Development of a scoring function for comparing simulated and experimental tumor spheroids

Progress continues in the field of cancer biology, yet much remains to be unveiled regarding the mechanisms of cancer invasion. In particular, complex biophysical mechanisms enable a tumor to remodel the surrounding extracellular matrix (ECM), allowing cells to invade alone or collectively. Tumor spheroids cultured in collagen represent a simplified, reproducible 3D model system, which is sufficiently complex to recapitulate the evolving organization of cells and interaction with the ECM that occur during invasion. Recent experimental approaches enable high resolution imaging and quantification of the internal structure of invading tumor spheroids. Concurrently, computational modeling enables simulations of complex multicellular aggregates based on first principles. The comparison between real and simulated spheroids represents a way to fully exploit both data sources, but remains a challenge. We hypothesize that comparing any two spheroids requires first the extraction of basic features from the raw data, and second the definition of key metrics to match such features. Here, we present a novel method to compare spatial features of spheroids in 3D. To do so, we define and extract features from spheroid point cloud data, which we simulated using Cells in Silico (CiS), a high-performance framework for large-scale tissue modeling previously developed by us. We then define metrics to compare features between individual spheroids, and combine all metrics into an overall deviation score. Finally, we use our features to compare experimental data on invading spheroids in increasing collagen densities. We propose that our approach represents the basis for defining improved metrics to compare large 3D data sets. Moving forward, this approach will enable the detailed analysis of spheroids of any origin, one application of which is informing in silico spheroids based on their in vitro counterparts. This will enable both basic and applied researchers to close the loop between modeling and experiments in cancer research

On the road to cellular digital twins of in vivo tumors

To this day, cancer remains an insufficiently understood disease plaguing humanity. In particular, the mechanisms driving tumor invasion still require extensive study. Current investigations address collective cellular behavior within tumors, which leads to solid or fluid tissue dynamics. Furthermore, the extracellular matrix (ECM) has come into focus as a driving force facilitating invasion. To complement the experimental studies, computational models are employed, and advances in computational power within HPC systems have enabled the simulation of macroscopic tissue arrangements. We hereby present our work using Cells in Silico (CiS), a high performance framework for large-scale tissue simulation previously developed by us. CiS is capable of simulating tissues composed of tens of millions of cells, while accurately representing many physical and biological properties. Our ultimate aim is to build a cellular digital twin of an in vivo tumor. Unfortunately, current in vivo measurement methods lack the required resolution for directly parameterizing our simulations. Therefore, we aim to parameterize CiS via a bottom-up approach, utilizing experimental data from multiple in vitro systems. We focused our first studies on tumor spheroids, a main workhorse of tumor analysis. Towards this, we developed a novel method to compare spatial features of spheroids in 3D.The comparison between real and simulated spheroids represents a way to fully exploit both data sources, but remains a challenging task. Towards this, we developed a novel method to compare spatial features of spheroids in 3D. To do so, we defined and extracted features from spheroid point cloud data, which we simulated using CiS. We then defined metrics to compare features between individual spheroids, and combined all metrics into an overall deviation score. Finally, we used our features to compare experimental data on invading spheroids in increasing collagen densities. We propose that our approach represents the basis for defining improved metrics to compare large 3D data sets. Moving forward, this approach will enable both basic and applied researchers to close the loop between modeling and experiments in cancer research

Towards cellular digital twins of in vivo tumors

To this day, cancer remains an insufficiently understood disease plaguing humanity. In particular, the mechanisms driving tumor invasion still require extensive study. Current investigations address collective cellular behavior within tumors, which leads to solid or fluid tissue dynamics. Furthermore, the extracellular matrix (ECM) has come into focus as a driving force facilitating invasion. Large scale tumor simulations at subcellular resolution represent a promising computational tool to complement the experimental studies, and advances in computational power within HPC systems have enabled the simulation of such macroscopic tissue arrangements. We hereby present our work using Cells in Silico (CiS), a high performance framework for large-scale tissue simulation previously developed by us. Combining a cellular Potts model and an agent-based layer, CiS is capable of simulating tissues composed of tens of millions of cells, while accurately representing many physical and biological properties. However, in order to realistically represent tumor dynamics, CiS needs to be parameterized. We present strategies and pitfalls in investigating multiple experimental data sources for this task. In particular, we highlight recent success utilizing a feature-extraction based deviation score method for the comparison of simulated and experimental tumor spheroids

Development of a scoring function for comparing simulated and experimental tumor spheroids.

Progress continues in the field of cancer biology, yet much remains to be unveiled regarding the mechanisms of cancer invasion. In particular, complex biophysical mechanisms enable a tumor to remodel the surrounding extracellular matrix (ECM), allowing cells to invade alone or collectively. Tumor spheroids cultured in collagen represent a simplified, reproducible 3D model system, which is sufficiently complex to recapitulate the evolving organization of cells and interaction with the ECM that occur during invasion. Recent experimental approaches enable high resolution imaging and quantification of the internal structure of invading tumor spheroids. Concurrently, computational modeling enables simulations of complex multicellular aggregates based on first principles. The comparison between real and simulated spheroids represents a way to fully exploit both data sources, but remains a challenge. We hypothesize that comparing any two spheroids requires first the extraction of basic features from the raw data, and second the definition of key metrics to match such features. Here, we present a novel method to compare spatial features of spheroids in 3D. To do so, we define and extract features from spheroid point cloud data, which we simulated using Cells in Silico (CiS), a high-performance framework for large-scale tissue modeling previously developed by us. We then define metrics to compare features between individual spheroids, and combine all metrics into an overall deviation score. Finally, we use our features to compare experimental data on invading spheroids in increasing collagen densities. We propose that our approach represents the basis for defining improved metrics to compare large 3D data sets. Moving forward, this approach will enable the detailed analysis of spheroids of any origin, one application of which is informing in silico spheroids based on their in vitro counterparts. This will enable both basic and applied researchers to close the loop between modeling and experiments in cancer research