Dataset Information

TMT 10-plex based quantitative proteomic analysis of drug resistant breast cancer cell lines

ABSTRACT: Breast cancer accounts for roughly 30% of all cancers in women worldwide, has a 15% death rate, and incidence rates are increasing at a rate of about 0.5% per year. Breast cancer comprises a heterogeneous group of tumor subtypes, whether defined by the histopathology of the primary tumor, the expression pattern of hormone receptors (estrogen and/or progesterone receptors; ER/PR) and epidermal growth factor receptor 2 (HER2), genetic alterations of transcriptomic traits. These patient-to-patient differences (as known as �쁦ntertumoral heterogeneity��, largely affect patient prognosis and treatment options. Alongside intertumoral heterogeneity, many studies reported that breast cancers heterogeneous consisting of many different cells or subclones of which different gene expression profiles within a patient�셲 primary tumor and individual metastases. These differences within the tumor are referred to as intratumor heterogeneity, which is caused by a combination of extrinsic factors from the tumor microenvironment and intrinsic parameters including genetic, epigenetic and transcriptomic traits, ability of proliferation, migration and invasion, cell plasticity, and the extent of stemness. These heterogeneities endow tumors with multiple capabilities and biological characteristics, making them more prone to metastasis, recurrence, and drug resistance. To overcome these facing challenges, understanding the proteome mechanisms behind transcriptome profiling from the aspect of treatment can help to improve resistance to cancer therapy. Recent proteomics technologies based on mass spectrometry enable an unbiased investigation of drug-induced changes in protein abundance and post-translational modifications. Several studies on resistance to chemotherapy have recently published data on mass spectrometry-based chemotherapeutic proteome profiling, which has the potential to discover molecular subtypes and related pathway features that may have been missed in prior transcriptome analyses. Nevertheless, few proteomics studies to date explore three types of drug-specific resistance of breast cancer signatures. In this study, we employed tandem mass tag (TMT) based proteomics technology to process the acquired mass spectrometry data to test the hypothesis that the chemotherapy in breast cancer cells may have distinct protein profiles that may result in their drug properties and new clinical implications. By unraveling the protein signatures across tamoxifen, doxorubicin, and paclitaxel and their relationship between drug-resistant cell lines and normal breast cancer cells, our study advances the understanding of drug-specific resistance and provides potential diagnostic and prognostic markers, as well as testable targets of therapy specific to breast cancer resistant cells.

INSTRUMENT(S): Q Exactive Plus

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Epithelial Cell, Breast Cancer Cell Line

DISEASE(S): Breast Cancer

SUBMITTER: Dohyun Han

LAB HEAD: Dohyun Han

PROVIDER: PXD030881 | Pride | 2022-04-04

REPOSITORIES: Pride

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Dataset's files

Source:

			Action	DRS
	20180902_breast_Drug_TMT10_F1.raw	Raw
	20180902_breast_Drug_TMT10_F10.raw	Raw
	20180902_breast_Drug_TMT10_F11.raw	Raw
	20180902_breast_Drug_TMT10_F12.raw	Raw
	20180902_breast_Drug_TMT10_F13.raw	Raw

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Publications

Discovery of Proteins Responsible for Resistance to Three Chemotherapy Drugs in Breast Cancer Cells Using Proteomics and Bioinformatics Analysis.

Cha Hyo Kyeong HK Cheon Seongmin S Kim Hyeyoon H Lee Kyung-Min KM Ryu Han Suk HS Han Dohyun D

Molecules (Basel, Switzerland) 20220308 6

Chemoresistance is a daunting obstacle to the effective treatment of breast cancer patients receiving chemotherapy. Although the mechanism of chemotherapy drug resistance has been explored broadly, the precise mechanism at the proteome level remains unclear. Especially, comparative studies between widely used anticancer drugs in breast cancer are very limited. In this study, we employed proteomics and bioinformatics approaches on chemoresistant breast cancer cell lines to understand the underlyi ...[more]

PMID: 35335125

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Project description:Breast cancer is an extremely heterogeneous disease. This heterogeneity can be observed at multiple levels, including gene expression, chromosomal aberrations, and disease pathology. A clear understanding of these differences is important since they impact upon treatment efficacy and clinical outcome. Many studies have shown that the tumor microenvironment also plays a critical role in cancer initiation and progression. Although genomic technologies have been used to gain a better understanding of the impact of gene expression heterogeneity on breast cancer outcome by identifying gene expression signatures associated with clinical outcome, histopathological breast cancer subtypes, and a variety of cancer--related pathways and processes, relatively little is known about the influence of of heterogeneity in the tumor microenvironment on these factors. We show that gene expression in the breast tumor microenvironment is highly heterogeneous, identifying at least six different classes of tumor stroma with distinct expression patterns and distinct biological processes. Two of these classes recapitulate the processes identified in the stroma--derived prognostic predictor, while the others are new classes of stroma associated with distinct clinical outcomes. One of these is associated with matrix remodeling and is strongly associated with the basal molecular subtype of breast cancer. The remainder are independent of the previously published molecular subtypes of breast cancer. Additionally, based on independent data from over 800 tumors, the combinations of stroma classes and breast cancer subtypes identify new subgroups of breast tumors that show better discrimination between good and poor outcome individuals than the molecular breast cancer subtypes or the stroma classes alone, suggesting a novel classification scheme for breast cancer . This further demonstrates an important role for the tumor microenvironment in defining breast cancer heterogeneity, with a consequent impact upon clinical outcome.