Isobavachalcone: A redox antifungal agent impairs the mitochondria protein of Cryptococcus neoformans.

Isobavachalcone (IBC) is a natural small molecule with various biological activities; however, its inhibitory effects on Cryptococcus neoformans remain unclear. In our study, IBC showed a good antifungal effect. Through in vitro experiments, its minimum inhibitory concentration was 0.5-1 µg/mL. It exhibited the same antifungal effect as Amphotericin B in brain and lung infections in in vivo experiments. IBC also showed a synergistic antifungal effect with emodin with lower toxicity, and C. neoformans did not develop drug resistance to IBC. In the mechanistic study, significantly damaged mitochondria of C. neoformans, a significant reduction in mitochondrial membrane potential and adenosine triphosphate production, and an increase in hydrogen peroxide (H2O2) caused by IBC were observed using transmission electron microscopy. Through drug affinity-responsive target stability combined with phenotype detection, riboflavin synthases of aconitase and succinate dehydrogenase were screened. Molecular docking, quantitative polymerase chain reaction experiments, target inhibitor and agonist intervention, molecular interaction measurements, and minimum inhibitory concentration detection of the constructed expression strains revealed that IBC targeted the activity of these two enzymes, interfered by the tricarboxylic acid cycle, inhibited the production of adenosine triphosphate, blocked electron transport, reduced mitochondrial membrane potential, and induced antioxidation imbalance and reactive oxygen species accumulation, thus producing an antifungal effect. Therefore, IBC is a promising lead drug and redox antifungal agent for C. neoformans.

A novel subtype based on driver methylation-transcription in lung adenocarcinoma.

AIMS: To identify driver methylation genes and a novel subtype of lung adenocarcinoma (LUAD) by multi-omics and elucidate its molecular features and clinical significance. METHODS: We collected LUAD patients from public databases, and identified driver methylation genes (DMGs) by MethSig and MethylMix algrothms. And novel driver methylation multi-omics subtypes were identified by similarity network fusion (SNF). Furthermore, the prognosis, tumor microenvironment (TME), molecular features and therapy efficiency among subtypes were comprehensively evaluated. RESULTS: 147 overlapped driver methylation were identified and validated. By integrating the mRNA expression and methylation of DMGs using SNF, four distinct patterns, termed as S1-S4, were characterized by differences in prognosis, biological features, and TME. The S2 subtype showed unfavorable prognosis. By comparing the characteristics of the DMGs subtypes with the traditional subtypes, S3 was concentrated in proximal-inflammatory (PI) subtype, and S4 was consisted of terminal respiratory unit (TRU) subtype and PI subtype. By analyzing TME and epithelial mesenchymal transition (EMT) features, increased immune infiltration and higher expression of immune checkpoint genes were found in S3 and S4. While S4 showed higher EMT score and expression of EMT associated genes, indicating S4 may not be as immunosensitive as the S3. Additionally, S3 had lower TIDE and higher IPS score, indicating its increased sensitivity to immunotherapy. CONCLUSION: The driver methylation-related subtypes of LUAD demonstrate prognostic predictive ability that could help inform treatment response and provide complementary information to the existing subtypes.

A bidirectional interpretable compound-protein interaction prediction framework based on cross attention.

The identification of compound-protein interactions (CPIs) plays a vital role in drug discovery. However, the huge cost and labor-intensive nature in vitro and vivo experiments make it urgent for researchers to develop novel CPI prediction methods. Despite emerging deep learning methods have achieved promising performance in CPI prediction, they also face ongoing challenges: (i) providing bidirectional interpretability from both the chemical and biological perspective for the prediction results; (ii) comprehensively evaluating model generalization performance; (iii) demonstrating the practical applicability of these models. To overcome the challenges posed by current deep learning methods, we propose a cross multi-head attention oriented bidirectional interpretable CPI prediction model (CmhAttCPI). First, CmhAttCPI takes molecular graphs and protein sequences as inputs, utilizing the GCW module to learn atom features and the CNN module to learn residue features, respectively. Second, the model applies cross multi-head attention module to compute attention weights for atoms and residues. Finally, CmhAttCPI employs a fully connected neural network to predict scores for CPIs. We evaluated the performance of CmhAttCPI on balanced datasets and imbalanced datasets. The results consistently show that CmhAttCPI outperforms multiple state-of-the-art methods. We constructed three scenarios based on compound and protein clustering and comprehensively evaluated the model generalization ability within these scenarios. The results demonstrate that the generalization ability of CmhAttCPI surpasses that of other models. Besides, the visualizations of attention weights reveal that CmhAttCPI provides chemical and biological interpretation for CPI prediction. Moreover, case studies confirm the practical applicability of CmhAttCPI in discovering anticancer candidates.

Outcomes and prognosis of non-small cell lung cancer patients who underwent curable surgery: a protocol for a real-world, retrospective, population-based and nationwide Chinese National Lung Cancer Cohort (CNLCC) study.

INTRODUCTION: Surgery is one of the main approaches for the comprehensive treatment of early and locally advanced non-small cell lung cancer (NSCLC). This study conducts a nationwide multicentre study to explore factors that could influence the outcomes of patients with I-IIIA NSCLC who underwent curable surgery in real-world scenarios. METHODS AND ANALYSIS: All patients diagnosed with NSCLC between January 2013 and December 2020 will be identified from 30 large public medical services centres in mainland China. The algorithm of natural language processing and artificial intelligence techniques were used to extract data from electronic health records of enrolled patients who fulfil the inclusion criteria. Six categories of parameters are collected and stored from the electronic records, then the parameters will be structured as a high-quality structured case report form. The code book will be compiled and each parameter will be classified and designated a code. In addition, the study retrieves the survival status and causes of death of patients from the Chinese Centre for Disease Control and Prevention. The primary endpoints are overall survival and the secondary endpoint is disease-free survival. Finally, an online platform is formed for data queries and the original records will be stored as secure electronic documents. ETHICS AND DISSEMINATION: The study has been approved by the Ethical Committee of the Chinese Academy of Medical Sciences. Study findings will be disseminated via presentations at conferences and publications in open-access journals. This study has been registered in the Chinese Trial Register (ChiCTR2100052773) on 11 May 2021, http://www.chictr.org.cn/showproj.aspx?proj=136659. TRIAL REGISTRATION NUMBER: ChiCTR2100052773.

Biologically Interpretable Deep Learning To Predict Response to Immunotherapy In Advanced Melanoma Using Mutations and Copy Number Variations.

Only 30-40% of advanced melanoma patients respond effectively to immunotherapy in clinical practice, so it is necessary to accurately identify the response of patients to immunotherapy pre-clinically. Here, we develop KP-NET, a deep learning model that is sparse on KEGG pathways, and combine it with transfer- learning to accurately predict the response of advanced melanomas to immunotherapy using KEGG pathway-level information enriched from gene mutation and copy number variation data. The KP-NET demonstrates best performance with AUROC of 0.886 on testing set and 0.803 on an unseen evaluation set when predicting responders (CR/PR/SD with PFS ≥6 mo) versus non-responders (PD/SD with PFS <6 mo) in anti-CTLA-4 treated melanoma patients. The model also achieves an AUROC of 0.917 and 0.833 in predicting CR/PR versus PD, respectively. Meanwhile, the AUROC is 0.913 when predicting responders versus non-responders in anti-PD-1/PD-L1 melanomas. Moreover, the KP-NET reveals some genes and pathways associated with response to anti-CTLA-4 treatment, such as genes PIK3CA, AOX1 and CBLB, and ErbB signaling pathway, T cell receptor signaling pathway, et al. In conclusion, the KP-NET can accurately predict the response of melanomas to immunotherapy and screen related biomarkers pre-clinically, which can contribute to precision medicine of melanoma.

E2EFP-MIL: End-to-end and high-generalizability weakly supervised deep convolutional network for lung cancer classification from whole slide image.

Efficient and accurate distinction of histopathological subtype of lung cancer is quite critical for the individualized treatment. So far, artificial intelligence techniques have been developed, whose performance yet remained debatable on more heterogenous data, hindering their clinical deployment. Here, we propose an end-to-end, well-generalized and data-efficient weakly supervised deep learning-based method. The method, end-to-end feature pyramid deep multi-instance learning model (E2EFP-MIL), contains an iterative sampling module, a trainable feature pyramid module and a robust feature aggregation module. E2EFP-MIL uses end-to-end learning to extract generalized morphological features automatically and identify discriminative histomorphological patterns. This method is trained with 1007 whole slide images (WSIs) of lung cancer from TCGA, with AUCs of 0.95-0.97 in test sets. We validated E2EFP-MIL in 5 real-world external heterogenous cohorts including nearly 1600 WSIs from both United States and China with AUCs of 0.94-0.97, and found that 100-200 training images are enough to achieve an AUC of >0.9. E2EFP-MIL overperforms multiple state-of-the-art MIL-based methods with high accuracy and low hardware requirements. Excellent and robust results prove generalizability and effectiveness of E2EFP-MIL in clinical practice. Our code is available at https://github.com/raycaohmu/E2EFP-MIL.

HiRAND: A novel GCN semi-supervised deep learning-based framework for classification and feature selection in drug research and development.

The prediction of response to drugs before initiating therapy based on transcriptome data is a major challenge. However, identifying effective drug response label data costs time and resources. Methods available often predict poorly and fail to identify robust biomarkers due to the curse of dimensionality: high dimensionality and low sample size. Therefore, this necessitates the development of predictive models to effectively predict the response to drugs using limited labeled data while being interpretable. In this study, we report a novel Hierarchical Graph Random Neural Networks (HiRAND) framework to predict the drug response using transcriptome data of few labeled data and additional unlabeled data. HiRAND completes the information integration of the gene graph and sample graph by graph convolutional network (GCN). The innovation of our model is leveraging data augmentation strategy to solve the dilemma of limited labeled data and using consistency regularization to optimize the prediction consistency of unlabeled data across different data augmentations. The results showed that HiRAND achieved better performance than competitive methods in various prediction scenarios, including both simulation data and multiple drug response data. We found that the prediction ability of HiRAND in the drug vorinostat showed the best results across all 62 drugs. In addition, HiRAND was interpreted to identify the key genes most important to vorinostat response, highlighting critical roles for ribosomal protein-related genes in the response to histone deacetylase inhibition. Our HiRAND could be utilized as an efficient framework for improving the drug response prediction performance using few labeled data.

A novel subtype to predict prognosis and treatment response with DNA driver methylation-transcription in ovarian cancer.

Aims: To identify a novel subtype with DNA driver methylation-transcriptomic multiomics and predict prognosis and therapy response in serous ovarian cancer (SOC). Methods: SOC cohorts with both mRNA and methylation were collected, and DNA driver methylation (DNAme) was identified with the MithSig method. A novel prognostic subtype was developed by integrating the information on DNAme and prognosis-regulated DNAme-associated mRNA by similarity network fusion. Results: 43 overlapped DNAme were identified in three independent cohorts. SOC patients were categorized into three distinct subtypes by integrated multiomics. There were differences in prognosis, tumor microenvironment and response to therapy among the subtypes. Conclusion: This study identified 43 DNAmes and proposes a novel subtype toward personalized chemotherapy and immunotherapy for SOC patients based on multiomics.

Ovarian cancer is a highly malignant gynecological disease. The high heterogeneity of ovarian cancer may contribute to chemotherapy resistance and immunotherapy insensitivity. Gene alterations and aberrant methylation occur in the process of tumor initiation and progression, but not all alterations are drivers of tumor development. In this study, we aim to find the DNA driver methylation (DNAme) that plays a decisive role in ovarian cancer development and obtain a novel multiomics molecular subtype related to DNAme integrated by multiple omics information. We identified 43 overlapping DNAme in three cohorts. The multiomics subtype associated with DNAme could predict ovarian cancer prognosis and treatment response.

Characterization of the Immune Cell Infiltration Landscape Uncovers Prognostic and Immunogenic Characteristics in Lung Adenocarcinoma.

The immune cell infiltration in TME has been reported to be associated with prognosis and immunotherapy efficiency of lung cancers. However, to date, the immune infiltrative landscape of lung adenocarcinoma (LUAD) has not been elucidated yet. Therefore, this study aimed to identify a new transcriptomic-based TME classification and develop a risk scoring system to predict the clinical outcomes of patients with LUAD. We applied "CIBERSORT" algorithm to analyze the transcriptomic data of LUAD samples and classified LUAD into four discrete subtypes according to the distinct immune cell infiltration patterns. Furthermore, we established a novel predictive tool (TMEscore) to quantify the immune infiltration patterns for each LUAD patient by principal component analysis. The TMEscore displayed as a reliable and independent prognostic biomarker for LUAD, with worse survival in TMEscrore-high patients and better survival in TMEscrore-low patients in both TCGA and other five GEO cohorts. In addition, enriched pathways and genomic alterations were also analyzed and compared in different TMEscore subgroups, and we observed that high TMEscore was significantly correlated with more aggressive molecular changes, while the low TMEscore subgroup enriched in immune active-related pathways. The TMEscore-low subtype showed overexpression of PD-1, CTLA4, and associations of other markers of sensitivity to immunotherapy, including TMB, immunophenoscore (IPS) analysis, and tumor immune dysfunction and exclusion (TIDE) algorithm. Conclusively, TMEscore is a promising and reliable biomarker to distinguish the prognosis, the molecular and immune characteristics, and the benefit from ICIs treatments in LUAD.

Improving ovarian cancer treatment decision using a novel risk predictive tool.

BACKGROUND: As a major component of the tumor tissue, the tumor microenvironment (TME) has been proven to associate with tumor progression and immunotherapy. Ovarian cancer accounts for the highest mortality rate among gynecologic malignancies. Its clinical treatment decision is highly correlated with the prognosis, underscoring the need to evaluate the prognosis and choose the proper clinical treatment through TME information. METHOD: This study constructs a score with TME information obtained by the CIBERSORT algorithm, which classifies the patients into high and low TMEscore groups with quantified TME infiltration patterns through the PCA algorithm. TMEscore was constructed by TCGA cohort and validated in GEO cohort. Univariate and multivariate Cox proportional hazards model analyses were used to demonstrate prognostic value of TMEscore in overall and stratified analysis. RESULT: TMEscore is highly correlated with survival and high TMEscore group has a better prognosis. In order to improve treatment decision, the expression of immune checkpoints, immunophenoscore (IPS) and ESTIMATE score showed a high TMEscore have a better immune microenvironment and respond better to immune checkpoint inhibitors (ICIs). Meanwhile, the mutation landscape between TMEscore groups was profiled, and 13 genes were found mutated differently between the two groups. Among them, BRCA1 has more mutations in the high TMEscore group and speculated that high TMEscore patients might be a beneficiary population of PARP inhibitors combined with immunotherapy. CONCLUSION: TMEscore based on TME with prognostic value and clinical value is proposed for the identification of targets treatment and immunotherapy strategies for ovarian cancer.

Identification and validation of cellular senescence patterns to predict clinical outcomes and immunotherapeutic responses in lung adenocarcinoma.

BACKGROUND: Aging and senescence can alter immune cell fitness and influence the efficacy of lung cancer treatments, especially immunotherapy. However, the correlations between cellular senescence and tumor microenvironment are still not clearly clarified and the value of cellular senescence-related genes in evaluating the immune infiltration and clinical outcomes of lung adenocarcinoma (LUAD) need further investigated. METHODS: We identified three cellular senescence clusters by NMF algorithm and correlated the cellular senescence clusters with the immune landscape in LUAD patients. A prognostic scoring system was established using random survival forest algorithm and validated in 4 external cohorts. Multivariate Cox regression analysis was performed to evaluate the prognostic value of the scoring system. Expression of LYPD3 was evaluated by immunohistochemistry in LUAD samples. RESULTS: Based on the mRNA expression profiles of 278 cellular senescence-related genes, three cellular senescence clusters with distinct prognosis were identified. We characterized three cellular senescence clusters by differences in biological processes, EMT score, expression of immunomodulatory genes, extent of intratumor heterogeneity and response to immunotherapy. Meanwhile, a cellular senescence-related scoring system (CSS) was established and validated as an independent prognostic factor and immunotherapy predictor of LUAD. Patients with low CSS was characterized by prolonged survival time. In response to anti-cancer drugs, patients with low CSS exhibited higher sensitivities to molecular drugs, such as Roscovitine (CDKs inhibitor), Lenaidornide (TNF-α inhibitor), MK2206 (Akt 1/2/3 inhibitor), and especially increased response to anti-PD-1/L1 immunotherapy. CONCLUSIONS: This study demonstrated the correlations between cellular senescence patterns and tumor immune landscape in LUAD, which enhanced our understanding of the tumor immune microenvironment and provided new insights for improving the outcome of immunotherapy for LUAD patients.

The Discovery of New Drug-Target Interactions for Breast Cancer Treatment.

Drug-target interaction (DTIs) prediction plays a vital role in probing new targets for breast cancer research. Considering the multifaceted challenges associated with experimental methods identifying DTIs, the in silico prediction of such interactions merits exploration. In this study, we develop a feature-based method to infer unknown DTIs, called PsePDC-DTIs, which fuses information regarding protein sequences extracted by pseudo-position specific scoring matrix (PsePSSM), detrended cross-correlation analysis coefficient (DCCA coefficient), and an FP2 format molecular fingerprint descriptor of drug compounds. In addition, the synthetic minority oversampling technique (SMOTE) is employed for dealing with the imbalanced data after Lasso dimensionality reduction. Then, the processed feature vectors are put into a random forest classifier to perform DTIs predictions on four gold standard datasets, including nuclear receptors (NR), G-protein-coupled receptors (GPCR), ion channels (IC), and enzymes (E). Furthermore, we explore new targets for breast cancer treatment using its risk genes identified from large-scale genome-wide genetic studies using PsePDC-DTIs. Through five-fold cross-validation, the average values of accuracy in NR, GPCR, IC, and E datasets are 95.28%, 96.19%, 96.74%, and 98.22%, respectively. The PsePDC-DTIs model provides us with 10 potential DTIs for breast cancer treatment, among which erlotinib (DB00530) and FGFR2 (hsa2263), caffeine (DB00201) and KCNN4 (hsa3783), as well as afatinib (DB08916) and FGFR2 (hsa2263) are found with direct or inferred evidence. The PsePDC-DTIs model has achieved good prediction results, establishing the validity and superiority of the proposed method.

Preclinical evaluation of MRG002, a novel HER2-targeting antibody-drug conjugate with potent antitumor activity against HER2-positive solid tumors.

BACKGROUND: ERBB2 is a proto-oncogene of multiple cancers including breast and gastric cancers with HER2 protein overexpression or gene amplification and has been proven clinically as a valid target for these cancers. HER2-targeting agents such as Herceptin®, Kadcyla® and ENHERTU® have been approved by the FDA for the treatment of breast cancer, but these drugs still face the challenge of acquired resistance and/or severe adverse reactions in clinical use. Therefore, there is significant unmet medical need for developing new agents that are more effective and safer for patients with advanced HER2-positive solid tumors including breast and gastric cancers. METHODS: We report here the making of MRG002, a novel HER2-targeted antibody drug conjugate (ADC), and preclinical characterization including pharmacology, pharmacodynamics and toxicology and discuss its potential as a novel agent for treating patients with HER2-positive solid tumors. RESULTS: MRG002 exhibited similar antigen binding affinity but much reduced antibody-dependent cellular cytotoxicity (ADCC) activity compared to trastuzumab. In addition to potent in vitro cytotoxicity, MRG002 showed tumor regression in both high- and medium-to-low HER2 expressing in vivo xenograft models. Furthermore, MRG002 showed enhanced antitumor activity when used in combination with an anti-PD-1 antibody. Main findings from toxicology studies are related to the payload and are consistent with literature report of other ADCs with monomethyl auristatinE. CONCLUSION: MRG002 has demonstrated a favorable toxicity profile and potent antitumor activities in the breast and gastric PDX models with varying levels of HER2 expression, and/or resistance to trastuzumab or T-DM1. A phase I clinical study of MRG002 in patients with HER2-positive solid tumors is ongoing (CTR20181778).

Cross-talk of pyroptosis and tumor immune landscape in lung adenocarcinoma.

BACKGROUND: Pyroptosis has been reported to exhibit a crucial effect on tumorigenesis, development and immune regulation in cancers. However, the potential roles of pyroptosis in the tumor immune landscape remain elusive in lung adenocarcinoma (LUAD) patients. METHODS: In this study, we curated 48 pyroptosis related genes (PRGs), used an unsupervised clustering method to determine pyroptosis patterns and comprehensively evaluated the pyroptosis patterns and tumor immune landscape of 500 LUAD patients. The Pyro score was developed using random survival forest algorithm, and multivariate Cox regression analysis was performed to evaluate the prognostic value of the Pyro score. RESULTS: Based on the mRNA expression profiles of 48 PRGs, three pyroptosis patterns were identified with distinct prognosis, biological pathways and immune landscape. We characterized three pyroptosis patterns by differences in epithelial mesenchymal transition (EMT), extent of intratumor heterogeneity (ITH), overall cell proliferation, neoantigen load, T-cell receptor (TCR) diversity, expression of immunomodulatory genes, and patient prognosis. Meanwhile, the Pyro score was established and validated as an independent prognostic factor and immunotherapy predictor for LUAD. Patients with low Pyro score were characterized by prolonged survival time, enhanced immune infiltration. In response to anti-cancer drugs, patients with low Pyro score exhibited higher sensitivities of drugs which targeted oncogenic related pathways, such as DNA damage repair (DDR) and IGF-1R pathways, and especially increased response to anti-PD-1/L1 immunotherapy. CONCLUSIONS: This study revealed the cross-talk between pyroptosis and the tumor immune landscape in LUAD. The comprehensive evaluation of pyroptosis patterns in individual LUAD patients enhances our understanding of the tumor immune landscape and provides a new way toward personalized immunotherapeutic strategies for LUAD patients.

Mediation Analysis Reveals Potential Biological Mechanism of Ascites Influencing Recurrence in Patients with Epithelial Ovarian Cancer.

OBJECTIVE: Ascites, an accumulation of peritoneal fluid, is associated with poor prognosis of certain cancers. The potential mechanism that ascites worsens prognosis has not been well understood. Lipids have been reported to correlate with the prognosis of patients with epithelial ovarian cancer (EOC). Therefore, we aimed here to investigate whether lipids mediate the effect of ascites on the recurrence of EOC. METHODS: We collected the demographic and pathological data of 437 previously untreated patients with EOC to investigate the influence of ascites on recurrence. To identify the mechanism that mediates the potential influence of ascites on recurrence, we used ultra-performance liquid chromatography coupled with mass spectrometry (UPLC-MS) to determine the plasma lipid profiles of 53 patients with EOC. We used mediation analysis to evaluate if lipids mediated the effects of ascites on the recurrence of EOC. RESULTS: Patients with ascites had a poorer prognosis, which was associated with higher levels of carbohydrate antigen-CA125 (CA125) and FIGO stage. We identified six different lipid metabolites that were associated with ascites and recurrence. Mediation analysis revealed that the lipids LysoPC(P-15:0), PC(P-34:4), and PC(38:6) may mediate the effects of ascites on recurrence. CONCLUSION: Our findings suggest that LysoPC(P-15:0), PC(P-34:4), and PC(38:6) mediate the effect of ascites on the prognosis of patients with EOC. We believe therefore that it is reasonable to consider metabolic interventions targeting the metabolism of LysoPC(P-15:0), PC(P-34:4), and PC(38:6) as a palliative treatment for patients with EOC with ascites. Further studies of more patients will be required to validate our findings.

Identification immunophenotyping of lung adenocarcinomas based on the tumor microenvironment.

The tumor immune microenvironment is heterogeneous, and its impact on treatment responses is not well understood. It is still a challenge to analyze the interaction between malignant cells and the tumor microenvironment to apply suitable immunotherapy in lung adenocarcinoma. We performed the nonnegative matrix factorization method to 513 messenger RNA expression profiles of lung adenocarcinomas (LUADs) from The Cancer Genome Atlas (TCGA) to obtain an immune-related expression pattern. Subsequently, we characterized the immune-related gene signatures and clinical and survival characteristics. We used 576 patients from Gene Expression Omnibus to confirm our findings. Of the patients in the training cohort, 51% had a high immune enrichment score, high expression of immune cell signaling, cytolytic activity, and interferon (IFN)-related signatures (all P < .05). We denoted these as the Immune Class. We further subdivided the Immune Class into two subclasses based on the tumor microenvironment. These were denoted the Active Immune Class and Exhausted Immune Class. The former showed significant IFN, T-cells, M1 macrophage signatures, and better prognosis (all P < .05), while the latter presented an exhausted immune response with activated stromal enrichment, M2 macrophage signatures, and immunosuppressive factors such as WNT/transforming growth factor-ß (all P < .05). Furthermore, we predicted the response of our immunophenotypes to immunological checkpoint inhibitors (P < .05). Our findings provide a novel insight into the immune-related state of LUAD and can identify the patients who will be receptive to suitable immunotherapeutic treatments.

Platelet-derived growth factor-D expression mediates the effect of differentiated degree on prognosis in epithelial ovarian cancer.

Platelet-derived growth factor-D (PDGF-D) can enhance invasion and metastasis in several human malignancies. Although several studies have been performed to investigate the association between clinicopathological characteristics and prognosis in epithelial ovarian cancer (EOC), the mediation effect of PDGF-D on above-mentioned association have been seldom assessed. In this study, we detected the PDGF-D expression from the tissues of patients with EOC and further collected clinicopathological characteristics and prognostic information to identify whether PDGF-D mediated the effect of differentiated degree on prognosis in patients with EOC. A total of 190 paraffin-embedded tissue samples from patients with EOC between July 2005 and December 2010 were collected. We performed a Kaplan-Meier analysis for the association between differentiated degree and prognosis followed by a causal mediation analysis. The analysis results indicated that differentiated degree was associated with prognosis and PDGF-D mediated the effect of differentiated degree on prognosis in patients with EOC, which might be a potential target for ovarian cancer treatment.

Ingenol mebutate gel for actinic keratosis.

BACKGROUND: Actinic keratosis is a common precursor to sun-related squamous-cell carcinoma. Treating actinic keratoses and the surrounding skin area (i.e., field therapy) can eradicate clinical and subclinical actinic keratoses. Topical field therapy currently requires weeks or months of treatment. We investigated the efficacy and safety of a new topical field therapy for actinic keratosis, ingenol mebutate gel (0.015% for face and scalp and 0.05% for trunk and extremities). METHODS: In four multicenter, randomized, double-blind studies, we randomly assigned patients with actinic keratoses on the face or scalp or on the trunk or extremities to receive ingenol mebutate or placebo (vehicle), self-applied to a 25-cm(2) contiguous field once daily for 3 consecutive days for lesions on the face or scalp or for 2 consecutive days for the trunk or extremities. Complete clearance (primary outcome) was assessed at 57 days, and local reactions were quantitatively measured. RESULTS: In a pooled analysis of the two trials involving the face and scalp, the rate of complete clearance was higher with ingenol mebutate than with placebo (42.2% vs. 3.7%, P<0.001). Local reactions peaked at day 4, with a mean maximum composite score of 9.1 on the local-skin-response scale (which ranges from 0 to 4 for six types of reaction, yielding a composite score of 0 to 24, with higher numbers indicating more severe reactions), rapidly decreased by day 8, and continued to decrease, approaching baseline scores by day 29. In a pooled analysis of the two trials involving the trunk and extremities, the rate of complete clearance was also higher with ingenol mebutate than with placebo (34.1% vs. 4.7%, P<0.001). Local skin reactions peaked between days 3 and 8 and declined rapidly, approaching baseline by day 29, with a mean maximum score of 6.8. Adverse events were generally mild to moderate in intensity and resolved without sequelae. CONCLUSIONS: Ingenol mebutate gel applied topically for 2 to 3 days is effective for field treatment of actinic keratoses. (Funded by LEO Pharma; ClinicalTrials.gov numbers, NCT00742391, NCT00916006, NCT00915551, and NCT00942604.).