Quantitative performance assessment of Ultivue multiplex panels in formalin-fixed, paraffin-embedded human and murine tumor specimens.

We present a rigorous validation strategy to evaluate the performance of Ultivue multiplex immunofluorescence panels. We have quantified the accuracy and precision of four different multiplex panels (three human and one mouse) in tumor specimens with varying levels of T cell density. Our results show that Ultivue panels are typically accurate wherein the relative difference in cell proportion between a multiplex image and a 1-plex image is less than 20% for a given biomarker. Ultivue panels exhibited relatively high intra-run precision (CV ≤ 25%) and relatively low inter-run precision (CV >> 25%) which can be remedied by using local intensity thresholding to gate biomarker positivity. We also evaluated the reproducibility of cell-cell distance estimates measured from multiplex images which show high intra- and inter-run precision. We introduce a new metric, multiplex labeling efficiency, which can be used to benchmark the overall fidelity of the multiplex data across multiple batch runs. Taken together our results provide a comprehensive characterization of Ultivue panels and offer practical guidelines for analyzing multiplex images.

Longitudinal multi-omics study of palbociclib resistance in HR-positive/HER2-negative metastatic breast cancer.

BACKGROUND: Cyclin-dependent kinase 4/6 inhibitor (CDK4/6) therapy plus endocrine therapy (ET) is an effective treatment for patients with hormone receptor-positive/human epidermal receptor 2-negative metastatic breast cancer (HR+/HER2- MBC); however, resistance is common and poorly understood. A comprehensive genomic and transcriptomic analysis of pretreatment and post-treatment tumors from patients receiving palbociclib plus ET was performed to delineate molecular mechanisms of drug resistance. METHODS: Tissue was collected from 89 patients with HR+/HER2- MBC, including those with recurrent and/or metastatic disease, receiving palbociclib plus an aromatase inhibitor or fulvestrant at Samsung Medical Center and Seoul National University Hospital from 2017 to 2020. Tumor biopsy and blood samples obtained at pretreatment, on-treatment (6 weeks and/or 12 weeks), and post-progression underwent RNA sequencing and whole-exome sequencing. Cox regression analysis was performed to identify the clinical and genomic variables associated with progression-free survival. RESULTS: Novel markers associated with poor prognosis, including genomic scar features caused by homologous repair deficiency (HRD), estrogen response signatures, and four prognostic clusters with distinct molecular features were identified. Tumors with TP53 mutations co-occurring with a unique HRD-high cluster responded poorly to palbociclib plus ET. Comparisons of paired pre- and post-treatment samples revealed that tumors became enriched in APOBEC mutation signatures, and many switched to aggressive molecular subtypes with estrogen-independent characteristics. We identified frequent genomic alterations upon disease progression in RB1, ESR1, PTEN, and KMT2C. CONCLUSIONS: We identified novel molecular features associated with poor prognosis and molecular mechanisms that could be targeted to overcome resistance to CKD4/6 plus ET. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03401359. The trial was posted on 18 January 2018 and registered prospectively.

Efficacy and Imaging-Enabled Pharmacodynamic Profiling of KRAS G12C Inhibitors in Xenograft and Genetically Engineered Mouse Models of Cancer.

KRAS is one of the most commonly mutated oncogenes in lung, colorectal, and pancreatic cancers. Recent clinical trials directly targeting KRAS G12C presented encouraging results for a large population of non-small cell lung cancer (NSCLC), but resistance to treatment is a concern. Continued exploration of new inhibitors and preclinical models is needed to address resistance mechanisms and improve duration of patient responses. To further enable the development of KRAS G12C inhibitors, we present a preclinical framework involving translational, non-invasive imaging modalities (CT and PET) and histopathology in a conventional xenograft model and a novel KRAS G12C knock-in mouse model of NSCLC. We utilized an in-house developed KRAS G12C inhibitor (Compound A) as a tool to demonstrate the value of this framework in studying in vivo pharmacokinetic/pharmacodynamic (PK/PD) relationship and anti-tumor efficacy. We characterized the Kras G12C-driven genetically engineered mouse model (GEMM) and identify tumor growth and signaling differences compared to its Kras G12D-driven counterpart. We also find that Compound A has comparable efficacy to sotorasib in the Kras G12C-driven lung tumors arising in the GEMM, but like observations in the clinic, some tumors inevitably progress on treatment. These findings establish a foundation for evaluating future KRAS G12C inhibitors that is not limited to xenograft studies and can be applied in a translationally relevant mouse model that mirrors human disease progression and resistance.

Volumetric imaging of optically cleared and fluorescently labeled animal tissue (VIOLA) for quantifying the 3D biodistribution of nanoparticles at cellular resolution in tumor tissue.

Nanoparticle (NP) technology holds significant promise to mediate targeted drug delivery to specific organs in the body. Understanding the 3D biodistribution of NPs in heterogeneous environments such as the tumor tissue can provide crucial information on efficacy, safety and potential clinical outcomes. Here we present a novel end-to-end workflow, VIOLA, which makes use of tissue clearing methodology in conjunction with high resolution imaging and advanced 3D image processing to quantify the spatiotemporal 3D biodistribution of fluorescently labeled ACCURIN® NPs. Specifically, we investigate the spatiotemporal biodistribution of NPs in three different murine tumor models (CT26, EMT6, and KPC-GEM) of increasing complexity and translational relevance. We have developed new endpoints to characterize NP biodistribution at multiple length scales. Our observations reveal that the macroscale NP biodistribution is spatially heterogeneous and exhibits a gradient with relatively high accumulation at the tumor periphery that progressively decreases towards the tumor core in all the tumor models. Microscale analysis revealed that NP extravasation from blood vessels increases in a time dependent manner and plateaus at 72 h post injection. Volumetric analysis and pharmacokinetic modeling of NP biodistribution in the vicinity of the blood vessels revealed that the local NP density exhibits a distance dependent spatiotemporal biodistribution which provide insights into the dynamics of NP extravasation in the tumor tissue. Our data represents a comprehensive analysis of NP biodistribution at multiple length scales in different tumor models providing unique insights into their spatiotemporal dynamics. Specifically, our results show that NPs exhibit a dynamic equilibrium with macroscale heterogeneity combined with microscale homogeneity.

Combinatorial immunotherapy induces tumor-infiltrating CD8+ T cells with distinct functional, migratory, and stem-like properties.

BACKGROUND: Programmed death (ligand) 1 (PD-(L)1) blockade and OX40/4-1BB costimulation have been separately evaluated in the clinic to elicit potent antitumor T cell responses. The precise mechanisms underlying single agent activity are incompletely understood. It also remains unclear if combining individual therapies leads to synergism, elicits novel immune mechanisms, or invokes additive effects. METHODS: We performed high-dimensional flow cytometry and single-cell RNA sequencing-based immunoprofiling of murine tumor-infiltrating lymphocytes (TILs) isolated from hosts bearing B16 or MC38 syngeneic tumors. This baseline infiltrate was compared to TILs after treatment with either anti-PD-(L)1, anti-OX40, or anti-4-1BB as single agents or as double and triple combinatorial therapies. Fingolimod treatment and CXCR3 blockade were used to evaluate the contribution of intratumoral versus peripheral CD8+ T cells to therapeutic efficacy. RESULTS: We identified CD8+ T cell subtypes with distinct functional and migratory signatures highly predictive of tumor rejection upon treatment with single agent versus combination therapies. Rather than reinvigorating terminally exhausted CD8+ T cells, OX40/4-1BB agonism expanded a stem-like PD-1loKLRG-1+Ki-67+CD8+ T cell subpopulation, which PD-(L)1 blockade alone did not. However, PD-(L)1 blockade synergized with OX40/4-1BB costimulation by dramatically enhancing stem-like TIL presence via a CXCR3-dependent mechanism. CONCLUSIONS: Our findings provide new mechanistic insights into the interplay between components of combinatorial immunotherapy, where agonism of select costimulatory pathways seeds a pool of stem-like CD8+ T cells more responsive to immune checkpoint blockade (ICB).

Pixelwise H-score: A novel digital image analysis-based metric to quantify membrane biomarker expression from immunohistochemistry images.

Immunohistochemistry (IHC) assays play a central role in evaluating biomarker expression in tissue sections for diagnostic and research applications. Manual scoring of IHC images, which is the current standard of practice, is known to have several shortcomings in terms of reproducibility and scalability to large scale studies. Here, by using a digital image analysis-based approach, we introduce a new metric called the pixelwise H-score (pix H-score) that quantifies biomarker expression from whole-slide scanned IHC images. The pix H-score is an unsupervised algorithm that only requires the specification of intensity thresholds for the biomarker and the nuclear-counterstain channels. We present the detailed implementation of the pix H-score in two different whole-slide image analysis software packages Visiopharm and HALO. We consider three biomarkers P-cadherin, PD-L1, and 5T4, and show how the pix H-score exhibits tight concordance to multiple orthogonal measurements of biomarker abundance such as the biomarker mRNA transcript and the pathologist H-score. We also compare the pix H-score to existing automated image analysis algorithms and demonstrate that the pix H-score provides either comparable or significantly better performance over these methodologies. We also present results of an empirical resampling approach to assess the performance of the pix H-score in estimating biomarker abundance from select regions within the tumor tissue relative to the whole tumor resection. We anticipate that the new metric will be broadly applicable to quantify biomarker expression from a wide variety of IHC images. Moreover, these results underscore the benefit of digital image analysis-based approaches which offer an objective, reproducible, and highly scalable strategy to quantitatively analyze IHC images.

Characterizing Intra-Tumor and Inter-Tumor Variability of Immune Cell Infiltrates in Murine Syngeneic Tumors.

Murine tumors are indispensable model systems in preclinical immuno-oncology research. While immunologic heterogeneity is well-known to be an important factor that can influence treatment outcome, there is a severe paucity of data concerning the nature of this heterogeneity in murine tumor models. Using serial sectioning methodology combined with IHC analysis and whole-slide image analysis, the depth-dependent variation in immune-cell abundance in tumor specimens was investigated at single-cell resolution. Specifically, intra- and intertumor variability in cell density of nine immune-cell biomarkers was quantified in multiple murine tumor models. The analysis showed that intertumor variability was typically the dominant source of variation in measurements of immune-cell densities. Statistical power analysis revealed the effect of group size and variance in immune-cell density on the predictive power of detecting a statistically meaningful fold-change in immune-cell density. Intertumor variability in the ratio of immune-cell densities showed distinct patterns in select tumor models and revealed the existence of strong correlations between select biomarker pairs. Furthermore, the relative proportion of immune cells at different depths across tumor samples was preserved in some but not all tumor models, thereby revealing the existence of compositional heterogeneity. Taken together, these results reveal novel insights into the nature of immunologic heterogeneity, which is not accessible through typical omics approaches.

Preclinical Evaluation of 89Zr-Df-IAB22M2C PET as an Imaging Biomarker for the Development of the GUCY2C-CD3 Bispecific PF-07062119 as a T Cell Engaging Therapy.

PURPOSE: A sensitive and specific imaging biomarker to monitor immune activation and quantify pharmacodynamic responses would be useful for development of immunomodulating anti-cancer agents. PF-07062119 is a T cell engaging bispecific antibody that binds to CD3 and guanylyl cyclase C, a protein that is over-expressed by colorectal cancers. Here, we used 89Zr-Df-IAB22M2C (89Zr-Df-Crefmirlimab), a human CD8-specific minibody to monitor CD8+ T cell infiltration into tumors by positron emission tomography. We investigated the ability of 89Zr-Df-IAB22M2C to track anti-tumor activity induced by PF-07062119 in a human CRC adoptive transfer mouse model (with injected activated/expanded human T cells), as well as the correlation of tumor radiotracer uptake with CD8+ immunohistochemical staining. PROCEDURES: NOD SCID gamma mice bearing human CRC LS1034 tumors were treated with four different doses of PF-07062119, or a non-targeted CD3 BsAb control, and imaged with 89Zr-Df-IAB22M2C PET at days 4 and 9. Following PET/CT imaging, mice were euthanized and dissected for ex vivo distribution analysis of 89Zr-Df-IAB22M2C in tissues on days 4 and 9, with additional data collected on day 6 (supplementary). Data were analyzed and reported as standard uptake value and %ID/g for in vivo imaging and ex vivo tissue distribution. In addition, tumor tissues were evaluated by immunohistochemistry for CD8+ T cells. RESULTS: The results demonstrated substantial mean uptake of 89Zr-Df-IAB22M2C (%ID/g) in PF-07062119-treated tumors, with significant increases in comparison to non-targeted BsAb-treated controls, as well as PF-07062119 dose-dependent responses over time of treatment. A moderate correlation was observed between tumor tissue radioactivity uptake and CD8+ cell density, demonstrating the value of the imaging agent for non-invasive assessment of intra-tumoral CD8+ T cells and the mechanism of action for PF-07062119. CONCLUSION: Immune-imaging technologies for quantitative cellular measures would be a valuable biomarker in immunotherapeutic clinical development. We demonstrated a qualification of 89Zr-IAB22M2C PET to evaluate PD responses (mice) to a novel immunotherapeutic.

Mouse lung automated segmentation tool for quantifying lung tumors after micro-computed tomography.

Unlike the majority of cancers, survival for lung cancer has not shown much improvement since the early 1970s and survival rates remain low. Genetically engineered mice tumor models are of high translational relevance as we can generate tissue specific mutations which are observed in lung cancer patients. Since these tumors cannot be detected and quantified by traditional methods, we use micro-computed tomography imaging for longitudinal evaluation and to measure response to therapy. Conventionally, we analyze microCT images of lung cancer via a manual segmentation. Manual segmentation is time-consuming and sensitive to intra- and inter-analyst variation. To overcome the limitations of manual segmentation, we set out to develop a fully-automated alternative, the Mouse Lung Automated Segmentation Tool (MLAST). MLAST locates the thoracic region of interest, thresholds and categorizes the lung field into three tissue categories: soft tissue, intermediate, and lung. An increase in the tumor burden was measured by a decrease in lung volume with a simultaneous increase in soft and intermediate tissue quantities. MLAST segmentation was validated against three methods: manual scoring, manual segmentation, and histology. MLAST was applied in an efficacy trial using a Kras/Lkb1 non-small cell lung cancer model and demonstrated adequate precision and sensitivity in quantifying tumor growth inhibition after drug treatment. Implementation of MLAST has considerably accelerated the microCT data analysis, allowing for larger study sizes and mid-study readouts. This study illustrates how automated image analysis tools for large datasets can be used in preclinical imaging to deliver high throughput and quantitative results.

Chemotherapy induces dynamic immune responses in breast cancers that impact treatment outcome.

To elucidate the effects of neoadjuvant chemotherapy (NAC), we conduct whole transcriptome profiling coupled with histopathology analyses of a longitudinal breast cancer cohort of 146 patients including 110 pairs of serial tumor biopsies collected before treatment, after the first cycle of treatment and at the time of surgery. Here, we show that cytotoxic chemotherapies induce dynamic changes in the tumor immune microenvironment that vary by subtype and pathologic response. Just one cycle of treatment induces an immune stimulatory microenvironment harboring more tumor infiltrating lymphocytes (TILs) and up-regulation of inflammatory signatures predictive of response to anti-PD1 therapies while residual tumors are immune suppressed at end-of-treatment compared to the baseline. Increases in TILs and CD8+ T cell proportions in response to NAC are independently associated with pathologic complete response. Further, on-treatment immune response is more predictive of treatment outcome than immune features in paired baseline samples although these are strongly correlated.

Information theoretic analysis of hyperspectral imaging systems with applications to fluorescence microscopy.

We present a general stochastic model for hyperspectral imaging data and derive analytical expressions for the Fisher information matrix for the underlying spectral unmixing problem. We investigate the linear mixing model as a special case and define a linear unmixing performance bound by using the Cramer-Rao inequality. As an application, we consider fluorescence imaging and show how the performance bound provides a spectral resolution limit that predicts how accurately a pair of spectrally similar fluorescent labels can be spectrally unmixed. We also report a novel result that shows how the spectral resolution limit can be overcome by exploiting the phenomenon of anti-Stokes shift fluorescence. In addition, we investigate how photon statistics, channel addition and channel splitting affect the performance bound. Finally by using the performance bound as a benchmark, we compare the performance of the least squares and the maximum likelihood estimators for spectral unmixing. For the imaging conditions tested here, our analysis shows that both estimators are unbiased and that the standard deviation of the maximum likelihood estimator is consistently closer to the performance bound than that of the least squares estimator. The results presented here are based on broad assumptions regarding the underlying data model and are applicable to hyperspectral data acquired with point detectors, sCMOS, CCD and EMCCD imaging detectors.

Myocarditis in Cynomolgus Monkeys Following Treatment with Immune Checkpoint Inhibitors.

PURPOSE: Immune checkpoint inhibitors (ICI) targeting PD1, PDL1, or CTLA4 are associated with immune-related adverse events (irAE) in multiple organ systems including myocarditis. The pathogenesis and early diagnostic markers for ICI-induced myocarditis are poorly understood, and there is currently a lack of laboratory animal model to enhance our understanding. We aimed to develop such a model using cynomolgus monkeys. EXPERIMENTAL DESIGN: Chinese-origin cynomolgus monkeys were dosed intravenously with vehicle or nivolumab 20 mg/kg plus ipilimumab 15 mg/kg once weekly and euthanized on day 29. RESULTS: Multiple organ toxicities were observed in cynomolgus monkeys, and were characterized by loose feces, lymphadenopathy, and mononuclear cell infiltrations of varying severity in heart, colon, kidneys, liver, salivary glands, and endocrine organs. Increased proliferation of CD4+ and CD8+ T lymphocytes as well as an increase in activated T cells and central memory T cells in the blood, spleen, and lymph nodes, were observed. Transcriptomic analysis suggested increased migration and activation of T cells and increased phagocytosis and antigen presentation in the heart. Mononuclear cell infiltration in myocardium was comprised primarily of T cells, with lower numbers of macrophages and occasional B cells, and was associated with minimal cardiomyocyte degeneration as well as increases in cardiac troponin-I and NT-pro-BNP. Morphologically, cardiac lesions in our monkey model are similar to the reported ICI myocarditis in humans. CONCLUSIONS: We have developed a monkey model characterized by multiple organ toxicities including myocarditis. This model may provide insight into the immune mechanisms and facilitate biomarker identification for ICI-associated irAEs.

Intensity-based axial localization approaches for multifocal plane microscopy.

Multifocal plane microscopy (MUM) can be used to visualize biological samples in three dimensions over large axial depths and provides for the high axial localization accuracy that is needed in applications such as the three-dimensional tracking of single particles and super-resolution microscopy. This report analyzes the performance of intensity-based axial localization approaches as applied to MUM data using Fisher information calculations. In addition, a new non-parametric intensity-based axial location estimation method, Multi-Intensity Lookup Algorithm (MILA), is introduced that, unlike typical intensity-based methods that make use of a single intensity value per data image, utilizes multiple intensity values per data image in determining the axial location of a point source. MILA is shown to be robust against potential bias induced by differences in the sub-pixel location of the imaged point source. The method's effectiveness on experimental data is also evaluated.

Investigating the usage of point spread functions in point source and microsphere localization.

Using a point spread function (PSF) to localize a point-like object, such as a fluorescent molecule or microsphere, represents a common task in single molecule microscopy image data analysis. The localization may differ in purpose depending on the application or experiment, but a unifying theme is the importance of being able to closely recover the true location of the point-like object with high accuracy. We present two simulation studies, both relating to the performance of object localization via the maximum likelihood fitting of a PSF to the object's image. In the first study, we investigate the integration of the PSF over an image pixel, which represents a critical part of the localization algorithm. Specifically, we explore how the fineness of the integration affects how well a point source can be localized, and find the use of too coarse a step size to produce location estimates that are far from the true location, especially when the images are acquired at relatively low magnifications. We also propose a method for selecting an appropriate step size. In the second study, we investigate the suitability of the common practice of using a PSF to localize a microsphere, despite the mismatch between the microsphere's image and the fitted PSF. Using criteria based on the standard errors of the mean and variance, we find the method suitable for microspheres up to 1 µm and 100 nm in diameter, when the localization is performed, respectively, with and without the simultaneous estimation of the width of the PSF.

Macrophage-Mediated Trogocytosis Leads to Death of Antibody-Opsonized Tumor Cells.

Understanding the complex behavior of effector cells such as monocytes or macrophages in regulating cancerous growth is of central importance for cancer immunotherapy. Earlier studies using CD20-specific antibodies have demonstrated that the Fcγ receptor (FcγR)-mediated transfer of the targeted receptors from tumor cells to these effector cells through trogocytosis can enable escape from antibody therapy, leading to the viewpoint that this process is protumorigenic. In the current study, we demonstrate that persistent trogocytic attack results in the killing of HER2-overexpressing breast cancer cells. Further, antibody engineering to increase FcγR interactions enhances this tumoricidal activity. These studies extend the complex repertoire of activities of macrophages to trogocytic-mediated cell death of HER2-overexpressing target cells and have implications for the development of effective antibody-based therapies. Mol Cancer Ther; 15(8); 1879-89. ©2016 AACR.

Two-color fluorescent analysis of connexin 36 turnover: relationship to functional plasticity.

Gap junctions formed of connexin 36 (Cx36, also known as Gjd2) show tremendous functional plasticity on several time scales. Changes in connexin phosphorylation modify coupling in minutes through an order of magnitude, but recent studies also imply involvement of connexin turnover in regulating cell-cell communication. We utilized Cx36 with an internal HaloTag to study Cx36 turnover and trafficking in cultured cells. Irreversible, covalent pulse-chase labeling with fluorescent HaloTag ligands allowed clear discrimination of newly formed and pre-existing Cx36. Cx36 in junctional plaques turned over with a half-life of 3.1 h, and the turnover rate was unchanged by manipulations of protein kinase A (PKA) activity. In contrast, changes in PKA activity altered coupling within 20 min. New Cx36 in cargo vesicles was added directly to existing gap junctions and newly made Cx36 was not confined to points of addition, but diffused throughout existing gap junctions. Existing connexins also diffused into photobleached areas with a half-time of less than 2 s. In conclusion, studies of Cx36-HaloTag revealed novel features of connexin trafficking and demonstrated that phosphorylation-based changes in coupling occur on a different time scale than turnover.

Investigation of the numerics of point spread function integration in single molecule localization.

The computation of point spread functions, which are typically used to model the image profile of a single molecule, represents a central task in the analysis of single molecule microscopy data. To determine how the accuracy of the computation affects how well a single molecule can be localized, we investigate how the fineness with which the point spread function is integrated over an image pixel impacts the performance of the maximum likelihood location estimator. We consider both the Airy and the two-dimensional Gaussian point spread functions. Our results show that the point spread function needs to be adequately integrated over a pixel to ensure that the estimator closely recovers the true location of the single molecule with an accuracy that is comparable to the best possible accuracy as determined using the Fisher information formalism. Importantly, if integration with an insufficiently fine step size is carried out, the resulting estimates can be significantly different from the true location, particularly when the image data is acquired at relatively low magnifications. We also present a methodology for determining an adequate step size for integrating the point spread function.

Quantitative Aspects of Single Molecule Microscopy.

Single molecule microscopy is a relatively new optical microscopy technique that allows the detection of individual molecules such as proteins in a cellular context. This technique has generated significant interest among biologists, biophysicists and biochemists, as it holds the promise to provide novel insights into subcellular processes and structures that otherwise cannot be gained through traditional experimental approaches. Single molecule experiments place stringent demands on experimental and algorithmic tools due to the low signal levels and the presence of significant extraneous noise sources. Consequently, this has necessitated the use of advanced statistical signal and image processing techniques for the design and analysis of single molecule experiments. In this tutorial paper, we provide an overview of single molecule microscopy from early works to current applications and challenges. Specific emphasis will be on the quantitative aspects of this imaging modality, in particular single molecule localization and resolvability, which will be discussed from an information theoretic perspective. We review the stochastic framework for image formation, different types of estimation techniques and expressions for the Fisher information matrix. We also discuss several open problems in the field that demand highly non-trivial signal processing algorithms.

An information-theoretic approach to designing the plane spacing for multifocal plane microscopy.

Multifocal plane microscopy (MUM) is a 3D imaging modality which enables the localization and tracking of single molecules at high spatial and temporal resolution by simultaneously imaging distinct focal planes within the sample. MUM overcomes the depth discrimination problem of conventional microscopy and allows high accuracy localization of a single molecule in 3D along the z-axis. An important question in the design of MUM experiments concerns the appropriate number of focal planes and their spacings to achieve the best possible 3D localization accuracy along the z-axis. Ideally, it is desired to obtain a 3D localization accuracy that is uniform over a large depth and has small numerical values, which guarantee that the single molecule is continuously detectable. Here, we address this concern by developing a plane spacing design strategy based on the Fisher information. In particular, we analyze the Fisher information matrix for the 3D localization problem along the z-axis and propose spacing scenarios termed the strong coupling and the weak coupling spacings, which provide appropriate 3D localization accuracies. Using these spacing scenarios, we investigate the detectability of the single molecule along the z-axis and study the effect of changing the number of focal planes on the 3D localization accuracy. We further review a software module we recently introduced, the MUMDesignTool, that helps to design the plane spacings for a MUM setup.

The level of HER2 expression is a predictor of antibody-HER2 trafficking behavior in cancer cells.

The receptor tyrosine kinase HER2 is known to play a central role in mitogenic signaling, motivating the development of targeted, HER2-specific therapies. However, despite the longstanding use of antibodies to target HER2, controversies remain concerning antibody/HER2 trafficking behavior in cancer cells. Understanding this behavior has direct relevance to the mechanism of action and effective design of such antibodies. In the current study, we analyzed the intracellular dynamics of trastuzumab, a marketed HER2-targeting antibody, in a panel of breast and prostate cancer cell lines that have a wide range of HER2 expression levels. Our results reveal distinct post-endocytic trafficking behavior of antibody-HER2 complexes in cells with different HER2 expression levels. In particular, HER2-overexpressing cells exhibit efficient HER2 recycling and limited reductions in HER2 levels upon antibody treatment, and consequently display a high level of antibody persistence on their plasma membrane. By contrast, in cells with low HER2 expression, trastuzumab treatment results in rapid antibody clearance from the plasma membrane combined with substantial decreases in HER2 levels and undetectable levels of recycling. A cell line with intermediate levels of HER2 expression exhibits both antibody recycling and clearance from the cell surface. Significantly, these analyses demonstrate that HER2 expression levels, rather than cell origin (breast or prostate), is a determinant of subcellular trafficking properties. Such studies have relevance to optimizing the design of antibodies to target HER2.