Leveraging synthetic lethality to uncover potential therapeutic target in gastric cancer.

Since trastuzumab was approved in 2012 for the first-line treatment of gastric cancer (GC), no significant advancement in GC targeted therapies has occurred. Synthetic lethality refers to the concept that simultaneous dysfunction of a pair of genes results in a lethal effect on cells, while the loss of an individual gene does not cause this effect. Through exploiting synthetic lethality, novel targeted therapies can be developed for the individualized treatment of GC. In this study, we proposed a computational strategy named Gastric cancer Specific Synthetic Lethality inference (GSSL) to identify synthetic lethal interactions in GC. GSSL analysis was used to infer probable synthetic lethality in GC using four accessible clinical datasets. In addition, prediction results were confirmed by experiments. GSSL analysis identified a total of 34 candidate synthetic lethal pairs, which included 33 unique targets. Among the synthetic lethal gene pairs, TP53-CHEK1 was selected for further experimental validation. Both computational and experimental results indicated that inhibiting CHEK1 could be a potential therapeutic strategy for GC patients with TP53 mutation. Meanwhile, in vitro experimental validation of two novel synthetic lethal pairs TP53-AURKB and ARID1A-EP300 further proved the universality and reliability of GSSL. Collectively, GSSL has been shown to be a reliable and feasible method for comprehensive analysis of inferring synthetic lethal interactions of GC, which may offer novel insight into the precision medicine and individualized treatment of GC.

Evolving therapeutic landscape of advanced hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common solid malignancies worldwide. A large proportion of patients with HCC are diagnosed at advanced stages and are only amenable to systemic therapies. We have witnessed the evolution of systemic therapies from single-agent targeted therapy (sorafenib and lenvatinib) to the combination of a checkpoint inhibitor plus targeted therapy (atezolizumab plus bevacizumab therapy). Despite remarkable advances, only a small subset of patients can obtain durable clinical benefit, and therefore substantial therapeutic challenges remain. In the past few years, emerging systemic therapies, including new molecular-targeted monotherapies (for example, donafenib), new immuno-oncology monotherapies (for example, durvalumab) and new combination therapies (for example, durvalumab plus tremelimumab), have shown encouraging results in clinical trials. In addition, many novel therapeutic approaches with the potential to offer improved treatment effects in patients with advanced HCC, such as sequential combination targeted therapy and next-generation adoptive cell therapy, have also been proposed and developed. In this Review, we summarize the latest clinical advances in the treatment of advanced HCC and discuss future perspectives that might inform the development of more effective therapeutics for advanced HCC.

Multi-region sequencing with spatial information enables accurate heterogeneity estimation and risk stratification in liver cancer.

BACKGROUND: Numerous studies have used multi-region sampling approaches to characterize intra-tumor heterogeneity (ITH) in hepatocellular carcinoma (HCC). However, conventional multi-region sampling strategies do not preserve the spatial details of samples, and thus, the potential influences of spatial distribution on patient-wise ITH (represents the overall heterogeneity level of the tumor in a given patient) have long been overlooked. Furthermore, gene-wise transcriptional ITH (represents the expression pattern of genes across different intra-tumor regions) in HCC is also under-explored, highlighting the need for a comprehensive investigation. METHODS: To address the problem of spatial information loss, we propose a simple and easy-to-implement strategy called spatial localization sampling (SLS). We performed multi-region sampling and sequencing on 14 patients with HCC, collecting a total of 75 tumor samples with spatial information and molecular data. Normalized diversity score and integrated heterogeneity score (IHS) were then developed to measure patient-wise and gene-wise ITH, respectively. RESULTS: A significant correlation between spatial and molecular heterogeneity was uncovered, implying that spatial distribution of sampling sites did influence ITH estimation in HCC. We demonstrated that the normalized diversity score had the ability to overcome sampling location bias and provide a more accurate estimation of patient-wise ITH. According to this metric, HCC tumors could be divided into two classes (low-ITH and high-ITH tumors) with significant differences in multiple biological properties. Through IHS analysis, we revealed a highly heterogenous immune microenvironment in HCC and identified some low-ITH checkpoint genes with immunotherapeutic potential. We also constructed a low-heterogeneity risk stratification (LHRS) signature based on the IHS results which could accurately predict the survival outcome of patients with HCC on a single tumor biopsy sample. CONCLUSIONS: This study provides new insights into the complex phenotypes of HCC and may serve as a guide for future studies in this field.

Clinical outcomes and potential therapies prediction of subgroups based on a ferroptosis-related long non-coding RNA signature for gastric cancer.

BACKGROUND: Gastric cancer (GC) is one of the most aggressive malignant tumors worldwide. Ferroptosis is a kind of iron-dependent cell death, which is proved to be closely related to tumor progression. In this study, we aim at constructing a ferroptosis-related lncRNAs signature to predict the prognosis of GC and explore potential therapies. METHODS: Ferroptosis-Related LncRNAs Signature for GC patients (FRLSG) was constructed through univariate Cox regression, the LASSO algorithm, and multivariate Cox regression. Kaplan-Meier analysis, receiver operating characteristic curves, and risk score plot were applied to verify the predictive power of FRLSG. Gene Set Enrichment Analysis (GSEA) and immune infiltration analyses were conducted to explore the potential clinical value of the FRLSG. In addition, drug sensitivity prediction was applied to identify chemotherapeutic drugs with potential therapeutic effect. RESULTS: Five ferroptosis-related lncRNAs (AC004816.1, AC005532.1, LINC01357, AL355574.1 and AL049840.4) were identified to construct FRLSG, whose expression level in GC were confirmed by experimental validation. Kaplan-Meier curve and ROC curve proved the reliability and effectiveness of the FRLSG in predicting the prognosis for GC patients. Several immune-related pathways were enriched in the high-FRLSG group, and further immune infiltration analyses demonstrated the high immune infiltration status of the high-FRLSG group. In addition, 19 and 24 candidate drugs with potential therapeutic effect were identified for the high- and low-FRLSG groups, respectively. CONCLUSIONS: FRLSG was an effective tool in predicting the prognosis of GC, which might help to prioritize potential therapeutics for GC patients.

Exploring synthetic lethal network for the precision treatment of clear cell renal cell carcinoma.

The emerging targeted therapies have revolutionized the treatment of advanced clear cell renal cell carcinoma (ccRCC) over the past 15 years. Nevertheless, lack of personalized treatment limits the development of effective clinical guidelines and improvement of patient prognosis. In this study, large-scale genomic profiles from ccRCC cohorts were explored for integrative analysis. A credible method was developed to identify synthetic lethality (SL) pairs and a list of 72 candidate pairs was determined, which might be utilized to selectively eliminate tumors with genetic aberrations using SL partners of specific mutations. Further analysis identified BRD4 and PRKDC as novel medical targets for patients with BAP1 mutations. After mapping these target genes to the comprehensive drug datasets, two agents (BI-2536 and PI-103) were found to have considerable therapeutic potentials in the BAP1 mutant tumors. Overall, our findings provided insight into the overview of ccRCC mutation patterns and offered novel opportunities for improving individualized cancer treatment.

An Immune Signature for Risk Stratification and Therapeutic Prediction in Helicobacter pylori-Infected Gastric Cancer.

Helicobacter pylori (HP) infection is the greatest risk factor for gastric cancer (GC). Increasing evidence has clarified that tumor immune microenvironment (TIME) is closely related to the prognosis and therapeutic efficacy of HP-positive (HP+) GC patients. In this study, we aimed to construct a novel immune-related signature for predicting the prognosis and immunotherapy efficacy of HP+ GC patients. A total of 153 HP+ GC from three different cohorts were included in this study. An Immune-Related prognostic Signature for HP+ GC patients (IRSHG) was established using Univariate Cox regression, the LASSO algorithm, and Multivariate Cox regression. Univariate and Multivariate analyses proved IRSHG was an independent prognostic predictor for HP+ GC patients, and an IRSHG-integrated nomogram was established to quantitatively assessthe prognostic risk. The low-IRSHG group exhibited higher copy number load and distinct mutation profiles compared with the high-IRSHG group. In addition, the difference of hallmark pathways and immune cells infiltration between the two groups was investigated. Notably, tumor immune dysfunction and exclusion (TIDE) analysis indicated that the low-IRSHG group had a higher sensitivity to anti-PD-1 immunotherapy, which was validated by an external pabolizumab treatment cohort. Moreover, 98 chemotherapeutic drugs and corresponding potential biomarkers were identified for two groups, and several drugs with potential ability to reverse IRSHG score were identified using CMap analysis. Collectively, IRSHG may serve as a promising biomarker for survival outcome as well as immunotherapy efficacy. Furthermore, it can also help to prioritize potential therapeutics for HP+ GC patients, providing new insight for the personalized treatment of HP-infected GC.

A stratification model of hepatocellular carcinoma based on expression profiles of cells in the tumor microenvironment.

BACKGROUND: A malignancy of the liver, hepatocellular carcinoma (HCC) is among the most common and second-leading causes of cancer-related deaths worldwide. A reliable prognosis model for guidance in choosing HCC therapies has yet to be established. METHODS: A consensus clustering approach was used to determine the number of immune clusters in the Cancer Genome Atlas and Liver Cancer-RIKEN, JP (LIRI_JP) datasets. The differentially expressed genes (DEGs) among these groups were identified based on RNA sequencing data. Then, to identify hub genes among signature genes, a co-expression network was constructed. The prognostic value and clinical characteristics of the immune clusters were also explored. Finally, the potential key genes for the immune clusters were determined. RESULTS: After conducting survival and correlation analyses of the DEGs, three immune clusters (C1, C2, and C3) were identified. Patients in C2 showed the longest survival time with the greatest abundance of tumor microenvironment (TME) cell populations. MGene mutations in Ffibroblast growth factor-19 (FGF19) and catenin (cadherin-associated protein),ß1(CTNNB1) were mostly observed in C2 and C3, respectively. The signature genes of C1, C2, and C3 were primarily enriched in 5, 23, and 26 pathways, respectively. CONCLUSIONS: This study sought to construct an immune-stratification model for the prognosis of HCC by dividing the expression profiles of patients from public datasets into three clusters and discovering the unique molecular characteristics of each. This stratification model provides insights into the immune and clinical characteristics of HCC subtypes, which is beneficial for the prognosis of HCC.

A survey of optimal strategy for signature-based drug repositioning and an application to liver cancer.

Pharmacologic perturbation projects, such as Connectivity Map (CMap) and Library of Integrated Network-based Cellular Signatures (LINCS), have produced many perturbed expression data, providing enormous opportunities for computational therapeutic discovery. However, there is no consensus on which methodologies and parameters are the most optimal to conduct such analysis. Aiming to fill this gap, new benchmarking standards were developed to quantitatively evaluate drug retrieval performance. Investigations of potential factors influencing drug retrieval were conducted based on these standards. As a result, we determined an optimal approach for LINCS data-based therapeutic discovery. With this approach, homoharringtonine (HHT) was identified to be a candidate agent with potential therapeutic and preventive effects on liver cancer. The antitumor and antifibrotic activity of HHT was validated experimentally using subcutaneous xenograft tumor model and carbon tetrachloride (CCL4)-induced liver fibrosis model, demonstrating the reliability of the prediction results. In summary, our findings will not only impact the future applications of LINCS data but also offer new opportunities for therapeutic intervention of liver cancer.

Integrative analysis of CRISPR screening data uncovers new opportunities for optimizing cancer immunotherapy.

BACKGROUND: In recent years, the application of functional genetic immuno-oncology screens has showcased the striking ability to identify potential regulators engaged in tumor-immune interactions. Although these screens have yielded substantial data, few studies have attempted to systematically aggregate and analyze them. METHODS: In this study, a comprehensive data collection of tumor immunity-associated functional screens was performed. Large-scale genomic data sets were exploited to conduct integrative analyses. RESULTS: We identified 105 regulator genes that could mediate resistance or sensitivity to immune cell-induced tumor elimination. Further analysis identified MON2 as a novel immune-oncology target with considerable therapeutic potential. In addition, based on the 105 genes, a signature named CTIS (CRISPR screening-based tumor-intrinsic immune score) for predicting response to immune checkpoint blockade (ICB) and several immunomodulatory agents with the potential to augment the efficacy of ICB were also determined. CONCLUSION: Overall, our findings provide insights into immune oncology and open up novel opportunities for improving the efficacy of current immunotherapy agents.

Mapping the landscape of synthetic lethal interactions in liver cancer.

Almost all the current therapies against liver cancer are based on the "one size fits all" principle and offer only limited survival benefit. Fortunately, synthetic lethality (SL) may provide an alternate route towards individualized therapy in liver cancer. The concept that simultaneous losses of two genes are lethal to a cell while a single loss is non-lethal can be utilized to selectively eliminate tumors with genetic aberrations. Methods: To infer liver cancer-specific SL interactions, we propose a computational pipeline termed SiLi (statistical inference-based synthetic lethality identification) that incorporates five inference procedures. Based on large-scale sequencing datasets, SiLi analysis was performed to identify SL interactions in liver cancer. Results: By SiLi analysis, a total of 272 SL pairs were discerned, which included 209 unique target candidates. Among these, polo-like kinase 1 (PLK1) was considered to have considerable therapeutic potential. Further computational and experimental validation of the SL pair TP53-PLK1 demonstrated that inhibition of PLK1 could be a novel therapeutic strategy specifically targeting those patients with TP53-mutant liver tumors. Conclusions: In this study, we report a comprehensive analysis of synthetic lethal interactions of liver cancer. Our findings may open new possibilities for patient-tailored therapeutic interventions in liver cancer.

Transcriptome analysis provides insight into the role of the melanin pathway in two differently pigmented strains of the turtle Pelodiscus sinensis.

Pelodiscus sinensis is the most common turtle species raised in East and Southeast Asia. The Japanese strain and the Qingxi black strain are important aquatic breeds of P. sinensis in China with unique characteristics in terms of production performance and appearance, including skin color. In this study, melanin content measure and histological analysis on skin samples of these two strains were carried out to compare their color characteristics. The results showed that Qingxi black turtles clearly have a greater ability to deposit melanin than the Japanese strain especially in the abdomen. Then, de novo transcriptome assembly and differential expression profiling analyses on the ventral skin from the two strains were performed to identify the genes responsible for the differences in skin color using the Illumina RNA-Seq system with three biological replicates. A total of 19,331 annotated unigenes were found by aligning to the reference genome of P. sinensis using TopHat v2.0.12. Differential expression analysis revealed that 670 genes were expressed differently, including 185 upregulated genes and 485 downregulated genes in Qingxi black strain using the DESeq R package (|log2FoldChange| ≥ 1, padj < 0.05). Sixteen differentially expressed genes (DEGs), which were randomly selected, were confirmed by quantitative real-time PCR (qRT-PCR). GO and KEGG analyses revealed four DEGs (agouti signaling protein, frizzled family receptor 1, phospholipase C, and protein kinase C) were related to melanogenesis pathway. Gene expression levels of the four DEGs as well as three genes from the tyrosinase gene family were measured by qRT-PCR. The results indicated that agouti signaling protein, tyrosinase-related protein, and dopachrome tautomerase could be the main genes responsible for the difference in abdominal skin color between the two turtle strains. This study provided valuable information for further analysis of the melanogenesis mechanisms in different varieties of P. sinensis.

Photo-accelerated "click" reaction between diarylsydnones and ring-strained alkynes for bioorthogonal ligation.

We constructed a library of diarylsydnone (DASyd) candidates in search of a photoclickable reaction toward alkynes, enabling an ultra-accelerated reactivity, while suppressing the background cycloaddition in the dark. The in vitro and in vivo protein labelling experiments revealed that the photo-accelerated DASyd-alkyne cycloaddition exhibits robust selectivity.

Efficient and selective sensing of Cu2+ and UO22+ by a europium metal-organic framework.

We report here the investigation of using a luminescent europium organic framework, [Eu2(MTBC)(OH)2(DMF)3(H2O)4]·2DMF·7H2O (denoted as compound 1), for detecting of both Cu2+ and UO22+ with high sensitivity. Based on the spectroscopy analysis, compound 1 could selectively respond to Cu2+ and UO22+ ions among other selected monovalent, divalent, trivalent metal cations based on a turn-off mechanism. The detection limit of compound 1 towards Cu2+ ion was as low as 17.2 µg/L, which is much lower than the maximum tolerable concentration of Cu2+ in drinking water (2 mg/L) defined by United States Environmental Protection Agency. On the other hand, the detection limit towards UO22+ ions is 309.2 µg/L, which could be used for detecting uranium in relative severely contaminated areas. The concentration-dependent luminescence intensity evolution process could be fully understood by the absorption kinetics and isotherm investigations. Furthermore, the quenching mechanism was elucidated by the UV-vis, excitation, luminescence, and lifetime studies. Compound 1, as the first MOF based luminescence probe for both Cu2+ and UO22+ ions, provides insight into developing MOF-based multifunctional sensors for both nonradioactive and radioactive elements.

Dual Colorimetric/Fluorometric Double-Throw pH-Switches: The Dimroth Rearrangement of N,9-Diaryl 8-Azaadenines.

A library of 12N,9-Diaryl 2-methyl-8-azaadenine (DAMA) compounds was designed and constructed through an aryl-pairing combination strategy for identifying a nucleobase-containing molecular switch that functions by the pH-regulated Dimroth rearrangement. By utilizing 2D thin-layer chromatography/mass spectrometry (2D-TLC-MS), the DAMA compounds were easily screened to identify which compounds could be used as molecular switches. The pH-switching ability of the DAMA was achieved by incorporating the acridine group as the key structural unit, as well as dual-modal colorimetric/fluorometric on/off properties as the probe functions. The real-time tracing of the switching process clearly indicated that the paired aromatics on both terminals of the DAMA molecule play a key role in tuning the switching kinetics.

Discovery of Fluorogenic Diarylsydnone-Alkene Photoligation: Conversion of ortho-Dual-Twisted Diarylsydnones into Planar Pyrazolines.

A small library of diarylsydnones (DASyds) was constructed based on aryl-pairing combinations and subjected to click reaction toward alkenes under photoirradiation with high efficiency. We were able to demonstrate the utility of DASyds for highly fluorescent turn-on ligation targeting the trans-cyclooct-4-en-1-ol moieties on protein.

Metabolic responses and arginine kinase expression of juvenile cuttlefish (Sepia pharaonis) under salinity stress.

The pharaoh cuttlefish Sepia pharaonis is particularly sensitive to environmental changes in its breeding environment. The breeding of S. pharaonis larvae was carried out in different salinities for 48h, and the changes in survival rate, histological structure, energy metabolism, and anti-oxidative stress parameters were investigated and correlated with arginine kinase (AK) expression changes in muscle and liver tissues. The suitable salinity for larvae cultivation ranged from 24 to 30‰, and the survival rate showed a significant decline at 21‰ salinity. Histological observations of muscle and liver showed that changes in salinity and osmotic pressure had an adverse effect on tissue structure. Measurements of glycogen and lactic acid levels suggested that S. pharaonis could dynamically adjust energy metabolism to provide additional energy under unsuitable salinity. The protein levels and enzyme activities of AK in muscle significantly increased at 21‰ salinity. The results were consistent with prompt replenishment of phosphoarginine stores during salinity stress to maintain a dynamic ATP balance, suggesting that AK plays an important role in the regulation of energy metabolism. This study provides insight into metabolic changes during salinity stress and sheds light on the functional role of AK in S. pharaonis.

A hydrolytically stable uranyl organic framework for highly sensitive and selective detection of Fe3+ in aqueous media.

We present a depleted uranium-based metal organic framework, UO2(C8H3O6N)·DMF, that exhibits highly sensitive and selective detection towards Fe3+ ions in aqueous media with an extremely low detection limit of 6.3 ppb. This work offers insight into exploring the potential applications of actinide-based metal organic frameworks in the area of chemical sensing with intrinsic advantages of high selectivity and sensitivity.