Radiation therapy in early-stage invasive breast cancer

Anticancer Potential of Postbiotic Derived from Lactobacillus brevis and Lactobacillus casei: In vitro Analysis of Breast Cancer Cell Line

Breast cancer has emerged as the most widespread and dangerous type of malignancy among women worldwide. Postbiotics have recently emerged as a promising novel adjunct in breast cancer therapy, due to their immunomodulatory effects and the potential to mitigate the adverse effects of conventional treatments. This study aims to investigate the therapeutic effects of postbiotics derived from Lactobacillus brevis (CSF2) and Lactobacillus casei (CFS5), specifically examining their ability to inhibit cell proliferation and induce apoptosis in MCF-7 breast cancer cells. In the current study, the anticancer activity of the cell-free supernatant of L. brevis and L. casei was investigated against MCF-7 cells using MTT assay, flow cytometry, and qRT-PCR technique. Both bacteria showed a high potential for the induction of cell death in MCF-7 cells. However, CFS2 cytotoxicity was significantly higher than CFS5. Flow cytometry results showed significant induction of early apoptosis in cells treated with both CFS2 and CFS5 within 48 h. The induction was notably higher in cells treated with CFS2 compared to CFS5. Overall, CFS2 therapy resulted in a greater increase in BAX and CASP9 gene expression, as well as an elevated BAX/BCL2 ratio within 48 h. These findings indicate that the CFS2 treatment showed a higher level of apoptotic activity than the CFS5 treatment. High biocompatibility was demonstrated following treatment with CFS2 and CFS5. These CFSs may serve as adjunctive medications for suppressing the proliferation of cancer cells. The results of the current study highlight the potential of postbiotics in cancer treatment and suggest that supernatants may serve as effective agents for suppressing cancer cell growth and viability.

A Clinical Decision Support System for Predicting Invasive Breast Cancer Recurrence: Preliminary Results

The mortality associated to breast cancer is in many cases related to metastasization and recurrence. Personalized treatment strategies are critical for the outcomes improvement of BC patients and the Clinical Decision Support Systems can have an important role in medical practice. In this paper, we present the preliminary results of a prediction model of the Breast Cancer Recurrence (BCR) within five and ten years after diagnosis. The main breast cancer-related and treatment-related features of 256 patients referred to Istituto Tumori “Giovanni Paolo II” of Bari (Italy) were used to train machine learning algorithms at the-state-of-the-art. Firstly, we implemented several feature importance techniques and then we evaluated the prediction performances of BCR within 5 and 10 years after the first diagnosis by means different classifiers. By using a small number of features, the models reached highly performing results both with reference to the BCR within 5 years and within 10 years with an accuracy of 77.50% and 80.39% and a sensitivity of 92.31% and 95.83% respectively, in the hold-out sample test. Despite validation studies are needed on larger samples, our results are promising for the development of a reliable prognostic supporting tool for clinicians in the definition of personalized treatment plans.

Automated Hypofractionated IMRT treatment planning for early-stage breast Cancer

Background

Hypofractionated whole-breast irradiation is a standard adjuvant therapy for early-stage breast cancer. This study evaluates the plan quality and efficacy of an in-house-developed automated radiotherapy treatment planning algorithm for hypofractionated whole-breast radiotherapy.

Methods

A cohort of 99 node-negative left-sided breast cancer patients completed hypofractionated whole-breast irradiation with six-field IMRT for 42.56 Gy in 16 daily fractions from year 2016 to 2018 at a tertiary center were re-planned with an in-house-developed algorithm. The automated plan-generating C#-based program is developed in a Varian ESAPI research mode. The dose-volume histogram (DVH) and other dosimetric parameters of the automated and manual plans were directly compared.

Results

The average time for generating an autoplan was 5 to 6 min, while the manual planning time ranged from 1 to 1.5 h. There was only a small difference in both the gantry angles and the collimator angles between the autoplans and the manual plans (ranging from 2.2 to 5.3 degrees). Autoplans and manual plans performed similarly well in hotspot volume and PTV coverage, with the autoplans performing slightly better in the ipsilateral-lung-sparing dose parameters but were inferior in contralateral-breast-sparing. The autoplan dosimetric quality did not vary with different breast sizes, but for manual plans, there was worse ipsilateral-lung-sparing (V_4Gy) in larger or medium-sized breasts than in smaller breasts. Autoplans were generally superior than manual plans in CI (1.24 ± 0.06 vs. 1.30 ± 0.09, p < 0.01) and MU (1010 ± 46 vs. 1205 ± 187, p < 0.01).

Conclusions

Our study presents a well-designed standardized fully automated planning algorithm for optimized whole-breast radiotherapy treatment plan generation. A large cohort of 99 patients were re-planned and retrospectively analyzed. The automated plans demonstrated similar or even better dosimetric quality and efficacy in comparison with the manual plans. Our result suggested that the autoplanning algorithm has great clinical applicability potential.

Differences in Expression of Mitochondrial Complexes Due to Genetic Variants May Alter Sensitivity to Radiation-Induced Cardiac Dysfunction

Radiation therapy is received by over half of all cancer patients. However, radiation doses may be constricted due to normal tissue side effects. In thoracic cancers, including breast and lung cancers, cardiac radiation is a major concern in treatment planning. There are currently no biomarkers of radiation-induced cardiotoxicity. Complex genetic modifiers can contribute to the risk of radiation-induced cardiotoxicities, yet these modifiers are largely unknown and poorly understood. We have previously reported the SS (Dahl salt-sensitive/Mcwi) rat strain is a highly sensitized model of radiation-induced cardiotoxicity compared to the more resistant Brown Norway (BN) rat strain. When rat chromosome 3 from the resistant BN rat strain is substituted into the SS background (SS.BN3 consomic), it significantly attenuates radiation-induced cardiotoxicity, demonstrating inherited genetic variants on rat chromosome 3 modify radiation sensitivity. Genes involved with mitochondrial function were differentially expressed in the hearts of SS and SS.BN3 rats 1 week after radiation. Here we further assessed differences in mitochondria-related genes between the sensitive SS and resistant SS.BN3 rats. We found mitochondrial-related gene expression differed in untreated hearts, while no differences in mitochondrial morphology were seen 1 week after localized heart radiation. At 12 weeks after localized cardiac radiation, differences in mitochondrial complex protein expression in the left ventricles were seen between the SS and SS.BN3 rats. These studies suggest that differences in mitochondrial gene expression caused by inherited genetic variants may contribute to differences in sensitivity to cardiac radiation.

Predicting Invasive Disease-Free Survival for Early-stage Breast Cancer Patients Using Follow-up Clinical Data

OBJECTIVE

Chinese women are seriously threatened by breast cancer with high morbidity and mortality. The lack of robust prognosis models results in difficulty for doctors to prepare an appropriate treatment plan that may prolong patient survival time. An alternative prognosis model framework to predict Invasive Disease-Free Survival (iDFS) for early-stage breast cancer patients, called MP4Ei, is proposed. MP4Ei framework gives an excellent performance to predict the relapse or metastasis breast cancer of Chinese patients in 5 years.

METHODS

MP4Ei is built based on statistical theory and gradient boosting decision tree framework. 5246 patients, derived from the Clinical Research Center for Breast (CRCB) in West China Hospital of Sichuan University, with early-stage (stage I-III) breast cancer are eligible for inclusion. Stratified feature selection, including statistical and ensemble methods, is adopted to select 23 out of the 89 patient features about the patient' demographics, diagnosis, pathology and therapy. Then 23 selected features as the input variables are imported into the XGBoost algorithm, with Bayesian parameter tuning and cross validation, to find out the optimum simplified model for 5-year iDFS prediction.

RESULTS

For eligible data, with 4196 patients (80%) for training, and with 1050 patients (20%) for testing, MP4Ei achieves comparable accuracy with AUC 0.8451, which has a significant advantage (p < 0.05).

CONCLUSION

This work demonstrates the complete iDFS prognosis model with very competitive performance.

SIGNIFICANCE

The proposed method in this paper could be used in clinical practice to predict patients' prognosis and future surviving state, which may help doctors make treatment plan.

A Hybrid Computer-aided-diagnosis System for Prediction of Breast Cancer Recurrence (HPBCR) Using Optimized Ensemble Learning

Cancer is a collection of diseases that involves growing abnormal cells with the potential to invade or spread to the body. Breast cancer is the second leading cause of cancer death among women. A method for 5-year breast cancer recurrence prediction is presented in this manuscript. Clinicopathologic characteristics of 579 breast cancer patients (recurrence prevalence of 19.3%) were analyzed and discriminative features were selected using statistical feature selection methods. They were further refined by Particle Swarm Optimization (PSO) as the inputs of the classification system with ensemble learning (Bagged Decision Tree: BDT). The proper combination of selected categorical features and also the weight (importance) of the selected interval-measurement-scale features were identified by the PSO algorithm. The performance of HPBCR (hybrid predictor of breast cancer recurrence) was assessed using the holdout and 4-fold cross-validation. Three other classifiers namely as supported vector machines, DT, and multilayer perceptron neural network were used for comparison. The selected features were diagnosis age, tumor size, lymph node involvement ratio, number of involved axillary lymph nodes, progesterone receptor expression, having hormone therapy and type of surgery. The minimum sensitivity, specificity, precision and accuracy of HPBCR were 77%, 93%, 95% and 85%, respectively in the entire cross-validation folds and the hold-out test fold. HPBCR outperformed the other tested classifiers. It showed excellent agreement with the gold standard (i.e. the oncologist opinion after blood tumor marker and imaging tests, and tissue biopsy). This algorithm is thus a promising online tool for the prediction of breast cancer recurrence.

Cordycepin, a Natural Antineoplastic Agent, Induces Apoptosis of Breast Cancer Cells via Caspase-dependent Pathways

Cordycepin, a major compound separated from Cordyceps sinensis, is known as a potential novel candidate for cancer therapy. Breast cancer, the most typical cancer diagnosed among women, remains a global health problem. In this study, the anti-breast cancer property of cordycepin and its underlying mechanisms was investigated. The direct effects of cordycepin on breast cancer cells both in in vitro and in vivo experiments were evaluated. Cordycepin exerted cytotoxicity in MCF-7 and MDA-MB-231 cells confirmed by reduced cell viability, inhibition of cell proliferation, enhanced lactate dehydrogenase release and reactive oxygen species accumulation, induced mitochondrial dysfunction and nuclear apoptosis in human breast cancer cells. Cordycepin increased the activation of pro-apoptotic proteins, including caspase-8, caspase-9, caspase-3 and Bax, and suppressed the expression of the anti-apoptotic protein, B-cell lymphoma 2 (Bcl-2). The inhibition on MCF-7-xenografted tumor growth in nude mice further confirmed cordycepin's anti-breast cancer effect. These aforementioned results reveal that cordycepin induces apoptosis in human breast cancer cells via caspase-dependent pathways. The data shed light on the possibility of cordycepin being a safe agent for breast cancer treatment.

Interventional therapy for human breast cancer in nude mice with 131I gelatin microspheres (¹³¹I-GMSs) following intratumoral injection

Introduction

The aim of this study was to investigate the effects of ¹³¹I gelatin microspheres (¹³¹I-GMS) on human breast cancer cells (MCF-7) in nude mice and the biodistribution of ¹³¹I-GMSs following intratumoral injections.

Methods

A total of 20 tumor-bearing mice were divided into a treatment group and control group and received intratumoral injections of 2.5 mci ¹³¹I-GMSs and nonradioactive GMSs, respectively. Tumor size was measured once per week. Another 16 mice received intratumoral injections of 0.4 mci ¹³¹I-GMSs and were subjected to single photon emission computed tomography (SPECT) scans and tissue radioactivity concentration measurements on day 1, 4, 8 and 16 postinjection. The 20 tumor-bearing mice received intratumoral injections of 0.4 mci [¹³¹I] sodium iodide solution and were subjected to SPECT scans and intratumoral radioactivity measurements at 1, 6, 24, 48 and 72 h postinjection. The tumors were collected for histological examination.

Results

The average tumor volume in the ¹³¹I-GMSs group on post-treatment day 21 decreased to 86.82 ± 63.6%, while it increased to 893.37 ± 158.12% in the control group (P < 0.01 vs. the ¹³¹I-GMSs group). ¹³¹I-GMSs provided much higher intratumoral retention of radioactivity, resulting in 19.93 ± 5.24% of the injected radioactivity after 16 days, whereas the control group retained only 1.83 ± 0.46% of the injected radioactivity within the tumors at 1 h postinjection.

Conclusions

¹³¹I-GMSs suppressed the growth of MCF-7 in nude mice and provided sustained intratumoral radioactivity retention. The results suggest the potential of ¹³¹I-GMSs for clinical applications in radiotherapy for breast cancer.