scholarly journals Microvascular proliferation in luminal A and basal-like breast cancer subtypes

2015 ◽  
Vol 68 (11) ◽  
pp. 891-897 ◽  
Author(s):  
Maria Ryssdal Kraby ◽  
Kristi Krüger ◽  
Signe Opdahl ◽  
Lars J Vatten ◽  
Lars A Akslen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Subtypes ◽  
Luminal A ◽  
Cancer Subtypes ◽  
Microvascular Proliferation ◽  
Basal Like Breast Cancer

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

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.

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

2020 ◽  
Vol 18 (1) ◽  
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 1114 differentially expressed genes in luminal A breast cancer and 1042 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.

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

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.

Comprehensive immunohistochemical analysis of RET, BCAR1, and BCAR3 expression in patients with Luminal A and B breast cancer subtypes

2022 ◽  
Author(s):  
Ana Carolina Pavanelli ◽  
Flavia Rotea Mangone ◽  
Piriya Yoganathan ◽  
Simone Aparecida Bessa ◽  
Suely Nonogaki ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Immunohistochemical Analysis ◽  
Breast Cancer Subtypes ◽  
Luminal A ◽  
Cancer Subtypes

scholarly journals Ki-67 (30-9) scoring and differentiation of Luminal A- and Luminal B-like breast cancer subtypes

2019 ◽  
Vol 178 (2) ◽  
pp. 451-458 ◽  
Author(s):  
Giuseppe Viale ◽  
Amy E. Hanlon Newell ◽  
Espen Walker ◽  
Greg Harlow ◽  
Isaac Bai ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Subtypes ◽  
Luminal A ◽  
Ki 67 ◽  
Cancer Subtypes ◽  
Luminal B

Response to neoadjuvant chemotherapy and outcome based on breast cancer subtype

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. e11516-e11516
Author(s):  
A. Guerrero-Zotano ◽  
J. Gavila ◽  
M. A. Climent ◽  
M. J. Juan ◽  
V. Guillem ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neoadjuvant Chemotherapy ◽  
Breast Cancer Subtypes ◽  
Nuclear Staining ◽  
Luminal A ◽  
Long Term Outcome ◽  
Cancer Subtypes ◽  
Luminal B ◽  
Her 2

e11516 Background: Gene expression profiling identifies several breast cancer subtypes with different chemosensitivity and outcome. We used immunohistochemistry surrogate markers to classify tumors according to known breast cancer subtypes and examined the relationship between neoadjuvant chemotherapy (NAC) response and long-term end points, including distant disease-free survival (DDFS) and overall survival (OS). Methods: Review of clinical and pathological data from 271 breast cancer patients treated in our institution with NAC between 1991–2008. Breast cancer subtypes were defined as follows: Luminal A: Estrogen receptor positive (ER+) and/or progesterone peceptor positive (PR+), human epidermal growth factor receptor 2-positive (Her-2+); Luminal B: ER+ and/or PR+,Her-2+; Basal: ER-,PR-,Her-2-;HER2: ER-,PR-,Her-2 +. ER and PR positive scored as positive if tumor cell nuclear staining was at least 2+. Her-2 scored as positive if test DAKO scored 3+ or FISH ratio Her-2/CEP-17>2.2. Results: 121 (45.8%) patients were classifed as Luminal A; 22 (8.1%) as Luminal B; 75 (27.7%) as Basal, and 50 (18.5%) as HER2. Most patients (63%) received NAC based on anthracyclines and taxanes. 36% Her-2+ patients were treated with NAC based on trastuzumab, and 43% received trastuzumab as adjuvant treatment. Response and outcome results are shown below (Table). Independently from subtype, only four patients out of 58 with pCR relapsed. Among patients who didn´t achieved pathologic complete response (pCR), basal and HER2 subtypes have the worst outcome (4 years SG 80% and 72% respectevely) compared with Luminal A (4 years SG: 94.7%), (log-rank p=0.009). Conclusions: Basal and HER2 tumor despite high chemosensitivity have worst long term outcome, particularly if pCR is not achieved after NAC. [Table: see text] No significant financial relationships to disclose.

Epigenetic aspects of triple-negative in patients with breast cancer.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 1041-1041
Author(s):  
Joaquina Martínez-Galan ◽  
Sandra Rios ◽  
Juan Ramon Delgado ◽  
Blanca Torres-Torres ◽  
Jesus Lopez-Peñalver ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Dna Methylation ◽  
Triple Negative ◽  
Breast Cancer Subtype ◽  
Regulation Of Gene Expression ◽  
Breast Cancer Subtypes ◽  
Luminal A ◽  
Cancer Subtypes ◽  
Luminal B

1041 Background: Identification of gene expression-based breast cancer subtypes is considered a critical means of prognostication. Genetic mutations along with epigenetic alterations contribute to gene-expression changes occurring in breast cancer. However, the reproducibility of differential DNA methylation discoveries for cancer and the relationship between DNA methylation and aberrant gene expression have not been systematically analysed. The present study was undertaken to dissect the breast cancer methylome and to deliver specific epigenotypes associated with particular breast cancer subtypes. Methods: By using Real Time QMSPCR SYBR green we analyzed DNA methylation in regulatory regions of 107 pts with breast cancer and analyzed association with prognostics factor in triple negative breast cancer and methylation promoter ESR1, APC, E-Cadherin, Rar B and 14-3-3 sigma. Results: We identified novel subtype-specific epigenotypes that clearly demonstrate the differences in the methylation profiles of basal-like and human epidermal growth factor 2 (HER2)-overexpressing tumors. Of the cases, 37pts (40%) were Luminal A (LA), 32pts (33%) Luminal B (LB), 14pts (15%) Triple-negative (TN), and 9pts (10%) HER2+. DNA hypermethylation was highly inversely correlated with the down-regulation of gene expression. Methylation of this panel of promoter was found more frequently in triple negative and HER2 phenotype. ESR1 was preferably associated with TN(80%) and HER2+(60%) subtype. With a median follow up of 6 years, we found worse overall survival (OS) with more frequent ESR1 methylation gene(p>0.05), Luminal A;ESR1 Methylation OS at 5 years 81% vs 93% when was ESR1 Unmethylation. Luminal B;ESR1 Methylation 86% SG at 5 years vs 92% in Unmethylation ESR1. Triple negative;ESR1 Methylation SG at 5 years 75% vs 80% in unmethylation ESR1. HER2;ESR1 Methylation SG at 5 years was 66.7% vs 75% in unmethylation ESR1. Conclusions: Our results provide evidence that well-defined DNA methylation profiles enable breast cancer subtype prediction and support the utilization of this biomarker for prognostication and therapeutic stratification of patients with breast cancer.

scholarly journals Evaluation of FGFR targeting in breast cancer through interrogation of patient-derived models

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Nicole J. Chew ◽  
Terry C. C. Lim Kam Sian ◽  
Elizabeth V. Nguyen ◽  
Sung-Young Shin ◽  
Jessica Yang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Therapeutic Target ◽  
Breast Cancer Subtype ◽  
Breast Cancer Subtypes ◽  
Cancer Tissue ◽  
Breast Cancers ◽  
Luminal A ◽  
Tissue Samples ◽  
Cancer Subtypes ◽  
Luminal B

Abstract Background Particular breast cancer subtypes pose a clinical challenge due to limited targeted therapeutic options and/or poor responses to the existing targeted therapies. While cell lines provide useful pre-clinical models, patient-derived xenografts (PDX) and organoids (PDO) provide significant advantages, including maintenance of genetic and phenotypic heterogeneity, 3D architecture and for PDX, tumor–stroma interactions. In this study, we applied an integrated multi-omic approach across panels of breast cancer PDXs and PDOs in order to identify candidate therapeutic targets, with a major focus on specific FGFRs. Methods MS-based phosphoproteomics, RNAseq, WES and Western blotting were used to characterize aberrantly activated protein kinases and effects of specific FGFR inhibitors. PDX and PDO were treated with the selective tyrosine kinase inhibitors AZD4547 (FGFR1-3) and BLU9931 (FGFR4). FGFR4 expression in cancer tissue samples and PDOs was assessed by immunohistochemistry. METABRIC and TCGA datasets were interrogated to identify specific FGFR alterations and their association with breast cancer subtype and patient survival. Results Phosphoproteomic profiling across 18 triple-negative breast cancers (TNBC) and 1 luminal B PDX revealed considerable heterogeneity in kinase activation, but 1/3 of PDX exhibited enhanced phosphorylation of FGFR1, FGFR2 or FGFR4. One TNBC PDX with high FGFR2 activation was exquisitely sensitive to AZD4547. Integrated ‘omic analysis revealed a novel FGFR2-SKI fusion that comprised the majority of FGFR2 joined to the C-terminal region of SKI containing the coiled-coil domains. High FGFR4 phosphorylation characterized a luminal B PDX model and treatment with BLU9931 significantly decreased tumor growth. Phosphoproteomic and transcriptomic analyses confirmed on-target action of the two anti-FGFR drugs and also revealed novel effects on the spliceosome, metabolism and extracellular matrix (AZD4547) and RIG-I-like and NOD-like receptor signaling (BLU9931). Interrogation of public datasets revealed FGFR2 amplification, fusion or mutation in TNBC and other breast cancer subtypes, while FGFR4 overexpression and amplification occurred in all breast cancer subtypes and were associated with poor prognosis. Characterization of a PDO panel identified a luminal A PDO with high FGFR4 expression that was sensitive to BLU9931 treatment, further highlighting FGFR4 as a potential therapeutic target. Conclusions This work highlights how patient-derived models of human breast cancer provide powerful platforms for therapeutic target identification and analysis of drug action, and also the potential of specific FGFRs, including FGFR4, as targets for precision treatment.

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