Integrative Genomic Analyses Yield Cell-Cycle Regulatory Programs with Prognostic Value

Cheng, Chao; Lou, Shaoke; Andrews, Erik H.; Ung, Matthew; Varn, Frederick S.

doi:10.1158/1541-7786.mcr-15-0368

Cited by 6 publications

(4 citation statements)

References 53 publications

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“…As shown, P53 targets only represent a small fraction (~4%) of the differentially expressed genes, suggesting that the majority of them are indirectly regulated by the P53 pathway. Additionally, according to our previous study, we provided the importance of using the indirect targets of transcriptional factor to estimate its pathway activity (48,49). Therefore, even though those P53 target genes were indirectly regulated by P53 pathway, it is reasonable to include them into P53 gene signature to enhance the power of statistical inference on P53 pathway activity for the future analysis.…”

Section: Resultsmentioning

confidence: 99%

A P53-Deficiency Gene Signature Predicts Recurrence Risk of Patients with Early-Stage Lung Adenocarcinoma

Zhao

Varn

Cai

et al. 2018

Cancer Epidemiology, Biomarkers &Amp; Prevention

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Background Lung cancer is associated with the highest mortality rate of all cancer types, and the most common histological subtype of lung cancer is adenocarcinoma. In order to apply more effective therapeutic treatment, molecular markers that are able to predict the recurrence risk of patients with adenocarcinoma are critically needed. Mutations in TP53 tumor suppressor gene have been found in approximately 50% of lung adenocarcinoma cases, but the presence of a TP53 mutation does not always associate with increased mortality. Methods The Cancer Genome Atlas (TCGA) RNA-seq data of lung adenocarcinoma were used to define a novel gene signature for P53 deficiency. This signature was then used to calculate a sample-specific P53-deficiency score based on a patient’s transcriptomic profile and tested in four independent lung adenocarcinoma microarray datasets. Results In all datasets, P53-deficiency score was a significant predictor for recurrence-free survival where high P53-deficiency score was associated with poor survival. The score was prognostic even after adjusting for several key clinical variables including age, tumor stage, smoking status, and P53 mutation status. Furthermore, the score was able to predict recurrence-free survival in patients with stage I adenocarcinoma, and was also associated with smoking status. Conclusions The P53 deficiency score was a better predictor of recurrence-free survival compared to P53 mutation status and provided additional prognostic values to established clinical factors. Impact The P53 deficiency score can be used to stratify early-stage patients into subgroups based on their risk of recurrence for aiding physicians to decide personalized therapeutic treatment.

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Section: Resultsmentioning

confidence: 99%

A P53-Deficiency Gene Signature Predicts Recurrence Risk of Patients with Early-Stage Lung Adenocarcinoma

Zhao

Varn

Cai

et al. 2018

Cancer Epidemiology, Biomarkers &Amp; Prevention

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“…As a family of transcription factors, E2Fs play important roles in GC by modulating the transcription of specific target genes, and their regulatory activity and biological effects can be reflected by their target gene expression. It has been reported that high E2F1 or E2F4 activity in liposarcoma patients is associated with unfavorable prognosis, with the core target gene sets of E2F1 containing 116 genes and the core target gene sets of E2F4 containing 199 genes, among which only 21 are shared ( 30 ). As we have shown, GSEA indicated that E2F1 was mainly involved in the cell cycle pathway; E2F4 , however, was mainly involved in ribosome pathway.…”

Section: Discussionmentioning

confidence: 99%

Comprehensive Analysis of E2F Family Members in Human Gastric Cancer

Yang

et al. 2021

Front. Oncol.

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Gastric cancer (GC) is the second most common cancer and the third most frequent cause of cancer-related deaths in China. E2Fs are a family of transcription factors reported to be involved in the tumor progression of various cancer types; however, the roles of individual E2Fs are still not known exactly in tumor progression of GC. In this study, we examined the expression of E2Fs to investigate their roles in tumor progression in GC patients using multiple databases, including ONCOMINE, GEPIA2, Kaplan-Meier plotter, cBioPortal, Metascape, LinkedOmics, GeneMANIA, STRING and UCSC Xena. We also performed real-time polymerase chain reaction (RT-PCR) to validate the expression levels of individual E2Fs in several GC cell lines. Our results demonstrated that the mRNA levels of E2F1/2/3/5/8 were significantly higher both in GC tissues and cell lines. The expression levels of E2F1 and E2F4 were correlated with poor overall survival (OS), decreased post-progression survival (PPS), and decreased progression-free survival (FP) in patients with GC. However, overexpression of E2F2, E2F5, E2F7 and E2F8 is significantly associated with disease-free survival and overall survival in patients with GC. In addition, higher E2F3 and E2F6 mRNA expression was found to increase GC patients’ OS and PPS. 224 of 415 patients with STAD (54%) had gene mutations that were associated with longer disease-free survival (DFS) but not OS. Cell cycle pathway was closely associated with mRNA level of more than half of E2Fs (E2F1/2/3/7/8). There were close and complicated interactions among E2F family members. Finally, our results indicated the gene expressions of E2Fs had a positive relationship with its copy numbers. Taken together, E2F1/2/3/5/8 can serve as biomarkers for GC patients with high prognostic value for OS of GC patients or therapeutic targets for GC.

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“…We note that our methods are distinct from con dence prediction scores that models inherently have, because our con dence score considers the effect of clinical features. We use three multi-gene signatures, including Oncotype DX, MammaPrint and an E2F4 signature [20][21][22], to demonstrate the potential of this framework to change the way of biomarker application. We use these signatures to construct classi cation models to predict patient response to neoadjuvant (pCR vs. RD, residual disease), and Cox regression models to predict patient recurrence-free survival in breast cancer.…”

Section: Introductionmentioning

confidence: 99%

A Framework to Predict the Applicability of Gene Signatures for Improving Prognostic Prediction

Yao

Tong

Cheng

2021

Preprint

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To improve cancer precision medicine, prognostic and predictive biomarkers are critically needed to aid physicians in deciding treatment strategies in a personalized fashion. Due to the heterogeneous nature of cancer, most biomarkers are expected to be valid only in a subset of patients. Furthermore, there is no current approach to determine the applicability of biomarkers. In this study, we propose a framework to improve the clinical application of biomarkers. As part of this framework, we develop a clinical outcome prediction model (CPM) and a predictability prediction model (PPM) for each biomarker and use these models to calculate a prognostic score (P-score) and a confidence score (C-score) for each patient. Each biomarker’s P-score indicates its association with patient clinical outcomes, while each C-score reflects the biomarker applicability of the biomarker’s CPM to a patient and therefore the confidence of the clinical prediction. We assessed the effectiveness of this framework by applying it to three biomarkers, Oncotype DX, MammaPrint, and an E2F4 signature, which have been used for predicting patient response, pathologic complete response versus residual disease to neoadjuvant chemotherapy (a classification problem), and recurrence-free survival (a Cox regression problem) in breast cancer, respectively. In both applications, our analyses indicated patients with higher C scores were more likely to be correctly predicted by the biomarkers, indicating the effectiveness of our framework. This framework provides a useful approach to develop and apply biomarkers in the context of cancer precision medicine.

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Integrative Genomic Analyses Yield Cell-Cycle Regulatory Programs with Prognostic Value

Cited by 6 publications

References 53 publications

A P53-Deficiency Gene Signature Predicts Recurrence Risk of Patients with Early-Stage Lung Adenocarcinoma

A P53-Deficiency Gene Signature Predicts Recurrence Risk of Patients with Early-Stage Lung Adenocarcinoma

Comprehensive Analysis of E2F Family Members in Human Gastric Cancer

A Framework to Predict the Applicability of Gene Signatures for Improving Prognostic Prediction

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