Extracellular matrix gene expression profiling using microfluidics for colorectal carcinoma stratification

DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data

Multi-omics data are good resources for prognosis and survival prediction; however, these are difficult to integrate computationally. We introduce DeepProg, a novel ensemble framework of deep-learning and machine-learning approaches that robustly predicts patient survival subtypes using multi-omics data. It identifies two optimal survival subtypes in most cancers and yields significantly better risk-stratification than other multi-omics integration methods. DeepProg is highly predictive, exemplified by two liver cancer (C-index 0.73-0.80) and five breast cancer datasets (C-index 0.68-0.73). Pan-cancer analysis associates common genomic signatures in poor survival subtypes with extracellular matrix modeling, immune deregulation, and mitosis processes. DeepProg is freely available at https://github.com/lanagarmire/DeepProg.

An Integrated Preprocessing Approach for Exploring Single-Cell Gene Expression in Rare Cells

Exploring the variability in gene expressions of rare cells at the single-cell level is critical for understanding mechanisms of differentiation in tissue function and development as well as for disease diagnostics and cancer treatment. Such studies, however, have been hindered by major difficulties in tracking the identity of individual cells. We present an approach that combines single-cell picking, lysing, reverse transcription and digital polymerase chain reaction to enable the isolation, tracking and gene expression analysis of rare cells. The approach utilizes a photocleavage bead-based microfluidic device to synthesize and deliver stable cDNA for downstream gene expression analysis, thereby allowing chip-based integration of multiple reactions and facilitating the minimization of sample loss or contamination. The utility of the approach was demonstrated with QuantStudio digital PCR by analyzing the radiation and bystander effect on individual IMR90 human lung fibroblasts. Expression levels of the Cyclin-dependent kinase inhibitor 1a (CDKN1A), Growth/differentiation factor 15 (GDF15), and Prostaglandin-endoperoxide synthase 2 (PTGS2) genes, previously shown to have different responses to direct and bystander irradiation, were measured across individual control, microbeam-irradiated or bystander IMR90 cells. In addition to the confirmation of accurate tracking of cell treatments through the system and efficient analysis of single-cell responses, the results enable comparison of activation levels of different genes and provide insight into signaling pathways within individual cells.

Tumour-on-a-chip: microfluidic models of tumour morphology, growth and microenvironment

Cancer remains one of the leading causes of death, albeit enormous efforts to cure the disease. To overcome the major challenges in cancer therapy, we need to have a better understanding of the tumour microenvironment (TME), as well as a more effective means to screen anti-cancer drug leads; both can be achieved using advanced technologies, including the emerging tumour-on-a-chip technology. Here, we review the recent development of the tumour-on-a-chip technology, which integrates microfluidics, microfabrication, tissue engineering and biomaterials research, and offers new opportunities for building and applying functional three-dimensional in vitro human tumour models for oncology research, immunotherapy studies and drug screening. In particular, tumour-on-a-chip microdevices allow well-controlled microscopic studies of the interaction among tumour cells, immune cells and cells in the TME, of which simple tissue cultures and animal models are not amenable to do. The challenges in developing the next-generation tumour-on-a-chip technology are also discussed.

The good and bad of targeting cancer-associated extracellular matrix

The maintenance of tissue homeostasis requires extracellular matrix (ECM) remodeling. Immune cells actively participate in regenerating damaged tissues contributing to ECM deposition and shaping. Dysregulated ECM deposition characterizes fibrotic diseases and cancer stromatogenesis, where a chronic inflammatory state sustains the ECM increase. In cancer, the ECM fosters several steps of tumor progression, providing pro-survival and proliferative signals, promoting tumor cell dissemination via collagen fibers or acting as a barrier to impede drug diffusion. Interfering with processes leading to chronic ECM deposition, as occurring in cancer, might allow the simultaneous targeting of both primary tumors and metastatic lesions. However, a note of caution comes from data showing that defective ECM deposition is associated with an exacerbated inflammatory and autoimmune phenotype and to lymphomagenesis. Immune cells display ITIM-inhibitory receptors recognizing collagens as counter ligands, which negatively regulate the immune response. This is in line with the idea that ECM components can provide homeostatic signals to immune cells to regulate and prevent unwanted activation, a concept particularly relevant in cancer where these mechanisms could be in place to keep infiltrating immune cells in a suppressive pro-tumoral state. In this context, the pharmacological targeting of myeloid cells, for which both direct and indirect roles in ECM deposition have been shown, can be a relevant option to this purpose.