Aromatase database

Aromatase, being the key enzyme in the biosynthesis of estrogen, has acquired much attention as estrogen is the primary molecule that promotes the growth of normal and cancerous breast epithelial cells. A class of drugs called Aromatase Inhibitors (AIs) considerably decrease the production of estrogen from androstenedione and testosterone and hence, they are widely used in the treatment of ER+ breast cancers in postmenopausal women as the locally produced estrogens are mainly responsible for cancerous growth in the breast tissue (Miki et al., 2007).

However, single nucleotide polymorphisms in the aromatase gene have been shown to interfere with the prognosis of breast cancer patients and the efficacy of the treatment using aromatase inhibitors like letrozole (Dunning et al., 2004). In particular, SNPs in coding and regulatory sequences play a major role in diseases. Among them, Single Amino acid Polymorphisms (SAPs) or non-synonymous SNPs (nsSNPs) are of great importance due to their effect on the protein (Ye et al., 2007). As we have started realizing the role and importance of SNPs to determine the efficacy of a drug, it would be advantageous if the predicted impact of SNPs on the protein is deposited on a common interface. This would primarily help validating the drug by knowing the efficacy of the drug on the proteins with single aminoacid polymorphism (SAP) even before clinical trials. Moreover, this would also be useful in determining the cause of decreased efficacy of existing drugs. Gaining knowledge in either of the scenarios, we would be able to modify the drug accordingly. Depositing such prediction data online would also enable research community including pharmaceutical industry to validate it in wet lab and in turn, the validated data may also be included in the database.

Submit to AIdb link would help gather information from the researchers throughout the world and eventually at the backend, it would help us to analyze the data for the impact of the nsSNPs, molecular interactions, aminoacid residues involved etc. Also, it would enable us to compare the impact of the nsSNPs on the synthetic and natural AIs.

Hence, the aim of this database is collect such data and make it available here.

References

Miki, Y., Suzuki, T., Tazawa, C., Yamaguchi, Y., Kitada, K., Honma, S., … Sasano, H. (2007). Aromatase localization in human breast cancer tissues: possible interactions between intratumoral stromal and parenchymal cells. Cancer Research, 67(8), 3945–3954. https://doi.org/67/8/3945 [pii]\r10.1158/0008-5472.CAN-06-3105

Dunning, A. M., Dowsett, M., Healey, C. S., Tee, L., Luben, R. N., Folkerd, E., … Ponder, B. A. (2004). Polymorphisms associated with circulating sex hormone levels in postmenopausal women. Journal of the National Cancer Institute, 96(12), 936–945. https://doi.org/10.1093/jnci/djh167Ye,

Z. Q., Zhao, S. Q., Gao, G., Liu, X. Q., Langlois, R. E., Lu, H., & Wei, L. (2007). Finding new structural and sequence attributes to predict possible disease association of single amino acid polymorphism (SAP). Bioinformatics , 23(12), 1444–1450. https://doi.org/10.1093/bioinformatics/btm119