Application of biomimetic nanovaccines in cancer immunotherapy: A useful strategy to help combat immunotherapy resistance.

Breakthroughs in actual clinical applications have begun through vaccine-based cancer immunotherapy, which uses the body's immune system, both humoral and cellular, to attack malignant cells and fight diseases. However, conventional vaccine approaches still face multiple challenges eliciting effective antigen-specific immune responses, resulting in immunotherapy resistance. In recent years, biomimetic nanovaccines have emerged as a promising alternative to conventional vaccine approaches by incorporating the natural structure of various biological entities, such as cells, viruses, and bacteria. Biomimetic nanovaccines offer the benefit of targeted antigen-presenting cell (APC) delivery, improved antigen/adjuvant loading, and biocompatibility, thereby improving the sensitivity of immunotherapy. This review presents a comprehensive overview of several kinds of biomimetic nanovaccines in anticancer immune response, including cell membrane-coated nanovaccines, self-assembling protein-based nanovaccines, extracellular vesicle-based nanovaccines, natural ligand-modified nanovaccines, artificial antigen-presenting cells-based nanovaccines and liposome-based nanovaccines. We also discuss the perspectives and challenges associated with the clinical translation of emerging biomimetic nanovaccine platforms for sensitizing cancer cells to immunotherapy.

Ferroptosis is a protective factor for the prognosis of cancer patients: a systematic review and meta-analysis.

BACKGROUND: Cancer is a leading global cause of death. Conventional cancer treatments like surgery, radiation, and chemotherapy have associated side effects. Ferroptosis, a nonapoptotic and iron-dependent cell death, has been identified and differs from other cell death types. Research has shown that ferroptosis can promote and inhibit tumor growth, which may have prognostic value. Given the unclear role of ferroptosis in cancer biology, this meta-analysis aims to investigate its impact on cancer prognosis. METHODS: This systematic review and meta-analysis conducted searches on PubMed, Embase, and the Cochrane Library databases. Eight retrospective studies were included to compare the impact of ferroptosis inhibition and promotion on cancer patient prognosis. The primary endpoints were overall survival (OS) and progression-free survival (PFS). Studies lacking clear descriptions of hazard ratios (HR) and 95% confidence intervals for OS and PFS were excluded. Random-effects meta-analysis and meta-regression were performed on the included study data to assess prognosis differences between the experimental and control groups. Meta-analysis results included HR and 95% confidence intervals. This study has been registered with PROSPERO, CRD 42023463720 on September 27, 2023. RESULTS: A total of 2,446 articles were screened, resulting in the inclusion of 5 articles with 938 eligible subjects. Eight studies were included in the meta-analysis after bias exclusion. The meta-analysis, after bias exclusion, demonstrated that promoting ferroptosis could increase cancer patients' overall survival (HR 0.31, 95% CI 0.21-0.44) and progression-free survival (HR 0.26, 95% CI 0.16-0.44) compared to ferroptosis inhibition. The results showed moderate heterogeneity, suggesting that biological activities promoting cancer cell ferroptosis are beneficial for cancer patient's prognosis. CONCLUSIONS: This systematic review and meta-analysis demonstrated that the promotion of ferroptosis yields substantial benefits for cancer prognosis. These findings underscore the untapped potential of ferroptosis as an innovative anti-tumor therapeutic strategy, capable of addressing challenges related to drug resistance, limited therapeutic efficacy, and unfavorable prognosis in cancer treatment. REGISTRATION: CRD42023463720.

Nanomedicine embraces cancer radio-immunotherapy: mechanism, design, recent advances, and clinical translation.

Cancer radio-immunotherapy, integrating external/internal radiation therapy with immuno-oncology treatments, emerges in the current management of cancer. A growing number of pre-clinical studies and clinical trials have recently validated the synergistic antitumor effect of radio-immunotherapy, far beyond the "abscopal effect", but it suffers from a low response rate and toxicity issues. To this end, nanomedicines with an optimized design have been introduced to improve cancer radio-immunotherapy. Specifically, these nanomedicines are elegantly prepared by incorporating tumor antigens, immuno- or radio-regulators, or biomarker-specific imaging agents into the corresponding optimized nanoformulations. Moreover, they contribute to inducing various biological effects, such as generating in situ vaccination, promoting immunogenic cell death, overcoming radiation resistance, reversing immunosuppression, as well as pre-stratifying patients and assessing therapeutic response or therapy-induced toxicity. Overall, this review aims to provide a comprehensive landscape of nanomedicine-assisted radio-immunotherapy. The underlying working principles and the corresponding design strategies for these nanomedicines are elaborated by following the concept of "from bench to clinic". Their state-of-the-art applications, concerns over their clinical translation, along with perspectives are covered.

Artificial intelligence in radiotherapy.

Radiotherapy is a discipline closely integrated with computer science. Artificial intelligence (AI) has developed rapidly over the past few years. With the explosive growth of medical big data, AI promises to revolutionize the field of radiotherapy through highly automated workflow, enhanced quality assurance, improved regional balances of expert experiences, and individualized treatment guided by multi-omics. In addition to independent researchers, the increasing number of large databases, biobanks, and open challenges significantly facilitated AI studies on radiation oncology. This article reviews the latest research, clinical applications, and challenges of AI in each part of radiotherapy including image processing, contouring, planning, quality assurance, motion management, and outcome prediction. By summarizing cutting-edge findings and challenges, we aim to inspire researchers to explore more future possibilities and accelerate the arrival of AI radiotherapy.

Non-coding genome in small cell lung cancer between theoretical view and clinical applications.

Small cell lung cancer (SCLC) is a highly aggressive cancer of the neuroendocrine system, characterized by poor differentiation, rapid growth, and poor overall survival (OS) of patients. Despite the recent advances in the treatment of SCLC recently, the 2-year survival rate of patients with the cancer is only 14-15%, occasioned by the acquired resistance to drugs and serious off-target effects. In humans, the coding region is only 2% of the total genome, and 20% of that is associated with human diseases. Beyond the coding genome are RNAs, promoters, enhancers, and other intricate elements. The non-coding regulatory regions, mainly the non-coding RNAs (ncRNAs), regulate numerous biological activities including cell proliferation, metastasis, and drug resistance. As such, they are potential diagnostic or prognostic biomarkers, and also potential therapeutic targets for SCLC. Therefore, understanding how non-coding elements regulate SCLC development and progression holds significant clinical implications. Herein, we summarized the recent discoveries on the relationship between the non-coding elements including long non-coding RNAs (lncRNA), microRNAs (miRNAs), circular RNA (circRNA), enhancers as well as promotors, and the pathogenesis of SCLC and their potential clinical applications.

Artificial intelligence-based prediction of clinical outcome in immunotherapy and targeted therapy of lung cancer.

Lung cancer accounts for the main proportion of malignancy-related deaths and most patients are diagnosed at an advanced stage. Immunotherapy and targeted therapy have great advances in application in clinics to treat lung cancer patients, yet the efficacy is unstable. The response rate of these therapies varies among patients. Some biomarkers have been proposed to predict the outcomes of immunotherapy and targeted therapy, including programmed cell death-ligand 1 (PD-L1) expression and oncogene mutations. Nevertheless, the detection tests are invasive, time-consuming, and have high demands on tumor tissue. The predictive performance of conventional biomarkers is also unsatisfactory. Therefore, novel biomarkers are needed to effectively predict the outcomes of immunotherapy and targeted therapy. The application of artificial intelligence (AI) can be a possible solution, as it has several advantages. AI can help identify features that are unable to be used by humans and perform repetitive tasks. By combining AI methods with radiomics, pathology, genomics, transcriptomics, proteomics, and clinical data, the integrated model has shown predictive value in immunotherapy and targeted therapy, which significantly improves the precision treatment of lung cancer patients. Herein, we reviewed the application of AI in predicting the outcomes of immunotherapy and targeted therapy in lung cancer patients, and discussed the challenges and future directions in this field.

Small cell lung cancer transformation: From pathogenesis to treatment.

Small cell lung cancer (SCLC) is a type of neuroendocrine tumor with high malignancy and poor prognosis. Besides the de novo SCLC, there is transformed SCLC, which has similar characteristics of pathological morphology, molecular characteristics, clinical manifestations and drug sensitivity. However, de novo SCLC and transformed SCLC have different pathogenesis and tumor microenvironment. SCLC transformation is one of the mechanisms of resistance to chemotherapy, immunotherapy, and targeted therapy in NSCLC. Two hypotheses have been used to explain the pathogenesis of SCLC transformation. Although SCLC transformation is not common in clinical practice, it has been repeatedly identified in many small patient series and case reports. It usually occurs in epidermal growth factor receptor (EGFR) mutant lung adenocarcinoma after treatment with tyrosine kinase inhibitors (TKIs). SCLC transformation can also occur in anaplastic lymphoma kinase (ALK)-positive lung cancer after treatment with ALK inhibitors and in wild-type EGFR or ALK NSCLC treated with immunotherapy. Chemotherapy was previously used to treat transformed SCLC, yet it is associated with an unsatisfactory prognosis. We comprehensively review the advancements in transformed SCLC, including clinical and pathological characteristics, and the potential effective treatment after SCLC transformation, aiming to give a better understanding of transformed SCLC and provide support for clinical uses.

Targeting lymph node delivery with nanovaccines for cancer immunotherapy: recent advances and future directions.

Although cancer immunotherapy is a compelling approach against cancer, its effectiveness is hindered by the challenge of generating a robust and durable immune response against metastatic cancer cells. Nanovaccines, specifically engineered to transport cancer antigens and immune-stimulating agents to the lymph nodes, hold promise in overcoming these limitations and eliciting a potent and sustained immune response against metastatic cancer cells. This manuscript provides an in-depth exploration of the lymphatic system's background, emphasizing its role in immune surveillance and tumor metastasis. Furthermore, it delves into the design principles of nanovaccines and their unique capability to target lymph node metastasis. The primary objective of this review is to provide a comprehensive overview of the current advancements in nanovaccine design for targeting lymph node metastasis, while also discussing their potential to enhance cancer immunotherapy. By summarizing the state-of-the-art in nanovaccine development, this review aims to shed light on the promising prospects of harnessing nanotechnology to potentiate cancer immunotherapy and ultimately improve patient outcomes.

LMP2-mRNA lipid nanoparticle sensitizes EBV-related tumors to anti-PD-1 therapy by reversing T cell exhaustion.

BACKGROUND: Targeting EBV-proteins with mRNA vaccines is a promising way to treat EBV-related tumors like nasopharyngeal carcinoma (NPC). We assume that it may sensitize tumors to immune checkpoint inhibitors. RESULTS: We developed an LMP2-mRNA lipid nanoparticle (C2@mLMP2) that can be delivered to tumor-draining lymph nodes. C2@mLMP2 exhibited high transfection efficiency and lysosomal escape ability and induced an increased proportion of CD8 + central memory T cells and CD8 + effective memory T cells in the spleen of the mice model. A strong synergistic anti-tumor effect of C2@mLMP2 in combination with αPD-1 was observed in tumor-bearing mice. The mechanism was identified to be associated with a reverse of CD8 + T cell exhaustion in the tumor microenvironment. The pathological analysis further proved the safety of the vaccine and the combined therapy. CONCLUSIONS: This is the first study proving the synergistic effect of the EBV-mRNA vaccine and PD-1 inhibitors for EBV-related tumors. This study provides theoretical evidence for further clinical trials that may expand the application scenario and efficacy of immunotherapy in NPC.

Computed tomography-based deep-learning prediction of induction chemotherapy treatment response in locally advanced nasopharyngeal carcinoma.

BACKGROUND: Deep learning methods have great potential to predict treatment response. The objective of this study was to evaluate and validate the predictive performance of the computed tomography (CT)-based model using deep learning features for identification of responders and nonresponders to induction chemotherapy (IC) in nasopharyngeal carcinoma (NPC). MATERIALS AND METHODS: All eligible patients were included retrospectively between January 2012 and December 2018, and assigned to the training (n = 208) or the testing cohort (n = 89). We extracted deep learning features of six pretrained convolutional neural networks (CNNs) via transfer learning method, and handcrafted radiomics features manually. Support vector machine (SVM) was adopted as the classifier. All predictive models were evaluated using the area under the receiver operating characteristics curve (AUC), by which an optimal model was selected. We also built clinical and clinical-radiological models for comparison. RESULTS: The model with features extracted from ResNet50 (RN-SVM) had optimal performance among all models with features extracted from pretrained CNNs with an AUC of 0.811, accuracy of 68.54%, sensitivity of 61.54%, specificity of 87.50%, positive predictive value (PPV) of 93.02%, and negative predictive value (NPV) of 45.65% in the testing cohort. The handcrafted radiomics model was slightly inferior to the RN-SVM model with an AUC of 0.663 and accuracy of 60.67% in the testing cohort. All the imaging-derived models had better predictive performance than the clinical model. CONCLUSION: The noninvasive deep learning method could provide efficient prediction of treatment response to IC in locally advanced NPC and might be a practicable approach in therapeutic strategy decision-making.

Safety and efficacy profile of Trastuzumab deruxtecan in solid cancer: pooled reanalysis based on clinical trials.

PURPOSE: This study aimed to explore the efficiency and safety of the new generation antibody-drug conjugate Trastuzumab deruxtecan (DS-8201a) in treating HER2-positive solid cancers. METHOD: By searching PubMed, Medline and Ovid for all clinical trials related to the safety and efficacy of DS-8201a. Event rates were calculated for all adverse events (AEs) to evaluate the safety of DS-8201a. Objective response rate (ORR) and progression-free survival (PFS) were summarized to assess the potency of DS-8201a. RESULT: The AEs with event rates greater than 30% regardless of grades were nausea, decreased appetite, vomiting, fatigue, anemia, decreased neutrophil count, alopecia and diarrhea. In the grade 3 or more, decreased neutrophil count, anemia and decreased white blood cell count were the only three AEs with event rates greater than 10% (20.3, 15.0 and 10.3%). The median PFS of patients with breast cancer, gastric cancer and other HER2-positive solid cancers were 9.0-22.1, 3.0-8.3 and 4.1-11.9 months. The median ORR was 37-79.9% in patients with breast and gastric cancer and 28.3-55% in patients with other HER2-positive cancers. CONCLUSION: DS-8201a plays an active role in treating HER2-positive cancers, especially breast and gastric cancer, which have HER2 amplification. The most common AEs of DS-8201a were related to gastrointestinal and hematological system. Decreased white blood cell count and appetite were the AEs occurred with high grades.

MRI-Based Computer-Aided Diagnostic Model to Predict Tumor Grading and Clinical Outcomes in Patients With Soft Tissue Sarcoma.

BACKGROUND: MRI acts as a potential resource for exploration and interpretation to identify tumor characterization by advanced computer-aided diagnostic (CAD) methods. PURPOSE: To evaluate and validate the performance of MRI-based CAD models for identifying low-grade and high-grade soft tissue sarcoma (STS) and for investigating survival prognostication. STUDY TYPE: Retrospective. SUBJECTS: A total of 540 patients (295 male/female: 295/245, median age: 42 years) with STSs. FIELD SEQUENCE: 5-T MRI with T1 WI sequence and fat-suppressed T2 -weighted (T2 FS) sequence. ASSESSMENT: Manual regions of interests (ROIs) were delineated for generation of radiomic features. Automatic segmentation and pretrained convolutional neural networks (CNNs) were performed for deep learning (DL) analysis. The last fully connected layer at the top of CNNs was removed, and the global max pooling was added to transform feature maps to numeric values. Tumor grade was determined on histological specimens. STATISTICAL TESTS: The support vector machine was adopted as the classifier for all MRI-based models. The DL signature was derived from the DL-MRI model with the highest area under the curve (AUC). The significant clinical variables, tumor location and size, integrated with radiomics and DL signatures were ready for construction of clinical-MRI nomogram to identify tumor grading. The prognostic value of clinical variables and these MRI-based signatures for overall survival (OS) was evaluated via Cox proportional hazard. RESULTS: The clinical-MRI differentiation nomogram represented an AUC of 0.870 in the training cohort, and an AUC of 0.855, accuracy of 79.01%, sensitivity of 79.03%, and specificity of 78.95% in the validation cohort. The prognostic model showed good performance for OS with 3-year C-index of 0.681 and 0.642 and 5-year C-index of 0.722 and 0.676 in the training and validation cohorts. DATA CONCLUSION: MRI-based CAD nomogram represents effective abilities in classification of low-grade and high-grade STSs. The MRI-based prognostic model yields favorable preoperative capacities to identify long-term survivals for STSs. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 4.

Use of Nomogram on Nutritional Assessment Indicators to Predict Clinical Outcomes in Patients Undergoing Surgical Resection for High-Grade Osteosarcoma.

Background: To evaluate the prognostic values of nutrition-associated indicators and develop nutritional models for prediction of different clinical outcomes in patients with high-grade osteosarcoma receiving surgical resection.Methods: Patients diagnosed as high-grade osteosarcomas were included between 2008 and 2018. Body mass index (BMI), Glasgow prognostic score (GPS), systematic inflammatory index (SII), and controlling nutritional score (CONUT) were calculated as nutrition-associated indicators. The primary outcome was overall survival (OS) as the long-term outcome, and the secondary outcome was the postoperative hospitalization duration as the short-term outcome. The prognostic values of nutrition-associated indicators were evaluated by univariate and multivariate analyses to recognize the potential predictors for construction of nomogram model with validation.Results: High GPS and CONUT yielded poor OS independently [GPS: HR (95% CI): 3.262 (2.035-5.229), p < 0.001; CONUT: HR (95% CI): 2.445 (1.508-3.964), p < 0.001]. The nomogram model for OS showed great prediction abilities and moderate calibration performance after integrating GPS and CONUT. CONUT was also identified as the independent predictor for hospitalization duration [OR (95% CI): 1.950 (1.145-3.321), p = 0.014].Conclusions: The CONUT score was considered as the significant predictor in prediction of OS and hospitalization duration. Appropriate management for nutritional status might optimize patients' prognoses with reference to nutrition-associated indicators.

Progression and application of CRISPR-Cas genomic editors.

Base editing technology is an efficient tool for genome editing, particularly in the correction of base mutations. Diverse base editing systems were developed according to the dCas9 or nCas9 linked with different deaminase or reverse transcriptase in the editors, including ABEs, CBEs, PEs and dual-functional of base editor (such as CGBE1, A&C-BEmax, ACBE, etc.). Currently, Base editing technology has been widely applied to various fields such as microorganisms, plants, animals and medicine for basic research and therapeutics. Here, we reviewed the advancement of base editing technology. We also discussed the application of base editors in different areas in the future.

The Value of Contrast-Enhanced Ultrasound Liver Imaging Reporting and Data System in the Diagnosis of Hepatocellular Carcinoma: A Meta-Analysis.

OBJECTIVES: The aim of this study is to systematically evaluate the diagnostic ability of the contrast-enhanced ultrasound (CEUS) liver imaging reporting and data system (LI-RADS) in hepatocellular carcinoma (HCC). METHODS: We searched relevant studies from PubMed, Medline, and Embase database. After literature search, duplicate removal, and data extraction, we calculated and analyzed the pooled sensitivity, pooled specificity, pooled odds ratios of diagnostic, pooled likelihood ratio (LR) of positive and negative, and area under the curve (AUC), accuracy, and F1 score to evaluate the diagnostic value of CEUS LI-RADS for HCC. RESULTS: Thirteen studies and 6491 patients were included in this analysis. The pooled sensitivity and pooled specificity were 0.72 (95% confidence interval [CI], 0.70-0.73) and 0.92 (95% CI, 0.91-0.93), respectively. The positive LR was 8.02 (95% CI, 4.93-13.06) and the negative LR was 0.31 (95% CI, 0.27-0.37). The pooled diagnostic odds ratio was 27.91 (95% CI, 15.39-50.63). The overall AUC was 0.8406 and the accuracy was 0.77. CONCLUSIONS: CEUS LI-RADS is an effective and promising method to diagnose HCC.

Use of radiomics based on 18F-FDG PET/CT and machine learning methods to aid clinical decision-making in the classification of solitary pulmonary lesions: an innovative approach.

PURPOSE: This study was designed and performed to assess the ability of 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) and computed tomography (CT) radiomics features combined with machine learning methods to differentiate between primary and metastatic lung lesions and to classify histological subtypes. Moreover, we identified the optimal machine learning method. METHODS: A total of 769 patients pathologically diagnosed with primary or metastatic lung cancers were enrolled. We used the LIFEx package to extract radiological features from semiautomatically segmented PET and CT images within the same volume of interest. Patients were randomly distributed in training and validation sets. Through the evaluation of five feature selection methods and nine classification methods, discriminant models were established. The robustness of the procedure was controlled by tenfold cross-validation. The model's performance was evaluated using the area under the receiver operating characteristic curve (AUC). RESULTS: Based on the radiomics features extracted from PET and CT images, forty-five discriminative models were established. Combined with appropriate feature selection methods, most classifiers showed excellent discriminative ability with AUCs greater than 0.75. In the differentiation between primary and metastatic lung lesions, the feature selection method gradient boosting decision tree (GBDT) combined with the classifier GBDT achieved the highest classification AUC of 0.983 in the PET dataset. In contrast, the feature selection method eXtreme gradient boosting combined with the classifier random forest (RF) achieved the highest AUC of 0.828 in the CT dataset. In the discrimination between squamous cell carcinoma and adenocarcinoma, the combination of GBDT feature selection method with GBDT classification had the highest AUC of 0.897 in the PET dataset. In contrast, the combination of the GBDT feature selection method with the RF classification had the highest AUC of 0.839 in the CT dataset. Most of the decision tree (DT)-based models were overfitted, suggesting that the classification method was not appropriate for practical application. CONCLUSION: 18F-FDG PET/CT radiomics features combined with machine learning methods can distinguish between primary and metastatic lung lesions and identify histological subtypes in lung cancer. GBDT and RF were considered optimal classification methods for the PET and CT datasets, respectively, and GBDT was considered the optimal feature selection method in our analysis.

The identification of gene signatures in patients with extranodal NK/T-cell lymphoma from a pair of twins.

BACKGROUND: There is no unified treatment standard for patients with extranodal NK/T-cell lymphoma (ENKTL). Cancer neoantigens are the result of somatic mutations and cancer-specific. Increased number of somatic mutations are associated with anti-cancer effects. Screening out ENKTL-specific neoantigens on the surface of cancer cells relies on the understanding of ENKTL mutation patterns. Hence, it is imperative to identify ENKTL-specific genes for ENKTL diagnosis, the discovery of tumor-specific neoantigens and the development of novel therapeutic strategies. We investigated the gene signatures of ENKTL patients. METHODS: We collected the peripheral blood of a pair of twins for sequencing to identify unique variant genes. One of the twins is diagnosed with ENKTL. Seventy samples were analyzed by Robust Multi-array Analysis (RMA). Two methods (elastic net and Support Vector Machine-Recursive Feature Elimination) were used to select unique genes. Next, we performed functional enrichment analysis and pathway enrichment analysis. Then, we conducted single-sample gene set enrichment analysis of immune infiltration and validated the expression of the screened markers with limma packages. RESULTS: We screened out 126 unique variant genes. Among them, 11 unique genes were selected by the combination of elastic net and Support Vector Machine-Recursive Feature Elimination. Subsequently, GO and KEGG analysis indicated the biological function of identified unique genes. GSEA indicated five immunity-related pathways with high signature scores. In patients with ENKTL and the group with high signature scores, a proportion of functional immune cells are all of great infiltration. We finally found that CDC27, ZNF141, FCGR2C and NES were four significantly differential genes in ENKTL patients. ZNF141, FCGR2C and NES were upregulated in patients with ENKTL, while CDC27 was significantly downregulated. CONCLUSION: We identified four ENKTL markers (ZNF141, FCGR2C, NES and CDC27) in patients with extranodal NK/T-cell lymphoma.

Safety and efficacy profile of mogamulizumab (Poteligeo) in the treatment of cancers: an update evidence from 14 studies.

BACKGROUND: CC chemokine receptor 4 (CCR4), the receptor for CCL22 and CCL17, is expressed on the surface of effector Tregs that have the highest suppressive effects on antitumor immune response. CCR4 is also widely expressed on the surface of tumor cells from patients with adult T-cell leukemia/lymphoma (ATL), peripheral T-cell lymphoma (PTCL) and cutaneous T-cell lymphoma (CTCL). Mogamulizumab is a humanized, IgG1 kappa monoclonal antibody that is directed against CCR4. By reducing the number of CCR4-positive Tregs and tumor cells, the mogamulizumab can reduce tumor burden and boost antitumor immunity to achieve antitumor effects. METHODS: We examined the PubMed and ClinicalTrials.gov until 1 February 2020. Considering variability in different studies, we selected the adverse events (AEs), overall survival (OS), progression-free survival (PFS), objective responses rate (ORR) and Hazard Ratio (HR) for PFS to evaluate the safety and efficacy profile of mogamulizumab. RESULTS: When patients were treated with mogamulizumab monotherapy, the most common all-grade AEs were lymphopenia, infusion reaction, fever, rash and chills while the most common grade ≥ 3 AEs were lymphopenia, neutropenia and rash. When patients were treated with combined therapy of mogamulizumab and other drugs, the most common all-grade AEs were neutropenia, anaemia, lymphopenia and gastrointestinal disorder, while the most common grade ≥ 3 AEs was lymphopenia. For patients treated with mogamulizumab monotherapy, the pooled ORR and mean PFS were 0.430 (95% CI: 0.393-0.469) and 1.060 months (95% CI: 1.043-1.077), respectively. For patients treated with combined therapy of mogamulizumab and other drugs, the pooled ORR was 0.203 (95% CI: 0.022-0.746) while the pooled PFS and OS were 2.093 months (95% CI: 1.602-2.584) and 6.591 months (95% CI: 6.014-7.167), respectively. CONCLUSIONS: Based on present evidence, we believed that mogamulizumab had clinically meaningful antitumor activity with acceptable toxicity which is a novel therapy in treating patients with cancers.

Integration of histopathological images and multi-dimensional omics analyses predicts molecular features and prognosis in high-grade serous ovarian cancer.

OBJECTIVE: This study used histopathological image features to predict molecular features, and combined with multi-dimensional omics data to predict overall survival (OS) in high-grade serous ovarian cancer (HGSOC). METHODS: Patients from The Cancer Genome Atlas (TCGA) were distributed into training set (n = 115) and test set (n = 114). In addition, we collected tissue microarrays of 92 patients as an external validation set. Quantitative features were extracted from histopathological images using CellProfiler, and utilized to establish prediction models by machine learning methods in training set. The prediction performance was assessed in test set and validation set. RESULTS: The prediction models were able to identify BRCA1 mutation (AUC = 0.952), BRCA2 mutation (AUC = 0.912), microsatellite instability-high (AUC = 0.919), microsatellite stable (AUC = 0.924), and molecular subtypes: proliferative (AUC = 0.961), differentiated (AUC = 0.952), immunoreactive (AUC = 0.941), mesenchymal (AUC = 0.918) in test set. The prognostic model based on histopathological image features could predict OS in test set (5-year AUC = 0.825) and validation set (5-year AUC = 0.703). We next explored the integrative prognostic models of image features, genomics, transcriptomics and proteomics. In test set, the models combining two omics had higher prediction accuracy, such as image features and genomics (5-year AUC = 0.834). The multi-omics model including all features showed the best prediction performance (5-year AUC = 0.911). According to risk score of multi-omics model, the high-risk and low-risk groups had significant survival differences (HR = 18.23, p < 0.001). CONCLUSIONS: These results indicated the potential ability of histopathological image features to predict above molecular features and survival risk of HGSOC patients. The integration of image features and multi-omics data may improve prognosis prediction in HGSOC patients.

Differential diagnostic ability of 18F-FDG PET/CT radiomics features between renal cell carcinoma and renal lymphoma.

BACKGROUND: The aim of this study is to determine the differential diagnostic value of texture parameters of PET/CT on renal cell carcinoma and renal lymphoma. METHODS: Twenty renal lymphoma and 18 renal cell carcinoma (RCC) patients were analyzed in this study. The pathological information and basic characteristics were extracted from the electronic medical record system of our hospital. We used LIFEx package to extract data from the radiomics images. Receiver operating characteristic analysis and binary logistic regression analysis was applied in determining the diagnostic accuracy of texture parameters as well as the synthetic parameter, of which the sensitivity and specificity was improved. RESULTS: There were 14 (two in Histogram, two in Grey Level Co-occurrence Matrix, five in Grey-Level Run Length Matrix, five in Grey-Level Zone Length Matrix) out of the texture parameters showing an area under the curve (AUC) >0.7 and P<0.05. Synthesized parameters of each section showed even higher differentiation ability, with AUC varying from 0.725 to 1.000. CONCLUSIONS: Texture analysis of 18F-FDG PET/CT could effectively differentiate between RCCs and renal lymphomas.