aSAH increases markedly with older age, ICH history, H-H grade and WFNS grade. Risk factors for 2-year dependent survival were associated with hypertension, diabetes and WFNS grade in older patients with aSAH. Operative treatment markedly decreased mortality but did not significantly decrease the morbidity of dependent survival compared to conservative treatment.

Epidermal growth factor receptor inhibitors as adjuvant treatment for patients with resected non-small cell lung cancer harboring EGFR mutation: a meta-analysis of randomized controlled clinical trials.

BACKGROUND: The epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) is still under investigation as adjuvant treatment for early-stage disease. Here, we performed a meta-analysis to evaluate the efficacy of adjuvant EGFR-TKI versus non-EGFR-TKI treatment in patients with completely resected non-small cell lung cancer (NSCLC) harboring EGFR mutation. METHODS: Two investigators independently extracted data from databases. A meta-analysis was performed following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The protocol was registered in PROSPERO (ID: CRD42022316481). The primary outcome was disease-free survival (DFS) in patients with EGFR mutation, measured as the hazard ratio (HR). Other outcomes (of subgroup analyses) included overall survival (OS) and DFS. RESULTS: After the systematic screening, eight studies with a total of 3098 patients with stage IB-IIIA NSCLC were included. The results show that in patients with EGFR mutation, the DFS in the adjuvant EGFR-TKI group was significantly superior to that in the control group, with a HR of 0.47 (95% confidence interval [CI]: 0.30-0.74; P = 0.001). In subgroup analyses of DFS, the benefit was observed in the EGFR-TKI group versus the chemotherapy group (HR 0.50, 95% CI 0.30-0.84; P = 0.009), the EGFR-TKI combined with chemotherapy group versus the chemotherapy group (HR 0.37, 95% CI 0.16-0.85; P = 0.02), and in stage IIA-IIIA NSCLC (HR 0.45, 95% CI 0.27-0.74; P = 0.002). However, the benefit of DFS did not translate into improved OS in the whole population (HR 0.79, 95% CI 0.54-1.14; P = 0.20). CONCLUSION: EGFR-TKIs prolonged DFS but not OS in patients with completely resected stage II-IIIA NSCLC harboring EGFR mutation. Longer follow-ups and new clinical trials that can result in changes in clinical practice are needed.

Cholesterol promotes EGFR-TKIs resistance in NSCLC by inducing EGFR/Src/Erk/SP1 signaling-mediated ERRα re-expression.

BACKGROUND: The use of epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) brings remarkable benefits for the survival of patients with advanced NSCLC harboring EGFR mutations. Unfortunately, acquired resistance seems to be inevitable and limits the application of EGFR-TKIs in clinical practice. This study reported a common molecular mechanism sustaining resistance and potential treatment options to overcome EGFR-TKIs resistance. METHODS: EGFR-TKIs resistant NSCLC cells were established and confirmed by MTT assay. Cholesterol content was detected and the promotional function of cholesterol on NSCLC growth was determined in vivo. Then, we identified ERRα expression as the downstream factor of cholesterol-mediated drug resistance. To dissect the regulatory mechanism, we conducted experiments, including immunofluorescence, co-immunoprecipitation, luciferase reporter assay and chromatin immunoprecipitation assay. RESULTS: Long-term exposure to EGFR-TKIs generate drug resistance with the characteristic of cholesterol accumulation in lipid rafts, which promotes EGFR and Src to interact and lead EGFR/Src/Erk signaling reactivation-mediated SP1 nuclear translocation and ERRα re-expression. Further investigation identifies ERRα as a target gene of SP1. Functionally, re-expression of ERRα sustains cell proliferation by regulating ROS detoxification process. Lovastatin, a drug used to decrease cholesterol level, and XCT790, an inverse agonist of ERRα, overcome gefitinib and osimertinib resistance both in vitro and in vivo. CONCLUSIONS: Our study indicates that cholesterol/EGFR/Src/Erk/SP1 axis-induced ERRα re-expression promotes survival of gefitinib and osimertinib-resistant cancer cells. Besides, we demonstrate the potential of lowing cholesterol and downregulation of ERRα as effective adjuvant treatment of NSCLC.

Targeting HNRNPU to overcome cisplatin resistance in bladder cancer.

PURPOSE: The overall response of cisplatin-based chemotherapy in bladder urothelial carcinoma (BUC) remains unsatisfactory due to the complex pathological subtypes, genomic difference, and drug resistance. The genes that associated with cisplatin resistance remain unclear. Herein, we aimed to identify the cisplatin resistance associated genes in BUC. EXPERIMENTAL DESIGN: The cytotoxicity of cisplatin was evaluated in six bladder cancer cell lines to compare their responses to cisplatin. The T24 cancer cells exhibited the lowest sensitivity to cisplatin and was therefore selected to explore the mechanisms of drug resistance. We performed genome-wide CRISPR screening in T24 cancer cells in vitro, and identified that the gene heterogeneous nuclear ribonucleoprotein U (HNRNPU) was the top candidate gene related to cisplatin resistance. Epigenetic and transcriptional profiles of HNRNPU-depleted cells after cisplatin treatment were analyzed to investigate the relationship between HNRNPU and cisplatin resistance. In vivo experiments were also performed to demonstrate the function of HNRNPU depletion in cisplatin sensitivity. RESULTS: Significant correlation was found between HNRNPU expression level and sensitivity to cisplatin in bladder cancer cell lines. In the high HNRNPU expressing T24 cancer cells, knockout of HNRNPU inhibited cell proliferation, invasion, and migration. In addition, loss of HNRNPU promoted apoptosis and S-phase arrest in the T24 cells treated with cisplatin. Data from The Cancer Genome Atlas (TCGA) demonstrated that HNRNPU expression was significantly higher in tumor tissues than in normal tissues. High HNRNPU level was negatively correlated with patient survival. Transcriptomic profiling analysis showed that knockout of HNRNPU enhanced cisplatin sensitivity by regulating DNA damage repair genes. Furthermore, it was found that HNRNPU regulates chemosensitivity by affecting the expression of neurofibromin 1 (NF1). CONCLUSIONS: Our study demonstrated that HNRNPU expression is associated with cisplatin sensitivity in bladder urothelial carcinoma cells. Inhibition of HNRNPU could be a potential therapy for cisplatin-resistant bladder cancer.

Development of a radiomics model to diagnose pheochromocytoma preoperatively: a multicenter study with prospective validation.

BACKGROUND: Preoperative diagnosis of pheochromocytoma (PHEO) accurately impacts preoperative preparation and surgical outcome in PHEO patients. Highly reliable model to diagnose PHEO is lacking. We aimed to develop a magnetic resonance imaging (MRI)-based radiomic-clinical model to distinguish PHEO from adrenal lesions. METHODS: In total, 305 patients with 309 adrenal lesions were included and divided into different sets. The least absolute shrinkage and selection operator (LASSO) regression model was used for data dimension reduction, feature selection, and radiomics signature building. In addition, a nomogram incorporating the obtained radiomics signature and selected clinical predictors was developed by using multivariable logistic regression analysis. The performance of the radiomic-clinical model was assessed with respect to its discrimination, calibration, and clinical usefulness. RESULTS: Seven radiomics features were selected among the 1301 features obtained as they could differentiate PHEOs from other adrenal lesions in the training (area under the curve [AUC], 0.887), internal validation (AUC, 0.880), and external validation cohorts (AUC, 0.807). Predictors contained in the individualized prediction nomogram included the radiomics signature and symptom number (symptoms include headache, palpitation, and diaphoresis). The training set yielded an AUC of 0.893 for the nomogram, which was confirmed in the internal and external validation sets with AUCs of 0.906 and 0.844, respectively. Decision curve analyses indicated the nomogram was clinically useful. In addition, 25 patients with 25 lesions were recruited for prospective validation, which yielded an AUC of 0.917 for the nomogram. CONCLUSION: We propose a radiomic-based nomogram incorporating clinically useful signatures as an easy-to-use, predictive and individualized tool for PHEO diagnosis.

A Noninvasive Tool Based on Magnetic Resonance Imaging Radiomics for the Preoperative Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer.

PURPOSE: This study aimed to identify patients with pathological complete response (pCR) and make better clinical decisions by constructing a preoperative predictive model based on tumoral and peritumoral volumes of multiparametric magnetic resonance imaging (MRI) obtained before neoadjuvant chemotherapy (NAC). METHODS: This study investigated MRI before NAC in 448 patients with nonmetastatic invasive ductal breast cancer (Sun Yat-sen Memorial Hospital, Sun Yat-sen University, n = 362, training cohort; and Sun Yat-sen University Cancer Center, n = 86, validation cohort). The tumoral and peritumoral volumes of interest (VOIs) were segmented and MRI features were extracted. The radiomic features were filtered via a random forest algorithm, and a supporting vector machine was used for modeling. The receiver operator characteristic curve and area under the curve (AUC) were calculated to assess the performance of the radiomics-based classifiers. RESULTS: For each MRI sequence, a total of 863 radiomic features were extracted and the top 30 features were selected for model construction. The radiomic classifiers of tumoral VOI and peritumoral VOI were both promising for predicting pCR, with AUCs of 0.96 and 0.97 in the training cohort and 0.89 and 0.78 in the validation cohort, respectively. The tumoral + peritumoral VOI radiomic model could further improve the predictive accuracy, with AUCs of 0.98 and 0.92 in the training and validation cohorts. CONCLUSIONS: The tumoral and peritumoral multiparametric MRI radiomics model can promisingly predict pCR in breast cancer using MRI images before surgery. Our results highlighted the potential value of the tumoral and peritumoral radiomic model in cancer management.

Remarkable effects of microbial factors on soil phosphorus bioavailability: A country-scale study.

Low soil phosphorus (P) bioavailability causes the widespread occurrence of P-limited terrestrial ecosystems around the globe. Exploring the factors influencing soil P bioavailability at large spatial scales is critical for managing these ecosystems. However, previous studies have mostly focused on abiotic factors. In this study, we explored the effects of microbial factors on soil P bioavailability of terrestrial ecosystems using a country-scale sampling effort. Our results showed that soil microbial biomass carbon (MBC) and acid phosphatase were important predictors of soil P bioavailability of agro- and natural ecosystems across China although they appeared less important than total soil P. The two microbial factors had a positive effect on soil P bioavailability of both ecosystem types and were able to mediate the effects of several abiotic factors (e.g., mean annual temperature). Meanwhile, we revealed that soil phytase could affect soil P bioavailability at the country scale via ways similar to those of soil MBC and acid phosphatase, a pattern being more pronounced in agroecosystems than in natural ecosystems. Moreover, we obtained evidence for the positive effects of microbial genes encoding these enzymes on soil P bioavailability at the country scale although their effect sizes varied between the two ecosystem types. Taken together, this study demonstrated the remarkable effects of microbial factors on soil P bioavailability at a large spatial scale, highlighting the importance to consider microbial factors in managing the widespread P-limited terrestrial ecosystems.

Assessment of quantitative dynamic contrast-enhanced MRI in distinguishing different histologic grades of breast phyllode tumor.

OBJECTIVES: To investigate whether quantitative DCE-MRI (qDCE-MRI) could help distinguish breast phyllodes tumor (PT) grades. MATERIALS AND METHODS: This retrospective study included 67 breast PTs (26 benign lesions, 25 borderline lesions, and 16 malignant lesions) from April 2016 to July 2020. MRI was performed with a 1.5-T MR system. Perfusion parameters (Ktrans, kep, ve, iAUC60) derived from qDCE-MRI, tumor size, and the mean ADC value were correlated with histologic grades using Spearman's rank correlation coefficient. Ktrans, kep, ve, and iAUC60 of three histologic grades were also calculated and compared. RESULTS: The Spearman correlation coefficient with histologic grade of the tumor size was 0.578 (p < 0.001); the ADC value was not correlated with histologic grades of breast PT (p = 0.059). The Ktrans, kep, ve, and iAUC60 of benign breast PTs were significantly lower than those of borderline breast PTs (p < 0.001) and lower than those of malignant breast PTs (p < 0.001). In comparison, the Ktrans, ve, and iAUC60 of borderline breast PTs were significantly lower than those of malignant breast PTs (p < 0.001, p < 0.001, p = 0.007, respectively). For ROC analysis, AUCs of Ktrans, ve, and iAUC60 were higher than tumor size and ADC value for differentiating three PT grades. CONCLUSION: Quantitative and semi-quantitative perfusion parameters (Ktrans, ve, and iAUC60, especially Ktrans) derived from qDCE-MRI showed better diagnosis efficiency than tumor size and ADC for grading breast PTs. Therefore, qDCE-MRI may be helpful for preoperative differentiating breast PT grades. KEY POINTS: • Quantitative dynamic contrast-enhanced MRI can be used as a complementary noninvasive method to improve the differential diagnosis of breast PT. • Ktrans, ve, and iAUC60 derived from qDCE-MRI showed better diagnosis efficiency than tumor size and ADC for grading breast PTs.

Overcoming anti-cancer drug resistance via restoration of tumor suppressor gene function.

The cytotoxic anti-cancer drugs cisplatin, paclitaxel, doxorubicin, 5-fluorouracil (5-FU), as well as targeted drugs including imatinib, erlotinib, and nivolumab, play key roles in clinical cancer treatment. However, the frequent emergence of drug resistance severely comprosises their anti-cancer efficacy. A number of studies indicated that loss of function of tumor suppressor genes (TSGs) is involved in the development of cancer drug resistance, apart from decreased drug influx, increased drug efflux, induction of anti-apoptosis mechanisms, alterations in tumor microenvironment, drug compartmentalization, enhanced DNA repair and drug inactivation. TSGs are involved in the pathogenesis of tumor formation through regulation of DNA damage repair, cell apoptosis, autophagy, proliferation, cell cycle progression, and signal transduction. Our increased understanding of TSGs in the past decades demonstrates that gene mutation is not the only reason that leads to the inactivation of TSGs. Loss of function of TSGs may be based on the ubiquitin-proteasome pathway, epigenetic and transcriptional regualtion, post-translation modifications like phosphorylation as well as cellular translocation of TSGs. As the above processes can constitute"druggable targets", these mechanisms provide novel therapeutic approaches in targeting TSGs. Some small molecule compounds targeting these approaches re-activated TSGs and reversed cancer drug resistance. Along this vein, functional restoration of TSGs is a novel and promising approach to surmount cancer drug resistance. In the current review, we draw a scenario based on the role of loss of function of TSGs in drug resistance, on mechanisms leading to inactivation of TSGs and on pharmacological agents acting on these mechanisms to overcome cancer drug resistance. This review discusses novel therapeutic strategies targeting TSGs and offers possible modalities to conquer cancer drug resistance.