A 16 Yin Yang gene expression ratio signature for ER+/node− breast cancer

Xu, Wayne; Jia, Gaofeng; Cai, Nianguang; Huang, Shujun; Davie, James R.; Pitz, Marshall; Banerji, Shantanu; Murphy, Leigh C.

doi:10.1002/ijc.30556

Cited by 7 publications

(12 citation statements)

References 44 publications

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“…Comparing cancer tissue samples with normal tissue has been historically used for discovery of DEGs in cancer; however, few studies have looked at them by way of two opposing groups of genes, the interaction of which would affect cancer progression. In this study, we hypothesized that two opposing effects in BC cancer cells, represented by Yin and Yang genes, determine the outcome of BC [13,14]. Yang genes are expressed more highly in normal breast tissues, while Yin genes are expressed more highly in breast tumors.…”

Section: Introductionmentioning

confidence: 99%

Integrative Analysis Reveals Subtype-Specific Regulatory Determinants in Triple Negative Breast Cancer

Huang

et al. 2019

Cancers

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Different breast cancer (BC) subtypes have unique gene expression patterns, but their regulatory mechanisms have yet to be fully elucidated. We hypothesized that the top upregulated (Yin) and downregulated (Yang) genes determine the fate of cancer cells. To reveal the regulatory determinants of these Yin and Yang genes in different BC subtypes, we developed a lasso regression model integrating DNA methylation (DM), copy number variation (CNV) and microRNA (miRNA) expression of 391 BC patients, coupled with miRNA–target interactions and transcription factor (TF) binding sites. A total of 25, 20, 15 and 24 key regulators were identified for luminal A, luminal B, Her2-enriched, and triple negative (TN) subtypes, respectively. Many of the 24 TN regulators were found to regulate the PPARA and FOXM1 pathways. The Yin Yang gene expression mean ratio (YMR) and combined risk score (CRS) signatures built with either the targets of or the TN regulators were associated with the BC patients’ survival. Previously, we identified FOXM1 and PPARA as the top Yin and Yang pathways in TN, respectively. These two pathways and their regulators could be further explored experimentally, which might help to identify potential therapeutic targets for TN.

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Section: Introductionmentioning

confidence: 99%

Integrative Analysis Reveals Subtype-Specific Regulatory Determinants in Triple Negative Breast Cancer

Huang

et al. 2019

Cancers

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“…Being able to capture and analyze these unique invading subpopulations in the act of doing so, together with their matched non-invasive counterparts, will be key in making progress towards understanding and treating cancer. While much work has been done in this field, some of these studies compare between different patients or utilize cell lines of different invasive properties [10][11][12][13][14] , which may be confounded by existing inter-tumor heterogeneity. While there have been other reports studying spatial heterogeneity within patient samples, these studies utilized the use of formalin fixed primary tumor tissue for the identification of genomic markers 15,16 .…”

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confidence: 99%

Heterogeneity at the invasion front of triple negative breast cancer cells

Yong

Ulintz

Cáceres

et al. 2020

Sci Rep

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Identifying better predictive and prognostic biomarkers for the diagnosis and treatment of triple negative breast cancer (TNBC) is complicated by tumor heterogeneity ranging from responses to therapy, mutational burden, and clonal evolution. To overcome the gap in our understanding of tumor heterogeneity, we hypothesized that isolating and studying the gene expression profile of invasive tumor cell subpopulations would be a crucial step towards achieving this goal. In this report, we utilized a fluidic device previously reported to be capable of supporting long-term three-dimensional growth and invasion dynamics of cancer cells. Live invading and matched non-invading SUM149 inflammatory breast cancer cells were enriched using this device and these two functionally distinct subpopulations were tested for differences in gene expression using a gene expression microarray. 305 target genes were identified to have altered expression in the invading cells compared to the non-invading tumoroid cells. Gene ontology analysis of the gene panel identified multiple biological roles ranging from extracellular matrix reorganization to modulation of the immune response and Rho signaling. Interestingly, the genes associated with the invasion front differ between different samples, consistent with inter- and intra-tumor heterogeneity. This work suggests the impact of heterogeneity in biomarker discovery should be considered as cancer therapy increasingly heads towards a personalized approach.

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“…For this reason multi-gene signatures have been extensively studied to provide prognostic and predictive information for breast cancer treatment. Today more than 30 different signatures have been reported [ 1 – 42 ]. Several signatures have become commercially available including the 70-gene signature (MammaPrint) [ 1 ], the 21-gene signature (OncotypeDx) [ 4 ], the 97-gene genomic grade index (GGI) [ 15 ], the EndoPredict assay [ 33 ], the breast cancer index [ 21 ], and the PAM50 assay [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, we have developed a 16 Yin Yang gene mean expression ratio (YMR-16) signature for ER-positive/node-negative breast cancer based on the hypothesis that two opposing effects (Yin and Yang) could determine cancer initiation and progression [ 42 ]. These 16 genes were identified among all human genes on the Illumina gene expression microarray platform.…”

Section: Introductionmentioning

confidence: 99%

“…These 16 genes were identified among all human genes on the Illumina gene expression microarray platform. In this study, we attempted to understand why there are so few overlapping genes among the previously reported multi-gene signatures, as well as to address our hypothesis that signatures share common functions or pathways [ 42 , 45 , 46 ]. We also evaluated the cohort of genes found to be common from multiple signatures in our Yin Yang gene Mean expression Ratio (YMR) model [ 42 ] and as a subtyping classifier.…”

Section: Introductionmentioning

confidence: 99%

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Genes and functions from breast cancer signatures

Huang

Murphy

2018

BMC Cancer

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BackgroundBreast cancer is a heterogeneous disease and personalized medicine is the hope for the improvement of the clinical outcome. Multi-gene signatures for breast cancer stratification have been extensively studied in the past decades and more than 30 different signatures have been reported. A major concern is the minimal overlap of genes among the reported signatures. We investigated the breast cancer signature genes to address our hypothesis that the genes of different signature may share common functions, as well as to use these previously reported signature genes to build better prognostic models.MethodsA total of 33 signatures and the corresponding gene lists were investigated. We first examined the gene frequency and the gene overlap in these signatures. Then the gene functions of each signature gene list were analysed and compared by the KEGG pathways and gene ontology (GO) terms. A classifier built using the common genes was tested using the METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) data. The common genes were also tested for building the Yin Yang gene mean expression ratio (YMR) signature using public datasets (GSE1456 and GSE2034).ResultsAmong a total of 2239 genes collected from the 33 breast cancer signatures, only 238 genes overlapped in at least two signatures; while from a total of 1979 function terms enriched in the 33 signature gene lists, 429 terms were common in at least two signatures. Most of the common function terms were involved in cell cycle processes. While there is almost no common overlapping genes between signatures developed for ER-positive (e.g. 21-gene signature) and those developed for ER-negative (e.g. basal signatures) tumours, they have common function terms such as cell death, regulation of cell proliferation. We used the 62 genes that were common in at least three signatures as a classifier and subtyped 1141 METABRIC cases including 144 normal samples into nine subgroups. These subgroups showed different clinical outcome. Among the 238 common genes, we selected those genes that are more highly expressed in normal breast tissue than in tumours as Yang genes and those more highly expressed in tumours than in normal as Yin genes and built a YMR model signature. This YMR showed significance in risk stratification in two datasets (GSE1456 and GSE2034).ConclusionsThe lack of significant numbers of overlapping genes among most breast cancer signatures can be partially explained by our discovery that these signature genes represent groups with similar functions. The genes collected from these previously reported signatures are valuable resources for new model development. The subtype classifier and YMR signature built from the common genes showed promising results.Electronic supplementary materialThe online version of this article (10.1186/s12885-018-4388-4) contains supplementary material, which is available to authorized users.

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A 16 Yin Yang gene expression ratio signature for ER+/node− breast cancer

Cited by 7 publications

References 44 publications

Integrative Analysis Reveals Subtype-Specific Regulatory Determinants in Triple Negative Breast Cancer

Integrative Analysis Reveals Subtype-Specific Regulatory Determinants in Triple Negative Breast Cancer

Heterogeneity at the invasion front of triple negative breast cancer cells

Genes and functions from breast cancer signatures

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