scholarly journals Claudin-low-like mouse mammary tumors show distinct transcriptomic patterns uncoupled from genomic drivers

2019 ◽  
Author(s):  
Christian Fougner ◽  
Helga Bergholtz ◽  
Raoul Kuiper ◽  
Jens Henrik Norum ◽  
Therese Sørlie

AbstractClaudin-low breast cancer is a molecular subtype associated with poor prognosis and without targeted treatment options. The claudin-low subtype is defined by certain biological characteristics, some of which may be clinically actionable, such as high immunogenicity. In mice, the medroxyprogesterone acetate (MPA) and 7,12-dimethylbenzanthracene (DMBA) induced mammary tumor model yields a heterogeneous set of tumors, a subset of which display claudin-low features. Neither the genomic characteristics of MPA/DMBA-induced claudin-low tumors, nor those of human claudin-low breast tumors, have been thoroughly explored.The transcriptomic characteristics and subtypes of MPA/DMBA-induced mouse mammary tumors were determined using gene expression microarrays. Somatic mutations and copy number aberrations in MPA/DMBA-induced tumors were identified from whole exome sequencing data. A publicly available dataset was queried to explore the genomic characteristics of human claudin-low breast cancer and to validate findings in the murine tumors.Half of MPA/DMBA-induced tumors showed a claudin-low-like subtype. All tumors carried mutations in known driver genes. While the specific genes carrying mutations varied between tumors, there was a consistent mutational signature with an overweight of T>A transversions in TG dinucleotides. Most tumors carried copy number aberrations with a potential oncogenic driver effect. Overall, several genomic events were observed recurrently, however none accurately delineated claudin-low-like tumors. Human claudin-low breast cancers carried a distinct set of genomic characteristics, in particular a relatively low burden of mutations and copy number aberrations. The gene expression characteristics of claudin-low-like MPA/DMBA-induced tumors accurately reflected those of human claudin-low tumors, including epithelial-mesenchymal transition phenotype, high level of immune activation and low degree of differentiation. There was an elevated expression of the immunosuppressive genes PTGS2 (encoding COX-2) and CD274 (encoding PD-L1) in human and murine claudin-low tumors. Our findings show that the claudin-low breast cancer subtype is not demarcated by specific genomic aberrations, but carries potentially targetable characteristics warranting further research.Author SummaryBreast cancer is comprised of several distinct disease subtypes with different etiologies, prognoses and therapeutic targets. The claudin-low breast cancer subtype is relatively poorly understood, and no specific treatment exists targeting its unique characteristics. Animal models accurately representing human disease counterparts are vital for developing novel therapeutics, but for the claudin-low breast cancer subtype, no such uniform model exists. Here, we show that exposing mice to the carcinogen DMBA and the hormone MPA causes a diverse range of mammary tumors to grow, and half of these have a gene expression pattern similar to that seen in human claudin-low breast cancer. These tumors have numerous changes in their DNA, with clear differences between each tumor, however no specific DNA aberrations clearly demarcate the claudin-low subtype. We also analyzed human breast cancers and show that human claudin-low tumors have several clear patterns in their DNA aberrations, but no specific features accurately distinguish claudin-low from non-claudin-low breast cancer. Finally, we show that both human and murine claudin-low tumors express high levels of genes associated with suppression of immune response. In sum, we highlight claudin-low breast cancer as a clinically relevant subtype with a complex etiology, and with potential unexploited therapeutic targets.

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Dongguo Li ◽  
Hong Xia ◽  
Zhen-ya Li ◽  
Lin Hua ◽  
Lin Li

Breast cancer is a heterogeneous disease with well-defined molecular subtypes. Currently, comparative genomic hybridization arrays (aCGH) techniques have been developed rapidly, and recent evidences in studies of breast cancer suggest that tumors within gene expression subtypes share similar DNA copy number aberrations (CNA) which can be used to further subdivide subtypes. Moreover, subtype-specific miRNA expression profiles are also proposed as novel signatures for breast cancer classification. The identification of mRNA or miRNA expression-based breast cancer subtypes is considered an instructive means of prognosis. Here, we conducted an integrated analysis based on copy number aberrations data and miRNA-mRNA dual expression profiling data to identify breast cancer subtype-specific biomarkers. Interestingly, we found a group of genes residing in subtype-specific CNA regions that also display the corresponding changes in mRNAs levels and their target miRNAs’ expression. Among them, the predicted direct correlation of BRCA1-miR-143-miR-145 pairs was selected for experimental validation. The study results indicated that BRCA1 positively regulates miR-143-miR-145 expression and miR-143-miR-145 can serve as promising novel biomarkers for breast cancer subtyping. In our integrated genomics analysis and experimental validation, a new frame to predict candidate biomarkers of breast cancer subtype is provided and offers assistance in order to understand the potential disease etiology of the breast cancer subtypes.


Cell Reports ◽  
2013 ◽  
Vol 5 (1) ◽  
pp. 216-223 ◽  
Author(s):  
Naif Zaman ◽  
Lei Li ◽  
Maria Luz Jaramillo ◽  
Zhanpeng Sun ◽  
Chabane Tibiche ◽  
...  

PLoS ONE ◽  
2013 ◽  
Vol 8 (2) ◽  
pp. e56761 ◽  
Author(s):  
Tiziana Triulzi ◽  
Patrizia Casalini ◽  
Marco Sandri ◽  
Manuela Ratti ◽  
Maria L. Carcangiu ◽  
...  

2018 ◽  
Vol 11 (1) ◽  
Author(s):  
Atif Ali Hashmi ◽  
Raeesa Mahboob ◽  
Saadia Mehmood Khan ◽  
Muhammad Irfan ◽  
Mariam Nisar ◽  
...  

2021 ◽  
pp. 1-14
Author(s):  
S. Raja Sree ◽  
A. Kunthavai

BACKGROUND: Breast cancer is a major disease causing panic among women worldwide. Since gene mutations are the root cause for cancer development, analyzing gene expressions can give more insights into various phenotype of cancer treatments. Breast Cancer subtype prediction from gene expression data can provide more information for cancer treatment decisions. OBJECTIVE: Gene expressions are complex for analysis due to its high dimensional nature. Machine learning algorithms such as k-Nearest Neighbors, Support Vector Machine (SVM) and Random Forest are used with selection of features for prediction of breast cancer subtypes. Prediction accuracy of the existing methods are affected due to high dimensional nature of gene expressions. The objective of the work is to propose an efficient algorithm for the prediction of breast cancer subtypes from gene expression. METHODS: For subtype prediction, a novel Hubness Weighted Support Vector machine algorithm (HWSVM) using bad hubness score as a weight measure to handle the outliers in the data has been proposed. Based on the various subtypes, features are projected into seven different feature sets and Ensemble based Hubness Aware Weighted Support Vector Machine (HWSVMEns) is implemented for breast cancer subtype prediction. RESULTS: The proposed algorithms have been compared with the classical SVM and other traditional algorithms such as Random Forest, k-Nearest Neighbor algorithms and also with various gene selection methods. CONCLUSIONS: Experimental results show that the proposed HWSVM outperforms other algorithms in terms of accuracy, precision, recall and F1 score due to the hubness weightage scheme and the ensemble approach. The experiments have shown an average accuracy of 92% across various gene expression datasets.


2014 ◽  
Vol 11 (2) ◽  
pp. 1-14 ◽  
Author(s):  
Markus List ◽  
Anne-Christin Hauschild ◽  
Qihua Tan ◽  
Torben A. Kruse ◽  
Jan Baumbach ◽  
...  

Summary Selecting the most promising treatment strategy for breast cancer crucially depends on determining the correct subtype. In recent years, gene expression profiling has been investigated as an alternative to histochemical methods. Since databases like TCGA provide easy and unrestricted access to gene expression data for hundreds of patients, the challenge is to extract a minimal optimal set of genes with good prognostic properties from a large bulk of genes making a moderate contribution to classification. Several studies have successfully applied machine learning algorithms to solve this so-called gene selection problem. However, more diverse data from other OMICS technologies are available, including methylation. We hypothesize that combining methylation and gene expression data could already lead to a largely improved classification model, since the resulting model will reflect differences not only on the transcriptomic, but also on an epigenetic level. We compared so-called random forest derived classification models based on gene expression and methylation data alone, to a model based on the combined features and to a model based on the gold standard PAM50. We obtained bootstrap errors of 10-20% and classification error of 1-50%, depending on breast cancer subtype and model. The gene expression model was clearly superior to the methylation model, which was also reflected in the combined model, which mainly selected features from gene expression data. However, the methylation model was able to identify unique features not considered as relevant by the gene expression model, which might provide deeper insights into breast cancer subtype differentiation on an epigenetic level.


Medicina ◽  
2021 ◽  
Vol 57 (3) ◽  
pp. 261
Author(s):  
Claudia Cava ◽  
Mirko Pisati ◽  
Marco Frasca ◽  
Isabella Castiglioni

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.


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 ◽  
...  

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.


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