Moving Immunotherapy Into Early-Stage Lung Cancer

Linehan, Anna; Forde, Patrick M.

doi:10.1097/ppo.0000000000000493

Cited by 6 publications

(6 citation statements)

References 29 publications

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“…Clinical studies showed that immunotherapy has a good effect on advanced NSCLC ( Garon et al, 2019 ). Moreover, early-stage surgically respectable LUAD may benefit from Immunotherapy ( Linehan and Forde, 2020 ). Bioinformatics analysis of immune checkpoint expression levels and immune cell infiltration is warranted to help predict immunotherapy efficacy and facilitate precision treatment of es-LUAD.…”

Section: Introductionmentioning

confidence: 99%

Cuproptosis-related gene signature correlates with the tumor immune features and predicts the prognosis of early-stage lung adenocarcinoma patients

Tang

Zhang

et al. 2022

Front. Genet.

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Background: Although a majority of early-stage lung adenocarcinoma (es-LUAD) patients have a favorable prognosis, there are still some cases with a risk of recurrence and metastasis. Cuproptosis is a new form of death that differs from other programmed cell death. However, no study has been reported for setting a prognostic model of es-LUAD using cuproptosis pattern-related genes.Methods: Using multiple R packages, the data from the GEO database was processed, and es-LUAD patients was classified into two patterns based on cuproptosis-related genes. Key differentially expressed genes (DEGs) in the two patterns were screened to construct a prognostic signature to assess differences in biological processes and immunotherapy responses in es-LUAD. Tumor microenvironment (TME) in es-LUAD was analyzed using algorithms such as TIMER and ssGSEA. Then, a more accurate nomogram was constructed by combining risk scores with clinical factors.Results: Functional enrichment analysis revealed that DEGs in two patterns were correlated with organelle fission, nuclear division, chromosome segregation, and cycle-related pathways. Univariate Cox regression and Lasso-Cox regression analyses identified six prognostic genes: ASPM, CCNB2, CDC45, CHEK1, NCAPG, and SPAG5. Based on the constructed model, we found that the high-risk group patients had higher expression of immune checkpoints (CTLA4, LAG3, PD-L1, TIGIT and TIM3), and a lower abundance of immune cells. Lastly, the nomogram was highly accurate in predicting the 1-, 3-, and 5-year survival status of patients with es-LUAD based on risk scores and clinical factors.Conclusion: The cuproptosis pattern-related signature can serve as a potential marker for clinical decision-making. It has huge potential in the future to guide the frequency of follow-up and adjuvant therapy for es-LUAD patients.

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Section: Introductionmentioning

confidence: 99%

Cuproptosis-related gene signature correlates with the tumor immune features and predicts the prognosis of early-stage lung adenocarcinoma patients

Tang

Zhang

et al. 2022

Front. Genet.

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“…According to the size, shape, progression rate, and imaging characteristics of pulmonary nodules, the benign and malignant properties and etiology of pulmonary nodules can be predicted. Pulmonary nodules less than 10 mm have only a 1% possibility of malignancy, while the malignant degree of pulmonary nodules reaching 20 mm is greatly increased [ 24 ]. If pulmonary nodules are detected, attention should be given to the nature of the nodules, and it is necessary to actively look for the cause and perform a differential diagnosis.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence Algorithm-Based Feature Extraction of Computed Tomography Images and Analysis of Benign and Malignant Pulmonary Nodules

Gao

Chen

Jiang

et al. 2022

Computational Intelligence and Neuroscience

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This study was aimed to explore the effect of CT image feature extraction of pulmonary nodules based on an artificial intelligence algorithm and the image performance of benign and malignant pulmonary nodules. In this study, the CT images of pulmonary nodules were collected as the research object, and the lung nodule feature extraction model based on expectation maximization (EM) was used to extract the image features. The Dice similarity coefficient, accuracy, benign and malignant nodule edges, internal signs, and adjacent structures were compared and analyzed to obtain the extraction effect of this feature extraction model and the image performance of benign and malignant pulmonary nodules. The results showed that the detection sensitivity of pulmonary nodules in this model was 0.955, and the pulmonary nodules and blood vessels were well preserved in the image. The probability of burr sign detection in the malignant group was 73.09% and that in the benign group was 8.41%. The difference was statistically significant ( P < 0.05 ). The probability of malignant component leaf sign (69.96%) was higher than that of a benign component leaf sign (0), and the difference was statistically significant ( P < 0.05 ). The probability of cavitation signs in the malignant group (59.19%) was higher than that in the benign group (3.74%), and the probability of blood vessel collection signs in the malignant group (74.89%) was higher than that in the benign group (11.21%), with statistical significance ( P < 0.05 ). The probability of the pleural traction sign in the malignant group was 17.49% higher than that in the benign group (4.67%), and the difference was statistically significant ( P < 0.05 ). In summary, the feature extraction effect of CT images based on the EM algorithm was ideal. Imaging findings, such as the burr sign, lobulation sign, vacuole sign, vascular bundle sign, and pleural traction sign, can be used as indicators to distinguish benign and malignant nodules.

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“… 13 Because of the limitation of adjuvant chemotherapy, MPR has been rare in lung cancer, with rates of 2%–7%, 12 , 14 , 15 , 16 , 17 but the incidence of MPR of neoadjuvant immunotherapy improved significantly, with a rate of 14%–83%. 18 , 19 The application of MPR has extended to the surrogate endpoint of neoadjuvant targeted therapy and immunotherapy clinical trials, such as the CTONG1103, 20 CheckMate‐159, 21 Nadim, 22 study, and so forth. Several trials have correlated MPR with DFS and OS in NSCLC.…”

Section: Rationale For Neoadjuvant Immunotherapy In Nsclcmentioning

confidence: 99%

“…In the era of targeted immunotherapy, the concept of MPR has been widely used, although its rationality has been questioned 13 . Because of the limitation of adjuvant chemotherapy, MPR has been rare in lung cancer, with rates of 2%–7%, 12,14–17 but the incidence of MPR of neoadjuvant immunotherapy improved significantly, with a rate of 14%–83% 18,19 . The application of MPR has extended to the surrogate endpoint of neoadjuvant targeted therapy and immunotherapy clinical trials, such as the CTONG1103, 20 CheckMate‐159, 21 Nadim, 22 study, and so forth.…”

Section: Rationale For Neoadjuvant Immunotherapy In Nsclcmentioning

confidence: 99%

The earlier, the better? A review of neoadjuvant immunotherapy in resectable non‐small‐cell lung cancer

Chen

et al. 2022

Chronic Diseases and Translational Medicine

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Immune checkpoint inhibitors (ICIs) have revolutionized the approach to advanced and locally advanced non‐small‐cell lung cancer (NSCLC). Antibodies blocking inhibitory immune checkpoints, such as programmed death 1 (PD‐1) and its ligand (PD‐L1), have remarkable antitumor efficacy and have been approved as a standard first‐ or second‐line treatment in non‐oncogene‐addicted advanced NSCLC. The successful application of immunotherapy in advanced lung cancer has motivated researchers to further evaluate its clinical role as a neoadjuvant setting for resectable NSCLC and for improved long‐term overall survival and curative rates. In this review, we discuss the efforts that incorporate ICIs into the treatment paradigm for surgically resectable lung cancer. We reviewed the early‐phase results from neoadjuvant clinical trials, the landscape of the majority of ongoing phase III trials, and discuss the prospects of ICIs as a curative therapy for resectable lung cancer. We also summarized the potential biomarkers and beneficiaries involved in the current study, as well as the remaining unresolved challenges for neoadjuvant immunotherapy.

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Moving Immunotherapy Into Early-Stage Lung Cancer

Cited by 6 publications

References 29 publications

Cuproptosis-related gene signature correlates with the tumor immune features and predicts the prognosis of early-stage lung adenocarcinoma patients

Cuproptosis-related gene signature correlates with the tumor immune features and predicts the prognosis of early-stage lung adenocarcinoma patients

Artificial Intelligence Algorithm-Based Feature Extraction of Computed Tomography Images and Analysis of Benign and Malignant Pulmonary Nodules

The earlier, the better? A review of neoadjuvant immunotherapy in resectable non‐small‐cell lung cancer

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