Live-single-cell phenotypic cancer biomarkers-future role in precision oncology?

Defining Biomarkers in Stem Cell-Derived Tissue Constructs for Drug and Disease Screening

The development of stem cell-derived tissue constructs (SCTCs) for clinical applications, including regenerative medicine, drug and disease screening offers significant hope for detecting and treating intractable disorders. SCTCs display a variety of biomarkers that can be used to understand biological mechanisms, assess drug interactions, and predict disease. Although SCTCs can be derived from patients and share the same genetic make-up, they are nevertheless distinct from human patients in many significant ways, which can undermine the clinical significance of measurements in SCTCs. This study defines biomarkers, how they apply to SCTCs, and clarifies specific ethical issues associated with the use of SCTCs for drug and disease screening.

Cellular subpopulations identified using an ensemble average of multiple dielectrophoresis measurements

While the average value measurement approach can successfully analyze and predict the general behavior and biophysical properties of an isogenic cell population, it fails when significant differences among individual cells are generated in the population by intracellular changes such as the cell cycle, or different cellular responses to certain stimuli. Detecting such single-cell differences in a cell population has remained elusive. Here, we describe an easy-to-implement and generalizable platform that measures the dielectrophoretic cross-over frequency of individual cells by decreasing measurement noise with a stochastic method and computing ensemble average statistics. This platform enables multiple, real-time, label-free detection of individual cells with significant dielectric variations over time within an isogenic cell population. Using a stochastic method in combination with the platform, we distinguished cell subpopulations from a mixture of drug-untreated and -treated isogenic cells. Furthermore, we demonstrate that our platform can identify drug-treated isogenic cells with different recovery rates.

Liprin-α1 Expression in Tumor-Infiltrating Lymphocytes Associates with Improved Survival in Patients with HPV-Positive Oropharyngeal Squamous Cell Carcinoma

BACKGROUND

Liprin-α1 is a scaffold protein involved in cell adhesion, motility, and invasion in malignancies. Liprin-α1 inhibits the expression of metastatic suppressor CD82 in cancers such as oral carcinoma, and the expression of these proteins has been known to correlate negatively. The role of these proteins has not been previously studied in human papillomavirus (HPV)-related head and neck cancers. Our aim was to assess the clinical and prognostic role of liprin-α1 and CD82 in HPV-positive oropharyngeal squamous cell carcinoma (OPSCC) in comparison to HPV-negative OPSCC.

METHODS

The data included 139 OPSCC patients treated at the Helsinki University Hospital (HUS) during 2012-2016. Immunohistochemistry was utilized in HPV determination and in biomarker assays. Overall survival (OS) was used in the survival analysis.

RESULTS

Stronger expression of liprin-α1 in tumor-infiltrating lymphocytes (TILs) was linked to lower cancer stage (p < 0.001) and HPV positivity (p < 0.001). Additionally, we found an association between elevated expression of liprin-α1 and weak expression of CD82 in tumor cells (p = 0.029). In survival analysis, we found significant correlation between favorable OS and stronger expression of liprin-α1 in TILs among the whole patient cohort (p < 0.001) and among HPV-positive patients (p = 0.042).

CONCLUSIONS

Increased liprin-α1 expression in the TILs is associated with favorable prognosis in OPSCC, especially among HPV-positive patients.

Biomarkers in Breast Cancer: An Old Story with a New End

Breast cancer is the second most frequent cancer in the world. It is a heterogeneous disease and the leading cause of cancer mortality in women. Advances in molecular technologies allowed for the identification of new and more specifics biomarkers for breast cancer diagnosis, prognosis, and risk prediction, enabling personalized treatments, improving therapy, and preventing overtreatment, undertreatment, and incorrect treatment. Several breast cancer biomarkers have been identified and, along with traditional biomarkers, they can assist physicians throughout treatment plan and increase therapy success. Despite the need of more data to improve specificity and determine the real clinical utility of some biomarkers, others are already established and can be used as a guide to make treatment decisions. In this review, we summarize the available traditional, novel, and potential biomarkers while also including gene expression profiles, breast cancer single-cell and polyploid giant cancer cells. We hope to help physicians understand tumor specific characteristics and support decision-making in patient-personalized clinical management, consequently improving treatment outcome.

Recent Trends in Metal Nanoparticles Decorated 2D Materials for Electrochemical Biomarker Detection

Technological advancements in the healthcare sector have pushed for improved sensors and devices for disease diagnosis and treatment. Recently, with the discovery of numerous biomarkers for various specific physiological conditions, early disease screening has become a possibility. Biomarkers are the body's early warning systems, which are indicators of a biological state that provides a standardized and precise way of evaluating the progression of disease or infection. Owing to the extremely low concentrations of various biomarkers in bodily fluids, signal amplification strategies have become crucial for the detection of biomarkers. Metal nanoparticles are commonly applied on 2D platforms to anchor antibodies and enhance the signals for electrochemical biomarker detection. In this context, this review will discuss the recent trends and advances in metal nanoparticle decorated 2D materials for electrochemical biomarker detection. The prospects, advantages, and limitations of this strategy also will be discussed in the concluding section of this review.

Best practices for the development and fit-for-purpose validation of biomarker methods: a conference report

This conference report summarized a full-day workshop, “best practices for the development and fit-for-purpose validation of biomarker methods,” which was held prior to the American Association of Pharmaceutical Scientists (AAPS) PharmSci360 Congress, San Antonio, TX, November 2019. The purpose of the workshop was to bring together thought leaders in biomarker assay development in order to identify which assay parameters and key statistical measures need to be considered when developing a biomarker assay. A diverse group of more than 40 scientists participated in the workshop. The workshop and subsequent working dinner stimulated robust discussion. While a consensus on best practices was not achieved, some common themes and major points to consider for biomarker assay development have been identified and agreed on. The focus of this conference report is to summarize the presentations and discussions which occurred at the workshop. Biomarker assay validation is a complex and an evolving area with discussions ongoing.

From imaging a single cell to implementing precision medicine: an exciting new era

In the age of high-throughput, single-cell biology, single-cell imaging has evolved not only in terms of technological advancements but also in its translational applications. The synchronous advancements of imaging and computational biology have produced opportunities of merging the two, providing the scientific community with tools towards observing, understanding, and predicting cellular and tissue phenotypes and behaviors. Furthermore, multiplexed single-cell imaging and machine learning algorithms now enable patient stratification and predictive diagnostics of clinical specimens. Here, we provide an overall summary of the advances in single-cell imaging, with a focus on high-throughput microscopy phenomics and multiplexed proteomic spatial imaging platforms. We also review various computational tools that have been developed in recent years for image processing and downstream applications used in biomedical sciences. Finally, we discuss how harnessing systems biology approaches and data integration across disciplines can further strengthen the exciting applications and future implementation of single-cell imaging on precision medicine.

Quantitative Approach for Protein Analysis in Small Cell Ensembles by an Integrated Microfluidic Chip with MALDI Mass Spectrometry

Increasing evidence has demonstrated that cells are individually heterogeneous. Advancing the technologies for single-cell analysis will improve our ability to characterize cells, study cell biology, design and screen drugs, and aid cancer diagnosis and treatment. Most current single-cell protein analysis approaches are based on fluorescent antibody-binding technology. However, this technology is limited by high background and cross-talk of multiple tags introduced by fluorescent labels. Stable isotope labels used in mass cytometry can overcome the spectral overlap of fluorophores. Nevertheless, the specificity of each antibody and heavy-metal-tagged antibody combination must be carefully validated to ensure detection of the intended target. Thus, novel single-cell protein analysis methods without using labels are urgently needed. Moreover, the labeling approach targets already known motifs, hampering the discovery of new biomarkers relevant to single-cell population variation. Here, we report a combined microfluidic and matrix-assisted laser desorption and ionization (MALDI) mass spectrometric approach for the analysis of protein biomarkers suitable for small cell ensembles. All necessary steps for cell analysis including cell lysis, protein capture, and digestion as well as MALDI matrix deposition are integrated on a microfluidic chip prior to the downstream MALDI-time-of-flight (TOF) detection. For proof of principle, this combined method is used to assess the amount of Bcl-2, an apoptosis regulator, in metastatic breast cancer cells (MCF-7) by using an isotope-labeled peptide as an internal standard. The proposed approach will eventually provide a new means for proteome studies in small cell ensembles with the potential for single-cell analysis and improve our ability in disease diagnosis, drug discovery, and personalized therapy.

What Is the Storage Effect, Why Should It Occur in Cancers, and How Can It Inform Cancer Therapy?

Intratumor heterogeneity is a feature of cancer that is associated with progression, treatment resistance, and recurrence. However, the mechanisms that allow diverse cancer cell lineages to coexist remain poorly understood. The storage effect is a coexistence mechanism that has been proposed to explain the diversity of a variety of ecological communities, including coral reef fish, plankton, and desert annual plants. Three ingredients are required for there to be a storage effect: (1) temporal variability in the environment, (2) buffered population growth, and (3) species-specific environmental responses. In this article, we argue that these conditions are observed in cancers and that it is likely that the storage effect contributes to intratumor diversity. Data that show the temporal variation within the tumor microenvironment are needed to quantify how cancer cells respond to fluctuations in the tumor microenvironment and what impact this has on interactions among cancer cell types. The presence of a storage effect within a patient’s tumors could have a substantial impact on how we understand and treat cancer.

OMICs Approaches in Diarrhetic Shellfish Toxins Research

Diarrhetic shellfish toxins (DSTs) are among the most prevalent marine toxins in Europe's and in other temperate coastal regions. These toxins are produced by several dinoflagellate species; however, the contamination of the marine trophic chain is often attributed to species of the genus Dinophysis. This group of toxins, constituted by okadaic acid (OA) and analogous molecules (dinophysistoxins, DTXs), are highly harmful to humans, causing severe poisoning symptoms caused by the ingestion of contaminated seafood. Knowledge on the mode of action and toxicology of OA and the chemical characterization and accumulation of DSTs in seafood species (bivalves, gastropods and crustaceans) has significantly contributed to understand the impacts of these toxins in humans. Considerable information is however missing, particularly at the molecular and metabolic levels involving toxin uptake, distribution, compartmentalization and biotransformation and the interaction of DSTs with aquatic organisms. Recent contributions to the knowledge of DSTs arise from transcriptomics and proteomics research. Indeed, OMICs constitute a research field dedicated to the systematic analysis on the organisms' metabolisms. The methodologies used in OMICs are also highly effective to identify critical metabolic pathways affecting the physiology of the organisms. In this review, we analyze the main contributions provided so far by OMICs to DSTs research and discuss the prospects of OMICs with regard to the DSTs toxicology and the significance of these toxins to public health, food safety and aquaculture.

Advances of single-cell genomics and epigenomics in human disease: where are we now?

Cellular heterogeneity is revolutionizing the way to study, monitor and dissect complex diseases. This has been possible with the technological and computational advances associated to single-cell genomics and epigenomics. Deeper understanding of cell-to-cell variation and its impact on tissue function will open new avenues for early disease detection, accurate diagnosis and personalized treatments, all together leading to the next generation of health care. This review focuses on the recent discoveries that single-cell genomics and epigenomics have facilitated in the context of human health. It highlights the potential of single-cell omics to further advance the development of personalized treatments and precision medicine in cancer, diabetes and chronic age-related diseases. The promise of single-cell technologies to generate new insights about the differences in function between individual cells is just emerging, and it is paving the way for identifying biomarkers and novel therapeutic targets to tackle age, complex diseases and understand the effect of life style interventions and environmental factors.

Quantitative scoring of epithelial and mesenchymal qualities of cancer cells using machine learning and quantitative phase imaging

SIGNIFICANCE

We introduce an application of machine learning trained on optical phase features of epithelial and mesenchymal cells to grade cancer cells' morphologies, relevant to evaluation of cancer phenotype in screening assays and clinical biopsies.

AIM

Our objective was to determine quantitative epithelial and mesenchymal qualities of breast cancer cells through an unbiased, generalizable, and linear score covering the range of observed morphologies.

APPROACH

Digital holographic microscopy was used to generate phase height maps of noncancerous epithelial (Gie-No3B11) and fibroblast (human gingival) cell lines, as well as MDA-MB-231 and MCF-7 breast cancer cell lines. Several machine learning algorithms were evaluated as binary classifiers of the noncancerous cells that graded the cancer cells by transfer learning.

RESULTS

Epithelial and mesenchymal cells were classified with 96% to 100% accuracy. Breast cancer cells had scores in between the noncancer scores, indicating both epithelial and mesenchymal morphological qualities. The MCF-7 cells skewed toward epithelial scores, while MDA-MB-231 cells skewed toward mesenchymal scores. Linear support vector machines (SVMs) produced the most distinct score distributions for each cell line.

CONCLUSIONS

The proposed epithelial-mesenchymal score, derived from linear SVM learning, is a sensitive and quantitative approach for detecting epithelial and mesenchymal characteristics of unknown cells based on well-characterized cell lines. We establish a framework for rapid and accurate morphological evaluation of single cells and subtle phenotypic shifts in imaged cell populations.

Application of Artificial Intelligence/Machine Vision & Learning for the Development of a Live Single-cell Phenotypic Biomarker Test to Predict Prostate Cancer Tumor Aggressiveness

To assess the usefulness and applications of machine vision (MV) and machine learning (ML) techniques that have been used to develop a single cell-based phenotypic (live and fixed biomarkers) platform that correlates with tumor biological aggressiveness and risk stratification, 100 fresh prostate samples were acquired, and areas of prostate cancer were determined by post-surgery pathology reports logged by an independent pathologist. The prostate samples were dissociated into single-cell suspensions in the presence of an extracellular matrix formulation. These samples were analyzed via live-cell microscopy. Dynamic and fixed phenotypic biomarkers per cell were quantified using objective MV software and ML algorithms. The predictive nature of the ML algorithms was developed in two stages. First, random forest (RF) algorithms were developed using 70% of the samples. The developed algorithms were then tested for their predictive performance using the blinded test dataset that contained 30% of the samples in the second stage. Based on the ROC (receiver operating characteristic) curve analysis, thresholds were set to maximize both sensitivity and specificity. We determined the sensitivity and specificity of the assay by comparing the algorithm-generated predictions with adverse pathologic features in the radical prostatectomy (RP) specimens. Using MV and ML algorithms, the biomarkers predictive of adverse pathology at RP were ranked and a prostate cancer patient risk stratification test was developed that distinguishes patients based on surgical adverse pathology features. The ability to identify and track large numbers of individual cells over the length of the microscopy experimental monitoring cycles, in an automated way, created a large biomarker dataset of primary biomarkers. This biomarker dataset was then interrogated with ML algorithms used to correlate with post-surgical adverse pathology findings. Algorithms were generated that predicted adverse pathology with >0.85 sensitivity and specificity and an AUC (area under the curve) of >0.85. Phenotypic biomarkers provide cellular and molecular details that are informative for predicting post-surgical adverse pathologies when considering tumor biopsy samples. Artificial intelligence ML-based approaches for cancer risk stratification are emerging as important and powerful tools to compliment current measures of risk stratification. These techniques have capabilities to address tumor heterogeneity and the molecular complexity of prostate cancer. Specifically, the phenotypic test is a novel example of leveraging biomarkers and advances in MV and ML for developing a powerful prognostic and risk-stratification tool for prostate cancer patients.

Elastic modulus and migration capability of drug treated leukemia cells K562

Leukemia is a commonly seen disease caused by abnormal differentiation of hematopoietic stem cells and blasting in bone marrow. Despite drugs are used to treat the disease clinically, the influence of these drugs on leukemia cells' biomechanical properties, which are closely related to complications like leukostasis or infiltration, is still unclear. Due to non-adherent and viscoelastic nature of leukemia cells, accurate measurement of their elastic modulus is still a challenging issue. In this study, we adopted rate-jump method together with optical tweezers indentation to accurately measure elastic modulus of leukemia cells K562 after phorbol 12-myristate 13-acetate (PMA), all-trans retinoic acid (ATRA), Cytoxan (CTX), and Dexamethasone (DEX) treatment, respectively. We found that compared to control sample, K562 cells treated by PMA showed nearly a threefold increase in elastic modulus. Transwell experiment results suggested that the K562 cells treated with PMA have the lowest migration capability. Besides, it was shown that the cytoskeleton protein gene α-tubulin and vimentin have a significant increase in expression after PMA treatment by qPCR. The results indicate that PMA has a significant influence on protein expression, stiffness, and migration ability of the leukemia cell K562, and may also play an important role in the leukostasis in leukemia.

Surface-Enhanced Raman Spectroscopy in Cancer Diagnosis, Prognosis and Monitoring

As medicine continues to advance our understanding of and knowledge about the complex and multifactorial nature of cancer, new major technological challenges have emerged in the design of analytical methods capable of characterizing and assessing the dynamic heterogeneity of cancer for diagnosis, prognosis and monitoring, as required by precision medicine. With this aim, novel nanotechnological approaches have been pursued and developed for overcoming intrinsic and current limitations of conventional methods in terms of rapidity, sensitivity, multiplicity, non-invasive procedures and cost. Eminently, a special focus has been put on their implementation in liquid biopsy analysis. Among optical nanosensors, those based on surface-enhanced Raman scattering (SERS) have been attracting tremendous attention due to the combination of the intrinsic prerogatives of the technique (e.g., sensitivity and structural specificity) and the high degree of refinement in nano-manufacturing, which translate into reliable and robust real-life applications. In this review, we categorize the diverse strategic approaches of SERS biosensors for targeting different classes of tumor biomarkers (cells, nucleic acids and proteins) by illustrating key recent research works. We will also discuss the current limitations and future research challenges to be addressed to improve the competitiveness of SERS over other methodologies in cancer medicine.

Landscape of granted US patents in personalized diagnostics for oncology from 2014 to 2018

INTRODUCTION

Personalized diagnostic testing (PDx) is a key component of the precision medicine toolkit and has shown the most development in cancer applications. Recent changes in the regulatory and legal landscapes regarding PDx development and commercialization have brought uncertainties to both intellectual property strategies and business model development. While the regulatory and legal uncertainties have been well-documented, there has been little reported analysis of the recent patent landscape and movement of IP into the PDx market. Areas covered: This article provides a snapshot landscape analysis of cancer-associated PDx US granted patents from 2014 to 2018, with a focus on claim types, biomarkers, and associated detection strategies, and assignee-specific IP portfolio analyses. Expert opinion: Patent-driven research is commonplace in the legal world for performing patentability, clearance, and validity analyses. The results from this review show that patent-driven analysis is also insightful for understanding strategies to build IP portfolios around biomarker and detection platforms, identifying partners and competitors, and driving PDx technologies into the market. This information is an important source of business intelligence and can provide companies or investors with valuable information for making strategic decisions in developing and commercializing PDx technologies.

An Integrative Data Mining and Omics-Based Translational Model for the Identification and Validation of Oncogenic Biomarkers of Pancreatic Cancer

Substantial alterations at the multi-omics level of pancreatic cancer (PC) impede the possibility to diagnose and treat patients in early stages. Herein, we conducted an integrative omics-based translational analysis, utilizing next-generation sequencing, transcriptome meta-analysis, and immunohistochemistry, combined with statistical learning, to validate multiplex biomarker candidates for the diagnosis, prognosis, and management of PC. Experiment-based validation was conducted and supportive evidence for the essentiality of the candidates in PC were found at gene expression or protein level by practical biochemical methods. Remarkably, the random forests (RF) model exhibited an excellent diagnostic performance and LAMC2, ANXA2, ADAM9, and APLP2 greatly influenced its decisions. An explanation approach for the RF model was successfully constructed. Moreover, protein expression of LAMC2, ANXA2, ADAM9, and APLP2 was found correlated and significantly higher in PC patients in independent cohorts. Survival analysis revealed that patients with high expression of ADAM9 (Hazard ratio (HR)_OS = 2.2, p-value < 0.001), ANXA2 (HR_OS = 2.1, p-value < 0.001), and LAMC2 (HR_DFS = 1.8, p-value = 0.012) exhibited poorer survival rates. In conclusion, we successfully explore hidden biological insights from large-scale omics data and suggest that LAMC2, ANXA2, ADAM9, and APLP2 are robust biomarkers for early diagnosis, prognosis, and management for PC.

Clinical Proof-of-concept of a Novel Platform Utilizing Biopsy-derived Live Single Cells, Phenotypic Biomarkers, and Machine Learning Toward a Precision Risk Stratification Test for Prostate Cancer Grade Groups 1 and 2 (Gleason 3 + 3 and 3 + 4)

OBJECTIVE

To examine the ability of a novel live primary-cell phenotypic (LPCP) test to predict postsurgical adverse pathology (P-SAP) features and risk stratify patients based on SAP features in a blinded study utilizing radical prostatectomy (RP) surgical specimens.

METHODS

Two hundred fifty-one men undergoing RP were enrolled in a prospective, multicenter (10), and proof-of-concept study in the United States. Fresh prostate samples were taken from known areas of cancer in the operating room immediately after RP. Samples were shipped and tested at a central laboratory. Utilizing the LPCP test, a suite of phenotypic biomarkers was analyzed and quantified using objective machine vision software. Biomarkers were objectively ranked via machine learning-derived statistical algorithms (MLDSA) to predict postsurgical adverse pathological features. Sensitivity and specificity were determined by comparing blinded predictions and unblinded RP surgical pathology reports, training MLDSAs on 70% of biopsy cells and testing MLDSAs on the remaining 30% of biopsy cells across the tested patient population.

RESULTS

The LPCP test predicted adverse pathologies post-RP with area under the curve (AUC) via receiver operating characteristics analysis of greater than 0.80 and distinguished between Prostate Cancer Grade Groups 1, 2, and 3/Gleason Scores 3 + 3, 3 + 4, and 4 + 3. Further, LPCP derived-biomarker scores predicted Gleason pattern, stage, and adverse pathology with high precision-AUCs>0.80.

CONCLUSION

Using MLDSA-derived phenotypic biomarker scores, the LPCP test successfully risk stratified Prostate Cancer Grade Groups 1, 2, and 3 (Gleason 3 + 3 and 7) into distinct subgroups predicted to have surgical adverse pathologies or not with high performance (>0.85 AUC).

Live-cell phenotypic-biomarker microfluidic assay for the risk stratification of cancer patients via machine learning

The risk stratification of prostate cancer and breast cancer tumours from patients relies on histopathology, selective genomic testing, or on other methods employing fixed formalin tissue samples. However, static biomarker measurements from bulk fixed-tissue samples provide limited accuracy and actionability. Here, we report the development of a live-primary-cell phenotypic-biomarker assay with single-cell resolution, and its validation with prostate cancer and breast cancer tissue samples for the prediction of post-surgical adverse pathology. The assay includes a collagen-I/fibronectin extracellular-matrix formulation, dynamic live-cell biomarkers, a microfluidic device, machine-vision analysis and machine-learning algorithms, and generates predictive scores of adverse pathology at the time of surgery. Predictive scores for the risk stratification of 59 prostate cancer patients and 47 breast cancer patients, with values for area under the curve in receiver-operating-characteristic curves surpassing 80%, support the validation of the assay and its potential clinical applicability for the risk stratification of cancer patients.

Potent effect of the MDM2 inhibitor AMG232 on suppression of glioblastoma stem cells

Testing new ways to identify untapped opportunities for glioblastoma therapies remains highly significant. Amplification and overexpression of MDM2 gene is frequent in glioblastoma and disrupting the MDM2-p53 interaction is a promising strategy to treat the cancer. RG7112 is the first-in class inhibitor and recently discovered AMG232 is the most potent MDM2 inhibitor known to date. Here, we compared the effects of these two clinical MDM2 inhibitors in six glioblastoma cell lines and ten patient-derived glioblastoma stem cells. Targeted sequencing of the TP53, MDM2 genes and whole transcriptome analysis were conducted to verify genetic status associated with sensitivity and resistance to the drugs. Although TP53 wild-type glioblastoma cell lines are similarly sensitive to AMG232 and RG7112, we found that four TP53 wild-type out of ten patient-derived glioblastoma cells are much more sensitive to AMG232 than RG7112 (average IC₅₀ of 76 nM vs. 720 nM). Among these, 464T stem cells containing MDM2 gene amplification were most sensitive to AMG232 with IC₅₀ of 5.3 nM. Moreover, AMG232 exhibited higher selectivity against p53 wild-type cells over p53 mutant stem cells compared to RG7112 (average selectivity of 512-fold vs. 16.5-fold). Importantly, we also found that AMG232 is highly efficacious in three-dimensional (3D) tumor spheroids growth and effectively inhibits the stemness-related factors, Nestin and ZEB1. Our data provide new evidence that glioblastoma stem cells have high susceptibility to AMG232 suggesting the potential clinical implications of MDM2 inhibition for glioblastoma treatment. These will facilitate additional preclinical and clinical studies evaluating MDM2 inhibitors in glioblastoma and direct further efforts towards developing better MDM2-targeted therapeutics.

Combining Multiplex SERS Nanovectors and Multivariate Analysis for In Situ Profiling of Circulating Tumor Cell Phenotype Using a Microfluidic Chip

Isolating and in situ profiling the heterogeneous molecular phenotype of circulating tumor cells are of great significance for clinical cancer diagnosis and personalized therapy. Herein, an on-chip strategy is proposed that combines size-based microfluidic cell isolation with multiple spectrally orthogonal surface-enhanced Raman spectroscopy (SERS) analysis for in situ profiling of cell membrane proteins and identification of cancer subpopulations. With the developed microfluidic chip, tumor cells are sieved from blood on the basis of size discrepancy. To enable multiplex phenotypic analysis, three kinds of spectrally orthogonal SERS aptamer nanovectors are designed, providing individual cells with composite spectral signatures in accordance with surface protein expression. Next, to statistically demultiplex the complex SERS signature and profile the cellular proteomic phenotype, a revised classic least square algorithm is employed to obtain the 3D phenotypic information at single-cell resolution. Combined with categorization algorithm partial least square discriminate analysis, cells from different human breast cancer subtypes can be reliably classified with high sensitivity and selectivity. The results demonstrate that this platform can identify cancer subtypes with the spectral information correlated to the clinically relevant surface receptors, which holds great potential for clinical cancer diagnosis and precision medicine.