scholarly journals Quantitative single-cell analysis of immunofluorescence protein multiplex images illustrates biomarker spatial heterogeneity within breast cancer subtypes

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Alison Min-Yan Cheung ◽  
Dan Wang ◽  
Kela Liu ◽  
Tyna Hope ◽  
Mayan Murray ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Single Cell ◽  
Diversity Index ◽  
Breast Cancer Subtype ◽  
Cellular Heterogeneity ◽  
Luminal A ◽  
Proliferating Cells ◽  
Protein Marker ◽  
Long Term Outcome ◽  
Luminal B

Abstract Background The extent of cellular heterogeneity in breast cancer could have potential impact on diagnosis and long-term outcome. However, pathology evaluation is limited to biomarker immunohistochemical staining and morphology of the bulk cancer. Inter-cellular heterogeneity of biomarkers is not usually assessed. As an initial evaluation of the extent of breast cancer cellular heterogeneity, we conducted quantitative and spatial imaging of Estrogen Receptor (ER), Progesterone Receptor (PR), Epidermal Growth Factor Receptor-2 (HER2), Ki67, TP53, CDKN1A (P21/WAF1), CDKN2A (P16INK4A), CD8 and CD20 of a tissue microarray (TMA) representing subtypes defined by St. Gallen surrogate classification. Methods Quantitative, single cell-based imaging was conducted using an Immunofluorescence protein multiplexing platform (MxIF) to study protein co-expression signatures and their spatial localization patterns. The range of MxIF intensity values of each protein marker was compared to the respective IHC score for the TMA core. Extent of heterogeneity in spatial neighborhoods was analyzed using co-occurrence matrix and Diversity Index measures. Results On the 101 cores from 59 cases studied, diverse expression levels and distributions were observed in MxIF measures of ER and PR among the hormonal receptor-positive tumor cores. As expected, Luminal A-like cancers exhibit higher proportions of cell groups that co-express ER and PR, while Luminal B-like (HER2-negative) cancers were composed of ER+, PR- groups. Proliferating cells defined by Ki67 positivity were mainly found in groups with PR-negative cells. Triple-Negative Breast Cancer (TNBC) exhibited the highest proliferative fraction and incidence of abnormal P53 and P16 expression. Among the tumors exhibiting P53 overexpression by immunohistochemistry, a group of TNBC was found with much higher MxIF-measured P53 signal intensity compared to HER2+, Luminal B-like and other TNBC cases. Densities of CD8 and CD20 cells were highest in HER2+ cancers. Spatial analysis demonstrated variability in heterogeneity in cellular neighborhoods in the cancer and the tumor microenvironment. Conclusions Protein marker multiplexing and quantitative image analysis demonstrated marked heterogeneity in protein co-expression signatures and cellular arrangement within each breast cancer subtype. These refined descriptors of biomarker expressions and spatial patterns could be valuable in the development of more informative tools to guide diagnosis and treatment.

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.

Towards cancer mega-cohorts: A novel homogenization algorithm applied to diverse breast cancer RNA-Seq datasets.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e13507-e13507
Author(s):  
Talal Ahmed ◽  
Mark Carty ◽  
Stephane Wenric ◽  
Raphael Pelossof
Keyword(s):  
Breast Cancer ◽  
Molecular Subtype ◽  
Breast Cancer Subtype ◽  
Model Performance ◽  
Test Results ◽  
Rna Seq ◽  
Luminal A ◽  
Test Set ◽  
Luminal B ◽  
Cancer Subtype

e13507 Background: Recent advances in transcriptomics have resulted in the emergence of several publicly available breast cancer RNA-Seq datasets, such as TCGA, SCAN-B, and METABRIC. However, molecular predictors cannot be applied across datasets without the correction of batch differences. In this study, we demonstrate a homogenization algorithm that allows the transfer of molecular subtype predictors from one RNA-Seq cohort to another. The algorithm only uses cohort-level RNA-Seq summary statistics, and therefore, does not require joint normalization of both datasets nor the transfer of patient information. Using this approach, we transferred a breast cancer subtype (Luminal A, Luminal B, HER2+, Basal) predictor trained on SCAN-B data to accurately predict subtypes from TCGA. Methods: First, we randomly split the TCGA cohort (n = 481 Luminal A, n = 189 Luminal B, n = 73 Her2+, n = 168 Basal) into two sets: TCGA-train and held-out TCGA-test (n = 455 and n = 456, respectively). Second, the SCAN-B cohort (n = 837) was homogenized with the TCGA-train set. Third, a molecular subtype predictor, based on a logistic regression model, was trained on homogenized SCAN-B RNA-Seq samples and used to predict the subtypes of TCGA-test RNA-Seq samples. For baseline comparison, a similar predictor trained on the non-homogenized SCAN-B cohort was tested on the TCGA-test set. The experimental framework was iterated 250 times. Reported P-values reflect a paired one-sided t-test. Results: To quantify model performance, we measured the average F1 score for each tumor subtype prediction from the held-out TCGA test set with and without cohort homogenization. The average F1 scores with vs. without homogenization were: Luminal A, 0.88 vs. 0.85 ( P< 1e-69); Luminal B, 0.74 vs. 0.51 ( P< 1e-183); Her2+, 0.73 vs. 0.53 ( P< 1e-99); Basal, 0.98 vs. 0.97 ( P< 1e-53). Overall, homogenization significantly outperformed no homogenization. Conclusions: We developed a novel homogenization algorithm that accurately transfers subtype predictors across diverse, independent breast cancer cohorts.

scholarly journals Identification of Breast Cancer Subtype-Specific Biomarkers by Integrating Copy Number Alterations and Gene Expression Profiles

Medicina ◽  
2021 ◽  
Vol 57 (3) ◽  
pp. 261
Author(s):  
Claudia Cava ◽  
Mirko Pisati ◽  
Marco Frasca ◽  
Isabella Castiglioni
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Copy Number ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Breast Cancer Subtype ◽  
Gene Expression Profiles ◽  
Copy Number Alterations ◽  
Luminal A ◽  
Luminal B

Background and Objectives: Breast cancer is a heterogeneous disease categorized into four subtypes. Previous studies have shown that copy number alterations of several genes are implicated with the development and progression of many cancers. This study evaluates the effects of DNA copy number alterations on gene expression levels in different breast cancer subtypes. Materials and Methods: We performed a computational analysis integrating copy number alterations and gene expression profiles in 1024 breast cancer samples grouped into four molecular subtypes: luminal A, luminal B, HER2, and basal. Results: Our analyses identified several genes correlated in all subtypes such as KIAA1967 and MCPH1. In addition, several subtype-specific genes that showed a significant correlation between copy number and gene expression profiles were detected: SMARCB1, AZIN1, MTDH in luminal A, PPP2R5E, APEX1, GCN5 in luminal B, TNFAIP1, PCYT2, DIABLO in HER2, and FAM175B, SENP5, SCAF1 in basal subtype. Conclusions: This study showed that computational analyses integrating copy number and gene expression can contribute to unveil the molecular mechanisms of cancer and identify new subtype-specific biomarkers.

Molecular analysis in breast cancer subtypes and correlation with pathologic complete response (pCR) to neoadjuvant chemotherapy.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. e22119-e22119
Author(s):  
Maria Gonzalez Cao ◽  
Carlota Costa ◽  
Miguel Angel Molina-Vila ◽  
Maria Teresa Cusido ◽  
Santiago Viteri Ramirez ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Neoadjuvant Chemotherapy ◽  
Breast Cancer Subtype ◽  
Pathologic Complete Response ◽  
Luminal A ◽  
Luminal B ◽  
Mutational Status ◽  
Cancer Subtype ◽  
Low Levels

e22119 Background: Although it is know that pCR following neoadjuvant chemotherapy is more frequent in some subtypes of breast cancer such as Triple Negative (TN) or erb2 tumors, the predictive role of gene expression and mutation status is not well defined in this setting. Methods: We analyzed samples from 41 patients (p) prospectively treated with neoadjuvant chemotherapy (sequential AC followed by weekly TXL, or inverse sequence, plus trastuzumab for erb2 positive p). Pathologic response (PR) was classified according to Miller-Payne and RCB criteria. Radiologic evaluation was performed by ultrasonography, dynamic MR and PET-TAC after each chemotherapy sequence. We performed expression analysis of AXL, BRCA1, RAP80, BIM, EZH2, ROR1, FGFR1, PTPN12, YAP, GAS6, beta-TRCP, HIF1 alpha and ZNF217 by RT-PCR, and mutational status of p53 and PI3K genes in pretreatment biopsies. Statistical analysis was performed using Mann-Whitney U and Pearson’s chi-squared tests. Results: pCR was detected in 5 p (3TN, 2 erb2) of 25 p (9 luminal A, 5 luminal B, 6 erb2 and 5 TN) evaluated for PR at time of submitting this abstract. TN tumors had lower levels of RAP80 (p=.0013), PTPN12 (p=.003), beta TRCP (p=.001), ZNF217 (p=.014) and YAP (p=.097). Luminal B tumors had low levels of YAP and the highest levels of FGFR1 (p=.09) and ZNF217 (p=.014). Luminal A tumors had high levels of beta-TRCP (p=.003). We found no differences in BRCA1, AXL, BIM, EZH2, ROR1, GAS6 and HIF1 levels by breast cancer subtype. P with low levels of FGFR1 (p=.087), HIF1alpha (p=.07) or EZH2 (p=.005) had higher probability of pCR. No pCR was observed in p with higher levels of AXL, EZH2, RAP80, GAS6, beta TRCP, HIF alpha. Four p had p53 mutations (1 luminal B, 1 erb2 and 2 TN) and 4 p had PI3K mutations (2 luminal A, 1 erb2, 1 luminal B). There was no correlation between p53 status and PR. P with PI3K mutations did not achieve pCR vs 46% of p with wild type PI3K (p=.23). Conclusions: Gene expression profile varies by breast cancer subtype. Chemosensitivity could be higher in tumors with lower levels of FGFR1, HIF1alpha or EZH2. Further results will be presented.

Identification of genes with clonal mutations for the tumorigenesis of breast cancer subtypes.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e13006-e13006
Author(s):  
Yee Him Cheung ◽  
Jie Wu ◽  
Nevenka Dimitrova
Keyword(s):  
Breast Cancer ◽  
Tumor Progression ◽  
Pearson Correlation ◽  
Breast Cancer Subtype ◽  
Copy Number Variations ◽  
Gene Expressions ◽  
Luminal A ◽  
Luminal B ◽  
Mutation Carriers ◽  
The Impact

e13006 Background: Current analyses on somatic mutations mainly involve counting samples as either carriers or non-carriers. Such a binary approach misses to take into account the fraction of tumor cells that carry a mutation, which is a significant measure of the influence of a mutation on the overall phenotype, such as gene expressions, subtype and pathway activity. Methods: In this study, we re-analyzed the 72,084 non-synonymous somatic mutation in 16,164 genes of 817 TCGA breast cancer samples (HER2+: 65, Luminal A: 415, Luminal B: 176, Basal: 136 and Normal-like: 25) using variant allele frequency (VAF) adjusted by sample purity derived from multiple methods. We obtained a gene-based VAF by choosing the maximum VAF among all mutations found in each gene for each sample. For each breast cancer subtype, we filtered for top 100 genes with highest number of mutation carriers and then ranked them by decreasing average VAF across all samples. We assume genes with higher average VAF are more likely to harbor clonal mutations in early tumor progression. We further evaluated the Pearson correlation between VAF of genes having at least ten mutation carriers and the expression levels of ESR1, PGR and ERBB2 in Luminal A subtype. Results: The top two genes with highest VAF for each of the four subtypes are as follows: (a) HER2+: ERBB2 (VAF = 0.39 ± 0.21, n = 4), KAT6A (0.31 ± 0.09, 4), (b) Luminal A: CTCF (0.41 ± 0.19, 13), MAP2K4 (0.37 ± 0.14, 24), (c) Luminal B: MAP2K4 (0.46 ± 0.11, 5), TP53 (0.46 ± 0.17, 64), and (d) Basal: SCN10A (0.46 ± 0.29, 5), MYH9 (0.43 ± 0.16, 5). The strongest correlations for Luminal A are: (i) SPEN (corr. = 0.85, n= 14), KMT2C (0.73, 24) and DMD (0.73, 11) with ESR1, (ii) DMD (0.69, 11) and NBL1 (0.67, 10) with PGR, and (iii) TBX3 (-0.51, 12) and MUC12 (0.47, 14) with ERBB2. Conclusions: While carrier count is effective for identifying genes prone to mutations, average VAF opens another perspective for uncovering genes that tend to harbor clonal mutations. Our work shows the potential of VAF analysis for identifying driver genes, understanding tumor progression and evaluating the impact of a mutation on a patient. As future work, we may improve the VAF estimates by adjusting for copy number variations and weight each variant by pathogenicity.

scholarly journals Predicting molecular subtypes of breast cancer using pathological images by deep convolutional neural network from public dataset

2020 ◽  
Author(s):  
Nam Nhut Phan ◽  
Chi-Cheng Huang ◽  
Eric Y Chuang
Keyword(s):  
Breast Cancer ◽  
Complex Disease ◽  
Molecular Subtypes ◽  
Expression Profiles ◽  
Breast Cancer Subtype ◽  
Molecular Taxonomy ◽  
Gene Expression Profiles ◽  
Deep Convolutional Neural Networks ◽  
Luminal A ◽  
Luminal B

AbstractBreast cancer is a heterogeneously complex disease. A number of molecular subtypes with distinct biological features lead to different treatment responses and clinical outcomes. Traditionally, breast cancer is classified into subtypes based on gene expression profiles; these subtypes include luminal A, luminal B, basal like, HER2-enriched, and normal-like breast cancer. This molecular taxonomy, however, could only be appraised through transcriptome analyses. Our study applies deep convolutional neural networks and transfer learning from three pre-trained models, namely ResNet50, InceptionV3 and VGG16, for classifying molecular subtypes of breast cancer using TCGA-BRCA dataset. We used 20 whole slide pathological images for each breast cancer subtype. The results showed that our scale training reached about 78% of accuracy for validation. This outcomes suggested that classification of molecular subtypes of breast cancer by pathological images are feasible and could provide reliable results

scholarly journals The methylenetetrahydrofolate reductase (MTHFR) C677T gene polymorphism is associated with breast cancer subtype susceptibility in southwestern China

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254267
Author(s):  
Zhen Li ◽  
Ji Zhang ◽  
Wei Zou ◽  
Qi Xu ◽  
Siyuan Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Protective Factor ◽  
Methylenetetrahydrofolate Reductase ◽  
Breast Cancer Subtype ◽  
Mthfr C677t ◽  
Southwestern China ◽  
C677t Polymorphism ◽  
Luminal A ◽  
Luminal B ◽  
Increased Risk

Methylenetetrahydrofolate reductase (MTHFR), a folate-dependent enzyme, is reportedly involved in several cancer types. The MTHFR C677T polymorphism influences many biological processes, including tumorigenesis. However, the association between the MTHFR C677T polymorphism and breast cancer (BC) subtypes is not fully understood. In this study, the MTHFR C677T polymorphism was genotyped in 490 individuals with or without BC from southwestern China. Analysis of the association between the MTHFR C677T polymorphism and BC revealed that there was a significant association between the MTHFR C677T polymorphism and triple-negative breast cancer (TNBC) (OR = 2.83, 95% CI: 1.12–9.51, P = 0.0401). Furthermore, the MTHFR C677T polymorphism can also serve as a protective factor in luminal A breast cancer (OR = 0.57, 95% CI: 0.34–0.94, P = 0.0258). Evaluation of the association between the MTHFR C677T polymorphism and clinical characteristics indicated that people who suffered from hypertension had an increased risk for BC (OR = 2.27; 95% CI: 1.08–4.6; P = 0.0264), especially TNBC (OR = 215.38; 95% CI: 2.45–84430.3; P = 0.0317). Our results suggest that the MTHFR C677T polymorphism is significantly associated with susceptibility to luminal B breast cancer and TNBC.

scholarly journals Re-definition of claudin-low as a breast cancer phenotype

2019 ◽  
Author(s):  
Christian Fougner ◽  
Helga Bergholtz ◽  
Jens Henrik Norum ◽  
Therese Sørlie
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Subtype ◽  
Breast Tumors ◽  
Intrinsic Subtype ◽  
Intrinsic Subtypes ◽  
Luminal A ◽  
Diverse Range ◽  
Luminal B ◽  
Cancer Phenotype ◽  
Definition Of

The claudin-low breast cancer subtype is defined by gene expression characteristics and encompasses a remarkably diverse range of breast tumors. Here, we investigate genomic, transcriptomic, and clinical features of claudin-low breast tumors. We show that claudin-low is not simply a subtype analogous to the intrinsic subtypes (basal-like, HER2-enriched, luminal A, luminal B and normal-like) as previously portrayed, but is a complex additional phenotype which may permeate breast tumors of various intrinsic subtypes. Claudin-low tumors were distinguished by low genomic instability, mutational burden and proliferation levels, and high levels of immune and stromal cell infiltration. In other aspects, claudin-low tumors reflected characteristics of their intrinsic subtype. Finally, we have developed an alternative method for identifying claudin-low tumors and thereby uncovered potential weaknesses in the established claudin-low classifier. In sum, these findings elucidate the heterogeneity in claudin-low breast tumors, and substantiate a re-definition of claudin-low as a cancer phenotype.Contact informationC.F. [email protected]. [email protected]. [email protected]. [email protected]

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