Identification of cancer driver genes based on hierarchical weak consensus model

Cancer is a complex gene mutation disease that derives from the accumulation of mutations during somatic cell evolution. With the advent of high-throughput technology, a large amount of omics data has been generated, and how to find cancer-related driver genes from a large number of omics data is a challenge. In the early stage, the researchers developed many frequency-based driver genes identification methods, but they could not identify driver genes with low mutation rates well. Afterwards, researchers developed network-based methods by fusing multi-omics data, but they rarely considered the connection among features. In this paper, after analyzing a large number of methods for integrating multi-omics data, a hierarchical weak consensus model for fusing multiple features is proposed according to the connection among features. By analyzing the connection between PPI network and co-mutation hypergraph network, this paper firstly proposes a new topological feature, called co-mutation clustering coefficient (CMCC). Then, a hierarchical weak consensus model is used to integrate CMCC, mRNA and miRNA differential expression scores, and a new driver genes identification method HWC is proposed. In this paper, the HWC method and current 7 state-of-the-art methods are compared on three types of cancers. The comparison results show that HWC has the best identification performance in statistical evaluation index, functional consistency and the partial area under ROC curve.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13755-024-00279-6.

Public neoantigens in breast cancer immunotherapy (Review)

Among women globally, breast cancer is the most prevalent cancer and the leading cause of cancer‑related death. Interestingly, though genetic mutations contribute to the disease, <15% of women diagnosed with breast cancer have a family history of the disease, suggesting a prevalence of sporadic genetic mutations in breast cancer development. In the rapidly rising field of cancer genomics, neoantigen‑based immunotherapy has come to the fore. The investigation of novel proteins arising from unique somatic mutations or neoantigens have opened a new pathway for both individualized and public cancer treatments. Because they are shared among individuals with similar genetic changes, public neoantigens provide an opportunity for 'off‑the‑shelf' anticancer therapies, potentially extending the benefits to a wider patient group. The present review aimed to highlight the role of shared or public neoantigens as therapeutic targets for patients with breast cancer, emphasizing common hotspot mutations of certain genes identified in breast cancer. The clinical utilization of public neoantigen‑based therapies for breast cancer treatment were also discussed.

SAP30, an oncogenic driver of progression, poor survival, and drug resistance in neuroblastoma

Neuroblastoma is the most devastating extracranial solid malignancy in children. Despite an intense treatment regimen, the prognosis for high-risk neuroblastoma patients remains poor, with less than 40% survival. So far, MYCN amplification status is considered the most prognostic factor but corresponds to only ∼25% of neuroblastoma patients. Therefore, it is essential to identify a better prognosis and therapy response marker in neuroblastoma patients. We applied robust bioinformatic data mining tools, such as weighted gene co-expression network analysis, cisTarget, and single-cell regulatory network inference and clustering on two neuroblastoma patient datasets. We found Sin3A-associated protein 30 (SAP30), a driver transcription factor positively associated with high-risk, progression, stage 4, and poor survival in neuroblastoma patient cohorts. Tumors of high-risk neuroblastoma patients and relapse-specific patient-derived xenografts showed higher SAP30 levels. The advanced pharmacogenomic analysis and CRISPR-Cas9 screens indicated that SAP30 essentiality is associated with cisplatin resistance and further showed higher levels in cisplatin-resistant patient-derived xenograft tumor cell lines. Silencing of SAP30 induced cell death in vitro and led to a reduced tumor burden and size in vivo. Altogether, these results indicate that SAP30 is a better prognostic and cisplatin-resistance marker and thus a potential drug target in high-risk neuroblastoma.

The Many Roads from Alternative Splicing to Cancer: Molecular Mechanisms Involving Driver Genes

Cancer driver genes are either oncogenes or tumour suppressor genes that are classically activated or inactivated, respectively, by driver mutations. Alternative splicing-which produces various mature mRNAs and, eventually, protein variants from a single gene-may also result in driving neoplastic transformation because of the different and often opposed functions of the variants of driver genes. The present review analyses the different alternative splicing events that result in driving neoplastic transformation, with an emphasis on their molecular mechanisms. To do this, we collected a list of 568 gene drivers of cancer and revised the literature to select those involved in the alternative splicing of other genes as well as those in which its pre-mRNA is subject to alternative splicing, with the result, in both cases, of producing an oncogenic isoform. Thirty-one genes fall into the first category, which includes splicing factors and components of the spliceosome and splicing regulators. In the second category, namely that comprising driver genes in which alternative splicing produces the oncogenic isoform, 168 genes were found. Then, we grouped them according to the molecular mechanisms responsible for alternative splicing yielding oncogenic isoforms, namely, mutations in cis splicing-determining elements, other causes involving non-mutated cis elements, changes in splicing factors, and epigenetic and chromatin-related changes. The data given in the present review substantiate the idea that aberrant splicing may regulate the activation of proto-oncogenes or inactivation of tumour suppressor genes and details on the mechanisms involved are given for more than 40 driver genes.

Multiomics approach towards characterization of tumor cell plasticity and its significance in precision and personalized medicine

Cellular plasticity refers to the ability of cells to change their identity or behavior, which can be advantageous in some cases (e.g., tissue regeneration) but detrimental in others (e.g., cancer metastasis). With a better understanding of cellular plasticity, the complexity of cancer cells, their heterogeneity, and their role in metastasis is being unraveled. The plasticity of the cells could also prove as a nemesis to their characterization. In this review, we have attempted to highlight the possibilities and benefits of using multiomics approach in characterizing the plastic nature of cancer cells. There is a need to integrate fragmented evidence at different levels of cellular organization (DNA, RNA, protein, metabolite, epigenetics, etc.) to facilitate the characterization of different forms of plasticity and cell types. We have discussed the role of cellular plasticity in generating intra-tumor heterogeneity. Different omics level evidence is being provided to highlight the variety of molecular determinants discovered using different techniques. Attempts have been made to integrate some of this information to provide a quantitative assessment and scoring of the plastic nature of the cells. However, there is a huge gap in our understanding of mechanisms that lead to the observed heterogeneity. Understanding of these mechanism(s) is necessary for finding targets for early detection and effective therapeutic interventions in metastasis. Targeting cellular plasticity is akin to neutralizing a moving target. Along with the advancements in precision and personalized medicine, these efforts may translate into better clinical outcomes for cancer patients, especially in metastatic stages.

Transient loss of Polycomb components induces an epigenetic cancer fate

Although cancer initiation and progression are generally associated with the accumulation of somatic mutations1,2, substantial epigenomic alterations underlie many aspects of tumorigenesis and cancer susceptibility3-6, suggesting that genetic mechanisms might not be the only drivers of malignant transformation7. However, whether purely non-genetic mechanisms are sufficient to initiate tumorigenesis irrespective of mutations has been unknown. Here, we show that a transient perturbation of transcriptional silencing mediated by Polycomb group proteins is sufficient to induce an irreversible switch to a cancer cell fate in Drosophila. This is linked to the irreversible derepression of genes that can drive tumorigenesis, including members of the JAK-STAT signalling pathway and zfh1, the fly homologue of the ZEB1 oncogene, whose aberrant activation is required for Polycomb perturbation-induced tumorigenesis. These data show that a reversible depletion of Polycomb proteins can induce cancer in the absence of driver mutations, suggesting that tumours can emerge through epigenetic dysregulation leading to inheritance of altered cell fates.

Autophagy-driven regulation of cisplatin response in human cancers: Exploring molecular and cell death dynamics

Despite the challenges posed by drug resistance and side effects, chemotherapy remains a pivotal strategy in cancer treatment. A key issue in this context is macroautophagy (commonly known as autophagy), a dysregulated cell death mechanism often observed during chemotherapy. Autophagy plays a cytoprotective role by maintaining cellular homeostasis and recycling organelles, and emerging evidence points to its significant role in promoting cancer progression. Cisplatin, a DNA-intercalating agent known for inducing cell death and cell cycle arrest, often encounters resistance in chemotherapy treatments. Recent studies have shown that autophagy can contribute to cisplatin resistance or insensitivity in tumor cells through various mechanisms. This resistance can be mediated by protective autophagy, which suppresses apoptosis. Additionally, autophagy-related changes in tumor cell metastasis, particularly the induction of Epithelial-Mesenchymal Transition (EMT), can also lead to cisplatin resistance. Nevertheless, pharmacological strategies targeting the regulation of autophagy and apoptosis offer promising avenues to enhance cisplatin sensitivity in cancer therapy. Notably, numerous non-coding RNAs have been identified as regulators of autophagy in the context of cisplatin chemotherapy. Thus, therapeutic targeting of autophagy or its associated pathways holds potential for restoring cisplatin sensitivity, highlighting an important direction for future clinical research.

In vivo assessment of the toxic impact of exposure to magnetic iron oxide nanoparticles (IONPs) using Drosophila melanogaster

Iron oxide nanoparticles (IONPs) have useful properties, such as strong magnetism and compatibility with living organisms which is preferable for medical applications such as drug delivery and imaging. However, increasing use of these materials, especially in medicine, has raised concerns regarding potential risks to human health. In this study, IONPs were coated with silicon dioxide (SiO2), citric acid (CA), and polyethylenimine (PEI) to enhance their dispersion and biocompatibility. Both coated and uncoated IONPs were assessed for genotoxic effects on Drosophila melanogaster. Results showed that uncoated IONPs induced genotoxic effects, including mutations and recombinations, while the coated IONPs demonstrated reduced or negligible genotoxicity. Additionally, bioinformatic analyses highlighted potential implications of induced recombination in various cancer types, underscoring the importance of understanding nanoparticle-induced genomic instability. This study highlights the importance of nanoparticle coatings in reducing potential genotoxic effects and emphasizes the necessity for comprehensive toxicity assessments in nanomaterial research.

Are we getting closer to a successful neoantigen cancer vaccine?

Although significant advances in immunotherapy have revolutionized the treatment of many cancer types over the past decade, the field of vaccine therapy, an important component of cancer immunotherapy, despite decades-long intense efforts, is still transmitting signals of promises and awaiting strong data on efficacy to proceed with regulatory approval. The field of cancer vaccines faces standard challenges, such as tumor-induced immunosuppression, immune response in inhibitory tumor microenvironment (TME), intratumor heterogeneity (ITH), permanently evolving cancer mutational landscape leading to neoantigens, and less known obstacles: neoantigen gain/loss upon immunotherapy, the timing and speed of appearance of neoantigens and responding T cell clonotypes and possible involvement of immune interference/heterologous immunity, in the complex interplay between evolving tumor epitopes and the immune system. In this review, we discuss some key issues related to challenges hampering the development of cancer vaccines, along with the current approaches focusing on neoantigens. We summarize currently well-known ideas/rationales, thus revealing the need for alternative vaccine approaches. Such a discussion should stimulate vaccine researchers to apply out-of-box, unconventional thinking in search of new avenues to deal with critical, often yet unaddressed challenges on the road to a new generation of therapeutics and vaccines.

Genetic Signature of Human Pancreatic Cancer and Personalized Targeting

Pancreatic cancer is a highly lethal disease with a 5-year survival rate of around 11-12%. Surgery, being the treatment of choice, is only possible in 20% of symptomatic patients. The main reason is that when it becomes symptomatic, IT IS the tumor is usually locally advanced and/or has metastasized to distant organs; thus, early diagnosis is infrequent. The lack of specific early symptoms is an important cause of late diagnosis. Unfortunately, diagnostic tumor markers become positive at a late stage, and there is a lack of early-stage markers. Surgical and non-surgical cases are treated with neoadjuvant and/or adjuvant chemotherapy, and the results are usually poor. However, personalized targeted therapy directed against tumor drivers may improve this situation. Until recently, many pancreatic tumor driver genes/proteins were considered untargetable. Chemical and physical characteristics of mutated KRAS are a formidable challenge to overcome. This situation is slowly changing. For the first time, there are candidate drugs that can target the main driver gene of pancreatic cancer: KRAS. Indeed, KRAS inhibition has been clinically achieved in lung cancer and, at the pre-clinical level, in pancreatic cancer as well. This will probably change the very poor outlook for this disease. This paper reviews the genetic characteristics of sporadic and hereditary predisposition to pancreatic cancer and the possibilities of a personalized treatment according to the genetic signature.

Cancer organoids: A platform in basic and translational research

An accumulation of previous work has established organoids as good preclinical models of human tumors, facilitating translation from basic research to clinical practice. They are changing the paradigm of preclinical cancer research because they can recapitulate the heterogeneity and pathophysiology of human cancers and more closely approximate the complex tissue environment and structure found in clinical tumors than in vitro cell lines and animal models. However, the potential applications of cancer organoids remain to be comprehensively summarized. In the review, we firstly describe what is currently known about cancer organoid culture and then discuss in depth the basic mechanisms, including tumorigenesis and tumor metastasis, and describe recent advances in patient-derived tumor organoids (PDOs) for drug screening and immunological studies. Finally, the present challenges faced by organoid technology in clinical practice and its prospects are discussed. This review highlights that organoids may offer a novel therapeutic strategy for cancer research.

Patterns of metastatic spread and tumor burden in unselected cancer patients using PET imaging: Implications for the oligometastatic spectrum theory

Introduction and background

Metastatic disease has been proposed as a continuum, with no clear cut-off between oligometastatic and polymetastatic disease. This study aims to quantify tumor burden and patterns of spread in unselected metastatic cancer patients referred for PET-based staging, response assessment of restaging.

Materials and methods

All oncological fluorodeoxyglucose (FDG-) and prostate-specific membrane antigen (PSMA-) positron emission tomography (PET) scans conducted at a single academic center in 2020 were analyzed. Imaging reports of all patients with metastatic disease were reviewed and assessed.

Results

For this study, 7,000 PET scans were screened. One third of PET scans (n = 1,754; 33 %) from 1,155 unique patients showed presence of metastatic disease from solid malignancies, of which 601 (52 %) and 554 (48 %) were classified as oligometastatic (maximum 5 metastases) and polymetastatic (>5 metastases), respectively. Lung and pleural cancer, skin cancer, and breast cancer were the most common primary tumor histologies with 132 (23.8 %), 88 (15.9 %), and 72 (13.0 %) cases, respectively. Analysis of the number of distant metastases showed a strong bimodal distribution of the metastatic burden with 26 % of patients having one solitary metastasis and 43 % of patients harboring >10 metastases. Yet, despite 43 % of polymetastatic patients having >10 distant metastases, their pattern of distribution was restricted to one or two organs in about two thirds of patients, and there was no association between the number of distant metastases and the number of involved organs.

Conclusion

The majority of metastatic cancer patients are characterized by either a solitary metastasis or a high tumor burden with >10 metastases, the latter was often associated with affecting a limited number of organs. These findings support both the spectrum theory of metastasis and the seed and soil hypothesis and can support in designing the next generation of clinical trials in the field of oligometastatic disease.

Drivers of cancer metastasis - Arise early and remain present

Cancer and its metastases arise from mutations of genes, drivers that promote a tumor's growth. Analyses of driver events provide insights into cancer cell history and may lead to a better understanding of oncogenesis. We reviewed 27 metastatic research studies, including pan-cancer studies, individual cancer studies, and phylogenetic analyses, and summarized our current knowledge of metastatic drivers. All of the analyzed studies had a high level of consistency of driver mutations between primary tumors and metastasis, indicating that most drivers appear early in cancer progression and are maintained in metastatic cells. Additionally, we reviewed data from around 50,000 metastatic cancer patients and compiled a list of genes altered in metastatic lesions. We performed Gene Ontology analysis and confirmed that the most significantly enriched processes in metastatic lesions were the epigenetic regulation of gene expression, signal transduction, cell cycle, programmed cell death, DNA damage, hypoxia and EMT. In this review, we explore the most recent discoveries regarding genetic factors in the advancement of cancer, specifically those that drive metastasis.

A functional identification platform reveals frequent, spontaneous neoantigen-specific T cell responses in patients with cancer

The clinical impact of tumor-specific neoantigens as both immunotherapeutic targets and biomarkers has been impeded by the lack of efficient methods for their identification and validation from routine samples. We have developed a platform that combines bioinformatic analysis of tumor exomes and transcriptional data with functional testing of autologous peripheral blood mononuclear cells (PBMCs) to simultaneously identify and validate neoantigens recognized by naturally primed CD4+ and CD8+ T cell responses across a range of tumor types and mutational burdens. The method features a human leukocyte antigen (HLA)-agnostic bioinformatic algorithm that prioritizes mutations recognized by patient PBMCs at a greater than 40% positive predictive value followed by a short-term in vitro functional assay, which allows interrogation of 50 to 75 expressed mutations from a single 50-ml blood sample. Neoantigens validated by this method include both driver and passenger mutations, and this method identified neoantigens that would not have been otherwise detected using an in silico prediction approach. These findings reveal an efficient approach to systematically validate clinically actionable neoantigens and the T cell receptors that recognize them and demonstrate that patients across a variety of human cancers have a diverse repertoire of neoantigen-specific T cells.

Interpreting the effect of mutations to protein binding sites from large-scale genomic screens

Predicting the functionality of missense mutations is extremely difficult. Large-scale genomic screens are commonly performed to identify mutational correlates or drivers of disease and treatment resistance, but interpretation of how these mutations impact protein function is limited. One such consequence of mutations to a protein is to impact its ability to bind and interact with partners or small molecules such as ATP, thereby modulating its function. Multiple methods exist for predicting the impact of a single mutation on protein-protein binding energy, but it is difficult in the context of a genomic screen to understand if these mutations with large impacts on binding are more common than statistically expected. We present a methodology for taking mutational data from large-scale genomic screens and generating functional and statistical insights into their role in the binding of proteins both with each other and their small molecule ligands. This allows a quantitative and statistical analysis to determine whether mutations impacting protein binding or ligand interactions are occurring more or less frequently than expected by chance. We achieve this by calculating the potential impact of any possible mutation and comparing an expected distribution to the observed mutations. This method is applied to examples demonstrating its ability to interpret mutations involved in protein-protein binding, protein-DNA interactions, and the evolution of therapeutic resistance.

Leveraging synthetic lethality to uncover potential therapeutic target in gastric cancer

Since trastuzumab was approved in 2012 for the first-line treatment of gastric cancer (GC), no significant advancement in GC targeted therapies has occurred. Synthetic lethality refers to the concept that simultaneous dysfunction of a pair of genes results in a lethal effect on cells, while the loss of an individual gene does not cause this effect. Through exploiting synthetic lethality, novel targeted therapies can be developed for the individualized treatment of GC. In this study, we proposed a computational strategy named Gastric cancer Specific Synthetic Lethality inference (GSSL) to identify synthetic lethal interactions in GC. GSSL analysis was used to infer probable synthetic lethality in GC using four accessible clinical datasets. In addition, prediction results were confirmed by experiments. GSSL analysis identified a total of 34 candidate synthetic lethal pairs, which included 33 unique targets. Among the synthetic lethal gene pairs, TP53-CHEK1 was selected for further experimental validation. Both computational and experimental results indicated that inhibiting CHEK1 could be a potential therapeutic strategy for GC patients with TP53 mutation. Meanwhile, in vitro experimental validation of two novel synthetic lethal pairs TP53-AURKB and ARID1A-EP300 further proved the universality and reliability of GSSL. Collectively, GSSL has been shown to be a reliable and feasible method for comprehensive analysis of inferring synthetic lethal interactions of GC, which may offer novel insight into the precision medicine and individualized treatment of GC.

Distinguishing between driver and passenger mechanisms of aging

Understanding why we age is a long-standing question, and many mechanistic theories of aging have been proposed. Owing to limitations in studying the aging process, including a lack of adequate quantitative measurements, its mechanistic basis remains a subject of debate. Here, I explore theories of aging from the perspective of causal relationships. Many aging-related changes have been observed and touted as drivers of aging, including molecular changes in the genome, telomeres, mitochondria, epigenome and proteins and cellular changes affecting stem cells, the immune system and senescent cell buildup. Determining which changes are drivers and not passengers of aging remains a challenge, however, and I discuss how animal models and human genetic studies have been used empirically to infer causality. Overall, our understanding of the drivers of human aging is still inadequate; yet with a global aging population, elucidating the causes of aging has the potential to revolutionize biomedical research.

Direct K-Ras Inhibitors to Treat Cancers: Progress, New Insights, and Approaches to Treat Resistance

Here we discuss approaches to K-Ras inhibition and drug resistance scenarios. A breakthrough offered a covalent drug against K-RasG12C. Subsequent innovations harnessed same-allele drug combinations, as well as cotargeting K-RasG12C with a companion drug to upstream regulators or downstream kinases. However, primary, adaptive, and acquired resistance inevitably emerge. The preexisting mutation load can explain how even exceedingly rare mutations with unobservable effects can promote drug resistance, seeding growth of insensitive cell clones, and proliferation. Statistics confirm the expectation that most resistance-related mutations are in cis, pointing to the high probability of cooperative, same-allele effects. In addition to targeted Ras inhibitors and drug combinations, bifunctional molecules and innovative tri-complex inhibitors to target Ras mutants are also under development. Since the identities and potential contributions of preexisting and evolving mutations are unknown, selecting a pharmacologic combination is taxing. Collectively, our broad review outlines considerations and provides new insights into pharmacology and resistance.

Enhancing the invasive traits of breast cancers by CYP1B1 via regulation of p53 to promote uPAR expression

Human cytochrome P450 1B1 (CYP1B1) catalyzes estrogen metabolism to produce metabolites that promote the progression of breast cancer. Since the invasive properties of cancer cells cause cancer relapse, which dramatically reduces patient survival, we investigated the new pro-invasive mechanism involving CYP1B1 in breast cancer. Exploring clinical data from invasive breast cancer patients revealed that CYP1B1 exhibits a potential correlation with urokinase-type plasminogen activator receptor (uPAR). Interestingly, uPAR mRNA expression was elevated in invasive breast cancer patients carrying TP53 genes with driver mutations, and our results showed that CYP1B1 activates the uPAR pathway following regulation of p53 according to its mutant status. CYP1B1 suppressed wild-type (WT) p53 whereas it induced the oncogenic gain-of-function mutant p53R280K, not only via transcriptional regulation but also the protein stabilization and activation following phosphorylation on Ser15 residue of p53R280K. Intriguingly, results from CYP1B1 polymorphic gene study and 4-hydroxyestradiol (4-OHE2) treatment showed that CYP1B1 regulates p53s and uPAR through its enzymatic activity. Furthermore, effects of DMBA and TMS on uPAR expression disappeared in HCT116p53-/- cells, indicating that p53 is critical for uPAR induction by CYP1B1. Collectively, our results demonstrate that CYP1B1 may reduce the relapse-free survival rate of breast cancer patients by inducing invasive traits in cancer cells via p53 regulation based on the mutation status of TP53 genes and further activation of the uPAR pathway. The elucidation of the previously unknown molecular mechanism of CYP1B1 may provide evidence for the development of effective anti-cancer therapeutic strategies that target the progression of cancer invasion.

The successive emergence of ERVL-MaLRs in primates

Although the ERVL-mammalian-apparent LTR retrotransposons (MaLRs) are the fourth largest family of transposable elements in the human genome, their evolutionary history and relationship have not been thoroughly studied. In this study, through RepeatMasker annotations of some representative species and construction of phylogenetic tree by sequence similarity, all primate-specific MaLR members are found to descend from MLT1A1 retrotransposon. Comparative genomic analysis, transposition-in-transposition inference, and sequence feature comparisons consistently show that each MaLR member evolved from its predecessor successively and had a limited activity period during primate evolution. Accordingly, a novel MaLR member was discovered as successor of MSTB1 in Tarsiiformes. At last, the identification of candidate precursor and intermediate THE1A elements provides further evidence for the previously proposed arms race model between ZNF430/ZNF100 and THE1B/THE1A. Taken together, this study sheds light on the evolutionary history of MaLRs and can serve as a foundation for future research on their interactions with zinc finger genes, gene regulation, and human health implications.

Identifying the personalized driver gene sets maximally contributing to abnormality of transcriptome phenotype in glioblastoma multiforme individuals

High heterogeneity in genome and phenotype of cancer populations made it difficult to apply population-based common driver genes to the diagnosis and treatment of cancer individuals. Characterizing and identifying the personalized driver mechanism for glioblastoma multiforme (GBM) individuals were pivotal for the realization of precision medicine. We proposed an integrative method to identify the personalized driver gene sets by integrating the profiles of gene expression and genetic alterations in cancer individuals. This method coupled genetic algorithm and random walk to identify the optimal gene sets that could explain abnormality of transcriptome phenotype to the maximum extent. The personalized driver gene sets were identified for 99 GBM individuals using our method. We found that genomic alterations in between one and seven driver genes could maximally and cumulatively explain the dysfunction of cancer hallmarks across GBM individuals. The driver gene sets were distinct even in GBM individuals with significantly similar transcriptomic phenotypes. Our method identified MCM4 with rare genetic alterations as previously unknown oncogenic genes, the high expression of which were significantly associated with poor GBM prognosis. The functional experiments confirmed that knockdown of MCM4 could significantly inhibit proliferation, invasion, migration, and clone formation of the GBM cell lines U251 and U118MG, and overexpression of MCM4 significantly promoted the proliferation, invasion, migration, and clone formation of the GBM cell line U87MG. Our method could dissect the personalized driver genetic alteration sets that are pivotal for developing targeted therapy strategies and precision medicine. Our method could be extended to identify key drivers from other levels and could be applied to more cancer types.

Integration of exome-seq and mRNA-seq using DawnRank, identified genes involved in innate immunity as drivers of breast cancer in the Indian cohort

Genetic heterogeneity influences the prognosis and therapy of breast cancer. The cause of disease progression varies and can be addressed individually. To identify the mutations and their impact on disease progression at an individual level, we sequenced exome and transcriptome from matched normal-tumor samples. We utilised DawnRank to prioritise driver genes and identify specific mutations in Indian patients. Mutations in the C3 and HLA genes were identified as drivers of disease progression, indicating the involvement of the innate immune system. We performed immune profiling on 16 matched normal/tumor samples using CIBERSORTx. We identified CD8+ve T cells, M2 macrophages, and neutrophils to be enriched in luminal A and T cells CD4+naïve, natural killer (NK) cells activated, T follicular helper (Tfh) cells, dendritic cells activated, and neutrophils in triple-negative breast cancer (TNBC) subtypes. Weighted gene co-expression network analysis (WGCNA) revealed activation of T cell-mediated response in ER positive samples and Interleukin and Interferons in ER negative samples. WGCNA analysis also identified unique pathways for each individual, suggesting that rare mutations/expression signatures can be used to design personalised treatment.

The ErbB Signaling Network and Its Potential Role in Endometrial Cancer

Endometrial cancer (EC) is the second most common malignancy of the female reproductive system worldwide. The updated EC classification emphasizes the significant role of various signaling pathways such as PIK3CA-PIK3R1-PTEN and RTK/RAS/β-catenin in EC pathogenesis. Some of these pathways are part of the EGF system signaling network, which becomes hyperactivated by various mechanisms and participates in cancer pathogenesis. In EC, the expression of ErbB receptors is significantly different, compared with the premenopausal and postmenopausal endometrium, mainly because of the increased transcriptional activity of ErbB encoding genes in EC cells. Moreover, there are some differences in ErbB-2 receptor profile among EC subgroups that could be explained by the alterations in pathophysiology and clinical behavior of various EC histologic subtypes. The fact that ErbB-2 receptor expression is more common in aggressive EC histologic subtypes (papillary serous and clear cell) could indicate a future role of ErbB-targeted therapies in well-defined EC subgroups with overexpression of ErbB receptors.

InDEP: an interpretable machine learning approach to predict cancer driver genes from multi-omics data

Cancer driver genes are critical in driving tumor cell growth, and precisely identifying these genes is crucial in advancing our understanding of cancer pathogenesis and developing targeted cancer drugs. Despite the current methods for discovering cancer driver genes that mainly rely on integrating multi-omics data, many existing models are overly complex, and it is difficult to interpret the results accurately. This study aims to address this issue by introducing InDEP, an interpretable machine learning framework based on cascade forests. InDEP is designed with easy-to-interpret features, cascade forests based on decision trees and a KernelSHAP module that enables fine-grained post-hoc interpretation. Integrating multi-omics data, InDEP can identify essential features of classified driver genes at both the gene and cancer-type levels. The framework accurately identifies driver genes, discovers new patterns that make genes as driver genes and refines the cancer driver gene catalog. In comparison with state-of-the-art methods, InDEP proved to be more accurate on the test set and identified reliable candidate driver genes. Mutational features were the primary drivers for InDEP's identifying driver genes, with other omics features also contributing. At the gene level, the framework concluded that substitution-type mutations were the main reason most genes were identified as driver genes. InDEP's ability to identify reliable candidate driver genes opens up new avenues for precision oncology and discovering new biomedical knowledge. This framework can help advance cancer research by providing an interpretable method for identifying cancer driver genes and their contribution to cancer pathogenesis, facilitating the development of targeted cancer drugs.

Potential immunosuppressive clonal hematopoietic mutations in tumor infiltrating immune cells in breast invasive carcinoma

A hallmark of cancer is a tumor cell's ability to evade immune destruction. Somatic mutations in tumor cells that prevent immune destruction have been extensively studied. However, somatic mutations in tumor infiltrating immune (TII) cells, to our knowledge, have not been previously studied. Understandably so since normal hematopoiesis prevents the accumulation of somatic mutations in immune cells. However, clonal hematopoiesis does result in the accumulation of somatic mutations in immune cells. These mutations cannot "drive" tumor growth, however, they may "facilitate" it by inhibiting an effective anti-tumor immune response. To identify potential immunosuppressive clonal hematopoietic (CH) mutations in TII cells, we analyzed exome and RNA sequencing data from matched tumor and normal blood samples, and single-cell RNA sequencing data, from breast cancer patients. We selected mutations that were somatic, present in TII cells, clonally expanded, potentially pathogenic, expressed in TII cells, unlikely to be a passenger mutation, and in immune response associated genes. We identified eight potential immunosuppressive CH mutations in TII cells. This work is a first step towards determining if immunosuppressive CH mutations in TII cells can affect the progression of solid tumors. Subsequent experimental confirmation could represent a new paradigm in the etiology of cancer.

Identifying cancer driver genes using a two-stage random walk with restart on a gene interaction network

Cancer development and progression are significantly influenced by cancer driver genes. Understanding cancer driver genes and their mechanisms of action is essential for developing effective cancer treatments. As a result, identifying driver genes is important for drug development, cancer diagnosis, and treatment. Here, we present an algorithm to discover driver genes based on the two-stage random walk with restart (RWR), and the modified method for calculating the transition probability matrix in random walk algorithm. First, we performed the first stage of RWR on the whole gene interaction network, in which we employ a new method for calculating the transition probability matrix and extracted the subnetwork based on nodes that had a high correlation with the seed nodes. The subnetwork was then applied to the second stage of RWR and the nodes were re-ranked in the subnetwork. Our approach outperformed existing methods in identifying driver genes. The outcome of the effect of three gene interaction networks, two rounds of random walk, and the seed nodes' sensitivity were all compared at the same time. In addition, we identified several potential driver genes, some of which are involved in driving cancer development. Overall, our method is efficient in various cancer types, significantly outperforms existing methods, and can identify possible driver genes.

Optimal cancer evasion in a dynamic immune microenvironment generates diverse post-escape tumor antigenicity profiles

The failure of cancer treatments, including immunotherapy, continues to be a major obstacle in preventing durable remission. This failure often results from tumor evolution, both genotypic and phenotypic, away from sensitive cell states. Here, we propose a mathematical framework for studying the dynamics of adaptive immune evasion that tracks the number of tumor-associated antigens available for immune targeting. We solve for the unique optimal cancer evasion strategy using stochastic dynamic programming and demonstrate that this policy results in increased cancer evasion rates compared to a passive, fixed strategy. Our foundational model relates the likelihood and temporal dynamics of cancer evasion to features of the immune microenvironment, where tumor immunogenicity reflects a balance between cancer adaptation and host recognition. In contrast with a passive strategy, optimally adaptive evaders navigating varying selective environments result in substantially heterogeneous post-escape tumor antigenicity, giving rise to immunogenically hot and cold tumors.

Widespread perturbation of ETS factor binding sites in cancer

Although >90% of somatic mutations reside in non-coding regions, few have been reported as cancer drivers. To predict driver non-coding variants (NCVs), we present a transcription factor (TF)-aware burden test based on a model of coherent TF function in promoters. We apply this test to NCVs from the Pan-Cancer Analysis of Whole Genomes cohort and predict 2555 driver NCVs in the promoters of 813 genes across 20 cancer types. These genes are enriched in cancer-related gene ontologies, essential genes, and genes associated with cancer prognosis. We find that 765 candidate driver NCVs alter transcriptional activity, 510 lead to differential binding of TF-cofactor regulatory complexes, and that they primarily impact the binding of ETS factors. Finally, we show that different NCVs within a promoter often affect transcriptional activity through shared mechanisms. Our integrated computational and experimental approach shows that cancer NCVs are widespread and that ETS factors are commonly disrupted.

Cellular and molecular features related to exceptional therapy response and extreme long-term survival in glioblastoma

Glioblastoma Multiforme (GBM) remains the most common malignant primary brain tumor with a dismal prognosis that rarely exceeds beyond 2 years despite extensive therapy, which consists of maximal safe surgical resection, radiotherapy, and/or chemotherapy. Recently, it has become clear that GBM is not one homogeneous entity and that both intra-and intertumoral heterogeneity contributes significantly to differences in tumoral behavior which may consequently be responsible for differences in survival. Strikingly and in spite of its dismal prognosis, small fractions of GBM patients seem to display extremely long survival, defined as surviving over 10 years after diagnosis, compared to the large majority of patients. Although the underlying mechanisms for this peculiarity remain largely unknown, emerging data suggest that still poorly characterized both cellular and molecular factors of the tumor microenvironment and their interplay probably play an important role. We hereby give an extensive overview of what is yet known about these cellular and molecular features shaping extreme long survival in GBM.

DNA damage response signaling: A common link between cancer and cardiovascular diseases

DNA damage response (DDR) signaling ensures genomic and proteomic homeostasis to maintain a healthy genome. Dysregulation either in the form of down- or upregulation in the DDR pathways correlates with various pathophysiological states, including cancer and cardiovascular diseases (CVDs). Impaired DDR is studied as a signature mechanism for cancer; however, it also plays a role in ischemia-reperfusion injury (IRI), inflammation, cardiovascular function, and aging, demonstrating a complex and intriguing relationship between cancer and pathophysiology of CVDs. Accordingly, there are increasing number of reports indicating higher incidences of CVDs in cancer patients. In the present review, we thoroughly discuss (1) different DDR pathways, (2) the functional cross talk among different DDR mechanisms, (3) the role of DDR in cancer, (4) the commonalities and differences of DDR between cancer and CVDs, (5) the role of DDR in pathophysiology of CVDs, (6) interventional strategies for targeting genomic instability in CVDs, and (7) future perspective.

Non-Association of Driver Alterations in PTEN with Differential Gene Expression and Gene Methylation in IDH1 Wildtype Glioblastomas

During oncogenesis, alterations in driver genes called driver alterations (DAs) modulate the transcriptome, methylome and proteome through oncogenic signaling pathways. These modulatory effects of any DA may be analyzed by examining differentially expressed mRNAs (DEMs), differentially methylated genes (DMGs) and differentially expressed proteins (DEPs) between tumor samples with and without that DA. We aimed to analyze these modulations with 12 common driver genes in Isocitrate Dehydrogenase 1 wildtype glioblastomas (IDH1-W-GBs). Using Cbioportal, groups of tumor samples with and without DAs in these 12 genes were generated from the IDH1-W-GBs available from "The Cancer Genomics Atlas Firehose Legacy Study Group" (TCGA-FL-SG) on Glioblastomas (GBs). For all 12 genes, samples with and without DAs were compared for DEMs, DMGs and DEPs. We found that DAs in PTEN were unassociated with any DEM or DMG in contrast to DAs in all other drivers, which were associated with several DEMs and DMGs. This contrasting PTEN-related property of being unassociated with differential gene expression or methylation in IDH1-W-GBs was unaffected by concurrent DAs in other common drivers or by the types of DAs affecting PTEN. From the lists of DEMs and DMGs associated with some common drivers other than PTEN, enriched gene ontology terms and insights into the co-regulatory effects of these drivers on the transcriptome were obtained. The findings from this study can improve our understanding of the molecular mechanisms underlying gliomagenesis with potential therapeutic benefits.

An evolution-based machine learning to identify cancer type-specific driver mutations

Identifying cancer type-specific driver mutations is crucial for illuminating distinct pathologic mechanisms across various tumors and providing opportunities of patient-specific treatment. However, although many computational methods were developed to predict driver mutations in a type-specific manner, the methods still have room to improve. Here, we devise a novel feature based on sequence co-evolution analysis to identify cancer type-specific driver mutations and construct a machine learning (ML) model with state-of-the-art performance. Specifically, relying on 28 000 tumor samples across 66 cancer types, our ML framework outperformed current leading methods of detecting cancer driver mutations. Interestingly, the cancer mutations identified by sequence co-evolution feature are frequently observed in interfaces mediating tissue-specific protein-protein interactions that are known to associate with shaping tissue-specific oncogenesis. Moreover, we provide pre-calculated potential oncogenicity on available human proteins with prediction scores of all possible residue alterations through user-friendly website (http://sbi.postech.ac.kr/w/cancerCE). This work will facilitate the identification of cancer type-specific driver mutations in newly sequenced tumor samples.

The Oncogenic FOXL2 C134W Mutation Is a Key Driver of Granulosa Cell Tumors

Adult-type granulosa cell tumors (AGCT) are the most common type of malignant ovarian sex cord-stromal tumors. Most AGCTs carry the somatic variant c.402C>G (p.C134W) affecting the transcription factor FOXL2. Germline dominant variants in FOXL2 are responsible for blepharophimosis syndrome, which is characterized by underdevelopment of the eyelid. In this work, we generated a mouse model harboring the C134W variant of FOXL2 to evaluate in vivo the poorly understood oncogenic role of FOXL2. The mutation was dominant regarding eyelid hypoplasia, reminiscent of blepharophimosis syndrome. Interestingly, Foxl2+/C134W female mice had reduced fertility and developed AGCTs through a progression from abnormal ovaries with aberrant granulosa cells to ovaries with stromal hyperplasia and atypia and on to tumors in adut mice. The genes dysregulated in mouse AGCTs exhibited the hallmarks of cancer and were consistent with a gain-of-function of the mutated allele affecting TGFβ signaling. A comparison of these data with previous results on human AGCTs indicated similar deregulated pathways. Finally, a mutational analysis of mouse AGCT transcriptomic data suggested the absence of additional driver mutations apart from FOXL2-C134W. These results provide a clear in vivo example in which a single mutational hit triggers tumor development associated with profound transcriptomic alterations.

SIGNIFICANCE

A newly generated mouse model carrying a FOXL2 mutation characteristic of adult-type granulosa cell tumors shows that FOXL2 C134W shifts the transcriptome towards a signature of granulosa cell cancer and drives tumorigenesis.

DeepSom: a CNN-based approach to somatic variant calling in WGS samples without a matched normal

MOTIVATION

Somatic mutations are usually called by analyzing the DNA sequence of a tumor sample in conjunction with a matched normal. However, a matched normal is not always available, for instance, in retrospective analysis or diagnostic settings. For such cases, tumor-only somatic variant calling tools need to be designed. Previously proposed approaches demonstrate inferior performance on whole-genome sequencing (WGS) samples.

RESULTS

We present the convolutional neural network-based approach called DeepSom for detecting somatic single nucleotide polymorphism and short insertion and deletion variants in tumor WGS samples without a matched normal. We validate DeepSom by reporting its performance on five different cancer datasets. We also demonstrate that on WGS samples DeepSom outperforms previously proposed methods for tumor-only somatic variant calling.

AVAILABILITY AND IMPLEMENTATION

DeepSom is available as a GitHub repository at https://github.com/heiniglab/DeepSom.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Knowledge mapping and current trends of global research on CRISPR in the field of cancer

Background: Gene editing tools using clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-related systems have revolutionized our understanding of cancer. The purpose of this study was to determine the distribution, collaboration, and direction of cancer research using CRISPR. Methods: Data from the Web of Science (WoS) Core Collection database were collected from 4,408 cancer publications related to CRISPR from 1 January 2013to 31 December 2022. The obtained data were analyzed using VOSviewer software for citation, co-citation, co-authorship, and co-occurrence analysis. Results: The number of annual publications has grown steadily over the past decade worldwide. The United States was shown, by far, to be the leading source of cancer publications, citations, and collaborations involving CRISPR than any other country, followed by China. Li Wei (Jilin University, China), and Harvard Medical School (Boston, MA, United States) were the author and institution with the most publications and active collaborations, respectively. The journal with the most contributions was Nature Communications (n = 147) and the journal with the most citations was Nature (n = 12,111). The research direction of oncogenic molecules, mechanisms, and cancer-related gene editing was indicated based on keyword analysis. Conclusion: The current study has provided a comprehensive overview of cancer research highlights and future trends of CRISPR, combined with a review of CRISPR applications in cancer to summarize and predict research directions and provide guidance to researchers.

Nuclear envelope, chromatin organizers, histones, and DNA: The many achilles heels exploited across cancers

In eukaryotic cells, the genome is organized in the form of chromatin composed of DNA and histones that organize and regulate gene expression. The dysregulation of chromatin remodeling, including the aberrant incorporation of histone variants and their consequent post-translational modifications, is prevalent across cancers. Additionally, nuclear envelope proteins are often deregulated in cancers, which impacts the 3D organization of the genome. Altered nuclear morphology, genome organization, and gene expression are defining features of cancers. With advances in single-cell sequencing, imaging technologies, and high-end data mining approaches, we are now at the forefront of designing appropriate small molecules to selectively inhibit the growth and proliferation of cancer cells in a genome- and epigenome-specific manner. Here, we review recent advances and the emerging significance of aberrations in nuclear envelope proteins, histone variants, and oncohistones in deregulating chromatin organization and gene expression in oncogenesis.

Pan-cancer functional analysis of somatic mutations in G protein-coupled receptors

G Protein-coupled receptors (GPCRs) are the most frequently exploited drug target family, moreover they are often found mutated in cancer. Here we used a dataset of mutations found in patient samples derived from the Genomic Data Commons and compared it to the natural human variance as exemplified by data from the 1000 genomes project. We explored cancer-related mutation patterns in all GPCR classes combined and individually. While the location of the mutations across the protein domains did not differ significantly in the two datasets, a mutation enrichment in cancer patients was observed among class-specific conserved motifs in GPCRs such as the Class A "DRY" motif. A Two-Entropy Analysis confirmed the correlation between residue conservation and cancer-related mutation frequency. We subsequently created a ranking of high scoring GPCRs, using a multi-objective approach (Pareto Front Ranking). Our approach was confirmed by re-discovery of established cancer targets such as the LPA and mGlu receptor families, but also discovered novel GPCRs which had not been linked to cancer before such as the P2Y Receptor 10 (P2RY10). Overall, this study presents a list of GPCRs that are amenable to experimental follow up to elucidate their role in cancer.

The Somatic Mutation Landscape of UDP-Glycosyltransferase (UGT) Genes in Human Cancers

The human UDP-glycosyltransferase (UGTs) superfamily has a critical role in the metabolism of anticancer drugs and numerous pro/anti-cancer molecules (e.g., steroids, lipids, fatty acids, bile acids and carcinogens). Recent studies have shown wide and abundant expression of UGT genes in human cancers. However, the extent to which UGT genes acquire somatic mutations within tumors remains to be systematically investigated. In the present study, our comprehensive analysis of the somatic mutation profiles of 10,069 tumors from 33 different TCGA cancer types identified 3427 somatic mutations in UGT genes. Overall, nearly 18% (1802/10,069) of the assessed tumors had mutations in UGT genes with huge variations in mutation frequency across different cancer types, ranging from over 25% in five cancers (COAD, LUAD, LUSC, SKCM and UCSC) to less than 5% in eight cancers (LAML, MESO, PCPG, PAAD, PRAD, TGCT, THYM and UVM). All 22 UGT genes showed somatic mutations in tumors, with UGT2B4, UGT3A1 and UGT3A2 showing the largest number of mutations (289, 307 and 255 mutations, respectively). Nearly 65% (2260/3427) of the mutations were missense, frame-shift and nonsense mutations that have been predicted to code for variant UGT proteins. Furthermore, about 10% (362/3427) of the mutations occurred in non-coding regions (5' UTR, 3' UTR and splice sites) that may be able to alter the efficiency of translation initiation, miRNA regulation or the splicing of UGT transcripts. In conclusion, our data show widespread somatic mutations of UGT genes in human cancers that may affect the capacity of cancer cells to metabolize anticancer drugs and endobiotics that control pro/anti-cancer signaling pathways. This highlights their potential utility as biomarkers for predicting therapeutic efficacy and clinical outcomes.

Integration of CRISPR/Cas9 with artificial intelligence for improved cancer therapeutics

Gene editing has great potential in treating diseases caused by well-characterized molecular alterations. The introduction of clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9)–based gene-editing tools has substantially improved the precision and efficiency of gene editing. The CRISPR/Cas9 system offers several advantages over the existing gene-editing approaches, such as its ability to target practically any genomic sequence, enabling the rapid development and deployment of novel CRISPR-mediated knock-out/knock-in methods. CRISPR/Cas9 has been widely used to develop cancer models, validate essential genes as druggable targets, study drug-resistance mechanisms, explore gene non-coding areas, and develop biomarkers. CRISPR gene editing can create more-effective chimeric antigen receptor (CAR)-T cells that are durable, cost-effective, and more readily available. However, further research is needed to define the CRISPR/Cas9 system’s pros and cons, establish best practices, and determine social and ethical implications. This review summarizes recent CRISPR/Cas9 developments, particularly in cancer research and immunotherapy, and the potential of CRISPR/Cas9-based screening in developing cancer precision medicine and engineering models for targeted cancer therapy, highlighting the existing challenges and future directions. Lastly, we highlight the role of artificial intelligence in refining the CRISPR system's on-target and off-target effects, a critical factor for the broader application in cancer therapeutics.

A New View of Activating Mutations in Cancer

A vast effort has been invested in the identification of driver mutations of cancer. However, recent studies and observations call into question whether the activating mutations or the signal strength are the major determinant of tumor development. The data argue that signal strength determines cell fate, not the mutation that initiated it. In addition to activating mutations, factors that can impact signaling strength include (i) homeostatic mechanisms that can block or enhance the signal, (ii) the types and locations of additional mutations, and (iii) the expression levels of specific isoforms of genes and regulators of proteins in the pathway. Because signal levels are largely decided by chromatin structure, they vary across cell types, states, and time windows. A strong activating mutation can be restricted by low expression, whereas a weaker mutation can be strengthened by high expression. Strong signals can be associated with cell proliferation, but too strong a signal may result in oncogene-induced senescence. Beyond cancer, moderate signal strength in embryonic neural cells may be associated with neurodevelopmental disorders, and moderate signals in aging may be associated with neurodegenerative diseases, like Alzheimer's disease. The challenge for improving patient outcomes therefore lies in determining signaling thresholds and predicting signal strength.

Neoplasia-associated Chromosome Translocations Resulting in Gene Truncation

Chromosomal translocations in cancer as well as benign neoplasias typically lead to the formation of fusion genes. Such genes may encode chimeric proteins when two protein-coding regions fuse in-frame, or they may result in deregulation of genes via promoter swapping or translocation of the gene into the vicinity of a highly active regulatory element. A less studied consequence of chromosomal translocations is the fusion of two breakpoint genes resulting in an out-of-frame chimera. The breaks then occur in one or both protein-coding regions forming a stop codon in the chimeric transcript shortly after the fusion point. Though the latter genetic events and mechanisms at first awoke little research interest, careful investigations have established them as neither rare nor inconsequential. In the present work, we review and discuss the truncation of genes in neoplastic cells resulting from chromosomal rearrangements, especially from seemingly balanced translocations.

Integrative Genomic Tests in Clinical Oncology

Many clinical decisions in oncology practice rely on the presence or absence of an alteration in a single genetic locus, be it a pathogenic variant in a hereditary cancer gene or activating mutation in a drug target. In addition, there are integrative tests that produce continuous variables and evaluate complex characteristics of the entire tumor genome. Microsatellite instability (MSI) analysis identifies tumors with the accumulation of mutations in short repetitive nucleotide sequences. This procedure is utilized in Lynch syndrome diagnostic pipelines and for the selection of patients for immunotherapy. MSI analysis is well-established for colorectal malignancies, but its applications in other cancer types lack standardization and require additional research. Homologous repair deficiency (HRD) indicates tumor sensitivity to PARP inhibitors and some cytotoxic drugs. HRD-related "genomic scars" are manifested by a characteristic pattern of allelic imbalances, accumulation of deletions with flanking homology, and specific mutation signatures. The detection of the genetic consequences of HRD is particularly sophisticated and expensive, as it involves either whole genome sequencing (WGS) or the utilization of large next-generation sequencing (NGS) panels. Tumor mutation burden (TMB) can be determined by whole exome sequencing (WES) or middle-throughput NGS multigene testing. Although TMB is regarded as an agnostic indicator of tumor sensitivity to immunotherapy, the clinical utility of this test is proven only for a few cancer types.

The Emerging Roles of Protein Interactions with O-GlcNAc Cycling Enzymes in Cancer

The dynamic O-GlcNAc modification of intracellular proteins is an important nutrient sensor for integrating metabolic signals into vast networks of highly coordinated cellular activities. Dysregulation of the sole enzymes responsible for O-GlcNAc cycling, O-GlcNAc transferase (OGT) and O-GlcNAcase (OGA), and the associated cellular O-GlcNAc profile is a common feature across nearly every cancer type. Many studies have investigated the effects of aberrant OGT/OGA expression on global O-GlcNAcylation activity in cancer cells. However, recent studies have begun to elucidate the roles of protein-protein interactions (PPIs), potentially through regions outside of the immediate catalytic site of OGT/OGA, that regulate greater protein networks to facilitate substrate-specific modification, protein translocalization, and the assembly of larger biomolecular complexes. Perturbation of OGT/OGA PPI networks makes profound changes in the cell and may directly contribute to cancer malignancies. Herein, we highlight recent studies on the structural features of OGT and OGA, as well as the emerging roles and molecular mechanisms of their aberrant PPIs in rewiring cancer networks. By integrating complementary approaches, the research in this area will aid in the identification of key protein contacts and functional modules derived from OGT/OGA that drive oncogenesis and will illuminate new directions for anti-cancer drug development.

Prostate cancer resistance leads to a global deregulation of translation factors and unconventional translation

Emerging evidence associates translation factors and regulators to tumorigenesis. However, our understanding of translational changes in cancer resistance is still limited. Here, we generated an enzalutamide-resistant prostate cancer (PCa) model, which recapitulated key features of clinical enzalutamide-resistant PCa. Using this model and poly(ribo)some profiling, we investigated global translation changes that occur during acquisition of PCa resistance. We found that enzalutamide-resistant cells exhibit an overall decrease in mRNA translation with a specific deregulation in the abundance of proteins involved in mitochondrial processes and in translational regulation. However, several mRNAs escape this translational downregulation and are nonetheless bound to heavy polysomes in enzalutamide-resistant cells suggesting active translation. Moreover, expressing these corresponding genes in enzalutamide-sensitive cells promotes resistance to enzalutamide treatment. We also found increased association of long non-coding RNAs (lncRNAs) with heavy polysomes in enzalutamide-resistant cells, suggesting that some lncRNAs are actively translated during enzalutamide resistance. Consistent with these findings, expressing the predicted coding sequences of known lncRNAs JPX, CRNDE and LINC00467 in enzalutamide-sensitive cells drove resistance to enzalutamide. Taken together, this suggests that aberrant translation of specific mRNAs and lncRNAs is a strong indicator of PCa enzalutamide resistance, which points towards novel therapeutic avenues that may target enzalutamide-resistant PCa.

A recurrent somatic missense mutation in GNAS gene identified in familial thyroid follicular cell carcinomas in German longhaired pointer dogs

BACKGROUND

We previously reported a familial thyroid follicular cell carcinoma (FCC) in a large number of Dutch German longhaired pointers and identified two deleterious germline mutations in the TPO gene associated with disease predisposition. However, the somatic mutation profile of the FCC in dogs has not been investigated at a genome-wide scale.

RESULTS

Herein, we comprehensively investigated the somatic mutations that potentially contribute to the inherited tumor formation and progression using high depth whole-genome sequencing. A GNAS p.A204D missense mutation was identified in 4 out of 7 FCC tumors by whole-genome sequencing and in 20 out of 32 dogs' tumors by targeted sequencing. In contrast to this, in the human TC, mutations in GNAS gene have lower prevalence. Meanwhile, the homologous somatic mutation in humans has not been reported. These findings suggest a difference in the somatic mutation landscape between TC in these dogs and human TC. Moreover, tumors with the GNAS p.A204D mutation had a significantly lower somatic mutation burden in these dogs. Somatic structural variant and copy number alterations were also investigated, but no potential driver event was identified.

CONCLUSION

This study provides novel insight in the molecular mechanism of thyroid carcinoma development in dogs. German longhaired pointers carrying GNAS mutations in the tumor may be used as a disease model for the development and testing of novel therapies to kill the tumor with somatic mutations in the GNAS gene.

Allostery: Allosteric Cancer Drivers and Innovative Allosteric Drugs

Here, we discuss the principles of allosteric activating mutations, propagation downstream of the signals that they prompt, and allosteric drugs, with examples from the Ras signaling network. We focus on Abl kinase where mutations shift the landscape toward the active, imatinib binding-incompetent conformation, likely resulting in the high affinity ATP outcompeting drug binding. Recent pharmacological innovation extends to allosteric inhibitor (GNF-5)-linked PROTAC, targeting Bcr-Abl1 myristoylation site, and broadly, allosteric heterobifunctional degraders that destroy targets, rather than inhibiting them. Designed chemical linkers in bifunctional degraders can connect the allosteric ligand that binds the target protein and the E3 ubiquitin ligase warhead anchor. The physical properties and favored conformational state of the engineered linker can precisely coordinate the distance and orientation between the target and the recruited E3. Allosteric PROTACs, noncompetitive molecular glues, and bitopic ligands, with covalent links of allosteric ligands and orthosteric warheads, increase the effective local concentration of productively oriented and placed ligands. Through covalent chemical or peptide linkers, allosteric drugs can collaborate with competitive drugs, degrader anchors, or other molecules of choice, driving innovative drug discovery.

Noncoding RNAs in pyroptosis and cancer progression: Effect, mechanism, and clinical application

Cell death is generally classified into two categories: regulated cell death (RCD) and accidental cell death (ACD). In particular, RCD is a kind of genetically controlled process, including programmed apoptotic death and programmed necrotic death. Pyroptosis, an inflammatory form of programmed necrotic death, causes inflammation in cells. The influence of pyroptosis on tumor is complicated. On the one hand, pyroptosis triggers antitumor response. On the other hand, pyroptosis may induce carcinogenesis. Pyroptosis is initiated by various factors, especially non-coding RNAs. In this review, we discuss the effects of ncRNAs on pyroptosis and the mechanisms by which ncRNAs initiate pyroptosis. Moreover, we introduce the influence of ncRNA on tumor resistance via pyroptosis. Additionally, we summarize how ncRNA-associated pyroptosis modulates the tumor microenvironment (TME) and thereafter triggers antitumor immune response. Finally, pyroptosis-related ncRNAs are promising diagnostic and immunotherapeutic biomarkers and therapeutic targets

AlphaFold, Artificial Intelligence (AI), and Allostery

AlphaFold has burst into our lives. A powerful algorithm that underscores the strength of biological sequence data and artificial intelligence (AI). AlphaFold has appended projects and research directions. The database it has been creating promises an untold number of applications with vast potential impacts that are still difficult to surmise. AI approaches can revolutionize personalized treatments and usher in better-informed clinical trials. They promise to make giant leaps toward reshaping and revamping drug discovery strategies, selecting and prioritizing combinations of drug targets. Here, we briefly overview AI in structural biology, including in molecular dynamics simulations and prediction of microbiota–human protein–protein interactions. We highlight the advancements accomplished by the deep-learning-powered AlphaFold in protein structure prediction and their powerful impact on the life sciences. At the same time, AlphaFold does not resolve the decades-long protein folding challenge, nor does it identify the folding pathways. The models that AlphaFold provides do not capture conformational mechanisms like frustration and allostery, which are rooted in ensembles, and controlled by their dynamic distributions. Allostery and signaling are properties of populations. AlphaFold also does not generate ensembles of intrinsically disordered proteins and regions, instead describing them by their low structural probabilities. Since AlphaFold generates single ranked structures, rather than conformational ensembles, it cannot elucidate the mechanisms of allosteric activating driver hotspot mutations nor of allosteric drug resistance. However, by capturing key features, deep learning techniques can use the single predicted conformation as the basis for generating a diverse ensemble.

Game-theoretic link relevance indexing on genome-wide expression dataset identifies putative salient genes with potential etiological and diapeutics role in colorectal cancer

Diapeutics gene markers in colorectal cancer (CRC) can help manage mortality caused by the disease. We applied a game-theoretic link relevance Index (LRI) scoring on the high-throughput whole-genome transcriptome dataset to identify salient genes in CRC and obtained 126 salient genes with LRI score greater than zero. The biomarkers database lacks preliminary information on the salient genes as biomarkers for all the available cancer cell types. The salient genes revealed eleven, one and six overrepresentations for major Biological Processes, Molecular Function, and Cellular components. However, no enrichment with respect to chromosome location was found for the salient genes. Significantly high enrichments were observed for several KEGG, Reactome and PPI terms. The survival analysis of top protein-coding salient genes exhibited superior prognostic characteristics for CRC. MIR143HG, AMOTL1, ACTG2 and other salient genes lack sufficient information regarding their etiological role in CRC. Further investigation in LRI methodology and salient genes to augment the existing knowledge base may create new milestones in CRC diapeutics.