Abstract SY01-04: High-end proteomics enables identification of breast cancer therapy resistance predictive proteins
Authors: Pasa-tolic Ljiljana , Luider Theo M. , Martens John WM , Foekens John A. , Umar Arzu 
Published: May 2019

Abstract
A spectrum of treatment options is available for breast cancer patients, the choice of which is mainly dependent on factors such as steroid hormone receptor and HER2/neu status, age, menopausal status, tumor size, grade, and lymph node status. Tamoxifen is an anti-estrogenic agent that has been widely and successfully used in the treatment of breast cancer over the past decades [1]. Tamoxifen inhibits the estrogen receptor α (ERα), which is expressed in approximately 70% of all breast tumors. When diagnosed at an early stage, breast cancer can be effectively treated with adjuvant tamoxifen. Next to endocrine treatment, adjuvant chemotherapy is often administered as well to improve survival. However, this success rate drops severely in advanced disease due to therapy-resistance. Approximately 50% of patients with metastatic breast cancer have no benefit from tamoxifen due to intrinsic resistance. While the other half of patients initially responds well to therapy and shows objective response (OR), eventually almost all patients develop progressive disease (PD) due to acquired resistance [2, 3]. First-line chemotherapy is more frequently used on ER- tumors, and on ER+ tumors where endocrine treatment failed or was no option to begin with. Despite the proven success of chemotherapy in primary tumors, in the advanced setting therapy-resistance eventually occurs. In general, patients with advanced disease only have a 5-year survival of approximately 20%.
The major issue in the treatment of breast cancer is that currently available clinical and biochemical markers can insufficiently predict the type of therapy response. Therefore, identification of new protein targets that can better predict therapy efficacy, and ultimately improve patient cure rate is crucial and a major focus of our research.
For our biomarker discovery studies we use primary breast tumor tissues from patients, of which we have long term clinical follow up. In addition, we perform laser capture microdissection on these heterogenic tissues to obtain relatively pure populations of tumor epithelial and stromal cells. By doing, so we end up with minute amounts of material, typically less than 1 μg protein, to be used in our comparative proteomics studies. This requires the use of robust proteomics platforms that offer exceptional sensitivity and dynamic range of detection in a high-throughput, accurate, and reproducible fashion.
One such technological platform is FTICR MS in combination with nLC, which has the possibility to separate in one single analysis large numbers of peptides (&gt;10³) with extremely high mass resolving power (&gt;100,000), and mass measurement accuracy (~1 ppm), with a dynamic range up to 10exp4, and a high peak capacity offered by nLC [4]. The nLC-FTICR platform in combination with the accurate mass and time (AMT) tag approach has allowed for high-throughput and sensitive studies of complex protein mixtures [5]. This approach initially utilizes conventional LC-MS/MS measurements to establish a reference database of AMT tags specific for a particular proteome sample (e.g. breast cancer tissue). Each tag consists of a theoretical mass calculated from the peptide sequence, an LC normalized elution time value, and an indicator of quality. The AMT tag database serves as a “look-up table” for identifying peptides in subsequent quantitative LC-MS analyses. Substituting routine LC-MS/MS analyses (shotgun approach) with LC-FTICR MS analyses (AMT tag approach) significantly increases overall throughput and sensitivity, while reducing sample requirements [6]. Additionally, quantitative intensity information related to the protein's abundance can be discerned from these MS analyses.
We have used the AMT tag approach for the identification of a putative tamoxifen therapy-resistance protein profile. In this study, primary tumor tissues responsive (n=27) and resistant (n=28) to first-line tamoxifen treatment were subjected to laser capture microdissection, and ~4,000 tumor cells were collected per tissue. nLC-FTICR MS was performed for protein biomarker discovery, and putative markers were verified through targeted MS/MS using Orbitrap. Differential abundance of our top candidate, EMMPRIN, was validated in an independent sample set using tissue microarray (TMA). High EMMPRIN levels were found in therapy-resistant tumors, which correlated with poor outcome on first-line tamoxifen treatment [7].
We are currently in the process of validating other putative profile proteins by means of immunohistochemistry using our TMA in cases where suitable antibodies are available. For other proteins, we are developing quantitative MS-based assays using multiple reaction monitoring (MRM). In addition, we are applying a similar comparative proteomics strategy for the identification of chemotherapy-resistance associated markers proteins.
In order to bring these validated marker proteins one step closer to clinical applicability, quantitative MRM assays may prove to be an invaluable tool for large-scale, multi-center validation studies.
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Citation Format: Ljiljana Pasa-tolic, Theo M. Luider, John WM Martens, John A. Foekens, Arzu Umar. High-end proteomics enables identification of breast cancer therapy resistance predictive proteins [abstract]. In: Proceedings of the AACR 101st Annual Meeting 2010; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr SY01-04