Behaviour of metastasis in relation to vascular index in patients with node-positive breast cancer treated with adjuvant tamoxifen

Breast cancer models: Engineering the tumor microenvironment

The mechanisms behind cancer initiation and progression are not clear. Therefore, development of clinically relevant models to study cancer biology and drug response in tumors is essential. In vivo models are very valuable tools for studying cancer biology and for testing drugs; however, they often suffer from not accurately representing the clinical scenario because they lack either human cells or a functional immune system. On the other hand, two-dimensional (2D) in vitro models lack the three-dimensional (3D) network of cells and extracellular matrix (ECM) and thus do not represent the tumor microenvironment (TME). As an alternative approach, 3D models have started to gain more attention, as such models offer a platform with the ability to study cell-cell and cell-material interactions parametrically, and possibly include all the components present in the TME. Here, we first give an overview of the breast cancer TME, and then discuss the current state of the pre-clinical breast cancer models, with a focus on the engineered 3D tissue models. We also highlight two engineering approaches that we think are promising in constructing models representative of human tumors: 3D printing and microfluidics. In addition to giving basic information about the TME in the breast tissue, this review article presents the state-of-the-art tissue engineered breast cancer models. STATEMENT OF SIGNIFICANCE: Involvement of biomaterials and tissue engineering fields in cancer research enables realistic mimicry of the cell-cell and cell-extracellular matrix (ECM) interactions in the tumor microenvironment (TME), and thus creation of better models that reflect the tumor response against drugs. Engineering the 3D in vitro models also requires a good understanding of the TME. Here, an overview of the breast cancer TME is given, and the current state of the pre-clinical breast cancer models, with a focus on the engineered 3D tissue models is discussed. This review article is useful not only for biomaterials scientists aiming to engineer 3D in vitro TME models, but also for cancer researchers willing to use these models for studying cancer biology and drug testing.

Prognosis in node-negative primary breast cancer: a neural network analysis of risk profiles using routinely assessed factors

BACKGROUND

The present study investigated complex time-dependent effects of routinely assessed factors on the risk of breast cancer recurrence over follow-up time, with a partial logistic artificial neural network (PLANN) model.

PATIENTS AND METHODS

PLANN was applied to data from 1793 patients with node-negative breast cancer, not submitted to any adjuvant treatment and with a minimal potential follow-up of 10 years.

RESULTS

The shape of the hazard function changed according to histology, which showed a time-dependent effect, partly modulated by estrogen receptors (ERs). Age and progesterone receptors (PgR) showed protective effects; the latter was more evident for short follow-up and high ER values. Tumour size and ER content showed time-dependent unfavourable effects at early and long follow-up times, respectively. Predicted values of disease recurrence probability at 2 years of follow-up showed that low steroid-receptor content, young age and large tumour size were associated with the highest risk of relapse. Although the oldest patients with high ER content seem to be those most protected overall, high risk predictions tend to spread also to higher steroid-receptor contents, intermediate ages and small tumour size, with an increase in follow-up time.

CONCLUSION

PLANN with suitable visualisation techniques provided thorough insights into the dynamics of breast cancer recurrence for improving individual risk staging of node-negative breast cancer patients.

Association of angiogenesis and disease outcome in node-positive breast cancer patients treated with adjuvant cyclophosphamide, doxorubicin, and fluorouracil: a Cancer and Leukemia Group B correlative science study from protocols 8541/8869

PURPOSE

Increased microvessel density (MVD), a reflection of tumor angiogenesis, is associated with diminished relapse-free and overall survival (OS) in several subsets of breast cancer patients. However, the utility of this assay in node-positive patients treated with adjuvant cyclophosphamide, doxorubicin, and fluorouracil (CAF) has not been well studied.

PATIENTS AND METHODS

Immunostaining for factor VIII-related antigen was performed on tissue sections from a subset of node-positive patients who received one of three dose/schedule regimens of CAF during participation in Cancer and Leukemia Group B protocol 8541. Sections from 577 cancers exhibited acceptable tumor and immunostaining quality and were included in the study. Each section was examined quantitatively for MVD as well as non-quantitatively by scoring the presence or absence of a prominent vascular pattern.

RESULTS

MVD counts were not associated with relapse-free or OS in univariate analysis. The presence of a prominent plexiform vascular pattern was correlated with decreased OS (P =.0085) in univariate analysis, but this pattern was not an independent prognostic indicator of survival in multivariate analysis. No apparent clinically important interactions between measures of angiogenesis, other prognostic factors, administration of tamoxifen, and chemotherapy dose were observed.

CONCLUSION

Assessment of angiogenesis does not provide useful information regarding prognosis in node-positive breast cancer patients treated with adjuvant CAF, nor do these measures predict which patients will benefit from dose intensification or addition of tamoxifen.