Lung cancer is the greatest contributor to tumor-derived death. Traditionally, platinum-based chemotherapies are the primary treatment for most patients. However, intrinsic drug resistance and side effects limit the efficacy of platinum-based chemotherapies. Previous studies demonstrated that Pol ζ can modulate cellular sensitivity to chemotherapy. The primary aim of this study was to investigate the potential role of the polymorphism of Pol ζ in platinum-based chemotherapy tolerance and side effects. A total of 663 patients who were newly histologically diagnosed with advanced NSCLC were enrolled. Their treatment response was classified into four categories: complete response (CR), partial response (PR), stable disease (SD) and progressive disease (PD). The gastrointestinal and hematological toxicity incidence was assessed twice a week during the entire first line of treatment. Thirteen SNPs of REV3 and REV7 were genotyped. The associations between SNPs and the treatment response or toxicity were analyzed with a logistic regression model. We discovered that five SNPs were correlated with the treatment response. Specifically, rs240969 was significantly associated with the treatment response, after a Bonferroni correction, in smokers and a combined cohort (P=0.048 and P=0.0082, respectively) as well as with rs3218573 in smokers (P=0.036). In addition, we discovered that the incidence of grade 3 or 4 gastrointestinal toxicity was significantly higher in patients carrying a G/G genotype of rs240966 or an A allele of rs456865. We also identified that five SNPs, namely rs240966, rs4945880, rs465646, rs2233025 and rs2336030, that were correlated with an increased risk of grade 3 or grade 4 hematologic toxicity. The REV3 and REV7 polymorphisms are in a catalytic subunit and an accessory subunit of Pol ζ, respectively, and participate in platinum-chemotherapy tolerance and side effects. Key words: REV3, REV7, Pol ζ, platinum-based chemotherapies, translesion synthesis, toxicityLung cancer is the highest contributor to cancer-related deaths, and non-small cell lung cancer (NSCLC) accounts for nearly 80% of all lung cancer deaths [1]. The incidence rate of lung cancer is rapidly rising due to tobacco use, air pollution, and other cancer-causing factors [2]. Although targeted therapy is very efficient and tremendously improves the progress-free survival (PFS) and overall survival (OS) of lung cancer patients [3][4][5], however,over 70% of patients lack the positive biomarkers that are considered necessary for platinum-based chemotherapies as the traditional front-line treatment [6,7]. The efficacy of platinum-based chemotherapies is severely limited by intrinsic drug resistance. In addition, while platinum can kill uncontrollably dividing tumor cells by coupling to DNA and terminating the replication of DNA, normal cells will also be inevitably damaged [8].Previous studies have shown that DNA repair systems play an essential role in platinum-based chemotherapy tolerance [8][9][10][11]. DNA inter-or intra-crosslinking cause...

Purpose: To predict early pathological response of breast cancer to neoadjuvant chemotherapy (NAC) based on quantitative, multi‐region analysis of dynamic contrast enhancement magnetic resonance imaging (DCE‐MRI). Methods: In this institution review board‐approved study, 35 patients diagnosed with stage II/III breast cancer were retrospectively investigated using DCE‐MR images acquired before and after the first cycle of NAC. First, principal component analysis (PCA) was used to reduce the dimensionality of the DCE‐MRI data with a high‐temporal resolution. We then partitioned the whole tumor into multiple subregions using k‐means clustering based on the PCA‐defined eigenmaps. Within each tumor subregion, we extracted four quantitative Haralick texture features based on the gray‐level co‐occurrence matrix (GLCM). The change in texture features in each tumor subregion between pre‐ and during‐NAC was used to predict pathological complete response after NAC. Results: Three tumor subregions were identified through clustering, each with distinct enhancement characteristics. In univariate analysis, all imaging predictors except one extracted from the tumor subregion associated with fast wash‐out were statistically significant (p< 0.05) after correcting for multiple testing, with area under the ROC curve or AUCs between 0.75 and 0.80. In multivariate analysis, the proposed imaging predictors achieved an AUC of 0.79 (p = 0.002) in leave‐one‐out cross validation. This improved upon conventional imaging predictors such as tumor volume (AUC=0.53) and texture features based on whole‐tumor analysis (AUC=0.65). Conclusion: The heterogeneity of the tumor subregion associated with fast wash‐out on DCE‐MRI predicted early pathological response to neoadjuvant chemotherapy in breast cancer.

Purpose: Monoscopic x‐ray imaging with on‐board kV devices is an attractive approach for real‐time image guidance in modern radiation therapy, but it falls short in providing reliable information along the direction of imaging x‐ray. By effectively taking consideration of projection data at prior times and/or angles through a Bayesian formalism, we develop a nonparametric algorithm for real‐time and full 3D tumor localization with a single x‐ray imager during treatment delivery.Methods: First, we construct the a priori probability density function using the 2D tumor locations on the projection images acquired during patient setup. Whenever an x‐ray image is acquired during the treatment delivery, the corresponding 2D tumor location on the imager is used to update the likelihood function. The unresolved third dimension is obtained by maximizing the posterior probability distribution. The algorithm does not involve optimization of any model parameter and therefore can be used in a ‘plug‐and‐play’ fashion. We validated the algorithm using the 3D tumor motion trajectories of a lung and a pancreas patient reproduced by a physical phantom. Continuous kV images were acquired over a full gantry rotation with the TrueBeam onboard imaging system. Three scenarios were considered: fluoroscopic setup, cone beam CT setup, and retrospective analysis. Results: The 3D localization error is < 1 mm on average and < 1.5 mm at 95 percentile in the lung and pancreas cases for all three scenarios. The difference in 3D localization error for different scenarios is small and is not statistically significant. Conclusions: The proposed algorithm eliminates the need for any population based model parameters in monoscopic image guided RT and allows accurate and real‐time 3D tumor localization on current standard Linacs with a single x‐ray imager.

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