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A chirality-dependent action of vitamin C in suppressing Kirsten rat sarcoma mutant tumor growth by the oxidative combination: Rationale for cancer therapeutics.

ABSTRACT: Kirsten rat sarcoma (KRAS) mutant cancers, which constitute the vast majority of pancreatic tumors, are characterized by their resistance to established therapies and high mortality rates. Here, we developed a novel and extremely effective combinational therapeutic approach to target KRAS mutant tumors through the generation of a cytotoxic oxidative stress. At high concentrations, vitamin C (VC) is known to provoke oxidative stress and selectively kill KRAS mutant cancer cells, although its effects are limited when it is given as monotherapy. We found that the combination of VC and the oxidizing drug arsenic trioxide (ATO) is an effective therapeutic treatment modality. Remarkably, its efficiency is dependent on chirality of VC as its enantiomer d-optical isomer of VC (d-VC) is significantly more potent than the natural l-optical isomer of VC. Thus, our results demonstrate that the oxidizing combination of ATO and d-VC is a promising approach for the treatment of KRAS mutant human cancers.

SUBMITTER: Wu X

PROVIDER: S-EPMC7365556 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectiveTo develop and validate radiomics models based on multiphasic CT in predicting Kirsten rat sarcoma virus (KRAS) gene mutation status in patients with colorectal cancer (CRC).Materials and methodsA total of 231 patients with pathologically confirmed CRC were retrospectively enrolled and randomly divided into training(n=184) and test groups(n=47) in a ratio of 4:1. A total of 1316 quantitative radiomics features were extracted from non-contrast phase (NCP), arterial-phase (AP) and venous-phase (VP) CT for each patient. Four steps were applied for feature selection including Spearman correlation analysis, variance threshold, least absolute contraction and selection operator, and multivariate stepwise regression analysis. Clinical and pathological characteristics were also assessed. Subsequently, three classification methods, logistic regression (LR), support vector machine (SVM) and random tree (RT) algorithm, were applied to develop seven groups of prediction models (NCP, AP, VP, AP+VP, AP+VP+NCP, AP&VP, AP&VP&NCP) for KRAS mutation prediction. The performance of these models was evaluated by receiver operating characteristics curve (ROC) analysis.ResultsAmong the three groups of single-phase models, the AP model, developed by LR algorithm, showed the best prediction performance with an AUC value of 0.811 (95% CI:0.685-0.938) in the test cohort. Compared with the single-phase models, the dual-phase (AP+VP) model with the LR algorithm showed better prediction performance (AUC=0.826, 95% CI:0.700-0.952). The performance of multiphasic (AP+VP+NCP) model with the LR algorithm (AUC=0.811, 95%CI: 0.679-0.944) is comparable to the model with the SVM algorithm (AUC=0.811, 95%CI: 0.695-0.918) in the test cohort, but the sensitivity, specificity, and accuracy of the multiphasic (AP+VP+NCP) model with the LR algorithm were 0.810, 0.808, 0.809 respectively, which were highest among these seven groups of prediction models in the test cohort.ConclusionThe CT radiomics models have the potential to predict KRAS mutation in patients with CRC; different phases may affect the predictive efficacy of radiomics model, of which arterial-phase CT is more informative. The combination of multiphasic CT images can further improve the performance of radiomics model.

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A chirality-dependent action of vitamin C in suppressing Kirsten rat sarcoma mutant tumor growth by the oxidative combination: Rationale for cancer therapeutics.

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