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The following publications from the Mayo PPII PGRN were not selected to be comprehensive, but rather to provide an overview – over a period of five years – of one facet of our work, studies of the pharmacogenomics of cancer treatment, including “endocrine therapy” , “chemotherapy” and “radiation therapy”. Gemcitabine metabolic pathway genetic polymorphisms and response in non-small cell lung
cancer patients. Pharmacogenet. Genomics
22(2):105-16. (2012).
Li, L., Schaid, D.J., Fridley, B.L., Kalari, K.R., Jenkins, G.D., Abo, R.P., Batzler, A., Moon, I.,
Pelleymounter, L., Eckloff, B.W., Wieben, E.D., Sun, Z., Pang, Y. and Wang, L.

This paper describes the merger of the use of LCLs to identify and interpret genetic variation associated
with a cellular phenotype, gemcitabine cytotoxicity, and the subsequent validation of these genetic
variants using samples from pancreatic cancer patient treated with gemcitabine. This is one of the few
examples that demonstrate the utility of the LCL model system and the use of this system to identify
candidates that were also associated with clinical response in cancer patients.

Glycine and a glycine dehydrogenase (GLDC
) SNP as citalopram/escitalopram response
biomarkers in depression: pharmacometabolomics-informed pharmacogenomics. Clin
Pharmacol Ther.
89(1):97-104. (2011)
Ji, Y., Hebbring, S., Zhu, H., Jenkins. G.D., Biernacka, J., Snyder, K., Drews, M., Fiehn, O., Zeng, Z.,
Schaid, D., Mrazek, D.A., Kaddurah-Daouk, R. and Weinshilboum, R.M.

This paper describes the “merging” of pharmacometabolomic and pharmacogenomic data, i.e.,
“pharmacometabolomics-informed pharmacogenomics”, to discover and replicate SNPs in an
unanticipated gene, GLDC, associated with SSRI response in depressed patients. This is one of the first
examples of the use of metabolomics to complement and “guide” genomic studies of drug response.
Genomics and drug response. NEJM 364 (12):1144-1153. (2011).
Wang, L., McLeod, H.L. and Weinshilboum, R.M.
We know that you prefer “peer reviewed” original research papers, but the fact that this review was
invited for inclusion in the NEJM Tenth Anniversary of the Genome Project “Genomics in Medicine”
series is an indication of the success of PGRN efforts to highlight pharmacogenomics as a leading edge
in the application of genomic science to medicine.
Functional genetic polymorphisms in the aromatase gene CYP19 vary the response of breast
cancer patients to neoadjuvant therapy with aromatase inhibitors. Cancer Res.
(2010) 70(1):319-

Wang, L., Ellsworth, K.A. Moon, I., Pelleymounter, L.L., Eckloff, B.W., Martin, Y.N., Fridley, B.L., Jenkins,
G.D., Batzler, A., Suman, V.J., Ravi, S., Dixon, J.M., Miller, W.R., Wieben, E.D., Buzdar, A.,
Weinshilboum, R.M. and Ingle, J.N.

This manuscript utilized tissue from Scottish women with breast cancer who were treated with aromatase
inhibitors in the neoadjuvant setting. We sequenced the CYP19 gene tissue from these patients – which
was also used to measure aromatase activity. Two linked CYP19 SNPs were found to be associated
with response to aromatase inhibitors and, in a replication study, they were also associated with
circulating levels of estrogen.
Genome-wide associations and functional genomic studies of musculoskeletal adverse events in
women receiving aromatase inhibitors. J. Clin. Oncol
. (2010) Nov1; 28(31):4674-82.Ingle, J.N., Schaid, D.J., Goss, P.E., Liu, M., Mushiroda, T., Chapman, J.-A., Kubo, M., Jenkins, G.D.,
Batzler, A., Shepherd, L., Pater, J., Wang, L., Ellis, M.J., Stearns, V., Rohrer, D., Goetz, M.P., Pritchard,
K.I., Flockhart, D.A., Nakamura, Y. and Weinshilboum, R.M.

This manuscript pursues the topic addressed in manuscripts (1) i.e., endocrine therapy of breast cancer
and the important role of aromatase inhibitors (AIs). A major adverse response to these agents is severe
musculoskeletal pain. This GWAS used DNA from the largest current AI study of breast cancer and
identified the relationship of the TCL1A gene to this adverse response, based both on GWAS data and
the results of extensive functional genomic studies.
Radiation Pharmacogenomics: A genome wide association approach to identify radiation
response biomarkers using human lymphoblastoid cell lines. Genome Res.

Niu N, Qin Y, Fridley BL, Hou J, Kalari KR, Zhu M, Wu TY, Jenkins GD, Batzler A, Wang L.

This manuscript used the same cell line system as described in article (2) to attempt to identify genetic
variations/ genes that might contribute to radiation response. The study has identified 5 candidate genes
which showed effect on radiation sensitivity after knockdown in various cancer cell lines. Those
candidates would be potential biomarkers for radiation response and would also provide novel
mechanistic insights into the biology underlying radio-sensitivity and radio-resistance.

FKBP51 acts as a scaffolding protein to regulate Akt phosphorylation. Cancer Cell

(2009) 16(3):259-66

Pei, H., Li, L., Fridley, B., Jenkins, G., Kalari, R., Lingle, W., Petersen, G., Lou, Z. and Wang, L.

This mechanistic follow-up of the preceding manuscript demonstrated that FKBP5 encodes a scaffolding
protein that “links” a phosphatase to AKT, a mechanism that plays a role in variation in response to a
series of cytotoxic antineoplastic agents including gemcitabine, taxanes and irinotecan.

Gemcitabine and cytosine arabinoside cytotoxicity: association with lymphoblastoid cell
expression. Cancer Res.
(2008) 68(17):7050-7058.
Li, L., Fridley, B.L., Kalari, K.R., Jenkins, G.D., Batzler, A., Safgren, S.L., Hildebrandt, M.A.T., Ames,
M.M., Schaid, D.J. and Wang, L.

This manuscript describes the use of a cell line-based model system consisting of data-rich
lymphoblastoid cell lines. In this study, expression array data were used to identify, and functionally
validate, novel candidate genes, NT5C3 and FKBP5, associated with response to the solid tumor
antineoplastic drug gemcitabine. This work led directly to basic mechanistic studies of FKBP5 which are
described in the following manuscript.


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