Montreal prognostic score: estimating survival of patients with non-small cell lung cancer using clinical biomarkers.
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ABSTRACT:
SUBMITTER: Gagnon B
PROVIDER: S-EPMC3798950 | biostudies-other | 2013 Oct
REPOSITORIES: biostudies-other
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Project description:IntroductionNon-small-cell lung cancer outcomes are poor but heterogeneous, even within stage groups. To improve prognostic precision we aimed to develop and validate a simple prognostic model using patient and disease variables.MethodsProspective registry and study data were analysed using Cox proportional hazards regression to derive a prognostic model (hospital 1, n=695), which was subsequently tested (Harrell's c-statistic for discrimination and Cox-Snell residuals for calibration) in two independent validation cohorts (hospital 2, n=479 and hospital 3, n=284).ResultsThe derived Lung Cancer Prognostic Index (LCPI) included stage, histology, mutation status, performance status, weight loss, smoking history, respiratory comorbidity, sex, and age. Two-year overall survival rates according to LCPI in the derivation and two validation cohorts, respectively, were 84, 77, and 68% (LCPI 1: score?9); 61, 61, and 42% (LCPI 2: score 10-13); 33, 32, and 14% (LCPI 3: score 14-16); 7, 16, and 5% (LCPI 4: score ?15). Discrimination (c-statistic) was 0.74 for the derivation cohort, 0.72 and 0.71 for the two validation cohorts.ConclusionsThe LCPI contributes additional prognostic information, which may be used to counsel patients, guide trial eligibility or design, or standardise mortality risk for epidemiological analyses.
| S-EPMC5572183 | biostudies-literature
Project description:BackgroundUse of exosomes as biomarkers in non-small cell lung cancer (NSCLC) is an intriguing approach in the liquid-biopsy era. Exosomes are nano-sized vesicles with membrane-bound proteins that reflect their originating cell. Prognostic biomarkers are needed to improve patient selection for optimal treatment. We here evaluate exosomes by protein phenotyping as a prognostic biomarker in NSCLC.MethodsExosomes from plasma of 276 NSCLC patients were phenotyped using the Extracellular Vesicle Array; 49 antibodies captured the proteins on the exosomes, and a cocktail of biotin-conjugated antibodies binding the general exosome markers CD9, CD81 and CD63 was used to visualise the captured exosomes. For each individual membrane-bound protein, results were analysed based on presence, in a concentration-dependent manner, and correlated to overall survival (OS).ResultsThe 49 proteins attached to the exosomal membrane were evaluated. NY-ESO-1, EGFR, PLAP, EpCam and Alix had a significant concentration-dependent impact on inferior OS. Due to multiple testing, NY-ESO-1 was the only marker that maintained a significant impact on inferior survival (hazard rate (HR) 1.78 95% (1.78-2.44); p = 0.0001) after Bonferroni correction. Results were adjusted for clinico-pathological characteristics, stage, histology, age, sex and performance status.ConclusionWe illustrate the promising aspects associated with the use of exosomal membrane-bound proteins as a biomarker and demonstrate that they are a strong prognostic biomarker in NSCLC.
| S-EPMC5423137 | biostudies-literature
Project description:Exosomal miRNAs are proposed as excellent candidate biomarkers for clinical applications. However, little is known about their potential roles as prognostic biomarkers in lung cancer. In this study, we explored the prognostic value of plasma exosomal microRNAs (miRNAs) for non-small-cell lung cancer (NSCLC). Using a quantitative polymerase chain reaction (qPCR) array panel, we analyzed 84 plasma exosomal miRNAs in 10 lung adenocarcinoma patients and 10 matched healthy controls. The qPCR array showed 30 aberrantly expressed exosomal miRNAs. Nine candidate miRNAs were selected based on differential expression and previous reports for further evaluating their prognostic roles in 196 NSCLC patients. Elevated levels of exosomal miR-23b-3p, miR-10b-5p and miR-21-5p were independently associated with poor overall survival (with hazard ratio [95% confidence interval]: 2.42 (1.45 - 4.04), P = 0.001; 2.22 (1.18 - 4.16), P = 0.013; 2.12 (1.28 - 3.49), P = 0.003, respectively). When compared to the clinical prognostic variables only model, adding the three exosomal miRNA signatures significantly improved survival predictive accuracy with an increase of time-dependent area under the receiver operating characteristic curve from 0.88 to 0.91 (P=0.015). Our results indicated that plasma exosomal miR-23b-3p, miR-10b-5p and miR-21-5p are promising non-invasive prognostic biomarkers of NSCLC.
| S-EPMC5355076 | biostudies-literature
Project description:Lung cancer is a leading cause of cancer-related death, and >80% of lung cancer diagnoses are non-small-cell lung cancer (NSCLC). However, when using current staging and prognostic indices, the prognosis can vary significantly. In the present study, we calculated a prognostic index for predicting overall survival (OS) in NSCLC patients. The data of 545 NSCLC patients were retrospectively reviewed. Univariate and multivariate Cox proportional hazards regression analyses were performed to evaluate the prognostic value of clinicopathological factors. Age (hazard ratio [HR] = 1.25, 95% confidence interval [CI] = 1.02-1.54), TNM stage (III, HR = 1.64, 95% CI = 1.08-2.48; IV, HR = 2.33, 95% CI = 1.48-3.69), lung lobectomy (HR = 1.96, 95% CI = 1.45-2.66), chemotherapy (HR = 1.42, 95% CI = 1.15-1.74), and pretreatment hemoglobin level (HR = 1.61, 95% CI = 1.28-2.02) were independent prognosticators. A prognostic index for NSCLC (PInscl, 0-6 points) was calculated based on age (≥65 years, 1 point), tumor-node-metastasis (TNM) stage (III, 1 point; IV, 2 points), lung lobectomy (no, 1 point), chemotherapy (no, 1 point), and pretreatment hemoglobin level (low, 1 point). In comparison with the "PInscl = 0" subgroup (survival time = 2.71 ± 1.86 years), the "PInscl = 2" subgroup (survival time = 1.86 ± 1.24 years), "PInscl = 3" subgroup (survival time = 1.45 ± 1.07 years), "PInscl = 4" subgroup (survival time = 1.17 ± 1.06 years), "PInscl = 5" subgroup (survival time = 0.81 ± 0.78 years), and "PInscl = 6" subgroup (survival time = 0.65 ± 0.56 years) exhibited significantly shorter survival times. Kaplan-Meier survival analysis showed that patients with higher PInscl scores had poorer OS than those with lower scores (log-rank test: χ2 = 155.82, P < 0.0001). The area under the curve of PInscl for predicting the 1-year OS was 0.73 (95 % CI = 0.69-0.77, P < 0.001), and the PInscl had a better diagnostic performance than the Karnofsky performance status or TNM stage (P < 0.01). In conclusion, the PInscl, which is calculated from age, TNM stage, lung lobectomy, chemotherapy, and pretreatment hemoglobin level, significantly predicted OS in NSCLC patients.
| S-EPMC7098984 | biostudies-literature
Project description:In the last decade, optimized treatment for non-small cell lung cancer had lead to improved prognosis, but the overall survival is still very short. To further understand the molecular basis of the disease we have to identify biomarkers related to survival. Here we present the development of an online tool suitable for the real-time meta-analysis of published lung cancer microarray datasets to identify biomarkers related to survival. We searched the caBIG, GEO and TCGA repositories to identify samples with published gene expression data and survival information. Univariate and multivariate Cox regression analysis, Kaplan-Meier survival plot with hazard ratio and logrank P value are calculated and plotted in R. The complete analysis tool can be accessed online at: www.kmplot.com/lung. All together 1,715 samples of ten independent datasets were integrated into the system. As a demonstration, we used the tool to validate 21 previously published survival associated biomarkers. Of these, survival was best predicted by CDK1 (p<1E-16), CD24 (p<1E-16) and CADM1 (p = 7E-12) in adenocarcinomas and by CCNE1 (p = 2.3E-09) and VEGF (p = 3.3E-10) in all NSCLC patients. Additional genes significantly correlated to survival include RAD51, CDKN2A, OPN, EZH2, ANXA3, ADAM28 and ERCC1. In summary, we established an integrated database and an online tool capable of uni- and multivariate analysis for in silico validation of new biomarker candidates in non-small cell lung cancer.
| S-EPMC3867325 | biostudies-literature
Project description:Lung cancer is the leading cause of cancer-related deaths worldwide. Despite growing efforts for its early detection by screening populations at risk, the majority of lung cancer patients are still diagnosed in an advanced stage. The management of lung cancer has dramatically improved in the last decade and is no longer based on the "one-fits-all" paradigm or the general histological classification of non-small cell versus small cell lung cancer. Emerging options of targeted therapies and immunotherapies have shifted the management of lung cancer to a more personalized treatment approach, significantly influencing the clinical course and outcome of the disease. Molecular biomarkers have emerged as valuable tools in the prognosis and prediction of therapy response. In this review, we discuss the relevant biomarkers used in the clinical management of lung tumors, from diagnosis to prognosis. We also discuss promising new biomarkers, focusing on non-small cell lung cancer as the most abundant type of lung cancer.
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Project description:Background: Systemic inflammation is a key factor in tumor growth. The Glasgow Prognostic Score (GPS) has a certain value in predicting the prognosis of lung cancer. However, these results still do not have a unified direction. Methods: A systematic review and meta-analysis were performed to investigate the relationship between GPS and the prognosis of patients with non-small cell lung cancer (NSCLC). We set patients as follows: GPS = 0 vs. GPS = 1 or 2, GPS = 0 vs. GPS = 1, GPS = 0 vs. GPS = 2. We collected the hazard ratio (HR) and the 95% confidence interval (CI). Results: A total of 21 studies were included, involving 7333 patients. We observed a significant correlation with GPS and poor OS in NSCLC patients (HRGPS=0 vs. GPS=1 or 2 = 1.62, 95% CI: 1.27-2.07, p ≤ .001; HRGPS=0 vs GPS=1 = 2.14, 95% CI:1.31-3.49, p ≤ .001; HRGPS=0 vs. GPS=2 = 2.64, 95% CI: 1.45-4.82, p ≤ .001). Moreover, we made a subgroup analysis of surgery and stage. The results showed that when divided into GPS = 0 group and GPS = 1 or 2 group, the effect of high GPS on OS was more obvious in surgery (HR = 1.79, 95% CI: 1.08-2.97, p = .024). When GPS was divided into two groups (GPS = 0 and GPS = 1 or 2), the III-IV stage, higher GPS is associated with poor OS (HR = 1.73, 95% CI: 1.43-2.09, p ≤ .001). In the comparison of GPS = 0 and GPS = 1 group (HR = 1.56, 95% CI: 1.05-2.31, p = .026) and the grouping of GPS = 0 and GPS = 2(HR = 2.23, 95% CI: 1.17-4.26, p = .015), we came to the same conclusion. Conclusion: For patients with NSCLC, higher GPS is associated with poor prognosis, and GPS may be a reliable prognostic indicator. The decrease of GPS after pretreatment may be an effective way to improve the prognosis of NSCLC.
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Project description:Introduction: Accumulating evidence showed that a large number of microRNAs (miRNAs) are abnormally expressed in lung cancer tissues and play critical roles in cancer development and progression. The aim of this study is to identify the differentially expressed miRNAs (DEMs) between non-small cell lung cancer (NSCLC) and normal lung tissues, and evaluate the prognostic value and potential target gene functional enrichment of the DEMs. Materials and Methods: We first downloaded the high-throughput miRNA data from The Cancer Genome Atlas Project (TCGA) database, and subsequently analyzed the data using bioinformatics analysis including limma package in R, Kaplan-Meier curve and Log-rank method, and several online analysis tools. Results: A total of 125 DEMs and 138 DEMs were respectively identified in lung adenocarcinoma (LUAD) tissues and lung squamous cell carcinoma (LUSC) tissues compared with their matched normal tissues. Moreover, we found that the prognostic function of the eight miRNAs (miR-375, miR-148a, miR-29b-1 and miR-584 for LUAD; miR-4746, miR-326, miR-93 and miR-671 for LUSC). Furthermore, the two four-miRNA signatures were constructed and found to be an independent prognostic factor for LUAD and LUSC patients, respectively. Additionally, our results indicated that the target genes of eight miRNAs may be involved in various pathways related to NSCLC, including PI3K-Akt, TGF-beta, FoxO, Ras, GPI-anchor biosynthesis and metabolic, Rap1, HIF-1 and proteasome. Conclusion: Overall, eight miRNAs were closely correlated with survival of NSCLC patients, and the constructed two four-miRNA signatures could be respectively used as prognostic markers in LUAD and LUSC patients.
| S-EPMC6843868 | biostudies-literature
Project description:Chromobox (CBX) family members are vital epigenetic regulators that repress the transcription of target genes through chromatin modification. Several studies have investigated the role of CBX family members in cancer. However, the function and prognostic value of diverse CBX family members in non-small-cell lung cancer remain largely unknown. In this study, we reveal that CBX family members are overexpressed in non-small-cell lung cancer tissue compared with normal lung tissue, with the exception of CBX6. Kaplan-Meier analysis demonstrated that high expressions of CBX1 and CBX3 are correlated with overall survival, disease-specific survival, disease-free interval, and progression-free interval for patients with lung adenocarcinoma (LUAD). Furthermore, regression model analysis suggests that CBX3 may be suitable as an independent prediction factor for overall survival and progression-free interval in patients with LUAD. In addition, CBX3 mRNA expression was found to be associated with tumor diameter and lymph node metastasis. Gene enrichment analysis suggests that CBX3 is involved in the cell cycle and P53 signaling pathways. Aberrant expression of CBX3 in LUAD is correlated with DNA copy number alteration. In summary, our data imply that CBX3 plays an important role in the promotion of LUAD and may thus have potential as a prognostic biomarker and molecular therapeutic target for the disease.
| S-EPMC7530393 | biostudies-literature
Project description:ObjectiveThis review aims to summarize the possibilities of recently discovered molecular diagnostic techniques in lung cancer, by evaluating their impact on diagnosis, monitoring, and prognosis in oligometastatic disease.BackgroundOligometastatic non-small cell lung cancer (OM-NSCLC) is currently defined based on morphological rather than biological features. Major advances in the detection of molecular biomarkers in cell-free tumoral DNA and the models of oncogene addiction make as feasible an early diagnosis and guide the therapeutic decision-making progress to improve the prognosis.MethodsThis narrative review EXAMINES current approaches of diagnosis, monitoring, and prognosis of OM-NSCLC and describes the fast-evolving therapeutic scenario of this disease. We provide an overview of the powerful capability of liquid biopsy techniques applied to blood and fluid and we focus on the technological advancement of circulant biomolecular factors in OM NSCLC pathology, starting from apparently simpler models such as oncogene addicted tumors to evaluate themselves in the light of treatment with immune-checkpoint inhibitors.ConclusionsA better understanding of spatial and temporal evolution of oligometastatic diseases would contribute to a more accurate diagnosis and tailored treatment. Data from prospective clinical trials in the early stage of disease, coupled with knowledge of genetic characteristics of lung tumors, are warranted. These efforts would lead to improving the possibility to eradicate the residual disease in these low burden tumoral settings, thus enhancing the definitive cure perspectives.
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