Effectiveness of combination with eribulin in third line of chemotherapy for triple negative breast cancer (case report)

In most cases triple negative breast cancer is characterized by an aggressive course of disease and early development of resistance to chemotherapy. Thereafter, the late-line treatment choice, usually after anthracyclines and taxanes, is problematic due to the limited amount of effective and low-toxic cytostatics. In our opinion, in this situation the use of eribulin which possesses unique antitumor action mechanisms is a good option. An illustrative case of a pronounced antitumor effect of eribulin in metastatic breast cancer with triple negative phenotype resistant to previous lines of chemotherapy is presented.

Dynamics of a Breast Cancer Model for Neutropenia Case due to Chemotherapy Effects

Breast cancer is a type of carcinoma with a high prevalence. The treatment of breast cancer through chemotherapy can cause a risk to healthy cells throughout the body. The neutrophil is one of the cells that is influenced by chemotherapy drugs. Chemotherapy-induced neutropenia is one of the most common toxic effects experienced by patients and often threatens chemotherapy to use efficiency. In this paper, we introduce an interaction model between blood components, i.e., neutrophil, lymphocytes, and albumin, with chemotherapy drugs. The model is important to understand the neutropenia effect due to chemotherapy in mathematical perspective and to calculate breast cancer patients’ survival level. Our model is a four-dimensional system of the first-order ODE with 13-dimensional parameter space. We focus our study for understanding the steady-state conditions and the bifurcations when the parameter values are varied. Here, we also study the role of albumin for reducing the neutropenia effects for breast cancer patients mathematically, where the results can be used as an alternative solution for treating neutropenia in a breast cancer case.

The Therapeutic Challenge of Disseminated Bone Marrow Metastasis From HR-Positive HER2-Negative Breast Cancer: Case Report and Review of the Literature

The efficacy and safety of the combination of endocrine therapy (ET) and CDK4/6 inhibitors for patients with hormone receptor (HR)-positive HER2-negative metastatic breast cancer (BC) presenting with visceral crisis or life-threatening conditions represent a challenge for daily clinical practice. Indeed, the peculiarity of this clinical presentation (signs and symptoms of rapidly progressive disease) does not allow to include such patients in a trial aiming for drug approval. On the basis of the scientific evidence available so far, chemotherapy represents the standard of care according to guidelines, on the basis of the more rapid activity in comparison with ET alone. Besides, the combination of ET and CDK4/6 inhibitors have demonstrated in clinical trials to have clinically impactful activity in a short time, thus suggesting a potential role in advanced tumors that require rapid response. Herein, we report the clinical history of a young woman with HR-positive HER2-negative metastatic BC and a pancytopenia due to carcinomatosis of the bone marrow receiving letrozole and leuprorelin plus the CDK4/6 inhibitor palbociclib, who significantly derived clinical benefit from treatment. Considering that these peculiar cases are excluded from clinical trials, the estimation of the magnitude of the benefit of the newer ET combination may potentially represent a practical question for large case series and real-world studies.

A Comparative Study of Two In Vivo PET Verification Methods in Clinical Cases

PurposePositron emission tomography (PET) range verification is an important method that can help improve the confidence in proton therapy for clinical applications. Two kinds of verification methods are implemented and compared based on clinical cases in this study.MethodThe study is conducted on 14 breast cancer patients following proton irradiation treatment. Verification is done by calculating the depth error between the numerically predicted values with the measured PET image along the beam direction. Point-based and segment-based methods are applied and compared. The verification results are presented as depth error means and standard deviations in a region of interest (ROI).ResultsThe mean value of the depth error of all 14 cases is within the range of [−3, 3] mm for both point-based and segment-based methods, and only one case result calculated by the point-based method is slightly beyond −3 mm. When comparing the mean depth error from the two methods, the paired t-test result shows that the p-value is 0.541, and the standard deviation of the segment-based method is smaller than that of the point-based method.ConclusionIn breast cancer case verification application, point-based and segment-based methods show no significant difference in the mean value of results. Both methods can quantify the accuracy of proton radiotherapy to the millimeter level.

Validity of a method for identifying disease subtypes that are etiologically heterogeneous

A focus of cancer epidemiologic research has become the identification of risk factors that influence specific subtypes of disease, a phenomenon known as etiologic heterogeneity. In previous work we developed a novel strategy to cluster tumor markers and identify disease subtypes that differ maximally with respect to known risk factors for use in the context of case-control studies. The method relies on the premise that unsupervised k-means clustering will find candidate solutions that are closely aligned with the sought-after etiologically distinct clusters, which may not be true in the presence of clusters of tumor markers that are not related to risk of disease. In this article, we investigate in detail the ability of the method to identify the “true” clusters in the presence of clusters that are unrelated to risk factors, what we term “counterfeit” clusters. We find that our method works when the strength of structure is larger in the clusters that truly represent etiologic heterogeneity than in the counterfeit clusters, but when this condition is not met, or when there are many tumor markers that simply represent noise, the method will not find the correct solution without first performing variable selection to identify the tumor markers most strongly related to the risk factors. We illustrate the results using data from a breast cancer case-control study.