Modeling small cell lung cancer (SCLC) biology through deterministic and stochastic mathematical models

Abstract

Mathematical cancer models are immensely powerful tools that are based in part on the fractal nature of biological structures, such as the geometry of the lung. Cancers of the lung provide an opportune model to develop and apply algorithms that capture changes and disease phenotypes. We reviewed mathematical models that have been developed for biological sciences and applied them in the context of small cell lung cancer (SCLC) growth, mutational heterogeneity, and mechanisms of metastasis. The ultimate goal is to develop the stochastic and deterministic nature of this disease, to link this comprehensive set of tools back to its fractalness and to provide a platform for accurate biomarker development. These techniques may be particularly useful in the context of drug development research, such as combination with existing omics approaches. The integration of these tools will be important to further understand the biology of SCLC and ultimately develop novel therapeutics.

Topological signatures in regulatory network enable phenotypic heterogeneity in small cell lung cancer

“…Our results highlight an important step in decoding the design principles of underlying regulatory network for phenotypic plasticity and heterogeneity in aggressive cancers such as SCLC. SCLC has no targeted therapy available till date (Subbiah et al, 2020), a limitation that is expected to be overcome by recent surge in high-throughput experimental data collection for SCLC and computational approaches to identify SCLC subtypes for clinical action (Salgia et al, 2018; Stewart et al, 2020; Tlemsani et al, 2020; Udyavar et al, 2017; Wooten et al, 2019). An abysmal, if any, correlation between mutational profiles and distinct SCLC subtypes (George et al, 2015), and multistability in SCLC network, argue for non-genetic causes of phenotypic heterogeneity.…”

Small Cell Lung Cancer Therapeutic Responses Through Fractal Measurements: From Radiology to Mitochondrial Biology

“…Furthermore, there is a dearth of genomic data in SCLC, primarily characterized by TP53 and RB1 alterations [28], which suggests that biomarkers should be pursued in areas of plentiful data such as CT scans, tissue immunohistochemical slides, and cell lines to determine how the self-similar and fractal nature of these images can act as a predictive model for clinical management. SCLC also has a unique fractal microenvironment that is self-similar at the cell level, tissue level, thoracic level, and metastatic site level, where the features of the metastatic tumors predicate themselves on the initial conditions of the primary site [27,29]. To demonstrate the clinical use of these measurements, we applied them across multiple scales from radiological to the cellular scale, where we measured the FD and LC of CT images, tissue, cells, and mitochondria.…”

Cell Fate Forecasting: A Data-Assimilation Approach to Predict Epithelial-Mesenchymal Transition

“…Predictions within a multicellular tissue are inherently more challenging because multicellular tissues can exhibit spatial heterogeneity in cell phenotype, both initially and as a function of time. Model development of the spatial interactions during the EMT process is complex, and this challenge is indeed an area of ongoing work within our lab ( 55 ) and others ( 18 , 56 , 57 , 58 ). As described by Hunt and colleagues ( 28 ), the EnKF can be further extended to account for spatial localization and interacting spatial dynamics, and we plan to extend the approach demonstrated here to multicellular tissues in the future as well.…”