Comparison of radiotherapy versus surgical resection following neoadjuvant chemoimmunotherapy in potentially resectable stage III non-small-cell lung cancer: A propensity score matching analysis.

BACKGROUND: Neoadjuvant chemoimmunotherapy followed by surgery is recommended for resectable non-small-cell lung cancer (NSCLC). However, a considerable proportion of patients do not undergo surgery and opt for alternative treatments such as radiotherapy. The efficacy of radiotherapy in this context remains unclear. METHODS: This retrospective study analyzed data from patients with stage III NSCLC who received neoadjuvant chemoimmunotherapy followed by either surgery or radiotherapy. Propensity score matching (PSM) was used to balance the heterogeneity between the groups. Efficacy outcomes, safety profiles, and disease recurrence patterns were assessed. RESULTS: In total, 175 patients were included; 50 underwent radiotherapy, and 125 underwent surgery. Prior to matching, radiotherapy was inferior to surgery in terms of progression-free survival (PFS; Hazard ratio [HR], 2.23; P = 0.008). Following a 1:1 PSM adjustment, each group consisted of 40 patients. The median PFS was 30.8 months in the radiotherapy group and not reached in the surgery group (HR, 1.46; P = 0.390). The 12- and 24-month PFS rates were 90.4 % and 69.0 % for the radiotherapy group compared to 94.1 % and 73.9 % for the surgery group, respectively. Subgroup analyses after PSM showed that patients with stage IIIA disease tend to benefit more from surgery than those with stage IIIB disease (HR, 3.00; P = 0.074). Grade 3-4 treatment-related adverse events (TRAEs) occurred in 62.5 % of patients in the radiotherapy group and 55.0 % in the surgery group, with no grade 5 TRAEs reported. The incidence of grade 3-4 treatment-related pneumonitis or pneumonia was 7.5 % and 2.5 % in the radiotherapy and surgery groups, respectively. CONCLUSION: Radiotherapy may be a viable alternative to surgery in patients with resectable NSCLC who do not undergo surgical resection after initial neoadjuvant chemoimmunotherapy, offering comparable efficacy and a manageable safety profile. Larger prospective studies are needed to validate these findings and optimize the treatment strategies for this patient population.

Tumor cell-intrinsic MELK enhanced CCL2-dependent immunosuppression to exacerbate hepatocarcinogenesis and confer resistance of HCC to radiotherapy.

BACKGROUND: The outcome of hepatocellular carcinoma (HCC) is limited by its complex molecular characteristics and changeable tumor microenvironment (TME). Here we focused on elucidating the functional consequences of Maternal embryonic leucine zipper kinase (MELK) in the tumorigenesis, progression and metastasis of HCC, and exploring the effect of MELK on immune cell regulation in the TME, meanwhile clarifying the corresponding signaling networks. METHODS: Bioinformatic analysis was used to validate the prognostic value of MELK for HCC. Murine xenograft assays and HCC lung metastasis mouse model confirmed the role of MELK in tumorigenesis and metastasis in HCC. Luciferase assays, RNA sequencing, immunopurification-mass spectrometry (IP-MS) and coimmunoprecipitation (CoIP) were applied to explore the upstream regulators, downstream essential molecules and corresponding mechanisms of MELK in HCC. RESULTS: We confirmed MELK to be a reliable prognostic factor of HCC and identified MELK as an effective candidate in facilitating the tumorigenesis, progression, and metastasis of HCC; the effects of MELK depended on the targeted regulation of the upstream factor miR-505-3p and interaction with STAT3, which induced STAT3 phosphorylation and increased the expression of its target gene CCL2 in HCC. In addition, we confirmed that tumor cell-intrinsic MELK inhibition is beneficial in stimulating M1 macrophage polarization, hindering M2 macrophage polarization and inducing CD8 + T-cell recruitment, which are dependent on the alteration of CCL2 expression. Importantly, MELK inhibition amplified RT-related immune effects, thereby synergizing with RT to exert substantial antitumor effects. OTS167, an inhibitor of MELK, was also proven to effectively impair the growth and progression of HCC and exert a superior antitumor effect in combination with radiotherapy (RT). CONCLUSIONS: Altogether, our findings highlight the functional role of MELK as a promising target in molecular therapy and in the combination of RT therapy to improve antitumor effect for HCC.

Gastroenteropancreatic neuroendocrine neoplasms: current development, challenges, and clinical perspectives.

Neuroendocrine neoplasms (NENs) are highly heterogeneous and potentially malignant tumors arising from secretory cells of the neuroendocrine system. Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) are the most common subtype of NENs. Historically, GEP-NENs have been regarded as infrequent and slow-growing malignancies; however, recent data have demonstrated that the worldwide prevalence and incidence of GEP-NENs have increased exponentially over the last three decades. In addition, an increasing number of studies have proven that GEP-NENs result in a limited life expectancy. These findings suggested that the natural biology of GEP-NENs is more aggressive than commonly assumed. Therefore, there is an urgent need for advanced researches focusing on the diagnosis and management of patients with GEP-NENs. In this review, we have summarized the limitations and recent advancements in our comprehension of the epidemiology, clinical presentations, pathology, molecular biology, diagnosis, and treatment of GEP-NETs to identify factors contributing to delays in diagnosis and timely treatment of these patients.

Boosting the sonodynamic performance of CoBiMn-layered double hydroxide nanoparticles via tumor microenvironment regulation for ultrasound imaging-guided sonodynamic therapy.

Sonodynamic therapy (SDT), a promising strategy for cancer treatment with the ability for deep tissue penetration, has received widespread attention in recent years. Sonosensitizers with intrinsic characteristics for tumor-specific curative effects, tumor microenvironment (TME) regulation and tumor diagnosis are in high demand. Herein, amorphous CoBiMn-layered double hydroxide (a-CoBiMn-LDH) nanoparticles are presented as multifunctional sonosensitizers to trigger reactive oxygen species (ROS) generation for ultrasound (US) imaging-guided SDT. Hydrothermal-synthesized CoBiMn-LDH nanoparticles are etched via a simple acid treatment to obtain a-CoBiMn-LDH nanoparticles with abundant defects. The a-CoBiMn-LDH nanoparticles give greater ROS generation upon US irradiation, reaching levels ~ 3.3 times and ~ 8.2 times those of the crystalline CoBiMn-LDH nanoparticles and commercial TiO2 sonosensitizer, respectively. This excellent US-triggered ROS generation performance can be attributed to the defect-induced narrow band gap and promoted electrons and holes (e-/h+) separation. More importantly, the presence of Mn4+ enables the a-CoBiMn-LDH nanoparticles to regulate the TME by decomposing H2O2 into O2 for hypoxia relief and US imaging, and consuming glutathione (GSH) for protection against ROS clearance. Biological mechanism analysis shows that a-CoBiMn-LDH nanoparticles modified with polyethylene glycol can serve as a multifunctional sonosensitizer to effectively kill cancer cells in vitro and eliminate tumors in vivo under US irradiation by activating p53, apoptosis, and oxidative phosphorylation-related signaling pathways.

Self-Illuminating Nanoagonist Simultaneously Induces Dual Cell Death Pathways via Death Receptor Clustering for Cancer Therapy.

Inducing death receptor 5 (DR5) clustering holds particular promise in tumor-specific therapeutics because it could trigger an apoptotic cascade in cancerous cells. Herein, we present a tumor microenvironment H2O2-responsive self-illuminating nanoagonist, which could induce dual tumor cell death pathways through enhancing DR5 clustering. By conjugating DR5 ligand peptides onto the surfaces of self-illuminating nanoparticles with cross-linking capacity, this strategy not only provides scaffolds for ligands to bind receptors but also cross-links them through photo-cross-linking. This strategy allows for efficient activation of DR5 downstream signaling, initiating the extrinsic apoptosis pathway and immunogenic cell death of tumor cells, and contributes to improved tumor-specific immune responses, resulting in enhanced antitumor efficacy and minimized systemic adverse effects.

Acidic/hypoxia dual-alleviated nanoregulators for enhanced treatment of tumor chemo-immunotherapy.

Chemotherapy plays a crucial role in triple-negative breast cancer (TNBC) treatment as it not only directly kills cancer cells but also induces immunogenic cell death. However, the chemotherapeutic efficacy was strongly restricted by the acidic and hypoxic tumor environment. Herein, we have successfully formulated PLGA-based nanoparticles concurrently loaded with doxorubicin (DOX), hemoglobin (Hb) and CaCO3 by a CaCO3-assisted emulsion method, aiming at the effective treatment of TNBC. We found that the obtained nanomedicine (DHCaNPs) exhibited effective drug encapsulation and pH-responsive drug release behavior. Moreover, DHCaNPs demonstrated robust capabilities in neutralizing protons and oxygen transport. Consequently, DHCaNPs could not only serve as oxygen nanoshuttles to attenuate tumor hypoxia but also neutralize the acidic tumor microenvironment (TME) by depleting lactic acid, thereby effectively overcoming the resistance to chemotherapy. Furthermore, DHCaNPs demonstrated a notable ability to enhance antitumor immune responses by increasing the frequency of tumor-infiltrating effector lymphocytes and reducing the frequency of various immune-suppressive cells, therefore exhibiting a superior efficacy in suppressing tumor growth and metastasis when combined with anti-PD-L1 (αPD-L1) immunotherapy. In summary, this study highlights that DHCaNPs could effectively attenuate the acidic and hypoxic TME, offering a promising strategy to figure out an enhanced chemo-immunotherapy to benefit TNBC patients.

Prediction of the Ki-67 expression level in head and neck squamous cell carcinoma with machine learning-based multiparametric MRI radiomics: a multicenter study.

BACKGROUND: This study aimed to develop and validate a machine learning (ML)-based fusion model to preoperatively predict Ki-67 expression levels in patients with head and neck squamous cell carcinoma (HNSCC) using multiparametric magnetic resonance imaging (MRI). METHODS: A total of 351 patients with pathologically proven HNSCC from two medical centers were retrospectively enrolled in the study and divided into training (n = 196), internal validation (n = 84), and external validation (n = 71) cohorts. Radiomics features were extracted from T2-weighted images and contrast-enhanced T1-weighted images and screened. Seven ML classifiers, including k-nearest neighbors (KNN), support vector machine (SVM), logistic regression (LR), random forest (RF), linear discriminant analysis (LDA), naive Bayes (NB), and eXtreme Gradient Boosting (XGBoost) were trained. The best classifier was used to calculate radiomics (Rad)-scores and combine clinical factors to construct a fusion model. Performance was evaluated based on calibration, discrimination, reclassification, and clinical utility. RESULTS: Thirteen features combining multiparametric MRI were finally selected. The SVM classifier showed the best performance, with the highest average area under the curve (AUC) of 0.851 in the validation cohorts. The fusion model incorporating SVM-based Rad-scores with clinical T stage and MR-reported lymph node status achieved encouraging predictive performance in the training (AUC = 0.916), internal validation (AUC = 0.903), and external validation (AUC = 0.885) cohorts. Furthermore, the fusion model showed better clinical benefit and higher classification accuracy than the clinical model. CONCLUSIONS: The ML-based fusion model based on multiparametric MRI exhibited promise for predicting Ki-67 expression levels in HNSCC patients, which might be helpful for prognosis evaluation and clinical decision-making.

Multiple Immunomodulatory Strategies Based on Targeted Regulation of Proprotein Convertase Subtilisin/Kexin Type 9 and Immune Homeostasis against Hepatocellular Carcinoma.

Immunotherapy is the most promising systemic therapy for hepatocellular carcinoma. However, the outcome remains poor. Proprotein convertase subtilisin/kexin type 9 (PCSK9) plays a role in altering cell-surface protein levels, potentially undermining the efficacy of immunotherapy against tumors. This highlights its potential as a target for antitumor therapy. Herein, CaCO3-based nanoparticles coencapsulated with DOX, an immunogenic cell death (ICD) inducer, and evolocumab was developed to enhanced the efficacy of immunotherapy. The obtained DOX/evolocumab-loaded CaCO3 nanoparticle (named DECP) exhibits a good capacity of acid neutralization and causes ICD of cancer cells. In addition, DECP is able to evaluate the cell-surface level of MHC-I, a biomarker that correlates positively with patients' overall survival. Upon intravenous injection, DECP accumulates within the tumor site, leading to growth inhibition of hepa1-6 bearing subcutaneous tumors. Specifically, DECP treatment causes augmented ratios of matured dendritic cells, tumor-infiltrating CD8+ T cells and natural killing cells, while concurrently depleting Foxp3+ regulatory T cells. Peritumoral delivery of DECP enhances the immune response of distant tumors and exhibits antitumor effects when combined with intravenous αPD-L1 therapy in a bilateral tumor model. This study presents CaCO3-based nanoparticles with multiple immunomodulatory strategies against hepatocellular carcinoma by targeting PCSK9 inhibition and modulating immune homeostasis in the unfavorable TME.

Hippo pathway in non-small cell lung cancer: mechanisms, potential targets, and biomarkers.

Lung cancer is the primary contributor to cancer-related deaths globally, and non-small cell lung cancer (NSCLC) constitutes around 85% of all lung cancer cases. Recently, the emergence of targeted therapy and immunotherapy revolutionized the treatment of NSCLC and greatly improved patients' survival. However, drug resistance is inevitable, and extensive research has demonstrated that the Hippo pathway plays a crucial role in the development of drug resistance in NSCLC. The Hippo pathway is a highly conserved signaling pathway that is essential for various biological processes, including organ development, maintenance of epithelial balance, tissue regeneration, wound healing, and immune regulation. This pathway exerts its effects through two key transcription factors, namely Yes-associated protein (YAP) and transcriptional co-activator PDZ-binding motif (TAZ). They regulate gene expression by interacting with the transcriptional-enhanced associate domain (TEAD) family. In recent years, this pathway has been extensively studied in NSCLC. The review summarizes a comprehensive overview of the involvement of this pathway in NSCLC, and discusses the mechanisms of drug resistance, potential targets, and biomarkers associated with this pathway in NSCLC.

Machine Learning Radiomics-Based Prediction of Non-sentinel Lymph Node Metastasis in Chinese Breast Cancer Patients with 1-2 Positive Sentinel Lymph Nodes: A Multicenter Study.

RATIONALE AND OBJECTIVES: This study aimed to construct a machine learning radiomics-based model using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images to evaluate non-sentinel lymph node (NSLN) metastasis in Chinese breast cancer (BC) patients who underwent total mastectomy (TM) and had 1-2 positive sentinel lymph nodes (SLNs). MATERIALS AND METHODS: In total, 494 patients were retrospectively enrolled from two hospitals, and were divided into the training (n = 286), internal validation (n = 122), and external validation (n = 86) cohorts. Features were extracted from DCE-MRI images for each patient and screened. Six ML classifies were trained and the best classifier was evaluated to calculate radiomics (Rad)-scores. A combined model was developed based on Rad-scores and clinical risk factors, then the calibration, discrimination, reclassification, and clinical usefulness were evaluated. RESULTS: 14 radiomics features were ultimately selected. The random forest (RF) classifier showed the best performance, with the highest average area under the curve (AUC) of 0.833 in the validation cohorts. The combined model incorporating RF-based Rad-scores, tumor size, lymphovascular invasion, and proportion of positive SLNs resulted in the best discrimination ability, with AUCs of 0.903, 0.890, and 0.836 in the training, internal validation, and external validation cohorts, respectively. Furthermore, the combined model significantly improved the classification accuracy and clinical benefit for NSLN metastasis prediction. CONCLUSION: A RF-based combined model using DCE-MRI images exhibited a promising performance for predicting NSLN metastasis in Chinese BC patients who underwent TM and had 1-2 positive SLNs, thereby aiding in individualized clinical treatment decisions.

Non-invasive prediction model of axillary lymph node status in patients with early-stage breast cancer: a feasibility study based on dynamic contrast-enhanced-MRI radiomics.

OBJECTIVES: Accurate axillary evaluation plays an important role in prognosis and treatment planning for breast cancer. This study aimed to develop and validate a dynamic contrast-enhanced (DCE)-MRI-based radiomics model for preoperative evaluation of axillary lymph node (ALN) status in early-stage breast cancer. METHODS: A total of 410 patients with pathologically confirmed early-stage invasive breast cancer (training cohort, N = 286; validation cohort, N = 124) from June 2018 to August 2022 were retrospectively recruited. Radiomics features were derived from the second phase of DCE-MRI images for each patient. ALN status-related features were obtained, and a radiomics signature was constructed using SelectKBest and least absolute shrinkage and selection operator regression. Logistic regression was applied to build a combined model and corresponding nomogram incorporating the radiomics score (Rad-score) with clinical predictors. The predictive performance of the nomogram was evaluated using receiver operator characteristic (ROC) curve analysis and calibration curves. RESULTS: Fourteen radiomic features were selected to construct the radiomics signature. The Rad-score, MRI-reported ALN status, BI-RADS category, and tumour size were independent predictors of ALN status and were incorporated into the combined model. The nomogram showed good calibration and favourable performance for discriminating metastatic ALNs (N + (≥1)) from non-metastatic ALNs (N0) and metastatic ALNs with heavy burden (N + (≥3)) from low burden (N + (1-2)), with the area under the ROC curve values of 0.877 and 0.879 in the training cohort and 0.859 and 0.881 in the validation cohort, respectively. CONCLUSIONS: The DCE-MRI-based radiomics nomogram could serve as a potential non-invasive technique for accurate preoperative evaluation of ALN burden, thereby assisting physicians in the personalized axillary treatment for early-stage breast cancer patients. ADVANCES IN KNOWLEDGE: This study developed a potential surrogate of preoperative accurate evaluation of ALN status, which is non-invasive and easy-to-use.

The outcomes of different regimens depend on the molecular subtypes of pulmonary large-cell neuroendocrine carcinoma: A retrospective study in China.

BACKGROUND: The optimal systemic treatment for pulmonary large-cell neuroendocrine carcinoma (LCNEC) remains controversial, and recent advances in LCNEC molecular subtype classification have provided potential strategies for assisting in treatment decisions. Our study aimed to investigate the impact of treatment regimens, molecular subtypes and their concordance on clinical outcomes of patients diagnosed with LCNEC. PATIENTS AND METHODS: All patients diagnosed with advanced pulmonary LCNEC in Peking Union Medical College Hospital (PUMCH) between January 2000 and October 2021 were enrolled in this retrospective study. The tumor samples were collected and sequenced using a tumor-specific gene panel, while clinical information was retrieved from the medical records system. The survival and therapeutic response were analyzed and compared between different subgroups classified by treatment regimen (SCLC or NSCLC-based), molecular subtype (type I or II) or the combination. RESULTS: In univariate subgroup analysis categorized only by treatment regimen or molecular subtype, there were no differences identified in DCR, ORR, PFS, or OS. Nevertheless, the group with consistent treatment regimen and molecular subtype exhibited significantly longer OS than that of the inconsistent group (median OS 37.7 vs. 8.3 months; p = 0.046). Particularly, the OS of patients with type II LCNEC treated with SCLC-based regimen was significantly prolonged than that of others (median 37.7 vs. 10.5 months; p = 0.039). CONCLUSIONS: Collectively, our study revealed the clinical outcomes of different treatment regimens for LCNEC patients highly depend on their molecular subtypes, highlighting the need for sequencing-guided therapy.

Development and Validation of a Dual-Energy CT-Based Model for Predicting the Number of Central Lymph Node Metastases in Clinically Node-Negative Papillary Thyroid Carcinoma.

RATIONALE AND OBJECTIVES: This study aimed to develop and validate a dual-energy CT (DECT)-based model for preoperative prediction of the number of central lymph node metastases (CLNMs) in clinically node-negative (cN0) papillary thyroid carcinoma (PTC) patients. MATERIALS AND METHODS: Between January 2016 and January 2021, 490 patients who underwent lobectomy or thyroidectomy, CLN dissection, and preoperative DECT examinations were enrolled and randomly allocated into the training (N = 345) and validation cohorts (N = 145). The patients' clinical characteristics and quantitative DECT parameters obtained on primary tumors were collected. Independent predictors of> 5 CLNMs were identified and integrated to construct a DECT-based prediction model, for which the area under the curve (AUC), calibration, and clinical usefulness were assessed. Risk group stratification was performed to distinguish patients with different recurrence risks. RESULTS: More than 5 CLNMs were found in 75 (15.3%) cN0 PTC patients. Age, tumor size, normalized iodine concentration (NIC), normalized effective atomic number (nZeff) and the slope of the spectral Hounsfield unit curve (λHu) in the arterial phase were independently associated with> 5 CLNMs. The DECT-based nomogram that incorporated predictors demonstrated favorable performance in both cohorts (AUC: 0.842 and 0.848) and significantly outperformed the clinical model (AUC: 0.688 and 0.694). The nomogram showed good calibration and added clinical benefit for predicting> 5 CLNMs. The KaplanMeier curves for recurrence-free survival showed that the high- and low-risk groups stratified by the nomogram were significantly different. CONCLUSION: The nomogram based on DECT parameters and clinical factors could facilitate preoperative prediction of the number of CLNMs in cN0 PTC patients.

Osimertinib versus comparator first-generation epidermal growth factor receptor tyrosine kinase inhibitors as first-line treatment in patients with advanced EGFR-mutated non-small cell lung cancer: a Chinese, multicenter, real-world cohort study.

Background: In the phase 3 FLAURA trial, osimertinib was compared with first-generation epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) as a first-line treatment for EGFR-mutant non-small cell lung cancer (NSCLC). Osimertinib showed longer progression-free survival (PFS), overall survival (OS), and a similar safety profile. However, more studies demonstrating the effectiveness and safety of osimertinib as a first-line strategy are needed in real-world populations. Methods: We enrolled 1,556 patients with EGFR-mutated stage IIIc-IV NSCLC from the CAPTRA-Lung database. All patients received either osimertinib (n=202) or a first-generation EGFR-TKI (n=1,354) as their initial treatment. To adjust for differences in baseline characteristics between two groups, 1:2 propensity score matching (PSM) was performed. Propensity scores included gender, age, Eastern Cooperative Oncology Group performance status score, smoking history, family history of tumor, pathology, EGFR mutations, and central nervous system (CNS) metastases. The standardized mean differences (SMD) before and after PSM were calculated to examine the balance of covariate distributions between two groups. Results: After PSM, 202 patients receiving osimertinib and 404 patients receiving first-generation EGFR-TKIs were finally identified. SMD of each matched variable is less than 0.10. The median PFS was 19.4 months [95% confidence interval (CI): 14.3-24.4] in the osimertinib arm and 10.9 months (95% CI: 9.3-12.5) in the comparator arm [hazard ratio (HR) for progression, 0.47; 95% CI: 0.38-0.59; P<0.001). The median OS was 40.5 months (95% CI: 27.1-54.0) vs. 34.3 months (95% CI: 30.6-38.0) in two groups, respectively (HR for death, 0.76; 95% CI: 0.58-1.00; P=0.045). The incidence of grade 3 adverse events (AEs) between the two groups was 1% and 4.2%, respectively. No grade 4 AEs and treatment-related deaths were reported in both groups. Conclusions: In real-world settings, osimertinib demonstrates longer PFS and OS, with a similar safety profile to that of comparator EGFR-TKIs when used as a first-line strategy in NSCLC patients.

Divergent tumor and immune cell reprogramming underlying immunotherapy response and immune-related adverse events in lung squamous cell carcinoma.

BACKGROUND: Lung squamous cell carcinoma (LUSC) remains a leading cause of cancer-related deaths with few therapeutic strategies. Immune checkpoint inhibitors (ICIs) have demonstrated promising efficacy in patients with LUSC. However, ICIs could also lead to a unique spectrum of immune-related adverse events (irAEs), which dampen the clinical outcome. In-depth characterization of the immune hallmarks of antitumor responses and irAEs remains an unmet need to maximize ICI-treatment benefits of patients. METHODS: We performed single-cell RNA sequencing (scRNA-seq) on pre-ICI and on-ICI treatment tumor biopsies. We used bulk RNA-seq data of matched pretreatment/on-treatment tumors and irAE affected organs to validate observations from scRNA-seq analysis. Two independent patient cohorts were collected to determine circulating tumor necrosis factor (TNF) protein expression levels. RESULTS: We found that increased proportions of a macrophage subcluster with highly expressed secreted phosphoprotein 1 (SPP1) and two tumor cell subclusters in irAE patients, whereas proportions of two cytotoxic CD8+ T cell subclusters were higher in patients with partial response (PR). TNF signaling pathway was conversely associated with treatment efficacy and irAE development in most macrophage and tumor cell subclusters. Cell-cell communications for TNF ligand-receptor pairs between macrophage/T cells and tumor cells were also bidirectionally remodeled in responders versus non-responders and irAE versus non-irAE patients. Bulk RNA-seq analysis on matched pretreatment/on-treatment tumors and irAE affected organs revealed remarkably enhanced macrophage abundance and TNF signaling pathway in on-treatment tumors and organs developed irAEs. Furthermore, we observed significantly increased circulating TNF protein in plasma or serum of irAE patients but not ICI responders, based on analysis of two independent LUSC patient cohorts and one published ICI patient cohort. CONCLUSIONS: Our data depicts specific reprogramming of macrophage, T cells and tumor cells associated with ICI response and irAEs, elucidates divergent roles of TNF signaling in antitumor immunity and irAEs, and highlights the significance of TNF expression in irAE development in the LUSC setting.

[CT-Based Weighted Radiomic Score Predicts Tumor Response to Immunotherapy in Non-Small Cell Lung Cancer].

Objective To develop a CT-based weighted radiomic model that predicts tumor response to programmed death-1(PD-1)/PD-ligand 1(PD-L1)immunotherapy in patients with non-small cell lung cancer.Methods The patients with non-small cell lung cancer treated by PD-1/PD-L1 immune checkpoint inhibitors in the Peking Union Medical College Hospital from June 2015 to February 2022 were retrospectively studied and classified as responders(partial or complete response)and non-responders(stable or progressive disease).Original radiomic features were extracted from multiple intrapulmonary lesions in the contrast-enhanced CT scans of the arterial phase,and then weighted and summed by an attention-based multiple instances learning algorithm.Logistic regression was employed to build a weighted radiomic scoring model and the radiomic score was then calculated.The area under the receiver operating characteristic curve(AUC)was used to compare the weighted radiomic scoring model,PD-L1 model,clinical model,weighted radiomic scoring + PD-L1 model,and comprehensive prediction model.Results A total of 237 patients were included in the study and randomized into a training set(n=165)and a test set(n=72),with the mean ages of(64±9)and(62±8)years,respectively.The AUC of the weighted radiomic scoring model reached 0.85 and 0.80 in the training set and test set,respectively,which was higher than that of the PD-L1-1 model(Z=37.30,P<0.001 and Z=5.69,P=0.017),PD-L1-50 model(Z=38.36,P<0.001 and Z=17.99,P<0.001),and clinical model(Z=11.40,P<0.001 and Z=5.76,P=0.016).The AUC of the weighted scoring model was not different from that of the weighted radiomic scoring + PD-L1 model and the comprehensive prediction model(both P>0.05).Conclusion The weighted radiomic scores based on pre-treatment enhanced CT images can predict tumor responses to immunotherapy in patients with non-small cell lung cancer.

Clinical significance of DNA damage response mutations in stage I and stage IIIa NSCLC.

BACKGROUND: DNA damage response (DDR) pathways are essential to sustain genomic stability and play a critical role in cancer development and progression. Here, we investigated the profile of DDR gene mutations in early-stage non-small cell lung cancer (NSCLC) and their prognostic values. METHODS: We first examined 74 DDR genes involved in seven DDR pathways and then focused on six specific genes: ATM, BRCA1, BRCA2, CHEK1, BARD1, and BRIP1. A total of 179 stage I and IIIa NSCLC patients who received curative resection in Peking Union Medical College Hospital and their corresponding samples were collected for DNA sequencing, immunohistochemistry and survival analysis. RESULTS: A total of 167 eligible patients were finally analyzed. Mutation frequencies were 82% and 26.3% for the selected 74 genes and six genes, respectively. Mismatch repair (MMR) and nucleotide excision repair (NER) alterations were observed more frequently in lung squamous cell carcinoma (LUSC) and smokers were more likely to develop the selected six DDR gene mutations than those who never smoked. Deleterious mutations in the six genes were independent prognostic indicators of significantly longer disease-free survival and overall survival. No association was found between DDR gene status and PD-L1 expression, CD8 positive lymphocyte and tumor-associated macrophage infiltration in tumor area. However, numbers of mutations were significantly increased among patients with DDR alterations. CONCLUSIONS: Deleterious mutations of these six genes were common in resected NSCLC and could serve as prognostic biomarkers.

Molecular subtypes and prognostic models for predicting prognosis of lung adenocarcinoma based on miRNA-related genes.

BACKGROUND: MicroRNAs (miRNAs) are crucial in cancer development and progression, and therapies targeting miRNAs demonstrate great therapeutic promise. AIM: We sought to predict the prognosis and therapeutic response of lung adenocarcinoma (LUAD) by classifying molecular subtypes and constructing a prognostic model based on miRNA-related genes. METHOD: This study was based on miRNA-mRNA action pairs and ceRNA networks in the Cancer Genome Atlas (TCGA) database. Three molecular subtypes were determined based on 64 miRNA-associated target genes identified in the ceRNA network. The S3 subtype had the best prognosis, and the S2 subtype had the worst prognosis. The S2 subtype had a higher tumor mutational load (TMB) and a lower immune score. The S2 subtype was more suitable for immunotherapy and sensitive to chemotherapy. The least absolute shrinkage and selection operator (LASSO) algorithm was performed to determine eight miRNA-associated target genes for the construction of prognostic models. RESULT: High-risk patients had a poorer prognosis, lower immune score, and lower response to immunotherapy. Robustness was confirmed in the Gene-Expression Omnibus (GEO) database cohort (GSE31210, GSE50081, and GSE37745 datasets). Overall, our study deepened the understanding of the mechanism of miRNA-related target genes in LUAD and provided new ideas for classification. CONCLUSION: Such miRNA-associated target gene characterization could be useful for prognostic prediction and contribute to therapeutic decision-making in LUAD.

Development and validation of convolutional neural network-based model to predict the risk of sentinel or non-sentinel lymph node metastasis in patients with breast cancer: a machine learning study.

Background: For patients with sentinel lymph node (SLN) metastasis and low risk of residual non-SLN (NSLN) metastasis, axillary lymph node (ALN) dissection could lead to overtreatment. This study aimed to develop and validate an automated preoperative deep learning-based tool to predict the risk of SLN and NSLN metastasis in patients with breast cancer (BC) using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images. Methods: In this machine learning study, we retrospectively enrolled 988 women with BC from three hospitals in Zhejiang, China between June 1, 2013 to December 31, 2021, June 1, 2017 to December 31, 2021, and January 1, 2019 to June 30, 2023, respectively. Patients were divided into the training set (n = 519), internal validation set (n = 129), external test set 1 (n = 296), and external test set 2 (n = 44). A convolutional neural network (CNN) model was proposed to predict the SLN and NSLN metastasis and was compared with clinical and radiomics approaches. The performance of different models to detect ALN metastasis was measured by the area under the curve (AUC), accuracy, sensitivity, and specificity. This study is registered at ChiCTR, ChiCTR2300070740. Findings: For SLN prediction, the top-performing model (i.e., the CNN algorithm) achieved encouraging predictive performance in the internal validation set (AUC 0.899, 95% CI, 0.887-0.911), external test set 1 (AUC 0.885, 95% CI, 0.867-0.903), and external test set 2 (AUC 0.768, 95% CI, 0.738-0.798). For NSLN prediction, the CNN-based model also exhibited satisfactory performance in the internal validation set (AUC 0.800, 95% CI, 0.783-0.817), external test set 1 (AUC 0.763, 95% CI, 0.732-0.794), and external test set 2 (AUC 0.728, 95% CI, 0.719-0.738). Based on the subgroup analysis, the CNN model performed well in tumour group smaller than 2.0 cm, with the AUC of 0.801 (internal validation set) and 0.823 (external test set 1). Of 469 patients with BC, the false positive rate of SLN prediction declined from 77.9% to 32.9% using CNN model. Interpretation: The CNN model can predict the SLN status of any detectable lesion size and condition of NSLN in patients with BC. Overall, the CNN model, employing ready DCE-MRI images could serve as a potential technique to assist surgeons in the personalized axillary treatment of in patients with BC non-invasively. Funding: National Key Research and Development projects intergovernmental cooperation in science and technology of China, National Natural Science Foundation of China, Natural Science Foundation of Zhejiang Province, and Zhejiang Medical and Health Science Project.