Retraction Note: Lysyl oxidase is essential for hypoxia-induced metastasis.

A Retraction to this paper has been published and can be accessed via a link at the top of the paper.

Targeting the tetraspanin CD81 reduces cancer invasion and metastasis.

Tetraspanins are an evolutionary conserved family of proteins involved in multiple aspects of cell physiology, including proliferation, migration and invasion, protein trafficking, and signal transduction; yet their detailed mechanism of action is unknown. Tetraspanins have no known natural ligands, but their engagement by antibodies has begun to reveal their role in cell biology. Studies of tetraspanin knockout mice and of germline mutations in humans have highlighted their role under normal and pathological conditions. Previously, we have shown that mice deficient in the tetraspanin CD81 developed fewer breast cancer metastases compared to their wild-type (WT) counterparts. Here, we show that a unique anti-human CD81 antibody (5A6) effectively halts invasion of triple-negative breast cancer (TNBC) cell lines. We demonstrate that 5A6 induces CD81 clustering at the cell membrane and we implicate JAM-A protein in the ability of this antibody to inhibit tumor cell invasion and migration. Furthermore, in a series of in vivo studies we demonstrate that this antibody inhibits metastases in xenograft models, as well as in syngeneic mice bearing a mouse tumor into which we knocked in the human CD81 epitope recognized by the 5A6 antibody.

Combining Acoustic Bioprinting with AI-Assisted Raman Spectroscopy for High-Throughput Identification of Bacteria in Blood.

Identifying pathogens in complex samples such as blood, urine, and wastewater is critical to detect infection and inform optimal treatment. Surface-enhanced Raman spectroscopy (SERS) and machine learning (ML) can distinguish among multiple pathogen species, but processing complex fluid samples to sensitively and specifically detect pathogens remains an outstanding challenge. Here, we develop an acoustic bioprinter to digitize samples into millions of droplets, each containing just a few cells, which are identified with SERS and ML. We demonstrate rapid printing of 2 pL droplets from solutions containing S. epidermidis, E. coli, and blood; when they are mixed with gold nanorods (GNRs), SERS enhancements of up to 1500× are achieved.We then train a ML model and achieve ≥99% classification accuracy from cellularly pure samples and ≥87% accuracy from cellularly mixed samples. We also obtain ≥90% accuracy from droplets with pathogen:blood cell ratios <1. Our combined bioprinting and SERS platform could accelerate rapid, sensitive pathogen detection in clinical, environmental, and industrial settings.

Gold Nanobipyramids as Second Near Infrared Optical Coherence Tomography Contrast Agents for in Vivo Multiplexing Studies.

Developing contrast-enhanced optical coherence tomography (OCT) techniques is important for specific imaging of tissue lesions, molecular imaging, cell-tracking, and highly sensitive microangiography and lymphangiography. Multiplexed OCT imaging in the second near-infrared (NIR-II) window is highly desirable since it allows simultaneous imaging and tracking of multiple biological events in high resolution with deeper tissue penetration in vivo. Here we demonstrate that gold nanobipyramids can function as OCT multiplexing contrast agents, allowing high-resolution imaging of two separate lymphatic flows occurring simultaneously from different drainage basins into the same lymph node in a live mouse. Contrast-enhanced multiplexed lymphangiography of a melanoma tumor in vivo shows that the peritumoral lymph flow upstream of the tumor is unidirectional, and tumor is accessible to such flow. Whereas the lymphatic drainage coming out from the tumor is multidirectional. We also demonstrate real-time tracking of the contrast agents draining from a melanoma tumor specifically to the sentinel lymph node of the tumor and the three-dimensional distribution of the contrast agents in the lymph node.

Plasmonic and Electrostatic Interactions Enable Uniformly Enhanced Liquid Bacterial Surface-Enhanced Raman Scattering (SERS).

Surface-enhanced Raman spectroscopy (SERS) is a promising cellular identification and drug susceptibility testing platform, provided it can be performed in a controlled liquid environment that maintains cell viability. We investigate bacterial liquid-SERS, studying plasmonic and electrostatic interactions between gold nanorods and bacteria that enable uniformly enhanced SERS. We synthesize five nanorod sizes with longitudinal plasmon resonances ranging from 670 to 860 nm and characterize SERS signatures of Gram-negative Escherichia coli and Serratia marcescens and Gram-positive Staphylococcus aureus and Staphylococcus epidermidis bacteria in water. Varying the concentration of bacteria and nanorods, we achieve large-area SERS enhancement that is independent of nanorod resonance and bacteria type; however, bacteria with higher surface charge density exhibit significantly higher SERS signal. Using cryo-electron microscopy and zeta potential measurements, we show that the higher signal results from attraction between positively charged nanorods and negatively charged bacteria. Our robust liquid-SERS measurements provide a foundation for bacterial identification and drug testing in biological fluids.

Toward rapid infectious disease diagnosis with advances in surface-enhanced Raman spectroscopy.

In a pandemic era, rapid infectious disease diagnosis is essential. Surface-enhanced Raman spectroscopy (SERS) promises sensitive and specific diagnosis including rapid point-of-care detection and drug susceptibility testing. SERS utilizes inelastic light scattering arising from the interaction of incident photons with molecular vibrations, enhanced by orders of magnitude with resonant metallic or dielectric nanostructures. While SERS provides a spectral fingerprint of the sample, clinical translation is lagged due to challenges in consistency of spectral enhancement, complexity in spectral interpretation, insufficient specificity and sensitivity, and inefficient workflow from patient sample collection to spectral acquisition. Here, we highlight the recent, complementary advances that address these shortcomings, including (1) design of label-free SERS substrates and data processing algorithms that improve spectral signal and interpretability, essential for broad pathogen screening assays; (2) development of new capture and affinity agents, such as aptamers and polymers, critical for determining the presence or absence of particular pathogens; and (3) microfluidic and bioprinting platforms for efficient clinical sample processing. We also describe the development of low-cost, point-of-care, optical SERS hardware. Our paper focuses on SERS for viral and bacterial detection, in hopes of accelerating infectious disease diagnosis, monitoring, and vaccine development. With advances in SERS substrates, machine learning, and microfluidics and bioprinting, the specificity, sensitivity, and speed of SERS can be readily translated from laboratory bench to patient bedside, accelerating point-of-care diagnosis, personalized medicine, and precision health.

Advances in the Characterization of Circulating Tumor Cells in Metastatic Breast Cancer: Single Cell Analyses and Interactions, and Patient-Derived Models for Drug Testing.

Metastasis is the major cause of breast cancer death worldwide. In metastatic breast cancer, circulating tumor cells (CTCs) can be captured from patient blood samples sequentially over time and thereby serve as surrogates to assess the biology of surviving cancer cells that may still persist in solitary or multiple metastatic sites following treatment. CTCs may thus function as potential real-time decision-making guides for selecting appropriate therapies during the course of disease or for the development and testing of new treatments. The heterogeneous nature of CTCs warrants the use of single cell platforms to better inform our understanding of these cancer cells. Current techniques for single cell analyses and techniques for investigating interactions between cancer and immune cells are discussed. In addition, methodologies for growing patient-derived CTCs in vitro or propagating them in vivo to facilitate CTC drug testing are reviewed. We advocate the use of CTCs in appropriate microenvironments to appraise the effectiveness of cancer chemotherapies, immunotherapies, and for the development of new cancer treatments, fundamental to personalizing and improving the clinical management of metastatic breast cancer.

T cell receptor sequencing of early-stage breast cancer tumors identifies altered clonal structure of the T cell repertoire.

Tumor-infiltrating T cells play an important role in many cancers, and can improve prognosis and yield therapeutic targets. We characterized T cells infiltrating both breast cancer tumors and the surrounding normal breast tissue to identify T cells specific to each, as well as their abundance in peripheral blood. Using immune profiling of the T cell beta-chain repertoire in 16 patients with early-stage breast cancer, we show that the clonal structure of the tumor is significantly different from adjacent breast tissue, with the tumor containing ∼2.5-fold greater density of T cells and higher clonality compared with normal breast. The clonal structure of T cells in blood and normal breast is more similar than between blood and tumor, and could be used to distinguish tumor from normal breast tissue in 14 of 16 patients. Many T cell sequences overlap between tissue and blood from the same patient, including ∼50% of T cells between tumor and normal breast. Both tumor and normal breast contain high-abundance "enriched" sequences that are absent or of low abundance in the other tissue. Many of these T cells are either not detected or detected with very low frequency in the blood, suggesting the existence of separate compartments of T cells in both tumor and normal breast. Enriched T cell sequences are typically unique to each patient, but a subset is shared between many different patients. We show that many of these are commonly generated sequences, and thus unlikely to play an important role in the tumor microenvironment.

Real-Time Detection of Circulating Tumor Cells in Living Animals Using Functionalized Large Gold Nanorods.

Optical coherence tomography (OCT) can be utilized with significant speckle reduction techniques and highly scattering contrast agents for non-invasive, contrast-enhanced imaging of living tissues at the cellular scale. The advantages of reduced speckle noise and improved targeted contrast can be harnessed to track objects as small as 2 µm in vivo, which enables applications for cell tracking and quantification in living subjects. Here we demonstrate the use of large gold nanorods as contrast agents for detecting individual micron-sized polystyrene beads and single myeloma cells in blood circulation using speckle-modulating OCT. This report marks the first time that OCT has been used to detect individual cells within blood in vivo. This technical capability unlocks exciting opportunities for dynamic detection and quantification of tumor cells circulating in living subjects.

Investigating circulating tumor cells and distant metastases in patient-derived orthotopic xenograft models of triple-negative breast cancer.

BACKGROUND: Circulating tumor cells (CTCs) represent a temporal "snapshot" of a patient's cancer and changes that occur during disease evolution. There is an extensive literature studying CTCs in breast cancer patients, and particularly in those with metastatic disease. In parallel, there is an increasing use of patient-derived models in preclinical investigations of human cancers. Yet studies are still limited demonstrating CTC shedding and metastasis formation in patient-derived models of breast cancer. METHODS: We used seven patient-derived orthotopic xenograft (PDOX) models generated from triple-negative breast cancer (TNBC) patients to study CTCs and distant metastases. Tumor fragments from PDOX tissue from each of the seven models were implanted into 57 NOD scid gamma (NSG) mice, and tumor growth and volume were monitored. Human CTC capture from mouse blood was first optimized on the marker-agnostic Vortex CTC isolation platform, and whole blood was processed from 37 PDOX tumor-bearing mice. RESULTS: Staining and imaging revealed the presence of CTCs in 32/37 (86%). The total number of CTCs varied between different PDOX tumor models and between individual mice bearing the same PDOX tumors. CTCs were heterogeneous and showed cytokeratin (CK) positive, vimentin (VIM) positive, and mixed CK/VIM phenotypes. Metastases were detected in the lung (20/57, 35%), liver (7/57, 12%), and brain (1/57, less than 2%). The seven different PDOX tumor models displayed varying degrees of metastatic potential, including one TNBC PDOX tumor model that failed to generate any detectable metastases (0/8 mice) despite having CTCs present in the blood of 5/5 tested, suggesting that CTCs from this particular PDOX tumor model may typify metastatic inefficiency. CONCLUSION: PDOX tumor models that shed CTCs and develop distant metastases represent an important tool for investigating TNBC.

Patient-derived xenografts of triple-negative breast cancer reproduce molecular features of patient tumors and respond to mTOR inhibition.

INTRODUCTION: Triple-negative breast cancer (TNBC) is aggressive and lacks targeted therapies. Phosphatidylinositide 3-kinase (PI3K)/mammalian target of rapamycin (mTOR) pathways are frequently activated in TNBC patient tumors at the genome, gene expression and protein levels, and mTOR inhibitors have been shown to inhibit growth in TNBC cell lines. We describe a panel of patient-derived xenografts representing multiple TNBC subtypes and use them to test preclinical drug efficacy of two mTOR inhibitors, sirolimus (rapamycin) and temsirolimus (CCI-779). METHODS: We generated a panel of seven patient-derived orthotopic xenografts from six primary TNBC tumors and one metastasis. Patient tumors and corresponding xenografts were compared by histology, immunohistochemistry, array comparative genomic hybridization (aCGH) and phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha (PIK3CA) sequencing; TNBC subtypes were determined. Using a previously published logistic regression approach, we generated a rapamycin response signature from Connectivity Map gene expression data and used it to predict rapamycin sensitivity in 1,401 human breast cancers of different intrinsic subtypes, prompting in vivo testing of mTOR inhibitors and doxorubicin in our TNBC xenografts. RESULTS: Patient-derived xenografts recapitulated histology, biomarker expression and global genomic features of patient tumors. Two primary tumors had PIK3CA coding mutations, and five of six primary tumors showed flanking intron single nucleotide polymorphisms (SNPs) with conservation of sequence variations between primary tumors and xenografts, even on subsequent xenograft passages. Gene expression profiling showed that our models represent at least four of six TNBC subtypes. The rapamycin response signature predicted sensitivity for 94% of basal-like breast cancers in a large dataset. Drug testing of mTOR inhibitors in our xenografts showed 77 to 99% growth inhibition, significantly more than doxorubicin; protein phosphorylation studies indicated constitutive activation of the mTOR pathway that decreased with treatment. However, no tumor was completely eradicated. CONCLUSIONS: A panel of patient-derived xenograft models covering a spectrum of TNBC subtypes was generated that histologically and genomically matched original patient tumors. Consistent with in silico predictions, mTOR inhibitor testing in our TNBC xenografts showed significant tumor growth inhibition in all, suggesting that mTOR inhibitors can be effective in TNBC, but will require use with additional therapies, warranting investigation of optimal drug combinations.

Single cell mutational analysis of PIK3CA in circulating tumor cells and metastases in breast cancer reveals heterogeneity, discordance, and mutation persistence in cultured disseminated tumor cells from bone marrow.

BACKGROUND: Therapeutic decisions in cancer are generally guided by molecular biomarkers or, for some newer therapeutics, primary tumor genotype. However, because biomarkers or genotypes may change as new metastases emerge, circulating tumor cells (CTCs) from blood are being investigated for a role in guiding real-time drug selection during disease progression, expecting that CTCs will comprehensively represent the full spectrum of genomic changes in metastases. However, information is limited regarding mutational heterogeneity among CTCs and metastases in breast cancer as discerned by single cell analysis. The presence of disseminated tumor cells (DTCs) in bone marrow also carry prognostic significance in breast cancer, but with variability between CTC and DTC detection. Here we analyze a series of single tumor cells, CTCs, and DTCs for PIK3CA mutations and report CTC and corresponding metastatic genotypes. METHODS: We used the MagSweeper, an immunomagnetic separation device, to capture live single tumor cells from breast cancer patients' primary and metastatic tissues, blood, and bone marrow. Single cells were screened for mutations in exons 9 and 20 of the PIK3CA gene. Captured DTCs grown in cell culture were also sequenced for PIK3CA mutations. RESULTS: Among 242 individual tumor cells isolated from 17 patients and tested for mutations, 48 mutated tumor cells were identified in three patients. Single cell analyses revealed mutational heterogeneity among CTCs and tumor cells in tissues. In a patient followed serially, there was mutational discordance between CTCs, DTCs, and metastases, and among CTCs isolated at different time points. DTCs from this patient propagated in vitro contained a PIK3CA mutation, which was maintained despite morphological changes during 21 days of cell culture. CONCLUSIONS: Single cell analysis of CTCs can demonstrate genotypic heterogeneity, changes over time, and discordance from DTCs and distant metastases. We present a cautionary case showing that CTCs from any single blood draw do not always reflect metastatic genotype, and that CTC and DTC analyses may provide independent clinical information. Isolated DTCs remain viable and can be propagated in culture while maintaining their original mutational status, potentially serving as a future resource for investigating new drug therapies.

Inhibition of protein translational machinery in triple-negative breast cancer as a promising therapeutic strategy.

Y-box binding protein-1 (YB-1) is a proto-oncogenic protein associated with protein translation regulation. It plays a crucial role in the development and progression of triple-negative breast cancer (TNBC). In this study, we describe a promising approach to inhibit YB-1 using SU056, a small-molecule inhibitor. SU056 physically interacts with YB-1 and reduces its expression, which helps to restrain the progression of TNBC. Proteome profiling analysis indicates that the inhibition of YB-1 by SU056 can alter the proteins that regulate protein translation, an essential process for cancer cell growth. Preclinical studies on human cells, mice, and patient-derived xenograft tumor models show the effectiveness of SU056. Moreover, toxicological studies have shown that SU056 treatment and dosing are well tolerated without any adverse effects. Overall, our study provides a strong foundation for the further development of SU056 as a potential treatment option for patients with TNBC by targeting YB-1.

Rapid genetic screening with high quality factor metasurfaces.

Genetic analysis methods are foundational to advancing personalized medicine, accelerating disease diagnostics, and monitoring the health of organisms and ecosystems. Current nucleic acid technologies such as polymerase chain reaction (PCR) and next-generation sequencing (NGS) rely on sample amplification and can suffer from inhibition. Here, we introduce a label-free genetic screening platform based on high quality (high-Q) factor silicon nanoantennas functionalized with nucleic acid fragments. Each high-Q nanoantenna exhibits average resonant quality factors of 2,200 in physiological buffer. We quantitatively detect two gene fragments, SARS-CoV-2 envelope (E) and open reading frame 1b (ORF1b), with high-specificity via DNA hybridization. We also demonstrate femtomolar sensitivity in buffer and nanomolar sensitivity in spiked nasopharyngeal eluates within 5 minutes. Nanoantennas are patterned at densities of 160,000 devices per cm2, enabling future work on highly-multiplexed detection. Combined with advances in complex sample processing, our work provides a foundation for rapid, compact, and amplification-free molecular assays.

A pharmacogenomic method for individualized prediction of drug sensitivity.

Identifying the best drug for each cancer patient requires an efficient individualized strategy. We present MATCH (Merging genomic and pharmacologic Analyses for Therapy CHoice), an approach using public genomic resources and drug testing of fresh tumor samples to link drugs to patients. Valproic acid (VPA) is highlighted as a proof-of-principle. In order to predict specific tumor types with high probability of drug sensitivity, we create drug response signatures using publically available gene expression data and assess sensitivity in a data set of >40 cancer types. Next, we evaluate drug sensitivity in matched tumor and normal tissue and exclude cancer types that are no more sensitive than normal tissue. From these analyses, breast tumors are predicted to be sensitive to VPA. A meta-analysis across breast cancer data sets shows that aggressive subtypes are most likely to be sensitive to VPA, but all subtypes have sensitive tumors. MATCH predictions correlate significantly with growth inhibition in cancer cell lines and three-dimensional cultures of fresh tumor samples. MATCH accurately predicts reduction in tumor growth rate following VPA treatment in patient tumor xenografts. MATCH uses genomic analysis with in vitro testing of patient tumors to select optimal drug regimens before clinical trial initiation.

Multiplex molecular analysis of CTCs.

Beyond enumeration, CTC characterization is expected to help guide therapeutic selection for personalized care of cancer patients. Different approaches may be used to simultaneously identify multiple CTC-specific markers for biological characterization; yet awareness of associated pitfalls is also important. We have focused this chapter on molecular profiling of CTCs following enrichment. We describe the MagSweeper technology that was specifically developed to isolate live and highly purified CTCs for pooled or single cell or pooled cell molecular analyses or for CTC growth in vitro or in vivo. However, most of what is discussed will apply to any multiplex analysis of CTCs, irrespective of the enrichment method.

Lysyl oxidase is essential for hypoxia-induced metastasis.

Metastasis is a multistep process responsible for most cancer deaths, and it can be influenced by both the immediate microenvironment (cell-cell or cell-matrix interactions) and the extended tumour microenvironment (for example vascularization). Hypoxia (low oxygen) is clinically associated with metastasis and poor patient outcome, although the underlying processes remain unclear. Microarray studies have shown the expression of lysyl oxidase (LOX) to be elevated in hypoxic human tumour cells. Paradoxically, LOX expression is associated with both tumour suppression and tumour progression, and its role in tumorigenesis seems dependent on cellular location, cell type and transformation status. Here we show that LOX expression is regulated by hypoxia-inducible factor (HIF) and is associated with hypoxia in human breast and head and neck tumours. Patients with high LOX-expressing tumours have poor distant metastasis-free and overall survivals. Inhibition of LOX eliminates metastasis in mice with orthotopically grown breast cancer tumours. Mechanistically, secreted LOX is responsible for the invasive properties of hypoxic human cancer cells through focal adhesion kinase activity and cell to matrix adhesion. Furthermore, LOX may be required to create a niche permissive for metastatic growth. Our findings indicate that LOX is essential for hypoxia-induced metastasis and is a good therapeutic target for preventing and treating metastases.

Isolating highly enriched populations of circulating epithelial cells and other rare cells from blood using a magnetic sweeper device.

The enumeration of rare circulating epithelial cells (CEpCs) in the peripheral blood of metastatic cancer patients has shown promise for improved cancer prognosis. Moving beyond enumeration, molecular analysis of CEpCs may provide candidate surrogate endpoints to diagnose, treat, and monitor malignancy directly from the blood samples. Thorough molecular analysis of CEpCs requires the development of new sample preparation methods that yield easily accessible and purified CEpCs for downstream biochemical assays. Here, we describe a new immunomagnetic cell separator, the MagSweeper, which gently enriches target cells and eliminates cells that are not bound to magnetic particles. The isolated cells are easily accessible and can be extracted individually based on their physical characteristics to deplete any cells nonspecifically bound to beads. We have shown that our device can process 9 mL of blood per hour and captures >50% of CEpCs as measured in spiking experiments. We have shown that the separation process does not perturb the gene expression of rare cells. To determine the efficiency of our platform in isolating CEpCs from patients, we have isolated CEpCs from all 47 tubes of 9-mL blood samples collected from 17 women with metastatic breast cancer. In contrast, we could not find any circulating epithelial cells in samples from 5 healthy donors. The isolated CEpCs are all stored individually for further molecular analysis.

Interpretable Classification of Bacterial Raman Spectra With Knockoff Wavelets.

Deep neural networks and other machine learning models are widely applied to biomedical signal data because they can detect complex patterns and compute accurate predictions. However, the difficulty of interpreting such models is a limitation, especially for applications involving high-stakes decision, including the identification of bacterial infections. This paper considers fast Raman spectroscopy data and demonstrates that a logistic regression model with carefully selected features achieves accuracy comparable to that of neural networks, while being much simpler and more transparent. Our analysis leverages wavelet features with intuitive chemical interpretations, and performs controlled variable selection with knockoffs to ensure the predictors are relevant and non-redundant. Although we focus on a particular data set, the proposed approach is broadly applicable to other types of signal data for which interpretability may be important.

Microfluidic live tracking and transcriptomics of cancer-immune cell doublets link intercellular proximity and gene regulation.

Cell-cell communication and physical interactions play a vital role in cancer initiation, homeostasis, progression, and immune response. Here, we report a system that combines live capture of different cell types, co-incubation, time-lapse imaging, and gene expression profiling of doublets using a microfluidic integrated fluidic circuit that enables measurement of physical distances between cells and the associated transcriptional profiles due to cell-cell interactions. We track the temporal variations in natural killer-triple-negative breast cancer cell distances and compare them with terminal cellular transcriptome profiles. The results show the time-bound activities of regulatory modules and allude to the existence of transcriptional memory. Our experimental and bioinformatic approaches serve as a proof of concept for interrogating live-cell interactions at doublet resolution. Together, our findings highlight the use of our approach across different cancers and cell types.