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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. Prognosis and prediction of adjuvant chemotherapy response in non-small cell lung cancer can have significant clinical impact. Here, the authors show that differential expression of a 29 extracellular matrix gene indicator, EPPI, can predict patient outcome.

Despite pronounced genomic and transcriptomic heterogeneity in non–small-cell lung cancer (NSCLC) not only between tumors, but also within a tumor, validation of clinically relevant gene signatures for prognostication has relied upon single-tissue samples, including 2 commercially available multigene tests (MGTs). Here we report an unanticipated impact of intratumor heterogeneity (ITH) on risk prediction of recurrence in NSCLC, underscoring the need for a better genomic strategy to refine prognostication. By leveraging label-free, inertial-focusing microfluidic approaches in retrieving circulating tumor cells (CTCs) at single-cell resolution, we further identified specific gene signatures with distinct expression profiles in CTCs from patients with differing metastatic potential. Notably, a refined prognostic risk model that reconciles the level of ITH and CTC-derived gene expression data outperformed the initial classifier in predicting recurrence-free survival (RFS). We propose tailored approaches to providing reliable risk estimates while accounting for ITH-driven variance in NSCLC.

Resistance to drug therapy is a major concern in cancer treatment. To probe clones resistant to chemotherapy, the current approach is to conduct pooled cell analysis. However, this can yield false negative outcomes, especially when we are analyzing a rare number of circulating tumor cells (CTCs) among an abundance of other cell types. Here, we develop a microfluidic device that is able to perform high throughput, selective picking and isolation of single CTC to 100% purity from a larger population of other cells. This microfluidic device can effectively separate the very rare CTCs from blood samples from as few as 1 in 20,000 white blood cells. We first demonstrate isolation of pure tumor cells from a mixed population and track variations of acquired T790M mutations before and after drug treatment using a model PC9 cell line. With clinical CTC samples, we then show that the isolated single CTCs are representative of dominant EGFR mutations such as T790M and L858R found in the primary tumor. With this single cell recovery device, we can potentially implement personalized treatment not only through detecting genetic aberrations at the single cell level, but also through tracking such changes during an anticancer therapy.

Cancer metastasis is the main attribute to cancer-related deaths. Furthermore, clinical reports have shown a strong correlation between the disease development and number of circulating tumor cells (CTCs) in the peripheral blood of cancer patients. Here, we present a label-free microdevice capable of isolating cancer cells from whole blood via their distinctively different physical properties such as deformability and size. The isolation efficiency is at least 80% for tests performed on breast and colon cancer cells. Viable isolated cells are also obtained which may give further insights to the understanding of the metastatic process. Contrasting with conventional biochemical techniques, the uniqueness of this microdevice lies in the mechanistic and efficient means of isolating viable cancer cells in blood. The microdevice has the potential to be used for routine monitoring of cancer development and cancer therapy in a clinical setting.

Sepsis is a major cause of mortality and morbidity in intensive care units. This case–control study aimed to investigate the haematology cell population data and extended inflammatory parameters for sepsis management. The study included three groups of patients: sepsis, non-sepsis, and healthy controls. Patients suspected of having sepsis underwent a Sequential Organ Failure Assessment (SOFA) evaluation and had blood drawn for blood cultures, complete peripheral blood counts (CBC), and measurements of various markers such as C-reactive protein (CRP), procalcitonin (PCT), and interleukin-6 (IL-6). We observed significant changes in numerous CBC parameters and extended inflammation parameters (EIPs), in addition to significant biochemical analysis markers CRP and IL-6 in sepsis cohorts. Multiple logistic regression analyses showed that combining different CBC parameters and EIPs were effective to profile these patients. Two different models have been developed using white blood cell counts and their extended parameters. Our findings indicate that the absolute counts of white blood cells, and the EIPs which reflect their activation states, are important for the prediction and assessment of sepsis, as the body responds to an insult that triggers an immune response. In an emergency situation, having timely updates on patient conditions becomes crucial for guiding the management process. Identifying trends in these specific patient groups will aid early diagnosis, complementing clinical signs and symptoms, especially as CBC is the most commonly ordered test in a diagnostic workup.

There are massive transcriptome profiles in the form of microarray. The challenge is that they are processed using diverse platforms and preprocessing tools, requiring considerable time and informatics expertise for cross-dataset analyses. If there exists a single, integrated data source, data-reuse can be facilitated for discovery, analysis, and validation of biomarker-based clinical strategy. Here, we present merged microarray-acquired datasets (MMDs) across 11 major cancer types, curating 8,386 patient-derived tumor and tumor-free samples from 95 GEO datasets. Using machine learning algorithms, we show that diagnostic models trained from MMDs can be directly applied to RNA-seq-acquired TCGA data with high classification accuracy. Machine learning optimized MMD further aids to reveal immune landscape across various carcinomas critically needed in disease management and clinical interventions. This unified data source may serve as an excellent training or test set to apply, develop, and refine machine learning algorithms that can be tapped to better define genomic landscape of human cancers.

Cytoadherence or sequestration is essential for the pathogenesis of the most virulent human malaria species, Plasmodium falciparum (P. falciparum). Similar to leukocyte-endothelium interaction in response to inflammation, cytoadherence of P. falciparum infected red blood cells (IRBCs) to endothelium occurs under physiological shear stresses in blood vessels and involves an array of molecule complexes which cooperate to form stable binding. Here, we applied single-molecule force spectroscopy technique to quantify the dynamic force spectra and characterize the intrinsic kinetic parameters for specific ligand-receptor interactions involving two endothelial receptor proteins: thrombospondin (TSP) and CD36. It was shown that CD36 mediated interaction was much more stable than that mediated by TSP at single molecule level, although TSP-IRBC interaction appeared stronger than CD36-IRBC interaction in the high pulling rate regime. This suggests that TSP-mediated interaction may initiate cell adhesion by capturing the fast flowing IRBCs whereas CD36 functions as the 'holder' for providing stable binding.

Library preparation for next-generation DNA sequencing (NGS) remains a key bottleneck in the sequencing process which can be relieved through improved automation and miniaturization. We describe a microfluidic device for automating laboratory protocols that require one or more column chromatography steps and demonstrate its utility for preparing Next Generation sequencing libraries for the Illumina and Ion Torrent platforms. Sixteen different libraries can be generated simultaneously with significantly reduced reagent cost and hands-on time compared to manual library preparation. Using an appropriate column matrix and buffers, size selection can be performed on-chip following end-repair, dA tailing, and linker ligation, so that the libraries eluted from the chip are ready for sequencing. The core architecture of the device ensures uniform, reproducible column packing without user supervision and accommodates multiple routine protocol steps in any sequence, such as reagent mixing and incubation; column packing, loading, washing, elution, and regeneration; capture of eluted material for use as a substrate in a later step of the protocol; and removal of one column matrix so that two or more column matrices with different functional properties can be used in the same protocol. The microfluidic device is mounted on a plastic carrier so that reagents and products can be aliquoted and recovered using standard pipettors and liquid handling robots. The carrier-mounted device is operated using a benchtop controller that seals and operates the device with programmable temperature control, eliminating any requirement for the user to manually attach tubing or connectors. In addition to NGS library preparation, the device and controller are suitable for automating other time-consuming and error-prone laboratory protocols requiring column chromatography steps, such as chromatin immunoprecipitation.

Single cell techniques permit the analysis of cellular properties that are obscured by studying the average behavior of cell populations. One way to determine how gene expression contributes to phenotypic differences among cells is to combine functional analysis with transcriptional profiling of single cells. Here we describe a microfluidic device for monitoring the responses of single cells to a ligand and then collecting cells of interest for transcriptional profiling or other assays. As a test, cells from the olfactory epithelium of zebrafish were screened by calcium imaging to identify sensory neurons that were responsive to the odorant L-lysine. Single cells were subsequently recovered for transcriptional profiling by qRT-PCR. Responsive cells all expressed TRPC2 but not OMP, consistent with known properties of amino-acid sensitive olfactory neurons. The device can be adapted for other areas in biology where there is a need to sort and analyze cells based on their signaling responses.

The need for efficient cell separation, an essential preparatory step in many biological and medical assays, has led to the recent development of numerous microscale separation techniques. This review describes the current state-of-the-art in microfluidics-based cell separation techniques. Microfluidics-based sorting offers numerous advantages, including reducing sample volumes, faster sample processing, high sensitivity and spatial resolution, low device cost, and increased portability. The techniques presented are broadly classified as being active or passive depending on the operating principles. The various separation principles are explained in detail along with popular examples demonstrating their application toward cell separation. Common separation metrics, including separation markers, resolution, efficiency, and throughput, of these techniques are discussed. Developing efficient microscale separation methods that offering greater control over cell population distribution will be important in realizing true point-of-care (POC) lab-on-a-chip (LOC) systems.