Synthetic lethality: A promising therapeutic strategy for hepatocellular carcinoma

The other side of the coin: protein deubiquitination by Ubiquitin-Specific Protease 1 in cancer progression and therapy

Reversible protein ubiquitination is a crucial factor in cellular homeostasis, with Ubiquitin-Specific Protease 1 (USP1) serving as a key deubiquitinase involved in DNA damage response (DDR) and repair mechanisms in cancer. While ubiquitin ligases have been extensively studied, research on the reverse process of ubiquitination, particularly the mechanisms involving USP1, remains relatively limited. USP1 is overexpressed in various cancers, influencing tumor initiation and progression by regulating multiple associated proteins. Inhibiting USP1 effectively suppresses tumor proliferation and migration and may help overcome resistance to cisplatin and PARP inhibitors. As a potential synthetic lethal target, USP1 demonstrates significant research potential. This review highlights the biological mechanisms of USP1 in cancer progression, the signaling pathways it regulates, and the latest advancements in USP1 inhibitors, while also analyzing the opportunities and challenges of targeting USP1. By adopting the perspective of "the other side of the coin," this review aims to underscore the crucial yet often overlooked role of the deubiquitinase USP1, contrasting it with the extensively studied ubiquitin ligases, and emphasizing its therapeutic potential in cancer treatment.

Advancing cancer therapy: new frontiers in targeting DNA damage response

Genomic instability is a core characteristic of cancer, often stemming from defects in DNA damage response (DDR) or increased replication stress. DDR defects can lead to significant genetic alterations, including changes in gene copy numbers, gene rearrangements, and mutations, which accumulate over time and drive the clonal evolution of cancer cells. However, these vulnerabilities also present opportunities for targeted therapies that exploit DDR deficiencies, potentially improving treatment efficacy and patient outcomes. The development of PARP inhibitors like Olaparib has significantly improved the treatment of cancers with DDR defects (e.g., BRCA1 or BRCA2 mutations) based on synthetic lethality. This achievement has spurred further research into identifying additional therapeutic targets within the DDR pathway. Recent progress includes the development of inhibitors targeting other key DDR components such as DNA-PK, ATM, ATR, Chk1, Chk2, and Wee1 kinases. Current research is focused on optimizing these therapies by developing predictive biomarkers for treatment response, analyzing mechanisms of resistance (both intrinsic and acquired), and exploring the potential for combining DDR-targeted therapies with chemotherapy, radiotherapy, and immunotherapy. This article provides an overview of the latest advancements in targeted anti-tumor therapies based on DDR and their implications for future cancer treatment strategies.

Metabolic vulnerabilities in cancer: A new therapeutic strategy

Cancer metabolism is now a key area for therapeutic intervention, targeting unique metabolic reprogramming crucial for tumor growth and survival. This article reviews the therapeutic potential of addressing metabolic vulnerabilities through glycolysis and glutaminase inhibitors, which disrupt cancer cell metabolism. Challenges such as tumor heterogeneity and adaptive resistance are discussed, with strategies including personalized medicine and predictive biomarkers to enhance treatment efficacy. Additionally, integrating diet and lifestyle changes with metabolic targeting underscores a holistic approach to improving therapy outcomes. The article also examines the benefits of incorporating these strategies into standard care, highlighting the potential for more tailored, safer treatments. In conclusion, exploiting metabolic vulnerabilities promises a new era in oncology, positioning metabolic targeting at the forefront of personalized cancer therapy and transforming patient care.

Inhibition of KIF20A enhances the immunotherapeutic effect of hepatocellular carcinoma by enhancing c-Myc ubiquitination

Immune therapy has significantly improved the prognosis of hepatocellular carcinoma (HCC) patients, yet its efficacy remains limited, underscoring the urgency to identify new therapeutic targets and biomarkers. Here, we investigated the pathological and physiological roles of KIF20A and assess its potential in enhancing HCC treatment efficacy when combined with PD-1 inhibitors. We initially assess KIF20A's oncogenic function using liver-specific KIF20A knockout (Kif20a CKO) mouse models and orthotopic xenografts. Subsequently, we establish a regulatory axis involving KIF20A, FBXW7, and c-Myc, validated through construction of c-Myc splicing mutants. Large-scale clinical immunohistochemistry (IHC) analyses confirm the pathological relevance of the KIF20A-FBXW7-c-Myc axis in HCC. We demonstrate that KIF20A overexpression correlates with poor prognosis in HCC by competitively inhibiting FBXW7-mediated degradation of c-Myc, thereby promoting glycolysis and enhancing tumor proliferation. Conversely, KIF20A downregulation suppresses these effects, impairing tumor growth through c-Myc downregulation. Notably, KIF20A inhibition attenuates c-Myc-induced MMR expression, associated with improved prognosis in HCC patients receiving PD-1 inhibitor therapy. Furthermore, in Kif20a CKO HCC mouse models, we observe synergistic effects between Kif20a knockout and anti-PD-1 antibodies, significantly enhancing immunotherapeutic efficacy against HCC. Our findings suggest that targeting the KIF20A-c-Myc axis could identify HCC patients likely to benefit from anti-PD-1 therapy. In conclusion, we propose that combining KIF20A inhibitors with anti-PD-1 treatment represents a promising therapeutic strategy for HCC, offering new avenues for clinical development and patient stratification.

Transcription factor 7 like 2 promotes metastasis in hepatocellular carcinoma via NEDD9-mediated activation of AKT/mTOR signaling pathway

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors of the digestive system, and the exact mechanism of HCC is still unclear. Transcription factor 7 like 2 (TCF7L2) plays a pivotal role in cell proliferation and stemness maintenance. However, the exact mechanism of TCF7L2 in HCC remains unclear.

METHODS

Clinical samples and public databases were used to analyze the expression and prognosis of TCF7L2 in HCC. The function of TCF7L2 in HCC was studied in vitro and in vivo. ChIP and luciferase assays were used to explore the molecular mechanism of TCF7L2. The relationship between TCF7L2 and NEDD9 was verified in HCC clinical samples by tissue microarrays.

RESULTS

The expression of TCF7L2 was upregulated in HCC, and high expression of TCF7L2 was associated with poor prognosis of HCC patients. Overexpression of TCF7L2 promoted the metastasis of HCC in vitro and in vivo, while Knockdown of TCF7L2 showed the opposite effect. Mechanically, TCF7L2 activated neural precursor cell expressed developmentally downregulated protein 9 (NEDD9) transcription by binding to the -1522/-1509 site of the NEDD9 promoter region, thereby increasing the phosphorylation levels of AKT and mTOR. The combination of TCF7L2 and NEDD9 could distinguish the survival of HCC patients.

CONCLUSIONS

This study demonstrated that TCF7L2 promotes HCC metastasis by activating AKT/mTOR pathway in a NEDD9-dependent manner, suggesting that potential of TCF7L2 and NEDD9 as prognostic markers and therapeutic targets for HCC.

Stratifin promotes the malignant progression of HCC via binding and hyperactivating AKT signaling

Hepatocellular carcinoma (HCC) is a highly aggressive malignant tumor with limited treatment options and poor prognosis. In this study, we reveal the pivotal role of Stratifin (SFN), also recognized as 14-3-3σ, in driving HCC progression. Our investigation underscores a substantial upregulation of SFN within HCC tissues, manifesting a significant association with worse prognostic outcomes among HCC patients. In vitro and in vivo experiments reveal that SFN overexpression significantly amplifies proliferation, mitigates sorafenib-induced effects on HCC cells, and enhances tumorigenesis. While SFN silencing exerts converse effects on HCC progression. Additionally, we unveil a critical interaction between SFN and AKT, where SFN boosts AKT kinase activity by disrupting the binding of PHLPP2 and AKT, thereby intensifying the malignant progression of HCC cells. In conclusion, this study identifies the oncogenic role of SFN and elucidates the regulatory mechanism of the SFN/AKT axis in HCC, which may provide valuable insights into the mechanisms of HCC progression and potential targets for therapeutic intervention.

Hepatocellular Carcinoma: Current Drug Therapeutic Status, Advances and Challenges

Hepatocellular carcinoma (HCC) is the most common form of liver cancer, accounting for ~90% of liver neoplasms. It is the second leading cause of cancer-related deaths and the seventh most common cancer worldwide. Although there have been rapid developments in the treatment of HCC over the past decade, the incidence and mortality rates of HCC remain a challenge. With the widespread use of the hepatitis B vaccine and antiviral therapy, the etiology of HCC is shifting more toward metabolic-associated steatohepatitis (MASH). Early-stage HCC can be treated with potentially curative strategies such as surgical resection, liver transplantation, and radiofrequency ablation, improving long-term survival. However, most HCC patients, when diagnosed, are already in the intermediate or advanced stages. Molecular targeted therapy, followed by immune checkpoint inhibitor immunotherapy, has been a revolution in HCC systemic treatment. Systemic treatment of HCC especially for patients with compromised liver function is still a challenge due to a significant resistance to immune checkpoint blockade, tumor heterogeneity, lack of oncogenic addiction, and lack of effective predictive and therapeutic biomarkers.

CMTM6 promotes hepatocellular carcinoma progression through stabilizing β-catenin

CMTM6, a regulator of PD-L1 stability, has been implicated in the development of various cancers. However, the expression and role of CMTM6 in hepatocellular carcinoma (HCC) remains controversial. Our study revealed a negative correlation between CMTM6 expression and HCC prognosis through bioinformatics analysis and immunofluorescence staining. CMTM6 expression was also positively associated with alpha-fetoprotein (AFP) levels, supporting its potential as a prognostic marker for HCC. Using Cmtm6 knockout mice, we found that Cmtm6 deficiency inhibited HCC formation and cell proliferation in primary liver cancer models induced by DEN and DEN/CCl4. In HCC cell lines, CMTM6 promoted cell proliferation and interacted with β-catenin, stabilizing it by preventing ubiquitination. In conclusion, our study suggested that CMTM6 upregulation promotes HCC cell proliferation through the β-catenin pathway, making it a potential therapeutic target for HCC treatment.

Predicting host-based, synthetic lethal antiviral targets from omics data

Traditional antiviral therapies often have limited effectiveness due to toxicity and the emergence of drug resistance. Host-based antivirals are an alternative, but can cause nonspecific effects. Recent evidence shows that virus-infected cells can be selectively eliminated by targeting synthetic lethal (SL) partners of proteins disrupted by viral infection. Thus, we hypothesized that genes depleted in CRISPR knockout (KO) screens of virus-infected cells may be enriched in SL partners of proteins altered by infection. To investigate this, we established a computational pipeline predicting antiviral SL drug targets. First, we identified SARS-CoV-2-induced changes in gene products via a large compendium of omics data. Second, we identified SL partners for each altered gene product. Last, we screened CRISPR KO data for SL partners required for cell viability in infected cells. Despite differences in virus-induced alterations detected by various omics data, they share many predicted SL targets, with significant enrichment in CRISPR KO-depleted datasets. Our comparison of SARS-CoV-2 and influenza infection data revealed potential broad-spectrum, host-based antiviral SL targets. This suggests that CRISPR KO data are replete with common antiviral targets due to their SL relationship with virus-altered states and that such targets can be revealed from analysis of omics datasets and SL predictions.

Multiple Roles of the RUNX Gene Family in Hepatocellular Carcinoma and Their Potential Clinical Implications

Hepatocellular carcinoma (HCC) is one of the most frequent cancers in humans, characterised by a high resistance to conventional chemotherapy, late diagnosis, and a high mortality rate. It is necessary to elucidate the molecular mechanisms involved in hepatocarcinogenesis to improve diagnosis and treatment outcomes. The Runt-related (RUNX) family of transcription factors (RUNX1, RUNX2, and RUNX3) participates in cardinal biological processes and plays paramount roles in the pathogenesis of numerous human malignancies. Their role is often controversial as they can act as oncogenes or tumour suppressors and depends on cellular context. Evidence shows that deregulated RUNX genes may be involved in hepatocarcinogenesis from the earliest to the latest stages. In this review, we summarise the topical evidence on the roles of RUNX gene family members in HCC. We discuss their possible application as non-invasive molecular markers for early diagnosis, prognosis, and development of novel treatment strategies in HCC patients.

Predicting host-based, synthetic lethal antiviral targets from omics data

Traditional antiviral therapies often have limited effectiveness due to toxicity and development of drug resistance. Host-based antivirals, while an alternative, may lead to non-specific effects. Recent evidence shows that virus-infected cells can be selectively eliminated by targeting synthetic lethal (SL) partners of proteins disrupted by viral infection. Thus, we hypothesized that genes depleted in CRISPR KO screens of virus-infected cells may be enriched in SL partners of proteins altered by infection. To investigate this, we established a computational pipeline predicting SL drug targets of viral infections. First, we identified SARS-CoV-2-induced changes in gene products via a large compendium of omics data. Second, we identified SL partners for each altered gene product. Last, we screened CRISPR KO data for SL partners required for cell viability in infected cells. Despite differences in virus-induced alterations detected by various omics data, they share many predicted SL targets, with significant enrichment in CRISPR KO-depleted datasets. Comparing data from SARS-CoV-2 and influenza infections, we found possible broad-spectrum, host-based antiviral SL targets. This suggests that CRISPR KO data are replete with common antiviral targets due to their SL relationship with virus-altered states and that such targets can be revealed from analysis of omics datasets and SL predictions.

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.

NSF4SL: negative-sample-free contrastive learning for ranking synthetic lethal partner genes in human cancers

MOTIVATION

Detecting synthetic lethality (SL) is a promising strategy for identifying anti-cancer drug targets. Targeting SL partners of a primary gene mutated in cancer is selectively lethal to cancer cells. Due to high cost of wet-lab experiments and availability of gold standard SL data, supervised machine learning for SL prediction has been popular. However, most of the methods are based on binary classification and thus limited by the lack of reliable negative data. Contrastive learning can train models without any negative sample and is thus promising for finding novel SLs.

RESULTS

We propose NSF4SL, a negative-sample-free SL prediction model based on a contrastive learning framework. It captures the characteristics of positive SL samples by using two branches of neural networks that interact with each other to learn SL-related gene representations. Moreover, a feature-wise data augmentation strategy is used to mitigate the sparsity of SL data. NSF4SL significantly outperforms all baselines which require negative samples, even in challenging experimental settings. To the best of our knowledge, this is the first time that SL prediction is formulated as a gene ranking problem, which is more practical than the current formulation as binary classification. NSF4SL is the first contrastive learning method for SL prediction and its success points to a new direction of machine-learning methods for identifying novel SLs.

AVAILABILITY AND IMPLEMENTATION

Our source code is available at https://github.com/JieZheng-ShanghaiTech/NSF4SL.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Multiple Perspectives Reveal the Role of DNA Damage Repair Genes in the Molecular Classification and Prognosis of Pancreatic Adenocarcinoma

Pancreatic adenocarcinoma (PAAD) is a highly heterogeneous and immunosuppressive cancer. This study investigated the diversity of DNA damage repair (DDR) and immune microenvironment in PAAD by transcriptomic and genomic analysis. Patients with PAAD were divided into two DDR-based subtypes with distinct prognosis and molecular characteristics. The differential expression genes were mostly enriched in DDR and immune-related pathways. In order to distinguish high- and low-risk groups clinically, a DDR- and immune-based 5-gene prognostic signature (termed DPRS) was established. Patients in the high-risk group had inferior prognosis, a low level of immune checkpoint gene expression and low sensitivity to DDR-associated inhibitors. Furthermore, single-cell sequencing was used to observe the performance of the DDR-based signature in a high dimension, and immunohistochemistry was used to verify the relationship between the genes we identified and the prognosis of patients with PAAD. In conclusion, the DDR heterogeneity of PAAD was demonstrated, and a novel DDR- and immune-based risk-scoring model was constructed, which indicated the feasibility of DPRS in predicting prognosis and drug response in PAAD patients.

Establishment and validation of a cholesterol metabolism-related prognostic signature for hepatocellular carcinoma

Hepatocellular carcinoma (HCC) represents the most important type of liver cancer, the 5-year survival rate for advanced HCC is 2%. The heterogeneity of HCC makes previous models fail to achieve satisfactory results. The role of Cholesterol-based metabolic reprogramming in cancer has attracted more and more attention. In this study, we screened cholesterol metabolism-related genes (CMRGs) based on a systematical analysis from TCGA and GEO database. Then, we constructed a prognostic signature based on the screened 5 CMRGs: FDPS, FABP5, ANXA2, ACADL and HMGCS2. The clinical value of the five CMRGs was validated by TCGA database and HPA database. HCC patients were assigned to the high-risk and low-risk groups on the basis of median risk score calculated by the five CMRGs. We evaluated the signature in TCGA database and validated in ICGC database. The results revealed that the prognostic signature had good prognostic performance, even among different clinicopathological subgroups. The function analysis linked CMRGs with KEGG pathway, such as cell adhesion molecules, drug metabolism-cytochrome P450 and other related pathways. In addition, patients in the high-risk group exhibited characteristics of high TP53 mutation, high immune checkpoints expression and high immune cell infiltration. Furthermore, based on the prognostic signature, we identified 25 most significant small molecule drugs as potential drugs for HCC patients. Finally, a nomogram combined risk score and TNM stage was constructed. These results indicated our prognostic signature has an excellent prediction performance. This study is expected to provide a potential diagnostic and therapeutic strategies for HCC.

Inhibiting Src-mediated PARP1 tyrosine phosphorylation confers synthetic lethality to PARP1 inhibition in HCC

Hepatocellular carcinoma (HCC), a heterogeneous cancer with high mortality, is resistant to single targeted therapy; thus, combination therapy based on synthetic lethality is a promising therapeutic strategy for HCC. Poly (adenosine diphosphate [ADP]-ribose) polymerase 1 (PARP1) is the most recognized target for synthetic lethality; however, the therapeutic effect of PARP1 inhibition on HCC is disappointing. Therefore, exploring new synthetic lethal partners for the efficient manipulation of HCC is urgently required. In this study, we identified Src and PARP1 as novel synthetic lethal partners, and the combination therapy produced significant anti-tumor effects without causing obvious side effects. Mechanistically, Src interacted with PARP1 and phosphorylated PARP1 at the Y992 residue, which further mediated resistance to PARP1 inhibition. Overall, this study revealed that Src-mediated PARP1 phosphorylation induced HCC resistance to PARP1 inhibitors and indicated a therapeutic window of the Y992 phosphorylation of PARP1 for HCC patients. Moreover, synthetic lethal therapy by co-targeting PARP1 and Src have the potential to broaden the strategies for HCC and might benefit HCC patients with high Src activation and resistance to PARP1 inhibitors alone.

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.

A 3D View of Colorectal Cancer Models in Predicting Therapeutic Responses and Resistance

Although there have been many advances in recent years for the treatment of colorectal cancer (CRC), it still remains the third most common cause of cancer-related deaths worldwide. Many patients with late stage CRC display resistance to multiple different therapeutics. An important aspect in developing effective therapeutics for CRC patients is understanding the interactions that take place in the tumor microenvironment (TME), as it has been shown to contribute to drug resistance in vivo. Much research over the past 100 years has focused on 2D monolayer cultures or in vivo studies, however, the efficacy in translating these to the clinic is very low. More recent studies are turning towards developing an effective 3D model of CRC that is clinically relevant, that can recapitulate the TME in vitro and bridge the gap between 2D cultures and in vivo studies, with the aim of reducing the use of animal models in the future. This review summarises the advantages and limitations of different 3D CRC models. It emphasizes how different 3D models may be optimised to study cellular and extracellular interactions that take place in the TME of CRC in an effort to allow the development of more translatable effective treatment options for patients.