As neovascularization is essential for tumor growth and metastasis, controlling angiogenesis is a promising tactic in limiting cancer progression. Melatonin has been studied for their inhibitory properties on angiogenesis in cancer. We performed an in vivo study to evaluate the effects of melatonin treatment on angiogenesis in breast cancer. Cell viability was measured by MTT assay after melatonin treatment in triple-negative breast cancer cells (MDA-MB-231). After, cells were implanted in athymic nude mice and treated with melatonin or vehicle daily, administered intraperitoneally 1 hour before turning the room light off. Volume of the tumors was measured weekly with a digital caliper and at the end of treatments animals underwent single photon emission computed tomography (SPECT) with Technetium-99m tagged vascular endothelial growth factor (VEGF) C to detect in vivo angiogenesis. In addition, expression of pro-angiogenic/growth factors in the tumor extracts was evaluated by membrane antibody array and collected tumor tissues were analyzed with histochemical staining. Melatonin in vitro treatment (1 mM) decreased cell viability (p<0.05). The breast cancer xenografts nude mice treated with melatonin showed reduced tumor size and cell proliferation (Ki-67) compared to control animals after 21 days of treatment (p<0.05). Expression of VEGF receptor 2 decreased significantly in the treated animals compared to that of control when determined by immunohistochemistry (p<0.05) but the changes were not significant on SPECT (p>0.05) images. In addition, there was a decrease of micro-vessel density (Von Willebrand Factor) in melatonin treated mice (p<0.05). However, semiquantitative densitometry analysis of membrane array indicated increased expression of epidermal growth factor receptor and insulin-like growth factor 1 in treated tumors compared to vehicle treated tumors (p<0.05). In conclusion, melatonin treatment showed effectiveness in reducing tumor growth and cell proliferation, as well as in the inhibition of angiogenesis.

The occurrence of metastasis, an important breast cancer prognostic factor, depends on cell migration/invasion mechanisms, which can be controlled by regulatory and effector molecules such as Rho-associated kinase protein (ROCK-1). Increased expression of this protein promotes tumor growth and metastasis, which can be restricted by ROCK-1 inhibitors. Melatonin has shown oncostatic, antimetastatic, and anti-angiogenic effects and can modulate ROCK-1 expression. Metastatic and nonmetastatic breast cancer cell lines were treated with melatonin as well as with specific ROCK-1 inhibitor (Y27632). Cell viability, cell migration/invasion, and ROCK-1 gene expression and protein expression were determined in vitro. In vivo lung metastasis study was performed using female athymic nude mice treated with either melatonin or Y27832 for 2 and 5 wk. The metastases were evaluated by X-ray computed tomography and single photon emission computed tomography (SPECT) and by immunohistochemistry for ROCK-1 and cytokeratin proteins. Melatonin and Y27632 treatments reduced cell viability and invasion/migration of both cell lines and decreased ROCK-1 gene expression in metastatic cells and protein expression in nonmetastatic cell line. The numbers of ‘hot’ spots (lung metastasis) identified by SPECT images were significantly lower in treated groups. ROCK-1 protein expression also was decreased in metastatic foci of treated groups. Melatonin has shown to be effective in controlling metastatic breast cancer in vitro and in vivo, not only via inhibition of the proliferation of tumor cells but also through direct antagonism of metastatic mechanism of cells rendered by ROCK-1 inhibition. When Y27632 was used, the effects were similar to those found with melatonin treatment.

Although cancer often is referred to as “a disease of the genes,” it is indisputable that the (epi)genetic properties of individual cancer cells are highly variable, even within the same tumor. Hence, preexisting resistant clones will emerge and proliferate after therapeutic selection that targets sensitive clones. Herein, the authors propose that quantitative image analytics, known as “radiomics,” can be used to quantify and characterize this heterogeneity. Virtually every patient with cancer is imaged radiologically. Radiomics is predicated on the beliefs that these images reflect underlying pathophysiologies, and that they can be converted into mineable data for improved diagnosis, prognosis, prediction, and therapy monitoring. In the last decade, the radiomics of cancer has grown from a few laboratories to a worldwide enterprise. During this growth, radiomics has established a convention, wherein a large set of annotated image features (1‐2000 features) are extracted from segmented regions of interest and used to build classifier models to separate individual patients into their appropriate class (eg, indolent vs aggressive disease). An extension of this conventional radiomics is the application of “deep learning,” wherein convolutional neural networks can be used to detect the most informative regions and features without human intervention. A further extension of radiomics involves automatically segmenting informative subregions (“habitats”) within tumors, which can be linked to underlying tumor pathophysiology. The goal of the radiomics enterprise is to provide informed decision support for the practice of precision oncology.

It is well-recognized that solid tumors are genomically, anatomically, and physiologically heterogeneous. In general, more heterogeneous tumors have poorer outcomes, likely due to the increased probability of harboring therapy-resistant cells and regions. It is hypothesized that the genomic and physiologic heterogeneity are related, because physiologically distinct regions will exert variable selection pressures leading to the outgrowth of clones with variable genomic/proteomic profiles. To investigate this, methods must be in place to interrogate and define, at the microscopic scale, the cytotypes that exist within physiologically distinct subregions ("habitats") that are present at mesoscopic scales. MRI provides a noninvasive approach to interrogate physiologically distinct local environments, due to the biophysical principles that govern MRI signal generation. Here, we interrogate different physiologic parameters, such as perfusion, cell density, and edema, using multiparametric MRI (mpMRI). Signals from six different acquisition schema were combined voxel-by-voxel into four clusters identified using a Gaussian mixture model. These were compared with histologic and IHC characterizations of sections that were coregistered using MRI-guided 3D printed tumor molds. Specifically, we identified a specific set of MRI parameters to classify viable-normoxic, viable-hypoxic, nonviable-hypoxic, and nonviable-normoxic tissue types within orthotopic 4T1 and MDA-MB-231 breast tumors. This is the first coregistered study to show that mpMRI can be used to define physiologically distinct tumor habitats within breast tumor models. Significance: This study demonstrates that noninvasive imaging metrics can be used to distinguish subregions within heterogeneous tumors with histopathologic correlation.