Dataset Information

Population-Based Brain Tumor Survival Analysis via Spatial- and Temporal-Smoothing.

ABSTRACT: In cancer research, population-based survival analysis has played an important role. In this article, we conduct survival analysis on patients with brain tumors using the SEER (Surveillance, Epidemiology, and End Results) database from the NCI (National Cancer Institute). It has been recognized that cancer survival models have spatial and temporal variations which are caused by multiple factors, but such variations are usually not "abrupt" (that is, they should be smooth). As such, spatially and temporally pooling all data and analyzing each spatial/temporal point separately are either inappropriate or ineffective. In this article, we develop and implement a spatial- and temporal-smoothing technique, which can effectively accommodate spatial/temporal variations and realize information borrowing across spatial/temporal points. Simulation demonstrates effectiveness of the proposed approach in improving estimation. Data on a total of 123,571 patients with brain tumors diagnosed between 1911 and 2010 from 16 SEER sites is analyzed. Findings different from separate estimation and simple pooling are made. Overall, this study may provide a practically useful way for modeling the survival of brain tumor (and other cancers) using population data.

SUBMITTER: Ma C

PROVIDER: S-EPMC6895900 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Population-Based Brain Tumor Survival Analysis via Spatial- and Temporal-Smoothing.

Ma Chenjin C Xue Yuan Y Ma Shuangge S

Cancers 20191105 11

In cancer research, population-based survival analysis has played an important role. In this article, we conduct survival analysis on patients with brain tumors using the SEER (Surveillance, Epidemiology, and End Results) database from the NCI (National Cancer Institute). It has been recognized that cancer survival models have spatial and temporal variations which are caused by multiple factors, but such variations are usually not "abrupt" (that is, they should be smooth). As such, spatially and ...[more]

PMID: 31694302

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Project description:Neuroimaging methods with enhanced spatial resolution such as functional magnetic resonance imaging (FMRI) suggest that the subcortical striatum plays a critical role in human reward processing. Analysis of FMRI data requires several preprocessing steps, some of which entail tradeoffs. For instance, while spatial smoothing can enhance statistical power, it may also bias localization towards regions that contain more gray than white matter. In a meta-analysis and reanalysis of an existing dataset, we sought to determine whether spatial smoothing could systematically bias the spatial localization of foci related to reward anticipation in the nucleus accumbens (NAcc). An activation likelihood estimate (ALE) meta-analysis revealed that peak ventral striatal ALE foci for studies that used smaller spatial smoothing kernels (i.e. <6mm FWHM) were more anterior than those identified for studies that used larger kernels (i.e. >7mm FWHM). Additionally, subtraction analysis of findings for studies that used smaller versus larger smoothing kernels revealed a significant cluster of differential activity in the left relatively anterior NAcc (Talairach coordinates: -10, 9, -1). A second meta-analysis revealed that larger smoothing kernels were correlated with more posterior localizations of NAcc activation foci (p<0.015), but revealed no significant associations with other potentially relevant parameters (including voxel volume, magnet strength, and publication date). Finally, repeated analysis of a representative dataset processed at different smoothing kernels (i.e., 0-12mm) also indicated that smoothing systematically yielded more posterior activation foci in the NAcc (p<0.005). Taken together, these findings indicate that spatial smoothing can systematically bias the spatial localization of striatal activity. These findings have implications both for historical interpretation of past findings related to reward processing and for the analysis of future studies.

Project description:BackgroundBrain metastasis is a common outcome in non-small cell lung cancer, and despite aggressive treatment, its clinical outcome is still frustrating. In recent years, immunotherapy has been developing rapidly, however, its therapeutic outcomes for primary lung cancer and brain metastases are not the same, suggesting that there may be differences in the immune microenvironment of primary lung cancer and brain metastases, however, we currently know little about these differences.MethodsSeventeen paired samples of NSCLC and their brain metastases and 45 other unpaired brain metastases samples were collected for the current study. Immunohistochemical staining was performed on all samples for the following markers: immune checkpoints CTLA-4, PD-1, PD-L1, B7-H3, B7-H4, IDO1, and EphA2; tumor-infiltrating lymphocytes (TILs) CD3, CD4, CD8, and CD20; tumor-associated microglia/macrophages (TAMs) CD68 and CD163; and tumor proliferation index Ki-67. The differences in expression of these markers were compared in 17 paired samples, and the effect of the expression level of these markers on the prognosis of patients was analyzed in lung adenocarcinoma brain metastases samples. Subsequently, multiplex immunofluorescence staining was performed in a typical lung-brain paired sample based on the aforementioned results. The multiplex immunofluorescence staining results revealed the difference in tumor immune microenvironment between primary NSCLC and brain metastases.ResultsIn 17 paired lesions, the infiltration of CTLA-4+ (P = 0.461), PD-1+ (P = 0.106), CD3+ (P = 0.045), CD4+ (P = 0.037), CD8+ (P = 0.008), and CD20+ (P = 0.029) TILs in brain metastases were significantly decreased compared with primary tumors. No statistically significant difference was observed in the CD68 (P = 0.954) and CD163 (P = 0.654) TAM infiltration between primary NSCLC and paired brain metastases. In all the brain metastases lesions, the expression of PD-L1 is related to the time interval of brain metastases in NSCLC. In addition, the Cox proportional hazards regression models showed high expression of B7-H4 (hazard ratio [HR] = 3.276, 95% confidence interval [CI] 1.335-8.041, P = 0.010) and CD68 TAM infiltration (HR = 3.775, 95% CI 1.419-10.044, P = 0.008) were independent prognosis factors for lung adenocarcinoma brain metastases patients.ConclusionsBoth temporal and spatial heterogeneity is present between the primary tumor and brain metastases of NCSLC. Brain metastases lesions exhibit a more immunosuppressive tumor immune microenvironment. B7-H4 and CD68+ TAMs may have potential therapeutic value for lung adenocarcinoma brain metastases patients.

Dataset Information

Population-Based Brain Tumor Survival Analysis via Spatial- and Temporal-Smoothing.

Publications

Population-Based Brain Tumor Survival Analysis via Spatial- and Temporal-Smoothing.

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