<i>survcomp</i>: an R/Bioconductor package for performance assessment and comparison of survival models

Schröder, Markus; Culhane, Aedín C.; Quackenbush, John; Haibe‐Kains, Benjamin

doi:10.1093/bioinformatics/btr511

Cited by 411 publications

(334 citation statements)

References 17 publications

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“…The maxstat package for maximally selected rank statistics was used to detect the optimal cutoff of each volume-dependent parameter. The survcomp packages for performance assessment, calculation of concordance index, and comparison of several Cox proportional hazards models were used for analyses (12,13). All tests were 2-sided, and the criterion for significance was a P level of less than 0.05.…”

Section: Resultsmentioning

confidence: 99%

Prognostic Value of Total Lesion Glycolysis by ¹⁸F-FDG PET/CT in Surgically Resected Stage IA Non–Small Cell Lung Cancer

Park

Cho

et al. 2014

J Nucl Med

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Despite the favorable prognosis of stage IA non-small cell lung cancer (NSCLC), the disease recurs after complete surgical resection in 20%-30% of patients. This study determined the prognostic value of various metabolic parameters of 18 F-FDG PET/CT in surgically resected stage IA NSCLC. Methods: We retrospectively reviewed 248 patients with stage IA NSCLC who underwent lobectomy and complete lymph node dissection after PET/CT. A region of interest was drawn on the primary lesion, and metabolic indices such as metabolic tumor volume, maximum standardized uptake value (SUV max ), and total lesion glycolysis (TLG) were measured using an SUV cutoff of 2.5. Results: The patients included 134 men and 114 women, and the mean age was 63.03 ± 10.01 y; 129 were stage T1a (#2 cm) and 119 were T1b (.2 cm). The median follow-up period was 36.6 mo. Recurrence took place in 15 patients. The mean (±SD) SUV max , metabolic tumor volume, and TLG were 4.55 ± 3.75, 5.92 ± 5.57, and 14.42 ± 17.35, respectively. The cutoffs of SUV max and TLG were 3.7 and 13.76, respectively. The 5-y overall survival (OS) was 95.1% in low-SUV max patients and 82.2% in high-SUV max patients (P 5 0.02). The 5-y OS was 93.7% in low-TLG patients and 78.3% in high-TLG patients (P 5 0.01). On multivariate analysis, TLG was a risk factor for OS (hazard ratio, 3.159; P 5 0.040), but SUV max showed marginal significance (P 5 0.064). The concordance index of the TLG model was 0.676 (95% CI, 0.541-0.812). Conclusion: TLG was a significant prognostic factor for OS in patients with stage IA NSCLC. Lungcanceri s the second most common cancer in both men and women and the most common cause of cancer death in the world (1). Non-small cell lung cancer (NSCLC) accounts for 80%-85% of all lung cancer cases. Oncologic treatment options for NSCLC typically include surgery, radiation, and chemotherapy, either alone or in combination. TNM stage has been considered the primary prognostic factor in NSCLC. However, tumor-and patientspecific factors vary even within the same disease stage, creating a heterogeneous population of patients, each with an individual prognosis that requires consideration of patient-and tumor-specific factors for best estimation (2,3). Recently, the incidence of earlystage NSCLC has increased as a result of the use of low-dose CT for screening. However, despite having early-stage disease, up to 20%-30% of patients with stage IA NSCLC relapse after surgical treatment. Therefore, other prognostic factors besides TNM stages are needed to identify patients at high risk for recurrence, predict prognosis, and recommend individualized adjuvant therapy (4).18 F-FDG PET relies on the fact that most neoplasms are highly metabolically active and therefore can be detected on a background of relatively less active normal tissues. When performed in addition to qualitative evaluation by visual inspection, 18 F-FDG PET can provide several semiquantitative measurements of radioactivity concentration in each tissue compartment, such as standardized uptake value (SUV...

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Section: Resultsmentioning

confidence: 99%

Prognostic Value of Total Lesion Glycolysis by ¹⁸F-FDG PET/CT in Surgically Resected Stage IA Non–Small Cell Lung Cancer

Park

Cho

et al. 2014

J Nucl Med

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“…Three biological replicates were used for each gender (male vs. female) and treatment (RU486‐treated vs. control). Kaplan–Meier statistics and hazard ratio were calculated using R packages “survival” and “survcomp” (Schroder, Culhane, Quackenbush & Haibe‐Kains, 2011; Therneau, 2014). A treatment was considered significant if both the G‐rho family tests p ‐value < .01 and the hazard ratio > 1.5.…”

Section: Methodsmentioning

confidence: 99%

Comparative transcriptomics across 14 Drosophila species reveals signatures of longevity

Avanesov

Porter

et al. 2018

Aging Cell

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SummaryLifespan varies dramatically among species, but the biological basis is not well understood. Previous studies in model organisms revealed the importance of nutrient sensing, mTOR, NAD/sirtuins, and insulin/IGF1 signaling in lifespan control. By studying life‐history traits and transcriptomes of 14 Drosophila species differing more than sixfold in lifespan, we explored expression divergence and identified genes and processes that correlate with longevity. These longevity signatures suggested that longer‐lived flies upregulate fatty acid metabolism, downregulate neuronal system development and activin signaling, and alter dynamics of RNA splicing. Interestingly, these gene expression patterns resembled those of flies under dietary restriction and several other lifespan‐extending interventions, although on the individual gene level, there was no significant overlap with genes previously reported to have lifespan‐extension effects. We experimentally tested the lifespan regulation potential of several candidate genes and found no consistent effects, suggesting that individual genes generally do not explain the observed longevity patterns. Instead, it appears that lifespan regulation across species is modulated by complex relationships at the system level represented by global gene expression.

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“…Detailed validation results are shown in Supplemental Figure S3. To perform the Fisher's combined method, we used an R/Bioconductor package "survcomp" (Schroder et al 2011). …”

Section: Active Tf Identificationmentioning

confidence: 99%

Reproducible combinatorial regulatory networks elucidate novel oncogenic microRNAs in non-small cell lung cancer

Mitra

Edmonds

Sun

et al. 2014

RNA

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While previous studies reported aberrant expression of microRNAs (miRNAs) in non-small cell lung cancer (NSCLC), little is known about which miRNAs play central roles in NSCLC's pathogenesis and its regulatory mechanisms. To address this issue, we presented a robust computational framework that integrated matched miRNA and mRNA expression profiles in NSCLC using feed-forward loops. The network consists of miRNAs, transcription factors (TFs), and their common predicted target genes. To discern the biological meaning of their associations, we introduced the direction of regulation. A network edge validation strategy using three independent NSCLC expression profiling data sets pinpointed reproducible biological regulations. Reproducible regulation, which may reflect the true molecular interaction, has not been applied to miRNA-TF coregulatory network analyses in cancer or other diseases yet. We revealed eight hub miRNAs that connected to a higher proportion of targets validated by independent data sets. Network analyses showed that these miRNAs might have strong oncogenic characteristics. Furthermore, we identified a novel miRNA-TF co-regulatory module that potentially suppresses the tumor suppressor activity of the TGF-β pathway by targeting a core pathway molecule (TGFBR2). Follow-up experiments showed two miRNAs (miR-9-5p and miR-130b-3p) in this module had increased expression while their target gene TGFBR2 had decreased expression in a cohort of human NSCLC. Moreover, we demonstrated these two miRNAs directly bind to the 3 ′ untranslated region of TGFBR2. This study enhanced our understanding of miRNA-TF co-regulatory mechanisms in NSCLC. The combined bioinformatics and validation approach we described can be applied to study other types of diseases.

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survcomp: an R/Bioconductor package for performance assessment and comparison of survival models

Cited by 411 publications

References 17 publications

Prognostic Value of Total Lesion Glycolysis by ¹⁸F-FDG PET/CT in Surgically Resected Stage IA Non–Small Cell Lung Cancer

Prognostic Value of Total Lesion Glycolysis by ¹⁸F-FDG PET/CT in Surgically Resected Stage IA Non–Small Cell Lung Cancer

Comparative transcriptomics across 14 Drosophila species reveals signatures of longevity

Reproducible combinatorial regulatory networks elucidate novel oncogenic microRNAs in non-small cell lung cancer

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survcomp: an R/Bioconductor package for performance assessment and comparison of survival models

Cited by 411 publications

References 17 publications

Prognostic Value of Total Lesion Glycolysis by 18F-FDG PET/CT in Surgically Resected Stage IA Non–Small Cell Lung Cancer

Prognostic Value of Total Lesion Glycolysis by 18F-FDG PET/CT in Surgically Resected Stage IA Non–Small Cell Lung Cancer

Comparative transcriptomics across 14 Drosophila species reveals signatures of longevity

Reproducible combinatorial regulatory networks elucidate novel oncogenic microRNAs in non-small cell lung cancer

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Product

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Prognostic Value of Total Lesion Glycolysis by ¹⁸F-FDG PET/CT in Surgically Resected Stage IA Non–Small Cell Lung Cancer

Prognostic Value of Total Lesion Glycolysis by ¹⁸F-FDG PET/CT in Surgically Resected Stage IA Non–Small Cell Lung Cancer