Breast--Cancer

One of the most common types of cancer among women is breast cancer. It represents one of the diseases leading to a high number of mortalities among women. On the other hand, prostate cancer is the second most frequent malignancy in men worldwide.
The early detection of prostate cancer is fundamental to reduce mortality and increase the survival rate. A comparison between six types of machine learning models as Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, k Nearest Neighbors, and Naïve Bayes has been performed. This research aims to identify the most efficient machine learning algorithms for identifying the most significant risk factors of prostate and breast cancers. For this reason, National Health Interview Survey (NHIS) and Prostate, Lung, Colorectal, and Ovarian (PLCO) datasets are used. A comprehensive comparison of risk factors leading to these two crucial cancers can significantly impact early detection and progressive improvement in survival.

One modality to improve quality of life, in Breast Cancer Survivors (BCS), is physical activity (PA). Less than 30% of BCS participate in PA. The purpose of this study is to explore BCS’s intention to exercise. Seventy-five BCS patients, undergoing treatment, completed a survey. The survey assessed the following: Health care practitioner influence, Demographics, Stages of Change (SOC), anxiety and depression, perceived barriers to exercise, past and current exercise, and the constructs of the Theory of Planned Behavior (TPB). BCS who were older, had less education, did not exercise before diagnosis, were in the pre-contemplation and contemplation stages, and/or exhibited a low Perceived Behavioral Control had a lower intention to exercise. Incorporating the findings from this research into an intervention may assist with increasing intention to exercise among BCS.

According to U.S. Breast Cancer Statistics, about 1 in 8 U.S. women will develop invasive breast cancer during their lifetime. Chemotherapeutics that are used on patients currently often lead to tumor resistance, bone marrow suppression and cachexia. This study evaluated a novel combination of three non-mutagenic compounds for their effectiveness against mammary tumor cells, toxicity towards immune cells, ability to provoke the expression of immunogenic cell death (ICD) markers, and killing in 3D tumor models. Methotrexate (MTX), 2-deoxyglucose (2DG), and wogonin (WGN) were combined at doses well below their EC50 values yet effectively killed human and mouse breast cancer cells. The combination inhibited cancer cell colony formation and induced a high degree of cell death in multiple malignant tumor cell lines. Importantly, the combination did not significantly inhibit the viability of peripheral-blood mononuclear cells (PBMCs), even when employed at 3X the concentration that killed cancer cells. In marked contrast, low-dose doxorubicin, a common therapeutic for breast cancers, significantly decreased PBMC viability and increased the percentage of cell death. Our novel combinatorial therapy (Trifecta) elicited the significant expression of three ICD hallmarks: calreticulin surface expression, ATP secretion, and HMGB-1 release. In all cases, Trifecta elicited an equal or greater degree of ICD-marker expression compared to doxorubicin, a known inducer of ICD. We show significant efficacy of Trifecta against human and mouse mammary 3D tumor models grown in Matrigel® ECM-complex containing culture medium, and reaffirm the marked resistance of tumorspheres towards the conventional chemotherapeutic doxorubicin. The effectiveness of Trifecta in an acceptable surrogate model for mouse studies bodes well for translation of our findings to the clinic. In conclusion, Trifecta has proven highly effective against tumor cells grown either as monolayers or tumorspheres, without significant cytotoxic effects towards proliferating immune cells. Furthermore, treatment with this combination elicits ICD, which has the potential to prime an adaptive immune response against tumor cells and prevent future relapse. The drugs chosen for our combination target metabolic pathways that cancer cells are heavily dependent upon and do not interact with or induce mutations in DNA. These properties place Trifecta at the forefront of developing anticancer therapies.