Genetic fusions favor tumorigenesis through degron loss in oncogenes

Comprehensive analysis of EphA2 in pan-cancer: A prognostic biomarker associated with cancer immunity

BACKGROUND

Several studies have reported a significant relationship between Ephrin receptor A2 (EphA2) and malignant progression in numerous cancers. However, there is a lack of comprehensive pan-cancer analysis on the prognostic value, mutation status, methylation landscape, and potential immunological function of EphA2.

METHOD

Using The Cancer Genome Atlas, Genotype Tissue Expression Database and GEO data, we analysed the differences in EphA2 expression between normal and tumour tissues and the effects of EphA2 on the prognosis of different tumours. Furthermore, using GSCALite, cBioPortal, TISDB, ULCLAN and TIMER 2.0 databases or platforms, we comprehensively analysed the potential oncogenic mechanisms or manifestations of EphA2 in 33 different tumour types, including tumour mutation status, DNA methylation status and immune cell infiltration. The correlation of EphA2 with immune checkpoints, tumour mutational burden, DNA microsatellite instability and DNA repair genes was also calculated. Finally, the effects of EphA2 inhibitors on the proliferation of human glioma and lung cancer cells were verified in cellular experiments.

RESULTS

EphA2 is differentially expressed in different tumours, and patients with overexpression have poorer overall survival. In addition, gene mutations, gene copy number variation and DNA/RNA methylation of EphA2 have been identified in various tumours. Moreover, EphA2 is positively associated with immune infiltration involving macrophages and CD8+ T cells. Further, EphA2 mRNA expression is significantly associated with immune checkpoint in various cancers, especially programmed death-ligand 1. Finally, the EphA2 inhibitor ALW-II-41-27 shows potent anti-tumour activity.

CONCLUSION

Our first pan-cancer study of EphA2 provides insight into the prognostic and immunological roles of EphA2 in different tumours, suggesting that EphA2 might be a potential biomarker for poor prognosis and immune infiltration in cancer.

Mechanistic patterns and clinical implications of oncogenic tyrosine kinase fusions in human cancers

Tyrosine kinase (TK) fusions are frequently found in cancers, either as initiating events or as a mechanism of resistance to targeted therapy. Partner genes and exons in most TK fusions are followed typical recurrent patterns, but the underlying mechanisms and clinical implications of these patterns are poorly understood. By developing Functionally Active Chromosomal Translocation Sequencing (FACTS), we discover that typical TK fusions involving ALK, ROS1, RET and NTRK1 are selected from pools of chromosomal rearrangements by two major determinants: active transcription of the fusion partner genes and protein stability. In contrast, atypical TK fusions that are rarely seen in patients showed reduced protein stability, decreased downstream oncogenic signaling, and were less responsive to inhibition. Consistently, patients with atypical TK fusions were associated with a reduced response to TKI therapies. Our findings highlight the principles of oncogenic TK fusion formation and selection in cancers, with clinical implications for guiding targeted therapy.

ELK3 destabilization by speckle-type POZ protein suppresses prostate cancer progression and docetaxel resistance

Accumulating evidence demonstrates that the activity regulation of ELK3, a member of the E26 transformation-specific oncogene family, is critical to regulating cell proliferation, migration, and survival in human cancers. However, the molecular mechanisms of how ELK3 induces chemoresistance in prostate cancer (PCa) have not been elucidated. In this study, we found that SPOP and ELK3 are an interacting partner. The interaction between SPOP and ELK3 resulted in increased ELK3 ubiquitination and destruction, assisted by checkpoint kinase-mediated ELK3 phosphorylation. Notably, the modulation of SPOP-mediated ELK3 protein stability affected the c-Fos-induced cell proliferation and invasion of PCa cells. The clinical involvement of the SPOP-ELK3 axis in PCa development was confirmed by an immunohistochemical assay on 123 PCa tissues, with an inverse correlation between increased ELK3 and decreased SPOP being present in ~80% of the specimens. This observation was supported by immunohistochemistry analysis using a SPOP-mutant PCa specimen. Finally, docetaxel treatment induced cell death by activating checkpoint kinase- and SPOP-mediated ELK3 degradation, while SPOP-depleted or SPOP-mutated PCa cells showed cell death resistance. Notably, this observation was correlated with the protein levels of ELK3. Taken together, our study reveals the precise mechanism of SPOP-mediated degradation of ELK3 and provides evidence that SPOP mutations contribute to docetaxel resistance in PCa.

Integrating Clinical Cancer and PTM Proteomics Data Identifies a Mechanism of ACK1 Kinase Activation

Beyond the most common oncogenes activated by mutation (mut-drivers), there likely exists a variety of low-frequency mut-drivers, each of which is a possible frontier for targeted therapy. To identify new and understudied mut-drivers, we developed a machine learning (ML) model that integrates curated clinical cancer data and posttranslational modification (PTM) proteomics databases. We applied the approach to 62,746 patient cancers spanning 84 cancer types and predicted 3,964 oncogenic mutations across 1,148 genes, many of which disrupt PTMs of known and unknown function. The list of putative mut-drivers includes established drivers and others with poorly understood roles in cancer. This ML model is available as a web application. As a case study, we focused the approach on nonreceptor tyrosine kinases (NRTK) and found a recurrent mutation in activated CDC42 kinase-1 (ACK1) that disrupts the Mig6 homology region (MHR) and ubiquitin-association (UBA) domains on the ACK1 C-terminus. By studying these domains in cultured cells, we found that disruption of the MHR domain helps activate the kinase while disruption of the UBA increases kinase stability by blocking its lysosomal degradation. This ACK1 mutation is analogous to lymphoma-associated mutations in its sister kinase, TNK1, which also disrupt a C-terminal inhibitory motif and UBA domain. This study establishes a mut-driver discovery tool for the research community and identifies a mechanism of ACK1 hyperactivation shared among ACK family kinases.

IMPLICATIONS

This research identifies a potentially targetable activating mutation in ACK1 and other possible oncogenic mutations, including PTM-disrupting mutations, for further study.

Mechanistic patterns and clinical implications of oncogenic tyrosine kinase fusions in human cancers

Tyrosine kinase (TK) fusions are frequently found in cancers, either as initiating events or as a mechanism of resistance to targeted therapy. Partner genes and exons in most TK fusions are typical and recurrent, but the underlying mechanisms and clinical implications of these patterns are poorly understood. Here, we investigated structures of > 8,000 kinase fusions and explore their generative mechanisms by applying newly developed experimental framework integrating high-throughput genome-wide gene fusion sequencing and clonal selection called Functionally Active Chromosomal Translocation Sequencing (FACTS). We discovered that typical oncogenic TK fusions recurrently seen in patients are selected from large pools of chromosomal rearrangements spontaneously occurring in cells based on two major determinants: active transcription of the fusion partner genes and protein stability. In contrast, atypical TK fusions that are rarely seen in patients showed reduced protein stability, decreased downstream oncogenic signaling, and were less responsive to inhibition. Consistently, patients with atypical TK fusions were associated with a reduced response to TKI therapies, as well as a shorter progression-free survival (PFS) and overall survival (OS) compared to patients with typical TK fusions. These findings highlight the principles of oncogenic TK fusion formation and their selection in cancers, with clinical implications for guiding targeted therapy.

DegronMD: Leveraging Evolutionary and Structural Features for Deciphering Protein-Targeted Degradation, Mutations, and Drug Response to Degrons

Protein-targeted degradation is an emerging and promising therapeutic approach. The specificity of degradation and the maintenance of cellular homeostasis are determined by the interactions between E3 ubiquitin ligase and degradation signals, known as degrons. The human genome encodes over 600 E3 ligases; however, only a small number of targeted degron instances have been identified so far. In this study, we introduced DegronMD, an open knowledgebase designed for the investigation of degrons, their associated dysfunctional events, and drug responses. We revealed that degrons are evolutionarily conserved and tend to occur near the sites of protein translational modifications, particularly in the regions of disordered structure and higher solvent accessibility. Through pattern recognition and machine learning techniques, we constructed the degrome landscape across the human proteome, yielding over 18,000 new degrons for targeted protein degradation. Furthermore, dysfunction of degrons disrupts the degradation process and leads to the abnormal accumulation of proteins; this process is associated with various types of human cancers. Based on the estimated phenotypic changes induced by somatic mutations, we systematically quantified and assessed the impact of mutations on degron function in pan-cancers; these results helped to build a global mutational map on human degrome, including 89,318 actionable mutations that may induce the dysfunction of degrons and disrupt protein degradation pathways. Multiomics integrative analysis unveiled over 400 drug resistance events associated with the mutations in functional degrons. DegronMD, accessible at https://bioinfo.uth.edu/degronmd, is a useful resource to explore the biological mechanisms, infer protein degradation, and assist with drug discovery and design on degrons.

Decoding Oncofusions: Unveiling Mechanisms, Clinical Impact, and Prospects for Personalized Cancer Therapies

Cancer-associated gene fusions, also known as oncofusions, have emerged as influential drivers of oncogenesis across a diverse range of cancer types. These genetic events occur via chromosomal translocations, deletions, and inversions, leading to the fusion of previously separate genes. Due to the drastic nature of these mutations, they often result in profound alterations of cellular behavior. The identification of oncofusions has revolutionized cancer research, with advancements in sequencing technologies facilitating the discovery of novel fusion events at an accelerated pace. Oncofusions exert their effects through the manipulation of critical cellular signaling pathways that regulate processes such as proliferation, differentiation, and survival. Extensive investigations have been conducted to understand the roles of oncofusions in solid tumors, leukemias, and lymphomas. Large-scale initiatives, including the Cancer Genome Atlas, have played a pivotal role in unraveling the landscape of oncofusions by characterizing a vast number of cancer samples across different tumor types. While validating the functional relevance of oncofusions remains a challenge, even non-driver mutations can hold significance in cancer treatment. Oncofusions have demonstrated potential value in the context of immunotherapy through the production of neoantigens. Their clinical importance has been observed in both treatment and diagnostic settings, with specific fusion events serving as therapeutic targets or diagnostic markers. However, despite the progress made, there is still considerable untapped potential within the field of oncofusions. Further research and validation efforts are necessary to understand their effects on a functional basis and to exploit the new targeted treatment avenues offered by oncofusions. Through further functional and clinical studies, oncofusions will enable the advancement of precision medicine and the drive towards more effective and specific treatments for cancer patients.

Characterization and evaluation of gene fusions as a measure of genetic instability and disease prognosis in prostate cancer

BACKGROUND

Prostate cancer (PCa) is one of the most prevalent cancers worldwide. The clinical manifestations and molecular characteristics of PCa are highly variable. Aggressive types require radical treatment, whereas indolent ones may be suitable for active surveillance or organ-preserving focal therapies. Patient stratification by clinical or pathological risk categories still lacks sufficient precision. Incorporating molecular biomarkers, such as transcriptome-wide expression signatures, improves patient stratification but so far excludes chromosomal rearrangements. In this study, we investigated gene fusions in PCa, characterized potential novel candidates, and explored their role as prognostic markers for PCa progression.

METHODS

We analyzed 630 patients in four cohorts with varying traits regarding sequencing protocols, sample conservation, and PCa risk group. The datasets included transcriptome-wide expression and matched clinical follow-up data to detect and characterize gene fusions in PCa. With the fusion calling software Arriba, we computationally predicted gene fusions. Following detection, we annotated the gene fusions using published databases for gene fusions in cancer. To relate the occurrence of gene fusions to Gleason Grading Groups and disease prognosis, we performed survival analyses using the Kaplan-Meier estimator, log-rank test, and Cox regression.

RESULTS

Our analyses identified two potential novel gene fusions, MBTTPS2,L0XNC01::SMS and AMACR::AMACR. These fusions were detected in all four studied cohorts, providing compelling evidence for the validity of these fusions and their relevance in PCa. We also found that the number of gene fusions detected in a patient sample was significantly associated with the time to biochemical recurrence in two of the four cohorts (log-rank test, p-value < 0.05 for both cohorts). This was also confirmed after adjusting the prognostic model for Gleason Grading Groups (Cox regression, p-values < 0.05).

CONCLUSIONS

Our gene fusion characterization workflow revealed two potential novel fusions specific for PCa. We found evidence that the number of gene fusions was associated with the prognosis of PCa. However, as the quantitative correlations were only moderately strong, further validation and assessment of clinical value is required before potential application.

In silico analysis of the profilaggrin sequence indicates alterations in the stability, degradation route, and intracellular protein fate in filaggrin null mutation carriers

Background: Loss of function mutation in FLG is the major genetic risk factor for atopic dermatitis (AD) and other allergic manifestations. Presently, little is known about the cellular turnover and stability of profilaggrin, the protein encoded by FLG. Since ubiquitination directly regulates the cellular fate of numerous proteins, their degradation and trafficking, this process could influence the concentration of filaggrin in the skin. Objective: To determine the elements mediating the interaction of profilaggrin with the ubiquitin-proteasome system (i.e., degron motifs and ubiquitination sites), the features responsible for its stability, and the effect of nonsense and frameshift mutations on profilaggrin turnover. Methods: The effect of inhibition of proteasome and deubiquitinases on the level and modifications of profilaggrin and processed products was assessed by immunoblotting. Wild-type profilaggrin sequence and its mutated variants were analysed in silico using the DEGRONOPEDIA and Clustal Omega tool. Results: Inhibition of proteasome and deubiquitinases stabilizes profilaggrin and its high molecular weight of presumably ubiquitinated derivatives. In silico analysis of the sequence determined that profilaggrin contains 18 known degron motifs as well as multiple canonical and non-canonical ubiquitination-prone residues. FLG mutations generate products with increased stability scores, altered usage of the ubiquitination marks, and the frequent appearance of novel degrons, including those promoting C-terminus-mediated degradation routes. Conclusion: The proteasome is involved in the turnover of profilaggrin, which contains multiple degrons and ubiquitination-prone residues. FLG mutations alter those key elements, affecting the degradation routes and the mutated products' stability.

Driving the degradation of oncofusion proteins for targeted cancer therapy

Oncofusion proteins drive the development of about 16.5% of human cancers {AuQ: Edit OK?}, functioning as the unique pathogenic factor in some cancers. The targeting of oncofusion proteins is an attractive strategy to treat malignant tumors. Recently, triggering the degradation of oncofusion proteins has been shown to hold great promise as a therapeutic strategy. Here, we review the recent findings on the mechanisms that maintain the high stability of oncofusion proteins. Then, we summarize strategies to target the degradation of oncofusion proteins through the ubiquitin-proteasome pathway, the autophagy-lysosomal pathway, and the caspase-dependent pathway. By examining oncofusion protein degradation in cancer, we not only gain better insight into the carcinogenic mechanisms that involve oncofusion proteins, but also raise the possibility of treating oncofusion-driven cancer.

Targeted protein posttranslational modifications by chemically induced proximity for cancer therapy

Post-translational modifications (PTMs) regulate all aspects of protein function. Therefore, upstream regulators of PTMs, such as kinases, acetyltransferases, or methyltransferases, are potential therapeutic targets for human diseases, including cancer. To date, multiple inhibitors and/or agonists of these PTM upstream regulators are in clinical use, while others are still in development. However, these upstream regulators control not only the PTMs of disease-related target proteins but also other disease-irrelevant substrate proteins. Thus, nontargeted perturbing activities may introduce unwanted off-target toxicity issues that limit the use of these drugs in successful clinical applications. Therefore, alternative drugs that solely regulate a specific PTM of the disease-relevant protein target may provide a more precise effect in treating disease with relatively low side effects. To this end, chemically induced proximity has recently emerged as a powerful research tool, and several chemical inducers of proximity (CIPs) have been used to target and regulate protein ubiquitination, phosphorylation, acetylation, and glycosylation. These CIPs have a high potential to be translated into clinical drugs and several examples such as PROTACs and MGDs are now in clinical trials. Hence, more CIPs need to be developed to cover all types of PTMs, such as methylation and palmitoylation, thus providing a full spectrum of tools to regulate protein PTM in basic research and also in clinical application for effective cancer treatment.

Systematic prediction of degrons and E3 ubiquitin ligase binding via deep learning

BACKGROUND

Degrons are short linear motifs, bound by E3 ubiquitin ligase to target protein substrates to be degraded by the ubiquitin-proteasome system. Mutations leading to deregulation of degron functionality disrupt control of protein abundance due to mistargeting of proteins destined for degradation and often result in pathologies. Targeting degrons by small molecules also emerges as an exciting drug design strategy to upregulate the expression of specific proteins. Despite their essential function and disease targetability, reliable identification of degrons remains a conundrum. Here, we developed a deep learning-based model named Degpred that predicts general degrons directly from protein sequences.

RESULTS

We showed that the BERT-based model performed well in predicting degrons singly from protein sequences. Then, we used the deep learning model Degpred to predict degrons proteome-widely. Degpred successfully captured typical degron-related sequence properties and predicted degrons beyond those from motif-based methods which use a handful of E3 motifs to match possible degrons. Furthermore, we calculated E3 motifs using predicted degrons on the substrates in our collected E3-substrate interaction dataset and constructed a regulatory network of protein degradation by assigning predicted degrons to specific E3s with calculated motifs. Critically, we experimentally verified that a predicted SPOP binding degron on CBX6 prompts CBX6 degradation and mediates the interaction with SPOP. We also showed that the protein degradation regulatory system is important in tumorigenesis by surveying degron-related mutations in TCGA.

CONCLUSIONS

Degpred provides an efficient tool to proteome-wide prediction of degrons and binding E3s singly from protein sequences. Degpred successfully captures typical degron-related sequence properties and predicts degrons beyond those from previously used motif-based methods, thus greatly expanding the degron landscape, which should advance the understanding of protein degradation, and allow exploration of uncharacterized alterations of proteins in diseases. To make it easier for readers to access collected and predicted datasets, we integrated these data into the website http://degron.phasep.pro/ .

Pancancer Analyses Reveal Genomics and Clinical Characteristics of the SETDB1 in Human Tumors

Background. Malignant tumor is one of the most common diseases that seriously affect human health. The prior literature has reported the biological function and potential therapeutic targets of SET domain bifurcated histone lysine methyltransferase 1 (SETDB1) as an oncogene. However, SETDB1 has rarely been analyzed from a pan-cancer perspective. Methods. Bioinformatics analysis tools and databases, including GeneCards, National Center for Biotechnology Information (NCBI), UniProt, Illustrator for Biological Sequences (IBS), Human Protein Atlas (HPA), GEPIA, TIMER2, Sangerbox 3.0, UALCAN, Kaplan-Meier (K-M) plotter, cBioPortal, Catalogue Of Somatic Mutations In Cancer (COSMIC), PhosphoSitePlus, TISIDB, STRING, and GeneMANIA, were utilized to clarify the biological functions and clinical significance of SETDB1 from a pan-cancer perspective. Results. In this study, the pan-cancer analysis demonstrated that SETDB1 showed significantly differential expression in most tumor tissues and paracancerous tissues, and SETDB1 expression was associated with clinicopathological features and clinical prognosis. We also found that SETDB1 mutations occurred in most tumors and were related to tumorigenesis. In addition, DNA methylation of SETDB1 primarily occurred at the cg10444928 site and was associated with prognosis in several human tumors. The predicted phosphorylation site of SETDB1 was Ser1006. We found that SETDB1 was significantly related to the specific tumor-infiltrating immune cell populations and expression of clinically targetable immune checkpoints and may be a promising immunotherapy target. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses also indicated that SETDB1 may function as crucial regulator in carcinogenesis of human cancers. Conclusions. SETDB1 is an important oncogene involved in tumorigenesis and tumor progression through different biological mechanisms. Furthermore, SETDB1 may be a potential therapeutic target for cancer treatment.

Quality control of protein complex composition

Eukaryotic cells possess hundreds of protein complexes that contain multiple subunits and must be formed at the correct time and place during development. Despite specific assembly pathways, cells frequently encounter complexes with missing or aberrant subunits that can disrupt important signaling events. Cells, therefore, employ several ubiquitin-dependent quality control pathways that can prevent, correct, or degrade flawed complexes. In this review, we will discuss our emerging understanding of such quality control of protein complex composition.