Browsing by Author "Kong, Feng-Ming Spring"

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    IDO Immune Status after Chemoradiation May Predict Survival in Lung Cancer Patients
    (American Association for Cancer Research, 2018-02-01) Wang, Weili; Huang, Lei; Jin, Jian-Yue; Jolly, Shruti; Zang, Yong; Wu, Huanmei; Yan, Li; Pi, Wenhu; Li, Lang; Mellor, Andrew L.; Kong, Feng-Ming Spring; Radiation Oncology, School of Medicine
    Host immunity influences the impact of radiotherapy (RT) in cancer, but mechanistic connections remain obscure. In this study, we investigated the relationship of indoleamine 2,3-dioxygenase (IDO) systemic activity on clinical outcomes in RT-treated non-small cell lung cancer (NSCLC). IDO-mediated production of kynurenine and the kynurenine:tryptophan ratio in patient blood serum were determined for stage III NSCLC patients at times before, during, and after RT administration and then correlated to overall survival (OS), progression-free survival, and disease progression rate in patients. We found the impact of RT on these serum IDO markers to be heterogeneous in patients. On average, kynurenine:tryptophan ratios were reduced during RT but restored after RT. Notably, both baseline levels of kynurenine:tryptophan and changes in the levels of kynurenine after RT were significantly associated with OS. When combined, favorable change and favorable baseline corresponded with very long-term OS (median OS was not reached after 57 months of median follow-up). Favorable change combined with unfavorable baseline still corresponded with a lack of distant metastases. Our results suggest that RT alters IDO-mediated immune status in NSCLC patients and that changes in this serum biomarker may be useful to predict outcomes and perhaps personalize RT dosage to improve survival.Significance: Radiotherapy appears to influence systemic IDO activity and to exert a significant impact on metastatic risk and overall survival, with possible implications for defining a biomarker to optimize radiation dose in patients to improve outcomes. Cancer Res; 78(3); 809-16. ©2017 AACR.
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    Modern Radiation Further Improves Survival in Non-Small Cell Lung Cancer: An Analysis of 288,670 Patients
    (Ivyspring, 2019-01-01) Cheng, Monica; Jolly, Shruti; Quarshie, William O.; Kapadia, Nirav; Vigneau, Fawn D.; Kong, Feng-Ming Spring; Radiation Oncology, School of Medicine
    Background: Radiation therapy plays an increasingly important role in the treatment of patients with non-small-cell lung cancer (NSCLC). The purpose of the present study is to assess the survival outcomes of radiotherapy treatment compared to other treatment modalities and to determine the potential role of advanced technologies in radiotherapy on improving survival. Methods: We used cancer incidence and survival data from the Surveillance, Epidemiology, and End Results database linked to U.S. Census data to compare survival outcomes of 288,670 patients with stage I-IV NSCLC treated between 1999 and 2008. The primary endpoint was overall survival. Results: Among the 288,670 patients diagnosed with stage I-IV NSCLC, 92,374 (32%) patients received radiotherapy-almost double the number receiving surgery (51,961, 18%). Compared to other treatment groups and across all stages of NSCLC, patients treated with radiotherapy showed greater median and overall survival than patients without radiation treatment (p < 0.0001). Radiotherapy had effectively improved overall survival regardless of age, gender, and histological categorization. Radiotherapy treatment received during the recent time period 2004 - 2008 is correlated with enhanced survival compared to the earlier time period 1999 - 2003. Conclusion: Radiation therapy was correlated with increased overall survival for all patients with primary NSCLC across stages. Combined surgery and radiotherapy treatment also correlates with improved survival, signaling the value of bimodal or multimodal treatments. Population-based increases in overall survival were seen in the recent time period, suggesting the potential role of advanced radiotherapeutic technologies in enhancing survival outcomes for lung cancer patients.
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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.