Gene set analysis of purine and pyrimidine antimetabolites cancer therapies

Fabrication of folic acid-conjugated pyrimidine-2(5H)-thione-encapsulated curdlan gum-PEGamine nanoparticles for folate receptor targeting breast cancer cells

In this study 5-((2-((3-methoxy benzylidene)-amino)-phenyl)-diazenyl)-4,6-diphenyl pyrimidine-2(5H)-thione was synthesized. The pharmacological applications of pyrimidine analogs are restricted due to their poor pharmacokinetic properties. As a solution, a microbial exopolysaccharide (curdlan gum) was used to synthesize folic acid-conjugated pyrimidine-2(5H)-thione-encapsulated curdlan gum-PEGamine nanoparticles (FA-Py-CG-PEGamine NPs). The results of physicochemical properties revealed that the fabricated FA-Py-CG-PEGamine NPs were between 100 and 400 nm in size with a majorly spherical shaped, crystalline nature, and the encapsulation efficiency and loading capacity were 79.04 ± 0.79 %, and 8.12 ± 0.39 % respectively. The drug release rate was significantly higher at pH 5.4 (80.14 ± 0.79 %) compared to pH 7.2. The cytotoxic potential of FA-Py-CG-PEGamine NPs against MCF-7 cells potentially reduced the number of cells after 24 h with 42.27 μg × mL-1 as IC50 value. The higher intracellular accumulation of pyrimidine-2(5H)-thione in MCF-7 cells leads to apoptosis, observed by AO/EBr staining and flow cytometry analysis. The highest pyrimidine-2(5H)-thione internalization in MCF-7 cells may be due to folate conjugated on the surface of curdlan gum nanoparticles. Further, internalized pyrimidine-2(5H)-thione increases the intracellular ROS level, leading to apoptosis and inducing the decalin in mitochondrial membrane potential. These outcomes demonstrated that the FA-Py-CG-PEGamine NPs were specificity-targeting folate receptors on the plasma membranes of MCF-7 Cells.

Fusobacterium nucleatum-triggered purine metabolic reprogramming drives tumorigenesis in head and neck carcinoma

BACKGROUND

Fusobacterium nucleatum (F. nucleatum) is a vital pro-oncogenic bacterium. Our previous study revealed that a high abundance of F. nucleatum in head and neck squamous cell carcinoma (HNSCC) is correlated with poor patient prognosis. However, the impact of F. nucleatum on metabolic reprogramming and tumor progression in HNSCC awaits more exploration.

METHODS

Liquid chromatography‒mass spectrometry (LC‒MS) was applied to analyze the altered metabolites in a head and neck carcinoma cell line (AMC-HN-8) after coculture with F. nucleatum for 24 hrs and 48 hrs. Both univariate and multivariate analyses were used to screen for differential metabolites. Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathway enrichment analysis was further used to explore the metabolic changes.

RESULTS

We observed a significantly altered metabolic profile in AMC-HN-8 cells over time after coculture with F. nucleatum. Among the several enriched pathways, the purine metabolic pathway was the most significantly enriched (P = 0.0005), with downregulation of purine degradation. Furthermore, uric acid, the end product of purine metabolism, significantly reversed F. nucleatum-triggered tumor progression and altered the intracellular reactive oxygen species (ROS) level. Moreover, the negative correlation between the serum uric acid level and the abundance of F. nucleatum was verified in 113 HNSCC patients (P = 0.0412, R = - 0.1924).

CONCLUSIONS

Our study revealed obviously aberrant purine metabolism driven by F. nucleatum in HNSCC, which was closely related to tumor progression and patient prognosis. These findings indicate the possibility of targeting F. nucleatum-induced purine metabolism reprogramming in the future treatment of HNSCC.

Dominant neoantigen verification in hepatocellular carcinoma by a single-plasmid system coexpressing patient HLA and antigen

BACKGROUND

Previous studies confirmed that most neoantigens predicted by algorithms do not work in clinical practice, and experimental validations remain indispensable for confirming immunogenic neoantigens. In this study, we identified the potential neoantigens with tetramer staining, and established the Co-HA system, a single-plasmid system coexpressing patient human leukocyte antigen (HLA) and antigen, to detect the immunogenicity of neoantigens and verify new dominant hepatocellular carcinoma (HCC) neoantigens.

METHODS

First, we enrolled 14 patients with HCC for next-generation sequencing for variation calling and predicting potential neoantigens. Then, the Co-HA system was established. To test the feasibility of the system, we constructed target cells coexpressing HLA-A*11:01 and the reported KRAS G12D neoantigen as well as specific T-cell receptor (TCR)-T cells. The specific cytotoxicity generated by this neoantigen was shown using the Co-HA system. Moreover, potential HCC-dominant neoantigens were screened out by tetramer staining and validated by the Co-HA system using methods including flow cytometry, enzyme-linked immunospot assay and ELISA. Finally, antitumor test in mouse mode and TCR sequencing were performed to further evaluate the dominant neoantigen.

RESULTS

First, 2875 somatic mutations in 14 patients with HCC were identified. The main base substitutions were C>T/G>A transitions, and the main mutational signatures were 4, 1 and 16. The high-frequency mutated genes included HMCN1, TTN and TP53. Then, 541 potential neoantigens were predicted. Importantly, 19 of the 23 potential neoantigens in tumor tissues also existed in portal vein tumor thrombi. Moreover, 37 predicted neoantigens restricted by HLA-A*11:01, HLA-A*24:02 or HLA-A*02:01 were performed by tetramer staining to screen out potential HCC-dominant neoantigens. HLA-A*24:02-restricted epitope 5'-FYAFSCYYDL-3' and HLA-A*02:01-restricted epitope 5'-WVWCMSPTI-3' demonstrated strong immunogenicity in HCC, as verified by the Co-HA system. Finally, the antitumor efficacy of 5'-FYAFSCYYDL-3'-specific T cells was verified in the B-NDG-B2m^tm1Fcrn^tm1(mB2m) mouse and their specific TCRs were successfully identified.

CONCLUSION

We found the dominant neoantigens with high immunogenicity in HCC, which were verified with the Co-HA system.

Crosstalk between xanthine oxidase (XO) inhibiting and cancer chemotherapeutic properties of comestible flavonoids- a comprehensive update

Gout is an inflammatory disease caused by metabolic disorder or genetic inheritance. People throughout the world are strongly dependent on ethnomedicine for the treatment of gout and some receive satisfactory curative treatment. The natural remedies as well as established drugs derived from natural sources or synthetically made exert their action by mechanisms that are closely associated with anticancer treatment mechanisms regarding inhibition of xanthine oxidase, feedback inhibition of de novo purine synthesis, depolymerization and disappearance of microtubule, inhibition of NF-ĸB activation, induction of TRAIL, promotion of apoptosis, and caspase activation and proteasome inhibition. Some anti-gout and anticancer novel compounds interact with same receptors for their action, e.g., colchicine and colchicine analogues. Dietary flavonoids, i.e., chrysin, kaempferol, quercetin, fisetin, pelargonidin, apigenin, luteolin, myricetin, isorhamnetin, phloretinetc etc. have comparable IC₅₀ values with established anti-gout drug and effective against both cancer and gout. Moreover, a noticeable number of newer anticancer compounds have already been isolated from plants that have been using by local traditional healers and herbal practitioners to treat gout. Therefore, the anti-gout plants might have greater potentiality to become selective candidates for screening of newer anticancer leads.

De Novo Purine Nucleotide Biosynthesis Pathway Is Required for Development and Pathogenicity in Magnaporthe oryzae

Purine nucleotides are indispensable compounds for many organisms and participate in basic vital activities such as heredity, development, and growth. Blocking of purine nucleotide biosynthesis may inhibit proliferation and development and is commonly used in cancer therapy. However, the function of the purine nucleotide biosynthesis pathway in the pathogenic fungus Magnaporthe oryzae is not clear. In this study, we focused on the de novo purine biosynthesis (DNPB) pathway and characterized MoAde8, a phosphoribosylglycinamide formyltransferase, catalyzing the third step of the DNPB pathway in M. oryzae. MoAde8 was knocked out, and the mutant (∆Moade8) exhibited purine auxotroph, defects in aerial hyphal growth, conidiation, and pathogenicity, and was more sensitive to hyperosmotic stress and oxidative stress. Moreover, ∆Moade8 caused decreased activity of MoTor kinase due to blocked purine nucleotide synthesis. The autophagy level was also impaired in ∆Moade8. Additionally, MoAde5, 7, 6, and 12, which are involved in de novo purine nucleotide biosynthesis, were also analyzed, and the mutants showed defects similar to the defects of ∆Moade8. In summary, de novo purine nucleotide biosynthesis is essential for conidiation, development, and pathogenicity in M. oryzae.

Metabolic Profiling of Thymic Epithelial Tumors Hints to a Strong Warburg Effect, Glutaminolysis and Precarious Redox Homeostasis as Potential Therapeutic Targets

Simple Summary

Thymomas and thymic carcinomas (TCs) are malignant thymic epithelial tumors (TETs) with poor outcome, if non-resectable. Metabolic signatures of TETs have not yet been studied and may offer new therapeutic options. This is the first metabolomics investigation on thymic epithelial tumors employing nuclear magnetic resonance spectroscopy of tissue samples. We could detect and quantify up to 37 metabolites in the major tumor subtypes, including acetylcholine that was not previously detected in other non-endocrine cancers. A metabolite-based cluster analysis distinguished three clinically relevant tumor subgroups, namely indolent and aggressive thymomas, as well as TCs. A metabolite-based metabolic pathway analysis also gave hints to activated metabolic pathways shared between aggressive thymomas and TCs. This finding was largely backed by enrichment of these pathways at the transcriptomic level in a large, publicly available, independent TET dataset. Due to the differential expression of metabolites in thymic epithelial tumors versus normal thymus, pathways related to proline, cysteine, glutathione, lactate and glutamine appear as promising therapeutic targets. From these findings, inhibitors of glutaminolysis and of the downstream TCA cycle are anticipated to be rational therapeutic strategies. If our results can be confirmed in future, sufficiently powered studies, metabolic signatures may contribute to the identification of new therapeutic options for aggressive thymomas and TCs.

Abstract

Thymomas and thymic carcinomas (TC) are malignant thymic epithelial tumors (TETs) with poor outcome, if non-resectable. Metabolic signatures of TETs have not yet been studied and may offer new therapeutic options. Metabolic profiles of snap-frozen thymomas (WHO types A, AB, B1, B2, B3, n = 12) and TCs (n = 3) were determined by high resolution magic angle spinning 1H nuclear magnetic resonance (HRMAS 1H-NMR) spectroscopy. Metabolite-based prediction of active KEGG metabolic pathways was achieved with MetPA. In relation to metabolite-based metabolic pathways, gene expression signatures of TETs (n = 115) were investigated in the public “The Cancer Genome Atlas” (TCGA) dataset using gene set enrichment analysis. Overall, thirty-seven metabolites were quantified in TETs, including acetylcholine that was not previously detected in other non-endocrine cancers. Metabolite-based cluster analysis distinguished clinically indolent (A, AB, B1) and aggressive TETs (B2, B3, TCs). Using MetPA, six KEGG metabolic pathways were predicted to be activated, including proline/arginine, glycolysis and glutathione pathways. The activated pathways as predicted by metabolite-profiling were generally enriched transcriptionally in the independent TCGA dataset. Shared high lactic acid and glutamine levels, together with associated gene expression signatures suggested a strong “Warburg effect”, glutaminolysis and redox homeostasis as potential vulnerabilities that need validation in a large, independent cohort of aggressive TETs. If confirmed, targeting metabolic pathways may eventually prove as adjunct therapeutic options in TETs, since the metabolic features identified here are known to confer resistance to cisplatin-based chemotherapy, kinase inhibitors and immune checkpoint blockers, i.e., currently used therapies for non-resectable TETs.

Determination of Breast Metabolic Phenotypes and Their Associations With Immunotherapy and Drug-Targeted Therapy: Analysis of Single-Cell and Bulk Sequences

Breast cancer is highly prevalent and fatal worldwide. Currently, breast cancer classification is based on the presence of estrogen, progesterone, and human epidermal growth factor 2. Because cancer and metabolism are closely related, we established a breast cancer classification system based on the metabolic gene expression profile. We performed typing of metabolism-related genes using The Cancer Genome Atlas-Breast Cancer and 2010 (YAU). We included 2,752 metabolic genes reported in previous literature, and the genes were further identified according to statistically significant variance and univariate Cox analyses. These prognostic metabolic genes were used for non-negative matrix factorization (NMF) clustering. Then, we identified characteristic genes in each metabolic subtype using differential analysis. The top 30 characteristic genes in each subtype were selected for signature construction based on statistical parameters. We attempted to identify standard metabolic signatures that could be used for other cohorts for metabolic typing. Subsequently, to demonstrate the effectiveness of the 90 Signature, NTP and NMF dimensional-reduction clustering were used to analyze these results. The reliability of the 90 Signature was verified by comparing the results of the two-dimensionality reduction clusters. Finally, the submap method was used to determine that the C1 metabolic subtype group was sensitive to immunotherapy and more sensitive to the targeted drug sunitinib. This study provides a theoretical basis for diagnosing and treating breast cancer.

CANT1 serves as a potential prognostic factor for lung adenocarcinoma and promotes cell proliferation and invasion in vitro

Background

Calcium-activated nucleotidase 1 (CANT1), functions as a calcium-dependent nucleotidase with a preference for UDP. However, the potential clinical value of CANT1 in lung adenocarcinoma (LA) has not been fully clarified. Thus, we sought to identify its potential biological function and mechanism through bioinformatics analysis and in vitro experiments in LA.

Methods

In the present study, we comprehensively investigated the prognostic role of CANT1 in LA patients through bioinformatics analysis and in vitro experiments. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were utilized to analyze the expression of CANT1 in LA patients and their clinical-prognostic value. The immunohistochemistry staining was obtained from the Human Protein Atlas (HPA). A Cox regression model was used to evaluate prognostic factors. Gene ontology (GO) and Gene set enrichment analysis (GSEA) was performed to explore the potential regulatory mechanism of CANT1 in the development of LA. Moreover, we also examined the relationship between CANT1 expression and DNA methylation. Finally, we did in vitro experiments to evaluate the biological behavior and role of CANT1 in LA cells (LACs).

Results

Our study showed that the CANT1 expression was significantly elevated in the LA tissues compared with the normal lung tissues. Increased CANT1 expression was significantly associated with the TN stage. A univariate Cox analysis indicated that high CANT1 expression levels were correlated with poor overall survival (OS) in LA. Besides, CANT1 expression was independently associated with OS in multivariate analysis. GO and GSEA analysis showed the enrichment of mitotic nuclear division, DNA methylation, and DNA damage. Then we found that the high expression of CANT1 is positively correlated with hypomethylation. The methylation level was associated with prognosis in LA patients. Finally, in vitro experiments indicated that knockdown of CANT1 resulted in decreased cell proliferation, invasion, and G1 phase cell-cycle arrest in LACs.

Conclusion

The present study suggested that CANT1 may serve as a potential prognosis biomarker in patients with LA. High CANT1 expression and promoter demethylation was associated with worse outcome. Finally, in vitro experiments verified the biological functions and behaviors of CANT1 in LA.

Calcium-activated nucleotides 1 (CANT1)-driven nuclear factor-k-gene binding (NF-ĸB) signaling pathway facilitates the lung cancer progression

Dysregulation of calcium-activated nucleotides 1 (CANT1) has been observed in different organs. Thus, its biological function in cancer has increasingly attracted researchers. The current work aims to study the CANT1 role in lung cancer and understand the underlying pathological mechanisms. High amplification of CANT1 was observed in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) tissues compared to normal tissues. The high-CANT1 patients showed a dismal prognosis in comparison with the low-CANT1 patients. Highly expressed CANT1 was significantly associated with the N stage of LUSC patients. Ectopic expression of CANT1 conspicuously increased the proliferation and viability of A549 cells. Conversely, CANT1 depletion resulted in adverse effects in H1299 cells. CANT1 depletion also resulted in the retardation of tumor growth in vivo. Mechanically, we found that CANT1 could elevate NF-ĸB (nuclear factor-k-gene binding) transcriptional activity in a concentration-dependent manner. This regulatory relationship was also established by the Western blot technique. Inhibiting NF-ĸB can significantly blunt the increased NF-κ-B Inhibitor-α (IκBα) expression caused by CANT1 overexpression in A549 cells. In conclusion, highly amplified CANT1 promotes the proliferation and viability of lung cancer cells. We also elucidate a new signaling axis of CANT1-NF-ĸB in lung cancer. This approach might be a promising strategy for lung cancer treatment.

Nucleotide de novo synthesis increases breast cancer stemness and metastasis via cGMP-PKG-MAPK signaling pathway

Metabolic reprogramming to fulfill the biosynthetic and bioenergetic demands of cancer cells has aroused great interest in recent years. However, metabolic reprogramming for cancer metastasis has not been well elucidated. Here, we screened a subpopulation of breast cancer cells with highly metastatic capacity to the lung in mice and investigated the metabolic alternations by analyzing the metabolome and the transcriptome, which were confirmed in breast cancer cells, mouse models, and patients' tissues. The effects and the mechanisms of nucleotide de novo synthesis in cancer metastasis were further evaluated in vitro and in vivo. In our study, we report an increased nucleotide de novo synthesis as a key metabolic hallmark in metastatic breast cancer cells and revealed that enforced nucleotide de novo synthesis was enough to drive the metastasis of breast cancer cells. An increased key metabolite of de novo synthesis, guanosine-5'-triphosphate (GTP), is able to generate more cyclic guanosine monophosphate (cGMP) to activate cGMP-dependent protein kinases PKG and downstream MAPK pathway, resulting in the increased tumor cell stemness and metastasis. Blocking de novo synthesis by silencing phosphoribosylpyrophosphate synthetase 2 (PRPS2) can effectively decrease the stemness of breast cancer cells and reduce the lung metastasis. More interestingly, in breast cancer patients, the level of plasma uric acid (UA), a downstream metabolite of purine, is tightly correlated with patient's survival. Our study uncovered that increased de novo synthesis is a metabolic hallmark of metastatic breast cancer cells and its metabolites can regulate the signaling pathway to promote the stemness and metastasis of breast cancer.

Identification of DNA methylation patterns and biomarkers for clear-cell renal cell carcinoma by multi-omics data analysis

Background

Tumorigenesis is highly heterogeneous, and using clinicopathological signatures only is not enough to effectively distinguish clear cell renal cell carcinoma (ccRCC) and improve risk stratification of patients. DNA methylation (DNAm) with the stability and reversibility often occurs in the early stage of tumorigenesis. Disorders of transcription and metabolism are also an important molecular mechanisms of tumorigenesis. Therefore, it is necessary to identify effective biomarkers involved in tumorigenesis through multi-omics analysis, and these biomarkers also provide new potential therapeutic targets.

Method

The discovery stage involved 160 pairs of ccRCC and matched normal tissues for investigation of DNAm and biomarkers as well as 318 cases of ccRCC including clinical signatures. Correlation analysis of epigenetic, transcriptomic and metabolomic data revealed the connection and discordance among multi-omics and the deregulated functional modules. Diagnostic or prognostic biomarkers were obtained by the correlation analysis, the Least Absolute Shrinkage and Selection Operator (LASSO) and the LASSO-Cox methods. Two classifiers were established based on random forest (RF) and LASSO-Cox algorithms in training datasets. Seven independent datasets were used to evaluate robustness and universality. The molecular biological function of biomarkers were investigated using DAVID and GeneMANIA.

Results

Based on multi-omics analysis, the epigenetic measurements uniquely identified DNAm dysregulation of cellular mechanisms resulting in transcriptomic alterations, including cell proliferation, immune response and inflammation. Combination of the gene co-expression network and metabolic network identified 134 CpG sites (CpGs) as potential biomarkers. Based on the LASSO and RF algorithms, five CpGs were obtained to build a diagnostic classifierwith better classification performance (AUC > 99%). A eight-CpG-based prognostic classifier was obtained to improve risk stratification (hazard ratio (HR) > 4; log-rank test, p-value < 0.01). Based on independent datasets and seven additional cancers, the diagnostic and prognostic classifiers also had better robustness and stability. The molecular biological function of genes with abnormal methylation were significantly associated with glycolysis/gluconeogenesis and signal transduction.

Conclusion

The present study provides a comprehensive analysis of ccRCC using multi-omics data. These findings indicated that multi-omics analysis could identify some novel epigenetic factors, which were the most important causes of advanced cancer and poor clinical prognosis. Diagnostic and prognostic biomarkers were identified, which provided a promising avenue to develop effective therapies for ccRCC.

Clinicopathological features and prognostic value of SOX11 in childhood acute lymphoblastic leukemia

Acute lymphoblastic leukemia is marked by aberrant transcriptional features that alter cell differentiation, self-renewal, and proliferative features. We sought to identify the transcription factors exhibiting altered and subtype-specific expression patterns in B-ALL and report here that SOX11, a developmental and neuronal transcription factor, is aberrantly expressed in the ETV6-RUNX1 and TCF3-PBX1 subtypes of acute B-cell leukemias. We show that a high expression of SOX11 leads to alterations of gene expression that are typically associated with cell adhesion, migration, and differentiation. A high expression is associated with DNA hypomethylation at the SOX11 locus and a favorable outcome. The results indicate that SOX11 expression marks a group of patients with good outcomes and thereby prompts further study of its use as a biomarker.

Potential Mechanisms Connecting Purine Metabolism and Cancer Therapy

Unrestricted cell proliferation is a hallmark of cancer. Purines are basic components of nucleotides in cell proliferation, thus impaired purine metabolism is associated with the progression of cancer. The de novo biosynthesis of purine depends on six enzymes to catalyze the conversion of phosphoribosylpyrophosphate to inosine 5'-monophosphate. These enzymes cluster around mitochondria and microtubules to form purinosome, which is a multi-enzyme complex involved in de novo purine biosynthesis and purine nucleotides requirement. In this review, we highlighted the purine metabolism and purinosome biology with emphasis on the therapeutic potential of manipulating of purine metabolism or purinosome in cancers. We also reviewed current advances in our understanding of mammalian target of rapamycin for regulating purinosome formation or purine metabolism in cancers and discussed the future prospects for targeting purinosome to treat cancers.

Computational discovery of transcription factors associated with drug response

This study integrates gene expression, genotype and drug response data in lymphoblastoid cell lines with transcription factor (TF)-binding sites from ENCODE (Encyclopedia of Genomic Elements) in a novel methodology that elucidates regulatory contexts associated with cytotoxicity. The method, GENMi (Gene Expression iN the Middle), postulates that single-nucleotide polymorphisms within TF-binding sites putatively modulate its regulatory activity, and the resulting variation in gene expression leads to variation in drug response. Analysis of 161 TFs and 24 treatments revealed 334 significantly associated TF-treatment pairs. Investigation of 20 selected pairs yielded literature support for 13 of these associations, often from studies where perturbation of the TF expression changes drug response. Experimental validation of significant GENMi associations in taxanes and anthracyclines across two triple-negative breast cancer cell lines corroborates our findings. The method is shown to be more sensitive than an alternative, genome-wide association study-based approach that does not use gene expression. These results demonstrate the utility of GENMi in identifying TFs that influence drug response and provide a number of candidates for further testing.

Reconstruction of Tissue-Specific Metabolic Networks Using CORDA

Human metabolism involves thousands of reactions and metabolites. To interpret this complexity, computational modeling becomes an essential experimental tool. One of the most popular techniques to study human metabolism as a whole is genome scale modeling. A key challenge to applying genome scale modeling is identifying critical metabolic reactions across diverse human tissues. Here we introduce a novel algorithm called Cost Optimization Reaction Dependency Assessment (CORDA) to build genome scale models in a tissue-specific manner. CORDA performs more efficiently computationally, shows better agreement to experimental data, and displays better model functionality and capacity when compared to previous algorithms. CORDA also returns reaction associations that can greatly assist in any manual curation to be performed following the automated reconstruction process. Using CORDA, we developed a library of 76 healthy and 20 cancer tissue-specific reconstructions. These reconstructions identified which metabolic pathways are shared across diverse human tissues. Moreover, we identified changes in reactions and pathways that are differentially included and present different capacity profiles in cancer compared to healthy tissues, including up-regulation of folate metabolism, the down-regulation of thiamine metabolism, and tight regulation of oxidative phosphorylation.

In vitro human cell line models to predict clinical response to anticancer drugs

In vitro human cell line models have been widely used for cancer pharmacogenomic studies to predict clinical response, to help generate pharmacogenomic hypothesis for further testing, and to help identify novel mechanisms associated with variation in drug response. Among cell line model systems, immortalized cell lines such as Epstein-Barr virus (EBV)-transformed lymphoblastoid cell lines (LCLs) have been used most often to test the effect of germline genetic variation on drug efficacy and toxicity. Another model, especially in cancer research, uses cancer cell lines such as the NCI-60 panel. These models have been used mainly to determine the effect of somatic alterations on response to anticancer therapy. Even though these cell line model systems are very useful for initial screening, results from integrated analyses of multiple omics data and drug response phenotypes using cell line model systems still need to be confirmed by functional validation and mechanistic studies, as well as validation studies using clinical samples. Future models might include the use of patient-specific inducible pluripotent stem cells and the incorporation of 3D culture which could further optimize in vitro cell line models to improve their predictive validity.

Population-based in vitro hazard and concentration-response assessment of chemicals: the 1000 genomes high-throughput screening study

BACKGROUND

Understanding of human variation in toxicity to environmental chemicals remains limited, so human health risk assessments still largely rely on a generic 10-fold factor (10½ each for toxicokinetics and toxicodynamics) to account for sensitive individuals or subpopulations.

OBJECTIVES

We tested a hypothesis that population-wide in vitro cytotoxicity screening can rapidly inform both the magnitude of and molecular causes for interindividual toxicodynamic variability.

METHODS

We used 1,086 lymphoblastoid cell lines from the 1000 Genomes Project, representing nine populations from five continents, to assess variation in cytotoxic response to 179 chemicals. Analysis included assessments of population variation and heritability, and genome-wide association mapping, with attention to phenotypic relevance to human exposures.

RESULTS

For about half the tested compounds, cytotoxic response in the 1% most "sensitive" individual occurred at concentrations within a factor of 10½ (i.e., approximately 3) of that in the median individual; however, for some compounds, this factor was > 10. Genetic mapping suggested important roles for variation in membrane and transmembrane genes, with a number of chemicals showing association with SNP rs13120371 in the solute carrier SLC7A11, previously implicated in chemoresistance.

CONCLUSIONS

This experimental approach fills critical gaps unaddressed by recent large-scale toxicity testing programs, providing quantitative, experimentally based estimates of human toxicodynamic variability, and also testable hypotheses about mechanisms contributing to interindividual variation.

Molecular insight into thiopurine resistance: transcriptomic signature in lymphoblastoid cell lines

Background

There has been considerable progress in the management of acute lymphoblastic leukemia (ALL) but further improvement is needed to increase long-term survival. The thiopurine agent 6-mercaptopurine (6-MP) used for ALL maintenance therapy has a key influence on clinical outcomes and relapse prevention. Genetic inheritance in thiopurine metabolism plays a major role in interindividual clinical response variability to thiopurines; however, most cases of thiopurine resistance remain unexplained.

Methods

We used lymphoblastoid cell lines (LCLs) from healthy donors, selected for their extreme thiopurine susceptibility. Thiopurine metabolism was characterized by the determination of TPMT and HPRT activity. We performed genome-wide expression profiling in resistant and sensitive cell lines with the goal of elucidating the mechanisms of thiopurine resistance.

Results

We determined a higher TPMT activity (+44%; P = 0.024) in resistant compared to sensitive cell lines, although there was no difference in HPRT activity. We identified a 32-gene transcriptomic signature that predicts thiopurine resistance. This signature includes the GTPBP4 gene coding for a GTP-binding protein that interacts with p53. A comprehensive pathway analysis of the genes differentially expressed between resistant and sensitive cell lines indicated a role for cell cycle and DNA mismatch repair system in thiopurine resistance. It also revealed overexpression of the ATM/p53/p21 pathway, which is activated in response to DNA damage and induces cell cycle arrest in thiopurine resistant LCLs. Furthermore, overexpression of the p53 target gene TNFRSF10D or the negative cell cycle regulator CCNG2 induces cell cycle arrest and may also contribute to thiopurine resistance. ARHGDIA under-expression in resistant cell lines may constitute a novel molecular mechanism contributing to thiopurine resistance based on Rac1 inhibition induced apoptosis and in relation with thiopurine pharmacodynamics.

Conclusion

Our study provides new insights into the molecular mechanisms underlying thiopurine resistance and suggests a potential research focus for developing tailored medicine.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-015-0150-6) contains supplementary material, which is available to authorized users.

Integrative gene set analysis: application to platinum pharmacogenomics

Integrative genomics has the potential to uncover relevant loci, as clinical outcome and response to chemotherapies are most likely not due to a single gene (or data type) but rather a complex relationship involving genetic variation, mRNA, DNA methylation, and copy number variation. In addition to this complexity, many complex phenotypes are thought to be controlled by the interplay of multiple genes within the same molecular pathway or gene set (GS). To address these two challenges, we propose an integrative gene set analysis approach and apply this strategy to a cisplatin (CDDP) pharmacogenomics study involving lymphoblastoid cell lines for which genome-wide SNP and mRNA expression data was collected. Application of the integrative GS analysis implicated the role of the RNA binding and cytoskeletal part GSs. The genes LMNB1 and CENPF, within the cytoskeletal part GS, were functionally validated with siRNA knockdown experiments, where the knockdown of LMNB1 and CENPF resulted in CDDP resistance in multiple cancer cell lines. This study demonstrates the utility of an integrative GS analysis strategy for detecting novel genes associated with response to cancer therapies, moving closer to tailored therapy decisions for cancer patients.

Pathway analysis of genome-wide data improves warfarin dose prediction

BACKGROUND

Many genome-wide association studies focus on associating single loci with target phenotypes. However, in the setting of rare variation, accumulating sufficient samples to assess these associations can be difficult. Moreover, multiple variations in a gene or a set of genes within a pathway may all contribute to the phenotype, suggesting that the aggregation of variations found over the gene or pathway may be useful for improving the power to detect associations.

RESULTS

Here, we present a method for aggregating single nucleotide polymorphisms (SNPs) along biologically relevant pathways in order to seek genetic associations with phenotypes. Our method uses all available genetic variants and does not remove those in linkage disequilibrium (LD). Instead, it uses a novel SNP weighting scheme to down-weight the contributions of correlated SNPs. We apply our method to three cohorts of patients taking warfarin: two European descent cohorts and an African American cohort. Although the clinical covariates and key pharmacogenetic loci for warfarin have been characterized, our association metric identifies a significant association with mutations distributed throughout the pathway of warfarin metabolism. We improve dose prediction after using all known clinical covariates and pharmacogenetic variants in VKORC1 and CYP2C9. In particular, we find that at least 1% of the missing heritability in warfarin dose may be due to the aggregated effects of variations in the warfarin metabolic pathway, even though the SNPs do not individually show a significant association.

CONCLUSIONS

Our method allows researchers to study aggregative SNP effects in an unbiased manner by not preselecting SNPs. It retains all the available information by accounting for LD-structure through weighting, which eliminates the need for LD pruning.