11 research outputs found
The estrogen effect; clinical and histopathological evidence of dichotomous influences in dogs with spontaneous mammary carcinomas.
The purpose of this study was to investigate the associations and explore the relationships between hormonal factors (serum estrogen, estrogen receptors and ovariohysterectomy) and other clinical/histological prognostic factors and their impact on outcome in dogs with mammary carcinomas. Data from two separate prospective studies on dogs with spontaneous mammary carcinomas were used for this research. All dogs underwent standardized diagnostic testing, staging, surgery and follow-up examinations. Serum estrogen was analyzed by competitive enzyme immunoassay or radioimmunoassay, and tumor estrogen receptor (ER) expression was analyzed by immunohistochemistry. A total of 159 dogs were included; 130 were spayed and 29 remained. High serum estrogen was associated with an overall longer time to metastasis (p = 0.021). When stratifying based on spay group, the effect was only significant in spayed dogs, (p = 0.019). Positive tumor ER expression was also associated with a longer time to metastasis (p = 0.025), but similar to above, only in dogs that were spayed (p = 0.049). Further subgroup analysis revealed that high serum estrogen was significantly associated with improved survival in dogs with ER positive tumors, but only in spayed dogs (p = 0.0052). Interestingly, the effect of spaying was the opposite in dogs with ER negative tumors; here, intact dogs with high serum estrogen but ER negative tumors had a significantly longer time to metastasis (p = 0.036). Low serum estrogen was associated with increased risk for the development of non-mammary tumors in the post-operative period (p = 0.012). These results highlight the dual effect of estrogen in cancer: Estrogen acts as a pro-carcinogen in ER positive mammary tumors, but a may have a protective effect in ER negative tumors, potentially via non-receptor mechanisms. The latter is supported by the decreased risk for non-mammary tumors in dogs with high serum estrogen, and explains the increased incidence of certain non-mammary tumors in in dogs spayed at an early age
Kaplan Meier estimates comparing vaccinated Group 2 to unvaccinated control Group 3 for (A) TTP (p = 0.34) and (B) LSS (p = 0.18).
<p>Kaplan Meier estimates comparing vaccinated Group 2 to unvaccinated control Group 3 for (A) TTP (p = 0.34) and (B) LSS (p = 0.18).</p
RNA loaded CD40-B cells stimulate antigen-specific IFN-γ responses in PBMCs.
<p>PBMCs were obtained from dogs at the time of diagnosis (pre) and 3 weeks post vaccination (post) and antigen-specific immune responses directed against (A) CDV-HA and (B) lymph node tumor antigens were determined by IFN-γ ELISPOT. *p<0.1, **p<0.05.</p
Kaplan Meier estimates for lymphoma-specific survival (LSS) of relapsed dogs who received salvage therapy, demonstrating statistically significant differences between vaccinated Group 2 to unvaccinated control Group 3 (p = 0.038).
<p>Kaplan Meier estimates for lymphoma-specific survival (LSS) of relapsed dogs who received salvage therapy, demonstrating statistically significant differences between vaccinated Group 2 to unvaccinated control Group 3 (p = 0.038).</p
Kaplan Meier estimates comparing intent-to-treat Group 1 to unvaccinated control Group 3 for (A) time-to-progression (TTP) (p = 0.16) and (B) lymphoma-specific survival (LSS) (p = 0.37).
<p>Kaplan Meier estimates comparing intent-to-treat Group 1 to unvaccinated control Group 3 for (A) time-to-progression (TTP) (p = 0.16) and (B) lymphoma-specific survival (LSS) (p = 0.37).</p
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Modeling molecular development of breast cancer in canine mammary tumors
Understanding the changes in diverse molecular pathways underlying the development of breast tumors is critical for improving diagnosis, treatment, and drug development. Here, we used RNA-profiling of canine mammary tumors (CMTs) coupled with a robust analysis framework to model molecular changes in human breast cancer. Our study leveraged a key advantage of the canine model, the frequent presence of multiple naturally occurring tumors at diagnosis, thus providing samples spanning normal tissue and benign and malignant tumors from each patient. We showed human breast cancer signals, at both expression and mutation level, are evident in CMTs. Profiling multiple tumors per patient enabled by the CMT model allowed us to resolve statistically robust transcription patterns and biological pathways specific to malignant tumors versus those arising in benign tumors or shared with normal tissues. We showed that multiple histological samples per patient is necessary to effectively capture these progression-related signatures, and that carcinoma-specific signatures are predictive of survival for human breast cancer patients. To catalyze and support similar analyses and use of the CMT model by other biomedical researchers, we provide FREYA, a robust data processing pipeline and statistical analyses framework