TP53 is a critical tumor-suppressor gene that is mutated in more than half of all human cancers. Mutations in TP53 not only impair its antitumor activity, but also confer mutant p53 protein oncogenic properties. The p53-targeted therapy approach began with the identification of compounds capable of restoring/reactivating wild-type p53 functions or eliminating mutant p53. Treatments that directly target mutant p53 are extremely structure and drug-species-dependent. Due to the mutation of wild-type p53, multiple survival pathways that are normally maintained by wild-type p53 are disrupted, necessitating the activation of compensatory genes or pathways to promote cancer cell survival. Additionally, because the oncogenic functions of mutant p53 contribute to cancer proliferation and metastasis, targeting the signaling pathways altered by p53 mutation appears to be an attractive strategy. Synthetic lethality implies that while disruption of either gene alone is permissible among two genes with synthetic lethal interactions, complete disruption of both genes results in cell death. Thus, rather than directly targeting p53, exploiting mutant p53 synthetic lethal genes may provide additional therapeutic benefits. Additionally, research progress on the functions of noncoding RNAs has made it clear that disrupting noncoding RNA networks has a favorable antitumor effect, supporting the hypothesis that targeting noncoding RNAs may have potential synthetic lethal effects in cancers with p53 mutations. The purpose of this review is to discuss treatments for cancers with mutant p53 that focus on directly targeting mutant p53, restoring wild-type functions, and exploiting synthetic lethal interactions with mutant p53. Additionally, the possibility of noncoding RNAs acting as synthetic lethal targets for mutant p53 will be discussed.

Hepatocellular carcinoma (HCC) is the most common subtype of liver cancer, and assessing its histopathological grade requires visual inspection by an experienced pathologist. In this study, the histopathological H&E images from the Genomic Data Commons Databases were used to train a neural network (inception V3) for automatic classification. According to the evaluation of our model by the Matthews correlation coefficient, the performance level was close to the ability of a 5-year experience pathologist, with 96.0% accuracy for benign and malignant classification, and 89.6% accuracy for well, moderate, and poor tumor differentiation. Furthermore, the model was trained to predict the ten most common and prognostic mutated genes in HCC. We found that four of them, including CTNNB1, FMN2, TP53, and ZFX4, could be predicted from histopathology images, with external AUCs from 0.71 to 0.89. The findings demonstrated that convolutional neural networks could be used to assist pathologists in the classification and detection of gene mutation in liver cancer.

Synthetic lethality is a lethal phenomenon in which the occurrence of a single genetic event is tolerable for cell survival, whereas the co-occurrence of multiple genetic events results in cell death. The main obstacle for synthetic lethality lies in the tumor biology heterogeneity and complexity, the inadequate understanding of synthetic lethal interactions, drug resistance, and the challenges regarding screening and clinical translation. Recently, DNA damage response inhibitors are being tested in various trials with promising results. This review will describe the current challenges, development, and opportunities for synthetic lethality in cancer therapy. The characterization of potential synthetic lethal interactions and novel technologies to develop a more effective targeted drug for cancer patients will be explored. Furthermore, this review will discuss the clinical development and drug resistance mechanisms of synthetic lethality in cancer therapy. The ultimate goal of this review is to guide clinicians at selecting patients that will receive the maximum benefits of DNA damage response inhibitors for cancer therapy.

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BackgroundCalmodulin (CaM) is a major calcium sensor in all eukaryotes. It binds calcium and modulates the activity of a wide range of downstream proteins in response to calcium signals. However, little is known about the CaM gene family in Solanaceous species, including the economically important species, tomato (Solanum lycopersicum), and the gene silencing model plant, Nicotiana benthamiana. Moreover, the potential function of CaM in plant disease resistance remains largely unclear.ResultsWe performed genome-wide identification of CaM gene families in Solanaceous species. Employing bioinformatics approaches, multiple full-length CaM genes were identified from tomato, N. benthamiana and potato (S. tuberosum) genomes, with tomato having 6 CaM genes, N. benthamiana having 7 CaM genes, and potato having 4 CaM genes. Sequence comparison analyses showed that three tomato genes, SlCaM3/4/5, two potato genes StCaM2/3, and two sets of N. benthamiana genes, NbCaM1/2/3/4 and NbCaM5/6, encode identical CaM proteins, yet the genes contain different intron/exon organization and are located on different chromosomes. Further sequence comparisons and gene structural and phylogenetic analyses reveal that Solanaceous species gained a new group of CaM genes during evolution. These new CaM genes are unusual in that they contain three introns in contrast to only a single intron typical of known CaM genes in plants. The tomato CaM (SlCaM) genes were found to be expressed in all organs. Prediction of cis-acting elements in 5' upstream sequences and expression analyses demonstrated that SlCaM genes have potential to be highly responsive to a variety of biotic and abiotic stimuli. Additionally, silencing of SlCaM2 and SlCaM6 altered expression of a set of signaling and defense-related genes and resulted in significantly lower resistance to Tobacco rattle virus and the oomycete pathogen, Pythium aphanidermatum.ConclusionsThe CaM gene families in the Solanaceous species tomato, N. benthamiana and potato were identified through a genome-wide analysis. All three plant species harbor a small set of genes that encode identical CaM proteins, which may manifest a strategy of plants to retain redundancy or enhanced quantitative gene function. In addition, Solanaceous species have evolved one new group of CaM genes during evolution. CaM genes play important roles in plant disease resistance to a variety of pathogens.

Economical nanocomposites based on π-stacking of N-acetyl glycosyl rhodamine B to graphene oxide (GO) are simply prepared. These "sweet" GO-materials are proven to be admirable for the fluorogenic recognition of specific intercellular sugar-based ligand-glycoprotein receptor interactions of interest.

<b><i>Background:</i></b> The preoperative selection of patients with intermediate-stage hepatocellular carcinoma (HCC) who are likely to have an objective response to first transarterial chemoembolization (TACE) remains challenging. <b><i>Objective:</i></b> To develop and validate a clinical-radiomic model (CR model) for preoperatively predicting treatment response to first TACE in patients with intermediate-stage HCC. <b><i>Methods:</i></b> A total of 595 patients with intermediate-stage HCC were included in this retrospective study. A tumoral and peritumoral (10 mm) radiomic signature (TPR-signature) was constructed based on 3,404 radiomic features from 4 regions of interest. A predictive CR model based on TPR-signature and clinical factors was developed using multivariate logistic regression. Calibration curves and area under the receiver operating characteristic curves (AUCs) were used to evaluate the model’s performance. <b><i>Results:</i></b> The final CR model consisted of 5 independent predictors, including TPR-signature (<i>p</i> &#x3c; 0.001), AFP (<i>p</i> = 0.004), Barcelona Clinic Liver Cancer System Stage B (BCLC B) subclassification (<i>p</i> = 0.01), tumor location (<i>p</i> = 0.039), and arterial hyperenhancement (<i>p</i> = 0.050). The internal and external validation results demonstrated the high-performance level of this model, with internal and external AUCs of 0.94 and 0.90, respectively. In addition, the predicted objective response via the CR model was associated with improved survival in the external validation cohort (hazard ratio: 2.43; 95% confidence interval: 1.60–3.69; <i>p</i> &#x3c; 0.001). The predicted treatment response also allowed for significant discrimination between the Kaplan-Meier curves of each BCLC B subclassification. <b><i>Conclusions:</i></b> The CR model had an excellent performance in predicting the first TACE response in patients with intermediate-stage HCC and could provide a robust predictive tool to assist with the selection of patients for TACE.

Recently, genetically targeted cancer therapies have been a topic of great interest. Synthetic lethality provides a new approach for the treatment of mutated genes that were previously considered unable to be targeted in traditional genotype-targeted treatments. The increasing researches and applications in the clinical setting made synthetic lethality a promising anticancer treatment option. However, the current understandings on different conditions of synthetic lethality have not been systematically assessed and the application of synthetic lethality in clinical practice still faces many challenges. Here, we propose a novel and systematic classification of synthetic lethality divided into gene level, pathway level, organelle level, and conditional synthetic lethality, according to the degree of specificity into its biological mechanism. Multiple preclinical findings of synthetic lethality in recent years will be reviewed and classified under these different categories. Moreover, synthetic lethality targeted drugs in clinical practice will be briefly discussed. Finally, we will explore the essential implications of this classification as well as its prospects in eliminating existing challenges and the future directions of synthetic lethality.