Dataset Information

A pooled treatment-curated breast cancer gene-expression dataset

ABSTRACT: Pre-clinical studies reported the immunogenic or immunomodulatory effects of traditional cancer therapies. However, the publicly available well-curated and harmonized breast cancer datasets, such as TCGA, METABRIC, and MetaGxBreast, lack careful curation of treatment regimens. Hence, limited exploration of the impact of therapies on the prognostic/predictive value of breast cancer biomarkers. Herein, we describe a pooled and treatment-curated gene-expression dataset to investigate the impact of treatments on the prognostic/predictive value of biomarkers. We searched the gene expression omnibus database to identify potential human breast cancer gene-expression datasets with anthracycline/taxane treatment. Published datasets with the detailed treatment regimen, clinical endpoint, clinical-pathological, and gene-expression data were extracted and harmonized. The dataset described herein would help researchers explore the interaction between gene-expression biomarkers and immunogenic/immunomodulatory treatments in breast cancer.

ORGANISM(S): Homo sapiens

PROVIDER: GSE205568 | GEO | 2024/03/09

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Purpose Aromatase inhibitors (AIs) have an established role in breast cancer treatment. Response rates are only 50-70% in the neoadjuvant setting and lower in advanced disease. Accurate biomarkers are urgently needed to predict response in these settings and to determine which individuals will benefit from adjuvant AI therapy. Participants and Methods Pre- and on-treatment (after 2 weeks and 3 months) biopsies were obtained from 89 post-menopausal women with ER+ breast cancer receiving neoadjuvant letrozole for transcript profiling. Dynamic clinical response was assessed by three-dimensional ultrasound measurements. Results The molecular response to letrozole was characterised and a four gene classifier of clinical response was established (accuracy of 96%) based upon the level of two genes prior to treatment (one associated with immune signalling, IL6ST and the other with apoptosis, NGFRAP1) and two proliferation genes (ASPM, MCM4) at 2 weeks of therapy. The four gene signature was found to be 91% accurate in a blinded, completely independent validation dataset of patients treated with anastrozole. Matched 2 week on-treatment biopsies improved predictive power over pre-treatment biopsies alone. This signature also significantly predicted recurrence free survival (p=0.029) and breast cancer specific survival (p=0.009). We demonstrate that the test can also be performed using quantitative PCR or immunohistochemistry. Conclusion A four gene predictive model of clinical response to AIs by two weeks has been generated and validated. Deregulated immune and apoptotic responses before treatment and a failure to reduce proliferation by 2 weeks are functional characteristics of breast tumours that do not respond to AIs. 25 Pre treatment, 25 two week and 25 three month on-treatment primary breast tumour samples from the same patients. Data was analysed with two previously generated datasets, GSE55374 which was also processed on Illumina HT-12v4 BeadChips (GPL10558) and GSE20181 on Affymetrix U133A GeneChips (GPL96)

Dataset Information

A pooled treatment-curated breast cancer gene-expression dataset

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets