Yimeng Li’s research while affiliated with First Affiliated Hospital of China Medical University and other places

Pralsetinib and selpercatinib have been approved as specific tyrosine kinase inhibitors (TKIs) for the treatment of patients with non-small cell lung cancer (NSCLC) harboring rearranged during transfection (RET) fusion and mutation. However, adverse events associated with pralsetinib and selpercatinib are not fully understood, especially in the real world. In this case, invasive aspergillosis that appeared concurrent with RET-TKI targeted therapy is proposed to be an additional adverse drug reaction (ADR) that was not mentioned in previous reports. Here, we describe the process of clinical diagnosis and treatment of invasive aspergillosis and attempt to explore its possible pathogenesis in association with RET-TKI targeted therapy, with the aim of providing clinicians a more in-depth understanding of the ADR associated with RET-TKIs, as well as to prevent serious outcomes caused by reduction or discontinuation of antitumor therapy.

In this study, we aimed to investigate disparities in the tumor immune microenvironment (TME) between primary and metastatic malignant melanoma (MM) using single-cell RNA sequencing (scRNA- seq ) and to identify metastasis-related T cell marker genes (MRTMGs) for predicting patient survival using machine learning techniques. We identified 6 distinct T cell clusters in 10×scRNA-seq data utilizing the Uniform Manifold Approximation and Projection (UMAP) algorithm. Four machine learning algorithms highlighted SRGN, PMEL, GPR143, EIF4A2, and DSP as pivotal MRTMGs, forming the foundation of the MRTMGs signature. A high MRTMGs signature was found to be correlated with poorer overall survival (OS) and suppression of antitumor immunity in MM patients. We developed a nomogram that combines the MRTMGs signature with the T stage and N stage, which accurately predicts 1-year, 3-year, and 5-year OS probabilities. Furthermore, in an immunotherapy cohort, a high MRTMG signature was associated with an unfavorable response to anti-programmed death 1 (PD-1) therapy. In conclusion, primary and metastatic MM display distinct TME landscapes with different T cell subsets playing crucial roles in metastasis. The MRTMGs signature, established through machine learning, holds potential as a valuable biomarker for predicting the survival of MM patients and their response to anti-PD-1 therapy.

Background Alectinib, a next-generation anaplastic lymphoma kinase tyrosine kinase inhibitor (ALK-TKI), has demonstrated noteworthy efficacy in the treatment of non-small cell lung cancer (NSCLC). Unfortunately, 53.3% of untreated patients receiving first-line treatment with alectinib developed resistance to alectinib. However, despite the widespread use of alectinib, studies on the efficacy and safety of continuing alectinib with other necessary therapies after progression of alectinib and possible population of benefit are still limited. Methods This retrospective cohort study included fifteen patients with ALK-positive NSCLC from nine institutions in China who experienced disease progression after first- or second-line treatment and continued to receive alectinib treatment between 2019 and 2022. This study aimed to evaluate the median progression-free survival (mPFS), objective response rate (ORR), median overall survival (mOS), and adverse events (AEs) of continuing alectinib combined with other therapies after the emergence of drug resistance. Results Among fifteen patients eligible for this study, all patients started continuing treatment with alectinib after oligoprogression or central nervous system (CNS) progression. The mPFS for the whole cohort receiving continuing alectinib with other necessary therapies was 8 months [95% confidence interval (CI): 4 to not applicable (NA)], with an ORR of 46.7%. The mOS was not reached. During continuing alectinib treatment, only one patient experienced grade 2 elevation of aspartate aminotransferase (AST) and serum glutamic-oxaloacetic transaminase (SGOT). Conclusions The continuation of alectinib treatment combined with other necessary therapies demonstrates favorable response and safety in patients with ALK-positive NSCLC who experienced oligoprogression or CNS progression following alectinib in first- or second-line therapy. Instead of immediately switching to another ALK-TKI, continuing alectinib combined with other necessary therapies may offer greater survival benefits to the patients.

Background Elderly cancer patients are more predisposed to developing nosocomial infections during anti-neoplastic treatment, and are associated with a bleaker prognosis. This study aimed to develop a novel risk classifier to predict the in-hospital death risk of nosocomial infections in this population. Methods Retrospective clinical data were collected from a National Cancer Regional Center in Northwest China. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was utilized to filter the optimal variables for model development and avoid model overfitting. Logistic regression analysis was performed to identify the independent predictors of the in-hospital death risk. A nomogram was then developed to predict the in-hospital death risk of each participant. The performance of the nomogram was evaluated using receiver operating characteristics (ROC) curve, calibration curve, and decision curve analysis (DCA). Results A total of 569 elderly cancer patients were included in this study, and the estimated in-hospital mortality rate was 13.9%. The results of multivariate logistic regression analysis showed that ECOG-PS (odds ratio [OR]: 4.41, 95% confidence interval [CI]: 1.95-9.99), surgery type (OR: 0.18, 95%CI: 0.04-0.85), septic shock (OR: 5.92, 95%CI: 2.43-14.44), length of antibiotics treatment (OR: 0.21, 95%CI: 0.09-0.50), and prognostic nutritional index (PNI) (OR: 0.14, 95%CI: 0.06-0.33) were independent predictors of the in-hospital death risk of nosocomial infections in elderly cancer patients. A nomogram was then constructed to achieve personalized in-hospital death risk prediction. ROC curves yield excellent discrimination ability in the training (area under the curve [AUC]=0.882) and validation (AUC=0.825) cohorts. Additionally, the nomogram showed good calibration ability and net clinical benefit in both cohorts. Conclusion Nosocomial infections are a common and potentially fatal complication in elderly cancer patients. Clinical characteristics and infection types can vary among different age groups. The risk classifier developed in this study could accurately predict the in-hospital death risk for these patients, providing an important tool for personalized risk assessment and clinical decision-making.

Pancreatic ductal adenocarcinoma (PDAC) is one of the lethal malignancies, with limited biomarkers identified to predict its prognosis and treatment response of immune checkpoint blockade (ICB). This study aimed to explore the predictive ability of T cell marker genes score (TMGS) to predict their overall survival (OS) and treatment response to ICB by integrating single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data. Multi-omics data of PDAC were applied in this study. The uniform manifold approximation and projection (UMAP) was utilized for dimensionality reduction and cluster identification. The non-negative matrix factorization (NMF) algorithm was applied to molecular subtypes clustering. The Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression was adopted for TMGS construction. The prognosis, biological characteristics, mutation profile, and immune function status between different groups were compared. Two molecular subtypes were identified via NMF: proliferative PDAC (C1) and immune PDAC (C2). Distinct prognoses and biological characteristics were observed between them. TMGS was developed based on 10 T cell marker genes (TMGs) through LASSO-Cox regression. TMGS is an independent prognostic factor of OS in PDAC. Enrichment analysis indicated that cell cycle and cell proliferation-related pathways are significantly enriched in the high-TMGS group. Besides, high-TMGS is related to more frequent KRAS, TP53, and CDKN2A germline mutations than the low-TMGS group. Furthermore, high-TMGS is significantly associated with attenuated antitumor immunity and reduced immune cell infiltration compared to the low-TMGS group. However, high TMGS is correlated to higher tumor mutation burden (TMB), a low expression level of inhibitory immune checkpoint molecules, and a low immune dysfunction score, thus having a higher ICB response rate. On the contrary, low TMGS is related to a favorable response rate to chemotherapeutic agents and targeted therapy. By combining scRNA-seq and bulk RNA-seq data, we identified a novel biomarker, TMGS, which has remarkable performance in predicting the prognosis and guiding the treatment pattern for patients with PDAC.

Objective Thymic epithelial tumors (TETs) are rare tumors that originated from thymic epithelial cells, with limited studies investigating their prognostic factors. This study aimed to investigate the prognostic factors of TETs and develop a new risk classifier to predict their overall survival (OS). Methods This retrospective study consisted of 1224 TETs patients registered in the Surveillance, Epidemiology, and End Results (SEER) database, and 75 patients from the First Affiliated Hospital of Xi’an Jiaotong University. The univariate and multivariate Cox regression analyses were adopted to select the best prognostic variables. A nomogram was developed to predict the OS of these patients. The discriminative and calibrated abilities of the nomogram were assessed using the receiver operating characteristics curve (ROC) and calibration curve. Decision curve analysis (DCA), net reclassification index (NRI), and integrated discrimination improvement (IDI) were adopted to assess its net clinical benefit and reclassification ability. Results The multivariate analysis revealed that age, sex, histologic type, TNM staging, tumor grade, surgery, radiation, and tumor size were independent prognostic factors of TETs, and a nomogram was developed to predict the OS of these patients based on these variables. The time-dependent ROC curves displayed that the nomogram yielded excellent performance in predicting the 12-, 36- and 60-month OS of these patients. Calibration curves presented satisfying consistencies between the actual and predicted OS. DCA illustrated that the nomogram will bring significant net clinical benefits to these patients compared to the classic TNM staging system. The estimated NRI and IDI showed that the nomogram could significantly increase the predictive ability of 12-, 36- and 60-month OS compared to the classic TNM staging system. Consistent findings were discovered in the internal and external validation cohorts. Conclusion The constructed nomogram is a reliable risk classifier to achieve personalized survival probability prediction of TETs, and could bring significant net clinical benefits to these patients.

Background: Lipid metabolism plays a pivotal role in cancer progression and metastasis. This study aimed to investigate the prognostic value of lipid metabolism-related genes (LMRGs) in early-stage lung adenocarcinoma (LUAD) and develop a lipid metabolism-related gene prognostic index (LMRGPI) to predict their overall survival (OS) and treatment response. Methods: A total of 774 early-stage LUAD patients were identified from The Cancer Genome Atlas (TCGA, 403 patients) database and Gene Expression Omnibus (GEO, 371 patients) database. The non-negative Matrix Factorization (NMF) algorithm was used to identify different population subtypes based on LMRGs. The Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate Cox regression analyses were used to develop the LMRGPI, with receiver operating characteristic (ROC) curves and concordance index being used to evaluate its performance. The characteristics of mutation landscape, enriched pathways, tumor microenvironment (TME), and treatment response between different LMRGPI groups were also investigated. Results: We identified two population subtypes based on LMRGs in the TCGA-LUAD cohort, with distinct prognosis, TME, and immune status being observed. LMRGPI was developed based on the expression levels of six LMRGs, including ANGPTL4, NPAS2, SLCO1B3, ACOXL, ALOX15, and B3GALNT1. Higher LMRGPI was correlated with poor OS both in TCGA and GSE68465 cohorts. Two nomograms were established to predict the survival probability of early-stage LUAD, with higher consistencies being observed between the predicted and actual OS. Higher LMRGPI was significantly correlated with more frequent TP53 mutation, higher tumor mutation burden (TMB), and up-regulation of CD274. Besides, patients with higher LMRGPI presented unremarkable responses for gefitinib, erlotinib, cisplatin, and vinorelbine, while they tend to have a favorable response for immune checkpoint inhibitors (ICIs). The opposite results were observed in the low-LMRGPI group. Conclusions: We comprehensively investigated the prognostic value of LMRGs in early-stage LUAD. Given its good prognostic ability, LMRGPI could serve as a promising biomarker to predict the OS and treatment response of these patients.

Background: Lung adenocarcinoma (LUAD) remains a lethal disease worldwide, with numerous studies exploring its potential prognostic markers using traditional RNA sequencing (RNA-seq) data. However, it cannot detect the exact cellular and molecular changes in tumor cells. This study aimed to construct a prognostic model for LUAD using single-cell RNA-seq (scRNA-seq) and traditional RNA-seq data. Methods: Bulk RNA-seq data were downloaded from The Cancer Genome Atlas (TCGA) database. LUAD scRNA-seq data were acquired from Gene Expression Omnibus (GEO) database. The uniform manifold approximation and projection (UMAP) was used for dimensionality reduction and cluster identification. Weighted Gene Correlation Network Analysis (WGCNA) was utilized to identify key modules and differentially expressed genes (DEGs). The non-negative Matrix Factorization (NMF) algorithm was used to identify different subtypes based on DEGs. The Cox regression analysis was used to develop the prognostic model. The characteristics of mutation landscape, immune status, and immune checkpoint inhibitors (ICIs) related genes between different risk groups were also investigated. Results: scRNA-seq data of four samples were integrated to identify 13 clusters and 9cell types. After applying differential analysis, NK cells, bladder epithelial cells, and bronchial epithelial cells were identified as significant cell types. Overall, 329 DEGs were selected for prognostic model construction through differential analysis and WGCNA. Besides, NMF identified two clusters based on DEGs in the TCGA cohort, with distinct prognosis and immune characteristics being observed. We developed a prognostic model based on the expression levels of six DEGs. A higher risk score was significantly correlated with poor survival outcomes but was associated with a more frequent TP53 mutation rate, higher tumor mutation burden (TMB), and up-regulation of PD-L1. Two independent external validation cohorts were also adopted to verify our results, with consistent results observed in them. Conclusion: This study constructed and validated a prognostic model for LUAD by integrating 10× scRNA-seq and bulk RNA-seq data. Besides, we observed two distinct subtypes in this population, with different prognosis and immune characteristics.