Methods of Novel Therapeutic Candidate Identification Through Gene Expression Analysis in Vascular-Related Diseases

[0001] This application claims the benefit of priority of U.S. Provisional Application Ser. No. 61/040,065, filed on Mar. 27, 2008, the disclosure of which is hereby incorporated by referenced in its entirety,

[0002] The present invention relates to assessment and identification of expression of genes related to vascular-related diseases. The present invention also includes methods of comparing gene expression patterns with respect to various disease states.

[0003] Pulmonary arterial hypertension (PAH) is an occlusive disease of the pulmonary arteries loading to serious hemodynamic abnormality, right heart failure, and premature death. The molecular mechanism behind PAH are still unclear. Without a more complete understanding of PAH and how its complex vascular dysfunctions relate to one another, patients will suffer from imprecise diagnosis and drug therapy that may be less than optimal. Despite recent advances and introduction of new clinically approved drugs, the 5-year survival from pulmonary hypertension remains an estimated 50% (Archer and Rich, 2000). Consequently, treatment for PAH, while recently improved, still offers significant and long-lasting improvement in only a minority of patients. A methodology to elucidate the molecular pathways associated with PAH could guide the development of new therapies for this disease.

[0004] Though platelets and other cells may have a role in PAH, pulmonary endothelial cells and pulmonary smooth muscle cells appear to be the primary sites of disease progression (Humbert et al 2004). Molecular pathways that show abnormality in pulmonary endothelial cells and pulmonary smooth muscle cells during PAH include endothelin-1 (Giaid et al 1993), serotonin & serotonin transporter (Marcos et al 2003), thromboxane (Walmrath et al 1997), nitric oxide synthase (Kobs and Chesler 2006), prostacyclins (Gailes, et al 2002), potassium channels (Mandegar et al 2002), BMP signaling (Eddahibi et al 2002), and survivin (McMurtry et al 2005). PAH impairs normal signaling and growth in both pulmonary endothelial and pulmonary smooth muscle cells, yet the cellular abnormalities seem to shift over time in unpredictable patterns that has thus far escaped concise definition (Michelakis, 2006).

[0005] PAH may be understood as proceeding in phases. In early PAH, endothelial apoptosis occurs, probably resulting in pulmonary arteriole plugging and an increase in pulmonary vascular pressure (Michelakis, 2006). In late PAH, chronic exposure to elevated pulmonary artery pressure together with dysfunctional endothelial signaling initiates hyperproliferation of smooth muscle cells (McMurtry et al 2005). Increased concentric pulmonary smooth muscle cell proliferation leads to ever increasing pulmonary artery pressure, right ventricular failure, and death.

[0006] Lung pathology in all PAH patients show thickening throughout the arterial wall or the pulmonary vascular bed. In some forms of the disease, the pulmonary vascular lesions are reversible (e.g. in newborns with congenital heart defects). In other patients, such as those with the idiopathic form, the lesions are irreversible. It is unknown how these variations in PAH relate to one another on a molecular basis (Pearl et al 2002).

[0007] Current therapies for PAH patients primarily target vascular tone. Treatments that aim at correcting potassium channel dysfunction (Machado et al 2001), nitric oxide impairment (Humbert et al 2004), prostacyclin impairment (Tuder et al 1999, Christman et al 1992), and endothelin-1 expression (Giaid et al 1993) have all been clinically available for several years. These therapies offer some relief from hemodynamic symptoms, but most patients show only a transient response. The proliferative disease continues to progress in most PAH patients, resulting in a five year mortality rate that remains at around 50% (Newman et al 2004).

[0008] Currently, there are no clinically available routine means to obtain endothelial and smooth muscle samples from the pulmonary arteries of pulmonary hypertension patients for diagnosis, disease staging or drug discovery. Applicant's earlier invention, described in U.S. Pat. No. 5,406,959, describes an endoarterial biopsy catheter that has demonstrated its safety and effectiveness in normal canines (Rothman, Mann et al., 1996), canines with experimentally-induced pulmonary hypertension (Rothman, Mann et al., 1998), and canines with single-sided lung transplant rejection (Rothman, Mann et al., 2003). Preliminary studies have also demonstrated the safety and efficacy of a catheter-based method to obtain endovascular samples from a porcine model of PAH.

[0009] Percutaneously-obtained pulmonary endoarterial biopsy samples were found to be of sufficient quantity and quality for porcine whole genome mRNA microarray analysis and microRNA analysis. Whole genome microarray analysis revealed time-sensitive variations in gene expression values as PAH progressed in the subject animal model. Genes previously shown to be associated with PAH displayed changes characteristic of the disease, and genes previously unassociated with PAH also displayed expression level dysregulation. These findings raise the possibility that the endoarterial biopsy catheter combined with microarray analysis may provide a valuable platform for the discovery of novel drug and biomarker targets in pulmonary hypertension and a platform to deliver individualized pharmacotranscriptomics.

[0010] MicroRNA analysis revealed pressure sensitive changes in microRNA expression. As our surgical shunt model of pulmonary hypertension progressed from a high flow low pressure (HFLP) manifestation to a high flow high pressure (HFHP) manifestation, different microRNAs became dysregulated either increasing or decreasing in expression relative to our baseline normal values.

[0011] Most new therapies promise to focus on arresting either the endothelial apoptosis that characterizes early PAH (angiopoetin-1 & endothelial nitric oxide synthase cell-base gene transfer (Zhao et al 2003; 2005), caspase inhibitors (Taraseviciene-Stewart et al 2001)) or the smooth muscle cell proliferation typical of late PAH (dichloroacetate (McMurtry et al 2004), simvastatin (Nishimura et al 2003), sidenafil (Wharton et al 2005), imatinib (Schermuly et al 2005), anti-survivin (McMurtry et al 2005), K+ channel replacement gene therapy (Pozeg et al 2003)).

[0012] Before administering therapies, however, it would be extremely valuable to determine which genes are dysregulated in each PAH patient at any stage of their individual disease progression. Without knowing what genes are aberrant during any point in the patient's disease course, targeted therapies may miss the mark in some patients. Life threatening side effects may emerge if the wrong cells, at the wrong time, are encouraged to die or proliferate in patients with compromised pulmonary vascular health.

[0013] A powerful method for determining the gene expression levels of thousands of genes simultaneously are DNA microarrays. Initially used for the classification of cancers that were difficult to discriminate histologically (Colub et al 1999, Bhattacharjee et al 2001, and Ramaswamy et al 2001), microarrays have been more recently applied to PAH (Geraci et al 2001). PAH microarray studies have been performed on whole lung homogenates in humans (Fantozzi et al 2005) and rats (Hoshikawa et al 2003), surgically-dissected pulmonary arteries in pigs (Medhora et al 2002), laser-microdissected pulmonary arteries in rats (Kwapiszewska et al 2005), and mononuclear peripheral blood in humans (Bull et al 2004). These studies have been performed to discover potentially novel PAH disease pathways, biomarkers, therapeutic targets and patient classification gene expression profiles.

[0014] To advance PAH microarray studies into practical clinical use, tissue procurement methodologies are required that do not require surgical explant or postmortem procurement, and peripheral blood has thus far proven to be inadequate to discriminate gene expression signatures between subgroups of PAH patients (Bull et al 2004; Bull et al 2007). To take advantage of the full power of microarray technologies in PAH patients, a safe and effective minimally invasive means for the repeat procurement of endovascular samples from living PAH patients is required.

[0015] The present invention provides for the use of a novel interventional catheter, an endoarterial biopsy catheter, to obtain serial biopsy specimens from hypertensive pulmonary vessels for analysis. The ability to procure endothelial and smooth muscle samples in a minimally invasive manner will allow physicians to use microarray profiling and other techniques to classify patients upon initial presentation according to their gene expression signatures, prescribe therapies that target genes empirically found to be dysregulated in each individual patient, and monitor and adjust PAH patient therapy according to subsequent biopsy findings. A greater understanding of the complex molecular pathways underlying each patient's PAH should enable more precise diagnosis and the delivery of more effective therapies. Also of importance is the ability to discover new uses for existing drugs as well as discovering new drug targets.

[0016] Individualized pharmacotranscriptomics based on endoarterial biopsy and microarray analysis represents a reasonable choice for researchers struggling with the complexities and contradictions of PAH and other vascular diseases. The huge literature generated from in vitro and animal studies falls short, at times, in addressing the actual facts of patient health. Many commentators describe this dilemma as the “bench-to-bedside gap”, where in vitro and animal laboratory data fails to model human disease circumstances (Aird, 2004). Bridging that gap through catheter-based access to the vasculature is a model that recapitulates the clinical and histopathological manifestations of a form of human pulmonary hypertension will likely enable closer correlations between animal studies and patient care, and serve as a model for other vascular-based diseases such as atherosclerosis, congestive heart failure, sickle cell disease, organ transplant rejection, connective tissue diseases, chronic obstructive pulmonary disease, pulmonary embolism, asthma, systemic inflammatory response, battlefield trauma, cancer, sepsis and acute respiratory distress syndrome. There is a need in the art to provide data from gene expression analyses in order to target novel candidates for use in treating or preventing PAH.

[0017] One aspect of the present invention provides for methods of treating an individual suffering from a vascular-related disease comprising the steps of:

[0018] a) obtaining a biopsy sample from the individual's pulmonary artery;

[0019] b) analyzing gene expression levels of the biopsy sample from the pulmonary artery of the individual and a non-diseased control;

[0020] c) comparing the gene expression levels between the biopsy sample from the pulmonary artery of the individual and the non-diseased control;

[0021] d) identifying at least one gene from step c) that is upregulated or downregulated in the biopsy sample based on the non-diseased control;

[0022] e) obtaining gene products from the genes identified in step c); and

[0023] f) selecting pharmaceutical agents which are known inhibitors of the gene products from the at least one unregulated gene or known promoters of the gene products from the at least one downregulated gene. An additional aspect to the present invention provides for the pharmaceutical agents selected for administration to the individual suffering from the vascular-related disease. In yet another aspect, the individual is categorized based on progression of the vascular-related disease, with the treatment being based on the timing of the disease.

[0024] Another aspect of the present invention provides for a means of comparing varying levels of gene expression based on an animal model for pulmonary arterial hypertension. In a preferred embodiment, the genes expressed in the animal model are genes found to be either unregulated or downregulated. In a more preferred embodiment, the upregulated or downregulated genes are time-dependent based on the time after exposure to the PAH.

[0025] Another aspect of the present invention provides for methods of identifying genes involved in the pathway of PAH based on differential gene expression studies to a time-dependent animal model for PAH. In one embodiment, the genes are compared to other known genes which are upregulated or downregulated in the known PAH pathway.

[0026] Yet another aspect of the present invention provides for methods of diagnosing a vascular-related disease in an individual comprising the steps of:

[0027] a) identifying at least one gene that is upregulated or downregulated in the vascular-related disease comprising the steps of:

• • [0028] 1) obtaining a biopsy sample from the individual's pulmonary artery during progression of the vascular-related disease;
  • [0029] 2) obtaining a pulmonary artery sample from a non-diseased control;
  • [0030] 3) extracting RNA from the samples in steps 1) and 2);
  • [0031] 4) obtaining gene products from the RNA extracted in step 3); and
  • [0032] 5) comparing gene expression levels from the biopsy sample with the non-diseased control, and

[0033] b) associating the genes unregulated in the biopsy sample with an inhibitor of the gene products for administration to the individual and genes downregulated in the biopsy sample with a promoter of the gene products for administration to the individual.

[0034] Another aspect of the present invention provides for methods of treating an individual having a vascular-related disease by targeting microRNAs comprising the following steps:

[0035] a) assessing a stage of the vascular-related disease in the individual;

[0036] b) identifying whether microRNAs are upregulated or downregulated;

[0037] c) selecting the microRNAs to target based on the stage of the vascular-related disease and whether the microRNAs are upregulated or downregulated; and

[0038] d) administering an agent known to inhibit an upregulated microRNA or an agent known to promote a downregulated microRNA to the individual. A variation of this embodiment provides for the stage of the vascular-related disease being based on flow rates and blood pressure within an artery of the individual.

[0039] Another aspect of the present invention provides for methods of therapeutically targeting microRNA dysregulated in PAH comprising the steps of:

• • [0040] (a) obtaining a biopsy sample from the pulmonary artery during the progression of PAH;
  • [0041] (b) obtaining a pulmonary artery sample from a non-diseased control;
  • [0042] (c) extracting RNA from the artery samples;
  • [0043] (d) converging the RNA to cDNA;
  • [0044] (e) comparing levels of microRNA expression at the two differing times;
  • [0045] (f) identifying microRNA dysregulated in PAH relative to baseline; and
  • [0046] (g) inhibiting upregulated microRNA or promoting downregulated microRNA identified in PAH biopsies.

[0051] The model described in the present invention is surgically-induced PAH in pigs. Our model mimics human Eisenmenger syndrome (a form of PAH related to congenital heart malformation) in both symptoms and pathology (Corno et al 2003). The size of the animals makes the pulmonary vessels available to the catheter, providing a ready transition to human clinical use. And finally, the commercial availability of whole genome porcine microarrays (Bai et al 2003) makes the species ideal for our purposes in the study, and renders the use of cross species microarrays unnecessary (Medhora et al 2002).

[0052] By obtaining pulmonary endovascular samples at early, intermediate and late time points in PAH progression, and analyzing these samples using porcine whole genome microarrays, a time-sensitive microarray based map of the underlying molecular biology of PAH may be obtained. Improved knowledge of the molecular mechanisms underlying PAH progression can lead to the identification of stage-specific biomarkers, new therapeutic targets for drug intervention, and novel signaling pathways involved in the pathogenesis of PAH. These novel target genes can then be validated using quantitative PCR and immunohistochemical stains on porcine endoarterial biopsy samples procured concurrently. At the same time, the combination of minimally invasive endoarterial biopsy and whole genome microarray analysis can serve as an animal model for subsequent studies in PAH patients.

[0053] The following examples provided in this disclosure provide a profile gene expression in pulmonary hypertensive pigs by surgical anastomosis of tee left pulmonary artery to the descending aorta. Endoarterial biopsy samples are collected from animals with a surgical shunt model of pulmonary hypertension at multiple time points over a 6-month time course. Gene expression analysis of the biopsy samples was a performed on porcine microarrays. Microarray analysis was performed to detect dysregulated genes previously unassociated with PAH, discover novel biomarkers of pulmonary hypertension and novel targets for therapeutic intervention and advance knowledge of the molecular mechanisms of pulmonary hypertension. These studies will also help validate a new platform for PAH diagnosis and drug discovery, endoarterial biopsy and microarray analysis, for eventual clinical practice.

[0054] In an animal model of PAH created by Antonio Corno and colleagues, pigs undergo surgery that redirects systemic circulation into the left pulmonary artery mimicking pulmonary hypertension secondary to congenital heart disease. The surgery elevates PA pressure and creates the same hemodynamic conditions that PAH patients experience. The present study investigates how the elevated pressure remodels the pulmonary vasculature. In Corno's studies, histology on necropsy confirmed intimal hyperplasia in the pulmonary arteries, evidence that the surgical shunt surgery described will cause endovascular remodeling (Corno et al 2003).

[0055] 20-30 kg Yucatan Micropigs (Sus scrofa, Yucatan micro breed) underwent surgical anastomosis of the left pulmonary artery to the descending aorta, resulting in left pulmonary arterial hypertension of at least ½ systemic levels. Animals are penned in the laboratory for no less than one weak prior to surgery and fed normal chow. On surgery day, animals were premedicated with 20 mg/kg intramuscular ketamine and 0.1 mg/kg intramuscular midazolam. After 0.25 mg of intramuscular atropine, anesthesia was induced with 1 mg/kg intravenous midazolam and 0.1 mg/kg intravenous fentanyl and maintained with 0.1 mg/kg/hr intravenous pancuronium bromide. Pigs were ventilated with an inspired oxygen fraction (FiO2) of 0.4, a tidal volume of 15 ml/kg, and a respiratory rate of 12 breaths/minute. One gram of intravenous cefazolin was given before and 2 hours after the surgical procedure. Surgical and catheter procedures were performed under general anesthesia with endo-tracheal intubation. Sedation medications and anesthetics were administered by an anesthesiologist. Intra-cardiac and intravascular pressures, EKG, and blood oxygen saturations were monitored continuously.

[0056] Under sterile conditions (the thoracic area was shaved and prepared with betadine, a left thoracotomy was performed through the fourth intercostal space, about 5 centimeters, to expose the great arteries. The main pulmonary artery (MPA) and its branches were identified and freed from surrounding tissue. Two clamps were placed on one proximal left pulmonary artery (LPA). The proximal LPA was sutured closed, using prolene, and the distal LPA was sutured end to side in a clamped region in the descending aorta (FIG. 1), using cardiovascular prolene. Pieces of LPA endoarterial tissue were taken for histology. The clamps were removed and an IV dose of 1 mg/kg Furosemide immediately given. Hemostasis was obtained with sutures and cautery. Direct needle blood pressures were recorded in the main pulmonary artery and in the newly anastomosed left pulmonary artery. The chest was closed with sutures, both subcutaneous and cutaneous layers, using prolene and surgical wire. The animals then were weaned from anesthesia and mechanical ventilation. Postoperative analgesia was provided with morphine four times a day and a 1-2 mg/kg dose from 0.25% solution of IV Bupivacaine, a local anesthetic. A circulating warming blanket was used during surgery and recovery.

[0057] Endoarterial biopsies were performed at baseline prior no surgery to obtain unaffected tissue, and at post-shunt timepoints to obtain hypertensive biopsy samples (FIG. 2). Follow up endoarterial biopsy procedures were scheduled 7, 21, 60 and 180 days after surgery. On each catheterization day, animals were premedicated with 10 mg/kg of IV propofol, were intubated and ventilated at a rate of 12 breaths/min and were maintained under anesthesia with 1.5% halpthane. A femoral artery line was placed for monitoring. To obtain biopsies from the hypertensive left lung, an 8F introducer is placed in a carotid artery, and a 7F endhole catheter is advanced into the aorta. An angiogram is performed to visualize the LPA-aortic anastomosis. The 7F endhole catheter is then threaded through the anastomosis with X-ray fluoroscopic guidance. An angiogram of the hypertensive left pulmonary vascular tree is then performed, and the catheter is advanced to the distal pulmonary artery selected for biopsy. A 0.038 in, 260 cm extra stiff Amplatz exchange guide wire will then be passed through the end-hole catheter. The end-hole catheter was exchanged for a long flexible 8F introducer sheath that is adapted with a radio opaque band at the distal end and shaped to conform to the vascular pathway A 7F angiographic catheter was advanced through the sheath in to a 2.5 mm to 3.0 mm distal pulmonary artery branch, where an angiogram was obtained. The angiographic catheter will serve as a guide to advance the stiff sheath into a small vessel targeted for biopsy and will then be exchanged for the endoarterial biopsy catheter.

[0058] The endoarterial biopsy catheter has an external diameter of 2.5 mm and is composed of two flexible polymeric tubes that slide relative to each other. The inner tube has a stainless steel distal end with a beveled opening that is designed to accommodate arterial tissue. A vacuum is coupled to the inner tube and channeled to the beveled opening. The outer tube terminates in a stainless steel cutting tube. The proximal ends of the two tubes are with a spring powered operating mechanism. To obtain the biopsy sample a vacuum is transmitted to the beveled opening of the inner tube, causing a tissue sample to be drawn in. The outer tube is then advanced over the inner tube, severing the tissue sample. With this design, the area of artery contacted by the outer periphery of the beveled opening is larger than the inner aperture connected to the vacuum, this maintaining the tissue sample with its orientation preserved. After each biopsy, the catheter was removed and the tissue sample was placed in the appropriate solution for further processing and analysis. After the biopsy procedures were completed, repeated angiograms were obtained to assess the degree of vascular injury. At the end of the procedure, the biopsy catheter and introducer sheath were removed and hemostasis was obtained by surgical repair of the carotid artery. The animals will then be weaned from anesthesia and mechanical ventilation. Postoperative analgesia was provided with morphine four times a day and or fentanyl patches and non-steroidal anti-inflammatory drugs four times a day.

[0059] For microarray analysis, biopsy samples are placed in a test tube containing RNAlater (Qiagen), flash frozen in dry ice, and kept frozen until RNA extraction. Additional samples are preserved in formalin, OCT freezing solution, or Bouin's solution for subsequent immunohistochemical and quantitative PCR analysis.

[0060] The Affymetrix GeneChip® Porcine Genome Array provides comprehensive coverage of the Sus scrofa transcriptome. The array contains 23,937 probe sets that interrogate approximately 23,256 transcripts from 20,201 Sus scrofa genes.

[0061] The sequence information for this array was selected from public data sources including UniGene Build 28 (August 2004), GenBank® mRNAs up to Aug. 24, 2004, and GenBank® porcine mitochondrial and rRNA sequences. Probe sets consist of up to eleven probe pairs. The array format consists of eleven micron features synthesized on the 100 format.

[0062] RNA was prepared from fresh frozen endoarterial biopsy samples in a segregated laboratory, specially prepared and cleaned regularly to destroy nucleases. Specimens were homogenized using QIAshredder column (Qiagen, Valencia, Calif.) utilized in a FastPrep FP120 Homogenizer (Thermo Electron Corporation, Waltham, Mass.). RNA was isolated using RNeasy Mini columns (Qiagen, Valencia, Calif.) as manufacturer's protocol. All total RNA was eluted in nuclease free water, and quantity was established by UV spectrophotometer. Final RNA integrity was evaluated by capillary electrophoresis on the Agilent 2100 Bioanalyzer (Agilent, Palo Alto, Calif.).

[0063] Before target production, the quality and quantity of each RNA sample was assessed using a 2100 BioAnalyzer (Agilent). Target was prepared and hybridized according to the “Affymetrix Technical Manual”. Total RNA (ug) was converted into cDNA using Reverse Transcriptase (Invitrogen) and a modified oligo (dT)24 primer than contains T7 promoter sequences (GeneSet). After first strand syntheses, residual RNA was degraded by the addition of RNaseH and a double-stranded cDNA molecule was generated using DNA Polymerase I and DNA Ligase. The cDNA will then be purified and concentrated using a phenol:chloroform extraction followed by ethanol precipitation. The cDNA products will then be incubated with T7 RNA Polymerase and biotinylated ribonucleotides using an In Vitro Transcription kit (Enzo Diagnostics). One-half of the cRNA product was purified using an RNeasy column (Qiagen) and quantified with a spectrophotometer. The cRNA target (20 ug) was incubated at 94° C. for 35 minutes in fragmentation buffer (Tris, MgOAc, KOAc). The fragmented cRNA was diluted in hybridization buffer (MES, NaCl, EDTA, Tween 20, Herring Sperm DNA, Acetylated BSA) containing biotin-labeled OligoB2 and Eukaryotic Hybridization Controls (Affymetrix). The hybridization cocktail was denatured at 99° C. for 5 minutes, incubated at 45° C. for 5 minutes and then injected into a GeneChip® cartridge. The GeneChip® array was incubated at 42° C. for at least 16 hours in a rotating oven at 60 rpm. GeneChips® were washed with a aeries of nonstringent (25° C.) and stringent (80° C.) solutions containing variable amounts of MES, Tween20 and SSPE. The microarrays will then be stained with Streptavidin Phycoerythrin and the fluorescent signal was amplified using a biotinylated antibody solution. Fluorescent images were detected in a GeneChip® Scanner 3000 and expression data was extracted using the GeneChip® Operating System v 1.1 (Affymetrix). All GeneChips® were scaled to a median intensity setting of 500. Gene expression levels were compared between biopsy samples taken from the control distal pulmonary vasculature (baseline LPA and RPA) and PAH distal pulmonary arteries (surgical shunt LPA).

[0064] After RNA preparation, array hybridization and scanning of the Porcine GeneChips® exactly as recommended by Affymetrix, the data produced are processed using the Affymetrix tools in the R-Bioconductor package called /Affy/. This tool set allows for various probe level analyses of the data as well as probe level quality control. The MAS5 algorithm was used, taking into account both the MM and PM probe data, and generating “Present” or “Absent” calls for each gene on each chip. If boxplots of the porcine probe level data reveal that any of the hybridizations are of low quality, the data from these chips was removed from any downstream analysis. MAS5 with the non-linear Quantiles normalization (Affy package /normalize.quantiles)/ was used with this data set to produce data almost free of artificial correlations. The Present/Absent calls are also used to remove from the analysis the genes that were never expressed in any of the samples examined (this is analogous to using a P-value for gauging a gene's data quality on a chip, and then filtering).

[0065] Based on the first dozen chips processed, after normalization and quality control ˜19,000 genes were moved on to the next stage of the analysis. The commercial package GeneSpring was used to assess differential gene expression and perform tests using clustering algorithms. Thus far, hierarchical clustering has revealed that the time-point replicates have the greatest similarity to one another.

[0066] Gene expression fold changes for 7, 21 60 and 180 days post-surgery relative to baseline were then loaded into GSEA (gens set enrichment analysis) or specially written PERL scripts which carry out KS (Kolmogorov-Smirnov) statistical analysis in order to identify novel therapeutic candidates.

[0067] During PAH the best therapeutic targets are those which are upregulated as the disease state progresses. Thus drugs which are known to counter the action of any upregulated genes and their products would be of the greatest potential therapeutic value. Therefore, lists of upregulated genes were then matched to drugs which target their gene products.

[0068] In addition, many drugs interact with multiple targets in the body's tissues. Lists of the targets (called genesets) for each of ˜2000 characterized drugs were obtained from the literature and online databases. These genesets were then used to search for drugs which would be most likely to have therapeutic value in PAH.

[0069] This was done by using KS statistics which computes the Kolmogorov-Smirnov score for a geneset for a particular drug within an ordered list. The KSscore task is used to examine the enrichment of a set of genes at the top of an ordered list. The KS score is high when the tags appear early (i.e. near the top) of the ordered list. The significance of the KS score for a particular test may be examined by computing KS scores for multiple sets of X query genes selected at random from the dataset (note that the KS score is not independent of the number of members of the query gene set). Using this approach we were able to identify in our messenger RNA expression dataset drugs which are currently used as therapeutics for PAH (see FIG. 1A). Importantly, using the same approach we were also able to identify additional potential therapeutics, within the existing pool of characterized drugs. This process identified existing drugs as novel therapeutics for PAH.

[0070] The porcine studies indicate that single endoarterial biopsy samples obtained in the porcine model of surgical shunt PAH contain sufficient RNA for microarray analysis, as we were able to analyze the mRNAs in whole genome porcine microarrays. We obtained endoarterial biopsies and measured pulmonary arterial pressure (PAP) at baseline prior to surgery, and at approximately 7, 21, 60 and 180 days post-PAH surgery from several animals. Porcine whole genome expression values were obtained for biological replicates with 2 samples from each time point. These replicates produced 5 sets of high quality replicated expression data (baseline, day 7, day 21, day 60 and day 180; see Table 1 for PAP data). Downstream data analysis was carried out using commercial (Ingenuity; GeneSpring) and free/open source software (R; Bioconductor; GSEA).

Mean expression values were obtained for each gene by averaging the gene expression values of the two biopsies at each timepoint. The resulting gene expression mean values were used to calculate fold changes between day 7, 21, 60 and day 180 gene expression relative to baseline.

[0071] Validity of the model was confirmed by examining the gene expression changes for selected genes previously found to be dysregulated in PAH (Table 2). Endothelin 1 end protein-tyrosine kinase Tie2 both displayed upregulation in accordance with explanted tissue from IPAH transplant recipients (Dewatcher et al 2006), and platelet-derived growth factor receptor alpha, serotonin receptors 2B and 1D, calmodulin, transcription factor STAT5b, voltage-dependent anion channels 1, 2 and 3, and RAS p21 protein activator 1 also increased in our model while tumor necrosis factor and plasminogen activator inhibitor-1 were found to be downregulated in our model in a similar fashion with IPAH explant tissue results (Fantozzi et al 2005). Survivin was unregulated in our model in a similar fashion to published findings (McMurtry et al 2005), and PYN and VAV-1 oncogenes, requiem homolog, inward rectifier K+ channel, and chloride channel 1 also increased while DEAD/H box polypeptide 3 and angiopoietin 1 displayed decreased expression in agreement with patient findings (Geraci et al 2001). We also observed decreased expression of peroxisome proliferator-activated receptor gamma (Ameshima et al 2003), and downregulated vascular endothelial growth factor B (Louzier et al 2003) in correspondence with previous results. The concordance between genes previously found to be aberrant in published PAH studies and altered gene expression in our model attest to the validity and potential usefulness of gene expression data derived from endoarterial biopsies. The time dependent nature of gene expression dysregulation found in our modal further demonstrates the utility of obtaining endoarterial biopsies at multiple time points is PAH progression.

[0072] While several of these genes have been previously associated with PAH (for example, KCBN1, CASP3, TLR4, IL1B, IL6, HMGCR, TOP1, FYN, PRKCA, EDNRB, PDGFRA, and HRT2B), several have not (for example, HSPE, YES1, CFTR, MAOA, MAOB, and CACND21), raising the intriguing possibility that known existing drugs that target upregulated genes previously unassociated with PAH may be effective in treatment of the disease.

[0073] Endoarterial biopsy samples percutaneously obtained during the progression of PAH were analyzed to correlate changes in microRNA expression to disease progression.

[0074] Data analysis was done in three stages. First, expression intensities were calculated for each miRNA probed on the array for all hybridizations (12 in total) using illumina's Beadstudio Version 3.0 software. Second, intensity values were quality controlled and normalized: quality control was carried out by using the illumina Beadstudio detection P-value set to <0.05 as a cutoff. This removed miRNAs which were effectively absent from the arrays (that is, were never detected). After this step, the initial 1145 miRNAS were reduced to 1094. All the arrays were then normalized using the normalize.quantiles routine from the Affy package in Bioconductor. This procedure accounted for any variation in hybridization intensity between the individual arrays

[0075] Finally, these normalized data were imported into GeneSpring and analysed for differentially expressed miRNAs. The groups of biological replicates ware described to the software and significantly differentially expressed genes determined on the basis of Welch t-tests and fold difference changes in expression level. The determination of miRNA targets genes was done a using a publicly available database of miRNA target sequences and a specially written PERL programming script.

[0076] The data was also looked at to reflect the stages of the disease (based on blood pressure and flow rates), as opposed to the time point or the individual pigs. Three groups were defined (1) Normal (baseline); (2) High Flow Low Pressure ‘HFLP’ and (3) High Flow High Pressure ‘HFHP’ (see Table 11). The groups were compared back so the baseline and the statistically significantly differentially regulated miRNAs determined (Tables 12, 13, 14 & 15).

[0077] Using illumina microRNA expression microarrays, fluctuations in the level of expression of ˜1200 microRNAs were determined during the onset and progression of PAH. Porcine and Homo sapiens miRNA sequences are very often highly conserved. Expression comparisons were done on a timepoint basis, taking in account the available replicates and the statistical significance of the expression changes. The data was also looked at to reflect the stages of the disease (based on blood pressure and flow rates), as opposed to the time point or the individual pigs. Three groups were defined (1) Normal (baseline); (2) High Flow Low Pressure ‘HFLP’ and (3) High Flow High Pressure ‘HFHP’. The groups were compared back to the baseline and the statistically significantly differentially regulated miRNAs determined.

[0078] The messenger RNA expression data set was analysed looking for expression changes in sets of genes with known target sites for particular miRNAs. miRNAs are known to negatively regulate gene expression at the level of translation by binding to upstream regions of mRNA and blocking events required for translation of the mRNA into protein. This was again done using Gene Set Enrichment Analysis (GSEA) and the publicly available miRNA genesets. “Cross-talk” is seen between the messenger RNA gene expression changes and the microRNA expression changes. The messenger RNA expression analysis directly revealed the differential expression of groups of genes competent to be regulated by these miRNA.

[0079] The use of gene expression data to shape individual drug therapies has been postulated as the next phase in personalized medicine. The bioinformatic processing of an individual's gene expression data can be used to generate a ranked list of therapies suitable for that individual. PAH disease pathology varies greatly over time, and is also likely to be specific for particular individuals. The analysis of the RNA in the PAH biopsy samples allows therapies to be tailored to the individual at that particular stage of the diseases progression.

[0080] The genes and biochemical pathways changing the most at the level of gene expression can be determined by comparing the PAH biopsy samples to a baseline control of normal healthy vasculature tissue. Observations show time-dependent extensive changes in gene expression with the progression of PAH. Known targets for approved PAH therapeutics can be seen Up-regulated in the diseased state.

[0081] Drug therapies can be ranked by using the known targets of drugs as genesets. These drug signature lists can then be used in a process such as Gene Set Enrichment Analysis, or KS statistics. KS Statistics returns a score for how well ranked a particular drug would be for a particular patient.

[0082] A drug is represented as the set of its known target genes; this can be in the dozens for some bioactive compounds. Genesets for ˜2000 drugs were assembled. KS Statistics yields a value (‘KS score’) representing the positional distribution of the set of query genes (here, the drug targets) within an ordered list of genes (genes induced in PAH). The ordered list is produced by looking at the fold change in a mRNAs expression between time X and the baseline, and sorting on this value. The gene with the greatest fold change is ranked as #1, second greatest fold change is ranked as #2, etc. KS score is computed in accordance with the Kolmogorov-Smirnov non-parametric rank statistic where X is the number of genes in the query gene set, Z is the number of genes in the ordered list, and Y=Z−X. A suitable baseline is generated using gene expression from artery samples from non-diseased controls. These samples can be obtained surgically, percutaneously or post-mortem.

[0083] This process can be repeated for all the PAH time points and the resulting table of KS scores for each drug hierarchically clustered. This reveals which drugs are potentially of the greatest therapeutic value for a patient.

[0084] This supports the idea of achieving personalized treatments for vascular-based diseases by generating individualized drug prescriptions based on the bioinformatic processing of gene expression data from endoarterial biopsy samples obtained from diseased arteries. Similarly, this enables personalized treatments for vascular-based diseases by generating lists of dysregulated microRNA from the bioinformatic processing of microRNA expression data from endoarterial biopsy samples from diseased arteries.

TABLE 1
Pulmonary arterial pressures obtained during endoarterial biopsy
catheterization for biopsy samples used in microarray analysis.
	Pulmonary Arterial Pressure mmHg
	Biopsy Sample	Systolic	Diastolic	Mean
	Baseline Pig #9	22	11	17
	Baseline Pig #10	25	16	18
	Day 21 Pig #2	95	66	82
	Day 21 Pig #6	41	26	33
	Day 60 Pig #6	87	59	74
	Day 60 Pig #5	49	27	38

TABLE 22
Selected genes previously associated with PAH similarly dysregulated in the porcine model.
							Fold	Fold	Fold	Fold
							Change	Change	Change	Change
							Day	Day	Day	Day
Gene							7/	21/	60/	180/
Symbol	Name	Baseline	Day 7	Day 21	Day 60	Day 180	Base	Base	Base	Base
VAV1	vav 1 oncogene	32.56	21.65	237.16	138.29	15.39	−1.50	7.28	4.25	−2.12
RASA1	RAS p21 protein	73.23	388.68	280.27	704.56	361.25	5.31	3.83	9.62	4.93
	activator 1
TIE2	protein-tyrosine	20.92	2.59	32.46	702.68	218.34	−8.08	1.55	33.59	10.44
	kinase Tie2
FYN	fyn proto-	43.33	180.38	404.77	531.03	166.17	4.16	9.34	12.26	3.83
	oncogene
VDAC1	voltage-dependent	1176.57	2350.33	6629.76	3144.14	2277.10	2.00	5.63	2.67	1.94
	anion channel 1
PDGFRA	platelet-derived	69.46	152.75	10.45	611.81	18.80	2.20	−6.64	8.81	−3.69
	growth factor
	receptor alpha
5-HT2B	serotonin 2B	49.96	19.03	166.45	142.91	488.77	−2.63	3.33	2.86	9.78
	receptor
KCNJ2	inwardly	389.66	83.23	1059.11	212.29	84.28	−4.68	2.72	−1.84	−4.62
	rectifying
	potassium
	channel
	KIR6.1
5-HT1D	serotonin 1D	14.17	42.48	8.32	61.48	4.95	3.00	−1.70	4.34	−2.86
	receptor
DPF2	requiem,	140.95	123.89	140.90	195.55	93.92	−1.14	−1.00	1.39	−1.50
	apoptosis
	response zinc
	finger gene
VDAC2	Voltage-	5683.09	2665.62	1620.37	4940.32	3643.38	−2.13	−3.51	−1.15	−1.56
	dependent
	anion channel 2
STAT5B	signal transducer	469.40	571.47	1327.53	808.09	626.07	1.22	2.83	1.72	1.33
	and activator of
	transcription 5b
AGPT	angiopoietin 1	17.825026	50.240685	11.806807	70.12715	3.4479218	2.82	−1.51	3.93	−5.17
BIRC5	apoptosis	169.33	751.37	140.45	31.26	44.69	4.44	−1.21	−5.42	−3.79
	inhibitor survivin
VDAC3	voltage-dependent	4092.99	750.86	5013.26	959.72	2018.55	−5.45	1.22	−4.26	−2.03
	anion channel 3
PLANH1	plasminogen	27760.742	43007.71	23685.375	28771.838	36086.305	1.55	−1.17	1.04	1.30
	activator
	inhibitor I
PPARG	peroxisome	177.80	12.86	18.96	194.20	62.40	−13.83	−9.38	1.09	−2.85
	proliferator-
	activated receptor
	gamma 2
CALM1	Calmodulin	3092.78	2436.23	867.25	1401.38	2252.81	−1.27	−3.57	−2.21	−1.37
APOE	apolipoprotein E	358.91	206.34	94.35	129.60	288.79	−1.74	−3.80	−2.77	−1.24

TABLE 3
Day 7 prescription.
		Fold	Fold	Fold	Fold
Probe ID	Name	Change D7/Base	Change D21/Base	Change D60/Base	Change D180/Base	Gene Symbol	DRUGS
Ssc.11018.1.A1_at	Mitogen-activated protein	39.80	−5.02	−17.27	−20.43	MAPK14	SCIO-469, RO-3201195
	kinase 14
Ssc.15986.1.S1_at	Insulin receptor	29.28	−16.85	−17.70	−19.01	INSR	insulin, insulin aspart,
							insulin glulisine, insulin
							lispro, insulin glargine
Ssc.873.1.S1_at	Cell division control protein 2	27.38	−2.37	−4.66	−7.76	CDC2	flavopiridol
Ssc.29928.1.A1_at	Histone deacetylase 11 (HD11)	21.56	−3.17	−2.91	−10.50	HDAC11	tributyrin, FXD101,
							pyroxamide, vorinostat, FR901228
Ssc.100.1.S1_at	Tumor necrosis factor	18.41	−1.70	−9.62	−3.15	TNF	adalimumab, etanercept,
	precursor (TNF-alpha)						infliximab, CDF870, golimumab,
							thalidomide
Ssc.19672.1.S1_at	RAC-alpha serine/threonine-	17.77	1.25	−19.62	−2.11	AKT1	enzastaurin
	protein kinase
Ssc.14475.3.S1_a_at	Peroxisome proliferator	13.83	−9.38	1.09	−2.85	PPARG	rosiglitatone, GI262570,
	activated receptor gamma						pioglitozone, tesaglitazar,
	(PPAR-gamma)						troglitazone
Ssc.14326.1.A1_at	Mitogen-activated protein	12.63	1.56	−1.80	−7.24	MAPK13	SCIO-469
	kinase 13
Ssc.25843.1.S1_at	Chloride channel protein 2	11.77	−17.04	−21.97	−9.92	CLCN2	lubiprostone
	(ClC-2
Ssc.16201.1.A1_at	Metabotropic glutamate	10.79	−35.68	−33.34	−47.92	GRM7	fasoracetam
	receptor
Ssc.11381.1.S1_at	Interferon-alpha/beta receptor	10.45	8.08	2.61	−1.30	IFNAR1	interferon beta-1a, interferon
	alpha chain						alfa-2b, interferon alfacon-1,
							PEG-interferon alfa-2a,
							interferon alfa-2a/ribavirin,
							pegintron, interferon beta-1b,
							IFNA2A
Ssc.5371.1.S1_a_at	DNA polymerase epsilon subunit	10.16	1.68	−1.37	−3.14	POLE2	gemcitabine
	B (DNA polymerase II subunit
	B)
Ssc.14471.1.S1_at	B-lymphocyte antigen CD19	9.54	3.30	−10.32	3.04	CD19	combotox, HD37-dgRTA, MT103
Ssc.24856.1.A1_at	phosphodiesterase 11A; cyclic	9.36	−2.70	−3.68	−2.68	PDE11A	dyphylline, nitroglycerin,
	nucleotide phosphodiesterase						aminophylline, dipyridamole,
	11A1						tolbutamide, tadalafil,
							theophylline, pentoxifylline
Ssc.189.1.S1_at	Diacylglycerol O-	9.16	−4.21	−36.07	−24.06	DGAT1	omacor
	acyltransferase 1
Ssc.16186.1.S1_at	T-cell surface glycoprotein	9.00	3.12	2.12	−5.66	CD3E	visilizumab, MT103, muromonab-
	CD3 epsilon						CD3
Ssc.15601.1.A1_s_at	Interleukin-1 beta precursor	8.18	4.65	−14.71	−2.84	IL1B	IL-1 trap
	(IL-1 beta)
Ssc.5538.1.S1_at	Carbonic anhydrase II	8.09	1.39	−2.25	−1.35	CA2	methazolamide,
	(Carbonate dehydratase II)						hydrochlorothiazide,
	(CA-II) (Carbonic anhydrase						acetazolamide,
	C)]						trichloromethiazide,
							dorzolamide, chlorothiazide,
							dorzolamide/timolol,
							brinzolamide, chlorthalidone,
							benzthiazide, sulfacetamide,
							topiramate
Ssc.113.1.S1_at	Interleukin-1 alpha (IL-1	8.01	−2.65	−1.59	−7.73	IL1A	IL-1 trap
	alpha)
Ssc.2895.1.S1_at	Serine/threonine-protein	7.48	−2.02	−2.45	−1.81	AURKB	AZD-1152
	kinase
Ssc.8219.1.A1_at	Histone deacetylase 8 (HD8)	7.21	−2.18	−12.09	−29.63	HDAC8	tributyrin, PXD101,
							pyroxamide, vorinostat, FR
							901228
Ssc.13473.1.A1_at	Ceramide glucosyltransferase	7.13	−2.88	−4.17	−6.14	UGCG	N-
							butyldeoxygalactonojirimycin,
							N-butyldeoxynojirimycin
Ssc.14129.1.A1_at	4-aminobutyrate	6.91	2.56	−21.17	−3.35	ABAT	valproic acid
	aminotransferase,
	mitochondrial (GABA
	transaminase)
Ssc.15379.1.S1_at	diacylglycerol O-	6.60	−1.51	−3.49	−2.13	DGAT2	omacor
	acyltransferase homolog 2;
	GS1999full
Ssc.15830.1.A1_at	Retinoic acid receptor beta	6.60	−3.28	−1.83	−1.10	RARB	etretinate, adapalene, 13-cis-
							retinoic acid, tazarotene,
							acitretin, retinoic acid, 9-
							cis-retinoic acid, fenretinide
Ssc.17928.1.A1_at	helicase (DNA) B; helicase B	6.40	−2.65	−4.38	−1.06	HELB	epirubicin
Ssc.19673.1.S1_at	T-cell surface glycoprotein	6.40	2.70	2.03	−11.77	CD3D	visilizumab, MT103
	CD3 delta
Ssc.17222.1.A1_at	mucin 1, transmembrane;	6.31	−6.10	−5.94	−13.48	MUC1	HuHMFG1
Ssc.55.1.S1_at	Epidermal growth factor	6.09	−5.46	−4.68	−4.52	EGFR	cetuximab, AEE 788,
	receptor						panitumumab, BMS-599626, ARRY-
							334543, XL647, canertinib,
							gefitinib, HKI-272, PD 153035,
							lapatinib, vandetanib,
							erlotinib
Ssc.19059.1.A1_at	Type-1 angiotensin II receptor	5.57	−2.57	1.50	−11.08	AGTR1	amlodipine/olmesartan
	(AT1) (AT1AR						medoxomil,
							losartan/hydrochlorothiazide,
							valsartan/hydrochlorothiazide,
							candesartan cilexetil,
							olmesartan medoxomil,
							irbesartan, losartan potassium,
							telmisartan, eprosartan,
							candesartan
							cilexetil/hydrochlorothiazide,
							hydrochlorothiazide/irbesartan,
							eprosartan/hydrochlorothiazide,
							hydrochlorothiazide/telmisartan,
							hydrochlorothiazide/olmesartan
							medoxomil, valsartan
Ssc.16162.1.S1_at	4-hydroxyphenylpyruvate	5.43	−8.93	−8.65	−3.84	HPD	nitisinone
	dioxygenase
Ssc.27603.1.S1_at	Endothelin B receptor	5.30	−3.27	15.99	1.23	EDNRB	bosentan, sitaxsentan,
							atrasentan
Ssc.16333.1.A1_at	Multidrug resistance protein 1	4.94	−2.99	−3.50	−22.89	ABCB1	XR9576, OC 144-093, valspodar
Ssc.12769.1.A1_at	Amiloride-sensitive cation	4.82	−13.74	−9.59	−10.72	ACCN1	amiloride,
	channel 1, neuronal						amiloride/hydrochlorothiazide
Ssc.17155.1.At_at	heparanase; heparanase-1	4.81	5.38	2.98	−1.83	HPSE	heparanase inhibitor PI-88
Ssc.15933.1.S1_s_at	Cytotoxic T-lymphocyte protein	4.76	−5.14	−6.63	−26.39	CTLA4	ipilimumab, ticilimumab
	4 (Cytotoxic T-lymphocyte-
	associated antigen 4) (CTLA-4)
	(CD152 antigen)
Ssc.15965.1.S1_at	Inward rectifier potassium	4.68	2.72	−1.84	−4.62	KCNJ2	nicorandil, amiodarone
	channel 2 (IRK1)
Ssc.26351.1.S1_at	cAMP-specific 3′,5′-cyclic	4.67	4.99	3.61	−1.15	PDE4D	dyphylline, nitroglycerin,
	phosphodiesterase 4D						arofylline, tetomilast, L
							869298, aminophylline,
							anagrelide, cilomilast,
							milrinone, rolipram,
							dipyridamole, L-826,141,
							roflumilast, tolbutamide,
							theophylline, pentoxifylline,
							caffeine
Ssc.15990.1.A1_at	Retinoic acid receptor RXR-	4.42	−4.18	−18.45	−24.58	RXRA	bexarotene, retinoic acid, 9-
	alpha						cis-retinoic acid
Ssc.2605.1.A1_at	Protein farnesyltransferase	4.39	−2.62	−7.00	−2.74	FNTB	lonafarnib, tipifarnib
	beta
Ssc.30373.1.A1_at	cGMP-specific 3′,5′-cyclic	4.27	1.52	2.34	4.09	PDE5A	dyphylline, nitroglycerin, DA-
	phosphodiesterase						8159, aminophylline,
							sildenafil, dipyridamole,
							aspirin/dipyridamole,
							vardenafil, tolbutamide,
							tadalafil, theophylline,
							pentoxifylline
Ssc.19233.1.S1_at	Collagen alpha 2(IX) chain	4.23	−1.14	−22.83	−2.12	COL9A2	collagenase
Ssc.1147.1.A1_at	Lipoprotein lipase	4.05	−5.49	3.78	−8.08	LPL	nicotinic acid,
							lovastatin/niacin
Ssc.13160.1.A1_at	Voltage-dependent L-type	4.03	−4.75	−1.11	−2.77	CACNA1F	MEM-1003, mibefradil,
	calcium channel alpha-1F						bepridil, nisoldipine,
	subunit						isradipine, nicardipine
Ssc.12748.1.A1_at	Catechol O-methyltransferase,	4.03	−3.20	−5.82	−21.93	COMT	carbidopa/entacapone/levodopa,
	membrane-bound form						BIA-3-202, tolcapone,
							entacapone
Ssc.24986.1.S1_at	Aldehyde dehydrogenase 1A1	4.00	−3.76	−2.17	−9.20	ALDH1A1	disulfiram, chlorpropamide
Ssc.15972.1.S1_at	Peroxisome proliferator	3.83	−1.07	−2.73	−1.60	PPARD	GW501516
	activated receptor delta
	(PPAR-delta)
Ssc.24509.1.A1_at	Gamma-aminobutyric-acid	3.76	−1.28	−1.98	−1.23	GABRP	alphadolone, sevoflurane,
	receptor pi subunit						isoflurane, isoniazid,
							felbamate, etomidate,
							halothane,
							fluoxetine/olanzapine,
							eszopiclone, zolpidem,
							lorazepam, olanzapine,
							zaleplon, secobarbital,
							phenobarbital, pentobarbital,
							desflurane, methoxyflurane,
							enflurane
Ssc.7176.1.A1_at	C—X—C chemokine receptor type	3.74	10.91	8.15	1.68	CXCR4	JM 3100
	4 (CXC-R4) (
Ssc.6570.1.S1_at	Delta-aminolevulinic acid	3.71	−3.43	−3.05	−1.66	ALAD	delta-aminolevulinic acid
	dehydratase]
Ssc.17485.1.S1_at	Guanylate cyclase soluble,	3.57	−38.82	−1.77	−7.04	GUCY1A2	nitroglycerin, isosorbide-5-
	alpha-2 chain						mononitrate, isosorbide
							dinitrate, nitroprusside,
							isosorbide
							dinitrate/hydralazine
Ssc.26200.1.S1_at	Thyroid hormone receptor beta-1	3.49	−1.56	7.96	2.52	THRB	3,5-diiodothyropropionic acid,
							amiodarone, thyroxine, L-
							triiodothyronine
Ssc.9595.1.S1_at	Beta platelet-derived growth	3.48	−3.60	1.40	1.65	PDGFRB	dasatinib, sunitinib,
	factor receptor						axitinib, KRN-951, imatinib,
							sorafenib, becaplermin
Ssc.108.1.S1_at	Oxytocin receptor (OT-R)	3.44	−8.53	−16.69	−4.47	OXTR	TT-235
Ssc.15801.1.A1_at	Protein kinase C, beta	3.36	6.36	3.53	−4.98	PRKCB1	enzastaurin, ruboxistaurin
Ssc.27928.1.S1_at	Opioid growth factor receptor	3.34	−3.33	−2.89	−3.51	OGFR	enkephalin, methionine
	(OGFr)
Ssc.12791.1.A1_at	3-hydroxy-3-methylglutaryl-	3.27	2.77	1.77	−2.56	HMGCR	aspirin/pravastatin,
	coenzyme A reductase (HMG-CoA						lovastatin/niacin,
	reductase						ezetimibe/simvastatin,
							amlodipine/atorvastatin,
							fluvastatin, cerivastatin,
							atorvastatin, pravastatin,
							simvastatin, lovastatin,
							rosuvastatin
Ssc.7933.1.A1_at	Cell division protein kinase 8	3.26	−1.63	−1.78	−3.05	CDK8	flavopiridol
Ssc.11147.1.S1_at	Aldehyde dehydrogenase,	3.18	1.13	2.00	−1.68	ALDH2	disulfiram, chlorpropamide
	mitochondrial (ALDH class 2)
	(ALDHI) (ALDH-E2)
Ssc.19608.1.S1_at	Retinoic acid receptor RXR-	3.15	−1.39	−1.73	−3.19	RXRG	bexarotene, retinoic acid, 9-
	gamma						cis-retinoic acid
Ssc.29260.1.A1_at	Granulocyte colony stimulating	3.15	−2.50	−4.36	−4.68	CSF3R	pegfilgrastim, filgrastim
	factor receptor (G-CSF-R)
	(CD114 antigen)]
Ssc.22797.1.S1_at	DNA topoisomerase II, beta	3.15	−1.87	1.02	−2.91	TOP2B	novobiocin, etoposide, CPI-
	isozyme						0004Na, pixantrone,
							becatecarin, elsamitrucin,
							AQ4N, BN 80927, tafluposide,
							mitoxantrone, norfloxacin,
							dexrazoxane, tirapazamine, TAS-
							103, XK469, gatifloxacin,
							valrubicin, gemifloxacin,
							moxifloxacin, nemorubicin,
							nalidixic acid, epirubicin,
							doxorubicin, daunorubicin
Ssc.16121.1.A1_at	Corticotropin releasing factor	3.03	−4.50	−17.00	−4.36	CRHR1	Crh, CRA0165, CRA1001,
	receptor 1						SSR125543A
Ssc.12630.1.A1_at	Sodium/potassium-transporting	2.97	2.81	−7.30	−2.03	ATP1A1	digoxin, omepratole,
	ATPase alpha-1 chain						ethacrynic acid, perphenazine
Ssc.13254.1.A1_at	Metabotropic glutamate	2.95	−15.40	−9.66	−3.57	GRM8	fasoracetam
	receptor 8
Ssc.30888.1.S1_at	Voltage-dependent L-type	2.92	−2.72	1.90	−7.82	CACNA1D	MEM-1003, mibefradil,
	calcium channel alpha-1D						bepridil, nisoldipine,
							isradipine, nicardipine
Ssc.9565.1.S1_at	Interferon-gamma receptor	2.92	1.63	1.41	−1.23	IFNGR1	interferon gamma-1b
	alpha
Ssc.15880.1.S1_at	Cysteinyl leukotriene receptor	2.86	1.14	−2.12	−3.42	CYSLTR2	montelukast, zafirlukast
	2 (CysLTR2)
Ssc.2753.1.S1_at	Serine/threonine-protein	2.77	−4.42	−7.50	−4.52	PLK1	BI 2536
	kinase PLK1
Ssc.16123.1.A1_at	cAMP-specific 3′,5′-cyclic	2.75	−3.97	−3.49	−1.46	PDE4A	arofylline, tetomilast, LB69298,
	phosphodiesterase 4A						anagrelide, cilomilast,
							milrinone, rolipram, L-826,141,
							amrinone, roflumilast,
							pentoxifylline, caffeine
Ssc.11383.1.A1_at	Glutamate receptor 3	2.61	1.85	−1.87	−2.43	GRIA3	talampanel, Org 24448,
							LY451395, tezampanel
Ssc.14403.1.S1_at	Sodium/potassium-transporting	2.61	−1.95	−8.51	−6.43	ATP1A2	digoxin, omeprazole,
	ATPase alpha-2 chain						ethacrynic acid, perphenazine
Ssc.21754.1.A1_at	Collagen alpha 1(VI) chain	2.57	−47.00	−5.79	−4.37	COL6A1	collagenase
Ssc.6498.1.A1_at	Mitogen-activated protein	2.41	−10.79	−5.78	−3.26	MAPK12	SCIO-469
	kinase 12 (Mitogen-activated
	protein kinase p38 gamma)
Ssc.16167.1.S1_at	Rho-associated protein kinase 1	2.40	2.32	2.83	3.00	ROCK1	fasudil, Y-27632
Ssc.12781.1.A1_at	Toll-like receptor 4	2.39	1.83	−1.52	−6.10	TLR4	TAK-242
Ssc.29366.1.A1_at	DNA topoisomerase I	2.37	−2.30	−2.87	−10.72	TOP1	elsamitrucin, T 0128, CT-2106,
							BN 80927, tafluposide, TAS-103,
							beta-lapachone, irinotecan,
							topotecan, 9-amino-20-
							camptothecin, rubitecan,
							gimatecan, karenitecin
Ssc.14485.1.S1_at	Parathyroid	2.28	−1.62	−3.40	−6.83	PTHR1	teriparatide
	hormone/parathyroid hormone-
	related peptide receptor
Ssc.12238.1.A1_at	Cysteinyl leukotriene receptor	2.28	−1.73	−1.34	−6.96	CYSLTR1	zeneca ZD 3523, montelukast,
	1 (CysLTR1)						zafirlukast
Ssc.3607.1.S1_at	Interferon-alpha/beta receptor	2.28	4.65	1.11	−1.56	IFNAR2	interferon beta-1a, interferon
	beta chain						alfa-2b, interferon alfacon-1,
							PEG-interferon alfa-2a,
							interferon alfa-2a/ribavirin,
							pegintron, interferon beta-1b,
							IFNA2A
Ssc.2548.1.S1_at	DNA polymerase epsilon p17]	2.27	1.93	−1.67	−1.21	POLE3	gemcitabine
Ssc.19706.1.A1_at	Mitogen-activated protein	2.25	−4.27	−2.31	−4.98	MAPK8	aplidine
	kinase 8
Ssc.15382.1.S1_at	Cannabinoid receptor 2 (CB2)	2.22	3.48	−1.37	−4.69	CNR2	BAY 38-7271, delta-9-
	(CB-2) (CX5)						tetrahydrocannabinol
Ssc.4756.1.A1_at	Adenosine A3 receptor	2.15	2.10	1.88	−1.81	ADORA3	adenosine, dyphylline,
							aminophylline, clofarabine,
							theophylline, caffeine
Ssc.23261.1.A1_at	Trifunctional purine	2.15	−3.54	−3.66	−5.67	GART	LY231514
	biosynthetic protein
	adenosine-3
Ssc.27293.1.A1_at	Hypoxanthine-guanine	2.15	−1.16	1.63	−7.14	HPRT1	6-mercaptopurine, thioguanine,
	phosphoribosyltransferase						azathioprine
Ssc.14476.1.S1_at	Interleukin-2 receptor alpha	2.07	−8.52	−8.73	−5.00	IL2RA	LMB-2, daclizumab,
							basiliximab, aldesleukin,
							denileukin diftitox
Ssc.27232.1.S1_at	Succinate semialdehyde	2.06	1.12	−2.84	−5.73	ALDH5A1	valproic acid
	dehydrogenase, mitochondrial
Ssc.10142.1.A1_at	Dihydropyrimidine	2.06	2.17	1.13	−2.68	DPYD	eniluracil
	dehydrogenase [NADP+]
Ssc.1908.1.S1_at	FKBP-rapamycin associated	1.99	1.13	−1.88	−1.46	FRAP1	AP23573, temsirolimus,
	protein (FRAP)						tacrolimus, everolimus
Ssc.204.1.S1_at	Cytochrome P450 3A4	1.97	−3.35	2.16	−1.42	CYP3A4	ketoconazole
Ssc.18459.1.S1_at	Amiloride-sensitive sodium	1.97	−2.41	1.43	−1.93	SCNN1A	triamterene/hydrochlorothiazide,
	channel alpha-						amiloride,
							amiloride/hydrochlorothiazide,
							triamterene
Ssc.24889.1.S1_at	Arachidonate 12-lipoxygenase,	1.97	2.83	−1.18	−2.78	ALOX12	sulfasalazine, balsalazide, 5-
	12S-type						aminosalicylic acid,
							masoprocol, verteporfin
Ssc.15748.2.S2_at	T lymphocyte activation	1.95	−1.24	1.54	−3.12	CD80	abatacept
	antigen CD80
Ssc.5826.1.A1_at	Macrophage colony stimulating	1.93	−4.97	−13.18	1.61	CSF1R	sunitinib
	factor I receptor (CD115
	antigen)
Ssc.15822.1.S1_at	Coagulation factor V	1.92	3.76	1.89	−1.75	F5	drotrecogin alfa
Ssc.9262.1.A1_at	Histamine H1 receptor	1.91	−4.10	−3.91	−1.86	HRH1	nitisinone
Ssc.62.2.S1_a_at	Interleukin-6 (IL-6) (	1.89	−6.32	2.77	−1.04	IL6	tocilizumab
Ssc.14258.1.S1_at	Amyloid beta A4 protein	1.87	3.33	1.34	−1.02	APP	AAB-001
Ssc.15878.1.S1_at	Serine/threonine protein	1.85	3.22	3.62	−1.49	PPP3CA	ISAtx-247, tacrolimus,
	phosphatase 2B						pimecrolimus, cyclosporin A
Ssc.19379.1.A1_at	Voltage-dependent L-type	1.83	−1.34	1.07	−3.27	CACNA1C	clevidipine, MEM-1003,
	calcium channel alpha-1C						amlodipine/olmesartan
	subunit						medoxomil,
							amlodipine/benazepril,
							diltiazem, verapamil,
							mibefradil, bepridil,
							enalapril/felodipine,
							amlodipine/atorvastatin,
							nisoldipine, isradipine,
							felodipine, nimodipine,
							nitrendipine, amlodipine,
							nicardipine, nifedipine,
							trandolapril/verapamil,
							diltiazem/enalapril
Ssc.10215.1.A1_at	High-affinity cAMP-specific	1.83	1.12	−8.48	−15.51	PDE8A	dyphylline, nitroglycerin,
	and IBMX-insensitive 3′,5′-						aminophylline, anagrelide,
	cyclic phosphodiesterase 8A						milrinone, dipyridamole,
							tolbutamide, theophylline,
							pentoxifylline
Ssc.20944.1.S1_at	Carbonic anhydrase XIV	1.82	−3.49	−5.34	−12.64	CA14	methazolamide,
							hydrochlorothiazide,
							acetazolamide,
							trichloromethiazide,
							chlorothiazide, chlorthalidone,
							benzthiazide, sulfacetamide,
							topiramate
Ssc.4125.1.A1_at	Histone deacetylase 5 (HD5)	1.82	−4.64	−4.18	−2.74	HDAC5	tributyrin, PXD101,
							pyroxamide, vorinostat, FR
							901228
Ssc.9272.1.S1_at	Tumor-associated calcium	1.81	3.94	176.50	2.07	TACSTD1	tucotuzumab celmoleukin
	signal transducer 1 (EPCAM
	antigen)
Ssc.6301.1.S1_at	Aromatic-L-amino-acid	1.76	1.80	−4.91	−1.88	DDC	carbidopa/entacapone/levodopa,
	decarboxylase						carbidopa/levodopa, S(−)-
							carbidopa, L-dopa
Ssc.15995.1.S1_at	Potassium voltage-gated	1.74	−26.30	−13.45	−2.79	KCNE1	nicorandil, amiodarone,
	channel subfamily E member 1						azimilide
Ssc.7581.1.A1_at	FL cytokine receptor precursor	1.69	1.04	−1.81	1.01	FLT3	CHIR-258, sorafenib,
							lestaurtinib, CGP 41251
Ssc.26325.1.S1_at	Cystic fibrosis transmembrane	1.60	−5.71	4.77	−8.99	CFTR	SP 303
	conductance regulator (CFTR)
Ssc.19691.1.S1_at	Platelet-activating factor	1.59	1.16	3.01	1.47	PLA2G7	darapladib
	acetylhydrolase
Ssc.24714.1.A1_at	Excitatory amino acid	1.58	−2.29	−7.61	−3.45	SLC1A2	riluzole
	transporter 2 (Sodium-
	dependent glutamate/aspartate
	transporter 2)
Ssc.22477.1.S1_at	Collagen alpha 1(IV) chain	1.58	−5.08	−1.78	1.04	COL4A1	collagenase
Ssc.227.1.S1_at	Potassium-transporting ATPase	1.56	−5.89	−51.53	−15.60	ATP4B	ilaprazole, TAK-390MR,
	beta						tenatoprazole, AGN 201904, AR-
							H047108, esomeprazole
							magnesium, omeprazole,
							lansoprazole,
							amoxicillin/clarithromycin/lans
							oprazole, rabeprazole,
							pantoprazole
Ssc.30147.1.A1_at	Fibroblast growth factor	1.56	1.13	−1.05	−1.18	FGFR2	palifermin
	receptor 2
Ssc.9523.1.A1_at	Methylated-DNA--protein-	1.55	−1.17	3.35	1.47	MGMT	O6-benzylguanine
	cysteine methyltransferase
Ssc.26466.1.A1_at	Integrin beta-3 (CD61 antigen	1.55	−1.49	−4.11	−2.73	ITGB3	TP 9201, EMD121974, tirofiban
Ssc.5592.1.S1_at	Protein	1.49	−3.49	−2.81	−3.68	FNTA	lonafarnib, tipifarnib
	farnesyltransferase/geranylgeranyltransferase
	type I alpha
Ssc.17986.1.A1_at	Poly [ADP-ribose] polymerase-1	1.47	−6.14	−4.64	−5.80	PARP1	INO-1001
Ssc.11051.1.S1_at	Cell division protein kinase 4	1.44	−7.67	−4.96	23.75	CDK4	PD-0332991, flavopiridol
Ssc.20818.1.S1_at	Interleukin-2 receptor beta	1.43	6.35	−1.17	−5.23	IL2RB	humanized MiK-Beta-1,
	chain						aldesleukin, denileukin
							diftitox
Ssc.16489.1.S1_at	Interleukin-7 receptor alpha	1.42	2.34	1.86	−6.31	IL7R	recombinant human interleukin-7
	chain
Ssc.10287.1.A1_at	Transforming growth factor	1.41	−4.54	1.43	1.67	TGFB2	AP-12009
	beta 2
Ssc.14375.1.A1_at	ribonucleotide reductase M2 B	1.40	−2.19	−5.17	−2.28	RRM2B	triapine, hydroxyurea
Ssc.16823.1.S1_at	P2Y purinoceptor 12 (P2Y12)	1.38	1.19	1.41	1.11	P2RY12	prasugrel, AZD 6140
	(P2Y12 platelet ADP receptor)						(Ticagrelor), clopidogrel
	(P2Y(ADP))
Ssc.11164.1.A1_at	DNA polymerase gamma subunit 1	1.37	−1.33	−3.67	−3.60	POLG	stavudine, vidarabine,
							zalcitabine
Ssc.10219.1.A1_at	Excitatory amino acid	1.36	1.24	−3.38	−1.03	SLC1A6	riluzole
	transporter 4
Ssc.3815.1.S1_at	RAC-beta serine/threonine-	1.35	−3.13	−3.28	−3.19	AKT2	enzastaurin
	protein kinase
Ssc.19619.1.S1_at	Proto-oncogene tyrosine-	1.35	−1.51	−3.32	−2.41	LCK	dasatinib
	protein kinase LCK
Ssc.2926.1.S1_at	Heme oxygenase 2	1.34	−1.43	−4.13	−1.81	HMOX2	tin mesoporphyrin
Ssc.11171.1.S1_at	Adenosine deaminase	1.32	−2.04	−3.23	1.12	ADA	pentostatin, vidarabine
Ssc.16621.1.A1_at	Excitatory amino acid	1.27	−7.18	−3.04	−8.29	SLC1A1	riluzole
	transporter 3
Ssc.11549.1.A1_at	Dual specificity mitogen-	1.25	−33.67	−11.33	−6.48	MAP2K1	PD 0325901
	activated protein kinase
	kinase 1
Ssc.6356.1.S1_at	Ornithine decarboxylase	1.24	−1.36	−1.74	−1.84	ODC1	tazarotene, eflornithine
Ssc.15999.1.A1_at	Vascular endothelial growth	1.24	1.24	18.52	−11.02	KDR	AEE 788, sunitinib, AZD 2171,
	factor receptor 2 (VEGFR-2)						pasopanib, XL647, CEP 7055,
							BMS-582664, KRN-951, vatalanib,
							sorafenib, vandetanib,
							pegaptanib
Ssc.9669.1.S1_at	Cell division protein kinase 5	1.21	−2.68	−17.06	−1.65	CDK5	flavopiridol
Ssc.115.1.S1_s_at	Heme oxygenase 1	1.18	−2.09	−3.40	−3.55	HMOX1	tin mesoporphyrin
Ssc.17224.1.S1_at	Toll-like receptor 8	1.17	6.49	3.13	−1.52	TLR8	resiquimod
Ssc.8046.1.A1_at	peptidylprolyl, isomerase A	1.16	−1.08	1.39	1.26	PPIA	M-methyl-4-Ile-cyclosporin
	isoform 1
Ssc.7297.1.S1_at	Amine oxidase [flavin-	1.16	6.42	7.02	−1.13	MAOB	safinamide, ladostigil,
	containing] B (MAO-B)						rasagiline, selegiline,
							dextroamphetamine,
							procainamide, tranylcypromine,
							phenelzine, isocarboxazid,
							benzphetamine
Ssc.28329.1.S1_at	DNA polymerase beta	1.14	1.19	1.07	5.25	POLB	nelarabine, clofarabine,
							stavudine, trifluridine,
							vidarabine, ralcitabine,
							entecavir
Ssc.12202.2.S1_at	Farnesyl pyrophosphate	1.12	−1.26	−2.68	−1.13	FDPS	YM 529, alendronic acid,
	synthetase						pamidronic acid
Ssc.19700.1.S1_at	Serine/threonine protein	1.11	1.13	1.34	1.09	PPP3CB	ISAtx-247, tacrolimus,
	phosphatase 2B catalytic						pimecrolimus, cyclosporin A
	subunit, beta
Ssc.8549.1.A1_at	Guanylate cyclase soluble,	1.11	1.84	−2.00	−2.59	GUCY1A3	nitroglycerin, isosorbide-5-
	alpha-3 chain						mononitrate, isosorbide
							dinitrate, nitroprusside,
							isosorbide
							dinitrate/hydralazine
Ssc.15374.1.S1_at	COL14A1 protein	1.10	−17.71	3.79	2.24	COL14A1	collagenase
Ssc.15901.1.S1_at	cGMP-inhibited 3′,5′-cyclic	1.10	−13.33	−3.16	−1.41	PDE3A	dyphylline, nitroglycerin,
	phosphodiesterase A						medorinone, aminophylline,
							cilostazol, dipyridamole,
							amrinone, tolbutamide,
							theophylline, pentoxifylline
Ssc.16000.1.A1_at	Vascular endothelial growth	1.08	−3.40	3.34	1.72	FLT1	sunitinib, axitinib, CEP 7055
	factor receptor 1
Ssc.20987.1.S1_at	Thrombopoietin receptor1	1.06	−1.58	−3.97	−5.87	MPL	SB-497115
Ssc.11149.1.S1_at	Carbonic anhydrase IX	1.04	−1.91	−1.33	−1.26	CA9	cG250, I 131 chimeric G250, Y
							90 chimeric G250,
							methazolamide,
							hydrochlorothiazide,
							acetazolamide,
							trichloromethiazide,
							chlorothiazide, chlorthalidone,
							benzthiazide, sulfacetamide,
							topiramate
Ssc.8726.1.A1_at	Amidophosphoribosyltransferase	1.03	1.24	4.16	−1.11	PPAT	6-mercaptopurine, thioguanine,
							azathioprine
Ssc.11406.1.A1_a_at	Interleukin-1 receptor, type I	1.03	2.70	−2.44	1.42	IL1R1	anakinra
Ssc.14506.1.S1_at	DNA topoisomerase II, alpha	1.01	3.80	−1.18	−14.30	TOP2A	novobiocin, etoposide, CPI-
							0004Na, pixantrone,
							becatecarin, elsamitrucin,
							AQ4N, BN 80927, tafluposide,
							mitoxantrone, norfloxacin,
							dexrazoxane, tirapazamine, TAS-
							103, gatifloxacin, valrubicin,
							gemifloxacin, moxifloxacin,
							nemorubicin, nalidixic acid,
							epirubicin, doxorubicin,
							daunorubicin

TABLE 4
Day 21 prescription.
		Fold	Fold	Fold	Fold
Probe ID	Name	Change D7/Base	Change D21/Base	Change D60/Base	Change D180/Base	Gene Symbol	Drugs
Ssc.23793.1.S1_at	T-cell surface antigen CD2	−3.27	79.25	93.34	5.20	CD2	alefacept, siplizumab
SscAffx.20.1.S1_at	T-cell surface glycoprotein	−2.19	14.07	10.96	3.62	CD3G	visilizumab, MT103
	CD3 gamma chain
Ssc.19532.1.S1_at	Guanylate cyclase soluble,	−4.28	12.74	3.04	2.13	GUCY1B3	nitroglycerin, isosorbide-5-
	beta-1 chain						mononitrate, isosorbide
							dinitrate, nitroprusside,
							isosorbide
							dinitrate/hydralazine
Ssc.7176.1.A1_at	C—X—C chemokine receptor type	3.74	10.91	8.15	1.68	CXCR4	JM 3100
	4 (CXC-R4) (CXCR-4) (CD184
	antigen).
Ssc.2714.1.S1_a_at	Proto-oncogene tyrosine-	−4.26	9.56	12.54	3.93	FYN	dasatinib
	protein kinase FYN
Ssc.15739.1.S1_at	Cytokine receptor common gamma	−1.12	9.42	1.90	−1.28	IL2RG	aldesleukin, denileukin
	chain (Interleukin-2						diftitox
	receptor gamma chain) (IL-2R
	gamma chain) (CD132 antigen)
Ssc.11381.1.S1_at	Interferon-alpha/beta receptor	10.45	8.08	2.61	−1.30	IFNAR1	interferon beta-1a, interferon
	alpha chain						alfa-2b, interferon alfacon-1,
							PEG-interferon alfa-2a,
							interferon alfa-2a/ribavirin,
							pegintron, interferon beta-1b,
							IFNA2A
Ssc.10256.1.A1_at	cAMP-specific 3′,5′-cyclic	−1.89	6.74	2.20	2.44	PDE4B	dyphylline, nitroglycerin,
	phosphodiesterase 4B						arofylline, tetomilast, L
							869298, aminophylline,
							anagrelide, cilomilast,
							milrinone, rolipram,
							dipyridamole, L-826,141,
							roflumilast, tolbutamide,
							theophylline, pentoxifylline,
							caffeine
Ssc.17224.1.S1_at	Toll-like receptor 8	1.17	6.49	3.13	−1.52	TLR8	resiquimod
Ssc.7297.1.S1_at	Amine oxidase [flavin-	1.16	6.42	7.02	−1.13	NAOB	safinamide, ladostigil,
	containing] B (Monoamine						rasagiline, selegiline,
	oxidase) (MAO-B).						dextroamphetamine,
							procainamide, tranylcypromine,
							phenelzine, isocarboxazid,
							benephetamine
Ssc.15801.1.A1_at	Protein kinase C, beta	3.36	6.36	3.53	−4.98	PRKCB1	enzastaurin, ruboxistaurin
Ssc.20818.1.S1_at	Interleukin-2 receptor beta	1.43	6.35	−1.17	−5.23	IL2RB	humanized MiK-Beta-1,
	chain (IL-2 receptor)						aldesleukin, denileukin
							diftitox
Ssc.12937.1.S1_at	Presenilin 1 (PS-1) (S182	−14.09	6.21	2.48	3.79	PSEN1	(R)-flurbiprofen
	protein).
Ssc.15932.1.S1_at	Integrin alpha-V	−6.15	5.79	2.94	3.14	ITGAV	abciximab, CNTO 95, EMD121974
Ssc.26328.1.S1_at	C-C chemokine receptor type 5	−2.53	5.61	3.25	1.29	CCR5	maraviroc, vicriviroc, SCH
	(CCR5) (CD195 antigen).						351125
Ssc.12845.1.S1_at	Cell division protein kinase 6	−6.56	5.40	4.77	5.13	CDK6	PD-0332991, flavopiridol
Ssc.17155.1.A1_at	heparanase; heparanase-1	4.81	5.38	2.98	−1.83	HPSE	heparanase inhibitor PI-88
Ssc.13460.1.A1_at	Histone deacetylase 9 (HD9)	−6.40	5.13	−1.79	5.72	HDAC9	tributyrin, PXD101,
	(HD7B) (HD7)						pyroxamide, vorinostat, FR
							901228
Ssc.24528.1.S1_at	Angiotensin-converting enzyme	−1.61	5.01	2.33	4.76	ACE	pentopril, perindoprilat,
							amlodipine/benazepril,
							lisinopril/hydrochlorothiazide,
							benazepril, enalapril,
							perindopril, captopril,
							enalapril/felodipine,
							hydrochlorothiazide/moexipril,
							benazepril/hydrochlorothiazide,
							hydrochlorothiazide/quinapril,
							fosinopril/hydrochlorothiazide,
							captopril/hydrochlorothiazide,
							enalapril/hydrochlorothiazide,
							ramipril, moexipril, quinapril,
							lisinopril, enalaprilat,
							trandolapril,
							trandolapril/verapamil,
							diltiazem/enalapril, fosinopril
Ssc.26351.1.S1_at	cAMP-specific 3′,5′-cyclic	4.67	4.99	3.61	−1.15	PDE4D	dyphylline, nitroglycerin,
	phosphodiesterase 4D						arofylline, tetomilast, L
							869298, aminophylline,
							anagrelide, cilomilast,
							milrinone, rolipram,
							dipyridamole, L-826,141,
							roflumilast, tolbutamide,
							theophylline, pentoxifylline,
							caffeine
Ssc.15601.1.A1_s_at	Interleukin-1 beta precursor,	8.18	4.65	−14.71	−2.84	IL1B	IL-1 trap
	(IL-1 beta)
Ssc.3607.1.S1_at	Interferon-alpha/beta receptor	2.28	4.65	1.11	−1.56	IFNAR2	interferon beta-1a, interferon
	beta chain						alfa-2b, interferon alfacon-1,
							PEG-interferon alfa-2a,
							interferon alfa-2a/ribavirin,
							pegintron, interferon beta-1b,
							IFNA2A
Ssc.20841.1.S1_at	Proto-oncogene tyrosine-	−1.13	4.37	−2.59	−1.93	SRC	dasatinib, AZM-475271
	protein kinase Src
Ssc.11200.1.S1_a_at	Proto-oncogene tyrosine-	−1.15	4.28	−3.41	−1.18	ABL1	imatinib, temozolomide
	protein kinase ABL1
Ssc.22974.1.A1_at	Metabotropic glutamate	−1.05	4.28	−2.05	−5.66	GRM1	fasoracetam
	receptor 1
Ssc.7111.1.A1_at	Ribonucleoside-diphosphate	−13.13	4.08	1.37	1.67	RRM2	gemcitabine, triapine,
	reductase M2 chain						hydroxyurea, fludarabine
	(Ribonucleotide reductase						phosphate
	small chain)
Ssc.9272.1.S1_at	Tumor-associated calcium	1.81	3.94	176.50	2.07	TACSTD1	tucotuzumab celmoleukin
	signal transducer 1 (EPCAM
	antigen)
Ssc.16160.1.S1_at	T lymphocyte activation	−1.55	3.88	−1.37	1.18	CD86	abatacept
	antigen CD86
Ssc.14506.1.S1_at	DNA topoisomerase II, alpha	1.01	3.80	−1.18	−14.30	TOP2A	novobiocin, etoposide, CPI-
	isozyme						0004Na, pixantrone,
							becatecarin, elsamitrucin,
							AQ4N, BN 80927, tafluposide,
							mitoxantrone, norfloxacin,
							dexrazoxane, tirapazamine, TAS-
							103, gatifloxacin, valrubicin,
							gemifloxacin, moxifloxacin,
							nemorubicin, nalidixic acid,
							epirubicin, doxorubicin,
							daunorubicin
Ssc.15822.1.S1_at	Coagulation factor V	1.92	3.76	1.89	−1.75	F5	drotrecogin alfa
	(Activated protein C
	cofactor).
Ssc.9034.1.A1_at	Proteinase activated receptor	−1.29	3.75	−1.40	1.37	F2R	chrysalin, argatroban,
	1 precursor (PAR-1) (Thrombin						bivalirudin
	receptor)
Ssc.15886.1.S1_at	Apopain precursor (Caspase-3)	−3.02	3.64	2.31	2.29	CASP3	IDN-6556
	(CASP-3)
Ssc.17518.1.S1_at	Adenosine A1 receptor	−3.16	3.44	−1.56	1.58	ADORA1	adenosine, dyphylline,
							aminophylline, clofarabine,
							theophylline, caffeine,
							tecadenoson
Ssc.14258.1.S1_at	Amyloid beta A4 protein	1.87	3.33	1.34	−1.02	APP	AAB-001
	precursor (APP) (ABPP)
Ssc.14471.1.S1_at	B-lymphocyte antigen CD19	9.54	3.30	−10.32	3.04	CD19	combotox, HD37-dgRTA, MT103
	precursor (Differentiation
	antigen CD19
Ssc.15878.1.S1_at	Serine/threonine protein	1.85	3.22	3.62	−1.49	PPP3CA	ISAtx-247, tacrolimus,
	phosphatase 2B catalytic						pimecrolimus, cyclosporin A
	subunit, alpha
Ssc.21108.1.S1_at	Complement C5	−17.35	3.21	618.80	9.28	C5	eculizumab
Ssc.16186.1.S1_at	T-cell surface glycoprotein	9.00	3.12	2.12	−5.66	CD3E	visilizumab, MT103, muromonab-
	CD3 epsilon chain (T-cell						CD3
	surface antigen T3/Leu-4
	epsilon chain)
Ssc.24966.1.S1_at	Purine nucleoside	−3.34	3.12	3.46	−1.14	NP	forodesine, 9-deaza-9-(3-
	phosphorylase (Inosine						thienylmethyl)guanine
	phosphorylase) (PNP).
Ssc.19873.1.S1_at	Collagen alpha 1(XVII) chain	−2.37	3.02	−1.55	2.18	COL17A1	collagenase
	(Bullous pemphigoid antigen 2)
Ssc.20904.1.A1_at	RAC-gamma serine/threonine-	−1.34	3.01	−2.18	1.23	AKT3	enzastaurin
	protein kinase (RAC-PK-gamma)
	(Protein kinase Akt-3)
	(Protein kinase B, gamma) (PKB
	gamma) (STK-2)
Ssc.26646.1.S1_at	Glutamate receptor 1	−1.10	2.91	−5.42	−3.43	GRIA1	talampanel, Org 24448,
							LY451395, tezampanel
Ssc.15312.1.S1_at	Histone deacetylase 4 (HD4)	−1.93	2.85	−2.41	1.20	HDAC4	tributyrin, PXD101,
							pyroxamide, vorinostat, FR
							901228
Ssc.24889.1.S1_at	Arachidonate 12-lipoxygenase,	1.97	2.83	−1.18	−2.78	ALOX12	sulfasalazine, balsalazide, 5-
	12S-type						aminosalicylic acid,
							masoprocol, verteportin
Ssc.12630.1.A1_at	Sodium/potassium-transporting	2.97	2.81	−7.30	−2.03	ATP1A1	digoxin, omeprazole,
	ATPase alpha-1 chain						ethacrynic acid, perphenazine
Ssc.3040.1.S1_at	Histone deacetylase 2 (HD2)	−3.24	2.79	4.94	4.72	HDAC2	tributyrin, PXD101,
							pyroxamide, vorinostat, FR901228
Ssc.12791.1.A1_at	3-hydroxy-3-methylglutaryl-	3.27	2.77	1.77	−2.56	HMGCR	aspirin/pravastatin,
	coenzyme A reductase (HMG-CoA						lovastatin/niacin,
	reductase)						ezetimibe/simvastatin,
							amlodipine/atorvastatin,
							fluvastatin, cerivastatin,
							atorvastatin, pravastatin,
							simvastatin, lovastatin,
							rosuvastatin
Ssc.20685.1.S1_at	Apoptosis regulator Bcl-2	−2.22	2.77	2.58	3.25	BCL2	oblimersen, (−)-gossypol
Ssc.15965.1.S1_at	Inward rectifier potassium	4.68	2.72	−1.84	−4.62	KCNJ2	nicorandil, amiodarone
	channel 2 (Potassium channel,
	inwardly rectifying, subfamily
	J, member 2) (Inward rectifier
	K+ channel Kir2.1) (Cardiac
	inward rectifier potassium
	channel) (IRK1).
Ssc.19673.1.S1_at	T-cell surface glycoprotein	6.40	2.70	2.03	−11.77	CD3D	visilizumab, MT103
	CD3 delta chain precursor (T-
	cell receptor T3 delta chain)
Ssc.16127.1.S1_at	Adrenocorticotropic hormone	−1.67	2.70	−2.51	−1.10	MC2R	cosyntropin, ACTH
	receptor (ACTH receptor)
	(ACTH-R)
Ssc.11406.1.A1_a_at	Interleukin-1 receptor, type I	1.03	2.70	−2.44	1.42	IL1R1	anakinra
	precursor (IL-1R-1) (IL-1R-
	alpha) (P80) (Antigen CD121a)
Ssc.19937.1.S1_at	Inosine-5′-monophosphate	1.00	2.69	−1.47	3.65	IMPDH2	thioguanine, VX-944,
	dehydrogenase 2 (IMP						interferon alfa-2a/ribavirin,
	dehydrogenase 2)						mycophenolic acid, ribavirin
Ssc.818.1.S1_at	RAF proto-oncogene	−1.40	2.56	1.58	1.98	RAF1	sorafenib
	serine/threonine-protein
	kinase
Ssc.14129.1.A1_at	4-aminobutyrate	6.91	2.56	−21.17	−3.35	ABAT	valproic acid
	aminotransferase,
	mitochondrial precursor
	(Gamma-amino-N-butyrate
	transaminase) (GABA
	transaminase)
Ssc.13186.1.S1_at	Cell division protein kinase 7	−1.08	2.38	4.34	1.24	CDK7	BMS-387032, flavopiridol
Ssc.16167.1.S1_at	Rho-associated protein kinase 1	2.40	2.32	2.83	3.00	ROCK1	fasudil, Y-27632
Ssc.6418.1.S1_at	Farnesyl-diphosphate	−1.25	2.31	1.33	1.08	FDFT1	TAK-475, zoledronic acid
	farnesyltransferase
Ssc.15829.1.S1_at	Retinoic acid receptor alpha	−1.73	2.23	−1.33	−1.16	RARA	etretinate, adapalene, arsenic
							trioxide, 13-cis-retinoic acid,
							tazarotene, acitretin, retinoic
							acid, 9-cis-retinoic acid
Ssc.10142.1.A1_at	Dihydropyrimidine	2.06	2.17	1.13	−2.68	DPYD	eniluracil
	dehydrogenase [NADP+] (DPD)
	(DHPDHase) (Dihydrouracil
	dehydrogenase) (Dihydrothymine
	dehydrogenase).
Ssc.23505.1.S1_at	Amine oxidase [flavin-	−1.86	2.17	1.08	1.20	MAOA	ladostigil, 1-
	containing] A (Monoamine						ethylphenoxathiin 10,10-
	oxidase) (MAO-A)						dioxide, dextroamphetamine,
							procainamide, tranylcypromine,
							phenelzine, isocarboxazid,
							benzphetamine, N-(2-
							indanyl)glycinamide
Ssc.20438.1.S1_at	Prostaglandin F2-alpha	−3.20	2.13	5.28	−38.97	PTGFR	tafluprost, travoprost,
	receptor (Prostanoid FP						isopropyl unoprostone,
	receptor) (PGF receptor) (PGF2						bimatoprost, latanoprost
	alpha receptor).
Ssc.4756.1.A1_at	Adenosine A3 receptor	2.15	2.10	1.88	−1.81	ADORA3	adenosine, dyphylline,
							aminophylline, clofarabine,
							theophylline, caffeine
Ssc.11302.1.S1_at	Collagen alpha 1(III) chain	−1.80	2.02	2.06	1.26	COL3A1	collagenase
Ssc.19400.2.A1_at	Presenilin 2 (PS-2) (STM-2)	−2.32	1.99	−5.30	−3.44	PSEN2	(R)-flurbiprofen
	(E5-1) (AD3LP) (AD5)
Ssc.3059.1.S1_at	Aldose reductase (AR)	−1.74	1.98	−1.66	2.49	AKR1B1	sorbinil, Zopolrestat (Alond,
	(Aldehyde reductase).						Pfizer), zenarestat (Fujisawa,
							Parke-Davis)
Ssc.18051.1.S1_at	cGMP-inhibited 3′,5′-cyclic	−3.16	1.96	3.41	2.32	PDE3B	dyphylline, nitroglycerin,
	phosphodiesterase B (Cyclic						medorinone, aminophylline,
	GMP inhibited						cilostazol, dipyridamole,
	phosphodiesterase B) (CGI-PDE						amrinone, tolbutamide,
	B) (CGIPDE1) (CGIP1)						theophylline, pentoxifylline
Ssc.2548.1.S1_at	DNA polymerase epsilon p17	2.27	1.93	−1.67	−1.21	POLE3	gemcitabine
	subunit (DNA polymerase
	epsilon subunit 3) (Chromatin
	accessibility complex 17)
	(HuCHRAC17) (CHRAC-17).
Ssc.11383.1.A1_at	Glutamate receptor 3 precursor	2.61	1.85	−1.87	−2.43	GRIA3	talampanel, Org 24448,
	(GluR-3) (GluR-C) (GluR-K3)						LY451395, tezampanel
	(Glutamate receptor
	ionotropic, AMPA 3)
Ssc.8549.1.A1_at	Guanylate cyclase soluble,	1.11	1.84	−2.00	−2.59	GUCY1A3	nitroglycerin, isosorbide-5-
	alpha-3 chain (GCS-alpha-3)						mononitrate, isosorbide
	(Soluble guanylate cyclase						dinitrate, nitroprusside,
	large subunit) (GCS-alpha-1).						isosorbide
							dinitrate/hydralazine
Ssc.12781.1.A1_at	Toll-like receptor 4	2.39	1.83	−1.52	−6.10	TLR4	TAK-242
Ssc.6301.1.S1_at	Aromatic-L-amino-acid	1.76	1.80	−4.91	−1.88	DDC	carbidopa/entacapone/levodopa,
	decarboxylase (AADC) (DOPA						carbidopa/levodopa, S(−)-
	decarboxylase)						carbidopa, L-dopa
Ssc.6801.1.S1_at	Proto-oncogene tyrosine-	−1.06	1.69	2.36	−1.82	YES1	dasatinib
	protein kinase YES
Ssc.5371.1.S1_a_at	DNA polymerase epsilon subunit	10.16	1.68	−1.37	−3.14	POLE2	gemcitabine
	B (DNA polymerase II subunit
	B).
Ssc.11572.1.A1_at	Histone deacetylase 3 (HD3)	−1.65	1.67	−1.55	2.36	HDAC3	tributyrin, PXD101,
	(RPD3-2) (SMAP45)						pyroxamide, MGCD0103,
							vorinostat, FR 901228
Ssc.23234.1.S1_at	collagen, type XXIV, alpha 1	−1.43	1.66	1.16	1.76	COL24A1	collagenase
Ssc.9565.1.S1_at	Interferon-gamma receptor	2.92	1.63	1.41	−1.23	IFNGR1	interferon gamma-1b
	alpha chain precursor (IFN-
	gamma-R1) (CD119 antigen)
Ssc.5021.1.S1_at	Glutamate decarboxylase, 65 kDa	−29.44	1.62	−10.79	−5.61	GAD2	valproic acid
	isoform (GAD-65) (65 kDa
	glutamic acid decarboxylase)
Ssc.14326.1.A1_at	Mitogen-activated protein	12.63	1.56	−1.80	−7.24	MAPK13	SCIO-469
	kinase 13 (Stress-activated
	protein kinase-4) (Mitogen-
	activated protein kinase p38
	delta) (MAP kinase p38 delta)
Ssc.10591.1.A1_at	Metabotropic glutamate	−7.83	1.54	−1.31	1.40	GRM5	fasoracetam
	receptor 5 precursor (mGluR5)
Ssc.30373.1.A1_at	cGMP-specific 3′,5′-cyclic	4.27	1.52	2.34	4.09	PDE5A	dyphylline, nitroglycerin, DA-
	phosphodiesterase						8159, aminophylline,
							sildenafil, dipyridamole,
							aspirin/dipyridamole,
							vardenafil, tolbutamide,
							tadalafil, theophylline,
							pentoxifylline
Ssc.6710.1.A1_at	Ribonucleoside-diphosphate	−1.90	1.44	2.01	−1.01	RRM1	gemcitabine, clofarabine,
	reductase M1 chain						fludarabine phosphate
	(Ribonucleotide reductase
	large chain)
Ssc.7139.1.S1_at	Dihydrofolate reductase	−1.13	1.41	−1.12	2.06	DHFR	pyrimethamine, trimethoprim,
							iclaprim, methotrexate,
							sulfisoxazole, triamterene,
							folic acid, trimetrexate,
							LY231514, PT 523
Ssc.5538.1.S1_at	Carbonic anhydrase II	8.09	1.39	−2.25	−1.35	CA2	methazolamide,
	(Carbonate dehydratase II)						hydrochlorothiazide,
	(CA-II) (Carbonic anhydrase C)						acetazolamide,
							trichloromethiazide,
							dorzolamide, chlorothiazide,
							dorzolamide/timolol,
							brinzolamide, chlorthalidone,
							benzthiazide, sulfacetamide,
							topiramate
Ssc.5569.1.S1_at	Thyroid hormone receptor alpha	−10.22	1.26	1.26	6.49	THRA	3,5-diiodothyropropionic acid,
	(C-erbA-alpha) (c-erbA-1)						amiodarone, thyroxine, L-
	(EAR-7) (EAR7)						triiodothyronine
Ssc.10360.1.S1_at	B-Raf proto-oncogene	−2.15	1.26	3.09	1.36	BRAF	sorafenib
	serine/threonine-protein
	kinase
Ssc.19672.1.S1_at	RAC-alpha serine/threonine-	17.77	1.25	−19.82	−2.11	AKT1	enzastaurin
	protein kinase (RAC-PK-alpha)
	(Protein kinase B) (PKB) (C-
	AKT)
Ssc.21011.1.S1_at	Collagen alpha 2(I) chain	−2.70	1.24	3.12	−1.01	COL1A2	collagenase
Ssc.5045.1.S1_at	3-beta-hydroxysteroid-	−1.55	1.24	2.19	2.08	EBP	SR 31747
	delta(8), delta(7)-isomerase
	(Cholestenol delta-isomerase)
	(Delta8-delta7 sterol
	isomerase) (D8-D7 sterol
	isomerase) (Emopamil-binding
	protein)
Ssc.8726.1.A1_at	Amidophosphoribosyltransferase	1.03	1.24	4.16	−1.11	PPAT	6-mercaptopurine, thioguanine,
	precursor (Glutamine						azathioprine
	phosphoribosylpyrophosphate
	amidotransferase) (ATASE)
	(GPAT)
Ssc.10219.1.A1_at	Excitatory amino acid	1.36	1.24	−3.38	−1.03	SLC1A6	riluzole
	transporter 4 (Sodium-
	dependent glutamate/aspartate
	transporter)
Ssc.15999.1.A1_at	Vascular endothelial growth	1.24	1.24	18.52	−11.02	KDR	AEE 788, sunitinib, AZD 2171,
	factor receptor 2 precursor						pazopanib, XL647, CEP 7055,
	(VEGFR-2) (Kinase insert						BMS-582664, KRN-951, vatalanib,
	domain receptor) (Protein-						sorafenib, vandetanib,
	tyrosine kinase receptor Flk-						pegaptanib
	1)
Ssc.9348.1.S1_at	Peroxisome proliferator	−1.05	1.22	−3.05	−1.52	PPARA	NS-220, tesaglitazar,
	activated receptor alpha						clofibrate, fenofibrate,
	(PPAR-alpha)						docosahexaenoic acid,
							gemfibrozil
Ssc.1498.1.S1_at	Proteasome subunit beta type 5	−1.81	1.19	1.38	5.94	PSMB5	bortezomib
	precursor (Proteasome epsilon
	chain) (Macropain epsilon
	chain) (Multicatalytic
	endopeptidase complex epsilon
	chain) (Proteasome subunit X)
	(Proteasome chain 6)
	(Proteasome subunit MB1)
Ssc.6934.1.A1_at	Thymidylate synthase (EC	−1.13	1.19	−1.11	−2.81	TYMS	flucytosine, 5-fluorouracil,
	2.1.1.45) (TS) (TSase) (OK/SW-						plevitrexed, nolatrexed,
	c1.29)						capecitabine, trifluridine,
							floxuridine, LY231514
Ssc.28329.1.S1_at	DNA polymerase beta	1.14	1.19	1.07	5.25	PCLB	nelarabine, clofarabine,
							stavudine, trifluridine,
							vidarabine, zalcitabine,
							entecavir
Ssc.16823.1.S1_at	P2Y purinoceptor 12 (P2Y12)	1.38	1.19	1.41	1.11	P2RY12	prasugrel, AZD 6140
	(P2Y12 platelet ADP receptor)						(Ticagrelor), clopidogrel
	(P2Y(ADP)) (ADP-glucose
	receptor) (ADPG-R) (P2Y(AC))
	(P2Y(cyc)) (P2T(AC)) (SP1999
Ssc.19691.1.S1_at	Platelet-activating factor	1.59	1.16	3.01	1.47	PLA2G7	darapladib
	acetylhydrolase precursor (EC
	3.1.1.47) (PAF
	acetylhydrolase) (PAF 2-
	acylhydrolase) (LDL-associated
	phospholipase A2) (LDL-PLA(2))
	(2-acetyl-1-
	alkylglycerophosphocholine
	esterase) (1-alkyl-2-
	acetylglycerophosphocholine
	esterase)
Ssc.15880.1.S1_at	Cysteinyl leukotriene receptor	2.86	1.14	−2.12	−3.42	CYSLTR2	montelukast, zafirlukast
	2 (CysLTR2) (PSEC0146) (HG57)
	(HPN321) (hGPCR21)
Ssc.11147.1.S1_at	Aldehyde dehydrogenase,	3.18	1.13	2.00	−1.68	ALDH2	disulfiram, chlorpropamide
	mitochondrial precursor (EC
	1.2.1.3) (ALDH class 2)
	(ALDHI) (ALDH-E2)
Ssc.1908.1.S1_at	FKBP-rapamycin associated	1.99	1.13	−1.88	−1.46	FRAP1	AP23573, temsirolimus,
	protein (FRAP) (Rapamycin						tacrolimus, everolimus
	target protein)
Ssc.19700.1.S1_at	Serine/threonine protein	1.11	1.13	1.34	1.09	PPP3CB	ISAtx-247, tacrolimus,
	phosphatase 2B catalytic						pimecrolimus, cyclosporin A
	subunit, beta
Ssc.30147.1.A1_at	Fibroblast growth factor	1.56	1.13	−1.05	−1.18	FGFR2	palifermin
	receptor 2 precursor (FGFR-2)
	(Keratinocyte growth factor
	receptor 2)
Ssc.27232.1.S1_at	Succinate semialdehyde	2.06	1.12	−2.84	−5.73	ALDH5A1	valproic acid
	dehydrogenase, mitochondrial
	precursor (NAD(+)-dependent
	succinic semialdehyde
	dehydrogenase)
Ssc.10215.1.A1_at	High-affinity cAMP-specific	1.83	1.12	−8.48	−15.51	PDE8A	dyphylline, nitroglycerin,
	and IBMX-insensitive 3′,5′-						aminophylline, anagrelide,
	cyclic phosphodiesterase 8A						milrinone, dipyridamole,
							tolbutamide, theophylline,
							pentoxifylline
Ssc.2767.2.S1_a_at	Prostaglandin E2 receptor, EP3	2.30	1.12	−1.59	−6.78	PTGER3	prostaglandin E1
	subtype (Prostanoid EP3
	receptor) (PGE receptor, EP3
	subtype)
Ssc.15955.1.S1_at	Antithrombin-III precursor	−1.89	1.12	−2.06	−2.42	SERPINC1	enoxaparin, SR-123781A,
	(ATIII) (PRO0309)						fondaparinux
Ssc.25040.1.S1_at	Serine/threonine-protein	−3.75	1.11	1.06	−2.45	CHEK1	UCN-01 (7-
	kinase Chk1						hydroxystaurosporine)
Ssc.14488.1.S1_at	Glutamate carboxypeptidase II	−1.04	1.10	1.02	1.69	FOLH1	capromab pendetide
	(Membrane glutamate
	carboxypeptidase)
Ssc.1.1.S1_at	3-oxo-5-alpha-steroid 4-	−1.77	1.05	−4.11	−1.09	SRD5A2	finasteride, dutasteride
	dehydrogenase 2 (Steroid 5-
	alpha-reductase 2) (SR type 2)
	(5 alpha-SR2)
Ssc.7581.1.A1_at	FL cytokine receptor precursor	1.69	1.04	−1.81	1.01	FLT3	CHIR-258, sorafenib,
	(Tyrosine-protein kinase						lestaurtinib, CGP 41251
	receptor FLT3) (Stem cell
	tyrosine kinase 1) (STK-1)
	(CD135 antigen)