Chronic wounds arise from interruption of normal healing due to many potential pathophysiological factors. Monitoring these multivariate factors can provide personalized diagnostic information for wound management, but current sensing technologies use complex laboratory tests or track a limited number of wound parameters. We report a flexible biosensing platform for multiplexed profiling of the wound microenvironment, inflammation, and infection state at the point of care. This platform integrates a sensor array for measuring inflammatory mediators [tumor necrosis factor–α, interleukin-6 (IL-6), IL-8, and transforming growth factor–β1], microbial burden (Staphylococcus aureus), and physicochemical parameters (temperature and pH) with a microfluidic wound exudate collector and flexible electronics for wireless, smartphone-based data readout. We demonstrate in situ multiplexed monitoring in a mouse wound model and also profile wound exudates from patients with venous leg ulcers. This technology may facilitate more timely and personalized wound management to improve chronic wound healing outcomes.

The prognosis and prediction of adjuvant chemotherapy (ACT) response in early-stage non-small cell lung cancer (NSCLC) patients remain poor in this era of personalized medicine. We hypothesize that extracellular matrix (ECM)-associated components could be potential markers for better diagnosis and prognosis due to their differential expression in 1,943 primary NSCLC tumors as compared to 303 normal lung tissues. Here we develop a 29-gene ECM-related prognostic and predictive indicator (EPPI). We validate a robust performance of the EPPI risk scoring system in multiple independent data sets, comprising a total of 2,071 early-stage NSCLC tumors. Patients are stratified according to the universal cutoff score based on the EPPI when applied in the clinical setting; the low-risk group has significantly better survival outcome. The functional EPPI gene set represents a potential genomic tool to improve patient selection in early-stage NSCLC to further derive the best benefits of ACT and prevent unnecessary treatment or ACT-associated morbidity.

As an alternative target to surgically resected tissue specimens, liquid biopsy has gained much attention over the past decade. Of the various circulating biomarkers, circulating tumor cells (CTCs) have particularly opened new windows into the metastatic cascade, with their functional, biochemical, and biophysical properties. Given the extreme rarity of intact CTCs and the associated technical challenges, however, analyses have been limited to bulk-cell strategies, missing out on clinically significant sources of information from cellular heterogeneity. With recent technological developments, it is now possible to probe genetic material of CTCs at the single-cell resolution to study spatial and temporal dynamics in circulation. Here, we discuss recent transcriptomic profiling efforts that enabled single-cell characterization of patient-derived CTCs spanning diverse cancer types. We further highlight how expression data of these putative biomarkers have advanced our understanding of metastatic spectrum and provided a basis for the development of CTC-based liquid biopsies to track, monitor, and predict the efficacy of therapy and any emergent resistance.

Recent sequencing efforts unveil genomic landscapes of tumor microenvironment. A key compartment in this niche is the extracellular matrix (ECM) and its related components – matrisome. Yet, little is known about the extent to which matrisome pattern is conserved in progressive tumors across diverse cancer types. Using integrative genomic approaches, we conducted multi-platform assessment of a measure of deregulated matrisome associated with tumor progression, termed as tumor matrisome index (TMI), in over 30,000 patient-derived samples. Combined quantitative analyses of genomics and proteomics reveal that TMI is closely associated with mutational load, tumor pathology, and predicts survival across different malignancies. Interestingly, we observed an enrichment of specific tumor-infiltrating immune cell populations, along with signatures predictive of resistance to immune checkpoint blockade immunotherapy, and clinically targetable immune checkpoints in TMI_high tumors. B7-H3 emerged as a particularly promising target for anti-tumor immunity in these tumors. Here, we show that matrisomal abnormalities could represent a potential clinically useful biomarker for prognostication and prediction of immunotherapy response.

As single-cell RNA sequencing (scRNA-seq) has emerged as a great tool for studying cellular heterogeneity within the past decade, the number of available scRNA-seq datasets also rapidly increased. However, reuse of such data is often problematic due to a small cohort size, limited cell types, and insufficient information on cell type classification. Here, we present a large integrated scRNA-seq dataset containing 224,611 cells from human primary non-small cell lung cancer (NSCLC) tumors. Using publicly available resources, we pre-processed and integrated seven independent scRNA-seq datasets using an anchor-based approach, with five datasets utilized as reference and the remaining two, as validation. We created two levels of annotation based on cell type-specific markers conserved across the datasets. To demonstrate usability of the integrated dataset, we created annotation predictions for the two validation datasets using our integrated reference. Additionally, we conducted a trajectory analysis on subsets of T cells and lung cancer cells. This integrated data may serve as a resource for studying NSCLC transcriptome at the single cell level.