AUTHOR=Zhang Hong , Wang Weili , Pi Wenhu , Bi Nan , DesRosiers Colleen , Kong Fengchong , Cheng Monica , Yang Li , Lautenschlaeger Tim , Jolly Shruti , Jin Jianyue , Kong Feng-Ming (Spring) 

TITLE=Genetic Variations in the Transforming Growth Factor-β1 Pathway May Improve Predictive Power for Overall Survival in Non-small Cell Lung Cancer

JOURNAL=Frontiers in Oncology

VOLUME=Volume 11 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.599719

DOI=10.3389/fonc.2021.599719

ISSN=2234-943X

ABSTRACT=Purpose: Transforming growth factor-beta1 (TGF-β1), a known immune suppressor, plays an important role in tumor progression and overall survival (OS) in many types of cancers. We hypothesize that the genetic variations of single nucleotide polymorphisms (SNPs) in the TGF-β1 pathway can predict survival in non-small cell lung cancer (NSCLC) patients after radiation therapy.
Materials and Methods: Fourteen functional SNPs in the TGF-β1 pathway were measured in 166 NSCLC patients enrolled in a multi-center clinical trial. Clinical factors including age, gender, ethnicity, smoking status, stage group, histology, Karnofsky Performance Status, equivalent dose at 2 Gy fractions (EQD2), and the use of chemotherapy were first tested under univariate Cox’s proportional hazards model. All significant clinical predictors were combined as a group of predictors, Clinical. The significant SNPs under Cox’s proportional hazards model were combined as a group of predictors, SNP. The predictive power of models using Clinical and Clinical plus SNP were compared with the cross-validation Concordance index (C-index) of random forest models. 
Results: Age, gender, stage group, smoking, histology and EQD2, were identified as significant clinical predictors Clinical. Among the 14 SNPs, BMP2:rs235756 (HR = 0.63; 95% CI:0.42-0.93; p = 0.022), SMAD9:rs7333607 (HR = 2.79; 95% CI 1.22–6.41; p = 0.015), SMAD3:rs12102171 (HR = 0.68; 95% CI: 0.46–1.00; p = 0.050) and SMAD4: rs12456284 (HR = 0.63; 95% CI: 0.43–0.92; p = 0.016) were identified as powerful predictors SNP. After adding SNP, the C-index increased from 84.1% to 87.6% at 24 months and from 79.4% to 84.4% at 36 months.
Conclusion: Genetic variations in the TGF-β1 pathway have the potential to improve the prediction accuracy for OS in NSCLC patients.