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Chronic infection with the Hepatitis B Virus (HBV) is a major cause of liver-related morbidity and mortality. One peculiar observation in cells infected with HBV (or with closely‑related animal hepadnaviruses) is the presence of viral DNA integration in the host cell genome, despite this form being a replicative dead-end for the virus. The frequent finding of somatic integration of viral DNA suggests an evolutionary benefit for the virus; however, the mechanism of integration, its functions, and the clinical implications remain unknown. Here we review the current body of knowledge of HBV DNA integration, with particular focus on the molecular mechanisms and its clinical implications (including the possible consequences of replication-independent antigen expression and its possible role in hepatocellular carcinoma). HBV DNA integration is likely to influence HBV replication, persistence, and pathogenesis, and so deserves greater attention in future studies.

Infection with the Hepatitis B Virus (HBV) is one of the strongest risk-factors for liver cancer (hepatocellular carcinoma, HCC). One of the reported drivers of HCC is the integration of HBV DNA into the host cell genome, which may induce pro-carcinogenic pathways. These reported pathways include: induction of chromosomal instability; generation of insertional mutagenesis in key cancer-associated genes; transcription of downstream cancer-associated cellular genes; and/or formation of a persistent source of viral protein expression (particularly HBV surface and X proteins). The contribution of each of these specific mechanisms towards carcinogenesis is currently unclear. Here, we review the current knowledge of specific sites of HBV DNA integration into the host genome, which sheds light on these mechanisms. We give an overview of previously-used methods to detect HBV DNA integration and the enrichment of integration events in specific functional and structural cellular genomic sites. Finally, we posit a theoretical model of HBV DNA integration during disease progression and highlight open questions in the field.

Background Despite the fact that tumor microenvironment (TME) and gene mutations are the main determinants of progression of the deadliest cancer in the world – lung cancer, their interrelations are not well understood. Digital pathology data provides a unique insight into the spatial composition of the TME. Various spatial metrics and machine learning approaches were proposed for prediction of either patient survival or gene mutations from this data. Still, these approaches are limited in the scope of analyzed features and in their explainability, and as such fail to transfer to clinical practice. Methods Here, we generated 23,199 image patches from 26 hematoxylin-and-eosin (H&E)-stained lung cancer tissue sections and annotated them into 9 different tissue classes. Using this dataset, we trained a deep neural network ARA-CNN. Next, we applied the trained network to segment 467 lung cancer H&E images from The Cancer Genome Atlas (TCGA) database. We used the segmented images to compute human-interpretable features reflecting the heterogeneous composition of the TME, and successfully utilized them to predict patient survival and cancer gene mutations. Results We achieved per-class AUC ranging from 0.72 to 0.99 for classifying tissue types in lung cancer with ARA-CNN. Machine learning models trained on the proposed human-interpretable features achieved a c-index of 0.723 in the task of survival prediction and AUC up to 73.5% for PDGFRB in the task of mutation classification. Conclusions We presented a framework that accurately predicted survival and gene mutations in lung adenocarcinoma patients based on human-interpretable features extracted from H&E slides. Our approach can provide important insights for designing novel cancer treatments, by linking the spatial structure of the TME in lung adenocarcinoma to gene mutations and patient survival. It can also expand our understanding of the effects that the TME has on tumor evolutionary processes. Our approach can be generalized to different cancer types to inform precision medicine strategies.

Discovering synthetic lethal (SL) gene partners of cancer genes is an important step in developing cancer therapies. However, identification of SL interactions is challenging, due to a large number of possible gene pairs, inherent noise and confounding factors in the observed signal. To discover robust SL interactions, we devised SLIDE-VIP, a novel framework combining eight statistical tests, including a new patient data-based test iSurvLRT. SLIDE-VIP leverages multi-omics data from four different sources: gene inactivation cell line screens, cancer patient data, drug screens and gene pathways. We applied SLIDE-VIP to discover SL interactions between genes involved in DNA damage repair, chromatin remodeling and cell cycle, and their potentially druggable partners. The top 883 ranking SL candidates had strong evidence in cell line and patient data, 250-fold reducing the initial space of 200K pairs. Drug screen and pathway tests provided additional corroboration and insights into these interactions. We rediscovered well-known SL pairs such as RB1 and E2F3 or PRKDC and ATM, and in addition, proposed strong novel SL candidates such as PTEN and PIK3CB. In summary, SLIDE-VIP opens the door to the discovery of SL interactions with clinical potential. All analysis and visualizations are available via the online SLIDE-VIP WebApp.

In hepatocellular carcinoma (HCC), somatic genome-wide DNA mutations are numerous, universal and heterogeneous. Some of these somatic mutations are drivers of the malignant process but the vast majority are passenger mutations. These passenger mutations can be deleterious to individual protein function but are tolerated by the cell or are offset by a survival advantage conferred by driver mutations. It is unknown if these somatic deleterious passenger mutations (DPMs) develop in the precancerous state of cirrhosis or if it is confined to HCC. Therefore, we studied four whole-exome sequencing datasets, including patients with non-cirrhotic liver (n = 12), cirrhosis without HCC (n = 6) and paired HCC with surrounding non-HCC liver (n = 74 paired samples), to identify DPMs. After filtering out putative germline mutations, we identified 187±22 DPMs per non-diseased tissue. DPMs number was associated with liver disease progressing to HCC, independent of the number of exonic mutations. Tumours contained significantly more DPMs compared to paired non-tumour tissue (258-293 per HCC exome). Cirrhosis- and HCC-associated DPMs do not occur predominantly in specific genes, chromosomes or biological pathways and the effect on tumour biology is presently unknown. Importantly, for the first time we have shown a significant increase in DPMs with HCC.

Synovial sarcoma is a rare and highly aggressive subtype of soft tissue sarcoma. The clinical challenge posed by advanced or metastatic synovial sarcoma, marked by limited treatment options and suboptimal outcomes, necessitates innovative approaches. The topoisomerase II (Topo II) inhibitor doxorubicin has remained the cornerstone systemic treatment for decades, and there is pressing need for improved therapeutic strategies for these patients. This study highlights the potential to enhance the cytotoxic effects of doxorubicin within well-characterized synovial sarcoma cell lines using the potent and selective DNA-PK inhibitor, peposertib. In vitro investigations unveil a p53-mediated synergistic anti-tumor effect when combining doxorubicin with peposertib. The in vitro findings were substantiated by pronounced anti-tumor effects in mice bearing subcutaneously implanted tumors. A well-tolerated regimen for the combined application was established using both pegylated liposomal doxorubicin (PLD) and unmodified doxorubicin. Notably, the combination of PLD and peposertib displayed enhanced anti-tumor efficacy compared to unmodified doxorubicin at equivalent doses, suggesting an improved therapeutic window—a critical consideration for clinical translation. Efficacy studies in two patient-derived xenograft models of synovial sarcoma, accurately reflecting human metastatic disease, further validate the potential of this combined therapy. These findings align with previous evidence showcasing the synergy between DNA-PK inhibition and Topo II inhibitors in diverse tumor models, including breast and ovarian cancers. Our study extends the potential utility of combined therapy to synovial sarcoma.

Hepatocellular carcinoma (HCC) is a prevalent primary liver cancer that is derived from hepatocytes and is characterised by high mortality rate and poor prognosis. While HCC is driven by cumulative changes in the hepatocyte genome, it is increasingly recognised that the liver microenvironment plays a pivotal role in HCC propensity, progression and treatment response. The microenvironmental stimuli that have been recognised as being involved in HCC pathogenesis are diverse and include intrahepatic cell subpopulations, such as immune and stellate cells, pathogens, such as hepatitis viruses, and non-cellular factors, such as abnormal extracellular matrix (ECM) and tissue hypoxia. Recently, a number of novel environmental influences have been shown to have an equally dramatic, but previously unrecognized, role in HCC progression. Novel aspects, including diet, gastrointestinal tract (GIT) microflora and circulating microvesicles, are now being recognized as increasingly important in HCC pathogenesis. This review will outline aspects of the HCC microenvironment, including the potential role of GIT microflora and microvesicles, in providing new insights into tumourigenesis and identifying potential novel targets in the treatment of HCC.

mRNA design is the critical first step in the development of a new vaccine or therapy. The mRNA primary sequence is assembled from multiple elements, including the coding sequence of the target protein or gene, flanking untranslated regions that enhance translation, and the poly(A) tail, which improves mRNA stability. The coding sequence can also be optimized to improve translation and stability, and the depletion of uridines and double-stranded RNA secondary structures can reduce reactogenicity. Here, we introduce mRNArchitect, a software toolkit to assist in the design of mRNA sequences according to user requirements. mRNArchitect can rapidly assemble and optimize mRNA sequences based on criteria including GC content, secondary structure, codon optimization, and Uridine depletion. The sequences generated by mRNArchitect can also be readily synthesized into DNA templates, and have been extensively validated across a wide variety of applications. We offer mRNArchitect as an open-source software toolkit to help scientists design new mRNA medicines.Competing Interest StatementT.R.M. H.M.G. and S.W.C. have received research funding, support for conference attendance, travel and accommodation from Oxford Nanopore Technologies. T.R.M. and S.W.C. have received research funding from Sartorius Stedim Australia, and Sanofi. T.R.M., H.M.G, R.Y.C., N.C. and S.W.C. have received support for conference attendance, travel and accommodation from Moderna. The other authors declare no competing interests.

Sequence design is the critical first step in developing a new mRNA medicine. The mRNA primary sequence is assembled from multiple elements, including the coding sequence of the target protein or gene, flanking untranslated regions that impact expression, and the poly(A) tail, which improves stability. The mRNA primary sequence can be optimized to improve the performance of an mRNA medicine, including increasing translation and stability, and reducing reactogenicity. Here, we introduce mRNArchitect, a software toolkit to assist in the design of mRNA sequences. mRNArchitect can rapidly assemble and optimize mRNA sequences based on user requirements, including GC content, secondary structure, codon optimization, and uridine depletion. The sequences generated by mRNArchitect can also be readily synthesized and have been experimentally validated across a variety of applications. We present mRNArchitect as a software toolkit to help scientists design new mRNA medicines. mRNArchitect is available via a web portal at www.basefacility.org.au/software/ and Github at https://github.com/BaseUQ/mRNArchitect. mRNArchitect is open-source and freely available under MIT License.

Discovering synthetic lethal (SL) gene partners of cancer genes is an important step in developing cancer therapies. However, identification of SL interactions is challenging, due to a large number of possible gene pairs, inherent noise and confounding factors in the observed signal. To discover robust SL interactions, we devised SLIDE-VIP, a novel framework combining eight statistical tests, including a new patient data-based test iSurvLRT. SLIDE-VIP leverages multi-omics data from four different sources: gene inactivation cell line screens, cancer patient data, drug screens and gene pathways. We applied SLIDE-VIP to discover SL interactions between genes involved in DNA damage repair, chromatin remodeling and cell cycle, and their potentially druggable partners. The top 883 ranking SL candidates had strong evidence in cell line and patient data, 250-fold reducing the initial space of 200K pairs. Drug screen and pathway tests provided additional corroboration and insights into these interactions. We rediscovered well-known SL pairs such as RB1 and E2F3 or PRKDC and ATM, and in addition, proposed strong novel SL candidates such as PTEN and PIK3CB. In summary, SLIDE-VIP opens the door to the discovery of SL interactions with clinical potential. All analysis and visualizations are available via the online SLIDE-VIP WebApp.