Basal-like Breast Cancer—Characteristics, Risks, and Associations

2012 ◽  
Vol 08 (01) ◽  
pp. 26
Author(s):  
Andrew Y Shuen ◽  
William D Foulkes ◽  
◽  
Keyword(s):  
Breast Cancer ◽  
Massively Parallel Sequencing ◽  
Target Identification ◽  
Response To Therapy ◽  
Breast Cancers ◽  
Luminal A ◽  
Luminal B ◽  
Predictors Of Response ◽  
Epidemiological Risk ◽  
Basal Like Breast Cancer

Breast cancer is a heterogeneous disease. Gene expression profiling has demonstrated the existence of at least five ‘intrinsic’ subtypes: luminal A, luminal B, human epidermal growth factor-2 receptor (HER2), normal-like, and basal-like. While the estrogen receptor (ER), progesterone receptor (PR), and HER2 remain the most important prognostic markers and predictors of response to therapy, interrogating thousands of genetic transcripts more accurately captures the biological complexity and clinical heterogeneity of this disease. Target identification through massively parallel sequencing is likely to bear fruit in the coming years. Attention to basal-like breast cancers has grown substantially due to their generally poor prognosis, lack of targeted therapies, and connection withBRCA1-related cancers. In this article, we discuss the basal-like subgroup with respect to its cellular origins, relationship toBRCA1, and associated epidemiological risk factors.

Inmunophenotypic profiles of male breast cancer

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 21180-21180
Author(s):  
T. Martin Gomez ◽  
B. Torio ◽  
I. Ruiz ◽  
F. Arranz ◽  
A. Arizcun
Keyword(s):  
Breast Cancer ◽  
Male Breast Cancer ◽  
In Situ Hybridisation ◽  
Case Series ◽  
Response To Therapy ◽  
Male Breast ◽  
Breast Cancers ◽  
Luminal A ◽  
Luminal B ◽  
Female Breast

21180 Background: Recently, inmunophenotypic characterization methods have allowed identification of female breast carcinomas into separate groups showing different behaviour and response to therapy: luminal A phenotype (RE +, HER2-neu - ), luminal B (RE +, HER2- neu + ), basal like (RE -, HER2-neu - ). In this study, we used immunohistochemistry to investigate the inmunophenotypic profile distribution of male breast cancer. Methods: all the male breast cancers were obtained from the files of the Departments of Pathology of Hospital Río Carrión in Palencia, Spain, since 1996. A total of 9 cases were reviewed to confirm the diagnosis and to characterize each tumour. The following CK immunohistochemistry was performed: 8/18 and 5 (Dako, Carpinteria, CA, USA) in a Dako autostainer. ER was interpreted as positive if > 10% of the cells were staining. Normal skin and tonsils were used as positive controls for the CK and a known breast cancer for the ER immunohistochemistry. Results: five cases expressed RE and were HER2-neu negative, so they have a luminal-A phenotype. The four cases that expressed the luminal-B pehnotype expressed RE and HER2-neu; we demonstrated gene amplification of the HER-neu gene using fluorescent in situ hybridisation (FISH) in those cases. Respect the CKs profile, all cases were positive for CK 8/18 and negative with CK 5, vimentin and p63, characteristic of luminal-like CK expression profile, according wiht the literature. Conclusions: this is the first case series of male breast cancer patients that provides inmunophenotypic profile data on this rare disease in only one center in Spain. We comunicate that the vast majority of these tumours express the phenotype of luminal-like CKs. None of our patients were basal-like tumours. The percentage expression of Her-2 parallels the finding in female breast cancers and this should be analysed for its predictive significance, according to new specific biological treatments. No significant financial relationships to disclose.

scholarly journals ER, PR, HER2, Ki-67 and CK5 in Early and Late Relapsing Breast Cancer—Reduced CK5 Expression in Metastases

2013 ◽  
Vol 7 ◽  
pp. BCBCR.S10701 ◽  
Author(s):  
Kristiina Joensuu ◽  
Marjut Leidenius ◽  
Mia Kero ◽  
Leif C. Andersson ◽  
Kathryn B. Horwitz ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Progression ◽  
Triple Negative ◽  
Significant Risk ◽  
Growth Factor Receptor ◽  
Primary Therapy ◽  
Breast Cancers ◽  
Luminal A ◽  
Ki 67 ◽  
Luminal B

Breast cancer can recur even decades after the primary therapy. Markers are needed to predict cancer progression and the risk of late recurrence. The estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER2), proliferation marker Ki-67, and cytokeratin CK5 were studied to find out whether their expression or occurrence in subgroups of breast cancers correlated with the time of recurrence. The expression of HER2, ER, PR, Ki-67, and CK5 was studied by IHC in 72 primary breast cancers and their corresponding recurrent/metastatic lesions. The patients were divided into three groups according to the time of the recurrence/metastasis: before two years, after 5 years, and after 10 years. Based on their IHC profiles, the tumors were divided into surrogates of the genetically defined subgroups of breast cancers and the subtype definitions were as follows: luminal A (ER or PR+HER2–), luminal B (ER or PR+HER2+), HER2 overexpressing (ER–PR–HER2+), triple-negative (ER–PR–HER2–), basal-like (ER–PR–HER2–CK5+), non-classified (ER–PR–HER2–CK5–) and luminobasal (ER or PR+CK5+). In multivariate analysis, tumor size and HER2 positivity were a significant risk of early cancer relapse. The metastases showed a significantly lower CK5 expression. CK5 positivity distinguished triple negative tumors into rapidly and slowly recurring cancers. The IHC subtype ER or PR+HER2– luminal A presented a significantly lower risk of early tumor recurrence. Ki-67 expression denoted early-relapsing tumors and correlated linearly with tumor progression, since Ki-67 positivity declined gradually from early-relapsing toward late-recurring cancers.

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.

scholarly journals Performing Data Mining to Explain the Correlation between the BIRC5 Gene and Prognosis in Breast Cancer Patients

2020 ◽  
Author(s):  
Hong Dongsheng ◽  
Zhang YanFang ◽  
Ye Ziqi ◽  
Chen Jing ◽  
Lu Xiaoyang
Keyword(s):  
Breast Cancer ◽  
Mrna Expression ◽  
Cancer Patients ◽  
Cancer Cell Line ◽  
Breast Cancers ◽  
Breast Cancer Patients ◽  
Luminal A ◽  
Luminal B ◽  
Kaplan Meier ◽  
Er Positive

Abstract Background: Breast cancer is the most commonly malignant cancers in women, and BIRC5 has been found to be overexpressed in a variety of human tumors. Its expression is associated with the prognosis of many cancers. However, whether BIRC5 mRNA could be used as an independent prognostic factor for breast cancer remains inconsistent in previous studies.Methods: Altered BIRC5 expression in normal tissue relative to various tumor tissue and in breast cancer patients with different molecular subtypes, clinical outcomes and chemotherapy responses were examined using the Oncomine, GOBO and Kaplan-Meier plotter datasets.Results: We found that many breast cancers had increased BIRC5 mRNA expression, and GOBO analysis showed that triple-negative cell lines displayed highest BIRC5 mRNA expression levels in the breast cancer cell line panel. Moreover, BIRC5 high mRNA expression was significantly associated with longer relapse-free survival (RFS) in all breast cancer patients. In particular, sub analysis revealed that high mRNA expression of BIRC5 was significantly associated with better survival in ER positive (HR = 2.05, p = 1e-16), but not in ER negative breast cancer (HR = 1.24, p = 0.1), furthermore, the results also demonstrated that BIRC5 high expression was significantly associated with longer RFS in luminal A (HR = 1.51, p = 3.1e-06) and luminal B (HR = 1.28, p = 0.026).Conclusions: In conclusion, BIRC5 is involved in the development and progression of breast cancer and may be a suitable prognostic marker for human breast cancer.

scholarly journals Low GATA3 expression distinguishes basal-like human breast cancer.

2022 ◽  
Author(s):  
Shahan Mamoor
Keyword(s):  
Breast Cancer ◽  
Survival Data ◽  
Human Breast Cancer ◽  
Molecular Subtypes ◽  
Human Breast ◽  
Luminal A ◽  
Luminal B ◽  
Dismal Prognosis ◽  
Gata3 Expression ◽  
Basal Like Breast Cancer

Patients diagnosed with basal-like breast cancer face a more aggressive disease course and more dismal prognosis than patients diagnosed with luminal A and luminal B breast cancer molecular subtypes (1-4). We mined published microarray data (5, 6) to understand in an unbiased fashion the most distinguishing transcriptional features of tumors from patients with basal or basal-like subtype breast cancer. We observed transcriptome-wide differential expression of the transcription factor GATA3 when comparing tumors of patients with basal-like breast cancer with that of other PAM50 molecular subtypes. GATA3 mRNA was present at significantly reduced quantities in the tumors of patients with basal-like breast cancer. Analysis of patient survival data revealed that GATA3 primary tumor expression was correlated with distant metastasis-free survival, with low GATA3 expression correlated with inferior survival outcomes. Low GATA3 expression appears to distinguish basal-like human breast cancer from the other molecular subtypes.

scholarly journals Poor Biological Factors and Prognosis of Interval Breast Cancers: Long-Term Results of Bahçeşehir (Istanbul) Breast Cancer Screening Project in Turkey

2020 ◽  
pp. 1103-1113
Author(s):  
Neslihan Cabioğlu ◽  
Sibel Özkan Gürdal ◽  
Arda Kayhan ◽  
Nilüfer Özaydın ◽  
Cennet Şahin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Screening ◽  
Breast Cancer Screening ◽  
Triple Negative ◽  
Long Term Results ◽  
Breast Cancers ◽  
Interval Cancers ◽  
Luminal A ◽  
Ki 67 ◽  
Luminal B

PURPOSE The Turkish Bahçeşehir Breast Cancer Screening Project was a 10-year, organized, population-based screening program carried out in Bahçeşehir county, Istanbul. Our aim was to examine the biologic features and outcome of screen-detected and interval breast cancers during the 10-year study period. METHODS Between 2009 and 2019, 2-view mammograms were obtained at 2-year intervals for women aged 40 to 69 years. Clinicopathological characteristics including ER, PR, HER2-neu, and Ki-67 status were analyzed for those diagnosed with breast cancer. RESULTS In 8,758 screened women, 131 breast cancers (1.5%) were detected. The majority of patients (82.3%) had prognostic stage 0-I disease. Contrarily, patients with interval cancers (n = 15; 11.4%) were more likely to have a worse prognostic stage (II-IV disease; odds ratio [OR], 3.59, 95% CI, 0.9 to 14.5) and high Ki-67 scores (OR, 3.14; 95% CI, 0.9 to 11.2). Interval cancers detected within 1 year were more likely to have a luminal B (57.1% v 31.9%) and triple-negative (14.3% v 1%) subtype and less likely to have a luminal A subtype (28.6% v 61.5%; P = .04). Patients with interval cancers had a poor outcome in 10-year disease-specific (DSS) and disease-free survival (DFS) compared with those with screen-detected cancers (DSS: 68.2% v 98.1%, P = .002; DFS: 78.6% v 96.5%, P = .011). CONCLUSION Our findings suggest the majority of screen-detected breast cancers exhibited a luminal A subtype profile with an excellent prognosis. However, interval cancers were more likely to have aggressive subtypes such as luminal B subtype or triple-negative cancers associated with a poor prognosis requiring other preventive strategies.

scholarly journals Prognostic features of signal transducer and activator of transcription 3 in an ER(+) breast cancer model system

2014 ◽  
Vol 13 ◽  
pp. CIN.S12493 ◽  
Author(s):  
Li-Yu D. Liu ◽  
Li-Yun Chang ◽  
Wen-Hung Kuo ◽  
Hsiao-Lin Hwa ◽  
Yi-Shing Lin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Poor Prognosis ◽  
Cancer Cell Line ◽  
Signal Transducer ◽  
Breast Cancers ◽  
Luminal A ◽  
Regulatory Pathways ◽  
Luminal B ◽  
Prognostic Features ◽  
Transcription 3

The aberrantly expressed signal transducer and activator of transcription 3 (STAT3) predicts poor prognosis, primarily in estrogen receptor positive (ER(+)) breast cancers. Activated STAT3 is overexpressed in luminal A subtype cells. The mechanisms contributing to the prognosis and/or subtype relevant features of STAT3 in ER(+) breast cancers are through multiple interacting regulatory pathways, including STAT3-MYC, STAT3-ERα, and STAT3-MYC-ERα interactions, as well as the direct action of activated STAT3. These data predict malignant events, treatment responses and a novel enhancer of tamoxifen resistance. The inferred crosstalk between ERα and STAT3 in regulating their shared target gene-METAP2 is partially validated in the luminal B breast cancer cell line-MCF7. Taken together, we identify a poor prognosis relevant gene set within the STAT3 network and a robust one in a subset of patients. VEGFA, ABL1, LYN, IGF2R and STAT3 are suggested therapeutic targets for further study based upon the degree of differential expression in our model.

scholarly journals Expression of glutamine metabolism-related proteins according to molecular subtype of breast cancer

2013 ◽  
Vol 20 (3) ◽  
pp. 339-348 ◽  
Author(s):  
Sewha Kim ◽  
Do Hee Kim ◽  
Woo-Hee Jung ◽  
Ja Seung Koo
Keyword(s):  
Breast Cancer ◽  
Molecular Subtypes ◽  
Molecular Subtype ◽  
Glutamine Metabolism ◽  
Breast Cancers ◽  
Breast Cancer Patients ◽  
Luminal A ◽  
Luminal B ◽  
Related Proteins ◽  
Glutaminase 1

The aim of this study was to investigate the expression of glutamine metabolism-related proteins to determine whether glutamine is metabolized differently according to breast cancer molecular subtype. We generated a tissue microarray of 702 breast cancer patients and performed immunohistochemical staining for glutamine metabolism-related proteins, including glutaminase 1 (GLS1 (GLS)), glutamate dehydrogenase (GDH (H6PD)), and amino acid transporter-2 (ASCT2 (SLC1A5)), which were separately evaluated in tumor and stroma compartments and then analyzed by breast cancer molecular subtypes. Breast cancers were classified as follows: 293 luminal A (41.7%), 166 luminal B (23.6%), 67 HER2 type (9.6%), and 176 TNBC (25.1%). HER2 type showed the highest stromal GLS1 (P=0.001), tumoral GDH (P=0.001), stromal GDH (P<0.001), and tumoral ASCT (P<0.001) expression. We identified differential expression of glutamine metabolism-related proteins according to molecular subtype of breast cancer. The highest glutamine metabolic activity was seen in HER2-type breast cancer.

scholarly journals DeepTRIAGE: Interpretable and Individualised Biomarker Scores using Attention Mechanism for the Classification of Breast Cancer Sub-types

2019 ◽  
Author(s):  
Adham Beykikhoshk ◽  
Thomas P. Quinn ◽  
Samuel C. Lee ◽  
Truyen Tran ◽  
Svetha Venkatesh
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Deep Learning ◽  
Clinical Decision Making ◽  
Response To Therapy ◽  
Molecular Signature ◽  
Luminal A ◽  
Tissue Samples ◽  
Luminal B

AbstractMotivationBreast cancer is a collection of multiple tissue pathologies, each with a distinct molecular signature that correlates with patient prognosis and response to therapy. Accurately differentiating between breast cancer sub-types is an important part of clinical decision-making. Already, this problem has been addressed using machine learning methods that separate tissue samples into distinct groups. However, there remains unexplained heterogeneity within the established sub-types that cannot be resolved by the commonly used classification algorithms. In this paper, we propose a novel deep learning architecture, calledDeepTRIAGE(Deep learning for the TRactable Individualised Analysis of Gene Expression), which not only classifies cancer sub-types with comparable accuracy, but simultaneously assigns each patient their own set of interpretable and individualised biomarker scores. These personalised scores describe how important each feature is in the classification of each patient, and can be analysed post-hoc to generate new hypotheses about intra-class heterogeneity.ResultsWe apply theDeepTRIAGEframework to classify the gene expression signatures of luminal A and luminal B breast cancer sub-types, and illustrate its use for genes and gene set (i.e., GO and KEGG) features. Using DeepTRIAGE, we find that the GINS1 gene and the kinetochore organisation GO term are the most important features for luminal sub-type classification. Through classification,DeepTRIAGEsimultaneously reveals heterogeneity within the luminal A biomarker scores that significantly associate with tumour stage, placing all luminal samples along a continuum of severity.Availability and implementationThe proposed model is implemented in Python using Py-Torch framework. The analysis is done in Python and R. All Methods and models are freely available fromhttps://github.com/adham/BiomarkerAttend.

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