No abstract

Breast cancer is the most common cancer in females and the leading cause of cancer death in women worldwide. Angiogenesis, the formation of new blood vessels, plays an important role in the development and progression of breast cancer. Vascular endothelial growth factor A (VEGFA), the key modulator of angiogenesis, is highly expressed in cancer tissue and correlates with its more aggressive features. Polymorphisms of VEGFA alter the levels of expression and subsequently influence the susceptibility and aggressiveness of breast cancer. Assessment of VEGFA polymorphisms may be used for the identification of patients suitable for anti-VEGFA therapy.

We present for the first time, to the best of our knowledge, the potential clinical implications of the combined assessment of ASMA fibroblasts and cytoplasmic HMGB1 in breast cancer.

Breast cancer is the most frequent type of cancer among women worldwide. Vascular endothelial growth factor (VEGF), the key modulator of angiogenesis, has been implicated in breast cancer susceptibility and aggressiveness. expression was determined in 99 breast cancer tissue samples using reverse transcription-polymerase chain reaction and the human epidermal growth factor receptor 2 (HER2) status was determined by immunohistochemistry. Subsequently, the associations of, HER2 and hormone receptor status with clinicopathological data were evaluated. High expression was found to be significantly correlated with the presence of lymphovascular invasion. In hormone receptor-positive/HER2-positive, HER2-positive and triple-negative breast cancer, high expression was correlated with the presence of axillary nodal metastasis and lower overall survival rates. Therefore, the assessment of the status along with the hormone receptor and HER2 status may help identify high-risk patients who may benefit from anti-VEGF treatment.

age-standardized incidence rate of female breast cancer per 100,000 was 28.5 (Imsamran et al., 2015). The incidence was approximately 3-times higher in Western Europe (96 per 100,000) (Ferlay et al., 2015). This difference may be due to differences in lifestyle, ethnicity, environmental factors, or socioeconomic status. The Breast Cancer Risk Assessment Tool is based on a statistical model known as the "Gail model" which is named after Dr. Mitchell Gail, Senior Investigator in the Biostatistics Branch of the National Cancer Institute's Division of Cancer Epidemiology and Genetics. The original Gail model used data from the Breast Cancer Detection Demonstration Project (BCDDP) and included five factors: age, age at first menstruation, age at first child, number of breast biopsies, and familial history of breast cancer in 1st degree relatives (Gail et al., 1989). The calculated result provides the risks of invasive and in situ carcinoma. The National Surgical Adjuvant Breast and Bowel Project (NSABP) modified this model by excluding the incidence of ductal carcinoma in situ

Dendritic cell (DC)–based T-cell activation is an alternative immunotherapy in breast cancer. The anti-programmed death ligand 1 (PD-L1) can enhance T-cell function. Nucleolin (NCL) is overexpressed in triple-negative breast cancer (TNBC). The regulation of PD-L1 expression through autophagy and the anti–PD-L1 peptide to help sensitize T cells for NCL-positive TNBC cell killing has not been evaluated. Results showed the worst clinical outcome in patients with high NCL and PD-L1. Self-differentiated myeloid-derived antigen-presenting cells reactive against tumors presenting NCL or SmartDCs-NCL producing GM-CSF and IL-4, could activate NCL-specific T cells. SmartDCs-NCL plus recombinant human ribosomal protein substrate 3 (RPS3) successfully induced maturation and activation of DCs characterized by the reduction of CD14 and the induction of CD11c, CD40, CD80, CD83, CD86, and HLA-DR. Interestingly, SmartDCs-NCL plus RPS3 in combination with anti–PD-L1 peptide revealed significant killing activity of the effector NCL-specific T cells against NCLHigh/PD-L1High MDA-MB-231 and NCLHigh/PD-L1High HCC70 TNBC cells at the effector: a target ratio of 5:1 in 2-D and 10:1 in the 3-D culture system; and increments of IFNγ by the ELISpot assay. No killing effect was revealed in MCF-10A normal mammary cells. Mechanistically, NCL-specific T-cell–mediated TNBC cell killing was through both apoptotic and autophagic pathways. Induction of autophagy by curcumin, an autophagic stimulator, inhibited the expression of PD-L1 and enhanced cytolytic activity of NCL-specific T cells. These findings provide the potential clinical approaches targeting NCLHigh/PD-L1High TNBC cells with NCL-specific T cells in combination with a PD-L1 inhibitor or autophagic stimulator.

Background and Purpose Web-based prognostic calculators have been developed to inform about the use of adjuvant systemic treatments in breast cancer. CancerMath and PREDICT are two examples of web-based prognostic tools that predict patient survival up to 15 years after an initial diagnosis of breast cancer. The aim of this study is to validate the use of CancerMath and PREDICT as prognostic tools in Thai breast cancer patients. Patients and Methods A total of 615 patients who underwent surgical treatment for stage I to III breast cancer from 2003 to 2011 at the Division of Head Neck and Breast Surgery, Department of Surgery, Siriraj Hospital, Mahidol University, Thailand were recruited. A model-predicted overall survival rate (OS) and the actual OS of the patients were compared. The efficacy of the model was evaluated using receiver-operating characteristic (ROC) analysis. Results For CancerMath, the predicted 5-year OS was 88.9% and the predicted 10-year OS was 78.3% (p<0.001). For PREDICT, the predicted 5-year OS was 83.1% and the predicted 10-year OS was 72.0% (p<0.001). The actual observed 5-year OS was 90.8% and the observed 10-year OS was 82.6% (p<0.001). CancerMath demonstrated better predictive performance than PREDICT in all subgroups for both 5- and 10-year OS. In addition, there was a marked difference between CancerMath and observed survival rates in patients who were older as well as patients who were stage N3. The area under the ROC curve for 5-year OS in CancerMath and 10-year OS was 0.74 (95% CI; 0.65–0.82) and 0.75 (95% CI; 0.68–0.82). In the PREDICT group, the area under the ROC curve for 5-year OS was 0.78 (95% CI; 0.71–0.85) and for 10-year OS, it was 0.78 (95% CI; 0.71–0.84). Conclusion CancerMath and PREDICT models both underestimated the OS in Thai breast cancer patients. Thus, a novel prognostic model for Thai breast cancer patients is required.

Extralymphatic filariasis is an uncommon phenomenon that can be caused by several lymphatic filarial species, including zoonotic filaria of animal origins. In this study, we report a case of a 64-year-old Thai woman who presented with a lump in her left breast that was diagnosed with invasive ductal carcinoma. At the same time, a small nodule was found in her right breast, via imaging study, without any abnormal symptoms. A core needle biopsy of the right breast nodule revealed a filarial-like nematode compatible with the adult stage of Brugia sp. A molecular identification of the nematode partial mt 12rRNA gene and ITS1 suggested the causative species as closely related to Brugia pahangi , a zoonotic lymphatic filaria of animals such as cats and dogs. The sequence of the partial mt 12rRNA and ITS1 gene in this patient was 94% and 99% identical to the previously reported sequence of mt 12rRNA and ITS1 genes of B. pahangi. The sequence of ITS1 gene is 99% similar to B. pahangi microfilaria from infected dogs in Bangkok, which was highly suspected of having a zoonotic origin. As far as we know, this is the first case report of B. pahangi filariasis presented with a breast mass concomitantly found in a patient with invasive ductal carcinoma. This raised serious concern regarding the zoonotic transmission of filariasis from natural animal reservoirs.