Potential predictive value of JAK2 expression for Pan-cancer response to PD-1 blockade immunotherapy

Peng, Jie; Xiao, Lushan; Dong, Zhong‐Yi; Li, Wenwen; Wang, Kun-Yuan; Wu, De‐Hua; Liu, Li

doi:10.21037/tcr.2018.04.09

Cited by 7 publications

(5 citation statements)

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“…et al proposed that JAK2 loss-of-function mutations lead to resistance to PD-1 blockade therapy due to the lack of reactive PD-L1 expression and response to interferon-gamma [65]. Moreover, IRF1 and JAK2 had been reported as biomarkers of the anti-PD-1 immunotherapy response in melanoma via clinical sample validation [64,66]. The CD8A is a cell surface glycoprotein on most cytotoxic T lymphocytes, which can mediate efficient cell-cell interactions within the immune system [67].…”

Section: Discussionmentioning

confidence: 99%

Identification of Potential Biomarkers for Anti-PD-1 Therapy in Melanoma by Weighted Correlation Network Analysis

Wang

Chai

et al. 2020

Genes

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Melanoma is the most malignant form of skin cancer, which seriously threatens human life and health. Anti-PD-1 immunotherapy has shown clinical benefits in improving patients’ overall survival, but some melanoma patients failed to respond. Effective therapeutic biomarkers are vital to evaluate and optimize benefits from anti-PD-1 treatment. Although the establishment of immunotherapy biomarkers is well underway, studies that identify predictors by gene network-based approaches are lacking. Here, we retrieved the existing datasets (GSE91061, GSE78220 and GSE93157, 79 samples in total) on anti-PD-1 therapy to explore potential therapeutic biomarkers in melanoma using weighted correlation network analysis (WGCNA), function validation and clinical corroboration. As a result, 13 hub genes as critical nodes were traced from the key module associated with clinical features. After receiver operating characteristic (ROC) curve validation by an independent dataset (GSE78220), six hub genes with diagnostic significance were further recovered. Moreover, these six genes were revealed to be closely associated not only with the immune system regulation, immune infiltration, and validated immunotherapy biomarkers, but also with excellent prognostic value and significant expression level in melanoma. The random forest prediction model constructed using these six genes presented a great diagnostic ability for anti-PD-1 immunotherapy response. Taken together, IRF1, JAK2, CD8A, IRF8, STAT5B, and SELL may serve as predictive therapeutic biomarkers for melanoma and could facilitate future anti-PD-1 therapy.

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Section: Discussionmentioning

confidence: 99%

Identification of Potential Biomarkers for Anti-PD-1 Therapy in Melanoma by Weighted Correlation Network Analysis

Wang

Chai

et al. 2020

Genes

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“…1,2 However, only~20% of patients with lung cancer respond to ICBs. 3 Several biomarkers, such as tumor-infiltrating lymphocytes (TILs), 4,5 PD-L1 expression, 6,7 gene expression profile (GEP), 8 tumor mutational burden (TMB), 9 high microsatellite instability (MSI-H), and neoantigen counts, 10,11 have been proposed to predict the response of patients to ICBs. However, these predictors have some deficiencies.…”

Section: Introductionmentioning

confidence: 99%

Deep neural network classification based on somatic mutations potentially predicts clinical benefit of immune checkpoint blockade in lung adenocarcinoma

et al. 2020

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Although several biomarkers have been proposed to predict the response of patients with lung adenocarcinoma (LUAD) to immune checkpoint blockade (ICB) therapy, existing challenges such as test platform uniformity, cutoff value definition, and low frequencies restrict their effective clinical application. Here, we attempted to use deep neural networks (DNNs) based on somatic mutations to predict the clinical benefit of ICB to LUAD patients undergoing immunotherapy. We used DNNs to train and validate the predictive model in three cohorts. Kaplan-Meier estimates determined the overall survival (OS) and progression-free survival (PFS) between specific subgroups. Then, we performed a relevant analysis on the multiple-dimension data types including immune cell infiltration, programmed death receptor 1 ligand (PD-L1) expression, and tumor mutational burden (TMB) from cohorts of LUAD public database and immunotherapeutic patients. Two classification groups (C1 and C2) in the training and two validation sets were identified for the efficacy of ICB via the DNN algorithm. Patients in C1 showed remarkably long OS and PFS to programmed death 1 (PD-1) inhibitors. The C1 group was significantly associated with increased expression of immune cell infiltration, immune checkpoints, activated T-effectors, and interferon gamma signature. C1 group also exhibited significantly higher TMB, neoantigens, transversion, or transition than the C2 group. This work provides novel insights that classification of DNNs using somatic mutations in LUAD could serve as a potentially predictive approach in developing a strategy for anti-PD-1/PD-L1 immunotherapy.

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“…JAK2 plays a critical role in the signaling of prolactin hormone, which may be involved in the development of medullary thyroid carcinoma. Peng, et al 19) examined the TCGA database containing 9315 tumor samples from 31 cancer types to study the relationship between the mRNA expression of JAK2 and PD-L1 expression. They found that high JAK2 expression was associated with high mRNA expression of PD-L1, which is likely a good indicator for immunotherapy response, including anti-PD-1/PD-L1 therapy.…”

Section: Discussionmentioning

confidence: 99%

Genetic Analysis of Locally Advanced Well-Differentiated Thyroid Cancer: Clinical Prognostic Implications

Jung¹,

Kang²,

Jeon³

et al. 2023

Korean J Otorhinolaryngol-Head Neck Surg

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Background and Objectives Despite the usually favorable prognosis of well-differentiated thyroid cancer (WDTC) following appropriate treatment, advanced T-staged WDTCs are associated with poor prognosis. This study focused on identifying genes associated with the prognosis of locally advanced WDTC by analyzing a The Cancer Genome Atlas cohort.Subjects and Method We analyzed the data of 501 patients with WDTC and classified them into two subgroups: pathological T4 stage (Cluster 1) or T1-3 stage (Cluster 2). We compared the mRNA expressions of thyroid cancer-related genes, and the somatic mutation frequencies of various cancer genes between the two subgroups.Results Cluster 1 included 23 patients (papillary=21/follicular-variant papillary thyroid cancer [FVPTC]=2) and Cluster 2 478 patients (papillary=371/FVPTC=100/others=7). Cluster 1 showed worse overall and disease-free survival than Cluster 2 (p<0.05 and p=0.12, respectively). Patients with pT4 stage had about a 1.8-fold increased risk of overall recurrence or death. MET, SERPINA1, TIMP1, PROS1, FN1, CDKN2A, and CDKN2B were significantly elevated while TG, DNAH9, TFF3, CRABP1, TPO, JAK2, KIT, KDR, and NFE2L2 were significantly lower in Cluster 1 (all, p<0.05 and adjusted p<0.05). A TERT, EIF1AX, and ATM showed significantly frequent somatic mutations in Cluster 1 when compared to Cluster 2. We also identified seven pathways related to 16 genetic markers.Conclusion Locally advanced WDTC presented 16 genetic alterations. We identified somatic mutations associated with local invasion transformation. Relevant pathways related to the 16 genetic markers could be therapeutic targets. Genetic analysis of locally advanced WDTC may be used to predict clinical applications in patient survival.

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Potential predictive value of JAK2 expression for Pan-cancer response to PD-1 blockade immunotherapy

Cited by 7 publications

References 0 publications

Identification of Potential Biomarkers for Anti-PD-1 Therapy in Melanoma by Weighted Correlation Network Analysis

Identification of Potential Biomarkers for Anti-PD-1 Therapy in Melanoma by Weighted Correlation Network Analysis

Deep neural network classification based on somatic mutations potentially predicts clinical benefit of immune checkpoint blockade in lung adenocarcinoma

Genetic Analysis of Locally Advanced Well-Differentiated Thyroid Cancer: Clinical Prognostic Implications

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