Final Report Summary - PATHSEC-BCA-BM (Pathway-based Secretomes in Breast Cancer Biomarker Discovery)
In the past decade, improvements in the characterization of human tumors have resulted in better survival rates for cancer patients. The understanding of cancer at the molecular level has increased greatly, motivating scientists to utilize targeted therapies instead of traditional treatments with cytotoxic drugs. However, less progress has been made in the development of clinical tools to monitor the disease during the course of therapy. The clinical impact of the increased molecular knowledge is limited by the requirement for repetitive tissue analysis. A challenge for achieving a successful management of cancer is therefore the discovery of secreted tumor biomarkers that represent useful surrogates for the disease and can be measured non-invasively.
The working hypothesis for the tumor biomarker discovery effort proposed here is that signaling pathways are altered during the tumorigenesis process, and that these alterations are translated into a differential secretion of proteins, which potentially can be exploited to discover secreted markers. By using a new proteomics approach capable of profiling the secreted sub-proteome (‘secretome’) of cells, we will generate secretome signatures to identify new tumor biomarkers in different breast cancer model systems, as well as in tumor tissue and human mammary epithelial cells (HMEC) engineered to activate or shut-down different pathways (Her-2, PI3K, Ras, PTEN, p53, BRCA,..). In addition, we will try to delineate which pathways are activated by the action or the resistance of an anti-Her2 drug using only secreted proteins. Once the breast cancer secretome signatures are obtained, we will analyze them with multivariate statistics and pathway analysis software. Once lists of potential pathway-based biomarkers correlating with different aspects of breast cancer prognosis or response to therapy have been established, we will take a list of potential biomarkers for validation using targeted quantitative assays developed using mass spectrometry.
Since this was among the firsts projects undertaken by our laboratory, we had to spend a considerable amount of time developing and implementing the experimental and data analysis methodology to generate reproducible quantitative cancer secretomes. First, we spent a few months optimizing our mass spectrometer (LTQ-Orbitrap Velos), implementing different acquisiton methods and setting up the data analysis software to do protein identification and quantification. Then, we optimized the dowsntream processing protocol to obtain secretomes from cancer cell lines. Finally, we set up pilot experiments with different cell lines to test the reproducibility of our experimental approach and to asseses whether we could detect biological differences in our secretomes over the expected technical variability of the mass spectrometic.
Once the experimental methodology based on tissue culture and mass spectrometry was optimized, we started genrating and analyzing the secretome inventories from the different breast cancer cell lines. We were able to do the quantitative secretome profiles of twenty breast cancer cell lines. Additionally, we generated a big secretome dataset from Her-2 expressing cells both sensitive and resistant to trastuzumab (a humanized anti-Her-2 antibody) both in the absence or the presence of the drug looking for a differential protein secretion to find new biomarkers of therapy response and resistance to anti-Her2 treatment.
The experimental design and statistical analysis involved with comprative secretome analysis proved to be challenging and we devoted a large effort during the whole project to optimize the multivariate statistical tools to derive candidate secretome-based biomarkers for breast cancer. Regarding objective 3, we have set up the methodology to perform targeted mass spectrometric assays to measure specific candidate biomarkers. This was achieved by implementing accurate inclusion lists in our LTQ-Orbitrap Velos mass spectrometer. A list of candidate biomarkers from objective 1 was tested using our targeted mass spectrometry methodology and we were able to measure accurately dozens of candidate biomarkers.
The main results achieved by the project are the following:
• Establishment of a reproducible pipeline for the quantitative proteomics analysis of cancer secretome inventories.
• Develpopment and implementation of experimental design and statistical tools for the identification of secretome-based candidate biomarkers for cancer.
• Generation and quantitative proteomics analysis of secretome inventories from breast cancer cell lines.
• Generation of secretome inventories from Her-2 expressing breast cancer cells that are sensitive / resistant to trastuzumab both in the absence or the presence of the drug.
• Establishment of a reproducible pipeline for the proteomics verification of candidate biomarkers in cancer secretome inventories by implementing accurate inclusion lists by targeted mass spectrometry.
This project has allowed the establisment of the Tumor Biomarkers laboratory at the Vall d’Hebron Institute of Oncology. Our objective is to facilitate individualized diagnostics and treatment of breast cancer patients by developing non-invasive diagonostics tools, which would represent a major milestone in the proteomics-based biomarker discovery field. The results obtained during this project have been published in leading peer-reviewed proteomic journals. Additonally, Given the translational nature of the Vall d’Hebron Institute of Oncology as a cancer research institute and its location in the Vall d’Hebron Hospital campus, the project has played a key role allowing our research group to blend with different ongoing activities related to clinical research , where the efforts are clearly oriented to the patient’s benefit.
The working hypothesis for the tumor biomarker discovery effort proposed here is that signaling pathways are altered during the tumorigenesis process, and that these alterations are translated into a differential secretion of proteins, which potentially can be exploited to discover secreted markers. By using a new proteomics approach capable of profiling the secreted sub-proteome (‘secretome’) of cells, we will generate secretome signatures to identify new tumor biomarkers in different breast cancer model systems, as well as in tumor tissue and human mammary epithelial cells (HMEC) engineered to activate or shut-down different pathways (Her-2, PI3K, Ras, PTEN, p53, BRCA,..). In addition, we will try to delineate which pathways are activated by the action or the resistance of an anti-Her2 drug using only secreted proteins. Once the breast cancer secretome signatures are obtained, we will analyze them with multivariate statistics and pathway analysis software. Once lists of potential pathway-based biomarkers correlating with different aspects of breast cancer prognosis or response to therapy have been established, we will take a list of potential biomarkers for validation using targeted quantitative assays developed using mass spectrometry.
Since this was among the firsts projects undertaken by our laboratory, we had to spend a considerable amount of time developing and implementing the experimental and data analysis methodology to generate reproducible quantitative cancer secretomes. First, we spent a few months optimizing our mass spectrometer (LTQ-Orbitrap Velos), implementing different acquisiton methods and setting up the data analysis software to do protein identification and quantification. Then, we optimized the dowsntream processing protocol to obtain secretomes from cancer cell lines. Finally, we set up pilot experiments with different cell lines to test the reproducibility of our experimental approach and to asseses whether we could detect biological differences in our secretomes over the expected technical variability of the mass spectrometic.
Once the experimental methodology based on tissue culture and mass spectrometry was optimized, we started genrating and analyzing the secretome inventories from the different breast cancer cell lines. We were able to do the quantitative secretome profiles of twenty breast cancer cell lines. Additionally, we generated a big secretome dataset from Her-2 expressing cells both sensitive and resistant to trastuzumab (a humanized anti-Her-2 antibody) both in the absence or the presence of the drug looking for a differential protein secretion to find new biomarkers of therapy response and resistance to anti-Her2 treatment.
The experimental design and statistical analysis involved with comprative secretome analysis proved to be challenging and we devoted a large effort during the whole project to optimize the multivariate statistical tools to derive candidate secretome-based biomarkers for breast cancer. Regarding objective 3, we have set up the methodology to perform targeted mass spectrometric assays to measure specific candidate biomarkers. This was achieved by implementing accurate inclusion lists in our LTQ-Orbitrap Velos mass spectrometer. A list of candidate biomarkers from objective 1 was tested using our targeted mass spectrometry methodology and we were able to measure accurately dozens of candidate biomarkers.
The main results achieved by the project are the following:
• Establishment of a reproducible pipeline for the quantitative proteomics analysis of cancer secretome inventories.
• Develpopment and implementation of experimental design and statistical tools for the identification of secretome-based candidate biomarkers for cancer.
• Generation and quantitative proteomics analysis of secretome inventories from breast cancer cell lines.
• Generation of secretome inventories from Her-2 expressing breast cancer cells that are sensitive / resistant to trastuzumab both in the absence or the presence of the drug.
• Establishment of a reproducible pipeline for the proteomics verification of candidate biomarkers in cancer secretome inventories by implementing accurate inclusion lists by targeted mass spectrometry.
This project has allowed the establisment of the Tumor Biomarkers laboratory at the Vall d’Hebron Institute of Oncology. Our objective is to facilitate individualized diagnostics and treatment of breast cancer patients by developing non-invasive diagonostics tools, which would represent a major milestone in the proteomics-based biomarker discovery field. The results obtained during this project have been published in leading peer-reviewed proteomic journals. Additonally, Given the translational nature of the Vall d’Hebron Institute of Oncology as a cancer research institute and its location in the Vall d’Hebron Hospital campus, the project has played a key role allowing our research group to blend with different ongoing activities related to clinical research , where the efforts are clearly oriented to the patient’s benefit.