Milou M F Schuurbiers, Freek A van Delft, Hendrik Koffijberg, Maarten J IJzerman, Kim Monkhorst, Marjolijn J L Ligtenberg, Daan van den Broek, Huub H van Rossum, Michel M van den Heuvel
{"title":"对转移性非小细胞肺癌免疫治疗无持久益处的早期预测血清肿瘤标志物算法的外部验证。","authors":"Milou M F Schuurbiers, Freek A van Delft, Hendrik Koffijberg, Maarten J IJzerman, Kim Monkhorst, Marjolijn J L Ligtenberg, Daan van den Broek, Huub H van Rossum, Michel M van den Heuvel","doi":"10.1177/14230380251316788","DOIUrl":null,"url":null,"abstract":"<p><p>BackgroundImmune checkpoint inhibitors (ICIs) provide a significant survival benefit in non-small cell lung cancer (NSCLC) patients; however, accurately predicting which patients will benefit remains a challenge. As previously shown, the STOP model, a machine learning model based on serum tumor markers, is capable of identifying non-responders after 6 weeks of ICIs.ObjectiveThis study aims to externally validate this model and to assess the predictive value in combination with radiological response assessment using RECIST criteria.MethodsIn a cohort of 242 metastatic NSCLC patients, CYFRA, CEA, and NSE were measured before start and after 6 weeks of ICI treatment. The ability of the STOP model to predict no durable benefit (NDB; progressive disease, death within 6 months or disease control of less than 6 months) was assessed using specificity and positive predictive value (PPV). Moreover, a combination of the STOP model with RECIST after 6-8 weeks of ICIs was investigated.ResultsThe STOP model achieved a specificity of 96% (95% CI 95%-97%) and a PPV of predicting NDB of 88.1% (95% CI 85.9%-90.3%). Combining the STOP model with RECIST improved specificity and PPV to 100% and predicted NDB on average 11.6 weeks (IQR 1.8-18.0 weeks) prior to developing radiologically defined progression.ConclusionsAfter 6 weeks of ICIs, the blood-based STOP model was capable of accurately predicting NDB in metastatic NSCLC patients, earlier than conventional radiological assessment. The combined serological and radiological response assessment creates an early opportunity to safely stop ICI treatment in patients who will not benefit, although the clinical utility of the assay is limited since the high specificity comes at the cost of a lower sensitivity.</p>","PeriodicalId":23364,"journal":{"name":"Tumor Biology","volume":"47 ","pages":"14230380251316788"},"PeriodicalIF":0.0000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"External validation of a serum tumor marker algorithm for early prediction of no durable benefit to immunotherapy in metastastic non-small cell lung carcinoma.\",\"authors\":\"Milou M F Schuurbiers, Freek A van Delft, Hendrik Koffijberg, Maarten J IJzerman, Kim Monkhorst, Marjolijn J L Ligtenberg, Daan van den Broek, Huub H van Rossum, Michel M van den Heuvel\",\"doi\":\"10.1177/14230380251316788\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>BackgroundImmune checkpoint inhibitors (ICIs) provide a significant survival benefit in non-small cell lung cancer (NSCLC) patients; however, accurately predicting which patients will benefit remains a challenge. As previously shown, the STOP model, a machine learning model based on serum tumor markers, is capable of identifying non-responders after 6 weeks of ICIs.ObjectiveThis study aims to externally validate this model and to assess the predictive value in combination with radiological response assessment using RECIST criteria.MethodsIn a cohort of 242 metastatic NSCLC patients, CYFRA, CEA, and NSE were measured before start and after 6 weeks of ICI treatment. The ability of the STOP model to predict no durable benefit (NDB; progressive disease, death within 6 months or disease control of less than 6 months) was assessed using specificity and positive predictive value (PPV). Moreover, a combination of the STOP model with RECIST after 6-8 weeks of ICIs was investigated.ResultsThe STOP model achieved a specificity of 96% (95% CI 95%-97%) and a PPV of predicting NDB of 88.1% (95% CI 85.9%-90.3%). Combining the STOP model with RECIST improved specificity and PPV to 100% and predicted NDB on average 11.6 weeks (IQR 1.8-18.0 weeks) prior to developing radiologically defined progression.ConclusionsAfter 6 weeks of ICIs, the blood-based STOP model was capable of accurately predicting NDB in metastatic NSCLC patients, earlier than conventional radiological assessment. The combined serological and radiological response assessment creates an early opportunity to safely stop ICI treatment in patients who will not benefit, although the clinical utility of the assay is limited since the high specificity comes at the cost of a lower sensitivity.</p>\",\"PeriodicalId\":23364,\"journal\":{\"name\":\"Tumor Biology\",\"volume\":\"47 \",\"pages\":\"14230380251316788\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Tumor Biology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/14230380251316788\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/3/17 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"Biochemistry, Genetics and Molecular Biology\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Tumor Biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/14230380251316788","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/3/17 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"Biochemistry, Genetics and Molecular Biology","Score":null,"Total":0}
引用次数: 0
摘要
免疫检查点抑制剂(ICIs)为非小细胞肺癌(NSCLC)患者提供了显著的生存益处;然而,准确预测哪些患者将受益仍然是一个挑战。如前所述,STOP模型是一种基于血清肿瘤标志物的机器学习模型,能够在6周的ICIs后识别无反应。目的对该模型进行外部验证,并结合RECIST标准评估放射反应的预测价值。方法在242例转移性NSCLC患者中,在ICI治疗开始前和6周后测量CYFRA、CEA和NSE。STOP模型预测无持久效益(NDB;使用特异性和阳性预测值(PPV)评估进展性疾病、6个月内死亡或疾病控制少于6个月的患者。此外,在6-8周的ICIs后,研究了STOP模型与RECIST的结合。结果STOP模型的特异性为96% (95% CI 95%-97%),预测NDB的PPV为88.1% (95% CI 85.9%-90.3%)。将STOP模型与RECIST相结合可将特异性和PPV提高到100%,并在放射学定义的进展发生前平均11.6周(IQR 1.8-18.0周)预测NDB。结论经过6周的ICIs后,基于血液的STOP模型能够准确预测转移性NSCLC患者的NDB,比传统的放射评估更早。血清学和放射学反应联合评估为不能获益的患者安全停止ICI治疗创造了早期机会,尽管该检测的临床效用有限,因为高特异性是以较低敏感性为代价的。
External validation of a serum tumor marker algorithm for early prediction of no durable benefit to immunotherapy in metastastic non-small cell lung carcinoma.
BackgroundImmune checkpoint inhibitors (ICIs) provide a significant survival benefit in non-small cell lung cancer (NSCLC) patients; however, accurately predicting which patients will benefit remains a challenge. As previously shown, the STOP model, a machine learning model based on serum tumor markers, is capable of identifying non-responders after 6 weeks of ICIs.ObjectiveThis study aims to externally validate this model and to assess the predictive value in combination with radiological response assessment using RECIST criteria.MethodsIn a cohort of 242 metastatic NSCLC patients, CYFRA, CEA, and NSE were measured before start and after 6 weeks of ICI treatment. The ability of the STOP model to predict no durable benefit (NDB; progressive disease, death within 6 months or disease control of less than 6 months) was assessed using specificity and positive predictive value (PPV). Moreover, a combination of the STOP model with RECIST after 6-8 weeks of ICIs was investigated.ResultsThe STOP model achieved a specificity of 96% (95% CI 95%-97%) and a PPV of predicting NDB of 88.1% (95% CI 85.9%-90.3%). Combining the STOP model with RECIST improved specificity and PPV to 100% and predicted NDB on average 11.6 weeks (IQR 1.8-18.0 weeks) prior to developing radiologically defined progression.ConclusionsAfter 6 weeks of ICIs, the blood-based STOP model was capable of accurately predicting NDB in metastatic NSCLC patients, earlier than conventional radiological assessment. The combined serological and radiological response assessment creates an early opportunity to safely stop ICI treatment in patients who will not benefit, although the clinical utility of the assay is limited since the high specificity comes at the cost of a lower sensitivity.
期刊介绍:
Tumor Biology is a peer reviewed, international journal providing an open access forum for experimental and clinical cancer research. Tumor Biology covers all aspects of tumor markers, molecular biomarkers, tumor targeting, and mechanisms of tumor development and progression.
Specific topics of interest include, but are not limited to:
Pathway analyses,
Non-coding RNAs,
Circulating tumor cells,
Liquid biopsies,
Exosomes,
Epigenetics,
Cancer stem cells,
Tumor immunology and immunotherapy,
Tumor microenvironment,
Targeted therapies,
Therapy resistance
Cancer genetics,
Cancer risk screening.
Studies in other areas of basic, clinical and translational cancer research are also considered in order to promote connections and discoveries across different disciplines.
The journal publishes original articles, reviews, commentaries and guidelines on tumor marker use. All submissions are subject to rigorous peer review and are selected on the basis of whether the research is sound and deserves publication.
Tumor Biology is the Official Journal of the International Society of Oncology and BioMarkers (ISOBM).