Why we do what we do. A brief analysis of cancer therapies

Impact of targeting the platelet-activating factor and its receptor in cancer treatment

The lipid mediator platelet-activating factor (PAF) and its receptor (PAFR) signaling play critical roles in a wide range of physiological and pathophysiological conditions, including cancer growth and metastasis. The ability of PAFR to interact with other oncogenic signaling cascades makes it a promising target for cancer treatment. Moreover, numerous natural and synthetic compounds, characterized by diverse pharmacological activities such as anti-inflammatory and anti-tumor effects, have been explored for their potential as PAF and PAFR antagonists. In this review, we provide comprehensive evidence regarding the PAF/PAFR signaling pathway, highlighting the effectiveness of various classes of PAF and PAFR inhibitors and antagonists across multiple cancer models. Notably, the synergistic effects of PAF and PAFR antagonists in enhancing the efficacy of chemotherapy and radiation therapy in several experimental cancer models are also discussed. Overall, the synthesis of literature review indicates that targeting the PAF/PAFR axis represents a promising approach for cancer treatment and also exerts synergy with chemotherapy and radiation therapy.

A novel machine learning-based cancer-specific cardiovascular disease risk score among patients with breast, colorectal, or lung cancer

BACKGROUND

Cancer patients have up to a 3-fold higher risk for cardiovascular disease (CVD) than the general population. Traditional CVD risk scores may be less accurate for them. We aimed to develop cancer-specific CVD risk scores and compare them with conventional scores in predicting 10-year CVD risk for patients with breast cancer (BC), colorectal cancer (CRC), or lung cancer (LC).

METHODS

We analyzed adults diagnosed with BC, CRC, or LC between 2005 and 2012. An machine learning (ML) Extreme Gradient Boosting algorithm ranked 40-50 covariates for predicting CVD for each cancer type using SHapley Additive exPlanations values. The top 10 ML-predictors were used to create predictive equations using logistic regression and compared with American College of Cardiology (ACC)/American Heart Association (AHA) Pooled Cohort Equations (PCE), Predicting Risk of cardiovascular disease EVENTs (PREVENT), and Systematic COronary Risk Evaluation-2 (SCORE2) using the area under the curve (AUC).

RESULTS

We included 10 339 patients: 55.5% had BC, 15.6% had CRC, and 29.7% had LC. The actual 10-year CVD rates were: BC 21%, CRC 10%, and LC 28%. The predictors derived from the ML algorithm included cancer-specific and socioeconomic factors. The cancer-specific predictive scores achieved AUCs of 0.84, 0.76, and 0.83 for BC, CRC, and LC, respectively, and outperformed PCE, PREVENT, and SCORE2, increasing the absolute AUC values by up to 0.31 points (with AUC ranging from 0 to 1). Similar results were found when excluding patients with cardiac history or advanced cancer from the analysis.

CONCLUSIONS

Cancer-specific CVD predictive scores outperform conventional scores and emphasize the importance of integrating cancer-related covariates for precise prediction.

Exploring Anti-Breast Cancer Effects of Live Pediococcus acidilactici and Its Cell-Free Supernatant Isolated from Human Breast Milk

Current breast cancer treatment options are limited by drug resistance and adverse side effects, which calls for the need for alternatives or complementary remedies. Probiotic bacteria isolated from human breast milk have been shown to possess proapoptotic and anti-inflammatory properties against breast mastitis in breastfeeding mothers and are being studied as possible anticancer regimens. Thus, this study aimed at exploring the effect of lactic acid bacteria isolated from human breast milk on MDA-MB 231 breast cancer cells. A total of twenty-two bacteria were isolated from four human breast milk samples. The isolates were characterized and identified using biochemical tests and Sanger sequencing, respectively. For in vitro experiments, we used isolated P. acidilactici to treat MDA-MB-231 cells, and an MTT assay was used to detect proliferation. RT-qPCR and wound healing assays were performed to determine the effect of the isolated P. acidilactici on breast cancer cytokine expression and migration. Exposure of MDA-MB 231 breast cancer cells to live P. acidilactici and its cell-free supernatant (CFS) for 24 h resulted in a reduction in cancer cell viability. Also, the expression of the cytokines IL-6, IL-8, and IL-10 in the breast cancer cells increased following exposure to P. acidilactici and its CFS for 24 and 72 h. Additionally, the levels of the SLUG gene remained unchanged while the TWIST1 gene was upregulated following exposure of the cancer cells to bacteria, indicating that P. acidilactici may promote epithelial-mesenchymal transition in breast cancer. Finally, the CFS significantly inhibited cancer cell mobility. These findings serve as a foundation to further investigate the usefulness of P. acidilactici as a potential therapeutic agent in breast cancer therapy.