Supplementary Figures 1-4 from Inhibition of Histone Deacetylase in Cancer Cells Slows Down Replication Forks, Activates Dormant Origins, and Induces DNA Damage

The clinical benefit of radiation therapy for patients with locally advanced stage III (N2) Non-Small Cell Lung Cancer (NSCLC) treated with surgery remains unclear due to a paucity of level 1 evidence. This study examined real-world evidence using a cohort of NSCLC patients from a multi-institution dataset to evaluate outcomes in patient treated with surgery alone, neoadjuvant or adjuvant radiation therapy. We extracted a sample of 415 fully-abstracted patients with pathologic stage III (N2) NSCLC defined at the initial surgical intervention performed within 6 months of initial diagnosis. Two- and five-year survival outcomes were evaluated for patients who received radiation neoadjuvant, adjuvant (PORT), or no radiation therapy. Sub-analyses and normalization of treatment groups were performed including use of chemotherapy, targeted therapy and IO therapy as well as age and gender using Cox proportionate hazards. Of the 415 patients evaluated, 54% (n=206) were treated with surgery alone, 16% (n=66) neoadjuvant radiation therapy and 34% (n=143) adjuvant radiation therapy. Chemotherapy use for patients across groups was balanced, 68% (n=140), 83% (n=56), 72% (n=103) for patient treated with surgery, neoadjuvant or adjuvant radiation therapy, respectively. Patients who received neoadjuvant radiation therapy had a 45% reduction in 2-year mortality risk (HR = .55, 95% CI 0.35 - 0.89), compared to patients treated with surgery alone (HR = 0.59, 95% CI .37 - 0.93). Radiation therapy did not improve 5-year mortality risk except in female patients, who show a 48% reduction in mortality risk at 5-years from radiation therapy pre- or post-operatively (HR = .52, 95% CI .29 - .94). There was no statistically significant difference in survival outcomes for patients treated with adjuvant radiation therapy, compared to surgery alone. Analysis of 415 abstracted patients this dataset suggests that the addition of preoperative radiation therapy improves 2 and 5-year survival for patients with pathologic stage III (N2) NSCLC, compared to patients treated with surgery alone. A significant survival benefit was not observed for adjuvant radiation therapy, however surgical margin status, performance status, and incidence of comorbidities was not included in the analysis due to the lack of granularity of the retrospective data.Abstract 3222; Table 1Surgery Only / No Radiation TherapyNeoadjuvant Radiation TherapyAdjuvant Radiation TherapyAll PatientsTotal (n)2-Year OS5-year OSTotal (n)2-Year OS5-Year OSTotal(n)2-Year OS5-Year OSTotal (n)2-Year OS5-Year OSAll patients20677%67%6692%86%14384%76%41582%71%Age >= 6015177%66%4994%78%10083%75%30082%71%Age < 605578%67%1712%71%4386%77%11583%71%Male10977%64%3090%70%7077%66%20979%66%Female9777%69%3694%81%7390%85%20685%77%Chemotherapy14076%74%5591%73%10380%70%29880%68%Neoadjuvant chemotherapy3093%87%4489%76%1090%80%8590%81%No chemotherapy6680%71%11100%91%4095%90%11787%79% Open table in a new tab

[This corrects the article DOI: 10.2196/jmir.5130.].

Andrew D. Patterson, Jessica A. Bonzo, Fei Li, Kristopher W. Krausz, Gabriel S. Eichler, Sadaf Aslam, Xenia Tigno, John N. Weinstein, Barbara C. Hansen, Jeffrey R. Idle, and Frank J. Gonzalez Laboratory of Metabolism, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, Genomics and Bioinformatics Group, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, Departments of Internal Medicine and Pediatrics, University of South Florida, Tampa, FL, Department of Clinical Pharmacology, University of Bern, Switzerland. Current affiliation: Department of Bioinformatics and Computational Biology, M. D. Anderson Cancer Center, Houston, TX (J.N.W.). Running Head: Metabolomic analysis identifies SLC6A20 dysfunction in T2DM Address Correspondence to: Frank J. Gonzalez, Tel: (301) 496-9067; Fax: (301) 496-8419 Email: fjgonz@helix.nih.gov

The quantity and complexity of the molecular-level data generated in both research and clinical settings require the use of sophisticated, powerful computational interpretation techniques. It is for this reason that bioinformatic analysis of complex molecular profiling data has become a fundamental technology in the development of personalized medicine. This chapter provides a high-level overview of the field of bioinformatics and outlines several, classic bioinformatic approaches. The highlighted approaches can be aptly applied to nearly any sort of high-dimensional genomic, proteomic, or metabolomic experiments. Reviewed technologies in this chapter include traditional clustering analysis, the Gene Expression Dynamics Inspector (GEDI), GoMiner (GoMiner), Gene Set Enrichment Analysis (GSEA), and the Learner of Functional Enrichment (LeFE).

To enhance understanding of the metabolic indicators of type 2 diabetes mellitus (T2DM) disease pathogenesis and progression, the urinary metabolomes of well characterized rhesus macaques (normal or spontaneously and naturally diabetic) were examined. High-resolution ultra-performance liquid chromatography coupled with the accurate mass determination of time-of-flight mass spectrometry was used to analyze spot urine samples from normal (n = 10) and T2DM (n = 11) male monkeys. The machine-learning algorithm random forests classified urine samples as either from normal or T2DM monkeys. The metabolites important for developing the classifier were further examined for their biological significance. Random forests models had a misclassification error of less than 5%. Metabolites were identified based on accurate masses (<10 ppm) and confirmed by tandem mass spectrometry of authentic compounds. Urinary compounds significantly increased (p < 0.05) in the T2DM when compared with the normal group included glycine betaine (9-fold), citric acid (2.8-fold), kynurenic acid (1.8-fold), glucose (68-fold), and pipecolic acid (6.5-fold). When compared with the conventional definition of T2DM, the metabolites were also useful in defining the T2DM condition, and the urinary elevations in glycine betaine and pipecolic acid (as well as proline) indicated defective re-absorption in the kidney proximal tubules by SLC6A20, a Na+-dependent transporter. The mRNA levels of SLC6A20 were significantly reduced in the kidneys of monkeys with T2DM. These observations were validated in the db/db mouse model of T2DM. This study provides convincing evidence of the power of metabolomics for identifying functional changes at many levels in the omics pipeline.

Abstract Histone acetylation is often altered in cancers and histone deacetylases inhibitors (HDACi) are actively pursued as anti-cancer agents. SAHA (suberoylanilide hydroxamic acid) is the first HDACi approved for clinical use. To monitor the effects of SAHA on DNA replication and genomic integrity in cancer cells, we detected broken DNA with COMET assays and analyzed phosphorylation of histone H2AX (gH2AX). Nuclear replication factories in single cells were visualized after pulse labeling with two thymidine analogs and confocal immunofluorescence microscopy. Nascent strand reverse transcriptase polymerase chain reaction (RT-PCR) in the human g-globin locus was used to assess the effects of SAHA on replication fork origin firing. In addition, cellular replicons were analyzed by genome-wide molecular combing. Our experiments prove that pharmacological concentrations of SAHA induce replication damage, as shown by single-cell and single-DNA molecule analyses. Molecular combing demonstrated a slow-down of replicons and activation of dormant replication origins in response to SAHA treatment. Similar results were obtained with siRNA-mediated HDAC3 depletion indicating the selectivity of the effect of SAHA. Activation of dormant origins at the g-globin locus control region was confirmed by nascent strand RT-PCR. Our findings demonstrate specific replication alterations and DNA damage in response to SAHA and emphasize the importance of chromatin acetylation for DNA replication in human cells. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 4862.

Abstract The development of multidrug resistance (MDR) to chemotherapy remains a major challenge in the treatment of cancer. Resistance exists against every effective anticancer drug and can develop by multiple mechanisms. These mechanisms can act individually or synergistically, leading to MDR, in which the cell becomes resistant to a variety of structurally and mechanistically unrelated drugs in addition to the drug initially administered. Although extensive work has been done to characterize MDR mechanisms in vitro, the translation of this knowledge to the clinic has not been successful. Therefore, identifying genes and mechanisms critical to the development of MDR in vivo and establishing a reliable method for analyzing highly homologous genes from small amounts of tissue is fundamental to achieving any significant enhancement in our understanding of MDR mechanisms and could lead to treatments designed to circumvent it. In this study, we use a previously established database that allows the identification of lead compounds in the early stages of drug discovery that are not ATP-binding cassette (ABC) transporter substrates. We believe this can serve as a model for appraising the accuracy and sensitivity of current methods used to analyze the expression profiles of ABC transporters. We found two platforms to be superior methods for the analysis of expression profiles of highly homologous gene superfamilies. This study also led to an improved database by revealing previously unidentified substrates for ABCB1, ABCC1, and ABCG2, transporters that contribute to MDR. [Mol Cancer Ther 2009;8(7):2057–66]

Global transcriptomic and proteomic profiling platforms have yielded important insights into the complex response to ionizing radiation (IR). Nonetheless, little is known about the ways in which small cellular metabolite concentrations change in response to IR. Here, a metabolomics approach using ultraperformance liquid chromatography coupled with electrospray time-of-flight mass spectrometry was used to profile, over time, the hydrophilic metabolome of TK6 cells exposed to IR doses ranging from 0.5 to 8.0 Gy. Multivariate data analysis of the positive ions revealed dose- and time-dependent clustering of the irradiated cells and identified certain constituents of the water-soluble metabolome as being significantly depleted as early as 1 h after IR. Tandem mass spectrometry was used to confirm metabolite identity. Many of the depleted metabolites are associated with oxidative stress and DNA repair pathways. Included are reduced glutathione, adenosine monophosphate, nicotinamide adenine dinucleotide, and spermine. Similar measurements were performed with a transformed fibroblast cell line, BJ, and it was found that a subset of the identified TK6 metabolites were effective in IR dose discrimination. The GEDI (Gene Expression Dynamics Inspector) algorithm, which is based on self-organizing maps, was used to visualize dynamic global changes in the TK6 metabolome that resulted from IR. It revealed dose-dependent clustering of ions sharing the same trends in concentration change across radiation doses. "Radiation metabolomics," the application of metabolomic analysis to the field of radiobiology, promises to increase our understanding of cellular responses to stressors such as radiation.

Interpretation of microarray data remains a challenge, and most methods fail to consider the complex, nonlinear regulation of gene expression. To address that limitation, we introduce Learner of Functional Enrichment (LeFE), a statistical/machine learning algorithm based on Random Forest, and demonstrate it on several diverse datasets: smoker/never smoker, breast cancer classification, and cancer drug sensitivity. We also compare it with previously published algorithms, including Gene Set Enrichment Analysis. LeFE regularly identifies statistically significant functional themes consistent with known biology.

Molecular crosstalk, including reciprocal stimulation, is theorized to take place between epithelial cancer cells and surrounding non-neoplastic stromal cells. This is the rationale for stromal therapy, which could eliminate support of a cancer by its genetically stable stroma. Epithelial-stromal crosstalk is so far poorly documented in vivo, and cell cultures and animal experiments may not provide accurate models. The current study details stromal-epithelial signalling pathways in 35 human colon cancers, and compares them with matched normal tissues using quantitative proteomic microarrays. Lysates prepared from separately microdissected epithelium and stroma were analysed using antibodies against 61 cell signalling proteins, most of which recognize activated phospho-isoforms. Analyses using unsupervised and supervised statistical methods suggest that cell signalling pathway profiles in stroma and epithelium appear more similar to each other in tumours than in normal colon. This supports the concept that coordinated crosstalk occurs between epithelium and stroma in cancer and suggests epithelial-mesenchymal transition. Furthermore, the data herein suggest that it is driven by cell proliferation pathways and that, specifically, several key molecules within the mitogen-activated protein kinase pathway may play an important role. Given recent findings of epithelial-mesenchymal transition in therapy-resistant tumour epithelium, these findings could have therapeutic implications for colon cancer.

AbstractMapping of protein signaling networks within tumors can identify new targets for therapy and provide a means to stratify patients for individualized therapy. Despite advances in combination chemotherapy, the overall survival for childhood rhabdomyosarcoma remains ∼60%. A critical goal is to identify functionally important protein signaling defects associated with treatment failure for the 40% nonresponder cohort. Here, we show, by phosphoproteomic network analysis of microdissected tumor cells, that interlinked components of the Akt/mammalian target of rapamycin (mTOR) pathway exhibited increased levels of phosphorylation for tumors of patients with short-term survival. Specimens (n = 59) were obtained from the Children's Oncology Group Intergroup Rhabdomyosarcoma Study (IRS) IV, D9502 and D9803, with 12-year follow-up. High phosphorylation levels were associated with poor overall and poor disease-free survival: Akt Ser473 (overall survival P < 0.001, recurrence-free survival P < 0.0009), 4EBP1 Thr37/46 (overall survival P < 0.0110, recurrence-free survival P < 0.0106), eIF4G Ser1108 (overall survival P < 0.0017, recurrence-free survival P < 0.0072), and p70S6 Thr389 (overall survival P < 0.0085, recurrence-free survival P < 0.0296). Moreover, the findings support an altered interrelationship between the insulin receptor substrate (IRS-1) and Akt/mTOR pathway proteins (P < 0.0027) for tumors from patients with poor survival. The functional significance of this pathway was tested using CCI-779 in a mouse xenograft model. CCI-779 suppressed phosphorylation of mTOR downstream proteins and greatly reduced the growth of two different rhabdomyosarcoma (RD embryonal P = 0.00008; Rh30 alveolar P = 0.0002) cell lines compared with controls. These results suggest that phosphoprotein mapping of the Akt/mTOR pathway should be studied further as a means to select patients to receive mTOR/IRS pathway inhibitors before administration of chemotherapy. [Cancer Res 2007;67(7):3431–40]

Proc Amer Assoc Cancer Res, Volume 47, 2006 1565 Introduction: In recent years, our research group has performed a number of experimental and computer studies that integrate different types of molecular data on the NCI-60, a diverse panel of human cancer cells from 9 tissues of origin. The NCI-60 panel has been used by the Developmental Therapeutics Program (DTP) of the NCI to screen >100,000 chemical compounds for anticancer activity since 1990. Using Pearson’s correlation as a metric, we previously shed light on the mechanisms and potential medical uses of a number of anticancer agents, including L-asparaginase, oxaliplatin, ‘MDR1-inverse’ compounds, and the ellipticiniums. The present study improves on that type of analysis by computing a more robust multi-gene correlation metric between drug growth inhibition data and biologically associated groups of genes, such as those in Gene Ontology (GO) categories or those with common Protein Family (Pfam) domains. Methods: To quantify the strength of association between anticancer drug activity patterns and mRNA transcriptional profiles, we developed a new algorithm (SCIMS) based on a stratified correlation metric and shrunken scoring scheme, with statistical significance computed using a false discovery rate (FDR) which adjusts for multiple comparisons. First, we validated the approach on a synthetic dataset of 100 drugs and 2000 genes. Then, we applied it to Affymetrix U133A microarray profiles of the NCI-60 and DTP GI50 data for 118 drugs with putatively known mechanisms of action. Because the program is compute-intensive, we ran it on the NIH’s Biowulf supercomputing cluster. Results: The results for the synthetic datasets showed >95% sensitivity and specificity for prediction of known associations. When applied to the NCI-60 cell line data, the SCIMS algorithm identified a number of robust associations that coincide with previously documented mechanisms of action, including a particularly strong one between the activity patterns of antifols and the ‘DNA processing’ GO category (FDR =.05). Conclusion: The results on the synthetic dataset suggest that the new algorithm is highly sensitive and specific, and the prediction of known mechanisms of action indicates utility of the algorithm for real data.

The aim of this study was to validate the use of transcriptional profiling as a means of characterizing the complex interactions of the thousands of genes that are expressed during fracture healing. Standard mid-diaphyseal tibia fractures were generated in C57/B6 murine tibiae and the transcriptional expression of approximately 13,000 genes was assessed. Three time points after fracture were assessed: day 3, representative of the inflammatory phase; day 10, representative of the peak of cartilage formation; and day 21, representative of the period of primary bone formation and coupled remodeling. A self-organizing mapping approach of the data revealed the temporal relationships between the expression of mRNAs for extracellular matrix proteins and the proteases that degrade the proteoglycan and collagenous matrices. A broad group of extracellular matrix protein mRNAs representative of basement membranes, blood vessels and cartilage all showed elevated expression over the first 21 days of fracture healing. The sorting of the data identified an orderly temporal expression of the metalloproteinases and ADAMTS during the progression of fracture healing with (MMP2/MMP14/TIMP2) and ADAMTS4 and 15 preceding the expression of (MMP9/MMP13). Based on their patterns of expression, relative to the known activities of the encoded proteolytic enzymes, our results suggest that the dissolution of cartilage protoeglycans proceeds before the underlying collagenous components of the matrix are removed. The exclusion of several mRNAs that are normally expressed by osteoclasts in the profiles of mRNAs from days 3 and 10 suggests that osteoclastic activity was largely absent during the early periods of cartilage tissue formation and that proteoglycan and specific collagenase activities, precedes or is prerequisite to later osteoclast infiltration into the remodeling tissues.

Genome-wide gene expression profile studies encompass increasingly large number of samples, posing a challenge to their presentation and interpretation without losing the notion that each transcriptome constitutes a complex biological entity. Much like pathologists who visually analyze information-rich histological sections as a whole, we propose here an integrative approach. We use a self-organizing maps-based software, the gene expression dynamics inspector (GEDI) to analyze gene expression profiles of various lung tumors. GEDI allows the comparison of tumor profiles based on direct visual detection of transcriptome patterns. Such intuitive "gestalt" perception promotes the discovery of interesting relationships in the absence of an existing hypothesis. We uncovered qualitative relationships between squamous cell tumors, small-cell tumors, and carcinoid tumor that would have escaped existing algorithmic classifications. These results suggest that GEDI may be a valuable explorative tool that combines global and gene-centered analyses of molecular profiles from large-scale microarray experiments.

Introduction: Colorectal carcinoma does not have a single specific biomarker that can detect and predict disease behaviour. Consequently, combinations of expression markers may be useful in diagnostics and prognostics. Moreover, it is likely that the complex interaction between tumour epithelium and stroma is a key facilitator in sustaining tumor growth.

Sequential screening is an iterative procedure that can greatly increase hit rates over random screening or non-iterative procedures. We studied the effects of three factors on enrichment rates: the method used to rank compounds, the molecular descriptor set and the selection of initial training set. The primary factor influencing recovery rates was the method of selecting the initial training set. Rates for recovering active compounds were substantially lower with the diverse training sets than they were with training sets selected by other methods. Because structure-activity information is incrementally enhanced in intermediate training sets, sequential screening provides significant improvement in the average rate of recovery of active compounds when compared with non-iterative selection procedures.