Next-generation characterization of the Cancer Cell Line Encyclopedia

Identification of AXL as a novel positive regulator of lipid raft in gastric cancer

Lipid rafts are highly heterogeneous and dynamic microdomains involved in molecule trafficking and signaling transduction. This study investigates the role of lipid rafts in gastric cancer and their key regulators. Analyzing FFPE samples from 111 gastric cancer patients, we found that high lipid raft levels predict poor prognosis. Modulating these levels in gastric cancer cell lines significantly impacted cell proliferation, migration, and invasion. Weighted Gene Co-expression Network Analysis identified AXL as a hub gene associated with lipid rafts. AXL knockdown experiments revealed its interaction with Caveolin-1, a caveolae lipid raft protein, which regulates lipid raft levels and promotes AKT and ERK signaling, enhancing cancer development and metastasis. In vivo tumorigenesis assays and survival analyses further supported these findings. This study underscores the significance of lipid rafts in gastric cancer and identifies AXL as a novel regulator, offering new insights into the molecular mechanisms of cancer progression and suggesting potential therapeutic targets.

LRP1 involvement in FHIT-regulated HER2 signaling in non-small cell lung cancer

The tumor suppressor fragile histidine triad (FHIT) is frequently lost in non-small cell lung cancer (NSCLC). We previously showed that a down-regulation of FHIT causes an up-regulation of the activity of HER2 associated to an epithelial-mesenchymal transition (EMT) and that lung tumor cells harboring a FHITlow/pHER2high phenotype are sensitive to anti-HER2 drugs. Here, we sought to decipher the FHIT-regulated HER2 signaling pathway in NSCLC. Transcriptomic analysis of tumor cells isolated from NSCLC revealed the endocytic receptor low density lipoprotein receptor-related protein 1 (LRP1), a central regulator of membrane trafficking and cell signaling, as a potential player of this signaling. In a cohort of 80 NSCLC assessed by immunohistochemistry, we found a significant association between a low FHIT expression and a high pHER2 and LRP1 expression by tumor cells. Experiments of FHIT silencing showed that FHIT regulated LRP1 expression both at the mRNA and protein levels in lung cell lines. Analyzing the relationship between LRP1 and HER2, we observed that an anti-HER2 targeted therapy reversed LRP1 overexpression induced by FHIT silencing whereas LRP1 silencing did not affect HER2 activity. Studying the functional role of LRP1, we showed that cell proliferation and invasion induced by FHIT silencing were LRP1-dependent. In addition, we found that the induction of vimentin upon FHIT inactivation was counteracted by LRP1 silencing. These results suggest that LRP1 acts downstream of HER2 to induce EMT and tumor progression following FHIT loss. Dual targeting of HER2 and LRP1 might represent a therapeutic strategy to more efficiently inhibit HER2 signaling in FHIT-negative NSCLC.

Mitochondrial signatures shape phenotype switching and apoptosis in response to PLK1 inhibitors

PLK1 inhibitors are emerging anticancer agents that are being tested as monotherapy and combination therapies for various cancers. Although PLK1 inhibition in experimental models has shown potent antitumor effects, translation to the clinic has been hampered by low antitumor activity and tumor relapse. Here, we report the identification of mitochondrial protein signatures that determine the sensitivity to approaches targeting PLK1 in human melanoma cell lines. In response to PLK1 inhibition or gene silencing, resistant cells adopt a pro-inflammatory and dedifferentiated phenotype, whereas sensitive cells undergo apoptosis. Mitochondrial DNA depletion and silencing of the ABCD1 transporter sensitize cells to PLK1 inhibition and attenuate the associated pro-inflammatory response. We also found that nonselective inhibitors of the p90 ribosomal S6 kinase (RSK) exert their antiproliferative and pro-inflammatory effects via PLK1 inhibition. Specific inhibition of RSK, on the other hand, is anti-inflammatory and promotes a program of antigen presentation. This study reveals the overlooked effects of PLK1 on phenotype switching and suggests that mitochondrial precision medicine can help improve the response to targeted therapies.

Overexpression and clinicopathological significance of zinc finger protein 71 in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most prevalent and aggressive forms of liver cancer, with high morbidity and poor prognosis due to late diagnosis and limited treatment options. Despite advances in understanding its molecular mechanisms, effective biomarkers for early detection and targeted therapy remain scarce. Zinc finger protein 71 (ZNF71), a zinc-finger protein, has been implicated in various cancers, yet its role in HCC remains largely unexplored. This gap in knowledge underscores the need for further investigation into the ZNF71 of potential as a diagnostic or therapeutic target in HCC.

AIM

To explore the expression levels, clinical relevance, and molecular mechanisms of ZNF71 in the progression of HCC.

METHODS

The study evaluated ZNF71 expression in 235 HCC specimens and 13 noncancerous liver tissue samples using immunohistochemistry. High-throughput datasets were employed to assess the differential expression of ZNF71 in HCC and its association with clinical and pathological features. The impact of ZNF71 on HCC cell line growth was examined through clustered regularly interspaced short palindromic repeat knockout screens. Co-expressed genes were identified and analyzed for enrichment using LinkedOmics and Sangerbox 3.0, focusing on significant correlations (P < 0.01, correlation coefficient ≥ 0.3). Furthermore, the relationship between ZNF71 expression and immune cell infiltration was quantified using TIMER2.0.

RESULTS

ZNF71 showed higher expression in HCC tissues vs non-tumorous tissues, with a significant statistical difference (P < 0.05). Data from the UALCAN platform indicated increased ZNF71 levels across early to mid-stage HCC, correlating with disease severity (P < 0.05). High-throughput analysis presented a standardized mean difference in ZNF71 expression of 0.55 (95% confidence interval [CI]: 0.34-0.75). The efficiency of ZNF71 mRNA was evaluated, yielding an area under the curve of 0.78 (95%CI: 0.75-0.82), a sensitivity of 0.63 (95%CI: 0.53-0.72), and a specificity of 0.82 (95%CI: 0.73-0.89). Diagnostic likelihood ratios were positive at 3.61 (95%CI: 2.41-5.41) and negative at 0.45 (95%CI: 0.36-0.56). LinkedOmics analysis identified strong positive correlations of ZNF71 with genes such as ZNF470, ZNF256, and ZNF285. Pathway enrichment analyses highlighted associations with herpes simplex virus type 1 infection, the cell cycle, and DNA replication. Negative correlations involved metabolic pathways, peroxisomes, and fatty acid degradation. TIMER2.0 analysis demonstrated positive correlations of high ZNF71 expression with various immune cell types, including CD4⁺ T cells, B cells, regulatory T cells, monocytes, macrophages, and myeloid dendritic cells.

CONCLUSION

ZNF71 is significantly upregulated in HCC, correlating with the disease’s clinical and pathological stages. It appears to promote HCC progression through mechanisms involving the cell cycle and metabolism and is associated with immune cell infiltration. These findings suggest that ZNF71 could be a novel target for diagnosing and treating HCC.

Intratumor heterogeneity in KRAS signaling shapes treatment resistance

KRAS mutations are linked to some of the deadliest forms of cancer. Pharmacological studies suggest that co-targeting KRAS with feedback/bypass pathways could lead to enhanced anti-tumor activity. The underlying premise is that cancers display a deep-rooted hypersensitivity to KRAS inactivation. Here, we investigate the role of intratumor heterogeneity in pancreatic ductal adenocarcinoma, focusing on oncogenic KRAS addiction and treatment resistance. Integrated analysis of single-cell and bulk RNA sequencing data reveals that most tumors display a mixture of cells with vastly different degrees of KRAS dependency. We identify distinct cell populations that vary in their gene expression patterns pertaining to the predicted level of KRAS signaling activity, cell growth, and differentiation commitment within each tumor. Selective targeting of mutant KRAS suppresses the growth of tumor cells with high RAS/mitogen-activated protein kinase (MAPK) activity while sparing pre-existing subsets with low RAS signaling activity, necessitating alternative treatments. Combination immunotherapy leads to durable tumor regression in preclinical models.

Harnessing the FBXW7 Somatic Mutant R465C for Targeted Protein Degradation

Targeted protein degradation (TPD) is a pharmacological strategy that eliminates specific proteins from cells by harnessing cellular proteolytic degradation machinery. In proteasome-dependent TPD, expanding the repertoire of E3 ligases compatible with this approach could enhance the applicability of this strategy across various biological contexts. In this study, we discovered that a somatic mutant of FBXW7, R465C, can be exploited by heterobifunctional compounds for targeted protein degradation. This work demonstrates the potential of utilizing mutant E3 ligases that occur exclusively in diseased cells for TPD applications.

De novo identification of universal cell mechanics gene signatures

Cell mechanical properties determine many physiological functions, such as cell fate specification, migration, or circulation through vasculature. Identifying factors that govern the mechanical properties is therefore a subject of great interest. Here, we present a mechanomics approach for establishing links between single-cell mechanical phenotype changes and the genes involved in driving them. We combine mechanical characterization of cells across a variety of mouse and human systems with machine learning-based discriminative network analysis of associated transcriptomic profiles to infer a conserved network module of five genes with putative roles in cell mechanics regulation. We validate in silico that the identified gene markers are universal, trustworthy, and specific to the mechanical phenotype across the studied mouse and human systems, and demonstrate experimentally that a selected target, CAV1, changes the mechanical phenotype of cells accordingly when silenced or overexpressed. Our data-driven approach paves the way toward engineering cell mechanical properties on demand to explore their impact on physiological and pathological cell functions.

Refined variant calling pipeline on RNA-seq data of breast cancer cell lines without matched-normal samples

OBJECTIVE

RNA-seq delivers valuable insights both to transcriptional patterns and mutational landscapes for transcribed genes. However, as tumour cell lines frequently lack their matched-normal counterpart, variant calling without the paired normal sample is still challenging. In order to exclude variants of common genetic variation without a matched-normal control, filtering strategies need to be developed to identify tumour relevant variants in cell lines.

RESULTS

Here, variants of 29 breast cancer cell lines were called on RNA-seq data via HaplotypeCaller. Low read depth sites, RNA-edit sites, and low complexity regions in coding regions were excluded. Common variants were filtered using 1000 genomes, gnomAD, and dbSNP data. Starting from hundred thousands of single nucleotide variants and small insertions and deletions, about thousand variants remained after filtering for each sample. Extracted variants were validated against the Catalogue of Somatic Mutations in Cancer (COSMIC) for 10 cell lines included in both data sets. Approximately half of the COSMIC variants were successfully called. Importantly, missing variants could mainly be attributed to sites with low read depth. Moreover, filtered variants also included all 10 cancer gene census COSMIC variants, a condensed hallmark variant set.

Antisense oligonucleotide-mediated TRA2β poison exon inclusion induces the expression of a lncRNA with anti-tumor effects

Upregulated expression of the oncogenic splicing factor TRA2β occurs in human tumors partly through decreased inclusion of its autoregulatory non-coding poison exon (PE). Here, we reveal that low TRA2β-PE inclusion negatively impacts patient survival across several tumor types. We demonstrate the ability of splice-switching antisense oligonucleotides (ASOs) to promote TRA2β-PE inclusion and lower TRA2β protein levels in pre-clinical cancer models. TRA2β-PE-targeting ASOs induce anti-cancer phenotypes and widespread transcriptomic alterations with functional impact on RNA processing, mTOR, and p53 signaling pathways. Surprisingly, the effect of TRA2β-PE-targeting ASOs on cell viability are not phenocopied by TRA2β knockdown. Mechanistically, we find that the ASO functions by both decreasing TRA2β protein and inducing the expression of TRA2β-PE-containing transcripts that act as long non-coding RNAs to sequester nuclear proteins. Finally, TRA2β-PE-targeting ASOs are toxic to preclinical 3D organoid and in vivo patient-derived xenograft models. Together, we demonstrate that TRA2β-PE acts both as a regulator of protein expression and a long-noncoding RNA to control cancer cell growth. Drugging oncogenic splicing factors using PE-targeting ASOs is a promising therapeutic strategy.

Integrated multi-omics analysis identifies a machine learning-derived signature for predicting prognosis and therapeutic vulnerability in clear cell renal cell carcinoma

AIMS

Clear cell renal cell carcinoma (ccRCC) shows considerable variation within and between tumors, presents varying treatment responses among patients, possibly due to molecular distinctions. This study utilized a multi-center and multi-omics analysis to establish and validate a prognosis and treatment vulnerability signature (PTVS) capable of effectively predicting patient prognosis and drug responsiveness.

MATERIALS AND METHODS

To address this complexity, we constructed an integrative multi-omics analysis using 10 clustering algorithms on ccRCC patient data. Afterwards, we applied bootstrapping in univariate Cox regression and the Boruta algorithm to pinpoint clinically relevant genes. Based on this, we developed a robust PTVS using seven machine learning algorithms.

KEY FINDINGS

Our analysis revealed two distinct ccRCC subtypes with differential prognostic implications, notably identifying subtype 2 with poorer outcomes. Patients in the low PTVS group exhibited superior prognosis statistics and an augmented sensitivity to immunotherapy, features consistent with a 'hot tumor' phenotype. Conversely, individuals within the high PTVS group exhibited diminished prognosis statistic and restricted advantages from immunotherapy. Importantly, the PTVS holds future potential as a notable biomarker for guiding personalized treatment strategies, with four prospective targets (CTSK, XDH, PKMYT1, and EGLN2) indicating therapeutic promise in patients scoring high on PTVS.

SIGNIFICANCE

The integrative analysis of multi-omics data profoundly enhances the molecular stratification of ccRCC, underscoring far-reaching impact of such comprehensive profiling on its therapeutic strategies.

Molecular subtypes of intrahepatic cholangiocarcinoma

Intrahepatic cholangiocarcinoma (iCCA) presents in two clinically distinct subtypes: large duct (LD-iCCA) and small duct (SD-iCCA). These subtypes exhibit significant molecular, genetic, and histopathological differences that impact patient prognosis and treatment responsiveness. This review advocates for a subtype-specific approach to iCCA research and clinical management, including tailored therapeutic strategies that consider distinct genetic profiles and tumor microenvironments. Current therapeutic approaches hold promise, yet efficacy varies by subtype. Additionally, subtype-specific molecular diagnostics, including DNA methylation-based classifiers and transcriptomic sequencing, have shown potential in refining iCCA subclassification, thereby guiding precision medicine efforts. This article outlines existing clinical trials, key research trajectories, and future directions for developing more effective subtype-adapted therapies for iCCA.

Intrinsic electrical activity drives small-cell lung cancer progression

Elevated or ectopic expression of neuronal receptors promotes tumour progression in many cancer types1,2; neuroendocrine (NE) transformation of adenocarcinomas has also been associated with increased aggressiveness3. Whether the defining neuronal feature, namely electrical excitability, exists in cancer cells and impacts cancer progression remains mostly unexplored. Small-cell lung cancer (SCLC) is an archetypal example of a highly aggressive NE cancer and comprises two major distinct subpopulations: NE cells and non-NE cells4,5. Here we show that NE cells, but not non-NE cells, are excitable, and their action potential firing directly promotes SCLC malignancy. However, the resultant high ATP demand leads to an unusual dependency on oxidative phosphorylation in NE cells. This finding contrasts with the properties of most cancer cells reported in the literature, which are non-excitable and rely heavily on aerobic glycolysis. Additionally, we found that non-NE cells metabolically support NE cells, a process akin to the astrocyte-neuron metabolite shuttle6. Finally, we observed drastic changes in the innervation landscape during SCLC progression, which coincided with increased intratumoural heterogeneity and elevated neuronal features in SCLC cells, suggesting an induction of a tumour-autonomous vicious cycle, driven by cancer cell-intrinsic electrical activity, which confers long-term tumorigenic capability and metastatic potential.

A hybrid machine learning framework for functional annotation of mitochondrial glutathione transport and metabolism proteins in cancers

BACKGROUND

Alterations of metabolism, including changes in mitochondrial metabolism as well as glutathione (GSH) metabolism are a well appreciated hallmark of many cancers. Mitochondrial GSH (mGSH) transport is a poorly characterized aspect of GSH metabolism, which we investigate in the context of cancer. Existing functional annotation approaches from machine (ML) or deep learning (DL) models based only on protein sequences, were unable to annotate functions in biological contexts.

RESULTS

We develop a flexible ML framework for functional annotation from diverse feature data. This hybrid ML framework leverages cancer cell line multi-omics data and other biological knowledge data as features, to uncover potential genes involved in mGSH metabolism and membrane transport in cancers. This framework achieves strong performance across functional annotation tasks and several cell line and primary tumor cancer samples. For our application, classification models predict the known mGSH transporter SLC25A39 but not SLC25A40 as being highly probably related to mGSH metabolism in cancers. SLC25A10, SLC25A50, and orphan SLC25A24, SLC25A43 are predicted to be associated with mGSH metabolism in multiple biological contexts and structural analysis of these proteins reveal similarities in potential substrate binding regions to the binding residues of SLC25A39.

CONCLUSION

These findings have implications for a better understanding of cancer cell metabolism and novel therapeutic targets with respect to GSH metabolism through potential novel functional annotations of genes. The hybrid ML framework proposed here can be applied to other biological function classifications or multi-omics datasets to generate hypotheses in various biological contexts. Code and a tutorial for generating models and predictions in this framework are available at: https://github.com/lkenn012/mGSH_cancerClassifiers .

Squamous cell cancers of the aero-upper digestive tract: A unified perspective on biology, genetics, and therapy

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.

Sideroflexin family genes were dysregulated and associated with tumor progression in prostate cancers

Sideroflexin (SFXN) family genes encode for a group of mitochondrial proteins involved in cellular processes such as iron homeostasis, amino acid metabolism, and energy production. Recent studies showed that they were aberrantly expressed in certain human cancers. However, there is a paucity of information about their expression in prostate cancer. In this study, we took a comprehensive approach to investigate their expression profiles in benign prostate tissue, prostate-derived cell lines, and prostate cancer tissues using multiple transcriptome datasets. Our results showed that SFXN1/3/4 genes were predominantly expressed in prostate tissue and cell lines. SFXN2/4 genes were significantly upregulated while the SFXN3 expression was significantly downregulated in malignant tissues compared to benign tissues. SFXN4 expression was identified as a diagnostic biomarker and prognostic factor for unfavorite survival outcomes. In advanced prostate cancers, SFXN2/4 expressions were positively correlated with the androgen receptor signaling activity but negatively correlated with the neuroendocrinal features. Further analysis discovered that SFXN5 expression was significantly elevated in neuroendocrinal prostate cancers. In conclusion, SFXN2/4 expressions are novel biomarkers in prostate cancer diagnosis and prognosis.

In-Depth Proteomic Analysis of Tissue Interstitial Fluid Reveals Biomarker Candidates Related to Varying Differentiation Statuses in Gastric Adenocarcinoma

The proteomic heterogeneity of gastric adenocarcinoma (GC) has been extensively investigated at the bulk tissue level, which can only provide an average molecular state. In this study, we collected an in-depth quantitative proteomic dataset of tissues and interstitial fluids (ISFs) from both poorly and non-poorly differentiated GC and presented a comprehensive analysis from several perspectives. Comparison of proteomes between ISFs and tissues revealed that ISF exhibited higher abundances of proteins associated with blood microparticles, protein-lipid complexes, immunoglobulin complexes, and high-density lipoprotein particles. Also, consistent and inconsistent protein abundance changes between them were revealed by a correlation analysis. Interestingly, a more pronounced difference between tumors and normal adjacent tissues was found at the ISF level, which accurately reflected tissue properties compared to those of bulk tissue. Two ISF-derived biomarker candidates, calsyntenin-1 (CLSTN1) and prosaposin (PSAP), were identified by distinguishing patients with different differentiation statuses and were further validated in serum samples. Additionally, the silencing of CLSTN1 and PSAP was demonstrated to suppress cell proliferation, migration, and invasion in poorly differentiated gastric cancer cell lines. In summary, the ISF proteome offers a new perspective on tumor biology. This study provides a valuable resource that significantly enhances the understanding of GC and may ultimately benefit clinical practice.

Synthetic lethality of mRNA quality control complexes in cancer

Synthetic lethality exploits the genetic vulnerabilities of cancer cells to enable a targeted, precision approach to treat cancer1. Over the past 15 years, synthetic lethal cancer target discovery approaches have led to clinical successes of PARP inhibitors2 and ushered several next-generation therapeutic targets such as WRN3, USP14, PKMYT15, POLQ6 and PRMT57 into the clinic. Here we identify, in human cancer, a novel synthetic lethal interaction between the PELO-HBS1L and SKI complexes of the mRNA quality control pathway. In distinct genetic contexts, including 9p21.3-deleted and high microsatellite instability (MSI-H) tumours, we found that phenotypically destabilized SKI complex leads to dependence on the PELO-HBS1L ribosomal rescue complex. PELO-HBS1L and SKI complex synthetic lethality alters the normal cell cycle and drives the unfolded protein response through the activation of IRE1, as well as robust tumour growth inhibition. Our results indicate that PELO and HBS1L represent novel therapeutic targets whose dependence converges upon SKI complex destabilization, a common phenotypic biomarker in diverse genetic contexts representing a significant population of patients with cancer.

EGFR inhibition augments the therapeutic efficacy of the NAT10 inhibitor Remodelin in Colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is the second leading cause of cancer-related death worldwide, and treatment options for advanced CRC are limited. The regulatory mechanisms of aberrant NAT10-mediated N4-acetylcytidine (ac4C) modifications in cancer progression remains poorly understood. Consequently, an integrated transcriptomic analysis is necessary to fully elucidate the role of NAT10-mediated ac4C modifications in CRC progression.

METHODS

NAT10 expression levels were analyzed in CRC samples and compared with those in corresponding normal tissues. The potential mechanisms of NAT10 in CRC were investigated using RNA sequencing, RNA immunoprecipitation sequencing, and acetylated RNA immunoprecipitation sequencing. Additional in vivo and in vitro experiments, including CCK-8 assays, colony formation and mouse xenograft models, were conducted to explore the biological role of NAT10-mediated ac4C modifications. We also evaluated and optimized a potential treatment strategy targeting NAT10.

RESULTS

We found that NAT10 is highly expressed in CRC samples and plays a pro-oncogenic role. NAT10 knockdown led to PI3K-AKT pathway inactivation, thereby inhibiting CRC progression. However, treatment with the NAT10 inhibitor Remodelin induced only a limited and reversible growth arrest in CRC cells. Further epigenetic and transcriptomic analysis revealed that NAT10 enhances the stability of ERRFI1 mRNA by binding to its coding sequence region in an ac4C-dependent manner. NAT10 knockdown decreased ERRFI1 expression, which subsequently activated the EGFR pathway and counteracted the inhibitory effects on CRC. Based on these findings, we demonstrated that dual inhibition of NAT10 and EGFR using Remodelin and the EGFR-specific monoclonal antibody cetuximab resulted in improved therapeutic efficacy compared to either drug alone. Moreover, we observed that 5-Fluorouracil promoted the interaction between NAT10 and UBR5, which increased the ubiquitin-mediated degradation of NAT10, leading to ERRFI1 downregulation and EGFR reactivation. Triple therapy with Remodelin, cetuximab, and 5-Fluorouracil enhanced tumor regression in xenograft mouse models of CRC with wild-type KRAS, NRAS and BRAF.

CONCLUSIONS

Our study elucidated the mechanism underlying 5-Fu-induced NAT10 downregulation, revealing that NAT10 inhibition destabilizes ERRFI1 mRNA through ac4C modifications, subsequently resulting in EGFR reactivation. A triple therapy regimen of Remodelin, cetuximab, and 5-Fu showed potential as a treatment strategy for CRC with wild-type KRAS, NRAS and BRAF.

Personalized prediction of anticancer potential of non-oncology drugs through learning from genome derived molecular pathways

Advances in cancer genomics have significantly expanded our understanding of cancer biology. However, the high cost of drug development limits our ability to translate this knowledge into precise treatments. Approved non-oncology drugs, comprising a large repository of chemical entities, offer a promising avenue for repurposing in cancer therapy. Herein we present CHANCE, a supervised machine learning model designed to predict the anticancer activities of non-oncology drugs for specific patients by simultaneously considering personalized coding and non-coding mutations. Utilizing protein-protein interaction networks, CHANCE harmonizes multilevel mutation annotations and integrates pharmacological information across different drugs into a single model. We systematically benchmarked the performance of CHANCE and show its predictions are better than previous model and highly interpretable. Applying CHANCE to approximately 5000 cancer samples indicated that >30% might respond to at least one non-oncology drug, with 11% non-oncology drugs predicted to have anticancer activities. Moreover, CHANCE predictions suggested an association between SMAD7 mutations and aspirin treatment response. Experimental validation using tumor cells derived from seven patients with pancreatic or esophageal cancer confirmed the potential anticancer activity of at least one non-oncology drug for five of these patients. To summarize, CHANCE offers a personalized and interpretable approach, serving as a valuable tool for mining non-oncology drugs in the precision oncology era.

Kinase Inhibitor-Induced Cell-Type Specific Vacuole Formation in the Absence of Canonical ATG5-Dependent Autophagy Initiation Pathway

Pyridinyl-imidazole class p38 MAPKα/β (MAPK14/MAPK11) inhibitors including SB202190 have been shown to induce cell-type specific defective autophagy resulting in micron-scale vacuole formation, cell death, and tumor suppression. We had earlier shown that this is an off-target effect of SB202190. Here we provide evidence that this vacuole formation is independent of ATG5-mediated canonical autophagosome initiation. While SB202190 interferes with autophagic flux in many cell lines parallel to vacuolation, autophagy-deficient DU-145 cells and CRISPR/Cas9 gene-edited ATG5-knockout A549 cells also undergo vacuolation upon SB202190 treatment. Late-endosomal GTPase RAB7 colocalizes with these compartments and RAB7 GTP-binding is essential for SB202190-induced vacuolation. A screen for modulators of SB202190-induced vacuolation revealed molecules including multi-kinase inhibitor sorafenib as inhibitors of vacuolation and sorafenib co-treatment enhanced cytotoxicity of SB202190. Moreover, VE-821, an ATR inhibitor was found to phenocopy the cell-type specific vacuolation response of SB202190. To identify the factors determining the cell-type specificity of vacuolation induced by SB-compounds and VE-821, we compared the transcriptomics data from vacuole-forming and non-vacuole-forming cancer cell lines and identified a gene expression signature that may define sensitivity of cells to these small-molecules. Further analyses using small molecule tools and the gene signature discovered here, could reveal novel mechanisms regulating this interesting anti-cancer phenotype.

The analysis of transcriptomic signature of TNBC-searching for the potential RNA-based predictive biomarkers to determine the chemotherapy sensitivity

Neoadjuvant chemotherapy is the foundation treatment for triple-negative breast cancer (TNBC) and frequently results in pathological complete response (pCR). However, there are large differences in clinical response and survival after neoadjuvant chemotherapy of TNBC patients. The aim was to identify genes whose expression significantly associates with the efficacy of neoadjuvant chemotherapy in patients with TNBC. Transcriptomes of 46 formalin-fixed paraffin-embedded (FFPE) tumor samples from TNBC patients were analyzed by RNA-seq by comparing 26 TNBCs with pCR versus 20 TNBCs with pathological partial remission (pPR). Subsequently, we narrowed down the list of genes to those that strongly correlated with drug sensitivity of 63 breast cancer cell lines based on Dependency Map Consortium data re-analysis. Furthermore, the list of genes was limited to those presenting specific expression in breast tumor cells as revealed in three large published single-cell RNA-seq breast cancer datasets. Finally, we analyzed which of the selected genes were significantly associated with overall survival (OS) in TNBC TCGA dataset. A total of 105 genes were significantly differentially expressed in comparison between pPR versus pCR. As revealed by PLSR analysis in breast cancer cell lines, out of 105 deregulated genes, 42 were associated with sensitivity to docetaxel, doxorubicin, paclitaxel, and/or cyclophosphamide. We found that 24 out of 42 sensitivity-associated genes displayed intermediate or strong expression in breast malignant cells using single-cell RNAseq re-analysis. Finally, 10 out of 24 genes were significantly associated with overall survival in TNBC TCGA dataset. Our RNA-seq-based findings suggest that there might be transcriptomic signature consisted of 24 genes specifically expressed in tumor malignant cells for predicting neoadjuvant response in FFPE samples from TNBC patients prior to treatment initiation. Additionally, nine out of 24 genes were potential survival predictors in TNBC. This group of 24 genes should be further investigated for its potential to be translated into a predictive test(s).

Mutant PP2A Induces IGFBP2 Secretion to Promote Development of High-Grade Uterine Cancer

Uterine serous carcinoma (USC) and uterine carcinosarcoma (UCS) tumors are uniquely aggressive, suggesting that the primary tumor is intrinsically equipped to disseminate and metastasize. Previous work identified mutational hotspots within PPP2R1A, which encodes the Aα scaffolding subunit of protein phosphatase 2A (PP2A), a heterotrimeric serine/threonine phosphatase. Two recurrent heterozygous PPP2R1A mutations, P179R and S256F, occur exclusively within high-grade subtypes of uterine cancer and can drive tumorigenesis and metastasis. Elucidation of the mechanisms by which PP2A Aα mutants promote tumor development and progression could help identify therapeutic opportunities. Here, we showed that expression of these mutants in USC/UCS cell lines enhanced tumor-initiating capacity, drove a hybrid epithelial-to-mesenchymal plasticity phenotype, and elevated secretion of the tumorigenic cytokine insulin growth factor (IGF) binding protein 2 (IGFBP2). Therapeutic targeting of the IGFBP2/IGF receptor 1 signaling axis using small molecules and genetic approaches resulted in marked tumor growth inhibition. Mechanistically, PP2A regulated IGFBP2 expression through the transcription factor, NF-κB, which harbors a B56 recognition motif. Collectively, these results identify a role for PP2A in regulating paracrine cancer cell signaling that can be targeted to block the initiation and metastasis of high-grade uterine cancer. Significance: Elevated IGFBP2 secretion by uterine cancer cells with heterozygous PPP2R1A mutations supports tumor progression and confers a vulnerability to IGFBP2/IGF1R inhibition as a therapeutic approach for this highly aggressive cancer subtype.

Transcript-specific enrichment enables profiling of rare cell states via single-cell RNA sequencing

Single-cell genomics technologies have accelerated our understanding of cell-state heterogeneity in diverse contexts. Although single-cell RNA sequencing identifies rare populations that express specific marker transcript combinations, traditional flow sorting requires cell surface markers with high-fidelity antibodies, limiting our ability to interrogate these populations. In addition, many single-cell studies require the isolation of nuclei from tissue, eliminating the ability to enrich learned rare cell states based on extranuclear protein markers. In the present report, we addressed these limitations by developing Programmable Enrichment via RNA FlowFISH by sequencing (PERFF-seq), a scalable assay that enables scRNA-seq profiling of subpopulations defined by the abundance of specific RNA transcripts. Across immune populations (n = 184,126 cells) and fresh-frozen and formalin-fixed, paraffin-embedded brain tissue (n = 33,145 nuclei), we demonstrated that programmable sorting logic via RNA-based cytometry can isolate rare cell populations and uncover phenotypic heterogeneity via downstream, high-throughput, single-cell genomics analyses.

Optimized biochemical method for human Polyphosphate quantification

Polyphosphate (polyP), a biopolymer composed of phosphates, impacts a wide range of biological functions and pathological conditions in all organisms. However, polyP's intricate physiology and structure in human cells have remained elusive, largely due to the lack of a reliable quantification method including its extraction. In this study, we assess critical points in the whole process: extraction, purification, and quantification polyP from human cell lines. We developed a highly efficient method that extracts between 3 and 100 times more polyP than previously achieved. Supported by Nuclear Magnetic Resonance (NMR), our approach confirms that mammalian polyP is primarily a linear unbranched polymer. We applied the optimized method to commonly used human cell lines, uncovering important variations of intracellular polyP that correlate with the expression levels of specific polyP converting enzymes. This study underscores the importance of employing several techniques for polyP characterization in parallel and provides a valuable and standardized tool for further exploration in this field.

Targeting lysine acetylation readers and writers

Lysine acetylation is a major post-translational modification in histones and other proteins that is catalysed by the 'writer' lysine acetyltransferases (KATs) and mediates interactions with bromodomains (BrDs) and other 'reader' proteins. KATs and BrDs play key roles in regulating gene expression, cell growth, chromatin structure, and epigenetics and are often dysregulated in disease states, including cancer. There have been accelerating efforts to identify potent and selective small molecules that can target individual KATs and BrDs with the goal of developing new therapeutics, and some of these agents are in clinical trials. Here, we summarize the different families of KATs and BrDs, discuss their functions and structures, and highlight key advances in the design and development of chemical agents that show promise in blocking the action of these chromatin proteins for disease treatment.

Dysregulation of the p300/CBP histone acetyltransferases in human cancer

p300 (E1A binding protein 300) and CBP (CREB-binding protein) are critical regulators of chromatin dynamics and gene expression, playing essential roles in various cellular processes, including proliferation, differentiation, apoptosis, and immune responses. These homologous histone acetyltransferases (HATs) function as transcriptional co-activators by acetylating histones and non-histone proteins. p300/CBP is essential for development, and dysregulation of p300 and CBP has been implicated in several human diseases, particularly cancer. Somatic mutations that inactivate p300/CBP are frequently observed across various cancer types. Additionally, other mutations leading to translocations or truncations of p300/CBP can result in enhanced catalytic activity, potentially representing novel gain-of-function mutations that promote tumor progression. In this review, we discuss the mechanisms underlying the regulation of p300/CBP HAT activity, its dysregulation in cancer, and the development of p300/CBP inhibitors and their potential in cancer therapies.

Identification of a novel immunogenic cell death-related classifier to predict prognosis and optimize precision treatment in hepatocellular carcinoma

Accumulating studies have highlighted the biological significance of immunogenic cell death (ICD) in cancer immunity. However, the influence of ICD on tumor microenvironment (TME) formation and immune response in Hepatocellular carcinoma (HCC) remains largely unexplored. In this study, we systematically analyzed the mRNA profiles of ICD-related genes in 1847 HCC patients and identified three molecular subtypes with significantly different immune features and prognostic stratification. A reliable risk model named ICD score was constructed via machine learning algorithms to assess the immunological status, therapeutic responses, and clinical outcomes of individual HCC patients. High ICD score indicated an immune-excluded TME phenotype, with lower anticancer immunity and shorter survival time. In contrast, low ICD score corresponded to abundant immune cell infiltration, high sensitivity to immunotherapy and a positive prognosis, indicating an "immune-hot" phenotype. Pan-cancer analysis further validated a negative association between ICD score and the immune cell infiltration levels. In conclusion, our findings revealed that the ICD score could serve as a robust prognostic biomarker to predict the benefits of immunotherapy and optimize the clinical decision-making of HCC patients.

Leveraging Network Target Theory for Efficient Prediction of Drug-Disease Interactions: A Transfer Learning Approach

Efficient virtual screening methods can expedite drug discovery and facilitate the development of innovative therapeutics. This study presents a novel transfer learning model based on network target theory, integrating deep learning techniques with diverse biological molecular networks to predict drug-disease interactions. By incorporating network techniques that leverage vast existing knowledge, the approach enables the extraction of more precise and informative drug features, resulting in the identification of 88,161 drug-disease interactions involving 7,940 drugs and 2,986 diseases. Furthermore, this model effectively addresses the challenge of balancing large-scale positive and negative samples, leading to improved performance across various evaluation metrics such as an Area under curve (AUC) of 0.9298 and an F1 score of 0.6316. Moreover, the algorithm accurately predicts drug combinations and achieves an F1 score of 0.7746 after fine-tuning. Additionally, it identifies two previously unexplored synergistic drug combinations for distinct cancer types in disease-specific biological network environments. These findings are further validated through in vitro cytotoxicity assays, demonstrating the potential of the model to enhance drug development and identify effective treatment regimens for specific diseases.

A compendium of Amplification-Related Gain Of Sensitivity genes in human cancer

While the effect of amplification-induced oncogene expression in cancer is known, the impact of copy-number gains on "bystander" genes is less understood. We create a comprehensive map of dosage compensation in cancer by integrating expression and copy number profiles from over 8000 tumors in The Cancer Genome Atlas and cell lines from the Cancer Cell Line Encyclopedia. Additionally, we analyze 17 cancer open reading frame screens to identify genes toxic to cancer cells when overexpressed. Combining these approaches, we propose a class of 'Amplification-Related Gain Of Sensitivity' (ARGOS) genes located in commonly amplified regions, yet expressed at lower levels than expected by their copy number, and toxic when overexpressed. We validate RBM14 as an ARGOS gene in lung and breast cancer cells, and suggest a toxicity mechanism involving altered DNA damage response and STING signaling. We additionally observe increased patient survival in a radiation-treated cancer cohort with RBM14 amplification.

Comprehensive pan-cancer analysis reveals NTN1 as an immune infiltrate risk factor and its potential prognostic value in SKCM

Netrin-1 (NTN1) is a laminin-related secreted protein involved in axon guidance and cell migration. Previous research has established a significant connection between NTN1 and nervous system development. In recent years, mounting evidence indicates that NTN1 also plays a crucial role in tumorigenesis and tumor progression. For instance, inhibiting Netrin-1 has been shown to suppress tumor growth and epithelial-mesenchymal transition (EMT) characteristics in endometrial cancer. To further elucidate the influence of genes on tumors, we utilized a variety of machine learning techniques and found that NTN1 is strongly linked to multiple cancer types, suggesting it as a potential therapeutic target. This study aimed to elucidate the role of NTN1 in pan-cancer using multi-omics data and explore its potential as a prognostic biomarker in SKCM. Analysis of the TCGA, GTEx, and UALCAN databases revealed significant differences in NTN1 expression at both the mRNA and protein levels. Prognostic value was evaluated through univariate Cox regression and Kaplan-Meier methods. Mutation and methylation analyses were conducted using the cBioPortal and SMART databases. We identified genes interacting with and correlated to NTN1 through STRING and GEPIA2, respectively. Subsequently, we performed GO and KEGG enrichment analyses. The results suggested that NTN1 might be involved in crucial biological processes and pathways related to cancer development and progression, including cell adhesion, axon guidance, immune response, and various signaling pathways. We then explored the correlation between NTN1 and immune infiltration as well as immunotherapy using the ESTIMATE package, TIMER2.0, TISIDB, TIDE, TIMSO, and TCIA. The relationship between NTN1 and tumor heterogeneity, stemness, DNA methyltransferases, and MMR genes was also examined. Lastly, we constructed a nomogram based on NTN1 in SKCM and investigated its association with drug sensitivity. NTN1 expression was significantly associated with tumor immune infiltration, molecular subtypes, and clinicopathological features in various cancers. Genetic analysis revealed that Deep deletions were the most common type of NTN1 alteration. Additionally, a positive correlation was observed between NTN1 CNAs and its expression levels. In most cancers, NTN1 showed positive correlations with immune and stromal scores, as well as with specific immune cell populations. Its predictive value for immunotherapy response was comparable to that of tumor mutational burden. Furthermore, NTN1 exhibited positive correlations with tumor heterogeneity, stemness, DNA methyltransferase genes, and MMR genes. In SKCM, NTN1 was identified as an independent risk factor and demonstrated potential associations with multiple drugs. NTN1 exhibits substantial clinical utility as a prognostic marker and indicator of immune response across various tumor types. This comprehensive analysis provides insights into its potential implications in pan-cancer research.

Characterization of tumor prognosis and sensitive chemotherapy drugs based on cuproptosis-related gene signature in ovarian cancer

BACKGROUND

Cuproptosis is a novel form of cell death, acting on the tricarboxylic acid cycle in mitochondrial respiration and mediated by protein lipoylation. Other cancer cell death processes, such as necroptosis, pyroptosis, and ferroptosis, have been shown to play crucial roles in the therapy and prognosis of ovarian cancer. However, the role of cuproptosis in ovarian cancer remains unclear.

METHODS

The expression profiles of 10 cuproptosis-related genes were extracted from GSE140082. Kaplan-Meier survival and Cox proportional hazards regression were used to identify prognostic genes for constructing risk models. Following this, Least Absolute Shrinkage and Selection Operator regression was employed to construct a risk score model. Next, a nomogram was constructed to predict overall survival in ovarian cancer. Ultimately, our analysis compared the two groups across various dimensions, including clinical characteristics, tumor progression, metabolism-related pathways, immune landscape, and drug sensitivity.

RESULTS

MTF1 and LIAS were identified as protective factors in ovarian cancer, with patients in the higher risk group being significantly associated with poorer survival. Furthermore, integrating the risk score with clinical characteristics in the nomogram demonstrated high specificity and sensitivity in predicting survival. A higher propotion of M2 macrophages, follicular helper T cells, and resting mast cells was observed in the high-risk group. Additionally, the IC50 values of Dasatinib, Bortezomib, Parthenolide, and Imatinib were significantly lower in the high-risk group.

CONCLUSIONS

The study highlights the prognostic significance of cuproptosis-related genes and provides new insights into developing pharmacological therapeutic strategies targeting cuproptosis for the prevention and treatment of ovarian cancer.

ACSL4 and polyunsaturated lipids support metastatic extravasation and colonization

Metastatic dissemination to distant organs demands that cancer cells possess high morphological and metabolic adaptability. However, contributions of the cellular lipidome to metastasis remain elusive. Here, we uncover a correlation between metastasis potential and ferroptosis susceptibility in multiple cancers. Metastases-derived cancer cells exhibited higher ferroptosis sensitivity and polyunsaturated fatty acyl (PUFA)-lipid contents than primary-tumor-derived cells from ovarian cancer patients. Metabolism-focused CRISPR screens in a mouse model for ovarian cancer distant metastasis established via two rounds of in vivo selection revealed the PUFA-lipid biosynthesis enzyme acyl-coenzyme A (CoA) synthetase long-chain family member 4 (ACSL4) as a pro-hematogenous metastasis factor. ACSL4 promotes metastatic extravasation by enhancing membrane fluidity and cellular invasiveness. While promoting metastasis, the high PUFA-lipid state creates dependencies on abhydrolase-domain-containing 6, acylglycerol lipase (ABHD6), enoyl-CoA delta isomerase 1 (ECI1), and enoyl-CoA hydratase 1 (ECH1)-rate-limiting enzymes preparing unsaturated fatty acids (UFAs) for β-oxidation. ACSL4/ECH1 co-inhibition achieved potent suppression of metastasis. Our work establishes the dual functions of PUFA-lipids in tumor progression and metastasis that may be exploitable for therapeutic development.

Post-transcriptional cross- and auto-regulation buffer expression of the human RNA helicases DDX3X and DDX3Y

The Y-linked gene DDX3Y and its X-linked homolog DDX3X survived the evolution of the human sex chromosomes from ordinary autosomes. DDX3X encodes a multifunctional RNA helicase, with mutations causing developmental disorders and cancers. We find that, among X-linked genes with surviving Y homologs, DDX3X is extraordinarily dosage sensitive. Studying cells of individuals with sex chromosome aneuploidy, we observe that when the number of Y Chromosomes increases, DDX3X transcript levels fall; conversely, when the number of X Chromosomes increases, DDX3Y transcript levels fall. In 46,XY cells, CRISPRi knockdown of either DDX3X or DDX3Y causes transcript levels of the homologous gene to rise. In 46,XX cells, chemical inhibition of DDX3X protein activity elicits an increase in DDX3X transcript levels. Thus, perturbation of either DDX3X or DDX3Y expression is buffered: by negative cross-regulation of DDX3X and DDX3Y in 46,XY cells and by negative auto-regulation of DDX3X in 46,XX cells. DDX3X-DDX3Y cross-regulation is mediated through mRNA destabilization-as shown by metabolic labeling of newly transcribed RNA-and buffers total levels of DDX3X and DDX3Y protein in human cells. We infer that post-transcriptional auto-regulation of the ancestral (autosomal) DDX3X gene transmuted into auto- and cross-regulation of DDX3X and DDX3Y as these sex-linked genes evolved from ordinary alleles of their autosomal precursor.

Computational frameworks transform antagonism to synergy in optimizing combination therapies

While drug combinations are increasingly important in disease treatment, predicting their therapeutic interactions remains challenging. This review systematically analyzes computational methods for predicting drug combination effects through multi-omics data integration. We comprehensively assess key algorithms including DrugComboRanker and AuDNNsynergy, and evaluate integration approaches encompassing kernel regression and graph networks. The review elucidates artificial intelligence applications in predicting drug synergistic and antagonistic effects.

Biologically relevant integration of transcriptomics profiles from cancer cell lines, patient-derived xenografts, and clinical tumors using deep learning

Cell lines and patient-derived xenografts are essential to cancer research; however, the results derived from such models often lack clinical translatability, as they do not fully recapitulate the complex cancer biology. Identifying preclinical models that sufficiently resemble the biological characteristics of clinical tumors across different cancers is critically important. Here, we developed MOBER, Multi-Origin Batch Effect Remover method, to simultaneously extract biologically meaningful embeddings while removing confounder information. Applying MOBER on 932 cancer cell lines, 434 patient-derived tumor xenografts, and 11,159 clinical tumors, we identified preclinical models with greatest transcriptional fidelity to clinical tumors and models that are transcriptionally unrepresentative of their respective clinical tumors. MOBER allows for transformation of transcriptional profiles of preclinical models to resemble the ones of clinical tumors and, therefore, can be used to improve the clinical translation of insights gained from preclinical models. MOBER is a versatile batch effect removal method applicable to diverse transcriptomic datasets, enabling integration of multiple datasets simultaneously.

In silico protein structural analysis of PRMT5 and RUVBL1 mutations arising in human cancers

DNA double strand breaks (DSBs) can be generated spontaneously during DNA replication and are repaired primarily by Homologous Recombination (HR). However, efficient repair requires chromatin remodeling to allow the recombination machinery access to the break. TIP60 is a complex conserved from yeast to humans that is required for histone acetylation and modulation of HR activity at DSBs. Two enzymatic activities within the TIP60 complex, KAT5 (a histone acetyltransferase) and RUVBL1 (an AAA+ ATPase) are required for efficient HR repair. Post-translational modification of RUVBL1 by the PRMT5 methyltransferase activates the complex acetyltransferase activity and facilitates error free HR repair. In S. pombe a direct interaction between PRMT5 and the acetyltransferase subunit of the TIP60 complex (KAT5) was also identified. The TIP60 complex has been partially solved experimentally in both humans and S. cerevisiae, but not S. pombe. Here, we used in silico protein structure analysis to investigate structural conservation between S. pombe and human PRMT5 and RUVBL1. We found that there is more similarity in structure conservation between S. pombe and human proteins than between S. cerevisiae and human. Next, we queried the COSMIC database to analyze how mutations occurring in human cancers affect the structure and function of these proteins. Artificial intelligence algorithms that predict how likely mutations are to promote cellular transformation and immortalization show that RUVBL1 mutations should have a more drastic effect than PRMT5. Indeed, in silico protein structural analysis shows that PRMT5 mutations are less likely to destabilize enzyme function. Conversely, most RUVBL1 mutations occur in a region required for interaction with its partner (RUVBL2). These data suggests that cancer mutations could destabilize the TIP60 complex. Sequence conservation analysis between S. pombe and humans shows that the residues identified in cancer cells are highly conserved, suggesting that this may be an essential process in eukaryotic DSB repair. These results shed light on mechanisms of DSB repair and also highlight how S. pombe remains a great model system for analyzing DSB repair processes that are tractable in human cells.

RSK1 is an exploitable dependency in myeloproliferative neoplasms and secondary acute myeloid leukemia

Myeloid malignancies are heterogenous disorders characterized by distinct molecular drivers but share convergence of oncogenic signaling pathways and propagation by ripe pro-inflammatory niches. Here, we establish a comprehensive transcriptional atlas across the spectrum of myeloproliferative neoplasms (MPN) and secondary acute myeloid leukemia (sAML) through RNA-sequencing of 158 primary samples encompassing CD34+ hematopoietic stem/progenitor cells and CD14+ monocytes. Supported by mass cytometry (CyTOF) profiling, we reveal aberrant networks of PI3K/AKT/mTOR signalling and NFκB-mediated hyper-inflammation. Combining ATAC-Seq, CUT&Tag, RNA-seq, and CyTOF, we demonstrate that targeting of ribosomal protein S6 kinase A1 (RSK1) suppresses NFκB activation and diminishes pro-inflammatory mediators including tumor necrosis factor (TNF) associated with MPN disease severity and transformation. We further evaluate a therapeutic approach utilizing a first-in-class RSK inhibitor, PMD-026, currently in Phase 2 development for breast cancer, for use in myeloid malignancies. Treatment with PMD-026 suppressed disease burden across seven syngeneic and patient-derived xenograft leukemia mouse models spanning the spectrum of driver and disease-modifying mutations. These findings uncover a therapeutic avenue for a conserved dependency across MPN and sAML.

Targeting the mitotic kinase NEK2 enhances CDK4/6 inhibitor efficacy by potentiating genome instability

Selective inhibitors that target cyclin-dependent kinases 4 and 6 (CDK4/6i) are approved by the U.S. Food and Drug Administration (FDA) for treatment of a subset of breast cancers and are being evaluated in numerous clinical trials for other cancers. Despite this advance, a subset of tumors are intrinsically resistant to these drugs and acquired resistance is nearly inevitable. Recent mechanistic evidence suggests that in addition to stalling the cell cycle, the antitumor effects of CDK4/6i involve the induction of chromosomal instability (CIN). Here, we exploit this mechanism by combining CDK4/6i with other instability-promoting agents to induce maladaptive CIN and irreversible cell fates. Specifically, dual targeting of CDK4/6 and the mitotic kinase NEK2 in vitro drives centrosome amplification and the accumulation of CIN that induces catastrophic mitoses, cell cycle exit, and cell death. Dual targeting also induces CIN in vivo and significantly decreases mouse tumor volume to a greater extent than either drug alone, without inducing overt toxicity. Importantly, we provide evidence that breast cancer cells are selectively dependent on NEK2, but nontransformed cells are not, in contrast with other mitotic kinases that are commonly essential in all cell types. These findings implicate NEK2 as a potential therapeutic target for breast cancer that could circumvent the dose-limiting toxicities that are commonly observed when blocking other mitotic kinases. Moreover, these data suggest that NEK2 inhibitors could be used to sensitize tumors to FDA-approved CDK4/6i for the treatment of breast cancers, improving their efficacy and providing a foundation for expanding the patient population that could benefit from CDK4/6i.

Comprehensive Cellular Senescence Evaluation to Aid Targeted Therapies

Drug resistance to a single agent is common in cancer-targeted therapies, and rational drug combinations are a promising approach to overcome this challenge. Many Food and Drug Administration-approved drugs can induce cellular senescence, which possesses unique vulnerabilities and molecular signatures. However, there is limited analysis on the effect of the combination of cellular-senescence-inducing drugs and targeted therapy drugs. Here, we conducted a comprehensive evaluation of cellular senescence using 7 senescence-associated gene sets. We quantified the cellular senescence states of ~10,000 tumor samples from The Cancer Genome Atlas and examined their associations with targeted drug responses. Our analysis revealed that tumors with higher cellular senescence scores exhibited increased sensitivity to targeted drugs. As a proof of concept, we experimentally confirmed that etoposide-induced senescence sensitized lung cancer cells to 2 widely used targeted drugs, erlotinib and dasatinib. Furthermore, we identified multiple genes whose dependencies were associated with senescence status across ~1,000 cancer cell lines, suggesting that cellular senescence generates unique vulnerabilities for therapeutic exploitation. Our study provides a comprehensive overview of drug response related to cellular senescence and highlights the potential of combining senescence-inducing agents with targeted therapies to improve treatment outcomes in lung cancer, revealing novel applications of cellular senescence in targeted cancer therapies.

Gastric Cancer Models Developed via GelMA 3D Bioprinting Accurately Mimic Cancer Hallmarks, Tumor Microenvironment Features, and Drug Responses

Current in vitro models for gastric cancer research, such as 2D cell cultures and organoid systems, often fail to replicate the complex extracellular matrix (ECM) found in vivo. For the first time, this study utilizes a gelatin methacryloyl (GelMA) hydrogel, a biomimetic ECM-like material, in 3D bioprinting to construct a physiologically relevant gastric cancer model. GelMA's tunable mechanical properties allow for the precise manipulation of cellular behavior within physiological ranges. Genetic and phenotypic analyses indicate that the 3D bioprinted GelMA (3Db) model accurately mimics the clinical tumor characteristics and reproduces key cancer hallmarks, such as cell proliferation, invasion, migration, angiogenesis, and the Warburg effect. Comparisons of gene expression and drug responses between the 3Db model and patient-derived xenograft models, both constructed from primary gastric cancer cells, validate the model's clinical relevance. The ability of the 3Db model to closely simulate in vivo conditions highlights its crucial role in identifying treatment targets and predicting patient-specific responses, showcasing its potential in high-throughput drug screening and clinical applications. This study is the first to report the pivotal role of GelMA-based 3D bioprinting in advancing gastric cancer research and regenerative medicine.

Immune-related glycosylation genes based classification predicts prognosis and therapy options of osteosarcoma

Osteosarcoma is the most common primary bone malignancy, with a very poor prognosis. Aberrant glycosylation is close involvement in osteosarcoma. Accordingly, this study aimed at investigating the role of glycosylation genes in the prognosis and therapy options of osteosarcoma. The microenvironment of osteosarcoma was assessed using estimate algorithm. A total of 20 immune-related glycosylation genes (IRGGs) was identified using Pearson correlation analysis. Accordingly, osteosarcoma patients were divided into C1 and C2 type using consensus clustering. Multiple algorithms (Xcell, MCP-counter, ssGSEA, epic, quantiseq), cancer immune cycle analysis, and GSVA were applied to estimate the immune, molecule and metabolism characteristics of osteosarcoma, indicating that C1 type was featured with high immune infiltration, high glycosylation, enriched MEK signaling, and good prognosis, while C2 type was characterized by more metastasis, enriched immunotherapy-positive gene signatures, high tumor mutation burden, and poor prognosis. Results from TIDE algorithm and immunotherapy datasets suggested the C2 type's preference of immune checkpoint inhibitors (ICIs), while data of GDSC, CMap analysis and cell experiments indicated that C1 type was sensitivity to MEK inhibitor PD0325901. In addition, univariate Cox and Lasso analysis was combined to establish an IRGGs' risk score containing 6 genes (B3GNT8, FUT7, GAL3ST4, GALNT14, HS3ST2, and MFNG). The data of DCA and ROC indicated its well prediction of prognosis in osteosarcoma. Finally, cellular location analysis showed that the 6 genes not only distributed in tumor cells but also in immune cells. In summary, the classification and risk score based on IRGGs effectively predicted the prognosis and therapy options of osteosarcoma. Further studies on IRGGs may contribute to the understanding of cancer immunity in osteosarcoma.

Protein phosphatase EYA1 regulates the dephosphorylation and turnover of BCL2L12 to promote glioma development

Glioma is the most prevalent and deadly type of intracranial tumor. Understanding the molecular drivers and their underlying mechanisms in glioma development is urgently needed. EYA1 is a unique protein phosphatase that drives gliomagenesis, yet its substrates remain largely uncharacterized. In this study, we identify BCL2L12 (BCL2-like 12), a critical oncoprotein in glioma, as a novel substrate of EYA1 phosphatase in glioma cells. Our findings demonstrate that EYA1 dephosphorylates BCL2L12 at threonine-33 (T33), which in turn protects BCL2L12 from ubiquitination and subsequent proteasomal degradation. Our results indicate that BCL2L12 partially mediates the oncogenic roles of EYA1 in promoting glioma cell proliferation, highlighting the significance of EYA1's dephosphorylation of BCL2L12 in tumor progression. Moreover, we validate a positive correlation between EYA1 and BCL2L12 protein levels in glioma patient samples. In summary, our study reveals how EYA1-BCL2L12 interaction functions in glioma development, implicating EYA1 as a potential therapeutic target for glioma treatment.

AMG 193, a Clinical Stage MTA-Cooperative PRMT5 Inhibitor, Drives Antitumor Activity Preclinically and in Patients with MTAP-Deleted Cancers

One of the most robust synthetic lethal interactions observed in multiple functional genomic screens has been the dependency on protein arginine methyltransferase 5 (PRMT5) in cancer cells with MTAP deletion. We report the discovery of the clinical stage MTA-cooperative PRMT5 inhibitor AMG 193, which preferentially binds PRMT5 in the presence of MTA and has potent biochemical and cellular activity in MTAP-deleted cells across multiple cancer lineages. In vitro, PRMT5 inhibition induces DNA damage, cell cycle arrest, and aberrant alternative mRNA splicing in MTAP-deleted cells. In human cell line and patient-derived xenograft models, AMG 193 induces robust antitumor activity and is well tolerated with no impact on normal hematopoietic cell lineages. AMG 193 synergizes with chemotherapies or the KRAS G12C inhibitor sotorasib in vitro and combination treatment in vivo substantially inhibits tumor growth. AMG 193 is demonstrating promising clinical activity, including confirmed partial responses in patients with MTAP-deleted solid tumors from an ongoing phase 1/2 study. Significance: AMG 193 preferentially inhibits the growth of MTAP-deleted tumor cells by inhibiting PRMT5 when in complex with MTA, thus sparing MTAP wild-type normal cells. AMG 193 shows promise as a targeted therapy in a clinically defined patient population.

Increased local DNA methylation disorder in AMLs with DNMT3A-destabilizing variants and its clinical implication

The mechanistic link between the complex mutational landscape of de novo methyltransferase DNMT3A and the pathology of acute myeloid leukemia (AML) has not been clearly elucidated so far. Motivated by a recent discovery of the significance of DNMT3A-destabilizing mutations (DNMT3AINS) in AML, we here investigate the common characteristics of DNMT3AINS AML methylomes through computational analyses. We present that methylomes of DNMT3AINS AMLs are considerably different from those of DNMT3AR882 AMLs in that they exhibit increased intratumor DNA methylation heterogeneity in bivalent chromatin domains. This epigenetic heterogeneity was associated with the transcriptional variability of developmental and membrane-associated factors shaping stem cell niche, and also was a predictor of the response of AML cells to hypomethylating agents, implying that the survival of AML cells depends on stochastic DNA methylations at bivalent domains. Altogether, our work provides a novel mechanistic model suggesting the genomic origin of the aberrant epigenomic heterogeneity in disease conditions.

Glycosylation profiling of triple-negative breast cancer: clinical and immune correlations and identification of LMAN1L as a biomarker and therapeutic target

Introduction

Breast cancer (BC) is the most prevalent malignant tumor in women, with triple-negative breast cancer (TNBC) showing the poorest prognosis among all subtypes. Glycosylation is increasingly recognized as a critical biomarker in the tumor microenvironment, particularly in BC. However, the glycosylation-related genes associated with TNBC have not yet been defined. Additionally, their characteristics and relationship with prognosis have not been deeply investigated.

Methods

Transcriptomic analyses were used to identify a glycosylation-related signature (GRS) associated with TNBC prognosis. A machine learning-based prediction model was constructed and validated across multiple independent datasets. The model's predictive capability was extended to evaluate the prognosis of TNBC individuals, tumor immune microenvironment and immunotherapy response. LMAN1L (Lectin, Mannose Binding 1 Like) was identified as a novel prognostic marker in TNBC, and its biological effects were validated through experimental assays.

Results

The GRS showed significant prognostic relevance for TNBC patients. The risk model effectively predicted molecular features, including immune cell infiltration and potential responses to immunotherapy. Experimental validation confirmed LMAN1L as a novel glycosylation-related prognostic gene, with low expression significantly inhibiting TNBC cell proliferation and migration.

Discussion

Our GRS risk model demonstrates robust predictive capability for TNBC prognosis and immunotherapy response. This model offers a promising strategy for personalized treatment and improved clinical outcomes in TNBC.

Pre-Clinical Rationale for Amcenestrant Combinations in HER2+/ER+ Breast Cancer

HER2-positive/oestrogen receptor-positive (HER2+/ER+) represents a unique breast cancer subtype. The use of individual HER2- or ER-targeting agents can lead to the acquisition of therapeutic resistance due to compensatory receptor crosstalk. New drug combinations targeting HER2 and ER could improve outcomes for patients with HER2+/ER+ breast cancer. In this study, the pre-clinical rationale is explored for combining amcenestrant (Amc), a selective oestrogen receptor degrader (SERD), with HER2-targeted therapies including trastuzumab, trastuzumab-emtansine (T-DM1) and tyrosine kinase inhibitors (TKIs). The combination of Amc and anti-HER2 therapies was investigated in a panel of four HER2+/ER+ cell lines: BT-474, MDA-MB-361, EFM-192a and a trastuzumab-resistant variant BT-474-T. Proliferation (IC50 and matrix combination assays) was determined using acid phosphatase assays. HER2/ER and intracellular signalling pathway protein levels/activity were investigated by western blot. Apoptosis was assessed using caspase 3/7 assays. Additivity and synergy were observed between Amc and the TKIs neratinib, lapatinib and tucatinib in all cell lines. Amc increased the anti-proliferative effect of trastuzumab in MDA-MB-361 and BT-474-T. Addition of Amc also increased anti-proliferative efficacy of T-DM1 in BT-474-T. TKI/Amc combinations reduced p-HER2 and ER levels and resulted in increased apoptosis. Higher ER expression in MDA-MB-361 and BT-474-T was associated with greater potential for synergy. In conclusion, the combination of Amc- and HER2-targeted treatments has potential as a therapeutic strategy for the treatment of HER2+/ER+ breast cancer and warrants further clinical investigation to validate safety and efficacy in patients.

Dysregulation of FURIN and Other Proprotein Convertase Genes in the Progression from HPV Infection to Cancer

Productive infections of oncogenic human papillomaviruses (HPVs) are closely linked to the differentiation of host epithelial cells, a process that the virus manipulates to create conditions favorable to produce virion progeny. This viral interference involves altering the expression of numerous host genes. Among these, proprotein convertases (PCs) have emerged as potential oncogenes due to their central role in cellular functions. Using RT-qPCR, aberrant PC gene expression was detected across the progression from early HPV infection stages to cancer. These findings demonstrated a progressive disruption of normal PC expression profiles, with FURIN consistently downregulated and other PCs upregulated, transitioning from the episomal stage to neoplastic and carcinoma phenotypes. This pattern of dysregulation was distinct from the broader trends observed in a variety of cancer types through bioinformatic analysis of publicly available transcriptomic datasets, where FURIN expression was predominantly upregulated compared to other PCs. Further bioinformatic investigations revealed a correlation between PC gene expression and cancer phenotype diversity, suggesting a potential link between the loss of normal PC gene expression patterns and the progression of HPV infections toward malignancy.

Utilizing liquid-liquid biopolymer regulators to predict the prognosis and drug sensitivity of hepatocellular carcinoma

BACKGROUND

Liquid-liquid phase separation (LLPS) is essential for the formation of membraneless organelles and significantly influences cellular compartmentalization, chromatin remodeling, and gene regulation. Previous research has highlighted the critical function of liquid-liquid biopolymers in the development of hepatocellular carcinoma (HCC).

METHODS

This study conducted a comprehensive review of 3,685 liquid-liquid biopolymer regulators, leading to the development of a LLPS related Prognostic Risk Score (LPRS) for HCC through bootstrap-based univariate Cox, Random Survival Forest (RSF), and LASSO analyses. A prognostic nomogram for HCC patients was developed using LPRS and other clinicopathological factors. We utilized SurvSHAP to identify key genes within the LPRS influencing HCC prognosis. To validate our findings, we collected 49 HCC cases along with adjacent tissue samples and confirmed the correlation between DCAF13 expression and HCC progression through qRT-PCR analysis and in vitro experiments.

RESULTS

LPRS was established with 8 LLPS-related genes (TXN, CBX2, DCAF13, SLC2A1, KPNA2, FTCD, MAPT, and SAC3D1). Further research indicated that a high LPRS is closely associated with vascular invasion, histological grade (G3-G4), and TNM stage (III-IV) in HCC, concurrently establishing LPRS as an independent risk factor for prognosis. A nomogram that integrates LPRS with TNM staging and patient age markedly improves the predictive accuracy of survival outcomes for HCC patients. Our findings suggest that increased DCAF13 expression in HCC plays a crucial role in cancer progression and angiogenesis. Navitoclax has emerged as a promising treatment for HCC patients with high LPRS levels, offering a novel therapeutic direction by targeting LLPS.

CONCLUSION

We have formulated a novel LPRS model that is capable of accurately predicting the clinical prognosis and drug sensitivity of HCC. DCAF13 might play a pivotal role in malignant progression mediated by LLPS.

Loss of SMAD1 in acute myeloid leukemia with KMT2A::AFF1 and KMT2A::MLLT3 fusion genes

Introduction

KMT2A-rearrangements define a subclass of acute leukemias characterized by a distinct gene expression signature linked to the dysfunctional oncogenic fusion proteins arising from various chromosomal translocations involving the KMT2A (also known as MLL1) gene. Research on the disease pathomechanism in KMT2A-rearranged acute leukemias has mainly focused on the upregulation of the stemness-related genes of the HOX-family and their co-factor MEIS1.

Results

Here we report the KMT2A::AFF1 and KMT2A::MLLT3 fusion gene-dependent downregulation of SMAD1, a TGF-β signaling axis transcription factor. SMAD1 expression is lost in the majority of AML patient samples and cell lines containing the two fusion genes KMT2A::AFF1 and KMT2A::MLLT3 compared to non-rearranged controls. Loss of SMAD1 expression is inducible by introducing the respective two KMT2A fusion genes into hematopoietic stem and progenitor cells. The loss of SMAD1 correlated with a markedly reduced amount of H3K4me3 levels at the SMAD1 promoter in tested cells with KMT2A::AFF1 and KMT2A::MLLT3. The expression of SMAD1 in cells with KMT2A::AFF1 fusion genes impacted the growth of cells in vitro and influenced engraftment of the KMT2A::AFF1 cell line MV4-11 in vivo. In MV4-11 cells SMAD1 expression caused a downregulation of HOXA9 and MEIS1, which was reinforced by TGF-β stimulation. Moreover, in MV4-11 cells SMAD1 presence sensitized cells for TGF-β mediated G1-arrest.

Conclusion

Overall, our data contributes to the understanding of the role of TGF-β signaling in acute myeloid leukemia with KMT2A::AFF1 by showing that SMAD1 loss can influence the growth dynamics and contribute to the pathogenic expression of disease driving factors.