The exomes of the NCI-60 panel: a genomic resource for cancer biology and systems pharmacology

Association of changes in expression of HDAC and SIRT genes after drug treatment with cancer cell line sensitivity to kinase inhibitors

Histone deacetylases (HDACs) and sirtuins (SIRTs) are important epigenetic regulators of cancer pathways. There is a limited understanding of how transcriptional regulation of their genes is affected by chemotherapeutic agents, and how such transcriptional changes affect tumour sensitivity to drug treatment. We investigated the concerted transcriptional response of HDAC and SIRT genes to 15 approved antitumor agents in the NCI-60 cancer cell line panel. Antitumor agents with diverse mechanisms of action induced upregulation or downregulation of multiple HDAC and SIRT genes. HDAC5 was upregulated by dasatinib and erlotinib in the majority of the cell lines. Tumour cell line sensitivity to kinase inhibitors was associated with upregulation of HDAC5, HDAC1, and several SIRT genes. We confirmed changes in HDAC and SIRT expression in independent datasets. We also experimentally validated the upregulation of HDAC5 mRNA and protein expression by dasatinib in the highly sensitive IGROV1 cell line. HDAC5 was not upregulated in the UACC-257 cell line resistant to dasatinib. The effects of cancer drug treatment on expression of HDAC and SIRT genes may influence chemosensitivity and may need to be considered during chemotherapy.

Chromosomal copy number and mutational status are required to authenticate ovarian cancer cell lines as appropriate cell models

BACKGROUND

The mutational status of ovarian cancer cell line IGROV-1 is inconsistent across the literature, suggestive of multiple clonal populations of the cell line. IGROV-1 has previously been categorised as an inappropriate model for high-grade serous ovarian cancer.

METHODS

IGROV-1 cells were obtained from the Netherlands Cancer Institute (IGROV-1-NKI) and the MD Anderson Cancer Centre (IGROV-1-MDA). Cell lines were STR fingerprinted and had their chromosomal copy number analysed and BRCA1/2 genes sequenced. Mutation status of ovarian cancer-related genes were extracted from the literature.

RESULTS

The IGROV-1-NKI cell line has a tetraploid chromosomal profile. In contrast, the IGROV-1-MDA cell line has pseudo-normal chromosomes. The IGROV-1-NKI and IGROV-MDA are both STR matches (80.7% and 84.6%) to the original IGROV-1 cells isolated in 1985. However, IGROV-1-NKI and IGROV-1-MDA are not an STR match to each other (78.1%) indicating genetic drift. The BRCA1 and BRCA2 gene sequences are 100% identical between IGROV-1-MDA and IGROV-1-NKI, including a BRCA1 heterozygous deleterious mutation. The IGROV-1-MDA cells are more resistant to cisplatin and olaparib than IGROV-1-NKI. IGROV-1 has a mutational profile consistent with both Type I (PTEN, PIK3CA and ARID1A) and Type II ovarian cancer (BRCA1, TP53) and is likely to be a Type II high-grade serous carcinoma of the SET (Solid, pseudo-Endometroid and Transitional cell carcinoma-like morphology) subtype.

CONCLUSIONS

Routine testing of chromosomal copy number as well as the mutational status of ovarian cancer related genes should become the new standard alongside STR fingerprinting to ensure that ovarian cancer cell lines are appropriate models.

Structural and mechanistic basis for nucleosomal H2AK119 deubiquitination by single-subunit deubiquitinase USP16

Epigenetic regulators have a crucial effect on gene expression based on their manipulation of histone modifications. Histone H2AK119 monoubiquitination (H2AK119Ub), a well-established hallmark in transcription repression, is dynamically regulated by the opposing activities of Polycomb repressive complex 1 (PRC1) and nucleosome deubiquitinases including the primary human USP16 and Polycomb repressive deubiquitinase (PR-DUB) complex. Recently, the catalytic mechanism for the multi-subunit PR-DUB complex has been described, but how the single-subunit USP16 recognizes the H2AK119Ub nucleosome and cleaves the ubiquitin (Ub) remains unknown. Here we report the cryo-EM structure of USP16-H2AK119Ub nucleosome complex, which unveils a fundamentally distinct mode of H2AK119Ub deubiquitination compared to PR-DUB, encompassing the nucleosome recognition pattern independent of the H2A-H2B acidic patch and the conformational heterogeneity in the Ub motif and the histone H2A C-terminal tail. Our work highlights the mechanism diversity of H2AK119Ub deubiquitination and provides a structural framework for understanding the disease-causing mutations of USP16.

CDKN2A copy number alteration in bladder cancer: Integrative analysis in patient-derived xenografts and cancer patients

Bladder cancer (BlCa) is an extensively heterogeneous disease that leads to great variability in tumor evolution scenarios and lifelong patient surveillance, emphasizing the need for modern, minimally invasive precision medicine. Here, we explored the clinical significance of copy number alterations (CNAs) in BlCa. CNA profiling was performed in 15 patient-derived xenografts (PDXs) and validated in The Cancer Genome Atlas BlCa (TCGA-BLCA; n = 408) and Lindgren et al. (n = 143) cohorts. CDKN2A copy number loss was identified as the most frequent CNA in bladder tumors, associated with reduced CDKN2A expression, tumors of a papillary phenotype, and prolonged PDX survival. The study's screening cohort consisted of 243 BlCa patients, and CDKN2A copy number was assessed in genomic DNA and cell-free DNA (cfDNA) from 217 tumors and 189 pre-treatment serum samples, respectively. CDKN2A copy number loss was correlated with superior disease-free and progression-free survival of non-muscle-invasive BlCa (NMIBC) patients. Moreover, a higher CDKN2A index (CDKN2A/LEP ratio) in pre-treatment cfDNA was associated with advanced tumor stage and grade and short-term NMIBC progression to invasive disease, while multivariate models fitted for CDKN2A index in pre-treatment cfDNA offered superior risk stratification of T1/high-grade and EORTC high-risk patients, enhancing prediction of treatment outcome. CDKN2A copy number status could serve as a minimally invasive tool to improve risk stratification and support personalized prognosis in BlCa.

scDM: A deep generative method for cell surface protein prediction with diffusion model

The executors of organismal functions are proteins, and the transition from RNA to protein is subject to post-transcriptional regulation; therefore, considering both RNA and surface protein expression simultaneously can provide additional evidence of biological processes. Cellular indexing of transcriptomes and epitopes by sequencing (CITE-Seq) technology can measure both RNA and protein expression in single cells, but these experiments are expensive and time-consuming. Due to the lack of computational tools for predicting surface proteins, we used datasets obtained with CITE-seq technology to design a deep generative prediction method based on diffusion models and to find biological discoveries through the prediction results. In our method, the scDM, which predicts protein expression values from RNA expression values of individual cells, uses a novel way of encoding the data into a model and generates predicted samples by introducing Gaussian noise to gradually remove the noise to learn the data distribution during the modelling process. Comprehensive evaluation across different datasets demonstrated that our predictions yielded satisfactory results and further demonstrated the effectiveness of incorporating information from single-cell multiomics data into diffusion models for biological studies. We also found that new directions for discovering therapeutic drug targets could be provided by jointly analysing the predictive value of surface protein expression and cancer cell drug scores.

MNDA, a PYHIN factor involved in transcriptional regulation and apoptosis control in leukocytes

Inflammation control is critical during the innate immune response. Such response is triggered by the detection of molecules originating from pathogens or damaged host cells by pattern-recognition receptors (PRRs). PRRs subsequently initiate intra-cellular signalling through different pathways, resulting in i) the production of inflammatory cytokines, including type I interferon (IFN), and ii) the initiation of a cascade of events that promote both immediate host responses as well as adaptive immune responses. All human PYRIN and HIN-200 domains (PYHIN) protein family members were initially proposed to be PRRs, although this view has been challenged by reports that revealed their impact on other cellular mechanisms. Of relevance here, the human PYHIN factor myeloid nuclear differentiation antigen (MNDA) has recently been shown to directly control the transcription of genes encoding factors that regulate programmed cell death and inflammation. While MNDA is mainly found in the nucleus of leukocytes of both myeloid (neutrophils and monocytes) and lymphoid (B-cell) origin, its subcellular localization has been shown to be modulated in response to genotoxic agents that induce apoptosis and by bacterial constituents, mediators of inflammation. Prior studies have noted the importance of MNDA as a marker for certain forms of lymphoma, and as a clinical prognostic factor for hematopoietic diseases characterized by defective regulation of apoptosis. Abnormal expression of MNDA has also been associated with altered levels of cytokines and other inflammatory mediators. Refining our comprehension of the regulatory mechanisms governing the expression of MNDA and other PYHIN proteins, as well as enhancing our definition of their molecular functions, could significantly influence the management and treatment strategies of numerous human diseases. Here, we review the current state of knowledge regarding PYHIN proteins and their role in innate and adaptive immune responses. Emphasis will be placed on the regulation, function, and relevance of MNDA expression in the control of gene transcription and RNA stability during cell death and inflammation.

Targeting the PD-L1 cytoplasmic domain and its regulatory pathways to enhance cancer immunotherapy

As a significant member of the immune checkpoint, programmed cell death 1 ligand 1 (PD-L1) plays a critical role in cancer immune escape and has become an important target for cancer immunotherapy. Clinically approved drugs mainly target the extracellular domain of PD-L1. Recently, the small cytoplasmic domain of PD-L1 has been reported to regulate PD-L1 stability and function through multiple pathways. Therefore, the intracellular domain of PD-L1 and its regulatory pathways could be promising targets for cancer therapy, expanding available strategies for combined immunotherapy. Here, we summarize the emerging roles of the PD-L1 cytoplasmic domain and its regulatory pathways. The conserved motifs, homodimerization, and posttranslational modifications of the PD-L1 cytoplasmic domain have been reported to regulate the membrane anchoring, degradation, nuclear translocation, and glycosylation of PD-L1. This summary provides a comprehensive understanding of the functions of the PD-L1 cytoplasmic domain and evaluates the broad prospects for targeted therapy.

Facile Green Synthesis of Iron Oxide Nanoparticles and Their Impact on Cytotoxicity, Antioxidative Properties and Bactericidal Activity

Background

Bioreductive processes are quite potent, effective and affordable for the synthesis of green nanoparticles (NPs), as compared to the physical and chemical methods. The present study aimed to evaluate the bactericidal, antioxidative and anticancer activity of turmeric rhizome-iron oxide nanoparticles (FeONPs) derived from the turmeric rhizome (Curcuma amada) using ferric chloride as a precursor.

Methods

With focusing on the manufacture of FeONPs via green approach, we characterized the NPs using FTIR, FT-Vis, DLS, and UV-Vis spectroscopy. The produced particles were tested for antibacterial, antioxidant, and anticancer properties. The synthesized NPs were also examined using the MDA-MB-231 human epithelial breast cancer cell line and NCI-60 cancer cell lines.

Results

The antioxidant activity of TR-FeONPs was concentration-dependent. The scavenging activity of TR-FeONPs was 76.09% at a concentration of 140 µg/ml. Using different concentrations of TR-FeONPs in the MTT assay against the MDA-MB-231 cell line indicated a reduction of less than 50% in cell viability at 125 µg/ml. Moreover, TR-FeONPs exhibited an effective bactericidal property. The gTR-FeONPs synthesized bioreductively were found to be effective in renal cancer, UO-31 cell line, with GI50 value of 66.64%.

Conclusion

Our study showcases a sustainable method based on green chemistry principles to produce FeONPs utilizing turmeric rhizome. We anticipate that the FeONPs produced through this biosynthesis process could serve as a promising drug delivery system in cancer treatment and as an effective antimicrobial agent against various diseases.

Integrative analysis confirms TPX2 as a novel biomarker for clinical implication, tumor microenvironment, and immunotherapy response across human solid tumors

Targeting Protein for Xenopus Kinesin Like Protein 2 (TPX2) serves as a microtubule associated protein for the regulation of spindle assembly and tumorigenesis. We aim to investigate the prognostic and immunological role of TPX2 in pan-cancer. TCGA database, Tumor Immune Single-cell Hub (TISCH), and Human Protein Atlas (HPA) were retrieved to evaluate the expression pattern of TPX2 as well as its diagnostic and prognostic value in solid tumors. Genomic alterations of TPX2 were assessed with cBioPortal database. In vitro experiments in lung adenocarcinoma (LUAD) were performed to confirm the potential role of TPX2. Overexpression of TPX2 was found in 22 types of cancers, and was positively related with copy number variations (CNV) and negative with methylation. Up-regulated TPX2 could predict worse outcomes in the majority of cancers. Single-cell analysis revealed that TPX2 was mainly distributed in malignant cells (especially in glioma) and proliferating T cells. Genomic alteration of TPX2 was common in different types of tumors, while with prognostic value in two types of cancers. Additionally, significant correlations were found between TPX2 expression and tumor microenvironment (including stromal cells and immune cells) as well as immune related genes across cancer types. Drug sensitivity analysis revealed that TPX2 could predict response to chemotherapy and immunotherapy. Functional analyses demonstrated close relationship of TPX2 with immune function and malignant phenotypes. Finally, it was confirmed that knockdown of TPX2 could reduce proliferation and migration ability of LUAD cells. In summary, TPX2 could serve as a diagnostic and prognostic biomarker and a potential immunotherapy marker.

Transcriptomics analysis reveals distinct mechanism of breast cancer stem cells regulation in mammospheres from MCF-7 and T47D cells

Luminal A breast cancer, constituting 70 % of breast cancer cases, presents a challenge due to the development of resistance and recurrence caused by breast cancer stem cells (BCSC). Luminal breast tumors are characterized by TP53 expression, a tumor suppressor gene involved in maintaining stem cell attributes in cancer. Although a previous study successfully developed mammospheres (MS) from MCF-7 (with wild-type TP53) and T47D (with mutant TP53) luminal breast cancer cells for BCSC enrichment, their transcriptomic profiles remain unclear. We aimed to elucidate the transcriptomic disparities between MS of MCF-7 and T47D cells using bioinformatics analyses of differentially expressed genes (DEGs), including the KEGG pathway, Gene Ontology (GO), drug-gene association, disease-gene association, Gene Set Enrichment Analysis (GSEA), DNA methylation analysis, correlation analysis of DEGs with immune cell infiltration, and association analysis of genes and small-molecule compounds via the Connectivity Map (CMap). Upregulated DEGs were enriched in metabolism-related KEGG pathways, whereas downregulated DEGs were enriched in the MAPK signaling pathway. Drug-gene association analysis revealed that both upregulated and downregulated DEGs were associated with fostamatinib. The KEGG pathway GSEA results indicated that the DEGs were enriched for oxidative phosphorylation, whereas the downregulated DEGs were negatively enriched for the p53 signaling pathway. Examination of DNA methylation revealed a noticeable disparity in the expression patterns of the PKM2, ERO1L, SLC6A6, EPAS1, APLP2, RPL10L, and NEDD4 genes when comparing cohorts with low- and high-risk breast cancer. Furthermore, a significant positive correlation was identified between SLC6A6 expression and macrophage presence, as well as MSN, and AKR1B1 expression and neutrophil and dentritic cell infiltration. CMap analysis unveiled SA-83851 as a potential candidate to counteract the effects of DEGs, specifically in cells harbouring mutant TP53. Further research, including in vitro and in vivo validations, is warranted to develop drugs targeting BCSCs.

Determinants for Antitumor and Protumor Effects of Programmed Cell Death

Cytotoxic anticancer therapies activate programmed cell death in the context of underlying stress and inflammatory signaling to elicit the emission of danger signals, cytokines, and chemokines. In a concerted manner, these immunomodulatory secretomes stimulate antigen presentation and T cell-mediated anticancer immune responses. In some instances, cell death-associated secretomes attract immunosuppressive cells to promote tumor progression. As it stands, cancer cell death-induced changes in the tumor microenvironment that contribute to antitumor or protumor effects remain largely unknown. This is complicated to examine because cell death is often subverted by tumors to circumvent natural, and therapy-induced, immunosurveillance. Here, we provide insights into important but understudied aspects of assessing the contribution of cell death to tumor elimination or cancer progression, including the role of tumor-associated genetics, epigenetics, and oncogenic factors in subverting immunogenic cell death. This perspective will also provide insights on how future studies may address the complex antitumor and protumor immunologic effects of cell death, while accounting for variations in tumor genetics and underlying microenvironment.

Patient-Derived Xenografts: A Valuable Preclinical Model for Drug Development and Biomarker Discovery

Patient-derived xenografts (PDXs), established by implanting patient tumor cells into immunodeficient mice, offer a platform for faithfully replicating human tumors. They closely mimic the histopathology, genomics, and drug sensitivity of patient tumors. This chapter highlights the versatile applications of PDXs, including studying tumor biology, metastasis, and chemoresistance, as well as their use in biomarker identification, drug screening, and personalized medicine. It also addresses challenges in using PDXs in cancer research, including variations in metastatic potential, lengthy establishment timelines, stromal changes, and limitations in immunocompromised models. Despite these challenges, PDXs remain invaluable tools guiding patient treatment and advancing preclinical drug development.

A comprehensive review on anticancer evaluation techniques

The development of effective anticancer strategies and the improvement of our understanding of cancer need analytical tools. Utilizing a variety of analytical approaches while investigating anti-cancer medicines gives us a thorough understanding of the traits and mechanisms concerned to cancer cells, which enables us to develop potent treatments to combat them. The importance of anticancer research may be attributed to various analytical techniques that contributes to the identification of therapeutic targets and the assessment of medication efficacy, which are crucial things in expanding our understanding of cancer biology. The study looks at methods that are often used in cancer research, including cell viability assays, clonogenic assay, flow cytometry, 2D electrophoresis, microarray, immunofluorescence, western blot caspase activation assay, bioinformatics, etc. The fundamentals, applications, and how each technique analytical advances our understanding of cancer are briefly reviewed.

Targeted Investigational Oncology Agents in the NCI-60: A Phenotypic Systems-based Resource

The NCI-60 human tumor cell line panel has proved to be a useful tool for the global cancer research community in the search for novel chemotherapeutics. The publicly available cell line characterization and compound screening data from the NCI-60 assay have significantly contributed to the understanding of cellular mechanisms targeted by new oncology agents. Signature sensitivity/resistance patterns generated for a given chemotherapeutic agent against the NCI-60 panel have long served as fingerprint presentations that encompass target information and the mechanism of action associated with the tested agent. We report the establishment of a new public NCI-60 resource based on the cell line screening of a large and growing set of 175 FDA-approved oncology drugs (AOD) plus >825 clinical and investigational oncology agents (IOA), representing a diverse set (>250) of therapeutic targets and mechanisms. This data resource is available to the public (https://ioa.cancer.gov) and includes the raw data from the screening of the IOA and AOD collection along with an extensive set of visualization and analysis tools to allow for comparative study of individual test compounds and multiple compound sets.

The neurological and non-neurological roles of the primary microcephaly-associated protein ASPM

Primary microcephaly (MCPH), is a neurological disorder characterized by small brain size that results in numerous developmental problems, including intellectual disability, motor and speech delays, and seizures. Hitherto, over 30 MCPH causing genes (MCPHs) have been identified. Among these MCPHs, MCPH5, which encodes abnormal spindle-like microcephaly-associated protein (ASPM), is the most frequently mutated gene. ASPM regulates mitotic events, cell proliferation, replication stress response, DNA repair, and tumorigenesis. Moreover, using a data mining approach, we have confirmed that high levels of expression of ASPM correlate with poor prognosis in several types of tumors. Here, we summarize the neurological and non-neurological functions of ASPM and provide insight into its implications for the diagnosis and treatment of MCPH and cancer.

Protein Kinase C at the Crossroad of Mutations, Cancer, Targeted Therapy and Immune Response

The frequent PKC dysregulations observed in many tumors have made these enzymes natural targets for anticancer applications. Nevertheless, this considerable interest in the development of PKC modulators has not led to the expected therapeutic benefits, likely due to the complex biological activities regulated by PKC isoenzymes, often playing ambiguous and protective functions, further driven by the occurrence of mutations. The structure, regulation and functions of PKCs have been extensively covered in other publications. Herein, we focused on PKC alterations mostly associated with complete functional loss. We also addressed the modest yet encouraging results obtained targeting PKC in selected malignancies and the more frequent negative clinical outcomes. The reported observations advocate the need for more selective molecules and a better understanding of the involved pathways. Furthermore, we underlined the most relevant immune mechanisms controlled by PKC isoforms potentially impacting the immune checkpoint inhibitor blockade-mediated immune recovery. We believe that a comprehensive examination of the molecular features of the tumor microenvironment might improve clinical outcomes by tailoring PKC modulation. This approach can be further supported by the identification of potential response biomarkers, which may indicate patients who may benefit from the manipulation of distinctive PKC isoforms.

Mouse Models to Examine Differentiated Thyroid Cancer Pathogenesis: Recent Updates

Although the overall prognosis of differentiated thyroid cancer (DTC), the most common endocrine malignancy, is favorable, a subset of patients exhibits aggressive features. Therefore, preclinical models that can be utilized to investigate DTC pathogenesis and novel treatments are necessary. Various mouse models have been developed based on advances in thyroid cancer genetics. This review focuses on recent progress in mouse models that have been developed to elucidate the molecular pathogenesis of DTC.

Benchmark of tools for in silico prediction of MHC class I and class II genotypes from NGS data

BACKGROUND

The Human Leukocyte Antigen (HLA) genes are a group of highly polymorphic genes that are located in the Major Histocompatibility Complex (MHC) region on chromosome 6. The HLA genotype affects the presentability of tumour antigens to the immune system. While knowledge of these genotypes is of utmost importance to study differences in immune responses between cancer patients, gold standard, PCR-derived genotypes are rarely available in large Next Generation Sequencing (NGS) datasets. Therefore, a variety of methods for in silico NGS-based HLA genotyping have been developed, bypassing the need to determine these genotypes with separate experiments. However, there is currently no consensus on the best performing tool.

RESULTS

We evaluated 13 MHC class I and/or class II HLA callers that are currently available for free academic use and run on either Whole Exome Sequencing (WES) or RNA sequencing data. Computational resource requirements were highly variable between these tools. Three orthogonal approaches were used to evaluate the accuracy on several large publicly available datasets: a direct benchmark using PCR-derived gold standard HLA calls, a correlation analysis with population-based allele frequencies and an analysis of the concordance between the different tools. The highest MHC-I calling accuracies were found for Optitype (98.0%) and arcasHLA (99.4%) on WES and RNA sequencing data respectively, while for MHC-II HLA-HD was the most accurate tool for both data types (96.2% and 99.4% on WES and RNA data respectively).

CONCLUSION

The optimal strategy for HLA genotyping from NGS data depends on the availability of either WES or RNA data, the size of the dataset and the available computational resources. If sufficient resources are available, we recommend Optitype and HLA-HD for MHC-I and MHC-II genotype calling respectively.

Patient-derived xenografts or organoids in the discovery of traditional and self-assembled drug for tumor immunotherapy

In addition to the rapid development of immune checkpoint inhibitors, there has also been a surge in the development of self-assembly immunotherapy drugs. Based on the immune target, traditional tumor immunotherapy drugs are classified into five categories, namely immune checkpoint inhibitors, direct immune modulators, adoptive cell therapy, oncolytic viruses, and cancer vaccines. Additionally, the emergence of self-assembled drugs with improved precision and environmental sensitivity offers a promising innovation approach to tumor immunotherapy. Despite rapid advances in tumor immunotherapy drug development, all candidate drugs require preclinical evaluation for safety and efficacy, and conventional evaluations are primarily conducted using two-dimensional cell lines and animal models, an approach that may be unsuitable for immunotherapy drugs. The patient-derived xenograft and organoids models, however, maintain the heterogeneity and immunity of the pathological tumor heterogeneity.

Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor

Mutational signature analysis is commonly performed in cancer genomic studies. Here, we present SigProfilerExtractor, an automated tool for de novo extraction of mutational signatures, and benchmark it against another 13 bioinformatics tools by using 34 scenarios encompassing 2,500 simulated signatures found in 60,000 synthetic genomes and 20,000 synthetic exomes. For simulations with 5% noise, reflecting high-quality datasets, SigProfilerExtractor outperforms other approaches by elucidating between 20% and 50% more true-positive signatures while yielding 5-fold less false-positive signatures. Applying SigProfilerExtractor to 4,643 whole-genome- and 19,184 whole-exome-sequenced cancers reveals four novel signatures. Two of the signatures are confirmed in independent cohorts, and one of these signatures is associated with tobacco smoking. In summary, this report provides a reference tool for analysis of mutational signatures, a comprehensive benchmarking of bioinformatics tools for extracting signatures, and several novel mutational signatures, including one putatively attributed to direct tobacco smoking mutagenesis in bladder tissues.

Multiomics surface receptor profiling of the NCI-60 tumor cell panel uncovers novel theranostics for cancer immunotherapy

BACKGROUND

Immunotherapy with immune checkpoint inhibitors (ICI) has revolutionized cancer therapy. However, therapeutic targeting of inhibitory T cell receptors such as PD-1 not only initiates a broad immune response against tumors, but also causes severe adverse effects. An ideal future stratified immunotherapy would interfere with cancer-specific cell surface receptors only.

METHODS

To identify such candidates, we profiled the surface receptors of the NCI-60 tumor cell panel via flow cytometry. The resulting surface receptor expression data were integrated into proteomic and transcriptomic NCI-60 datasets applying a sophisticated multiomics multiple co-inertia analysis (MCIA). This allowed us to identify surface profiles for skin, brain, colon, kidney, and bone marrow derived cell lines and cancer entity-specific cell surface receptor biomarkers for colon and renal cancer.

RESULTS

For colon cancer, identified biomarkers are CD15, CD104, CD324, CD326, CD49f, and for renal cancer, CD24, CD26, CD106 (VCAM1), EGFR, SSEA-3 (B3GALT5), SSEA-4 (TMCC1), TIM1 (HAVCR1), and TRA-1-60R (PODXL). Further data mining revealed that CD106 (VCAM1) in particular is a promising novel immunotherapeutic target for the treatment of renal cancer.

CONCLUSION

Altogether, our innovative multiomics analysis of the NCI-60 panel represents a highly valuable resource for uncovering surface receptors that could be further exploited for diagnostic and therapeutic purposes in the context of cancer immunotherapy.

Harnessing preclinical models for the interrogation of ovarian cancer

Ovarian cancer (OC) is a heterogeneous malignancy with various etiology, histopathology, and biological feature. Despite accumulating understanding of OC in the post-genomic era, the preclinical knowledge still undergoes limited translation from bench to beside, and the prognosis of ovarian cancer has remained dismal over the past 30 years. Henceforth, reliable preclinical model systems are warranted to bridge the gap between laboratory experiments and clinical practice. In this review, we discuss the status quo of ovarian cancer preclinical models which includes conventional cell line models, patient-derived xenografts (PDXs), patient-derived organoids (PDOs), patient-derived explants (PDEs), and genetically engineered mouse models (GEMMs). Each model has its own strengths and drawbacks. We focus on the potentials and challenges of using these valuable tools, either alone or in combination, to interrogate critical issues with OC.

Pharmacogenomics of in vitro response of the NCI-60 cancer cell line panel to Indian natural products

BACKGROUND

Indian natural products have been anecdotally used for cancer treatment but with limited efficacy. To better understand their mechanism, we examined the publicly available data for the activity of Indian natural products in the NCI-60 cell line panel.

METHODS

We examined associations of molecular genomic features in the well-characterized NCI-60 cancer cell line panel with in vitro response to treatment with 75 compounds derived from Indian plant-based natural products. We analyzed expression measures for annotated transcripts, lncRNAs, and miRNAs, and protein-changing single nucleotide variants in cancer-related genes. We also examined the similarities between cancer cell line response to Indian natural products and response to reference anti-tumor compounds recorded in a U.S. National Cancer Institute (NCI) Developmental Therapeutics Program database.

RESULTS

Hierarchical clustering based on cell line response measures identified clustering of Phyllanthus and cucurbitacin products with known anti-tumor agents with anti-mitotic mechanisms of action. Curcumin and curcuminoids mostly clustered together. We found associations of response to Indian natural products with expression of multiple genes, notably including SLC7A11 involved in solute transport and ATAD3A and ATAD3B encoding mitochondrial ATPase proteins, as well as significant associations with functional single nucleotide variants, including BRAF V600E.

CONCLUSION

These findings suggest potential mechanisms of action and novel associations of in vitro response with gene expression and some cancer-related mutations that increase our understanding of these Indian natural products.

H3K4 demethylase KDM5B regulates cancer cell identity and epigenetic plasticity

The H3K4 demethylase KDM5B is overexpressed in multiple cancer types, and elevated expression levels of KDM5B is associated with decreased survival. However, the underlying mechanistic contribution of dysregulated expression of KDM5B and H3K4 demethylation in cancer is poorly understood. Our results show that loss of KDM5B in multiple types of cancer cells leads to increased proliferation and elevated expression of cancer stem cell markers. In addition, we observed enhanced tumor formation following KDM5B depletion in a subset of representative cancer cell lines. Our findings also support a role for KDM5B in regulating epigenetic plasticity, where loss of KDM5B in cancer cells with elevated KDM5B expression leads to alterations in activity of chromatin states, which facilitate activation or repression of alternative transcriptional programs. In addition, we define KDM5B-centric epigenetic and transcriptional patterns that support cancer cell plasticity, where KDM5B depleted cancer cells exhibit altered epigenetic and transcriptional profiles resembling a more primitive cellular state. This study also provides a resource for evaluating associations between alterations in epigenetic patterning upon depletion of KDM5B and gene expression in a diverse set of cancer cells.

Transcriptome Analysis of Liver Cancer Cell Huh-7 Treated With Metformin

Metformin is a kind of widely used antidiabetic drug that regulates glucose homeostasis by inhibiting liver glucose production and increasing muscle glucose uptake. Recently, some studies showed that metformin exhibits anticancer properties in a variety of cancers. Although several antitumor mechanisms have been proposed for metformin action, its mode of action in human liver cancer remains not elucidated. In our study, we investigated the underlying molecular mechanisms of metformin's antitumor effect on Huh-7 cells of hepatocellular carcinoma (HCC) in vitro. RNA sequencing was performed to explore the effect of metformin on the transcriptome of Huh-7 cells. The results revealed that 4,518 genes (with log2 fold change > 1 or < −1, adjusted p-value < 0.05) were differentially expressed in Huh-7 cells with treatment of 25-mM metformin compared with 0-mM metformin, including 1,812 upregulated and 2,706 downregulated genes. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses identified 54 classical pathways that were significantly enriched, and 16 pathways are closely associated with cancer, such as cell cycle, DNA replication, extracellular matrix–receptor interaction, and so on. We selected 11 differentially expressed genes, which are closely associated with HCC, to validate their differential expressions through a quantitative real-time reverse transcription-polymerase chain reaction. The result exhibited that the genes of fatty acid synthase, mini-chromosome maintenance complex components 6 and 5, myristoylated alanine-rich C-kinase substrate, fatty acid desaturase 2, C-X-C motif chemokine ligand 1, bone morphogenetic protein 4, S-phase kinase-associated protein 2, kininogen 1, and proliferating cell nuclear antigen were downregulated, and Dual-specificity phosphatase-1 is significantly upregulated in Huh-7 cells with treatment of 25-mM metformin. These differentially expressed genes and pathways might play a crucial part in the antitumor effect of metformin and might be potential targets of metformin treating HCC. Further investigations are required to evaluate the metformin mechanisms of anticancer action in vivo.

Cancer Conformational Landscape Shapes Tumorigenesis

During tumorigenesis, DNA mutations in protein coding sequences can alter amino acid sequences which can change the structures of proteins. While the 3D structure of mutated proteins has been studied with atomic resolution, the precise impact of somatic mutations on the 3D proteome during malignant transformation remains unknown because methods to reveal in vivo protein structures in high throughput are limited. Here, we measured the accessibility of the lysine ε-amine for chemical modification across proteomes using covalent protein painting (CPP) to indirectly determine alterations in the 3D proteome. CPP is a novel, high-throughput quantitative mass spectrometric method that surveyed a total of 8052 lysine sites across the 60 cell lines of the well-studied anticancer cell line panel (NCI60). Overall, 5.2 structural alterations differentiated any cancer cell line from the other 59. Structural aberrations in 98 effector proteins correlated with the selected presence of 90 commonly mutated proteins in the NCI60 cell line panel, suggesting that different tumor genotypes reshape a limited set of effector proteins. We searched our dataset for druggable conformational aberrations and identified 49 changes in the cancer conformational landscape that correlated with the growth inhibition profiles of 300 drug candidates out of 50,000 small molecules. We found that alterations in heat shock proteins are key predictors of anticancer drug efficacy, which implies that the proteostasis network may have a general but hitherto unrecognized role in maintaining malignancy. Individual lysine sites may serve as biomarkers to guide drug selection or may be directly targeted for anticancer drug development.

Telomerase Reverse Transcriptase Promoter Mutations in Human Hepatobiliary, Pancreatic and Gastrointestinal Cancer Cell Lines

BACKGROUND/AIM

The promoter region of the telomerase reverse transcriptase (TERT) gene is a regulatory element capable of affecting TERT expression, telomerase activity, and telomerase length. Mutations within the TERT promoter region are the most common mutations in many cancers. In this study, we characterized the TERT promoter mutation status in hepatobiliary, pancreatic, and gastrointestinal cancer cell lines.

MATERIALS AND METHODS

TERT promoter mutation status was assessed by digital PCR in 12 liver cancer, 5 cholangiocarcinoma (CCA), 12 pancreatic cancer, 17 gastrointestinal cancer, and 3 healthy control cell lines.

RESULTS

The C228T promoter mutation was detected in 9 liver cancer lines, and the C250T TERT mutation was detected in 1 oesophageal squamous cell carcinoma line.

CONCLUSION

The C228T promoter mutation is specific to liver cancer cell lines among various gastrointestinal cancer cell lines. These data will contribute to future research on the tumorigenic mechanisms and clinical use of digital PCR to detect mutations.

Prediction of severe adverse events, modes of action and drug treatments for COVID-19's complications

Following SARS-CoV-2 infection, some COVID-19 patients experience severe host driven adverse events. To treat these complications, their underlying etiology and drug treatments must be identified. Thus, a novel AI methodology MOATAI-VIR, which predicts disease-protein-pathway relationships and repurposed FDA-approved drugs to treat COVID-19's clinical manifestations was developed. SARS-CoV-2 interacting human proteins and GWAS identified respiratory failure genes provide the input from which the mode-of-action (MOA) proteins/pathways of the resulting disease comorbidities are predicted. These comorbidities are then mapped to their clinical manifestations. To assess each manifestation's molecular basis, their prioritized shared proteins were subject to global pathway analysis. Next, the molecular features associated with hallmark COVID-19 phenotypes, e.g. unusual neurological symptoms, cytokine storms, and blood clots were explored. In practice, 24/26 of the major clinical manifestations are successfully predicted. Three major uncharacterized manifestation categories including neoplasms are also found. The prevalence of neoplasms suggests that SARS-CoV-2 might be an oncovirus due to shared molecular mechanisms between oncogenesis and viral replication. Then, repurposed FDA-approved drugs that might treat COVID-19's clinical manifestations are predicted by virtual ligand screening of the most frequent comorbid protein targets. These drugs might help treat both COVID-19's severe adverse events and lesser ones such as loss of taste/smell.

Prospective clinical trial testing COXEN-based gene expression models of chemosensitivity in dogs with spontaneous osteosarcoma

BACKGROUND

This study is a prospective clinical trial in dogs with osteosarcoma testing a gene expression model (GEM) predicting the chemosensitivity of tumors to carboplatin (CARBO) or doxorubicin (DOX) developed using the COXEN method.

PATIENTS AND METHODS

Sixty dogs with appendicular osteosarcoma were enrolled in this trial. RNA isolation and gene expression profiling were conducted with 2 biopsies for 54/63 screened tumors, and with a single biopsy for 9 tumors. Resulting gene expression data were used for calculation of a COXEN score for CARBO and DOX based on a previous study showing the significance of this predictor on patient outcome utilizing retrospective data (BMC Bioinformatics 17:93). Dogs were assigned adjuvant CARBO, DOX or the combination based on the results of the COXEN score following surgical removal of the tumor via amputation and were monitored for disease progression by chest radiograph every 2 months.

RESULTS

The COXEN predictor of chemosensitivity to CARBO or DOX was not a significant predictor of progression-free interval or overall survival for the trial participants. The calculation of DOX COXEN score using gene expression data from two independent biopsies of the same tumor were highly correlated (P < 0.0001), whereas the calculated CARBO COXEN score was not (P = 0.3039).

CONCLUSION

The COXEN predictor of chemosensitivity to CARBO or DOX is not a significant predictor of outcome when utilized in this prospective study. This trial represents the first prospective trial of a GEM predictor of chemosensitivity and establishes pet dogs with cancer as viable surrogates for prospective trials of prognostic indicators.

Post-translational modification of RAS proteins

Mutations of RAS genes drive cancer more frequently than any other oncogene. RAS proteins integrate signals from a wide array of receptors and initiate downstream signaling through pathways that control cellular growth. RAS proteins are fundamentally binary molecular switches in which the off/on state is determined by the binding of GDP or GTP, respectively. As such, the intrinsic and regulated nucleotide-binding and hydrolytic properties of the RAS GTPase were historically believed to account for the entirety of the regulation of RAS signaling. However, it is increasingly clear that RAS proteins are also regulated by a vast array of post-translational modifications (PTMs). The current challenge is to understand what are the functional consequences of these modifications and which are physiologically relevant. Because PTMs are catalyzed by enzymes that may offer targets for drug discovery, the study of RAS PTMs has been a high priority for RAS biologists.

Putative N-glycoprotein markers of MCF-7/ADR cancer stem cells from N-glycoproteomics characterization of the whole cell lysate

Breast cancer is one of the most malignant diseases among females. N-glycoproteomics studies have shown that N-glycosylation alteration of tumor cells is the key player of cancer progression, multidrug resistance (MDR) and high mortality. Cancer stem cells (CSCs) have the remarkable potential of self-renewing and differentiation which leads to drug resistance and metastasis. To investigate the differentially expressed N-glycosylation in adriamycin-resistant breast cancer stem cells MCF-7/ADR CSCs (relative to MCF-7 CSCs) and find the putative biomarkers, 1:1 paired ZIC-HILIC-enriched and stable isotopic diethyl labelled (SIDE) intact N-glycopeptides from MCF-7/ADR CSCs and MCF-7 CSCs were analyzed with C18-RPLC-ESI-MS/MS (HCD with stepped NCE); differentially expressed intact N-glycopeptides (DEGPs) were identified and quantified via search engine GPSeeker. With control of spectrum-level FDR≤1%, 5515 intact N-glycopeptides were identified (1737 N-glycosites, 1705 peptide backbones and 1516 intact N-glycoproteins; 181 putative N-glycan linkages and 68 monosaccharide compositions). Among 5515 intact N-glycopeptide IDs, 3864 were identified with glycoform score≥1, i.e., one or more structure-diagnostic fragment ions were observed to distinguish sequence isomers. With the three technical replicates and the criteria of fold change≥1.5 and p value<0.05, 380 DEGPs (corresponding to 153 intact N-glycoproteins) were found along with 293 down-regulated and 87 up-regulated. For these 153 intact N-glycoproteins, the molecular functions and biological processes of were comprehensively discussed, and side-to-side comparison of differential expression results with other method were also made.

ARIH1 signaling promotes anti-tumor immunity by targeting PD-L1 for proteasomal degradation

Cancer expression of PD-L1 suppresses anti-tumor immunity. PD-L1 has emerged as a remarkable therapeutic target. However, the regulation of PD-L1 degradation is not understood. Here, we identify several compounds as inducers of PD-L1 degradation using a high-throughput drug screen. We find EGFR inhibitors promote PD-L1 ubiquitination and proteasomal degradation following GSK3α-mediated phosphorylation of Ser279/Ser283. We identify ARIH1 as the E3 ubiquitin ligase responsible for targeting PD-L1 to degradation. Overexpression of ARIH1 suppresses tumor growth and promotes cytotoxic T cell activation in wild-type, but not in immunocompromised mice, highlighting the role of ARIH1 in anti-tumor immunity. Moreover, combining EGFR inhibitor ES-072 with anti-CTLA4 immunotherapy results in an additive effect on both tumor growth and cytotoxic T cell activation. Our results delineate a mechanism of PD-L1 degradation and cancer escape from immunity via EGFR-GSK3α-ARIH1 signaling and suggest GSK3α and ARIH1 might be potential drug targets to boost anti-tumor immunity and enhance immunotherapies.

A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research

Recent advances in sequencing and biotechnological methodologies have led to the generation of large volumes of molecular data of different omics layers, such as genomics, transcriptomics, proteomics and metabolomics. Integration of these data with clinical information provides new opportunities to discover how perturbations in biological processes lead to disease. Using data-driven approaches for the integration and interpretation of multi-omics data could stably identify links between structural and functional information and propose causal molecular networks with potential impact on cancer pathophysiology. This knowledge can then be used to improve disease diagnosis, prognosis, prevention, and therapy. This review will summarize and categorize the most current computational methodologies and tools for integration of distinct molecular layers in the context of translational cancer research and personalized therapy. Additionally, the bioinformatics tools Multi-Omics Factor Analysis (MOFA) and netDX will be tested using omics data from public cancer resources, to assess their overall robustness, provide reproducible workflows for gaining biological knowledge from multi-omics data, and to comprehensively understand the significantly perturbed biological entities in distinct cancer types. We show that the performed supervised and unsupervised analyses result in meaningful and novel findings.

Integrative pan cancer analysis reveals epigenomic variation in cancer type and cell specific chromatin domains

Epigenetic mechanisms contribute to the initiation and development of cancer, and epigenetic variation promotes dynamic gene expression patterns that facilitate tumor evolution and adaptation. While the NCI-60 panel represents a diverse set of human cancer cell lines that has been used to screen chemical compounds, a comprehensive epigenomic atlas of these cells has been lacking. Here, we report an integrative analysis of 60 human cancer epigenomes, representing a catalog of activating and repressive histone modifications. We identify genome-wide maps of canonical sharp and broad H3K4me3 domains at promoter regions of tumor suppressors, H3K27ac-marked conventional enhancers and super enhancers, and widespread inter-cancer and intra-cancer specific variability in H3K9me3 and H4K20me3-marked heterochromatin domains. Furthermore, we identify features of chromatin states, including chromatin state switching along chromosomes, correlation of histone modification density with genetic mutations, DNA methylation, enrichment of DNA binding motifs in regulatory regions, and gene activity and inactivity. These findings underscore the importance of integrating epigenomic maps with gene expression and genetic variation data to understand the molecular basis of human cancer. Our findings provide a resource for mining epigenomic maps of human cancer cells and for identifying epigenetic therapeutic targets.

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.

CellMiner Cross-Database (CellMinerCDB) version 1.2: Exploration of patient-derived cancer cell line pharmacogenomics

CellMiner Cross-Database (CellMinerCDB, discover.nci.nih.gov/cellminercdb) allows integration and analysis of molecular and pharmacological data within and across cancer cell line datasets from the National Cancer Institute (NCI), Broad Institute, Sanger/MGH and MD Anderson Cancer Center (MDACC). We present CellMinerCDB 1.2 with updates to datasets from NCI-60, Broad Cancer Cell Line Encyclopedia and Sanger/MGH, and the addition of new datasets, including NCI-ALMANAC drug combination, MDACC Cell Line Project proteomic, NCI-SCLC DNA copy number and methylation data, and Broad methylation, genetic dependency and metabolomic datasets. CellMinerCDB (v1.2) includes several improvements over the previously published version: (i) new and updated datasets; (ii) support for pattern comparisons and multivariate analyses across data sources; (iii) updated annotations with drug mechanism of action information and biologically relevant multigene signatures; (iv) analysis speedups via caching; (v) a new dataset download feature; (vi) improved visualization of subsets of multiple tissue types; (vii) breakdown of univariate associations by tissue type; and (viii) enhanced help information. The curation and common annotations (e.g. tissues of origin and identifiers) provided here across pharmacogenomic datasets increase the utility of the individual datasets to address multiple researcher question types, including data reproducibility, biomarker discovery and multivariate analysis of drug activity.

Molecular genomic features associated with in vitro response of the NCI-60 cancer cell line panel to natural products

Natural products remain a significant source of anticancer chemotherapeutics. The search for targeted drugs for cancer treatment includes consideration of natural products, which may provide new opportunities for antitumor cytotoxicity as single agents or in combination therapy. We examined the association of molecular genomic features in the well-characterized NCI-60 cancer cell line panel with in vitro response to treatment with 1302 small molecules which included natural products, semisynthetic natural product derivatives, and synthetic compounds based on a natural product pharmacophore from the Developmental Therapeutics Program of the US National Cancer Institute's database. These compounds were obtained from a variety of plant, marine, and microbial species. Molecular information utilized for the analysis included expression measures for 23059 annotated transcripts, lncRNAs, and miRNAs, and data on protein-changing single nucleotide variants in 211 cancer-related genes. We found associations of expression of multiple genes including SLFN11, CYP2J2, EPHX1, GPC1, ELF3, and MGMT involved in DNA damage repair, NOTCH family members, ABC and SLC transporters, and both mutations in tyrosine kinases and BRAF V600E with NCI-60 responses to specific categories of natural products. Hierarchical clustering identified groups of natural products, which correlated with a specific mechanism of action. Specifically, several natural product clusters were associated with SLFN11 gene expression, suggesting that potential action of these compounds may involve DNA damage. The associations between gene expression or genome alterations of functionally relevant genes with the response of cancer cells to natural products provide new information about potential mechanisms of action of these identified clusters of compounds with potentially similar biological effects. This information will assist in future drug discovery and in design of new targeted cancer chemotherapy agents.

DRIM: A Web-Based System for Investigating Drug Response at the Molecular Level by Condition-Specific Multi-Omics Data Integration

Pharmacogenomics is the study of how genes affect a person's response to drugs. Thus, understanding the effect of drug at the molecular level can be helpful in both drug discovery and personalized medicine. Over the years, transcriptome data upon drug treatment has been collected and several databases compiled before drug treatment cancer cell multi-omics data with drug sensitivity (IC 50, AUC) or time-series transcriptomic data after drug treatment. However, analyzing transcriptome data upon drug treatment is challenging since more than 20,000 genes interact in complex ways. In addition, due to the difficulty of both time-series analysis and multi-omics integration, current methods can hardly perform analysis of databases with different data characteristics. One effective way is to interpret transcriptome data in terms of well-characterized biological pathways. Another way is to leverage state-of-the-art methods for multi-omics data integration. In this paper, we developed Drug Response analysis Integrating Multi-omics and time-series data (DRIM), an integrative multi-omics and time-series data analysis framework that identifies perturbed sub-pathways and regulation mechanisms upon drug treatment. The system takes drug name and cell line identification numbers or user's drug control/treat time-series gene expression data as input. Then, analysis of multi-omics data upon drug treatment is performed in two perspectives. For the multi-omics perspective analysis, IC 50-related multi-omics potential mediator genes are determined by embedding multi-omics data to gene-centric vector space using a tensor decomposition method and an autoencoder deep learning model. Then, perturbed pathway analysis of potential mediator genes is performed. For the time-series perspective analysis, time-varying perturbed sub-pathways upon drug treatment are constructed. Additionally, a network involving transcription factors (TFs), multi-omics potential mediator genes, and perturbed sub-pathways is constructed, and paths to perturbed pathways from TFs are determined by an influence maximization method. To demonstrate the utility of our system, we provide analysis results of sub-pathway regulatory mechanisms in breast cancer cell lines of different drug sensitivity. DRIM is available at: http://biohealth.snu.ac.kr/software/DRIM/.

Overlapping variants in the blood, tissues and cell lines for patients with intracranial meningiomas are predominant in stem cell-related genes

OBJECTIVE

Bulk tissue genomic analysis of meningiomas identified common somatic mutations, however, it often excluded blood-related variants. In contrast, genomic characterisation of primary cell lines that can provide critical information regarding growth and proliferation, have been rare. In our work, we identified the variants that are present in the blood, tissues and corresponding cell lines that are likely to be predictive, tumorigenic and progressive.

METHOD

Whole-exome sequencing was used to identify variants and distinguish related pathways that exist in 42 blood, tissues and corresponding cell lines (BTCs) samples for patients with intracranial meningiomas. Conventional sequencing was used for the confirmation of variants. Integrative analysis of the gene expression for the corresponding samples was utilised for further interpretations.

RESULTS

In total, 926 BTC variants were detected, implicating 845 genes. A pathway analysis of all BTC genes with damaging variants indicated the 'cell morphogenesis involved in differentiation' stem cell-related pathway to be the most frequently affected pathway. Concordantly, five stem cell-related genes, GPRIN2, ALDH3B2, ASPN, THSD7A and SIGLEC6, showed BTC variants in at least five of the patients. Variants that were heterozygous in the blood and homozygous in the tissues or the corresponding cell lines were rare (average: 1.3 ± 0.3%), and included variants in the RUNX2 and CCDC114 genes. An analysis comparing the variants detected only in tumours with aggressive features indicated a total of 240 BTC genes, implicating the 'homophilic cell adhesion via plasma membrane adhesion molecules' pathway, and identifying the stem cell-related transcription coactivator NCOA3/AIB1/SRC3 as the most frequent BTC gene. Further analysis of the possible impact of the poly-Q mutation present in the NCOA3 gene indicated associated deregulation of 15 genes, including the up-regulation of the stem cell related SEMA3D gene and the angiogenesis related VEGFA gene.

CONCLUSION

Stem cell-related pathways and genes showed high prevalence in the BTC variants, and novel variants in stem cell-related genes were identified for meningioma. These variants can potentially be used as predictive, tumorigenic and progressive biomarkers for meningioma.

ABTB2 Regulatory Variant as Predictor of Epirubicin-Based Neoadjuvant Chemotherapy in Luminal A Breast Cancer

Background: Epirubicin combined with docetaxel is the cornerstone of neoadjuvant chemotherapy (NAC) for breast cancer. The efficacy of NAC for luminal A breast cancer patients is very limited, and single nucleotide polymorphism is one of the most important factors that influences the efficacy. Our study is aimed to explore genetic markers for the efficacy of epirubicin combined with docetaxel for NAC in patients with luminal A breast cancer.

Methods: A total of 421 patients with two stages of luminal A breast cancer were enrolled in this study from 2 centers. Among them 231 patients were included in the discovery cohort and 190 patients are in the replication cohort. All patients received epirubicin 75 mg/m² and docetaxel 75 mg/m² on day 1, in a 21-day cycle, a cycle for 2–6 cycles. Before treatment, 2 ml of peripheral blood was collected from each patient to isolate genomic DNA. Fourteen functional variants potentially regulating epirubicin/docetaxel response genes were prioritized by CellMiner and bioinformatics approaches. Moreover, biological assays were performed to determine the effect of genetic variations on response to chemotherapy.

Results: The patients carrying rs6484711 variant A allele suffered a poor response to epirubicin and docetaxel for NAC (OR = 0.37, 95% CI: 0.18–0.74, P = 0.005) in combined stage. Moreover, expression quantitative trait loci (eQTL) analyses and luciferase reporter assays revealed that rs6484711 A allele significantly increased the expression of ABTB2. Subsequent biological assays illustrated that upregulation of ABTB2 significantly reduced the apoptosis rate of breast cancer cells and enhanced the chemo-resistance to epirubicin.

Conclusions: Our study demonstrated rs6484711 polymorphism regulating ABTB2 expression might predict efficacy to epirubicin based NAC in luminal A breast cancer patients. These results provided valuable information about potential role of genetic variations in individualized chemotherapy.

Efficacy of anti-CD147 chimeric antigen receptors targeting hepatocellular carcinoma

Chimeric antigen receptor (CAR) therapy is a promising immunotherapeutic strategy for treating multiple refractory blood cancers, but further advances are required for solid tumor CAR therapy. One challenge is identifying a safe and effective tumor antigen. Here, we devise a strategy for targeting hepatocellular carcinoma (HCC, one of the deadliest malignancies). We report that T and NK cells transduced with a CAR that recognizes the surface marker, CD147, also known as Basigin, can effectively kill various malignant HCC cell lines in vitro, and HCC tumors in xenograft and patient-derived xenograft mouse models. To minimize any on-target/off-tumor toxicity, we use logic-gated (log) GPC3–synNotch-inducible CD147-CAR to target HCC. LogCD147-CAR selectively kills dual antigen (GPC3⁺CD147⁺), but not single antigen (GPC3^-CD147⁺) positive HCC cells and does not cause severe on-target/off-tumor toxicity in a human CD147 transgenic mouse model. In conclusion, these findings support the therapeutic potential of CD147-CAR-modified immune cells for HCC patients.

Chimeric antigen receptor (CAR)-based therapy for the treatment of liver cancer represents a promising therapeutic strategy. Here the authors show that CD147-targeting CAR-NK or CAR-T can induce anti-tumor activity against hepatocellular carcinoma in vitro and in vivo.

Mouse-human co-clinical trials demonstrate superior anti-tumour effects of buparlisib (BKM120) and cetuximab combination in squamous cell carcinoma of head and neck

Background

Recurrent and/or metastatic squamous cell carcinoma of head and neck (R/M SCCHN) is a common cancer with high recurrence and mortality. Current treatments have low response rates (RRs).

Methods

Fifty-three patients with R/M SCCHN received continuous oral buparlisib. In parallel, patient-derived xenografts (PDXs) were established in mice to evaluate resistance mechanisms and efficacy of buparlisib/cetuximab combination. Baseline and on-treatment tumour genomes and transcriptomes were sequenced. Based on the integrated clinical and PDX data, 11 patients with progression under buparlisib monotherapy were treated with a combination of buparlisib and cetuximab.

Results

For buparlisib monotherapy, disease control rate (DCR) was 49%, RR was 3% and median progression-free survival (PFS) and overall survival (OS) were 63 and 143 days, respectively. For combination therapy, DCR was 91%, RR was 18% and median PFS and OS were 111 and 206 days, respectively. Four PDX models were originated from patients enrolled in the current clinical trial. While buparlisib alone did not inhibit tumour growth, combination therapy achieved tumour inhibition in three of seven PDXs. Genes associated with apoptosis and cell-cycle arrest were expressed at higher levels with combination treatment than with buparlisib or cetuximab alone.

Conclusions

The buparlisib/cetuximab combination has significant promise as a treatment strategy for R/M SCCHN.

Clinical Trial Registration

NCT01527877.

Long-Term Glucose Restriction with or without β-Hydroxybutyrate Enrichment Distinctively Alters Epithelial-Mesenchymal Transition-Related Signalings in Ovarian Cancer Cells

The beneficial impacts of the ketogenic diet and metabolic reprograming were recently reported for ovarian cancer patients. In this study, the effects of glucose restriction with or without beta-hydroxybutyrate (bHB) enrichment were studied in drug-resistant CD133high A2780CP and CD133low SK-OV-3 ovarian cancer cells to scrutinize the impact of experimental ketosis on ATP production, epithelial to mesenchymal transition (EMT), and related signaling pathways including Wnt, Hippo, and Hedgehog. Cells were adapted and maintained for a month with restricted levels of glucose (250 mg/l) with or without the therapeutic concentration of bHB (5 mM). Quantitative PCR, Western blot analysis, flow cytometry, chemiluminescence, and wound healing assay were used in this study. Glucose restriction and bHB enrichment reduced the stemness marker and diminished In Vitro migration in both cell lines. Glucose restriction significantly reduced ATP levels in both cells, but bHB enrichment was partially compensated for the ATP levels solely in SK-OV-3 cells. Glucose restriction mainly inhibited the Wnt pathway in the CD133high A2780CP cells, but the Hedgehog pathway was the main target in CD133low SK-OV-3 cells. In Conclusion, Prior targeted evaluations of key genes' expression would help to predict the distinctive impacts of metabolic fuels and to optimize the efficacy of ketogenic diets.

The LL-100 Cell Lines Panel: Tool for Molecular Leukemia-Lymphoma Research

Certified cell line models provide ideal experimental platforms to answer countless scientific questions. The LL-100 panel is a cohort of cell lines that are broadly representative of all leukemia–lymphoma entities (including multiple myeloma and related diseases), rigorously authenticated and validated, and comprehensively annotated. The process of the assembly of the LL-100 panel was based on evidence and experience. To expand the genetic characterization across all LL-100 cell lines, we performed whole-exome sequencing and RNA sequencing. Here, we describe the conception of the panel and showcase some exemplary applications with a focus on cancer genomics. Due diligence was paid to exclude cross-contaminated and non-representative cell lines. As the LL-100 cell lines are so well characterized and readily available, the panel will be a valuable resource for identifying cell lines with mutations in cancer genes, providing superior model systems. The data also add to the current knowledge of the molecular pathogenesis of leukemia–lymphoma. Additional efforts to expand the breadth of available high-quality cell lines are clearly warranted.

Human genes differ by their UV sensitivity estimated through analysis of UV-induced silent mutations in melanoma

We hypothesized that human genes differ by their sensitivity to ultraviolet (UV) exposure. We used somatic mutations detected by genome-wide screens in melanoma and reported in the Catalog Of Somatic Mutations In Cancer. As a measure of UV sensitivity, we used the number of silent mutations generated by C>T transitions in pyrimidine dimers of a given transcript divided by the number of potential sites for this type of mutations in the transcript. We found that human genes varied by UV sensitivity by two orders of magnitude. We noted that the melanoma-associated tumor suppressor gene CDKN2A was among the top five most UV-sensitive genes in the human genome. Melanoma driver genes have a higher UV-sensitivity compared with other genes in the human genome. The difference was more prominent for tumor suppressors compared with oncogene. The results of this study suggest that differential sensitivity of human transcripts to UV light may explain melanoma specificity of some driver genes. Practical significance of the study relates to the fact that differences in UV sensitivity among human genes need to be taken into consideration whereas predicting melanoma-associated genes by the number of somatic mutations detected in a given gene.

Optimization of a WGA-Free Molecular Tagging-Based NGS Protocol for CTCs Mutational Profiling

Molecular characterization of Circulating Tumor Cells (CTCs) is still challenging, despite attempts to minimize the drawbacks of Whole Genome Amplification (WGA). In this paper, we propose a Next-Generation Sequencing (NGS) optimized protocol based on molecular tagging technology, in order to detect CTCs mutations while skipping the WGA step. MDA-MB-231 and MCF-7 cell lines, as well as leukocytes, were sorted into pools (2–5 cells) using a DEPArray™ system and were employed to set up the overall NGS procedure. A substantial reduction of reagent volume for the preparation of libraries was performed, in order to fit the limited DNA templates directly derived from cell lysates. Known variants in TP53, KRAS, and PIK3CA genes were detected in almost all the cell line pools (35/37 pools, 94.6%). No additional alterations, other than those which were expected, were found in all tested pools and no mutations were detected in leukocytes. The translational value of the optimized NGS workflow is confirmed by sequencing CTCs pools isolated from eight breast cancer patients and through the successful detection of variants. In conclusion, this study shows that the proposed NGS molecular tagging approach is technically feasible and, compared to traditional NGS approaches, has the advantage of filtering out the artifacts generated during library amplification, allowing for the reliable detection of mutations and, thus, making it highly promising for clinical use.

Mining Drug-Target Associations in Cancer: Analysis of Gene Expression and Drug Activity Correlations

Cancer is a complex disease affecting millions of people worldwide, with over a hundred clinically approved drugs available. In order to improve therapy, treatment, and response, it is essential to draw better maps of the targets of cancer drugs and possible side interactors. This study presents a large-scale screening method to find associations of cancer drugs with human genes. The analysis is focused on the current collection of Food and Drug Administration (FDA)-approved drugs (which includes about one hundred chemicals). The approach integrates global gene-expression transcriptomic profiles with drug-activity profiles of a set of 60 human cell lines obtained for a collection of chemical compounds (small bioactive molecules). Using a standardized expression for each gene versus standardized activity for each drug, Pearson and Spearman correlations were calculated for all possible pairwise gene-drug combinations. These correlations were used to build a global bipartite network that includes 1007 gene-drug significant associations. The data are integrated into an open web-tool called GEDA (Gene Expression and Drug Activity) which includes a relational view of cancer drugs and genes, disclosing the putative indirect interactions found for FDA-approved drugs as well as the known targets of these drugs. The results also provide insight into the complex action of pharmaceuticals, presenting an alternative view to address predicted pleiotropic effects of the drugs.

Establishment and characterization of patient-derived xenografts as paraclinical models for head and neck cancer

Background

We investigated whether head and neck squamous cell carcinoma (HNSCC) patient-derived xenografts (PDXs) reaffirm patient responses to anti-cancer therapeutics.

Methods

Tumors from HNSCC patients were transplanted into immunodeficient mice and propagated via subsequent implantation. We evaluated established PDXs by histology, genomic profiling, and in vivo anti-cancer efficacy testing to confirm them as the authentic in vivo platform.

Results

From 62 HNSCCs, 15 (24%) PDXs were established. The primary cancer types were tongue (8), oropharynx (3), hypopharynx (1), ethmoid sinus cancer (1), supraglottic cancer (1), and parotid gland (1); six PDXs (40%) were established from biopsy specimens from advanced HNSCC. PDXs mostly retained donor characteristics and remained stable across passages. PIK3CA (H1047R), HRAS (G12D), and TP53 mutations (H193R, I195T, R248W, R273H, E298X) and EGFR, CCND1, MYC, and PIK3CA amplifications were identified. Using the acquisition method, biopsy showed a significantly higher engraftment rate when compared with that of surgical resection (100% [6/6] vs. 16.1% [9/56], P < 0.001). Specimens obtained from metastatic sites showed a significantly higher engraftment rate than did those from primary sites (100% [9/9] vs. 11.3% [6/53], P < 0.001). Three PDX models from HPV-positive tumors were established, as compared to 12 from HPV-negative (15.8% [3/19] and 27.9% [12/43] respectively, P = 0.311), suggesting that HPV positivity tends to show a low engraftment rate. Drug responses in PDX recapitulated the clinical responses of the matching patients with pan-HER inhibitors and pan-PI3K inhibitor.

Conclusions

Genetically and clinically annotated HNSCC PDXs could be useful preclinical tools for evaluating biomarkers, therapeutic targets, and new drug discovery.

The ability of miRNAs to induce mesenchymal-to-epithelial transition (MET) in cancer cells is highly dependent upon genetic background

Understanding of the molecular basis of host cell-miRNA interactions is prerequisite to the successful application of miRNAs as potential therapeutic agents. We studied the morphological and molecular consequences of over expression of three sequence divergent miRNAs previously implicated in the mesenchymal-to-epithelial transition process (MET) in three distinct mesenchymal-like cancer cell lines. The ability of miRNAs to induce morphological changes characteristic of MET positively correlated with induced changes in the expression of genes previously implicated in the process. Variability in the responses of different mesenchymal-like cells to over expression of the same miRNAs was attributable to inherent differences in trans-regulatory profiles pre-disposing these cells to miRNA-induced MET. Collectively our results indicate that miRNA-mediated regulation of MET is a highly integrated process that is significantly modulated by the molecular background of individual cells.

Computational Methods for the Integrative Analysis of Genomics and Pharmacological Data

Since the pioneering NCI-60 panel of the late'80's, several major screenings of genetic profiling and drug testing in cancer cell lines have been conducted to investigate how genetic backgrounds and transcriptional patterns shape cancer's response to therapy and to identify disease-specific genes associated with drug response. Historically, pharmacogenomics screenings have been largely heterogeneous in terms of investigated cell lines, assay technologies, number of compounds, type and quality of genomic data, and methods for their computational analysis. The analysis of this enormous and heterogeneous amount of data required the development of computational methods for the integration of genomic profiles with drug responses across multiple screenings. Here, we will review the computational tools that have been developed to integrate cancer cell lines' genomic profiles and sensitivity to small molecule perturbations obtained from different screenings.