Response to Pre-Operative Radiotherapy in Relation to Gene Expression Patterns in Breast Cancer Patients

Abstract Motivation High-throughput molecular profiles of human cells have been used in predictive computational approaches for stratification of healthy and malignant phenotypes and identification of their biological states. In this regard, pathway activities have been used as biological features in unsupervised and supervised learning schemes. Results We developed SIGN (Similarity Identification in Gene expressioN), a flexible open-source R package facilitating the use of pathway activities and their expression patterns to identify similarities between biological samples. We defined a new measure, the transcriptional similarity coefficient, which captures similarity of gene expression patterns, instead of quantifying overall activity, in biological pathways between the samples. To demonstrate the utility of SIGN in biomedical research, we establish that SIGN discriminates subtypes of breast tumors and patients with good or poor overall survival. SIGN outperforms the best models in DREAM challenge in predicting survival of breast cancer patients using the data from the Molecular Taxonomy of Breast Cancer International Consortium. In summary, SIGN can be used as a new tool for interrogating pathway activity and gene expression patterns in unsupervised and supervised learning schemes to improve prognostic risk estimation for cancer patients by the biomedical research community. Availability and implementation An open-source R package is available (https://cran.r-project.org/web/packages/SIGN/).

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Differential gene expression patterns in HER2 positive metastatic breast cancer patients according to hormone receptor status

Annals of Oncology ◽

10.1093/annonc/mdx424.049 ◽

2017 ◽

Vol 28 ◽

pp. vi38-vi39

Author(s):

C. Omarini ◽

S. Kaleci ◽

G. Guaitoli ◽

S. Bettelli ◽

C. Caprera ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Metastatic Breast Cancer ◽

Expression Patterns ◽

Metastatic Breast ◽

Hormone Receptor Status ◽

Gene Expression Patterns ◽

Breast Cancer Patients ◽

Her2 Positive ◽

Differential Gene

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Haplotypes associated to gene expression in breast cancer: can they lead us to the susceptibility markers?

10.1101/248047 ◽

2018 ◽

Author(s):

Hege Edvardsen ◽

Bettina Kulle ◽

Anya Tsalenko ◽

Grethe Irene Grenaker Alnӕs ◽

Fredrik Ekeberg Johansen ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Cancer Patients ◽

Haplotype Analysis ◽

Cancer Susceptibility ◽

Expression Patterns ◽

Breast Cancer Patients ◽

Haplotype Distribution ◽

Significant Difference ◽

History Of

AbstractWe have undertaken a systematic haplotype analysis of the positional type of biclusters analysing samples collected from 164 breast cancer patients and 86 women with no known history of breast cancer. We present here the haplotypes and LD patterns in more than 80 genes distributed across all chromosomes and how they differ between cases and controls. We aim by this to 1) identify genes with different haplotype distribution or LD patterns between breast cancer patients and controls and 2) to evaluate the intratumoral mRNA expression patterns in breast cancer associated particularly to the cancer susceptibility haplotypes. A significant difference in haplotype distribution between cases and controls was observed for a total of 35 genes including ABCC1, AKT2, NFKB1, TGFBR2 and XRCC4. In addition we see a negative correlation between LD patterns in cases and controls for neighboring markers in 8 genes such as CDKN1A, EPHX1 and XRCC1.

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Association between the nucleosome footprint of plasma DNA and neoadjuvant chemotherapy response for breast cancer

npj Breast Cancer ◽

10.1038/s41523-021-00237-5 ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Xu Yang ◽

Geng-Xi Cai ◽

Bo-Wei Han ◽

Zhi-Wei Guo ◽

Ying-Song Wu ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Neoadjuvant Chemotherapy ◽

Cancer Patients ◽

Cell Receptor ◽

Chemotherapy Response ◽

Breast Cancer Patients ◽

Plasma Dna ◽

Transcription Start Sites ◽

Response To Chemotherapy

AbstractGene expression signatures have been used to predict the outcome of chemotherapy for breast cancer. The nucleosome footprint of cell-free DNA (cfDNA) carries gene expression information of the original tissues and thus may be used to predict the response to chemotherapy. Here we carried out the nucleosome positioning on cfDNA from 85 breast cancer patients and 85 healthy individuals and two cancer cell lines T-47D and MDA-MB-231 using low-coverage whole-genome sequencing (LCWGS) method. The patients showed distinct nucleosome footprints at Transcription Start Sites (TSSs) compared with normal donors. In order to identify the footprints of cfDNA corresponding with the responses to neoadjuvant chemotherapy in patients, we mapped on nucleosome positions on cfDNA of patients with different responses: responders (pretreatment, n = 28; post-1 cycle, post-3/4 cycles, and post-8 cycles of treatment, n = 12) and nonresponders (pretreatment, n = 10; post-1 cycle, post-3/4 cycles, and post-8 cycles of treatment, n = 10). The coverage depth near TSSs in plasma cfDNA differed significantly between responders and nonresponders at pretreatment, and also after neoadjuvant chemotherapy treatment cycles. We identified 232 TSSs with differential footprints at pretreatment and 321 after treatment and found enrichment in Gene Ontology terms such as cell growth inhibition, tumor suppressor, necrotic cell death, acute inflammatory response, T cell receptor signaling pathway, and positive regulation of vascular endothelial growth factor production. These results suggest that cfDNA nucleosome footprints may be used to predict the efficacy of neoadjuvant chemotherapy for breast cancer patients and thus may provide help in decision making for individual patients.

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Abnormal Long Non-Coding RNAs Expression Patterns Have the Potential Ability for Predicting Survival and Treatment Response in Breast Cancer

Genes ◽

10.3390/genes12070996 ◽

2021 ◽

Vol 12 (7) ◽

pp. 996

Author(s):

Ana Carolina Pavanelli ◽

Flavia Rotea Mangone ◽

Luciana R. C. Barros ◽

Juliana Machado-Rugolo ◽

Vera L. Capelozzi ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Patients ◽

Predictive Value ◽

Expression Patterns ◽

Survival Rates ◽

In Silico Analysis ◽

Cancer Prognosis ◽

Response To Treatment ◽

Breast Cancer Patients ◽

Non Coding Rnas

Abnormal long non-coding RNAs (lncRNAs) expression has been documented to have oncogene or tumor suppressor functions in the development and progression of cancer, emerging as promising independent biomarkers for molecular cancer stratification and patients’ prognosis. Examining the relationship between lncRNAs and the survival rates in malignancies creates new scenarios for precision medicine and targeted therapy. Breast cancer (BRCA) is a heterogeneous malignancy. Despite advances in its molecular classification, there are still gaps to explain in its multifaceted presentations and a substantial lack of biomarkers that can better predict patients’ prognosis in response to different therapeutic strategies. Here, we performed a re-analysis of gene expression data generated using cDNA microarrays in a previous study of our group, aiming to identify differentially expressed lncRNAs (DELncRNAs) with a potential predictive value for response to treatment with taxanes in breast cancer patients. Results revealed 157 DELncRNAs (90 up- and 67 down-regulated). We validated these new biomarkers as having prognostic and predictive value for breast cancer using in silico analysis in public databases. Data from TCGA showed that compared to normal tissue, MIAT was up-regulated, while KCNQ1OT1, LOC100270804, and FLJ10038 were down-regulated in breast tumor tissues. KCNQ1OT1, LOC100270804, and FLJ10038 median levels were found to be significantly higher in the luminal subtype. The ROC plotter platform results showed that reduced expression of these three DElncRNAs was associated with breast cancer patients who did not respond to taxane treatment. Kaplan–Meier survival analysis revealed that a lower expression of the selected lncRNAs was significantly associated with worse relapse-free survival (RFS) in breast cancer patients. Further validation of the expression of these DELncRNAs might be helpful to better tailor breast cancer prognosis and treatment.

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