Browsing by Subject "Non-small-cell lung cancer"

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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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    Radiation-induced lung toxicity in non-small-cell lung cancer: Understanding the interactions of clinical factors and cytokines with the dose-toxicity relationship
    (Elsevier, 2017-10) Hawkins, Peter G.; Boonstra, Philip S.; Hobson, Stephen; Hearn, Jason W.D.; Hayman, James A.; Haken, Randall K. Ten; Matuszak, Martha M.; Stanton, Paul; Kalemkerian, Gregory P.; Ramnath, Nithya; Lawrence, Theodore S.; Schipper, Matthew J.; Kong, Feng-Ming (Spring); Jolly, Shruti; Radiation Oncology, School of Medicine
    BACKGROUND AND PURPOSE: Current methods to estimate risk of radiation-induced lung toxicity (RILT) rely on dosimetric parameters. We aimed to improve prognostication by incorporating clinical and cytokine data, and to investigate how these factors may interact with the effect of mean lung dose (MLD) on RILT. MATERIALS AND METHODS: Data from 125 patients treated from 2004 to 2013 with definitive radiotherapy for stages I-III NSCLC on four prospective clinical trials were analyzed. Plasma levels of 30 cytokines were measured pretreatment, and at 2 and 4weeks midtreatment. Penalized logistic regression models based on combinations of MLD, clinical factors, and cytokine levels were developed. Cross-validated estimates of log-likelihood and area under the receiver operating characteristic curve (AUC) were used to assess accuracy. RESULTS: In prognosticating grade 3 or greater RILT by MLD alone, cross-validated log-likelihood and AUC were -28.2 and 0.637, respectively. Incorporating clinical features and baseline cytokine levels increased log-likelihood to -27.6 and AUC to 0.669. Midtreatment cytokine data did not further increase log-likelihood or AUC. Of the 30 cytokines measured, higher levels of 13 decreased the effect of MLD on RILT, corresponding to a lower odds ratio for RILT per Gy MLD, while higher levels of 4 increased the association. CONCLUSIONS: Although the added prognostic benefit from cytokine data in our model was modest, understanding how clinical and biologic factors interact with the MLD-RILT relationship represents a novel framework for understanding and investigating the multiple factors contributing to radiation-induced toxicity.
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    Spotlight on the treatment of ALK-rearranged non-small-cell lung cancer.
    (Future Medicine, 2017-12) Mamdani, Hirva; Jalal, Shadia I.; Medicine, School of Medicine
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    Weighted-Support Vector Machine Learning Classifier of Circulating Cytokine Biomarkers to Predict Radiation-Induced Lung Fibrosis in Non-Small-Cell Lung Cancer Patients
    (Frontiers Media, 2021-02-01) Yu, Hao; Lam, Ka-On; Wu, Huanmei; Green, Michael; Wang, Weili; Jin, Jian-Yue; Hu, Chen; Jolly, Shruti; Wang, Yang; Kong, Feng-Ming Spring; BioHealth Informatics, School of Informatics and Computing
    Background: Radiation-induced lung fibrosis (RILF) is an important late toxicity in patients with non-small-cell lung cancer (NSCLC) after radiotherapy (RT). Clinically significant RILF can impact quality of life and/or cause non-cancer related death. This study aimed to determine whether pre-treatment plasma cytokine levels have a significant effect on the risk of RILF and investigate the abilities of machine learning algorithms for risk prediction. Methods: This is a secondary analysis of prospective studies from two academic cancer centers. The primary endpoint was grade≥2 (RILF2), classified according to a system consistent with the consensus recommendation of an expert panel of the AAPM task for normal tissue toxicity. Eligible patients must have at least 6 months' follow-up after radiotherapy commencement. Baseline levels of 30 cytokines, dosimetric, and clinical characteristics were analyzed. Support vector machine (SVM) algorithm was applied for model development. Data from one center was used for model training and development; and data of another center was applied as an independent external validation. Results: There were 57 and 37 eligible patients in training and validation datasets, with 14 and 16.2% RILF2, respectively. Of the 30 plasma cytokines evaluated, SVM identified baseline circulating CCL4 as the most significant cytokine associated with RILF2 risk in both datasets (P = 0.003 and 0.07, for training and test sets, respectively). An SVM classifier predictive of RILF2 was generated in Cohort 1 with CCL4, mean lung dose (MLD) and chemotherapy as key model features. This classifier was validated in Cohort 2 with accuracy of 0.757 and area under the curve (AUC) of 0.855. Conclusions: Using machine learning, this study constructed and validated a weighted-SVM classifier incorporating circulating CCL4 levels with significant dosimetric and clinical parameters which predicts RILF2 risk with a reasonable accuracy. Further study with larger sample size is needed to validate the role of CCL4, and this SVM classifier in RILF2.