Breast Cancer Molecular Subtyping in Practice: A Real-World Study of the APIS Breast Cancer Subtyping Assay in a Consecutive Series of Breast Core Biopsies.

The APIS Breast Cancer Subtyping Kit is an mRNA-based assessment of the seven parameters including three biomarkers routinely assessed in all the newly diagnosed breast cancers (BC), oestrogen receptor (ER), progesterone receptor (PR) and HER-2 and an additional four genes that create a novel proliferation signature, MKI67, PCNA, CCNA2 and KIF23. Taken together, the data are used to produce a molecular subtype for every sample. The kit was evaluated against the current standard protocol of immunohistochemistry (IHC) and/or in situ hybridisation (ISH) in breast cancer patients. The data were presented at the weekly breast multidisciplinary team (MDT) meeting. A total of 98 consecutive cases of pre-operative breast cancer core biopsies and two core biopsies of nodal metastases yielding 100 cases were assessed. IHC and APIS results were available for 100 and 99 cases. ER was concordant in 97% cases, PR was concordant in 89% and HER-2 results were concordant with IHC/ISH in 100% of the cases. Ki-67 IHC was discordant in 3% of cases when compared with MK167 alone but discordant in 24% when compared with the four-gene proliferation signature. In conclusion, our study indicates that the APIS Breast Cancer Subtyping Kit is highly concordant when compared to the results produced for ER/PR/HER-2 by IHC and/or ISH. The assay could play a role in the routine assessment of newly diagnosed breast cancer (BC) specimens.

Glioma growth modeling based on the effect of vital nutrients and metabolic products.

Glioma brain tumors exhibit considerably aggressive behavior leading to high mortality rates. Mathematical modeling of tumor growth aims to explore the interactions between glioma cells and tissue microenvironment, which affect tumor evolution. Leveraging this concept, we present a three-dimensional model of glioma spatio-temporal evolution based on existing continuum approaches, yet incorporating novel factors of the phenomenon. The proposed model involves the interactions between different tumor cell phenotypes and their microenvironment, investigating how tumor growth is affected by complex biological exchanges. It focuses on the separate and combined effect of vital nutrients and cellular wastes on tumor expansion, leading to the formation of cell populations with different metabolic, proliferative, and diffusive profiles. Several simulations were performed on a virtual and a real glioma, using combinations of proliferation and diffusion rates for different evolution times. The model results were validated on a glioma model available in the literature and a real case of tumor progression. The experimental observations indicate that our model estimates quite satisfactorily the expansion of each region and the overall tumor growth. Based on the individual results, the proposed model may provide an important research tool for patient-specific simulation of different tumor evolution scenarios and reliable estimation of glioma evolution. Graphical Abstract Outline of the mathematical model functionality and application to glioma growth with indicative results.

Advances in the treatment of soft tissue sarcoma: focus on eribulin.

Eribulin mesylate is a synthetic derivative of halichondrin B isolated from a marine sponge. Its mechanism of action is through microtubule inhibition, which is different from that of taxanes. Eribulin has been approved for the treatment of metastatic breast cancer and more recently for non-operable or metastatic liposarcoma in patients who have received prior anthracycline chemotherapy. The major side effects of eribulin are bone marrow suppression including neutropenia, leukopenia, anemia, and fatigue/weakness, which can be well managed. In this article, we reviewed evidence from the latest published data on eribulin and its use in the treatment of soft tissue sarcomas. We explored the drug's mechanism of action, pharmacodynamics, pharmacokinetics, and metabolism. Lastly, we reviewed all preclinical studies as well as clinical trials that investigated eribulin.

In Surgical Colon Cancer Patients Extended-Duration Thromboprophylaxis (30 days) with the Highest Dose of Tinzaparin (4,500 IU s.c./q.d.) Normalizes the Postoperative VEGF Levels.

Background/Purpose: In colon cancer (CC) patients preoperative (pre-op) levels of VEGF-A165 (VEGF) is a strong predictor for disease recurrence. Elevated postoperative (post-op) VEGF levels could have undesirable effects by enhancing tumor growth and metastasis formation. It has been suggested that thromboprophylaxis with a Low Molecular Weight Heparin (LMWH) in surgical cancer patients, further to thromboembolic protection, may exert some anti-neoplastic properties, as well. The aim of our study was to assess the potential impact of the LMWH Tinzaparin (Innohep® - Leo Pharma, Copenhagen, Denmark), given at different doses and for different perioperative (peri-op) periods, upon the post-op variability of serum VEGF levels in surgical CC patients. Methods: A total of 54 consecutive CC patients who underwent a curative resection were randomized in four groups according to their peri-op thromboprophylaxis scheme, which was based on administrating Tinzaparin in different doses and at different periods, as follows: group I: 3,500 IU for 10 days, group II: 3,500 IU for 30 days, group III: 4,500 IU for 10 days and group IV: 4,500 IU for 30 days. Serum VEGF concentrations were evaluated on the pre-op day (Day 0) and on the 10th and 30th post-op days (Day 10 and Day 30, respectively). For statistical analyses the mixed design ANOVA was used. P < 0.05 was considered significant. Results: On Day 0, VEGF didn't differ between groups I, II, III and IV (p>0.05, for every comparison). On Day 10, VEGF was increased in all groups. Between Day 10 and Day 30, VEGF remained stable in groups I (p=0.031) and II (p=1.000) and increased significantly in group III (p=0.005). On the contrary, VEGF decreased significantly in group IV (p<0.001). The most remarkable finding was observed when we compared VEGF between Day 0 and Day 30: while in groups I, II and III, VEGF remained significantly higher compared to Day 0 (p<0.001, p=0.041 and p<0.001, respectively), on the contrary, in group IV (extended-duration with the highest dose of 4,500 IU of tinzaparin) it was comparable to Day 0 (p=1.000). Conclusions: In surgical CC patients only the recommended thromboprophylaxis scheme with the highest prophylactic dose of Tinzaparin (4,500 IU) for extended-duration (30 days) normalizes VEGF levels at the end of the first post-op month by reducing them to the pre-op levels.

A Glycolysis-Based In Silico Model for the Solid Tumor Growth.

Cancer-tumor growth is a complex process depending on several biological factors, such as the chemical microenvironment of the tumor, the cellular metabolic profile, and its proliferation rate. Several mathematical models have been developed for identifying the interactions between tumor cells and tissue microenvironment, since they play an important role in tumor formation and progression. Toward this direction we propose a new continuum model of avascular glioma-tumor growth, which incorporates a new factor, namely, the glycolytic potential of cancer cells, to express the interactions of three different tumor-cell populations (proliferative, hypoxic, and necrotic) with their tissue microenvironment. The glycolytic potential engages three vital nutrients, i.e., oxygen, glucose, and lactate, which provide cells with the necessary energy for their survival and proliferation. Extensive simulations are performed for different evolution times and various proliferation rates, in order to investigate how the tumor growth is affected. According to medical experts, the experimental observations indicate that the model predicts quite satisfactorily the overall tumor growth as well as the expansion of each region separately. Following extensive evaluation, the proposed model may provide an essential tool for patient-specific tumor simulation and reliable prediction of glioma spatiotemporal expansion.

Mathematical modelling of spatio-temporal glioma evolution.

BACKGROUND: Gliomas are the most common types of brain cancer, well known for their aggressive proliferation and the invasive behavior leading to a high mortality rate. Several mathematical models have been developed for identifying the interactions between glioma cells and tissue microenvironment, which play an important role in the mechanism of the tumor formation and progression. METHODS: Building and expanding on existing approaches, this paper develops a continuous three-dimensional model of avascular glioma spatio-temporal evolution. The proposed spherical model incorporates the interactions between the populations of four different glioma cell phenotypes (proliferative, hypoxic, hypoglychemic and necrotic) and their tissue microenvironment, in order to investigate how they affect tumor growth and invasion in an isotropic and homogeneous medium. The model includes two key variables involved in the proliferation and invasion processes of cancer cells; i.e. the extracellular matrix and the matrix-degradative enzymes concentrations inside the tumor and its surroundings. Additionally, the proposed model focuses on innovative features, such as the separate and independent impact of two vital nutrients, namely oxygen and glucose, in tumor growth, leading to the formation of cell populations with different metabolic profiles. The model implementation takes under consideration the variations of particular factors, such as the local cell proliferation rate, the variable conversion rates of cells from one category to another and the nutrient-dependent thresholds of conversion. All model variables (cell densities, ingredients concentrations) are continuous and described by reaction-diffusion equations. RESULTS: Several simulations were performed using combinations of growth and invasion rates, for different evolution times. The model results were evaluated by medical experts and validated on experimental glioma models available in the literature, revealing high agreement between simulated and experimental results. CONCLUSIONS: Based on the experimental validation, as well as the evaluation by clinical experts, the proposed model may provide an essential tool for the patient-specific simulation of different tumor evolution scenarios and reliable prognosis of glioma spatio-temporal progression.