Integration of spatial transcriptomic (GeoMx Digital Spatial Profiler) and single-cell RNA sequencing data from multiple gastric cancers identifies spatially resolved expression-based intratumoral heterogeneity, associated with distinct immune microenvironments. We uncovered two separate evolutionary trajectories associated with specific molecular subtypes, clinical prognoses, stromal neighborhoods, and genetic drivers. Tumor-stroma interfaces emerged as a unique state of tumor ecology.

Protein Tyrosine Phosphatase Non-Receptor Type 6 (PTPN6) plays a crucial regulatory role in cellular processes and has been implicated in oncogenesis. This pan-cancer analysis aimed to elucidate PTPN6's involvement across various cancer types, with a particular emphasis on its association with tumor immunity. We analyzed PTPN6 expression data from open access databases using various statistical techniques, including survival analysis, genetic heterogeneity analysis, immune profiling, single-cell analysis, drug sensitivity analysis, and protein interaction analysis. We also conducted in vitro experiments utilizing colorectal cancer cell lines to validate PTPN6's functional role. PTPN6 exhibited distinct expression patterns across cancers, and its prognostic significance was apparent in several cancer types, particularly in glioblastoma, sarcoma, and melanoma. We observed correlations between PTPN6 and immune genes/cell infiltration in these cancers, suggesting a potential role in modulating the tumor immune microenvironment. Single-cell analysis revealed that PTPN6 is predominantly localized in macrophages, B cells, and dendritic cells within the tumor microenvironment, implying its involvement in regulating immune cell function. Enrichment analysis highlighted PTPN6's role in immune-related pathways. Drug sensitivity analysis identified specific drugs, including PAC-1, SNX-2112, BELINOSTAT, VORINOSTAT, TPCA-1, and PHA-893,888, whose efficacy may be influenced by PTPN6 expression. Knocking down PTPN6 expression inhibited the proliferation and migration of colorectal cancer cells in vitro, confirming its oncogenic role in this cancer type. This pan-cancer analysis establishes PTPN6's multifaceted influence on tumor immunity and its potential as a biomarker and therapeutic target.

Single-cell RNA sequencing (scRNA-seq) is a powerful tool for characterizing tumor heterogeneity, yet accurately identifying malignant cells remains challenging. Here, we propose scMalignantFinder, a machine learning tool specifically designed to distinguish malignant cells from their normal counterparts using a data- and knowledge-driven strategy. To develop the tool, multiple cancer datasets were collected, and the initially annotated malignant cells were calibrated using nine carefully curated pan-cancer gene signatures, resulting in over 400,000 single-cell transcriptomes for training. The union of differentially expressed genes across datasets was taken as the features for model construction to comprehensively capture tumor transcriptional diversity. scMalignantFinder outperformed existing automated methods across two gold-standard and eleven patient-derived scRNA-seq datasets. The capability to predict malignancy probability empowers scMalignantFinder to capture dynamic characteristics during tumor progression. Furthermore, scMalignantFinder holds the potential to annotate malignant regions in tumor spatial transcriptomics. Overall, we provide an efficient tool for detecting heterogeneous malignant cell populations.

DNA amplification has intrigued scientists for decades. Since its discovery, significant progress has been made in understanding the mechanisms promoting DNA amplification and their associated function(s). While DNA copy gains were once thought to be regulated purely by stochastic processes, recent findings have revealed the important role of epigenetic modifications in driving these amplifications and their integration into the genome. Furthermore, advances in genomic technology have enabled detailed characterization of these genomic events in terms of size, structure, formation, and regulation. This review highlights how our understanding of DNA amplifications has evolved over time, tracing its trajectory from initial discovery to the contemporary landscape. We describe how recent discoveries have started to uncover how these genomic events occur by controlled biological processes rather than stochastic mechanisms, presenting opportunities for therapeutic modulation.

As found in human lung squamous cell carcinoma (LUSC), STING1 involved in ER-Golgi intermediate compartment (ERGIC) could coordinate immune responses to ectopic DNA triggered by DNA-targeted chemotherapy. ERGIC STING1 is considered to compete with nuclear STING1 to decline aryl hydrocarbon receptor (AhR)-chromosomal instability (CIN)-triggered chronic STING activation which could cause therapeutic resistance. Moreover, GSTP1 was proved to inhibit ERGIC-STING1 via promoting S-glutathione modification of STING1. Hence, a potent GSTP1-targeted Pt(IV) hybrid NBDHEX-DN604, was designed via conjugating a GSTP1 inhibitor NBDHEX to the axial position of Pt(IV) prodrug. As mentioned, hypoxia is mainly observed in malignant tumors and develops acquired drug resistance. In vitro bio-properties of hypoxic SK-MES-1/cDDP cells demonstrated that NBDHEX-DN604 could reverse chemo-immuno resistance via intercepting GSTP1 to activate ERGIC STING1, leading to the decrease of nuclear STING1. The mechanistic data indicated that NBDHEX-DN604 could elevate ERGIC STING1 to mitigate nuclear STING1-mediated AhR-TLS-CIN-chronic activation. Meanwhile, NBDHEX-DN604 was found to decline STING1-AhR-CIN to circumvent chemo-immuno resistance, resulting in predominant in vivo antitumor effect in HY-KLN-205/cDDP-inoculated BALB/c mice. The data provide a novel rationale for the mixed chemo-immunotherapy of NBDHEX-DN604 as a potent Pt(IV) therapeutic method for patients with resistant LUSC.

ATIP3-deficient breast cancers represent a subset of aggressive tumors with limited therapeutic options and poor prognosis. Here, we screened a panel of cell cycle kinase inhibitors to identify novel targets for these tumors. We show that loss of ATIP3 sensitizes breast cancer cells to WEE1 inhibition, resulting in aberrant mitoses characterized by detachment of centromere proteins from DNA and chromosome pulverization. This phenotype arises from excessive replication stress and DNA damage in S-phase, combined with premature mitotic entry driven by untimely CDK1 activation. Mechanistically, we identify DNA2 helicase/nuclease as a key mediator of chromosome pulverization. Importantly, the heightened sensitivity of ATIP3-deficient cells to WEE1 inhibition provides a strong rationale for clinical exploration of WEE1-targeted therapies. Furthermore, combining WEE1 and PKMYT1 inhibitors enhances therapeutic efficacy, offering a promising strategy for personalized treatment in ATIP3-deficient breast cancers.

Non-small cell lung cancer (NSCLC) is the most common type of cancer, with lung adenocarcinoma (LUAD) as the predominant subtype. Despite advancements in targeted therapies, many NSCLC patients still experience poor outcomes due to treatment resistance and disease progression. Genomic instability (GI), a hallmark of cancer, defined as the increased tendency of DNA mutations and alterations, is closely linked to cancer initiation, progression, and resistance to therapy. Emerging evidence suggests that long non-coding RNAs (lncRNAs)-molecules longer than 200 nucleotides that do not encode proteins but regulate gene expression-play critical roles in cancer biology and are associated with GI. However, the relationship between GI and lncRNA expression in LUAD remains poorly understood.

In this study, we analyzed the transcript profiles of lncRNAs and mRNAs from LUAD samples in The Cancer Genome Atlas (TCGA) database and classified them based on their Homologous Recombination Deficiency (HRD) score. The HRD score is an unweighted sum of three independent DNA-based measures of genomic instability: loss of heterozygosity, telomeric allelic imbalance, and large-scale transitions. We then performed a differential gene expression analysis to identify lncRNAs and mRNAs that were either upregulated or downregulated in samples with high HRD scores compared to those with low HRD scores. Following this, we conducted a correlation analysis to assess the significance of the association between HRD scores and the expression of both lncRNAs and mRNAs.

We identified 30 differentially expressed lncRNAs and 200 mRNAs associated with genomic instability. Using an RNA interactome database from sequencing experiments, we found evidence of interactions between GI-associated lncRNAs (GI-lncRNAs) and GI-associated mRNAs (GI-mRNAs). Further investigation showed that some GI-lncRNAs play regulatory and functional roles in LUAD and other diseases. We also found that GI-lncRNAs have potential as prognostic biomarkers, particularly when integrated with HRD stratification. The expression of specific GI-lncRNAs was associated with primary therapy response and immune infiltration in LUAD. Additionally, we identified existing drugs that could modulate GI-lncRNAs, offering potential therapeutic strategies to address GI in LUAD.

Our findings suggest that GI-associated lncRNAs could serve as valuable biomarkers for LUAD prognosis and therapeutic response. Furthermore, modulating these lncRNAs presents potential treatment avenues to address genomic instability in LUAD.

Originally identified for its involvement in bone remodeling, accumulating data emerged in the past years indicating that receptor activator of nuclear factor κB ligand (RANKL) actually acts as a multifunctional soluble molecule that influences various physiological and pathological processes. Regarding its role in carcinogenesis, while direct effects on tumor cell behavior have been precisely characterized, the impact of the RANKL/RANK system (and its inhibition) on the intratumoral immune landscape remains unclear.

After various in silico/in situ/in vitro analyses, the immunotherapeutic efficacy of RANKL blockade (alone and in combination with immune checkpoint inhibitors (anti-programmed cell death protein-1 (PD-1)) or doxorubicin/paclitaxel-based chemotherapy) was investigated using different syngeneic mouse models of triple-negative breast cancer (4T1, 67NR and E0771). Isolated from retrieved tumors, 14 immune cell (sub)populations, along with the activation status of antigen-presenting cells, were thoroughly analyzed in each condition. Finally, the impact of RANKL on the functionality of both dendritic cells (DC) and plasmacytoid dendritic cells (pDC) was determined.

A drastic tumor growth inhibition was reproductively observed following RANKL inhibition. Strikingly, this antitumor activity was not detected in immunocompromised mice, demonstrating its dependence on the adaptive immune responses and justifying the diverse enriched signatures linked to immune cell regulation/differentiation detected in RANKLhigh-expressing human neoplasms. Interestingly, neoadjuvant chemotherapy (but not PD-1 checkpoint inhibition) potentiated the anticancer effects of RANKL blockade by priming effector T cells and increasing their infiltration within the tumor microenvironment. Mechanistically, we highlighted that RANKL indirectly promotes regulatory T cell differentiation and suppressive function by inhibiting the mTOR signaling pathway on antigen-presenting cells.

Taken together, this study provides insight into the role of RANKL/RANK axis in immune tolerance, demonstrates the significant impact of RANKL-dependent impairment of T cell-DC/pDC crosstalk on tumor development and, ultimately, supports that this ligand could be an interesting actionable target for cancer immunotherapy.

Head and neck squamous cell carcinoma (HNSCC) remains a prevalent and lethal malignancy, with a five-year survival rate of just 50% for cases of locally advanced disease. Chromosomal aberrations, particularly the deletion of the short arm of chromosome 3 (3p), have been strongly associated with poor prognosis and more aggressive tumor phenotypes. The tumor microenvironment (TME) plays a pivotal role in tumor progression and resistance to therapy. This study aims to elucidate the impact of 3p deletion on the TME, immune cell infiltration, and treatment response in HNSCC, to identify novel therapeutic targets to improve patient outcomes. We analyzed single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) and bulk transcriptome data from The Cancer Genome Atlas (TCGA). Pseudo-time trajectory and cell-cell communication analyses were performed with the Monocle and CellChat packages. The Wilcoxon test was used to evaluate the differential gene expression between wild-type (wt) and mutant (mut) groups. Prognostic models were developed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and Cox regression analyses to find the genes related to survival, with survival analysis conducted via Kaplan-Meier curves. Gene set enrichment analysis (GSEA) was employed to investigate pathway dysregulation, and immune cell infiltration was assessed using various immune scoring methodologies to explore the differences immune environment. The Tumor Immune Dysfunction and Exclusion (TIDE) database was utilized to predict the potential efficacy of immune checkpoint inhibitors. mRNA and protein expression levels of SPP1 were examined by RT-qPCR and Western blotting, while cell proliferation was assessed using the CCK8 assay. The mut group demonstrated significant alterations in cellular composition, characterized by increased endothelial cells and macrophages and decreased fibroblasts and CD8 + T cells, indicative of an immunosuppressive TME. Differential expression analysis revealed downregulation of immune pathways, including antigen processing and presentation, T cell receptor signaling, and B cell receptor signaling pathways in the mut group, along with enhanced metabolic activity in glycolysis and lipid metabolism. The prognostic model identified nine key genes associated with poor survival in HNSCC. The mut group exhibited poorer overall survival and a more immunosuppressive microenvironment compared to the wt group, which correlated with the outcomes observed in high-risk versus low-risk groups. High-risk patients also showed a diminished response to immunotherapy compared to low-risk patients. Additionally, SPP1 emerged as a critical gene associated with chemotherapy resistance and macrophage M2 polarization. This study demonstrates that 3p deletion significantly reshapes the TME, contributing to poor prognosis in HNSCC by fostering an immunosuppressive environment and enhancing chemoresistance. These findings highlight the potential for developing targeted therapies that address the genetic and immunological landscape of HNSCC.

Intratumoral heterogeneity drives therapy resistance and relapses in advanced stage cancers, such as ovarian cancer. Here, we present a live cell imaging assay using patient-derived ovarian cancer organoids for real time capture and quantification of natural killer cell-mediated apoptotic events in >500 organoids simultaneously. Our assay revealed significant inter- and intratumor response heterogeneity and identified a rare resistant organoid population, opening avenues to test immunomodulatory strategies that overcome resistance.

Chromosome number abnormalities is one of the hallmarks of cancer. DNA copy number alterations (CNA) are studied using various genome-wide methods. In our study, we investigated CNA in human pituitary tumors using three platforms CytoSNP-850 K microarrays, low-pass whole-genome sequencing (average × 7 coverage, LPWGS), and Infinium Methylation EPIC array. Virtual karyotypes based on each dataset were generated using open-source software packages for each sample. Concordant CNA profiles were found for most of tumor. Surprisingly, substantial discrepancies between results from SNP arrays and LPWGS/EPIC arrays were identified in 20% of tumors, for which discrimination of true karyotype was required. B-allelic frequency data from SNP arrays was crucial to adjust normal ploidy level as ultimately verified with FISH. The discrepancy between virtual karyotypes was more pronounced the more CNAs were found. When CNAs covered around half of genome, the level of normal/diploid copy number was incorrectly set with methods, based solely on signal intensity/read-counts coverage. To conclude, CNA analyses with methods such as LPWGS and methylation arrays in highly aneuploid tumors are prone to a bias from improper normal ploidy level setting. These methods are commonly used therefore, we aimed to aware the scientific community about this underestimated methodological problem.

Many regulatory factors impact the expression of individual genes including, but not limited, to microRNA, long non-coding RNA (lncRNA), transcription factors (TFs), cis-methylation, copy number variation (CNV), and single-nucleotide polymorphisms (SNPs). While each mechanism can influence gene expression substantially, the relative importance of each mechanism at the level of individual genes and tissues is poorly understood. Here, we present the integrative Models of Estimated gene expression (iModEst), which details the relative contribution of different regulators to the gene expression of 16,000 genes and 21 tissues within The Cancer Genome Atlas (TCGA). Specifically, we derive predictive models of gene expression using tumour data and test their predictive accuracy in cancerous and tumour-adjacent tissues. Our models can explain up to 70% of the variance in gene expression across 43% of the genes within both tumour and tumour-adjacent tissues. We confirm that TF expression best predicts gene expression in both tumour and tumour-adjacent tissue whereas methylation predictive models in tumour tissues does not transfer well to tumour adjacent tissues. We find new patterns and recapitulate previously reported relationships between regulator and gene-expression, such as CNV-predicted FGFR2 expression and SNP-predicted TP63 expression. Together, iModEst offers an interactive, comprehensive atlas of individual regulator-gene-tissue expression relationships as well as relationships between regulators.

Adult-type diffuse gliomas arise from glial or progenitor cells. These tumors are currently classified as astrocytoma isocitrate dehydrogenase (IDH)-mutant or IDH-mutant oligodendroglioma with co-deletion of chromosomal arms 1p and 19q, both of which could be either slow-growing tumors, or glioblastoma (GBM), which is a more aggressive tumor. Despite advances in diagnosis and treatment, the median survival time after GBM diagnosis remains low at approximately 15 months, with a 5-year overall survival (OS) rate of 6.8 %. Therefore, new biomarker and therapeutic target discoveries are required to improve prognosis. Mucin 1 (MUC1) and MUC4 are membrane-bound mucins and potential biomarkers of several tumors. However, the role of these mucins in adult gliomas has not been well explored. In this retrospective study, in silico analysis of data from patients with adult-type diffuse glioma revealed differential methylation and expression patterns of MUC1 and MUC4 between GBM and non-GBM groups. In the GBM group, decreased methylation and elevated expression of MUC1 were observed (r = -0.25, p < 0.0001), whereas increased methylation and decreased expression of MUC4 were observed (r = -0.13, p = 0.1344). Conversely, in the non-GBM group, MUC1 exhibited higher methylation and lower expression (r = -0.27, p < 0.0001), whereas MUC4 showed lower methylation and higher expression (r = -0.32, p < 0.0001). The expression of these genes influenced OS in adult patients with glioma (p = 0.0344), with high MUC1 and low MUC4 expression associated with worse OS. MUC1 and MUC4 expression correlated with that of MUC20 in both GBM (r = 0.54) and non-GBM (r = 0.53) groups (p < 0.0001). Functional enrichment analysis identified the biological roles of MUC1-co-expressed genes as involvement in innate immunity, antigen processing, and proinflammatory responses in both the non-GBM and GBM groups, and integrin-based signaling pathways in the GBM group. MUC4-co-expressed genes are involved in ion transport in GBM patients. Using molecular docking, we observed that MUC1 domains physically interact with immune response-related proteins, such as receptors for advanced glycation end products (RAGE), major histocompatibility complex II (MHC-II), and extracellular matrix receptor integrin alpha 2 (ITGA2). To our knowledge, this is the first retrospective study and in silico analysis demonstrating the relevance and correlation of MUC1 and MUC4 in adult gliomas. These findings elucidate the molecular mechanisms underlying adult-type diffuse glioma progression and highlight MUC1 and MUC4 as potential prognostic markers and therapeutic targets for glioma management.

Somatic copy number alterations (sCNAs) are valuable phylogenetic markers for inferring evolutionary relationships among tumor cell subpopulations. Advances in single-cell DNA sequencing technologies are making it possible to obtain such sCNAs datasets at ever-larger scales. However, existing methods for reconstructing phylogenies from sCNAs are often too slow for large datasets. We propose two new distance-based methods, DICE-bar and DICE-star, for reconstructing single-cell tumor phylogenies from sCNA data. Using carefully simulated datasets, we find that DICE-bar matches or exceeds the accuracies of all other methods on noise-free datasets and that DICE-star shows exceptional robustness to noise and outperforms all other methods on noisy datasets. Both methods are also orders of magnitude faster than many existing methods. Our experimental analysis also reveals how noise/error in copy number inference, as expected for real datasets, can drastically impact the accuracies of most methods. We apply DICE-star, the most accurate method on error-prone datasets, to several real single-cell breast and ovarian cancer datasets and find that it rapidly produces phylogenies of equivalent or greater reliability compared with existing methods.

Aneuploidy is prevalent in cancer and associates with fitness advantage and poor patient prognosis. Yet, experimentally induced aneuploidy initially leads to adverse effects and impaired proliferation, suggesting that cancer cells must adapt to aneuploidy. We performed in vitro evolution of cells with extra chromosomes and obtained cell lines with improved proliferation and gene expression changes congruent with changes in aneuploid cancers. Integrated analysis of cancer multi-omics data and model cells revealed increased expression of DNA replicative and repair factors, reduced genomic instability, and reduced lysosomal degradation. We identified E2F4 and FOXM1 as transcription factors strongly associated with adaptation to aneuploidy in vitro and in cancers and validated this finding. The adaptation to aneuploidy also coincided with specific copy number aberrations that correlate with poor patient prognosis. Chromosomal engineering mimicking these aberrations improved aneuploid cell proliferation, while loss of previously present extra chromosomes impaired it. The identified common adaptation strategies suggest replication stress, genomic instability, and lysosomal stress as common liabilities of aneuploid cancers.

Non-canonical roles for growth factors in the nucleus have been previously described, but their mechanism of action and biological roles remain enigmatic. Platelet-derived growth factor B (PDGFB) can drive formation of low-grade glioma and here we show that it localizes to the nucleus of human glioma cells where it binds chromatin to preserve genome stability and cell lineage. Failure of PDGFB to localize to the nucleus leads to chromosomal abnormalities, aberrant heterochromatin architecture and accelerated tumorigenesis. Furthermore, nuclear localization of PDGFB is reliant upon the expression levels and mutation status of isocitrate dehydrogenase (IDH). Unexpectedly, we identified macrophages as the predominant source of PDGFB in human, finding that immune-derived PDGFB can localize to the nucleus of glioma cells. Collectively, these studies show that immune derived PDGFB enters the nucleus of glioma cells to maintain genomic stability, while identifying a new mechanism by which IDH mutations promote gliomagenesis.

S-acylation, which includes S-palmitoylation, is the only known reversible lipid-based post-translational protein modification. S-palmitoylation is mediated by palmitoyl acyltransferases (PATs), a family of 23 enzymes commonly referred to as zDHHCs, which catalyze the addition of palmitate to cysteine residues on specific target proteins. Aberrant S-palmitoylation events have been linked to the pathogenesis of multiple human diseases. While there have been advances in elucidating the molecular mechanisms underlying the pathogenesis of various skin conditions, there remain gaps in the knowledge, specifically with respect to the contribution of S-palmitoylation to the maintenance of skin barrier function. Towards this goal, we performed PubMed literature searches relevant to S-palmitoylation in skin to define current knowledge and areas that may benefit from further research studies. Furthermore, to identify alterations in gene products that are S-palmitoylated, we utilized bioinformatic tools such as SwissPalm and analyzed relevant data from publicly available databases such as cBioportal. Since the targeting of S-palmitoylated targets may offer an innovative treatment perspective, we surveyed small molecules inhibiting zDHHCs, including 2-bromopalmitate (2-BP) which is associated with off-target effects, and other targeting strategies. Collectively, our work aims to advance both basic and clinical research on skin barrier function with a focus on zDHHCs and relevant protein targets that may contribute to the pathogenesis of skin conditions such as atopic dermatitis, psoriasis, and skin cancers including melanoma.

Molecular subtypes, such as defined by The Cancer Genome Atlas (TCGA), delineate a cancer's underlying biology, bringing hope to inform a patient's prognosis and treatment plan. However, most approaches used in the discovery of subtypes are not suitable for assigning subtype labels to new cancer specimens from other studies or clinical trials. Here, we address this barrier by applying five different machine learning approaches to multi-omic data from 8,791 TCGA tumor samples comprising 106 subtypes from 26 different cancer cohorts to build models based upon small numbers of features that can classify new samples into previously defined TCGA molecular subtypes-a step toward molecular subtype application in the clinic. We validate select classifiers using external datasets. Predictive performance and classifier-selected features yield insight into the different machine-learning approaches and genomic data platforms. For each cancer and data type we provide containerized versions of the top-performing models as a public resource.

Squamous cell cancers (SCCs) of the head and neck, esophagus, and lung, referred to as aero-upper digestive SCCs, are prevalent in the United States and worldwide. Their incidence and mortality are projected to increase at alarming rates, posing diagnostic, prognostic, and therapeutic challenges. These SCCs share certain epigenetic, genomic, and genetic alterations, immunologic properties, environmental exposures, as well as lifestyle and nutritional risk factors, which may underscore common complex gene-environmental interactions across them. This review focuses upon the frequent shared epigenetic, genomic, and genetic alterations, emerging preclinical model systems, and how this collective knowledge can be leveraged into perspectives on standard of care therapies and mechanisms of resistance, nominating new potential directions in translational therapeutics.

Mislocalization of overexpressed CENP-A (Cse4 in budding yeast) contributes to chromosomal instability (CIN) in yeasts, flies, and human cells. Overexpression of CENP-A is observed in many cancers and this correlates with poor prognosis. Here, we show that altered stoichiometry of histone H3 and expression of oncohistone mutation H3 E97K contributes to mislocalization of Cse4 and CIN. Oncohistone mutations in the globular domain of histone H3 such as H3 E97K occur in several cancers; however, their functional effects remain unexplored. We demonstrated that strains with reduced gene dosage of histone H3 (hht1Δ and hht2Δ) or oncohistone H3 E97K mutation exhibit enhanced Cse4-H4 interaction, an in vivo change in the conformational state of Cse4, and this contributes to mislocalization of Cse4. Oncohistone H3 E97K mutant protein was unstable and exhibited defects in interaction with histone H4. Notably, mislocalization of Cse4 and CIN phenotypes were observed in hht1Δ and oncohistone H3 E97K mutants expressing endogenous Cse4. In summary, our studies highlight the importance of histone H3 stoichiometry in preventing mislocalization of Cse4 for chromosomal stability and suggest that oncohistone H3 mutations may contribute to CIN in human cancers.

Mutations in the TP53 gene and chromosomal instability (CIN) are two of the most common alterations in cancer. CIN, marked by changes in chromosome numbers and structure, drives tumor development, but is poorly tolerated in healthy cells, where developmental and tissue homeostasis mechanisms typically eliminate cells with chromosomal abnormalities. Mechanisms that allow cancer cells to acquire and adapt to CIN remain largely unknown. Tumor suppressor protein p53, often referred to as the "guardian of the genome", plays a critical role in maintaining genomic stability. In cancer, CIN strongly correlates with TP53 mutations, and recent studies suggest that p53 prevents the propagation of cells with abnormal karyotypes arising from mitotic errors. Furthermore, p53 dysfunction is frequent in cells that underwent whole-genome doubling (WGD), a process that facilitates CIN onset, promotes aneuploidy tolerance, and is associated with poor patient prognosis across multiple cancer types. This review summarizes current insights into p53's role in protecting cells from chromosome copy number alterations and discusses the implications of its dysfunction for the adaption and propagation of cancer cells.

Many tumors evolve through cellular mutation and selection, where subpopulations of cells (subclones) with shared ancestry compete for dominance. Introduction of next generation sequencing enables subclone identification using small somatic. However, there are several advantages to marking subclones with structural variants: they have greater functional impact, play a crucial role in late-stage tumors, and provide a more complete view of genomic instability driving tumor evolution. Here, we present SVCFit, a scalable method to estimate the cellular prevalence of somatic deletions, duplications and inversions. We demonstrate that cellular prevalence estimation can be improved by incorporating distinct read patterns for each structural variant type. Additionally, this improvement is achieved without prior knowledge of tumor purity, which is often inaccurate. Using a combination of simulated data and patient-derived metastatic samples with known mixture proportions, we show that our algorithm achieves significantly greater accuracy than state-of-the-art in estimating the structural variants cellular prevalence (p<0.05). The speed of SVCFit estimation from cost-effective bulk whole-genome sequencing (WGS) makes it well-suited for analyzing large cohorts of sequenced tumor samples, enhancing the accessibility of SV-based clonal reconstruction.

A major goal of cancer biology is to understand the mechanisms driven by somatically acquired mutations. Two distinct methodologies-one analyzing mutation clustering within protein sequences and 3D structures, the other leveraging protein-protein interaction network topology-offer complementary strengths. We present NetFlow3D, a unified, end-to-end 3D structurally-informed protein interaction network propagation framework that maps the multiscale mechanistic effects of mutations. Built upon the Human Protein Structurome, which incorporates the 3D structures of every protein and the binding interfaces of all known protein interactions, NetFlow3D integrates atomic, residue, protein and network-level information: It clusters mutations on 3D protein structures to identify driver mutations and propagates their impacts anisotropically across the protein interaction network, guided by the involved interaction interfaces, to reveal systems-level impacts. Applied to 33 cancer types, NetFlow3D identifies 2 times more 3D clusters and incorporates 8 times more proteins in significantly interconnected network modules compared to traditional methods.

Cell-cell fusion is a tightly controlled process in the human body known to be involved in fertilization, placental development, muscle growth, bone remodeling, and viral response. Fusion between cancer cells results first in a whole-genome doubled state, which may be followed by the generation of aneuploidies; these genomic alterations are known drivers of tumor evolution. The role of cell-cell fusion in cancer progression and treatment response has been understudied due to limited experimental systems for tracking and analyzing individual fusion events. To meet this need, we developed a molecular toolkit to map the origins and outcomes of individual cell fusion events within a tumor cell population. This platform, ClonMapper Duo ('CMDuo'), identifies cells that have undergone cell-cell fusion through a combination of reporter expression and engineered fluorescence-associated index sequences paired to randomly generated nucleotide barcodes. scRNA-seq of the indexed barcodes enables the mapping of each set of parental cells and fusion progeny throughout the cell population. In triple-negative breast cancer cells CMDuo uncovered subclonal transcriptomic hybridization and unveiled distinct cell-states which arise in direct consequence of homotypic cell-cell fusion. CMDuo is a platform that enables mapping of cell-cell fusion events in high-throughput single cell data and enables the study of cell fusion in disease progression and therapeutic response.

Using rare cancer predisposition alleles derived from The Cancer Genome Atlas (TCGA) and high cancer prevalence (14% of participants) in All of Us (version 6), we assessed the impact of these rare alleles on cancer occurrence in six broad groups of genetic similarity provided by All of Us: African/African American (AFR), Admixed American/Latino (AMR), East Asian (EAS), European (EUR), Middle Eastern (MID), or South Asian (SAS). We observed that germline susceptibility to cancer consistently replicates in EUR-like participants but less so in other participants. We found that All of Us participants from the EUR (p = 1.8 × 10-7), AFR (p = 0.018), and MID (p = 0.0083) genetic similarity groups who carry a rare pathogenic mutation are more likely to have cancer than those without a rare pathogenic mutation. With the advent of combining medical records and genetic mutations, we also performed a phenome-wide association study (PheWAS) to assess the effect of pathogenic variants on additional phenotypes. This analysis again showed several associations between predisposition variants and cancer in EUR-like participants, but fewer in those of the other genetic similarity groups. As All of Us grows to 1 million participants, our projections suggest sufficient power (>99%) to detect cancer-associated variants that are common, but limited power (∼28%) to detect rare mutations when using the entire cohort. This study provides preliminary insights into genetic predispositions to cancer across a diverse cohort and demonstrates the value of All of Us as a resource for cancer research.

Here, we have discussed the molecular mechanisms of p53-responsive microRNAs dysregulation in response to genotoxic stress in diffuse large B-cell lymphoma (DLBCL) patients. The role of micro ribonucleic acids (microRNAs) in p53-signaling cellular stress has been studied. MicroRNAs are the small non-coding RNAs, which regulate genes expression at post-transcriptional level. Many of them play a crucial role in carcinogenesis and may act as oncogenes or suppressor of tumor growth. The understanding of the effect of p53-responsive microRNA dysregulation on oncogenesis achieved in recent decades opens wide opportunities for the diagnosis, prediction and of microRNA-based cancer therapy. Development of new bioinformatics tools and databases for microRNA supports DLBCL research. We overview the studies on the role of miRNAs in regulating gene expression associated with tumorigenesis processes, with particular emphasis on their role as tumor growth-suppressing factors. The starting point is a brief description of the classical microRNA biogenesis pathway and the role of p53 in regulating the expression of these molecules. We analyze various molecular mechanisms leading to this dysregulation, including mutations in the TP53 gene, DNA methylation, changes in host-genes expression or microRNA gene copy number, mutations in microRNA and microRNA biogenesis genes.

Patients with lateral lymph node metastasis (LLNM) may experience higher locoregional recurrence rates and poorer prognoses compared to those without LLNM, highlighting the need for effective preoperative stratification to reliably assess risk LLNM. In this study, we collected PTMC samples from Peking Union Medical College Hospital and employed data-independent acquisition mass spectrometry proteomics technique to identify protein profiles in PTMC tissues with and without LLNM. Pseudo temporal analysis and single sample gene set enrichment analysis were conducted in combination with The Cancer Genome Atlas Thyroid Carcinoma for functional coordination analysis and the construction of a prediction model based on random forest. Non-negative matrix factorization (NMF) clustering was utilized to classify molecular subtypes of PTMC. Our findings revealed that the differential activation of pathways such as MAPK and PI3K was critical in enhancing the lateral lymph node metastatic potential of PTMC. We successfully screened biomarkers via machine learning and public databases, creating an effective prediction model for metastasis. Additionally, we explored the mechanism of metastasis-associated PTMC subtypes via NMF clustering. These insights into LLNM mechanisms in PTMC may contribute to future biomarker screening and the identification of therapeutic targets.

Chromosomal instability (CIN) refers to an increased propensity of cells to acquire structural and numerical chromosomal abnormalities during cell division, which contributes to tumour genetic heterogeneity. CIN has long been recognized as a hallmark of cancer, and evidence over the past decade has strongly linked CIN to tumour evolution, metastasis, immune evasion and treatment resistance. Until recently, the mechanisms by which CIN propels cancer progression have remained elusive. Beyond the generation of genomic copy number heterogeneity, recent work has unveiled additional tumour-promoting consequences of abnormal chromosome segregation. These mechanisms include complex chromosomal rearrangements, epigenetic reprogramming and the induction of cancer cell-intrinsic inflammation, emphasizing the multifaceted role of CIN in cancer.

Chromosomal aneuploidy, that is, numerical chromosome aberrations, is one of the molecular hallmarks of cancer. However, when neoplasms are studied with sequencing- and array-based approaches, chromosome numbers and ploidy states are typically inferred from bulk DNA data. Furthermore, published molecular estimates of neoplasia-associated aneuploidy often also include genomic imbalances resulting from various types of structural rearrangement, which likely result from other mechanisms than numerical chromosome aberrations. We thus analyzed chromosome numbers using single-cell cytogenetic data from 83,862 tumors, and show that both benign and malignant tumors are highly heterogeneous with regard to deviations from the normal, diploid state. Focusing on the chromosome numbers in 112 specific tumor types, defined by both exact morphologic diagnosis and organ location and from which data from ≥50 cases were available, we found two major clusters: one predominated by near-diploid neoplasms and one by neoplasms with extensive aneuploidy and one or more whole genome doublings. The former cluster included most benign solid tumors, myeloid neoplasms, and malignant gene fusion-associated solid tumors, whereas the latter was predominated by malignant solid tumors and lymphomas. For 16 malignant tumor types, the distribution of chromosome numbers could be compared to TCGA ploidy level data. Cytogenetic and molecular data correlated well, but the former indicates a higher level of clonal heterogeneity. The results presented here suggest shared pathogenetic mechanisms in certain tumor types and provide a reference for molecular analyses.

Missing an entire chromosome or chromosome arm in normal diploid cells has a deleterious impact on cell viability, which may contribute to the development of specific birth defects. Nevertheless, the effects of chromosome loss in human cells have remained unexplored due to the lack of suitable model systems. Here, we developed an efficient, selection-free approach to generate partial monosomy in human induced pluripotent stem cells (iPSCs). The introduction of Cas9 proteins and a pair of gRNAs induces over megabase-sized interstitial chromosomal deletions. Using human chromosome 21 (HSA21) as a model, partial monosomy 21q (PM21q) iPSC lines with deletions ranging from 4.5 to 27.9 Mb were isolated. A 33.6 Mb deletion, encompassing all protein-coding genes on 21q, was also achieved, establishing the first 21q monosomy human iPSC line. Transcriptome and proteome analyses revealed that the abundances of mRNA and protein encoded by the majority of genes in the monosomic regions are half of the diploid expression level, indicating an absence of dosage compensation. The ability to generate customized partial monosomy cell lines on an isogenic, karyotypically normal background should facilitate the gain of novel insights into the impact of chromosome loss on cellular fitness.

In non-small cell lung cancers, the non-squamous and squamous subtypes (nsqNSCLC and sqNSCLC) exhibit disparities in pathophysiology, tumor immunology, and potential genomic correlates affecting responses to immune checkpoint inhibitor (ICI)-based treatments. In our in-house training cohort (n=85), the presence of the LRP1B deleterious mutation (LRP1B-del) was associated with longer and shorter progression-free survival (PFS) on ICIs alone in nsqNSCLCs and sqNSCLCs, respectively (Pinteraction=0.008). These results were validated using a larger public ICI cohort (n=208, Pinteraction<0.001). Multiplex immunofluorescence staining revealed an association between LRP1B-del and increased and decreased numbers of tumor-infiltrating CD8+ T cells in nsqNSCLCs (P=0.040) and sqNSCLCs (P=0.014), respectively. In the POPLAR/OAK cohort, nsqNSCLCs with LRP1B-del demonstrated improved PFS benefits from atezolizumab over docetaxel (hazard ratio (HR) =0.70, P=0.046), whereas this benefit was negligible in those without LRP1B-del (HR=1.05, P=0.64). Conversely, sqNSCLCs without LRP1B-del benefited more from atezolizumab (HR=0.60, P=0.002) than those with LRP1B-del (HR=1.30, P=0.31). Consistent results were observed in the in-house CHOICE-01 cohort, in which nsqNSCLCs with LRP1B-del and sqNSCLCs without LRP1B-del benefited more from toripalimab plus chemotherapy than from chemotherapy alone (Pinteraction=0.008). This multi-cohort study delineates the antithetical impacts of LRP1B-del in nsqNSCLCs and sqNSCLCs on predicting the benefits from ICI alone or with chemotherapy over chemotherapy alone. Our findings highlight the distinct clinical utility of LRP1B-del in guiding treatment choices for nsqNSCLCs and sqNSCLCs, emphasizing the necessity for a detailed analysis based on pathological subtypes when investigating biomarkers for cancer therapeutics.

Rapid evaporative ionization mass spectrometry (REIMS) coupled with a monopolar handpiece used for surgical resection and combined with chemometrics has been previously explored by our research group (Mangraviti et al. in Int J Mol Sci 23(18):10562, 2022) to identify several mammary gland pathologies. Here, the increased sample size allowed the construction of three statistical models to distinguish between benign and malignant canine mammary tumours (CMTs), facilitating a more in-depth investigation of changes in cellular metabolic phenotype during neoplastic transformation and biological behaviour. The results demonstrate that REIMS is effective in identifying neoplastic tissues with an accuracy of 97%, with differences in MS spectra characterized by the relative abundance of phospholipids compared to triglycerides more commonly identified in normal mammary glands. The increased rate of phospholipid synthesis represents an informative feature for tumour recognition, with phosphatidylcholine and phosphatidylethanolamine, the two major phospholipid species identified here together with sphingolipids, playing a crucial role in carcinogenesis. REIMS technology allowed the classification of different histotypes of benign CMTs with an accuracy score of 95%, distinguishing them from normal glands based on the increase in sphingolipids, glycolipids, phospholipids, and arachidonic acid, demonstrating the close association between cancer and inflammation. Finally, dysregulation of fatty acid metabolism with increased signalling for saturated, mono- and polyunsaturated fatty acids characterized the metabolic phenotype of neoplastic cells and their malignant transformation, supporting the increased formation of new organelles for cell division. Further investigations on a more significant number of tumour histotypes will allow for the creation of a more extensive database and lay the basis for how understanding metabolic alterations in the tumour microenvironment can improve surgical precision.

Profiling tumors with single-cell RNA sequencing has the potential to identify recurrent patterns of transcription variation related to cancer progression, and to produce therapeutically relevant insights. However, strong intertumor heterogeneity can obscure more subtle patterns that are shared across tumors. Here we introduce a statistical method, generalized binary covariance decomposition (GBCD), to address this problem. We show that GBCD can decompose transcriptional heterogeneity into interpretable components-including patient-specific, dataset-specific and shared components relevant to disease subtypes-and that, in the presence of strong intertumor heterogeneity, it can produce more interpretable results than existing methods. Applied to data on pancreatic ductal adenocarcinoma, GBCD produced a refined characterization of existing tumor subtypes, and identified a gene expression program prognostic of poor survival independent of tumor stage and subtype. The gene expression program is enriched for genes involved in stress responses, and suggests a role for the integrated stress response in pancreatic ductal adenocarcinoma.

Mitochondria are pivotal in cellular energy metabolism and have garnered significant attention for their roles in cancer progression and therapy resistance. Despite this, the functional diversity of mitochondria across various cancer types remains inadequately characterized. This study seeks to fill this knowledge gap by introducing and validating MitoScore-a novel metric designed to quantitatively assess mitochondrial function across a wide array of cancers. Our investigation evaluates the capacity of MitoScore not only to distinguish between tumor and adjacent normal tissues but also to serve as a predictive marker for clinical outcomes. We analyzed gene expression data from 24 cancer types and corresponding normal tissues using the TCGA database. MitoScore was calculated by summing the normalized expression levels of six mitochondrial genes known to be consistently altered across multiple cancers. Differential gene expression was assessed using DESeq2, with a focus on identifying significant changes in mitochondrial function. MitoScore's associations with tumor proliferation, hypoxia, aneuploidy, and clinical outcomes were evaluated using Spearman's correlation, linear regression, and Kaplan-Meier survival analyses. MitoScore was significantly higher in tumor tissues compared to normal tissues across most cancer types (p < 0.001). It positively correlated with tumor proliferation rates (r = 0.46), hypoxia scores (r = 0.61), and aneuploidy (r = 0.44), indicating its potential as a marker of aggressive tumor behavior. High MitoScore was also associated with poorer prognosis in several cancer types, suggesting its utility as a predictive biomarker for clinical outcomes. This study introduces MitoScore, a metric for mitochondrial activity often elevated in tumors and linked to poor prognosis. It correlates positively with hypoxia and negatively with stromal and immune infiltration, highlighting mitochondria's role in the tumor microenvironment. MitoScore's association with genomic instability, such as aneuploidy, suggests mitochondrial dysfunction contributes to cancer progression. Despite challenges in mitochondrial-targeted therapies, MitoScore may identify tumors responsive to such treatments, warranting further research for clinical application.

The HOXB13/IL17RB gene expression biomarker has been shown to predict response to adjuvant and extended endocrine therapy in patients with early-stage ER+ HER2- breast tumors. HOXB13 gene expression is the primary determinant driving the prognostic and endocrine treatment-predictive performance of the biomarker. Currently, there is limited data on HOXB13 expression in HER2+ and ER- breast cancers. Herein, we studied the expression of HOXB13 in large cohorts of HER2+ and ER- breast cancers.

We investigated gene expression, genomic copy number, mutational signatures, and clinical outcome data in the TGGA and METABRIC breast cancer cohorts. Genomic-based gene amplification data was validated with tri-colored fluorescence in situ hybridization.

In the TCGA breast cancer cohort, HOXB13 gene expression was significantly higher in HER2+ versus HER2- breast cancers, and its expression was also significantly higher in the ER- versus ER+ breast cancers. HOXB13 is frequently co-gained or co-amplified with ERBB2. Joint copy gains of HOXB13 and ERBB2 occurred with low-level co-gains or high-level co-amplifications (co-amp), the latter of which is associated with an interstitial loss that includes the tumor suppressor BRCA1. ERBB2/HOXB13 co-amp tumors with interstitial BRCA1 loss exhibit a mutational signature associated with APOBEC deaminase activity and copy number signatures associated with chromothripsis and genomic instability. Among ERBB2-amplified tumors of different tissue origins, ERBB2/HOXB13 co-amp with a BRCA1 loss appeared to be enriched in breast cancer compared to other tumor types. Lastly, patients with ERBB2/HOXB13 co-amplified and BRCA1 lost tumors displayed a significantly shorter progression-free survival (PFS) than those with ERBB2-only amplifications. The difference in PFS was restricted to the ER- subset patients and this difference in PFS was not solely driven by HOXB13 gene expression.

HOXB13 is frequently co-gained with ERBB2 at both low-copy number level or as complex high-level amplification with relative BRCA1 loss. ERBB2/HOXB13 amplified, BRCA1-lost tumors are strongly enriched in breast cancer, and patients with such breast tumors experience a shortened PFS.

The DNA ploidy and the S phase fraction (SPF) are associated with advanced tumors progression. The present study aimed to investigate the clinicopathological behavior of oral squamous cell carcinoma(OSCC) and the prognostication of the DNA ploidy and the SPF on its clinical outcomes.

This a prospective longitudinal study of 93 OSCC patients (186 specimens). Using high resolution flowcytometry, DNA analysis and SPF were performed for the primary tumor, field of cancerization and a control normal field. The patients were followed clinically for the development of recurrence or second primary tumor(SPT).

All the 93 patients are associated with wide field of cancerization. Out of the total number of cases, 28 (30%) patients developed recurrence. Of them 17 (18.3%) had regional recurrence, and eight (8.6%) had local recurrence. Second primary tumors were identified in 14 (15.1%) of the cases. Nodal metastasis, overall recurrence and SPTs were significantly associated with DNA aneuploidy and high SPF. High SPF and DNA hyperploidy showed a 28% increased risk of local recurrence (P.value = 0.032).

In a series of 93 oral cancers who had wide field of cancerization, 30% of the patients developed recurrence, and 15% developed second primary tumors. The DNA ploidy and the SPF were independent prognosticators for the prediction of OSCC outcomes.

Aberrant expression of the BAP1 (BRCA associated protein 1) tumor suppressor gene is a prominent risk factor for several tumor types and is important in tumor evolution and progression. Here we performed integrated multi-omics analyses using data from The Cancer Genome Atlas for 33 cancer types and over 10 000 individuals to identify alterations leading to BAP1 disruption. We combined existing variant calls and new calls derived from a de novo local realignment pipeline across multiple independent variant callers, increasing somatic variant detection by 41% from 182 to 257, including 11 indels ≥40 bp. The expanded detection of mutations highlights the power of new tools to uncover longer indels and impactful mutations. We developed an expression-based BAP1 activity score and identified a transcriptional profile associated with BAP1 disruption in cancer. BAP1 has been proposed to play a critical role in controlling tumor plasticity and normal cell fate. Leveraging human and mouse liver datasets, BAP1 loss in normal cells resulted in lower BAP1 activity scores and lower scores were associated with a less-differentiated phenotype in embryonic cells. Together, our expanded BAP1 mutant samples revealed a transcriptional signature in cancer cells, supporting BAP1's influences on cellular plasticity and cell identity maintenance.