Dataset Information

In-depth proteomic analysis for discovery of response prediction markers in breast cancer chemotherpy using needle biopsy FFPE tissue

ABSTRACT: Breast cancer is the most frequently diagnosed female cancer accounting for 23 % of the total cases and the second leading cause of cancer mortality in the world, particularly in western countries. Since GEPARDUO trial reported the therapeutic benefit of combined doxorubicin and cyclophosphamide regimen in sequential administration with docetaxel, the combination regimen has become a standard therapeutic strategy in neoadjuvant systemic therapy for patients with operable breast cancers regardless of an intrinsic subtype. Although approximately 70% of entire patients are currently receiving the chemotherapy regimen, pathologic complete response (pCR) rate is still low, ranging from 23% to 32.7% due to the high heterogeneity of breast cancers. Therefore, the need for a marker predictive of response to a particular cytotoxic regimen, especially before neoadjuvant chemotherapy, is becoming all the more necessary to optimize therapeutic efficacy and to avoid unnecessary complications caused by systemic therapy. In the study, here we generated the first high-coverage proteomic data for needle biopsy FFPE sample being characterized with identical clinical conditions including chemotherapeutic regimens and the stage classification.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Breast

DISEASE(S): Breast Cancer

SUBMITTER: Dohyun Han

LAB HEAD: Dohyun Han

PROVIDER: PXD013431 | Pride | 2021-09-08

REPOSITORIES: Pride

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			Action	DRS
	20170618_NAC_S10_40246_F1.raw	Raw
	20170618_NAC_S10_40246_F2.raw	Raw
	20170618_NAC_S10_40246_F3.raw	Raw
	20170618_NAC_S10_40246_F4.raw	Raw
	20170618_NAC_S10_40246_F5.raw	Raw

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Publications

Combined the SMAC mimetic and BCL2 inhibitor sensitizes neoadjuvant chemotherapy by targeting necrosome complexes in tyrosine aminoacyl-tRNA synthase-positive breast cancer.

Lee Kyung-Min KM Lee Hyebin H Han Dohyun D Moon Woo Kyung WK Kim Kwangsoo K Oh Hyeon Jeong HJ Choi Jinwoo J Hwang Eun Hye EH Kang Seong Eun SE Im Seock-Ah SA Lee Kyung-Hun KH Ryu Han Suk HS

Breast cancer research : BCR 20201125 1

<h4>Background</h4>Chemotherapy is the standard treatment for breast cancer; however, the response to chemotherapy is disappointingly low. Here, we investigated the alternative therapeutic efficacy of novel combination treatment with necroptosis-inducing small molecules to overcome chemotherapeutic resistance in tyrosine aminoacyl-tRNA synthetase (YARS)-positive breast cancer.<h4>Methods</h4>Pre-chemotherapeutic needle biopsy of 143 invasive ductal carcinomas undergoing the same chemotherapeutic ...[more]

PMID: 33239070

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Project description:A gene expression signature characterizes expression data from breast cancer samples of patients with pathological complete response (pCR) or residual disease (RD) following the neoadjuvant trial. Several gene expression profiles have been reported to predict breast cancer response to neoadjuvant chemotherapy. These studies often consider breast cancer as a homogeneous entity, although higher rates of pathologic complete response (pCR) are known to occur within the basal-like subclass. We postulated that profiles with higher predictive accuracy could be derived from a subset analysis of basal-like tumors in isolation. Using a previously described ‘‘intrinsic’’ signature to differentiate breast tumor subclasses, we identified 50 basal-like tumors from two independent clinical trials associated with gene expression profile data. 24 tumor data sets (included in this GEO submission) were derived from a 119-patient neoadjuvant trial at our institution and an additional 26 tumor data sets were identified from a published data set (Hess et al. J Clin Oncol 24:4236–4244, 2006). The combined 50 basal-like tumors were partitioned to form a 37 sample training set with 13 sequestered for validation. Clinical surveillance occurred for a mean of 26 months. We identified a 23-gene profile which predicted pCR in basal-like breast cancers with 92% predictive accuracy in the sequestered validation data set. Furthermore, distinct cluster of patients with high rates of cancer recurrence was observed based on cluster analysis with the 23-gene signature. Disease-free survival analysis of these three clusters revealed significantly reduced survival in the patients of this high recurrence cluster. We identified a 23- gene signature which predicts response of basal-like breast cancer to neoadjuvant chemotherapy as well as disease-free survival. This signature is independent of tissue collection method and chemotherapeutic regimen. Keywords: Disease state analysis

Project description:Abstract Introduction Response rates to chemotherapy remain highly variable in breast cancer patients. We set out to identify genes associated with chemotherapy resistance. We analyzed what is currently the largest single-institute set of gene expression profiles derived from breast cancers prior to a single neoadjuvant chemotherapy regimen (dose-dense doxorubicine and cyclophophamide). Methods We collected, gene expression-profiled and analyzed 178 HER2-negative breast tumor biopsies (M-bM-^@M-^XNKI datasetM-bM-^@M-^Y). We employed a recently developed approach for detecting imbalanced differential signal (DIDS) in order to identify markers of resistance to treatment. In contrast to traditional methods, DIDS is able to identify markers that show aberrant expression in only a small subgroup of the non-responder samples. Results We found a number of markers of resistance to anthracycline-based chemotherapy. We validated our findings by the analysis of three external datasets, which contained 456 HER2-negative samples in total. Since these external sets included patients who received differing treatment regimens we could only validate markers of general chemotherapy resistance. There was a highly significant overlap in the markers identified in the NKI dataset and the other three datasets. Five resistance markers, SERPINA6, BEX1, AGTR1, SLC26A3, and LAPTM4B, were identified in three of the four datasets (p-value overlap <1e-6). These five genes identified resistant tumors that could not have been identified by merely taking ER-status or proliferation into account. Conclusion The identification of these genes might lead to a better understanding of the mechanisms involved in (clinically) observed chemotherapy resistance and could possibly assist in the recognition of breast cancers in which chemotherapy does not contribute to response or survival. We collected, gene expression profiled and analyzed 178 HER2-negative breast tumor biopsies, obtained from patients scheduled to undergo neoadjuvant therapy.