Abstract:
Cancer marker sets consisting of particular genes differentially expressed in tumours provide improved accuracy of predicting effectiveness of paclitaxel or paclitaxel-like drug treatment against a cancer. These sets are further useful for screening drug candidates for paclitaxel-like cancer treatment activity. The cancer marker sets may be used in a clinical setting to provide information about the likelihood that a cancer patient would or would not respond to paclitaxel or paclitaxel-like drug treatment.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    This application claims the benefit of U.S. Provisional Patent Application Ser. No. 61/563,929 filed Nov. 28, 2011, the entire contents of which is herein incorporated by reference. 
     
    
     FIELD OF THE INVENTION 
       [0002]    The present invention is related to cancer, more particularly to methods and markers for predicting whether paclitaxel would be effective for treating a tumour in a patient, and to methods and markers for screening drug candidates for paclitaxel-like tumour treating activity. 
       BACKGROUND OF THE INVENTION 
       [0003]    Cancer is the second most common cause of death in the Western world, where the lifetime risk of developing cancer is approximately 40%. The overall annual costs of cancer, measured in direct medical expenses and lost productivity, is increasing at an exponential rate. In 2008 costs were estimated to be $228 billion in the United States alone (La Thangue 2011). In general, one cancer drug is only effective in a small fraction (10-30%) of cancer patients (Sarker 2007). Therefore, predictive biomarker-driven cancer therapy could lead to a reduction in unnecessary treatment (reducing healthcare cost) and adverse effects. 
         [0004]    Predictive biomarkers for drug response are sets of genes/proteins whose modulated levels could be used to determine whether a patient would or would not respond to a particular drug. Paclitaxel is a drug that targets a cancer cell&#39;s essential cell-cycle processes, and has become a first line drug for treating various cancers, for example breast cancer, ovarian cancer and prostate cancer. However, similar to other cancer drugs, only a small fraction of patients respond to paclitaxel treatment, for example only 20% of ER+ breast cancer patients and 30% of ERN triple negative breast cancer patients respond to paclitaxel. Therefore, it would be useful to have biomarkers to predict whether a patient would respond or not to treatment with paclitaxel. Current efforts have been made to identify such biomarkers; however, prediction rates are in the range of 50-60% (Hatzis 2011), which is still too low to be truly useful. 
         [0005]    Recently, an algorithm (Multiple Survival Screening (MSS)) has been developed for identifying high-quality cancer prognostic markers and this algorithm was applied for identifying robust marker sets for breast cancer prognosis (Li 2010; Wang 2010). 
         [0006]    There is a need to find new markers and develop new tests which are able to more accurately and robustly predict which patients would respond or not respond to paclitaxel or paclitaxel-like drug treatment. 
       SUMMARY OF THE INVENTION 
       [0007]    It has now been found that marker sets consisting of particular genes differentially expressed in tumours advantageously provide improved accuracy of predicting effectiveness of paclitaxel or paclitaxel-like drug treatment against a cancer. These sets are further useful for screening drug candidates for paclitaxel-like tumour treatment activity. The marker sets of the present invention may be used in a clinical setting to provide information about the likelihood that a cancer patient would or would not respond to paclitaxel or paclitaxel-like drug treatment. 
         [0008]    In one aspect of the present invention, there is provided a method of determining likelihood that a tumour in a patient would be treatable with paclitaxel or a paclitaxel-like drug, the method comprising: obtaining a gene expression list of a sample of the tumour or an extract of the tumour having message RNA therein of the patient; determining a gene expression profile of the sample from the gene expression list for genes of a gene marker set; and, comparing the gene expression profile of the sample to standardized “good” and “bad” profiles of the marker set to determine whether the gene expression profile of the sample predicts that the tumour is treatable or not treatable with paclitaxel or a paclitaxel-like drug, wherein “good” indicates that the tumour is likely treatable with paclitaxel or a paclitaxel-like drug and “bad” indicates that the tumour is not likely treatable with paclitaxel or a paclitaxel-like drug. 
         [0009]    In a second aspect of the invention, there is provided a method of screening a chemical compound as a drug candidate with paclitaxel-like tumour-treating activity, the method comprising: determining a gene expression profile for genes of a gene marker set of a tumor sample treated with the chemical compound; and, comparing the gene expression profile of the sample to standardized “good” and “bad” profiles of the marker set to determine whether the gene expression profile of the sample predicts that the chemical compound would have paclitaxel-like tumour-treating activity, wherein “good” indicates that the chemical compound is likely to have paclitaxel-like tumour-treating activity and “bad” indicates that the tumour is not likely to have paclitaxel-like tumour-treating activity. 
         [0010]    In methods of the present invention, the gene marker set is one or more of Set 1, Set 2, Set 3, Set 4, Set 5 and Set 6, wherein 
       Set 1: 
       [0011]      
         [0000]    
       
         
               
               
               
             
               
               
               
             
           
               
                   
               
               
                 Gene Name 
                 EntrezGene ID 
                 Full Name of Gene 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 HELLS 
                 3070 
                 Helicase, lymphoid-specific 
               
               
                 CDC2 
                 983 
                 Cell division cycle 2, G1 to S and G2 to M 
               
               
                 PLEKHF1 
                 79156 
                 Pleckstrin homology domain containing, family F (with FYVE domain) 
               
               
                   
                   
                 member 1 
               
               
                 IGFBP3 
                 3486 
                 Insulin-like growth factor binding protein 3 
               
               
                 CASP3 
                 836 
                 Caspase 3, apoptosis-related cysteine peptidase 
               
               
                 HRK 
                 8739 
                 Harakiri, BCL2 interacting protein (contains only BH3 domain) 
               
               
                 PCSK6 
                 5046 
                 Proprotein convertase subtilisin/kexin type 6 
               
               
                 PLAGL1 
                 5325 
                 Pleiomorphic adenoma gene-like 1 
               
               
                 NME5 
                 8382 
                 Non-metastatic cells 5, protein expressed in (nucleoside-diphosphate 
               
               
                   
                   
                 kinase) 
               
               
                 PROP1 
                 5626 
                 PROP paired-like homeobox 1 
               
               
                 NOD2 
                 64127 
                 Nucleotide-binding oligomerization domain containing 2 
               
               
                 CD38 
                 952 
                 CD38 molecule 
               
               
                 ATP7A 
                 538 
                 ATPase, Cu++ transporting, alpha polypeptide (Menkes syndrome) 
               
               
                 INDO 
                 3620 
                 Indoleamine-pyrrole 2,3 dioxygenase 
               
               
                 PIM2 
                 11040 
                 Pim-2 oncogene 
               
               
                 ECT2 
                 1894 
                 Epithelial cell transforming sequence 2 oncogene 
               
               
                 CASP8AP2 
                 9994 
                 CASP8 associated protein 2 
               
               
                 STK17B 
                 9262 
                 Serine/threonine kinase 17b 
               
               
                 PRKDC 
                 5591 
                 Protein kinase, DNA-activated, catalytic polypeptide 
               
               
                 CRADD 
                 8738 
                 CASP2 and RIPK1 domain containing adaptor with death domain 
               
               
                 BECN1 
                 8678 
                 Beclin 1 (coiled-coil, myosin-like BCL2 interacting protein) 
               
               
                 CAPN10 
                 11132 
                 Calpain 10 
               
               
                 PRUNE2 
                 158471 
                 Prune homolog 2 ( Drosophila ) 
               
               
                 SKP2 
                 6502 
                 S-phase kinase-associated protein 2 (p45) 
               
               
                 ANL1 
                 25 
                 V-abl Abelson murine leukemia viral oncogene homolog 1 
               
               
                 CLN3 
                 1201 
                 Ceroid-lipofuscinosis, neuronal 3, juvenile (Batten, Spielmeyer-Vogt 
               
               
                   
                   
                 disease) 
               
               
                 CTSB 
                 1508 
                 Cathepsin B 
               
               
                 MUC2 
                 4583 
                 Mucin 2, oligomeric mucus/gel-forming 
               
               
                 NUP62 
                 23636 
                 Nucleoporin 62 kDa 
               
               
                 APOE 
                 348 
                 Apolipoprotein E 
               
               
                   
               
             
          
         
       
     
       Set 2: 
       [0012]      
         [0000]    
       
         
               
               
               
             
               
               
               
             
           
               
                   
               
               
                 Gene Name 
                 EntrezGene ID 
                 Full Name of Gene 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 CENPE 
                 1062 
                 Centromere protein E, 312 kDa 
               
               
                 CENPF 
                 1063 
                 Centromere protein F, 350/400 ka (mitosin) 
               
               
                 AURKB 
                 9212 
                 Aurora kinase B 
               
               
                 TTK 
                 7272 
                 TTK protein kinase 
               
               
                 CDCA8 
                 55143 
                 Cell division cycle associated 8 
               
               
                 SKP1 
                 6500 
                 S-phase kinase-associated protein 1 
               
               
                 CCNA2 
                 890 
                 Cyclin A2 
               
               
                 CAMK2G 
                 818 
                 Calcium/calmodulin-dependent protein kinase (CaM kinase) 
               
               
                   
                   
                 II gamma 
               
               
                 INHBA 
                 3624 
                 Inhibin, beta A 
               
               
                 CDC2 
                 983 
                 Cell division cycle 2, G1 to S and G2 to M 
               
               
                 ERCC6L 
                 54821 
                 Excision repair cross-complementing rodent repair 
               
               
                   
                   
                 deficiency, complementation group 6-like 
               
               
                 BUB1B 
                 701 
                 BUB1 budding uninhibited by benzimidazoles 1 homolog 
               
               
                   
                   
                 beta (yeast) 
               
               
                 NCAPD3 
                 23310 
                 Non-SMC condensin II complex, subunit D3 
               
               
                 CDC25A 
                 993 
                 Cell division cycle 25 homolog A ( S.   pombe ) 
               
               
                 DCC1 
                 79075 
                 Defective in sister chromatid cohesion homolog 1 ( S.   
               
               
                   
                   
                   cerevisiae ) 
               
               
                 PSMB9 
                 5698 
                 Proteasome (prosome, macropain) subunit, beta type, 9 
               
               
                   
                   
                 (large multifunctional peptidase 2) 
               
               
                 DLG7 
                 9787 
                 Discs, large homolog 7 ( Drosophila ) 
               
               
                 CHEK1 
                 1111 
                 CHK1 checkpoint homolog ( S.   pombe ) 
               
               
                 CLASP1 
                 23332 
                 Cytoplasmic linker associated protein 1 
               
               
                 SMC2 
                 10592 
                 Structural maintenance of chromosomes 2 
               
               
                 ZWINT 
                 11130 
                 ZW10 interactor 
               
               
                 SKP2 
                 6502 
                 S-phase kinase-associated protein 2 (p45) 
               
               
                 NCAPG 
                 64151 
                 Non-SMC condensin I complex, subunit G 
               
               
                 DBF4 
                 10926 
                 DBF4 homolog ( S.   cerevisiae ) 
               
               
                 CDC20 
                 991 
                 Cell division cycle 20 homolog ( S.   cerevisiae ) 
               
               
                 STMN1 
                 3925 
                 Stathmin 1/oncoprotein 18 
               
               
                 MDM2 
                 4193 
                 Mdm2, transformed 3T3 cell double minute 2, p53 binding 
               
               
                   
                   
                 protein (mouse) 
               
               
                 TXNL4B 
                 54957 
                 Thioredoxin-like 4B 
               
               
                 ABL1 
                 25 
                 V-abl Abelson murine leukemia viral oncogene homolog 1 
               
               
                 NUMA1 
                 4926 
                 Nuclear mitotic apparatus protein 1 
               
               
                   
               
             
          
         
       
     
       Set 3: 
       [0013]      
         [0000]    
       
         
               
               
               
             
               
               
               
             
           
               
                   
               
               
                   
                 EntrezGene 
                   
               
               
                 Gene Name 
                 ID 
                 Full Name of Gene 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 CCL2 
                 6347 
                 Chemokine (C—C motif) ligand 2 
               
               
                 TAP1 
                 6890 
                 Transporter 1, ATP-binding cassette,  
               
               
                   
                   
                 sub-family B (MDR/TAP) 
               
               
                 CD163 
                 9332 
                 CD163 molecule 
               
               
                 IFIH1 
                 64135 
                 Interferon induced with helicase C domain 1 
               
               
                 SERPINE1 
                 5054 
                 Serpin peptidase inhibitor, clade E (nexin,  
               
               
                   
                   
                 plasminogen activator inhibitor type 1),  
               
               
                   
                   
                 member 1 
               
               
                 RSAD2 
                 91543 
                 Radical S-adenosyl methionine domain  
               
               
                   
                   
                 containing 2 
               
               
                 DHX58 
                 79132 
                 DEXH (Asp-Glu-X-His) box polypeptide 58 
               
               
                 VWF 
                 7450 
                 Von Willebrand factor 
               
               
                 TNFRSF17 
                 608 
                 Tumor necrosis factor receptor superfamily,  
               
               
                   
                   
                 member 17 
               
               
                 TNFRSF4 
                 7293 
                 Tumor necrosis factor receptor superfamily,  
               
               
                   
                   
                 member 4 
               
               
                 PSG9 
                 5678 
                 Pregnancy specific beta-1-glycoprotein 9 
               
               
                 CCR4 
                 1233 
                 Chemokine (C—C motif) receptor 4 
               
               
                 FXN 
                 2395 
                 Frataxin 
               
               
                 PARP1 
                 142 
                 Poly (ADP-ribose) polymerase family,  
               
               
                   
                   
                 member 1 
               
               
                 C1QB 
                 713 
                 Complement component 1, q subcomponent,  
               
               
                   
                   
                 B chain 
               
               
                 PRKDC 
                 5591 
                 Protein kinase, DNA-activated, catalytic  
               
               
                   
                   
                 polypeptide 
               
               
                 CD38 
                 952 
                 CD38 molecule 
               
               
                 APOE 
                 348 
                 Apolipoprotein E 
               
               
                 FKBP1A 
                 2280 
                 FK506 binding protein 1A, 12 kDa 
               
               
                 IL4 
                 3565 
                 Interleukin 4 
               
               
                 PCSK6 
                 5046 
                 Proprotein convertase subtilisin/kexin type 6 
               
               
                 BECN1 
                 8678 
                 Beclin 1 (coiled-coil, myosin-like BCL2  
               
               
                   
                   
                 interacting protein) 
               
               
                 PSMB9 
                 5698 
                 Proteasome (prosome, macropain) subunit,  
               
               
                   
                   
                 beta type, 9 (large multifunctional  
               
               
                   
                   
                 peptidase 2) 
               
               
                 GALNT2 
                 2590 
                 UDP-N-acetyl-alpha-D-galactosamine:  
               
               
                   
                   
                 polypeptide N-acetylgalactosaminyltransferase 
               
               
                   
                   
                 2 (GalNAc-T2) 
               
               
                 KLK13 
                 26085 
                 Kallikrein-related peptidase 13 
               
               
                 LAX1 
                 54900 
                 Lymphocyte transmembrane adaptor 1 
               
               
                 GCH1 
                 2643 
                 GTP cyclohydrolase 1 (dopa-responsive  
               
               
                   
                   
                 dystonia) 
               
               
                 CLN3 
                 1201 
                 Ceroid-lipofuscinosis, neuronal 3, juvenile  
               
               
                   
                   
                 (Batten, Spielmeyer-Vogt disease) 
               
               
                 C2 
                 717 
                 Complement component 2 
               
               
                 PSG1 
                 5669 
                 Pregnancy specific beta-1-glycoprotein 1 
               
               
                   
               
             
          
         
       
     
       Set 4: 
       [0014]      
         [0000]    
       
         
               
               
               
             
               
               
               
             
           
               
                   
               
               
                   
                 EntrezGene  
                   
               
               
                 Gene Name 
                 ID 
                 Full Name of Gene 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 API5 
                 8539 
                 Apoptosis inhibitor 5 
               
               
                 AGT 
                 183 
                 Angiotensinogen (serpin peptidase inhibitor,  
               
               
                   
                   
                 clade A, member 8) 
               
               
                 SAP30BP 
                 29115 
                 SAP30 binding protein 
               
               
                 BNIP3 
                 664 
                 BCL2/adenovirus E1B 19 kDa interacting  
               
               
                   
                   
                 protein 3 
               
               
                 GLI3 
                 2737 
                 GLI-Kruppel family member GLI3 (Greig 
               
               
                   
                   
                 cephalopolysyndactyly syndrome) 
               
               
                 UNC5B 
                 219699 
                 Unc-5 homolog B ( C. elegans ) 
               
               
                 PDE1B 
                 5153 
                 Phosphodiesterase 1B, calmodulin-dependent 
               
               
                 MSX1 
                 4487 
                 Msh homeobox 1 
               
               
                 HIP1 
                 3092 
                 Huntingtin interacting protein 1 
               
               
                 PDCD10 
                 11235 
                 Programmed cell death 10 
               
               
                 PPARD 
                 5467 
                 Peroxisome proliferator-activated receptor  
               
               
                   
                   
                 delta 
               
               
                 LOC283871  
                 283871 
                 Hypothetical protein LOC283871 
               
               
                 RRAGA 
                 10670 
                 Ras-related GTP binding A 
               
               
                 ERBB3 
                 2065 
                 V-erb-b2 erythroblastic leukemia viral  
               
               
                   
                   
                 oncogene homolog 3 (avian) 
               
               
                 IHPK2 
                 51447 
                 Inositol hexaphosphate kinase 2 
               
               
                 EEF1A2 
                 1917 
                 Eukaryotic translation elongation factor 1  
               
               
                   
                   
                 alpha 2 
               
               
                 PERP 
                 64065 
                 PERP, TP53 apoptosis effector 
               
               
                 ATP6AP1 
                 537 
                 ATPase, H+ transporting, lysosomal  
               
               
                   
                   
                 accessory protein 1 
               
               
                 ING4 
                 51147 
                 Inhibitor of growth family, member 4 
               
               
                 NLRP2 
                 55655 
                 NLR family, pyrin domain containing 2 
               
               
                 FXR1 
                 8087 
                 Fragile X mental retardation, autosomal  
               
               
                   
                   
                 homolog 1 
               
               
                 C16orf5 
                 29965 
                 Chromosome 16 open reading frame 5 
               
               
                 BLCAP 
                 10904 
                 Bladder cancer associated protein 
               
               
                 VEGFA 
                 7422 
                 Vascular endothelial growth factor A 
               
               
                 ESR1 
                 2099 
                 Estrogen receptor 1 
               
               
                 TRAF5 
                 7188 
                 TNF receptor-associated factor 5 
               
               
                 FIS1 
                 51024 
                 Fission 1 (mitochondrial outer membrane)  
               
               
                   
                   
                 homolog ( S. cerevisiae ) 
               
               
                 SFRP1 
                 6422 
                 Secreted frizzled-related protein 1 
               
               
                 COMP 
                 1311 
                 Cartilage oligomeric matrix protein 
               
               
                 CDKN2A 
                 1029 
                 Cyclin-dependent kinase inhibitor 2A  
               
               
                   
                   
                 (melanoma, p16, inhibits CDK4) 
               
               
                   
               
             
          
         
       
     
       Set 5: 
       [0015]      
         [0000]    
       
         
               
               
               
             
               
               
               
             
           
               
                   
               
               
                   
                 EntrezGene  
                   
               
               
                 Gene Name 
                 ID 
                 Full Name of Gene 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 PERP 
                 64065 
                 PERP, TP53 apoptosis effector 
               
               
                 KAL1 
                 3730 
                 Kallmann syndrome 1 sequence 
               
               
                 EFS 
                 10278 
                 Embryonal Fyn-associated substrate 
               
               
                 CLDN3 
                 1365 
                 Claudin 3 
               
               
                 CD36 
                 948 
                 CD36 molecule (thrombospondin receptor) 
               
               
                 ITGA6 
                 3655 
                 Integrin, alpha 6 
               
               
                 CXCL12 
                 6387 
                 Chemokine (C—X—C motif) ligand 12  
               
               
                   
                   
                 (stromal cell-derived factor 1) 
               
               
                 PCDHB3 
                 56132 
                 Protocadherin beta 3 
               
               
                 RHOB 
                 388 
                 Ras homolog gene family, member B 
               
               
                 ITGB1 
                 3688 
                 Integrin, beta 1 (fibronectin receptor, beta  
               
               
                   
                   
                 polypeptide, antigen CD29 includes MDF2,  
               
               
                   
                   
                 MSK12) 
               
               
                 GMDS 
                 2762 
                 GDP-mannose 4,6-dehydratase 
               
               
                 DLG1 
                 1739 
                 Discs, large homolog 1 ( Drosophila ) 
               
               
                 COL19A1 
                 1310 
                 Collagen, type XIX, alpha 1 
               
               
                 SIGLEC8 
                 27181 
                 Sialic acid binding Ig-like lectin 8 
               
               
                 PPARD 
                 5467 
                 Peroxisome proliferator-activated receptor  
               
               
                   
                   
                 delta 
               
               
                 IGFALS 
                 3483 
                 Insulin-like growth factor binding protein,  
               
               
                   
                   
                 acid labile subunit 
               
               
                 LAMA4 
                 3910 
                 Laminin, alpha 4 
               
               
                 STAB1 
                 23166 
                 Stabilin 1 
               
               
                 PTPRM 
                 5797 
                 Protein tyrosine phosphatase, receptor type, M 
               
               
                 SPAM1 
                 6677 
                 Sperm adhesion molecule 1 (PH-20  
               
               
                   
                   
                 hyaluronidase, zona pellucida binding) 
               
               
                 AGT 
                 183 
                 Angiotensinogen (serpin peptidase inhibitor,  
               
               
                   
                   
                 clade A, member 8) 
               
               
                 ZYX 
                 7791 
                 Zyxin 
               
               
                 PCDH7 
                 5099 
                 Protocadherin 7 
               
               
                 PCDHGB5 
                 56101 
                 Protocadherin gamma subfamily B, 5 
               
               
                 MADCAM1 
                 8174 
                 Mucosal vascular addressin cell adhesion  
               
               
                   
                   
                 molecule 1 
               
               
                 COMP 
                 1311 
                 Cartilage oligomeric matrix protein 
               
               
                 PVRL2 
                 5819 
                 Poliovirus receptor-related 2 (herpesvirus  
               
               
                   
                   
                 entry mediator B) 
               
               
                 LAMA5 
                 3911 
                 Laminin, alpha 5 
               
               
                 PCDHB17 
                 54661 
                 Protocadherin beta 17 pseudogene 
               
               
                 ITGA8 
                 8516 
                 Integrin, alpha 8 
               
               
                   
               
             
          
         
       
     
       Set 6: 
       [0016]      
         [0000]    
       
         
               
               
               
             
               
               
               
             
           
               
                   
               
               
                   
                 EntrezGene  
                   
               
               
                 Gene Name 
                 ID 
                 Full Name of Gene 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 PDE1B 
                 5153 
                 Phosphodiesterase 1B, calmodulin-dependent 
               
               
                 ITGA6 
                 3655 
                 Integrin, alpha 6 
               
               
                 CCND1 
                 595 
                 Cyclin D1 
               
               
                 DEK 
                 7913 
                 DEK oncogene (DNA binding) 
               
               
                 MSX1 
                 4487 
                 Msh homeobox 1 
               
               
                 CHAF1B 
                 8208 
                 Chromatin assembly factor 1, subunit B (p60) 
               
               
                 TLK1 
                 9874 
                 Tousled-like kinase 1 
               
               
                 SLC25A36 
                 55186 
                 Solute carrier family 25, member 36 
               
               
                 RPS6KB1 
                 6198 
                 Ribosomal protein S6 kinase, 70 kDa,  
               
               
                   
                   
                 polypeptide 1 
               
               
                 USP1 
                 7398 
                 Ubiquitin specific peptidase 1 
               
               
                 AGT 
                 183 
                 Angiotensinogen (serpin peptidase inhibitor,  
               
               
                   
                   
                 clade A, member 8) 
               
               
                 PRKRA 
                 8575 
                 Protein kinase, interferon-inducible double  
               
               
                   
                   
                 stranded RNA dependent activator 
               
               
                 MTMR15 
                 22909 
                 Myotubularin related protein 15 
               
               
                 CHRNA3 
                 1136 
                 Cholinergic receptor, nicotinic, alpha 3 
               
               
                 C16orf5 
                 29965 
                 Chromosome 16 open reading frame 5 
               
               
                 PPARD 
                 5467 
                 Peroxisome proliferator-activated receptor  
               
               
                   
                   
                 delta 
               
               
                 FGB 
                 2244 
                 Fibrinogen beta chain 
               
               
                 ANXA2P2 
                 304 
                 Annexin A2 pseudogene 2 
               
               
                 HSPB1 
                 3315 
                 Heat shock 27 kDa protein 1 
               
               
                 ANXA2 
                 302 
                 Annexin A2 
               
               
                 ESR1 
                 2099 
                 Estrogen receptor 1 
               
               
                 SMAD2 
                 4087 
                 SMAD family member 2 
               
               
                 STAB1 
                 23166 
                 Stabilin 1 
               
               
                 FANCE 
                 2178 
                 Fanconi anemia, complementation group E 
               
               
                 NFATC4 
                 4776 
                 Nuclear factor of activated T-cells,  
               
               
                   
                   
                 cytoplasmic, calcineurin-dependent 4 
               
               
                 ERBB3 
                 2065 
                 V-erb-b2 erythroblastic leukemia viral  
               
               
                   
                   
                 oncogene homolog 3 (avian) 
               
               
                 ERAP1 
                 51752 
                 Endoplasmic reticulum aminopeptidase 1 
               
               
                 TOR1B 
                 27348 
                 Torsin family 1, member B (torsin B) 
               
               
                 HPS5 
                 11234 
                 Hermansky-Pudlak syndrome 5 
               
               
                 RPA3 
                 6119 
                 Replication protein A3, 14 kDa 
               
               
                   
               
             
          
         
       
     
         [0017]    The genes in the marker sets of the present invention are individually known and are individually known to be differentially expressed in tumour cells. How they are differentially expressed and whether their differential expression generally correlates to “good” or “bad” paclitaxel tumour-treating activity can also be determined from publicly available datasets. However, the specific combination of the genes in each marker set of the present invention unexpectedly provides for more robust marker sets having improved accuracy for prediction of whether or not paclitaxel is likely to be effective in treating the tumour. The marker sets of the present invention consisting of the specific combination of genes that gives rise to the improved predictive accuracy may be generated using the Multiple Survival Screening (MSS) method previously developed (Li 2010; Wang 2010). 
         [0018]    Paclitaxel is a mitotic inhibitor. It stabilizes microtubules and as a result, interferes with the normal breakdown of microtubules during cell division. Paclitaxel-treated cells have defects in mitotic spindle assembly, chromosome segregation, and cell division. Unlike other tubulin-targeting drugs such as colchicine that inhibit microtubule assembly, paclitaxel stabilizes the microtubule polymer and protects it from disassembly. Chromosomes are thus unable to achieve a metaphase spindle configuration. This blocks progression of mitosis, and prolonged activation of the mitotic checkpoint triggers apoptosis or reversion to the G-phase of the cell cycle without cell division. The ability of paclitaxel to inhibit spindle function is generally attributed to its suppression of microtubule dynamics, however that suppression of dynamics occurs at concentrations lower than those needed to block mitosis. At the higher therapeutic concentrations, paclitaxel appears to suppress microtubule detachment from centrosomes, a process normally activated during mitosis. The binding site for paclitaxel has been identified on the beta-tubulin subunit. Paclitaxel-like drugs have a similar mechanism of action as paclitaxel. Paclitaxel-like drugs include, for example, paclitaxel derivatives (e.g. DHA-paclitaxel, PG-paclitaxel) and other taxanes (e.g. docetaxel). 
         [0019]    The sample comprises a sample of the tumour of the patient or an extract thereof, which contains the genes in the marker set or message RNA that hybridizes to the genes in the marker set. Preferably, the sample comprises a sample of the tumour of the patient. The tumour is preferably a breast tumour, ovarian tumor, lung tumour or prostate tumour, more preferably a breast tumour (e.g. estrogen receptor positive (ER+); estrogen receptor negative (ERN triple negative), etc). 
         [0020]    Preferably, three marker sets are used together to make predictions. Thus, gene expression profiles of the sample are preferably determined for the genes in each of Sets 1, 2 and 3, or each of Sets 4, 5 and 6. Sets 1, 2 and 3 are particularly useful for determining the effectiveness of paclitaxel for treating ER+ tumours. Sets 4, 5 and 6 are particularly useful for determining the effectiveness of paclitaxel for treating ERN triple negative tumours. In this case, the gene expression profiles are compared to standardized “good” and “bad” profiles of each respective gene marker set to determine whether each of the gene expression profiles predicts that the effectiveness of paclitaxel is “good” or “bad”. If all three marker sets predict that the effectiveness is “good” then the patient is predicted to be a suitable candidate for paclitaxel cancer treatment. If all three marker sets predict that the effectiveness is “bad” then the patient is predicted to be a bad candidate for paclitaxel cancer treatment. If one or two of the marker sets predict that the effectiveness is “good” or one or two of the marker sets predict that the effectiveness is “bad” then the patient is predicted to be an uncertain candidate for paclitaxel cancer treatment. Using all three marker sets improves accuracy of the prediction. 
         [0021]    In a particular embodiment, each gene in the gene expression profile has a gene expression value and a modified gene expression profile is obtained by multiplying the gene expression value by its marker-factor. Standardized “good” and “bad” profiles are determined by computing standardized centroids for both “good” and “bad” classes using prediction analysis for microarrays method (Tibshirani 2002). Modified class centroids of the marker set are obtained by multiplying the standardized centroids for each class by the marker-factor. The modified gene expression profile of the sample is compared to each modified class centroid to determine if paclitaxel effectiveness is “good” or “bad”. The class whose centroid is closest to the modified gene expression profile, in Pearson correlation distance, is predicted to be the class for the sample. 
         [0022]    Gene expression profiles of a patient&#39;s tumour may be readily obtained by any number of methods known in the art, for example microarray analysis, individual gene or RNA screening (e.g. by PCR or real time PCR), diagnostic panels, mini chips, NanoString chips, RNA-seq chips, protein chips, ELISA tests, etc. In a preferred embodiment, a sample may be obtained from a patient by any suitable means, for example, with a syringe or other fluid and/or tissue separation means. The sample may be screened against a microarray on which gene probes of the marker sets are printed. An output of the gene expression profile of the sample is preferably obtained before comparing the gene expression profile to the standardized “good” and “bad” profiles of the marker set. To obtain the output, message RNA in the sample may be hybridized to the genes on the microarray, the hybridized microarray may be scanned to get all the readouts of marker genes for the sample, the readouts may be normalized and the gene expression profile of the marker set for the sample is thereby obtained. Detailed information for making microarray gene chip, scanning and normalization of array data is generally known in the art and can be found in the publicly available literature (http://en.wikipedia.org/wiki/DNA_microarray). It is also possible to obtain the gene expression profile by RNA-sequencing and related sequencing technologies as these technologies become more accessible (http://en.wikipedia.org/wiki/RNA-Seq). 
         [0023]    In another embodiment, kits or commercial packages are provided, which comprise gene probes for each of the genes in a gene marker set of the present invention along with instructions for obtaining a gene expression profile of a sample for the gene marker set. The kit or commercial package may further comprise instructions for comparing the gene expression profile of the sample to standardized “good” and “bad” profiles of the marker set to determine whether the gene expression profile of the sample predicts that paclitaxel effectiveness is “good” or “bad”. Preferably, the kit or commercial package comprises gene probes for at least three gene marker sets of the present invention. The kit or commercial package may further comprise means for obtaining a sample of a tumour having message RNA therein from a patient, for example suitable syringes, fluid and/or tissue separation means, etc. In addition to the gene probes, the kit or commercial package may further comprise reagents and/or equipment useful for screening the sample against the gene probes for obtaining the gene expression profile of the sample. Various standard elements of such kits or commercial packages are generally known in the art. 
         [0024]    Further features of the invention will be described or will become apparent in the course of the following detailed description. 
     
    
     DESCRIPTION OF PREFERRED EMBODIMENTS 
     Example 1 
     Generation of Paclitaxel Response Marker Sets for ER+ Breast Cancer 
       [0025]    To develop ER+ cancer marker sets of the present invention, the Multiple Survival Screening (MSS) method (Li 2010; Wang 2010) was used. In applying this method, a training set of 260 ER+ breast cancer samples was selected from a public metadata set (GEO GSE4779, GSE20194, GSE20271, GSE22093 and GSE23988). Each patient has been treated with paclitaxel and followed-up pathologically to determine who is responsive to the treatment. The primary tumors prior to any drug treatment have been microarray profiled. The datasets contain information about gene expression profiles for patient primary tumours and the information of response/non-response for paclitaxel treatment for each patient. Datasets identify whether each of these genes is up-regulated or down-regulated in tumours and correlates these genes with responsiveness to paclitaxel treatment (i.e. “good” vs. “bad”). 
         [0026]    100 samples from the datasets were randomly selected in which 70 were samples that did not respond to paclitaxel treatment (“bad”) and 30 were samples that did respond to paclitaxel treatment (“good”). Array-wide single-gene based clustering (using fuzzy clustering method, http://stat.ethz.ch/R-manual/R-patched/library/cluster/html/fanny.html) of responsive/non-responsive was conducted to obtain effectiveness genes, which are genes whose differential expression values are correlated with effective paclitaxel treatment. It is not relevant whether the expression of each gene is upregulated or downregulated so long as the differential expression is correlated to effective paclitaxel treatment. Selection of samples and array-wide single-gene based clustering analyses (using fuzzy clustering method, http://stat.ethz.ch/R-manual/R-patched/library/cluster/html/fanny.html) were repeated 100 times, and the effectiveness genes (which have P value &lt;0.05 in more than 75 out of the 100 times) from each of the 100 repetitions were merged. 
         [0027]    Using the effectiveness gene set, Gene Ontology (GO) analysis (using GO annotation software, David, http://david.abcc.ncifcrf.gov/) was performed to identify only those genes that belong to GO terms that are known to be associated with cancer, such as apoptosis, response to wounding, DNA replication and transcription repair, mitosis and immune response. Table 1 lists the ER+ cancer-related GO term gene sets. Two million distinct random-gene-sets were generated by randomly picking 30 genes from each ER+ cancer-related GO term gene set. 
         [0000]    
       
         
               
               
               
             
           
               
                   
                 TABLE 1 
               
               
                   
                   
               
               
                   
                 GO Term 
                 Number of genes 
               
               
                   
                   
               
             
             
               
                   
                 Apoptosis 
                 68 
               
               
                   
                 Response to wounding 
                 60 
               
               
                   
                 DNA replication and transcription repair 
                 53 
               
               
                   
                 Mitosis 
                 63 
               
               
                   
                 Immune response 
                 63 
               
               
                   
                   
               
             
          
         
       
     
         [0028]    Of 83 samples (58 with no response to paclitaxel treatment and 25 that responded to paclitaxel treatment) selected from the dataset to form the training set, 36 random datasets were generated. For a given GO term gene set, paclitaxel effectiveness screening was then conducted using the 2 million random-gene-sets against all the 36 random datasets. For each random dataset, the statistical significance of the correlation between the expression values of each random-gene-set (30 genes) and paclitaxel effectiveness status (“good” or “bad”) was examined by fuzzy clustering analysis (using fuzzy clustering method, http://stat.ethz.ch/R-manual/R-patched/library/cluster/html/fanny.html). If the P value was less than a cut-off for an effectiveness screening using one random-gene-set against one random dataset, that random-gene-set was said to have passed. When a few thousands of random-gene-sets had passed 32 or more random datasets (the detailed parameters are shown in Table 2), the random-gene-sets that had passed were retained for further analysis. The genes in the retained random-gene-sets were then ranked based on their frequency of appearance in the passed random-gene-sets. The top 30 genes were chosen as a potential-marker-set. A similar effectiveness screening of random-gene-sets against random datasets was performed for each of the other selected GO term gene sets. Only apoptosis, mitosis and immune response GO term gene sets were used to generate the ER+ marker sets. 
         [0000]    
       
         
               
             
               
               
               
               
             
               
               
               
               
             
           
               
                 TABLE 2 
               
             
             
               
                   
               
               
                 Parameters for Screening of the Marker Sets 
               
             
          
           
               
                   
                 Number of Passed  
                 Number of Passed  
                 Cut-off  
               
               
                   
                 Sample Sets 
                 Gene Sets 
                 P value 
               
               
                   
               
             
          
           
               
                 Apoptosis 
                 32 
                 1586 
                 0.01 
               
               
                 Mitosis 
                 32 
                 4370 
                 0.005 
               
               
                 Immune response 
                 34 
                 2959 
                 0.05 
               
               
                   
               
             
          
         
       
     
         [0029]    For each GO term gene set used, another 1 million distinct random-gene-sets were generated and the clustering process using the random datasets mentioned above was repeated. If the gene members for the top 30 were substantially the same as those in the potential-marker-set generated by the first screening, then the potential-marker-set is stable and can be used as a real ER+ cancer marker set. If the genes for the two potential marker sets were not substantially the same, then these GO term genes are unsuitable for finding a real marker set and the potential marker set was dropped from further analysis. 
         [0030]    In this way, three ER+ cancer marker sets were generated having stable signatures, one related to apoptosis (Set 1), one related to mitosis (Set 2) and one related to immune response (Set 3). The genes, EntrezGene ID and full names of the genes in each of the three marker sets are given above. More details of each gene, including the nucleotide sequence of each gene, are known in the art and may be conveniently found in the National Center for Biotechnology Information (NCBI) Databases at http://www.ncbi.nlm.nih.gov/. 
       Example 2 
     Generation of Paclitaxel Response Marker Sets for ERN Breast Cancer 
       [0031]    To develop ERN (estrogen receptor negative) cancer marker sets of the present invention, the Multiple Survival Screening (MSS) method (Li 2010; Wang 2010) was used. In applying this method, a training set of 202 ERN breast cancer samples was selected from GSE25066 dataset (Hatzis 2011). The dataset contains information which is the same as those described above (the ER+ datasets). 153 samples from the dataset were randomly selected in which 100 were samples that did not respond to paclitaxel treatment (“bad”) and 53 were samples that did respond to paclitaxel treatment (“good”). Array-wide single-gene based fuzzy clustering (using fuzzy clustering method, http://stat.ethz.ch/R-manual/R-patched/library/cluster/html/fanny.html) screening of responsive/non-responsive samples was performed to obtain effectiveness genes, which are genes whose differential expression values are correlated with effective paclitaxel treatment. It is not relevant whether the expression of each gene is upregulated or downregulated so long as the differential expression is correlated to effective paclitaxel treatment. Selection of samples and array-wide screening were repeated 3 times, and effectiveness genes (P value &lt;0.05) from each of the 3 repetitions were merged. Using the effectiveness gene set, Gene Ontology (GO) analysis (using GO annotation software, David, http://david.abcc.ncifcrf.gov/) was performed to identify only those genes that belong to GO terms that are known to be associated with cancer, such as apoptosis, cell cycle, cell adhesion, response, DNA repair &amp; replication and mitosis. Table 3 lists the ERN cancer-related GO term gene sets. Two million distinct random-gene-sets were generated by randomly picking 30 genes from each ERN cancer-related GO term gene set. 
         [0000]    
       
         
               
               
               
             
           
               
                   
                 TABLE 3 
               
               
                   
                   
               
               
                   
                 GO Term 
                 Number of genes 
               
               
                   
                   
               
             
             
               
                   
                 Apoptosis 
                 82 
               
               
                   
                 Cell cycle 
                 88 
               
               
                   
                 Cell adhesion 
                 47 
               
               
                   
                 Response to stimulus 
                 61 
               
               
                   
                 DNA repair &amp; replication 
                 53 
               
               
                   
                 Mitosis 
                 45 
               
               
                   
                   
               
             
          
         
       
     
         [0032]    Of 152 samples (99 with no response to paclitaxel treatment and 53 that responded to paclitaxel treatment) selected from the dataset to form the training set, 36 random datasets were generated. For a given GO term gene set, paclitaxel effectiveness screening was then conducted using the 1 million random-gene-sets against all the 36 random datasets. For each random dataset, the statistical significance of the correlation between the expression values of each random-gene-set (30 genes) and paclitaxel effectiveness status (“good” or “bad”) was examined by fuzzy clustering analysis (using fuzzy clustering method, http://stat.ethz.ch/R-manual/R-patched/library/cluster/html/fanny.html). If the P value was less than a cut-off for an effectiveness screening using one random-gene-set against one random dataset, that random-gene-set was said to have passed. When a few thousands of random-gene-sets had passed 32 or more random datasets (the detailed parameters are shown in Table 4), the random-gene-sets that had passed were retained for further analysis. The genes in the retained random-gene-sets were then ranked based on their frequency of appearance in the passed random-gene-sets. The top 30 genes were chosen as a potential-marker-set. A similar effectiveness screening of random-gene-sets against random datasets was performed for each of the other selected GO term gene sets. Only apoptosis, cell adhesion and response GO term gene sets were used to generate the ERN marker sets. 
         [0000]    
       
         
               
             
               
               
               
               
               
             
               
               
               
               
               
             
           
               
                 TABLE 4 
               
             
             
               
                   
               
               
                 Parameters for Screening of the Marker Sets 
               
             
          
           
               
                   
                   
                 Number of Passed  
                 Number of Passed  
                 Cut-off  
               
               
                   
                   
                 Sample Sets 
                 Gene Sets 
                 P value 
               
               
                   
                   
               
             
          
           
               
                   
                 Apoptosis 
                 36 
                 4454 
                 0.005 
               
               
                   
                 Cell adhesion 
                 36 
                 5779 
                 0.05 
               
               
                   
                 Response to 
                 36 
                 10682 
                 0.005 
               
               
                   
                 stimulus 
               
               
                   
                   
               
             
          
         
       
     
         [0033]    For each GO term gene set used, another 1 million distinct random-gene-sets were generated and the survival screening process using the random datasets mentioned above was repeated. If the gene members for the top 30 were substantially the same as those in the potential-marker-set generated by the first screening, then the potential-marker-set is stable and can be used as a real ERN cancer marker set. If the genes for the two potential marker sets were not substantially the same, then these GO term genes are unsuitable for finding a real marker set and the potential marker set was dropped from further analysis. 
         [0034]    In this way, three ERN cancer marker sets were generated having stable signatures, one related to apoptosis (Set 4), one related to cell adhesion (Set 5) and one related to response to stimulus (Set 6). The genes, EntrezGene ID and full names of the genes in each of the three marker sets are given above. More details of each gene, including the nucleotide sequence of each gene, are known in the art and may be conveniently found in the National Center for Biotechnology Information (NCBI) Databases at http://www.ncbi.nlm.nih.gov/. 
       Example 3 
     Validating Effectiveness of the Marker Sets in Predicting Paclitaxel Effectiveness for Treating Breast Cancer 
       [0035]    The effectiveness of the marker sets generated in Examples 1 and 2 was validated against datasets containing breast cancer gene expression data from sample populations. Sets 1, 2 and 3 from Example 1 were validated against metadata from public data (GSE4779, GSE20194, GSE20271, GSE22093 and GSE23988) and against the GSE25066 dataset (Hatzis 2011). Sets 4, 5 and 6 from Example 2 were validated against the GSE25066 dataset (ERN, 87% triple negative) (Hatzis 2011), the GSE20174 dataset (triple negative) (Zeidler-Erdely 2010), and the GSE20194 dataset (triple negative) (Popovici 2010; Shi 2010). 
         [0036]    To perform the validation for a given test dataset containing ‘n’ samples, the gene expression profile of the marker set was extracted. For each gene expression value its marker-factor was multiplied to obtain a modified gene expression profile of the testing sample. Standardized centroids were computed for both “good” and “bad” classes from n−1 samples for the marker set using the Prediction Analysis for Microarrays (PAM) method (Tibshirani 2002). The marker-factor of each gene was multiplied to the class centroids to get modified class centroids of the marker set. For predicting the paclitaxel response of the targeted testing sample using the marker set, the modified gene expression profile of the sample was compared to each of these modified class centroids. The class whose centroid that it is closest to, in Pearson correlation distance, is the predicted class for that sample. If the sample is predicted to be unresponsive to paclitaxel treatment (i.e. “bad”), it is denoted as 0, otherwise it is denoted as 1. If all three marker sets (Sets 1, 2 and 3, or Sets 4, 5 and 6) predict that a particular sample is unresponsive to paclitaxel (i.e. denoted as 0 for all 3 marker sets), the sample is assigned to a paclitaxel unresponsive group (i.e. “bad”). If all three marker sets predict that a particular sample is responsive to paclitaxel (i.e. denoted as 1 for all 3 marker sets), the sample is assigned to a paclitaxel responsive group (i.e. “good”). If a sample is not assigned to either of these groups, it is assigned to an indeterminate group. 
         [0037]    This validation process was carried out in each of the test datasets. Table 5 shows the accuracy for Sets 1, 2 and 3 in predicting the paclitaxel unresponsive group in the metadata from public data dataset and the GSE25066 dataset. Table 6 shows the accuracy for Sets 4, 5 and 6 in predicting the paclitaxel unresponsive group in the GSE25066 dataset, the GSE20174 dataset and the GSE20194 dataset. The accuracy of the marker sets against the test datasets is remarkably high, and much higher than the 50-60% that can be achieved using current prior art marker sets (Hatzis 2011). 
         [0000]    
       
         
               
             
               
               
               
             
           
               
                 TABLE 5 
               
             
             
               
                   
               
               
                 Accuracy of Sets 1, 2 and 3 
               
             
          
           
               
                   
                   
                 Accuracy (paclitaxel  
               
               
                 Dataset 
                 No. of Samples 
                 unresponsive group) 
               
               
                   
               
               
                 Metadata from public data  
                 260 
                 95.4% 
               
               
                 (training part: GSE4779, 
                   
                   
               
               
                 GSE20194, GSE20271, 
                   
                   
               
               
                 GSE22093 and GSE23988) 
                   
                   
               
               
                 Metadata from public data  
                 111 
                 97.2% 
               
               
                 (test part: GSE4779, 
                   
                   
               
               
                 GSE20194, GSE20271, 
                   
                   
               
               
                 GSE22093 and GSE23988) 
                   
                   
               
               
                 GSE25066 
                 290 
                 96.3% 
               
               
                   
               
             
          
         
       
     
         [0000]    
       
         
               
             
               
               
               
               
             
               
               
               
               
             
           
               
                 TABLE 6 
               
             
             
               
                   
               
               
                 Accuracy of Sets 4, 5 and 6 
               
             
          
           
               
                   
                   
                   
                 Accuracy (paclitaxel  
               
               
                   
                 Dataset 
                 No. of Samples 
                 unresponsive group) 
               
               
                   
                   
               
             
          
           
               
                   
                 GSE25066 (training) 
                 202 
                 91% 
               
               
                   
                 GSE20174 
                 59 
                 91% 
               
               
                   
                 GSE20194 
                 70 
                 88% 
               
               
                   
                   
               
             
          
         
       
     
       REFERENCES 
       [0038]    The contents of the entirety of each of which are incorporated by this reference.
   Cui Q, Ma Y, Jaramillo M, Bari H, Awan A, Yang S, Zhang S, Liu L, Lu M, O&#39;Connor-McCourt M, Purisima E O, Wang E. (2007) A map of human cancer signaling. Molecular Systems Biology. 3:152, 13 pages.   Fuzzy Analysis Clustering version 1.14.0. (2011) http://stat.ethz.ch/R-manual/R-patched/library/cluster/html/fanny.html.   GO annotation software, David. http://david.abcc.ncifcrf.gov/.   Hatzis C, et al. (2011) A Genomic Predictor of Response and Survival Following Taxane-Anthracycline Chemotherapy for Invasive Breast Cancer. JAMA. 305(18): 1873-1881.   La Thangue NB,  Kerr  D J. (2011) Predictive biomarkers: a paradigm shift towards personalized cancer medicine.  Nat. Rev. Clin. Oncol.  8, 587-596.   Li J, Lenferink AEG, Deng Y, Collins C, Cui Q, Purisima EO, O&#39;Connor-McCourt M D, Wang E. (2010) Identification of high-quality cancer prognostic markers and metastasis network modules. Nature Communications. 1:34, DOI: 10.1038/ncomms1033.   National Center for Biotechnology Information (NCBI) Databases. http://www.ncbi.nlm.nih.gov/.   Popovici V, Chen W, Gallas B G, Hatzis C, et al. (2010) Effect of training-sample size and classification difficulty on the accuracy of genomic predictors.  Breast Cancer Res.  12(1), R5.   Sarker D, Workman P. (2007) Pharmacodynamic biomarkers for molecular cancer therapeutics.  Adv. Cancer Res.  96, 213-268.   Shi L, Campbell G, Jones W D, Campagne F, et al. (2010) The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models.  Nat Biotechnol.  28(8), 827-38.   Tibshirani R, Hastie T, Narasimhan B, Chu G. (2002) Diagnosis of multiple cancer types by shrunken centroids of gene expression.  PNAS.  99, 6567-6572.   Wang E, Li J, Deng Y, Lenferink AEG, O&#39;Connor-McCourt M D, Purisima EO. (2010) Process for Tumour Characteristic and Marker Set Identification, Tumour Classification and Marker Sets for Cancer. International Patent Application WO 2010/118520 published Oct. 21, 2010.   Wikipedia, the free encyclopedia. (2010a) DNA Microarray. http://en.wikipedia.org/wiki/DNA_microarray.   Wikipedia, the free encyclopedia. (2010b) RNA-Seq. http://en.wikipedia.org/wiki/RNA-Seq.   Zeidler-Erdely P C, Kashon M L, Li S, Antonini J M. (2010) Response of the mouse lung transcriptome to welding fume: effects of stainless and mild steel fumes on lung gene expression in NJ and C57BL/6J mice. Respir Res. 11(1), 70 (18 pages).   
 
         [0054]    Other advantages that are inherent to the structure are obvious to one skilled in the art. The embodiments are described herein illustratively and are not meant to limit the scope of the invention as claimed. Variations of the foregoing embodiments will be evident to a person of ordinary skill and are intended by the inventor to be encompassed by the following claims.