Engineering Multi-Component Bioluminescent Imaging Platforms and Transcriptome Analyses in Models of Metastatic Breast Cancer

Cancer metastases are driven by complex interactions among tumor cells, immune cells, and other cell types. Dissecting the roles of these cells is complicated by the inherent heterogeneity of the tumor environment. Currently we lack the models needed to profile cancer metastasis and monitor disease progression. Improved cancer models can be used in conjunction with noninvasive imaging probes to further visualize specific cell types and cellular interactions that are important to cancer progression in vivo. Bioluminescence is well suited for sensitive, noninvasive imaging in disease models. Bioluminescence relies on enzymes (luciferases) that generate light via the chemical oxidation of small molecule substrates (luciferins). No external excitation source is required, and enough tissue-penetrant light is released, enabling sensitive detection of cells and other biological features. Despite its broad applicability, bioluminescence has historically been limited to monitoring one cell population at a time and lacks the spatial resolution necessary to “see” cellular interactions relevant to cancer progression. This dissertation bridges the need for better methods to study metastatic progression by developing platforms that (1) enable multicomponent bioluminescence imaging in vivo, (2) recapitulate disease progression with new metastatic cancer models, (3) profile comprehensive transcriptome analyses of metastatic disease, and (4) facilitate tumor-immune cell monitoring through engineered imaging probes. Collectively, the imaging methods and models developed in this dissertation enable a more detailed examination of tumor heterogeneity and metastatic disease progression in vivo

Multiplexed bioluminescence imaging with a substrate unmixing platform.

Bioluminescent tools can illuminate cellular features in whole organisms. Multi-component tracking remains challenging, though, owing to a lack of well-resolved probes and long imaging times. To address the need for more rapid, quantitative, and multiplexed bioluminescent readouts, we developed an analysis pipeline featuring sequential substrate administration and serial image acquisition. Light output from each luciferin is layered on top of the previous image, with minimal delay between substrate delivery. A MATLAB algorithm was written to analyze bioluminescent images generated from the rapid imaging protocol and deconvolute (i.e., unmix) signals from luciferase-luciferin pairs. Mixtures comprising three to five luciferase reporters were readily distinguished in under 50 min; this same experiment would require days using conventional workflows. We further showed that the algorithm can be used to accurately quantify luciferase levels in heterogeneous mixtures. Based on its speed and versatility, the multiplexed imaging platform will expand the scope of bioluminescence technology

Rapid Multicomponent Bioluminescence Imaging via Substrate Unmixing

Studies of biological function demand probes that can report on processes in real time and in physiological environments. Bioluminescent tools are uniquely suited for this purpose, as they enable sensitive imaging in cells and tissues. Bioluminescent reporters can also be monitored continuously over time without detriment, as excitation light is not required. Rather, light emission derives from luciferase-luciferin reactions. Several engineered luciferases and luciferins have expanded the scope of bioluminescence imaging in recent years. Multicomponent tracking remains challenging, though, due to a lack of streamlined methods to visualize combinations of bioluminescent reporters. Conventional approaches image one luciferase at a time. Consequently, short-term changes in cell growth or gene expression cannot be easily captured. Here, we report a strategy for rapid, multiplexed imaging with a wide range of luciferases and luciferins. Sequential addition of orthogonal luciferins, followed by substrate unmixing, enabled facile detection of multiple luciferases in vitro and in vivo. Multicomponent imaging in mice was also achieved on the minutes-to-hours time scale

Transcriptome analysis of heterogeneity in mouse model of metastatic breast cancer.

BackgroundCancer metastasis is a complex process involving the spread of malignant cells from a primary tumor to distal organs. Understanding this cascade at a mechanistic level could provide critical new insights into the disease and potentially reveal new avenues for treatment. Transcriptome profiling of spontaneous cancer models is an attractive method to examine the dynamic changes accompanying tumor cell spread. However, such studies are complicated by the underlying heterogeneity of the cell types involved. The purpose of this study was to examine the transcriptomes of metastatic breast cancer cells using the well-established MMTV-PyMT mouse model.MethodsOrgan-derived metastatic cell lines were harvested from 10 female MMTV-PyMT mice. Cancer cells were isolated and sorted based on the expression of CD44low/EpCAMhigh or CD44high/EpCAMhigh surface markers. RNA from each cell line was extracted and sequenced using the NextSeq 500 Illumina platform. Tissue-specific genes were compared across the different metastatic and primary tumor samples. Reads were mapped to the mouse genome using STAR, and gene expression was quantified using RSEM. Single-cell RNA-seq (scRNA-seq) was performed on select samples using the ddSeq platform by BioRad and analyzed using Seurat v3.2.3. Monocle2 was used to infer pseudo-time progression.ResultsComparison of RNA sequencing data across all cell populations produced distinct gene clusters. Differential gene expression patterns related to CD44 expression, organ tropism, and immunomodulatory signatures were observed. scRNA-seq identified expression profiles based on tissue-dependent niches and clonal heterogeneity. These cohorts of data were narrowed down to identify subsets of genes with high expression and known metastatic propensity. Dot plot analyses further revealed clusters expressing cancer stem cell and cancer dormancy markers. Changes in relevant genes were investigated across pseudo-time and tissue origin using Monocle2. These data revealed transcriptomes that may contribute to sub-clonal evolution and treatment evasion during cancer progression.ConclusionsWe performed a comprehensive transcriptome analysis of tumor heterogeneity and organ tropism during breast cancer metastasis. These data add to our understanding of metastatic progression and highlight targets for breast cancer treatment. These markers could also be used to image the impact of tumor heterogeneity on metastases

Transcriptome analysis of heterogeneity in mouse model of metastatic breast cancer.

BackgroundCancer metastasis is a complex process involving the spread of malignant cells from a primary tumor to distal organs. Understanding this cascade at a mechanistic level could provide critical new insights into the disease and potentially reveal new avenues for treatment. Transcriptome profiling of spontaneous cancer models is an attractive method to examine the dynamic changes accompanying tumor cell spread. However, such studies are complicated by the underlying heterogeneity of the cell types involved. The purpose of this study was to examine the transcriptomes of metastatic breast cancer cells using the well-established MMTV-PyMT mouse model.MethodsOrgan-derived metastatic cell lines were harvested from 10 female MMTV-PyMT mice. Cancer cells were isolated and sorted based on the expression of CD44low/EpCAMhigh or CD44high/EpCAMhigh surface markers. RNA from each cell line was extracted and sequenced using the NextSeq 500 Illumina platform. Tissue-specific genes were compared across the different metastatic and primary tumor samples. Reads were mapped to the mouse genome using STAR, and gene expression was quantified using RSEM. Single-cell RNA-seq (scRNA-seq) was performed on select samples using the ddSeq platform by BioRad and analyzed using Seurat v3.2.3. Monocle2 was used to infer pseudo-time progression.ResultsComparison of RNA sequencing data across all cell populations produced distinct gene clusters. Differential gene expression patterns related to CD44 expression, organ tropism, and immunomodulatory signatures were observed. scRNA-seq identified expression profiles based on tissue-dependent niches and clonal heterogeneity. These cohorts of data were narrowed down to identify subsets of genes with high expression and known metastatic propensity. Dot plot analyses further revealed clusters expressing cancer stem cell and cancer dormancy markers. Changes in relevant genes were investigated across pseudo-time and tissue origin using Monocle2. These data revealed transcriptomes that may contribute to sub-clonal evolution and treatment evasion during cancer progression.ConclusionsWe performed a comprehensive transcriptome analysis of tumor heterogeneity and organ tropism during breast cancer metastasis. These data add to our understanding of metastatic progression and highlight targets for breast cancer treatment. These markers could also be used to image the impact of tumor heterogeneity on metastases

Macromolecular assembly of bioluminescent protein nanoparticles for enhanced imaging.

Bioluminescence imaging has advantages over fluorescence imaging, such as minimal photobleaching and autofluorescence, and greater signal-to-noise ratios in many complex environments. Although significant achievements have been made in luciferase engineering for generating bright and stable reporters, the full capability of luciferases for nanoparticle tracking has not been comprehensively examined. In biocatalysis, enhanced enzyme performance after immobilization on nanoparticles has been reported. Thus, we hypothesized that by assembling luciferases onto a nanoparticle, the resulting complex could lead to substantially improved imaging properties. Using a modular bioconjugation strategy, we attached NanoLuc (NLuc) or Akaluc bioluminescent proteins to a protein nanoparticle platform (E2), yielding nanoparticles NLuc-E2 and Akaluc-E2, both with diameters of ∼45 ​nm. Although no significant differences were observed between different conditions involving Akaluc and Akaluc-E2, free NLuc at pH 5.0 showed significantly lower emission values than free NLuc at pH 7.4. Interestingly, NLuc immobilization on E2 nanoparticles (NLuc-E2) emitted increased luminescence at pH 7.4, and at pH 5.0 showed over two orders of magnitude (>200-fold) higher luminescence (than free NLuc), expanding the potential for imaging detection using the nanoparticle even upon endocytic uptake. After uptake by macrophages, the resulting luminescence with NLuc-E2 nanoparticles was up to 7-fold higher than with free NLuc at 48 ​h. Cells incubated with NLuc-E2 could also be imaged using live bioluminescence microscopy. Finally, biodistribution of nanoparticles into lymph nodes was detected through imaging using NLuc-E2, but not with conventionally-labeled fluorescent E2. Our data demonstrate that NLuc-bound nanoparticles have advantageous properties that can be utilized in applications ranging from single-cell imaging to in vivo biodistribution

Efficacy and Molecular Mechanisms of Differentiated Response to the Aurora and Angiogenic Kinase Inhibitor ENMD-2076 in Preclinical Models of p53-Mutated Triple-Negative Breast Cancer

PurposeTriple-negative breast cancer (TNBC) is a subtype associated with poor prognosis and for which there are limited therapeutic options. The purpose of this study was to evaluate the efficacy of ENMD-2076 in p53-mutated TNBC patient-derived xenograft (PDX) models and describe patterns of terminal cell fate in models demonstrating sensitivity, intrinsic resistance, and acquired resistance to ENMD-2076.Experimental designp53-mutated, TNBC PDX models were treated with ENMD-2076 and evaluated for mechanisms of sensitivity or resistance to treatment. Correlative tissue testing was performed on tumor tissue to assess for markers of proliferation, apoptosis, senescence, and pathways of resistance after treatment and at the time of acquired resistance.ResultsSensitivity to ENMD-2076 200 mg/kg daily was associated with induction of apoptosis while models exhibiting intrinsic or acquired resistance to treatment presented with a senescent phenotype. Response to ENMD-2076 was accompanied by an increase in p53 and p73 levels, even within the background of mutant p53. Treatment with ENMD-2076 resulted in a decrease in pAurA and an increase in pHH3. We observed a TNBC subtype switch from the luminal androgen receptor to the basal-like subtype at acquired resistance.ConclusionENMD-2076 has antitumor activity in preclinical models of p53-mutated TNBC. Increased levels of p53 and p73 correlated with sensitivity whereas senescence was associated with resistance to ENMD-2076. The novel finding of a TNBC subtype switch at time of acquired resistance may provide mechanistic insights into the biologic effects of selective pressure of anticancer treatments on TNBC. ENMD-2076 is currently being evaluated in a Phase 2 clinical trial in patients with metastatic, previously treated TNBC where these biologic correlates can be further explored