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Tong X, Qu X, Wang M. A Four-Gene-Based Prognostic Model Predicts Overall Survival in Patients With Cutaneous Melanoma. Front Oncol 2021; 11:639874. [PMID: 33842346 PMCID: PMC8024561 DOI: 10.3389/fonc.2021.639874] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 01/29/2021] [Indexed: 01/29/2023] Open
Abstract
Background Cutaneous melanoma (CM) is one of the most aggressive cancers with highly metastatic ability. To make things worse, there are limited effective therapies to treat advanced CM. Our study aimed to investigate new biomarkers for CM prognosis and establish a novel risk score system in CM. Methods Gene expression data of CM from Gene Expression Omnibus (GEO) datasets were downloaded and analyzed to identify differentially expressed genes (DEGs). The overlapped DEGs were then verified for prognosis analysis by univariate and multivariate COX regression in The Cancer Genome Atlas (TCGA) datasets. Based on the gene signature of multiple survival associated DEGs, a risk score model was established, and its prognostic and predictive role was estimated through Kaplan-Meier (K-M) analysis and log-rank test. Furthermore, the correlations between prognosis related genes expression and immune infiltrates were analyzed via Tumor Immune Estimation Resource (TIMER) site. Results A total of 103 DEGs were obtained based on GEO cohorts, and four genes were verified in TCGA datasets. Subsequently, four genes (ADAMDEC1, GNLY, HSPA13, and TRIM29) model was developed by univariate and multivariate Cox regression analyses. The K-M plots showed that the high-risk group was associated with shortened survival than that in the low-risk group (P < 0.0001). Multivariate analysis suggested that the model was an independent prognostic factor (high-risk vs. low-risk, HR= 2.06, P < 0.001). Meanwhile, the high-risk group was prone to have larger breslow depth (P< 0.001) and ulceration (P< 0.001). Conclusions The four-gene risk score model functions well in predicting the prognosis and treatment response in CM and will be useful for guiding therapeutic strategies for CM patients. Additional clinical trials are needed to verify our findings.
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Affiliation(s)
- Xiaoxia Tong
- Cancer Institute, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Xiaofei Qu
- Cancer Institute, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Mengyun Wang
- Cancer Institute, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
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Goldberg SB, Schalper KA, Gettinger SN, Mahajan A, Herbst RS, Chiang AC, Lilenbaum R, Wilson FH, Omay SB, Yu JB, Jilaveanu L, Tran T, Pavlik K, Rowen E, Gerrish H, Komlo A, Gupta R, Wyatt H, Ribeiro M, Kluger Y, Zhou G, Wei W, Chiang VL, Kluger HM. Pembrolizumab for management of patients with NSCLC and brain metastases: long-term results and biomarker analysis from a non-randomised, open-label, phase 2 trial. Lancet Oncol 2020; 21:655-663. [PMID: 32251621 PMCID: PMC7380514 DOI: 10.1016/s1470-2045(20)30111-x] [Citation(s) in RCA: 316] [Impact Index Per Article: 79.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Revised: 02/07/2020] [Accepted: 02/12/2020] [Indexed: 12/17/2022]
Abstract
BACKGROUND We did a phase 2 trial of pembrolizumab in patients with non-small-cell lung cancer (NSCLC) or melanoma with untreated brain metastases to determine the activity of PD-1 blockade in the CNS. Interim results were previously published, and we now report an updated analysis of the full NSCLC cohort. METHODS This was an open-label, phase 2 study of patients from the Yale Cancer Center (CT, USA). Eligible patients were at least 18 years of age with stage IV NSCLC with at least one brain metastasis 5-20 mm in size, not previously treated or progressing after previous radiotherapy, no neurological symptoms or corticosteroid requirement, and Eastern Cooperative Oncology Group performance status less than two. Modified Response Evaluation Criteria in Solid Tumors (mRECIST) criteria was used to evaluate CNS disease; systemic disease was not required for participation. Patients were treated with pembrolizumab 10 mg/kg intravenously every 2 weeks. Patients were in two cohorts: cohort 1 was for those with PD-L1 expression of at least 1% and cohort 2 was patients with PD-L1 less than 1% or unevaluable. The primary endpoint was the proportion of patients achieving a brain metastasis response (partial response or complete response, according to mRECIST). All treated patients were analysed for response and safety endpoints. This study is closed to accrual and is registered with ClinicalTrials.gov, NCT02085070. FINDINGS Between March 31, 2014, and May 21, 2018, 42 patients were treated. Median follow-up was 8·3 months (IQR 4·5-26·2). 11 (29·7% [95% CI 15·9-47·0]) of 37 patients in cohort 1 had a brain metastasis response. There were no responses in cohort 2. Grade 3-4 adverse events related to treatment included two patients with pneumonitis, and one each with constitutional symptoms, colitis, adrenal insufficiency, hyperglycaemia, and hypokalaemia. Treatment-related serious adverse events occurred in six (14%) of 42 patients and were pneumonitis (n=2), acute kidney injury, colitis, hypokalaemia, and adrenal insufficiency (n=1 each). There were no treatment-related deaths. INTERPRETATION Pembrolizumab has activity in brain metastases from NSCLC with PD-L1 expression at least 1% and is safe in selected patients with untreated brain metastases. Further investigation of immunotherapy in patients with CNS disease from NSCLC is warranted. FUNDING Merck and the Yale Cancer Center.
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Affiliation(s)
- Sarah B Goldberg
- Department of Medicine (Medical Oncology), Yale School of Medicine, New Haven, CT, USA.
| | - Kurt A Schalper
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Scott N Gettinger
- Department of Medicine (Medical Oncology), Yale School of Medicine, New Haven, CT, USA
| | - Amit Mahajan
- Department of Radiology, Yale School of Medicine, New Haven, CT, USA
| | - Roy S Herbst
- Department of Medicine (Medical Oncology), Yale School of Medicine, New Haven, CT, USA
| | - Anne C Chiang
- Department of Medicine (Medical Oncology), Yale School of Medicine, New Haven, CT, USA
| | - Rogerio Lilenbaum
- Department of Medicine (Medical Oncology), Yale School of Medicine, New Haven, CT, USA
| | - Frederick H Wilson
- Department of Medicine (Medical Oncology), Yale School of Medicine, New Haven, CT, USA
| | - Sacit Bulent Omay
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - James B Yu
- Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT, USA
| | - Lucia Jilaveanu
- Department of Medicine (Medical Oncology), Yale School of Medicine, New Haven, CT, USA
| | - Thuy Tran
- Department of Medicine (Medical Oncology), Yale School of Medicine, New Haven, CT, USA
| | - Kira Pavlik
- Department of Medicine (Medical Oncology), Yale School of Medicine, New Haven, CT, USA
| | - Elin Rowen
- Department of Medicine (Medical Oncology), Yale School of Medicine, New Haven, CT, USA
| | - Heather Gerrish
- Department of Medicine (Medical Oncology), Yale School of Medicine, New Haven, CT, USA
| | - Annette Komlo
- Department of Medicine (Medical Oncology), Yale School of Medicine, New Haven, CT, USA
| | - Richa Gupta
- Department of Medicine (Medical Oncology), Yale School of Medicine, New Haven, CT, USA
| | - Hailey Wyatt
- Department of Medicine (Medical Oncology), Yale School of Medicine, New Haven, CT, USA
| | - Matthew Ribeiro
- Department of Medicine (Medical Oncology), Yale School of Medicine, New Haven, CT, USA
| | - Yuval Kluger
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Geyu Zhou
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale School of Medicine, New Haven, CT, USA
| | - Wei Wei
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Veronica L Chiang
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Harriet M Kluger
- Department of Medicine (Medical Oncology), Yale School of Medicine, New Haven, CT, USA
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