Abstract

Despite the recognized advances in the treatment of breast cancer, it still accounts for 15% of all cancer-related deaths. 90% of breast cancer deaths are due to unpredicted metastasis. There is neither successful treatment for metastatic patients nor a specific test to predict or detect secondary lesions. Patients with primary tumor will be either over-treated with cytotoxic side effects or under-treated and risk recurrence. This necessitates the need for personalized treatment, which is hard to offer for such heterogeneous disease. Obstacles in treating breast cancer metastasis are mainly due to the gaps exist in the understanding of the molecular mechanism of metastasis. The linear model of metastasis is supported by several observations that reflect an early crosstalk between the primary and secondary tumor, which in turn makes the secondary microenvironment fertile for the growth of disseminated cells. This communication occurs through circulation and utilizes molecules which have not been identified to date. Identifying such molecules may help in detecting initial stages of tumor colonization and predict the target organ of metastasis. Furthermore, these molecules may help to provide a personalized therapy that aims to tailor treatment according to the biology of the individual tumor. Advances in proteomics allows for more reproducible and sensitive biomarker discovery. Proteomic biomarkers are often more translatable to the clinic compared to biomarkers identified using other omics approaches. Further, protein biomarkers can be found in biological fluids making them a non-invasive way to treat or investigate cancer patients. We present in this manuscript our study of the use of a proteomic approach on blood serum samples of metastatic and non-metastatic patients using LC-MS/MS quantitative analysis machine to identify molecules that could be associated with different stages of breast cancer metastasis. We focused on the deferential expression of low molecular weight biomolecules known to reflect disease-specific signatures. We manually analyzed 2500 individual small biomolecules in each serum sample of total of 51 samples. Comparisons between different sample types (from stage I and III Breast Cancer patients in this case) allows for the detection of unique short peptide biomarkers present in one sample type. We built a multi-biomarker model with more sensitivity and specificity to identify the stage of the tumor and applied them on blinded set of samples to validate prediction power. We hope that our study will provide insights for future work on the collection, analysis, and understanding of role of molecules in metastatic breast cancer.

Degree

PhD

College and Department

Life Sciences; Physiology and Developmental Biology

Rights

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