Addendum: The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity

Prediction of anticancer drug sensitivity using an interpretable model guided by deep learning

BACKGROUND

The prediction of drug sensitivity plays a crucial role in improving the therapeutic effect of drugs. However, testing the effectiveness of drugs is challenging due to the complex mechanism of drug reactions and the lack of interpretability in most machine learning and deep learning methods. Therefore, it is imperative to establish an interpretable model that receives various cell line and drug feature data to learn drug response mechanisms and achieve stable predictions between available datasets.

RESULTS

This study proposes a new and interpretable deep learning model, DrugGene, which integrates gene expression, gene mutation, gene copy number variation of cancer cells, and chemical characteristics of anticancer drugs to predict their sensitivity. This model comprises two different branches of neural networks, where the first involves a hierarchical structure of biological subsystems that uses the biological processes of human cells to form a visual neural network (VNN) and an interpretable deep neural network for human cancer cells. DrugGene receives genotype input from the cell line and detects changes in the subsystem states. We also employ a traditional artificial neural network (ANN) to capture the chemical structural features of drugs. DrugGene generates final drug response predictions by combining VNN and ANN and integrating their outputs into a fully connected layer. The experimental results using drug sensitivity data extracted from the Cancer Drug Sensitivity Genome Database and the Cancer Treatment Response Portal v2 reveal that the proposed model is better than existing prediction methods. Therefore, our model achieves higher accuracy, learns the reaction mechanisms between anticancer drugs and cell lines from various features, and interprets the model's predicted results.

CONCLUSIONS

Our method utilizes biological pathways to construct neural networks, which can use genotypes to monitor changes in the state of network subsystems, thereby interpreting the prediction results in the model and achieving satisfactory prediction accuracy. This will help explore new directions in cancer treatment. More available code resources can be downloaded for free from GitHub ( https://github.com/pangweixiong/DrugGene ).

Pathway-based signatures predict patient outcome, chemotherapy benefit and synthetic lethal dependencies in invasive lobular breast cancer

BACKGROUND

Invasive Lobular Carcinoma (ILC) is a morphologically distinct breast cancer subtype that represents up to 15% of all breast cancers. Compared to Invasive Breast Carcinoma of No Special Type (IBC-NST), ILCs exhibit poorer long-term outcome and a unique pattern of metastasis. Despite these differences, the systematic discovery of robust prognostic biomarkers and therapeutically actionable molecular pathways in ILC remains limited.

METHODS

Pathway-centric multivariable models using statistical machine learning were developed and tested in seven retrospective clinico-genomic cohorts (n = 996). Further external validation was performed using a new RNA-Seq clinical cohort of aggressive ILCs (n = 48).

RESULTS AND CONCLUSIONS

mRNA dysregulation scores of 25 pathways were strongly prognostic in ILC (FDR-adjusted P < 0.05). Of these, three pathways including Cell-cell communication, Innate immune system and Smooth muscle contraction were also independent predictors of chemotherapy response. To aggregate these findings, a multivariable machine learning predictor called PSILC was developed and successfully validated for predicting overall and metastasis-free survival in ILC. Integration of PSILC with CRISPR-Cas9 screening data from breast cancer cell lines revealed 16 candidate therapeutic targets that were synthetic lethal with high-risk ILCs. This study provides interpretable prognostic and predictive biomarkers of ILC which could serve as the starting points for targeted drug discovery for this disease.

Intestinal Tuft Cells: Morphology, Function, and Implications for Human Health

Tuft cells are a rare and morphologically distinct chemosensory cell type found throughout many organs, including the gastrointestinal tract. These cells were identified by their unique morphologies distinguished by large apical protrusions. Ultrastructural data have begun to describe the molecular underpinnings of their cytoskeletal features, and tuft cell-enriched cytoskeletal proteins have been identified, although the connection of tuft cell morphology to tuft cell functionality has not yet been established. Furthermore, tuft cells display variations in function and identity between and within tissues, leading to the delineation of distinct tuft cell populations. As a chemosensory cell type, they display receptors that are responsive to ligands specific for their environment. While many studies have demonstrated the tuft cell response to protists and helminths in the intestine, recent research has highlighted other roles of tuft cells as well as implicated tuft cells in other disease processes including inflammation, cancer, and viral infections. Here, we review the literature on the cytoskeletal structure of tuft cells. Additionally, we focus on new research discussing tuft cell lineage, ligand-receptor interactions, tuft cell tropism, and the role of tuft cells in intestinal disease. Finally, we discuss the implication of tuft cell-targeted therapies in human health and how the morphology of tuft cells may contribute to their functionality.

SLMO2 is a potential prognostic and immunological biomarker in human pan-cancer

SLMO2 is a lipid transporter that transports phosphatidylserine to the interior of mitochondria, also known as PRELID3B, which plays an important role in lipid metabolism. It has also been reported to be involved in the growth process of breast and lung tumors. However, its functions and underlying mechanisms in cancer progress remain elusive, and the potential as pan-cancer biomarker and therapeutic target remains unexplored. Using the TCGA project and GEO database, we performed pan-cancer analysis of SLMO2, which including the expression pattern, prognostic value, mutation landscape, methylation modification, protein-protein interaction network and the relationship between SLMO2 expression and immune infiltration. KEGG enrichment analysis was also performed to predict function and relevant cellular pathways of SLMO2. In addition, proliferation and migration assays were performed to detect the proliferation and metastasis capacity of breast cancer and lung cancer cells. In our study, we found that SLMO2 was overexpressed in pan-cancer and the elevated expression of SLMO2 was correlated with poorer prognosis. SLMO2 mutations were distributed in a variety of tumors and correlated with prognosis. Promoter methylation analysis showed that SLMO2 methylation levels were lower in most tumors compared with normal tissues, while a few tumors showed increased methylation levels of SLMO2. SLMO2 expression was also positively correlated with immune infiltration of MDSCs. Further pathway enrichment analysis indicated that SLMO2 was involved in regulating of cytoplasmic transport and other oncogenic processes. In vitro experiments have shown that SLMO2 promotes the proliferation and migration of breast cancer and lung cancer cells. In conclusion, our findings suggested that SLMO2 was a potential prognostic and immunological marker in pan-cancer. This study suggested a potential strategy for targeting SLMO2 to treat tumors, including manipulating tumor growth or the tumor microenvironment, especially the infiltration of MDSC.

Current advances in comprehensive omics data mining for oncology and cancer research

The availability of a large amount of multiomics data enables data-driven discovery studies on cancers. High-throughput data on mutations, gene/protein expression, immune scores (tumor-infiltrating cells), drug screening, and RNAi (shRNAs and CRISPRs) screening are major integrated components of patient samples and cell line datasets. Improvements in data access and user interfaces make it easy for general scientists to carry out their data mining practices on integrated multiomics data platforms without computational expertise. Here, we summarize the extent of data integration and functionality of several portals and software that provide integrated multiomics data mining platforms for all cancer studies. Recent progress includes programming interfaces (APIs) for customized data mining. Precalculated datasets assist noncomputational users in quickly browsing data associations. Furthermore, stand-alone software provides fast calculations and smart functions, guiding optimal sampling and filtering options for the easy discovery of significant data associations. These efforts improve the utility of cancer omics big data for noncomputational users at all levels of cancer research. In the present review, we aim to provide analytical information guiding general scientists to find and utilize data mining tools for their research.

Adapting Nanopore Sequencing Basecalling Models for Modification Detection via Incremental Learning and Anomaly Detection

We leverage machine learning approaches to adapt nanopore sequencing basecallers for nucleotide modification detection. We first apply the incremental learning technique to improve the basecalling of modification-rich sequences, which are usually of high biological interests. With sequence backbones resolved, we further run anomaly detection on individual nucleotides to determine their modification status. By this means, our pipeline promises the single-molecule, single-nucleotide and sequence context-free detection of modifications. We benchmark the pipeline using control oligos, further apply it in the basecalling of densely-modified yeast tRNAs and E.coli genomic DNAs, the cross-species detection of N6-methyladenosine (m6A) in mammalian mRNAs, and the simultaneous detection of N1-methyladenosine (m1A) and m6A in human mRNAs. Our IL-AD workflow is available at: https://github.com/wangziyuan66/IL-AD.

Identification of aneuploidy-related gene signature to predict survival in head and neck squamous cell carcinomas

BACKGROUND

To parse the characteristics of aneuploidy related riskscore (ARS) model in head and neck squamous cell carcinomas (HNSC) and their predictive ability on patient prognosis.

METHODS

Molecular subtyping of HNSC specimens was clustered by Copy Number Variation (CNV) data from The Cancer Genome Atlas (TCGA) dataset applying consistent clustering, followed by immune condition evaluation, differentially expressed genes (DEGs) analysis and DEGs function annotation. Weighted gene co-expression network analysis (WGCNA), protein-protein interaction, Univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO) and stepwise multivariate Cox regression analysis were implemented to construct an ARS model. A nomogram for clinic practice was designed by rms package. Immunotherapy evaluation and drug sensitivity prediction were also carried out.

RESULTS

We stratified HNSC patients into three different molecular subgroups, with the best prognosis in C1 cluster among 3 clusters. C1 cluster displayed greatest immune infiltration status. The most DEGs between C1 and C2 groups, mainly enriched in cell cycle and immune function. We constructed a nine-gene ARS model (ICOS, IL21R, CCR7, SELL, CYTIP, ZAP70, CCR4, S1PR4 and CD79A) that effectively differentiates between high- and low-risk patients. Patients in low ARS group showed a higher sensitivity to immunotherapy. A nomogram built by integrating ARS and clinic-pathological characteristics helped predict clinic survival benefit. Drug sensitivity evaluation found that 4/9 inhibitor drugs (MK-8776, AZD5438, PD-0332991, PHA-665752) acted on the cell cycle.

CONCLUSIONS

We classified 3 molecular subtypes for HNSC patients and established an ARS prognostic model, which offered a prospective direction for prognosis in HNSC.

Transcription factors direct epigenetic reprogramming at specific loci in human cancers

The characterization of epigenetic changes during cancer development and progression led to notable insights regarding the roles of cancer-specific epigenetic reprogramming. Recent studies showed that transcription factors (TFs) are capable to regulate epigenetic reprogramming at specific loci in different cancer types through their DNA-binding activities. However, the causal association of dynamic histone modification change mediated by TFs is still not well elucidated. Here we evaluated the impacts of 636 transcription factor binding activities on histone modification in 24 cancer types. We performed Instrumental Variables analysis by using genetic lesions of TFs as our instrumental proxies, which previously discovered to be associated with histone mark activities. As a result, we showed a total of 6 EpiTFs as strong directors of epigenetic reprogramming of histone modification in cancers, which alters the molecular and clinical phenotypes of cancer. Together our findings highlight a causal mechanism driven by the TFs and genome-wide histone modification, which is relevant to multiple status of oncogenesis.

Cell-Autonomous Cxcl1 Sustains Tolerogenic Circuitries and Stromal Inflammation via Neutrophil-Derived TNF in Pancreatic Cancer

We have shown that KRAS-TP53 genomic coalteration is associated with immune-excluded microenvironments, chemoresistance, and poor survival in pancreatic ductal adenocarcinoma (PDAC) patients. By treating KRAS-TP53 cooperativity as a model for high-risk biology, we now identify cell-autonomous Cxcl1 as a key mediator of spatial T-cell restriction via interactions with CXCR2+ neutrophilic myeloid-derived suppressor cells in human PDAC using imaging mass cytometry. Silencing of cell-intrinsic Cxcl1 in LSL-KrasG12D/+;Trp53R172H/+;Pdx-1Cre/+(KPC) cells reprograms the trafficking and functional dynamics of neutrophils to overcome T-cell exclusion and controls tumor growth in a T cell-dependent manner. Mechanistically, neutrophil-derived TNF is a central regulator of this immunologic rewiring, instigating feed-forward Cxcl1 overproduction from tumor cells and cancer-associated fibroblasts (CAF), T-cell dysfunction, and inflammatory CAF polarization via transmembrane TNF-TNFR2 interactions. TNFR2 inhibition disrupts this circuitry and improves sensitivity to chemotherapy in vivo. Our results uncover cancer cell-neutrophil cross-talk in which context-dependent TNF signaling amplifies stromal inflammation and immune tolerance to promote therapeutic resistance in PDAC.

SIGNIFICANCE

By decoding connections between high-risk tumor genotypes, cell-autonomous inflammatory programs, and myeloid-enriched/T cell-excluded contexts, we identify a novel role for neutrophil-derived TNF in sustaining immunosuppression and stromal inflammation in pancreatic tumor microenvironments. This work offers a conceptual framework by which targeting context-dependent TNF signaling may overcome hallmarks of chemoresistance in pancreatic cancer. This article is highlighted in the In This Issue feature, p. 1275.

Integrated CRISPR screening and drug profiling identifies combination opportunities for EGFR, ALK, and BRAF/MEK inhibitors

Anti-tumor efficacy of targeted therapies is variable across patients and cancer types. Even in patients with initial deep response, tumors are typically not eradicated and eventually relapse. To address these challenges, we present a systematic screen for targets that limit the anti-tumor efficacy of EGFR and ALK inhibitors in non-small cell lung cancer and BRAF/MEK inhibitors in colorectal cancer. Our approach includes genome-wide CRISPR screens with or without drugs targeting the oncogenic driver ("anchor therapy"), and large-scale pairwise combination screens of anchor therapies with 351 other drugs. Interestingly, targeting of a small number of genes, including MCL1, BCL2L1, and YAP1, sensitizes multiple cell lines to the respective anchor therapy. Data from drug combination screens with EGF816 and ceritinib indicate that dasatinib and agents disrupting microtubules act synergistically across many cell lines. Finally, we show that a higher-order-combination screen with 26 selected drugs in two resistant EGFR-mutant lung cancer cell lines identified active triplet combinations.

Allograft inflammatory factor 1 is a potential diagnostic, immunological, and prognostic biomarker in pan-cancer

BACKGROUND

Allograft Inflammatory Factor 1 (AIF-1) is a member of the allograft inflammatory factor gene family and plays an essential role in the occurrence and development of malignant tumors. However, little is known about the expression pattern, predictive value, and biological function of AIF-1 across cancers.

MATERIALS AND METHODS

We first analyzed AIF-1 expression across cancers based on data from public databases. Univariate Cox regression and Kaplan-Meier analyses were used to explore the predictive value of AIF-1 expression in various cancers. Moreover, gene set enrichment analysis (GSEA) was applied to determine the cancer hallmarks associated with AIF-1 expression. Spearman correlation analysis was performed to investigate the association between AIF-1 expression and tumor microenvironment scores, immune cell infiltration, immune-related genes, TMB, MSI, and DNA methyltransferases.

RESULTS

AIF-1 expression was upregulated in most cancer types and exhibited prognosis-predictive ability. AIF-1 expression was positively correlated with immune infiltrating cells and immune checkpoint-related genes in most cancers. Additionally, the promoter methylation level of AIF-1 was different in distinct tumors. High methylation levels of AIF-1 were associated with a worse prognosis in UCEC and melanoma, whereas they were associated with a better prognosis in GBM, KIRC, OV, and UVM. Finally, we found that AIF-1 was significantly highly expressed in KIRC tissues. Functionally, silencing AIF-1 dramatically decreased proliferation, migration, and invasion abilities.

CONCLUSION

Our results reveal that AIF-1 acts as a robust tumor biomarker and is closely correlated with tumor immune infiltration. Furthermore, AIF-1 may function as an oncogene and promote tumor progression in KIRC.

Combination of furosemide, gold, and dopamine as a potential therapy for breast cancer

Breast cancer is one of the leading causes of death in women worldwide. Initially, it develops in the epithelium of the ducts or lobules of the breast glandular tissues with limited growth and the potential to metastasize. It is a highly heterogeneous malignancy; however, the common molecular mechanisms could help identify new targeted drugs for treating its subtypes. This study uses computational drug repositioning approaches to explore fresh drug candidates for breast cancer treatment. We also implemented reversal gene expression and gene expression-based signatures to explore novel drug candidates computationally. The drug activity profiles and related gene expression changes were acquired from the DrugBank, PubChem, and LINCS databases, and then in silico drug screening, molecular dynamics (MD) simulation, replica exchange MD simulations, and simulated annealing molecular dynamics (SAMD) simulations were conducted to discover and verify the valid drug candidates. We have found that compounds like furosemide, gold, and dopamine showed significant outcomes. Furthermore, the expression of genes related to breast cancer was observed to be reversed by these shortlisted drugs. Therefore, we postulate that combining furosemide, gold, and dopamine would be a potential combination therapy measurement for breast cancer patients.

Sustained activation of non-canonical NF-κB signalling drives glycolytic reprogramming in doxorubicin-resistant DLBCL

DLBCL is the most common lymphoma with high tumor heterogeneity. Treatment refractoriness and relapse from R-CHOP therapy in patients remain a clinical problem. Activation of the non-canonical NF-κB pathway is associated with R-CHOP resistance. However, downstream targets of non-canonical NF-κB mediating R-CHOP-induced resistance remains uncharacterized. Here, we identify the common mechanisms underlying both intrinsic and acquired resistance that are induced by doxorubicin, the main cytotoxic component of R-CHOP. We performed global transcriptomic analysis of (1) a panel of resistant versus sensitive and (2) isogenic acquired doxorubicin-resistant DLBCL cell lines following short and chronic exposure to doxorubicin respectively. Doxorubicin-induced stress in resistant cells activates a distinct transcriptional signature that is enriched in metabolic reprogramming and oncogenic signalling. Selective and sustained activation of non-canonical NF-κB signalling in these resistant cells exacerbated their survival by augmenting glycolysis. In response to doxorubicin, p52-RelB complexes transcriptionally activated multiple glycolytic regulators with prognostic significance through increased recruitment at their gene promoters. Targeting p52-RelB and their targets in resistant cells increased doxorubicin sensitivity in vitro and in vivo. Collectively, our study uncovered novel molecular drivers of doxorubicin-induced resistance that are regulated by non-canonical NF-κB pathway. We reveal new avenues of therapeutic targeting for R-CHOP-treated refractory/relapsed DLBCL patients.

EWS::FLI1 and HOXD13 Control Tumor Cell Plasticity in Ewing Sarcoma

PURPOSE

Propagation of Ewing sarcoma requires precise regulation of EWS::FLI1 transcriptional activity. Determining the mechanisms of fusion regulation will advance our understanding of tumor progression. Here we investigated whether HOXD13, a developmental transcription factor that promotes Ewing sarcoma metastatic phenotypes, influences EWS::FLI1 transcriptional activity.

EXPERIMENTAL DESIGN

Existing tumor and cell line datasets were used to define EWS::FLI1 binding sites and transcriptional targets. Chromatin immunoprecipitation and CRISPR interference were employed to identify enhancers. CUT&RUN and RNA sequencing defined binding sites and transcriptional targets of HOXD13. Transcriptional states were investigated using bulk and single-cell transcriptomic data from cell lines, patient-derived xenografts, and patient tumors. Mesenchymal phenotypes were assessed by gene set enrichment, flow cytometry, and migration assays.

RESULTS

We found that EWS::FLI1 creates a de novo GGAA microsatellite enhancer in a developmentally conserved regulatory region of the HOXD locus. Knockdown of HOXD13 led to widespread changes in expression of developmental gene programs and EWS::FLI1 targets. HOXD13 binding was enriched at established EWS::FLI1 binding sites where it influenced expression of EWS::FLI1-activated genes. More strikingly, HOXD13 bound and activated EWS::FLI1-repressed genes, leading to adoption of mesenchymal and migratory cell states that are normally suppressed by the fusion. Single-cell analysis confirmed that direct transcriptional antagonism between HOXD13-mediated gene activation and EWS::FLI1-dependent gene repression defines the state of Ewing sarcoma cells along a mesenchymal axis.

CONCLUSIONS

Ewing sarcoma tumors are comprised of tumor cells that exist along a mesenchymal transcriptional continuum. The identity of cells along this continuum is, in large part, determined by the competing activities of EWS::FLI1 and HOXD13. See related commentary by Weiss and Bailey, p. 4360.

Integrative analyses of prognosis, tumor immunity, and ceRNA network of the ferroptosis-associated gene FANCD2 in hepatocellular carcinoma

Extensive evidence has revealed that ferroptosis plays a vital role in HCC development and progression. Fanconi anemia complementation group D2 (FANCD2) has been reported to serve as a ferroptosis-associated gene and has a close relationship with tumorigenesis and drug resistance. However, the impact of the FANCD2-related immune response and its mechanisms in HCC remains incompletely understood. In the current research, we evaluated the prognostic significance and immune-associated mechanism of FANCD2 based on multiple bioinformatics methods and databases. The results demonstrated that FANCD2 was commonly upregulated in 15/33 tumors, and only the high expression of FANCD2 in HCC was closely correlated with worse clinical outcomes by OS and DFS analyses. Moreover, ncRNAs, including two major types, miRNAs and lncRNAs, were closely involved in mediating FANCD2 upregulation in HCC and were established in a ceRNA network by performing various in silico analyses. The DUXAP8-miR-29c-FANCD2 and LINC00511-miR-29c-FANCD2 axes were identified as the most likely ncRNA-associated upstream regulatory axis of FANCD2 in HCC. Finally, FANCD2 expression was confirmed to be positively related to HCC immune cell infiltration, immune checkpoints, and IPS analysis, and GSEA results also revealed that this ferroptosis-associated gene was primarily involved in cancer-associated pathways in HCC. In conclusion, our investigations indicate that ncRNA-related modulatory overexpression of FANCD2 might act as a promising prognostic and immunotherapeutic target against HCC.

Aberrant expression of SPAG6 and NM23 predicts poor prognosis of human osteosarcoma

Objective: To investigate the expression and clinical significance of sperm-associated antigen 6 and NM23 proteins in human osteosarcoma.

Methods: The specimens of conventional osteosarcoma with follow-up from 42 Chinese patients were analyzed in this study, and 12 cases of osteochondroma were considered controls. The expression of SPAG6 and NM23 was inspected using immunohistochemical staining, qRT-PCR, and Western blotting methods.

Results: The positive expression rate of SPAG6 protein (71.43%) in 42 cases of osteosarcoma tissue was significantly higher than that (33.33%) in 12 cases of osteochondroma tissues (p &lt; 0.05), while the positive rate of NM23 protein (35.71%) in osteosarcoma tissue was lower than that (58.33%) in osteochondroma tissue (p &lt; 0.05). The mRNA and protein levels of SPAG6 were significantly higher than those of the adjacent normal tissues, while the expression of NM23 was lower in osteosarcoma tissues than that in the controls (p &lt; 0.05 for all). There was a positive relationship between the expression of SPAG6 and pathological grade, metastasis, and Enneking stage (p &lt; 0.05 for all). The overall survival rate of osteosarcoma patients with SPAG6 positive expression was significantly lower than that with SPAG6 negative expression. The relationship between the expression of NM23 and pathological grade, metastasis, and Enneking stage was negative (p &lt; 0.05 for all). The overall survival rate of the osteosarcoma patients with NM23 positive expression was higher than that of the patients with NM23 negative expression (p &lt; 0.05).

Conclusion: Overexpression of SPAG6 and low expression of NM23 are negatively related to pathological grade, metastasis, and Enneking stage and prognosis of osteosarcoma patients. This suggested that SPAG6 and NM23 should be considered candidate prognostic biomarkers for patients with osteosarcoma.

Construction and validation of a novel ten miRNA-pair based signature for the prognosis of clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is the most predominate pathological subtype of renal cell carcinoma, causing a recurrence or metastasis rate as high as 20% to 40% after operation, for which effective prognostic signature is urgently needed.

METHODS

The mRNA and miRNA profiles of ccRCC specimens were collected from the Cancer Genome Atlas. MiRNA-pair risk score (miPRS) for each miRNA pair was generated as a signature and validated by univariate and multivariate Cox proportional hazards regression analysis. Functional enrichment was performed, and immune cells infiltration, as well as tumor mutation burden (TMB), and immunophenoscore (IPS) were evaluated between high and low miPRS groups. Target gene-prediction and differentially expressed gene-analysis were performed based on databases of miRDB, miRTarBase, and TargetScan. Multivariate Cox proportional hazards regression analysis was adopted to establish the prognostic model and Kaplan-Meier survival analysis was performed.

FINDINGS

A novel 10 miRNA-pair based signature was established. Area under the time-dependent receiver operating curve proved the performance of the signature in the training, validation, and testing cohorts. Higher TMB, as well as the higher CTLA4-negative PD1-negative IPS, were discovered in high miPRS patients. A prognostic model was built based on miPRS (1 year-, 5 year-, 10 year- ROC-AUC=0.92, 0.84, 0.82, respectively).

INTERPRETATION

The model based on miPRS is a novel and valid tool for predicting the prognosis of ccRCC.

FUNDING

This study was supported by research grants from the China National Natural Scientific Foundation (81903972, 82002018, and 82170752) and Shanghai Sailing Program (19YF1406700 and 20YF1406000).

In Vivo Modeling of Human Breast Cancer Using Cell Line and Patient-Derived Xenografts

Historically, human breast cancer has been modeled largely in vitro using long-established cell lines primarily in two-dimensional culture, but also in three-dimensional cultures of varying cellular and molecular complexities. A subset of cell line models has also been used in vivo as cell line-derived xenografts (CDX). While outstanding for conducting detailed molecular analysis of regulatory mechanisms that may function in vivo, results of drug response studies using long-established cell lines have largely failed to translate clinically. In an attempt to address this shortcoming, many laboratories have succeeded in developing clinically annotated patient-derived xenograft (PDX) models of human cancers, including breast, in a variety of host systems. While immunocompromised mice are the predominant host, the immunocompromised rat and pig, zebrafish, as well as the chicken egg chorioallantoic membrane (CAM) have also emerged as potential host platforms to help address perceived shortcomings of immunocompromised mice. With any modeling platform, the two main issues to be resolved are criteria for "credentialing" the models as valid models to represent human cancer, and utility with respect to the ability to generate clinically relevant translational research data. Such data are beginning to emerge, particularly with the activities of PDX consortia such as the NCI PDXNet Program, EuroPDX, and the International Breast Cancer Consortium, as well as a host of pharmaceutical companies and contract research organizations (CRO). This review focuses primarily on these important aspects of PDX-related research, with a focus on breast cancer.

Comprehensive Analysis of Prognostic Value and Immune Infiltration of NLRC4 and CASP1 in Colorectal Cancer

Introduction

Nod-like receptor C4 (NLRC4) is a member of the Nod-like receptor (NLR) family, and its expression mediates the activation of caspase-1 (CASP1). Abnormal expression of NLRC4 and CASP1 is associated with multiple tumors. However, the expression differences, prognostic value and immune correlation of NLRC4 and CASP1 in colorectal cancer (CRC) remain to be determined.

Methods

In this study, TCGA, CCLE, HPA, PrognoScan, STRING and GeneMANIA databases were used to analyze differences in expression, prognostic value, genetic alterations and immune cell infiltration of NLRC4 and CASP1 in CRC patients. Then, we further validated the expression of NLRC4 and CASP1 in CRC using immunohistochemistry (IHC).

Results

NLRC4 and CASP1 were expressed low in CRC tissues and CRC cell lines. The expression of NLRC4 was significantly related to the patient’s gender and lymph node metastasis. NLRC4 and CASP1 down-regulated expression was observably correlated with poor survival and diverse immune cells infiltration in CRC patients. NLRC4 and CASP1 have a gene mutation alteration. NLRC4 and CASP1 had a significant positive correlation in CRC.

Conclusion

This study will provide new ideas for the prognosis and treatment in CRC. NLRC4 and CASP1 are expected to be novel biomarkers and potential immunotherapy targets in CRC patients.

Context dependent role of p53 during the interaction of hepatocellular carcinoma and endothelial cells

BACKGROUND

During the progression of hepatocellular carcinoma (HCC), several angiogenic factors are overexpressed in the hepatic microenvironment, which play a critical role in governing the phenotype of the endothelial cells. Mutation in the p53 gene (TP53) is a common event in HCC that may dysregulate the angiogenic signals. However, their functional messages remain largely unexplored at the onset of metastasis.

METHODS

Role of p53 was studied by siRNA mediated silencing of p53 in HepG2 cells (WTp53), collecting and analyzing their conditioned medium, followed by indirect co-culture with endothelial cells (HUVECs). Gene and protein expression in HCC cells and endothelial cells was studied by RT-qPCR and western blotting respectively. β-catenin protein expression and localization were analyzed by immunocytochemistry.

RESULTS

We have studied a cell-to-cell interaction model to investigate the crosstalk of endothelial and hepatoma cells by either knocking down p53 or by using p53 null low metastatic HCC cell line. In the absence of p53, the HCC cells influence the migration and vascular network formation of endothelial cells through paracrine signaling of VEGF. Secretory VEGF activated the VEGF receptor-2 along with the survival signaling in endothelial cells. However, the β-catenin signal is upregulated in endothelial cells only during interaction with metastatic set up irrespective of absence and presence of p53, indicating context-dependent participation of p53 during communication between hepatoma cells and endothelial cells.

CONCLUSION

This study highlights that the role of p53 on cellular responses during interaction of hepatocellular carcinoma and endothelial cells is distinct to cell types and context.

CLEC12A plays an important role in immunomodulatory function and prognostic significance of patients with acute myeloid leukemia

The physiological function and prognostic significance of C-type lectin domain family 12 member A (CLEC12A) in acute myeloid leukemia (AML) patients are unclear. CLEC12A transcriptional expression in a variety of tumors from several public databases was collected and compared. We found that CLEC12A was highly expressed in AML cell lines and in tissues from AML patients and a higher CLEC12A expression in leukemia stem cells. CLEC12A low expression was associated with poor prognosis in the chemotherapy-only group and high CLEC12A expression may benefit from autologous or allogeneic hematopoietic stem cell transplantation (HSCT). CLEC12A expression was positively correlated with infiltrating levels of type 2 macrophages and monocytes and negatively associated with NK cells and regulatory T cells in AML. CLEC12A high was positively associated with immune checkpoint genes as well as macrophage associated genes. CLEC12A is an ideal chimeric antigen receptor T-cell (CAR-T) therapy target for AML and its expression level was closely linked to treatment response and patients' survival outcome. CLEC12A plays an important immunomodulatory role in AML.

Power in Numbers: Harnessing Combinatorial and Integrated Screens to Advance Nanomedicine

Nanocarriers have significant potential to advance personalized medicine through targeted drug delivery. However, to date, efforts to improve nanoparticle accumulation at target disease sites have largely failed to translate clinically, stemming from an incomplete understanding of nano-bio interactions. While progress has been made to evaluate the effects of specific physical and chemical nanoparticle properties on trafficking and uptake, there is much to be gained from controlling these properties singularly and in combination to determine their interactions with different cell types. We and others have recently begun leveraging library-based nanoparticle screens to study structure-function relationships of lipid- and polymer-based drug delivery systems to guide nanoparticle design. These combinatorial screening efforts are showing promise in leading to the successful identification of critical characteristics that yield improved and specific accumulation at target sites. However, there is a crucial need to equally consider the influence of biological complexity on nanoparticle delivery, particularly in the context of clinical translation. For example, tissue and cellular heterogeneity presents an additional dimension to nanoparticle trafficking, uptake, and accumulation; applying imaging and screening tools as well as bioinformatics may further expand our understanding of how nanoparticles engage with cells and tissues. Given recent advances in the fields of omics and machine learning, there is substantial promise to revolutionize nanocarrier development through the use of integrated screens, harnessing the combinatorial parameter space afforded both by nanoparticle libraries and clinically annotated biological data sets in combination with high throughput in vivo studies.

Landscape of Epidermal Growth Factor Receptor Heterodimers in Brain Metastases

Simple Summary

HER2+ breast cancer patients are treated with agents that tag HER2+ tumour cells for elimination by the immune system, down-modulate HER2 activity and/or block the formation of HER2 dimers, including the neuregulin-1 receptor, HER2-HER3. HER2-targeted therapies prolong survival by lowering the risk of relapse, but do not prevent brain metastases. The reasons for this are not fully understood. We quantified HER2-HER3 dimers in 203 brain metastases, and 34 primary breast tumour samples. Dimer frequency was relatively high in brain metastases from breast, ovarian, lung and kidney cancers, and in brain metastases compared to patient-matched breast tumours; but did not reliably correlate with HER2/HER3 expression or activation. In in vitro experiments, pertuzumab failed to suppress HER2-HER3 dimers in HER2+ breast cancer cells provided with a saturating concentration of neuregulin-1. These findings may provide insights about the differences in intracranial versus extracranial efficacy of HER2-targeted therapies.

Abstract

HER2+ breast cancer patients have an elevated risk of developing brain metastases (BM), despite adjuvant HER2-targeted therapy. The mechanisms underpinning this reduced intracranial efficacy are unclear. We optimised the in situ proximity ligation assay (PLA) for detection of the high-affinity neuregulin-1 receptor, HER2-HER3 (a key target of pertuzumab), in archival tissue samples and developed a pipeline for high throughput extraction of PLA data from fluorescent microscope image files. Applying this to a large BM sample cohort (n = 159) showed that BM from breast, ovarian, lung and kidney cancers have higher HER2-HER3 levels than other primary tumour types (melanoma, colorectal and prostate cancers). HER2 status, and tumour cell membrane expression of pHER2(Y^1221/1222) and pHER3(Y¹²²²) were positively, but not exclusively, associated with HER2-HER3 frequency. In an independent cohort (n = 78), BM had significantly higher HER2-HER3 levels than matching primary tumours (p = 0.0002). For patients who had two craniotomy procedures, HER2-HER3 dimer levels were lower in the consecutive lesion (n = 7; p = 0.006). We also investigated the effects of trastuzumab and pertuzumab on five different heterodimers in vitro: HER2-EGFR, HER2-HER4, HER2-HER3, HER3-HER4, HER3-EGFR. Treatment significantly altered the absolute frequencies of individual complexes in SKBr3 and/or MDA-MB-361 cells, but in the presence of neuregulin-1, the overall distribution was not markedly altered, with HER2-HER3 and HER2-HER4 remaining predominant. Together, these findings suggest that markers of HER2 and HER3 expression are not always indicative of dimerization, and that pertuzumab may be less effective at reducing HER2-HER3 dimerization in the context of excess neuregulin.

Human Epididymis Protein 4 and Lewis y Enhance Chemotherapeutic Resistance in Epithelial Ovarian Cancer Through the p38 MAPK Pathway

INTRODUCTION

Ovarian cancer has a high mortality rate due to difficulties in early detection and chemotherapy resistance. Human epididymal protein 4 (HE4) has been adopted as a novel serum biomarker for early ovarian cancer diagnosis, and the presence of Lewis y antigen modifications on HE4 in ovarian cancer cell lines has been detected in previous studies. The aim of this study was to analyze the expression of HE4 and Lewis y antigen in human ovarian cancer in order to find a correlation between them, as well as with the clinical pathological parameters of patients with ovarian cancer.

METHODS

Immunohistochemistry was used to detect the respective expression of these compounds in two patient groups (chemotherapy-resistant and chemotherapy-sensitive) containing a total of 95 patients. Then, a bioinformatic approach was adopted and online large sample databases (TCGA, CCLE, and GTEx; Metascape, Cytoscape) were used to explore the potential mechanisms of action of these compounds.

RESULTS

The results of this study demonstrate that high HE4 and Lewis y expression could be used as markers for chemotherapy resistance and poor prognosis in patients with ovarian cancer. These two expression events were widely correlated in various cancer tissues and are thought to act by activating the p38 mitogen-activated protein kinases (MAPK) pathway and inducing Vascular Endothelial Growth Factor A (VEGFA), Prostaglandin-Endoperoxide Synthase 2 (PTGS2), Early Growth Response 1 (EGR1), and Hypoxia-Inducible Factor 1-Alpha (HIFI1A), thereby promoting malignant biological behavior and resistance in ovarian cancer.

CONCLUSIONS

These findings not only reveal the possible mechanism by which HE4 and Lewis y antigen affect ovarian cancer but also identify a four-gene signature that could be very useful in ovarian cancer detection and/or the development of new targeted therapies.

Dynamic expression of SNAI2 in prostate cancer predicts tumor progression and drug sensitivity

Prostate cancer is a highly heterogeneous disease, understanding the crosstalk between complex genomic and epigenomic alterations will aid in developing targeted therapeutics. We demonstrate that, even though snail family transcriptional repressor 2 (SNAI2) is frequently amplified in prostate cancer, it is epigenetically silenced in this disease, with dynamic changes in SNAI2 levels showing distinct clinical relevance. Integrative clinical data from 18 prostate cancer cohorts and experimental evidence showed that gene fusion between transmembrane serine protease 2 (TMPRSS2) and ETS transcription factor ERG (ERG) (TMPRSS2–ERG fusion) is involved in the silencing of SNAI2. We created a silencer score to evaluate epigenetic repression of SNAI2, which can be reversed by treatment with DNA methyltransferase inhibitors and histone deacetylase inhibitors. Silencing of SNAI2 facilitated tumor cell proliferation and luminal differentiation. Furthermore, SNAI2 has a major influence on the tumor microenvironment by reactivating tumor stroma and creating an immunosuppressive microenvironment in prostate cancer. Importantly, SNAI2 expression levels in part determine sensitivity to the cancer drugs dasatinib and panobinostat. For the first time, we defined the distinct clinical relevance of SNAI2 expression at different disease stages. We elucidated how epigenetic silencing of SNAI2 controls the dynamic changes of SNAI2 expression that are essential for tumor initiation and progression and discovered that restoring SNAI2 expression by treatment with panobinostat enhances dasatinib sensitivity, indicating a new therapeutic strategy for prostate cancer.

Lung Cancer Models Reveal Severe Acute Respiratory Syndrome Coronavirus 2-Induced Epithelial-to-Mesenchymal Transition Contributes to Coronavirus Disease 2019 Pathophysiology

INTRODUCTION

Coronavirus disease 2019 is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which enters host cells through the cell surface proteins ACE2 and TMPRSS2.

METHODS

Using a variety of normal and malignant models and tissues from the aerodigestive and respiratory tracts, we investigated the expression and regulation of ACE2 and TMPRSS2.

RESULTS

We find that ACE2 expression is restricted to a select population of epithelial cells. Notably, infection with SARS-CoV-2 in cancer cell lines, bronchial organoids, and patient nasal epithelium induces metabolic and transcriptional changes consistent with epithelial-to-mesenchymal transition (EMT), including up-regulation of ZEB1 and AXL, resulting in an increased EMT score. In addition, a transcriptional loss of genes associated with tight junction function occurs with SARS-CoV-2 infection. The SARS-CoV-2 receptor, ACE2, is repressed by EMT through the transforming growth factor-β, ZEB1 overexpression, and onset of EGFR tyrosine kinase inhibitor resistance. This suggests a novel model of SARS-CoV-2 pathogenesis in which infected cells shift toward an increasingly mesenchymal state, associated with a loss of tight junction components with acute respiratory distress syndrome-protective effects. AXL inhibition and ZEB1 reduction, as with bemcentinib, offer a potential strategy to reverse this effect.

CONCLUSIONS

These observations highlight the use of aerodigestive and, especially, lung cancer model systems in exploring the pathogenesis of SARS-CoV-2 and other respiratory viruses and offer important insights into the potential mechanisms underlying the morbidity and mortality of coronavirus disease 2019 in healthy patients and patients with cancer alike.

Applications of Machine Learning to Predict Cisplatin Resistance in Lung Cancer

Purpose

Lung cancer, mainly lung adenocarcinoma, lung squamous cell carcinoma and small cell lung cancer, has the highest incidence and cancer-related mortality worldwide. Platinum-based chemotherapy plays an important role in the treatment of various lung cancer subtypes, but not all patients benefit from this treatment regimen; thus, it is worth identifying lung cancer patients who are resistant or sensitive to platinum-based therapy.

Methods

The drug response and sequencing data of 170 lung cancer cell lines were downloaded from the Genomics of Drug Sensitivity in Cancer (GDSC) database, and support vector machines (SVMs) and beam search were used to select an optimal gene panel that can predict the sensitivity of cell lines to cisplatin. Then, we used available cell line data to explore the potential mechanisms.

Results

In this work, the drug response and sequencing data of 170 lung cancer cell lines were downloaded from the GDSC database, and SVMs and beam search were used to screen a panel of genes related to lung cancer cell line resistance to cisplatin. A final panel of nine genes (PLXNC1, KIAA0649, SPTBN4, SLC14A2, F13A1, COL5A1, SCN2A, PLEC, and ALMS1) was identified, and achieved an area under the curve (AUC) of 0.873 ± 0.004. The natural logarithm of the half maximal inhibitory concentration (lnIC50) values of the mutant-type (panel-MT) group was significantly higher than that of the wild-type (panel-WT) group, regardless of the lung cancer subtype. The differentially expressed pathways between the two groups may explain this difference.

Conclusion

In this study, we found that a panel of nine genes can accurately predict sensitivity to cisplatin, which may provide individualized treatment recommendations to improve the prognosis of patients with lung cancer.

Co-Adjuvant Therapy Efficacy of Catechin and Procyanidin B2 with Docetaxel on Hormone-Related Cancers In Vitro

Prostate (PC) and breast cancer (BC) are heterogeneous hormonal cancers. Treatment resistance and adverse effects are the main limitations of conventional chemotherapy treatment. The use of sensitizing agents could improve the effectiveness of chemotherapeutic drugs as well as obviate these limitations. This study analyzes the effect of single catechin (CAT), procyanidin B2 (ProB2) treatment as well as the co-adjuvant treatment of each of these compounds with docetaxel (DOCE). We used PC- and BC-derived cell lines (PC3, DU-145, T47D, MCF-7 and MDA-MB-231). The short and long-term pro-apoptotic, anti-proliferative and anti-migratory effects were analyzed. RT-qPCR was used to discover molecular bases of the therapeutic efficacy of these compounds. ProB2 treatment induced a two- to five-fold increase in anti-proliferative and pro-apoptotic effects compared to single DOCE treatment, and also had a more sensitizing effect than DOCE on DU145 cells. Regarding BC cells, ProB2- and CAT-mediated sensitization to DOCE anti-proliferative and pro-apoptotic effects was cell-independent and cell-dependent, respectively. Combined treatment led to high-efficacy effects on MCF-7 cells, which were associated to the up-regulation of CDKN1A, BAX, caspase 9 and E-cadherin mRNA under combined treatment compared to single DOCE treatment. CAT and ProB2 can enhance the efficacy of DOCE therapy on PC and BC cells by the sensitizing mechanism.

Genomic and Transcriptomic Characterization of Canine Osteosarcoma Cell Lines: A Valuable Resource in Translational Medicine

Osteosarcoma (OSA) represents the most common primary bone tumor in dogs and is characterized by a highly aggressive behavior. Cell lines represent one of the most suitable and reproducible pre-clinical models, and therefore the knowledge of their molecular landscape is mandatory to investigate oncogenic mechanisms and drug response. The present study aims at determining variants, putative driver genes, and gene expression aberrations by integrating whole-exome and RNA sequencing. For this purpose, eight canine OSA cell lines and one matched pair of primary tumor and normal tissue were analyzed. Overall, cell lines revealed a mean tumor mutational burden of 9.6 mutations/Mb (range 3.9–16.8). Several known oncogenes and tumor suppressor genes, such as ALK, MYC, and MET, were prioritized as having a likely role in canine OSA. Mutations in eight genes, previously described as human OSA drivers and including TP53, PTCH1, MED12, and PI3KCA, were retrieved in our cell lines. When variants were cross-referenced with human OSA driver mutations, the E273K mutation of TP53 was identified in the Wall cell line and tumor sample. The transcriptome profiling detected two possible p53 inactivation mechanisms in the Wall cell line on the one hand, and in D17 and D22 on the other. Moreover, MET overexpression, potentially leading to MAPK/ERK pathway activation, was observed in D17 and D22 cell lines. In conclusion, our data provide the molecular characterization of a large number of canine OSA cell lines, allowing future investigations on potential therapeutic targets and associated biomarkers. Notably, the Wall cell line represents a valuable model to empower prospective in vitro studies both in human and in dogs, since the TP53 driver mutation was maintained during cell line establishment and was widely reported as a mutation hotspot in several human cancers.

Comprehensive analysis of prognostic value and immune infiltration of kindlin family members in non-small cell lung cancer

BACKGROUND

Kindlin Family Members have been reported to be aberrantly expressed in various human cancer types and involved in tumorigenesis, tumor progression, and chemoresistance. However, their roles in non-small cell lung cancer (NSCLC) remain poorly elucidated.

METHODS

We analyzed the prognostic value and immune infiltration of Kindlins in NSCLC through Oncomine, GEPIA, UALCAN, CCLE, Kaplan‑Meier plotter, cBioPortal, TIMER, GeneMANIA, STRING, and DAVID database. Additionally, the mRNA expression levels of Kindlins were verified in 30 paired NSCLC tissues and NSCLC cell lines by real-time PCR.

RESULTS

The expression level of FERMT1 was remarkably increased in NSCLC tissues and NSCLC cell lines, while FERMT2 and FERMT3 were reduced. Kindlins expressions were associated with individual cancer stages and nodal metastasis. We also found that higher expression level of FERMT1 was obviously correlated with worse overall survival (OS) in patients with NSCLC, while higher FERMT2 was strongly associated with better overall survival (OS) and first progression (FP). Additionally, the expression of FERMT2 and FERMT3 were obviously correlated with the immune infiltration of diverse immune cells. Functional enrichment analysis has shown that Kindlins may be significantly correlated with intracellular signal transduction, ATP binding and the PI3K-Akt signaling pathway in NSCLC.

CONCLUSIONS

The research provides a new perspective on the distinct roles of Kindlins in NSCLC and likely has important implications for future novel biomarkers and therapeutic targets in NSCLC.

Recruitment of KMT2C/MLL3 to DNA Damage Sites Mediates DNA Damage Responses and Regulates PARP Inhibitor Sensitivity in Cancer

When recruited to promoters, histone 3 lysine 4 (H3K4) methyltransferases KMT2 (KMT2A-D) activate transcription by opening chromatin through H3K4 methylation. Here, we report that KMT2 mutations occur frequently in non-small cell lung cancer (NSCLC) and are associated with high mutation loads and poor survival. KMT2C regulated DNA damage responses (DDR) through direct recruitment to DNA damage sites by Ago2 and small noncoding DNA damage response RNA, where it mediates H3K4 methylation, chromatin relaxation, secondary recruitment of DDR factors, and amplification of DDR signals along chromatin. Furthermore, by disrupting homologous recombination (HR)-mediated DNA repair, KMT2C/D mutations sensitized NSCLC to Poly(ADP-ribose) polymerase inhibitors (PARPi), whose efficacy is unclear in NSCLC due to low BRCA1/2 mutation rates. These results demonstrate a novel, transcription-independent role of KMT2C in DDR and identify high-frequency KMT2C/D mutations as much-needed biomarkers for PARPi therapies in NSCLC and other cancers with infrequent BRCA1/2 mutations. SIGNIFICANCE: This study uncovers a critical role for KMT2C in DDR via direct recruitment to DNA damage sites, identifying high-frequency KMT2C/D mutations as biomarkers for response to PARP inhibition in cancer.

Widespread Alternative Splicing Changes in Metastatic Breast Cancer Cells

Aberrant alternative splicing (AS) is a hallmark of cancer and a potential target for novel anti-cancer therapeutics. Breast cancer-associated AS events are known to be linked to disease progression, metastasis, and survival of breast cancer patients. To identify altered AS programs occurring in metastatic breast cancer, we perform a global analysis of AS events by using RNA-mediated oligonucleotide annealing, selection, and ligation coupled with next-generation sequencing (RASL-seq). We demonstrate that, relative to low-metastatic, high-metastatic breast cancer cells show different AS choices in genes related to cancer progression. Supporting a global reshape of cancer-related splicing profiles in metastatic breast cancer we found an enrichment of RNA-binding motifs recognized by several splicing regulators, which have aberrant expression levels or activity during breast cancer progression, including SRSF1. Among SRSF1-regulated targets we found DCUN1D5, a gene for which skipping of exon 4 in its pre-mRNA introduces a premature termination codon (PTC), thus generating an unstable transcript degraded by nonsense-mediated mRNA decay (NMD). Significantly, distinct breast cancer subtypes show different DCUN1D5 isoform ratios with metastatic breast cancer expressing the highest level of the NMD-insensitive DCUN1D5 mRNA, thus showing high DCUN1D5 expression levels, which are ultimately associated with poor overall and relapse-free survival in breast cancer patients. Collectively, our results reveal global AS features of metastatic breast tumors, which open new possibilities for the treatment of these aggressive tumor types.

Advances in bulk and single-cell multi-omics approaches for systems biology and precision medicine

Multi-omics allows the systematic understanding of the information flow across different omics layers, while single omics can mainly reflect one aspect of the biological system. The advancement of bulk and single-cell sequencing technologies and related computational methods for multi-omics largely facilitated the development of system biology and precision medicine. Single-cell approaches have the advantage of dissecting cellular dynamics and heterogeneity, whereas traditional bulk technologies are limited to individual/population-level investigation. In this review, we first summarize the technologies for producing bulk and single-cell multi-omics data. Then, we survey the computational approaches for integrative analysis of bulk and single-cell multimodal data, respectively. Moreover, the databases and data storage for multi-omics, as well as the tools for visualizing multimodal data are summarized. We also outline the integration between bulk and single-cell data, and discuss the applications of multi-omics in precision medicine. Finally, we present the challenges and perspectives for multi-omics development.

The dark side of daylight: photoaging and the tumor microenvironment in melanoma progression

Continued thinning of the atmospheric ozone, which protects the earth from damaging ultraviolet radiation (UVR), will result in elevated levels of UVR reaching the earth's surface, leading to a drastic increase in the incidence of skin cancer. In addition to promoting carcinogenesis in skin cells, UVR is a potent extrinsic driver of age-related changes in the skin known as "photoaging." We are in the preliminary stages of understanding of the role of intrinsic aging in melanoma, and the tumor-permissive effects of photoaging on the skin microenvironment remain largely unexplored. In this Review, we provide an overview of the impact of UVR on the skin microenvironment, addressing changes that converge or diverge with those observed in intrinsic aging. Intrinsic and extrinsic aging promote phenotypic changes to skin cell populations that alter fundamental processes such as melanogenesis, extracellular matrix deposition, inflammation, and immune response. Given the relevance of these processes in cancer, we discuss how photoaging might render the skin microenvironment permissive to melanoma progression.

Norrin mediates tumor-promoting and -suppressive effects in glioblastoma via Notch and Wnt

Glioblastoma multiforme (GBM) contains a subpopulation of cells, GBM stem cells (GSCs), that maintain the bulk tumor and represent a key therapeutic target. Norrin is a Wnt ligand that binds Frizzled class receptor 4 (FZD4) to activate canonical Wnt signaling. Although Norrin, encoded by NDP, has a well-described role in vascular development, its function in human tumorigenesis is largely unexplored. Here, we show that NDP expression is enriched in neurological cancers, including GBM, and its levels positively correlated with survival in a GBM subtype defined by low expression of ASCL1, a proneural factor. We investigated the function of Norrin and FZD4 in GSCs and found that it mediated opposing tumor-suppressive and -promoting effects on ASCL1lo and ASCL1hi GSCs. Consistent with a potential tumor-suppressive effect of Norrin suggested by the tumor outcome data, we found that Norrin signaling through FZD4 inhibited growth in ASCL1lo GSCs. In contrast, in ASCL1hi GSCs Norrin promoted Notch signaling, independently of WNT, to promote tumor progression. Forced ASCL1 expression reversed the tumor-suppressive effects of Norrin in ASCL1lo GSCs. Our results identify Norrin as a modulator of human brain cancer progression and reveal an unanticipated Notch-mediated function of Norrin in regulating cancer stem cell biology. This study identifies an unanticipated role of Norrin in human brain cancer progression. In addition, we provide preclinical evidence suggesting Norrin and canonical Wnt signaling as potential therapeutic targets for GBM subtype-restricted cancer stem cells.

Bioinformatics Approaches for Anti-cancer Drug Discovery

Drug discovery is important in cancer therapy and precision medicines. Traditional approaches of drug discovery are mainly based on in vivo animal experiments and in vitro drug screening, but these methods are usually expensive and laborious. In the last decade, omics data explosion provides an opportunity for computational prediction of anti-cancer drugs, improving the efficiency of drug discovery. High-throughput transcriptome data were widely used in biomarkers' identification and drug prediction by integrating with drug-response data. Moreover, biological network theory and methodology were also successfully applied to the anti-cancer drug discovery, such as studies based on protein-protein interaction network, drug-target network and disease-gene network. In this review, we summarized and discussed the bioinformatics approaches for predicting anti-cancer drugs and drug combinations based on the multi-omic data, including transcriptomics, toxicogenomics, functional genomics and biological network. We believe that the general overview of available databases and current computational methods will be helpful for the development of novel cancer therapy strategies.

Lung cancer models reveal SARS-CoV-2-induced EMT contributes to COVID-19 pathophysiology

COVID-19 is an infectious disease caused by SARS-CoV-2, which enters host cells via the cell surface proteins ACE2 and TMPRSS2. Using a variety of normal and malignant models and tissues from the aerodigestive and respiratory tracts, we investigated the expression and regulation of ACE2 and TMPRSS2. We find that ACE2 expression is restricted to a select population of highly epithelial cells. Notably, infection with SARS-CoV-2 in cancer cell lines, bronchial organoids, and patient nasal epithelium, induces metabolic and transcriptional changes consistent with epithelial to mesenchymal transition (EMT), including upregulation of ZEB1 and AXL, resulting in an increased EMT score. Additionally, a transcriptional loss of genes associated with tight junction function occurs with SARS-CoV-2 infection. The SARS-CoV-2 receptor, ACE2, is repressed by EMT via TGFbeta, ZEB1 overexpression and onset of EGFR TKI inhibitor resistance. This suggests a novel model of SARS-CoV-2 pathogenesis in which infected cells shift toward an increasingly mesenchymal state, associated with a loss of tight junction components with acute respiratory distress syndrome-protective effects. AXL-inhibition and ZEB1-reduction, as with bemcentinib, offers a potential strategy to reverse this effect. These observations highlight the utility of aerodigestive and, especially, lung cancer model systems in exploring the pathogenesis of SARS-CoV-2 and other respiratory viruses, and offer important insights into the potential mechanisms underlying the morbidity and mortality of COVID-19 in healthy patients and cancer patients alike.

Exploring the 'cold/hot' properties of traditional Chinese medicine by cell temperature measurement

Context: It is common sense that chewing a mint leaf can cause a cooling feeling, while chewing ginger root will produce a burning feeling. In Traditional Chinese Medicine (TCM), this phenomenon is referred to as 'cold/hot' properties of herbs. Herein, it is reported that TCM with different "cold/hot" properties have different effects on the variation of cells.Objective: To explore the intrinsic 'cold/hot' properties of TCM from the perspective of cellular and molecular biology.Materials and methods: A375 cells were selected using Cancer Cell Line Encyclopaedia (CCLE) analysis and western blots. Hypaconitine and baicalin were selected by structural similarity analysis from 56 and 140 compounds, respectively. A wireless thermometry system was used to measure cellular temperature change induced by different compounds. Alteration of intracellular calcium influx was investigated by means of calcium imaging.Results: The IC₅₀ values of GSK1016790A, HC067047, hypaconitine, and baicalin for A375 cells are 8.363 nM, 816.4 μM, 286.4 μM and 29.84 μM, respectively. And, 8 μM hypaconitine induced obvious calcium influx while 8 μM baicalin inhibited calcium influx induced by TRPV4 activation. Cellular temperature elevated significantly when treated with GSK1016790A or hypaconitine, while the results were reversed when cells were treated with HC067047 or baicalin.Discussion and conclusions: The changes in cellular temperature are speculated to be caused by the alteration of intracellular calcium influx mediated by TRPV4. In addition, the 'cold/hot' properties of compounds in TCM can be classified by using cellular temperature detection.

MEK is a promising target in the basal subtype of bladder cancer

While many resources exist for the drug screening of bladder cancer cell lines in 2D culture, it is widely recognized that screening in 3D culture is more representative of in vivo response. Importantly, signaling changes between 2D and 3D culture can result in changes to drug response. To address the need for 3D drug screening of bladder cancer cell lines, we screened 17 bladder cancer cell lines using a library of 652 investigational small-molecules and 3 clinically relevant drug combinations in 3D cell culture. Our goal was to identify compounds and classes of compounds with efficacy in bladder cancer. Utilizing established genomic and transcriptomic data for these bladder cancer cell lines, we correlated the genomic molecular parameters with drug response, to identify potentially novel groups of tumors that are vulnerable to specific drugs or classes of drugs. Importantly, we demonstrate that MEK inhibitors are a promising targeted therapy for the basal subtype of bladder cancer, and our data indicate that drug screening of 3D cultures provides an important resource for hypothesis generation.

Prediction of cancer dependencies from expression data using deep learning

Detecting cancer dependencies is key to disease treatment. Recent efforts have mapped gene dependencies and drug sensitivities in hundreds of cancer cell lines. These data allow us to learn for the first time models of tumor vulnerabilities and apply them to suggest novel drug targets. Here we devise novel deep learning methods for predicting gene dependencies and drug sensitivities from gene expression measurements. By combining dimensionality reduction strategies, we are able to learn accurate models that outperform simpler neural networks or linear models.

Top-ranked expressed gene transcripts of human protein-coding genes investigated with GTEx dataset

With considerable accumulation of RNA-Seq transcriptome data, we have extended our understanding about protein-coding gene transcript compositions. However, alternatively compounded patterns of human protein-coding gene transcripts would complicate gene expression data processing and interpretation. It is essential to exhaustively interrogate complex mRNA isoforms of protein-coding genes with an unified data resource. In order to investigate representative mRNA transcript isoforms to be utilized as transcriptome analysis references, we utilized GTEx data to establish a top-ranked transcript isoform expression data resource for human protein-coding genes. Distinctive tissue specific expression profiles and modulations could be observed for individual top-ranked transcripts of protein-coding genes. Protein-coding transcripts or genes do occupy much higher expression fraction in transcriptome data. In addition, top-ranked transcripts are the dominantly expressed ones in various normal tissues. Intriguingly, some of the top-ranked transcripts are noncoding splicing isoforms, which imply diverse gene regulation mechanisms. Comprehensive investigation on the tissue expression patterns of top-ranked transcript isoforms is crucial. Thus, we established a web tool to examine top-ranked transcript isoforms in various human normal tissue types, which provides concise transcript information and easy-to-use graphical user interfaces. Investigation of top-ranked transcript isoforms would contribute understanding on the functional significance of distinctive alternatively spliced transcript isoforms.

Pevonedistat (MLN4924): mechanism of cell death induction and therapeutic potential in colorectal cancer

Pevonedistat (MLN4924), a selective inhibitor of the NEDD8-activating enzyme E1 regulatory subunit (NAE1), has demonstrated significant therapeutic potential in several malignancies. Although multiple mechanisms-of-action have been identified, how MLN4924 induces cell death and its potential as a combinatorial agent with standard-of-care (SoC) chemotherapy in colorectal cancer (CRC) remains largely undefined. In an effort to understand MLN4924-induced cell death in CRC, we identified p53 as an important mediator of the apoptotic response to MLN4924. We also identified roles for the extrinsic (TRAIL-R2/caspase-8) and intrinsic (BAX/BAK) apoptotic pathways in mediating the apoptotic effects of MLN4924 in CRC cells, as well as a role for BID, which modulates a cross-talk between these pathways. Depletion of the anti-apoptotic protein FLIP, which we identify as a novel mediator of resistance to MLN4924, enhanced apoptosis in a p53-, TRAIL-R2/DR5-, and caspase-8-dependent manner. Notably, TRAIL-R2 was involved in potentiating the apoptotic response to MLN4924 in the absence of FLIP, in a ligand-independent manner. Moreoever, when paired with SoC chemotherapies, MLN4924 demonstrated synergy with the irinotecan metabolite SN38. The cell death induced by MLN4924/SN38 combination was dependent on activation of mitochondria through BAX/BAK, but in a p53-independent manner, an important observation given the high frequency of TP53 mutation(s) in advanced CRC. These results uncover mechanisms of cell death induced by MLN4924 and suggest that this second-generation proteostasis-disrupting agent may have its most widespread activity in CRC, in combination with irinotecan-containing treatment regimens.

Overexpression of annexin A5 might guide the gemtuzumab ozogamicin treatment choice in patients with pediatric acute myeloid leukemia

Background:

Acute myeloid leukemia (AML) is a common hematological malignancy. Gemtuzumab ozogamicin (GO), a humanized anti-CD33 antibody conjugated with the potent anti-tumor antibiotic calicheamicin, represents a promising targeted therapy for AML. Annexin A5 (ANXA5) is a proposed marker for the clinical prognosis of AML to guide treatment choice.

Methods:

In total, 253 patients with pediatric AML were enrolled and divided into two treatment groups: conventional chemotherapy alone and conventional chemotherapy in combination with GO. Univariate, multivariate, and Kaplan–Meier survival analyses were conducted to assess risk factors and clinical outcomes, and to estimate hazard ratios (HRs) and their 95% confidence interval. The level of statistical significance was set at p < 0.05.

Results:

In the GO treatment group, high ANXA5 expression was considered a favorable prognostic factor for overall survival (OS) and event-free survival (EFS). Multivariate analysis showed that high ANXA5 expression was an independent favorable factor for OS (HR = 0.629, p = 0.084) and EFS (HR = 0.544, p = 0.024) distinct from the curative effect of GO treatment. When all patients were again divided into two groups, this time based on the median expression of ANXA5, patients undergoing chemotherapy combined with GO had significantly better OS (p = 0.0012) and EFS (p = 0.0003) in the ANXA5 high-expression group. Gene set enrichment analysis identified a relevant series of pathways associated with glutathione metabolism, leukocyte transendothelial migration, and hematopoietic cell lineage.

Conclusion:

The expression level of ANXA5 can help optimize the treatment regimen for individual patients, and patients with overexpression of ANXA5 may circumvent poor outcomes from chemotherapy combined with GO.

Metabolomic profiling of goslings with visceral gout reveals a distinct metabolic signature

1.The objective of the experiment was to analyse serum profiles of goslings with visceral gout and compare them with those of healthy individuals to identify differentially-abundant metabolites as potential biomarkers. 2.Untargeted gas chromatography and time-of-flight mass spectrometry (GC-TOF-MS) metabolomic profiling was used to compare the serum metabolome of 15 goslings (Anser cygnoides) with gout and 15 healthy goslings (control). 3.Goslings with gout had a metabolic profile distinct from that of the controls, with 45 metabolite levels differing significantly (VIP > 1; P < 0.05) between both groups. Nine metabolites (hydrocortisone, glucose, trans-4-hydroxy-L-proline, galactose, 2-deoxy-D-galactose, beta-mannosylglycerate, d-glucoheptose, zymosterol, and hypoxanthine) were selected through receiver operating characteristics (ROC) analysis (area under curve (AUC) score ≥0.85) as potential biomarkers. Pathway analysis revealed that metabolites with differing levels were mainly involved in galactose, arginine and proline and purine metabolisms. 4.These results provided new insights into the pathogenesis of gout. Increased xanthine and hypoxanthine with decreased hydrocortisone provide promising biomarkers for gosling gout diagnosis. The findings suggested that hepatic metabolic disorders frequently occur in the development of avian gout.

Obesity/Type 2 Diabetes-Associated Liver Tumors Are Sensitive to Cyclin D1 Deficiency

Type 2 diabetes, which is mainly linked to obesity, is associated with increased incidence of liver cancer. We have previously found that in various models of obesity/diabetes, hyperinsulinemia maintains heightened hepatic expression of cyclin D1, suggesting a plausible mechanism linking diabetes and liver cancer progression. Here we show that cyclin D1 is greatly elevated in human livers with diabetes and is among the most significantly upregulated genes in obese/diabetic liver tumors. Liver-specific cyclin D1 deficiency protected obese/diabetic mice against hepatic tumorigenesis, whereas lean/nondiabetic mice developed tumors irrespective of cyclin D1 status. Cyclin D1 dependency positively correlated with liver cancer sensitivity to palbociclib, an FDA-approved CDK4 inhibitor, which was effective in treating orthotopic liver tumors under obese/diabetic conditions. The antidiabetic drug metformin suppressed insulin-induced hepatic cyclin D1 expression and protected against obese/diabetic hepatocarcinogenesis. These results indicate that the cyclin D1-CDK4 complex represents a potential selective therapeutic vulnerability for liver tumors in obese/diabetic patients. SIGNIFICANCE: Obesity/diabetes-associated liver tumors are specifically vulnerable to cyclin D1 deficiency and CDK4 inhibition, suggesting that the obese/diabetic environment confers cancer-selective dependencies that can be therapeutically exploited.

A single nucleotide variant of human PARP1 determines response to PARP inhibitors

The introduction of novel cancer drugs and innovative treatments brings great hope for cancer patients, but also an urgent need to match drugs to suitable patients, since certain drugs that benefit one patient may actually harm others. The newly developed poly-ADP ribose polymerase (PARP) inhibitors (PARPis) are a group of pharmacological enzyme inhibitors used clinically for multiple indications. Several forms of cancer tend to be PARP dependent, making PARP an attractive target for cancer therapy. Specifically, PARPis are commonly used in BRCA-associated breast cancers patients, since unrepaired single-strand breaks are converted into double-strand breaks and BRCA-associated tumors cannot repair them by homologous recombination so that PARPi leads to tumor cell death, by a mechanism called “Synthetic Lethality”. Unfortunately, not all patients respond to PARPi, and it is not currently possible to predict who will or will not respond. Here, we present a specific genomic marker, which reflects a single-nucleotide polymorphism of human PARP1 and correlates in vitro with response to PARPi, throughout all indications. In addition, we report that this SNP is associated with re-shaping mRNA, and mRNA levels, and influences the final protein structure to expose new binding sites while hiding others. The status of the SNP is therefore critical to patients’ care, as it relates responses to PARPi to the PARP1-SNP carried.

Three-Dimensional Cell Culture Based on Magnetic Fields to Assemble Low-Grade Ovarian Carcinoma Cell Aggregates Containing Lymphocytes

There is a limited number of established ovarian cancer cell lines matching the low-grade serous histotype available for research purposes. Three-dimensional (3D) culture systems provide in vitro models with better tissue-like characteristics than two-dimensional (2D) systems. The goal in the study was to characterize the growth of a given low-grade serous ovarian carcinoma cell line in a 3D culture system conducted in a magnetic field. Moreover, the culture system was evaluated in respect to the assembly of malignant cell aggregates containing lymphocytes. CAISMOV24 cell line alone or mixed with human peripheral blood mononuclear cells (PBMC) were cultured using a commercially available 3D culture system designed for 24 well plates. Resulting cell aggregates revealed the intrinsic capacity of CAISMOV24 cells to assemble structures morphologically defined as papillary, and reflected molecular characteristics usually found in ovarian carcinomas. The contents of lymphocytes into co-cultured cell aggregates were significantly higher (p < 0.05) when NanoShuttle-conjugated PBMC were employed compared with non-conjugated PBMC. Moreover, lymphocyte subsets NK, T-CD4, T-CD8 and T-regulatory were successfully retrieved from co-cultured cell aggregates at 72h. Thus, the culture system allowed CAISMOV24 cell line to develop papillary-like cell aggregates containing lymphocytes.

Exploring Integrative Analysis Using the BioMedical Evidence Graph

PURPOSE

The analysis of cancer biology data involves extremely heterogeneous data sets, including information from RNA sequencing, genome-wide copy number, DNA methylation data reporting on epigenetic regulation, somatic mutations from whole-exome or whole-genome analyses, pathology estimates from imaging sections or subtyping, drug response or other treatment outcomes, and various other clinical and phenotypic measurements. Bringing these different resources into a common framework, with a data model that allows for complex relationships as well as dense vectors of features, will unlock integrated data set analysis.

METHODS

We introduce the BioMedical Evidence Graph (BMEG), a graph database and query engine for discovery and analysis of cancer biology. The BMEG is unique from other biologic data graphs in that sample-level molecular and clinical information is connected to reference knowledge bases. It combines gene expression and mutation data with drug-response experiments, pathway information databases, and literature-derived associations.

RESULTS

The construction of the BMEG has resulted in a graph containing > 41 million vertices and 57 million edges. The BMEG system provides a graph query-based application programming interface to enable analysis, with client code available for Python, Javascript, and R, and a server online at bmeg.io. Using this system, we have demonstrated several forms of cross-data set analysis to show the utility of the system.

CONCLUSION

The BMEG is an evolving resource dedicated to enabling integrative analysis. We have demonstrated queries on the system that illustrate mutation significance analysis, drug-response machine learning, patient-level knowledge-base queries, and pathway level analysis. We have compared the resulting graph to other available integrated graph systems and demonstrated the former is unique in the scale of the graph and the type of data it makes available.

Modulation of proliferation factors in lung adenocarcinoma with an analysis of the transcriptional consequences of genomic EGFR activation

Genes of the pre-replication, pre-initiation and replisome complexes duplicate the genome from many sites once in a normal cell cycle. This study examines complex components in lung adenocarcinoma (LUAD) closely, correlating changes in the genome and transcriptome with proliferation and overall survival. Molecular subtypes (The Cancer Genome Atlas (TCGA), 2014) based on copy number, DNA methylation, and mRNA expression had variable proliferation levels, the highest correlating with decreased survival. A pattern of increased expression typified by POLE2 and POLQ was found for multiple replication factors over thirty-seven tumor types. EGFR altered cases unanticipatedly inversely correlated with proliferation factor expression in LUAD, Colon adenocarcinoma, and Cancer Cell Line Encyclopedia cell lines, but not in glioblastoma or breast cancer. Activation mutations did not uniformly correlate with proliferation, most cases were pre-metastatic. A gene expression profile was identified, and pathway involvement considered. Significantly, results suggest EGFR over expression and activation are early alterations that likely stall the replication complex through PCNA phosphorylation creating replication stress responsible for DNA damage response and further mutation, but does not promote increased proliferation itself. An argument is presented that the mechanism driving lethality in this tumor cohort could differ from over proliferation seen in other LUAD.

Multi-Omics Characterization of the Spontaneous Mesenchymal-Epithelial Transition in the PMC42 Breast Cancer Cell Lines

Epithelial–mesenchymal plasticity (EMP), encompassing epithelial–mesenchymal transition (EMT) and mesenchymal–epithelial transition (MET), are considered critical events for cancer metastasis. We investigated chromosomal heterogeneity and chromosomal instability (CIN) profiles of two sister PMC42 breast cancer (BC) cell lines to assess the relationship between their karyotypes and EMP phenotypic plasticity. Karyotyping by GTG banding and exome sequencing were aligned with SWATH quantitative proteomics and existing RNA-sequencing data from the two PMC42 cell lines; the mesenchymal, parental PMC42-ET cell line and the spontaneously epithelially shifted PMC42-LA daughter cell line. These morphologically distinct PMC42 cell lines were also compared with five other BC cell lines (MDA-MB-231, SUM-159, T47D, MCF-7 and MDA-MB-468) for their expression of EMP and cell surface markers, and stemness and metabolic profiles. The findings suggest that the epithelially shifted cell line has a significantly altered ploidy of chromosomes 3 and 13, which is reflected in their transcriptomic and proteomic expression profiles. Loss of the TGFβR2 gene from chromosome 3 in the epithelial daughter cell line inhibits its EMT induction by TGF-β stimulus. Thus, integrative ‘omics’ characterization established that the PMC42 system is a relevant MET model and provides insights into the regulation of phenotypic plasticity in breast cancer.