Dataset Information

Proteomics and peptidomics of breast cancer subtypes

ABSTRACT: In our study, we applied high-throughput mass spectrometry, bioinformatic, and machine learning approaches to investigate the proteome, including noncoding RNA (ncRNA)–derived micropeptides, of different breast cancer subtypes. In total, 19 tumor and 18 paired nontumor samples were used to obtain the proteome and peptidome. Among these, 5 samples were classified as LABC (plus 5 paired nontumor samples), 4 as LBBC (HER2−) (plus 4 paired nontumor samples), 4 as HER2+ breast cancer (plus 3 paired nontumor samples), and 6 as TNBC (plus 6 paired nontumor samples). We identified that each subtype depends on different protein expression patterns to sustain its malignancy,and also alterations in pathways and processes that can be associated with each subtype and its biological and clinical behaviors. Regarding subtype biomarkers, our panels achieved performances with at least 75% of sensibility and 92% of specificity. In the validation cohort, the panels obtained acceptable to outstanding performances (AUC = 0.740 to 1.00). We identified 58 peptides expressed on breast tissue, including 27 differentially expressed MPs in tumor compared with nontumor samples and MPs exhibiting tumor or subtype specificity. These peptides presented physicochemical features compatible with the canonical proteome and were predicted to influence the tumor immune environment and participate in cell communication, metabolism, and signaling processes. In addition, some MPs presented potential as anticancer, antiinflammatory, and antiangiogenic molecules.

INSTRUMENT(S): Orbitrap Fusion Lumos

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Breast

DISEASE(S): Breast Cancer

SUBMITTER: Michel Batista

LAB HEAD: Enilze Maria de Souza Fonseca Ribeiro

PROVIDER: PXD057759 | Pride | 2024-11-12

REPOSITORIES: Pride

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			Action	DRS
	20210217_69120_AlexandreUFPR_N932_1.raw	Raw
	20210217_69120_AlexandreUFPR_N932_2.raw	Raw
	20210217_69220_AlexandreUFPR_N957_1.raw	Raw
	20210217_69220_AlexandreUFPR_N957_2.raw	Raw
	20210217_69420_AlexandreUFPR_N986_1.raw	Raw

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Publications

Peptidomics and Machine Learning-based Evaluation of Noncoding RNA-Derived Micropeptides in Breast Cancer: Expression Patterns and Functional/Therapeutic Insights.

de Azevedo Alexandre Luiz Korte ALK Gomig Talita Helen Bombardelli THB Batista Michel M de Oliveira Jaqueline Carvalho JC Cavalli Iglenir João IJ Gradia Daniela Fiori DF Ribeiro Enilze Maria de Souza Fonseca EMSF

Laboratory investigation; a journal of technical methods and pathology 20241010 12

Breast cancer is a highly heterogeneous disease characterized by different subtypes arising from molecular alterations that give the disease different phenotypes, clinical behaviors, and prognostic. The noncoding RNA (ncRNA)-derived micropeptides (MPs) represent a novel layer of complexity in cancer study once they can be biologically active and can present potential as biomarkers and also in therapeutics. However, few large-scale studies address the expression of these peptides at the peptidomi ...[more]

PMID: 39393531

Publication: 1/2

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Project description:Breast cancer is a heterogeneous disease for which prognosis and treatment strategies are largely governed by the receptor status (estrogen, progesterone and Her2-neu) of the tumor cells. Gene expression profiling of whole breast tumors further stratifies breast cancer into several molecular subtypes which also co-segregate with the receptor status of the tumor cells. We postulated that cancer associated fibroblasts (CAFs) within the tumor stroma may exhibit subtype specific gene expression profiles and thus contribute to the biology of the disease in a subtype specific manner. Several studies have reported gene expression profile differences between CAFs and normal breast fibroblasts but in none of these studies were the results stratified based on tumor subtypes. To address whether gene expression in breast cancer associated fibroblasts varies between breast cancer subtypes, we compared the gene expression profiles of early passage primary CAFs isolated from twenty human breast cancer samples representing three main subtypes; seven ER+, seven triple negative (TNBC) and six Her2+. We observed significant expression differences between CAFs derived from Her2+ breast cancer and CAFs from TNBC and ER+ cancers, particularly in pathways associated with cytoskeleton and integrin signaling. In the case of Her2+ breast cancer, the signaling pathways found to be selectively up regulated in CAFs may contribute to the more invasive properties and unfavorable prognosis of Her2+ breast cancer. These data demonstrate that in addition to the distinct molecular profiles that characterize the neoplastic cells, CAF gene expression is also differentially regulated in distinct subtypes of breast cancer. We isolated CAFs from twenty primary breast cancer samples representing three main subtypes (ER+ (n=7), TNBC (n=7), Her2+ (n=6)) and performed gene expression profile analyses on RNA isolated from these early passage CAFs. Those samples were done in two batches with 4 samples repeated in both batches. One TNBC sample was found to be an outlier and not used in the analysis.

Project description:Abstract BACKGROUND: The basal-like breast cancer (BLBC) subtype is characterized by positive staining for basal mammary epithelial cytokeratin markers, lack of hormone receptor and HER2 expression, and poor prognosis with currently no approved molecularly-targeted therapies. The oncogenic signaling pathways driving basal-like tumorigenesis are not fully elucidated. METHODS: One hundred sixteen unselected breast tumors were subjected to integrated analysis of phosphoinositide 3-kinase (PI3K) pathway related molecular aberrations by immunohistochemistry, mutation analysis, and gene expression profiling. Incidence and relationships between molecular biomarkers were characterized. Findings for select biomarkers were validated in an independent series. Synergistic cell killing in vitro and in vivo tumor therapy was investigated in breast cancer cell lines and mouse xenograft models, respectively. RESULTS: Sixty-four % of cases had an oncogenic alteration to PIK3CA, PTEN, or INPP4B; when including upstream kinases HER2 and EGFR, 75 % of cases had one or more aberration including 97 % of estrogen receptor (ER)-negative tumors. PTEN-loss was significantly associated to stathmin and EGFR overexpression, positivity for the BLBC markers cytokeratin 5/14, and the BLBC molecular subtype by gene expression profiling, informing a potential therapeutic combination targeting these pathways in BLBC. Combination treatment of BLBC cell lines with the EGFR-inhibitor gefitinib plus the PI3K pathway inhibitor LY294002 was synergistic, and correspondingly, in an in vivo BLBC xenograft mouse model, gefitinib plus PI3K-inhibitor PWT-458 was more effective than either monotherapy and caused tumor regression. CONCLUSIONS: Our study emphasizes the importance of PI3K/PTEN pathway activity in ER-negative and basal-like breast cancer and supports the future clinical evaluation of combining EGFR and PI3K pathway inhibitors for the treatment of BLBC. Gene expression profiles were generated for 95 breast tumors using Agilent Human 44K v5 microarrays following the manufacturer protocol. Stratagene Universal Human Reference RNA was used as the common control sample.