BCL10 as a new candidate gene for immune response in pigs: cloning, expression and association analysis

BCL10 is an apoptotic regulatory molecule identified through its direct involvement in t(1; 14)(p22; q32) of mucosa-associated lymphoid tissue lymphoma, and was implicated in the pathogenesis of this and several other tumour types. BCL10 was recognized as an antigen receptor-specific regulator of NF-kappaB, which showed close association with immune responses. In this study, we cloned and characterized BCL10 from the porcine spleen and analysed its genomic structure. BCL10 was mapped to SSC4q21-q23 by the IMpRH panels, it is closely linked to the marker S0161 and SW1461. This gene has three exons and two introns. Reverse transcriptase-polymerase chain reaction analyses showed that BCL10 was widely expressed in all the examined tissues. Transient transfection indicated that porcine BCL10 was located in cytoplasm in Pig Kidney Epithelial cells. BCL10 gene displays the opposite expression trend between the two treatments mimic virus and bacteria of polyriboinosinic-polyribocytidylic acid (Poly I:C) and lipopolysaccharide (LPS). The level of the BCL10 mRNA was up-regulated during 12-24 h and peaking at 48 h when treated with LPS, whereas it was down-regulated during 0-48 h and highest at 0 h (cells without treating with Poly I:C) when treated with Poly I:C. One single nucleotide polymorphism (SNP) site was identified in the 3'-untranslated region of porcine BCL10. Association analysis revealed that this SNP was significantly associated with intermediate cell mass (eosinophile granulocyte, basophile granulocyte and histoleucocyte) percentage, absolute intermediate cell mass count and mean red blood cell volume of 0-day-old pigs, and red blood cell count of 17-day-old pigs (P < 0.05), and also had significant associations with red blood cell count and haemoglobin concentration of 32-day-old pigs (P < 0.01).

Novel Stably Transplantable Xenograft Models of Human Breast Cancer for Evaluation of Experimental Therapeutics

In translational breast cancer research, our ability to evaluate clinical responses of human tumors to new therapeutic agents is severely limited experimentally. For example, it is not possible to evaluate the response of a single tumor to multiple candidate therapeutic agents. Conversely, the limited number of well-characterized in vivo preclinical human tumor models currently available precludes evaluation of multiple clinically relevant tumors with candidate therapeutic agents. These limitations severely impinge on our ability to develop and test novel therapeutic agents, particularly those that may target tumor-initiating “cancer stem cells”, which are relatively resistant to chemotherapy and radiation and may be responsible for disease recurrence and metastases.Historically, in vivo experimental therapeutic research has relied on either genetically engineered animal models, or “xenograft” transplantation models in which established human cancer cell lines are transplanted into immunocompromised host mice. However, each type of model has significant limitations. We sought to circumvent some of these limitations by propagating a cohort of human tumors as stably transplantable xenograft tissue lines grown in the absence of engineered or immortalized fibroblasts by transplanting clinical biopsies directly into the mammary fat pad of SCID/Beige immunocompromised mice (lacking B-cell, T-cell, and NK cell function) without intervening culture in vitro.Thus far, we have established 13 independent stably transplantable xenograft lines representing nine “triple-negative” (ER-PR-HER2-), two HER2+, and two ER+ breast cancers. Established xenograft lines show phenotypic similarity to the primary tumor with respect to histology and gene expression. Xenografts are being characterized genetically by whole genome sequencing as well as for the diversity of tumor-initiating cell types present. These models are proving useful for the evaluation of experimental therapeutics for their ability to inhibit tumor growth, and for their ability to target the subset cancer cells capable of regenerating tumors upon transplantation, with the goals of overcoming chemoresistance, preventing disease recurrence, and eliminating metastases.

Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 1159.

Comparison of Synchrotron Images of DCIS of the Breast with Their Pathologic Findings

Background: Synchrotron radiation x-ray imaging have opened a new door to explore breast tissue below the micron scale providing detailed internal structures of the samples with a great magnification and an excellent resolution. Using phase contrast technique, we got monochromated synchrotron images of DCIS (Ductal Carcinoma In Situ) of the breast tissue section. To figure out relation with their optical microscopic features, we compared the synchrotron images of the DCIS with their histopathologic findings of the same stained section.Material and Methods: A x-ray microscope was installed on 1B2 beamline of Pohang Light Source, a third generation synchrotron radiation facility with operating energy of 2.5 GeV in Pohang, Korea. The x-ray energy was set at 11.1 keV, and the x-ray beam was monochromatized by a W/B4C monochromator. Zernike phase-shifter was adapted for phase contrast x-ray microscopy. Formalin-fixed 5µm-thick breast tissue sample was attached onto the Kapton film, and positioned 25 m away from the beam source. The synchrotron image of sample was converted into a visual image on the CsI(TI) scintillation crystal, and this visual image was captured by a full frame CCD camera. After scanning, we patched these images one and another to show the large area of the tissue section. For the comparative analysis with their synchrotron image, synchrotron-scanned breast tissue sections were stained, and the histopathologic findings of the samples were captured by image analyzer. The magnifying power of this microscope was 100x.Results: The monochromated x-ray microscopic images of DCIS of the breast tissue were obtained with a good resolution. These images revealed various structures of DCIS lesion with a good contrast and high visibility by phase contrast technique. They include prominent and intact periductal basement membrane - a hallmark of DCIS lesion, and stippled fine microcalcifications which were indistinct in the histologic section, and a shadow of intense peritumoral inflammatory cell infiltration. But the minute changes of each cancer cell showing the features of cancer tissue such as, cellular differentiation and nuclear grade, were not identified well.Discussion: The x-ray microscopic imaging of DCIS of the breast tissue section with synchrotron radiation showed a good correspondence with the histopathologic findings of their stained tissue sections. And the synchrotron image of DCIS had some unique radiologic features differentiating from histopathogic findings. Therefore the synchrotron images of the DCIS lesion appear to have a new possibility of use for the clinical and research purposes in near future.

Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 5015.

Crosstalk between Estrogen Receptor alpha 36, a Dominant Extra-Nuclear ER, and HER2 Promotes Tumor Cell Survival in Breast Cancer

The ER-α36 protein is a 36kDa novel splice variant of the full length 66kDa ER-α. ER-α36 lacks both transcriptional activation domains of ER-α66 but retains the DNA-binding domain and partial dimerization and ligand-binding domains. It has been shown that ER-α36 could trigger membrane-initiated signaling in ER-α36 over-expressing 293 cells, suggesting that ER-α36 might have functions linked with activation of intracellular signaling.To further explore the mechanism(s) of ER-α36 activation of intracellular signaling, in particular the possible relationship between ER-α36 and HER2 in breast cancer, we performed a pilot study. First, we examined the correlation between ER-α36 and HER2 by immunohistochemistry (IHC) using a specific anti-ER-α36 antibody designed against a unique epitope located in the C-tail of ER-a36 which is not present in ER-α66. The results showed that the expression of ER-α36 was highly associated with HER2 in a cohort of 249 breast tumors (P&lt;0.05). Then, we examined whether ER-α36 physically interacts with HER2 in breast cancer cell lines using co-immunoprecipitation (Co-IP) and Western blotting (WB) experiments. We found that ER-α36 interacts with HER2 in both MCF7/HER2 and BT474 cell lines. Second, we explored whether ER-α36 influences the activation of HER2 and its downstream signaling by knocking down ER-α36 expression in both MCF7/HER2 and BT474 cell lines. We found that knockdown of ER-α36 by siRNA abrogated the activation of P-HER2, as well as Akt and MAPK, in both MCF7/HER2 and BT474 cells. Finally, we asked the key question whether ER-α36 was associated with cell proliferation/survival. Strikingly, we found that when both MCF7/HER2 and BT474 cells were deprived of ER-α36 by siRNA, they died quickly, whereas control cells grew normally.In conclusion, our preliminary data suggests that there might be a crosstalk between ER-α36 and HER2 signaling, and that this crosstalk might promote tumor cell survival through HER2/Akt and/or HER2/MAPK signaling pathways in breast cancer. Thus, ER-α36 may be a valuable biomarker and a novel therapeutic target in breast cancer.

Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 2121.

Prognostic Factors and Long-Term Survivors in Inflammatory Breast Cancer

Background: Inflammatory Breast Cancer (IBC) carries a worse overall survival and demonstrates distinct clinicopathologic qualities which require refinement of prognostic indicators specific to IBC. We hypothesized that given IBC's typically rapid course, nodal status would not be a significant prognostic factor. We also investigated the impact of treatment with radiation and surgery on IBC survival.Material and Methods: Retrospective data from the Surveillance, Epidemiology, and End Results of the National Cancer Institute (SEER 13 and 17 registries) were used to evaluate the prognostic effects of nodal status, surgical resection, and radiation on mortality of patients with IBC. We compared survival between patients with or without positive lymph node status and treatment with radiation, surgery, and those patients treated with both. Data were subjected to multivariate statistical analysis.Results: A total of 3755 IBC patients were diagnosed and reported to SEER between 1995 and 2005. IBC survival appears independent of lymph node status with mean survival times of 34 months for node negative patients and 32 months for node positive patients (figure 1, p= 0.11). We identified 735 cases with adequate local treatment records and follow-up information. In this subset we compared survival between groups that received both surgery and radiation (SR), groups that received one of these therapies independently (S or R respectively), to those without either therapy (NT, no therapy) given positive lymph node status at diagnosis (fig. 2, p&lt;0.001).Discussion: Our data suggests that the prognosis of IBC is not related to lymph node status at the time of diagnosis. Because patients with IBC amenable to surgical intervention or radiation are likely to have more favorable biology then those who progress on chemotherapy, it remains difficult to quantify the impact of local therapies. NCCN guidelines currently recommend first-line chemotherapy before moving to local treatments; therefore one limitation of our study is the assumption that centers reporting data to SEER follow NCCN guidelines. Of special interest is the substantial number of patients that show prolonged survival with local therapies. This supports previous reports that about 30% of patients who complete chemotherapy, surgery and radiation therapy may survive five years or more after IBC diagnosis.

Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 4039.

Specific and sensitive detection of Ralstonia solanacearum in soil with quantitative, real-time PCR assays

AIMS

The aim of this study was to develop a sensitive and an effective method suitable for large-scale detection and quantification of Ralstonia solanacearum in soil.

METHODS AND RESULTS

Based on the specific sequence of R. solanacearum strain G1000, the primer pair R.sol1-R.sol2 and the TaqMan probe Rs-pro were designed, and specific and sensitive PCR detection methods were successfully established. The detection limit was 100 fg microl(-1) DNA in conventional PCR and 1.2 fg microl(-1) in real-time PCR. By combining real-time PCR with the modified protocols to extract DNA from soil, it was possible to achieve real-time detection of R. solanacearum in soil, and the degree of sensitivity was 100 fg microl(-1). To detect inhibition in soil samples, an exogenous internal positive control (IPC) was included preventing false negative results, and IPC was successfully amplified from all samples tested. The methodology developed was used to detect the presence of R. solanacearum in tobacco fields in China.

CONCLUSIONS

The real-time PCR combined with the protocol to extract DNA from soil led to the development of a specific, sensitive and rapid detection method for R. solanacearum in soil.

SIGNIFICANCE AND IMPACT OF THE STUDY

The real-time PCR improves the detection sensitivity and specificity and provides an important tool for routine detection of R. solanacearum in soil samples and for epidemiological and ecological studies.

Antibunching single-photon emission and blinking suppression of CdSe/ZnS quantum dots

We demonstrated that by properly coupling to silver nanoprisms, single CdSe/ZnS semiconductor quantum dots (QDs) exhibited suppressed blinking behavior, an enhanced fluorescence intensity ( approximately 2.5 fold), increased radiative decay rates ( approximately 12.5 fold), and antibunching single-photon emission. All these modifications significantly promote the overall performance of the proposed single-photon sources based on colloidal semiconductor QDs.

Subpopulations of stem-like cells in side population cells from the human bladder transitional cell cancer cell line T24

Cancer stem cells can be isolated from human tumours using specific cell surface markers. Bladder cancer cells, however, lack specific cell surface markers, making this approach impracticable. In this study an alternative method was used, involving isolation of side population cells to explore the stem cell characteristics of bladder cancer. Side population cells were isolated from the bladder transitional cell cancer cell line T24 and examined for potential stem cell characteristics related to proliferation, cell cycle distribution, self-renewal and differentiation. It was observed that T24 side population cells have stronger proliferative and colony formation abilities than non-side population cells. Side population cells were also more resistant to chemotherapy and radiotherapy, which may be due to expression of the ATP-binding cassette half-transporter, sub-family G, member 2 protein. Overall, the results suggest that side population cells from the human bladder transitional cell cancer cell line T24 harbour stem-like cells.

Regularized estimation for the accelerated failure time model

SUMMARY

In the presence of high-dimensional predictors, it is challenging to develop reliable regression models that can be used to accurately predict future outcomes. Further complications arise when the outcome of interest is an event time, which is often not fully observed due to censoring. In this article, we develop robust prediction models for event time outcomes by regularizing the Gehan's estimator for the accelerated failure time (AFT) model (Tsiatis, 1996, Annals of Statistics 18, 305-328) with least absolute shrinkage and selection operator (LASSO) penalty. Unlike existing methods based on the inverse probability weighting and the Buckley and James estimator (Buckley and James, 1979, Biometrika 66, 429-436), the proposed approach does not require additional assumptions about the censoring and always yields a solution that is convergent. Furthermore, the proposed estimator leads to a stable regression model for prediction even if the AFT model fails to hold. To facilitate the adaptive selection of the tuning parameter, we detail an efficient numerical algorithm for obtaining the entire regularization path. The proposed procedures are applied to a breast cancer dataset to derive a reliable regression model for predicting patient survival based on a set of clinical prognostic factors and gene signatures. Finite sample performances of the procedures are evaluated through a simulation study.