A PARylation-phosphorylation cascade promotes TOPBP1 loading and RPA-RAD51 exchange in homologous recombination.

The efficiency of homologous recombination (HR) in the repair of DNA double-strand breaks (DSBs) is closely associated with genome stability and tumor response to chemotherapy. While many factors have been functionally characterized in HR, such as TOPBP1, their precise regulation remains unclear. Here, we report that TOPBP1 interacts with the RNA-binding protein HTATSF1 in a cell-cycle- and phosphorylation-dependent manner. Mechanistically, CK2 phosphorylates HTATSF1 to facilitate binding to TOPBP1, which promotes S-phase-specific TOPBP1 recruitment to damaged chromatin and subsequent RPA/RAD51-dependent HR, genome integrity, and cancer-cell viability. The localization of HTATSF1-TOPBP1 to DSBs is potentially independent of the transcription-coupled RNA-binding and processing capacity of HTATSF1 but rather relies on the recognition of poly(ADP-ribosyl)ated RPA by HTATSF1, which can be blunted with PARP inhibitors. Together, our study provides a mechanistic insight into TOPBP1 loading at HR-prone DSB sites via HTATSF1 and reveals how RPA-RAD51 exchange is tuned by a PARylation-phosphorylation cascade.

Language model enables end-to-end accurate detection of cancer from cell-free DNA.

We present a language model Affordable Cancer Interception and Diagnostics (ACID) that can achieve high classification performance in the diagnosis of cancer exclusively from using raw cfDNA sequencing reads. We formulate ACID as an autoregressive language model. ACID is pretrained with language sentences that are obtained from concatenation of raw sequencing reads and diagnostic labels. We benchmark ACID against three methods. On testing set subjected to whole-genome sequencing, ACID significantly outperforms the best benchmarked method in diagnosis of cancer [Area Under the Receiver Operating Curve (AUROC), 0.924 versus 0.853; P < 0.001] and detection of hepatocellular carcinoma (AUROC, 0.981 versus 0.917; P < 0.001). ACID can achieve high accuracy with just 10 000 reads per sample. Meanwhile, ACID achieves the best performance on testing sets that were subjected to bisulfite sequencing compared with benchmarked methods. In summary, we present an affordable, simple yet efficient end-to-end paradigm for cancer detection using raw cfDNA sequencing reads.

Scalable batch-correction approach for integrating large-scale single-cell transcriptomes.

Integration of accumulative large-scale single-cell transcriptomes requires scalable batch-correction approaches. Here we propose Fugue, a simple and efficient batch-correction method that is scalable for integrating super large-scale single-cell transcriptomes from diverse sources. The core idea of the method is to encode batch information as trainable parameters and add it to single-cell expression profile; subsequently, a contrastive learning approach is used to learn feature representation of the additive expression profile. We demonstrate the scalability of Fugue by integrating all single cells obtained from the Human Cell Atlas. We benchmark Fugue against current state-of-the-art methods and show that Fugue consistently achieves improved performance in terms of data alignment and clustering preservation. Our study will facilitate the integration of single-cell transcriptomes at increasingly large scale.

A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings.

Advancement in single-cell RNA sequencing leads to exponential accumulation of single-cell expression data. However, there is still lack of tools that could integrate these unlimited accumulations of single-cell expression data. Here, we presented a universal approach iSEEEK for integrating super large-scale single-cell expression via exploring expression rankings of top-expressing genes. We developed iSEEEK with 11.9 million single cells. We demonstrated the efficiency of iSEEEK with canonical single-cell downstream tasks on five heterogenous datasets encompassing human and mouse samples. iSEEEK achieved good clustering performance benchmarked against well-annotated cell labels. In addition, iSEEEK could transfer its knowledge learned from large-scale expression data on new dataset that was not involved in its development. iSEEEK enables identification of gene-gene interaction networks that are characteristic of specific cell types. Our study presents a simple and yet effective method to integrate super large-scale single-cell transcriptomes and would facilitate translational single-cell research from bench to bedside.

Experimental Study on Emergency Psychophysiological and Behavioral Reactions to Coal Mining Accidents.

Effective emergency responses are crucial for preventing coal mine accidents and mitigating injuries. This paper aims to investigate the characteristics of emergency psychophysiological reactions to coal mine accidents and to explore the potential of key indicators for identifying emergency behavioral patterns. Initially, virtual reality technology facilitated a simulation experiment for emergency escape during coal mine accidents. Subsequently, the characteristics of emergency reactions were analyzed through correlation analysis, hypothesis testing, and analysis of variance. The significant changes in physiological indicators were then taken as input features and fed into the three classifiers of machine learning algorithms. These classifications ultimately led to the identification of behavioral patterns, including agility, defensiveness, panic, and rigidity, that individuals may exhibit during a coal mine accident emergency. The study results revealed an intricate relationship between the mental activities induced by accident stimuli and the resulting physiological changes and behavioral performances. During the virtual reality simulation of a coal mine accident, subjects were observed to experience significant physiological changes in electrodermal activity, heart rate variability, electromyogram, respiration, and skin temperature. The random forest classification model, based on SCR + RANGE + IBI + SDNN + LF/HF, outperformed all other models, achieving accuracies of up to 92%. These findings hold promising implications for early warning systems targeting abnormal psychophysiological and behavioral reactions to emergency accidents, potentially serving as a life-saving measure in perilous situations and fostering the sustainable growth of the coal mining industry.

Intrinsically Stretchable Electroluminescent Elastomers with Self-Confinement Effect for Highly Efficient Non-Blended Stretchable OLEDs.

Ultra-flexible stretchable organic light-emitting diodes (OLEDs) are emerging as a basic component of flexible electronics and human-machine interfaces. However, the brightness and efficiency of stretchable OLEDs remain still far inferior to their rigid counterparts, owing to the scarcity of satisfactory stretchable electroluminescent materials. Herein, we explore a general concept based on the self-confinement effect to dramatically improve the stretchability of elastomers, without affecting electroluminescent properties. The balanced rigid/flexible chain dynamics under self-confinement significantly reduces the modulus of the elastomers, resulting in the maximum strain reaching 806 %. Ultra-flexible stretchable OLEDs have been constructed based on the resulting ISEEs, achieving unprecedented high-performance non-blended stretchable OLEDs. The results suggest an effective molecular design strategy for highly deformable stretchable displays and flexible electronics.

Genetic alterations in gastric amphicrine carcinomas and comparison with gastric mixed neuroendocrine-non-neuroendocrine neoplasms.

Gastric amphicrine carcinoma, in which endocrine and epithelial cell features are present within the same cells, is often confused with gastric mixed neuroendocrine-non-neuroendocrine neoplasm (MiNEN). In this study, we performed high-resolution copy number (CN) profiling and whole exome sequencing (WES) of formalin-fixed and paraffin-embedded (FFPE) tissues from eight gastric amphicrine carcinomas and compared the molecular features with those of the adenocarcinoma and neuroendocrine carcinoma (NEC) components of eight gastric MiNENs. The most frequent high-level CN variant was a gain of 20q13.12-20q13.2, which was found in five gastric amphicrine carcinomas. Amplifications of MYT1, NTSR1, and ZBTB46 located in this region were demonstrated by qPCR and immunohistochemistry. The CN characteristics of gastric amphicrine carcinomas were different from those of MiNENs in hierarchical clustering analysis, suggesting that amphicrine carcinoma is a separate entity from MiNEN. Moreover, the CN level of C5 (complement C5) was higher in amphicrine carcinoma than in both the adenocarcinoma and the NEC component of MiNENs, suggesting that amphicrine carcinomas might benefit more from C5 inhibitors than MiNENs. WES showed frequent somatic mutations of TP53 (37.5%, 3/8) and APC (25.0%, 2/8) in amphicrine carcinoma. There were no specific mutation characteristics to distinguish amphicrine carcinoma from MiNEN. An integrated KEGG pathway analysis showed that the estrogen signaling pathway was enriched in amphicrine carcinomas, which might be associated with the high morbidity of male patients. In summary, our study revealed the unique CN and mutation characteristics of gastric amphicrine carcinoma and differentiated these characteristics from those of MiNENs. These data provide a foundation for further studies on the development and progression of amphicrine carcinoma.

Development of an AI system for accurately diagnose hepatocellular carcinoma from computed tomography imaging data.

BACKGROUND AND AIMS: Computed tomography (CT) scan is frequently used to detect hepatocellular carcinoma (HCC) in routine clinical practice. The aim of this study is to develop a deep-learning AI system to improve the diagnostic accuracy of HCC by analysing liver CT imaging data. METHODS: We developed a deep-learning AI system by training on CT images from 7512 patients at Henan Provincial Peoples' Hospital. Its performance was validated on one internal test set (Henan Provincial Peoples' Hospital, n = 385) and one external test set (Henan Provincial Cancer Hospital, n = 556). The area under the receiver-operating characteristic curve (AUROC) was used as the primary classification metric. Accuracy, sensitivity, specificity, precision, negative predictive value and F1 metric were used to measure the performance of AI systems and radiologists. RESULTS: AI system achieved high performance in identifying HCC patients, with AUROC of 0.887 (95% CI 0.855-0.919) on the internal test set and 0.883 (95% CI 0.855-0.911) on the external test set. For internal test set, accuracy was 81.0% (76.8-84.8%), sensitivity was 78.4% (72.4-83.7%), specificity was 84.4% (78.0-89.6%) and F1 (harmonic average of precision and recall rate) was 0.824. For external test set, accuracy was 81.3% (77.8-84.5%), sensitivity was 89.4% (85.0-92.8%), specificity was 74.0% (68.5-78.9%) and F1 was 0.819. Compared with radiologists, AI system achieved comparable accuracy and F1 metric on internal test set (0.853 versus 0.818, P = 0.107; 0.863 vs. 0.824, P = 0.082) and external test set (0.805 vs. 0.793, P = 0.663; 0.810 vs. 0.814, P = 0.866). The predicted HCC risk scores by AI system in HCC patients with multiple tumours and high fibrosis stage were higher than those with solitary tumour and low fibrosis stage (tumour number: 0.197 vs. 0.138, P = 0.006; fibrosis stage: 0.183 vs. 0.127, P < 0.001). Radiologists' review showed that the accuracy of saliency heatmaps predicted by algorithms was 92.1% (95% CI: 89.2-95.0%). CONCLUSIONS: AI system achieved high performance in the detection of HCC compared with a group of specialised radiologists. Further investigation by prospective clinical trials was necessitated to verify this model.

Total body irradiation-induced colon damage is prevented by nitrate-mediated suppression of oxidative stress and homeostasis of the gut microbiome.

Inorganic dietary nitrate plays vital roles in biological functions via the exogenous NO3-/NO2-/NO pathway under hypoxia and ischemia. We previously verified the antioxidative effects of inorganic nitrate in a mouse model of total body irradiation (TBI). Accordingly, in this study, we evaluated the effects of inorganic nitrate on prevention of TBI-induced colon injury and dysbiosis of the gut microbiome. Nitrate significantly rescued the abnormal biological indexes (body weight, white blood cell, red blood cell, platelet, hemoglobin level and intestinal canal lengths) induced by TBI. Then, we detected oxidative stress and DNA damage indexes (phospho-histone H2AX and p53 binding protein 1), which were both increased by irradiation (IR) and alleviated by nitrate. IR-induced apoptosis and senescence were ameliorated by inorganic nitrate. The distribution of the gut microbiome differed for mice with TBI and those receiving inorganic nitrate. The average abundance of Lactobacillus significantly increased, and that of Bacteroidales decreased at the genus level in the nitrate group compared with that in the IR alone group. At 30 days after TBI, the abundances of Bacteroides and Faecalibaculum decreased, whereas that of Lactobacillus increased in the IR + nitrate group compared with that in the IR alone group. Inorganic nitrate efficiently prevents TBI-induced colon epithelium injury and maintains the homeostasis of the gut microbiome. Thus, our results showed that inorganic nitrate might be a promising treatment for TBI induced colon injury.

Tumor specific methylome in Chinese high-grade serous ovarian cancer characterized by gene expression profile and tumor genotype.

OBJECTIVE: Extensive genetic and limited epigenetics have been characterized by the Cancer Genome Atlas (TCGA) among Western High-grade serous ovarian cancer (HGSOC). The present study aimed to characterize Chinese HGSOC at genome scale. METHODS: We used reduced representation bisulfite sequencing to investigate whole-genome and tumor-specific DNA methylation in 21 HGSOC tumors paired with their normal tissues, followed by a replication study involving additional 41 HGSOC patients. Altered methylation patterns in HGSOC were further characterized by gene expression profiles and whole-exome sequencing data. RESULTS: Comparing HGSOC tumors with normal tissues we observed global hypomethylation but with more specific hypermethylation in gene promoter. Totally, we revealed 159,881 differentially methylated regions (DMRs) and 4060 differentially expressed genes (DEGs). By integrating DNA methylation and mRNA expression data, we identified 153 negative (mainly in the upstream region) and 115 positive (mainly in the CDS regions) DMRs-DEGs correlated pairs, respectively. The negatively correlated DMRs-DEGs underlined Wnt and cell adhesion molecule binding as critical canonical pathways disrupted by DNA methylation. Eleven DMRs (in CAPS, FZD7, CDKN2A, PON3, KLF4, etc.), accompanied with a global DNA methylation marker, were validated in the replication samples. Whole-exome sequencing presented a relatively less dominated TP53 mutation in Chinese HGSOC compared to TCGA dataset. Unsupervised analysis of the three-level omics data identified differential methylation and expression subgroups based on tumor genetics, one of which presented increased DNA methylation and significantly associated with TP53 mutation. CONCLUSIONS: Our individual and integrated analyses contribute details about the tissue-specific genetic and DNA methylation landscape of Chinese HGSOC.

Identification of genomic alterations in oesophageal squamous cell cancer.

Oesophageal cancer is one of the most aggressive cancers and is the sixth leading cause of cancer death worldwide. Approximately 70% of global oesophageal cancer cases occur in China, with oesophageal squamous cell carcinoma (ESCC) being the histopathological form in the vast majority of cases (>90%). Currently, there are limited clinical approaches for the early diagnosis and treatment of ESCC, resulting in a 10% five-year survival rate for patients. However, the full repertoire of genomic events leading to the pathogenesis of ESCC remains unclear. Here we describe a comprehensive genomic analysis of 158 ESCC cases, as part of the International Cancer Genome Consortium research project. We conducted whole-genome sequencing in 17 ESCC cases and whole-exome sequencing in 71 cases, of which 53 cases, plus an additional 70 ESCC cases not used in the whole-genome and whole-exome sequencing, were subjected to array comparative genomic hybridization analysis. We identified eight significantly mutated genes, of which six are well known tumour-associated genes (TP53, RB1, CDKN2A, PIK3CA, NOTCH1, NFE2L2), and two have not previously been described in ESCC (ADAM29 and FAM135B). Notably, FAM135B is identified as a novel cancer-implicated gene as assayed for its ability to promote malignancy of ESCC cells. Additionally, MIR548K, a microRNA encoded in the amplified 11q13.3-13.4 region, is characterized as a novel oncogene, and functional assays demonstrate that MIR548K enhances malignant phenotypes of ESCC cells. Moreover, we have found that several important histone regulator genes (MLL2 (also called KMT2D), ASH1L, MLL3 (KMT2C), SETD1B, CREBBP and EP300) are frequently altered in ESCC. Pathway assessment reveals that somatic aberrations are mainly involved in the Wnt, cell cycle and Notch pathways. Genomic analyses suggest that ESCC and head and neck squamous cell carcinoma share some common pathogenic mechanisms, and ESCC development is associated with alcohol drinking. This study has explored novel biological markers and tumorigenic pathways that would greatly improve therapeutic strategies for ESCC.

Printed supercapacitors: materials, printing and applications.

Supercapacitors hold great promise for future electronic systems that are moving towards being flexible, portable, and highly integrated, due to their superior power density, stability and cycle lives. Printed electronics represents a paradigm shift in the manufacturing of supercapacitors in that it provides a whole range of simple, low-cost, time-saving, versatile and environmentally-friendly manufacturing technologies for supercapacitors with new and desirable structures (micro-, asymmetric, flexible, etc.), thus unleashing the full potential of supercapacitors for future electronics. In this review, we start by introducing the structural features of printed supercapacitors, followed by a summary of materials related to printed supercapacitors, including electrodes, electrolytes, current collectors and substrates; then the approaches to improve the performance of printed supercapacitors by tuning printing processes are discussed; next a summary of the recent developments of printed supercapacitors is given in terms of specific printing methods utilized; finally, challenges and future research opportunities of this exciting research direction are presented.

Diagnosis of thyroid cancer using deep convolutional neural network models applied to sonographic images: a retrospective, multicohort, diagnostic study.

BACKGROUND: The incidence of thyroid cancer is rising steadily because of overdiagnosis and overtreatment conferred by widespread use of sensitive imaging techniques for screening. This overall incidence growth is especially driven by increased diagnosis of indolent and well-differentiated papillary subtype and early-stage thyroid cancer, whereas the incidence of advanced-stage thyroid cancer has increased marginally. Thyroid ultrasound is frequently used to diagnose thyroid cancer. The aim of this study was to use deep convolutional neural network (DCNN) models to improve the diagnostic accuracy of thyroid cancer by analysing sonographic imaging data from clinical ultrasounds. METHODS: We did a retrospective, multicohort, diagnostic study using ultrasound images sets from three hospitals in China. We developed and trained the DCNN model on the training set, 131 731 ultrasound images from 17 627 patients with thyroid cancer and 180 668 images from 25 325 controls from the thyroid imaging database at Tianjin Cancer Hospital. Clinical diagnosis of the training set was made by 16 radiologists from Tianjin Cancer Hospital. Images from anatomical sites that were judged as not having cancer were excluded from the training set and only individuals with suspected thyroid cancer underwent pathological examination to confirm diagnosis. The model's diagnostic performance was validated in an internal validation set from Tianjin Cancer Hospital (8606 images from 1118 patients) and two external datasets in China (the Integrated Traditional Chinese and Western Medicine Hospital, Jilin, 741 images from 154 patients; and the Weihai Municipal Hospital, Shandong, 11 039 images from 1420 patients). All individuals with suspected thyroid cancer after clinical examination in the validation sets had pathological examination. We also compared the specificity and sensitivity of the DCNN model with the performance of six skilled thyroid ultrasound radiologists on the three validation sets. FINDINGS: Between Jan 1, 2012, and March 28, 2018, ultrasound images for the four study cohorts were obtained. The model achieved high performance in identifying thyroid cancer patients in the validation sets tested, with area under the curve values of 0·947 (95% CI 0·935-0·959) for the Tianjin internal validation set, 0·912 (95% CI 0·865-0·958) for the Jilin external validation set, and 0·908 (95% CI 0·891-0·925) for the Weihai external validation set. The DCNN model also showed improved performance in identifying thyroid cancer patients versus skilled radiologists. For the Tianjin internal validation set, sensitivity was 93·4% (95% CI 89·6-96·1) versus 96·9% (93·9-98·6; p=0·003) and specificity was 86·1% (81·1-90·2) versus 59·4% (53·0-65·6; p<0·0001). For the Jilin external validation set, sensitivity was 84·3% (95% CI 73·6-91·9) versus 92·9% (84·1-97·6; p=0·048) and specificity was 86·9% (95% CI 77·8-93·3) versus 57·1% (45·9-67·9; p<0·0001). For the Weihai external validation set, sensitivity was 84·7% (95% CI 77·0-90·7) versus 89·0% (81·9-94·0; p=0·25) and specificity was 87·8% (95% CI 81·6-92·5) versus 68·6% (60·7-75·8; p<0·0001). INTERPRETATION: The DCNN model showed similar sensitivity and improved specificity in identifying patients with thyroid cancer compared with a group of skilled radiologists. The improved technical performance of the DCNN model warrants further investigation as part of randomised clinical trials. FUNDING: The Program for Changjiang Scholars and Innovative Research Team in University in China, and National Natural Science Foundation of China.

Whole-Genome Sequencing Reveals Diverse Models of Structural Variations in Esophageal Squamous Cell Carcinoma.

Comprehensive identification of somatic structural variations (SVs) and understanding their mutational mechanisms in cancer might contribute to understanding biological differences and help to identify new therapeutic targets. Unfortunately, characterization of complex SVs across the whole genome and the mutational mechanisms underlying esophageal squamous cell carcinoma (ESCC) is largely unclear. To define a comprehensive catalog of somatic SVs, affected target genes, and their underlying mechanisms in ESCC, we re-analyzed whole-genome sequencing (WGS) data from 31 ESCCs using Meerkat algorithm to predict somatic SVs and Patchwork to determine copy-number changes. We found deletions and translocations with NHEJ and alt-EJ signature as the dominant SV types, and 16% of deletions were complex deletions. SVs frequently led to disruption of cancer-associated genes (e.g., CDKN2A and NOTCH1) with different mutational mechanisms. Moreover, chromothripsis, kataegis, and breakage-fusion-bridge (BFB) were identified as contributing to locally mis-arranged chromosomes that occurred in 55% of ESCCs. These genomic catastrophes led to amplification of oncogene through chromothripsis-derived double-minute chromosome formation (e.g., FGFR1 and LETM2) or BFB-affected chromosomes (e.g., CCND1, EGFR, ERBB2, MMPs, and MYC), with approximately 30% of ESCCs harboring BFB-derived CCND1 amplification. Furthermore, analyses of copy-number alterations reveal high frequency of whole-genome duplication (WGD) and recurrent focal amplification of CDCA7 that might act as a potential oncogene in ESCC. Our findings reveal molecular defects such as chromothripsis and BFB in malignant transformation of ESCCs and demonstrate diverse models of SVs-derived target genes in ESCCs. These genome-wide SV profiles and their underlying mechanisms provide preventive, diagnostic, and therapeutic implications for ESCCs.

Alterations in Enteric Virome Are Associated With Colorectal Cancer and Survival Outcomes.

BACKGROUND & AIMS: Patients with colorectal cancer (CRC) have a different gut microbiome signature than individuals without CRC. Little is known about the viral component of CRC-associated microbiome. We aimed to identify and validate viral taxonomic markers of CRC that might be used in detection of the disease or predicting outcome. METHODS: We performed shotgun metagenomic analyses of viromes of fecal samples from 74 patients with CRC (cases) and 92 individuals without CRC (controls) in Hong Kong (discovery cohort). Viral sequences were classified by taxonomic alignment against an integrated microbial reference genome database. Viral markers associated with CRC were validated using fecal samples from 3 separate cohorts: 111 patients with CRC and 112 controls in Hong Kong, 46 patients with CRC and 63 controls in Austria, and 91 patients with CRC and 66 controls in France and Germany. Using abundance profiles of CRC-associated virome genera, we constructed random survival forest models to identify those associated with patient survival times. RESULTS: The diversity of the gut bacteriophage community was significantly increased in patients with CRC compared with controls. Twenty-two viral taxa discriminated cases from controls with an area under the receiver operating characteristic curve of 0.802 in the discovery cohort. The viral markers were validated in 3 cohorts, with area under the receiver operating characteristic curves of 0.763, 0.736, and 0.715, respectively. Clinical subgroup analysis showed that dysbiosis of the gut virome was associated with early- and late-stage CRC. A combination of 4 taxonomic markers associated with reduced survival of patients with CRC (log-rank test, P = 8.1 × 10-6) independently of tumor stage, lymph node metastases, or clinical parameters. We found altered interactions between bacteriophages and oral bacterial commensals in fecal samples from patients with CRC compared with controls. CONCLUSIONS: In a metagenomic analysis of fecal samples from patients and controls, we identified virome signatures associated with CRC. These data might be used to develop tools to identify individuals with CRC or predict outcomes.

Oncogenes without a Neighboring Tumor-Suppressor Gene Are More Prone to Amplification.

Focal copy number gains or losses are important genomic hallmarks of cancer. The genomic distribution of oncogenes and tumor-suppressor genes (TSG) in relation to focal copy number aberrations is unclear. Our analysis revealed that the mean distance of TSGs from oncogenes was significantly shorter than that of noncancer genes, suggesting that oncogenes and TSGs tend to be in close physical proximity in the human genome. Such relationship was conserved in mouse and drosophila. Pan-cancer analysis using data from The Cancer Genome Atlas indicated that oncogenes without a nearby TSG are more prone to amplification. In conclusion, our study provides evidence for the nonrandom distribution of oncogenes and TSGs across different species. Our data also support that the existence of a neighboring TSG can suppress amplification of an oncogene, shedding new light on a previously unappreciated protective mechanism of TSGs.

Genomic analyses reveal mutational signatures and frequently altered genes in esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is one of the most common cancers worldwide and the fourth most lethal cancer in China. However, although genomic studies have identified some mutations associated with ESCC, we know little of the mutational processes responsible. To identify genome-wide mutational signatures, we performed either whole-genome sequencing (WGS) or whole-exome sequencing (WES) on 104 ESCC individuals and combined our data with those of 88 previously reported samples. An APOBEC-mediated mutational signature in 47% of 192 tumors suggests that APOBEC-catalyzed deamination provides a source of DNA damage in ESCC. Moreover, PIK3CA hotspot mutations (c.1624G>A [p.Glu542Lys] and c.1633G>A [p.Glu545Lys]) were enriched in APOBEC-signature tumors, and no smoking-associated signature was observed in ESCC. In the samples analyzed by WGS, we identified focal (<100 kb) amplifications of CBX4 and CBX8. In our combined cohort, we identified frequent inactivating mutations in AJUBA, ZNF750, and PTCH1 and the chromatin-remodeling genes CREBBP and BAP1, in addition to known mutations. Functional analyses suggest roles for several genes (CBX4, CBX8, AJUBA, and ZNF750) in ESCC. Notably, high activity of hedgehog signaling and the PI3K pathway in approximately 60% of 104 ESCC tumors indicates that therapies targeting these pathways might be particularly promising strategies for ESCC. Collectively, our data provide comprehensive insights into the mutational signatures of ESCC and identify markers for early diagnosis and potential therapeutic targets.

Characterizing genomic differences of human cancer stratified by the TP53 mutation status.

The key roles of the TP53 mutation in cancer have been well established. TP53 is the most frequently mutated gene, and its inactivation is widespread among human cancer types. However, the landscape of genomic alterations in human cancers stratified by the TP53 mutation has not yet been described. We obtained somatic mutation and copy number change data of 6551 regular-mutated samples from the Cancer Genome Atlas (TCGA) and compared significantly mutated genes (SMGs), copy number alterations, mutational signatures and mutational strand asymmetries between cancer samples with and without the TP53 mutation. We identified 126 SMGs, 30 of which were statistically significant in both the TP53 mutant and wild-type groups. Several SMGs, such as VHL, SMAD4 and PTEN, showed a mutation bias towards the TP53 wild-type group, whereas ATRX, IDH1 and RB1 were more prevalent in the TP53 mutant group. Five mutational signatures were extracted from the combined TCGA dataset on which mutational asymmetry analysis was performed, revealing that the TP53 mutant group exhibited substantially greater replication and transcription biases. Furthermore, we found that alterations of multiple genes in a merged mutually exclusive network composed of BRAF, EGFR, PAK1, PIK3CA, PTEN, APC and TERT were related to shortened survival in the TP53 wild-type group. In summary, we characterized the genomic differences and similarities underlying human cancers stratified by the TP53 mutation and identified multi-gene alterations of a merged mutually exclusive network to be a poor prognostic factor for the TP53 wild-type group.