scholarly journals A Computational Framework for Prediction and Analysis of Cancer Signaling Dynamics from RNA Sequencing Data—Application to the ErbB Receptor Signaling Pathway

Cancers ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2878
Author(s):  
Hiroaki Imoto ◽  
Suxiang Zhang ◽  
Mariko Okada
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Cell ◽  
Gene Expression Data ◽  
Cancer Cell Line ◽  
Erbb Receptor ◽  
Expression Data ◽  
Rna Seq ◽  
Signaling Dynamics ◽  
Gene Expression Levels

A current challenge in systems biology is to predict dynamic properties of cell behaviors from public information such as gene expression data. The temporal dynamics of signaling molecules is critical for mammalian cell commitment. We hypothesized that gene expression levels are tightly linked with and quantitatively control the dynamics of signaling networks regardless of the cell type. Based on this idea, we developed a computational method to predict the signaling dynamics from RNA sequencing (RNA-seq) gene expression data. We first constructed an ordinary differential equation model of ErbB receptor → c-Fos induction using a newly developed modeling platform BioMASS. The model was trained with kinetic parameters against multiple breast cancer cell lines using autologous RNA-seq data obtained from the Cancer Cell Line Encyclopedia (CCLE) as the initial values of the model components. After parameter optimization, the model proceeded to prediction in another untrained breast cancer cell line. As a result, the model learned the parameters from other cells and was able to accurately predict the dynamics of the untrained cells using only the gene expression data. Our study suggests that gene expression levels of components within the ErbB network, rather than rate constants, can explain the cell-specific signaling dynamics, therefore playing an important role in regulating cell fate.

scholarly journals Coexpression and coregulation analysis of time-series gene expression data in estrogen-induced breast cancer cell

2013 ◽  
Vol 8 (1) ◽  
Author(s):  
Anirban Bhar ◽  
Martin Haubrock ◽  
Anirban Mukhopadhyay ◽  
Ujjwal Maulik ◽  
Sanghamitra Bandyopadhyay ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Time Series ◽  
Cancer Cell ◽  
Breast Cancer Cell ◽  
Gene Expression Data ◽  
Expression Data ◽  
Time Series Gene Expression ◽  
Analysis Of Time Series

scholarly journals Comparing alternative pipelines for cross-platform microarray gene expression data integration with RNA-seq data in breast cancer

2016 ◽  
Author(s):  
Alina Frolova ◽  
Vladyslav Bondarenko ◽  
Maria Obolenska
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Data Integration ◽  
Gene Expression Data ◽  
Statistical Power ◽  
Meta Analysis ◽  
Expression Data ◽  
Rna Seq ◽  
Microarray Gene Expression ◽  
Cross Platform

AbstractBackgroundAccording to major public repositories statistics an overwhelming majority of the existing and newly uploaded data originates from microarray experiments. Unfortunately, the potential of this data to bring new insights is limited by the effects of individual study-specific biases due to small number of biological samples. Increasing sample size by direct microarray data integration increases the statistical power to obtain a more precise estimate of gene expression in a population of individuals resulting in lower false discovery rates. However, despite numerous recommendations for gene expression data integration, there is a lack of a systematic comparison of different processing approaches aimed to asses microarray platforms diversity and ambiguous probesets to genes correspondence, leading to low number of studies applying integration.ResultsHere, we investigated five different approaches of the microarrays data processing in comparison with RNA-seq data on breast cancer samples. We aimed to evaluate different probesets annotations as well as different procedures of choosing between probesets mapped to the same gene. We show that pipelines rankings are mostly preserved across Affymetrix and Illumina platforms. BrainArray approach based on updated annotation and redesigned probesets definition and choosing probeset with the maximum average signal across the samples have best correlation with RNA-seq, while averaging probesets signals as well as scoring the quality of probes sequences mapping to the transcripts of the targeted gene have worse correlation. Finally, randomly selecting probeset among probesets mapped to the same gene significantly decreases the correlation with RNA-seq.ConclusionWe show that methods, which rely on actual probesets signal intensities, are advantageous to methods considering biological characteristics of the probes sequences only and that cross-platform integration of datasets improves correlation with the RNA-seq data. We consider the results obtained in this paper contributive to the integrative analysis as a worthwhile alternative to the classical meta-analysis of the multiple gene expression datasets.

A gp78/AMFR protein-driven gene signature that predicts breast cancer outcome.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 553-553
Author(s):  
Sandeep K. Singhal ◽  
Jung Byun ◽  
Samson Park ◽  
Tingfen Yan ◽  
Ryan Yancey ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Protein Expression ◽  
Gene Expression Data ◽  
Independent Predictor ◽  
Response To Therapy ◽  
Expression Data ◽  
Rna Seq ◽  
Gene Expression Signatures ◽  
Gene Modules

553 Background: gp78, also known as the autocrine motility factor receptor (AMFR) or RNF45, is a polytopic RING-type E3 ubiquitin ligase resident to the endoplasmic reticulum (ER) that plays major role in the cellular response to stress by regulating ER homeostasis and signaling through its participation in the unfolded protein response (UPR) and ER associated degradation. We used machine learning (ML) and statistical modeling (SM) to assess gp78 as a protein biomarker that is an independent predictor of breast cancer (bc) survival exclusively in women of self-reported African descent as opposed to European ancestry. Methods: We examined a cohort of racially diverse 555 BC bc patients who underwent surgery for their primary BC in Greenville, NC using ML and SM approach. We leveraged the availability of RNA-seq gene expression data on a portion of our bc cohort (N=136 of 555) to construct gene expression signatures. Results: Using antibodies developed in the Weissman lab and established methods for quantitative IHC, we have found that gp78 expression is significantly increased in the tumors of bc patients compared to normal breast epithelia. In addition, we found that gp78 is expressed at significantly higher levels in bc of non-Hispanic black women (NHB) compared to non-Hispanic white women (NHW) (p=0.0038), and that bc subtypes known to be more aggressive and associated with higher grades like, Basal (p=1.6e-12), Luminal B (p=2.3e-4) and HER2(8.3e-4), display significantly higher levels of gp78 compare to Luminal A. Moreover, Kaplan-Meier survival curve analyses show that gp78 protein expression is more significantly associated with poor survival in NHB women (HR:1.65, p=0.073) compared to NHW women (HR:2.01, p=0.004). Finally, multivariate analysis reveals that gp78 protein expression, based on quantitative IHC, is an independent predictor of poor bc survival exclusively in women of African (NHB) ancestry (HR:1.99, p=0.017). We leveraged the availability of RNA-seq gene expression data on a portion of our bc cohort to construct gene expression signatures or gene modules. An analysis of pooled publicly available data from 845 patients that underwent neoadjuvant chemotherapy for bc (primarily taxane and anthracycline based), reveals that gp78 gene modules are highly predictive of patient response to therapy. gp78-derived gene modules show both high fold difference and significance in predicting response to therapy (AUC:0.72) which is very similar to other multi-gene panels that are currently in clinical use including Prosigna, MammaPrint, and Oncotype Dx. Conclusions: Our results show that gp78/AMFR is an independent predictor of bc survival and response to therapy, based on race, thus implicating a role for this protein, and potentially the UPR, as underlying biological differences in tumor properties linked to genetic ancestry.

scholarly journals Multi-Omics Analysis of Cancer Cell Lines with High/Low Ferroptosis Scores and Development of a Ferroptosis-Related Model for Multiple Cancer Types

2021 ◽  
Vol 9 ◽  
Author(s):  
Guangyao Shan ◽  
Huan Zhang ◽  
Guoshu Bi ◽  
Yunyi Bian ◽  
Jiaqi Liang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Cancer Cell ◽  
Gene Expression Data ◽  
Cancer Cell Lines ◽  
Expression Data ◽  
Rna Seq ◽  
Multiple Cancer ◽  
Related Model ◽  
Cancer Types

Background: Ferroptosis is a newly identified regulated cell death characterized by iron-dependent lipid peroxidation and subsequent membrane oxidative damage, which has been implicated in multiple types of cancers. The multi-omics differences between cancer cell lines with high/low ferroptosis scores remain to be elucidated.Methods and Materials: We used RNA-seq gene expression, gene mutation, miRNA expression, metabolites, copy number variation, and drug sensitivity data of cancer cell lines from DEPMAP to detect multi-omics differences associated with ferroptosis. Based on the gene expression data of cancer cell lines, we performed LASSO-Logistic regression analysis to build a ferroptosis-related model. Lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), esophageal cancer (ESCA), bladder cancer (BLCA), cervical cancer (CESC), and head and neck cancer (HNSC) patients from the TCGA database were used as validation cohorts to test the efficacy of this model.Results: After stratifying the cancer cell lines into high score (HS) and low score (LS) groups according to the median of ferroptosis scores generated by gene set variation analysis, we found that IC50 of 66 agents such as oxaliplatin (p < 0.001) were significantly different, among which 65 were higher in the HS group. 851 genes such as KEAP1 and NRAS were differentially muted between the two groups. Differentially expressed genes, miRNAs and metabolites were also detected—multiple items such as IL17F (logFC = 6.58, p < 0.001) differed between the two groups. Unlike the TCGA data generated by bulk RNA-seq, the gene expression data in DEPMAP are from pure cancer cells, so it could better reflect the traits of tumors in cancer patients. Thus, we built a 15-signature model (AUC = 0.878) based on the gene expression data of cancer cell lines. The validation cohorts demonstrated a higher mutational rate of NFE2L2 and higher expression levels of 12 ferroptosis-related genes in HS groups.Conclusion: This article systemically analyzed multi-omics differences between cancer cell lines with high/low ferroptosis scores and a ferroptosis-related model was developed for multiple cancer types. Our findings could improve our understanding of the role of ferroptosis in cancer and provide new insight into treatment for malignant tumors.

scholarly journals Immune microenvironment and intrinsic subtyping in hormone receptor-positive/HER2-negative breast cancer

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Gaia Griguolo ◽  
Maria Vittoria Dieci ◽  
Laia Paré ◽  
Federica Miglietta ◽  
Daniele Giulio Generali ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Gene Expression Data ◽  
Hormone Receptor ◽  
Tumor Biology ◽  
Expression Data ◽  
Immune Microenvironment ◽  
Immune Infiltrate ◽  
Hormone Receptor Positive ◽  
Her2 Breast Cancer

AbstractLittle is known regarding the interaction between immune microenvironment and tumor biology in hormone receptor (HR)+/HER2− breast cancer (BC). We here assess pretreatment gene-expression data from 66 HR+/HER2− early BCs from the LETLOB trial and show that non-luminal tumors (HER2-enriched, Basal-like) present higher tumor-infiltrating lymphocyte levels than luminal tumors. Moreover, significant differences in immune infiltrate composition, assessed by CIBERSORT, were observed: non-luminal tumors showed a more proinflammatory antitumor immune infiltrate composition than luminal ones.

Sign in / Sign up

Export Citation Format

Share Document