Chemical Complementarity of Blood-Sourced, Breast Cancer-Related TCR CDR3s and the CMV UL29 and IE1 Antigens is Associated with Worse Overall Survival.
Pooja Neerumalla, Rahul Jain, Michael T Aboujaoude, Tabitha R Hudock, Joanna J Song, Bryan H Cao, Andrea Chobrutskiy, Boris I Chobrutskiy, George Blanck
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引用次数: 0
Abstract
Cytomegalovirus (CMV) infection is common and becomes a particular concern in immunocompromised patients. Understanding the potential role CMV plays in breast cancer patients' disease progression is important for providing more patient-specific treatments. In this study, we analyzed whether a breast cancer patient's blood-sourced T-cell receptor (TCR) complementarity determining-3 (CDR3) amino acid (AA) sequences could provide an indication of the impact of a systemic CMV infection. Specifically, we assessed the chemical complementarity of patient TCR CDR3 AAs and CMV antigens to determine whether patients with greater complementarity also represented different survival probabilities. Initially, we examined five distinct CMV antigens, of which two, IE1 and UL29, represented TCR (TRA+ RB)-CDR3-CMV antigen complementarity scores (CSs) whereby cases representing the upper 50th percentile of CSs had a worse overall survival (log-rank p = 5.034E-3, for IE1). Then, an analysis of CSs representing previously identified, TCR IE1 epitopes indicated that greater TRB CDR3-IE1 epitope complementarities represented a worse OS (log-rank p = 0.0111). These results raise the question of whether a systemic, anti-CMV response leads to increased systemic inflammation, which is either directly or indirectly supportive of tumor growth; or are patients succumbing to a direct impact of CMV functions on tumor growth or metastasis?
期刊介绍:
Biochemical Genetics welcomes original manuscripts that address and test clear scientific hypotheses, are directed to a broad scientific audience, and clearly contribute to the advancement of the field through the use of sound sampling or experimental design, reliable analytical methodologies and robust statistical analyses.
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