Adoptive transfer of gene-engineered CD4+ helper T cells induces potent primary and secondary tumor rejection

Because CD4(+) T cells play a key role in aiding cellular immune responses, we wanted to assess whether increasing numbers of gene-engineered antigen-restricted CD4(+) T cells could enhance an antitumor response mediated by similarly gene-engineered CD8(+) T cells. In this study, we have used retroviral transduction to generate erbB2-reactive mouse T-cell populations composed of various proportions of CD4(+) and CD8(+) cells and then determined the antitumor reactivity of these mixtures. Gene-modified CD4(+) and CD8(+) T cells were shown to specifically secrete Tc1 (T cytotoxic-1) or Tc2 cytokines, proliferate, and lyse erbB2(+) tumor targets following antigen ligation in vitro. In adoptive transfer experiments using severe combined immunodeficient (scid) mice, we demonstrated that injection of equivalent numbers of antigen-specific engineered CD8(+) and CD4(+) T cells led to significant improvement in survival of mice bearing established lung metastases compared with transfer of unfractionated (largely CD8(+)) engineered T cells. Transferred CD4(+) T cells had to be antigen-specific (not just activated) and secrete interferon gamma (IFN-gamma) to potentiate the antitumor effect. Importantly, antitumor responses in these mice correlated with localization and persistence of gene-engineered T cells at the tumor site. Strikingly, mice that survived primary tumor challenge could reject a subsequent re-challenge. Overall, this study has highlighted the therapeutic potential of using combined transfer of antigen-specific gene-modified CD8(+) and CD4(+) T cells to significantly enhance T-cell adoptive transfer strategies for cancer therapy.

Hormone replacement therapy and breast cancer risk in California

Numerous studies have documented increased breast cancer risks with hormone replacement therapy (HRT), but these do not give a woman her specific absolute risk for the remainder of her life. This article estimates the magnitude of the effect of HRT on breast cancer incidence in California and calculates a woman's cumulative risk of breast cancer with different formulations and durations of HRT use. The effects of HRT on the underlying breast cancer incidence were estimated using the attributable fraction method, applying HRT prevalence data from the 2001 California Health Interview Survey and published rates of higher relative risk (RR) from HRT use from the Women's Health Initiative (WHI) study and Million Women's Survey (MWS). The annual number of breast cancers potentially attributable to HRT in California was estimated, along with individual cumulative risk of breast cancer for various ages to 79 years according to HRT use, duration, and formulation. Using the WHI data, 829 of 19,000 breast cancers (4.3%) in California may be attributable to HRT This figure increases to 3401 (17.4%) when the MWS RRs are applied. Use of estrogen-only HRT or short-term (approximately 5 years) use of combined HRT has a minimal effect on the cumulative risk calculated to the age of 79 years; application of the MWS data to a Californian woman commencing HRT at the age of 50 years (no HRT, 8.5%; estrogen only, 8.6%; combined, 9.1%). Prolonged (approximately 10 years) use of combined HRT increases the cumulative risk to 10.3%. This article demonstrates that HRT will generate a small additional risk of breast cancer in an individual. The reduction in perimenopausal symptoms may be considered sufficient to warrant this extra risk. However, this view needs to be balanced because the small increases in individual risk will be magnified, producing a noticeable change in population cancer caseload where HRT use is high.

A histone deacetylase inhibitor, azelaic bishydroxamic acid, shows cytotoxicity on Epstein-Barr virus-transformed B-cell lines - A potential therapy for posttransplant lymphoproliferative disease

Background. Posttransplant lymphoproliferative disease (PTLD), driven by the presence of Epstein-Barr virus (EBV), is becoming an increasingly important clinical problem after solid organ transplantation. The use of immunosuppressive therapy leads to the inhibition of the cytotoxic T cells that normally control the EBV latently infected B cells. The prognosis for many patients with PTLD is poor, and the optimal treatment strategy is not well defined. Method. This study investigates the use of a histone deacetylase inhibitor, azelaic bishydroxamic acid (ABRA), for its ability to effectively kill EBV-transformed lymphoblastoid cell lines. Results. In vitro treatment of lymphoblastoid cell lines with ABRA showed that they were effectively killed by low doses of the drug (ID50 2-5 mug/ml) within 48 hr. As well as being effective against polyclonal B-cell lines, ABHA was also shown to be toxic to seven of eight clonal Burkitt's lymphoma cell lines, indicating that the drug may also be useful in the treatment of late-occurring clonal PTLD. In addition, ABHA treatment did not induce EBV replication or affect EBV latent gene expression. Conclusion. These studies suggest that ABHA effectively kills both polyclonal and clonal B-cell lines and has potential in the treatment of PTLD.

Relationship of XIST expression and responses of ovarian cancer to chemotherapy

Expression profiling to characterize cancer pharmacology has become a new approach to discover novel molecular targets for prognostic markers and cancer therapy. In a study to compare the global RNA expression profiles between primary and recurrent ovarian tumors from the same patient, we have identified XIST (inactive X chromosome-specific transcripts) as the most differentially expressed gene that was down-regulated in the recurrent tumor. XIST encodes a spliced noncoding polyadenylated transcript that is unique in being expressed exclusively from the inactive X chromosome and is involved in the X-inactivation process. Subsequent characterization of XIST expression in a panel of female cancer cell lines showed that the expression level of XIST correlates significantly with Taxol sensitivity. The clinical relevance of this observation is demonstrated by the strong association between XIST RNA levels and disease-free periods of ovarian cancer patients in a group of 21 ovarian cancer cases with Taxol in the therapeutic regiments. Cytogenetic studies on ovarian cancer cell lines indicated that loss of inactive X chromosome is one mechanism for the loss of XIST transcripts in the cell lines. Our data suggest that XIST expression may be a potential marker for chemotherapeutic responses in ovarian cancer.

Model-based clustering in gene expression microarrays: An application to breast cancer data

In microarray studies, the application of clustering techniques is often used to derive meaningful insights into the data. In the past, hierarchical methods have been the primary clustering tool employed to perform this task. The hierarchical algorithms have been mainly applied heuristically to these cluster analysis problems. Further, a major limitation of these methods is their inability to determine the number of clusters. Thus there is a need for a model-based approach to these. clustering problems. To this end, McLachlan et al. [7] developed a mixture model-based algorithm (EMMIX-GENE) for the clustering of tissue samples. To further investigate the EMMIX-GENE procedure as a model-based -approach, we present a case study involving the application of EMMIX-GENE to the breast cancer data as studied recently in van 't Veer et al. [10]. Our analysis considers the problem of clustering the tissue samples on the basis of the genes which is a non-standard problem because the number of genes greatly exceed the number of tissue samples. We demonstrate how EMMIX-GENE can be useful in reducing the initial set of genes down to a more computationally manageable size. The results from this analysis also emphasise the difficulty associated with the task of separating two tissue groups on the basis of a particular subset of genes. These results also shed light on why supervised methods have such a high misallocation error rate for the breast cancer data.