Combining immune checkpoint inhibitors with chemotherapy in advanced solid tumours: A review

Larroquette, Mathieu; Domblides, Charlotte; Lefort, Félix; Lasserre, M.; Quivy, Amandine; Sionneau, Baptiste; Bertolaso, Pauline; Gross‐Goupil, Marine; Ravaud, Alain; Daste, Amaury

doi:10.1016/j.ejca.2021.09.013

Cited by 41 publications

(30 citation statements)

References 89 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, the high-risk group may be more sensitive to immune checkpoint inhibitors against PD1 and CLTA4 . Most patients with lung cancer are diagnosed in an advanced stage [ 93 ]; thus, chemotherapy still serves as an important therapeutic method for them [ 94 ]. The high-risk group has a lower IC50 for six common chemotherapy drugs for lung cancer, which may be a reference option for chemotherapy drugs.…”

Section: Discussionmentioning

confidence: 99%

A ferroptosis-related gene signature for overall survival prediction and immune infiltration in lung squamous cell carcinoma

et al. 2022

View full text Add to dashboard Cite

Background: Ferroptosis is associated with cancer initiation and progression. However, the molecular mechanism and prognostic value of ferroptosis-related genes in lung squamous cell carcinoma (LUSC) are poorly understood. Methods: The mRNA expression profiles, methylation data, and clinical information of patients with LUSC were downloaded from TCGA and GEO database. Ferroptosis-related differentially expressed genes (DEGs) were identified between cancerous and non-cancerous tissues, and their prognostic value was systemically investigated by bioinformatic analyses. Results: A ferroptosis-related gene signature (ALOX5, TFRC, PHKG2, FADS2, NOX1) was constructed using multivariate Cox regression analysis and represented as a risk score. Overall survival (OS) probability was significantly lower in the high-risk group than in the low-risk group (P < 0.001), and receiver operating characteristic curve showed a good predictive capacity (AUC = 0.739). The risk score was an independent prognostic factor for LUSC. A nomogram was constructed to predict the OS probabilities at 1, 3, and 5 years. High-risk score was associated with increased immune infiltration, lower methylation levels, higher immune checkpoint genes expression levels, and better chemotherapy response. Cell adhesion molecules, focal adhesion, and extra-cellular matrix receptor interaction were the main pathways in the high-risk group. The signature was validated using the TCGA test cohort, entire TCGA cohort, GSE30219, GSE157010, GSE73403, and GSE4573 datasets. The gene disorders in patients with LUSC were validated using real-time PCR and single-cell RNA sequencing analysis. Conclusions: A ferroptosis-related gene signature was constructed to predict OS probability in LUSC. This could facilitate novel therapeutic methods and guide individualized therapy.

show abstract

Section: Discussionmentioning

confidence: 99%

A ferroptosis-related gene signature for overall survival prediction and immune infiltration in lung squamous cell carcinoma

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Intriguingly, CD4+ T cells have been identified as having direct antitumor cytolytic function [ 54 ]. The efficiency of blockade therapy based on immune checkpoints is primarily dependent on the expression of genes associated with immune checkpoints and T cell-dependent immune response [ 55 ]. Unsurprisingly, in the low-risk group, the immune checkpoint genes were expressed more, particularly PD-L1, CTLA4, and LAG3, showing that the low-risk patients could be more benefited from the immune checkpoint blockade therapy.…”

Section: Discussionmentioning

confidence: 99%

Identification and Development of an Age-Related Classification and Signature to Predict Prognosis and Immune Landscape in Osteosarcoma

Hong

et al. 2022

Journal of Oncology

View full text Add to dashboard Cite

Background. In childhood and adolescence, the prevailing bone tumor is osteosarcoma associated with frequent recurrence and lung metastasis. This research focused on predicting the survival and immune landscape of osteosarcoma by developing a prognostic signature and establishing aging-related genes (ARGs) subtypes. Methods. The training group comprised of the transcriptomic and associated clinical data of 84 patients with osteosarcoma accessed at the TARGET database and the validation group consisted of 53 patients from GSE21257. The aging-related subtypes were identified using unsupervised consensus clustering analysis. The ARG signature was developed utilizing multivariate Cox analysis and LASSO regression. The prognostic value was assessed using the univariate and multivariate Cox analyses, Kaplan-Meier plotter, time-dependent ROC curve, and nomogram. The functional enrichment analyses were performed by GSEA, GO, and KEGG analysis, while the ssGSEA, ESTIMATE, and CIBERSORT analyses were conducted to reveal the immune landscape in osteosarcoma. Results. The two clusters of osteosarcoma patients formed based on 543 ARGs, depicted a considerable difference in the tumor microenvironment, and the overall survival and immune cell infiltration rate varied as well. Among these, the selected 23 ARGs were utilized for the construction of an efficient predictive prognostic signature for the overall survival prediction. The testing in the validation group of osteosarcoma patients confirmed the status of the high-risk score as an independent indicator for poor prognosis, which was already identified as such using the univariate and multivariate Cox analyses. Furthermore, the ARG signature could distinguish different immune-related functions, infiltration status of immune cells, and tumor microenvironment, as well as predict the immunotherapy response of osteosarcoma patients. Conclusion. The aging-related subtypes were identified and a prognostic signature was developed in this research, which determined different prognoses and allowed for treatment of osteosarcoma patients to be tailored. Additionally, the immunotherapeutic response of individuals with osteosarcoma could also be predicted by the ARG signature.

show abstract

“…The mechanism of standard chemotherapy is to act directly on cancer cells to induce tumour lethality. The main effect of immunotherapy including anti-programmed death-1 (PD-1) or anti-programmed death-ligand 1 (PD-L1) is to enhance CD8+ cytotoxic T-cell immune response with the aim of reinvigorating the immune system against cancer cells ( 4 ). Recently, the combination of immunotherapy with standard chemotherapy to enhance their mechanism of action has shown benefits in phase III randomized control trials ( 5 ).…”

Section: Introductionmentioning

confidence: 99%

“…The tumor micro-environment involves complex interaction between intracellular and extracellular metabolites, which would affect the anti-tumour efficiency of chemo-immunotherapy ( 4 , 6 ). Metabolism has been proposed as a mechanism potentially affecting the response and the toxicity to immunotherapy and chemotherapy ( 7 , 8 ).…”

Section: Introductionmentioning

confidence: 99%

Metabolomics profiling in prediction of chemo-immunotherapy efficiency in advanced non-small cell lung cancer

Mei

Zhang

et al. 2023

Front. Oncol.

View full text Add to dashboard Cite

BackgroundTo explore potential metabolomics biomarker in predicting the efficiency of the chemo-immunotherapy in patients with advanced non-small cell lung cancer (NSCLC).MethodsA total of 83 eligible patients were assigned to receive chemo-immunotherapy. Serum samples were prospectively collected before the treatment to perform metabolomics profiling analyses under the application of gas chromatography mass spectrometry (GC-MS). The key metabolites were identified using projection to latent structures discriminant analysis (PLS-DA). The key metabolites were used for predicting the chemo-immunotherapy efficiency in advanced NSCLC patients.ResultsSeven metabolites including pyruvate, threonine, alanine, urea, oxalate, elaidic acid and glutamate were identified as the key metabolites to the chemo-immunotherapy response. The receiver operating characteristic curves (AUC) were 0.79 (95% CI: 0.69-0.90), 0.60 (95% CI: 0.48-0.73), 0.69 (95% CI: 0.57-0.80), 0.63 (95% CI: 0.51-0.75), 0.60 (95% CI: 0.48-0.72), 0.56 (95% CI: 0.43-0.67), and 0.67 (95% CI: 0.55-0.80) for the key metabolites, respectively. A binary logistic regression was used to construct a combined biomarker model to improve the discriminating efficiency. The AUC was 0.86 (95% CI: 0.77-0.94) for the combined biomarker model. Pathway analyses showed that urea cycle, glucose-alanine cycle, glycine and serine metabolism, alanine metabolism, and glutamate metabolism were the key metabolic pathway to the chemo-immunotherapy response in patients with advanced NSCLC.ConclusionMetabolomics analyses of key metabolites and pathways revealed that GC-MS could be used to predict the efficiency of chemo-immunotherapy. Pyruvate, threonine, alanine, urea, oxalate, elaidic acid and glutamate played a central role in the metabolic of PD patients with advanced NSCLC.

show abstract

Combining immune checkpoint inhibitors with chemotherapy in advanced solid tumours: A review

Cited by 41 publications

References 89 publications

A ferroptosis-related gene signature for overall survival prediction and immune infiltration in lung squamous cell carcinoma

A ferroptosis-related gene signature for overall survival prediction and immune infiltration in lung squamous cell carcinoma

Identification and Development of an Age-Related Classification and Signature to Predict Prognosis and Immune Landscape in Osteosarcoma

Metabolomics profiling in prediction of chemo-immunotherapy efficiency in advanced non-small cell lung cancer

Contact Info

Product

Resources

About