Patients with cancer have an increased risk of venous thromboembolism (VTE). Guidelines suggest to use risk assessment tools to guide decisions about thromboprophylaxis, but current tools have modest discriminatory ability. Genetic information from the germline or tumor has the potential to improve VTE prediction. Here, we provide a clinical overview of the current role of genetics in cancer-associated VTE.

Germline mutations, such as factor V Leiden and prothrombin G20210A, are associated with a 2- to 2.5-fold increased VTE risk in patients with cancer. Tumor-specific somatic mutations also contribute to VTE risk, such as ALK rearrangements increasing the risk in nonsmall cell lung cancer and IDH1 mutations decreasing the risk in gliomas. Other somatic mutations associated with VTE independent of tumor type include KRAS , STK11 , MET , KEAP1 , CTNNB1 , and CDKN2B . Incorporating data on germline or somatic mutations in risk scores improves discriminatory ability compared with the Khorana score.

Specific germline and somatic mutations are associated with an increased VTE risk in patients with cancer and potentially improve performance of clinical risk scores. The increasing and widespread use of genetic testing in cancer care provides an opportunity for further development of prediction models incorporating genetic predictors.

Otto Warburg originally proposed that cancer arose from a two-step process. The first step involved a chronic insufficiency of mitochondrial oxidative phosphorylation (OxPhos), while the second step involved a protracted compensatory energy synthesis through lactic acid fermentation. His extensive findings showed that oxygen consumption was lower while lactate production was higher in cancerous tissues than in non-cancerous tissues. Warburg considered both oxygen consumption and extracellular lactate as accurate markers for ATP production through OxPhos and glycolysis, respectively. Warburg's hypothesis was challenged from findings showing that oxygen consumption remained high in some cancer cells despite the elevated production of lactate suggesting that OxPhos was largely unimpaired. New information indicates that neither oxygen consumption nor lactate production are accurate surrogates for quantification of ATP production in cancer cells. Warburg also did not know that a significant amount of ATP could come from glutamine-driven mitochondrial substrate level phosphorylation in the glutaminolysis pathway with succinate produced as end product, thus confounding the linkage of oxygen consumption to the origin of ATP production within mitochondria. Moreover, new information shows that cytoplasmic lipid droplets and elevated aerobic lactic acid fermentation are both biomarkers for OxPhos insufficiency. Warburg's original hypothesis can now be linked to a more complete understanding of how OxPhos insufficiency underlies dysregulated cancer cell growth. These findings can also address several questionable assumptions regarding the origin of cancer thus allowing the field to advance with more effective therapeutic strategies for a less toxic metabolic management and prevention of cancer.

Alternative splicing is a fundamental mechanism that generates functionally distinct proteins from individual genes, contributing to gene regulation and proteomic diversity. In Drosophila, the bunched (bun) gene, a member of the TSC-22 domain gene family, undergoes alternative splicing, yielding diverse protein isoforms involved in crucial biological processes. Nevertheless, the specific roles and regulatory mechanisms of each isoform remain elusive. Here, we employed CRISPR/Cas9 technology to introduce targeted deletions within the endogenous locus of the bun gene, resulting in the removal of either long or short isoforms. We discovered that the short isoforms demonstrated a growth-suppressive role, whereas the long isoforms exhibited a growth-promoting effect. Surprisingly, the long isoforms exhibited a remarkable dual functionality, as both deletion and amplification of long isoform expression impede the excess growth induced by Hippo pathway inactivation. Mechanistically, ectopically expressed Bun long isoforms act as the transcriptional suppressor by competitively binding to targets' promoter regions in conjunction with Yorkie/Scalloped (Yki/Sd), thereby inhibiting its transcriptional outputs and ultimately leading to the growth suppression. These findings unveil the intricate interaction between distinct spliced isoforms of Bun and oncogenic outcomes, highlighting Bun long isoforms as the critical transcription suppressor regulating Hippo pathway inactivation-mediated growth and tumorigenesis in Drosophila.

Mutational burden tests are essential for detecting signals of positive selection in cancer driver discovery by comparing observed mutation rates with background mutation rates (BMRs). However, accurate BMR estimation is challenging due to the diversity of mutational processes across genomes, complicating driver discovery efforts. Existing methods rely on various genomic regions and features for BMR estimation but lack a model that integrates both intergenic intervals and functional genomic elements on a comprehensive set of genomic features. Here, we introduce eMET (element-specific Mutation Estimator with boosted Trees), which employs 1372 (epi)genomic features from intergenic data and fine-tunes it with element-specific data through transfer learning. Applied to PCAWG somatic mutations, eMET significantly improves BMR accuracy and has potential to enhance driver discovery. Additionally, we provide an extensive analysis of BMR estimation, examining different machine learning models, genomic interval strategies, feature categories, and dimensionality reduction techniques.

Oxidative stress and chronic inflammation are important drivers in the pathogenesis and progression of many chronic diseases, such as cancers of the breast, kidney, lung, and others, autoimmune diseases (rheumatoid arthritis), cardiovascular diseases (hypertension, atherosclerosis, arrhythmia), neurodegenerative diseases (Alzheimer's disease, Parkinson's disease, Huntington's disease), mental disorders (depression, schizophrenia, bipolar disorder), gastrointestinal disorders (inflammatory bowel disease, colorectal cancer), and other disorders. With the increasing demand for less toxic and more tolerable therapies, flavonoids have the potential to effectively modulate the responsiveness to conventional therapy and radiotherapy. Flavonoids are polyphenolic compounds found in fruits, vegetables, grains, and plant-derived beverages. Six of the twelve structurally different flavonoid subgroups are of dietary significance and include anthocyanidins (e.g. pelargonidin, cyanidin), flavan-3-ols (e.g. epicatechin, epigallocatechin), flavonols (e.g. quercetin, kaempferol), flavones (e.g. luteolin, baicalein), flavanones (e.g. hesperetin, naringenin), and isoflavones (daidzein, genistein). The health benefits of flavonoids are related to their structural characteristics, such as the number and position of hydroxyl groups and the presence of C2C3 double bonds, which predetermine their ability to chelate metal ions, terminate ROS (e.g. hydroxyl radicals formed by the Fenton reaction), and interact with biological targets to trigger a biological response. Based on these structural characteristics, flavonoids can exert both antioxidant or prooxidant properties, modulate the activity of ROS-scavenging enzymes and the expression and activation of proinflammatory cytokines (e.g., interleukin-1beta (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α)), induce apoptosis and autophagy, and target key signaling pathways, such as the nuclear factor erythroid 2-related factor 2 (Nrf2) and Bcl-2 family of proteins. This review aims to briefly discuss the mutually interconnected aspects of oxidative and inflammatory mechanisms, such as lipid peroxidation, protein oxidation, DNA damage, and the mechanism and resolution of inflammation. The major part of this article discusses the role of flavonoids in alleviating oxidative stress and inflammation, two common components of many human diseases. The results of epidemiological studies on flavonoids are also presented.

Efficiently identifying cancer driver genes plays a key role in the cancer development, diagnosis and treatment. Current unsupervised driver gene identification methods typically integrate multi-omics data into gene function networks and employ network embedding algorithms to learn gene features. Additionally, they consider mutual exclusivity and mutation frequency as crucial concepts in identifying driver genes. However, existing approaches neglect the possible important implications of mutual exclusivity in the embedding space. Furthermore, they simply assume that all driver genes exhibit high mutation frequencies. Fortunately, we explored the mutual exclusivity implanted in the learned features and have verified that the Euclidean distances between learned features are strongly related to the mutual exclusivity and they can reveal more information for the mutual exclusivity. Thus, we designed an unsupervised driver gene predicting framework DriverMEDS based on the above idea and a novel driver mutation scoring strategy. First, we design a feature clustering algorithm to generate gene modules. In each module, the Euclidean distances of learned features are used to calculate a module importance score for each gene based on the related mutual exclusivity. Then, following the fact that most of driver genes have intermediate mutation frequencies, a driver mutation scoring function is designed for each gene to optimize the existing mutation frequency scoring strategy. Finally, the weighted sum of the module importance score and the driver mutation score is used to prioritize the genes. The experiment results and analysis show that DriverMEDS could detect novel cancer driver genes and relevant function modules, and outperforms other five state-of-the-art methods for cancer driver identification.

Genomic analyses of cell-free DNA (cfDNA) in plasma are enabling noninvasive blood-based biomarker approaches to cancer detection and disease monitoring. Current approaches for identification of circulating tumour DNA typically use targeted tumour-specific mutations or methylation analyses. An emerging approach is based on the recognition of altered genome-wide cfDNA fragmentation in patients with cancer. Recent studies have revealed a multitude of characteristics that can affect the compendium of cfDNA fragments across the genome, collectively called the 'cfDNA fragmentome'. These changes result from genomic, epigenomic, transcriptomic and chromatin states of an individual and affect the size, position, coverage, mutation, structural and methylation characteristics of cfDNA. Identifying and monitoring these changes has the potential to improve early detection of cancer, especially using highly sensitive multi-feature machine learning approaches that would be amenable to broad use in populations at increased risk. This Review highlights the rapidly evolving field of genome-wide analyses of cfDNA characteristics, their comparison to existing cfDNA methods, and recent related innovations at the intersection of large-scale sequencing and artificial intelligence. As the breadth of clinical applications of cfDNA fragmentome methods have enormous public health implications for cancer screening and personalized approaches for clinical management of patients with cancer, we outline the challenges and opportunities ahead.

Accurate identification of single-nucleotide variants (SNVs) is paramount for disease diagnosis. Despite the facile design of DNA hybridization probes, their limited specificity poses challenges in clinical applications. Here, a differential reaction pathway probe (DRPP) based on a dynamic DNA reaction network is presented. DRPP leverages differences in reaction intermediate concentrations between SNV and WT groups, directing them into distinct reaction pathways. This generates a strong pulse-like signal for SNV and a weak unidirectional increase signal for wild-type (WT). Through the application of machine learning to fluorescence kinetic data analysis, the classification of SNV and WT signals is automated with an accuracy of 99.6%, significantly exceeding the 80.7% accuracy of conventional methods. Additionally, sensitivity for variant allele frequency (VAF) is enhanced down to 0.1%, representing a ten-fold improvement over conventional approaches. DRPP accurately identified D614G and N501Y SNVs in the S gene of SARS-CoV-2 variants in patient swab samples with accuracy over 99% (n = 82). It determined the VAF of ovarian cancer-related mutations KRAS-G12R, NRAS-G12C, and BRAF-V600E in both tissue and blood samples (n = 77), discriminating cancer patients and healthy individuals with significant difference (p < 0.001). The potential integration of DRPP into clinical diagnostics, along with rapid amplification techniques, holds promise for early disease diagnostics and personalized diagnostics.

Carcinomatosis is one of the leading threats to human public fitness. HNF1B is a critical transcription element in vertebrate proliferation and oncogenesis, which has been shown to play roles in reactive oxygen species (ROS) metabolism. Our previous results have identified HNF1B as a tumor suppressor that could inhibit the malignant progression of prostate cancer. Yet there is no pan-carcinomatosis analysis of HNF1B, which could help us better understand common and unique underlying mechanisms in mankind knubs to enhance novel and competent treatment. Here, in our research, we evaluated the utterance pattern and explored the function of HNF1B in 33 knub categories using the data from the Cancer Genome Atlas Program (TCGA), Gene Expression Omnibus (GEO), and CLNICAL PROTEOMICTUMOR ANALYSIS CONSORTIUM (CPTAC) dataset. We found different HNF1B roles in various cancer types. HNF1B was upregulated in CHOL, STAD, KIRP, and THCA, and was downregulated in GBM, KICH, COAD, KIRC, LUSC, SARC, PAAD, and TGCT. Prognostic analyses indicated that higher HNF1B displayed better illness outcomes in BLCA, READ, and PRAD, while poorer outcomes in LUSC and THYM. HNF1B mutation was most frequent in endometrial cancer but was not associated with disease prognosis. It was discovered that HNF1B utterance relevant to endothelial cell penetration status in BLCA, ESCA, LUAD, LUSC, and TGCT, and carcinomatosis-associated fibroblast infiltration was observed in ESCA, KIRC, LIHC, and TGCT. Moreover, functional enrichment analysis disclosed that metabolism-related functions were implicated in the function of HNF1B. Taken together, our pan- carcinomatosis analysis showed the complicated roles of HNF1B in a variety of carcinomatoses, being able to improve the extensive comprehension of HNF1B's role in tumorigenesis.

The prognosis of hepatocellular carcinoma (HCC) is poor and there is no stable and reliable molecular biomarker for evaluation. This study attempted to find reliable prognostic markers from tumor mutational profiles.

A total of 362 HCC samples with whole-exome sequencing were collected as discovery datasets, and 200 samples with targeted sequencing were used for validation of the relevant results. All HCC samples were obtained from previously published studies. Bayesian non-negative matrix factorization was used to extract mutational signatures, and multivariate Cox regression models were utilized to identify the prognostic role of mutational factors. Gene set enrichment analysis was employed to discover potential signaling pathways associated with specific mutational groups.

In the HCC discovery dataset, a total of four mutational signatures (i.e., signatures 4, 6, 16, and 22) were extracted, of which signature 16 characterized by T>C mutations was observed to be associated with favorable HCC prognosis, and this correlation was also found in the validation dataset. Further analysis showed that patients with ARID1A mutations exhibited inferior survival outcomes in both discovery and validation datasets. Mechanistic exploration revealed that the presence of signature 16 was associated with better immune infiltration and tumor immunogenicity, while patients with ARID1A mutations were away from these favorable immunological features.

By integrating somatic mutation data and clinical information of HCC, this study identified that signature 16 and ARID1A mutations were associated with better and worse outcomes respectively, providing a basis for prognosis prediction and clinical treatment strategies of HCC.

Glioblastoma multiforme (GBM) is the most prevalent and aggressive primary brain tumor in adults, characterized by a poor prognosis and significant resistance to existing treatments. Despite progress in therapeutic strategies, the median overall survival remains approximately 15 months. A hallmark of GBM is its intricate molecular profile, driven by disruptions in multiple signaling pathways, including PI3K/AKT/mTOR, Wnt, NF-κB, and TGF-β, critical to tumor growth, invasion, and treatment resistance. This review examines the epidemiology, molecular mechanisms, and therapeutic prospects of targeting these pathways in GBM, highlighting recent insights into pathway interactions and discovering new therapeutic targets to improve patient outcomes.

This review comprehensively analyzes the current landscape of tumor-agnostic therapies in oncology. Tumor-agnostic therapies are designed to target specific molecular alterations rather than the primary site of the tumor, representing a shift in cancer treatment. We discuss recent approvals by regulatory agencies such as the FDA and EMA, highlighting therapies that have demonstrated efficacy across multiple cancer types sharing common alterations. We delve into the trial methodologies that underpin these approvals, emphasizing innovative designs such as basket trials and umbrella trials. These methodologies present unique advantages, including increased efficiency in patient recruitment and the ability to assess drug efficacy in diverse populations rapidly. However, they also entail certain challenges, including the need for robust biomarkers and the complexities of regulatory requirements. Moreover, we examine the promising prospects for developing therapies for rare cancers that exhibit common molecular targets typically associated with more prevalent malignancies. By synthesizing these insights, this review underscores the transformative potential of tumor-agnostic therapies in oncology. It offers a pathway for personalized cancer treatment that transcends conventional histology-based classification.

Langerhans cell histiocytosis (LCH) is a rare myeloid neoplasm that predominantly affects young children.

To investigate genetic alterations and their correlation with clinical characteristics and prognosis in pediatric LCH.

We performed targeted sequencing to detect mutations in LCH lesions from pediatric patients.

A total of 30 genomic alterations in 5 genes of the MAPK pathway were identified in 187 of 223 patients (83.9%). BRAF V600E (B-Raf proto-oncogene, serine/threonine kinase) was the most common mutation (51.6%), followed by MAP2K1 (mitogen-activated protein kinase kinase 1) alterations (17.0%) and other BRAF mutations (13.0%). ARAF (A-Raf proto-oncogene, serine/threonine kinase) and KRAS (KRAS proto-oncogene, GTPase) mutations were relatively rare (2.2% and 0.9%, respectively). Additionally, FNBP1 (formin-binding protein 1)::BRAF fusion and MAP3K10 (mitogen-activated protein kinase kinase 10) mutations A17T and R823C were identified in 1 case each, with possible constitutive activation of ERK1/2 phosphorylation. BRAF V600E was more frequent in patients with risk organ involvement, while MAP2K1 mutation was more prevalent in patients with single-system LCH (P = .001). BRAF V600E was associated with craniofacial bone, skin, liver, spleen, and ear involvement (all P < .05). Patients with other BRAF mutations had a higher proportion of spinal column involvement (P = .006). Univariate analysis showed a significant difference in progression-free survival among the 4 molecular subgroups for patients treated with first-line therapy (P = .02). According to multivariate analysis, risk organ involvement was the strongest independent adverse prognostic factor (hazard ratio, 8.854; P < .001); BRAF or MAP2K1 mutation was not an independent prognostic factor.

Most pediatric patients with LCH carry somatic mutations involving the MAPK pathway, correlating with clinical characteristics and outcomes for first-line chemotherapy.

High-risk human papillomaviruses are well-established drivers of several cancer types including cervical, head and neck, penile as well as anal cancers. While the E6 and E7 viral oncoproteins have proven to be critical for malignant transformation, evidence is also beginning to emerge suggesting that both host pathways and additional viral genes may also be pivotal for malignant transformation. Here, we focus on the role of host APOBEC genes, which have an important role in molecular editing including in the response to the viral DNA and their role in HPV-driven carcinogenesis. Further, we also discuss data developed suggesting the existence of HPV-derived miRNAs in HPV + tumors and their potential role in regulating the host transcriptome. Collectively, while recent advances in these two areas have added complexity to the working model of papillomavirus-induced oncogenesis, these discoveries have also shed a light onto new areas of research that will be required to fully understand the process.

Cyclic nucleotides are synthesized by adenylyl and/or guanylyl cyclase, and downstream of this synthesis, the cyclic nucleotide phosphodiesterase families (PDEs) specifically hydrolyze cyclic nucleotides. PDEs control cyclic adenosine-3',5'monophosphate (cAMP) and cyclic guanosine-3',5'-monophosphate (cGMP) intracellular levels by mediating their quick return to the basal steady state levels. This often takes place in subcellular nanodomains. Thus, PDEs govern short-term protein phosphorylation, long-term protein expression, and even epigenetic mechanisms by modulating cyclic nucleotide levels. Consequently, their involvement in both health and disease is extensively investigated. PDE inhibition has emerged as a promising clinical intervention method, with ongoing developments aiming to enhance its efficacy and applicability. In this comprehensive review, we extensively look into the intricate landscape of PDEs biochemistry, exploring their diverse roles in various tissues. Furthermore, we outline the underlying mechanisms of PDEs in different pathophysiological conditions. Additionally, we review the application of PDE inhibition in related diseases, shedding light on current advancements and future prospects for clinical intervention. SIGNIFICANCE STATEMENT: Regulating PDEs is a critical checkpoint for numerous (patho)physiological conditions. However, despite the development of several PDE inhibitors aimed at controlling overactivated PDEs, their applicability in clinical settings poses challenges. In this context, our focus is on pharmacodynamics and the structure activity of PDEs, aiming to illustrate how selectivity and efficacy can be optimized. Additionally, this review points to current preclinical and clinical evidence that depicts various optimization efforts and indications.

Distinguishing tumor maintenance genes from initiation, progression, and passenger genes is critical for developing effective therapies. We employed a functional genomic approach using the Lazy Piggy transposon to identify tumor maintenance genes in vivo and applied this to sonic hedgehog (SHH) medulloblastoma (MB). Combining Lazy Piggy screening in mice and transcriptomic profiling of human MB, we identified the voltage-gated potassium channel KCNB2 as a candidate maintenance driver. KCNB2 governs cell volume of MB-propagating cells (MPCs), with KCNB2 depletion causing osmotic swelling, decreased plasma membrane tension, and elevated endocytic internalization of epidermal growth factor receptor (EGFR), thereby mitigating proliferation of MPCs to ultimately impair MB growth. KCNB2 is largely dispensable for mouse development and KCNB2 knockout synergizes with anti-SHH therapy in treating MB. These results demonstrate the utility of the Lazy Piggy functional genomic approach in identifying cancer maintenance drivers and elucidate a mechanism by which potassium homeostasis integrates biomechanical and biochemical signaling to promote MB aggression.

Somatic driver mutations play important roles in cancer and must be precisely identified to advance our understanding of tumorigenesis and its promotion and progression. However, identifying somatic driver mutations remains challenging in Homo sapiens genomics due to the random nature of mutations and the high cost of qualitative experiments. Building on the powerful sequence interpretation capabilities of language models, we propose a self-attention-based contextualized pretrained language model for somatic driver mutation identification. We pretrained the model with the Homo sapiens reference genome to equip it with the ability to understand genome sequences and then fine-tuned it for oncogene and tumor suppressor gene prediction tasks, enabling it to extract features related to driver genes from the original genome sequence. The fine-tuned model was used to obtain the mutations' carcinogenic effect characteristics to further identify whether the mutation is a driver or a passenger. Compared with other computational algorithms, our method achieved excellent somatic driver mutation identification performance on the test set, with an absolute improvement of 4.31% in AUROC over the best comparison method. The strong performance of our method indicates that it can provide new insights into the discovery of cancer drivers.

8-Oxo-7,8-dihydro-2'-deoxyguanosine (dOG), the dominant oxidative product of 2'-deoxyguanosine (dG) under high levels of reactive oxygen species, usually serves as a biomarker for oxidative stress and a risk assessment factor for various diseases. Due to the extremely low abundance of dOG and the susceptibility of dOG detection to the interference of spurious oxidation, research on related biological processes is limited by insufficient sensitivity and specificity. In this work, an ultrasensitive and reliable approach for genome-wide dOG quantification was developed through chemical labeling-assisted high-performance liquid chromatography-tandem mass spectrometry with the introduction of glycosylase pretreatment. Upon derivatization by a novel labeling reagent rhodamine B ethylenediamine, the detection sensitivity of dOG was enhanced by 100-fold, and the detection limit was as low as 25 amol, which was superior to those of reported mass spectrometry-based methods. Potassium ferricyanide, as a single-electron oxidant, was shown to possess strong selectivity for dOG versus dG, improving the labeling specificity and reducing the interference from dG. The spurious oxidation during sample pretreatment was systematically explored and minimized, and a control assay of glycosylase pretreatment was proposed to further improve the quantitative accuracy of dOG. Precise quantification of endogenous dOG in different cells was achieved with less than 500 ng of genomic DNA. This method was successfully applied to the assessment of the overall level of oxidative damage under the treatment of glycosylase inhibitors, potentially contributing to the exploration of the complex role of dOG in physiological status and disease phenotype.

The Systemic Evolutionary Theory of the Origin of Cancer (SETOC) is a recently proposed theory founded on two primary principles: the cooperative and endosymbiotic process of cell evolution as described by Lynn Margulis, and the integration of complex systems operating in eukaryotic cells, which is a core concept in systems biology. The SETOC proposes that malignant transformation occurs when cells undergo a continuous adaptation process in response to long-term injuries, leading to tissue remodeling, chronic inflammation, fibrosis, and ultimately cancer. This process involves a maladaptive response, wherein the 'endosymbiotic contract' between the nuclear-cytoplasmic system (derived from the primordial archaeal cell) and the mitochondrial system (derived from the primordial α-proteobacterium) gradually breaks down. This ultimately leads to uncoordinated behaviors and functions in transformed cells. The decoupling of the two cellular subsystems causes transformed cells to acquire phenotypic characteristics analogous to those of unicellular organisms, as well as certain biological features of embryonic development that are normally suppressed. These adaptive changes enable cancer cells to survive in the harsh tumor microenvironment characterized by low oxygen concentrations, inadequate nutrients, increased catabolic waste, and increased acidity. De-endosymbiosis reprograms the sequential metabolic functions of glycolysis, the TCA cycle, and oxidative phosphorylation (OxPhos). This leads to increased lactate fermentation (Warburg effect), respiratory chain dysfunction, and TCA cycle reversal. Here, we present an updated version of the SETOC that incorporates the fundamental principles outlined by this theory and integrates the epistemological approach used to develop it.

The mutational landscape of TP53, a tumor suppressor mutated in about half of all cancers, includes over 2,000 known missense mutations. To fully leverage TP53 mutation status for personalized medicine, a thorough understanding of the functional diversity of these mutations is essential. We conducted a deep mutational scan using saturation genome editing with CRISPR-mediated homology-directed repair to engineer 9,225 TP53 variants in cancer cells. This high-resolution approach, covering 94.5% of all cancer-associated TP53 missense mutations, precisely mapped the impact of individual mutations on tumor cell fitness, surpassing previous deep mutational scan studies in distinguishing benign from pathogenic variants. Our results revealed even subtle loss-of-function phenotypes and identified promising mutants for pharmacological reactivation. Moreover, we uncovered the roles of splicing alterations and nonsense-mediated messenger RNA decay in mutation-driven TP53 dysfunction. These findings underscore the power of saturation genome editing in advancing clinical TP53 variant interpretation for genetic counseling and personalized cancer therapy.

Synthetic lethality is a genetic phenomenon whereby the simultaneous presence of two different genetic alterations impairs cellular viability. Importantly, targeting synthetic lethal interactions offers potential therapeutic strategies for cancers with alterations in pathways that might otherwise be considered undruggable. High-throughput screening methods based on modern CRISPR-Cas9 technologies have emerged and become crucial for identifying novel synthetic lethal interactions with the potential for translation into biologically rational cancer therapeutic strategies as well as associated predictive biomarkers of response capable of guiding patient selection. Spurred by the clinical success of PARP inhibitors in patients with BRCA-mutant cancers, novel agents targeting multiple synthetic lethal interactions within DNA damage response pathways are in clinical development, and rational strategies targeting synthetic lethal interactions spanning alterations in epigenetic, metabolic and proliferative pathways have also emerged and are in late preclinical and/or early clinical testing. In this Review, we provide a comprehensive overview of established and emerging technologies for synthetic lethal drug discovery and development and discuss promising therapeutic strategies targeting such interactions.

The applications of single-cell and spatial technologies in recent times have revolutionized the present understanding of cellular states and the cellular heterogeneity inherent in complex biological systems. These advancements offer unprecedented resolution in the examination of the functional genomics of individual cells and their spatial context within tissues. In this review, we have comprehensively discussed the historical development and recent progress in the field of single-cell and spatial genomics. We have reviewed the breakthroughs in single-cell multi-omics technologies, spatial genomics methods, and the computational strategies employed toward the analyses of single-cell atlas data. Furthermore, we have highlighted the advances made in constructing cellular atlases and their clinical applications, particularly in the context of disease. Finally, we have discussed the emerging trends, challenges, and opportunities in this rapidly evolving field.

Immune checkpoint blockade (ICB) has revolutionized cancer treatment; however, the mechanisms determining patient response remain poorly understood. Here, we used machine learning to predict ICB response from germline and somatic biomarkers and interpreted the learned model to uncover putative mechanisms driving superior outcomes. Patients with higher infiltration of T-follicular helper cells had responses even in the presence of defects in the MHC class-I (MHC-I). Further investigation uncovered different ICB responses in tumors when responses were reliant on MHC-I versus MHC-II neoantigens. Despite similar response rates, MHC II-reliant responses were associated with significantly longer durable clinical benefits (discovery: median overall survival of 63.6 vs. 34.5 months; P = 0.0074; validation: median overall survival of 37.5 vs. 33.1 months; P = 0.040). Characteristics of the tumor immune microenvironment reflected MHC neoantigen reliance, and analysis of immune checkpoints revealed LAG3 as a potential target in MHC II-reliant but not MHC I-reliant responses. This study highlights the value of interpretable machine learning models in elucidating the biological basis of therapy responses.

The current dogma in cancer biology contends that cancer is an identity problem: mutations in a cell's DNA cause it to "go rogue" and proliferate out of control. However, this largely ignores the role of cell-cell interaction and fails to explain phenomena such as cancer reversion, the existence of cancers without mutations, and foreign-body carcinogenesis. In this proof-of-concept paper, we draw on criminology to propose that cancer may alternatively be conceptualized as a relational problem: Although a cell's genetics is essential, the influence of its interaction with other cells is equally important in determining its phenotype. We create a simple agent-based network model of interactions among normal and cancer cells to demonstrate this idea. We find that both high mutation rates and low levels of connectivity among cells can promote oncogenesis. Viewing cancer as a breakdown in communication networks among cells in a tissue complements the gene-centric paradigm nicely and provides a novel perspective for understanding and treating cancer.

Among members of the mitogen-activated protein kinase (MAPK) family, c-Jun N-terminal kinases (JNKs) are vital for cellular responses to stress, inflammation, and apoptosis. Recent advances have highlighted their important implications in cancer biology, where dysregulated JNK signalling plays a role in the growth, progression, and metastasis of tumors. The present understanding of JNK kinase and its function in the etiology of cancer is summarized in this review. By modifying a number of downstream targets, such as transcription factors, apoptotic regulators, and cell cycle proteins, JNKs exert diverse effects on cancer cells. Apoptosis avoidance, cell survival, and proliferation are all promoted by abnormal JNK activation in many types of cancer, which leads to tumor growth and resistance to treatment. JNKs also affect the tumour microenvironment by controlling the generation of inflammatory cytokines, angiogenesis, and immune cell activity. However, challenges remain in deciphering the context-specific roles of JNK isoforms and their intricate crosstalk with other signalling pathways within the complex tumor environment. Further research is warranted to delineate the precise mechanisms underlying JNK-mediated tumorigenesis and to develop tailored therapeutic strategies targeting JNK signalling to improve cancer management. The review emphasizes the role of JNK kinases in cancer biology, as well as their potential as pharmaceutical targets for precision oncology therapy and cancer resistance. Also, this review summarizes all the available promising JNK inhibitors that are suggested to promote the responsiveness of cancer cells to cancer treatment.

The endoplasmic reticulum (ER) is crucial for maintaining calcium balance, lipid biosynthesis, and protein folding. Disruptions in ER homeostasis, often due to the accumulation of misfolded or unfolded proteins, lead to ER stress, which plays a significant role in various diseases, especially cancer. Urological cancers, which account for high male mortality worldwide, pose a persistent challenge due to their incurability and tendency to develop drug resistance. Among the numerous dysregulated biological mechanisms, ER stress is a key factor in the progression and treatment response of these cancers. This review highlights the dual role of aberrant ER stress activation in urologic cancers, affecting both tumor growth and therapeutic outcomes. While ER stress can support tumor growth through pro-survival autophagy, it primarily inhibits cancer progression via apoptosis and pro-death autophagy. Interestingly, ER stress can paradoxically aid cancer progression through mechanisms such as exosome-mediated immune evasion. Additionally, the review examines how pharmacological interventions, particularly with phytochemicals, can stimulate ER stress-mediated tumor suppression. Key regulators, including PERK, IRE1α, and ATF6, are discussed for their roles in upregulating CHOP levels and triggering apoptosis. In conclusion, a deeper understanding of ER stress in urological cancers not only clarifies the complex interactions between cellular stress and cancer progression but also provides new opportunities for innovative therapeutic strategies.

Hormone therapy resistance and the ensuing aggressive tumor progression present a significant clinical challenge. However, the mechanisms underlying the induction of tumor malignancy upon inhibition of steroid hormone signaling remain poorly understood. Here, we demonstrate that Drosophila malignant epithelial tumors show a similar reduction in ecdysone signaling, the main steroid hormone pathway. Our analysis of ecdysone-induced downstream targets reveals that overexpression of the nuclear receptor E75, particularly facilitates the malignant transformation of benign tumors. Genome-wide DNA binding profiles and biochemistry data reveal that E75 not only binds to the transcription factors of both Hippo and Notch pathways, but also exhibits widespread co-binding to their target genes, thus contributing to tumor malignancy. We further validated these findings by demonstrating that depletion of NR1D2, the mammalian homolog of E75, inhibits the activation of Hippo and Notch target genes, impeding glioblastoma progression. Together, our study unveils a novel mechanism by which hormone inhibition promotes tumor malignancy, and describes an evolutionarily conserved role of the oncogene E75/NR1D2 in integration of Hippo and Notch pathway activity during tumor progression.

This review explores the progression of pancreatic intraepithelial neoplasia (PanIN) to pancreatic ductal adenocarcinoma through a dual lens of intrinsic molecular alterations and extrinsic microenvironmental influences. PanIN development begins with Kirsten rat sarcoma viral oncogene (KRAS) mutations driving PanIN initiation. Key additional mutations in cyclin-dependent kinase inhibitor 2A (CDKN2A), tumor protein p53 (TP53), and mothers against decapentaplegic homolog 4 (SMAD4) disrupt cell cycle control and genomic stability, crucial for PanIN progression from low-grade to high-grade dysplasia. Additional molecular alterations in neoplastic cells, including epigenetic modifications and chromosomal alterations, can further contribute to neoplastic progression. In parallel with these alterations in neoplastic cells, the microenvironment, including fibroblast activation, extracellular matrix remodeling, and immune modulation, plays a pivotal role in PanIN initiation and progression. Crosstalk between neoplastic and stromal cells influences nutrient support and immune evasion, contributing to tumor development, growth, and survival. This review underscores the intricate interplay between cell-intrinsic molecular drivers and cell-extrinsic microenvironmental factors, shaping PanIN predisposition, initiation, and progression. Future research aims to unravel these interactions to develop targeted therapeutic strategies and early detection techniques, aiming to alleviate the severe impact of pancreatic cancer by addressing both genetic predispositions and environmental influences.

WDFY2 is a protein that may provide valuable insights into the mechanisms underlying human tumors and aid in the development of novel therapies. Despite its potential importance, the role of WDFY2 in pan-cancer has not been systematically investigated. In this study, we comprehensively explored the expression pattern and function of WDFY2 across 33 cancers using various databases, including TCGA, CPTAC and GEO datasets. Our results indicate that WDFY2 is downregulated in most cancer types, including BRCA, KIRP, KICH, LUAD, KIRC, PCPG, PRAD, THCA, ACC, OV, TGCT and UCS, while it is upregulated in CESC, CHOL, COAD, HNSC, LUSC, READ, STAD and UCEC. Prognostic analyses showed that higher levels of WDFY2 were associated with worse disease outcomes in ACC, BLCA, COAD, READ, SARC, MESO and OV. WDFY2 mutations were most frequent in colorectal cancer but were not associated with disease prognosis. We also found that WDFY2 expression correlated with monocyte infiltration status in SKCM, endothelial cell infiltration in COAD, KIRC, MESO, OV and THCA, and cancer-associated fibroblast infiltration in COAD, LUAD and OV. Additionally, functional enrichment analysis revealed that WDFY2 is involved in metabolism. Overall, our comprehensive analysis sheds light on the role of WDFY2 in various cancers, providing a better understanding of its role in tumorigenesis.

A fundamental challenge in biomedicine is understanding the mechanisms predisposing individuals to disease. While previous research has suggested that switch-like gene expression is crucial in driving biological variation and disease susceptibility, a systematic analysis across multiple tissues is still lacking. By analyzing transcriptomes from 943 individuals across 27 tissues, we identified 1,013 switch-like genes. We found that only 31 (3.1%) of these genes exhibit switch-like behavior across all tissues. These universally switch-like genes appear to be genetically driven, with large exonic genomic structural variants explaining five (~18%) of them. The remaining switch-like genes exhibit tissue-specific expression patterns. Notably, tissue-specific switch-like genes tend to be switched on or off in unison within individuals, likely under the influence of tissue-specific master regulators, including hormonal signals. Among our most significant findings, we identified hundreds of concordantly switched-off genes in the stomach and vagina that are linked to gastric cancer (41-fold, p<10-4) and vaginal atrophy (44-fold, p<10-4), respectively. Experimental analysis of vaginal tissues revealed that low systemic levels of estrogen lead to a significant reduction in both the epithelial thickness and the expression of the switch-like gene ALOX12. We propose a model wherein the switching off of driver genes in basal and parabasal epithelium suppresses cell proliferation therein, leading to epithelial thinning and, therefore, vaginal atrophy. Our findings underscore the significant biomedical implications of switch-like gene expression and lay the groundwork for potential diagnostic and therapeutic applications.

We aimed to provide an updated narrative review with respect to the RET pathogenic variants and their implications at the clinical and molecular level in the diagnosis of medullary thyroid cancer (MTC)/multiple endocrine neoplasia (MEN) type 2, particularly with respect to the presence of cutaneous lichen amyloidosis (CLA). We searched English-language, in extenso original articles with no timeline nor study design restriction that were published on PubMed. A traditional interplay stands for CLA and MTC in MEN2 (not MEN3) confirmation. While the connection has been reported for more than three decades, there is still a large gap in understanding and addressing it. The majority of patients with MEN2A-CLA have RET pathogenic variants at codon 634; hence, it suggests an involvement of this specific cysteine residue in both disorders (most data agree that one-third of C634-positive subjects have CLA, but the ranges are between 9% and 50%). Females seem more prone to MEN2-CLA than males. Non-C634 germline RET pathogenic variants included (at a low level of statistical evidence) the following: RET V804M mutation in exon 14 for MTC-CLA (CLA at upper back); RET S891A mutation in exon 15 binding OSMR variant G513D (familial MTC and CLA comprising the lower legs to thighs, upper back, shoulders, arms, and forearms); and C611Y (CLA at interscapular region), respectively. Typically, CLA is detected at an early age (from childhood until young adulthood) before the actual MTC identification unless RET screening protocols are already applied. The time frame between CLA diagnosis and the identification of RET pathogenic variants was between 5 and 60 years according to one study. The same RET mutation in one family is not necessarily associated with the same CLA presentation. In MTC/MEN2 subjects, the most affected CLA area was the scapular region of the upper back. Alternatively, another hypothesis highlighted the fact that CLA is secondary to long-term prurit/notalgia paresthetica (NP) in MTC/MEN2. OSMR p. G513D may play a role in modifying the evolutionary processes of CLA in subjects co-harboring RET mutations (further studies are necessary to sustain this aspect). Awareness in CLA-positive patients is essential, including the decision of RET testing in selected cases.

Mixed-lineage kinase 3 (MLK3) is a serine/threonine kinase of the MAPK Kinase kinase (MAP3K) family that plays critical roles in various biological processes, including cancer. Upon activation, MLK3 differentially activates downstream MAPKs, such as JNK, p38, and ERK. In addition, it regulates various non-canonical signaling pathways, such as β-catenin, AMPK, Pin1, and PAK1, to regulate cell proliferation, apoptosis, invasion, and metastasis. Recent studies have also uncovered other potentially diverse roles of MLK3 in malignancy, which include metabolic reprogramming, cancer-associated inflammation, and evasion of cancer-related immune surveillance. The role of MLK3 in cancer is complex and cancer-specific, and an understanding of its function at the molecular level aligned specifically with the cancer hallmarks will have profound therapeutic implications for diagnosing and treating MLK3-dependent cancers. This review summarizes the current knowledge about the effect of MLK3 on the hallmarks of cancer, providing insights into its potential as a promising anticancer drug target.

A fundamental challenge in biomedicine is understanding the mechanisms predisposing individuals to disease. While previous research has suggested that switch-like gene expression is crucial in driving biological variation and disease susceptibility, a systematic analysis across multiple tissues is still lacking. By analyzing transcriptomes from 943 individuals across 27 tissues, we identified 1,013 switch-like genes. We found that only 31 (3.1%) of these genes exhibit switch-like behavior across all tissues. These universally switch-like genes appear to be genetically driven, with large exonic genomic structural variants explaining five (~18%) of them. The remaining switch-like genes exhibit tissue-specific expression patterns. Notably, tissue-specific switch-like genes tend to be switched on or off in unison within individuals, likely under the influence of tissue-specific master regulators, including hormonal signals. Among our most significant findings, we identified hundreds of concordantly switched-off genes in the stomach and vagina that are linked to gastric cancer (41-fold, p<10-4) and vaginal atrophy (44-fold, p<10-4), respectively. Experimental analysis of vaginal tissues revealed that low systemic levels of estrogen lead to a significant reduction in both the epithelial thickness and the expression of the switch-like gene ALOX12. We propose a model wherein the switching off of driver genes in basal and parabasal epithelium suppresses cell proliferation therein, leading to epithelial thinning and, therefore, vaginal atrophy. Our findings underscore the significant biomedical implications of switch-like gene expression and lay the groundwork for potential diagnostic and therapeutic applications.

In recent decades, machine-learning (ML) technologies have advanced the management of high-dimensional and complex cancer data by developing reliable and user-friendly automated diagnostic tools for clinical applications. Immunohistochemistry (IHC) is an essential staining method that enables the identification of cellular origins by analyzing the expression of specific antigens within tissue samples. The aim of this study was to identify a model that could predict histopathological diagnoses based on specific immunohistochemical markers.

The XGBoost learning model was applied, where the input variable (target variable) was the histopathological diagnosis and the predictors (independent variables influencing the target variable) were the immunohistochemical markers.

Our study demonstrated a precision rate of 85.97% within the dataset, indicating a high level of performance and suggesting that the model is generally reliable in producing accurate predictions.

This study demonstrated the feasibility and clinical efficacy of utilizing the probabilistic decision tree algorithm to differentiate tumor diagnoses according to immunohistochemistry profiles.

Receptor tyrosine kinases (RTKs), a category of transmembrane receptors, have gained significant clinical attention in oncology due to their central role in cancer pathogenesis. Genetic alterations, including mutations, amplifications, and overexpression of certain RTKs, are critical in creating environments conducive to tumor development. Following their discovery, extensive research has revealed how RTK dysregulation contributes to oncogenesis, with many cancer subtypes showing dependency on aberrant RTK signaling for their proliferation, survival and progression. These findings paved the way for targeted therapies that aim to inhibit crucial biological pathways in cancer. As a result, RTKs have emerged as primary targets in anticancer therapeutic development. Over the past two decades, this has led to the synthesis and clinical validation of numerous small molecule tyrosine kinase inhibitors (TKIs), now effectively utilized in treating various cancer types. In this manuscript we aim to provide a comprehensive understanding of the RTKs in the context of cancer. We explored the various alterations and overexpression of specific receptors across different malignancies, with special attention dedicated to the examination of current RTK inhibitors, highlighting their role as potential targeted therapies. By integrating the latest research findings and clinical evidence, we seek to elucidate the pivotal role of RTKs in cancer biology and the therapeutic efficacy of RTK inhibition with promising treatment outcomes.

Hereditary breast and ovarian cancer is a well-known genetic condition, inherited mainly in an autosomal dominant way, which elevates the risk of developing malignancies at a young age in heterozygous carriers. Advances in new generation sequencing have enabled medical professionals to determine whether a patient is harbouring mutations in moderate- or high penetrance susceptibility genes. We conducted a retrospective analysis among 275 patients who underwent genetic counselling and multigene panel testing for hereditary breast and ovarian cancer syndrome in our department. From these patients 74.5% (205/275) were affected by some type of malignancy, while the remaining 25.5% (70/275) had a positive family history of different cancers, suggesting a genetic predisposition. These tests confirmed a genetic variant in 29.8% and 28.6% of these patient groups respectively. The results also mirrored our general knowledge concerning the genetic background of hereditary breast and ovarian cancer, as variants in either one of the BRCA1 and BRCA2 genes proved to be the most common cause among our patients with 41.5%. Our test also detected a novel mutation in the CDH1 gene and three patients with double heterozygosity in two different susceptibility genes. This study demonstrates the relevance of genetic counselling and non-BRCA gene sequencing among cancer patients and patients who fulfil the criteria for genetic testing, while also providing important details about the genetic profile of Hungarian patients.

Osteogenesis imperfecta type 10 (OI10) is caused by loss of function codon variants in the gene SERPINH1 that encodes heat shock protein 47 (HSP47), rather than in a gene specifying bone formation. The HSP47 variants disrupt the folding of both collagen and the endonuclease IRE1α (inositol-requiring enzyme 1α) that splices X-Box Binding Protein 1 (XBP1) mRNA. Besides impairing bone development, variants likely affect osteoclast differentiation. Three distinct biochemical scaffold play key roles in the differentiation and regulated cell death of osteoclasts. These scaffolds consist of non-templated protein modifications, ordered lipid arrays, and protein filaments. The scaffold components are specified genetically, but assemble in response to extracellular perturbagens, pathogens, and left-handed Z-RNA helices encoded genomically by flipons. The outcomes depend on interactions between RIPK1, RIPK3, TRIF, and ZBP1 through short interaction motifs called RHIMs. The causal HSP47 nonsynonymous substitutions occur in a novel variant leucine repeat region (vLRR) that are distantly related to RHIMs. Other vLRR protein variants are causal for a variety of different mendelian diseases. The same scaffolds that drive mendelian pathology are associated with many other complex disease outcomes. Their assembly is triggered dynamically by flipons and other context-specific switches rather than by causal, mendelian, codon variants.