NCI-60 whole exome sequencing and pharmacological CellMiner analyses

Sarcoma_CellminerCDB: A tool to interrogate the genomic and functional characteristics of a comprehensive collection of sarcoma cell lines

Sarcomas are a diverse group of rare malignancies composed of multiple different clinical and molecular subtypes. Due to their rarity and heterogeneity, basic, translational, and clinical research in sarcoma has trailed behind that of other cancers. Outcomes for patients remain generally poor due to an incomplete understanding of disease biology and a lack of novel therapies. To address some of the limitations impeding preclinical sarcoma research, we have developed Sarcoma_CellMinerCDB, a publicly available interactive tool that merges publicly available sarcoma cell line data and newly generated omics data to create a comprehensive database of genomic, transcriptomic, methylomic, proteomic, metabolic, and pharmacologic data on 133 annotated sarcoma cell lines. The reproducibility, functionality, biological relevance, and therapeutic applications of Sarcoma_CellMinerCDB described herein are powerful tools to address and generate biological questions and test hypotheses for translational research. Sarcoma_CellMinerCDB (https://discover.nci.nih.gov/SarcomaCellMinerCDB) aims to contribute to advancing the preclinical study of sarcoma.

Safety and efficacy of canakinumab treatment for undifferentiated autoinflammatory diseases: the data of a retrospective cohort two-centered study

Introduction

The blockade of interleukine-1 (anakinra and canakinumab) is a well-known highly effective tool for monogenic autoinflammatory diseases (AIDs), such as familial Mediterranean fever, tumor necrosis factor receptor-associated periodic syndrome, hyperimmunoglobulinaemia D syndrome, and cryopyrin-associated periodic syndrome, but this treatment has not been assessed for patients with undifferentiated AIDs (uAIDs). Our study aimed to assess the safety and efficacy of canakinumab for patients with uAIDs.

Methods

Information on 32 patients with uAIDs was retrospectively collected and analyzed. Next-generation sequencing and Federici criteria were used for the exclusion of the known monogenic AID.

Results

The median age of the first episode was 2.5 years (IQR: 1.3; 5.5), that of the disease diagnosis was 5.7 years (IQR: 2.5;12.7), and that of diagnostic delay was 1.1 years (IQR: 0.4; 6.1). Patients had variations in the following genes: IL10, NLRP12, STAT2, C8B, LPIN2, NLRC4, PSMB8, PRF1, CARD14, IFIH1, LYST, NFAT5, PLCG2, COPA, IL23R, STXBP2, IL36RN, JAK1, DDX58, LACC1, LRBA, TNFRSF11A, PTHR1, STAT4, TNFRSF1B, TNFAIP3, TREX1, and SLC7A7. The main clinical features were fever (100%), rash (91%; maculopapular predominantly), joint involvement (72%), splenomegaly (66%), hepatomegaly (59%), lymphadenopathy (50%), myalgia (28%), heart involvement (31%), intestinal involvement (19%); eye involvement (9%), pleuritis (16%), ascites (6%), deafness, hydrocephalia (3%), and failure to thrive (25%). Initial treatment before canakinumab consisted of non-biologic therapies: non-steroidal anti-inflammatory drugs (NSAID) (91%), corticosteroids (88%), methotrexate (38%), intravenous immunoglobulin (IVIG) (34%), cyclosporine A (25%), colchicine (6%) cyclophosphamide (6%), sulfasalazine (3%), mycophenolate mofetil (3%), hydroxychloroquine (3%), and biologic drugs: tocilizumab (62%), sarilumab, etanercept, adalimumab, rituximab, and infliximab (all 3%). Canakinumab induced complete remission in 27 patients (84%) and partial remission in one patient (3%). Two patients (6%) were primary non-responders, and two patients (6%) further developed secondary inefficacy. All patients with partial efficacy or inefficacy were switched to tocilizumab (n = 4) and sarilumab (n = 1). The total duration of canakinumab treatment was 3.6 (0.1; 8.7) years. During the study, there were no reported Serious Adverse Events (SAEs). The patients experienced non-frequent mild respiratory infections at a rate that is similar as before canakinumab is administered. Additionally, one patient developed leucopenia, but it was not necessary to stop canakinumab for this patient.

Conclusion

The treatment of patients with uAIDs using canakinumab was safe and effective. Further randomized clinical trials are required to confirm the efficacy and safety.

YAP at the Crossroads of Biomechanics and Drug Resistance in Human Cancer

Biomechanical forces are of fundamental importance in biology, diseases, and medicine. Mechanobiology is an emerging interdisciplinary field that studies how biological mechanisms are regulated by biomechanical forces and how physical principles can be leveraged to innovate new therapeutic strategies. This article reviews state-of-the-art mechanobiology knowledge about the yes-associated protein (YAP), a key mechanosensitive protein, and its roles in the development of drug resistance in human cancer. Specifically, the article discusses three topics: how YAP is mechanically regulated in living cells; the molecular mechanobiology mechanisms by which YAP, along with other functional pathways, influences drug resistance of cancer cells (particularly lung cancer cells); and finally, how the mechanical regulation of YAP can influence drug resistance and vice versa. By integrating these topics, we present a unified framework that has the potential to bring theoretical insights into the design of novel mechanomedicines and advance next-generation cancer therapies to suppress tumor progression and metastasis.

CellMinerCDB: NCATS Is a Web-Based Portal Integrating Public Cancer Cell Line Databases for Pharmacogenomic Explorations

Major advances have been made in the field of precision medicine for treating cancer. However, many open questions remain that need to be answered to realize the goal of matching every patient with cancer to the most efficacious therapy. To facilitate these efforts, we have developed CellMinerCDB: National Center for Advancing Translational Sciences (NCATS; https://discover.nci.nih.gov/rsconnect/cellminercdb_ncats/), which makes available activity information for 2,675 drugs and compounds, including multiple nononcology drugs and 1,866 drugs and compounds unique to the NCATS. CellMinerCDB: NCATS comprises 183 cancer cell lines, with 72 unique to NCATS, including some from previously understudied tissues of origin. Multiple forms of data from different institutes are integrated, including single and combination drug activity, DNA copy number, methylation and mutation, transcriptome, protein levels, histone acetylation and methylation, metabolites, CRISPR, and miscellaneous signatures. Curation of cell lines and drug names enables cross-database (CDB) analyses. Comparison of the datasets is made possible by the overlap between cell lines and drugs across databases. Multiple univariate and multivariate analysis tools are built-in, including linear regression and LASSO. Examples have been presented here for the clinical topoisomerase I (TOP1) inhibitors topotecan and irinotecan/SN-38. This web application provides both substantial new data and significant pharmacogenomic integration, allowing exploration of interrelationships.

SIGNIFICANCE

CellMinerCDB: NCATS provides activity information for 2,675 drugs in 183 cancer cell lines and analysis tools to facilitate pharmacogenomic research and to identify determinants of response.

National Cancer Institute (NCI) Program for Natural Product Discovery: Exploring NCI-60 Screening Data of Natural Product Samples with Artificial Neural Networks

National Cancer Institute (NCI) Program for Natural Product Discovery is a new initiative aimed at creating new technologies for natural product-based drug discovery. Here, we present the development of a neural network-based bioinformatics platform for visualization and analysis of natural product high-throughput screening data using the NCI's 60 human tumor cell anticancer drug screen. We demonstrate how the tool enables visualization of similar patterns of response that can be parsed both chemically and taxonomically, grouping NCI-60 biological profiles in one easy-to-use bioinformatics interface.

Large inherent variability in data derived from highly standardised cell culture experiments

Cancer drug development is hindered by high clinical attrition rates, which are blamed on weak predictive power by preclinical models and limited replicability of preclinical findings. However, the technically feasible level of replicability remains unknown. To fill this gap, we conducted an analysis of data from the NCI60 cancer cell line screen (2.8 million compound/cell line experiments), which is to our knowledge the largest depository of experiments that have been repeatedly performed over decades. The findings revealed profound intra-laboratory data variability, although all experiments were executed following highly standardised protocols that avoid all known confounders of data quality. All compound/ cell line combinations with > 100 independent biological replicates displayed maximum GI50 (50% growth inhibition) fold changes (highest/ lowest GI50) > 5% and 70.5% displayed maximum fold changes > 1000. The highest maximum fold change was 3.16 × 10¹⁰ (lowest GI50: 7.93 ×10^-10 µM, highest GI50: 25.0 µM). FDA-approved drugs and experimental agents displayed similar variation. Variability remained high after outlier removal, when only considering experiments that tested drugs at the same concentration range, and when only considering NCI60-provided quality-controlled data. In conclusion, high variability is an intrinsic feature of anti-cancer drug testing, even among standardised experiments in a world-leading research environment. Awareness of this inherent variability will support realistic data interpretation and inspire research to improve data robustness. Further research will have to show whether the inclusion of a wider variety of model systems, such as animal and/ or patient-derived models, may improve data robustness.

Seleno-Analogs of Scaffolds Resembling Natural Products a Novel Warhead toward Dual Compounds

Nowadays, oxidative cell damage is one of the common features of cancer and Alzheimer's disease (AD), and Se-containing molecules, such as ebselen, which has demonstrated strong antioxidant activity, have demonstrated well-established preventive effects against both diseases. In this study, a total of 39 Se-derivatives were synthesized, purified, and spectroscopically characterized by NMR. Antioxidant ability was tested using the DPPH assay, while antiproliferative activity was screened in breast, lung, prostate, and colorectal cancer cell lines. In addition, as a first approach to evaluate their potential anti-Alzheimer activity, the in vitro acetylcholinesterase inhibition (AChEI) was tested. Regarding antioxidant properties, compound 13a showed concentration- and time-dependent radical scavenging activity. Additionally, compounds 14a and 17a showed high activity in the melanoma and ovarian cancer cell lines, with LD₅₀ values below 9.2 µM. Interestingly, in the AChEI test, compound 14a showed almost identical inhibitory activity to galantamine along with a 3-fold higher in vitro BBB permeation (Pe = 36.92 × 10^-6 cm/s). Molecular dynamics simulations of the aspirin derivatives (14a and 14b) confirm the importance of the allylic group instead of the propargyl one. Altogether, it is concluded that some of these newly synthesized Se-derivatives, such as 14a, might become very promising candidates to treat both cancer and AD.

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.

SCLC-CellMiner: A Resource for Small Cell Lung Cancer Cell Line Genomics and Pharmacology Based on Genomic Signatures

CellMiner-SCLC (https://discover.nci.nih.gov/SclcCellMinerCDB/) integrates drug sensitivity and genomic data, including high-resolution methylome and transcriptome from 118 patient-derived small cell lung cancer (SCLC) cell lines, providing a resource for research into this "recalcitrant cancer." We demonstrate the reproducibility and stability of data from multiple sources and validate the SCLC consensus nomenclature on the basis of expression of master transcription factors NEUROD1, ASCL1, POU2F3, and YAP1. Our analyses reveal transcription networks linking SCLC subtypes with MYC and its paralogs and the NOTCH and HIPPO pathways. SCLC subsets express specific surface markers, providing potential opportunities for antibody-based targeted therapies. YAP1-driven SCLCs are notable for differential expression of the NOTCH pathway, epithelial-mesenchymal transition (EMT), and antigen-presenting machinery (APM) genes and sensitivity to mTOR and AKT inhibitors. These analyses provide insights into SCLC biology and a framework for future investigations into subtype-specific SCLC vulnerabilities.

Vilsmeier reagent, NaHSe and diclofenac acid chloride: one-pot synthesis of a novel selenoindolinone with potent anticancer activity

An effective and straightforward synthesis of 3-seleno functionalized indolinone (5) involving Vilsmeier reagent is presented. Likewise, a procedure to achieve lactamization of diclofenac with excellent yields by using hydrides is also ascertained. Compound 5 exhibited impressive growth inhibition in most of the cell lines in an NCI-60 panel, particularly towards resistant breast cancer cells.

Cytochrome P450 1B1 polymorphism drives cancer cell stemness and patient outcome in head-and-neck carcinoma

Background

Cytochrome P450 1B1 (CYP1B1) is mostly expressed in tumours and displays unusual properties. Its two polymorphic forms were differently associated with anticancer drug sensitivity. We decipher here the role of this polymorphism in anticancer drug efficacy in vitro, in vivo and in the clinical setting.

Methods

From head-and-neck squamous cell carcinoma cell lines not expressing CYP1B1, we generated isogenic derivatives expressing the two forms. Proliferation, invasiveness, stem cell characteristics, sensitivity to anticancer agents and transcriptome were analysed. Tumour growth and chemosensitivity were studied in vivo. A prospective clinical trial on 121 patients with advanced head-and-neck cancers was conducted, and a validation-retrospective study was conducted.

Results

Cell lines expressing the variant form displayed high rates of in vitro proliferation and invasiveness, stemness features and resistance to DNA-damaging agents. In vivo, tumours expressing the variant CYP1B1 had higher growth rates and were markedly drug-resistant. In the clinical study, overall survival was significantly associated with the genotypes, wild-type patients presenting a longer median survival (13.5 months) than the variant patients (6.3 months) (p = 0.0166).

Conclusions

This frequent CYP1B1 polymorphism is crucial for cancer cell proliferation, migration, resistance to chemotherapy and stemness properties, and strongly influences head-and-neck cancer patients’ survival.

TraPS-VarI: Identifying genetic variants altering phosphotyrosine based signalling motifs

Patient stratification and individualized therapeutic strategies rely on the established knowledge of genotype-specific molecular and cellular alterations of biological and therapeutic significance. Whilst almost all approved drugs have been developed based on the Reference Sequence protein database (RefSeq), the latest genome sequencing studies establish the substantial prevalence of non-synonymous genetic mutations in the general population, including stop-insertion and frame shift mutations within the coding regions of membrane proteins. While the availability of individual genotypes are becoming increasingly common, the biological and clinical interpretations of mutations among individual genomes is largely lagging behind. Lately, transmembrane proteins of haematopoietic (myeloid and lymphoid) derived immune cells have attracted much attention as important targets for cancer immunotherapies. As such, the signalling properties of haematological transmembrane receptors rely on the membrane-proximal phosphotyrosine based sequence motifs (TBSMs) such as ITAM (immunoreceptor tyrosine-based activation motif), ITIM (immunoreceptor tyrosine-based inhibition motif) and signal transducer and activator of transcription 3 (STAT3)-recruiting YxxQ motifs. However, mutations that alter the coding regions of transmembrane proteins, resulting in either insertion or deletion of crucial signal modulating TBSMs, remains unknown. To conveniently identify individual cell line-specific or patient-specific membrane protein altering mutations, we present the Transmembrane Protein Sequence Variant Identifier (TraPS-VarI). TraPS-VarI is an annotation tool for accurate mapping of the effect of an individual’s mutation in the transmembrane protein sequence, and to identify the prevalence of TBSMs. TraPS-VarI is a biologist and clinician-friendly algorithm with a web interface and an associated database browser (https://www.traps-vari.org/).

SLFN11 is a general target for enhancing the sensitivity of cancer to chemotherapy (DNA-damaging agents)

In patients with cancer, drug tolerance often occurs during the use of chemotherapy drugs, seriously affecting patient prognosis and survival. Therefore, scientists began to study the factors that affect chemotherapy drug sensitivity, and the high correlation between Schlafen-11 (SLFN11) and sensitivity to chemical drugs (mainly DNA-damaging agents, DDAs) has received increasing attention since it was discovered through bioinformatics analyses. Regarding the mechanism, SLFN11 may sensitise cells to chemotherapy drugs by preventing DNA damage repair. In recent years, SLFN11 has gradually become a hot research topic, and the results are enriching our understanding of this molecule. Indeed, the biological functions of SLFN11 under normal physiological conditions and in cancer, changes in its expression levels and mechanisms promoting apoptosis within the context of chemotherapeutic interventions have gradually been uncovered. Studies to date provide knowledge and the experimental and theoretical bases underlying SLFN11 and its effects on sensitivity to chemotherapy drugs. This review summarises the existing research on SLFN11 with the aim of achieving a more comprehensive understanding and furthering the development of strategies to target SLFN11 in the treatment of cancer.

Establishment and genomic characterization of gingivobuccal carcinoma cell lines with smokeless tobacco associated genetic alterations and oncogenic PIK3CA mutation

Smokeless tobacco associated Gingivobuccal squamous cell carcinoma (GB-SCC) is a major public health problem but available oral cancer cell lines are mostly from smoking associated tongue SCC raising the need for pertinent GB-SCC cell line models. As part of the International Cancer Genome Consortium (ICGC) Project, 4 novel cell lines, namely, Indian Tata Memorial Centre Oral Cancer (ITOC) -01 to -04 were established and characterized with conventional methods, karyotyping, ultrastructure, in vivo tumourigenicity, Whole exome sequencing (WES) and RNA sequencing. These hyperploid cell lines form xenografts in mice and show metabolically active and necrotic areas on fluorodeoxyglucose-positron emission tomography (FDG-PET) imaging. WES of ITOC cell lines recapitulate the genomic tumor profile of ICGC GB-SCC database. We further identified smokeless tobacco associated genetic alterations (PCLO, FAT3 and SYNE2) and oncogenic PIK3CA mutation in GB-SCC cell lines. Transcriptome profiling identified deregulation of pathways commonly altered in cancer and down-regulation of arachidonic acid metabolism pathway, implying its possible role in GB-SCC. Clinical application of high throughput sequencing data depends on relevant cell line models to validate potential targets. Extensively characterized, these oral SCC cell lines are particularly suited for mechanistic studies and pre-clinical drug development for smokeless tobacco associated oral cancer.

CellMinerCDB for Integrative Cross-Database Genomics and Pharmacogenomics Analyses of Cancer Cell Lines

CellMinerCDB provides a web-based resource (https://discover.nci.nih.gov/cellminercdb/) for integrating multiple forms of pharmacological and genomic analyses, and unifying the richest cancer cell line datasets (the NCI-60, NCI-SCLC, Sanger/MGH GDSC, and Broad CCLE/CTRP). CellMinerCDB enables data queries for genomics and gene regulatory network analyses, and exploration of pharmacogenomic determinants and drug signatures. It leverages overlaps of cell lines and drugs across databases to examine reproducibility and expand pathway analyses. We illustrate the value of CellMinerCDB for elucidating gene expression determinants, such as DNA methylation and copy number variations, and highlight complexities in assessing mutational burden. We demonstrate the value of CellMinerCDB in selecting drugs with reproducible activity, expand on the dominant role of SLFN11 for drug response, and present novel response determinants and genomic signatures for topoisomerase inhibitors and schweinfurthins. We also introduce LIX1L as a gene associated with mesenchymal signature and regulation of cellular migration and invasiveness.

Bioinformatics Approaches to Predict Drug Responses from Genomic Sequencing

Fulfilling the promises of precision medicine will depend on our ability to create patient-specific treatment regimens. Therefore, being able to translate genomic sequencing into predicting how a patient will respond to a given drug is critical. In this chapter, we review common bioinformatics approaches that aim to use sequencing data to predict sample-specific drug susceptibility. First, we explain the importance of customized drug regimens to the future of medical care. Second, we discuss the different public databases and community efforts that can be leveraged to develop new methods for identifying new predictive biomarkers. Third, we cover the basic methods that are currently used to identify markers or signatures of drug response, without any prior knowledge of the drug's mechanism of action. We further discuss how one can integrate knowledge about drug targets, mechanisms, and predictive markers to better estimate drug response in a diverse set of samples. We begin this section with a primer on popular methods to identify targets and mechanism of action for new small molecules. This discussion also includes a set of computational methods that incorporate other drug features, which do not relate to drug-induced genetic changes or sequencing data such as drug structures, side-effects, and efficacy profiles. Those additional drug properties can aid in gaining higher accuracy for the identification of drug target and mechanism of action. We then progress to discuss using these targets in combination with disease-specific expression patterns, known pathways, and genetic interaction networks to aid drug choice. Finally, we conclude this chapter with a general overview of machine learning methods that can integrate multiple pieces of sequencing data along with prior drug or biological knowledge to drastically improve response prediction.

Synthesis and Biological Evaluation of New Madecassic Acid Derivatives Targeting ERK Cascade Signaling

In the present study, a series of novel madecassic acid derivatives was synthesized and screened against the National Cancer Institute's 60 human cancer cell line panel. Among them, compounds 5, 12, and 17 displayed potent and highly differential antiproliferative activity against 80% of the tumor cells harboring the B-Raf^V600E mutation within the nanomolar range. Structure-activity analysis revealed that a 5-membered A ring containing an α,β-unsaturated aldehyde substituted at C-23 with a 2-furoyl group seems to be crucial to produce this particular growth inhibition signature. In silico analysis of the cytotoxicity pattern of these compounds identified two highly correlated clinically approved drugs with known B-Raf^V600E inhibitory activity. Follow-up analysis revealed inhibition of the ERK signaling pathway through the reduction of cellular Raf protein levels is a key mechanism of action of these compounds. In particular, 17 was the most potent compound in suppressing tumor growth of B-Raf^V600E-mutant cell lines and displayed the highest reduction of Raf protein levels among the tested compounds. Taken together, this study revealed that modifications of madecassic acid structure can provide molecules with potent anticancer activity against cell lines harboring the clinically relevant B-Raf^V600E mutation, with compound 17 identified as a promising lead for the development of new anticancer drugs.

STAT3-enhancing germline mutations contribute to tumor-extrinsic immune evasion

Immune evasion and the suppression of antitumor responses during cancer progression are considered hallmarks of cancer and are typically attributed to tumor-derived factors. Although the molecular basis for the crosstalk between tumor and immune cells is an area of active investigation, whether host-specific germline variants can dictate immunosuppressive mechanisms has remained a challenge to address. A commonly occurring germline mutation (c.1162G>A/rs351855 G/A) in the FGFR4 (CD334) gene enhances signal transducer and activator of transcription 3 (STAT3) signaling and is associated with poor prognosis and accelerated progression of multiple cancer types. Here, using rs351855 SNP-knockin transgenic mice and Fgfr4-knockout mice, we reveal the genotype-specific gain of immunological function of suppressing the CD8/CD4+FOXP3+CD25+ regulatory T cell ratio in vivo. Furthermore, using knockin transgenic mouse models for lung and breast cancers, we establish the host-specific, tumor-extrinsic functions of STAT3-enhancing germline variants in impeding the tumor infiltration of CD8 T cells. Thus, STAT3-enhancing germline receptor variants contribute to immune evasion through their pleiotropic functions in immune cells.

Robust in-silico identification of cancer cell lines based on next generation sequencing

Cancer cell lines (CCL) are important tools for cancer researchers world-wide. However, handling of cancer cell lines is error-prone, and critical errors such as misidentification and cross-contamination occur more often than acceptable. Based on the fact that CCL today very often are sequenced (partly or entirely) anyway as part of the studies performed, we developed Uniquorn, a computational method that reliably identifies CCL samples based on variant profiles derived from whole exome or whole genome sequencing. Notably, Uniquorn does neither require a particular sequencing technology nor downstream analysis pipeline but works robustly across different NGS platforms and analysis steps. We evaluated Uniquorn by comparing more than 1900 CCL profiles from three large CCL libraries, embracing 1585 duplicates, against each other. In this setting, our method achieves a sensitivity of 97% and specificity of 99%. Errors are strongly associated to low quality mutation profiles. The R-package Uniquorn is freely available as Bioconductor-package.

Distribution bias and biochemical characterization of TOP1MT single nucleotide variants

Mitochondrial topoisomerase I (TOP1MT) is a type IB topoisomerase encoded in the nucleus of vertebrate cells. In contrast to the other five human topoisomerases, TOP1MT possesses two high frequency single nucleotide variants (SNVs), rs11544484 (V256I, Minor Allele Frequency = 0.27) and rs2293925 (R525W, MAF = 0.45), which tend to be mutually exclusive across different human ethnic groups and even more clearly in a cohort of 129 US patients with breast cancer and in the NCI-60 cancer cell lines. We expressed these two TOP1MT variants and the double-variant (V256I-R525W) as recombinant proteins, as well as a less common variant E168G (rs200673353, MAF = 0.001), and studied their biochemical properties by magnetic tweezers-based supercoil relaxation and classical DNA relaxation assays. Variants showed reduced DNA relaxation activities, especially the V256I variant towards positively supercoiled DNA. We also found that the V256I variant was enriched to MAF = 0.64 in NCI-60 lung carcinoma cell lines, whereas the TOP1MT R525W was enriched to MAF = 0.65 in the NCI-60 melanoma cell lines. Moreover, TOP1MT expression correlated with the 256 variants in the NCI-60 lung carcinoma cell lines, valine with high expression and isoleucine with low expression. Our results are discussed in the context of evolution between the nuclear and mitochondrial topoisomerases and potential cancer predisposition.

Targeting DNA repair and replication stress in the treatment of ovarian cancer

Approximately half of high-grade serous epithelial ovarian cancers incur alterations in genes of homologous recombination (BRCA1, BRCA2, RAD51C, Fanconi anemia genes), and the rest incur alterations in other DNA repair pathways at high frequencies. Such cancer-specific gene alterations can confer selective sensitivity to DNA damaging agents such as cisplatin and carboplatin, topotecan, etoposide, doxorubicin, and gemcitabine. Originally presumed to inhibit DNA repair, PARP inhibitors that have recently been approved by the FDA for the treatment of advanced ovarian cancer also act as DNA damaging agents by inducing PARP-DNA complexes. These DNA damaging agents induce different types of DNA lesions that require various DNA repair genes for the repair, but commonly induce replication fork slowing or stalling, also referred to as replication stress. Replication stress activates DNA repair checkpoint proteins (ATR, CHK1), which prevent further DNA damage. Hence, targeting DNA repair genes or DNA repair checkpoint genes augments the anti-tumor activity of DNA damaging agents. This review describes the rational basis for using DNA repair and DNA repair checkpoint inhibitors as single agents. The review also presents the strategies combining these inhibitors with DNA damaging agents for ovarian cancer therapy based on specific gene alterations.

Phosphorylated fraction of H2AX as a measurement for DNA damage in cancer cells and potential applications of a novel assay

Phosphorylated H2AX (γ-H2AX) is a sensitive marker for DNA double-strand breaks (DSBs), but the variability of H2AX expression in different cell and tissue types makes it difficult to interpret the meaning of the γ-H2AX level. Furthermore, the assays commonly used for γ-H2AX detection utilize laborious and low-throughput microscopy-based methods. We describe here an ELISA assay that measures both phosphorylated H2AX and total H2AX absolute amounts to determine the percentage of γ-H2AX, providing a normalized value representative of the amount of DNA damage. We demonstrate the utility of the assay to measure DSBs introduced by either ionizing radiation or DNA-damaging agents in cultured cells and in xenograft models. Furthermore, utilizing the NCI-60 cancer cell line panel, we show a correlation between the basal fraction of γ-H2AX and cellular mutation levels. This additional application highlights the ability of the assay to measure γ-H2AX levels in many extracts at once, making it possible to correlate findings with other cellular characteristics. Overall, the γ-H2AX ELISA represents a novel approach to quantifying DNA damage, which may lead to a better understanding of mutagenic pathways in cancer and provide a useful biomarker for monitoring the effectiveness of DNA-damaging anticancer agents.

The NCI-60 Methylome and Its Integration into CellMiner

A unique resource for systems pharmacology and genomic studies is the NCI-60 cancer cell line panel, which provides data for the largest publicly available library of compounds with cytotoxic activity (∼21,000 compounds), including 108 FDA-approved and 70 clinical trial drugs as well as genomic data, including whole-exome sequencing, gene and miRNA transcripts, DNA copy number, and protein levels. Here, we provide the first readily usable genome-wide DNA methylation database for the NCI-60, including 485,577 probes from the Infinium HumanMethylation450k BeadChip array, which yielded DNA methylation signatures for 17,559 genes integrated into our open access CellMiner version 2.0 (https://discover.nci.nih.gov/cellminer). Among new insights, transcript versus DNA methylation correlations revealed the epithelial/mesenchymal gene functional category as being influenced most heavily by methylation. DNA methylation and copy number integration with transcript levels yielded an assessment of their relative influence for 15,798 genes, including tumor suppressor, mitochondrial, and mismatch repair genes. Four forms of molecular data were combined, providing rationale for microsatellite instability for 8 of the 9 cell lines in which it occurred. Individual cell line analyses showed global methylome patterns with overall methylation levels ranging from 17% to 84%. A six-gene model, including PARP1, EP300, KDM5C, SMARCB1, and UHRF1 matched this pattern. In addition, promoter methylation of two translationally relevant genes, Schlafen 11 (SLFN11) and methylguanine methyltransferase (MGMT), served as indicators of therapeutic resistance or susceptibility, respectively. Overall, our database provides a resource of pharmacologic data that can reinforce known therapeutic strategies and identify novel drugs and drug targets across multiple cancer types. Cancer Res; 77(3); 601-12. ©2016 AACR.

Using Pharmacogenomic Databases for Discovering Patient-Target Genes and Small Molecule Candidates to Cancer Therapy

With multiple omics strategies being applied to several cancer genomics projects, researchers have the opportunity to develop a rational planning of targeted cancer therapy. The investigation of such numerous and diverse pharmacogenomic datasets is a complex task. It requires biological knowledge and skills on a set of tools to accurately predict signaling network and clinical outcomes. Herein, we describe Web-based in silico approaches user friendly for exploring integrative studies on cancer biology and pharmacogenomics. We briefly explain how to submit a query to cancer genome databases to predict which genes are significantly altered across several types of cancers using CBioPortal. Moreover, we describe how to identify clinically available drugs and potential small molecules for gene targeting using CellMiner. We also show how to generate a gene signature and compare gene expression profiles to investigate the complex biology behind drug response using Connectivity Map. Furthermore, we discuss on-going challenges, limitations and new directions to integrate molecular, biological and epidemiological information from oncogenomics platforms to create hypothesis-driven projects. Finally, we discuss the use of Patient-Derived Xenografts models (PDXs) for drug profiling in vivo assay. These platforms and approaches are a rational way to predict patient-targeted therapy response and to develop clinically relevant small molecules drugs.

A ribonucleotide reductase inhibitor with deoxyribonucleoside-reversible cytotoxicity

Ribonucleotide Reductase (RNR) is the sole enzyme that catalyzes the reduction of ribonucleotides into deoxyribonucleotides. Even though RNR is a recognized target for antiproliferative molecules, and the main target of the approved drug hydroxyurea, few new leads targeted to this enzyme have been developed. We have evaluated a recently identified set of RNR inhibitors with respect to inhibition of the human enzyme and cellular toxicity. One compound, NSC73735, is particularly interesting; it is specific for leukemia cells and is the first identified compound that hinders oligomerization of the mammalian large RNR subunit. Similar to hydroxyurea, it caused a disruption of the cell cycle distribution of cultured HL-60 cells. In contrast to hydroxyurea, the disruption was reversible, indicating higher specificity. NSC73735 thus defines a potential lead candidate for RNR-targeted anticancer drugs, as well as a chemical probe with better selectivity for RNR inhibition than hydroxyurea.

Role of Merlin/NF2 inactivation in tumor biology

Merlin (Moesin-ezrin-radixin-like protein, also known as schwannomin) is a tumor suppressor protein encoded by the neurofibromatosis type 2 gene NF2. Loss of function mutations or deletions in NF2 cause neurofibromatosis type 2 (NF2), a multiple tumor forming disease of the nervous system. NF2 is characterized by the development of bilateral vestibular schwannomas. Patients with NF2 can also develop schwannomas on other cranial and peripheral nerves, as well as meningiomas and ependymomas. The only potential treatment is surgery/radiosurgery, which often results in loss of function of the involved nerve. There is an urgent need for chemotherapies that slow or eliminate tumors and prevent their formation in NF2 patients. Interestingly NF2 mutations and merlin inactivation also occur in spontaneous schwannomas and meningiomas, as well as other types of cancer including mesothelioma, glioma multiforme, breast, colorectal, skin, clear cell renal cell carcinoma, hepatic and prostate cancer. Except for malignant mesotheliomas, the role of NF2 mutation or inactivation has not received much attention in cancer, and NF2 might be relevant for prognosis and future chemotherapeutic approaches. This review discusses the influence of merlin loss of function in NF2-related tumors and common human cancers. We also discuss the NF2 gene status and merlin signaling pathways affected in the different tumor types and the molecular mechanisms that lead to tumorigenesis, progression and pharmacological resistance.

Correlation between Gene Variants, Signaling Pathways, and Efficacy of Chemotherapy Drugs against Colon Cancers

Efficacies, toxicities, and resistance mechanisms of chemotherapy drugs, such as oxaliplatin and 5-fluorouracil (5-FU), vary widely among various categories and subcategories of colon cancers. By understanding the differences in the drug efficacy and resistance at the level of protein–protein networks, we identified the correlation between the drug activity of oxaliplatin/5-FU and gene variations from the US National Cancer Institute-60 human cancer cell lines. The activity of either of these drugs is correlated with specific amino acid variant(s) of KRAS and other genes from the signaling pathways of colon cancer progression. We also discovered that the activity of a non-DNA-binding novel platinum drug, phosphaplatin, is comparable with oxaliplatin and 5-FU when it was tested against colon cancer cell lines. Our strategy that combines the knowledge from pharmacogenomics across cell lines with the molecular information from specific cancer cells is beneficial for predicting the outcome of a possible combination therapy for personalized treatment.

Understanding the Genetic Mechanisms of Cancer Drug Resistance Using Genomic Approaches

A major obstacle in precision cancer medicine is the inevitable resistance to targeted therapies. Tremendous effort and progress has been made over the past few years to understand the biochemical and genetic mechanisms underlying drug resistance, with the goal to eventually overcome such daunting challenges. Diverse mechanisms, such as secondary mutations, oncogene bypass, and epigenetic alterations, can all lead to drug resistance, and the number of known involved genes is growing rapidly, thus providing many possibilities to overcome resistance. The finding of these mechanisms and genes invariably requires the application of genomic and functional genomic approaches to tumors or cancer models. In this review, we briefly highlight the major drug-resistance mechanisms known today, and then focus primarily on the technological approaches leading to the advancement of this field.

Quality control material for the detection of somatic mutations in fixed clinical specimens by next-generation sequencing

BACKGROUND

Targeted next generation sequencing (NGS) technology to assess the mutational status of multiple genes on formalin-fixed, paraffin embedded (FFPE) tumors is rapidly being adopted in clinical settings, where quality control (QC) practices are required. Establishing reliable FFPE QC materials for NGS can be challenging and/or expensive. Here, we established a reliable and cost-effective FFPE QC material for routine utilization in the Ion AmpliSeq™ Cancer Hotspot Panel v2 (CHP2) assay.

METHODS

The performance characteristics of the CHP2 assay were determined by sequencing various cell line mixtures and 55 different FFPE tumors on the Ion Torrent PGM platform. A FFPE QC material was prepared from a mixture of cell lines derived from different cancers, comprising single nucleotide variants and small deletions on actionable genes at different allelic frequencies.

RESULTS

The CHP2 assay performed with high precision and sensitivity when custom variant calling pipeline parameters where established. In addition, all expected somatic variants in the QC material were consistently called at variant frequencies ranging from 9.1 % (CV = 11.1 %) to 37.9 % (CV = 2.8 %).

CONCLUSIONS

The availability of a reliable and cost-effective QC material is instrumental in assessing the performance of this or any targeted NGS assay that detects somatic variants in fixed solid tumor specimens.

Using CellMiner 1.6 for Systems Pharmacology and Genomic Analysis of the NCI-60

The NCI-60 cancer cell line panel provides a premier model for data integration, and systems pharmacology being the largest publicly available database of anticancer drug activity, genomic, molecular, and phenotypic data. It comprises gene expression (25,722 transcripts), microRNAs (360 miRNAs), whole-genome DNA copy number (23,413 genes), whole-exome sequencing (variants for 16,568 genes), protein levels (94 genes), and cytotoxic activity (20,861 compounds). Included are 158 FDA-approved drugs and 79 that are in clinical trials. To improve data accessibility to bioinformaticists and non-bioinformaticists alike, we have developed the CellMiner web-based tools. Here, we describe the newest CellMiner version, including integration of novel databases and tools associated with whole-exome sequencing and protein expression, and review the tools. Included are (i) "Cell line signature" for DNA, RNA, protein, and drugs; (ii) "Cross correlations" for up to 150 input genes, microRNAs, and compounds in a single query; (iii) "Pattern comparison" to identify connections among drugs, gene expression, genomic variants, microRNA, and protein expressions; (iv) "Genetic variation versus drug visualization" to identify potential new drug:gene DNA variant relationships; and (v) "Genetic variant summation" designed to provide a synopsis of mutational burden on any pathway or gene group for up to 150 genes. Together, these tools allow users to flexibly query the NCI-60 data for potential relationships between genomic, molecular, and pharmacologic parameters in a manner specific to the user's area of expertise. Examples for both gain- (RAS) and loss-of-function (PTEN) alterations are provided.

Zebrafish: a new companion for translational research in oncology

In an era of high-throughput "omic" technologies, the unprecedented amount of data that can be generated presents a significant opportunity but simultaneously an even greater challenge for oncologists trying to provide personalized treatment. Classically, preclinical testing of new targets and identification of active compounds against those targets have entailed the extensive use of established human cell lines, as well as genetically modified mouse tumor models. Patient-derived xenografts in zebrafish may in the near future provide a platform for selecting an appropriate personalized therapy and together with zebrafish transgenic tumor models represent an alternative vehicle for drug development. The zebrafish is readily genetically modified. The transparency of zebrafish embryos and the recent development of pigment-deficient zebrafish afford researchers the valuable capacity to observe directly cancer formation and progression in a live vertebrate host. The zebrafish is amenable to transplantation assays that test the serial passage of fluorescently labeled tumor cells as well as their capacity to disseminate and/or metastasize. Progress achieved to date in genetic engineering and xenotransplantation will establish the zebrafish as one of the most versatile animal models for cancer research. A model organism that can be used in transgenesis, transplantation assays, single-cell functional assays, and in vivo imaging studies make zebrafish a natural companion for mice in translational oncology research.

Using drug response data to identify molecular effectors, and molecular "omic" data to identify candidate drugs in cancer

The current convergence of molecular and pharmacological data provides unprecedented opportunities to gain insights into the relationships between the two types of data. Multiple forms of large-scale molecular data, including but not limited to gene and microRNA transcript expression, DNA somatic and germline variations from next-generation DNA and RNA sequencing, and DNA copy number from array comparative genomic hybridization are all potentially informative when one attempts to recognize the panoply of potentially influential events both for cancer progression and therapeutic outcome. Concurrently, there has also been a substantial expansion of the pharmacological data being accrued in a systematic fashion. For cancer cell lines, the National Cancer Institute cell line panel (NCI-60), the Cancer Cell Line Encyclopedia (CCLE), and the collaborative Genomics of Drug Sensitivity in Cancer (GDSC) databases all provide subsets of these forms of data. For the patient-derived data, The Cancer Genome Atlas (TCGA) provides analogous forms of genomic information along with treatment histories. Integration of these data in turn relies on the fields of statistics and statistical learning. Multiple algorithmic approaches may be chosen, depending on the data being considered, and the nature of the question being asked. Combining these algorithms with prior biological knowledge, the results of molecular biological studies, and the consideration of genes as pathways or functional groups provides both the challenge and the potential of the field. The ultimate goal is to provide a paradigm shift in the way that drugs are selected to provide a more targeted and efficacious outcome for the patient.