A phenotypic switch in the dispersal strategy of breast cancer cells selected for metastatic colonization

Stochastic Fluctuations Drive Non-genetic Evolution of Proliferation in Clonal Cancer Cell Populations

Evolutionary dynamics allows us to understand many changes happening in a broad variety of biological systems, ranging from individuals to complete ecosystems. It is also behind a number of remarkable organizational changes that happen during the natural history of cancers. These reflect tumour heterogeneity, which is present at all cellular levels, including the genome, proteome and phenome, shaping its development and interrelation with its environment. An intriguing observation in different cohorts of oncological patients is that tumours exhibit an increased proliferation as the disease progresses, while the timescales involved are apparently too short for the fixation of sufficient driver mutations to promote explosive growth. Here, we discuss how phenotypic plasticity, emerging from a single genotype, may play a key role and provide a ground for a continuous acceleration of the proliferation rate of clonal populations with time. We address this question by combining the analysis of real-time growth of non-small-cell lung carcinoma cells (N-H460) together with stochastic and deterministic mathematical models that capture proliferation trait heterogeneity in clonal populations to elucidate the contribution of phenotypic transitions on tumour growth dynamics.

Low Doses of Celecoxib Might Promote Phenotype Switching in Cutaneous Melanoma Treated with Dabrafenib-Preliminary Study

Background: Cutaneous melanoma is a heterogeneous tumor with a rapidly switching molecular and cellular phenotype. The invasive phenotype switching characterized by MITFlow/AXLhigh predicts early resistance to multiple targeted drugs in melanoma. Celecoxib proved to be a valuable adjuvant in cutaneous melanoma in preclinical studies. Our in vitro study evaluated for the first time whether celecoxib could prevent phenotype switching in two human melanoma cell lines treated with dabrafenib. Methods: All in vitro experiments were carried out on BRAF-V600E-positive A375 and SK-MEL-28 human melanoma cell lines, and subjected to a celecoxib and dabrafenib drug combination for 72 h. Melanoma cells were already in the MITFlow/AXLhigh end of the spectrum. Of main interest was the evaluation of the key proteins expressed in phenotype switching (TGF-β, MITF, AXL, YAP, TAZ), as well as cell death mechanisms correlated with oxidative stress production. Results: Celecoxib significantly enhanced the apoptotic effect of dabrafenib in each melanoma cell line compared to the dabrafenib group (p < 0.0001). Even though celecoxib promoted low MITF expression, this was correlated with high receptor tyrosine kinase AXL levels in A375 and SK-MEL-28 cell lines (p < 0.0001), a positive marker for the phenotype switch to an invasive state. Conclusion: This preliminary study highlighted that celecoxib might promote MITFlow/AXLhigh expression in cutaneous melanoma treated with dabrafenib, facilitating phenotype switching in vitro. Our results need further confirmation, as this finding could represent an important limitation of celecoxib as an antineoplastic drug.

Fully automated platelet differential interference contrast image analysis via deep learning

Platelets mediate arterial thrombosis, a leading cause of myocardial infarction and stroke. During injury, platelets adhere and spread over exposed subendothelial matrix substrates of the damaged blood vessel wall. The mechanisms which govern platelet activation and their interaction with a range of substrates are therefore regularly investigated using platelet spreading assays. These assays often use differential interference contrast (DIC) microscopy to assess platelet morphology and analysis performed using manual annotation. Here, a convolutional neural network (CNN) allowed fully automated analysis of platelet spreading assays captured by DIC microscopy. The CNN was trained using 120 generalised training images. Increasing the number of training images increases the mean average precision of the CNN. The CNN performance was compared to six manual annotators. Significant variation was observed between annotators, highlighting bias when manual analysis is performed. The CNN effectively analysed platelet morphology when platelets spread over a range of substrates (CRP-XL, vWF and fibrinogen), in the presence and absence of inhibitors (dasatinib, ibrutinib and PRT-060318) and agonist (thrombin), with results consistent in quantifying spread platelet area which is comparable to published literature. The application of a CNN enables, for the first time, automated analysis of platelet spreading assays captured by DIC microscopy.