Using Bioinformatic Approaches to Identify Pathways Targeted by Human Leukemogens

Reuben Thomas, Jimmy Phuong, Cliona M. McHale and Luoping Zhang
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Reuben Thomas: Genes and Environment Laboratory, School of Public Health, University of California, Berkeley, CA 94720, USA
Jimmy Phuong: Genes and Environment Laboratory, School of Public Health, University of California, Berkeley, CA 94720, USA
Cliona M. McHale: Genes and Environment Laboratory, School of Public Health, University of California, Berkeley, CA 94720, USA
Luoping Zhang: Genes and Environment Laboratory, School of Public Health, University of California, Berkeley, CA 94720, USA

IJERPH, 2012, vol. 9, issue 7, 1-25

Abstract: We have applied bioinformatic approaches to identify pathways common to chemical leukemogens and to determine whether leukemogens could be distinguished from non-leukemogenic carcinogens. From all known and probable carcinogens classified by IARC and NTP, we identified 35 carcinogens that were associated with leukemia risk in human studies and 16 non-leukemogenic carcinogens. Using data on gene/protein targets available in the Comparative Toxicogenomics Database (CTD) for 29 of the leukemogens and 11 of the non-leukemogenic carcinogens, we analyzed for enrichment of all 250 human biochemical pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The top pathways targeted by the leukemogens included metabolism of xenobiotics by cytochrome P450, glutathione metabolism, neurotrophin signaling pathway, apoptosis, MAPK signaling, Toll-like receptor signaling and various cancer pathways. The 29 leukemogens formed 18 distinct clusters comprising 1 to 3 chemicals that did not correlate with known mechanism of action or with structural similarity as determined by 2D Tanimoto coefficients in the PubChem database. Unsupervised clustering and one-class support vector machines, based on the pathway data, were unable to distinguish the 29 leukemogens from 11 non-leukemogenic known and probable IARC carcinogens. However, using two-class random forests to estimate leukemogen and non-leukemogen patterns, we estimated a 76% chance of distinguishing a random leukemogen/non-leukemogen pair from each other.

Keywords: leukemogen; pathway; Comparative Toxicogenomics Database; carcinogen; clustering (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2012
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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