Radiomic signatures associated with tumor immune heterogeneity predict survival in locally recurrent nasopharyngeal carcinoma.

BACKGROUND: The prognostic value of traditional clinical indicators for locally recurrent nasopharyngeal carcinoma (lrNPC) is limited due to their inability to reflect intratumor heterogeneity. We aimed to develop a radiomic signature to reveal tumor immune heterogeneity and predict survival in lrNPC. METHODS: This multicenter, retrospective study included 921 patients with lrNPC. A machine learning signature and nomogram based on pretreatment MRI features were developed for predicting overall survival (OS) in a training cohort and validated in two independent cohorts. A clinical nomogram and an integrated nomogram were constructed for comparison. Nomogram performance was evaluated by concordance index (C-index) and receiver operating characteristic curve analysis. Accordingly, patients were classified into risk groups. The biological characteristics and immune infiltration of the signature were explored by RNA sequencing (RNA-seq) analysis. RESULTS: The machine learning signature and nomogram demonstrated comparable prognostic ability to a clinical nomogram, achieving C-indexes of 0.729, 0.718, and 0.731 in the training, internal, and external validation cohorts, respectively. Integration of the signature and clinical variables significantly improved the predictive performance. The proposed signature effectively distinguished patients between risk groups with significantly distinct OS rates. Subgroup analysis indicated the recommendation of local salvage treatments for low-risk patients. Exploratory RNA-seq analysis revealed differences in interferon response and lymphocyte infiltration between risk groups. CONCLUSIONS: An MRI-based radiomic signature predicted OS more accurately. The proposed signature associated with tumor immune heterogeneity may serve as a valuable tool to facilitate prognostic stratification and guide individualized management for lrNPC patients.

The development of a novel zeolite-based assay for efficient and deep plasma proteomic profiling.

Plasma proteins are considered the most informative source of biomarkers for disease diagnosis and monitoring. Mass spectrometry (MS)-based proteomics has been applied to identify biomarkers in plasma, but the complexity of the plasma proteome and the extremely large dynamic range of protein abundances in plasma make the clinical application of plasma proteomics highly challenging. We designed and synthesized zeolite-based nanoparticles to deplete high-abundance plasma proteins. The resulting novel plasma proteomic assay can measure approximately 3000 plasma proteins in a 45 min chromatographic gradient. Compared to those in neat and depleted plasma, the plasma proteins identified by our assay exhibited distinct biological profiles, as validated in several public datasets. A pilot investigation of the proteomic profile of a hepatocellular carcinoma (HCC) cohort identified 15 promising protein features, highlighting the diagnostic value of the plasma proteome in distinguishing individuals with and without HCC. Furthermore, this assay can be easily integrated with all current downstream protein profiling methods and potentially extended to other biofluids. In conclusion, we established a robust and efficient plasma proteomic assay with unprecedented identification depth, paving the way for the translation of plasma proteomics into clinical applications.

Construction and application of nasopharyngeal carcinoma-specific big data platform based on electronic health records.

OBJECTIVE: To establish a nasopharyngeal carcinoma-specific big data platform based on electronic health records (EHRs) to provide data support for real-world study of nasopharyngeal carcinoma. METHODS: A multidisciplinary expert team was established for this project. Based on industry standards and practical feasibility, the team designed the nasopharyngeal carcinoma data element standards including 14 modules and 640 fields. Data from patients diagnosed with nasopharyngeal carcinoma who visited Southern Hospital after 1999 were extracted from 15 EHRs systems and were cleaned, structured, and standardized using information technologies such as machine learning and natural language processing. In addition, a series of measures such as quality control and data encryption were taken to ensure data quality and patient privacy. At the platform application level, 10 functional modules were designed according to the needs of nasopharyngeal carcinoma research. RESULTS: As of 1 October 2022, the Big Data platform has included 11,617patients, of whom 8228 (70.83 %) were male and 3389 (29.17 %) were female, with a median age of 48 years (interquartile range, 40 years). The data in the platform were validated to have a high level of completeness and accuracy, especially for key variables such as social demographics, laboratory tests and vital signs. Currently, six projects involving risk factors, early diagnosis, treatment efficacy and prevention of treatment-related toxic reactions have been conducted on the platform. CONCLUSIONS: We have established a high-quality NPC-specific big data platform by integrating heterogeneous data from multiple sources in the EHR. The platform provides an effective tool and strong data support for real-world studies of nasopharyngeal carcinoma, which helps to improve research efficiency, reduce costs, and improve the quality of research results. We expect to promote multicenter nasopharyngeal carcinoma data sharing in the future to facilitate the generation of high-quality real-world evidence in nasopharyngeal carcinoma. This article may provide some reference value for other comprehensive hospitals to establish a big data platform for nasopharyngeal carcinoma.

Radiomic signatures reveal multiscale intratumor heterogeneity associated with tissue tolerance and survival in re-irradiated nasopharyngeal carcinoma: a multicenter study.

BACKGROUND: Post-radiation nasopharyngeal necrosis (PRNN) is a severe adverse event following re-radiotherapy for patients with locally recurrent nasopharyngeal carcinoma (LRNPC) and associated with decreased survival. Biological heterogeneity in recurrent tumors contributes to the different risks of PRNN. Radiomics can be used to mine high-throughput non-invasive image features to predict clinical outcomes and capture underlying biological functions. We aimed to develop a radiogenomic signature for the pre-treatment prediction of PRNN to guide re-radiotherapy in patients with LRNPC. METHODS: This multicenter study included 761 re-irradiated patients with LRNPC at four centers in NPC endemic area and divided them into training, internal validation, and external validation cohorts. We built a machine learning (random forest) radiomic signature based on the pre-treatment multiparametric magnetic resonance images for predicting PRNN following re-radiotherapy. We comprehensively assessed the performance of the radiomic signature. Transcriptomic sequencing and gene set enrichment analyses were conducted to identify the associated biological processes. RESULTS: The radiomic signature showed discrimination of 1-year PRNN in the training, internal validation, and external validation cohorts (area under the curve (AUC) 0.713-0.756). Stratified by a cutoff score of 0.735, patients with high-risk signature had higher incidences of PRNN than patients with low-risk signature (1-year PRNN rates 42.2-62.5% vs. 16.3-18.8%, P < 0.001). The signature significantly outperformed the clinical model (P < 0.05) and was generalizable across different centers, imaging parameters, and patient subgroups. The radiomic signature had prognostic value concerning its correlation with PRNN-related deaths (hazard ratio (HR) 3.07-6.75, P < 0.001) and all causes of deaths (HR 1.53-2.30, P < 0.01). Radiogenomics analyses revealed associations between the radiomic signature and signaling pathways involved in tissue fibrosis and vascularity. CONCLUSIONS: We present a radiomic signature for the individualized risk assessment of PRNN following re-radiotherapy, which may serve as a noninvasive radio-biomarker of radiation injury-associated processes and a useful clinical tool to personalize treatment recommendations for patients with LANPC.

Exploring the impact of emissions trading schemes on income inequality between urban and rural areas.

While the Paris Agreement and 2030 Agenda for Sustainable Development are the two most important global governance agendas, in practice they have been implemented in isolation. This calls for the need to focus on the potential policy synergies between emission reduction policies and Sustainable Development Goals (SDGs). This paper therefore aims to explore whether the emissions trading scheme (ETS) policy-one of the most effective ways to fulfill the target determined by the Paris Agreement, would facilitate reducing income inequality (SDG10). By combining a difference-in-difference estimation and propensity score matching technique based on a balanced panel dataset for 31 Chinese provinces from 2010 to 2018, we quantify the impact of ETS policy on income inequality between urban and rural areas in China. The results demonstrate that compared with the regions without ETS, the Theil index between rural and urban areas with ETS decreased by 0.018 on average in China. In addition, the ETS would perform better in regions with low urbanization level and high coal dependence. Hence it is vital to speed up the establishment of a unified ETS market in China. This is particularly true for inner underdeveloped regions in China. These findings proven to be robust according to a series of tests challenge the view that SDG 10 has the least relevance to climate action and suggest rethinking the effectiveness and applicability of ETS. Therefore, our research can not only serve as a reference for the development of ETS in China and elsewhere, but also inform decision makers who are seeking for policy synergies between the Paris Agreement and SDGs.

Comprehensive and clinically accurate head and neck cancer organs-at-risk delineation on a multi-institutional study.

Accurate organ-at-risk (OAR) segmentation is critical to reduce radiotherapy complications. Consensus guidelines recommend delineating over 40 OARs in the head-and-neck (H&N). However, prohibitive labor costs cause most institutions to delineate a substantially smaller subset of OARs, neglecting the dose distributions of other OARs. Here, we present an automated and highly effective stratified OAR segmentation (SOARS) system using deep learning that precisely delineates a comprehensive set of 42 H&N OARs. We train SOARS using 176 patients from an internal institution and independently evaluate it on 1327 external patients across six different institutions. It consistently outperforms other state-of-the-art methods by at least 3-5% in Dice score for each institutional evaluation (up to 36% relative distance error reduction). Crucially, multi-user studies demonstrate that 98% of SOARS predictions need only minor or no revisions to achieve clinical acceptance (reducing workloads by 90%). Moreover, segmentation and dosimetric accuracy are within or smaller than the inter-user variation.

Exploring prognostic value and regulation network of PPP1R1A in hepatocellular carcinoma.

Novel and accurate biomarkers are needed for early detection and progression evaluation of hepatocellular carcinoma (HCC). Protein phosphatase 1 regulatory subunit 1A (PPP1R1A) has been studied in cancer biology; however, the expression pattern and biological function of PPP1R1A in HCC are unclear. The differentially expressed genes (DEGs) in HCC were screened by The Cancer Genome Atlas (TCGA) database. Real-time PCR and immunohistochemistry (IHC) assay were used to detect the expression of PPP1R1A in BALB/c mice, human normal tissues and corresponding tumor tissues, especially HCC. Then, Kaplan-Meier analysis of patients with HCC was performed to evaluate the relationship between PPP1R1A expression and prognosis. The transcriptional regulatory network of PPP1R1A was constructed based on the differentially expressed mRNAs, microRNAs and transcription factors (TFs). To explore the downstream regulation of PPP1R1A, the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis and immune infiltration score were performed. A total of 4 DEGs were screened out. PPP1R1A was differentially distributed and expressed in BALB/c mice and human tissues. PPP1R1A expression was higher in normal tissues than that in tumor tissues, and patients with higher PPP1R1A expression had better clinical outcome in HCC. In addition, we constructed miR-21-3p/TAL1/PPP1R1A transcriptional network. Furthermore, PPP1R1A may modulate the activation of PI3K-Akt pathway, cell cycle, glycogen metabolism and the recruitment of M2 macrophage in HCC. This study may help to clarify the function and mechanism of PPP1R1A in HCC and provide a potential biomarker for tumor prevention and treatment.

COMMD10 inhibits tumor progression and induces apoptosis by blocking NF-κB signal and values up BCLC staging in predicting overall survival in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is the third leading cause of cancer mortality worldwide. Currently, there is limited knowledge of dysregulation of cellular proliferation and apoptosis that contribute to the malignant phenotype in HCC. Copper metabolism gene MURR1 domain 10 (COMMD10) is initially identified as a suppressor gene in the pathogenesis of HCC in our observations. Here we aimed to explore its function and prognostic value in the progression of HCC. METHODS: Functional experiments were performed to explore the role of COMMD10 in HCC. The molecular mechanisms of COMMD10 were determined by luciferase assay, immunofluorescence, and immunoprecipitation. The nomogram was based on a retrospective and multicenter study of 516 patients who were pathologically diagnosed with HCC from three Chinese hospitals. The predictive accuracy and discriminative ability of the nomogram were determined by a C-index and calibration curve and were compared with COMMD10 and the Barcelona Clinic Liver Cancer (BCLC) staging system. The primary endpoint was overall survival (OS). RESULTS: COMMD10 expression was significantly lower in HCC than that in normal liver tissues. In vitro and in vivo experiments revealed that COMMD10 suppressed cell proliferation and induced apoptosis in HCC. Mechanistically, COMMD10 inhibits TNFα mediated ubiquitination of IκBα and p65 nuclear translocation through the combination of COMMD10-N terminal to the Rel homology domain of p65, which inhibited NF-κB activity and increased expression of cleaved caspase9/3 in HCC. Clinically, COMMD10 stratifies early-stage HCC patients into two risk groups with significantly different OS. Additionally, the nomogram based on COMMD10 and BCLC stage yielded more accuracy than BCLC stage alone for predicting OS of HCC patients in three cohorts. CONCLUSIONS: COMMD10 suppresses proliferation and promotes apoptosis by inhibiting NF-κB signaling and values up BCLC staging in predicting OS, which provides evidence for the identification of potential therapeutic targets and the accurate prediction of prognosis for patients with HCC.