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Genomics and Metabolomics Advances in Cancer

Writer and Curator: Larry H. Bernstein, MD, FCAP

 

Genomics

Unraveling the clonal hierarchy of somatic genomic aberrations

D Prandi, SC Baca, A Romanel, CE Barbieri, Juan-Miguel Mosquera, et al.
Genome Biology 2014, 15:439
http://genomebiology.com/2014/15/8/439

Defining the chronology of molecular alterations may identify milestones in carcinogenesis. To unravel the temporal evolution of aberrations from clinical tumors, we developed CLONET, which upon estimation of tumor admixture and ploidy infers the clonal hierarchy of genomic aberrations. Comparative analysis across 100 sequenced genomes from prostate, melanoma, and lung cancers established diverse evolutionary hierarchies, demonstrating the early disruption of tumor-specific pathways. The analyses highlight the diversity of clonal evolution within and across tumor types that might be informative for risk stratification and patient selection for targeted therapies. CLONET addresses heterogeneous clinical samples seen in the setting of precision medicine.

The Transcription Factor Titration Effect Dictates Level of Gene Expression

RC Brewster, FM Weinert, HG Garcia, D Song, M Rydenfelt, and R Phillips
Cell,  Mar 13, 2014;156: 1312–1323
http://dx.doi.org/10.1016/j.cell.2014.02.022

Models of transcription are often built around a picture of RNA polymerase and transcription factors (TFs) acting on a single copy of a promoter. However, most TFs are shared between multiple genes with varying binding affinities. Beyond that, genes often exist at high copy number—in multiple identical copies on the chromosome or on plasmids or viral vectors with copy numbers in the hundreds. Using a thermodynamic model, we characterize the interplay between TF copy number and the demand for that TF. We demonstrate the parameter-free predictive power of this model as a function of the copy number of the TF and the number and affinities of the available specific binding sites; such predictive control is important for the understanding of transcription and the desire to quantitatively design the output of genetic circuits. Finally, we use these experiments to dynamically measure plasmid copy number through the cell cycle.

Telomere dynamics in human mesenchymal stem cells after exposure to acute oxidative stress

M Harbo, S Koelvraa, N Serakinci, L Bendixa
DNA Repair 2012.  http://dx.doi.org/10.1016/j.dnarep.2012.06.003

A gradual shortening of telomeres due to replication can be measured using the standard telomere restriction fragments (TRF) assay and other methods by measuring the mean length of all the telomeres in a cell. In contrast, stress-induced telomere shortening, which is believed to be just as important for causing cellular senescence, cannot be measured properly using these methods. Stress-induced telomere shortening caused by, e.g. oxidative damage happens in a stochastic manner leaving just a few single telomeres critically short. It is now possible to visualize these few ultra-short telomeres due to the advantages of the newly developed Universal single telomere length assay (STELA), and we therefore believe that this method should be considered the method of choice when measuring the length of telomeres after exposure to oxidative stress. In order to test our hypothesis, cultured human mesenchymal stem cells, either primary or hTERT immortalized, were exposed to sub-lethal doses of hydrogen peroxide, and the short term effect on telomere dynamics was monitored by Universal STELA and TRF measurements. Both telomere measures were then correlated with the percentage of senescent cells estimated by senescence-associated β-galactosidase staining. The exposure to acute oxidative stress resulted in an increased number of ultra-short telomeres, which correlated strongly with the percentage of senescent cells, whereas a correlation between mean telomere length and the percentage of senescent cells was absent. Based on the findings in the present study, it seems reasonable to conclude that Universal STELA is superior to TRF in detecting telomere damage caused by exposure to oxidative stress. The choice of method should therefore be considered carefully in studies examining stress-related telomere shortening as well as in the emerging field of lifestyle studies involving telomere length measurements.

tDNA insulators and the emerging role of TFIIIC in genome organization

Kevin Van Bortle and Victor G. Corces
Transcription Dec 12, 2012; 3(6): 1-8. www.landesbioscience.com

Recent findings provide evidence that tDNAs function as chromatin insulators from yeast to humans. TFIIIC, a transcription factor that interacts with the B-box in tDNAs as well as thousands of ETC sites in the genome, is responsible for insulator function. Though tDNAs are capable of enhancer-blocking and barrier activities for which insulators are defined, new insights into the relationship between insulators and chromatin structure suggest that TFIIIC serves a complex role in genome organization. We review the role of tRNA genes and TFIIIC as chromatin insulators, and highlight recent findings that have broadened our understanding of insulators in genome biology.

Structure and organization of insulators in eukaryotes. (A) From yeast to mammals, in organisms in which it has been studied, the TFIIIC protein interacts with the B-box sequence in tRNA genes or sites in the genome named ETC sites.

Synthetic CpG islands reveal DNA sequence determinants of chromatin structure

E Wachter, T Quante, C Merusi, A Arczewska, F Stewart, S Webb, A Bird
eLife 2014;3:e03397. http://dx.doi.org:/10.7554/eLife.03397.001

The mammalian genome is punctuated by CpG islands (CGIs), which differ sharply from the bulk genome by being rich in G + C and the dinucleotide CpG. CGIs often include transcription initiation sites and display ‘active’ histone marks, notably histone H3 lysine 4 methylation. In embryonic stem cells (ESCs) some CGIs adopt a ‘bivalent’ chromatin state bearing simultaneous ‘active’ and ‘inactive’ chromatin marks. To determine whether CGI chromatin is developmentally programmed at specific genes or is imposed by shared features of CGI DNA, we integrated artificial CGI-like DNA sequences into the ESC genome. We found that bivalency is the default chromatin structure for CpG-rich, G + C-rich DNA. A high CpG density alone is not sufficient for this effect, as A + T-rich sequence settings invariably provoke de novo DNA methylation leading to loss of CGI signature chromatin. We conclude that both CpG-richness and G + C-richness are required for induction of signature chromatin structures at CGIs.

Locus-specific mutation databases: pitfalls and good practice based on the p53 experience

Thierry Soussi, Chikashi Ishioka, Mireille Claustres and Christophe Béroud
NATURE REVIEWS | CANCER JAN 2006; 6: 83-90.

Between 50,000 and 60,000 mutations have been described in various genes that are associated with a wide variety of diseases. Reporting, storing and analysing these data is an important challenge as such data provide invaluable information for both clinical medicine and basic science.

The practical value of mutation analysis All studies performed to date show that mutations are, in general, not randomly distributed. Hot-spot regions have been demonstrated, corresponding to a region of DNA that is susceptible to mutations (such as CpG dinucleotides), a codon encoding a key residue in the biological function of the protein, or both (BOX 1). Identification of these hot-spot regions and natural mutants is essential to define crucial regions in an unknown protein.

Locus-specific databases have been developed to exploit this huge volume of data. The p53 mutation database is a paradigm, as it constitutes the largest collection of somatic mutations (22,000). However, there are several biases in this database that can lead to serious erroneous interpretations. We describe several rules for mutation database management that could benefit the entire scientific community.

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

A Subramaniana, P Tamayo, VK  Mootha, S Mukherjee, BL Ebert, et al.
PNAS  Oct 25, 2005; 102(43): 15545–15550
http://pnas.org/cgi/doi/10.1073/pnas.0506580102

Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

Mutational landscape and significance across 12 major cancer types

C Kandoth, MD McLellan, F Vandin, Kai Ye, B Niu, C Lu, et al.
NATURE  OCT 2013; 502: 333-337. http://dx.doi.org:/10.1038/nature12634

The Cancer Genome Atlas (TCGA) has used the latest sequencing and analysis methods to identify somatic variants across thousands of tumours. Here we present data and analytical results for point mutations and small insertions/deletions from 3,281 tumours across 12 tumour types as part of the TCGA Pan-Cancer effort. We illustrate the distributions of mutation frequencies, types and contexts across tumour types, and establish their links to tissues of origin, environmental/ carcinogen influences, and DNA repair defects. Using the integrated data sets, we identified 127 significantly mutated genes from well-known(for example, mitogen-activated protein kinase, phosphatidylinositol-3-OH kinase,Wnt/b-catenin and receptor tyrosine kinase signalling pathways, and cell cycle control) and emerging (for example, histone, histone modification, splicing, metabolism and proteolysis) cellular processes in cancer. The average number of mutations in these significantly mutated genes varies across tumour types; most tumours have two to six, indicating that the numberof driver mutations required during oncogenesis is relatively small. Mutations in transcriptional factors/regulators show tissue specificity, whereas histone modifiers are often mutated across several cancer types. Clinical association analysis identifies genes having a significant effect on survival, and investigations of mutations with respect to clonal/subclonal architecture delineate their temporal orders during tumorigenesis. Taken together, these results lay the groundwork for developing new diagnostics and individualizing cancer treatment.

Molecular insights into RNA and DNA helicase evolution from the determinants of  specificity for a DEAD-box RNA helicase

Anna L. Mallam, David J. Sidote and Alan M. Lambowitz
eLife 2014; http://dx.doi.org:/10.7554/eLife.04630

How different helicase families with a conserved catalytic ‘helicase core’ evolved to function on varied RNA and DNA substrates by diverse mechanisms remains unclear. Here, we used Mss116, a yeast DEAD-box protein that utilizes ATP to locally unwind dsRNA, to investigate helicase specificity and mechanism. Our results define the molecular basis for the substrate specificity of a DEAD-box protein. Additionally, they show that Mss116 has ambiguous substrate-binding properties and interacts with all four NTPs and both RNA and DNA. The efficiency of unwinding correlates with the stability of the ‘closed-state’ helicase core, a complex with nucleotide and nucleic acid that forms as duplexes are unwound. Crystal structures reveal that core stability is modulated by family-specific interactions that favor certain substrates. This suggests how present-day  helicases diversified from an ancestral core with broad specificity by retaining core closure as a common catalytic mechanism while optimizing substrate-binding interactions for different cellular functions.

Identification of human TERT elements necessary for telomerase recruitment to telomeres

Jens C Schmidt, Andrew B Dalby, Thomas R Cech
eLife 2014; http://dx.doi.org/10.7554/eLife.03563

Human chromosomes terminate in telomeres, repetitive DNA sequences bound by the shelterin complex. Shelterin protects chromosome ends, prevents recognition by the DNA damage machinery, and recruits telomerase. A patch of  amino acids, termed the TEL-patch, on the OB-fold domain of the shelterin  component TPP1 is essential to recruit telomerase to telomeres. In contrast, the site on telomerase that interacts with the TPP1 OB-fold is not well defined. Here we identify separation-of-function mutations in the TEN-domain of human telomerase reverse transcriptase (hTERT) that disrupt the interaction of telomerase with TPP1 in vivo and in vitro but have very little effect on the catalytic activity of telomerase. Suppression of a TEN-domain mutation with a compensatory charge-swap mutation in the TEL-patch indicates that their association is direct. Our findings define the interaction interface required for telomerase recruitment to telomeres, an important step towards developing modulators of this interaction as therapeutics for human disease.

Metabolomics

Single Cell Profiling of Circulating Tumor Cells: Transcriptional Heterogeneity and Diversity from Breast Cancer Cell Lines

MN Mindrinos, G Bhanot, SH Dairkee, RW Davis, SS Jeffrey
PLoS ONE 7(5): e33788. http://dx.doi.org:/doi:10.1371/journal.pone.0033788

Background: To improve cancer therapy, it is critical to target metastasizing cells. Circulating tumor cells (CTCs) are rare cells found in the blood of patients with solid tumors and may play a key role in cancer dissemination. Uncovering CTC phenotypes offers a potential avenue to inform treatment. However, CTC transcriptional profiling is limited by leukocyte contamination; an approach to surmount this problem is single cell analysis. Here we demonstrate feasibility of performing high dimensional single CTC profiling, providing early insight into CTC heterogeneity and allowing comparisons to breast cancer cell lines widely used for drug discovery.
Methodology/Principal Findings: We purified CTCs using the MagSweeper, an immunomagnetic enrichment device that isolates live tumor cells from unfractionated blood. CTCs that met stringent criteria for further analysis were obtained from 70% (14/20) of primary and 70% (21/30) of metastatic breast cancer patients; none were captured from patients with nonepithelial cancer (n = 20) or healthy subjects (n = 25). Microfluidic-based single cell transcriptional profiling of 87 cancer associated and reference genes showed heterogeneity among individual CTCs, separating them into two major subgroups, based on 31 highly expressed genes. In contrast, single cells from seven breast cancer cell lines were tightly clustered together by sample ID and ER status. CTC profiles were distinct from those of cancer cell lines, questioning the suitability of such lines for drug discovery efforts for late stage cancer therapy.
Conclusions/Significance: For the first time, we directly measured high dimensional gene expression in individual CTCs without the common practice of pooling such cells. Elevated transcript levels of genes associated with metastasis NPTN, S100A4, S100A9, and with epithelial mesenchymal transition: VIM, TGFß1, ZEB2, FOXC1, CXCR4, were striking compared to cell lines. Our findings demonstrate that profiling CTCs on a cell-by-cell basis is possible and may facilitate the application of ‘liquid biopsies’ to better model drug discovery

Simplifying Disease Complexity part 6 – Bringing Metabolomics into Practice
Dr. Kirk Beebe, Director of Application Science, Metabolon, Inc.

n the previous editions of this 6-part series, we’ve explored numerous example of how metabolomics is bringing success to areas such as cancer, metabolic disease, cardiovascular, and rare disease research. Although we did not devote attention to every area of biology or therapeutic area, the intent of this broad series was not only to convey how metabolomics can be used in a specific area of research (e.g. cancer), but actually, how metabolomics is a central science for interrogating any biological question. So, although it may seem like an oversimplification, to understand whether metabolomics could be used in a research setting one need only ask themselves, “Do I have a biological question that would benefit from a hypothesis-free approach?, am I interested in exploring my system for potential new discoveries? Or do I need a biomarker/better biomarker?

As described in our first part, metabolites have been and continue to be a staple for clinical and in vivo decision making (e.g. cholesterol, glucose, bilirubin, creatinine, thyroid hormone, newborn screening for inborn errors of metabolism (IEMs)). In short, this utility is fundamental to the foundations of biology since metabolism is central to all kingdoms of life and contemporary biology is driven to maintain metabolic homeostasis to maintain the phenotype. An unappreciated point that we leave this series with is that this fundamental nature (the connection of metabolism to the phenotype) confers an important advantage of metabolism for deriving biomarkers and understanding the underlying physiology.

Metabolites are a diagnostic data stream.

Whether a phenotype is driven by a single mutation or a combination of genetic differences, environmental influences or the microbiota, metabolism provides a systems-level diagnostic.

That is, no matter the source of the physiological or phenotypic change (i.e. genes, microbiota, environmental), the change will almost invariably register within metabolism. Thus, modern metabolomic approaches offer the opportunity to more deeply interrogate the “metabolome” to discover more sensitive and specific biomarkers and understand the basis of disease and drug response.

As such, metabolomics has the potential to be able to integrate systems on a number of levels. It is useful through its ability to enrich genomics, transcriptomics and proteomics, thus integrating a number of data streams that provide knowledge and contribute to informed decision-making and patient management1. Using metabolomics, individual tissues can be queried but less invasive sample types (e.g., blood, urine, feces, and/or saliva) can also yield biomarkers and mechanistic insight. The integration of the individual tissues at the level of these more accessible samples can offer an overview of the entire system and inform on important biological pathways. Finally, although the focus of this series was on what metabolomics can bring to biomarker and other related research areas, it should be noted that a combination of metabolomics with other scientific approaches will undoubtedly broaden insight and produce verifiable, validatable biomarkers that track with efficacy and therapy.

As we close this series, we hope that we have conveyed 4 critical points – 1) metabolism is central to biology and hence, key in research and biomarker discovery, 2) the reason for this is due to the fundamental nature of metabolism being central to the development of all life and being the focal point of contemporary biology’s drive to maintain homeostasis, 3) metabolomic is the most powerful way to survey metabolism by offering a simultaneous read-out if hundreds of reactions and pathways, and 4) metabolomics as a practical tool has only recently emerged.

And it is on this last point that we leave the reader with some final considerations. We imagine that, after careful review of the information outlined in this series, many readers will be motivated to explore the use of metabolomics in their research. However, as outlined throughout this series, mature technologies have only recently arisen. Nevertheless, there are many laboratories that perform some version of “metabolomics”. Although the experimental goal often dictates the precise approach, there are 5 critical features  that a metabolomic technology must harbor in order for it to achieve a similar purpose as mature omic technologies (e.g. DNA sequencers) in terms of depth of coverage and data quality. These minimally include:

1. Must be based on an authenticated chemical library
   2. Must have procedures for eliminated noise from the data
   5. Must have a mechanism to identify novel metabolites
   6. Must have robust QC process from sample preparation through statistical analysis
   4. Must provide a mechanism to abstract information/interpret the data

References

1. Eckhart, A.D., Beebe, K. & Milburn, M. Metabolomics as a key integrator for “omic” advancement of personalized medicine and future therapies. Clin Transl Sci 5, 285-288

(2012).

2. Evans, A., Mitchell, M., Dai, H. & DeHaven, C.D. Categorizing Ion –Features in Liquid Chromatography/Mass Spectrometry Metobolomics Data. Metabolomics 2 (2012).
3. DeHaven, C.D., Evans, A., Dai, H. & Lawton, K.A. in Metabolomics. (ed. U. Roessner) (InTech, 2012).
4. Dehaven, C.D., Evans, A.M., Dai, H. & Lawton, K.A. Organization of GC/MS and LC/MS metabolomics data into chemical libraries. J Cheminform 2, 9 (2010).
5. Evans, A.M., DeHaven, C.D., Barrett, T., Mitchell, M. & Milgram, E. Integrated, nontargeted ultrahigh performance liquid chromatography/electrospray ionization tandem mass spectrometry platform for the identification and relative quantification of the small-molecule complement of biological systems. Anal Chem 81, 6656-6667 (2009).

Prediction of intracellular metabolic states from extracellular metabolomic data

MK Aurich, G Paglia, Ottar Rolfsson, S Hrafnsdottir, M  Magnusdottir, MM, et al.

Metabolomics Aug 14, 2014;  http://dx.doi.org:/10.1007/s11306-014-0721-3
http://link.springer.com/article/10.1007/s11306-014-0721-3/fulltext.html#Sec1

http://link.springer.com/static-content/images/404/
art%253A10.1007%252Fs11306-014-0721-3/MediaObjects/11306_2014_721_Fig1_HTML.gif

Metabolic models can provide a mechanistic framework to analyze information-rich omics data sets, and are increasingly being used to investigate metabolic alternations in human diseases. An expression of the altered metabolic pathway utilization is the selection of metabolites consumed and released by cells. However, methods for the inference of intracellular metabolic states from extracellular measurements in the context of metabolic models remain underdeveloped compared to methods for other omics data. Herein, we describe a workflow for such an integrative analysis emphasizing on extracellular metabolomics data. We demonstrate, using the lymphoblastic leukemia cell lines Molt-4 and CCRF-CEM, how our methods can reveal differences in cell metabolism. Our models explain metabolite uptake and secretion by predicting a more glycolytic phenotype for the CCRF-CEM model and a more oxidative phenotype for the Molt-4 model, which was supported by our experimental data. Gene expression analysis revealed altered expression of gene products at key regulatory steps in those central metabolic pathways, and literature query emphasized the role of these genes in cancer metabolism. Moreover, in silico gene knock-outs identified unique control points for each cell line model, e.g., phosphoglycerate dehydrogenase for the Molt-4 model. Thus, our workflow is well suited to the characterization of cellular metabolic traits based on extracellular metabolomic data, and it allows the integration of multiple omics data sets into a cohesive picture based on a defined model context.

Genomics and Cancer

Identification of Gene Networks Associated with Acute Myeloid Leukemia by Comparative Molecular Methylation and Expression Profiling

M Dellett, KA O’Hagan, HA Alexandra Colyer and KI Mills
Biomarkers in Cancer 2010:2 43–55  http://www.la-press.com.

Around 80% of acute myeloid leukemia (AML) patients achieve a complete remission, however many will relapse and ultimately die of their disease. The association between karyotype and prognosis has been studied extensively and identified patient cohorts as having favourable [e.g. t(8; 21), inv (16)/t(16; 16), t(15; 17)], intermediate [e.g. cytogenetically normal (NK-AML)] or adverse risk [e.g. complex karyotypes]. Previous studies have shown that gene expression profiling signatures can classify the sub-types of AML, although few reports have shown a similar feature by using methylation markers. The global methylation patterns in 19 diagnostic AML samples were investigated using the Methylated CpG Island Amplification Microarray (MCAM) method and CpG island microarrays containing 12,000 CpG sites. The first analysis, comparing favourable and intermediate cytogenetic risk groups, revealed significantly differentially methylated CpG sites (594 CpG islands) between the two subgroups. Mutations in the NPM1 gene occur at a high frequency (40%) within the NK-AML subgroup and are associated with a more favourable prognosis in these patients. A second analysis comparing the NPM1 mutant and wild-type research study subjects again identified distinct methylation profiles between these two subgroups. Network and pathway analysis revealed possible molecular mechanisms associated with the different risk and/or mutation sub-groups. This may result in a better classification of the risk groups, improved monitoring targets, or the identification of novel molecular therapies.

Molecular Imaging of Proteases in Cancer

Yunan Yang, Hao Hong, Yin Zhang and Weibo Cai
Cancer Growth and Metastasis 2009:2 13–27. http://www.la-press.com

Proteases play important roles during tumor angiogenesis, invasion, and metastasis. Various molecular imaging techniques have been employed for protease imaging: optical (both fluorescence and bioluminescence), magnetic resonance imaging (MRI), single-photon emission computed tomography (SPECT), and positron emission tomography (PET). In this review, we will summarize the current status of imaging proteases in cancer with these techniques. Optical imaging of proteases, in particular with fluorescence, is the most intensively validated and many of the imaging probes are already commercially available. It is generally agreed that the use of activatable probes is the most accurate and appropriate means for measuring protease activity. Molecular imaging of proteases with other techniques (i.e. MRI, SPECT, and PET) has not been well-documented in the literature which certainly deserves much future effort. Optical imaging and molecular MRI of protease activity has very limited potential for clinical investigation. PET/SPECT imaging is suitable for clinical investigation; however the optimal probes for PET/SPECT imaging of proteases in cancer have yet to be developed. Successful development of protease imaging probes with optimal in vivo stability, tumor targeting efficacy, and desirable pharmacokinetics for clinical translation will eventually improve cancer patient management. Not limited to cancer, these protease-targeted imaging probes will also have broad applications in other diseases such as arthritis, atherosclerosis, and myocardial infarction.

Evolutionarily conserved genetic interactions with budding and fission yeast MutS identify orthologous relationships in mismatch repair-deficient cancer cells

E Tosti, JA Katakowski, S Schaetzlein, Hyun-Soo Kim, CJ Ryan, M Shales, et al.
Genome Medicine 2014, 6:68. http://genomemedicine.com/content/6/9/68

Background: The evolutionarily conserved DNA mismatch repair (MMR) system corrects base-substitution and insertion-deletion mutations generated during erroneous replication. The mutation or inactivation of many MMR factors strongly predisposes to cancer, where the resulting tumors often display resistance to standard chemotherapeutics. A new direction to develop targeted therapies is the harnessing of synthetic genetic interactions, where the simultaneous loss of two otherwise non-essential factors leads to reduced cell fitness or death. High-throughput screening in human cells to directly identify such interactors for disease-relevant genes is now widespread, but often requires extensive case-by-case optimization. Here we asked if conserved genetic interactors (CGIs) with MMR genes from two evolutionary distant yeast species (Saccharomyces cerevisiae and Schizosaccharomyzes pombe) can predict orthologous genetic relationships in higher eukaryotes.
Methods: High-throughput screening was used to identify genetic interaction profiles for the MutSα and MutSβ heterodimer subunits (msh2Δ, msh3Δ, msh6Δ) of fission yeast. Selected negative interactors with MutSβ (msh2Δ/msh3Δ) were directly analyzed in budding yeast, and the CGI with SUMO-protease Ulp2 further examined after RNA interference/drug treatment in MSH2-deficient and -proficient human cells.
Results: This study identified distinct genetic profiles for MutSα and MutSβ, and supports a role for the latter in recombinatorial DNA repair. Approximately 28% of orthologous genetic interactions with msh2Δ/msh3Δ are conserved in both yeasts, a degree consistent with global trends across these species. Further, the CGI between budding/fission yeast msh2 and SUMO-protease Ulp2 is maintained in human cells (MSH2/SENP6), and enhanced by Olaparib, a PARP inhibitor that induces the accumulation of single-strand DNA breaks. This identifies SENP6 as a promising new target for the treatment of MMR-deficient cancers.
Conclusion: Our findings demonstrate the utility of employing evolutionary distance in tractable lower eukaryotes to predict orthologous genetic relationships in higher eukaryotes. Moreover, we provide novel insights into the genome maintenance functions of a critical DNA repair complex and propose a promising targeted treatment for MMR deficient tumors.

Cancer Genome Landscapes

B Vogelstein, N Papadopoulos, VE Velculescu, S Zhou, LA Diaz Jr., KW Kinzler, et al.
Science 339, 1546 (2013); http://dx.doi.org:/10.1126/science.1235122

Over the past decade, comprehensive sequencing efforts have revealed the genomic landscapes of common forms of human cancer. For most cancer types, this landscape consists of a small number of “mountains” (genes altered in a high percentage of tumors) and a much larger number of “hills” (genes altered infrequently). To date, these studies have revealed ~140 genes that, when altered by intragenic mutations, can promote or “drive” tumorigenesis. A typical tumor contains two to eight of these “driver gene” mutations; the remaining mutations are passengers that confer no selective growth advantage. Driver genes can be classified into 12 signaling pathways that regulate three core cellular processes: cell fate, cell survival, and genome maintenance. A better understanding of these pathways is one of the most pressing needs in basic cancer research. Even now, however, our knowledge of cancer genomes is sufficient to guide the development of more effective approaches for reducing cancer morbidity and mortality.

Approaches for establishing the function of regulatory genetic variants involved in disease

Julian Charles Knight
Genome Medicine 2014, 6:92.  http://genomemedicine.com/content/6/10/92

The diversity of regulatory genetic variants and their mechanisms of action reflect the complexity and context-specificity of gene regulation. Regulatory variants are important in human disease and defining such variants and establishing mechanism is crucial to the interpretation of disease-association studies. This review describes approaches for identifying and functionally characterizing regulatory variants, illustrated using examples from common diseases. Insights from recent advances in resolving the functional epigenomic regulatory landscape in which variants act are highlighted, showing how this has enabled functional annotation of variants and the generation of hypotheses about mechanism of action. The utility of quantitative trait mapping at the transcript, protein and metabolite level to define association of specific genes with particular variants and further inform disease associations are reviewed. Establishing mechanism of action is an essential step in resolving functional regulatory variants, and this review describes how this is being facilitated by new methods for analyzing allele-specific expression, mapping chromatin interactions and advances in genome editing. Finally, integrative approaches are discussed together with examples highlighting how defining the mechanism of action of regulatory variants and identifying specific modulated genes can maximize the translational utility of genome-wide association studies to understand the pathogenesis of diseases and discover new drug targets or opportunities to repurpose existing drugs to treat them.

Biomarkers

TRIM29 as a Novel Biomarker in Pancreatic Adenocarcinoma

Hongli Sun, Xianwei Dai, and Bing Han
Disease Markers 2014, Article ID 317817, 7 pages
http://dx.doi.org/10.1155/2014/317817

Background and Aim. Tripartite motif-containing 29 (TRIM29) is structurally a member of the tripartite motif family of proteins and is involved in diverse human cancers. However, its role in pancreatic cancer remains unclear.
Methods. The expression pattern of TRIM29 in pancreatic ductal adenocarcinoma was assessed by immunocytochemistry. Multivariate logistic regression analysis was used to investigate the association between TRIM29 and clinical characteristics. In vitro analyses by scratch wound healing assay and invasion assays were performed using the pancreatic cancer cell lines.
Results. Immunohistochemical analysis showed TRIM29 expression in pancreatic cancer tissues was significantly higher (𝑛 = 186) than that in matched adjacent nontumor tissues. TRIM29 protein expression was significantly correlated with lymph node metastasis (𝑃 = 0.019). Patients with positive TRIM29 expression showed both shorter overall survival and shorter recurrence-free survival than those with negative TRIM29 expression. Multivariate analysis revealed that TRIM29 was an independent factor for pancreatic cancer over survival (HR = 2.180, 95% CI: 1.324–4.198, 𝑃 = 0.011). In vitro, TRIM29 knockdown resulted in inhibition of pancreatic cancer cell proliferation, migration, and invasion.
Conclusions. Our results indicate that TRIM29 promotes tumor progression and may be a novel prognostic marker for pancreatic ductal adenocarcinoma.

Bioinformatic identification of proteins with tissue-specific expression for biomarker discovery

I Prassas, CC Chrystoja, S Makawita1, and EP Diamandis
BMC Medicine 2012, 10:39. http://www.biomedcentral.com/1741-7015/10/39

Background: There is an important need for the identification of novel serological biomarkers for the early detection of cancer. Current biomarkers suffer from a lack of tissue specificity, rendering them vulnerable to nondisease-specific increases. The present study details a strategy to rapidly identify tissue-specific proteins using bioinformatics.
Methods: Previous studies have focused on either gene or protein expression databases for the identification of candidates. We developed a strategy that mines six publicly available gene and protein databases for tissue-specific proteins, selects proteins likely to enter the circulation, and integrates proteomic datasets enriched for the cancer secretome to prioritize candidates for further verification and validation studies.
Results: Using colon, lung, pancreatic and prostate cancer as case examples, we identified 48 candidate tissuespecific biomarkers, of which 14 have been previously studied as biomarkers of cancer or benign disease. Twenty six candidate biomarkers for these four cancer types are proposed.
Conclusions: We present a novel strategy using bioinformatics to identify tissue-specific proteins that are potential cancer serum biomarkers. Investigation of the 26 candidates in disease states of the organs is warranted

The Serum Glycome to Discriminate between Early-Stage Epithelial Ovarian Cancer and Benign Ovarian Diseases

K Biskup, E Iona Braicu, J Sehouli, R Tauber, and V Blanchard
Disease Markers 2014, Article ID 238197, 10 pages
http://dx.doi.org/10.1155/2014/238197

Epithelial ovarian cancer (EOC) is the sixth most common cause of cancer deaths in women because the diagnosis occurs mostly when the disease is in its late-stage. Current diagnostic methods of EOC show only a moderate sensitivity, especially at an early-stage of the disease; hence, novel biomarkers are needed to improve the diagnosis. We recently reported that serum glycome modifications observed in late-stage EOC patients by MALDI-TOF-MS could be combined as a glycan score named GLYCOV that was calculated from the relative areas of the 11 N-glycan structures that were significantly modulated. Here, we evaluated the ability of GLYCOV to recognize early-stage EOC in a cohort of 73 individuals comprised of 20 early-stage primary serous EOC, 20 benign ovarian diseases (BOD), and 33 age-matched healthy controls. GLYCOV was able to recognize stage I EOC whereas CA125 values were statistically significant only for stage II EOC patients. In addition, GLYCOV was more sensitive and specific compared to CA125 in distinguishing early-stage EOC from BOD patients, which is of high relevance to clinicians as it is difficult for them to diagnose malignancy prior to operation.

The Clinicopathological Significance of miR-133a in Colorectal Cancer

Timothy Ming-Hun Wan, Colin Siu-Chi Lam, Lui Ng, Ariel Ka-Man Chow, et al.
Disease Markers  2014, Article ID 919283, 8 pages http://dx.doi.org/10.1155/2014/919283

This study determined the expression of microRNA-133a (MiR-133a) in colorectal cancer (CRC) and adjacent normal mucosa samples and evaluated its clinicopathological role in CRC. The expression of miR-133a in 125 pairs of tissue samples was analyzed by quantitative real-time polymerase chain reaction (qRT-PCR) and correlated with patient’s clinicopathological data by statistical analysis. Endogenous expression levels of several potential target genes were determined by qRT-PCR and correlated using Pearson’s method. MiR-133a was downregulated in 83.2% of tumors compared to normal mucosal tissue. Higher miR-133a expression in tumor tissues was associated with development of distant metastasis, advanced Dukes and TNM staging, and poor survival. The unfavorable prognosis of higher miR-133a expression was accompanied by dysregulation of potential miR-133a target genes, LIM and SH3 domain protein 1 (LASP1), Caveolin-1 (CAV1), and Fascin-1 (FSCN1). LASP1 was found to possess a negative correlation (𝛾 = −0.23), whereas CAV1 exhibited a significant positive correlation (𝛾 = 0.27), and a stronger correlation was found in patients who developed distant metastases (𝛾 = 0.42). In addition, a negative correlation of FSCN1 was only found in nonmetastatic patients. In conclusion, miR-133a was downregulated in CRC tissues, but its higher expression correlated with adverse clinical characteristics and poor prognosis.

The Clinical Significance of PR, ER, NF-𝜅B, and TNF-𝛼 in Breast Cancer

Xian-Long Zhou, Wei Fan, Gui Yang, and Ming-Xia Yu
Disease Markers 2014, Article ID 494581, 7 pages http://dx.doi.org/10.1155/2014/494581

Objectives. To investigate the expression of estrogen (ER), progesterone receptors (PR), nuclear factor-𝜅B (NF-𝜅B), and tumor necrosis factor-𝛼 (TNF-𝛼) in human breast cancer (BC), and the correlation of these four parameters with clinicopathological features of BC.
Methods and Results. We performed an immunohistochemical SABC method for the identification of ER, PR, NF-𝜅B, and TNF-𝛼 expression in 112 patients with primary BC.The total positive expression rate of ER, PR, NF-𝜅B, and TNF-𝛼 was 67%, 76%, 84%, and 94%, respectively. The expressions of ER and PR were correlated with tumor grade, TNM stage, and lymph node metastasis (𝑃 < 0.01, resp.), but not with age, tumor size, histological subtype, age at menarche, menopause status, number of pregnancies, number of deliveries, and family history of cancer. Expressions of ER and PR were both correlated with NF-𝜅B and TNF-𝛼 expression (𝑃 < 0.05, resp.). Moreover, there was significant correlation between ER and PR (𝑃 < 0.0001) as well as between NF-𝜅B and TNF-𝛼 expression (𝑃 < 0.05).
Conclusion. PR and ER are highly expressed, with significant correlation with NF-𝜅B and TNF-𝛼 expression in breast cancer. The important roles of ER and PR in invasion and metastasis of breast cancer are probably associated with NF-𝜅B and TNF-𝛼 expression.

Serum Protein Profile at Remission Can Accurately Assess Therapeutic Outcomes and Survival for Serous Ovarian Cancer

J Wang, A Sharma, SA Ghamande, S Bush, D Ferris, W Zhi, et la.
PLoS ONE 8(11): e78393. http://dx.doi.org:/10.1371/journal.pone.0078393

Background: Biomarkers play critical roles in early detection, diagnosis and monitoring of therapeutic outcome and recurrence of cancer. Previous biomarker research on ovarian cancer (OC) has mostly focused on the discovery and validation of diagnostic biomarkers. The primary purpose of this study is to identify serum biomarkers for prognosis and therapeutic outcomes of ovarian cancer. Experimental Design: Forty serum proteins were analyzed in 70 serum samples from healthy controls (HC) and 101 serum samples from serous OC patients at three different disease phases: post diagnosis (PD), remission (RM) and recurrence (RC). The utility of serum proteins as OC biomarkers was evaluated using a variety of statistical methods including survival analysis.
Results: Ten serum proteins (PDGF-AB/BB, PDGF-AA, CRP, sFas, CA125, SAA, sTNFRII, sIL-6R, IGFBP6 and MDC) have individually good area-under-the-curve (AUC) values (AUC = 0.69–0.86) and more than 10 three-marker combinations have excellent AUC values (0.91–0.93) in distinguishing active cancer samples (PD & RC) from HC. The mean serum protein levels for RM samples are usually intermediate between HC and OC patients with active cancer (PD & RC). Most importantly, five proteins (sICAM1, RANTES, sgp130, sTNFR-II and sVCAM1) measured at remission  can classify, individually and in combination, serous OC patients into two subsets with significantly different overall survival (best HR = 17, p,1023).
Conclusion: We identified five serum proteins which, when measured at remission, can accurately predict the overall survival of serous OC patients, suggesting that they may be useful for monitoring the therapeutic outcomes for ovarian cancer.

Serum Clusterin as a Tumor Marker and Prognostic Factor for Patients with Esophageal Cancer

Wei Guo, Xiao Ma, Christine Xue, Jianfeng Luo, Xiaoli Zhu, et al.
Disease Markers 2014, Article ID 168960, 7 pages http://dx.doi.org/10.1155/2014/168960

Background. Recent studies have revealed that clusterin is implicated in many physiological and pathological processes, including tumorigenesis. However, the relationship between serum clusterin expression and esophageal squamous cell carcinoma (ESCC) is unclear.
Methods. The serum clusterin concentrations of 87 ESCC patients and 136 healthy individuals were examined. An independent-samples Mann-Whitney 𝑈 test was used to compare serum clusterin concentrations of ESCC patients to those of healthy controls. Univariate analysis was conducted using the log-rank test and multivariate analyses were performed using the Cox proportional hazards model. Results. In healthy controls, the mean clusterin concentration was 288.8 ± 75.1 𝜇g/mL, while in the ESCC patients, the mean clusterin concentration was higher at 412.3±159.4 𝜇g/mL (𝑃 < 0.0001). The 1-, 2-, and 4-year survival rates for the 87 ESCC patients were 89.70%, 80.00%, and 54.50%. Serum clusterin had an optimal diagnostic cut-off point (serum clusterin concentration = 335.5 𝜇g/mL) for esophageal squamous cell carcinoma with sensitivity of 71.26% and specificity of 77.94%. And higher serum clusterin concentration (>500 𝜇g/mL) indicated better prognosis (𝑃 = 0.030).
Conclusions. Clusterin may play a key role during tumorigenesis and tumor progression of ESCC and it could be applied in clinical work as a tumor marker and prognostic factor.

Septin 9 methylated DNA is a sensitive and specific blood test for colorectal cancer

JD Warren, Wei Xiong, AM Bunker, CP Vaughn, LV Furtado, et al.
BMC Medicine 2011, 9:133. http://www.biomedcentral.com/1741-7015/9/133

Background: About half of Americans 50 to 75 years old do not follow recommended colorectal cancer (CRC) screening guidelines, leaving 40 million individuals unscreened. A simple blood test would increase screening compliance, promoting early detection and better patient outcomes. The objective of this study is to demonstrate the performance of an improved sensitivity blood-based Septin 9 (SEPT9) methylated DNA test for colorectal cancer. Study variables include clinical stage, tumor location and histologic grade.
Methods: Plasma samples were collected from 50 untreated CRC patients at 3 institutions; 94 control samples were collected at 4 US institutions; samples were collected from 300 colonoscopy patients at 1 US clinic prior to endoscopy. SEPT9 methylated DNA concentration was tested in analytical specimens, plasma of known CRC cases, healthy control subjects, and plasma collected from colonoscopy patients.
Results: The improved SEPT9 methylated DNA test was more sensitive than previously described methods; the test had an overall sensitivity for CRC of 90% (95% CI, 77.4% to 96.3%) and specificity of 88% (95% CI, 79.6% to 93.7%), detecting CRC in patients of all stages. For early stage cancer (I and II) the test was 87% (95% CI, 71.1% to 95.1%) sensitive. The test identified CRC from all regions, including proximal colon (for example, the cecum) and had a 12% false-positive rate. In a small prospective study, the SEPT9 test detected 12% of adenomas with a false-positive rate of 3%.
Conclusions: A sensitive blood-based CRC screening test using the SEPT9 biomarker specifically detects a majority of CRCs of all stages and colorectal locations. The test could be offered to individuals of average risk for CRC who are unwilling or unable to undergo colonoscopy.

Matrix Metalloproteinases in Cancer: Prognostic Markers and Therapeutic Targets

Pia Vihinen And Veli-Matti K¨Ah¨Ari
Int. J. Cancer 2002; 99: 157–166 http://dx.doi.org:/10.1002/ijc.10329

Degradation of extracellular matrix is crucial for malignant tumour growth, invasion, metastasis and angiogenesis. Matrix metalloproteinases (MMPs) are a family of zinc-dependent neutral endopeptidases collectively capable of degrading essentially all matrix components. Elevated levels of distinct MMPs can be detected in tumour tissue or serumof patients with advanced cancer and their role as prognostic indicators in cancer is studied. In addition, therapeutic intervention of tumour growth and invasion based on inhibition of MMP activity is under intensive investigation and several MMP inhibitors are in clinical trials in cancer. In this review, we discuss the current view on the feasibility of MMPs as prognostic markers and as targets for therapeutic intervention in cancer.

Mass Spectrometric Screening of Ovarian Cancer with Serum Glycans

Jae-Han Kim, Chang Won Park, Dalho Um, Ki Hwang Baek, Yohahn Jo, et al.
Disease Markers  2014, Article ID 634289, 9 pages
http://dx.doi.org/10.1155/2014/634289

development of novel biomarkers based on the glycomic analysis. In this study, N-linked glycans from human serum were quantitatively profiled by matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) and compared between healthy controls and ovarian cancer patients. A training set consisting of 40 healthy controls and 40 ovarian cancer cases demonstrated an inverse correlation between 𝑃 value of ANOVA and area under the curve (AUC) of each candidate biomarker peak from MALDI-TOF MS, providing standards for the classification. A multi-biomarker panel composed of 15 MALDI-TOF MS peaks resulted in AUC of 0.89, 80∼90% sensitivity, and 70∼83% specificity in the training set. The performance of the biomarker panel was validated in a separate blind test set composed of 23 healthy controls and 37 ovarian cancer patients, leading to 81∼84% sensitivity and 83% specificity with cut-off values determined by the training set. Sensitivity of CA-125, the most widely used ovarian cancer marker, was 74%in the training set and 78% in the test set, respectively. These results indicate that MALDI-TOF MS-mediated serum N-glycan analysis could provide critical information for the screening of ovarian cancer.

Large, Collaborative Lung Cancer Trial Goes for Precision Medicine Goal

News | June 30, 2014 | Lung Cancer Targets

By Anna Azvolinsky, PhD

In a new biomarker-focused clinical trial, five therapies will be tested to develop new, precision medicine approaches to treat squamous cell lung cancer. The Lung Cancer Master Protocol (Lung-MAP)/SWOG S1400 phase 2/3 clinical trial, brings together the National Cancer Institute (NCI), the Foundation for the National Institutes of Health (FNIH), SWOG Cancer Research, five pharmaceutical companies (Amgen, AstraZeneca, Genentech, MedImmune, and Pfizer), Foundation Medicine (a molecular informatics company), and Friends of Cancer Research, a non-profit foundation.

The trial aims to enroll about 10,000 patients total and will cost about $160 million, of which the NCI is contributing $25 million.

Lung-MAP is unique as this is the first public-private partnership in drug development that includes the NCI, the Food and Drug Administration (FDA), U.S. oncology cooperative groups, and a number of patient advocacy groups according to one of the study investigators, David Gandara, MD, chair of the SWOG lung committee, and thoracic oncologist at the UC Davis Cancer Center. “Funds are made available for every aspect of the trial,” said Gandara. “There is nothing in the history of oncology or drug development like it.”

The clinical trial seeks to identify molecular aberrations in patients with advanced squamous cell lung cancer that can be targeted either by existing therapies or through the development of new ones. The innovation of this trial is a master protocol that will rely on the strength of numbers—up to 1000 patients per year at more than 200 sites throughout the U.S. for more than 200 cancer-related genetic alterations. Testing results will then dictate which experimental trial arm is most appropriate for which patient. Unlike a trial that seeks to enroll patients harboring just one mutation, which limits the access for many patients, the Lung-MAP design better ensures that a patient who is screened will be eligible for a targeted therapy trial arm.

This type of umbrella trial design is particularly suitable for squamous cell lung cancer. Thus far, has not been defined by one or several driver mutations. Instead, these tumors are made of a spectrum of genetic aberrations that are each relatively rare within the squamous lung cancer patient population, making enrollment into targeted therapy clinical trials difficult. According to the NCI, Lung-MAP “aims to establish a model of clinical testing that more efficiently meets the needs of both patients and drug developers,” facilitating more efficient matching of a patient to an investigational targeted therapy trial.

Lung-MAP was specifically designed for squamous cell lung cancer because this lung cancer subtype represents the greatest unmet need for new treatment, Gandara told OncoTherapy Network:

“All of the dramatic advances that have been made in the treatment of lung cancer over the last ten years have occurred in adenocarcinoma, a lung cancer subtype with several recently recognized and ‘druggable oncogenes’ such as EGFR mutations or ALK translocations. However, there have been essentially no advances in squamous cell lung cancer.”

But, recent genome-wide studies have identified several gene alterations in squamous cell lung cancer that are also druggable, including PI3K, FGFR, and CDK mutations, said Gandara. The trial is initially testing four targeted therapies: Genentech’s GDC-0032 (a PI3 kinase inhibitor), Pfizer’s palbociclib (an oral cyclin-dependent-kinase 4/6 inhibitor, AZD4547), an oral fibroblast growth factor receptor inhibitor from AstraZeneca, and rilotumumab, Amgen’s antibody against the human hepatocyte growth factor.

The fifth agent is, MEDI4736, an immune checkpoint inhibitor antibody targeting PD-L1. Patients whose tumors do not harbor a mutation suitable for targeting with one of the four targeted therapies will be enrolled in the MED4736 sub-study.

Once a patient is matched to a specific trial sub-study, randomization will determine whether the patient receives the experimental therapy or standard of care chemotherapy. The planned trial endpoints for each sub-study are overall survival and progression-free survival.

“I cannot overemphasize the importance of the FDA’s participation in this project, since each of these sub-studies is designed to result in approval of a paired biomarker and new drug if that sub-study meets the requirements for improved effectiveness,” said Gandara.

– See more at: http://www.oncotherapynetwork.com/lung-cancer-targets/large-collaborative-lung-cancer-trial-goes-precision-medicine-goal

The BATTLE Trial: Personalizing Therapy for Lung Cancer

Kim, RS. Herbst, II. Wistuba, JJ Lee, GR. Blumenschein Jr., A Tsao, DJ. Stewart, et al.

Authors’ Affiliations: 1Departments of Thoracic/Head and Neck Medical Oncology, 2Pathology, 3Biostatistics, and 4Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas; 5Winship Cancer Center, Emory University, Atlanta, Georgia; 6Dana-Farber Cancer Institute, Boston, Massachusetts; and 7University of Maryland, Baltimore, Maryland.

Corresponding Author:

Waun K. Hong, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030. Phone: 713-794-1441; Fax: 1-713-792-4654; E-mail:whong@mdanderson.org

The Biomarker-integrated Approaches of Targeted Therapy for Lung Cancer Elimination (BATTLE) trial represents the first completed prospective, biopsy-mandated, biomarker-based, adaptively randomized study in 255 pretreated lung cancer patients. Following an initial equal randomization period, chemorefractory non–small cell lung cancer (NSCLC) patients were adaptively randomized to erlotinib, vandetanib, erlotinib plus bexarotene, or sorafenib, based on relevant molecular biomarkers analyzed in fresh core needle biopsy specimens. Overall results include a 46% 8-week disease control rate (primary end point), confirm prespecified hypotheses, and show an impressive benefit from sorafenib among mutant-KRAS patients. BATTLE establishes the feasibility of a new paradigm for a personalized approach to lung cancer clinical trials.

(ClinicalTrials.gov numbers:NCT00409968, NCT00411671, NCT00411632, NCT00410059, and   NCT00410189.

Significance: The BATTLE study is the first completed prospective, adaptively randomized study in heavily pretreated NSCLC patients that mandated tumor profiling with “real-time” biopsies, taking a substantial step toward realizing personalized lung cancer therapy by integrating real-time molecular laboratory findings in delineating specific patient populations for individualized treatment. Cancer Discovery; 1(1); 44–53. © 2011 AACR.

Read the Commentary on this article by Sequist et al., p. 14
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Pharmacometabolomics in Drug Discovery & Development: Applications and Challenges

Yang and F. Marotta
Metabolomics 2012, 2:5 http://dx.doi.org/10.4172/2153-0769.1000e122

Recently, the concept of pharmaco-metabolomics is mentioned more frequently as an emerging discipline to study the effect of drugs on the whole pattern of small endogenous molecules and in applying the profiles of metabolomics for drug development. For the latter part, metabolomics is majorly used to differentiate patients into responder or non-responder groups in an effort to decrease large inter-individual variation in clinical trials. It is a novel approach that combines metabolite profile and chemo-metrics to model and predict drug targets, efficacy, pharmacokinetics and toxicity on both individual and population basis. It attracts many scientists’ attention because of its intrinsic advantages and promising potentials in drug discovery and development. Considering personalized drug treatment is the desired goal for current drug development, pharmaco-metabolomics provide an effective and inexpensive strategy to evaluate drug efficacy and toxicology, which may make personalized medicine realistic both from scientific and financial perspectives. Furthermore, the FDA also realized that metabolomics coupling with other “Omics” approaches could be a valuable tool in evaluating general toxicology and could eventually replace the use of animals after addressing certain challenges.

Networking metabolites and diseases

Pascal Braun, Edward Rietman, and Marc Vidal
PNAS  July 22, 2008; 105(29): 9849–9850

Diseasome and Drug-Target Network

Recently, Goh et al. constructed a ‘‘diseasome’’ network in which two diseases are linked to each other if they share at least one gene, in which mutations are associated with both diseases. In the resulting network, related disease families cluster tightly together, thus phenotypically defining functional modules. Importantly, for the first time this study applied concepts from network biology to human diseases, thus opening the door for discovering causal relationships between  disregulated networks and resulting ailments.

Subsequently Yilderim et al. linked drugs to protein targets in a drug–target network, which could then be overlaid with the diseasome network. One notable finding was the recent trend toward the development of new compounds directly targeted at disease gene products, whereas previous drugs, often found by trial and error, appear to target proteins only indirectly related to the actual disease molecular mechanisms. An important question that remains in this emerging field of network analysis consists of investigating the extent to which directly targeting the product of mutated genes is an efficient approach or whether targeting network properties instead, and thereby accounting for indirect nonlinear effects of system perturbations by drugs, may prove more fruitful. However, to answer such questions it is important to have a good understanding of the various influences that can lead to diseases.