Research Notes: Evaluation of recombinant Newcastle disease virus expressing gB protein of infectious laryngotracheitis virus as bivalent in ovo vaccine.

Infectious laryngotracheitis (ILT) and Newcastle disease (ND) are 2 highly infectious avian respiratory diseases that have caused significant economic losses in the poultry industry worldwide. In ovo vaccination is administered during the late stage of incubation and is an attractive immunization method for poultry industry. However, most of the avian live vaccine strains that are safe for use after hatching are highly pathogenic to chicken embryos and therefore unsafe for in ovo vaccination. Previously, a recombinant Newcastle disease virus (NDV) strain, rTS-gB, expressing the gB protein of Infectious laryngotracheitis virus (ILTV), was demonstrated to be safe and immunogenic as a bivalent vaccine for hatched birds. In this study, we evaluated the safety and protective efficacy of rTS-gB as an in ovo vaccine. This vaccine strain was found to be safe for in ovo vaccination, with a hatchability and survival rate of 93.3% in chickens vaccinated in ovo with rTS-gB. In ovo vaccination with rTS-gB induced effective ILTV- and NDV-specific antibody responses in birds and conferred complete protection against virulent NDV and ILTV challenges. Furthermore, shedding of the challenged ILTV in cloacal and tracheal samples from in ovo vaccinated chickens was greatly reduced. These results indicate that the rNDV strain rTS-gB is a safe and highly immunogenic in ovo vaccine candidate against both ND and ILT.

Deep learning-based diagnosis and survival prediction of patients with renal cell carcinoma from primary whole slide images.

There is an urgent clinical demand to explore novel diagnostic and prognostic biomarkers for renal cell carcinoma (RCC). We proposed deep learning-based artificial intelligence strategies. The study included 1752 whole slide images from multiple centres. Based on the pixel-level of RCC segmentation, the diagnosis diagnostic model achieved an area under the receiver operating characteristic curve (AUC) of 0.977 (95% CI 0.969-0.984) in the external validation cohort. In addition, our diagnostic model exhibited excellent performance in the differential diagnosis of RCC from renal oncocytoma, which achieved an AUC of 0.951 (0.922-0.972). The graderisk for the recognition of high-grade tumour achieved AUCs of 0.840 (0.805-0.871) in the Cancer Genome Atlas (TCGA) cohort, 0.857 (0.813-0.894) in the Shanghai General Hospital (General) cohort, and 0.894 (0.842-0.933) in the Clinical Proteomic Tumor Analysis Consortium (CPTAC) cohort, for the recognition of high-grade tumour. The OSrisk for predicting 5-year survival status achieved an AUC of 0.784 (0.746-0.819) in the TCGA cohort, which was further verified in the independent general cohort and the CPTAC cohort, with AUCs of 0.774 (0.723-0.820) and 0.702 (0.632-0.765), respectively. Moreover, the competing-risk nomogram (CRN) showed its potential to be a prognostic indicator, with a hazard ratio (HR) of 5.664 (3.893-8.239, p<0.0001), outperforming other traditional clinical prognostic indicators. Kaplan-Meier survival analysis further illustrated that our CRN could significantly distinguish patients with high survival risk. Deep learning-based artificial intelligence could be a useful tool for clinicians to diagnose and predict the prognosis of RCC patients, thus improving the process of individualised treatment.

Deep learning-based pathological prediction of lymph node metastasis for patient with renal cell carcinoma from primary whole slide images.

BACKGROUND: Metastasis renal cell carcinoma (RCC) patients have extremely high mortality rate. A predictive model for RCC micrometastasis based on pathomics could be beneficial for clinicians to make treatment decisions. METHODS: A total of 895 formalin-fixed and paraffin-embedded whole slide images (WSIs) derived from three cohorts, including Shanghai General Hospital (SGH), Clinical Proteomic Tumor Analysis Consortium (CPTAC) and Cancer Genome Atlas (TCGA) cohorts, and another 588 frozen section WSIs from TCGA dataset were involved in the study. The deep learning-based strategy for predicting lymphatic metastasis was developed based on WSIs through clustering-constrained-attention multiple-instance learning method and verified among the three cohorts. The performance of the model was further verified in frozen-pathological sections. In addition, the model was also tested the prognosis prediction of patients with RCC in multi-source patient cohorts. RESULTS: The AUC of the lymphatic metastasis prediction performance was 0.836, 0.865 and 0.812 in TCGA, SGH and CPTAC cohorts, respectively. The performance on frozen section WSIs was with the AUC of 0.801. Patients with high deep learning-based prediction of lymph node metastasis values showed worse prognosis. CONCLUSIONS: In this study, we developed and verified a deep learning-based strategy for predicting lymphatic metastasis from primary RCC WSIs, which could be applied in frozen-pathological sections and act as a prognostic factor for RCC to distinguished patients with worse survival outcomes.

Deep learning-based multi-model prediction for disease-free survival status of patients with clear cell renal cell carcinoma after surgery: a multicenter cohort study.

BACKGROUND: Although separate analysis of individual factor can somewhat improve the prognostic performance, integration of multimodal information into a single signature is necessary to stratify patients with clear cell renal cell carcinoma (ccRCC) for adjuvant therapy after surgery. METHODS: A total of 414 patients with whole slide images, computed tomography images, and clinical data from three patient cohorts were retrospectively analyzed. The authors performed deep learning and machine learning algorithm to construct three single-modality prediction models for disease-free survival of ccRCC based on whole slide images, cell segmentation, and computed tomography images, respectively. A multimodel prediction signature (MMPS) for disease-free survival were further developed by combining three single-modality prediction models and tumor stage/grade system. Prognostic performance of the prognostic model was also verified in two independent validation cohorts. RESULTS: Single-modality prediction models performed well in predicting the disease-free survival status of ccRCC. The MMPS achieved higher area under the curve value of 0.742, 0.917, and 0.900 in three independent patient cohorts, respectively. MMPS could distinguish patients with worse disease-free survival, with HR of 12.90 (95% CI: 2.443-68.120, P <0.0001), 11.10 (95% CI: 5.467-22.520, P <0.0001), and 8.27 (95% CI: 1.482-46.130, P <0.0001) in three different patient cohorts. In addition, MMPS outperformed single-modality prediction models and current clinical prognostic factors, which could also provide complements to current risk stratification for adjuvant therapy of ccRCC. CONCLUSION: Our novel multimodel prediction analysis for disease-free survival exhibited significant improvements in prognostic prediction for patients with ccRCC. After further validation in multiple centers and regions, the multimodal system could be a potential practical tool for clinicians in the treatment for ccRCC patients.

Association of the m6 A reader IGF2BP3 with tumor progression and brain-specific metastasis in breast cancer.

BACKGROUND: This study aimed to determine the role of insulin-like growth factor 2 mRNA-binding protein 3 (IGF2BP3), an N6 -methyladinosine reader, in the progression and distant metastasis of breast cancer. METHODS: IGF2BP3 expression was assessed in 152 pairs of breast cancer and adjacent normal tissue (ANT) by real-time quantitative polymerase chain reaction and in 561 cases of breast cancer and 163 cases of ANT by immunohistochemistry. Survival curves were estimated using the Kaplan-Meier method and then compared statistically using the log-rank test. The prognostic role of IGF2BP3 was determined by Cox regression analysis. RESULTS: Analysis of public gene data sets revealed that IGF2PB3 predicted distant metastasis in breast cancer and was highly correlated with brain metastasis. In the clinical retrospective cohort, the positive rate of IGF2BP3 increased gradually with breast cancer progression. Positive IGF2BP3 expression was related to poor distant metastasis-free survival (DMFS, p = .030) and Cox regression analysis identified IGF2BP3 as an independent risk factor for DMFS (hazard ratio, 1.876; 95% confidence interval, 1.128-3.159; p = .019). Positive IGF2BP3 expression was markedly related to breast cancer brain metastasis (p = .011) but not to lung and bone metastasis. Moreover, patients with IGF2BP3-positive brain metastasis had lower survival than patients with IGF2BP3-negative brain metastasis (p = .041). Gene expression profiling results indicated that high IGF2BP3 expression was associated with the PD-1 checkpoint pathway, HER2-HER3 signaling, and epithelial-mesenchymal transition. CONCLUSIONS: IGF2BP3 may serve as a novel predictive biomarker and a potential therapeutic target for breast cancer brain metastasis, which warrants further investigation. PLAIN LANGUAGE SUMMARY: As an m6 A reader, IGF2BP3 is dysregulated and implicated in various cancers but its role in breast cancer has not been fully clarified. In this study, we found that IGF2BP3 was upregulated in breast cancer and IGF2BP3 expression increased gradually during breast cancer progression. IGF2BP3 expression exerted no effect on the overall survival and breast cancer-specific survival of breast cancer patients; however, IGF2BP3-positive patients were more likely to develop distant metastasis than IGF2BP3-negative patients. In addition, IGF2BP3 was associated with brain-specific metastasis in breast cancer patients. These findings warrant further investigation because they provide a rationale for novel predictive or therapeutic approaches.

A bi-allelic REC114 loss-of-function variant causes meiotic arrest and nonobstructive azoospermia.

Nonobstructive azoospermia (NOA), the most severe manifestation of male infertility, lacks a comprehensive understanding of its genetic etiology. Here, a bi-allelic loss-of-function variant in REC114 (c.568C > T: p.Gln190*) were identified through whole exome sequencing (WES) in a Chinese NOA patient. Testicular histopathological analysis and meiotic chromosomal spread analysis were conducted to assess the stage of spermatogenesis arrested. Co-immunoprecipitation (Co-IP) and Western blot (WB) were used to investigate the influence of variant in vitro. In addition, our results revealed that the variant resulted in truncated REC114 protein and impaired interaction with MEI4, which was essential for meiotic DNA double-strand break (DSB) formation. As far as we know, this study presents the first report that identifies REC114 as the causative gene for male infertility. Furthermore, our study demonstrated indispensability of the REC114-MEI4 complex in maintaining DSB homoeostasis, and highlighted that the disruption of the complex due to the REC114 variant may underline the mechanism of NOA.

Preliminary exploration of the effects of environmental factors on the microsatellite status of BRAF-mutated colorectal cancer.

BACKGROUND: To investigate the expression of EBV products and frequency of gallstone disease (GD) among different microsatellite status in colorectal cancer (CRC) with BRAFV600E mutation. METHODS: We collected 30 CRC patients with BRAFV600E mutation and 10 BRAF ( -) CRC patients as well as 54 healthy subjects. Tumor tissue samples were collected to detect the mutation of BRAF, KRAS, and TP53. Microsatellite status was determined by immunohistochemistry and PCR. EBER in situ hybridization was performed to detect EBV. In addition, we also collected clinical information about the patients. RESULTS: We found that although EBV products were detected in CRC, there were no significant differences in the EBV distribution between the different BRAF groups. Our study demonstrated that BRAFV600E mutation and BRAFV600E with MSI were significantly more frequent in the right CRC. Furthermore, the KRAS mutation rate in the BRAF-wild-type group was proved to be significantly higher than that in the BRAF mutation group. In addition, we revealed that BRAF mutation and MSI were independent risk factors of TNM stage. The frequency of GD was higher in CRC patients than in general population, and although there was no significant difference between CRC with or without BRAFV600E mutation, the highest frequency of GD was found in MSS CRC with BRAFV600E mutation. CONCLUSIONS: EBV plays a role in CRC, but is not a determinant of different microsatellite status in CRC with BRAFV600E mutation. The frequency of GD in MSS CRC with BRAFV600E mutation is significantly higher than that in the general population.

Quantitative proteomics profiling reveals the inhibition of trastuzumab antitumor efficacy by phosphorylated RPS6 in gastric carcinoma.

BACKGROUND: The anti-HER2 antibody trastuzumab is a standard treatment for gastric carcinoma with HER2 overexpression, but not all patients benefit from treatment with HER2-targeted therapies due to intrinsic and acquired resistance. Thus, more precise predictors for selecting patients to receive trastuzumab therapy are urgently needed. METHODS: We applied mass spectrometry-based proteomic analysis to 38 HER2-positive gastric tumor biopsies from 19 patients pretreated with trastuzumab (responders n = 10; nonresponders, n = 9) to identify factors that may influence innate sensitivity or resistance to trastuzumab therapy and validated the results in tumor cells and patient samples. RESULTS: Statistical analyses revealed significantly lower phosphorylated ribosomal S6 (p-RPS6) levels in responders than nonresponders, and this downregulation was associated with a durable response and better overall survival after anti-HER2 therapy. High p-RPS6 levels could trigger AKT/mTOR/RPS6 signaling and inhibit trastuzumab antitumor efficacy in nonresponders. We demonstrated that RPS6 phosphorylation inhibitors in combination with trastuzumab effectively suppressed HER2-positive GC cell survival through the inhibition of the AKT/mTOR/RPS6 axis. CONCLUSIONS: Our findings provide for the first time a detailed proteomics profile of current protein alterations in patients before anti-HER2 therapy and present a novel and optimal predictor for the response to trastuzumab treatment. HER2-positive GC patients with low expression of p-RPS6 are more likely to benefit from trastuzumab therapy than those with high expression. However, those with high expression of p-RPS6 may benefit from trastuzumab in combination with RPS6 phosphorylation inhibitors.

Screening and identification of prognostic genes associated with eosinophilic features of clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma, which is characterized by transparent cytoplasm. However, some ccRCC also show eosinophilic cytoplasm, and the molecular difference between eosinophilic and clear subtypes is unclear. In this study, we uncovered that under an optical microscope ccRCC with eosinophilic features has a poor prognosis. Eosinophilic ccRCC tends to have a higher histologic grade. Eosinophilic ccRCC has 16 genes significantly up-regulated compared with ccRCC, of which seven genes have multi-cohort validation prognostic value. Immune infiltration analysis suggested a low number of M1 macrophages and NK tissue-resident cells in eosinophilic ccRCC. Enrichment analysis suggests that ccRCC with eosinophilic features may be closely associated with the transport and metabolism of many substances. The findings of this study have important implications for the study of the malignant transformation of ccRCC.

Identification of Embryonic Chicken Proteases Activating Newcastle Disease Virus and Their Roles in the Pathogenicity of Virus Used as In Ovo Vaccine.

In ovo vaccination is an attractive immunization approach for chickens. However, most live Newcastle disease virus (NDV) vaccine strains used safely after hatching are unsafe as in ovo vaccines due to their high pathogenicity for chicken embryos. The mechanism for viral pathogenicity in chicken embryos is poorly understood. Our previous studies reported that NDV strain TS09-C was a safe in ovo vaccine, and the F protein cleavage site (FCS) containing three basic amino acids (3B-FCS) was the crucial determinant of the attenuation of TS09-C in chicken embryos. Here, five trypsin-like proteases that activated NDV in chicken embryos were identified. The F protein with 3B-FCS was sensitive to the proteases Tmprss4, Tmprss9, and F7, was present in fewer tissue cells of chicken embryos, which limited the viral tropism, and was responsible for the attenuation of NDV with 3B-FCS, while the F protein with FCS containing two basic amino acids could be cleaved not only by Tmprss4, Tmprss9, and F7 but also by Prss23 and Cfd, was present in most tissue cells, and thereby was responsible for broad tissue tropism and high pathogenicity of virus in chicken embryos. Furthermore, when mixed with the protease inhibitors aprotinin and camostat, NDV with 2B-FCS exhibited greatly weakened pathogenicity in chicken embryos. Thus, our results extend the understanding of the molecular mechanism of NDV pathogenicity in chicken embryos and provide a novel molecular target for the rational design of in ovo vaccines, ensuring uniform and effective vaccine delivery and earlier induction of immune protection by the time of hatching. IMPORTANCE As an attractive immunization approach for chickens, in ovo vaccination can induce a considerable degree of protection by the time of hatching, provide support in closing the window in which birds are susceptible to infection, facilitate fast and uniform vaccine delivery, and reduce labor costs by the use of mechanized injectors. The commercial live Newcastle disease virus (NDV) vaccine strains are not safe for in ovo vaccination and cause the death of chicken embryos. The mechanism for viral pathogenicity in chicken embryos is poorly understood. In the present study, we identified five trypsin-like proteases that activate NDV in chicken embryos and elucidated their roles in the tissue tropism and pathogenicity of NDV used as in ovo vaccine. Finally, we revealed the molecular basis for the pathogenicity of NDV in chicken embryos and provided a novel strategy for the rational design of in ovo ND vaccines.

Ultrasonographic evaluation of the rete testis thickness: a promising approach to differentiate obstructive from nonobstructive azoospermia.

This study aimed to evaluate the ability of rete testis thickness (RTT) and testicular shear wave elastography (SWE) to differentiate obstructive azoospermia (OA) from nonobstructive azoospermia (NOA). We assessed 290 testes of 145 infertile males with azoospermia and 94 testes of 47 healthy volunteers at Shanghai General Hospital (Shanghai, China) between August 2019 and October 2021. The testicular volume (TV), SWE, and RTT were compared among patients with OA and NOA and healthy controls. The diagnostic performances of the three variables were evaluated using the receiver operating characteristic curve. The TV, SWE, and RTT in OA differed significantly from those in NOA (all P ≤ 0.001) but were similar to those in healthy controls. Males with OA and NOA were similar at TVs of 9-11 cm 3 ( P = 0.838), with sensitivity, specificity, Youden index, and area under the curve of 50.0%, 84.2%, 0.34, and 0.662 (95% confidence interval [CI]: 0.502-0.799), respectively, for SWE cut-off of 3.1 kPa; and 94.1%, 79.2%, 0.74, and 0.904 (95% CI: 0.811-0.996), respectively, for RTT cut-off of 1.6 mm. The results showed that RTT performed significantly better than SWE in differentiating OA from NOA in the TV overlap range. In conclusion, ultrasonographic RTT evaluation proved a promising diagnostic approach to differentiate OA from NOA, particularly in the TV overlap range.

The feasibility of proteomics sequencing based immune-related prognostic signature for predicting clinical outcomes of bladder cancer patients.

BACKGROUND: Bladder cancer (BCa) shows its potential immunogenity in current immune-checkpoint inhibitor related immunotherapies. However, its therapeutic effects are improvable and could be affected by tumor immune microenvironment. Hence it is interesting to find some more prognostic indicators for BCa patients concerning immunotherapies. METHODS: In the present study, we retrospect 129 muscle-invasive BCa (MIBC) patients with radical cystectomy in Shanghai General Hospital during 2007 to 2018. Based on the results of proteomics sequencing from 9 pairs of MIBC tissue from Shanghai General Hospital, we focused on 13 immune-related differential expression proteins and their related genes. An immune-related prognostic signature (IRPS) was constructed according to Cancer Genome Atlas (TCGA) dataset. The IRPS was verified in ArrayExpress (E-MTAB-4321) cohort and Shanghai General Hospital (General) cohort, separately. A total of 1010 BCa patients were involved in the study, including 405 BCa patients in TCGA cohort, 476 BCa patients in E-MTAB-4321 cohort and 129 MIBC patients in General cohort. RESULT: It can be indicated that high IRPS score was related to poor 5-year overall survival and disease-free survival. The IRPS score was also evaluated its immune infiltration. We found that the IRPS score was adversely associated with GZMB, IFN-γ, PD-1, PD-L1. Additionally, higher IRPS score was significantly associated with more M2 macrophage and resting mast cell infiltration. CONCLUSION: The study revealed a novel BCa prognostic signature based on IRPS score, which may be useful for BCa immunotherapies.

Robust Prediction of Prognosis and Immunotherapeutic Response for Clear Cell Renal Cell Carcinoma Through Deep Learning Algorithm.

It is of great urgency to explore useful prognostic markers and develop a robust prognostic model for patients with clear-cell renal cell carcinoma (ccRCC). Three independent patient cohorts were included in this study. We applied a high-level neural network based on TensorFlow to construct the robust model by using the deep learning algorithm. The deep learning-based model (FB-risk) could perform well in predicting the survival status in the 5-year follow-up, which could also significantly distinguish the patients with high overall survival risk in three independent patient cohorts of ccRCC and a pan-cancer cohort. High FB-risk was found to be partially associated with negative regulation of the immune system. In addition, the novel phenotyping of ccRCC based on the F-box gene family could robustly stratify patients with different survival risks. The different mutation landscapes and immune characteristics were also found among different clusters. Furthermore, the novel phenotyping of ccRCC based on the F-box gene family could perform well in the robust stratification of survival and immune response in ccRCC, which might have potential for application in clinical practices.

Predicting Clear Cell Renal Cell Carcinoma Survival Using Kurtosis of Cytoplasm in the Hematoxylin Channel from Histology Slides.

PURPOSE: Grade-dependent decrease of lipid storage in clear cell renal cell carcinoma (ccRCC) leads to morphology changes in HE sections. This study investigated the role of cytoplasmic features in frozen sections of ccRCC on prognosis using the digital pathology approach. METHODS: We established an automatic pipeline that performed tumor region selection, stain vector normalization, nuclei segmentation, and feature extraction based on the pathologic data from Shanghai General Hospital and The Cancer Genome Atlas database. Extracted features were subjected to survival analysis. RESULTS: Kurtosis of the cytoplasm in the hematoxylin channel was correlated with progression-free survival (HR 0.10, 95% CI: 0.04-0.24, p=6.52 ∗ 10-7) and overall survival (HR 0.11, 95% CI: 0.05-0.31, p=1.72 ∗ 10-5) in ccRCC, which outperformed other texture features in this analysis. Multivariate Cox regression analysis revealed that low kurtosis of cytoplasm in the hematoxylin channel was an independent predictor for a shorter progression-free survival time (p=0.044) and overall survival time (p = 0.01). Kaplan-Meier survival analysis of progression-free survival and overall survival also showed a significantly worse prognosis in patients with low kurtosis of the cytoplasm in the hematoxylin channel (both p < 0.0001). Lower kurtosis of cytoplasm in the hematoxylin channel was associated with higher pathologic grade, less cholesterol ester, and more mitochondrial DNA content. CONCLUSION: Kurtosis of the cytoplasm in the hematoxylin channel predicts survival in clear cell renal cell carcinoma.

Machine learning-based pathomics signature could act as a novel prognostic marker for patients with clear cell renal cell carcinoma.

BACKGROUND: Traditional histopathology performed by pathologists through naked eyes is insufficient for accurate survival prediction of clear cell renal cell carcinoma (ccRCC). METHODS: A total of 483 whole slide images (WSIs) data from three patient cohorts were retrospectively analyzed. We performed machine learning algorithm to identify optimal digital pathological features and constructed machine learning-based pathomics signature (MLPS) for ccRCC patients. Prognostic performance of the prognostic model was also verified in two independent validation cohorts. RESULTS: MLPS could significantly distinguish ccRCC patients with high survival risk, with hazard ratio of 15.05, 4.49 and 1.65 in three independent cohorts, respectively. Cox regression analysis revealed that the MLPS could act as an independent prognostic factor for ccRCC patients. Integration nomogram based on MLPS, tumour stage system and tumour grade system improved the current survival prediction accuracy for ccRCC patients, with area under curve value of 89.5%, 90.0%, 88.5% and 85.9% for 1-, 3-, 5- and 10-year disease-free survival prediction. DISCUSSION: The machine learning-based pathomics signature could act as a novel prognostic marker for patients with ccRCC. Nevertheless, prospective studies with multicentric patient cohorts are still needed for further verifications.

A Novel Nomogram Based on Machine Learning-Pathomics Signature and Neutrophil to Lymphocyte Ratio for Survival Prediction of Bladder Cancer Patients.

Traditional histopathology performed by pathologists through naked eyes is insufficient for accurate survival prediction of bladder cancer (BCa). In addition, how neutrophil to lymphocyte ratio (NLR) could be used for prognosis prediction of BCa patients has not been fully understood. In this study, we collected 508 whole slide images (WSIs) of hematoxylin-eosin strained BCa slices and NLR value from the Shanghai General Hospital and The Cancer Genome Atlas (TCGA), which were further processed for nuclear segmentation. Cross-verified prediction models for predicting clinical prognosis were constructed based on machine learning methods. Six WSIs features were selected for the construction of pathomics-based prognosis model, which could automatically distinguish BCa patients with worse survival outcomes, with hazard ratio value of 2.19 in TCGA cohort (95% confidence interval: 1.63-2.94, p <0.0001) and 3.20 in General cohort (95% confidence interval: 1.75-5.87, p = 0.0014). Patients in TCGA cohort with high NLR exhibited significantly worse clinical survival outcome when compared with patients with low NLR (HR = 2.06, 95% CI: 1.29-3.27, p <0.0001). External validation in General cohort also revealed significantly poor prognosis in BCa patients with high NLR (HR = 3.69, 95% CI: 1.83-7.44 p <0.0001). Univariate and multivariate cox regression analysis proved that both the MLPS and the NLR could act as independent prognostic factor for overall survival of BCa patients. Finally, a novel nomogram based on MLPS and NLR was constructed to improve their clinical practicability, which had excellent agreement with actual observation in 1-, 3- and 5-year overall survival prediction. Decision curve analyses both in the TCGA cohort and General cohort revealed that the novel nomogram acted better than both the tumor grade system in prognosis prediction. Our novel nomogram based on MLPS and NLR could act as an excellent survival predictor and provide a scalable and cost-effective method for clinicians to facilitate individualized therapy. Nevertheless, prospective studies are still needed for further verifications.

PD-1-Positive Tumor-Associated Macrophages Define Poor Clinical Outcomes in Patients With Muscle Invasive Bladder Cancer Through Potential CD68/PD-1 Complex Interactions.

Tumor-associated macrophages (TAMs) regulate tumor immunity. Previous studies have shown that the programmed cell death protein 1 (PD-1)-positive TAMs have an M2 macrophage phenotype. CD68 is a biomarker of TAMs and is considered to be a poor prognostic marker of several malignancies. Our results show that PD-1-positive TAMs can be a negative survival indicator in patients with muscle-invasive bladder cancer (MIBC), and that the mechanistic effects could result due to a combination of PD-1 and CD68 activity. We analyzed 22 immune cell types using data from 402 patients with MIBC from the TCGA database, and found that a high immune score and M2 TAMs were strongly associated with poor clinical outcomes in patients with MIBC. Further, we analyzed resected samples from 120 patients with MIBC and found that individuals with PD-1-positive TAMs showed a reduction in 5-year overall survival and disease-free survival. Additionally, PD-1-positive TAMs showed a significant association with higher programmed death-ligand 1 (PD-L1) expression, the Ki67 index, the pT stage and fewer CD8-positive T cells. Through the co-immunoprecipitation (co-IP) assay of THP-1 derived macrophages, we found that CD68 can bind to PD-1. The binding of CD68 and PD-1 can induce M2 polarization of THP-1 derived macrophages and promote cancer growth. The anti-CD68 treatment combined with peripheral blood mononuclear cells (PBMC) showed obvious synergy effects on inhibiting the proliferation of T24 cells. Together, these results indicate for the first time that CD68/PD-1 may be a novel target for the prognosis of patients with MIBC.

Clinical use of machine learning-based pathomics signature for diagnosis and survival prediction of bladder cancer.

Traditional histopathology performed by pathologists by the naked eye is insufficient for accurate and efficient diagnosis of bladder cancer (BCa). We collected 643 H&E-stained BCa images from Shanghai General Hospital and The Cancer Genome Atlas (TCGA). We constructed and cross-verified automatic diagnosis and prognosis models by performing a machine learning algorithm based on pathomics data. Our study indicated that high diagnostic efficiency of the machine learning-based diagnosis model was observed in patients with BCa, with area under the curve (AUC) values of 96.3%, 89.2%, and 94.1% in the training cohort, test cohort, and external validation cohort, respectively. Our diagnosis model also performed well in distinguishing patients with BCa from patients with glandular cystitis, with an AUC value of 93.4% in the General cohort. Significant differences were found in overall survival in TCGA cohort (hazard ratio (HR) = 2.09, 95% confidence interval (CI): 1.56-2.81, P < .0001) and the General cohort (HR = 5.32, 95% CI: 2.95-9.59, P < .0001) comparing patients with BCa of high risk vs low risk stratified by risk score, which was proved to be an independent prognostic factor for BCa. The integration nomogram based on our risk score and clinicopathologic characters displayed higher prediction accuracy than current tumor stage/grade systems, with AUC values of 77.7%, 83.8%, and 81.3% for 1-, 3-, and 5-y overall survival prediction of patients with BCa. However, prospective studies are still needed for further verifications.

Clinical use of a machine learning histopathological image signature in diagnosis and survival prediction of clear cell renal cell carcinoma.

Due to the complicated histopathological characteristics of renal neoplasms, traditional distinguishing of clear cell renal cell carcinoma (ccRCC) by naked eyes of experienced pathologist remains labor intensive and time consuming. Here, we extracted quantitative features of hematoxylin-eosin-stained images using CellProfiler and performed machine learning method to develop and verify a novel computational recognition of digital pathology for diagnosis and prognosis of ccRCC patients in the training, test and external validation cohort. The diagnostic model based on digital pathology could accurately distinguish ccRCC from normal renal tissues, with area under the curve (AUC) of 96.0%, 94.5% and 87.6% in the training, test and external validation cohorts, respectively. It could also accurately distinguish ccRCC from other pathological types of renal cancer, with AUC values of 97.0% and 81.4% in the Cancer Genome Atlas (TCGA) cohort and General cohort. We next developed and verified a computational recognition prognosis model with risk score. There was a significant difference in disease-free survival comparing patients with high vs low risk score in training cohort (hazard ratio = 2.72, P < .0001) and validation cohort (hazard ratio = 9.50, P = .0091). The integrated nomogram based on our computational recognition risk score and clinicopathologic factors demonstrated excellent survival prediction for ccRCC patients, with increased accuracy by 6.6% in patients from Shanghai General Hospital and by 2.5% in patients from TCGA cohort when compared to current tumor stages/grade systems. These results indicate the potential clinical use of our machine learning histopathological image signature in diagnosis and survival prediction of ccRCC.