The NCI Transcriptional Pharmacodynamics Workbench: A Tool to Examine Dynamic Expression Profiling of Therapeutic Response in the NCI-60 Cell Line Panel

Identification and multi-omics analysis of essential coding and long non-coding genes in colorectal cancer

Essential genes are indispensable for the survival of cancer cell. CRISPR/Cas9-based pooled genetic screens have distinguished the essential genes and their functions in distinct cellular processes. Nevertheless, the landscape of essential genes at the single cell levels and the effect on the tumor microenvironment (TME) remains limited. Here, we identified 396 essential protein-coding genes (ESPs) by integration of 8 genome-wide CRISPR loss-of-function screen datasets of colorectal cancer (CRC) cell lines and single-cell RNA sequencing (scRNA-seq) data of CRC tissues. Then, 29 essential long non-coding genes (ESLs) were predicted using Hypergeometric Test (HT) and Personalized PageRank (PPR) algorithms based on ESPs and co-expressed network constructed from scRNA-seq. CRISPR/Cas9 knockout experiment verified the effect of several ESPs and ESLs on the survival of CRC cell line. Furthermore, multi-omics features of ESPs and ESLs were illustrated by examining their expression patterns and transcription factor (TF) regulatory network at the single cell level, as well as DNA mutation and DNA methylation events at bulk level. Finally, through integrating multiple intracellular regulatory networks with cell-cell communication network (CCN), we elucidated that CD47 and MIF are regulated by multiple CRC essential genes, and the anti-cancer drugs sunitinib can interfere the expression of them potentially. Our findings provide a comprehensive asset of CRC ESPs and ESLs, sheding light on the mining of potential therapy targets for CRC.

Reversal gene expression assessment for drug repurposing, a case study of glioblastoma

BACKGROUND

Glioblastoma (GBM) is a rare brain cancer with an exceptionally high mortality rate, which illustrates the pressing demand for more effective therapeutic options. Despite considerable research efforts on GBM, its underlying biological mechanisms remain unclear. Furthermore, none of the United States Food and Drug Administration (FDA) approved drugs used for GBM deliver satisfactory survival improvement.

METHODS

This study presents a novel computational pipeline by utilizing gene expression data analysis for GBM for drug repurposing to address the challenges in rare disease drug development, particularly focusing on GBM. The GBM Gene Expression Profile (GGEP) was constructed with multi-omics data to identify drugs with reversal gene expression to GGEP from the Integrated Network-Based Cellular Signatures (iLINCS) database.

RESULTS

We prioritized the candidates via hierarchical clustering of their expression signatures and quantification of their reversal strength by calculating two self-defined indices based on the GGEP genes' log2 foldchange (LFC) that the drug candidates could induce. Among five prioritized candidates, in-vitro experiments validated Clofarabine and Ciclopirox as highly efficacious in selectively targeting GBM cancer cells.

CONCLUSIONS

The success of this study illustrated a promising avenue for accelerating drug development by uncovering underlying gene expression effect between drugs and diseases, which can be extended to other rare diseases and non-rare diseases.

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.

Reversal Gene Expression Assessment for Drug Repurposing, a Case Study of Glioblastoma

Glioblastoma (GBM) is a rare brain cancer with an exceptionally high mortality rate, which illustrates the pressing demand for more effective therapeutic options. Despite considerable research efforts on GBM, its underlying biological mechanisms remain unclear. Furthermore, none of the United States Food and Drug Administration (FDA) approved drugs used for GBM deliver satisfactory survival improvement. This study presents a novel computational pipeline by utilizing gene expression data analysis for GBM for drug repurposing to address the challenges in rare disease drug development, particularly focusing on GBM. The GBM Gene Expression Profile (GGEP) was constructed with multi-omics data to identify drugs with reversal gene expression to GGEP from the Integrated Network-Based Cellular Signatures (iLINCS) database. We prioritized the candidates via hierarchical clustering of their expression signatures and quantification of their reversal strength by calculating two self-defined indices based on the GGEP genes' log2 foldchange (LFCs) that the drug candidates could induce. Among eight prioritized candidates, in-vitro experiments validated Clofarabine and Ciclopirox as highly efficacious in selectively targeting GBM cancer cells. The success of this study illustrated a promising avenue for accelerating drug development by uncovering underlying gene expression effect between drugs and diseases, which can be extended to other rare diseases and non-rare diseases.

Integrated transcriptomics- and structure-based drug repositioning identifies drugs with proteasome inhibitor properties

Computational pharmacogenomics can potentially identify new indications for already approved drugs and pinpoint compounds with similar mechanism-of-action. Here, we used an integrated drug repositioning approach based on transcriptomics data and structure-based virtual screening to identify compounds with gene signatures similar to three known proteasome inhibitors (PIs; bortezomib, MG-132, and MLN-2238). In vitro validation of candidate compounds was then performed to assess proteasomal proteolytic activity, accumulation of ubiquitinated proteins, cell viability, and drug-induced expression in A375 melanoma and MCF7 breast cancer cells. Using this approach, we identified six compounds with PI properties ((-)-kinetin-riboside, manumycin-A, puromycin dihydrochloride, resistomycin, tegaserod maleate, and thapsigargin). Although the docking scores pinpointed their ability to bind to the β5 subunit, our in vitro study revealed that these compounds inhibited the β1, β2, and β5 catalytic sites to some extent. As shown with bortezomib, only manumycin-A, puromycin dihydrochloride, and tegaserod maleate resulted in excessive accumulation of ubiquitinated proteins and elevated HMOX1 expression. Taken together, our integrated drug repositioning approach and subsequent in vitro validation studies identified six compounds demonstrating properties similar to proteasome inhibitors.

The Role of IRF9 Upregulation in Modulating Sensitivity to Olaparib and Platinum-Based Chemotherapies in Breast Cancer

Poly(ADP-ribose) polymerase (PARP) inhibitors are targeted therapies that accumulate DNA damage by interfering with DNA repair mechanisms and are approved for treating several cancers with BRCA1/2 mutations. In this study, we utilized CRISPR-dCas9 interference screening to identify genes regulating sensitivity to PARP inhibitors in breast cancer cell lines. Our findings indicated that the interferon (IFN) signaling gene IRF9 was critically involved in modulating sensitivity to these inhibitors. We revealed that the loss of IRF9 leads to increased resistance to the PARP inhibitor in MDA-MB-468 cells, and a similar desensitization was observed in another breast cancer cell line, MDA-MB-231. Further analysis indicated that while the basal expression of IRF9 did not correlate with the response to the PARP inhibitor olaparib, its transcriptional induction was significantly associated with increased sensitivity to the DNA-damaging agent cisplatin in the NCI-60 cell line panel. This finding suggests a mechanistic link between IRF9 induction and cellular responses to DNA damage. Additionally, data from the METABRIC patient tissue study revealed a complex network of IFN-responsive gene expressions postchemotherapy, with seven upregulated genes, including IRF9, and three downregulated genes. These findings underscore the intricate role of IFN signaling in the cellular response to chemotherapy. Collectively, our CRISPR screening data and subsequent bioinformatic analyses suggest that IRF9 is a novel biomarker for sensitivity to DNA-damaging agents, such as olaparib and platinum-based chemotherapeutic agents. Our findings for IRF9 not only enhance our understanding of the genetic basis of drug sensitivity, but also elucidate the role of IRF9 as a critical effector within IFN signaling pathways, potentially influencing the association between the host immune system and chemotherapeutic efficacy.

ATF3-CBS signaling axis coordinates ferroptosis and tumorigenesis in colorectal cancer

The induction of ferroptosis is promising for cancer therapy. However, the mechanisms enabling cancer cells to evade ferroptosis, particularly in low-cystine environments, remain elusive. Our study delves into the intricate regulatory mechanisms of Activating transcription factor 3 (ATF3) on Cystathionine β-synthase (CBS) under cystine deprivation stress, conferring resistance to ferroptosis in colorectal cancer (CRC) cells. Additionally, our findings establish a positively correlation between this signaling axis and CRC progression, suggesting its potential as a therapeutic target. Mechanistically, ATF3 positively regulates CBS to resist ferroptosis under cystine deprivation stress. In contrast, the suppression of CBS sensitizes CRC cells to ferroptosis through targeting the mitochondrial tricarboxylic acid (TCA) cycle. Notably, our study highlights that the ATF3-CBS signaling axis enhances ferroptosis-based CRC cancer therapy. Collectively, the findings reveal that the ATF3-CBS signaling axis is the primary feedback pathway in ferroptosis, and blocking this axis could be a potential therapeutic approach for colorectal cancer.

Molecular basis of GDF15 induction and suppression by drugs in cardiomyocytes and cancer cells toward precision medicine

GDF15 has recently emerged as a key driver of the development of various disease conditions including cancer cachexia. Not only the tumor itself but also adverse effects of chemotherapy have been reported to contribute to increased GDF15. Although regulation of GDF15 transcription by BET domain has recently been reported, the molecular mechanisms of GDF15 gene regulation by drugs are still unknown, leaving uncertainty about the safe and effective therapeutic strategies targeting GDF15. We screened various cardiotoxic drugs and BET inhibitors for their effects on GDF15 regulation in human cardiomyocytes and cancer cell lines and analyzed in-house and public gene signature databases. We found that DNA damaging drugs induce GDF15 in cardiomyocytes more strongly than drugs with other modes of action. In cancer cells, GDF15 induction varied depending on drug- and cell type-specific gene signatures including mutations in PI3KCA, TP53, BRAF and MUC16. GDF15 suppression by BET inhibition is particularly effective in cancer cells with low activity of the PI3K/Akt axis and high extracellular concentrations of pantothenate. Our findings provide insights that the risk for GDF15 overexpression and concomitant cachexia can be reduced by a personalized selection of anticancer drugs and patients for precision medicine.

Microsatellite instability states serve as predictive biomarkers for tumors chemotherapy sensitivity

There is an urgent need for markers to predict the efficacy of different chemotherapy drugs. Herein, we examined whether microsatellite instability (MSI) status can predict tumor multidrug sensitivity and explored the underlying mechanisms. We downloaded data from several public databases. Drug sensitivity was compared between the high microsatellite instability (MSI-H) and microsatellite-stable/low microsatellite instability (MSS/MSI-L) groups. In addition, we performed pathway enrichment analysis and cellular chemosensitivity assays to explore the mechanisms by which MSI status may affect drug sensitivity and assessed the differences between drug-treated and control cell lines. We found that multiple MSI-H tumors were more sensitive to a variety of chemotherapy drugs than MSS/MSI-L tumors, and especially for CRC, chemosensitivity is enhanced through the downregulation of DDR pathways such as NHEJ. Additional DNA damage caused by chemotherapeutic drugs results in further downregulation of DDR pathways and enhances drug sensitivity, forming a cycle of increasing drug sensitivity.

Mitochondrial RNA methyltransferase TRMT61B is a new, potential biomarker and therapeutic target for highly aneuploid cancers

Despite being frequently observed in cancer cells, chromosomal instability (CIN) and its immediate consequence, aneuploidy, trigger adverse effects on cellular homeostasis that need to be overcome by anti-stress mechanisms. As such, these safeguard responses represent a tumor-specific Achilles heel, since CIN and aneuploidy are rarely observed in normal cells. Recent data have revealed that epitranscriptomic marks catalyzed by RNA-modifying enzymes change under various stress insults. However, whether aneuploidy is associated with such RNA modifying pathways remains to be determined. Through an in silico search for aneuploidy biomarkers in cancer cells, we found TRMT61B, a mitochondrial RNA methyltransferase enzyme, to be associated with high levels of aneuploidy. Accordingly, TRMT61B protein levels are increased in tumor cell lines with an imbalanced karyotype as well as in different tumor types when compared to control tissues. Interestingly, while TRMT61B depletion induces senescence in melanoma cell lines with low levels of aneuploidy, it leads to apoptosis in cells with high levels. The therapeutic potential of these results was further validated by targeting TRMT61B in transwell and xenografts assays. We show that TRM61B depletion reduces the expression of several mitochondrial encoded proteins and limits mitochondrial function. Taken together, these results identify a new biomarker of aneuploidy in cancer cells that could potentially be used to selectively target highly aneuploid tumors.

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.

Pharmacometabolomics reveals urinary diacetylspermine as a biomarker of doxorubicin effectiveness in triple negative breast cancer

Triple-negative breast cancer (TNBC) patients receive chemotherapy treatment, including doxorubicin, due to the lack of targeted therapies. Drug resistance is a major cause of treatment failure in TNBC and therefore, there is a need to identify biomarkers that determine effective drug response. A pharmacometabolomics study was performed using doxorubicin sensitive and resistant TNBC patient-derived xenograft (PDX) models to detect urinary metabolic biomarkers of treatment effectiveness. Evaluation of metabolite production was assessed by directly studying tumor levels in TNBC-PDX mice and human subjects. Metabolic flux leading to biomarker production was determined using stable isotope-labeled tracers in TNBC-PDX ex vivo tissue slices. Findings were validated in 12-h urine samples from control (n = 200), ER+/PR+ (n = 200), ER+/PR+/HER2+ (n = 36), HER2+ (n = 81) and TNBC (n = 200) subjects. Diacetylspermine was identified as a urine metabolite that robustly changed in response to effective doxorubicin treatment, which persisted after the final dose. Urine diacetylspermine was produced by the tumor and correlated with tumor volume. Ex vivo tumor slices revealed that doxorubicin directly increases diacetylspermine production by increasing tumor spermidine/spermine N¹-acetyltransferase 1 expression and activity, which was corroborated by elevated polyamine flux. In breast cancer patients, tumor diacetylspermine was elevated compared to matched non-cancerous tissue and increased in HER2+ and TNBC compared to ER+ subtypes. Urine diacetylspermine was associated with breast cancer tumor volume and poor tumor grade. This study describes a pharmacometabolomics strategy for identifying cancer metabolic biomarkers that indicate drug response. Our findings characterize urine diacetylspermine as a non-invasive biomarker of doxorubicin effectiveness in TNBC.

Curcumin alters distinct molecular pathways in breast cancer subtypes revealed by integrated miRNA/mRNA expression analysis

BACKGROUND

Curcumin is well known for its anticancer properties. Its cytotoxic activity has been documented in several cancer cell lines, including breast cancer. The pleiotropic activity of curcumin as an antioxidant, an antiangiogenic, antiproliferative, and pro-apoptotic, is due to its diverse targets, such as signaling pathways, protein/enzyme, or noncoding gene.

AIM

This study aimed to identify key miRNAs and mRNAs induced by curcumin in breast cancer cells MCF7, T47D (hormone positive), versus MDA-MB231 (hormone negative) using comparative analysis of global gene expression profiles.

METHODS

RNA was isolated and subjected to mRNA and miRNA library sequencing to study the global gene expression profile of curcumin-treated breast cancer cells. The differential expression of gene and miRNA was performed using the DESeq R package. The enriched pathways were studied using cluster profileR, and integrated miRNA-mRNA analysis was carried out using miRtarvis and miRmapper tools.

RESULTS

Curcumin treatment led to upregulation of 59% TSGs in MCF7, 21% in MDA-MB-231 cells, and 36% TSGs in T47D, and downregulation of 57% oncogenes in MCF7, 76% in MDA-MB-231, and 91% in T47D. Similarly, curcumin treatment led to upregulation of 32% TSmiRs in MCF7, 37.5% in MDA-MB231, and 62.5% in T47D, and downregulation of 77% oncomiRs in MCF7, 50% in MDA-MB231 and 28.6% in T47D. Integrated analysis of miRNA-mRNA led to the identification of a common NFKB pathway altered by curcumin in all three cell lines. Analysis of uniquely enriched pathway revealed non-integrin membrane-ECM interactions and laminin interactions in MCF7; extracellular matrix organization and degradation in MDA-MB-231 and cell cycle arrest and G2/M transition in T47D.

CONCLUSION

Curcumin regulates miRNA and mRNA in a cell type-specific manner. The integrative analysis led to the detection of miRNAs and mRNAs pairs, which can be used as biomarkers associated with carcinogenesis, diagnostic, and treatment response in breast cancer.

Connecting omics signatures and revealing biological mechanisms with iLINCS

There are only a few platforms that integrate multiple omics data types, bioinformatics tools, and interfaces for integrative analyses and visualization that do not require programming skills. Here we present iLINCS ( http://ilincs.org ), an integrative web-based platform for analysis of omics data and signatures of cellular perturbations. The platform facilitates mining and re-analysis of the large collection of omics datasets (>34,000), pre-computed signatures (>200,000), and their connections, as well as the analysis of user-submitted omics signatures of diseases and cellular perturbations. iLINCS analysis workflows integrate vast omics data resources and a range of analytics and interactive visualization tools into a comprehensive platform for analysis of omics signatures. iLINCS user-friendly interfaces enable execution of sophisticated analyses of omics signatures, mechanism of action analysis, and signature-driven drug repositioning. We illustrate the utility of iLINCS with three use cases involving analysis of cancer proteogenomic signatures, COVID 19 transcriptomic signatures and mTOR signaling.

The structural maintenance of chromosomes 5 is a possible biomarker for individualized treatment of colorectal cancer

BACKGROUND

Although the understanding of resistance to oxaliplatin (OXA) chemotherapy in colorectal cancer (CRC) has been sought for many years, drug tolerance remains a major challenge for cancer therapy. Revealing the molecular mechanism of OXA resistance could help to explain the poor prognosis of patients.

METHODS

Gene expression omnibus (GEO) database was searched, GSE83129, which contains RNA profiling in metastatic CRC patients treated first-line with OXA, was chosen for the following analysis. Differential expressed genes (DEGs) between the adenocarcinoma and adjacent_normal team, respectively, in the OXA responders and no-responders were analyzed. The Gene Ontology (GO) and hub genes in the protein-protein interaction (PPI) network were used for the molecular mechanism of OXA resistance. Tumor-related databases were used for the clinical relevance of the structural maintenance of chromosomes 5 (SMC5) in CRC. The in vitro assays were used to detect the molecular function of SMC5 in CRC cells. Quantitative real-time PCR (qRT-PCR) and western blot were used to detect the expression of the structural maintenance of chromosomes 5/6 (SMC5/6) complex components upon OXA and raltitrexed (RTX) treatment. CCK-8 was used to detect the cell viability of cells with different treatment.

RESULTS

SMC5 was downregulated in CRC tissues of OXA no-response patients. Lower expression of SMC5 was correlated with a poor prognosis in CRC patients, improved this gene expression, inhibited the CRC cell growth and invasion in vitro. Furthermore, SMC5 was downregulated upon OXA treatment in CRC cells, while RTX would reverse its expression, and the combination of these two drugs restored the SMC5 level to the normal situation. Finally, RTX treatment enhanced the OXA cytotoxicity.

CONCLUSION

SMC5 is a tumor suppressor, that low expression of this gene is benefit for the development of CRC. Combination treatment with RTX and OXA may be more suitable for those OXA no-responders with lower SMC5.

Replication stress defines distinct molecular subtypes across cancers

Endogenous replication stress is a major driver of genomic instability. Current assessments of replication stress are low throughput precluding its comprehensive assessment across tumors. Here we develop and validate a transcriptional profile of replication stress by leveraging established cellular characteristics that portend replication stress. The repstress gene signature defines a subset of tumors across lineages characterized by activated oncogenes, aneuploidy, extrachromosomal DNA amplification, immune evasion, high genomic instability, and poor survival, and importantly predicts response to agents targeting replication stress more robustly than previously reported transcriptomic measures of replication stress. Repstress score profiles the dual roles of replication stress during tumorigenesis and in established cancers and defines distinct molecular subtypes within cancers that may be more vulnerable to drugs targeting this dependency. Altogether, our study provides a molecular profile of replication stress, providing novel biological insights of the replication stress phenotype, with clinical implications.

Prognosis and Therapeutic Efficacy Prediction of Adrenocortical Carcinoma Based on a Necroptosis-Associated Gene Signature

Background. Adrenocortical carcinoma (ACC) is a rare and poor prognosis malignancy. Necroptosis is a special type of cell apoptosis, which is regulated in caspase-independent pathways and mainly induced through the activation of receptor-interacting protein kinase 1, receptor-interacting protein kinase 3, and mixed lineage kinase domain-like pseudokinase. A precise predictive tool based on necroptosis is needed to improve the level of diagnosis and treatment. Method. Four ACC cohorts were enrolled in this study. The Cancer Genome Atlas ACC (TCGA-ACC) cohort was used as the training cohort; three datasets (GSE19750, GSE33371, and GSE49278) from Gene Expression Omnibus (GEO) platform were combined as the GEO testing cohort after removing of batch effect. Forty-nine necroptosis-associated genes were obtained from a prior study and further filtered by least absolute shrinkage and selection operator Cox regression analysis; corresponding coefficients were used to calculate the necroptosis-associated gene score (NAGs). Patients in the TCGA-ACC cohort were equally divided into two groups with the mean value of NAGs. We investigated the associations between NAGs groups and clinicopathological feature distribution and overall survival (OS) in ACC, the molecular mechanisms, and the value of NAGs in therapy prediction. A nomogram risk model was established to quantify risk stratification for ACC patients. Finally, the results were confirmed in the GEO-combined cohort. Result. Patients in the TCGA-ACC cohort were divided into high and low NAGs groups. The high NAGs group had more fatal cases and advanced stage patients than the low NAGs group ( $P<0.001$ , hazard ratio $\left(\text{HR}\right)=13.97$ , 95% confidence interval (95% CI): 4.168–46.844; survival rate: low NAGs, 7.69% vs. high NAGs, 61.53%). NAGs were validated to be negatively correlated with OS ( $R=-0.48$ , $P<0.001$ ) and act as an independent factor in ACC with high discriminative efficacy ( $P<0.001$ , $\text{HR}=11.76$ , 95% CI: 2.86–48.42). In addition, a high predictive efficacy nomogram risk model was established combining NAGs with tumor stage. Higher mutation rates were observed in the high NAGs group, and the mutation of TP53 may lead to a high T cell infiltration level among the NAGs groups. Patients belonged to the high NAGs are more sensitive to the chemotherapy of cisplatin, gemcitabine, paclitaxel, and etoposide (all $P<0.05$ ). Ultimately, the same statistical algorithms were conducted in the GEO-combined cohort, and the crucial role of NAGs prediction value was further validated. Conclusion. We constructed a necroptosis-associated gene signature, revealed the prognostic value between ACC and it, systematically explored the molecular alterations among patients with different NAGs, and manifested the value of drug sensitivity prediction in ACC.

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.

Chemotherapy-induced COX-2 upregulation by cancer cells defines their inflammatory properties and limits the efficacy of chemoimmunotherapy combinations

Cytotoxic therapies, besides directly inducing cancer cell death, can stimulate immune-dependent tumor growth control or paradoxically accelerate tumor progression. The underlying mechanisms dictating these opposing outcomes are poorly defined. Here, we show that cytotoxic therapy acutely upregulates cyclooxygenase (COX)-2 expression and prostaglandin E2 (PGE2) production in cancer cells with pre-existing COX-2 activity. Screening a compound library of 1280 approved drugs, we find that all classes of chemotherapy drugs enhance COX-2 transcription whilst arresting cancer cell proliferation. Genetic manipulation of COX-2 expression or its gene promoter region uncover how augmented COX-2/PGE2 activity post-treatment profoundly alters the inflammatory properties of chemotherapy-treated cancer cells in vivo. Pharmacological COX-2 inhibition boosts the efficacy of the combination of chemotherapy and PD-1 blockade. Crucially, in a poorly immunogenic breast cancer model, only the triple therapy unleashes tumor growth control and significantly reduces relapse and spontaneous metastatic spread in an adjuvant setting. Our findings suggest COX-2/PGE2 upregulation by dying cancer cells acts as a major barrier to cytotoxic therapy-driven tumor immunity and uncover a strategy to improve the outcomes of immunotherapy and chemotherapy combinations.

Systemic Efficacy of Sirolimus via the ERBB Signaling Pathway in Breast Cancer

Rapamycin, also known as sirolimus, inhibits the mTOR pathway in complex diseases such as cancer, and its downstream targets are ribosomal S6 kinases (RPS6K). Sirolimus is involved in regulating cell growth and cell survival through roles such as the mediation of epidermal growth factor signaling. However, the systemic efficacy of sirolimus in pathway regulation is unclear. The purpose of this study is to determine systemic drug efficacy using computational methods and drug-induced datasets. We suggest a computational method using gene expression datasets induced by sirolimus and an inverse algorithm that simultaneously identifies parameters referring to gene–gene interactions. We downloaded two sirolimus-induced microarray gene expression datasets and used a computational method to obtain the most enriched pathway, then adopted an inverse algorithm to discover the gene–gene interactions of that pathway. In the results, RPS6KB1 was a target gene of sirolimus and was associated with genes in the pathway. The common gene interactions from two datasets were a hub gene, RPS6KB1, and 10 related genes (AKT3, CBLC, MAP2K7, NRG1/2, PAK3, PIK3CD/G, PRKCG, and SHC3) in the epidermal growth factor (ERBB) signaling pathway.

Pharmacological Targeting of STING-Dependent IL-6 Production in Cancer Cells

Activation of the STING pathway upon genotoxic treatment of cancer cells has been shown to lead to anti-tumoral effects, mediated through the acute production of interferon (IFN)-β. Conversely, the pathway also correlates with the expression of NF-κB-driven pro-tumorigenic genes, but these associations are only poorly defined in the context of genotoxic treatment, and are thought to correlate with a chronic engagement of the pathway. We demonstrate here that half of the STING-expressing cancer cells from the NCI60 panel rapidly increased expression of pro-tumorigenic IL-6 upon genotoxic DNA damage, often independent of type-I IFN responses. While preferentially dependent on canonical STING, we demonstrate that genotoxic DNA damage induced by camptothecin (CPT) also drove IL-6 production through non-canonical STING signaling in selected cancer cells. Consequently, pharmacological inhibition of canonical STING failed to broadly inhibit IL-6 production induced by CPT, although this could be achieved through downstream ERK1/2 inhibition. Finally, prolonged inhibition of canonical STING signaling was associated with increased colony formation of MG-63 cells, highlighting the duality of STING signaling in also restraining the growth of selected cancer cells. Collectively, our findings demonstrate that genotoxic-induced DNA damage frequently leads to the rapid production of pro-tumorigenic IL-6 in cancer cells, independent of an IFN signature, through canonical and non-canonical STING activation; this underlines the complexity of STING engagement in human cancer cells, with frequent acute pro-tumorigenic activities induced by DNA damage. We propose that inhibition of ERK1/2 may help curb such pro-tumorigenic responses to DNA-damage, while preserving the anti-proliferative effects of the STING-interferon axis.

TPWshiny: an interactive R/Shiny app to explore cell line transcriptional responses to anti-cancer drugs

SUMMARY

The NCI Transcriptional Pharmacodynamics Workbench (NCI TPW) is an extensive compilation of directly measured transcriptional responses to anti-cancer agents across the well-characterized NCI-60 cancer cell lines. The NCI TPW data are publicly available through a web interface that allows limited user interaction with the data. We developed 'TPWshiny' as a standalone, easy to install, R application to facilitate more interactive data exploration.With no programming skills required, TPWshiny provides an intuitive and comprehensive graphical interface to help researchers understand the response of tumor cell lines to 15 therapeutic agents. The data are presented in interactive scatter plots, heatmaps, time series and Venn diagrams. Data can be queried by drug concentration, time point, gene and tissue type. Researchers can download the data for further analysis.

AVAILABILITY AND IMPLEMENTATION

Users can download the ready-to-use, self-extracting package for Windows or macOS, and R source code from the project website (https://brb.nci.nih.gov/TPWshiny/). TPWshiny documentation and additional information can be found on the project website.

Q-omics: Smart Software for Assisting Oncology and Cancer Research

The rapid increase in collateral omics and phenotypic data has enabled data-driven studies for the fast discovery of cancer targets and biomarkers. Thus, it is necessary to develop convenient tools for general oncologists and cancer scientists to carry out customized data mining without computational expertise. For this purpose, we developed innovative software that enables user-driven analyses assisted by knowledge-based smart systems. Publicly available data on mutations, gene expression, patient survival, immune score, drug screening and RNAi screening were integrated from the TCGA, GDSC, CCLE, NCI, and DepMap databases. The optimal selection of samples and other filtering options were guided by the smart function of the software for data mining and visualization on Kaplan-Meier plots, box plots and scatter plots of publication quality. We implemented unique algorithms for both data mining and visualization, thus simplifying and accelerating user-driven discovery activities on large multiomics datasets. The present Q-omics software program (v0.95) is available at http://qomics.sookmyung.ac.kr.

DeepCellState: An autoencoder-based framework for predicting cell type specific transcriptional states induced by drug treatment

Drug treatment induces cell type specific transcriptional programs, and as the number of combinations of drugs and cell types grows, the cost for exhaustive screens measuring the transcriptional drug response becomes intractable. We developed DeepCellState, a deep learning autoencoder-based framework, for predicting the induced transcriptional state in a cell type after drug treatment, based on the drug response in another cell type. Training the method on a large collection of transcriptional drug perturbation profiles, prediction accuracy improves significantly over baseline and alternative deep learning approaches when applying the method to two cell types, with improved accuracy when generalizing the framework to additional cell types. Treatments with drugs or whole drug families not seen during training are predicted with similar accuracy, and the same framework can be used for predicting the results from other interventions, such as gene knock-downs. Finally, analysis of the trained model shows that the internal representation is able to learn regulatory relationships between genes in a fully data-driven manner.

Phase II study of the histone deacetylase inhibitor vorinostat (Suberoylanilide Hydroxamic Acid; SAHA) in recurrent or metastatic transitional cell carcinoma of the urothelium - an NCI-CTEP sponsored: California Cancer Consortium trial, NCI 6879

BACKGROUND

Until the advent of T cell check point inhibitors standard second-line therapy for patients with metastatic urothelial cancer (mUC) was undefined. Histone deacetylase inhibitors (HDACi) have anti-cancer activity in a variety of tumor models including modulation of apoptosis in bladder cancer cell lines. We evaluated the efficacy and toxicity of the HDACi vorinostat in patients with mUC failing first-line platinum-based therapy either in the adjuvant/neoadjuvant setting or for recurrent/advanced disease.

METHODS

Vorinostat was given orally 200 mg twice daily continuously until progression or unacceptable toxicity. The primary end point was RECIST response rate (RR); a RR > 20% was deemed interesting in a 2-stage design requiring one response in the first 12 patients to proceed to 2nd stage for a total of 37 subjects. CT or MRI scan imaging occurred every 6 weeks.

RESULTS

Fourteen patients were accrued characterized by: median age 66 years (43-84); Caucasian (79%); males (86%); and Karnofsky performance status ≥90 (50%). Accrual was terminated in the first stage as no responses were observed. Best response was stable disease (3 patients). Progression was observed in 8 patients. Two patients came off therapy prior to re-imaging and a 3rd patient died while on treatment and was not assessed for response. Median number of cycles was 2 (range 1-11). Median disease-free survival and overall survival times were 1.1 (0.8, 2.1) & 3.2 (2.1, 14.5) months, respectively. Toxicities were predominantly cytopenias and thrombocytopenic bleeding. Two pts. had grade 5 toxicity unlikely related to treatment. Two pts. had grade 4 and 6 had grade 3 toxicities observed. Two patients with stable disease remained on therapy for 6+ cycles.

CONCLUSIONS

Vorinostat on this dose-schedule had limited efficacy and significant toxicity resulting in a unfavorable risk:benefit ratio in patients with mUC. NCT00363883.

Integrating transcriptomics, proteomics, glycomics and glycoproteomics to characterize paclitaxel resistance in breast cancer cells

Chemoresistance is a major factor driving breast cancer (BC) relapse and the high rates of cancer-related deaths. Aberrant levels of glycans are closely correlated with chemoresistance. The essential functions of glycans in chemoresistance is not systematically studied. In this study, an integrated strategy with a combination of transcriptomics, proteomics, glycomics and glycoproteomics was applied to explore the dysregulation of glycogenes, glycan structures and glycoproteins in chemoresistance of breast cancer cells. In paclitaxel (PTX) resistant MCF7 cells, 19 differentially expressed N-glycan-related proteins were identified, of which MGAT4A was the most significantly down-regulated, consistent with decrease in MGAT4A expression at mRNA level in PTX treated BC cells. Glycomic analysis consistently revealed suppressed levels of multi-antennary branching structures using MALDI-TOF/TOF-MS and lectin microarray. Several target glycoproteins bearing suppressed levels of multi-antennary branching structures were identified, and ERK signaling pathway was strongly suppressed in PTX resistant MCF7 cells. Our findings demonstrated the aberrant levels of multi-antennary branching structures and their target glycoproteins on PTX resistance. Systematically integrative multi-omic analysis is expected to facilitate the discovery of the aberrant glycosyltransferases, N-glycosylation and glycoproteins in tumor progression and chemoresistance. SIGNIFICANCE: An integrated strategy with a combination of transcriptomics, proteomics, glycomics and glycoproteomics is crucial to understand the association between glycans and chemoresistance in BC. In this multi-omic analysis, we identified unique glycan-related protein, glycan and glycoprotein signatures defining PTX chemoresistance in BC. This study might provide valuable information to understand molecular mechanisms underlying chemoresistance in BC.

Bioinformatic analysis linking genomic defects to chemosensitivity and mechanism of action

A joint analysis of the NCI60 small molecule screening data, their genetically defective genes, and mechanisms of action (MOA) of FDA approved cancer drugs screened in the NCI60 is proposed for identifying links between chemosensitivity, genomic defects and MOA. Self-Organizing-Maps (SOMs) are used to organize the chemosensitivity data. Student's t-tests are used to identify SOM clusters with enhanced chemosensitivity for tumor cell lines with versus without genetically defective genes. Fisher's exact and chi-square tests are used to reveal instances where defective gene to chemosensitivity associations have enriched MOAs. The results of this analysis find a relatively small set of defective genes, inclusive of ABL1, AXL, BRAF, CDC25A, CDKN2A, IGF1R, KRAS, MECOM, MMP1, MYC, NOTCH1, NRAS, PIK3CG, PTK2, RPTOR, SPTBN1, STAT2, TNKS and ZHX2, as possible candidates for roles in chemosensitivity for compound MOAs that target primarily, but not exclusively, kinases, nucleic acid synthesis, protein synthesis, apoptosis and tubulin. These results find exploitable instances of enhanced chemosensitivity of compound MOA's for selected defective genes. Collectively these findings will advance the interpretation of pre-clinical screening data as well as contribute towards the goals of cancer drug discovery, development decision making, and explanation of drug mechanisms.

Nuclear factor erythroid 2-related factor 2 modulates HER4 receptor in ovarian cancer cells to influence their sensitivity to tyrosine kinase inhibitors

Aim:

Nuclear factor erythroid 2-related factor 2 (NRF2) is a key component in the cell’s response to oxidative and electrophilic stress and is a transcription factor regulating the expression of a collection of anti-oxidative and cytoprotective genes. Human epidermal growth factor receptor 4 (HER4/erbB4) regulates growth and differentiation in many cancer types. Here, NRF2 and HER4 receptor interactions were investigated in a panel of ovarian cancer cell lines.

Methods:

Pharmacological [tert-butylhydroquinone (tBHQ) and retinoid/rexinoid, bexarotene] and genetic [small interfering RNA (siRNA)] manipulations were used to activate or inhibit NRF2 function in the cell line panel (PE01, OVCAR3, SKOV3). Activity of the HER-targeted tyrosine kinase inhibitors, erlotinib (ERL) and lapatinib (LAP), was evaluated after NRF2 activation.

Results:

While tBHQ increased the levels of both phosphorylated-NRF2 (pNRF2) and HER4 in PE01, OVCAR3 and SKOV3 cells, bexatorene and NRF2-target siRNA treatment decreased pNRF2 and total HER4 levels. The tBHQ-dependent pharmacological activation of NRF2 attenuated the therapeutic effectiveness of ERL and LAP. Analyses of gene expression data from a HER4 driven reporter system and in vitro or in vivo cancer models, support NRF2 regulation of HER4 expression.

Conclusions:

These results support the presence of signaling interaction between the NRF2 and HER4 receptor pathways and suggest that intervention modulating this cross-talk could have anticancer therapeutic value.

Chromatin Remodeling and Immediate Early Gene Activation by SLFN11 in Response to Replication Stress

Schlafen 11 (SLFN11) was recently discovered as a cellular restriction factor against replication stress. Here, we show that SLFN11 increases chromatin accessibility genome wide, prominently at active promoters in response to replication stress induced by the checkpoint kinase 1 (CHK1) inhibitor prexasertib or the topoisomerase I (TOP1) inhibitor camptothecin. Concomitantly, SLFN11 selectively activates cellular stress response pathways by inducing the transcription of the immediate early genes (IEGs), including JUN, FOS, EGR1, NFKB2, and ATF3, together with the cell cycle arrest genes CDKN1A (p21^WAF1) and GADD45. Both chromatin remodeling and IEG activation require the putative ATPase and helicase activity of SLFN11, whereas canonical extrinsic IEG activation is SLFN11 independent. SLFN11-dependent IEG activation by camptothecin is also observed across 55 non-isogenic NCI-60 cell lines. We conclude that SLFN11 acts as a global regulator of chromatin structure and an intrinsic IEG activator with the potential to engage the innate immune activation in response to replicative stress.

mTOR/EGFR/iNOS/MAP2K1/FGFR/TGFB1 Are Druggable Candidates for N-(2,4-Difluorophenyl)-2',4'-Difluoro-4-Hydroxybiphenyl-3-Carboxamide (NSC765598), With Consequent Anticancer Implications

Background

The application of computational and multi-omics approaches has aided our understanding of carcinogenesis and the development of therapeutic strategies. NSC765598 is a novel small molecule derivative of salicylanilide. This study aims to investigate the ligand-protein interactions of NSC765598 with its potential targets and to evaluate its anticancer activities in vitro.

Methods

We used multi-computational tools and clinical databases, respectively, to identify the potential drug target for NSC765598 and analyze the genetic profile and prognostic relevance of the targets in multiple cancers. We evaluated the in vitro anticancer activities against the National Cancer Institute 60 (NCI60) human tumor cell lines and used molecular docking to study the ligand-protein interactions. Finally, we used the DTP-COMPARE algorithm to compare the NSC765598 anticancer fingerprints with NCI standard agents.

Results

We identified mammalian target of rapamycin (mTOR)/epidermal growth factor receptor (EGFR)/inducible nitric oxide synthase (iNOS)/mitogen-activated protein 2 kinase 1 (MAP2K1)/fibroblast growth factor receptor (FGFR)/transforming growth factor-β1 (TGFB1) as potential targets for NSC765598. The targets were enriched in cancer-associated pathways, were overexpressed and were of prognostic relevance in multiple cancers. Among the identified targets, genetic alterations occurred most frequently in EGFR (7%), particularly in glioblastoma, esophageal squamous cell cancer, head and neck squamous cell cancer, and non–small-cell lung cancer, and were associated with poor prognoses and survival of patients, while other targets were less frequently altered. NSC765598 displayed selective antiproliferative and cytotoxic preferences for NSCLC (50% growth inhibition (GI₅₀) = 1.12–3.95 µM; total growth inhibition (TGI) = 3.72–16.60 μM), leukemia (GI₅₀ = 1.20–3.10 µM; TGI = 3.90–12.70 μM), melanoma (GI₅₀ = 1.45–3.59 µM), and renal cancer (GI₅₀ = 1.38–3.40 µM; TGI = 4.84–13.70 μM) cell lines, while panels of colon, breast, ovarian, prostate, and central nervous system (CNS) cancer cell lines were less sensitive to NSC765598. Interestingly, NSC765598 docked well into the binding cavity of the targets by conventional H-bonds, van der Waal forces, and a variety of π-interactions, with higher preferences for EGFR (ΔG = −11.0 kcal/mol), NOS2 (ΔG = −11.0 kcal/mol), and mTOR (ΔG = −8.8 kcal/mol). NSC765598 shares similar anti-cancer fingerprints with NCI standard agents displayed acceptable physicochemical values and met the criteria of drug-likeness.

Conclusion

NSC765598 displayed significant anticancer and potential multi-target properties, thus serve as a novel candidate worthy of further preclinical studies.

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.

Predicting chemosensitivity using drug perturbed gene dynamics

Background

One of the current directions of precision medicine is the use of computational methods to aid in the diagnosis, prognosis, and treatment of disease based on data driven approaches. For instance, in oncology, there has been a particular focus on development of algorithms and biomarkers that can be used for pre-clinical and clinical applications. In particular large-scale omics-based models to predict drug sensitivity in in vitro cancer cell line panels have been used to explore the utility and aid in the development of these models as clinical tools. Additionally, a number of web-based interfaces have been constructed for researchers to explore the potential of drug perturbed gene expression as biomarkers including the NCI Transcriptional Pharmacodynamic Workbench. In this paper we explore the influence of drug perturbed gene dynamics of the NCI Transcriptional Pharmacodynamics Workbench in computational models to predict in vitro drug sensitivity for 15 drugs on the NCI60 cell line panel.

Results

This work presents three main findings. First, our models show that gene expression profiles that capture changes in gene expression after 24 h of exposure to a high concentration of drug generates the most accurate predictive models compared to the expression profiles under different dosing conditions. Second, signatures of 100 genes are developed for different gene expression profiles; furthermore, when the gene signatures are applied across gene expression profiles model performance is substantially decreased when gene signatures developed using changes in gene expression are applied to non-drugged gene expression. Lastly, we show that the gene interaction networks developed on these signatures show different network topologies and can be used to inform selection of cancer relevant genes.

Conclusion

Our models suggest that perturbed gene signatures are predictive of drug response, but cannot be applied to predict drug response using unperturbed gene expression. Furthermore, additional drug perturbed gene expression measurements in in vitro cell lines could generate more predictive models; but, more importantly be used in conjunction with computational methods to discover important drug disease relationships.

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/.

Elucidation of Mechanisms of Topotecan-Induced Cell Death in Human Breast MCF-7 Cancer Cells by Gene Expression Analysis

Topotecan is a clinically active anticancer agent for the management of various human tumors. While the principal mechanism of tumor cell killing by topotecan is due to its interactions with topoisomerase I and formation of DNA double-strand breaks, recent studies suggest that mechanisms involving generation of reactive free radicals and induction of oxidative stress may play a significant role in topotecan-dependent tumor cell death. We have shown that topotecan generates a topotecan radical following one-electron oxidation by a peroxidase-hydrogen peroxide system which reacts with reduced glutathione and cysteine, forming the glutathiyl and cysteinyl radicals, respectively. While little is known how these events are involved in topotecan-induced tumor cell death, we have now examined the effects of topotecan short (1 h) and long (24 h) exposure on global gene expression patterns using gene expression microarray analysis in human breast MCF-7 cancer cells, a wild-type p53 containing cell line. We show here that topotecan treatment significantly down-regulated estrogen receptor alpha (ERα/ESR1) and antiapoptotic BCL2 genes in addition to many other p53-regulated genes. Furthermore, 8-oxoguanine DNA glycosylase (OGG1), ferredoxin reductase (FDXR), methionine sulfoxide reductase (MSR), glutathione peroxidases (GPx), and glutathione reductase (GSR) genes were also differentially expressed by topotecan treatment. The differential expression of these genes was observed in a wild-type p53-containing breast ZR-75-1 tumor cell line following topotecan treatment. The involvement of reactive oxygen free radical sensor genes, the oxidative DNA damage (OGG1) repair gene and induction of pro-apoptotic genes suggest that reactive free radical species play a role in topotecan-induced tumor cell death.

Turning liabilities into opportunities: Off-target based drug repurposing in cancer

Targeted drugs and precision medicine have transformed the landscape of cancer therapy and significantly improved patient outcomes in many cases. However, as therapies are becoming more and more tailored to smaller patient populations and acquired resistance is limiting the duration of clinical responses, there is an ever increasing demand for new drugs, which is not easily met considering steadily rising drug attrition rates and development costs. Considering these challenges drug repurposing is an attractive complementary approach to traditional drug discovery that can satisfy some of these needs. This is facilitated by the fact that most targeted drugs, despite their implicit connotation, are not singularly specific, but rather display a wide spectrum of target selectivity. Importantly, some of the unintended drug "off-targets" are known anticancer targets in their own right. Others are becoming recognized as such in the process of elucidating off-target mechanisms that in fact are responsible for a drug's anticancer activity, thereby revealing potentially new cancer vulnerabilities. Harnessing such beneficial off-target effects can therefore lead to novel and promising precision medicine approaches. Here, we will discuss experimental and computational methods that are employed to specifically develop single target and network-based off-target repurposing strategies, for instance with drug combinations or polypharmacology drugs. By illustrating concrete examples that have led to clinical translation we will furthermore examine the various scientific and non-scientific factors that cumulatively determine the success of these efforts and thus can inform the future development of new and potentially lifesaving off-target based drug repurposing strategies for cancers that constitute important unmet medical needs.

Sorafenib Inhibits Ribonucleotide Reductase Regulatory Subunit M2 (RRM2) in Hepatocellular Carcinoma Cells

The main curative treatments for hepatocellular carcinoma (HCC) are surgical resection and liver transplantation, which only benefits 15% to 25% of patients. In addition, HCC is highly refractory and resistant to cytotoxic chemotherapy. Although several multi-kinase inhibitors, such as sorafenib, regorafenib, and lenvatinib, have been approved for treating advanced HCC, only a short increase of median overall survival in HCC patients was achieved. Therefore, there is an urgent need to design more effective strategies for advanced HCC patients. Human ribonucleotide reductase is responsible for the conversion of ribonucleoside diphosphate to 2′-deoxyribonucleoside diphosphate to maintain the homeostasis of nucleotide pools. In this study, mining the cancer genomics and proteomics data revealed that ribonucleotide reductase regulatory subunit M2 (RRM2) serves as a prognosis biomarker and a therapeutic target for HCC. The RNA sequencing (RNA-Seq) analysis and public microarray data mining found that RRM2 was a novel molecular target of sorafenib in HCC cells. In vitro experiments validated that sorafenib inhibits RRM2 expression in HCC cells, which is positively associated with the anticancer activity of sorafenib. Although both RRM2 knockdown and sorafenib induced autophagy in HCC cells, restoration of RRM2 expression did not rescue HCC cells from sorafenib-induced autophagy and growth inhibition. However, long-term colony formation assay indicated that RRM2 overexpression partially rescues HCC cells from the cytotoxicity of sorafenib. Therefore, this study identifies that RRM2 is a novel target of sorafenib, partially contributing to its anticancer activity in HCC cells.

Bioinformatics Tools and Resources for Cancer Immunotherapy Study

In recent years, cancer immunotherapy has emerged as a highly promising approach to treat patients with cancer, as the patient's own immune system is harnessed to attack cancer cells. However, the application of these approaches is still limited to a minority of patients with cancer and it is difficult to predict which patients will derive the greatest clinical benefit.One of the challenges faced by the biomedical community in the search of more effective biomarkers is the fact that translational research efforts involve collecting and accessing data at many different levels: from the type of material examined (e.g., cell line, animal models, clinical samples) to multiple data type (e.g., pharmacodynamic markers, genetic sequencing data) to the scale of a study (e.g., small preclinical study, moderate retrospective study on stored specimen sets, clinical trials with large cohorts).This chapter reviews several publicly available bioinformatics tools and data resources for high throughput molecular analyses applied to a range of data types, including those generated from microarray, whole-exome sequencing (WES), RNA-seq, DNA copy number, and DNA methylation assays, that are extensively used for integrative multidimensional data analysis and visualization.

A phase I pharmacokinetic study of belinostat in patients with advanced cancers and varying degrees of liver dysfunction

AIMS

The histone deacetylase inhibitor belinostat has activity in various cancers. Because belinostat is metabolized by the liver, reduced hepatic clearance could lead to excessive drug accumulation and increased toxicity. Safety data in patients with liver dysfunction are needed for this drug to reach its full potential in the clinic.

METHODS

We performed a phase 1 trial to determine the safety, maximum tolerated dose (MTD) and pharmacokinetics of belinostat in patients with advanced cancer and varying degrees of liver dysfunction.

RESULTS

Seventy-two patients were enrolled and divided into cohorts based on liver function. In patients with mild dysfunction, the MTD was the same as the recommended phase 2 dose (1000 mg/m2 /day). Belinostat was well tolerated in patients with moderate and severe liver dysfunction, although the trial was closed before the MTD in these cohorts could be determined. The mean clearance of belinostat was 661 mL/min/m2 in patients with normal liver function, compared to 542, 505 and 444 mL/min/m2 in patients with mild, moderate and severe hepatic dysfunction. Although this trial was not designed to assess clinical activity, of the 47 patients evaluable for response, 13 patients (28%) experienced stable disease.

CONCLUSION

While a statistically significant difference in clearance indicates increased belinostat exposure with worsening liver function, no relationship was observed between belinostat exposure and toxicity. An assessment of belinostat metabolites revealed significant differences in metabolic pathway capability in patients with differing levels of liver dysfunction. Further studies are needed to establish formal dosing guidelines in this patient population.

Quantitative Proteome Landscape of the NCI-60 Cancer Cell Lines

Here we describe a proteomic data resource for the NCI-60 cell lines generated by pressure cycling technology and SWATH mass spectrometry. We developed the DIA-expert software to curate and visualize the SWATH data, leading to reproducible detection of over 3,100 SwissProt proteotypic proteins and systematic quantification of pathway activities. Stoichiometric relationships of interacting proteins for DNA replication, repair, the chromatin remodeling NuRD complex, β-catenin, RNA metabolism, and prefoldins are more evident than that at the mRNA level. The data are available in CellMiner (discover.nci.nih.gov/cellminercdb and discover.nci.nih.gov/cellminer), allowing casual users to test hypotheses and perform integrative, cross-database analyses of multi-omic drug response correlations for over 20,000 drugs. We demonstrate the value of proteome data in predicting drug response for over 240 clinically relevant chemotherapeutic and targeted therapies. In summary, we present a novel proteome resource for the NCI-60, together with relevant software tools, and demonstrate the benefit of proteome analyses.

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High-quality NCI-60 proteotypes created using pressure cycling technology and SWATH-MS

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Proteotypes improve drug response prediction in multi-omics regression analysis

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∼3000 measured proteins allow investigation into protein complex stoichiometry

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CellMinerCDB (discover.nci.nih.gov/cellminercdb) portal allows dataset exploration

Identification of pharmacodynamic biomarkers and common molecular mechanisms of response to genotoxic agents in cancer cell lines

PURPOSE

Genotoxic agents (GAs) including cisplatin, doxorubicin, gemcitabine, and topotecan are often used in cancer treatment. However, the response to GAs is variable among patients and predictive biomarkers are inadequate to select patients for treatment. Accurate and rapid pharmacodynamics measures of response can, thus, be useful for monitoring therapy and improve clinical outcomes.

METHODS

This study focuses on integrating a database of genome-wide response to treatment (The NCI Transcriptional Pharmacodynamics Workbench) with a database of baseline gene expression (GSE32474) for the NCI-60 cell lines to identify mechanisms of response and pharmacodynamic (PD) biomarkers.

RESULTS AND CONCLUSIONS

Our analysis suggests that GA-induced endoplasmic reticulum (ER) stress may signal for GA-induced cell death. Reducing the uptake of GA, activating DNA repair, and blocking ER-stress induction cooperate to prevent GA-induced cell death in the GA-resistant cells. ATF3, DDIT3, CARS, and PPP1R15A appear as possible candidate PD biomarkers for monitoring the progress of GA treatment. Further validation studies on the proposed intrinsic drug-resistant mechanism and candidate genes are needed using in vivo data from either patient-derived xenograft models or clinical chemotherapy trials.

The POU-Domain Transcription Factor Oct-6/POU3F1 as a Regulator of Cellular Response to Genotoxic Stress

DNA damage and the generation of reactive oxygen species (ROS) are key mechanisms of apoptotic cell death by commonly used genotoxic drugs. However, the complex cellular response to these pharmacologic agents remains yet to be fully characterized. Several studies have described the role of transcription factor octamer-1 (Oct-1)/Pit-1, Oct-1/2, and Unc-86 shared domain class 2 homeobox 1 (POU2F1) in the regulation of the genes important for cellular response to genotoxic stress. Evaluating the possible involvement of other POU family transcription factors in these pathways, we revealed the inducible expression of Oct-6/POU3F1, a regulator of neural morphogenesis and epidermal differentiation, in cancer cells by genotoxic drugs. The induction of Oct-6 occurs at the transcriptional level via reactive oxygen species (ROS) and ataxia telangiectasia mutated- and Rad3-related (ATR)-dependent mechanisms, but in a p53 independent manner. Moreover, we provide evidence that Oct-6 may play a role in the regulation of cellular response to DNA damaging agents. Indeed, by using the shRNA approach, we demonstrate that in doxorubicin-treated H460 non-small-cell lung carcinoma (NSCLC) cells, Oct-6 depletion leads to a reduced G2-cell cycle arrest and senescence, but also to increased levels of intracellular ROS and DNA damage. In addition, we could identify p21 and catalase as Oct-6 target genes possibly mediating these effects. These results demonstrate that Oct-6 is expressed in cancer cells after genotoxic stress, and suggests its possible role in the control of ROS, DNA damage response (DDR), and senescence.