Gene expression panel (TheraPrint) analyzed as predictors of response to neoadjuvant chemotherapy (NCT) in patients (pts) with stage II-III and inflammatory breast cancer (BC).

e21013 Background: TheraPrint is a microarray-based gene expression panel of 125 genes identified as potential targets for prognosis and therapeutic response. These genes may hold the key to a greater level of personalized prognosis and therapy for BC pts. The aim of the current study was to assess the clinical relevance of the TheraPrint genes for either predictive and/or prognostic value in 2 patient cohorts treated with NCT. Methods: The 1st patient cohort are 68 Stage II-III BC pts treated with NCT. Expression data from Agilent full genome arrays, containing the MammaPrint, BluePrint and TheraPrint diagnostic profiles/probes (Somlo et al, 2009). Median FU 2.3 years. The 2nd patient cohort are 230 Stage I-III BC pts treated with NCT. Expression data from Affymetrix probe sets was publically available (Iwamoto et al, 2011). Median FU 5.2 years. To identify genes that are differentially expressed between responders (pCR/RCBI) and non-responders, a supervised analysis was performed. The analysis was performed across all pts and also within groups of HER2+ and HER2-. Univariate t-tests were performed, with results filtered by permutation p-value (p<0.05) and fold change of >1.5. Global test was also reported. In addition, survival data analysis was performed across all pts. Results: Overlapping genes between the 2 datasets that were significantly differentially expressed between responders and non-responders include: BCL2 (down-regulated) and CDH3, GRB7, KRT6B, KRT17 (up-regulated). When analysing the HER2- subgroup, 3 genes turned out to be differentially expressed between responders and non-responders in the 2 datasets: FLT1, PIK3R1 (down-regulated) and KRT6B (up-regulated). For the HER2+ subgroup, only one gene overlapped for the 2 datasets: IL2RA (up-regulated). The top canonical pathways for the significant genes have been analyzed, and in addition correlation of the TheraPrint gene expression with survival for these pt groups. Conclusions: This study has identified several genes from a panel of 125 TheraPrint genes with statistically significant correlation between expression and response to NCT.

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Gene expression panel (TheraPrint) analyzed as predictors of response to neoadjuvant chemotherapy (NCT) in patients (pts) with stage II-III and inflammatory breast cancer (BC).

Journal of Clinical Oncology ◽

10.1200/jco.2012.30.27_suppl.8 ◽

2012 ◽

Vol 30 (27_suppl) ◽

pp. 8-8 ◽

Cited By ~ 2

Author(s):

George Somlo ◽

Femke De Snoo ◽

Justine Peeters ◽

Lisette Stork-Sloots ◽

Iris Simon ◽

...

Keyword(s):

Gene Expression ◽

Survival Data ◽

Stage Ii ◽

Differentially Expressed ◽

P Value ◽

Expression Data ◽

Global Test ◽

Patient Cohort ◽

Predictors Of Response ◽

Canonical Pathways

8 Background: TheraPrint is a microarray-based gene expression panel of 125 genes identified as potential targets for prognosis and therapeutic response. These genes may hold the key to a greater level of personalized prognosis and therapy for BC pts. The aim of the current study was to assess the clinical relevance of the TheraPrint genes for either predictive and/or prognostic value in two patient cohorts treated with NCT. Methods: The 1st patient cohort are 68 Stage II-III BC pts treated with NCT. Expression data from Agilent full genome arrays, containing the MammaPrint, BluePrint and TheraPrint diagnostic profiles/probes (Somlo et al, 2009). Median FU 2.3 years. The 2nd patient cohort are 230 Stage I-III BC pts treated with NCT. Expression data from Affymetrix probe sets was publically available (Iwamoto et al, 2011). Median FU 5.2 years. To identify genes that are differentially expressed between responders (pCR/RCBI) and non-responders, a supervised analysis was performed. The analysis was performed across all pts and also within groups of HER2+ and HER2-. Univariate t-tests were performed, with results filtered by permutation p-value (p<0.05) and fold change of >1.5. Global test was also reported. In addition, survival data analysis was performed across all pts. Results: Overlapping genes between the 2 datasets that were significantly differentially expressed between responders and non-responders include: BCL2 (down-regulated) and CDH3, GRB7, KRT6B, KRT17 (up-regulated). When analysing the HER2- subgroup, 3 genes turned out to be differentially expressed between responders and non-responders in the 2 datasets: FLT1, PIK3R1 (down-regulated) and KRT6B (up-regulated). For the HER2+ subgroup, only one gene overlapped for the 2 datasets: IL2RA (up-regulated). The top canonical pathways for the significant genes have been analyzed, and in addition correlation of the TheraPrint gene expression with survival for these pt groups. Conclusions: This study has identified several genes from a panel of 125 TheraPrint genes with statistically significant correlation between expression and response to NCT.

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Gene Set Correlation Analysis and Visualization Using Gene Expression Data

Current Bioinformatics ◽

10.2174/1574893615999200629124444 ◽

2020 ◽

Vol 15 ◽

Author(s):

Chen-An Tsai ◽

James J. Chen

Keyword(s):

Gene Expression ◽

Correlation Analysis ◽

Gene Expression Data ◽

Differentially Expressed Gene ◽

Differentially Expressed ◽

Superior Performance ◽

Expression Data ◽

Gene Set ◽

Gene Sets ◽

Set Correlation

Background: Gene set enrichment analyses (GSEA) provide a useful and powerful approach to identify differentially expressed gene sets with prior biological knowledge. Several GSEA algorithms have been proposed to perform enrichment analyses on groups of genes. However, many of these algorithms have focused on identification of differentially expressed gene sets in a given phenotype. Objective: In this paper, we propose a gene set analytic framework, Gene Set Correlation Analysis (GSCoA), that simultaneously measures within and between gene sets variation to identify sets of genes enriched for differential expression and highly co-related pathways. Methods: We apply co-inertia analysis to the comparisons of cross-gene sets in gene expression data to measure the costructure of expression profiles in pairs of gene sets. Co-inertia analysis (CIA) is one multivariate method to identify trends or co-relationships in multiple datasets, which contain the same samples. The objective of CIA is to seek ordinations (dimension reduction diagrams) of two gene sets such that the square covariance between the projections of the gene sets on successive axes is maximized. Simulation studies illustrate that CIA offers superior performance in identifying corelationships between gene sets in all simulation settings when compared to correlation-based gene set methods. Result and Conclusion: We also combine between-gene set CIA and GSEA to discover the relationships between gene sets significantly associated with phenotypes. In addition, we provide a graphical technique for visualizing and simultaneously exploring the associations of between and within gene sets and their interaction and network. We then demonstrate integration of within and between gene sets variation using CIA and GSEA, applied to the p53 gene expression data using the c2 curated gene sets. Ultimately, the GSCoA approach provides an attractive tool for identification and visualization of novel associations between pairs of gene sets by integrating co-relationships between gene sets into gene set analysis.

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Identification of key genes unique to the luminal A and basal-like breast cancer subtypes via bioinformatic analysis

10.21203/rs.3.rs-28637/v1 ◽

2020 ◽

Author(s):

Rong Jia ◽

Zhongxian Li ◽

Wei Liang ◽

Yucheng Ji ◽

Yujie Weng ◽

...

Keyword(s):

Breast Cancer ◽

Differentially Expressed Genes ◽

Survival Data ◽

Breast Cancer Subtypes ◽

Differentially Expressed ◽

Expression Data ◽

Luminal A ◽

Cancer Subtypes ◽

Protein Protein Interaction ◽

Basal Like Breast Cancer

Abstract Background Breast cancer subtypes are statistically associated with prognosis. The search for markers of breast tumor heterogeneity and the development of precision medicine for patients are the current focuses of the field. Methods We used a bioinformatic approach to identify key disease-causing genes unique to the luminal A and basal-like subtypes of breast cancer. First, we retrieved gene expression data for luminal A breast cancer, basal-like breast cancer, and normal breast tissue samples from The Cancer Genome Atlas database. The differentially expressed genes unique to the 2 breast cancer subtypes were identified and subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. We constructed protein–protein interaction networks of the differentially expressed genes. Finally, we analyzed the key modules of the networks, which we combined with survival data to identify the unique cancer genes associated with each breast cancer subtype. Results We identified 1,114 differentially expressed genes in luminal A breast cancer and 1,042 differentially expressed genes in basal-like breast cancer, of which the subtypes shared 500. We observed 614 and 542 differentially expressed genes unique to luminal A and basal-like breast cancer, respectively. Through enrichment analyses, protein–protein interaction network analysis, and module mining, we identified 8 key differentially expressed genes unique to each subtype. Analysis of the gene expression data in the context of the survival data revealed that high expression of NMUR1 and NCAM1 in luminal A breast cancer statistically correlated with poor prognosis, whereas the low expression levels of CDC7 , KIF18A , STIL , and CKS2 in basal-like breast cancer statistically correlated with poor prognosis. Conclusions NMUR1 and NCAM1 are novel key disease-causing genes for luminal A breast cancer, and STIL is a novel key disease-causing gene for basal-like breast cancer. These genes are potential targets for clinical treatment.

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Generation of patterns from gene expression data by assigning confidence to differentially expressed genes

Bioinformatics ◽

10.1093/bioinformatics/16.8.685 ◽

2000 ◽

Vol 16 (8) ◽

pp. 685-698 ◽

Cited By ~ 60

Author(s):

E. Manduchi ◽

G. R. Grant ◽

S. E. McKenzie ◽

G. C. Overton ◽

S. Surrey ◽

...

Keyword(s):

Gene Expression ◽

Differentially Expressed Genes ◽

Gene Expression Data ◽

Differentially Expressed ◽

Expression Data

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Can We Assume the Gene Expression Profile as a Proxy for Signaling Network Activity?

Biomolecules ◽

10.3390/biom10060850 ◽

2020 ◽

Vol 10 (6) ◽

pp. 850 ◽

Cited By ~ 2

Author(s):

Mehran Piran ◽

Reza Karbalaei ◽

Mehrdad Piran ◽

Jehad Aldahdooh ◽

Mehdi Mirzaie ◽

...

Keyword(s):

Gene Expression ◽

Signaling Pathways ◽

Differentially Expressed Genes ◽

Expression Profile ◽

Signaling Network ◽

Differentially Expressed ◽

Network Activity ◽

Gene Products ◽

Expression Data ◽

Gene Pairs

Studying relationships among gene products by expression profile analysis is a common approach in systems biology. Many studies have generalized the outcomes to the different levels of central dogma information flow and assumed a correlation of transcript and protein expression levels. However, the relation between the various types of interaction (i.e., activation and inhibition) of gene products to their expression profiles has not been widely studied. In fact, looking for any perturbation according to differentially expressed genes is the common approach, while analyzing the effects of altered expression on the activity of signaling pathways is often ignored. In this study, we examine whether significant changes in gene expression necessarily lead to dysregulated signaling pathways. Using four commonly used and comprehensive databases, we extracted all relevant gene expression data and all relationships among directly linked gene pairs. We aimed to evaluate the ratio of coherency or sign consistency between the expression level as well as the causal relationships among the gene pairs. Through a comparison with random unconnected gene pairs, we illustrate that the signaling network is incoherent, and inconsistent with the recorded expression profile. Finally, we demonstrate that, to infer perturbed signaling pathways, we need to consider the type of relationships in addition to gene-product expression data, especially at the transcript level. We assert that identifying enriched biological processes via differentially expressed genes is limited when attempting to infer dysregulated pathways.

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HisCoM-PAGE: Hierarchical Structural Component Models for Pathway Analysis of Gene Expression Data

Genes ◽

10.3390/genes10110931 ◽

2019 ◽

Vol 10 (11) ◽

pp. 931 ◽

Cited By ~ 4

Author(s):

Mok ◽

Kim ◽

Lee ◽

Choi ◽

Lee ◽

...

Keyword(s):

Gene Expression ◽

Pancreatic Cancer ◽

Gene Expression Data ◽

Pathway Analysis ◽

Structural Component ◽

Biological Data ◽

Gene Set Enrichment Analysis ◽

Expression Data ◽

Global Test ◽

Causal Pathways

Although there have been several analyses for identifying cancer-associated pathways, based on gene expression data, most of these are based on single pathway analyses, and thus do not consider correlations between pathways. In this paper, we propose a hierarchical structural component model for pathway analysis of gene expression data (HisCoM-PAGE), which accounts for the hierarchical structure of genes and pathways, as well as the correlations among pathways. Specifically, HisCoM-PAGE focuses on the survival phenotype and identifies its associated pathways. Moreover, its application to real biological data analysis of pancreatic cancer data demonstrated that HisCoM-PAGE could successfully identify pathways associated with pancreatic cancer prognosis. Simulation studies comparing the performance of HisCoM-PAGE with other competing methods such as Gene Set Enrichment Analysis (GSEA), Global Test, and Wald-type Test showed HisCoM-PAGE to have the highest power to detect causal pathways in most simulation scenarios.

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Genes differentially expressed in medulloblastoma and fetal brain

Physiological Genomics ◽

10.1152/physiolgenomics.1999.1.2.83 ◽

1999 ◽

Vol 1 (2) ◽

pp. 83-91 ◽

Cited By ~ 50

Author(s):

E. M. C. MICHIELS ◽

E. OUSSOREN ◽

M. VAN GROENIGEN ◽

E. PAUWS ◽

P. M. M. BOSSUYT ◽

...

Keyword(s):

Gene Expression ◽

Brain Tumor ◽

Northern Blot ◽

Fetal Brain ◽

Differentially Expressed ◽

P Value ◽

Test Statistics ◽

Childhood Brain Tumor ◽

Germinal Layer ◽

Test Statistic

Michiels, E. M. C., E. Oussoren, M. van Groenigen, E. Pauws, P. M. M. Bossuyt, P. A. Voûte, and F. Baas. Genes differentially expressed in medulloblastoma and fetal brain. Physiol. Genomics 1: 83–91, 1999.—Serial analysis of gene expression (SAGE) was used to identify genes that might be involved in the development or growth of medulloblastoma, a childhood brain tumor. Sequence tags from medulloblastoma (10229) and fetal brain (10692) were determined. The distributions of sequence tags in each population were compared, and for each sequence tag, pairwise χ2 test statistics were calculated. Northern blot was used to confirm some of the results obtained by SAGE. For 16 tags, the χ2 test statistic was associated with a P value < 10−4. Among those transcripts with a higher expression in medulloblastoma were the genes for ZIC1 protein and the OTX2 gene, both of which are expressed in the cerebellar germinal layers. The high expression of these two genes strongly supports the hypothesis that medulloblastoma arises from the germinal layer of the cerebellum. This analysis shows that SAGE can be used as a rapid differential screening procedure.

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Transcriptomic Identification of Floral Transition and Development-Associated Genes in Styrax japonicus

Forests ◽

10.3390/f11010010 ◽

2019 ◽

Vol 11 (1) ◽

pp. 10

Author(s):

Wei Li ◽

Zhengzhao Xu ◽

Cuiping Zhang ◽

Xinqiang Jiang ◽

Kuiling Wang

Keyword(s):

Gene Expression ◽

Protein Kinase ◽

Flower Development ◽

Floral Development ◽

Differentially Expressed ◽

Cdna Libraries ◽

Expression Data ◽

Kegg Database ◽

Pathway Enrichment ◽

Gene Expression Dynamics

Styrax japonicus (S. japonicus) is an important flowering tree species in temperate regions, and it is regarded as a nectariferous plant. However, there have been few studies to date analyzing floral development in this species. In order to understand gene expression dynamics during S. japonicus flower development, we; therefore, prepared cDNA libraries from three distinct stages of S. japonicus. Illumina sequencing generated 31,471 differentially expressed unigenes during flower development. We additionally conducted pathway enrichment analyses using the GO and KEGG database in order to assess the functions of genes differentially expressed during different stages of the floral development process, revealing these genes to be associated with pathways including phytohormone signaling, Transcription factor, protein kinase, and circadian rhythms. In total, 4828 TF genes, 8402 protein kinase genes, and 78 DEGs related to hormone pathways were identified in flower development stages. Six genes were selected for confirmation of expression levels using quantitative real-time PCR. The gene expression data presented herein represent the most comprehensive dataset available regarding the flowering of S. japonicus, thus offering a reference for future studies of the flowering of this and other Styracaceae species.

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Association Between Gene Expression Profiles and Commonly Mutated Genes In The Hematopoietic Stem Cells Of Patients With Myelodysplastic Syndromes

Blood ◽

10.1182/blood.v122.21.2779.2779 ◽

2013 ◽

Vol 122 (21) ◽

pp. 2779-2779 ◽

Cited By ~ 1

Author(s):

Andrea Pellagatti ◽

Moritz Gerstung ◽

Elli Papaemmanuil ◽

Luca Malcovati ◽

Aristoteles Giagounidis ◽

...

Keyword(s):

Gene Expression ◽

Differentially Expressed Genes ◽

Gene Expression Data ◽

Expression Profiles ◽

Gene Mutations ◽

Gene Expression Profiles ◽

Differentially Expressed ◽

Expression Data ◽

Common Gene ◽

Mutational Status

Abstract A particular profile of gene expression can reflect an underlying molecular abnormality in malignancy. Distinct gene expression profiles and deregulated gene pathways can be driven by specific gene mutations and may shed light on the biology of the disease and lead to the identification of new therapeutic targets. We selected 143 cases from our large-scale gene expression profiling (GEP) dataset on bone marrow CD34+ cells from patients with myelodysplastic syndromes (MDS), for which matching genotyping data were obtained using next-generation sequencing of a comprehensive list of 111 genes involved in myeloid malignancies (including the spliceosomal genes SF3B1, SRSF2, U2AF1 and ZRSR2, as well as TET2, ASXL1and many other). The GEP data were then correlated with the mutational status to identify significantly differentially expressed genes associated with each of the most common gene mutations found in MDS. The expression levels of the mutated genes analyzed were generally lower in patients carrying a mutation than in patients wild-type for that gene (e.g. SF3B1, ASXL1 and TP53), with the exception of RUNX1 for which patients carrying a mutation showed higher expression levels than patients without mutation. Principal components analysis showed that the main directions of gene expression changes (principal components) tend to coincide with some of the common gene mutations, including SF3B1, SRSF2 and TP53. SF3B1 and STAG2 were the mutated genes showing the highest number of associated significantly differentially expressed genes, including ABCB7 as differentially expressed in association with SF3B1 mutation and SULT2A1 in association with STAG2 mutation. We found distinct differentially expressed genes associated with the four most common splicing gene mutations (SF3B1, SRSF2, U2AF1 and ZRSR2) in MDS, suggesting that different phenotypes associated with these mutations may be driven by different effects on gene expression and that the target gene may be different. We have also evaluated the prognostic impact of the GEP data in comparison with that of the genotype data and importantly we have found a larger contribution of gene expression data in predicting progression free survival compared to mutation-based multivariate survival models. In summary, this analysis correlating gene expression data with genotype data has revealed that the mutational status shapes the gene expression landscape. We have identified deregulated genes associated with the most common gene mutations in MDS and found that the prognostic power of gene expression data is greater than the prognostic power provided by mutation data. AP and MG contributed equally to this work. JB and PJC are co-senior authors. Disclosures: No relevant conflicts of interest to declare.

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