Transcriptome and Exome Analyses of Hepatocellular Carcinoma Reveal Patterns to Predict Cancer Recurrence in Liver Transplant Patients

Utility Analyses of AVITI Sequencing Chemistry

Background

DNA sequencing is a critical tool in modern biology. Over the last two decades, it has been revolutionized by the advent of massively parallel sequencing, leading to significant advances in the genome and transcriptome sequencing of various organisms. Nevertheless, challenges with accuracy, lack of competitive options and prohibitive costs associated with high throughput parallel short-read sequencing persist.

Results

Here, we conduct a comparative analysis using matched DNA and RNA short-reads assays between Element Biosciences' AVITI and Illumina's NextSeq 550 chemistries. Similar comparisons were evaluated for synthetic long-read sequencing for RNA and targeted single-cell transcripts between the AVITI and Illumina's NovaSeq 6000. For both DNA and RNA short-read applications, the study found that the AVITI produced significantly higher per sequence quality scores. For PCR-free DNA libraries, we observed an average 89.7% lower experimentally determined error rate when using the AVITI chemistry, compared to the NextSeq 550. For short-read RNA quantification, AVITI platform had an average of 32.5% lower error rate than that for NextSeq 550. With regards to synthetic long-read mRNA and targeted synthetic long read single cell mRNA sequencing, both platforms' respective chemistries performed comparably in quantification of genes and isoforms. The AVITI displayed a marginally lower error rate for long reads, with fewer chemistry-specific errors and a higher mutation detection rate.

Conclusion

These results point to the potential of the AVITI platform as a competitive candidate in high-throughput short read sequencing analyses when juxtaposed with the Illumina NextSeq 550.

Therapeutic targeting at genome mutations of liver cancer by the insertion of HSV1 thymidine kinase through Cas9-mediated editing

BACKGROUND

Liver cancer is one of the most lethal malignancies for humans. The treatment options for advanced-stage liver cancer remain limited. A new treatment is urgently needed to reduce the mortality of the disease.

METHODS

In this report, we developed a technology for mutation site insertion of a suicide gene (herpes simplex virus type 1- thymidine kinase) based on type II CRISPR RNA-guided endonuclease Cas9-mediated genome editing to treat liver cancers.

RESULTS

We applied the strategy to 3 different mutations: S45P mutation of catenin beta 1, chromosome breakpoint of solute carrier family 45 member 2-alpha-methylacyl-CoA racemase gene fusion, and V235G mutation of SAFB-like transcription modulator. The results showed that the herpes simplex virus type 1-thymidine kinase insertion rate at the S45P mutation site of catenin beta 1 reached 77.8%, while the insertion rates at the breakpoint of solute carrier family 45 member 2 - alpha-methylacyl-CoA racemase gene fusion were 95.1%-98.7%, and the insertion at V235G of SAFB-like transcription modulator was 51.4%. When these targeting reagents were applied to treat mouse spontaneous liver cancer induced by catenin beta 1S45P or solute carrier family 45 member 2-alpha-methylacyl-CoA racemase, the mice experienced reduced tumor burden and increased survival rate. Similar results were also obtained for the xenografted liver cancer model: Significant reduction of tumor volume, reduction of metastasis rate, and improved survival were found in mice treated with the targeting reagent, in comparison with the control-treated groups.

CONCLUSIONS

Our studies suggested that mutation targeting may hold promise as a versatile and effective approach to treating liver cancers.

Pushing the limits of treatment for hepatocellular carcinoma

PURPOSE OF REVIEW

We review existing and newer strategies for treatment and surveillance of hepatocellular carcinoma (HCC) both pre and postliver transplantation.

SUMMARY

HCC is rising in incidence and patients are often diagnosed at later stages. Consequently, there is a need for treatment strategies which include collaboration of multiple specialties. Combinations of locoregional, systemic, and surgical therapies are yielding better postliver transplantation (post-LT) outcomes for patients with HCC than previously seen. Tumor biology (tumor size, number, location, serum markers, response to therapy) can help identify patients who are at high risk for HCC recurrence posttransplantation and may expand transplant eligibility for some patients.

Long-read single-cell sequencing reveals expressions of hypermutation clusters of isoforms in human liver cancer cells

The protein diversity of mammalian cells is determined by arrays of isoforms from genes. Genetic mutation is essential in species evolution and cancer development. Accurate long-read transcriptome sequencing at single-cell level is required to decipher the spectrum of protein expressions in mammalian organisms. In this report, we developed a synthetic long-read single-cell sequencing technology based on LOOPSeq technique. We applied this technology to analyze 447 transcriptomes of hepatocellular carcinoma (HCC) and benign liver from an individual. Through Uniform Manifold Approximation and Projection analysis, we identified a panel of mutation mRNA isoforms highly specific to HCC cells. The evolution pathways that led to the hyper-mutation clusters in single human leukocyte antigen molecules were identified. Novel fusion transcripts were detected. The combination of gene expressions, fusion gene transcripts, and mutation gene expressions significantly improved the classification of liver cancer cells versus benign hepatocytes. In conclusion, LOOPSeq single-cell technology may hold promise to provide a new level of precision analysis on the mammalian transcriptome.

Transcriptomic sequencing analysis of key long noncoding RNAs and mRNAs expression profiles in postoperative recurrence of hepatocellular carcinoma

BACKGROUND

Recurrence is the main cause of death in hepatocellular carcinoma (HCC) patients after liver resection.

OBJECTIVE

The long non-coding RNAs (lncRNAs) have been reported participated in progression and prognosis of HCC, however, the vital role of lncRNA in postoperative recurrence of HCC has rarely been systematically identified.

METHODS

RNA-sequencing (RNA-seq) was performed between orthotopic model of HCC and hepatoma postoperative recurrent model to comprehensively analyze the integrated transcriptome expression profiles of lncRNA and mRNA. Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) was then conducted to quantify the expression levels of DElncRNAs and their target mRNAs.

RESULTS

In our study, 211 lncRNAs (P-value < 0.05) and 1125 mRNAs (P-adjust < 0.05) were significantly differentially expressed (DE) between two groups. Moreover, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that DElncRNAs and DEmRNAs were mainly enriched in lipid metabolism, including Arachidonic acid metabolism, PPAR signaling pathway, Steroid hormone biosynthesis, Linoleic acid metabolism, Inflammatory mediator regulation of TRP channels, and Fatty acid degradation. Furthermore, we constructed lncRNA-mRNA interaction networks and protein-protein interaction (PPI) network, and verified by qRT-PCR, suggesting that increased DEIncRNAs (XLOC_063499 and XLOC_042016) may prevent HCC recurrence after surgery by upregulating on targeted cytochrome P450 (CYP) family genes in the lipid metabolism pathway, such as cyp3a16, cyp3a44, cyp2c39, cyp2c40 and cyp2c68.

CONCLUSION

Overall, Our findings provided new insights for further investigation of biological function in lncRNA related HCC recurrence.

Machine learning methods for accurately predicting survival and guiding treatment in stage I and II hepatocellular carcinoma

Accurately predicting survival in patients with early hepatocellular carcinoma (HCC) is essential for making informed decisions about treatment and prognosis. Herein, we have developed a machine learning (ML) model that can predict patient survival and guide treatment decisions. We obtained patient demographic information, tumor characteristics, and treatment details from the SEER database. To analyze the data, we employed a Cox proportional hazards (CoxPH) model as well as 3 ML algorithms: neural network multitask logistic regression (N-MLTR), DeepSurv, and random survival forest (RSF). Our evaluation relied on the concordance index (C-index) and Integrated Brier Score (IBS). Additionally, we provided personalized treatment recommendations regarding surgery and chemotherapy choices and validated models' efficacy. A total of 1136 patients with early-stage (I, II) hepatocellular carcinoma (HCC) who underwent liver resection or transplantation were randomly divided into training and validation cohorts at a ratio of 3:7. Feature selection was conducted using Cox regression analyses. The ML models (NMLTR: C-index = 0.6793; DeepSurv: C-index = 0.7028; RSF: C-index = 0.6890) showed better discrimination in predicting survival than the standard CoxPH model (C-index = 0.6696). Patients who received recommended treatments had higher survival rates than those who received unrecommended treatments. ML-based surgery treatment recommendations yielded higher hazard ratios (HRs): NMTLR HR = 0.36 (95% CI: 0.25-0.51, P < .001), DeepSurv HR = 0.34 (95% CI: 0.24-0.49, P < .001), and RSF HR = 0.37 (95% CI: 0.26-0.52, P = <.001). Chemotherapy treatment recommendations were associated with significantly improved survival for DeepSurv (HR: 0.57; 95% CI: 0.4-0.82, P = .002) and RSF (HR: 0.66; 95% CI: 0.46-0.94, P = .020). The ML survival model has the potential to benefit prognostic evaluation and treatment of HCC. This novel analytical approach could provide reliable information on individual survival and treatment recommendations.

European Society for Organ Transplantation Consensus Statement on Biomarkers in Liver Transplantation

Currently, one-year survival following liver transplantation (LT) exceeds 90% in large international registries, and LT is considered definitive treatment for patients with end-stage liver disease and liver cancer. Recurrence of disease, including hepatocellular carcinoma (HCC), significantly hampers post-LT outcomes. An optimal approach to immunosuppression (IS), including safe weaning, may benefit patients by mitigating the effect on recurrent diseases, as well as reducing adverse events associated with over-/under-IS, including chronic kidney disease (CKD). Prediction of these outcome measures-disease recurrence, CKD, and immune status-has long been based on relatively inaccurate clinical models. To address the utility of new biomarkers in predicting these outcomes in the post-LT setting, the European Society of Organ Transplantation (ESOT) and International Liver Transplant Society (ILTS) convened a working group of experts to review literature pertaining to primary disease recurrence, development of CKD, and safe weaning of IS. Summaries of evidence were presented to the group of panelists and juries to develop guidelines, which were discussed and voted in-person at the Consensus Conference in Prague November 2022. The consensus findings and recommendations of the Liver Working Group on new biomarkers in LT, clinical applicability, and future needs are presented in this article.

Transcriptome analysis creates a new era of precision medicine for managing recurrent hepatocellular carcinoma

The high incidence of hepatocellular carcinoma (HCC) recurrence negatively impacts outcomes of patients treated with curative intent despite advances in surgical techniques and other locoregional liver-targeting therapies. Over the past few decades, the emergence of transcriptome analysis tools, including real-time quantitative reverse transcription PCR, microarrays, and RNA sequencing, has not only largely contributed to our knowledge about the pathogenesis of recurrent HCC but also led to the development of outcome prediction models based on differentially expressed gene signatures. In recent years, the single-cell RNA sequencing technique has revolutionized our ability to study the complicated crosstalk between cancer cells and the immune environment, which may benefit further investigations on the role of different immune cells in HCC recurrence and the identification of potential therapeutic targets. In the present article, we summarized the major findings yielded with these transcriptome methods within the framework of a causal model consisting of three domains: primary cancer cells; carcinogenic stimuli; and tumor microenvironment. We provided a comprehensive review of the insights that transcriptome analyses have provided into diagnostics, surveillance, and treatment of HCC recurrence.

Association of metabolic dysfunction-associated fatty liver disease with kidney disease

Non-alcoholic fatty liver disease (NAFLD) is characterized by the accumulation of fat in more than 5% of hepatocytes in the absence of excessive alcohol consumption and other secondary causes of hepatic steatosis. In 2020, the more inclusive term metabolic (dysfunction)-associated fatty liver disease (MAFLD) - defined by broader diagnostic criteria - was proposed to replace the term NAFLD. The new terminology and revised definition better emphasize the pathogenic role of metabolic dysfunction and uses a set of definitive, inclusive criteria for diagnosis. Diagnosis of MAFLD is based on evidence of hepatic steatosis (as assessed by liver biopsy, imaging techniques or blood biomarkers and scores) in persons who are overweight or obese and have type 2 diabetes mellitus or metabolic dysregulation, regardless of the coexistence of other liver diseases or excessive alcohol consumption. The known association between NAFLD and chronic kidney disease (CKD) and our understanding that CKD can occur as a consequence of metabolic dysfunction suggests that individuals with MAFLD - who by definition have fatty liver and metabolic comorbidities - are at increased risk of CKD. In this Perspective article, we discuss the clinical associations between MAFLD and CKD, the pathophysiological mechanisms by which MAFLD may increase the risk of CKD and the potential drug treatments that may benefit both conditions.