Table 2: Systems Methods to Understand Physiological Stages of Metastasis.

Physiological stages of metastasis present unique questions, as well as experimental avenues to explore the progression of metastasis.

Systems Methods to Understand Stages of Metastasis
Stage	Techniques
Tumor Dissemination	i. Organoids: Creating spheroids mimicking primary tumor mass to study early dissemination events. Variations typically include genetic perturbations in tumor cells, microenvironmental factors (rigidity, matrix etc.), metabolic environ, as well as presence of soluble factors (Cheung and Ewald, 2016; Cheung et al., 2016; Lehmann et al., 2017; Zanoni et al., 2016). ii. SC-Seq: RNA profiling at single cell resolution to study tumor evolution, test for placement on phenotypic continuum of dissemination iii. Spatial Seq: Laser ablated, or spatial deconstruction by other means, to identify spatial correlates of altered transcriptome in the spheroid. iv. Invasion Parametrization: Organoid characterization (e.g. shape analysis, biosensor activities) driven by microscopy for high throughput screening, or mechanistic insights into dissemination (Shirure et al., 2018) v. Intravital imaging: Owing to a predetermined site of orthotopic xenotransplant to study this stage, intravital imaging is useful in studying early disseminating events (Ilina et al., 2018; Vennin et al., 2016) vi. Computational Modeling: Rich parameters are now available to mechanistically model signaling pathways to understand cellular state in the phenotypic continuum (Hatzikirou et al., 2012)
Stromal Invasion	i. Intravital imaging: Imaging live in an animal through a window (sometimes in reporter hosts) to observe interaction of disseminating cancer cells with the stromal compartment (Entenberg et al., 2017) ii. Ligand-Receptor Association: Typically through SC-Seq, wherein expression of ligands and receptors could identify heterotypic interactions in stroma (Tirosh et al., 2016) iii. Stromal invasion assays: Quantitative methods to systematically study genetic and environmental drivers of stromal invasion (Kshitiz Gupta, 2019) iv. Stromal secretome analysis: Single cell secretome analyses by immune and other cell type in stroma can reveal cancer-stroma crosstalk (Neelakantan et al., 2017; Xue et al., 2015) v. Spatiotemporal Computational Modeling: Heterotypic cellular interactions can be modeled using spatially defined modeling with partial differential equations, or cellular automatons (Tape, 2016; Tape et al., 2016)
Intravasation/Extravasation	i. 3D vasculature models: Tissue models to study cancer-vasculature interactions(Zervantonakis et al., 2012) ii. Permeability measurements: Study effect of cancer secreted factors on vascular permeability in high throughput (Harney et al., 2015; Uhl et al., 2018) iii. Phenotypic screens of intravasation: Barcoded KO libraries selected for intravasation potential (Jeon et al., 2014; Zervantonakis et al., 2012) iv. Microfluidics: Approaches to mimic vasculature and lymphatics, and incorporate defined mechanical parameters into observation (Shirure et al., 2018)
Premetastatic Niche	i. Exosomes: Microvesicles secreted by cancer cells bearing microRNA and other components, which could prepare niches in other tissues for micrometastases (Costa-Silva et al., 2015; Hoshino et al., 2015). ii. Highly multiplexed imaging cytometry: Spatially resolved histological analysis with CyTof(Giesen et al., 2014). iii. Biomimetic niches: Biomaterial based approaches to study cancer cell interactions with perturbed environment mimicking premetastatic niches (Aguado et al., 2018)
Secondary Metastasis	i. Staging analyses by Genomics: Understanding tumor subtypes and staging by genomic profiling in comparison to histological profiling (Court et al., 2015; Enokida et al., 2005; Goldsmith et al., 2019; Villani et al., 2017) ii. SC-Seq/Metabolomics/Genomics: Characterization of the metastatic nodes at multiple granularities and scale can reveal the evolution of secondary node formation (Enokida et al., 2005; Goldsmith et al., 2019) iii. Single cell secretome: Identifying the molecular communication between neighboring cells and metastatic cancer cells (Blanco et al., 2012; Li et al., 2016a) iv. MRS/PET: functional imaging can reveal environmental factors correlating with metastasis (Herzog et al.,2013; Koush et al., 2019)
Dormancy/Senescence	i. Tissue/ whole organism multiplexed histology: Identifying dormant cells unaffected by therapy (Qutaish et al., 2018; Roy et al., 2009) ii. SC-Seq: Comparative transcriptomics to reveal senescent, stem-like, or dormant cells(Marlow and Dontu, 2015; Milanovic et al., 2018) iii. In vitro, or ex-vivo models: Studying emergence of dormancy by ex-vivo tissue culture models (Clark et al., 2018; Marlow and Dontu, 2015; Wheeler et al., 2014) iv. Mechanistic computational models: Cellular automata, and other models to predict emergence of a senescent phenotype (Poleszczuk et al., 2015) v. Cell line variants: Selection of clones from metastatic sources showing latency (Marlow and Dontu, 2015)
Drug Resistance	i. Multivariate statistical analysis and regimen optimization: Systemic variation of drug regimen, combination etc to study a response (Korkut et al., 2015; Lee et al., 2012) ii. Organs on chip: Miniature tissue mimetics to systemically test drug response in varied microenvironments (Li et al., 2019; Ronaldson-Bouchard and Vunjak-Novakovic, 2018; Saengwimol et al., 2018) iii. Cancer specific drug kinetics prediction: Systems analysis of drug response by simulating signaling or metabolic state of cells (Kochanczyk et al., 2017; Persi et al., 2018) iv. Deep learning and machine learning: Artificial intelligence has found many applications in network analysis, compound property and prediction of drug activity (Camacho et al., 2018; Chen et al., 2018)