Regulatory T (T(reg)) cells mediate homeostatic peripheral tolerance by suppressing autoreactive T cells. Failure of host antitumor immunity may be caused by exaggerated suppression of tumor-associated antigen-reactive lymphocytes mediated by T(reg) cells; however, definitive evidence that T(reg) cells have an immunopathological role in human cancer is lacking. Here we show, in detailed studies of CD4(+)CD25(+)FOXP3(+) T(reg) cells in 104 individuals affected with ovarian carcinoma, that human tumor T(reg) cells suppress tumor-specific T cell immunity and contribute to growth of human tumors in vivo. We also show that tumor T(reg) cells are associated with a high death hazard and reduced survival. Human T(reg) cells preferentially move to and accumulate in tumors and ascites, but rarely enter draining lymph nodes in later cancer stages. Tumor cells and microenvironmental macrophages produce the chemokine CCL22, which mediates trafficking of T(reg) cells to the tumor. This specific recruitment of T(reg) cells represents a mechanism by which tumors may foster immune privilege. Thus, blocking T(reg) cell migration or function may help to defeat human cancer.

The HER2-targeted therapy trastuzumab is widely used for the treatment of patients with metastatic breast tumors overexpressing HER2. However, an objective response is observed in only 12% to 24% of patients treated with trastuzumab as a single agent and initial responders regress in <6 months (1-3). The reason for the clinical failure of trastuzumab in this setting remains unclear. Here we show that local lymph node-positive disease progression in 89% of breast cancer patients with HER2-positive tumors involves the HER2 oncogenic variant HER2Δ16. We further show that ectopic expression of HER2Δ16, but not wild-type HER2, promotes receptor dimerization, cell invasion, and trastuzumab resistance of NIH3T3 and MCF-7 tumor cell lines. The potentiated metastatic and oncogenic properties of HER2Δ16 were mediated through direct coupling of HER2Δ16 to Src kinase. Cotargeting of HER2Δ16 and Src kinase with the singleagent tyrosine kinase inhibitor dasatinib resulted in Src inactivation, destabilization of HER2Δ16, and suppressed tumorigenicity. Activated Src kinase was also observed in 44% of HER2Δ16-expressing breast carcinomas underscoring the potential clinical implications of coupled HER2Δ16 and Src signaling. Our results suggest that HER2Δ16 expression is an important genetic event driving trastuzumab-refractory breast cancer. We propose that successful targeted therapeutics for intervention of aggressive HER2-positive breast cancers will require a strategy to suppress HER2Δ16 oncogenic signaling. One possibility involves a therapeutic strategy employing single-agent tyrosine kinase inhibitors to disengage the functionally coupled oncogenic HER2Δ16 and Src tyrosine kinase pathways.

Although crosstalk between cell-surface and nuclear receptor signaling pathways has been implicated in the development and progression of endocrine-regulated cancers, evidence of direct coupling of these signaling pathways has remained elusive. Here we show that estrogen promotes an association between extranuclear estrogen receptor A (ER) and the epidermal growth factor receptor (EGFR) family member ERBB4. Ectopically expressed as well as endogenous ERBB4 interacts with and potentiates ER transactivation, indicating that the ERBB4/ER interaction is functional. Estrogen induces nuclear translocation of the proteolytic processed ERBB4 intracellular domain (4ICD) and nuclear translocation of 4ICD requires functional ligand-bound ER. The nuclear ER/4ICD complex is selectively recruited to estrogen-inducible gene promoters such as progesterone receptor (PgR) and stromal cell-derived factor 1 (SDF-1) but not to trefoil factor 1 precursor (pS2). Consistent with 4ICD-selective promoter binding, suppression of ERBB4 expression by interfering RNA shows that 4ICD coactivates ER transcription at the PgR and SDF-1 but not the pS2 promoter. Significantly, ERBB4 itself is an estrogen-inducible gene and the ERBB4 promoter harbors a consensus estrogen response element (ERE) half-site with overlapping activator protein-1 elements that bind ER and 4ICD in response to estrogen. Using a cell proliferation assay and a small interfering RNA approach, we show that ERBB4 expression is required for the growth-promoting action of estrogen in the T47D breast cancer cell line. Our results indicate that ERBB4 is a unique coregulator of ER, directly coupling extranuclear and nuclear estrogen actions in breast cancer. We propose that the contribution of an autocrine ERBB4/ER signaling pathway to tumor growth and therapeutic response should be considered when managing patients with ER-positive breast cancer. (Cancer Res 2006; 66(16): 7991-8)

Despite the great success of genome-wide association studies (GWAS) in identification of the common genetic variants associated with complex diseases, the current GWAS have focused on single-SNP analysis. However, single-SNP analysis often identifies only a few of the most significant SNPs that account for a small proportion of the genetic variants and offers only a limited understanding of complex diseases. To overcome these limitations, we propose gene and pathway-based association analysis as a new paradigm for GWAS. As a proof of concept, we performed a comprehensive gene and pathway-based association analysis of 13 published GWAS. Our results showed that the proposed new paradigm for GWAS not only identified the genes that include significant SNPs found by single-SNP analysis, but also detected new genes in which each single SNP conferred a small disease risk; however, their joint actions were implicated in the development of diseases. The results also showed that the new paradigm for GWAS was able to identify biologically meaningful pathways associated with the diseases, which were confirmed by a gene-set-rich analysis using gene expression data.

Current GWAS have primarily focused on testing association of single SNPs. To only test for association of single SNPs has limited utility and is insufficient to dissect the complex genetic structure of many common diseases. To meet conceptual and technical challenges raised by GWAS, we propose gene and pathway-based GWAS as complementary to the current single SNP-based GWAS. This publication develops three statistics for testing association of genes and pathways with disease: linear combination test, quadratic test and decorrelation test which take correlations among SNPs within a gene or genes within a pathway into account. The null distribution of the proposed statistics is examined and the statistics are applied to GWAS of rheumatoid arthritis in the Wellcome Trust Case Control Consortium and the North American Rheumatoid Arthritis Consortium studies. The preliminary results show that the proposed gene and pathway-based GWAS offer several remarkable features. First, not only can they identify the genes that have large genetic effects, but also they can detect new genes in which each single SNP conferred a small amount of disease risk, and their joint actions can be implicated in the development of diseases. Second, gene and pathway-based analysis can allow the formation of the core of pathway definition of complex diseases and unravel the functional bases of an association finding. Third, replication of association findings at the gene or pathway level is much easier than replication at the individual SNP level.

Telomeres play a central role in cellular aging, and shorter telomere length has been associated with age-related disorders including diabetes. However, a causal link between telomere shortening and diabetes risk has not been established. In a well-characterized longitudinal cohort of American Indians participating in the Strong Heart Family Study, we examined whether leukocyte telomere length (LTL) at baseline predicts incident diabetes independent of known diabetes risk factors. Among 2,328 participants free of diabetes at baseline, 292 subjects developed diabetes during an average 5.5 years of follow-up. Compared with subjects in the highest quartile (longest) of LTL, those in the lowest quartile (shortest) had an almost twofold increased risk of incident diabetes (hazard ratio [HR] 1.83 [95% CI 1.26–2.66]), whereas the risk for those in the second (HR 0.87 [95% CI 0.59–1.29]) and the third (HR 0.95 [95% CI 0.65–1.38]) quartiles was statistically nonsignificant. These findings suggest a nonlinear association between LTL and incident diabetes and indicate that LTL could serve as a predictive marker for diabetes development in American Indians, who suffer from disproportionately high rates of diabetes.

Phosphoinositide-3 kinase (PI3K)/Akt signaling is activated by growth factors such as insulin and epidermal growth factor (EGF) and regulates several functions such as cell cycling, apoptosis, cell growth, and cell migration. Here, we find that Kank is an Akt substrate located downstream of PI3K and a 14-3-3–binding protein. The interaction between Kank and 14-3-3 is regulated by insulin and EGF and is mediated through phosphorylation of Kank by Akt. In NIH3T3 cells expressing Kank, the amount of actin stress fibers is reduced, and the coexpression of 14-3-3 disrupted this effect. Kank also inhibits insulin-induced cell migration via 14-3-3 binding. Furthermore, Kank inhibits insulin and active Akt-dependent activation of RhoA through binding to 14-3-3. Based on these findings, we hypothesize that Kank negatively regulates the formation of actin stress fibers and cell migration through the inhibition of RhoA activity, which is controlled by binding of Kank to 14-3-3 in PI3K–Akt signaling.

Although great progress in genome-wide association studies (GWAS) has been made, the significant SNP associations identified by GWAS account for only a few percent of the genetic variance, leading many to question where and how we can find the missing heritability. There is increasing interest in genome-wide interaction analysis as a possible source of finding heritability unexplained by current GWAS. However, the existing statistics for testing interaction have low power for genome-wide interaction analysis. To meet challenges raised by genome-wide interactional analysis, we have developed a novel statistic for testing interaction between two loci (either linked or unlinked). The null distribution and the type I error rates of the new statistic for testing interaction are validated using simulations. Extensive power studies show that the developed statistic has much higher power to detect interaction than classical logistic regression. The results identified 44 and 211 pairs of SNPs showing significant evidence of interactions with FDR<0.001 and 0.001