Effect of histology on the efficacy of immune checkpoint inhibitors in advanced non-small cell lung cancer: A systematic review and meta-analysis

Li, Feng; Zhai, Suokai; Lv, Zhuoheng; Yuan, Ligong; Wang, Shuaibo; Jin, Donghui; Yi, Hang; Fu, Li; Mao, Yousheng

doi:10.3389/fonc.2022.968517

Cited by 6 publications

(4 citation statements)

References 81 publications

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“…The application of cancer immunotherapy has achieved clinical advances and proven antitumor effects for melanoma, NSCLC renal cell carcinoma, head and neck squamous cell carcinoma, urothelial cell carcinoma, colorectal cancer, and others [ 13 , 42 , 43 ]. The effect of ICIs on histological types of NSCLC has been reported, and a meta-analysis revealed that both non-LUSC and LUSC responded to ICIs, with LUSC showing a greater response [ 13 ]. However, another meta-analysis of smaller size reported that non-LUSC showed a greater response to ICI treatment in previously untreated patients [ 43 ].…”

Section: Discussionmentioning

confidence: 99%

“…A recent meta-analysis of lung ICI monotherapy found that the hazard ratio (HR) for OS and the 95% confidence interval (CI) for non-LUSC and LUSC were 0.80, 95% CI 0.73–0.87 and 0.71, 95% CI 0.65–0.77, respectively. In addition, the HR (95% CI) for PFS was 0.90 (0.76–1.07) for non-LUSC and 0.65 (0.56–0.77) for LUSC [ 13 ]. These combined results for ICI monotherapy showed better OS and PFS for LUSC.…”

Section: Introductionmentioning

confidence: 99%

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Immune Gene Networks from Lung Cancer Patients Treated with Immune Checkpoint Inhibitors

Kim,

Kang,

Jekarl

2024

Biomedicines

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The association between immune checkpoint inhibitors (ICIs) and immune gene networks in squamous lung cancer (LUSC) and lung adenocarcinoma (LUAD) was studied. Immune gene networks were constructed using RNA-seq data from the gene expression omnibus (GEO) database. Datasets with more than 10 samples of normal control and tumor tissues were selected; of these, GSE87340, GSE120622, and GSE111907 were suitable for analysis. Gene set enrichment for pathway analysis was performed. For immune gene network construction, 998 unique immune genes were selected from 21 pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG). Gene function annotation was performed based on the KEGG, Gene Ontology, and Reactome databases. Tumor tissues showed decreased coagulation, hematopoiesis, and innate immune pathways, whereas complement- and coagulation-related genes were prominent in the tumor immune gene network. The average numbers of neighbors, clustering coefficients, network diameters, path lengths, densities, and heterogeneities were highest for normal tissue, followed by LUAD and LUSC. Decreased coagulation genes, which were prominent in tumor immune networks, imply functional attenuation. LUAD was deviated from normal tissue, based on network parameters. Tumor tissues showed decreased immune function, and the deviation of LUSC from normal tissue might explain LUSC’s better therapeutic response to ICI treatment.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Immune Gene Networks from Lung Cancer Patients Treated with Immune Checkpoint Inhibitors

Kim,

Kang,

Jekarl

2024

Biomedicines

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“…According to the World Health Organization guidelines, MpBC is classified into various subtypes based on its cytopathological features and cell differentiation [1]. In this particular case, squamous cell differentiation may have contributed to the remarkable response to IO, as IO has been proven effective in squamous carcinomas across different tumor subtypes, such as cervical, lung, and head and neck cancers [18][19][20][21]. However, due to financial constraints, we lacked information on the expression of TILs and TMB in this case, which are important factors that could have shed light on the underlying mechanisms behind this dramatic response.…”

Section: Discussionmentioning

confidence: 99%

Long-Lasting Complete Remission in a Patient With Metastatic Metaplastic Breast Cancer Treated With Immune Checkpoint Inhibitor and Chemotherapy: A Case Report and a Review of the Literature

Al-Awadhi,

Alnaqbi,

Albawardi

2024

Cureus

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Metaplastic breast cancer (MpBC) is a rare form of breast cancer known for suboptimal response to chemotherapy, high recurrence rate, poor prognosis, and limited treatment options. Recent studies have reported that MpBC has high expression of programmed death ligand 1 and tumor-infiltrating lymphocytes, indicating the potential effectiveness of immunotherapy (IO) in MpBC. In addition, several reports have demonstrated the activity of IO in MpBC. In this case report, we present a case of recurrent MpBC that achieved durable, rapid, complete remission with atezolizumab (anti-PD-L1) and nab-paclitaxel with a continued response even after discontinued therapy.

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“…Treatments and prognoses vary for NSCLC depending on the histological subtypes ( 4 ). For example, Li et al ( 5 ) found that in comparison to non-SQ-NSCLC, ICI monotherapy for SQ-NSCLC led to a noticeably greater survival rate. In addition, Baine et al ( 6 ) showed that SCC is independently linked to an increased risk of death, and patients with SCC who have received SBRT treatment are at an elevated risk of both local and distant failure.…”

Section: Introductionmentioning

confidence: 99%

Advancing NSCLC pathological subtype prediction with interpretable machine learning: a comprehensive radiomics-based approach

Kuang,

Zhang,

Zhang

et al. 2024

Front. Med.

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ObjectiveThis research aims to develop and assess the performance of interpretable machine learning models for diagnosing three histological subtypes of non-small cell lung cancer (NSCLC) utilizing CT imaging data.MethodsA retrospective cohort of 317 patients diagnosed with NSCLC was included in the study. These individuals were randomly segregated into two groups: a training set comprising 222 patients and a validation set with 95 patients, adhering to a 7:3 ratio. A comprehensive extraction yielded 1,834 radiomic features. For feature selection, statistical methodologies such as the Mann–Whitney U test, Spearman’s rank correlation, and one-way logistic regression were employed. To address data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was utilized. The study designed three distinct models to predict adenocarcinoma (ADC), squamous cell carcinoma (SCC), and large cell carcinoma (LCC). Six different classifiers, namely Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, eXtreme Gradient Boosting (XGB), and LightGBM, were deployed for model training. Model performance was gauged through accuracy metrics and the area under the receiver operating characteristic (ROC) curves (AUC). To interpret the diagnostic process, the Shapley Additive Explanations (SHAP) approach was applied.ResultsFor the ADC, SCC, and LCC groups, 9, 12, and 8 key radiomic features were selected, respectively. In terms of model performance, the XGB model demonstrated superior performance in predicting SCC and LCC, with AUC values of 0.789 and 0.848, respectively. For ADC prediction, the Random Forest model excelled, showcasing an AUC of 0.748.ConclusionThe constructed machine learning models, leveraging CT imaging, exhibited robust predictive capabilities for SCC, LCC, and ADC subtypes of NSCLC. These interpretable models serve as substantial support for clinical decision-making processes.

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Effect of histology on the efficacy of immune checkpoint inhibitors in advanced non-small cell lung cancer: A systematic review and meta-analysis

Cited by 6 publications

References 81 publications

Immune Gene Networks from Lung Cancer Patients Treated with Immune Checkpoint Inhibitors

Immune Gene Networks from Lung Cancer Patients Treated with Immune Checkpoint Inhibitors

Long-Lasting Complete Remission in a Patient With Metastatic Metaplastic Breast Cancer Treated With Immune Checkpoint Inhibitor and Chemotherapy: A Case Report and a Review of the Literature

Advancing NSCLC pathological subtype prediction with interpretable machine learning: a comprehensive radiomics-based approach

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