Cancer Tissue Engineering: A Novel 3D Polystyrene Scaffold for In Vitro Isolation and Amplification of Lymphoma Cancer Cells from Heterogeneous Cell Mixtures

Isolation and amplification of primary lymphoma cells in vitro setting is technically and biologically challenging task. To optimize culture environment and mimic in vivo conditions, lymphoma cell lines were used as a test case and were grown in 3-dimension (3D) using a novel 3D tissue culture polystyrene scaffold with neonatal stromal cells to represent a lymphoma microenvironment. In this model, the cell proliferation was enhanced more than 200-fold or 20,000% neoplastic surplus in 7 days when less than 1% lymphoma cells were cocultured with 100-fold excess of neonatal stroma cells, representing 3.2-fold higher proliferative rate than 2D coculture model. The lymphoma cells grew and aggregated to form clusters during 3D coculture and did not maintained the parental phenotype to grow in single-cell suspension. The cluster size was over 5-fold bigger in the 3D coculture by day 4 than 2D coculture system and contained less than 0.00001% of neonatal fibroblast trace. This preliminary data indicate that novel 3D scaffold geometry and coculturing environment can be customized to amplify primary cancer cells from blood or tissues related to hematological cancer and subsequently used for personalized drug screening procedures

Prediction of clinical outcomes using the pyrolysis, gas chromatography, and differential mobility spectrometry (Py-GC-DMS) system

AbstractBiological and molecular heterogeneity of human diseases especially cancers contributes to variations in treatment response, clinical outcome, and survival. The addition of new disease- and condition-specific biomarkers to existing clinical markers to track cancer heterogeneity provides possibilities for further assisting clinicians in predicting clinical outcomes and making choices of treatment options. Ionization patterns derived from biological specimens can be adapted for use with existing clinical markers for early detection, patient risk stratification, treatment decision making, and monitoring disease progression. In order to demonstrate the application of pyrolysis, gas chromatography, and differential mobility spectrometry (Py-GC-DMS) for human diseases to predict the outcome of diseases, we analyzed the ionized spectral signals generated by instrument ACB2000 (ACBirox universal detector 2000, ACBirox LLC, NJ, USA) from the serum samples of Mantle Cell Lymphoma (MCL) patients. Here, we have used mantle cell lymphoma as a disease model for a conceptual study only and based on the ionization patterns of the analyzed serum samples, we developed a multivariate algorithm comprised of variable selection and reduction steps followed by receiver operating characteristic curve (ROC) analysis to predict the probability of a good or poor clinical outcome as a means of estimating the likely success of a particular treatment option. Our preliminary study performed with small cohort provides a proof of concept demonstrating the ability of this system to predict the clinical outcome for human diseases with high accuracy suggesting the promising application of pyrolysis, gas chromatography, and differential mobility spectrometry (Py-GC-DMS) in the field of medicine