Addressing cellular heterogeneity in tumor and circulation for refined prognostication.

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.

Personalized Treatment Through Detection and Monitoring of Genetic Aberrations in Single Circulating Tumor Cells.

Circulating tumor cells (CTCs) present a viable alternative to access tumor materials other than primary biopsies in cancer. This disease is among the most widespread in the world and is difficult to target because of its complex nature, challenges in getting quality samples and dynamic temporal changes in response to treatment. Conventional methods of detection and monitoring the disease profile do not suffice to be able to target the heterogeneity that exists at the cellular level. CTCs have been identified as a possible substitute for tumor tissue samples, and can be used to complement current disease management. Challenges in CTCs molecular analysis lie in the purity of the sample, which is masked by the presence of large quantities of white blood cells (WBCs) . In this chapter, we present a microfluidic biochip platform that performs secondary purification to isolate single CTCs efficiently. Studying single CTCs will allow for sensitive detection of critical mutations and addressing intercellular variances that will be otherwise missed easily due to low mutation frequencies when evaluating bulk cell retrieval. Using the biochip, we isolated single CTCs, and conducted personalized integrated EGFR mutational analysis using conventional polymerase chain reaction (PCR) and Sanger sequencing. We also demonstrated that high quality next generation sequencing (NGS) libraries can be readily generated from these samples. In our initial study, we revealed that the dominant EGFR mutations such as L858R and T790M could be detected in Non Small Cell Lung Cancer (NSCLC) patients with low CTC counts. We envision the biochip will enable efficient isolation of rare single cells from samples. This technology coupled with downstream molecular characterization of CTCs will aid in realizing the personalized medicine for cancer patients.

Exploring Extended White Blood Cell Parameters for the Evaluation of Sepsis among Patients Admitted to Intensive Care Units.

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.

Complementary Sequential Circulating Tumor Cell (CTC) and Cell-Free Tumor DNA (ctDNA) Profiling Reveals Metastatic Heterogeneity and Genomic Changes in Lung Cancer and Breast Cancer.

INTRODUCTION: Circulating tumor cells (CTCs) and cell-free tumor DNA (ctDNA) are tumor components present in circulation. Due to the limited access to both CTC enrichment platforms and ctDNA sequencing in most laboratories, they are rarely analyzed together. METHODS: Concurrent isolation of ctDNA and single CTCs were isolated from lung cancer and breast cancer patients using the combination of size-based and CD45-negative selection method via DropCell platform. We performed targeted amplicon sequencing to evaluate the genomic heterogeneity of CTCs and ctDNA in lung cancer and breast cancer patients. RESULTS: Higher degrees of genomic heterogeneity were observed in CTCs as compared to ctDNA. Several shared alterations present in CTCs and ctDNA were undetected in the primary tumor, highlighting the intra-tumoral heterogeneity of tumor components that were shed into systemic circulation. Accordingly, CTCs and ctDNA displayed higher degree of concordance with the metastatic tumor than the primary tumor. The alterations detected in circulation correlated with worse survival outcome for both lung and breast cancer patients emphasizing the impact of the metastatic phenotype. Notably, evolving genetic signatures were detected in the CTCs and ctDNA samples during the course of treatment and disease progression. CONCLUSIONS: A standardized sample processing and data analysis workflow for concurrent analysis of CTCs and ctDNA successfully dissected the heterogeneity of metastatic tumor in circulation as well as the progressive genomic changes that may potentially guide the selection of appropriate therapy against evolving tumor clonality.

Microdevice for the isolation and enumeration of cancer cells from blood.

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.

Compendiums of cancer transcriptomes for machine learning applications.

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.

Pan-cancer analysis connects tumor matrisome to immune response.

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 TMIhigh 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.

A merged lung cancer transcriptome dataset for clinical predictive modeling.

The Gene Expression Omnibus (GEO) database is an excellent public source of whole transcriptomic profiles of multiple cancers. The main challenge is the limited accessibility of such large-scale genomic data to people without a background in bioinformatics or computer science. This presents difficulties in data analysis, sharing and visualization. Here, we present an integrated bioinformatics pipeline and a normalized dataset that has been preprocessed using a robust statistical methodology; allowing others to perform large-scale meta-analysis, without having to conduct time-consuming data mining and statistical correction. Comprising 1,118 patient-derived samples, the normalized dataset includes primary non-small cell lung cancer (NSCLC) tumors and paired normal lung tissues from ten independent GEO datasets, facilitating differential expression analysis. The data has been merged, normalized, batch effect-corrected and filtered for genes with low variance via multiple open source R packages integrated into our workflow. Overall this dataset (with associated clinical metadata) better represents the diseased population and serves as a powerful tool for early predictive biomarker discovery.

An extracellular matrix-related prognostic and predictive indicator for early-stage non-small cell lung cancer.

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.

Microfluidic enrichment for the single cell analysis of circulating tumor cells.

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.

A microfluidic device to sort cells based on dynamic response to a stimulus.

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.

A microfluidic device for preparing next generation DNA sequencing libraries and for automating other laboratory protocols that require one or more column chromatography steps.

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.

Molecular mechanistic insights into the endothelial receptor mediated cytoadherence of Plasmodium falciparum-infected erythrocytes.

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.

Manipulation and isolation of single cells and nuclei.

The heterogeneous behavior of cells within a cell population makes measurements at the multicellular level insensitive to changes in single cells. Single-cell and single-nucleus analyses are therefore important to address this deficiency which will aid in the understanding of fundamental biology at both the cellular and subcellular levels. Recent technological advancements have enabled the development of new methodologies capable of handling these new challenges. This review highlights various techniques used in single-cell and single-nucleus manipulation and isolation. In particular, the applications related to microfluidics, electrical, optical, and physical methods will be discussed. Ultimately, it is hoped that these techniques will enable fundamental tests to be conducted on single cells and nuclei. One important potential outcome is that this will contribute not only towards detection and isolation of diseased cells but also more accurate diagnosis and prognosis of human diseases.

Versatile label free biochip for the detection of circulating tumor cells from peripheral blood in cancer patients.

The isolation of circulating tumor cells (CTCs) using microfluidics is attractive as the flow conditions can be accurately manipulated to achieve an efficient separation. CTCs are rare events within the peripheral blood of metastatic cancer patients which makes them hard to detect. The presence of CTCs is likely to indicate the severity of the disease and increasing evidences show its use for prognostic and treatment monitoring purposes. We demonstrated an effective separation using a microfluidic device to utilize the unique differences in size and deformability of cancer cells to blood cells. Using physical structures placed in the path of blood specimens in a microchannel, CTCs which are generally larger and stiffer are retained while most blood constituents are removed. The placements of the structures are optimized by computational analysis to enhance the isolation efficiency. With blood specimens from metastatic lung cancer patients, we confirmed the successful detection of CTCs. The operations for processing blood are straightforward and permit multiplexing of the microdevices to concurrently work with different samples. The microfluidic device is optically transparent which makes it simple to be integrated to existing laboratory microscopes and immunofluorescence staining can be done in situ to distinguish cancer cells from hematopoietic cells. This also minimizes the use of expensive staining reagents, given the small size of the microdevice. Identification of CTCs will aid in the detection of malignancy and disease stage as well as understanding the phenotypic and genotypic expressions of cancer cells.

Microfluidics for cell separation.

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.