Dataset Information

Interleukin-10 haplotype may predict survival and relapse in resected non-small cell lung cancer.

ABSTRACT: IL-10 is associated with tumor malignancy via immune escape. We hypothesized that IL-10 haplotypes categorized by IL-10 promoter polymorphisms at -1082A>G, -819C>T, and -592C>A might influence IL-10 expression and give rise to non-small cell lung cancer (NSCLC) patients with poor outcomes and relapse. We collected adjacent normal tissues from 385 NSCLC patients to determine IL-10 haplotypes by direct sequencing and polymerase chain reaction restriction fragment length polymorphism (PCR-RFLP). Of the 385 tumors, 241 were available to evaluate IL-10 mRNA expression levels by real-time RT-PCR. The influence of IL-10 haplotypes on overall survival (OS) and relapse free survival (RFS) were determined by Kaplan-Meier and multivariate Cox regression analysis. The results showed that IL-10 mRNA levels were significantly higher in tumors with the non-ATA haplotype than with the ATA haplotype (P?=?0.004). Patients with the non-ATA haplotype had shorter OS and RFS periods than did patients with the ATA haplotype. This may be associated with the observation that the number of tumor-infiltrating lymphocytes was decreased in the tumors with higher levels of IL-10. Consistently, T cells from the peripheral blood of the patients with non-ATA haplotype were more susceptible to apoptosis and less cytotoxic to tumor cells, compared to those from the patients with ATA haplotype. The results suggest that IL-10 can promote tumor malignancy via promoting T cell apoptosis and tumor cell survival, and IL-10 haplotype evaluated by PCR-RFLP or direct sequencing may be used to predict survival and relapse in resected NSCLC, helping clinicians to make appropriate decisions on treatment of the patients.

SUBMITTER: Wang YC

PROVIDER: S-EPMC3407146 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Interleukin-10 haplotype may predict survival and relapse in resected non-small cell lung cancer.

Wang Yaw-Cheng YC Sung Wen-Wei WW Wu Tzu-Chin TC Wang Lee L Chien Wen-Pin WP Cheng Ya-Wen YW Chen Chih-Yi CY Shieh Shwn-Huey SH Lee Huei H

PloS one 20120727 7

IL-10 is associated with tumor malignancy via immune escape. We hypothesized that IL-10 haplotypes categorized by IL-10 promoter polymorphisms at -1082A>G, -819C>T, and -592C>A might influence IL-10 expression and give rise to non-small cell lung cancer (NSCLC) patients with poor outcomes and relapse. We collected adjacent normal tissues from 385 NSCLC patients to determine IL-10 haplotypes by direct sequencing and polymerase chain reaction restriction fragment length polymorphism (PCR-RFLP). Of ...[more]

PMID: 22848356

Similar Datasets

Project description:BackgroundThe frequent recurrence of early-stage non-small-cell lung cancer (NSCLC) is generally attributable to metastatic disease undetected at complete resection. Management of such patients depends on prognostic staging to identify the individuals most likely to have occult disease. We aimed to develop and validate a practical, reliable assay that improves risk stratification compared with conventional staging.MethodsA 14-gene expression assay that uses quantitative PCR, runs on formalin-fixed paraffin-embedded tissue samples, and differentiates patients with heterogeneous statistical prognoses was developed in a cohort of 361 patients with non-squamous NSCLC resected at the University of California, San Francisco. The assay was then independently validated by the Kaiser Permanente Division of Research in a masked cohort of 433 patients with stage I non-squamous NSCLC resected at Kaiser Permanente Northern California hospitals, and on a cohort of 1006 patients with stage I-III non-squamous NSCLC resected in several leading Chinese cancer centres that are part of the China Clinical Trials Consortium (CCTC).FindingsKaplan-Meier analysis of the Kaiser validation cohort showed 5 year overall survival of 71·4% (95% CI 60·5-80·0) in low-risk, 58·3% (48·9-66·6) in intermediate-risk, and 49·2% (42·2-55·8) in high-risk patients (p(trend)=0·0003). Similar analysis of the CCTC cohort indicated 5 year overall survivals of 74·1% (66·0-80·6) in low-risk, 57·4% (48·3-65·5) in intermediate-risk, and 44·6% (40·2-48·9) in high-risk patients (p(trend)<0·0001). Multivariate analysis in both cohorts indicated that no standard clinical risk factors could account for, or provide, the prognostic information derived from tumour gene expression. The assay improved prognostic accuracy beyond National Comprehensive Cancer Network criteria for stage I high-risk tumours (p<0·0001), and differentiated low-risk, intermediate-risk, and high-risk patients within all disease stages.InterpretationOur practical, quantitative-PCR-based assay reliably identified patients with early-stage non-squamous NSCLC at high risk for mortality after surgical resection.FundingUCSF Thoracic Oncology Laboratory and Pinpoint Genomics.

Project description:Adjuvant chemotherapy reduces recurrences of non-small-cell lung cancer (NSCLC). To determine which patients need adjuvant chemotherapy, we assessed factors associated with time to relapse (TTR).In 230 resected stage I-II NSCLCs, we correlated immunohistochemistry scores for factors associated with cell growth rate, growth regulation, hypoxia, cell survival, and cell death with TTR.With a median follow-up of 82 months (1-158) for those alive and relapse free at last follow-up, median time to recurrence was not reached. The 2- and 5-year probabilities of maintaining freedom from recurrence were 80.7% (95% confidence interval, 75.3%, 86.4%) and 74.6% (95% confidence interval, 68.6%, 81.2%), respectively. TTR curves flattened at an apparent cure rate of 70%. In multicovariate Cox models, factors correlating with shorter TTR were membranous carbonic anhydrase IX (mCAIX) staining (any versus none, hazard ratio = 2.083, p = 0.023) and node stage (N1 versus N0, hazard ratio = 2.591, p = 0.002). mCAIX scores correlated positively with tumor size, grade, squamous histology, necrosis, mitoses, Ki67, p53, nuclear DNA methyltransferase 1, and cytoplasmic enhancer-of-split-and-hairy-related protein, and they correlated inversely with papillary histology, epidermal growth factor receptor mutation (trend), copper transporter-1, and cytoplasmic hypoxia-inducible factor-1?, vascular endothelial growth factor, DNA methyltransferase 1, and excision repair cross-complementing rodent repair deficiency, complementation group 1.Nodal stage and mCAIX immunohistochemistry were the strongest independent predictors of shorter TTR in resected NSCLCs. mCAIX correlated with tumor size, markers of tumor proliferation and necrosis, and tumor genetic characteristics, and it paradoxically correlated inversely with the hypoxia markers, hypoxia-inducible factor-1? and vascular endothelial growth factor. Presence of mCAIX could help determine patients with high risk of recurrence who might require adjuvant chemotherapy.

Project description:Non-small-cell lung cancer (NSCLC) represents approximately 80-85% of lung cancer diagnoses and is the leading cause of cancer-related death worldwide. Recent studies indicate that image-based radiomics features from positron emission tomography/computed tomography (PET/CT) images have predictive power for NSCLC outcomes. To this end, easily calculated functional features such as the maximum and the mean of standard uptake value (SUV) and total lesion glycolysis (TLG) are most commonly used for NSCLC prognostication, but their prognostic value remains controversial. Meanwhile, convolutional neural networks (CNN) are rapidly emerging as a new method for cancer image analysis, with significantly enhanced predictive power compared to hand-crafted radiomics features. Here we show that CNNs trained to perform the tumor segmentation task, with no other information than physician contours, identify a rich set of survival-related image features with remarkable prognostic value. In a retrospective study on pre-treatment PET-CT images of 96 NSCLC patients before stereotactic-body radiotherapy (SBRT), we found that the CNN segmentation algorithm (U-Net) trained for tumor segmentation in PET and CT images, contained features having strong correlation with 2- and 5-year overall and disease-specific survivals. The U-Net algorithm has not seen any other clinical information (e.g. survival, age, smoking history, etc.) than the images and the corresponding tumor contours provided by physicians. In addition, we observed the same trend by validating the U-Net features against an extramural data set provided by Stanford Cancer Institute. Furthermore, through visualization of the U-Net, we also found convincing evidence that the regions of metastasis and recurrence appear to match with the regions where the U-Net features identified patterns that predicted higher likelihoods of death. We anticipate our findings will be a starting point for more sophisticated non-intrusive patient specific cancer prognosis determination. For example, the deep learned PET/CT features can not only predict survival but also visualize high-risk regions within or adjacent to the primary tumor and hence potentially impact therapeutic outcomes by optimal selection of therapeutic strategy or first-line therapy adjustment.

Project description:Background:The clinical outcomes of patients with resected T1-3N0-2M0 non-small cell lung cancer (NSCLC) with the same tumor-node-metastasis (TNM) stage are diverse. Although other prognostic factors and prognostic prediction tools have been reported in many published studies, a convenient, accurate and specific prognostic prediction software for clinicians has not been developed. The purpose of our research was to develop this type of software that can analyze subdivided T and N staging and additional factors to predict prognostic risk and the corresponding mean and median survival time and 1-5-year survival rates of patients with resected T1-3N0-2M0 NSCLC. Results:Using a Cox proportional hazard regression model, we determined the independent prognostic factors and obtained a prognostic index (PI) eq. PI?=???ixi.=0.379X1-0.403X2-0.267X51-0.167X61-0.298X62?+?0.460X71?+?0.617X72-0.344X81-0.105X91-0.243X92?+?0.305X101?+?0.508X102?+?0.754X103?+?0.143X111?+?0.170X112?+?0.434X113-0.327X122-0.247X123?+?0.517X133?+?0.340X134?+?0.457X143?+?0.419X144?+?0.407X145. Using the PI equation, we determined the PI value of every patient. According to the quantile of the PI value, patients were divided into three risk groups: low-, intermediate-, and high-risk groups with significantly different survival rates. Meanwhile, we obtained the mean and median survival times and 1-5-year survival rates of the three groups. We developed the RNSCLC-PRSP software which is freely available on the web at http://www.rnsclcpps.com with all major browsers supported to determine the prognostic risk and associated survival of patients with resected T1-3N0-2?M0 non-small cell lung cancer. Conclusions:After prognostic factor analysis, prognostic risk grouping and corresponding survival assessment, we developed a novel software program. It is practical and convenient for clinicians to evaluate the prognostic risk and corresponding survival of patients with resected T1-3N0-2M0 NSCLC. Additionally, it has guiding significance for clinicians to make decisions about complementary treatment for patients.

Dataset Information

Interleukin-10 haplotype may predict survival and relapse in resected non-small cell lung cancer.

Publications

Interleukin-10 haplotype may predict survival and relapse in resected non-small cell lung cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure