Construction of an integrated prognostic classifier model for predicting the efficacy of immune checkpoint inhibitor therapy in non-small cell lung cancer.
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ABSTRACT:
SUBMITTER: Lin Z
PROVIDER: S-EPMC7427306 | biostudies-literature | 2020 Aug
REPOSITORIES: biostudies-literature
Publications
Construction of an integrated prognostic classifier model for predicting the efficacy of immune checkpoint inhibitor therapy in non-small cell lung cancer.
Lin Zhihuan Z Zhang Yaxiong Y Zhang Li L
Cancer communications (London, England) 20200708 8
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Project description:Background Nicotinamide adenine dinucleotide (NAD+) metabolism is involved in a series of cancer pathogenesis processes, and is considered a promising therapeutic target for cancer treatment. However, a comprehensive analysis of NAD+ metabolism events on immune regulation and cancer survival has not yet been conducted. Here, we constructed a prognostic NAD+ metabolism-related gene signature (NMRGS) associated with immune checkpoint inhibitor (ICI) efficacy in glioma. Methods 40 NAD+ metabolism-related genes (NMRGs) were obtained from the Reactome database and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Glioma cases with transcriptome data and clinical information were obtained from Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA). NMRGS was constructed based on the calculated risk score using univariate analysis, Kaplan–Meier analysis, multivariate Cox regression, and nomogram. This NMRGS was verified in training (CGGA693) and validation (TCGA and CGGA325) cohorts. The immune characteristics, mutation profile, and response to ICI therapy were subsequently analyzed for different NMRGS subgroups. Results Six NAD+ metabolism-related genes, including CD38, nicotinamide adenine dinucleotide kinase (NADK), nicotinate phosphoribosyltransferase (NAPRT), nicotinamide/nicotinic acid mononucleotide adenylyltransferase 3 (NMNAT3), poly(ADP-Ribose) polymerase family member 6 (PARP6), and poly(ADP-Ribose) polymerase family member 9 (PARP9), were ultimately used to construct a comprehensive risk model for glioma patients. Patients in the NMRGS-high group showed a poorer survival outcome than those in the NMRGS-low group. The area under curve (AUC) indicated that NMRGS has good potential in glioma prognostic prediction. A nomogram with improved accuracy was established based on independent prognostic factors (NMRGS score, 1p19q codeletion status, and WHO grade). Furthermore, patients in the NMRGS-high group showed a more immunosuppressive microenvironment, higher tumor mutation burden (TMB), higher human leucocyte antigen (HLA) expression and a more therapeutic response to ICI therapy. Conclusions This study constructed a prognostic NAD+ metabolism-related signature associated with the immune landscape in glioma, which can be used for guiding individualized ICI therapy.
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| S-EPMC8473642 | biostudies-literature
Project description:Breast cancer has overtaken lung cancer as the most frequently diagnosed cancer type and is the leading cause of death for women worldwide. It has been demonstrated in published studies that long non-coding RNAs (lncRNAs) involved in genomic stability are closely associated with the progression of breast cancer, and remarkably, genomic stability has been shown to predict the response to immune checkpoint inhibitors (ICIs) in cancer therapy, especially colorectal cancer. Therefore, it is of interest to explore somatic mutator-derived lncRNAs in predicting the prognosis and ICI efficacy in breast cancer patients. In this study, the lncRNA expression data and somatic mutation data of breast cancer patients from The Cancer Genome Atlas (TCGA) were downloaded and analyzed thoroughly. Univariate and multivariate Cox proportional hazards analyses were used to generate the genomic instability-related lncRNAs in a training set, which was subsequently used to analyze a testing set and combination of the two sets. The qRT-PCR was conducted in both normal mammary and breast cancer cell lines. Furthermore, the Kaplan-Meier and receiver operating characteristic (ROC) curves were applied to validate the predictive effect in the three sets. Finally, the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to evaluate the association between genomic instability-related lncRNAs and immune checkpoints. As a result, a six-genomic instability-related lncRNA signature (U62317.4, MAPT-AS1, AC115837.2, EGOT, SEMA3B-AS1, and HOTAIR) was identified as the independent prognostic risk model for breast cancer patients. Compared with the normal mammary cells, the qRT-PCR showed that HOTAIR was upregulated while MAPT-AS1, EGOT, and SEMA3B-AS1 were downregulated in breast cancer cells. The areas under the ROC curves at 3 and 5 years were 0.711 and 0.723, respectively. Moreover, the patients classified in the high-risk group by the prognostic model had abundant negative immune checkpoint molecules. In summary, this study suggested that the prognostic model comprising six genomic instability-related lncRNAs may provide survival prediction. It is necessary to identify patients who are suitable for ICIs to avoid severe immune-related adverse effects, especially autoimmune diseases. This model may predict the ICI efficacy, facilitating the identification of patients who may benefit from ICIs.
| S-EPMC9389369 | biostudies-literature
Project description:Immune checkpoint inhibitor (ICI) therapy has revolutionised the treatment of various cancer types. ICIs reinstate T-cell function to elicit an anti-cancer immune response. The resulting immune response can however have off-target effects which manifest as autoimmune type serious immune-related adverse events (irAE) in ~10-55% of patients treated. It is currently challenging to predict both who will experience irAEs and to what severity. Identification of patients at high risk of serious irAE would revolutionise patient care. While the pathogenesis driving irAE development is still unclear, host genetic factors are proposed to be key determinants of these events. This review presents current evidence supporting the role of the host genome in determining risk of irAE. We summarise the spectrum and timing of irAEs following treatment with ICIs and describe currently reported germline genetic variation associated with expression of immuno-modulatory factors within the cancer immunity cycle, development of autoimmune disease and irAE occurrence. We propose that germline genetic determinants of host immune function and autoimmune diseases could also explain risk of irAE development. We also endorse genome-wide association studies of patients being treated with ICIs to identify genetic variants that can be used in polygenic risk scores to predict risk of irAE.
| S-EPMC9789157 | biostudies-literature
Project description:Programmed death-ligand 1 (PD-L1) expression is a predictor of immune checkpoint inhibitor (ICI) treatment efficacy. The clinical efficacy of ICIs for non-small-cell lung cancer (NSCLC) patients harboring major mutations, such as EGFR or ALK mutations, is limited. We genotyped 190 patients with advanced lung adenocarcinomas who received nivolumab or pembrolizumab monotherapy, and examined the efficacy in NSCLC patients with or without major mutations. Among the patients enrolled in the genotyping study, 47 patients harbored EGFR mutations, 25 patients had KRAS mutations, 5 patients had a HER2 mutation, 6 patients had a BRAF mutation, and 7 patients had ALK rearrangement. The status of PD-L1 expression was evaluated in 151 patients, and the rate of high PD-L1 expression (?50%) was significantly higher in patients with ALK mutations. The progression-free survival was 0.6 (95% CI: 0.2-2.1) months for ALK-positive patients and 1.8 (95% CI: 1.2-2.1) months for EGFR-positive patients. All patients with ALK rearrangement showed disease progression within three months from the initiation of anti-PD-1 treatment. Our data suggested that ICI treatment was significantly less efficacious in patients with ALK rearrangement than in patients with EGFR mutations, and PD-L1 expression was not a critical biomarker for ICI treatment for patients with one of these mutations.
| S-EPMC7178012 | biostudies-literature
Project description:BACKGROUND Immune-checkpoint inhibitors have propelled the field of therapeutics for small cell lung cancer (SCLC) treatment, but are only beneficial to some patients. The objective of this study was to identify valid biomarkers for good potential response to immunotherapy. MATERIAL AND METHODS We performed an integrated analysis of the available datasets from the Gene Expression Omnibus (GEO) projects, Cancer Cell Line Encyclopedia (CCLE), TISIDB database, and Lung Cancer Explorer (LCE) database. Six prognosis-related genes (MCM2, EZH2, CENPK, CHEK1, CDKN2A, and EXOSC2) were identified utilizing the meta workflow of data analysis methods. We performed subclass mapping to compare their expression profiles to other datasets of patients who responded to immunotherapy. A drug sensitivity predictive model was used to predict the chemotherapeutic response to cisplatin and etoposide. RESULTS Our results showed that the expression of the 6 key genes was significantly associated with the overall survival of patients with SCLC. Lower expression of these 6 genes was correlated to the response to anti-PD-1 treatment. Additionally, low expression of MCM2, EZH2, CENPK, and CHEK1 was correlated with increased sensitivity to cisplatin, but not etoposide. CONCLUSIONS Overall, our data showed that MCM2, EZH2, CENPK, CHEK1, CDKN2A, and EXOSC2 are potential prognostic and predictive biomarkers for response to immune-checkpoint inhibitor treatment in patients with SCLC. Further studies with large sample sizes are required to validate our findings and to explore the detailed mechanisms underlying the role of these genes in SCLC.
| S-EPMC8570048 | biostudies-literature
Project description:Lung cancer is one of the most common and mortal malignancies, usually with a poor prognosis in its advanced or recurrent stages. Recently, immune checkpoint inhibitors (ICIs) immunotherapy has revolutionized the treatment of human cancers including lung adenocarcinoma (LUAD), and significantly improved patients' prognoses. However, the prognostic and predictive outcomes differ because of tumor heterogeneity. Here, we present an effective method, GDPLichi (Genes of DNA damage repair to predict LUAD immune checkpoint inhibitors response), as the signature to predict the LUAD patient's response to the ICIs. GDPLichi utilized only 7 maker genes from 8 DDR pathways to construct the predictive model and classified LUAD patients into two subgroups: low- and high-risk groups. The high-risk group was featured by worse prognosis and decreased B cells, CD8+ T cells, CD8+ central memory T cells, hematopoietic stem cells (HSC), myeloid dendritic cells (MDC), and immune scores as compared to the low-risk group. However, our research also suggests that the high-risk group was more sensitive to ICIs, which might be explained by increased TMB, neoantigen, immune checkpoint molecules, and immune suppression genes' expression, but lower TIDE score as compared to the low-risk group. This conclusion was verified in three other LUAD cohort datasets (GSE30219, GSE31210, GSE50081).
| S-EPMC8713481 | biostudies-literature
Project description:There are multiple approved indications for immune checkpoint inhibitors (ICI) in patients with advanced solid tumors. Polypharmacy, defined as the use of ≥ 5 medications, is common among cancer patients. The impact of these non-oncologic medications on ICI efficacy or the development of side effects, specifically immune related adverse events (irAEs), is unclear. Recent clinical studies investigating the connection between concomitant medications and ICI efficacy have produced conflicting results. A systematic literature search was performed on PubMed to identify published clinical studies evaluating the impact of metformin, angiotensin-converting-enzyme inhibitor (ACEi), angiotensin receptor blockers (ARBs) and aspirin on ICI outcomes and toxicity in patients with advanced solid tumors. Clinical outcomes assessed included overall response rate, progression free survival, overall patient survival and the development of adverse events, specifically irAEs. A total of 10 retrospective studies were identified. Most studies reported a small percentage (range 8% to 42%) of their study population taking the concomitant medications of interest. Collectively, the studies did not identify a significant impact on ICI efficacy with concomitant medication use. In addition, the impact on irAEs was rarely reported in these studies but no significant group effect on reported toxicities or irAEs was found. This review provides a comprehensive analysis of current clinical studies and illustrates potential alterations in the tumor microenvironment induced by the medications. Given the high occurrence of polypharmacy among patients with advanced cancer, gaining a better understanding of the impact of non-oncologic medications on immunotherapy is necessary to improve ICI efficacy and reduce toxicity.
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Project description:BackgroundThe gene ENPEP encodes glutamyl aminopeptidase, which can cut N-terminal aspartic acid from angiotensin II, and is related to tumorigenesis and immune microenvironment, however, the association between the expression of ENPEP and benefits of immune checkpoint inhibitors (ICIs) has had no investigation.MethodsWe assess the immunotherapeutic predictive performance of ENPEP expression and mutation in multiple cohorts, including one discovery cohort (Pender cohort), four validation cohorts (Hugo cohort; Liu cohort; Mariathasan cohort; Zhao cohort), and one mutation cohort (Miao cohort). Cohorts from The Cancer Genome Atlas (TCGA) were used to explore mechanism and analysis prognosis.ResultsIn the discovery cohort, patients with lower ENPEP expression had superior response rates (47.2% vs. 36.1%) and over-all survival (OS) (HR [95% CI] = 0.61 [0.39-0.96]; p = 0.032) compared with those with higher ENPEP expression. The association between ENPEP and immunotherapy efficacy was consistently observed in validation cohorts (Hugo: OS HR [95% CI] = 0.41 [0.11-1.45], p = 0.158; Liu: OS HR [95% CI] = 0.73 [0.44-1.20], p = 0.211; Mariathasan: OS HR [95% CI] = 0.84 [0.65-1.09], p = 0.181; Zhao: OS HR [95% CI] = 0.20 [0.04-1.01], p = 0.033; Pooled cohort: OS HR [95% CI] = 0.76 [0.61-0.95], p = 0.015), and in the mutation cohort (ENPEP mutation vs. wild type (WT), OS HR [95% CI] = 0.46 [0.26-0.93], p = 0.017). Reliably, ENPEP is associated with M2 macrophage infiltration and activation in TCGA.ConclusionsOur results demonstrated ENPEP is a potential biomarker to classify patients' response to ICIs treatment.
| S-EPMC8817092 | biostudies-literature
Project description:ImportanceDerived neutrophils/(leukocytes minus neutrophils) ratio (dNLR) and lactate dehydrogenase (LDH) level have been correlated with immune checkpoint inhibitor (ICI) outcomes in patients with melanoma.ObjectiveTo determine whether pretreatment dNLR and LDH are associated with resistance to ICIs in patients with advanced non-small cell lung cancer (NSCLC).Design, setting, and participantsMulticenter retrospective study with a test (n = 161) and a validation set (n = 305) treated with programmed death 1/programmed death ligand 1 (PD-1/PD-L1) inhibitors in 8 European centers, and a control cohort (n = 162) treated with chemotherapy only. Complete blood cell counts, LDH, and albumin levels were measured before ICI treatment. A lung immune prognostic index (LIPI) based on dNLR greater than 3 and LDH greater than upper limit of normal (ULN) was developed, characterizing 3 groups (good, 0 factors; intermediate, 1 factor; poor, 2 factors).Main outcomes and measuresThe primary end point was overall survival (OS). Secondary end points were progression-free survival (PFS) and disease control rate (DCR).ResultsIn the pooled ICI cohort (N = 466), 301 patients (65%) were male, 422 (90%) were current or former smokers, and 401 (87%) had performance status of 1 or less; median age at diagnosis was 62 (range, 29-86) years; 270 (58%) had adenocarcinoma and 159 (34%) had squamous histologic subtype. Among 129 patients with PD-L1 data, 96 (74%) had PD-L1 of at least 1% by immunohistochemical analysis, and 33 (26%) had negative results. In the test cohort, median PFS and OS were 3 (95% CI, 2-4) and 10 (95% CI, 8-13) months, respectively. A dNLR greater than 3 and LDH greater than ULN were independently associated with OS (hazard ratio [HR] 2.22; 95% CI, 1.23-4.01 and HR, 2.51; 95% CI, 1.32-4.76, respectively). Median OS for poor, intermediate, and good LIPI was 3 months (95% CI, 1 month to not reached [NR]), 10 months (95% CI, 8 months to NR), and 34 months (95% CI, 17 months to NR), respectively, and median PFS was 2.0 (95% CI, 1.7-4.0), 3.7 (95% CI, 3.0-4.8), and 6.3 (95% CI, 5.0-8.0) months (both P < .001). Disease control rate was also correlated with dNLR greater than 3 and LDH greater than ULN. Results were reproducible in the ICI validation cohort for OS, PFS, and DCR, but were nonsignificant in the chemotherapy cohort.Conclusions and relevancePretreatment LIPI, combining dNLR greater than 3 and LDH greater than ULN, was correlated with worse outcomes for ICI, but not for chemotherapy, suggesting that LIPI can serve as a potentially useful tool when selecting ICI treatment, raising the hypothesis that the LIPI might be useful for identifying patients unlikely to benefit from treatment with an ICI.
| S-EPMC5885829 | biostudies-literature