Drug-loaded nanoparticles for cancer therapy: a high-throughput multicellular agent-based modeling study.

Interactions between biological systems and nanomaterials have become an important area of study due to the application of nanomaterials in medicine. In particular, the application of nanomaterials for cancer diagnosis or treatment presents a challenging opportunity due to the complex biology of this disease spanning multiple time and spatial scales. A system-level analysis would benefit from mathematical modeling and computational simulation to explore the interactions between anticancer drug-loaded nanoparticles (NPs), cells, and tissues, and the associated parameters driving this system and a patient's overall response. Although a number of models have explored these interactions in the past, few have focused on simulating individual cell-NP interactions. This study develops a multicellular agent-based model of cancer nanotherapy that simulates NP internalization, drug release within the cell cytoplasm, "inheritance" of NPs by daughter cells at cell division, cell pharmacodynamic response to the intracellular drug, and overall drug effect on tumor dynamics. A large-scale parallel computational framework is used to investigate the impact of pharmacokinetic design parameters (NP internalization rate, NP decay rate, anticancer drug release rate) and therapeutic strategies (NP doses and injection frequency) on the tumor dynamics. In particular, through the exploration of NP "inheritance" at cell division, the results indicate that cancer treatment may be improved when NPs are inherited at cell division for cytotoxic chemotherapy. Moreover, smaller dosage of cytostatic chemotherapy may also improve inhibition of tumor growth when cell division is not completely inhibited. This work suggests that slow delivery by "heritable" NPs can drive new dimensions of nanotherapy design for more sustained therapeutic response.

Digitize your Biology! Modeling multicellular systems through interpretable cell behavior.

Cells are fundamental units of life, constantly interacting and evolving as dynamical systems. While recent spatial multi-omics can quantitate individual cells' characteristics and regulatory programs, forecasting their evolution ultimately requires mathematical modeling. We develop a conceptual framework-a cell behavior hypothesis grammar-that uses natural language statements (cell rules) to create mathematical models. This allows us to systematically integrate biological knowledge and multi-omics data to make them computable. We can then perform virtual "thought experiments" that challenge and extend our understanding of multicellular systems, and ultimately generate new testable hypotheses. In this paper, we motivate and describe the grammar, provide a reference implementation, and demonstrate its potential through a series of examples in tumor biology and immunotherapy. Altogether, this approach provides a bridge between biological, clinical, and systems biology researchers for mathematical modeling of biological systems at scale, allowing the community to extrapolate from single-cell characterization to emergent multicellular behavior.

MYC Deregulation and PTEN Loss Model Tumor and Stromal Heterogeneity of Aggressive Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) patients have a poor prognosis and few treatment options. Mouse models of TNBC are important for development of new therapies, however, few mouse models represent the complexity of TNBC. Here, we develop a female TNBC murine model by mimicking two common TNBC mutations with high co-occurrence: amplification of the oncogene MYC and deletion of the tumor suppressor PTEN. This Myc;Ptenfl model develops heterogeneous triple-negative mammary tumors that display histological and molecular features commonly found in human TNBC. Our research involves deep molecular and spatial analyses on Myc;Ptenfl tumors including bulk and single-cell RNA-sequencing, and multiplex tissue-imaging. Through comparison with human TNBC, we demonstrate that this genetic mouse model develops mammary tumors with differential survival and therapeutic responses that closely resemble the inter- and intra-tumoral and microenvironmental heterogeneity of human TNBC, providing a pre-clinical tool for assessing the spectrum of patient TNBC biology and drug response.

Robust biomarker discovery through multiplatform multiplex image analysis of breast cancer clinical cohorts.

Spatial profiling of tissues promises to elucidate tumor-microenvironment interactions and enable development of spatial biomarkers to predict patient response to immunotherapy and other therapeutics. However, spatial biomarker discovery is often carried out on a single patient cohort or imaging technology, limiting statistical power and increasing the likelihood of technical artifacts. In order to analyze multiple patient cohorts profiled on different platforms, we developed methods for comparative data analysis from three disparate multiplex imaging technologies: 1) cyclic immunofluorescence data we generated from 102 breast cancer patients with clinical follow-up, in addition to publicly available 2) imaging mass cytometry and 3) multiplex ion-beam imaging data. We demonstrate similar single-cell phenotyping results across breast cancer patient cohorts imaged with these three technologies and identify cellular abundance and proximity-based biomarkers with prognostic value across platforms. In multiple platforms, we identified lymphocyte infiltration as independently associated with longer survival in triple negative and high-proliferation breast tumors. Then, a comparison of nine spatial analysis methods revealed robust spatial biomarkers. In estrogen receptor-positive disease, quiescent stromal cells close to tumor were more abundant in good prognosis tumors while tumor neighborhoods of mixed fibroblast phenotypes were enriched in poor prognosis tumors. In triple-negative breast cancer (TNBC), macrophage proximity to tumor and B cell proximity to T cells were greater in good prognosis tumors, while tumor neighborhoods of vimentin-positive fibroblasts were enriched in poor prognosis tumors. We also tested previously published spatial biomarkers in our ensemble cohort, reproducing the positive prognostic value of isolated lymphocytes and lymphocyte occupancy and failing to reproduce the prognostic value of tumor-immune mixing score in TNBC. In conclusion, we demonstrate assembly of larger clinical cohorts from diverse platforms to aid in prognostic spatial biomarker identification and validation.

A multi-omic analysis of MCF10A cells provides a resource for integrative assessment of ligand-mediated molecular and phenotypic responses.

The phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix proteins. While these signals are normally tightly controlled, their dysregulation leads to phenotypic and molecular states associated with diverse diseases. To develop a detailed understanding of the linkage between molecular and phenotypic changes, we generated a comprehensive dataset that catalogs the transcriptional, proteomic, epigenomic and phenotypic responses of MCF10A mammary epithelial cells after exposure to the ligands EGF, HGF, OSM, IFNG, TGFB and BMP2. Systematic assessment of the molecular and cellular phenotypes induced by these ligands comprise the LINCS Microenvironment (ME) perturbation dataset, which has been curated and made publicly available for community-wide analysis and development of novel computational methods ( synapse.org/LINCS_MCF10A ). In illustrative analyses, we demonstrate how this dataset can be used to discover functionally related molecular features linked to specific cellular phenotypes. Beyond these analyses, this dataset will serve as a resource for the broader scientific community to mine for biological insights, to compare signals carried across distinct molecular modalities, and to develop new computational methods for integrative data analysis.

A framework for multiplex imaging optimization and reproducible analysis.

Multiplex imaging technologies are increasingly used for single-cell phenotyping and spatial characterization of tissues; however, transparent methods are needed for comparing the performance of platforms, protocols and analytical pipelines. We developed a python software, mplexable, for reproducible image processing and utilize Jupyter notebooks to share our optimization of signal removal, antibody specificity, background correction and batch normalization of the multiplex imaging with a focus on cyclic immunofluorescence (CyCIF). Our work both improves the CyCIF methodology and provides a framework for multiplexed image analytics that can be easily shared and reproduced.

Theoretical and experimental analysis of negative dielectrophoresis-induced particle trajectories.

Many biomedical analysis applications require trapping and manipulating single cells and cell clusters within microfluidic devices. Dielectrophoresis (DEP) is a label-free technique that can achieve flexible cell trapping, without physical barriers, using electric field gradients created in the device by an electrode microarray. Little is known about how fluid flow forces created by the electrodes, such as thermally driven convection and electroosmosis, affect DEP-based cell capture under high conductance media conditions that simulate physiologically relevant fluids such as blood or plasma. Here, we compare theoretical trajectories of particles under the influence of negative DEP (nDEP) with observed trajectories of real particles in a high conductance buffer. We used 10-µm diameter polystyrene beads as model cells and tracked their trajectories in the DEP microfluidic chip. The theoretical nDEP trajectories were in close agreement with the observed particle behavior. This agreement indicates that the movement of the particles was highly dominated by the DEP force and that contributions from thermal- and electroosmotic-driven flows were negligible under these experimental conditions. The analysis protocol developed here offers a strategy that can be applied to future studies with different applied voltages, frequencies, conductivities, and polarization properties of the targeted particles and surrounding medium. These findings motivate further DEP device development to manipulate particle trajectories for trapping applications.

An omic and multidimensional spatial atlas from serial biopsies of an evolving metastatic breast cancer.

Mechanisms of therapeutic resistance and vulnerability evolve in metastatic cancers as tumor cells and extrinsic microenvironmental influences change during treatment. To support the development of methods for identifying these mechanisms in individual people, here we present an omic and multidimensional spatial (OMS) atlas generated from four serial biopsies of an individual with metastatic breast cancer during 3.5 years of therapy. This resource links detailed, longitudinal clinical metadata that includes treatment times and doses, anatomic imaging, and blood-based response measurements to clinical and exploratory analyses, which includes comprehensive DNA, RNA, and protein profiles; images of multiplexed immunostaining; and 2- and 3-dimensional scanning electron micrographs. These data report aspects of heterogeneity and evolution of the cancer genome, signaling pathways, immune microenvironment, cellular composition and organization, and ultrastructure. We present illustrative examples of how integrative analyses of these data reveal potential mechanisms of response and resistance and suggest novel therapeutic vulnerabilities.

The impact of tumor epithelial and microenvironmental heterogeneity on treatment responses in HER2+ breast cancer.

Despite the availability of multiple human epidermal growth factor receptor 2-targeted (HER2-targeted) treatments, therapeutic resistance in HER2+ breast cancer remains a clinical challenge. Intratumor heterogeneity for HER2 and resistance-conferring mutations in the PIK3CA gene (encoding PI3K catalytic subunit α) have been investigated in response and resistance to HER2-targeting agents, while the role of divergent cellular phenotypes and tumor epithelial-stromal cell interactions is less well understood. Here, we assessed the effect of intratumor cellular genetic heterogeneity for ERBB2 (encoding HER2) copy number and PIK3CA mutation on different types of neoadjuvant HER2-targeting therapies and clinical outcome in HER2+ breast cancer. We found that the frequency of cells lacking HER2 was a better predictor of response to HER2-targeted treatment than intratumor heterogeneity. We also compared the efficacy of different therapies in the same tumor using patient-derived xenograft models of heterogeneous HER2+ breast cancer and single-cell approaches. Stromal determinants were better predictors of response than tumor epithelial cells, and we identified alveolar epithelial and fibroblastic reticular cells as well as lymphatic vessel endothelial hyaluronan receptor 1-positive (Lyve1+) macrophages as putative drivers of therapeutic resistance. Our results demonstrate that both preexisting and acquired resistance to HER2-targeting agents involve multiple mechanisms including the tumor microenvironment. Furthermore, our data suggest that intratumor heterogeneity for HER2 should be incorporated into treatment design.

Enzalutamide response in a panel of prostate cancer cell lines reveals a role for glucocorticoid receptor in enzalutamide resistant disease.

Representative in vitro model systems that accurately model response to therapy and allow the identification of new targets are important for improving our treatment of prostate cancer. Here we describe molecular characterization and drug testing in a panel of 20 prostate cancer cell lines. The cell lines cluster into distinct subsets based on RNA expression, which is largely driven by functional Androgen Receptor (AR) expression. KLK3, the AR-responsive gene that encodes prostate specific antigen, shows the greatest variability in expression across the cell line panel. Other common prostate cancer associated genes such as TMPRSS2 and ERG show similar expression patterns. Copy number analysis demonstrates that many of the most commonly gained (including regions containing TERC and MYC) and lost regions (including regions containing TP53 and PTEN) that were identified in patient samples by the TCGA are mirrored in the prostate cancer cell lines. Assessment of response to the anti-androgen enzalutamide shows a distinct separation of responders and non-responders, predominantly related to status of wild-type AR. Surprisingly, several AR-null lines responded to enzalutamide. These AR-null, enzalutamide-responsive cells were characterized by high levels of expression of glucocorticoid receptor (GR) encoded by NR3C1. Treatment of these cells with the anti-GR agent mifepristone showed that they were more sensitive to this drug than enzalutamide, as were several of the enzalutamide non-responsive lines. This is consistent with several recent reports that suggest that GR expression is an alternative signaling mechanism that can bypass AR blockade. This study reinforces the utility of large cell line panels for the study of cancer and identifies several cell lines that represent ideal models to study AR-null cells that have upregulated GR to sustain growth.

Transcriptional signatures in histologic structures within glioblastoma tumors may predict personalized drug sensitivity and survival.

BACKGROUND: Glioblastoma is a rapidly fatal brain cancer that exhibits extensive intra- and intertumoral heterogeneity. Improving survival will require the development of personalized treatment strategies that can stratify tumors into subtypes that differ in therapeutic vulnerability and outcomes. Glioblastoma stratification has been hampered by intratumoral heterogeneity, limiting our ability to compare tumors in a consistent manner. Here, we develop methods that mitigate the impact of intratumoral heterogeneity on transcriptomic-based patient stratification. METHODS: We accessed open-source transcriptional profiles of histological structures from 34 human glioblastomas from the Ivy Glioblastoma Atlas Project. Principal component and correlation network analyses were performed to assess sample inter-relationships. Gene set enrichment analysis was used to identify enriched biological processes and classify glioblastoma subtype. For survival models, Cox proportional hazards regression was utilized. Transcriptional profiles from 156 human glioblastomas were accessed from The Cancer Genome Atlas to externally validate the survival model. RESULTS: We showed that intratumoral histologic architecture influences tumor classification when assessing established subtyping and prognostic gene signatures, and that indiscriminate sampling can produce misleading results. We identified the cellular tumor as a glioblastoma structure that can be targeted for transcriptional analysis to more accurately stratify patients by subtype and prognosis. Based on expression from cellular tumor, we created an improved risk stratification gene signature. CONCLUSIONS: Our results highlight that biomarker performance for diagnostics, prognostics, and prediction of therapeutic response can be improved by analyzing transcriptional profiles in pure cellular tumor, which is a critical step toward developing personalized treatment for glioblastoma.

Individual Cells Can Resolve Variations in Stimulus Intensity along the IGF-PI3K-AKT Signaling Axis.

Cells sense and respond to signals in their local environment by activating signaling cascades that lead to phenotypic changes. Differences in these signals can be discriminated at the population level; however, single cells have been thought to be limited in their capacity to distinguish ligand doses due to signaling noise. We describe here the rational development of a genetically encoded FoxO1 sensor, which serves as a down-stream readout of insulin growth factor-phosphatidylinositol 3-kinase IGF-PI3K-AKT signaling pathway activity. With this reporter, we tracked individual cell responses to multiple IGF-I doses, pathway inhibitors, and repeated treatments. We observed that individual cells can discriminate multiple IGF-I doses, and these responses are sustained over time, are reproducible at the single-cell level, and display cell-to-cell heterogeneity. These studies imply that cell-to-cell variation in signaling responses is biologically meaningful and support the endeavor to elucidate mechanisms of cell signaling at the level of the individual cell.

Annot: a Django-based sample, reagent, and experiment metadata tracking system.

BACKGROUND: In biological experiments, comprehensive experimental metadata tracking - which comprises experiment, reagent, and protocol annotation with controlled vocabulary from established ontologies - remains a challenge, especially when the experiment involves multiple laboratory scientists who execute different steps of the protocol. Here we describe Annot, a novel web application designed to provide a flexible solution for this task. RESULTS: Annot enforces the use of controlled vocabulary for sample and reagent annotation while enabling robust investigation, study, and protocol tracking. The cornerstone of Annot's implementation is a json syntax-compatible file format, which can capture detailed metadata for all aspects of complex biological experiments. Data stored in this json file format can easily be ported into spreadsheet or data frame files that can be loaded into R ( https://www.r-project.org/ ) or Pandas, Python's data analysis library ( https://pandas.pydata.org/ ). Annot is implemented in Python3 and utilizes the Django web framework, Postgresql, Nginx, and Debian. It is deployed via Docker and supports all major browsers. CONCLUSIONS: Annot offers a robust solution to annotate samples, reagents, and experimental protocols for established assays where multiple laboratory scientists are involved. Further, it provides a framework to store and retrieve metadata for data analysis and integration, and therefore ensures that data generated in different experiments can be integrated and jointly analyzed. This type of solution to metadata tracking can enhance the utility of large-scale datasets, which we demonstrate here with a large-scale microenvironment microarray study.

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer.

Understanding the impact of the microenvironment on the phenotype of cells is a difficult problem due to the complex mixture of both soluble growth factors and matrix-associated proteins in the microenvironment in vivo. Furthermore, readily available reagents for the modeling of microenvironments in vitro typically utilize complex mixtures of proteins that are incompletely defined and suffer from batch to batch variability. The microenvironment microarray (MEMA) platform allows for the assessment of thousands of simple combinations of microenvironment proteins for their impact on cellular phenotypes in a single assay. The MEMAs are prepared in well plates, which allows the addition of individual ligands to separate wells containing arrayed extracellular matrix (ECM) proteins. The combination of the soluble ligand with each printed ECM forms a unique combination. A typical MEMA assay contains greater than 2,500 unique combinatorial microenvironments that cells are exposed to in a single assay. As a test case, the breast cancer cell line MCF7 was plated on the MEMA platform. Analysis of this assay identified factors that both enhance and inhibit the growth and proliferation of these cells. The MEMA platform is highly flexible and can be extended for use with other biological questions beyond cancer research.

Microenvironment-Mediated Mechanisms of Resistance to HER2 Inhibitors Differ between HER2+ Breast Cancer Subtypes.

Extrinsic signals are implicated in breast cancer resistance to HER2-targeted tyrosine kinase inhibitors (TKIs). To examine how microenvironmental signals influence resistance, we monitored TKI-treated breast cancer cell lines grown on microenvironment microarrays composed of printed extracellular matrix proteins supplemented with soluble proteins. We tested ∼2,500 combinations of 56 soluble and 46 matrix microenvironmental proteins on basal-like HER2+ (HER2E) or luminal-like HER2+ (L-HER2+) cells treated with the TKIs lapatinib or neratinib. In HER2E cells, hepatocyte growth factor, a ligand for MET, induced resistance that could be reversed with crizotinib, an inhibitor of MET. In L-HER2+ cells, neuregulin1-ß1 (NRG1ß), a ligand for HER3, induced resistance that could be reversed with pertuzumab, an inhibitor of HER2-HER3 heterodimerization. The subtype-specific responses were also observed in 3D cultures and murine xenografts. These results, along with bioinformatic pathway analysis and siRNA knockdown experiments, suggest different mechanisms of resistance specific to each HER2+ subtype: MET signaling for HER2E and HER2-HER3 heterodimerization for L-HER2+ cells.

Mutant p53-associated myosin-X upregulation promotes breast cancer invasion and metastasis.

Mutations of the tumor suppressor TP53 are present in many forms of human cancer and are associated with increased tumor cell invasion and metastasis. Several mechanisms have been identified for promoting dissemination of cancer cells with TP53 mutations, including increased targeting of integrins to the plasma membrane. Here, we demonstrate a role for the filopodia-inducing motor protein Myosin-X (Myo10) in mutant p53-driven cancer invasion. Analysis of gene expression profiles from 2 breast cancer data sets revealed that MYO10 was highly expressed in aggressive cancer subtypes. Myo10 was required for breast cancer cell invasion and dissemination in multiple cancer cell lines and murine models of cancer metastasis. Evaluation of a Myo10 mutant without the integrin-binding domain revealed that the ability of Myo10 to transport ß1 integrins to the filopodia tip is required for invasion. Introduction of mutant p53 promoted Myo10 expression in cancer cells and pancreatic ductal adenocarcinoma in mice, whereas suppression of endogenous mutant p53 attenuated Myo10 levels and cell invasion. In clinical breast carcinomas, Myo10 was predominantly expressed at the invasive edges and correlated with the presence of TP53 mutations and poor prognosis. These data indicate that Myo10 upregulation in mutant p53-driven cancers is necessary for invasion and that plasma-membrane protrusions, such as filopodia, may serve as specialized metastatic engines.

Metabolomics of human breast cancer: new approaches for tumor typing and biomarker discovery.

Breast cancer is the most common cancer in women worldwide, and the development of new technologies for better understanding of the molecular changes involved in breast cancer progression is essential. Metabolic changes precede overt phenotypic changes, because cellular regulation ultimately affects the use of small-molecule substrates for cell division, growth or environmental changes such as hypoxia. Differences in metabolism between normal cells and cancer cells have been identified. Because small alterations in enzyme concentrations or activities can cause large changes in overall metabolite levels, the metabolome can be regarded as the amplified output of a biological system. The metabolome coverage in human breast cancer tissues can be maximized by combining different technologies for metabolic profiling. Researchers are investigating alterations in the steady state concentrations of metabolites that reflect amplified changes in genetic control of metabolism. Metabolomic results can be used to classify breast cancer on the basis of tumor biology, to identify new prognostic and predictive markers and to discover new targets for future therapeutic interventions. Here, we examine recent results, including those from the European FP7 project METAcancer consortium, that show that integrated metabolomic analyses can provide information on the stage, subtype and grade of breast tumors and give mechanistic insights. We predict an intensified use of metabolomic screens in clinical and preclinical studies focusing on the onset and progression of tumor development.

Integration of metabolomics and expression of glycerol-3-phosphate acyltransferase (GPAM) in breast cancer-link to patient survival, hormone receptor status, and metabolic profiling.

Changes in lipid metabolism are an important but not well-characterized hallmark of cancer. On the basis of our recent findings of lipidomic changes in breast cancer, we investigated glycerol-3-phosphate acyltransferase (GPAM), a key enzyme in the lipid biosynthesis of triacylglycerols and phospholipids. GPAM protein expression was evaluated and linked to metabolomic and lipidomic profiles in a cohort of human breast carcinomas. In addition, GPAM mRNA expression was analyzed using the GeneSapiens in silico transcriptiomics database. High cytoplasmic GPAM expression was associated with hormone receptor negative status (p = 0.013). On the protein (p = 0.048) and mRNA (p = 0.001) levels, increased GPAM expression was associated with a better overall survival. Metabolomic analysis by GC-MS showed that sn-glycerol-3-phosphate, the substrate of GPAM, was elevated in breast cancer compared to normal breast tissue. LC-MS based lipidomic analysis identified significantly higher levels of phospholipids, especially phosphatidylcholines in GPAM protein positive tumors. In conclusion, our results suggest that GPAM is expressed in human breast cancer with associated changes in the cellular metabolism, in particular an increased synthesis of phospholipids, the major structural component of cellular membranes.

GTI: a novel algorithm for identifying outlier gene expression profiles from integrated microarray datasets.

BACKGROUND: Meta-analysis of gene expression microarray datasets presents significant challenges for statistical analysis. We developed and validated a new bioinformatic method for the identification of genes upregulated in subsets of samples of a given tumour type ('outlier genes'), a hallmark of potential oncogenes. METHODOLOGY: A new statistical method (the gene tissue index, GTI) was developed by modifying and adapting algorithms originally developed for statistical problems in economics. We compared the potential of the GTI to detect outlier genes in meta-datasets with four previously defined statistical methods, COPA, the OS statistic, the t-test and ORT, using simulated data. We demonstrated that the GTI performed equally well to existing methods in a single study simulation. Next, we evaluated the performance of the GTI in the analysis of combined Affymetrix gene expression data from several published studies covering 392 normal samples of tissue from the central nervous system, 74 astrocytomas, and 353 glioblastomas. According to the results, the GTI was better able than most of the previous methods to identify known oncogenic outlier genes. In addition, the GTI identified 29 novel outlier genes in glioblastomas, including TYMS and CDKN2A. The over-expression of these genes was validated in vivo by immunohistochemical staining data from clinical glioblastoma samples. Immunohistochemical data were available for 65% (19 of 29) of these genes, and 17 of these 19 genes (90%) showed a typical outlier staining pattern. Furthermore, raltitrexed, a specific inhibitor of TYMS used in the therapy of tumour types other than glioblastoma, also effectively blocked cell proliferation in glioblastoma cell lines, thus highlighting this outlier gene candidate as a potential therapeutic target. CONCLUSIONS/SIGNIFICANCE: Taken together, these results support the GTI as a novel approach to identify potential oncogene outliers and drug targets. The algorithm is implemented in an R package (Text S1).