Next-generation characterization of the Cancer Cell Line Encyclopedia

A CRISPR activation screen identifies FBXO22 supporting targeted protein degradation

Targeted protein degradation (TPD) represents a potent chemical biology paradigm that leverages the cellular degradation machinery to pharmacologically eliminate specific proteins of interest. Although multiple E3 ligases have been discovered to facilitate TPD, there exists a compelling requirement to diversify the pool of E3 ligases available for such applications. Here we describe a clustered regularly interspaced short palindromic repeats (CRISPR)-based transcriptional activation screen focused on human E3 ligases, with the goal of identifying E3 ligases that can facilitate heterobifunctional compound-mediated target degradation. Through this approach, we identified a candidate proteolysis-targeting chimera (PROTAC), 22-SLF, that induces the degradation of FK506-binding protein 12 when the transcription of FBXO22 gene is activated. Subsequent mechanistic investigations revealed that 22-SLF interacts with C227 and/or C228 in F-box protein 22 (FBXO22) to achieve target degradation. Lastly, we demonstrated the versatility of FBXO22-based PROTACs by effectively degrading additional endogenous proteins, including bromodomain-containing protein 4 and the echinoderm microtubule-associated protein-like 4-anaplastic lymphoma kinase fusion protein.

Synthetic augmentation of cancer cell line multi-omic datasets using unsupervised deep learning

Integrating diverse types of biological data is essential for a holistic understanding of cancer biology, yet it remains challenging due to data heterogeneity, complexity, and sparsity. Addressing this, our study introduces an unsupervised deep learning model, MOSA (Multi-Omic Synthetic Augmentation), specifically designed to integrate and augment the Cancer Dependency Map (DepMap). Harnessing orthogonal multi-omic information, this model successfully generates molecular and phenotypic profiles, resulting in an increase of 32.7% in the number of multi-omic profiles and thereby generating a complete DepMap for 1523 cancer cell lines. The synthetically enhanced data increases statistical power, uncovering less studied mechanisms associated with drug resistance, and refines the identification of genetic associations and clustering of cancer cell lines. By applying SHapley Additive exPlanations (SHAP) for model interpretation, MOSA reveals multi-omic features essential for cell clustering and biomarker identification related to drug and gene dependencies. This understanding is crucial for developing much-needed effective strategies to prioritize cancer targets.

Development and Validation of a Novel Four Gene-Pairs Signature for Predicting Prognosis in DLBCL Patients

Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin's lymphoma. Because individual clinical outcomes of DLBCL in response to standard therapy differ widely, new treatment strategies are being investigated to improve therapeutic efficacy. In this study, we identified a novel signature for stratification of DLBCL useful for prognosis prediction and treatment selection. First, 408 prognostic gene sets were selected from approximately 2500 DLBCL samples in public databases, from which four gene-pair signatures consisting of seven prognostic genes were identified by Cox regression analysis. Then, the risk score was calculated based on these gene-pairs and we validated the risk score as a prognostic predictor for DLBCL patient outcomes. This risk score demonstrated independent predictive performance even when combined with other clinical parameters and molecular subtypes. Evaluating external DLBCL cohorts, we demonstrated that the risk-scoring model based the four gene-pair signatures leads to stable predictive performance, compared with nine existing predictive models. Finally, high-risk DLBCL showed high resistance to DNA damage caused by anticancer drugs, suggesting that this characteristic is responsible for the unfavorable prognosis of high-risk DLBCL patients. These results provide a novel index for classifying the biological characteristics of DLBCL and clearly indicate the importance of genetic analyses in the treatment of DLBCL.

DNA Damage and Inflammatory Response of p53 Null H358 Non-Small Cell Lung Cancer Cells to X-Ray Exposure Under Chronic Hypoxia

Hypoxia-induced radioresistance limits therapeutic success in cancer. In addition, p53 mutations are widespread in tumors including non-small cell lung carcinomas (NSCLCs), and they might modify the radiation response of hypoxic tumor cells. We therefore analyzed the DNA damage and inflammatory response in chronically hypoxic (1% O2, 48 h) p53 null H358 NSCLC cells after X-ray exposure. We used the colony-forming ability assay to determine cell survival, γH2AX immunofluorescence microscopy to quantify DNA double-strand breaks (DSBs), flow cytometry of DAPI-stained cells to measure cell cycle distribution, ELISAs to quantify IL-6 and IL-8 secretion in cell culture supernatants, and RNA sequencing to determine gene expression. Chronic hypoxia increased the colony-forming ability and radioresistance of H358 cells. It did not affect the formation or resolution of X-ray-induced DSBs. It reduced the fraction of cells undergoing G2 arrest after X-ray exposure and delayed the onset of G2 arrest. Hypoxia led to an earlier enhancement in cytokines secretion rate after X-irradiation compared to normoxic controls. Gene expression changes were most pronounced after the combined exposure to hypoxia and X-rays and pertained to senescence and different cell death pathways. In conclusion, hypoxia-induced radioresistance is present despite the absence of functional p53. This resistance is related to differences in clonogenicity, cell cycle regulation, cytokine secretion, and gene expression under chronic hypoxia, but not to differences in DNA DSB repair kinetics.

Mapping in silico genetic networks of the KMT2D tumour suppressor gene to uncover novel functional associations and cancer cell vulnerabilities

BACKGROUND

Loss-of-function (LOF) alterations in tumour suppressor genes cannot be directly targeted. Approaches characterising gene function and vulnerabilities conferred by such mutations are required.

METHODS

Here, we computationally map genetic networks of KMT2D, a tumour suppressor gene frequently mutated in several cancer types. Using KMT2D loss-of-function (KMT2DLOF) mutations as a model, we illustrate the utility of in silico genetic networks in uncovering novel functional associations and vulnerabilities in cancer cells with LOF alterations affecting tumour suppressor genes.

RESULTS

We revealed genetic interactors with functions in histone modification, metabolism, and immune response and synthetic lethal (SL) candidates, including some encoding existing therapeutic targets. Notably, we predicted WRN as a novel SL interactor and, using recently available WRN inhibitor (HRO761 and VVD-133214) treatment response data, we observed that KMT2D mutational status significantly distinguishes treatment-sensitive MSI cell lines from treatment-insensitive MSI cell lines.

CONCLUSIONS

Our study thus illustrates how tumour suppressor gene LOF alterations can be exploited to reveal potentially targetable cancer cell vulnerabilities.

ADM: adaptive graph diffusion for meta-dimension reduction

Dimension reduction is essential for analyzing high-dimensional data, with various techniques developed to address diverse data characteristics. However, individual methods often struggle to capture all intricate patterns and complex structures simultaneously. To overcome this limitation, we introduce ADM (Adaptive graph Diffusion for Meta-dimension reduction), a novel meta-dimension reduction method grounded in graph diffusion theory. ADM integrates results from multiple dimension reduction techniques, leveraging their individual strengths while mitigating their specific weaknesses.ADM utilizes dynamic Markov processes to transform Euclidean space results into an information space, revealing intrinsic nonlinear manifold structures that are hard to capture by conventional methods. A critical advancement in ADM is its adaptive diffusion mechanism, which dynamically selects optimal diffusion time scales for each sample, enabling effective representation of multi-scale structures. This approach generates robust, high-quality low-dimensional representations that capture both local and global data structures while reducing noise and technique-specific distortions. We demonstrate ADM's efficacy on simulated and real-world datasets, including various omics data types. Results show that ADM provides clearer separation between biological groups and reveals more meaningful patterns compared to existing methods, advancing the analysis and visualization of complex biological data.

Antibody-Drug Conjugates Targeting the EGFR Ligand Epiregulin Elicit Robust Anti-Tumor Activity in Colorectal Cancer

As colorectal cancer (CRC) remains a leading cause of cancer-related death, identifying therapeutic targets and approaches is essential to improve patient outcomes. The EGFR ligand epiregulin (EREG) is highly expressed in RAS wildtype and mutant CRC with minimal expression in normal tissues, making it an attractive target for antibody-drug conjugate (ADC) development. In this study, we produced and purified an EREG monoclonal antibody (mAb), H231, that had high specificity and affinity for human and mouse EREG. H231 also internalized to lysosomes, which is important for ADC payload release. ImmunoPET and ex vivo biodistribution studies showed significant tumor uptake of 89Zr-labeled H231 with minimal uptake in normal tissues. H231 was conjugated to either cleavable dipeptide or tripeptide chemical linkers attached to the DNA-alkylating payload duocarmycin DM, and cytotoxicity of EREG ADCs was assessed in a panel of CRC cell lines. EREG ADCs incorporating tripeptide linkers demonstrated the highest potency in EREG-expressing CRC cells irrespective of RAS mutations. Preclinical safety and efficacy studies showed EREG ADCs were well-tolerated, neutralized EGFR pathway activity, caused significant tumor growth inhibition or regression, and increased survival in CRC cell line and patient-derived xenograft models. These data suggest EREG is a promising target for the development of ADCs for treating CRC and other cancer types that express high levels of EREG. While the efficacy of clinically approved anti-EGFR mAbs are largely limited by RAS mutational status, EREG ADCs may show promise for both RAS mutant and wildtype patients, thus improving existing treatment options.

A framework for target discovery in rare cancers

While large-scale functional genetic screens have uncovered numerous cancer dependencies, rare cancers are poorly represented in such efforts and the landscape of dependencies in many rare cancers remains obscure. We performed genome-scale CRISPR knockout screens in an exemplar rare cancer, TFE3-translocation renal cell carcinoma (tRCC), revealing previously unknown tRCC-selective dependencies in pathways related to mitochondrial biogenesis, oxidative metabolism, and kidney lineage specification. To generalize to other rare cancers in which experimental models may not be readily available, we employed machine learning to infer gene dependencies in a tumor or cell line based on its transcriptional profile. By applying dependency prediction to alveolar soft part sarcoma (ASPS), a distinct rare cancer also driven by TFE3 translocations, we discovered and validated that MCL1 represents a dependency in ASPS but not tRCC. Finally, we applied our model to predict gene dependencies in tumors from the TCGA (11,373 tumors; 28 lineages) and multiple additional rare cancers (958 tumors across 16 types, including 13 distinct subtypes of kidney cancer), nominating potentially actionable vulnerabilities in several poorly-characterized cancer types. Our results couple unbiased functional genetic screening with a predictive model to establish a landscape of candidate vulnerabilities across cancers, including several rare cancers currently lacking in potential targets.

Microtubule poleward flux as a target for modifying chromosome segregation errors

Cancer cells often display errors in chromosome segregation, some of which result from improper chromosome alignment at the spindle midplane. Chromosome alignment is facilitated by different rates of microtubule poleward flux between sister kinetochore fibers. However, the role of the poleward flux in supporting mitotic fidelity remains unknown. Here, we introduce the hypothesis that the finely tuned poleward flux safeguards against lagging chromosomes and micronuclei at mitotic exit by promoting chromosome alignment in metaphase. We used human untransformed RPE-1 cells depleted of KIF18A/kinesin-8 as a system with reduced mitotic fidelity, which we rescued by three mechanistically independent treatments, comprising low-dose taxol or codepletion of the spindle proteins HAUS8 or NuMA. The rescue of mitotic errors was due to shortening of the excessively long overlaps of antiparallel microtubules, serving as a platform for motor proteins that drive the flux, which in turn slowed down the overly fast flux and improved chromosome alignment. In contrast to the prevailing view, the rescue was not accompanied by reduction of overall microtubule growth rates. Instead, speckle microscopy revealed that the improved chromosome alignment in the rescue treatments was associated with slower growth and flux of kinetochore microtubules. In a similar manner, a low-dose taxol treatment rescued mitotic errors in a high-grade serous ovarian carcinoma cell line OVKATE. Collectively, our results highlight the potential of targeting microtubule poleward flux to modify chromosome instability and provide insight into the mechanism through which low doses of taxol rescue certain mitotic errors in cancer cells.

Genetically encoded fluorescent reporter for polyamines

Polyamines are abundant and evolutionarily conserved metabolites that are essential for life. Dietary polyamine supplementation extends life-span and health-span. Dysregulation of polyamine homeostasis is linked to Parkinson's disease and cancer, driving interest in therapeutically targeting this pathway. However, measuring cellular polyamine levels, which vary across cell types and states, remains challenging. We introduce a first-in-class genetically encoded polyamine reporter for real-time measurement of polyamine concentrations in single living cells. This reporter utilizes the polyamine-responsive ribosomal frameshift motif from the OAZ1 gene. We demonstrate broad applicability of this approach and reveal dynamic changes in polyamine levels in response to genetic and pharmacological perturbations. Using this reporter, we conducted a genome-wide CRISPR screen and uncovered an unexpected link between mitochondrial respiration and polyamine import, which are both risk factors for Parkinson's disease. By offering a new lens to examine polyamine biology, this reporter may advance our understanding of these ubiquitous metabolites and accelerate therapy development.

TFF3 and PVRL2 co-targeting identified by multi-omics approach as an effective cancer immunosuppression strategy

BACKGROUND

The immunosuppressive tumour microenvironment (TME) plays a critical role in cancer progression and relapse by significantly influencing cancer pathogenesis through autocrine and paracrine signalling. Trefoil factor 3 (TFF3), a secreted protein, has been implicated in modulating the TME to promote cancer advancement. Herein, we investigated the potential association between TFF3 and key immunosuppressive TME components to distinguish a co-targetable oncotherapeutic strategy.

METHODS

The TFF3-PVRL2 association were identified and investigated by integrating multiple bioinformatic-tools. The virtual compound screening for PVRL2 inhibitors was done with EasyVS. The TFF3-PVRL2 protein-level correlation was validated by immunoblotting, and the effectiveness of co-inhibiting TFF3 and PVRL2 was assessed using siRNA and AMPC (a TFF3 inhibitor).

RESULTS

Analysis of the TISIDB database revealed a positive correlation between TFF3 and PVRL2 mRNA levels across multiple cancer types. This correlation was confirmed at the protein level through immunoblot analysis. Further evaluation using TCGA pan-cancer datasets demonstrated that TFF3 and PVRL2 interact to establish an immunosuppressive TME, promoting cancer progression in BRCA, LUAD, PAAD, PRAD, and STAD. Enrichment analyses of positively correlated genes, PPI network hub proteins, and ceRNA networks involving TFF3 and PVRL2, conducted using LinkedOmics, STRING, and Cytoscape, provided insights into their potential co-functions in cancer. A cell-based assay was performed to evaluate the combined therapeutic efficacy of targeting both, TFF3 and PVRL2 and virtual screening identified potential drugs for inhibiting PVRL2.

CONCLUSION

PVRL2 has emerged as a promising immunoinhibitory target with significant associations with TFF3 and represents a key co-targetable molecule for effective oncotherapeutic strategies.

Pan-Cancer Drug Sensitivity Prediction from Gene Expression using Deep Learning

Cancer is a group of complex diseases, with tumor heterogeneity, durable drug efficacy, emerging resistance, and host toxicity presenting major challenges to the development of effective cancer therapeutics. While traditionally used methods have remained limited in their capacity to overcome these challenges in cancer drug development, efforts have been made in recent years toward applying "big data" to cancer research and precision oncology. By curating, standardizing, and integrating data from various databases, we developed deep learning architectures that use perturbation and baseline transcriptional signatures to predict efficacious small molecule compounds and genetic dependencies in cancer. A series of internal validations followed by prospective validation in prostate cancer cell lines were performed to ensure consistent performance and model applicability. We report SensitivitySeq, a novel bioinformatics tool for prioritizing small molecule compounds and gene dependencies in silico to drive the development of targeted therapies for cancer. To the best of our knowledge, this is the first supervised deep learning approach, validated in vitro, to predict drug sensitivity using baseline cancer cell line gene expression alongside cell line-independent perturbation-response consensus signatures.

Nuclear localization of MTHFD2 is required for correct mitosis progression

Subcellular compartmentalization of metabolic enzymes establishes a unique metabolic environment that elicits specific cellular functions. Indeed, the nuclear translocation of certain metabolic enzymes is required for epigenetic regulation and gene expression control. Here, we show that the nuclear localization of the mitochondrial enzyme methylenetetrahydrofolate dehydrogenase 2 (MTHFD2) ensures mitosis progression. Nuclear MTHFD2 interacts with proteins involved in mitosis regulation and centromere stability, including the methyltransferases KMT5A and DNMT3B. Loss of MTHFD2 induces severe methylation defects and impedes correct mitosis completion. MTHFD2 deficient cells display chromosome congression and segregation defects and accumulate chromosomal aberrations. Blocking the catalytic nuclear function of MTHFD2 recapitulates the phenotype observed in MTHFD2 deficient cells, whereas restricting MTHFD2 to the nucleus is sufficient to ensure correct mitotic progression. Our discovery uncovers a nuclear role for MTHFD2, supporting the notion that translocation of metabolic enzymes to the nucleus is required to meet precise chromatin needs.

Integrative ensemble modelling of cetuximab sensitivity in colorectal cancer patient-derived xenografts

Patient-derived xenografts (PDXs) are tumour fragments engrafted into mice for preclinical studies. PDXs offer clear advantages over simpler in vitro cancer models - such as cancer cell lines (CCLs) and organoids - in terms of structural complexity, heterogeneity, and stromal interactions. Here, we characterise 231 colorectal cancer PDXs at the genomic, transcriptomic, and epigenetic levels, along with their response to cetuximab, an EGFR inhibitor used clinically for metastatic colorectal cancer. After evaluating the PDXs' quality, stability, and molecular concordance with publicly available patient cohorts, we present results from training, interpreting, and validating the integrative ensemble classifier CeSta. This model takes in input the PDXs' multi-omic characterisation and predicts their sensitivity to cetuximab treatment, achieving an area under the receiver operating characteristics curve > 0.88. Our study demonstrates that large PDX collections can be leveraged to train accurate, interpretable drug sensitivity models that: (1) better capture patient-derived therapeutic biomarkers compared to models trained on CCL data, (2) can be robustly validated across independent PDX cohorts, and (3) could contribute to the development of future therapeutic biomarkers.

Integrating multi-omics data reveals neuroblastoma subtypes in the tumor microenvironment

Neuroblastoma (NB) is a severe pediatric tumor originating from the developing sympathetic nervous system, characterized by diverse clinical outcomes, including spontaneous regression and aggressive metastasis. This variability suggests the existence of different NB subtypes, necessitating accurate classification for effective targeted treatment. In this study, we employed the similarity network fusion (SNF) algorithm and identified three NB subtypes, including mesenchymal-like (MES), MYCN-like (MYCN), and neurogenic-like (Neurogenic). The MES subtype exhibited the highest activation of immune-related pathways. The MYCN subtype demonstrated the worst prognosis, with enrichment in cell growth and proliferation pathways. Conversely, the Neurogenic subtype showed the best prognosis, with enrichment in sympathetic nervous system development processes. Through single-cell RNA sequencing (scRNA-seq) analysis, we examined the tumor microenvironments of these distinct NB subtypes, revealing divergent differentiation trajectories for adrenergic cells within the MYCN and Neurogenic subtypes. We also identified a significant presence of naïve T cells in the MES subtype, as well as mesenchymal cell subtypes associated with the unique plasticity observed in both the MES and MYCN subtypes. Drug sensitivity prediction analysis suggested that the MES subtype may respond favorably to MEK inhibitors, while the MYCN subtype may be susceptible to Bcl-2 inhibitors. Our integrative multi-omics approach enabled precise stratification of NB into biologically distinct subtypes, potentially facilitating the development of subtype-specific therapeutic strategies for improved patient management and survival outcomes.

MDM4 exon skipping upon dysfunctional ribosome assembly

Recent studies revealed how nucleolar stress enhances MDM4 exon skipping and activates p53 via the ribosomal protein L22 (RPL22; eL22). Tumor-associated L22 mutations lead to full-length MDM4 synthesis, overcoming tumor suppression by p53. This forum article explores how MDM4 splicing patterns integrate stress signaling to take p53-dependent cell fate decisions.

Deciphering Folate Receptor alphaGene Expression and mRNA Signatures in Ovarian Cancer: Implications for Precision Therapies

Antibody-drug conjugates targeting folate receptor alpha (FRα) are a promising treatment for platinum-resistant ovarian cancer (OC) with high FRα expression. Challenges persist in accurately assessing FRα expression levels. Our study aimed to better elucidate FRα gene expression and identify mRNA signatures in OC. We pooled OC gene expression data from 16 public datasets, encompassing 1832 OC and 30 normal ovarian tissues. Additional data included DNA copy number and methylation data from TCGA and protein data from 363 cancer cell lines from the Broad Institute Cancer Cell Line Encyclopedia. FOLR1 mRNA expression was significantly correlated with protein expression in pan-cancer cell lines and ovarian cancer cell lines. FOLR1 expression was higher in OC samples than in normal ovarian tissues (OR = 3.88, p = 6.97 × 10-12). Patients with high FOLR1 expression were more likely to be diagnosed with serous histology, FIGO stage III-IV, and high-grade tumors; however, nearly similar percentages of patients with low FOLR1 expression were also diagnosed with these features. FOLR1 mRNA expression was not correlated with platinum sensitivity or complete surgery, nor with prognosis. However, we identified a 187-gene signature associated with high FOLR1 expression that was significantly associated with improved survival (HR = 0.71, p = 1.18 × 10-6), independently from clinicopathological features. We identified a gene expression signature correlated to high FRα expression and OC prognosis, which may be used to refine therapeutic strategies targeting FRα in OC. These findings warrant validation in larger cohorts.

Elevated ITGA3 expression serves as a novel prognostic biomarker and regulates tumor progression in cervical cancer

Patients with advanced and recurrent cervical cancer often lack satisfactory treatment outcomes. Thus, it is necessary to seek reliable biomarkers that provide the ability to identify the disease at an early stage and predict the patient prognosis, providing new strategies for the treatment of cervical cancer. The sequencing data of ITGA3 were retrieved from public datasets. Immune infiltration and sensitivity of potential immunotherapy and chemotherapy have been analyzed between two subgroups. Functional analysis was applied to excavate the related pathways of ITGA3 in cervical cancer. Furthermore, the impact of ITGA3 in tumor progression has been verified in vitro. The results revealed that the level of ITGA3 was upregulated in cervical cancer, and was positively correlated with worse prognosis. The tumor microenvironment of patients in the high-risk group was immunosuppressed. Patients in high-risk group may not benefit from immunotherapy, but be may be sensitive to several chemotherapy drugs. Notably, the angiogenesis, epithelial mesenchymal transition, and PI3K pathway were increased in high-risk group. Collectively, ITGA3 is a marker of poor prognosis and promotes tumor progression by regulating PI3K/AKT pathway in cervical cancer. Our results provide new insights for potential molecular targeted therapy and prognostic prediction of cervical cancer.

moPepGen: Rapid and Comprehensive Identification of Non-canonical Peptides

Gene expression is a multi-step transformation of biological information from its storage form (DNA) into functional forms (protein and some RNAs). Regulatory activities at each step of this transformation multiply a single gene into a myriad of proteoforms. Proteogenomics is the study of how genomic and transcriptomic variation creates this proteomic diversity, and is limited by the challenges of modeling the complexities of gene-expression. We therefore created moPepGen, a graph-based algorithm that comprehensively generates non-canonical peptides in linear time. moPepGen works with multiple technologies, in multiple species and on all types of genetic and transcriptomic data. In human cancer proteomes, it enumerates previously unobservable noncanonical peptides arising from germline and somatic genomic variants, noncoding open reading frames, RNA fusions and RNA circularization. By enabling efficient detection and quantitation of previously hidden proteins in both existing and new proteomic data, moPepGen facilitates all proteogenomics applications. It is available at: https://github.com/uclahs-cds/package-moPepGen.

Identification of APBB1 as a substrate for anaplastic lymphoma kinase

Anaplastic lymphoma kinase (ALK) is a well-known oncogene involved in various malignancies such as anaplastic large cell lymphoma, lung cancer and neuroblastoma. Several substrates for fused ALK have been identified and their biological functions have been described. However, the lack of a comprehensive identification of ALK substrates limits our understanding of the biological roles of receptor ALK. Thus, this study aimed to identify novel ALK substrates and characterize their biological functions. We screened the interactors of the kinase domain of receptor ALK using proximity-dependent biotin identification and identified 43 interactors. We narrowed down the candidates by evaluating whether these interactors were downstream of ALK in a neuroblastoma cell line, NB-1. Amongst these, we identified amyloid beta precursor protein-binding family B member 1 (APBB1) as an ALK downstream molecule involved in NB-1 cell viability. Finally, we assessed the kinase-substrate relationship between ALK and APBB1 and found that ALK phosphorylated multiple tyrosine residues in APBB1 both in-cell and in-tube assays, with tyrosine 269 as a major target. In conclusion, we successfully identified a new substrate for receptor ALK. Our results may help further elucidate the molecular mechanism of ALK downstream signalling in neuroblastoma.

Quercetin inhibits endothelial & hepatocellular carcinoma cell crosstalk via reducing extracellular vesicle-mediated VEGFR2 mRNA transfer

This study aims to investigate the regulatory effects of quercetin extracellular vesicles (EVs)-mediated expression of vascular endothelial growth factor receptor 2 (VEGFR2) in hepatocellular carcinoma (HCC)-derived circulating tumor cells (CTCs) and the underlying mechanisms. CTCs were isolated from patients with pathologically diagnosed HCC, with VEGFR2 expression visualized by fluorescence in situ hybridization (FISH). The human HCC cell line Huh-7 and SK-HEP-1 were used for in vitro studies to assess EVs uptake, VEGFR2 mRNA transfer, invasion, migration, cancer stem cell (CSC) properties, and VEGF secretion. Results showed that VEGFR2 mRNA was commonly expressed in HCC-CTCs, with a higher incidence in biphenotypic CTCs. Its expression was limited in HCC cell lines, but present in certain liver cells. In vitro experiments confirmed that VEGFR2 mRNA could be transferred to HCC cells via EVs from primary tumor endothelial cells (PTECs), which was impaired by quercetin treatment. Quercetin significantly reduced VEGFR2 mRNA and protein expression in HCC cells, weakened their invasive and metastatic capacities, and diminished VEGFR2-mediated CSC properties. In vivo, quercetin reduced VEGF secretion, impaired angiogenesis, slowed tumor growth, and decreased the number and proportion of VEGFR2-positive CTCs. In summary, VEGFR2 mRNA is present in HCC-CTCs, potentially sourced from PTECs-derived EVs. Quercetin effectively inhibits VEGFR2 expression, impacting HCC cell invasion, metastasis, and CSC characteristics. Besides, it reduces VEGFR2-positive CTCs in vivo. These effects support its therapeutic potential in HCC treatment by targeting the angiogenesis and tumor dissemination pathway.

NanoTrans: an integrated computational framework for comprehensive transcriptome analysis with nanopore direct RNA sequencing

Nanopore direct RNA sequencing (DRS) provides the direct access to native RNA strands with full-length information, shedding light on rich qualitative and quantitative properties of gene expression profiles. Here with NanoTrans, we present an integrated computational framework that comprehensively covers all major DRS-based application scopes, including isoform clustering and quantification, poly(A) tail length estimation, RNA modification profiling, and fusion gene detection. In addition to its merit in providing such a streamlined one-stop solution, NanoTrans also shines in its workflow-orientated modular design, batch processing capability, all-in-one tabular and graphic report output, as well as automatic installation and configuration supports. Finally, by applying NanoTrans to real DRS datasets of yeast, Arabidopsis, as well as human embryonic kidney and cancer cell lines, we further demonstrate its utility, effectiveness, and efficacy across a wide range of DRS-based application settings.

Hyrtios sp.-associated Cladosporium sp. UR3 as a potential source of antiproliferative metabolites

BACKGROUND

Sponge-associated microorganisms are promising resources for the production of bioactive compounds with cytotoxic potential. The main goal of our study is to isolate the fungal endophytes from the Red Sea sponge Hyrtios sp. followed by investigating their cytotoxicity against number of cell lines.

RESULTS

The fungal strain UR3 was isolated from the Red Sea sponge using Sabouraud dextrose agar media. It was identified based on partial 18 S rRNA gene and ITS sequence analyses as Cladosporium sp. UR3. The in vitro cytotoxic potential of the ethyl acetate extract of the fungal isolate was evaluated using MTT assay against three cancer cell lines: CACO2, MCF7, and HEPG2. Metabolomics profiling of the obtained ethyl acetate extract using LC-HR-ESI-MS, along with molecular docking and pharmacological network studies for the dereplicated compounds were performed to explore its chemical profile and the possible cytotoxic mechanism of the sponge-associated fungi.

CONCLUSION

These results highlighted the role of sponge-associated fungi as a fruitful resource for the discovery of cytotoxic metabolites.

Proteome-wide copy-number estimation from transcriptomics

Protein copy numbers constrain systems-level properties of regulatory networks, but proportional proteomic data remain scarce compared to RNA-seq. We related mRNA to protein statistically using best-available data from quantitative proteomics and transcriptomics for 4366 genes in 369 cell lines. The approach starts with a protein's median copy number and hierarchically appends mRNA-protein and mRNA-mRNA dependencies to define an optimal gene-specific model linking mRNAs to protein. For dozens of cell lines and primary samples, these protein inferences from mRNA outmatch stringent null models, a count-based protein-abundance repository, empirical mRNA-to-protein ratios, and a proteogenomic DREAM challenge winner. The optimal mRNA-to-protein relationships capture biological processes along with hundreds of known protein-protein complexes, suggesting mechanistic relationships. We use the method to identify a viral-receptor abundance threshold for coxsackievirus B3 susceptibility from 1489 systems-biology infection models parameterized by protein inference. When applied to 796 RNA-seq profiles of breast cancer, inferred copy-number estimates collectively re-classify 26-29% of luminal tumors. By adopting a gene-centered perspective of mRNA-protein covariation across different biological contexts, we achieve accuracies comparable to the technical reproducibility of contemporary proteomics.

Clinical proteomics towards multiomics in cancer

Recent technological advancements in mass spectrometry (MS)-based proteomics technologies have accelerated its application to study greater and greater numbers of human tumor specimens. Over the last several years, the Clinical Proteomic Tumor Analysis Consortium, the International Cancer Proteogenome Consortium, and others have generated MS-based proteomic profiling data combined with corresponding multiomics data on thousands of human tumors to date. Proteomic data sets in the public domain can be re-examined by other researchers with different questions in mind from what the original studies explored. In this review, we examine the increasing role of proteomics in studying cancer, along with the potential for previous studies and their associated data sets to contribute to improving the diagnosis and treatment of cancer in the clinical setting. We also explore publicly available proteomics and multi-omics data from cancer cell line models to show how such data may aid in identifying therapeutic strategies for cancer subsets.

Functional genomics reveals an off-target dependency of drug synergy in gastric cancer therapy

BACKGROUND

Integrating molecular-targeted agents into combination chemotherapy is transformative for enhancing treatment outcomes in cancer. However, realizing the full potential of this approach requires a clear comprehension of the genetic dependencies underlying drug synergy. While the interactions between conventional chemotherapeutics are well-explored, the interplay of molecular-targeted agents with conventional chemotherapeutics remains a frontier in cancer treatment. Hence, we leveraged a powerful functional genomics approach to decode genomic dependencies that drive synergy in molecular-targeted agent/chemotherapeutic combinations in gastric adenocarcinoma, addressing a critical need in gastric cancer therapy.

METHODS

We screened pharmacological interactions between fifteen molecular-targeted agent/conventional chemotherapeutic pairs in gastric adenocarcinoma cells, and examined the genome-scale genetic dependencies of synergy integrating genome-wide CRISPR screening with the shRNA-based signature assay. We validated the synergy in cell death using fluorescence-based and lysis-dependent inference of cell death kinetics assay, and validated the genetic dependencies by single-gene knockout experiments.

RESULTS

Our combination screen identified SN-38/erlotinib as the drug pair with the strongest synergism. Functional genomics assays unveiled a genetic dependency signature of SN-38/erlotinib identical to SN-38. Remarkably, the enhanced cell death with improved kinetics induced by SN-38/erlotinib was attributed to erlotinib's off-target effect, inhibiting ABCG2, rather than its on-target effect on EGFR.

CONCLUSION

In the era of precision medicine, where emphasis on primary drug targets prevails, our research challenges this paradigm by showcasing a robust synergy underpinned by an off-target dependency. Further dissection of the intricate genetic dependencies that underlie synergy can pave the way to developing more effective combination strategies in gastric cancer therapy.

Identification and characterization of ADAR1 mutations and changes in gene expression in human cancers

ADAR1 (Adenosine deaminase action on RNA1) is involved in post-transcriptional RNA editing. ADAR1 mutations have been identified in many cancers but its role in tumor formation is still not well understood. Here we used available cancer genomes deposited on CSOMIC and cBioPortal to identify and characterize mutations and changes in ADAR1 expression in cancer cells. We identify several high frequency substitutions including one at R767 which is located in one of the dsRNA interacting domains. In silico protein structure analysis suggest the R767 mutations affect the protein stability and are likely to destabilize interaction with dsRNA. Gene expression analysis shows that in samples with under-expressed ADAR1, there is a statistically significant increase in expression of BLCAP (Bladder Cancer Associated Protein). Although BLCAP was initially identified in bladder cancers, more recent evidence shows that it is a tumor suppressor and BLCAP mutations have been detected in many cancer cells. Epistatic analysis using the cBioPortal mutual exclusivity calculator for the TCGA pan-cancer data shows that co-mutations between ADAR1 and other genes regulated by it are likely in cancer cells except for PTEN, AKT1 and BLCAP. This suggests that when ADAR1 function is impaired, PTEN, AKT1 and BLCAP become essential for survival of cancer cells. We also identified several samples with high mutation burden between ADAR1 and other genes regulated primarily in endometrial cancers. Finally, we show that the deaminase domain is highly conserved in metazoans and mutations within conserved residues do occur in human cancers suggesting that destabilization of the enzyme function is contributing to cancer development.

Systematic mapping of mitochondrial calcium uniporter channel (MCUC)-mediated calcium signaling networks

The mitochondrial calcium uniporter channel (MCUC) mediates mitochondrial calcium entry, regulating energy metabolism and cell death. Although several MCUC components have been identified, the molecular basis of mitochondrial calcium signaling networks and their remodeling upon changes in uniporter activity have not been assessed. Here, we map the MCUC interactome under resting conditions and upon chronic loss or gain of mitochondrial calcium uptake. We identify 89 high-confidence interactors that link MCUC to several mitochondrial complexes and pathways, half of which are associated with human disease. As a proof-of-concept, we validate the mitochondrial intermembrane space protein EFHD1 as a binding partner of the MCUC subunits MCU, EMRE, and MCUB. We further show a MICU1-dependent inhibitory effect of EFHD1 on calcium uptake. Next, we systematically survey compensatory mechanisms and functional consequences of mitochondrial calcium dyshomeostasis by analyzing the MCU interactome upon EMRE, MCUB, MICU1, or MICU2 knockdown. While silencing EMRE reduces MCU interconnectivity, MCUB loss-of-function leads to a wider interaction network. Our study provides a comprehensive and high-confidence resource to gain insights into players and mechanisms regulating mitochondrial calcium signaling and their relevance in human diseases.

Increased Gremlin1 Expression in Pancreatic Ductal Adenocarcinoma Promotes a Fibrogenic Stromal Microenvironment

OBJECTIVE

Pancreatic ductal adenocarcinoma (PDAC) microenvironment is primarily composed of cancer-associated fibroblasts and immune cells. Gremlin1 (Grem1) is a profibrogenic factor that promotes tumorigenesis in several cancers. However, the role of Grem1 in the PDAC microenvironment is not defined.

MATERIALS AND METHODS

We correlated Grem1 levels with activated stroma and immune cells in human PDAC using The Cancer Genome Atlas RNA-sequencing data and characterized expression of Grem1 transcripts and isoforms in pancreatic cell lines and PDAC tissues. We assessed the role of Grem1 in the microenvironment by in vitro studies.

RESULTS

Grem1 expression is associated with an activated stroma and increased M1 and M2 macrophages. Only full length Grem1 variant 1 and isoform 1 were detectable in human pancreatic cells, and remarkably high levels of Grem1 were observed in pancreatic fibroblasts. Immunohistochemistry detected Grem1 protein in PDAC tumor and stromal cells, which correlated with infiltrating macrophages in PDAC tumors. Grem1 knockdown in cancer-associated fibroblasts suppressed transforming growth factor β-induced extracellular matrix proteins. Grem1 recombinant protein treatment in vitro increased M1 and M2 macrophages.

CONCLUSIONS

Grem1 acts as a profibrogenic factor in the PDAC microenvironment via modulation of fibroblasts and macrophages. Grem1 may have the potential to be developed as a therapeutic target for PDAC.

Molecular signature of immune-related new survival predictions for subtype of renal cell carcinomas

Background

Kidney renal papillary cell carcinoma (KIRP), kidney chromophobe (KICH), and kidney renal clear cell carcinoma (KIRC) are three most common subtypes of renal cell carcinomas (RCC), and its development is a multifaceted process that intricately involves the interplay of numerous genes. Despite recent advances in research on renal cell carcinoma, the prognosis of KIRC patients remains dismal. Therefore, there is an urgent need to explore new prognostic biomarkers and treatment strategies to help clinicians choose more effective treatment methods and accurately predict long-term efficacy. Our study aimed to systematically evaluate the gene expression profiles of three RCC subtypes, especially KIRC, and to identify survival-related biomarker.

Methods

In our present study, we systematically evaluate the genes expression profile difference among three subtypes of RCC, and identify the survival-related key genes signature based on GEPIA2. GeneMANIA was used to identify the functionality-related differentially expressed genes (DEGs). Furthermore, focusing on KIRC, we intersected functionality-related and survival-related DEGs based on two datasets.

Results

We ascertained five DEGs (ANK3, FREM2, KIF13B, MPP7 and SOX6) as key survival-related genes in KIRC. High levels of these five DEGs expressions were strongly associated with favorable prognosis, but not correlated to metastasis. Downregulation of these five DEGs expressions was closely associated with immunomodulators, chemokines, and infiltrating levels of different immune cells, which indicated that these five DEGs were key immune-related novel prognostic biomarkers for KIRC.

Conclusions

The five identified DEGs serve as potential novel prognostic biomarkers for KIRC. However, the crucial factors that lead to the downregulation and functional inactivation of these five key genes need to be explored in future studies.

Empowering biomedical discovery with AI agents

We envision "AI scientists" as systems capable of skeptical learning and reasoning that empower biomedical research through collaborative agents that integrate AI models and biomedical tools with experimental platforms. Rather than taking humans out of the discovery process, biomedical AI agents combine human creativity and expertise with AI's ability to analyze large datasets, navigate hypothesis spaces, and execute repetitive tasks. AI agents are poised to be proficient in various tasks, planning discovery workflows and performing self-assessment to identify and mitigate gaps in their knowledge. These agents use large language models and generative models to feature structured memory for continual learning and use machine learning tools to incorporate scientific knowledge, biological principles, and theories. AI agents can impact areas ranging from virtual cell simulation, programmable control of phenotypes, and the design of cellular circuits to developing new therapies.

Enhancer of Zeste Homolog 2 Protects Mucosal Melanoma from Ferroptosis via the KLF14-SLC7A11 Signaling Pathway

BACKGROUND

Mucosal melanoma (MM) is epidemiologically, biologically, and molecularly distinct from cutaneous melanoma. Current treatment strategies have failed to significantly improve the prognosis for MM patients. This study aims to identify therapeutic targets and develop combination strategies by investigating the mechanisms underlying the tumorigenesis and progression of MM.

METHODS

We analyzed the copy number amplification of enhancer of zeste homolog 2 (EZH2) in 547 melanoma patients and investigated its correlation with clinical prognosis. Utilizing cell lines, organoids, and patient-derived xenograft models, we assessed the impact of EZH2 on cell proliferation and sensitivity to ferroptosis. Further, we explored the mechanisms of ferroptosis resistance associated with EZH2 by conducting RNA sequencing and chromatin immunoprecipitation sequencing.

RESULTS

EZH2 copy number amplification was closely associated with malignant phenotype and poor prognosis in MM patients. EZH2 was essential for MM cell proliferation in vitro and in vivo. Moreover, genetic perturbation of EZH2 rendered MM cells sensitized to ferroptosis. Combination treatment of EZH2 inhibitor with ferroptosis inducer significantly inhibited the growth of MM. Mechanistically, EZH2 inhibited the expression of Krüpple-Like factor 14 (KLF14), which binds to the promoter of solute carrier family 7 member 11 (SLC7A11) to repress its transcription. Loss of EZH2 therefore reduced the expression of SLC7A11, leading to reduced intracellular SLC7A11-dependent glutathione synthesis to promote ferroptosis.

CONCLUSION

Our findings not only establish EZH2 as a biomarker for MM prognosis but also highlight the EZH2-KLF14-SLC7A11 axis as a potential target for MM treatment.

Chromatin interaction maps identify oncogenic targets of enhancer duplications in cancer

As a major type of structural variants, tandem duplication plays a critical role in tumorigenesis by increasing oncogene dosage. Recent work has revealed that noncoding enhancers are also affected by duplications leading to the activation of oncogenes that are inside or outside of the duplicated regions. However, the prevalence of enhancer duplication and the identity of their target genes remains largely unknown in the cancer genome. Here, by analyzing whole-genome sequencing data in a non-gene-centric manner, we identify 881 duplication hotspots in 13 major cancer types, most of which do not contain protein-coding genes. We show that the hotspots are enriched with distal enhancer elements and are highly lineage-specific. We develop a HiChIP-based methodology that navigates enhancer-promoter contact maps to prioritize the target genes for the duplication hotspots harboring enhancer elements. The methodology identifies many novel enhancer duplication events activating oncogenes such as ESR1, FOXA1, GATA3, GATA6, TP63, and VEGFA, as well as potentially novel oncogenes such as GRHL2, IRF2BP2, and CREB3L1 In particular, we identify a duplication hotspot on Chromosome 10p15 harboring a cluster of enhancers, which skips over two genes, through a long-range chromatin interaction, to activate an oncogenic isoform of the NET1 gene to promote migration of gastric cancer cells. Focusing on tandem duplications, our study substantially extends the catalog of noncoding driver alterations in multiple cancer types, revealing attractive targets for functional characterization and therapeutic intervention.

Developmental-status-aware transcriptional decomposition establishes a cell state panorama of human cancers

BACKGROUND

Cancer cells evolve under unique functional adaptations that unlock transcriptional programs embedded in adult stem and progenitor-like cells for progression, metastasis, and therapeutic resistance. However, it remains challenging to quantify the stemness-aware cell state of a tumor based on its gene expression profile.

METHODS

We develop a developmental-status-aware transcriptional decomposition strategy using single-cell RNA-sequencing-derived tissue-specific fetal and adult cell signatures as anchors. We apply our method to various biological contexts, including developing human organs, adult human tissues, experimentally induced differentiation cultures, and bulk human tumors, to benchmark its performance and to reveal novel biology of entangled developmental signaling in oncogenic processes.

RESULTS

Our strategy successfully captures complex dynamics in developmental tissue bulks, reveals remarkable cellular heterogeneity in adult tissues, and resolves the ambiguity of cell identities in in vitro transformations. Applying it to large patient cohorts of bulk RNA-seq, we identify clinically relevant cell-of-origin patterns and observe that decomposed fetal cell signals significantly increase in tumors versus normal tissues and metastases versus primary tumors. Across cancer types, the inferred fetal-state strength outperforms published stemness indices in predicting patient survival and confers substantially improved predictive power for therapeutic responses.

CONCLUSIONS

Our study not only provides a general approach to quantifying developmental-status-aware cell states of bulk samples but also constructs an information-rich, biologically interpretable, cell-state panorama of human cancers, enabling diverse translational applications.

Chemoproteogenomic stratification of the missense variant cysteinome

Cancer genomes are rife with genetic variants; one key outcome of this variation is widespread gain-of-cysteine mutations. These acquired cysteines can be both driver mutations and sites targeted by precision therapies. However, despite their ubiquity, nearly all acquired cysteines remain unidentified via chemoproteomics; identification is a critical step to enable functional analysis, including assessment of potential druggability and susceptibility to oxidation. Here, we pair cysteine chemoproteomics-a technique that enables proteome-wide pinpointing of functional, redox sensitive, and potentially druggable residues-with genomics to reveal the hidden landscape of cysteine genetic variation. Our chemoproteogenomics platform integrates chemoproteomic, whole exome, and RNA-seq data, with a customized two-stage false discovery rate (FDR) error controlled proteomic search, which is further enhanced with a user-friendly FragPipe interface. Chemoproteogenomics analysis reveals that cysteine acquisition is a ubiquitous feature of both healthy and cancer genomes that is further elevated in the context of decreased DNA repair. Reference cysteines proximal to missense variants are also found to be pervasive, supporting heretofore untapped opportunities for variant-specific chemical probe development campaigns. As chemoproteogenomics is further distinguished by sample-matched combinatorial variant databases and is compatible with redox proteomics and small molecule screening, we expect widespread utility in guiding proteoform-specific biology and therapeutic discovery.

Single‑cell RNA sequencing reveals heterogeneity in ovarian cancer and constructs a prognostic signature for prognostic prediction and immunotherapy

BACKGROUND

Ovarian cancer (OC) is one of the cancers with a high incidence at present, which poses a severe threat to women's health. This study focused on identifying the heterogeneity among malignant epithelial cell OC and constructing an effective prognostic signature to predict prognosis and immunotherapy according to a multidisciplinary study.

METHODS

The InterCNV algorithm was used to identify the heterogeneity of OC based on the scRNA-seq and bulk RNA-seq data. Six algorithms selected EMTscore. An effective prognostic signature was conducted using the COX and Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithms. The texting datasets were used to assess the accuracy of the prognostic signature. We evaluated different immune characteristics and immunotherapy response differences among other risk groups.

RESULTS

A prognostic signature including 14 genes was established. The patients in the high-risk group have poor survival outcomes. We also found that the patients in the low-risk group have higher immune cell infiltration, enrichment of immune checkpoints, and immunotherapy response, suggesting that the patients in the low-risk group may be more sensitive to immunotherapy. Finally, the laboratory test results showed that KREMEN2 was identified as a novel biomarker and therapeutic target for OC patients.

CONCLUSIONS

Our study established a GRG signature consisting of 16 genes based on the scRNA-seq and bulk RNA-seq data, which provides a new perspective on the prediction of prognosis and treatment strategy for OC.

Comprehensive pan-cancer analysis of KLRB1-CLEC2D pair and identification of small molecule inhibitors to disrupt their interaction

The interplay between immune checkpoints KLRB1 and CLEC2D is crucial for tumor progression and immune evasion, yet the interaction dynamics are not fully understood. This study aims to elucidate the interaction across various cancers and identify small molecule inhibitors that can disrupt it. We perform a comprehensive pan-cancer analysis of the KLRB1-CLEC2D pair, including mRNA expression patterns, pathological stages, survival outcomes, and single-cell omics, immune infiltration, copy number variations, and DNA methylation profiles. Our findings reveal a consistently higher CLEC2D/KLRB1 ratio in most cancer types compared to normal tissues, and this ratio also increased with advancing pathological stages. Lower KLRB1 expression correlated with higher mortality in most cancers, opposite to CLEC2D. Expression variations were attributed to differential lymphocyte infiltration, CNV, and DNA methylation. Structure-based virtual screening analysis identified compounds including forsythiaside A and RGD peptides as effective inhibitors of the KLRB1-CLEC2D interaction, validated through microscale thermophoresis. This research advances understanding of the KLRB1-CLEC2D interaction within the tumor microenvironment and introduces novel therapeutic strategies to modulate this interaction.

Multivariate analysis of metabolic state vulnerabilities across diverse cancer contexts reveals synthetically lethal associations

Targeting the distinct metabolic needs of tumor cells has recently emerged as a promising strategy for cancer therapy. The heterogeneous, context-dependent nature of cancer cell metabolism, however, poses challenges to identifying effective therapeutic interventions. Here, we utilize various unsupervised and supervised multivariate modeling approaches to systematically pinpoint recurrent metabolic states within hundreds of cancer cell lines, elucidate their association with tumor lineage and growth environments, and uncover vulnerabilities linked to their metabolic states across diverse genetic and tissue contexts. We validate key findings via analysis of data from patient-derived tumors and pharmacological screens and by performing genetic and pharmacological experiments. Our analysis uncovers synthetically lethal associations between the tumor metabolic state (e.g., oxidative phosphorylation), driver mutations (e.g., loss of tumor suppressor PTEN), and actionable biological targets (e.g., mitochondrial electron transport chain). Investigating the mechanisms underlying these relationships can inform the development of more precise and context-specific, metabolism-targeted cancer therapies.

Expression-Driven Genetic Dependency Reveals Targets for Precision Medicine

Cancer cells are heterogeneous, each harboring distinct molecular aberrations and are dependent on different genes for their survival and proliferation. While successful targeted therapies have been developed based on driver DNA mutations, many patient tumors lack druggable mutations and have limited treatment options. Here, we hypothesize that new precision oncology targets may be identified through "expression-driven dependency", whereby cancer cells with high expression of a targeted gene are more vulnerable to the knockout of that gene. We introduce a Bayesian approach, BEACON, to identify such targets by jointly analyzing global transcriptomic and proteomic profiles with genetic dependency data of cancer cell lines across 17 tissue lineages. BEACON identifies known druggable genes, e.g., BCL2, ERBB2, EGFR, ESR1, MYC, while revealing new targets confirmed by both mRNA- and protein-expression driven dependency. Notably, the identified genes show an overall 3.8-fold enrichment for approved drug targets and enrich for druggable oncology targets by 7 to 10-fold. We experimentally validate that the depletion of GRHL2, TP63, and PAX5 effectively reduce tumor cell growth and survival in their dependent cells. Overall, we present the catalog of express-driven dependency targets as a resource for identifying novel therapeutic targets in precision oncology.

P16INK4A drives RB1 degradation by UTP14A-catalyzed K810 ubiquitination

P16INK4A expression is inversely associated with RB1 expression in cancer cells, and P16INK4A inhibits CDK4-catalyzed RB1 phosphorylation. How P16INK4A and RB1 coordinately express and regulate the cell cycle remains to be studied. In the present study, we found that P16INK4A upregulated the E3 ligase UTP14A, which led to the ubiquitination of RB1 at K810 and RB1 degradation. P16INK4A loss consistently disrupted the UTP14A-mediated degradation of RB1 and caused RB1 accumulation. Functionally, P16INK4A loss inhibited RB1 ubiquitination in a cell cycle progression-independent fashion and inhibited proteome-scale ubiquitination in a cell cycle progression-dependent manner. Our findings indicate that there is a negative feedback loop between P16INK4A and RB1 expression and that disruption of this loop may partially rescue the biological outcomes of P16INK4A loss. We also revealed a hitherto unknown function for P16 INK4A in regulating proteome-scale ubiquitination by inhibiting cell proliferation, which may be useful for the development of anticancer drugs.

Temporal regulation of acetylation status determines PARP1 role in DNA damage response and metabolic homeostasis

Poly(ADP-ribose) polymerase 1 (PARP1) is an abundant nuclear protein involved in DNA repair, chromatin structure, and transcription. However, the regulation of its different functions remains poorly understood. Here, we report the role of PARP1 acetylation status in modulating its DNA repair and transactivation functions. We demonstrate that histone deacetylase 5 (HDAC5) determines PARP1 acetylation at Lys498 and Lys521 sites. HDAC5-mediated deacetylation at Lys498 site regulates PARP1 DNA damage response and facilitates efficient recruitment of DNA repair factors at damaged sites, thereby promoting cell survival. Additionally, HDAC5-mediated deacetylation at Lys521 site promotes PARP1 coactivator function, resulting in induction of proliferative and metabolic genes in an activating transcription factor 4-dependent manner. Thus, PARP1 induces metabolic adaptation to spur malignant phenotype. Our studies in mouse tumor models suggest that pharmacological inhibition of PARP1 enzymatic activity does not block tumor progression robustly as transactivation function remains unperturbed. These findings provide key mechanistic insights into PARP1 regulation and expand its role in tumor development.

Functional Assessments of Gynecologic Cancer Models Highlight Differences Between Single-Node Inhibitors of the PI3K/AKT/mTOR Pathway and a Pan-PI3K/mTOR Inhibitor, Gedatolisib

Background/Objectives: The PI3K/AKT/mTOR (PAM) pathway is frequently activated in gynecological cancers. Many PAM inhibitors selectively target single PAM pathway nodes, which can lead to reduced efficacy and increased drug resistance. To address these limitations, multiple PAM pathway nodes may need to be inhibited. Gedatolisib, a well-tolerated panPI3K/mTOR inhibitor targeting all Class I PI3K isoforms, mTORC1 and mTORC2, could represent an effective treatment option for patients with gynecologic cancers. Methods: Gedatolisib and other PAM inhibitors (e.g., alpelisib, capivasertib, and everolimus) were tested in endometrial, ovarian, and cervical cancer cell lines by using cell viability, cell proliferation, and flow cytometry assays. Xenograft studies evaluated gedatolisib in combination with a CDK4/6 inhibitor (palbociclib) or an anti-estrogen (fulvestrant). A pseudo-temporal transcriptomic trajectory of endometrial cancer clinical progression was computationally modeled employing data from 554 patients to correlate non-clinical studies with a potential patient group. Results: Gedatolisib induced a substantial decrease in PAM pathway activity in association with the inhibition of cell cycle progression and the decreased cell viability in vitro. Compared to single-node PAM inhibitors, gedatolisib exhibited greater growth-inhibitory effects in almost all cell lines, regardless of the PAM pathway mutations. Gedatolisib combined with either fulvestrant or palbociclib inhibited tumor growth in endometrial and ovarian cancer xenograft models. Conclusions: Gedatolisib in combination with other therapies has shown an acceptable safety profile and promising preliminary efficacy in clinical studies with various solid tumor types. The non-clinical data presented here support the development of gedatolisib combined with CDK4/6 inhibitors and/or hormonal therapy for gynecologic cancer treatment.

Targeting the Sodium-Potassium Pump as a Therapeutic Strategy in Acute Myeloid Leukemia

Tissue-specific differences in the expression of paralog genes, which are not essential in most cell types due to the buffering effect of the partner pair, can make for highly selective gene dependencies. To identify selective paralogous targets for acute myeloid leukemia (AML), we integrated the Cancer Dependency Map with numerous datasets characterizing protein-protein interactions, paralog relationships, and gene expression in cancer models. In this study, we identified ATP1B3 as a context-specific, paralog-related dependency in AML. ATP1B3, the β-subunit of the sodium-potassium pump (Na/K-ATP pump), interacts with the α-subunit ATP1A1 to form an essential complex for maintaining cellular homeostasis and membrane potential in all eukaryotic cells. When ATP1B3's paralog ATP1B1 is poorly expressed, elimination of ATP1B3 leads to the destabilization of the Na/K-ATP pump. ATP1B1 expression is regulated through epigenetic silencing in hematopoietic lineage cells through histone and DNA methylation in the promoter region. Loss of ATP1B3 in AML cells induced cell death in vitro and reduced leukemia burden in vivo, which could be rescued by stabilizing ATP1A1 through overexpression of ATP1B1. Thus, ATP1B3 is a potential therapeutic target for AML and other hematologic malignancies with low expression of ATP1B1. Significance: ATP1B3 is a lethal selective paralog dependency in acute myeloid leukemia that can be eliminated to destabilize the sodium-potassium pump, inducing cell death.

Golgi-localized Ring Finger Protein 121 is necessary for MYCN-driven neuroblastoma tumorigenesis

MYCN amplification predicts poor prognosis in childhood neuroblastoma. To identify MYCN oncogenic signal dependencies we performed N-ethyl-N-nitrosourea (ENU) mutagenesis on the germline of neuroblastoma-prone TH-MYCN transgenic mice to generate founders which had lost tumorigenesis. Sequencing of the mutant mouse genomes identified the Ring Finger Protein 121 (RNF121WT) gene mutated to RNFM158R associated with heritable loss of tumorigenicity. While the RNF121WT protein localised predominantly to the cis-Golgi Complex, the RNF121M158R mutation in Helix 4 of its transmembrane domain caused reduced RNF121 protein stability and absent Golgi localisation. RNF121WT expression markedly increased during TH-MYCN tumorigenesis, whereas hemizygous RNF121WT gene deletion reduced TH-MYCN tumorigenicity. The RNF121WT-enhanced growth of MYCN-amplified neuroblastoma cells depended on RNF121WT transmembrane Helix 5. RNF121WT directly bound MYCN protein and enhanced its stability. High RNF121 mRNA expression associated with poor prognosis in human neuroblastoma tissues and another MYC-driven malignancy, laryngeal cancer. RNF121 is thus an essential oncogenic cofactor for MYCN and a target for drug development.

An automated network-based tool to search for metabolic vulnerabilities in cancer

The development of computational tools for the systematic prediction of metabolic vulnerabilities of cancer cells constitutes a central question in systems biology. Here, we present gmctool, a freely accessible online tool that allows us to accomplish this task in a simple, efficient and intuitive environment. gmctool exploits the concept of genetic Minimal Cut Sets (gMCSs), a theoretical approach to synthetic lethality based on genome-scale metabolic networks, including a unique database of synthetic lethals computed from Human1, the most recent metabolic reconstruction of human cells. gmctool introduces qualitative and quantitative improvements over our previously developed algorithms to predict, visualize and analyze metabolic vulnerabilities in cancer, demonstrating a superior performance than competing algorithms. A detailed illustration of gmctool is presented for multiple myeloma (MM), an incurable hematological malignancy. We provide in vitro experimental evidence for the essentiality of CTPS1 (CTPS synthase) and UAP1 (UDP-N-Acetylglucosamine Pyrophosphorylase 1) in specific MM patient subgroups.

Transipedia.org: k-mer-based exploration of large RNA sequencing datasets and application to cancer data

Indexing techniques relying on k-mers have proven effective in searching for RNA sequences across thousands of RNA-seq libraries, but without enabling direct RNA quantification. We show here that arbitrary RNA sequences can be quantified in seconds through their decomposition into k-mers, with a precision akin to that of conventional RNA quantification methods. Using an index of the Cancer Cell Line Encyclopedia (CCLE) collection consisting of 1019 RNA-seq samples, we show that k-mer indexing offers a powerful means to reveal non-reference sequences, and variant RNAs induced by specific gene alterations, for instance in splicing factors.

Microarray analysis points to LMNB1 and JUN as potential target genes for predicting metastasis promotion by etoposide in colorectal cancer

Etoposide is a second-line chemotherapy agent widely used for metastatic colorectal cancer. However, we discovered that etoposide treatment induced greater motility potential in four colorectal cancer cell lines. Therefore, we used microarrays to test the mRNA of these cancer cell lines to investigate the mechanisms of etoposide promoting colorectal cancer metastasis. Differentially expressed genes (DEGs) were identified by comparing the gene expression profiles in samples from etoposide-treated cells and untreated cells in all four colorectal cancer cell lines. Next, these genes went through the Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway analysis. Among the top 10 genes including the upregulated and downregulated, eight genes had close interaction according to the STRING database: FAS, HMMR, JUN, LMNB1, MLL3, PLK2, STAG1 and TBL1X. After etoposide treatment, the cell cycle, metabolism-related and senescence signaling pathways in the colorectal cancer cell lines were significantly downregulated, whereas necroptosis and oncogene pathways were significantly upregulated. We suggest that the differentially expressed genes LMNB1 and JUN are potential targets for predicting colorectal cancer metastasis. These results provide clinical guidance in chemotherapy, and offer direction for further research in the mechanism of colorectal cancer metastasis.

DEK regulates B-cell proliferative capacity and is associated with aggressive disease in low-grade B-cell lymphomas

This study sheds light on the pivotal role of the oncoprotein DEK in B-cell lymphoma. We reveal DEK expression correlates with increased tumor proliferation and inferior overall survival in cases diagnosed with low-grade B-cell lymphoma (LGBCL). We also found significant correlation between DEK expression and copy number alterations in LGBCL tumors, highlighting a novel mechanism of LGBCL pathogenesis that warrants additional exploration. To interrogate the mechanistic role of DEK in B-cell lymphoma, we generated a DEK knockout cell line model, which demonstrated DEK depletion caused reduced proliferation and altered expression of key cell cycle and apoptosis-related proteins, including Bcl-2, Bcl-xL, and p53. Notably, DEK depleted cells showed increased sensitivity to apoptosis-inducing agents, including venetoclax and staurosporine, which underscores the therapeutic potential of targeting DEK in B-cell lymphomas. Overall, our study contributes to a better understanding of DEK's role as an oncoprotein in B-cell lymphomas, highlighting its potential as both a promising therapeutic target and a novel biomarker for aggressive LGBCL. Further research elucidating the molecular mechanisms underlying DEK-mediated tumorigenesis could pave the way for improved treatment strategies and better clinical outcomes for patients with B-cell lymphoma.

Leveraging Patient-Derived Organoids for Personalized Liver Cancer Treatment

Primary liver cancer (PLC) is a primary cause of cancer-related death worldwide, and novel treatments are needed due to the limited options available for treatment and tumor heterogeneity. 66 surgically removed PLC samples were cultured using the self-developed 2:2 method, and the final success rate for organoid culture was 40.9%. Organoid performance has been evaluated using comprehensive molecular measurements, such as whole-exome and RNA sequencing, as well as anticancer drug testing. Multiple organoids and their corresponding tumor tissues contained several of the same mutations, with all pairs sharing conventional TP53 mutations. Regarding copy number variations and gene expression, significant correlations were observed between the organoids and their corresponding parental tumor tissues. Comparisons at the molecular level provided us with an assessment of organoid-to-tumor concordance, which, in combination with drug sensitivity testing provided direct guidance for treatment selection. Finally, we were able to determine an appropriate pharmacological regimen for a patient with ICC, demonstrating the clinical practicality in tailoring patient-specific drug regimens. Our study provides an organoid culture technology that can cultivate models that retain most of the molecular characteristics of tumors and can be used for drug sensitivity testing, demonstrating the broad potential application of organoid technology in precision medicine for liver cancer treatment.