Aristolochic acids (AAs) have long been considered as a potent carcinogen due to its nephrotoxicity. Aristolochic acid I (AAI) reacts with DNA to form covalent aristolactam (AL)-DNA adducts, leading to subsequent A to T transversion mutation, commonly referred as AA mutational signature. Previous research inferred that AAs were widely implicated in liver cancer throughout Asia. In this study, we explored whether AAs exposure was the main cause of liver cancer in the context of HBV infection in mainland China. Totally 1256 liver cancer samples were randomly retrieved from 3 medical centers and a refined bioanalytical method was used to detect AAI-DNA adducts. 5.10% of these samples could be identified as AAI positive exposure. Whole genome sequencing suggested 8.41% of 107 liver cancer patients exhibited the dominant AA mutational signature, indicating a relatively low overall AAI exposure rate. In animal models, long-term administration of AAI barely increased liver tumorigenesis in adult mice, opposite from its tumor-inducing role when subjected to infant mice. Furthermore, AAI induced dose-dependent accumulation of AA-DNA adduct in target organs in adult mice, with the most detected in kidney instead of liver. Taken together, our data indicate that AA exposure was not the major threat of liver cancer in adulthood.

Transmembrane proteins have critical biological functions and play a role in a multitude of cellular processes including cell signaling, transport of molecules and ions across membranes. Approximately 60% of transmembrane proteins are considered as drug targets. Missense mutations in such proteins can lead to many diverse diseases and disorders, such as neurodegenerative diseases and cystic fibrosis. However, there are limited studies on mutations in transmembrane proteins. In this work, we first design a new feature encoding method, termed weight attenuation position-specific scoring matrix (WAPSSM), which builds upon the protein evolutionary information. Then, we propose a new mutation prediction algorithm (cascade XGBoost) by leveraging the idea learned from consensus predictors and gcForest. Multi-level experiments illustrate the effectiveness of WAPSSM and cascade XGBoost algorithms. Finally, based on WAPSSM and other three types of features, in combination with the cascade XGBoost algorithm, we develop a new transmembrane protein mutation predictor, named MutTMPredictor. We benchmark the performance of MutTMPredictor against several existing predictors on seven datasets. On the 546 mutations dataset, MutTMPredictor achieves the accuracy (ACC) of 0.9661 and the Matthew\'s Correlation Coefficient (MCC) of 0.8950. While on the 67,584 dataset, MutTMPredictor achieves an MCC of 0.7523 and area under curve (AUC) of 0.8746, which are 0.1625 and 0.0801 respectively higher than those of the existing best predictor (fathmm). Besides, MutTMPredictor also outperforms two specific predictors on the Pred-MutHTP datasets. The results suggest that MutTMPredictor can be used as an effective method for predicting and prioritizing missense mutations in transmembrane proteins. The MutTMPredictor webserver and datasets are freely accessible at http://csbio.njust.edu.cn/bioinf/muttmpredictor/ for academic use.

Next Generation Sequencing (NGS) has dramatically improved the flexibility and outcomes of cancer research and clinical trials, providing highly sensitive and accurate high-throughput platforms for large-scale genomic testing. In contrast to whole-genome (WGS) or whole-exome sequencing (WES), targeted genomic sequencing (TS) focuses on a panel of genes or targets known to have strong associations with pathogenesis of disease and/or clinical relevance, offering greater sequencing depth with reduced costs and data burden. This allows targeted sequencing to identify low frequency variants in targeted regions with high confidence, thus suitable for profiling low-quality and fragmented clinical DNA samples. As a result, TS has been widely used in clinical research and trials for patient stratification and the development of targeted therapeutics. However, its transition to routine clinical use has been slow. Many technical and analytical obstacles still remain and need to be discussed and addressed before large-scale and cross-centre implementation. Gold-standard and state-of-the-art procedures and pipelines are urgently needed to accelerate this transition. In this review we first present how TS is conducted in cancer research, including various target enrichment platforms, the construction of target panels, and selected research and clinical studies utilising TS to profile clinical samples. We then present a generalised analytical workflow for TS data discussing important parameters and filters in detail, aiming to provide the best practices of TS usage and analyses.

Familial Adenomatous Polyposis (FAP) is the second most common inherited predisposition to colorectal cancer (CRC) associated with the development of hundreds to thousands of adenomas in the colon and rectum. Mutations in APC are found in ~ 80% polyposis patients with FAP. In the remaining 20% no genetic diagnosis can be provided suggesting other genes or mechanisms that render APC inactive may be responsible. Copy number variants (CNVs) remain to be investigated in FAP and may account for disease in a proportion of polyposis patients. A cohort of 56 polyposis patients and 40 controls were screened for CNVs using the 2.7M microarray (Affymetrix) with data analysed using ChAS (Affymetrix). A total of 142 CNVs were identified unique to the polyposis cohort suggesting their involvement in CRC risk. We specifically identified CNVs in four unrelated polyposis patients among CRC susceptibility genes APC, DCC, MLH1 and CTNNB1 which are likely to have contributed to disease development in these patients. A recurrent deletion was observed at position 18p11.32 in 9% of the patients screened that was of particular interest. Further investigation is necessary to fully understand the role of these variants in CRC risk given the high prevalence among the patients screened.

Defects in the completion of cell division by cytokinesis have long been proposed to foster carcinogenesis by engendering chromosome instability, but few tumor suppressor mechanisms controlling this process have so far been identified. Here, we identify a carboxyl (C)-terminal region of the high-mobility group protein HMG20b that is essential for cytokinesis, and report that it is inactivated by a cancer-associated mutation. We find that a C-terminal region of HMG20b spanning residues 173-317 is necessary and sufficient not only for its localization to cytokinetic structures, but also for its interaction with the tumor suppressor BRCA2, implicated in the abscission step of cytokinesis. Indeed, expression of this C-terminal HMG20b region suffices to restore cytokinesis in HMG20b-depleted cells. The non-conservative substitution of HMG20b residue Ala247 with Pro, reported in human lung cancer, disrupts these activities of HMG20b, impairing cytokinesis in a trans-dominant manner. Our findings provide fresh insight into the mechanism by which the HMG20b-BRCA2 complex controls mitotic cell division, and implicate heterozygous HMG20b mutations affecting cytokinesis regulation in the genesis of human cancers.