The pharmacogenetics of lithium response depends upon clinical co-morbidity.

BACKGROUND: Based on results from randomized, controlled clinical trials, lithium monotherapy or lithium with the addition of an antipsychotic remains a first-line treatment option for both acute and long-term mood stabilization in bipolar mania. However, response to lithium is poor in bipolar patients who exhibit clinical characteristics such as rapid cycling and mixed manic states, suggesting that they may have a biologically and genetically distinct form of bipolar disorder. A test that could predict response to lithium based upon genetic factors would have significant clinical value. METHODS: Eight clinical characteristics were assessed in 92 lithium responders and 92 nonresponders; all probands were from families recruited for linkage studies. Lithium response was rated retrospectively from a standardized interviews and medical records. Eight candidate genes were selected from those reported to be associated with susceptibility to illness, lithium response, or lithium mechanism of action. Sixty-seven single nucleotide polymorphisms (SNPs) were genotyped in these subjects and analyzed for association with the defined clinical characteristics. RESULTS: Using q-value analysis for multiplicity correction, we found significant interactions between lithium response and SNPs (rs1387923 and rs1565445) in the gene encoding neurotrophic tyrosine kinase receptor type 2 (NTRK2) and suicidal ideation, and between SNP rs2064721 in the gene encoding inositol polyphosphate-1-phosphatase (INPP1) and post-traumatic stress disorder. CONCLUSION: These data support the idea that response to lithium has a multi-genetic etiology dependent upon manifestations of other clinical co-diagnoses.

CACNA1C polymorphisms are associated with the efficacy of calcium channel blockers in the treatment of hypertension.

Retrospective pharmacogenetic analysis was performed on 120 Caucasian subjects. Subjects were obtained in collaboration with the Estonian Genome Project and Egeen Inc. (CA, USA), who provided blinded medical record and genetic data to the researchers, respectively. Subjects selected from the Estonian Genome Project had a diagnosis of hypertension confirmed by at least two blood pressure measurements and multiple follow-up measurements for assessing calcium channel blocker antihypertensive treatment outcome. Treatment outcome was scored positive if at least three follow-up blood pressure measurements were nonhypertensive and no more than one follow-up measurement was hypertensive (>140/90). The genotypes of 62 single nucleotide polymorphisms (SNPs) in the calcium channel, voltage-dependent, L type, alpha 1C subunit (CACNA1C) gene were obtained for each subject from a blood sample. Univariate analyses with multiple test correction were conducted using family-wise error rate and false discovery rate methods. Three SNPs in CANCA1C had significant associations with antihypertensive outcome, combining to yield a positive treatment outcome of less than 15 to 80%.

A multimarker model to predict outcome in tamoxifen-treated breast cancer patients.

PURPOSE: This study was designed to produce a model to predict outcome in tamoxifen-treated breast cancer patients based on clinicopathologic features and multiple molecular markers. EXPERIMENTAL DESIGN: This was a retrospective study of 324 stage I to III female breast cancer patients treated with tamoxifen for whom standard clinicopathologic data and tumor tissue microarrays were available. Nine molecular markers were studied by semiquantitative immunohistochemistry and/or fluorescence in situ hybridization. Cox proportional hazards analysis was used to determine the contributions of each variable to disease-specific and overall survival, and machine learning was used to produce a model to predict patient outcome. RESULTS: On a univariate basis, the following features were significantly associated with worse survival: high pathologic tumor or nodal class, histologic grade, epidermal growth factor receptor, ERBB2, MYC, or TP53; absent estrogen receptor (ER) or progesterone receptor; and low BCL2. CCND1 and CDKN1B did not reach statistical significance. On a multivariate basis, nodal class, ER, and MYC were statistically significant as independent factors for survival. However, the benefit of ER-positive status was moderated by BCL2, ERBB2, and progesterone receptor. BCL2 and TP53 also interacted as an independent risk factor. A kernel partial least squares polynomial model was developed with an area under the receiver operating characteristic curve of 0.90. CONCLUSIONS: Our data show the predictive value of BCL2, ERBB2, MYC, and TP53 in addition to the standard hormone receptors and clinicopathologic features, and they show the importance of conditional interpretation of certain molecular markers. Our multimarker predictive model performed significantly better than standard guidelines.