Molecular profiling approaches for identifying novel biomarkers

Bibliography

• PETRICOIN EF III, HACKETT JL, LESKO LJ et al.: Medical applications of microarray technologies: a regulatory science perspective. Nat. Genet. Sapp]. (2002) 32:474–479.
   PubMed Web of Science ®Google Scholar
• IDEKER T, GALITSKI T, HOOD L: A new approach to decoding life: systems biology. Annu. Rev Genomics Hum. Genet. (2001) 2:343–372.
   PubMed Web of Science ®Google Scholar
• BIOMARKERS DEFINITIONS WORKING GROUP: Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Chit. Pharmacol The]: (2001) 69:89–95.
   PubMed Web of Science ®Google Scholar
• THE FOOD AND DRUG MODERNIZATION ACT OF 1997: Title 21 Code of Federal Regulations. Part 314, Subpart H, Section 314.500.
   Google Scholar
• COLBURN WA: Biomarkers in drug discovery and development: from target identification through drug marketing. Chit. Pharmacol (2003) 43 (4):329–341.
   Google Scholar
• SCHADT EE, MONKS SA, FRIEND SH: A new paradigm for drug discovery: integrating clinical, genetic, genomic and molecular phenotype data to identify drug targets. Biochem. Soc. Trans. (2003) 31(2)437–443.
   Google Scholar
• FRANK R, HARGREAVES R: Clinical biomarkers in drug discovery and development. Nat. Rev Drug Discov. (2003) 2(7):566–580.
   PubMed Web of Science ®Google Scholar
• ••An excellent review of current advances inthe clinical biomarker field.
   Google Scholar
• MORRISH PK, RAKSHI JS, BAILEY DL, SAWLE GV, BROOKS DJ: Measuring the rate of progression and estimating the preclinical period of Parkinson's disease with [18F]dopa PET. I Neurol Neurosurg. Psychiatry (1998) 64(3):314–319.
   PubMed Web of Science ®Google Scholar
• DU AT, SCHUFF N, ZHU XP et al.: Atrophy rates of entorhinal cortex in AD and normal aging. Neurology (2003) 60(3):481–486.
   PubMed Web of Science ®Google Scholar
• WAGNER JA: Overview of biomarkers and surrogate endpoints in drug development. Dis. Markers (2002) 18(2):41–46.
   PubMed Web of Science ®Google Scholar
• WARING JF, DAI X, HE Y, LUM P, ROBERTS CJ, ULRICH R: 'If you build it, they will come': design, construction and use of a compendium of expression profiles for mechanism of toxicity prediction. In: Toxicogenomics: Principles and Applications. Hamadeh HK, Afshari CA (Eds), Wiley Publishers, NY. In Press.
   Google Scholar
• WARING JF, ULRICH RG: The impact of genomics-based technologies on drug safety evaluation. Annu. Rev Phannacol Toxicol (2000) 40:335–352.
   PubMed Web of Science ®Google Scholar
• CASTLE AL, CARVER MP, MENDRICK DL: Toxicogenomics: a new revolution in drug safety. Drug Discov. Today (2002) 7(13)728–736.
   Google Scholar
• ULRICH R, FRIEND SH: Toxicogenomics and drug discovery: will new technologies help us produce better drugs? Nat. Rev Drug Discov. (2002) 1(1):84–88.
   PubMed Web of Science ®Google Scholar
• GERHOLD DL, JENSEN RV, GULLANS SR: Better therapeutics through microarrays. Nat. Genet. Sapp]. (2002) 32:547–552.
   PubMed Web of Science ®Google Scholar
• TUGWOOD JD, HOLLINS LE, COCKERILL MJ: Genomics and the search for novel biomarkers in toxicology. Biomarkers (2003) 8:79–92.
   PubMed Web of Science ®Google Scholar
• SISTARE FD: Development and application of interspecies biomarkers in nonclinical safety evaluations. In: Biomarkers M Clinical Drug Development Bloom JC, Dean RA (Eds), Marcel Dekker, Inc., NY (2003):97–117.
   Google Scholar
• MACGREGORJT: The future of regulatory toxicology: impact of the biotechnology revolution. Toxica Se/. (2003) 75:236–248.
   Google Scholar
• MORRIS AJ: Information and observer disagreement in histopathology. Histopathology (1994) 25:123–128.
   PubMed Web of Science ®Google Scholar
• BOORMAN GA, HASEMAN JK, WATERS MD, HARDISTY JF, SILLS RC: Quality review procedures necessary for rodent pathology databases and toxicogenomic studies: the National Toxicology Program Experience. Toxicol Pathol (2002) 30:88–92.
   PubMed Web of Science ®Google Scholar
• FURNESS PN: The use of digital images inpathology. Pathol (1997) 183:253–263.
   Web of Science ®Google Scholar
• KRIETE A, ANDERSON MK, LOVE B et al: Combined histomorphometric and gene-expression profiling applied to toxicology. Genome Biol. (2003) 4(5):R32.
   PubMedGoogle Scholar
• PANDURO A, SHALABY F, WEINER FR, BIEMPICA L, ZERN MA, SHAFRITZ DA: Transcriptional switch from albumin to a-fetoprotein and changes in transcription of other genes during carbon tetrachloride induced liver regeneration. Biochemistry (1986) 25(6):1414–1420.
   PubMed Web of Science ®Google Scholar
• CLARKE H, EGAN DA, HEFFERNAN M et al.: a-glutathione s-transferase (a-GST) release, an early indicator of carbon tetrachloride hepatotoxicity in the rat. Hum. Exp. Toxicol (1997) 16(3):154–157.
   PubMed Web of Science ®Google Scholar
• ROLAN P, ATKINSON AJ, LESKO LJ: Use of biomarkers from drug discovery through clinical practice: report of the Ninth European Federation of Pharmaceutical Sciences Conference on Optimizing Drug Development. Gin. Pharmacol The]: (2003) 73:284–291.
   Web of Science ®Google Scholar
• NUWAYSIR EF, BITTNER M, TRENT J, BARRETT JC, AFSHARI CA: Microarrays and toxicology: the advent of toxicogenomics. Mol. Carcinog. (1999) 24(3):153–159.
   PubMed Web of Science ®Google Scholar
• OSCARSON M: Pharmacogenetics of drug metabolizing enzymes: importance for personalized medicine. Gin. Chem. Lab. Med. (2003) 41(4):573–580.
   PubMed Web of Science ®Google Scholar
• ROGERS JF, NAFZIGER AN, BERTINO JS Jr: Pharmacogenetics affects dosing, efficacy, and toxicity of cytochrome P450-metabolized drugs. Am. Med. (2002) 113(9):746–750.
   PubMed Web of Science ®Google Scholar
• LIOTTA L, PETRICOIN E: Molecular profiling of human cancer. Nat. Rev Genet. (2000) 1:48–56.
   PubMed Web of Science ®Google Scholar
• DEPRIMO SE, WONG LM, KHATRY DB: Expression profiling of blood samples from an 5U5416 Phase III metastatic colorectal cancer clinical trial: a novel strategy for biomarker identification. BMC Cancer (2003) 3(1):3.
   PubMedGoogle Scholar
• ALIZADEH AA, EISEN MB, DAVIS RE et al.: Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature (2000) 403:503–511.
   PubMed Web of Science ®Google Scholar
• VAN I VEER LJ, DAI H, VAN DE VIJVER MJ et al.: Gene expression profiling predicts clinical outcome of breast cancer. Nature (2002) 415(6871):530–536.
   Google Scholar
• VAN DE VIJVER MJ, HE YD, VAN 7 VEER LJ et al: A gene-expression signature as a predictor of survival in breast cancer. N Engl. J. Med. (2002) 347:1999–2009.
   PubMed Web of Science ®Google Scholar
• BUCKHAULTS P, ZHANG Z, CHEN YC et al.: Identifying tumor origin using a gene expression-based classification map. Cancer Res. (2003) 63:4144–4149.
   PubMed Web of Science ®Google Scholar
• RADMACHER MD, MCSHANE LM, SIMON R: A paradigm for class prediction using gene expression profiles. .1. Comput. Biol. (2002) 9(3):505–511.
   Google Scholar
• THOMAS RS, RANK DR, PENN SG et al.: Identification of toxicologically predictive gene sets using cDNA microarrays. MoL Pharmacol (2001) 60(6):1189–1194.
   PubMed Web of Science ®Google Scholar
• DENISON MS, WHITLOCK JP Jr: Xenobiotic-inducible transcription of cytochrome P450 genes. J. Biol. Chem. (1995) 270(31):18175–18178.
   PubMed Web of Science ®Google Scholar
• HAMADEH HK, BUSHEL PR, JAYADEV S et al.: Gene expression analysis reveals chemical-specific profiles. Toxicol. Sci. (2002) 67(2):219–231.
   PubMed Web of Science ®Google Scholar
• HAMADEH HK, BUSHEL PR, JAYADEV S et al.: Prediction of compound signature using high density gene expression profiling. Toxicol. Sci. (2002) 67(2):232–240.
   PubMed Web of Science ®Google Scholar
• WARING JF, CIURLIONIS R, JOLLY RA, HEINDEL M, ULRICH RG: Microarray analysis of hepatotoxins in vitro reveals a correlation between gene expression profiles and mechanisms of toxicity. Toxicol. Lett. (2001) 120(1-3):359–368.
   PubMed Web of Science ®Google Scholar
• WARING JF, JOLLY RA, CIURLIONIS R et al.: Clustering of hepatotwdns based on mechanism of toxicity using gene expression profiles. Toxicol. AppL Pharmacol. (2001) 175(1):28–42.
   PubMed Web of Science ®Google Scholar
• HUANG Q, DUNN RT II, JAYADEV S et al: Assessment of cisplatin-induced nephrotoxicity by microarray technology. Toxica. Sci. (2001) 63(2):196–207.
   PubMed Web of Science ®Google Scholar
• BAN M, HETTICH D, HUGUET N: Nephrotoxicity mechanism of cis-platinum (II) diamine dichloride in mice. Toxicol. Lett. (1994) 71(2):161–168.
   PubMed Web of Science ®Google Scholar
• SAFIRSTEIN R, MILLER P, GUTTENPLAN JB: Uptake and metabolism of cisplatin by ratkidney. Kidney Int. (1984) 25(5):753–758.
   PubMed Web of Science ®Google Scholar
• BURCZYNSKI ME, MCMILLIAN M, CIERVO J et al: Toxicogenomics-based discrimination of toxic mechanism in HepG2 human hepatoma cells. Toxica. Sci. (2000) 58(2):399–415.
   PubMed Web of Science ®Google Scholar
• ABRAHAM SC, WU TT, KLIMSTRA DS et al: Distinctive molecular genetic alterations in sporadic and familial adenomatouspolyposis-associated pancreatoblastomas: frequent alterations in the APC/P-catenin pathway and chromosome 11p. Am. PathoL (2001) 159(5):1619–1627.
   PubMed Web of Science ®Google Scholar
• DESMAZE C, SORIA JC, FREULET-MARRIERE MA, MATHIEU N, SABATIER L: Telomere-driven genomic instability in cancer cells. Cancer Lett. (2003) 194(2):173–182.
   PubMedGoogle Scholar
• GILES RH, VAN ES JH, CLEVERS H: Caught up in a Wnt storm: Wnt signaling in cancer. Biochim. Biophys. Acta (2003) 1653(1):1–24.
   PubMed Web of Science ®Google Scholar
• BIGNOLD LP: Initiation of genetic instability and tumour formation: a review and hypothesis of a nongenotoxic mechanism. Cell. Mol Life Sci. (2003) 60(6):1107–1117.
   PubMed Web of Science ®Google Scholar
• ROGERS LK, MOORTHY B, SMITH CV: Acetaminophen binds to mouse hepatic and renal DNA at human therapeutic doses. Chem. Res. Toxicol. (1997) 10(4):470–476.
   PubMed Web of Science ®Google Scholar
• JAESCHKE H, KNIGHT TR, BAIT ML: The role of oxidant stress and reactive nitrogen species in acetaminophen hepatotoxicity. Toxicol. Lett. (2003) 144(3):279–288.
   PubMed Web of Science ®Google Scholar
• MYTILINEOU C, KRAMER BC, YABUT JA: Glutathione depletion and oxidative stress. Parkinsonism Relat. Disord. (2002) 8(6):385–387.
   PubMed Web of Science ®Google Scholar
• SEARFOSS GH, JORDAN WH, CALLIGARO DO et al.: Adipsin: a biomarker of gastrointestinal toxicity mediated by a functional y-secretase inhibitor. J. Biol. Chem. (2003) 278:46107–46116.
   PubMed Web of Science ®Google Scholar
• BUTTE A: The use and analysis of microarray data. Nat. Rev. Drug Discov. (2002) 1(12):951–960.
   PubMed Web of Science ®Google Scholar
• ••This review provides a clear overview ofmicroarray technology and data analysis options.
   Google Scholar
• SIMON R, RADMACHER MD, DOBBIN K, MCSHANE LM: Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. Natl. Cancer Inst. (2003) 95(1):14–18.
   Google Scholar
• ••This paper provides a nice summary of thecomplicating factors in interpreting microarray data.
   Google Scholar
• DUDOIT S, FRIDLYAND J, SPEED TP: Comparison of discrimination methods for the classification of tumours using gene expression data. J. Am. Stat. Assoc. (2002) 97:77–87.
   Web of Science ®Google Scholar
• •This study details differences among computational methods used to analyse microarray data.
   Google Scholar
• DING CH: Unsupervised feature selection via two-way ordering in gene expression analysis. Bioinformatics (2003) 19(101259–1266.
   Google Scholar
• JORNSTEN R, YU B: Simultaneous gene clustering and subset selection for sample classification via MDL. Bioinformatics (2003) 19(9):1100–1109.
   PubMed Web of Science ®Google Scholar
• KLUGER Y, BASRI R, CHANG JT, GERSTEIN M: Spectral biclustering of microarray data: coclustering genes and conditions. Genome Res. (2003) 13(4):703–716.
   PubMed Web of Science ®Google Scholar
• BURA E, PFEIFFER RM: Graphical methods for class prediction using dimension reduction techniques on DNA microarray data. Bioinformatics (2003) 19(101252–1258.
   Google Scholar
• GOLUB TR, SLONIM DK, TAMAYO P et al: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science (1999) 286(5439):531–537.
   PubMed Web of Science ®Google Scholar
• ALBRECHT A, VINTERBO SA, OHNO-MACHADO L: An Epicurean learning approach togene-expression data classification. Artif IntelL Med. (2003) 28(1):75–87.
   PubMed Web of Science ®Google Scholar
• LI W, YANG Y: ZMI's law in importance of genes for cancer classification using microarray data. Theor: Biol. (2002) 219(4):539–551.
   PubMed Web of Science ®Google Scholar
• PEDDADA SD, LOBENHOFEREK, LI L, AFSHARI CA, WEINBERG CR, UMBACH DM: Gene selection and clustering for time-course and dose-response microarray experiments using order-restricted inference. Bioinformatics (2003) 19(4834–841.
   Google Scholar
• BRAZMA A, HINGAMP P, QUACKENBUSH J et al: Minimum information about a microarray experiment (MIAME) — toward standards for microarray data. Nat. Genet. (2001) 29(4):365–371.
   PubMed Web of Science ®Google Scholar