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]

scholarly journals Heterogeneity of Breast Cancer Associations with Common Genetic Variants inFGFR2according to the Intrinsic Subtypes in Southern Han Chinese Women

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Huiying Liang ◽  
Xuexi Yang ◽  
Lujia Chen ◽  
Hong Li ◽  
Anna Zhu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Triple Negative ◽  
Chinese Women ◽  
Intrinsic Subtype ◽  
Han Chinese ◽  
Dependent Manner ◽  
Intrinsic Subtypes ◽  
Luminal A ◽  
Luminal B ◽  
Common Genetic Variants

GWAS have identified variation in theFGFR2locus as risk factors for breast cancer. Validation studies, however, have shown inconsistent results by ethnics and pathological characteristics. To further explore this inconsistency and investigate the associations ofFGFR2variants with breast cancer according to intrinsic subtype (Luminal-A, Luminal-B, ER−&PR−&HER2+, and triple negative) among Southern Han Chinese women, we genotyped rs1078806, rs1219648, rs2420946, rs2981579, and rs2981582 polymorphisms in 609 patients and 882 controls. Significant associations with breast cancer risk were observed for rs2420946, rs2981579, and rs2981582 with OR (95% CI) per risk allele of 1.19 (1.03–1.39), 1.24 (1.07–1.43), and 1.17 (1.01–1.36), respectively. In subtype specific analysis, above three SNPs were significantly associated with increased Luminal-A risk in a dose-dependent mannerPtrend<0.01; however, only rs2981579 was associated with Luminal-B, and none were linked to ER−&PR− subtypes (ER−&PR−&HER2+ and triple negative). Haplotype analyses also identified common haplotypes significantly associated with luminal-like subtypes (Luminal-A and Luminal-B), but not with ER−&PR− subtypes. Our results suggest that associations ofFGFR2SNPs with breast cancer were heterogeneous according to intrinsic subtype. Future studies stratifying patients by their intrinsic subtypes will provide new insights into the complex genetic mechanisms underlying breast cancer.

Breast cancer intrinsic subtypes by PAM50 in older women.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 1524-1524
Author(s):  
Emily Oldham Jenkins ◽  
Allison Mary Deal ◽  
Carey K. Anders ◽  
Aleix Prat ◽  
Charles M. Perou ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Tumor Biology ◽  
Continuous Variable ◽  
Intrinsic Subtype ◽  
Intrinsic Subtypes ◽  
Luminal A ◽  
Prognostic Information ◽  
Luminal B ◽  
Kaplan Meier ◽  
Treatment Data

1524 Background: Breast cancer (BC) incidence dramatically increases with age and the number of older BC patients (pts) in the U.S. is rising. Although immunohistochemical (IHC) data confirm that the incidence of luminal BC increases with age while the incidence of triple negative (TN) BC decreases, age-specific data on the frequency of BC subtypes defined by gene expression is limited. We characterized the incidence of BC intrinsic subtypes using gene microarrays according to age. Methods: Data from 2,150 pts were pooled from publicly available microarray datasets to determine the incidence of PAM50 breast cancer intrinsic subtypes (Luminal A [LumA], Luminal B [LumB], HER2-enriched [Her2E], Basal-like [Basal] and Normal-like [Norm]) by age. Adjuvant treatment data (none, chemotherapy, endocrine therapy or both) were available for 1,741 samples. Relapse-free (RFS) and overall survival (OS) differences were determined using the Kaplan-Meier method. Results: PAM50 intrinsic subtypes by age are tabulated below. The incidence of luminal (A, B, A+B) tumors increased with age (p<0.01, p<0.0001, p<0.0001, respectively), while the percentage of basal tumors decreased (p<0.0001). Basal tumors represented 13% of pts aged > 70 years (yrs); of the 70% with available IHC data, 74% were TNBC. Age as a continuous variable was not associated with RFS (p = 0.37). Subtype alone (n=2031), and after controlling for treatment (n=1645), was significantly associated with RFS (both p<0.0001). The addition of age to a multivariate analysis added no prognostic information. Conclusions: Though more favorable subtypes increase with age, older BC pts still have a substantial percentage of high-risk subtypes. Age alone was not a significant factor in outcome. Tumor biology as defined by intrinsic subtype is an important clinical predictor. [Table: see text]

scholarly journals Reparameterization of PAM50 expression identifies novel breast tumor dimensions and leads to discovery of a breast cancer susceptibility locus at 12q15

2017 ◽  
Author(s):  
Michael J Madsen ◽  
Stacey Knight ◽  
Carol Sweeney ◽  
Rachel Factor ◽  
Mohamed Salama ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Susceptibility ◽  
Expression Patterns ◽  
Breast Tumors ◽  
Two Dimensions ◽  
Three Dimensions ◽  
Tumor Characteristics ◽  
Intrinsic Subtypes ◽  
Luminal A ◽  
Luminal B

AbstractIt is well-known that breast tumors exhibit different expression patterns that can be used to assign intrinsic subtypes – the PAM50 assay, for example, categorizes tumors into: Luminal A, Luminal B, HER2-enriched and Basal-like – yet tumors are often more complex than categorization can describe. We used 911 sporadic breast tumors to reparameterize expression from the PAM50 genes to five orthogonal tumor dimensions using principal components (PC). Three dimensions captured intrinsic subtype, two dimensions were novel, and all replicated in 945 TCGA tumors. By definition dimensions are independent, an important attribute for inclusion in downstream studies exploring effects of tumor diversity. One application where tumor subtyping has failed to provide impact is susceptibility genetics. Germline genetic heterogeneity reduces power for gene-finding. The identification of heritable tumor characteristics has potential to increase homogeneity. We compared 238 breast tumors from high-risk pedigrees not attributable to BRCA1 or BRCA2 to 911 sporadic breast tumors. Two PC dimensions were significantly enriched in the pedigrees (intrinsic subtypes were not). We performed proof-of-concept gene-mapping in one enriched pedigree and identified a 0.5 Mb genomewide significant region at 12q15 that segregated to the 8 breast cancer cases with the most extreme PC tumors through 32 meioses (p=2.6×10−8). In conclusion, our study: suggests a new approach to describe tumor diversity; supports the hypothesis that tumor characteristics are heritable providing new avenues for germline studies; and proposes a new breast cancer locus. Reparameterization of expression patterns may similarly inform other studies attempting to model the effects of tumor heterogeneity.

scholarly journals Robust and interpretable PAM50 reclassification exhibits survival advantage for myoepithelial and immune phenotypes

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
James C. Mathews ◽  
Saad Nadeem ◽  
Arnold J. Levine ◽  
Maryam Pouryahya ◽  
Joseph O. Deasy ◽  
...  
Keyword(s):  
Survival Rates ◽  
Intrinsic Subtype ◽  
Cell Phenotype ◽  
Intrinsic Subtypes ◽  
Her2 Gene ◽  
Luminal A ◽  
Gene Group ◽  
Luminal B ◽  
Mammary Cell

Abstract We introduce a classification of breast tumors into seven classes which are more clearly defined by interpretable mRNA signatures along the PAM50 gene set than the five traditional PAM50 intrinsic subtypes. Each intrinsic subtype is partially concordant with one of our classes, and the two additional classes correspond to division of the classes concordant with the Luminal B and the Normal intrinsic subtypes along expression of the Her2 gene group. Our Normal class shows similarity with the myoepithelial mammary cell phenotype, including TP63 expression (specificity: 80.8% and sensitivity: 82.8%), and exhibits the best overall survival (89.6% at 5 years). Though Luminal A tumors are traditionally considered the least aggressive, our analysis shows that only the Luminal A tumors which are now classified as myoepithelial have this phenotype, while tumors in our luminal class (concordant with Luminal A) may be more aggressive than previously thought. We also find that patients with basal tumors surviving to 48 months exhibit favorable continued survival rates when certain markers for B lymphocytes are present and poor survival rates when they are absent, which is consistent with recent findings.

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.

Stage, biology, and age.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. e11512-e11512
Author(s):  
Hee Jeong Kim ◽  
Beom Seok Ko ◽  
Jong Han Yu ◽  
jong Won Lee ◽  
Byung Ho Sohn ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Triple Negative ◽  
Medical Center ◽  
Intrinsic Subtype ◽  
Young Age ◽  
Tnm Stage ◽  
Intrinsic Subtypes ◽  
Luminal A ◽  
Survival Difference

e11512 Background: Breast cancer subtypes are prognostic and predictive for patients. In this study, prognostic value of TNM stage, intrinsic subtype, and age were compared. Methods: We analyzed results from 7,626 breast cancer patients registered on the Asan medical center database between 1999 and 2009. We compared survival according to the TNM stage, intrinsic subtype using ER, PR, Her2- immunohistochemical staining, and age. Results: Luminal A subtype showed the best survival rates while triple negative subtype showed the worst survival rate amongst intrinsic subtypes. Survival analysis showed that Stage I triple negative breast cancer showed better survival compared to Stage III Luminal A subtype breast cancer (89.0% vs 76.6% P<0.001). Survival differences between intrinsic subtypes were more significant in lymph node positive breast cancer compared to lymph node negative breast cancer. Age did not affect survival between stages and intrinsic subtypes except for the young age subgroup (≤35), for whom there was no survival difference amongst intrinsic subtypes. Conclusions: Staging of breast cancer showed a more direct correlation to survival than known prognosis for intrinsic subtypes, as advanced, good prognostic intrinsic subtype breast cancer had worse survival than early, worse prognostic intrinsic subtype breast cancer. However for the young age group (≤35), the survival of all intrinsic subtypes were similar.

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.

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.

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