Pub Date : 2024-05-23DOI: 10.3389/fonc.2024.1357898
K. Sreter, Maria Joana Pereira Catarata, Maximilian von Laffert, Armin Frille
Lung cancer remains the leading cause of cancer death globally. More than 50% of new cases are diagnosed in an advanced or metastatic stage, thus contributing to the poor survival of such patients. Mutations in the KRAS (Kirsten rat sarcoma virus) gene occur in nearly a third of lung adenocarcinoma and have for decades been deemed an ‘undruggable’ target. Yet, in recent years, a growing number of small molecules, such as the GTPase inhibitors, has been investigated in clinical trials of lung cancer patients harboring KRAS mutations, yielding promising results with improved outcomes. Currently, there are only two approved targeted therapies (adagrasib and sotorasib) for advanced or metastatic KRAS-mutated NSCLC from the second-line setting onwards. In this narrative review, we will focus on KRAS, its molecular basis, the role of its co-mutations, clinical evidence for its inhibition, putative mutation to resistance, and future strategies to overcome resistance to KRAS inhibition.
{"title":"Resistance to KRAS inhibition in advanced non-small cell lung cancer","authors":"K. Sreter, Maria Joana Pereira Catarata, Maximilian von Laffert, Armin Frille","doi":"10.3389/fonc.2024.1357898","DOIUrl":"https://doi.org/10.3389/fonc.2024.1357898","url":null,"abstract":"Lung cancer remains the leading cause of cancer death globally. More than 50% of new cases are diagnosed in an advanced or metastatic stage, thus contributing to the poor survival of such patients. Mutations in the KRAS (Kirsten rat sarcoma virus) gene occur in nearly a third of lung adenocarcinoma and have for decades been deemed an ‘undruggable’ target. Yet, in recent years, a growing number of small molecules, such as the GTPase inhibitors, has been investigated in clinical trials of lung cancer patients harboring KRAS mutations, yielding promising results with improved outcomes. Currently, there are only two approved targeted therapies (adagrasib and sotorasib) for advanced or metastatic KRAS-mutated NSCLC from the second-line setting onwards. In this narrative review, we will focus on KRAS, its molecular basis, the role of its co-mutations, clinical evidence for its inhibition, putative mutation to resistance, and future strategies to overcome resistance to KRAS inhibition.","PeriodicalId":507440,"journal":{"name":"Frontiers in Oncology","volume":"43 26","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141103527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-23DOI: 10.3389/fonc.2024.1361694
Haiqin Xie, Yudi Zhang, Licong Dong, Heng Lv, Xuechen Li, Chenyang Zhao, Yun Tian, Lu Xie, Wangjie Wu, Qi Yang, Li Liu, Desheng Sun, Li Qiu, Linlin Shen, Yusen Zhang
Soft tissue tumors (STTs) are benign or malignant superficial neoplasms arising from soft tissues throughout the body with versatile pathological types. Although Ultrasonography (US) is one of the most common imaging tools to diagnose malignant STTs, it still has several drawbacks in STT diagnosis that need improving.The study aims to establish this deep learning (DL) driven Artificial intelligence (AI) system for predicting malignant STTs based on US images and clinical indexes of the patients.We retrospectively enrolled 271 malignant and 462 benign masses to build the AI system using 5-fold validation. A prospective dataset of 44 malignant masses and 101 benign masses was used to validate the accuracy of system. A multi-data fusion convolutional neural network, named ultrasound clinical soft tissue tumor net (UC-STTNet), was developed to combine gray scale and color Doppler US images and clinic features for malignant STTs diagnosis. Six radiologists (R1-R6) with three experience levels were invited for reader study.The AI system achieved an area under receiver operating curve (AUC) value of 0.89 in the retrospective dataset. The diagnostic performance of the AI system was higher than that of one of the senior radiologists (AUC of AI vs R2: 0.89 vs. 0.84, p=0.022) and all of the intermediate and junior radiologists (AUC of AI vs R3, R4, R5, R6: 0.89 vs 0.75, 0.81, 0.80, 0.63; p <0.01). The AI system also achieved an AUC of 0.85 in the prospective dataset. With the assistance of the system, the diagnostic performances and inter-observer agreement of the radiologists was improved (AUC of R3, R5, R6: 0.75 to 0.83, 0.80 to 0.85, 0.63 to 0.69; p<0.01).The AI system could be a useful tool in diagnosing malignant STTs, and could also help radiologists improve diagnostic performance.
{"title":"Deep learning driven diagnosis of malignant soft tissue tumors based on dual-modal ultrasound images and clinical indexes","authors":"Haiqin Xie, Yudi Zhang, Licong Dong, Heng Lv, Xuechen Li, Chenyang Zhao, Yun Tian, Lu Xie, Wangjie Wu, Qi Yang, Li Liu, Desheng Sun, Li Qiu, Linlin Shen, Yusen Zhang","doi":"10.3389/fonc.2024.1361694","DOIUrl":"https://doi.org/10.3389/fonc.2024.1361694","url":null,"abstract":"Soft tissue tumors (STTs) are benign or malignant superficial neoplasms arising from soft tissues throughout the body with versatile pathological types. Although Ultrasonography (US) is one of the most common imaging tools to diagnose malignant STTs, it still has several drawbacks in STT diagnosis that need improving.The study aims to establish this deep learning (DL) driven Artificial intelligence (AI) system for predicting malignant STTs based on US images and clinical indexes of the patients.We retrospectively enrolled 271 malignant and 462 benign masses to build the AI system using 5-fold validation. A prospective dataset of 44 malignant masses and 101 benign masses was used to validate the accuracy of system. A multi-data fusion convolutional neural network, named ultrasound clinical soft tissue tumor net (UC-STTNet), was developed to combine gray scale and color Doppler US images and clinic features for malignant STTs diagnosis. Six radiologists (R1-R6) with three experience levels were invited for reader study.The AI system achieved an area under receiver operating curve (AUC) value of 0.89 in the retrospective dataset. The diagnostic performance of the AI system was higher than that of one of the senior radiologists (AUC of AI vs R2: 0.89 vs. 0.84, p=0.022) and all of the intermediate and junior radiologists (AUC of AI vs R3, R4, R5, R6: 0.89 vs 0.75, 0.81, 0.80, 0.63; p <0.01). The AI system also achieved an AUC of 0.85 in the prospective dataset. With the assistance of the system, the diagnostic performances and inter-observer agreement of the radiologists was improved (AUC of R3, R5, R6: 0.75 to 0.83, 0.80 to 0.85, 0.63 to 0.69; p<0.01).The AI system could be a useful tool in diagnosing malignant STTs, and could also help radiologists improve diagnostic performance.","PeriodicalId":507440,"journal":{"name":"Frontiers in Oncology","volume":"31 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141106317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-23DOI: 10.3389/fonc.2024.1381354
Serafin Castellano-Damaso, Felisa Vázquez-Gómez, Jose Luis Moreno-Carrasco, Begoña Arce, Pedro Borrego, Alvaro Lassaletta
Dissemination in pediatric low-grade glioma may occur in about 4%–10% of patients according to retrospective cohort studies. Due to its low incidence, there is no consensus on treatment for these patients. According to the constitutional activation of the MAPK/ERK pathway in these tumors, MEK inhibitors such as trametinib have been used successfully in the relapsed setting. Skin toxicity is frequent in patients receiving trametinib, normally mild to moderate, but sometimes severe, needing to discontinue the drug, limiting the efficacy in the tumor. There is not much information in the literature regarding whether reducing the dose of trametinib is able to maintain efficacy while, at the same time, decreasing toxicity. Here, we present an adolescent, with severe skin toxicity, whose trametinib dose was reduced by 50% and efficacy on the tumor continued while skin toxicity significantly decreased.
{"title":"Continuous response despite reduced dose of trametinib as single agent in an adolescent with a relapsed disseminated pediatric low-grade glioma KIAA1549-BRAF fusion positive: a case report and review of the literature","authors":"Serafin Castellano-Damaso, Felisa Vázquez-Gómez, Jose Luis Moreno-Carrasco, Begoña Arce, Pedro Borrego, Alvaro Lassaletta","doi":"10.3389/fonc.2024.1381354","DOIUrl":"https://doi.org/10.3389/fonc.2024.1381354","url":null,"abstract":"Dissemination in pediatric low-grade glioma may occur in about 4%–10% of patients according to retrospective cohort studies. Due to its low incidence, there is no consensus on treatment for these patients. According to the constitutional activation of the MAPK/ERK pathway in these tumors, MEK inhibitors such as trametinib have been used successfully in the relapsed setting. Skin toxicity is frequent in patients receiving trametinib, normally mild to moderate, but sometimes severe, needing to discontinue the drug, limiting the efficacy in the tumor. There is not much information in the literature regarding whether reducing the dose of trametinib is able to maintain efficacy while, at the same time, decreasing toxicity. Here, we present an adolescent, with severe skin toxicity, whose trametinib dose was reduced by 50% and efficacy on the tumor continued while skin toxicity significantly decreased.","PeriodicalId":507440,"journal":{"name":"Frontiers in Oncology","volume":"18 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141105062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The purpose of this retrospective study was to establish a combined model based on ultrasound (US)-radiomics and clinical factors to predict patients with stage I cervical cancer (CC) before surgery.A total of 209 CC patients who had cervical lesions found by transvaginal sonography (TVS) from the First Affiliated Hospital of Anhui Medical University were retrospectively reviewed, patients were divided into the training set (n = 146) and internal validation set (n = 63), and 52 CC patients from Anhui Provincial Maternity and Child Health Hospital and Nanchong Central Hospital were taken as the external validation set. The clinical independent predictors were selected by univariate and multivariate logistic regression analyses. US-radiomics features were extracted from US images. After selecting the most significant features by univariate analysis, Spearman’s correlation analysis, and the least absolute shrinkage and selection operator (LASSO) algorithm, six machine learning (ML) algorithms were used to build the radiomics model. Next, the ability of the clinical, US-radiomics, and clinical US-radiomics combined model was compared to diagnose stage I CC. Finally, the Shapley additive explanations (SHAP) method was used to explain the contribution of each feature.Long diameter of the cervical lesion (L) and squamous cell carcinoma-associated antigen (SCCa) were independent clinical predictors of stage I CC. The eXtreme Gradient Boosting (Xgboost) model performed the best among the six ML radiomics models, with area under the curve (AUC) values in the training, internal validation, and external validation sets being 0.778, 0.751, and 0.751, respectively. In the final three models, the combined model based on clinical features and rad-score showed good discriminative power, with AUC values in the training, internal validation, and external validation sets being 0.837, 0.828, and 0.839, respectively. The decision curve analysis validated the clinical utility of the combined nomogram. The SHAP algorithm illustrates the contribution of each feature in the combined model.We established an interpretable combined model to predict stage I CC. This non-invasive prediction method may be used for the preoperative identification of patients with stage I CC.
这项回顾性研究的目的是建立一个基于超声（US）放射组学和临床因素的联合模型，以预测宫颈癌（CC）I期患者的术前情况。研究回顾性分析了安徽医科大学第一附属医院经阴道超声检查（TVS）发现宫颈病变的209例CC患者，将患者分为训练集（146例）和内部验证集（63例），并将安徽省妇幼保健院和南充市中心医院的52例CC患者作为外部验证集。通过单变量和多变量逻辑回归分析筛选出临床独立预测因子。从 US 图像中提取 US 放射组学特征。通过单变量分析、斯皮尔曼相关性分析和最小绝对收缩和选择算子（LASSO）算法筛选出最重要的特征后，使用六种机器学习（ML）算法建立放射组学模型。接下来，比较了临床模型、US-放射组学模型和临床US-放射组学组合模型诊断I期CC的能力。宫颈病变长径（L）和鳞状细胞癌相关抗原（SCCa）是 I 期 CC 的独立临床预测指标。在六个ML放射组学模型中，eXtreme Gradient Boosting（Xgboost）模型表现最佳，其训练集、内部验证集和外部验证集的曲线下面积（AUC）值分别为0.778、0.751和0.751。在最后的三个模型中，基于临床特征和 rad-score 的组合模型显示出良好的判别能力，训练集、内部验证集和外部验证集的 AUC 值分别为 0.837、0.828 和 0.839。决策曲线分析验证了组合提名图的临床实用性。我们建立了一个可解释的综合模型来预测 I 期 CC。这种无创预测方法可用于术前识别 I 期 CC 患者。
{"title":"An interpretable clinical ultrasound-radiomics combined model for diagnosis of stage I cervical cancer","authors":"Xianyue Yang, Chuanfen Gao, Nian Sun, X. Qin, Xiaoling Liu, Chaoxue Zhang","doi":"10.3389/fonc.2024.1353780","DOIUrl":"https://doi.org/10.3389/fonc.2024.1353780","url":null,"abstract":"The purpose of this retrospective study was to establish a combined model based on ultrasound (US)-radiomics and clinical factors to predict patients with stage I cervical cancer (CC) before surgery.A total of 209 CC patients who had cervical lesions found by transvaginal sonography (TVS) from the First Affiliated Hospital of Anhui Medical University were retrospectively reviewed, patients were divided into the training set (n = 146) and internal validation set (n = 63), and 52 CC patients from Anhui Provincial Maternity and Child Health Hospital and Nanchong Central Hospital were taken as the external validation set. The clinical independent predictors were selected by univariate and multivariate logistic regression analyses. US-radiomics features were extracted from US images. After selecting the most significant features by univariate analysis, Spearman’s correlation analysis, and the least absolute shrinkage and selection operator (LASSO) algorithm, six machine learning (ML) algorithms were used to build the radiomics model. Next, the ability of the clinical, US-radiomics, and clinical US-radiomics combined model was compared to diagnose stage I CC. Finally, the Shapley additive explanations (SHAP) method was used to explain the contribution of each feature.Long diameter of the cervical lesion (L) and squamous cell carcinoma-associated antigen (SCCa) were independent clinical predictors of stage I CC. The eXtreme Gradient Boosting (Xgboost) model performed the best among the six ML radiomics models, with area under the curve (AUC) values in the training, internal validation, and external validation sets being 0.778, 0.751, and 0.751, respectively. In the final three models, the combined model based on clinical features and rad-score showed good discriminative power, with AUC values in the training, internal validation, and external validation sets being 0.837, 0.828, and 0.839, respectively. The decision curve analysis validated the clinical utility of the combined nomogram. The SHAP algorithm illustrates the contribution of each feature in the combined model.We established an interpretable combined model to predict stage I CC. This non-invasive prediction method may be used for the preoperative identification of patients with stage I CC.","PeriodicalId":507440,"journal":{"name":"Frontiers in Oncology","volume":"120 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141105451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-23DOI: 10.3389/fonc.2024.1431520
Chenchen Niu, Dong Ren, Bella Lingjia Liu
{"title":"Editorial: Novel therapeutic targets of gastric carcinogenesis: from basic research to drug development and clinical application","authors":"Chenchen Niu, Dong Ren, Bella Lingjia Liu","doi":"10.3389/fonc.2024.1431520","DOIUrl":"https://doi.org/10.3389/fonc.2024.1431520","url":null,"abstract":"","PeriodicalId":507440,"journal":{"name":"Frontiers in Oncology","volume":"58 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141102887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-23DOI: 10.3389/fonc.2024.1366449
Qingliu He, Chengcheng Wei, Li Cao, Pu Zhang, Zhuang Wei, Fangzhen Cai
Kidney cancer is a prevalent malignancy with an increasing incidence worldwide. Blood cell indices and inflammation-related markers have shown huge potential as biomarkers for predicting cancer incidences, but that is not clear in kidney cancer. Our study aims to investigate the correlations of blood cell indices and inflammation-related markers with kidney cancer risk.We performed a population-based cohort prospective analysis using data from the UK Biobank. A total of 466,994 participants, free of kidney cancer at baseline, were included in the analysis. The hazard ratios (HRs) and 95% confidence intervals (CIs) for kidney cancer risk were calculated using Cox proportional hazards regression models. Restricted cubic spline models were used to investigate nonlinear longitudinal associations. Stratified analyses were used to identify high-risk populations. The results were validated through sensitivity analyses.During a mean follow-up of 12.4 years, 1,710 of 466,994 participants developed kidney cancer. The Cox regression models showed that 13 blood cell indices and four inflammation-related markers were associated with kidney cancer incidence. The restricted cubic spline models showed non-linear relationships with kidney cancer. Finally, combined with stratified and sensitivity analyses, we found that the mean corpuscular hemoglobin concentration (MCHC), red blood cell distribution width (RDW), platelet distribution width (PDW), systemic immune-inflammation index (SII), and product of platelet count and neutrophil count (PPN) were related to enhanced kidney cancer risk with stable results.Our findings identified that three blood cell indices (MCHC, RDW, and PDW) and two inflammation-related markers (SII and PPN) were independent risk factors for the incidence of kidney cancer. These indexes may serve as potential predictors for kidney cancer and aid in the development of targeted screening strategies for at-risk individuals.
{"title":"Blood cell indices and inflammation-related markers with kidney cancer risk: a large-population prospective analysis in UK Biobank","authors":"Qingliu He, Chengcheng Wei, Li Cao, Pu Zhang, Zhuang Wei, Fangzhen Cai","doi":"10.3389/fonc.2024.1366449","DOIUrl":"https://doi.org/10.3389/fonc.2024.1366449","url":null,"abstract":"Kidney cancer is a prevalent malignancy with an increasing incidence worldwide. Blood cell indices and inflammation-related markers have shown huge potential as biomarkers for predicting cancer incidences, but that is not clear in kidney cancer. Our study aims to investigate the correlations of blood cell indices and inflammation-related markers with kidney cancer risk.We performed a population-based cohort prospective analysis using data from the UK Biobank. A total of 466,994 participants, free of kidney cancer at baseline, were included in the analysis. The hazard ratios (HRs) and 95% confidence intervals (CIs) for kidney cancer risk were calculated using Cox proportional hazards regression models. Restricted cubic spline models were used to investigate nonlinear longitudinal associations. Stratified analyses were used to identify high-risk populations. The results were validated through sensitivity analyses.During a mean follow-up of 12.4 years, 1,710 of 466,994 participants developed kidney cancer. The Cox regression models showed that 13 blood cell indices and four inflammation-related markers were associated with kidney cancer incidence. The restricted cubic spline models showed non-linear relationships with kidney cancer. Finally, combined with stratified and sensitivity analyses, we found that the mean corpuscular hemoglobin concentration (MCHC), red blood cell distribution width (RDW), platelet distribution width (PDW), systemic immune-inflammation index (SII), and product of platelet count and neutrophil count (PPN) were related to enhanced kidney cancer risk with stable results.Our findings identified that three blood cell indices (MCHC, RDW, and PDW) and two inflammation-related markers (SII and PPN) were independent risk factors for the incidence of kidney cancer. These indexes may serve as potential predictors for kidney cancer and aid in the development of targeted screening strategies for at-risk individuals.","PeriodicalId":507440,"journal":{"name":"Frontiers in Oncology","volume":"29 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141104047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-23DOI: 10.3389/fonc.2024.1399693
Hua Feng, Shuxian Shang, Kun Chen, Xuan Sun, Xueping Yue
There is evidence of a modest reduction in skin cancer risk among metformin users. However, no studies have further examined the effects of metformin on melanoma survival and safety outcomes. This study aimed to quantitatively summarize any influence of metformin on the overall survival (OS) and immune-related adverse effects (irAEs) in melanoma patients.Selection criteria: The inclusion criteria were designed based on the PICOS principles. Information sources: PubMed, EMBASE, Cochrane Library, and Web of Science were searched for relevant literature published from the inception of these databases until November 2023 using ‘Melanoma’ and ‘Metformin’ as keywords. Survival outcomes were OS, progression-free survival (PFS), recurrence-free survival (RFS), and mortality; the safety outcome was irAEs. Risk of bias and data Synthesis: The Cochrane tool for assessing the risk of bias in randomized trial 2 (RoB2) and methodological index for non-randomized studies (MINORS) were selected to assess the risk of bias. The Cochrane Q and I2 statistics based on Stata 15.1 SE were used to test the heterogeneity among all studies. Funnel plot, Egger regression, and Begg tests were used to evaluate publication bias. The leave-one-out method was selected as the sensitivity analysis tool.A total of 12 studies were included, involving 111,036 melanoma patients. The pooled HR for OS was 0.64 (95% CI [0.42, 1.00], p = 0.004, I2 = 73.7%), HR for PFS was 0.89 (95% CI [0.70, 1.12], p = 0.163, I2 = 41.4%), HR for RFS was 0.62 (95% CI [0.26, 1.48], p = 0.085, I2 = 66.3%), and HR for mortality was 0.53 (95% CI [0.46, 0.63], p = 0.775, I2 = 0.0%). There was no significant difference in irAEs incidence (OR = 1.01; 95% CI [0.42, 2.41]; p = 0.642) between metformin and no metformin groups.The improvement in overall survival of melanoma patients with metformin may indirectly result from its diverse biological targets and beneficial effects on multiple systemic diseases. While we could not demonstrate a specific improvement in the survival of melanoma patients, the combined benefits and safety of metformin for patients taking the drug are worthy of recognition.https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42024518182.
有证据表明，二甲双胍使用者患皮肤癌的风险略有降低。然而，目前还没有研究进一步探讨二甲双胍对黑色素瘤生存率和安全性结果的影响。本研究旨在定量总结二甲双胍对黑色素瘤患者总生存期（OS）和免疫相关不良反应（irAEs）的影响：纳入标准根据 PICOS 原则设计。信息来源以 "黑色素瘤 "和 "二甲双胍 "为关键词，检索了 PubMed、EMBASE、Cochrane Library 和 Web of Science 等数据库从建立之初到 2023 年 11 月期间发表的相关文献。生存期结果为OS、无进展生存期（PFS）、无复发生存期（RFS）和死亡率；安全性结果为irAEs。偏倚风险与数据综合：评估偏倚风险时选用了Cochrane随机试验2偏倚风险评估工具（RoB2）和非随机研究方法学指数（MINORS）。使用基于 Stata 15.1 SE 的 Cochrane Q 和 I2 统计量来检验所有研究之间的异质性。漏斗图、Egger 回归和 Begg 检验用于评估发表偏倚。共纳入12项研究，涉及111036名黑色素瘤患者。汇总的OS HR为0.64（95% CI [0.42，1.00]，p = 0.004，I2 = 73.7%），PFS HR为0.89（95% CI [0.70，1.12]，p = 0.163，I2 = 41.4%），RFS 的 HR 为 0.62（95% CI [0.26，1.48]，p = 0.085，I2 = 66.3%），死亡率的 HR 为 0.53（95% CI [0.46，0.63]，p = 0.775，I2 = 0.0%）。二甲双胍组和未使用二甲双胍组的irAEs发生率无明显差异（OR = 1.01；95% CI [0.42，2.41]；p = 0.642）。虽然我们无法证明二甲双胍对黑色素瘤患者生存率的具体改善作用，但二甲双胍对服药患者的综合益处和安全性是值得肯定的。https://www.crd.york.ac.uk/PROSPERO/，标识符为 CRD42024518182。
{"title":"Impact of metformin on melanoma: a meta-analysis and systematic review","authors":"Hua Feng, Shuxian Shang, Kun Chen, Xuan Sun, Xueping Yue","doi":"10.3389/fonc.2024.1399693","DOIUrl":"https://doi.org/10.3389/fonc.2024.1399693","url":null,"abstract":"There is evidence of a modest reduction in skin cancer risk among metformin users. However, no studies have further examined the effects of metformin on melanoma survival and safety outcomes. This study aimed to quantitatively summarize any influence of metformin on the overall survival (OS) and immune-related adverse effects (irAEs) in melanoma patients.Selection criteria: The inclusion criteria were designed based on the PICOS principles. Information sources: PubMed, EMBASE, Cochrane Library, and Web of Science were searched for relevant literature published from the inception of these databases until November 2023 using ‘Melanoma’ and ‘Metformin’ as keywords. Survival outcomes were OS, progression-free survival (PFS), recurrence-free survival (RFS), and mortality; the safety outcome was irAEs. Risk of bias and data Synthesis: The Cochrane tool for assessing the risk of bias in randomized trial 2 (RoB2) and methodological index for non-randomized studies (MINORS) were selected to assess the risk of bias. The Cochrane Q and I2 statistics based on Stata 15.1 SE were used to test the heterogeneity among all studies. Funnel plot, Egger regression, and Begg tests were used to evaluate publication bias. The leave-one-out method was selected as the sensitivity analysis tool.A total of 12 studies were included, involving 111,036 melanoma patients. The pooled HR for OS was 0.64 (95% CI [0.42, 1.00], p = 0.004, I2 = 73.7%), HR for PFS was 0.89 (95% CI [0.70, 1.12], p = 0.163, I2 = 41.4%), HR for RFS was 0.62 (95% CI [0.26, 1.48], p = 0.085, I2 = 66.3%), and HR for mortality was 0.53 (95% CI [0.46, 0.63], p = 0.775, I2 = 0.0%). There was no significant difference in irAEs incidence (OR = 1.01; 95% CI [0.42, 2.41]; p = 0.642) between metformin and no metformin groups.The improvement in overall survival of melanoma patients with metformin may indirectly result from its diverse biological targets and beneficial effects on multiple systemic diseases. While we could not demonstrate a specific improvement in the survival of melanoma patients, the combined benefits and safety of metformin for patients taking the drug are worthy of recognition.https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42024518182.","PeriodicalId":507440,"journal":{"name":"Frontiers in Oncology","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141103185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-23DOI: 10.3389/fonc.2024.1379013
Y. Hoshi, T. Enokida, Shingo Tamura, Torahiko Nakashima, S. Okano, T. Fujisawa, Masanobu Sato, A. Wada, Hideki Tanaka, N. Takeshita, N. Tanaka, Ryutaro Onaga, T. Kishida, H. Uryu, Shingo Sakashita, Takahiro Asakage, Makoto Tahara
Olfactory neuroblastoma (ONB) is a rare malignant tumor of the head and neck. Due to its rarity, standard systemic therapy for this condition has yet to be established. In particular, the use of immune checkpoint inhibitors (ICIs) for the recurrent or metastatic (R/M) ONB population remains unclear.We retrospectively evaluated 11 patients with R/M ONB who received any systemic chemotherapy at two Japanese institutions (National Cancer Center Hospital East and Kyushu Medical Center) between January 2002 and March 2022 and analyzed outcomes by use of anti-PD-1 antibody (nivolumab or pembrolizumab) monotherapy.Of the 11 patients, 6 received ICI (ICI-containing treatment group) and the remaining 5 were treated with systemic therapy but not including ICI (ICI-non-containing treatment group). Overall survival (OS) was significantly longer in the ICI-containing group (median OS: not reached vs. 6.4 months, log-rank p-value: 0.035). The fraction of ICI systemic therapy in the entire treatment period of this group reached 85.9%. Four patients (66.7%) in the ICI-containing treatment group experienced immune-related adverse events (irAE), with grades of 1/2. No irAE of grade 3 or more was seen, and no patient required interruption or discontinuation of treatment due to toxicity.ICI monotherapy appears to be effective and to contribute to prolonged survival in R/M ONB.
嗅神经母细胞瘤（ONB）是一种罕见的头颈部恶性肿瘤。由于其罕见性，该病的标准系统疗法尚未确立。我们回顾性评估了2002年1月至2022年3月期间在日本两家机构（国立癌症中心东医院和九州医疗中心）接受过任何系统化疗的11例R/M ONB患者，并通过使用抗PD-1抗体（nivolumab或pembrolizumab）单药治疗分析了疗效。在11名患者中，6人接受了ICI治疗（含ICI治疗组），其余5人接受了全身治疗，但不包括ICI（不含ICI治疗组）。含 ICI 治疗组的总生存期（OS）明显更长（中位 OS：未达 6.4 个月 vs. 6.4 个月，log-rank p 值：0.035）。该组患者在整个治疗期间接受 ICI 系统治疗的比例达到 85.9%。含 ICI 治疗组中有四名患者（66.7%）出现了免疫相关不良事件（irAE），级别为 1/2。ICI单药治疗似乎有效，并有助于延长R/M ONB的生存期。
{"title":"Efficacy of anti-PD-1 monotherapy for recurrent or metastatic olfactory neuroblastoma","authors":"Y. Hoshi, T. Enokida, Shingo Tamura, Torahiko Nakashima, S. Okano, T. Fujisawa, Masanobu Sato, A. Wada, Hideki Tanaka, N. Takeshita, N. Tanaka, Ryutaro Onaga, T. Kishida, H. Uryu, Shingo Sakashita, Takahiro Asakage, Makoto Tahara","doi":"10.3389/fonc.2024.1379013","DOIUrl":"https://doi.org/10.3389/fonc.2024.1379013","url":null,"abstract":"Olfactory neuroblastoma (ONB) is a rare malignant tumor of the head and neck. Due to its rarity, standard systemic therapy for this condition has yet to be established. In particular, the use of immune checkpoint inhibitors (ICIs) for the recurrent or metastatic (R/M) ONB population remains unclear.We retrospectively evaluated 11 patients with R/M ONB who received any systemic chemotherapy at two Japanese institutions (National Cancer Center Hospital East and Kyushu Medical Center) between January 2002 and March 2022 and analyzed outcomes by use of anti-PD-1 antibody (nivolumab or pembrolizumab) monotherapy.Of the 11 patients, 6 received ICI (ICI-containing treatment group) and the remaining 5 were treated with systemic therapy but not including ICI (ICI-non-containing treatment group). Overall survival (OS) was significantly longer in the ICI-containing group (median OS: not reached vs. 6.4 months, log-rank p-value: 0.035). The fraction of ICI systemic therapy in the entire treatment period of this group reached 85.9%. Four patients (66.7%) in the ICI-containing treatment group experienced immune-related adverse events (irAE), with grades of 1/2. No irAE of grade 3 or more was seen, and no patient required interruption or discontinuation of treatment due to toxicity.ICI monotherapy appears to be effective and to contribute to prolonged survival in R/M ONB.","PeriodicalId":507440,"journal":{"name":"Frontiers in Oncology","volume":"25 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141104422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-23DOI: 10.3389/fonc.2024.1325157
Silvia Galbiati, A. Bettiga, G. Colciago, C. Senti, Francesco Trevisani, Giulia Villa, Ilaria Marzinotto, Michele Ghidini, R. Passalacqua, Francesco Montorsi, A. Salonia, R. Vago
Urothelial Bladder Cancer (BC) is the ninth most common cancer worldwide. It is classified into Non Muscle Invasive (NMIBC) and Muscle Invasive Bladder Cancer (MIBC), which are characterized by frequent recurrences and progression rate, respectively. The diagnosis and monitoring are obtained through invasive methods as cystoscopy and post-surgery biopsies. Thus, a panel of biomarkers able to discriminate BC based on grading or staging represents a significant step forward in the patients’ workup. In this perspective, long non-coding RNAs (lncRNAs) are emerged as reliable candidates as potential biomarker given their specific and regulated expression. In the present work we propose two lncRNAs, the Small Ubiquitin Modifier 1 pseudogene 3 (SUMO1P3), a poorly characterized pseudogene, and the Urothelial Carcinoma Associated 1 (UCA1) as candidates to monitor the BC progression.This study was a retrospective trial enrolling NMIBC and MIBC patients undergoing surgical intervention: the expression of the lncRNA SUMO1P3 and UCA1 was evaluated in urine from 113 subjects (cases and controls). The receiver operating characteristic curve analysis was used to evaluate the performance of single or combined biomarkers in discriminating cases from controls.SUMO1P3 and UCA1 expression in urine was able to significantly discriminate low grade NMIBC, healthy control and benign prostatic hyperplasia subjects versus high grade NMIBC and MIBC patients. We also demonstrated that miR-320a, which binds SUMO1P3, was reduced in high grade NMIBC and MIBC patients and the SUMO1P3/miR-320a ratio was used to differentiate cases versus controls, showing a statistically significant power. Finally, we provided an automated method of RNA extraction coupled to ddPCR analysis in a perspective of clinical application.We have shown that the lncRNA SUMO1P3 is increased in urine from patients with high grade NMIBC and MIBC and that it is likely to be good candidate to predict bladder cancer progression if used alone or in combination with UCA1 or with miRNA320a.
{"title":"The long noncoding RNA SUMO1P3 as urinary biomarker for monitoring bladder cancer progression","authors":"Silvia Galbiati, A. Bettiga, G. Colciago, C. Senti, Francesco Trevisani, Giulia Villa, Ilaria Marzinotto, Michele Ghidini, R. Passalacqua, Francesco Montorsi, A. Salonia, R. Vago","doi":"10.3389/fonc.2024.1325157","DOIUrl":"https://doi.org/10.3389/fonc.2024.1325157","url":null,"abstract":"Urothelial Bladder Cancer (BC) is the ninth most common cancer worldwide. It is classified into Non Muscle Invasive (NMIBC) and Muscle Invasive Bladder Cancer (MIBC), which are characterized by frequent recurrences and progression rate, respectively. The diagnosis and monitoring are obtained through invasive methods as cystoscopy and post-surgery biopsies. Thus, a panel of biomarkers able to discriminate BC based on grading or staging represents a significant step forward in the patients’ workup. In this perspective, long non-coding RNAs (lncRNAs) are emerged as reliable candidates as potential biomarker given their specific and regulated expression. In the present work we propose two lncRNAs, the Small Ubiquitin Modifier 1 pseudogene 3 (SUMO1P3), a poorly characterized pseudogene, and the Urothelial Carcinoma Associated 1 (UCA1) as candidates to monitor the BC progression.This study was a retrospective trial enrolling NMIBC and MIBC patients undergoing surgical intervention: the expression of the lncRNA SUMO1P3 and UCA1 was evaluated in urine from 113 subjects (cases and controls). The receiver operating characteristic curve analysis was used to evaluate the performance of single or combined biomarkers in discriminating cases from controls.SUMO1P3 and UCA1 expression in urine was able to significantly discriminate low grade NMIBC, healthy control and benign prostatic hyperplasia subjects versus high grade NMIBC and MIBC patients. We also demonstrated that miR-320a, which binds SUMO1P3, was reduced in high grade NMIBC and MIBC patients and the SUMO1P3/miR-320a ratio was used to differentiate cases versus controls, showing a statistically significant power. Finally, we provided an automated method of RNA extraction coupled to ddPCR analysis in a perspective of clinical application.We have shown that the lncRNA SUMO1P3 is increased in urine from patients with high grade NMIBC and MIBC and that it is likely to be good candidate to predict bladder cancer progression if used alone or in combination with UCA1 or with miRNA320a.","PeriodicalId":507440,"journal":{"name":"Frontiers in Oncology","volume":"116 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141105643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-23DOI: 10.3389/fonc.2024.1383096
Zhu Xu, Qin Wang, Yiyao Zhang, Xiaolan Li, Mei Wang, Yuhong Zhang, Yaxin Pei, Kezhen Li, Man Yang, Liping Luo, Chuan Wu, Weidong Wang
Tertiary lymphoid structures (TLS) is a particular component of tumor microenvironment (TME). However, its biological mechanisms in colorectal cancer (CRC) have not yet been understood. We desired to reveal the TLS gene signature in CRC and evaluate its role in prognosis and immunotherapy response.The data was sourced from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Based on TLS-related genes (TRGs), the TLS related subclusters were identified through unsupervised clustering. The TME between subclusters were evaluated by CIBERSORT and xCell. Subsequently, developing a risk model and conducting external validation. Integrating risk score and clinical characteristics to create a comprehensive nomogram. Further analyses were conducted to screen TLS-related hub genes and explore the relationship between hub genes, TME, and biological processes, using random forest analysis, enrichment and variation analysis, and competing endogenous RNA (ceRNA) network analysis. Multiple immunofluorescence (mIF) and immunohistochemistry (IHC) were employed to characterize the existence of TLS and the expression of hub gene.Two subclusters that enriched or depleted in TLS were identified. The two subclusters had distinct prognoses, clinical characteristics, and tumor immune infiltration. We established a TLS-related prognostic risk model including 14 genes and validated its predictive power in two external datasets. The model’s AUC values for 1-, 3-, and 5-year overall survival (OS) were 0.704, 0.737, and 0.746. The low-risk group had a superior survival rate, more abundant infiltration of immune cells, lower tumor immune dysfunction and exclusion (TIDE) score, and exhibited better immunotherapy efficacy. In addition, we selected the top important features within the model: VSIG4, SELL and PRRX1. Enrichment analysis showed that the hub genes significantly affected signaling pathways related to TLS and tumor progression. The ceRNA network: PRRX1-miRNA (hsa-miR-20a-5p, hsa-miR-485–5p) -lncRNA has been discovered. Finally, IHC and mIF results confirmed that the expression level of PRRX1 was markedly elevated in the TLS- CRC group.We conducted a study to thoroughly describe TLS gene signature in CRC. The TLS-related risk model was applicable for prognostic prediction and assessment of immunotherapy efficacy. The TLS-hub gene PRRX1, which had the potential to function as an immunomodulatory factor of TLS, could be a therapeutic target for CRC.
{"title":"Exploiting tertiary lymphoid structures gene signature to evaluate tumor microenvironment infiltration and immunotherapy response in colorectal cancer","authors":"Zhu Xu, Qin Wang, Yiyao Zhang, Xiaolan Li, Mei Wang, Yuhong Zhang, Yaxin Pei, Kezhen Li, Man Yang, Liping Luo, Chuan Wu, Weidong Wang","doi":"10.3389/fonc.2024.1383096","DOIUrl":"https://doi.org/10.3389/fonc.2024.1383096","url":null,"abstract":"Tertiary lymphoid structures (TLS) is a particular component of tumor microenvironment (TME). However, its biological mechanisms in colorectal cancer (CRC) have not yet been understood. We desired to reveal the TLS gene signature in CRC and evaluate its role in prognosis and immunotherapy response.The data was sourced from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Based on TLS-related genes (TRGs), the TLS related subclusters were identified through unsupervised clustering. The TME between subclusters were evaluated by CIBERSORT and xCell. Subsequently, developing a risk model and conducting external validation. Integrating risk score and clinical characteristics to create a comprehensive nomogram. Further analyses were conducted to screen TLS-related hub genes and explore the relationship between hub genes, TME, and biological processes, using random forest analysis, enrichment and variation analysis, and competing endogenous RNA (ceRNA) network analysis. Multiple immunofluorescence (mIF) and immunohistochemistry (IHC) were employed to characterize the existence of TLS and the expression of hub gene.Two subclusters that enriched or depleted in TLS were identified. The two subclusters had distinct prognoses, clinical characteristics, and tumor immune infiltration. We established a TLS-related prognostic risk model including 14 genes and validated its predictive power in two external datasets. The model’s AUC values for 1-, 3-, and 5-year overall survival (OS) were 0.704, 0.737, and 0.746. The low-risk group had a superior survival rate, more abundant infiltration of immune cells, lower tumor immune dysfunction and exclusion (TIDE) score, and exhibited better immunotherapy efficacy. In addition, we selected the top important features within the model: VSIG4, SELL and PRRX1. Enrichment analysis showed that the hub genes significantly affected signaling pathways related to TLS and tumor progression. The ceRNA network: PRRX1-miRNA (hsa-miR-20a-5p, hsa-miR-485–5p) -lncRNA has been discovered. Finally, IHC and mIF results confirmed that the expression level of PRRX1 was markedly elevated in the TLS- CRC group.We conducted a study to thoroughly describe TLS gene signature in CRC. The TLS-related risk model was applicable for prognostic prediction and assessment of immunotherapy efficacy. The TLS-hub gene PRRX1, which had the potential to function as an immunomodulatory factor of TLS, could be a therapeutic target for CRC.","PeriodicalId":507440,"journal":{"name":"Frontiers in Oncology","volume":"35 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141103970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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