Dysfunction of p53 is observed in the many malignant tumors. In cervical cancer, p53 is inactivated by degradation through the complex with human papilloma virus (HPV) oncoprotein E6 and ...E6-associated protein (E6AP), an E3 ubiquitin protein ligase. In endometrial cancer, overexpression of p53 in immunohistochemistry is a significant prognostic factor. A discrepancy between p53 overexpression and TP53 mutations is observed in endometrioid endometrial cancer, indicating that the accumulation of p53 protein can be explained by not only gene mutations but also dysregulation of the factors such as ERβ and MDM2. Furthermore, the double-positive expression of immunoreactive estrogen receptor (ER) β and p53 proteins is closely associated with the incidence of metastasis and/or recurrence. High-grade serous ovarian carcinoma (HGSC) arises from secretary cells in the fallopian tube. The secretary cell outgrowth (SCOUT) with TP53 mutations progresses to HGSC via the p53 signature, serous intraepithelial lesion (STIL), and serous intraepithelial carcinoma (STIC), indicating that TP53 mutation is associated with carcinogenesis of HGSC. Clinical application targeting p53 has been approved for some malignant tumors. Gene therapy by the adenovirus-mediated p53 gene transfer system is performed for head and neck cancer. A clinical phase III trial using MDM2/X inhibitors, idasanutlin (RG7388) combined with cytarabine, is being performed involving relapse/refractory acute myeloid leukemia patients. The use of adenoviruses as live vectors which encode wild-type p53 has given promising results in cervical cancer patients.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Radiotherapy is a treatment method that irradiates lesions by utilizing the difference in radiosensitivity between cancer cells and normal cells. CT imaging, treatment planning, and irradiation are ...important steps in radiotherapy. However, there are some problems hidden in each step. In collaboration with Department of Systems Science, Graduate School of Informatics at Kyoto University, we are currently working on a project that will change the way radiotherapy is conducted. In this talk, we will introduce some of the results.
•The prediction and classification performance for gamma passing rate was assessed.•3D dosiomics feature was extracted from dose distribution on patient at treatment plans.•Prediction and ...classification model was built by using plan and 3D dosiomics features.•Dosiomics model has the potential to predict and classify gamma passing rate.•The combination of both features improved the prediction and classification performance.
The purpose of this study was to predict and classify the gamma passing rate (GPR) value by using new features (3D dosiomics features and combined with plan and dosiomics features) together with a machine learning technique for volumetric modulated arc therapy (VMAT) treatment plans.
A total of 888 patients who underwent VMAT were enrolled comprising 1255 treatment plans. Further, 24 plan complexity features and 851 dosiomics features were extracted from the treatment plans. The dataset was randomly split into a training/validation (80%) and test (20%) dataset. The three models for prediction and classification using XGBoost were as follows: (i) plan complexity features-based prediction method (plan model); (ii) 3D dosiomics feature-based prediction model (dosiomics model); (iii) a combination of both the previous models (hybrid model). The prediction performance was evaluated by calculating the mean absolute error (MAE) and the correlation coefficient (CC) between the predicted and measured GPRs. The classification performance was evaluated by calculating the area under curve (AUC) and sensitivity.
MAE and CC at γ2%/2 mm in the test dataset were 4.6% and 0.58, 4.3% and 0.61, and 4.2% and 0.63 for the plan model, dosiomics model, and hybrid model, respectively. AUC and sensitivity at γ2%/2 mm in test dataset were 0.73 and 0.70, 0.81 and 0.90, and 0.83 and 0.90 for the plan model, dosiomics model, and hybrid model, respectively.
A combination of both plan and dosiomics features with machine learning technique can improve the prediction and classification performance for GPR.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Aequorin consists of apoprotein (apoAequorin) and (S)‐2‐peroxycoelenterazine (CTZ‐OOH) and is considered to be a transient‐state complex of an enzyme (apoAequorin) and a substrate (coelenterazine and ...molecular oxygen) in the enzymatic reaction. The degradation process of CTZ‐OOH in aequorin was characterized under various conditions of protein denaturation. By acid treatment, the major product from CTZ‐OOH was coelenteramine (CTM), but not coelenteramide (CTMD), and no significant luminescence was observed. The counterparts of CTM from CTZ‐OOH were identified as 4‐hydroxyphenylpyruvic acid (4HPPA) and 4‐hydroxyphenylacetic acid (4HPAA) by liquid chromatography/electrospray ionization–time‐of‐flight mass spectrometry (LC/ESI‐TOF‐MS). In the luminescence reaction of aequorin with Ca2+, similar amounts of 4HPPA and 4HPAA were detected, indicating that CTM is formed by two pathways from CTZ‐OOH through dioxetanone anion and not by hydrolysis from CTMD.
The formation of coelenteramine (CTM) from (S)‐2‐peroxycoelenterazine (CTZ‐OOH) in aequorin was investigated under protein denaturing conditions. Under acidic conditions, 4‐hydroxyphenylpyruvic acid (4HPPA) and 4‐hydroxyphenylacetic acid were identified as the counterparts of CTM by LC/ESI‐TOF‐MS analysis. This study shows for the first time that CTM and 4HPPA can be generated from CTZ‐OOH without light emission.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Personalized medicine is increasingly becoming a subject of intense interest, and studies on the utilization of data mining for implementing personalized medicine are on the rise. The increasing view ...that medical images are minable data on patients' personal traits have led to increased focus on quantitative analysis of these data, a field known as radiomics. Radiomics is a hybrid of the term "radiology" and the suffix "-omics", and aims to integrate and comprehensively analyze biological data. Researchers extract high-dimensional quantitative image information, known as the radiomic features, which are not detectable upon visual examination of regions of interest containing tumors. This information is subsequently linked to clinical, histopathological and molecular biological data and investigated regarding tumor phenotype, prognosis and treatment efficacy. The aim of this study is to predict prognosis for early-stage lung cancer patients after stereotactic body radiotherapy in multi-institution by CT-based radiomic features.
Coelenterazine (CTZ) is known as luciferin (a substrate) for the luminescence reaction with luciferase (an enzyme) in marine organisms and is unstable in aqueous solutions. The dehydrogenated form of ...CTZ (dehydrocoelenterazine, dCTZ) is stable and thought to be a storage form of CTZ and a recycling intermediate from the condensation reaction of coelenteramine and 4-hydroxyphenylpyruvic acid to CTZ. In this study, the enzymatic conversion of dCTZ to CTZ was successfully achieved using NAD(P)H:FMN oxidoreductase from the bioluminescent bacterium Vibrio fischeri ATCC 7744 (FRase) in the presence of NADH (the FRase–NADH reaction). CTZ reduced from dCTZ in the FRase–NADH reaction was identified by HPLC and LC/ESI-TOF-MS analyses. Thus, dCTZ can be enzymatically converted to CTZ in vitro. Furthermore, the concentration of dCTZ could be determined by the luminescence activity using the CTZ-utilizing luciferases (Gaussia luciferase or Renilla luciferase) coupled with the FRase–NADH reaction.
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•Dehydrocoelenterazine (dCTZ) is a dehydrogenated form of coelenterazine (CTZ).•dCTZ can be reduced enzymatically to CTZ using FMN reductase (FRase) and NADH.•FMNH2 produced in the FRase–NADH reaction can reduce dCTZ analogs.•dCTZ could be detected using Renilla luciferase with the FRase–NADH reaction.•dCTZ concentrations can be measured with high sensitivity via bioluminescence.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Background and purpose
Volumetric‐modulated arc therapy (VMAT) is a complex rotational therapy technique in which highly conformal dose distribution can be realized by varying the speed of gantry ...rotation, multileaf collimator (MLC) shape, and dose rate. However, the complexity of the technique creates a discrepancy between the calculated and measured doses. Thus, to mitigate the plan complexity in VMAT, this study aimed to develop an algorithm and evaluate its usefulness by conducting a feasibility study.
Materials and methods
A total of 50 patients who underwent VMAT between September 2015 and December 2020 were arbitrarily selected for this study. Specifically, patients with less than 85% gamma passing rate (GPR) at 5%/1 mm or 3%/2 mm criterion were selected randomly. Using the GPR prediction model, problematic MLC positions that contribute to a decrease in GPR were identified. Those problematic MLC positions were optimized using a limited nonlinear algorithm under mechanical limitations. Additionally, the dose prescription for the target was re‐normalized. The VMAT modulated complexity score (MCSv), averaged aperture area (AA), and monitor unit per gray (MU/Gy) were evaluated as plan complexity parameters. Calculated doses in patient geometry were evaluated for the target and its surrounding region. In addition, an ArcCHECK cylindrical diode array was used to measure the dose, and GPRs at 5%/1 mm and 3%/2 mm criteria were evaluated to analyze the difference between the mitigated and original plans. The difference was calculated using the mean ± standard deviation.
Results
The differences between the MCSv, AA, and MU/cGy values for the mitigated and original plans were 0.8 ± 1.7 (×10–2), 42.7 ± 57.9, and ‐5.6 ± 8.5, respectively. Regarding the calculated dose, the dose volume parameters were consistent within 1% for the target and the surrounding region. The differences between the mitigated and original plans were 1.8 ± 2.9% and 1.3 ± 1.8% for GPRs at 5%/1 mm and 3%/2 mm, respectively.
Conclusions
This feasibility study resulted in the development of an algorithm with the potential to mitigate plan complexity and improve the GPR for VMAT under minor leaf position modifications.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Third-party punishment is a common mechanism to promote cooperation in humans. Theoretical models of evolution of cooperation predict that punishment maintains cooperation if it is sufficiently ...frequent. On the other hand, empirical studies have found that participants frequently punishing others do not success in comparison with those not eager to punish others, suggesting that punishment is suboptimal and thus should not be frequent. That being the case, our question is what mechanism, if any, can sustain cooperation even if punishment is rare. The present study proposes that one possible mechanism is risk-averse social learning. Using the method of evolutionary game dynamics, we investigate the effect of risk attitude of individuals on the question. In our framework, individuals select a strategy based on its risk, i.e., the variance of the payoff, as well as its expected payoff; risk-averse individuals prefer to select a strategy with low variable payoff. Using the framework, we examine the evolution of cooperation in two-player social dilemma games with punishment. We study two models: cooperators and defectors compete, while defectors may be punished by an exogenous authority; and cooperators, defectors, and cooperative punishers compete, while defectors may be punished by the cooperative punishers. We find that in both models, risk-averse individuals achieve stable cooperation with significantly low frequency of punishment. We also examine three punishment variants: in each game, all defectors are punished; only one of defectors is punished; and only a defector who exploits a cooperator or a cooperative punisher is punished. We find that the first and second variants effectively promote cooperation. Comparing the first and second variants, each can be more effective than the other depending on punishment frequency.
Purpose
The dosimetric accuracies of volumetric modulated arc therapy (VMAT) plans were predicted using plan complexity parameters via machine learning.
Methods
The dataset consisted of 600 cases of ...clinical VMAT plans from a single institution. The predictor variables (n = 28) for each plan included complexity parameters, machine type, and photon beam energy. Dosimetric measurements were performed using a helical diode array (ArcCHECK), and the dosimetric accuracy of the passing rates for a 5% dose difference (DD5%) and gamma index of 3%/3 mm (γ3%/3 mm) were predicted using three machine learning models: regression tree analysis (RTA), multiple regression analysis (MRA), and neural networks (NNs). First, the prediction models were applied to 500 cases of the VMAT plans. Then, the dosimetric accuracy was predicted using each model for the remaining 100 cases (evaluation dataset). The error between the predicted and measured passing rates was evaluated.
Results
For the 600 cases, the mean ± standard deviation of the measured passing rates was 92.3% ± 9.1% and 96.8% ± 3.1% for DD5% and γ3%/3 mm, respectively. For the evaluation dataset, the mean ± standard deviation of the prediction errors for DD5% and γ3%/3 mm was 0.5% ± 3.0% and 0.6% ± 2.4% for RTA, 0.0% ± 2.9% and 0.5% ± 2.4% for MRA, and –0.2% ± 2.7% and –0.2% ± 2.1% for NN, respectively.
Conclusions
NNs performed slightly better than RTA and MRA in terms of prediction error. These findings may contribute to increasing the efficiency of patient‐specific quality‐assurance procedures.
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