A model of dormant-emergent metastatic breast cancer progression enabling exploration of biomarker signatures

Breast cancer mortality predominantly results from dormant micrometastases that emerge as fatal outgrowths years after initial diagnosis. In order to gain insights concerning factors associated with emergence of liver metastases, we recreated spontaneous dormancy in an all-human ex vivo hepatic microphysiological system (MPS). Seeding this MPS with small numbers (<0.05% by cell count) of the aggressive MDA-MB-231 breast cancer cell line, two populations formed: actively proliferating ("growing"; EdU+), and spontaneously quiescent ("dormant"; EdU-). Following treatment with a clinically standard chemotherapeutic, the proliferating cells were eliminated and only quiescent cells remained; this residual dormant population could then be induced to a proliferative state ("emergent"; EdU+) by physiologically-relevant inflammatory stimuli, lipopolysaccharide (LPS) and epidermal growth factor (EGF). Multiplexed proteomic analysis of the MPS effluent enabled elucidation of key factors and processes that correlated with the various tumor cell states, and candidate biomarkers for actively proliferating (either primary or secondary emergence) versus dormant metastatic cells in liver tissue. Dormancy was found to be associated with signaling reflective of cellular quiescence even more strongly than the original tumor-free liver tissue, whereas proliferative nodules presented inflammatory signatures. Given the minimal tumor burden, these markers likely represent changes in the tumor microenvironment rather than in the tumor cells. A computational decision tree algorithm applied to these signatures indicated the potential of this MPS for clinical discernment of each metastatic stage from blood protein analysis.


INTRODUCTION
Once breast cancer advances to clinically evident metastatic disease, death invariably ensues. Upon diagnosis, the vast majority of breast cancer patients present with no evidence of disseminated disease. However, tumor cells escape into the circulation early during primary tumor development (1) and in some instances establish as small, clinically silent dormant micro-metastases in secondary ectopic sites, which emerge years later as lethal, clinically overt metastatic growths (2). As a result, following removal of the primary mass, prophylactic chemotherapy is often administered to eradicate any undetected disseminated tumor cells circulating throughout the body. While this approach has reduced recurrence and mortality by a third, there is significant morbidity and even mortality in the universal application of adjuvant chemotherapy. Furthermore, the established dormant micro-metastases are typically resistant to such treatments, which mainly act upon actively cycling cells (3,4). Triple-negative breast cancer (TNBC) is a salient example wherein 25% of patients die from recurrence within 5-years of diagnosis despite prophylactic treatment (5). With respect to ectopic sites, evidence of breast to liver metastases is particularly foreboding with a median survival of 4 -23 months after detection (6-8).
This treatment paradox has driven the search for defined non-invasive biomarkers or molecular signatures of secondary dissemination and outgrowth. It is imperative to discern the status of these micro-metastases -whether such cells are a beginning to emerge as lethal macro-metastases or simply remaining as dormant, clinically silent cells/nodules. This is challenging as the vanishingly small number of cells at the earliest stages are unlikely to produce sufficient signals for detection within the body. It is precisely this dilution of signals that has obstructed the development of cancer screening protocols for early detection using tumor cell-derived biomarkers. We propose that it is most fruitful to detect surrogate biomarkers that reflect the homeostasis of the tumor microenvironment being one of either suppressive dormancy or active outgrowth. As the surrounding tissue will be orders of magnitude greater than the actual tumor cell count early in emergence, the dilution of candidate biomarkers in whole body fluids should be proportionally less. To date, only a handful of reliable biomarkers have been approved (9) and these markers are usually correlative and not mechanistically related to disease in ways that would inform therapeutic options. It is difficult to predict recurrence, yet pinpointing novel biomarkers as tools for the early detection and monitoring of metastatic recurrence would be clinically beneficial.
The surrounding tumor microenvironment, particularly the inflammatory/immune system, plays a key role in regulating metastatic resistance and recurrence (10

Cytochrome P450 assay
The activity of four cytochrome P450 (CYP) enzymes was measured using a previously validated CYP cocktail assay (17).  Table S3). Standard and sample diluents consisted of WE media in the presence of 0.75% bovine serum albumin (Sigma-Aldrich).

Multiplex immunoassays
All samples and multiplex immunoassay panels were run simultaneously to avoid confounding differences from day-to-day and operator variability.  Table S3.

Cancer cell detection and quantification
Images of the scaffolds were taken using an Olympus BX51 with a 2x objective (PlanApo NA = 0.08). An Olympus CCD camera along with Magnafire image acquisition software was used to acquire digital images. The percentage of MDA-MB-231-RFP + cells on the scaffolds was determined using MetaMorph software (Molecular Devices LLC). Images of the entire scaffolds were inclusively thresholded for RFP + regions and the positive region reported as a percentage of total scaffold area.

Click-iT PLUS EdU assay
Active proliferation was assessed using the Click- Functions were utilized to identify up-or down-regulated pathways. Pathways associated with an activation z-score greater than 1.7 or less than -1.7 were considered considerably altered.  Table S3).
Statistics: All graphs and heatmaps were generated using GraphPad Prism version 7 (GraphPad Software Inc). Wilcoxon rank sum tests were performed using MATLAB version 2016b (Mathworks Inc). Analytes identified as significantly different were those with a median fold change greater than 1.5 and a p-value < 0.05. Volcano plots were generated using the ggplot2 version 2.2.1 and ggrepel (version 0.6.5) packages in R (version 3.4.0).

Receiver operator characteristics (ROC) curves for area under the curve (AUC) univariate
predictions were generated using the perfcurve function in MATLAB with default parameters and decision trees were fit using the fitctree function. The standard CART algorithm was used to select the best split predictor at each node with gini's diversity index as the split criterion and a minimum parent size of 2. Candidate decision trees were generated using 4fold cross-validation and the reported error was based on the entire data set.

Recapitulation of dormant-emergent breast cancer metastasis progression in a liver MPS.
Key to the use of primary cell-derived organs is the functional consistency of cells over time and between donors. The demographics and characteristics of the patient donors utilized within this study are summarized in supplemental Fig. S1A and supplemental Table   S1. Hepatic niche function and health was unaffected by patient donor background and was Distinct from and as an advance on our previous studies (11, 12), the main question herein was to determine if these persisting cells were indeed viable, reversibly-growth arrested cells and not merely (pre-)apoptotic or senescent cells that remained trapped within the 3D hepatic tissue. To investigate this, the persisting cells were exposed to the pathophysiologically relevant inflammatory stimulus of LPS/EGF. These cues are particularly pertinent to the liver -LPS is a common portal blood contaminant from the gastrointestinal tract that fluctuates between 0.01 to 1 ng/ml within the liver (18) that can drive the outgrowth of breast cancer cells (19, 20), and EGF is a growth factor produced immediately upon damage to the liver, and all carcinomas express the EGF receptor (EGFR) and produce cognate ligands (21). Stimulation with these extracellular signals provoked the persisting cells to emerge and outgrow (RFP + /EdU + ) ( Table S2). Additionally, the phenotypes of growing, shown on day 7. and dormant, on day 13, were maintained through day 15 (supplemental

Differential signaling profiles of dormant and outgrowing metastatic niches in the liver MPS
Significant progress has been made in understanding cellular activity through the molecular analysis of signaling pathways (22). Circulating effluent from the metastatic MPS essentially acts as the 'blood' of the system. Sampling and assaying this medium for secreted and soluble signaling molecules enabled us to characterize the cell-to-cell crosstalk and microenvironmental homeostasis associated with each metastatic niche. Four multiplex immunoassay panels were used to analyze circulating effluent from each scenario on day 15 (i.e. growing, dormant, and emergent metastatic niches, as well as the tumor-free hepatic niche). Accounting for signal overlap between panels and levels detectable above background, a total of 77 unique analytes out of 101 were successfully measured above the limit of detection (LoD) ( Fig. 2A Selected analytes were those with a p < 0.05 and a fold change > 1.5; analytes with a p < 0.05 but a fold change < 1.5 are not discussed but are listed in supplemental Tables S9 -S11. First, we assessed the change in signal abundance within each metastatic scenario relative to the hepatic niche (Fig. 2B,C). Interestingly, the presence of a small number of growing MDA-MB-231 cells (< 1% of total cell mass) altered the signaling profile of the hepatic niche (Fig. 2B, supplemental Table S9) -34 were increased and 33 were reduced.
Although the analyte levels in the small number of specimens studied (4-6 donors) did not reach statistical significance, elevations (> 1.5-fold change) of common breast cancerassociated markers (PAI-1 and RANTES) and metastatic promoters (IP-10 and MCP-2) were observed (supplemental Table S9).
For a more clinically relevant assessment to detect tumors that would warrant intervention, we queried analyte abundance in the actively outgrowing tumor cells (growing and emergent metastatic niches) to that of non-proliferating (dormant and tumor-free hepatic niches) (Fig. 2E, supplemental Table S11). In validation of the metastatic MPS model, the majority of the significantly abundant analytes were either breast cancer-associated markers (uPA, PAI-1, RANTES, MCP-1) or those with a strong association with breast cancer progression (IL-6, Gro-α/-β, MIP-1α, IL-1RA, SDF-1α/β, TECK, MIG).
The growing and emergent metastatic niches largely exhibited similar signaling profiles, particularly compared to the dormant metastatic niche (Fig. 2B, supplemental Table S10); this was expected as both situations have proliferating tumor cell nodules. For the emergent metastatic niche, stimulation returned 34 out of 41 signals that were reduced in the dormant metastatic niche following chemotherapeutic treatment (supplemental Table S9).
Indeed, the emergent metastatic niche was associated with a striking abundance of analytes  Table S10).

Distinct functional pathways are activated within each metastatic niche in the liver MPS
IPA was employed to investigate the systemic impact of the different signaling microenvironments within metastatic niches captured in Fig. 2. IPA predictions revealed alterations in several biological mechanisms and functions amongst the experimental metastatic stages based upon fold variations in analyte levels (Fig. 3). In validation of the metastatic MPS, analysis of the growing MDA-MB-231 cells revealed activation of cancer pathways, specifically metastasis of cells, and lipid metabolism pathways, specifically triacylglycerol concentrations (Fig. 3A). The applicability of metastatic cancer pathways is evident. The latter is linked to enhanced/reactivation of lipid biosynthesis as part of cancerassociated metabolic reprogramming to glycolysis (35), which is a frequent response of metastasizing tumor cells colonizing and surviving in the liver due the low oxygen and glucose levels (36). Fig. 3 Consistent with previous data, IPA indicated numerous biological pathways were downregulated in the dormant metastatic niche (Fig. 3B,C, supplemental Fig. S7A).

Suggested location for
Dramatically reduce were those associated with cell movement, migration and proliferation of cancer cells as well as the activity of resident liver cells, both parenchymal and NPCs.
One pathway relating to carbohydrate synthesis was markedly unregulated (Fig. 3B Table S12).
Overall, these data demonstrated that profiling the circulating effluent from the metastatic MPS can discriminate between livers that contain dormant or outgrowing (either primary or secondary emergence) breast cancer cells. While the accuracy of the decisions trees generated are very high, this is not unexpected given the limited numbers of samples and consistency of experimentation, given that the MPS system is robust and relevant to cancer progression. Thus, these serve as examples that require refinement with a diversity of primary cancer cells and subsequent validation in human specimens.

DISCUSSION
Metastatic breast cancer remains a largely incurable and fatal disease. The danger stems from tumor cells that disseminate early and lay as clinically silent, dormant cells/nodules, which may awaken to outgrow after a long and variable period of latency.
These metastatic relapses occur in only one-third or fewer women who received a seemingly curative removal of the primary tumor. In the absence of our ability to detect these There is promise that this can be applied clinically as we could define biomarkers with high AUC values, and generate diagnostic decision tree models discerning each metastatic stage. Most all of the molecules identified have been associated with metastases (being detected at the later, less treatable stage of macro-metastases) and reduced overall survival. However, it is premature to discuss specific biomarkers at present as the AUC values and decision trees currently serve as proofs of principle. This is for a number of reasons. First, herein we used a singular cancer cell line, of a specific subtype (the highly recurrent triple negative phenotype). Second, the dilutional and isolated organ issues addressed above will impact the translation to the clinical situation. However, that highly predictive decision trees can be readily obtained argues for moving forward with further biomarker studies. Importantly, we emphasize this is a validation of 'fit for purpose' and are not stating the molecules listed within are new candidates to be considered for clinical use.
Moreover, we acknowledge our model unlikely to translate in all aspects because of the low predictive power caused by the small donor number. Validation in animals is not readily attainable due to the lack of reliable and tunable spontaneous dormancy models, the stochastic nature of true metastases from primary tumor growths, and the inability to control emergence. Translation to persons will be hampered by the long latency between primary tumors and metastatic recurrence. Still, the predictability of the MPS suggests that a future clinical utility involves the incorporation and investigation of patient-derived cells post-hoc.
Lastly, it must be noted that many of the identified factors may be causative for emergence and not merely indicative, which could provide for interventions to prevent outgrowth.
This MPS approximates the earliest, and clinically silent stages of metastatic seeding, dormancy, outgrowth and emergence in an all human context and thus captures this situation uniquely from animal models and human specimens, but it is still not without significant limitations. The cellular complement is complex and but not fully complete lacking some of the hematopoietic immune cells that would populate such an invaded liver, and the connections to other organs that would modify any tissue responses. Further, the time-scale of days to weeks rather than months to years would also impact extrapolation to the patient situation. Thus, any signatures or potentially causal linkages would need to be validated in human specimens; the decision tree analyses presented herein only serve to demonstrate that unique signatures may be possible. Given the daunting challenges of validation in that the specimens would need to be assessed in the absence of knowledge about whether any given patient even harbored micrometastases, such prior work in a tractable system such as this one, is requisite to focus the translational study. Further, we feel that this system can also be used as a first step in therapeutic interventions to determine whether potential causal signals are valid targets for further study.
The future utility of this metastatic MPS is innumerable. It is easily adaptable to other cancer types, such as metastatic prostate (49) or metastatic colorectal cancer, which has an abysmal 5-year survival of only 10% (50). This model is also poised to capture many of the relevant features of inflammation-mediated tumor outgrowth. New iterations extend to a multi-organ MPS (51), which connects the metastatic hepatic niche to relevant inflammatory organs, to further pursue drivers of emergence, such as the gut (52) or adipose fat (53) as well as to a primary tumor to model the entire metastatic cascade from the initial escape through to immediate outgrowth or delayed emergence at the secondary ectopic site.
Moreover, MPSs are making large strides in bridging the gap between bench level preclinical experiments and clinical trials in humans (15,(54)(55)(56)(57). As the liver is the principle site of drug metabolism, the MPS may also serve as an all-human model to replace mice in the pre-clinical evaluation of therapies against recurrence that eradicate or maintain a dormant state turning metastasis into chronic, manageable disease.