Browsing by Author "Lycan, Thomas"

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    c-Met Mediated Cytokine Network Promotes Brain Metastasis of Breast Cancer by Remodeling Neutrophil Activities
    (MDPI, 2023-05-05) Liu, Yin; Smith, Margaret R.; Wang, Yuezhu; D’Agostino, Ralph, Jr.; Ruiz, Jimmy; Lycan, Thomas; Kucera, Gregory L.; Miller, Lance D.; Li, Wencheng; Chan, Michael D.; Farris, Michael; Su, Jing; Song, Qianqian; Zhao, Dawen; Chandrasekaran, Arvind; Xing, Fei; Biostatistics and Health Data Science, School of Medicine
    The brain is one of the most common metastatic sites among breast cancer patients, especially in those who have Her2-positive or triple-negative tumors. The brain microenvironment has been considered immune privileged, and the exact mechanisms of how immune cells in the brain microenvironment contribute to brain metastasis remain elusive. In this study, we found that neutrophils are recruited and influenced by c-Met high brain metastatic cells in the metastatic sites, and depletion of neutrophils significantly suppressed brain metastasis in animal models. Overexpression of c-Met in tumor cells enhances the secretion of a group of cytokines, including CXCL1/2, G-CSF, and GM-CSF, which play critical roles in neutrophil attraction, granulopoiesis, and homeostasis. Meanwhile, our transcriptomic analysis demonstrated that conditioned media from c-Met high cells significantly induced the secretion of lipocalin 2 (LCN2) from neutrophils, which in turn promotes the self-renewal of cancer stem cells. Our study unveiled the molecular and pathogenic mechanisms of how crosstalk between innate immune cells and tumor cells facilitates tumor progression in the brain, which provides novel therapeutic targets for treating brain metastasis.
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    Genomic Signature for Initial Brain Metastasis Velocity (iBMV) in Non-Small-Cell Lung Cancer Patients: The Elusive Biomarker to Predict the Development of Brain Metastases?
    (MDPI, 2025-03-15) Glynn, Sarah E.; Lanier, Claire M.; Choi, Ariel R.; D'Agostino, Ralph, Jr.; Farris, Michael; Abdulhaleem, Mohammed; Wang, Yuezhu; Smith, Margaret; Ruiz, Jimmy; Lycan, Thomas; Petty, William Jeffrey; Cramer, Christina K.; Tatter, Stephen B.; Laxton, Adrian W.; White, Jaclyn J.; Su, Jing; Whitlow, Christopher T.; Soto-Pantoja, David R.; Xing, Fei; Jiang, Yuming; Chan, Michael; Helis, Corbin A.; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Background/Objectives: No prior studies have attempted to identify a biomarker for initial brain metastasis velocity (iBMV), with limited studies attempting to correlate genomic data with the development of brain metastases. Methods: Patients with non-small-cell lung cancer (NSCLC) who underwent next-generation sequencing (NGS) were identified in our departmental database. iBMV was calculated by dividing the number of BMs by the interval of time between primary cancer and BM diagnosis. Two-sample t-testing was used to identify mutations statistically associated with iBMV (p < 0.1). A value of +1 was assigned to each mutation with a positive association ("deleterious genes"), and a value of -1 to each with an inverse association ("protective genes"). The sum of these values was calculated to define iBMV risk scores of -1, 0 and 1. Pearson correlation test was used to determine the association between iBMV risk score and calculated iBMV, and a competing risk analysis assessed for death as a competing risk to the development of BMs. Results: A total of 312 patients were included in the analysis, 218 of whom (70%) developed brain metastases. "Deleterious genes" included ARID1A, BRAF, CDK4, GNAQ, MLH1, MSH6, PALB2, RAD51D, RB1 and TSC1; "protective genes" included ARAF, IDH1, MYC, and PTPN11. iBMV risk scores of 1, 0 and -1, predicted an 88%, 61% and 65% likelihood of developing a BM (p < 0.01). A competing risk analysis found a significant association between iBMV risk scores of 1 vs. 0 and 1 vs. -1, and the likelihood of developing a BM using death as a competing risk. Overall survival (OS) at 1 and 2 years for patients with iBMV risk scores of 1, 0 and -1 was 72% vs. 84% vs. 85% and 46% vs. 69% vs. 70% (p < 0.02). Conclusions: Development of a genomic signature for iBMV via non-invasive liquid biopsy appears feasible in NSCLC patients. Patients with a positive iBMV risk score were more likely to develop brain metastases. Validation of this signature could lead to a biomarker with the potential to guide treatment recommendations and surveillance schedules.
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    Prognostic Mutational Signatures of NSCLC Patients treated with chemotherapy, immunotherapy and chemoimmunotherapy
    (Springer Nature, 2023-03-27) Smith, Margaret R.; Wang, Yuezhu; D’Agostino, Ralph, Jr.; Liu, Yin; Ruiz, Jimmy; Lycan, Thomas; Oliver, George; Miller, Lance D.; Topaloglu, Umit; Pinkney, Jireh; Abdulhaleem, Mohammed N.; Chan, Michael D.; Farris, Michael; Su, Jing; Mileham, Kathryn F.; Xing, Fei; Biostatistics and Health Data Science, School of Medicine
    Different types of therapy are currently being used to treat non-small cell lung cancer (NSCLC) depending on the stage of tumor and the presence of potentially druggable mutations. However, few biomarkers are available to guide clinicians in selecting the most effective therapy for all patients with various genetic backgrounds. To examine whether patients' mutation profiles are associated with the response to a specific treatment, we collected comprehensive clinical characteristics and sequencing data from 524 patients with stage III and IV NSCLC treated at Atrium Health Wake Forest Baptist. Overall survival based Cox-proportional hazard regression models were applied to identify mutations that were "beneficial" (HR < 1) or "detrimental" (HR > 1) for patients treated with chemotherapy (chemo), immune checkpoint inhibitor (ICI) and chemo+ICI combination therapy (Chemo+ICI) followed by the generation of mutation composite scores (MCS) for each treatment. We also found that MCS is highly treatment specific that MCS derived from one treatment group failed to predict the response in others. Receiver operating characteristics (ROC) analyses showed a superior predictive power of MCS compared to TMB and PD-L1 status for immune therapy-treated patients. Mutation interaction analysis also identified novel co-occurring and mutually exclusive mutations in each treatment group. Our work highlights how patients' sequencing data facilitates the clinical selection of optimized treatment strategies.