Incorporating Single-nucleotide Polymorphisms Into the Lyman Model to Improve Prediction of Radiation Pneumonitis

Abstract

Purpose To determine whether single nucleotide polymorphisms (SNPs) in genes associated with DNA repair, cell cycle, transforming growth factor beta, tumor necrosis factor and receptor, folic acid metabolism, and angiogenesis can significantly improve the fit of the Lyman-Kutcher-Burman (LKB) normal-tissue complication probability (NTCP) model of radiation pneumonitis (RP) risk among patients with non-small cell lung cancer (NSCLC). Methods and Materials Sixteen SNPs from 10 different genes (XRCC1, XRCC3, APEX1, MDM2, TGFβ, TNFα, TNFR, MTHFR, MTRR, and VEGF) were genotyped in 141 NSCLC patients treated with definitive radiotherapy, with or without chemotherapy. The LKB model was used to estimate the risk of severe (Grade ≥3) RP as a function of mean lung dose (MLD), with SNPs and patient smoking status incorporated into the model as dose-modifying factors. Multivariate (MV) analyses were performed by adding significant factors to the MLD model in a forward stepwise procedure, with significance assessed using the likelihood-ratio test. Bootstrap analyses were used to assess the reproducibility of results under variations in the data. Results Five SNPs were selected for inclusion in the multivariate NTCP model based on MLD alone. SNPs associated with an increased risk of severe RP were in genes for TGFβ, VEGF, TNFα, XRCC1 and APEX1. With smoking status included in the MV model, the SNPs significantly associated with increased risk of RP were in genes for TGFβ, VEGF, and XRCC3. Bootstrap analyses selected a median of 4 SNPs per model fit, with the 6 genes listed above selected most often. Conclusions This study provides evidence that SNPs can significantly improve the predictive ability of the Lyman MLD model. With a small number of SNPs, it was possible to distinguish cohorts with >50% risk versus <10% risk of RP when exposed to high MLDs.

Treatment planning evaluation and optimization should be biologically and not dose/volume based

“…Development of radio-genomics databases have helped pave the way for models factoring in single-nucleotide polymorphisms to improve predictive power of NTCP models. 5 Imaging based biomarkers 6 may be used to improve models. Monte Carlo approaches to TCP/NTCP estimations and in-silico modeling of outcomes and mechanisms are very promising.…”

Contrasting analytical and data-driven frameworks for radiogenomic modeling of normal tissue toxicities in prostate cancer

“…In the present work, as a proof-of-concept, we introduce SNPs and CNVs as genetic variables of interest and combine them with dosimetric and clinical variables using analytical [19] and datadriven frameworks [20], denoted as radiogenomic modeling [21].…”

“…These could be gains (CNV >2) or losses (CNV <2) [18]. The role of CNVs has only recently been implicated in inflammation-related diseases [18] and has not yet been investigated in RITs.In the present work, as a proof-of-concept, we introduce SNPs and CNVs as genetic variables of interest and combine them with dosimetric and clinical variables using analytical [19] and datadriven frameworks [20], denoted as radiogenomic modeling [21]. …”

Personalized Radiation Therapy (PRT) for Lung Cancer

“…Tucker et al have incorporated SNPs into a normal-tissue complication probability (NTCP) model to predict radiation pneumonitis (RP) risk [57]. Five SNPs (in genes for TGFβ, VEGF, TNFα, XRCC1 and APEX1) were selected from 16 SNPs from 10 different genes (XRCC1, XRCC3, APEX1, MDM2, TGFβ, TNFα, TNFR, MTHFR, MTRR, and VEGF) to incorporate into a mean lung dose based NTCP model from 141 NSCLC patients.…”