Abstract P3-03-28: Integrative Bioinformatics Approach Reveals Potential Liquid Biopsy-based Biomarkers for Breast Cancer Metastasis Monitoring

Abstract Background: Despite recent advances in screening and therapeutic strategies, breast cancer remains the first leading cause of cancer death among females. The 5-year survival rate is high as 95% at localized stages but drops significantly to 31% once the cancer metastasizes. Therefore, developing predictive tools of metastatic recurrence is an unmet clinical need to design preventive clinical trials. In this study, we explored potential protein-based biomarkers associated with metastatic recurrence in the patients with non-metastatic breast cancer who underwent curative intent local and systemic therapies and further assessed their functions using publicly available dataset to identify potential biomarker candidates to further develop blood-based protein signatures for a future study. Methods: This study used a public dataset (GEO ID: GSE102484), which is a microarray dataset that contains primary tumor samples (n = 683) sourced from subjects with (n = 95) and without (n = 582) distant recurrence (metastasis) of breast cancer (met and non-met, respectively). Subjects with stage IV tumor (n = 6) were excluded from this study. Our approach began with two sets of differentially expressed genes (DEGs). The first set of DEGs are from all the samples in the dataset regardless of breast cancer subtypes, while the second set of DEGs are from triple negative breast cancer (TNBC) samples only. To identify subjects with TNBC, basal-like breast cancer samples were selected using PAM50 algorithm with genefu R package. To select potential blood biomarker candidates, we developed an integrative bioinformatics pipeline. Briefly, 1) Differential expression analysis between met and non-met were performed using limma R package. We then selected DEGs with log2 fold change ≥ 0.58 and false discovery rate (FDR) < 0.1, 2) Genes with housekeeping/hematopoietic markers were removed, 3) The genes that can be detectable in human bloods were selected using the human secretome database from the Human Protein Atlas (HPA), and 4) The functional enrichment analysis of gene ontology (GO) and pathways were performed using topGO and pathfindr packages, respectively. This pipeline was applied to the two sets of DEGs separately. Results: The differential expression analysis of all breast cancer samples identified 134 significantly upregulated genes in met and the selection process with the pipeline resulted in 12 marker candidates including CD93, HPRT1, MMP1, EPHB4, HSPD1, MAP4, CCDC80, MMP11, SAFB2, CALD1, CRISP3, and GAPDH. Subsequent GO enrichment analysis identified functions associated with these markers including angiogenesis, calcium ion binding, and extracellular matrix and structure organizations. Nucleotide metabolism was observed significance in pathway. We then focused on the TNBC samples from the dataset and identified 162 significantly 162 significantly upregulated genes in met from TNBC. The functional enrichment analysis of the 162 genes revealed significant enrichment of GO biological processes in met, including regulation of apoptotic process, angiogenesis, and calcium ion binding. We also found that FoxO signaling pathway, mTOR signaling pathway, and MAPK signaling pathway significantly enriched in met. Out of the 12 marker candidates from the 134 upregulated genes, 8 marker candidates including CALD1, CD93, EPHB4, SAFB2, HSPD1, MAP4, CCDC80, and HPRT1 were also found in TNBC. Conclusion: Through an integrative computational analysis, we identified 12 markers associated with breast cancer metastasis, of which 8 were specifically linked to metastasis in TNBC. These markers are involved in diverse functions and pathways that contribute to the metastatic process in breast cancer. Future studies will focus on validating these biomarkers using liquid biopsy samples from breast cancer patients, potentially offering a less invasive diagnostic approach. Citation Format: Hideki Hideki, Cynthia Jinno, HyoYoung Kim, Jina Kim, Sunao Tanaka, Takeo Fujii, Sungyong You, Hideki Furuya. Integrative Bioinformatics Approach Reveals Potential Liquid Biopsy-based Biomarkers for Breast Cancer Metastasis Monitoring [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P3-03-28.