Growth differentiation factor 15 (GDF15) predicts relapse free and overall survival in unresected locally advanced non-small cell lung cancer treated with chemoradiotherapy

生长分化因子 15 (GDF15) 可预测接受放化疗治疗的未切除局部晚期非小细胞肺癌的无复发生存率和总生存率

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作者:Fiorella Di Pastena, Gregory Pond, Evangelia E Tsakiridis, Andre Gouveia, Elham Ahmadi, Olga-Demetra Biziotis, Amr Ali, Anand Swaminath, Gordon Okawara, Peter M Ellis, Bassam Abdulkarim, Naseer Ahmed, Andrew Robinson, Wilson Roa, Mario Valdes, Peter Kavsak, Marcin Wierzbicki, James Wright, Gregory S

Conclusions

GDF15 is a plasma marker that responds to the treatment of unresected LA-NSCLC with cCRT and metformin. GDF15 levels correspond with tumor volume and increased GDF15 levels predict for poor RFS and OS. These results require validation in larger clinical trial datasets.

Methods

Patients were randomized to treatment with platinum-based chemotherapy and concurrent chest radiotherapy (60-66 Gy), with or without metformin (2000 mg/d). The trial collected tumor volume parameters, survival outcomes, and patient blood plasma at baseline, during (weeks 1 and 6) and 6 months after cCRT. Plasma GDF15 levels were assayed with the ELISA method. Statistical analyses explored associations between GDF15, survival outcomes, and radiotherapy tumor volumes.

Results

Baseline plasma levels of GDF15 were elevated in study patients, they increased during cCRT (p < 0.001), and the addition of metformin was associated with a further increase (week 6, p = 0.033). Baseline GDF15 levels correlated with the radiotherapy gross target volume (GTV, p < 0.01), while week 1 of radiotherapy levels correlated with radiotherapy planned target volume (PTV, p < 0.006). In multivariate analysis, baseline plasma GDF15 was prognostic for poor relapse-free (RFS) and overall survival (OS) (p = 0.005 and p = 0.002, respectively). Conclusions: GDF15 is a plasma marker that responds to the treatment of unresected LA-NSCLC with cCRT and metformin. GDF15 levels correspond with tumor volume and increased GDF15 levels predict for poor RFS and OS. These results require validation in larger clinical trial datasets.

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