Multi-organ immunity on a 3D-printed multi-tissue chip with tubing-free impeller pump.

Multi-organ-on-chip systems (MOOCs) have the potential to mimic communication between organ systems and reveal mechanisms of health and disease. However, many existing MOOCs are challenging for non-experts to implement, due to complex tubing, electronics, or pump mechanisms. In addition, few MOOCs have incorporated immune organs such as the lymph node (LN), limiting their applicability to critical events such as vaccination. Here we developed a 3D-printed, user-friendly device and companion tubing-free impeller pump to co-culture two or more tissue samples, including a LN, under a recirculating common media. Native tissue structure and immune function were incorporated by maintaining slices of murine LN tissue ex vivo in 3D- printed mesh supports for at least 24 hr. In a two-compartment model of a LN and an upstream injection site, vaccination of the multi-tissue chip was similar to in vivo vaccination in terms of locations of antigen accumulation and acute changes in activation markers and gene expression in the LN. We anticipate that in the future, this flexible platform will enable models of multi-organ immune responses throughout the body.

A Step Toward Personalized Surgical Decision Making: Machine Learning Predicts 1 Versus Numerous Melanoma Lymph Node Metastases Using RNA-sequencing.

OBJECTIVE: Develop a predictive model to identify patients with 1 pathologic lymph node (pLN) versus >1 pLN using machine learning applied to gene expression profiles and clinical data as input variables. BACKGROUND: Standard management for clinically detected melanoma lymph node metastases is complete therapeutic LN dissection (TLND). However, >40% of patients with a clinically detected melanoma lymph node will only have 1 pLN on final review. Recent data suggest that targeted excision of just the single enlarged LN may provide excellent regional control, with less morbidity than TLND. The selection of patients for less morbid surgery requires accurate identification of those with only 1 pLN. METHODS: The Cancer Genome Atlas database was used to identify patients who underwent TLND for melanoma. Pathology reports in The Cancer Genome Atlas were reviewed to identify the number of pLNs. Patients were included for machine learning analyses if RNA sequencing data were available from a pLN. After feature selection, the top 20 gene expression and clinical input features were used to train a ridge logistic regression model to predict patients with 1 pLN versus >1 pLN using 10-fold cross-validation on 80% of samples. The model was then tested on the remaining holdout samples. RESULTS: A total of 153 patients met inclusion criteria: 64 with one pLN (42%) and 89 with >1 pLNs (58%). Feature selection identified 1 clinical (extranodal extension) and 19 gene expression variables used to predict patients with 1 pLN versus >1 pLN. The ridge logistic regression model identified patient groups with an accuracy of 90% and an area under the receiver operating characteristic curve of 0.97. CONCLUSIONS: Gene expression profiles together with clinical variables can distinguish melanoma metastasis patients with 1 pLN versus >1 pLN. Future models trained using positron emission tomography/computed tomography imaging, gene expression, and relevant clinical variables may further improve accuracy and may predict patients who can be managed with a targeted LN excision rather than a complete TLND.