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

Natural history models for lung Cancer: A scoping review

INTRODUCTION

Natural history models (NHMs) of lung cancer (LC) simulate the disease's natural progression providing a baseline for assessing the impact of interventions. NHMs have been increasingly used to inform public health policies, highlighting their utility. The objective of this scoping review was to summarize existing LC NHMs, identify their limitations, and propose a framework for future NHM development.

METHODS

We searched MEDLINE, Embase, Web of Science, and IEEE Xplore from their inception to October 5, 2023, for peer-reviewed, full-length articles with an LC NHM. Model characteristics, their applications, data sources used, and limitations were extracted and narratively synthesized.

RESULTS

From 238 publications, 69 publications were included in our review, corresponding to 22 original LC NHMs and 47 model applications. The majority of the models (n = 15, 68 %) used a microsimulation approach. NHM parameters were predominately informed by cancer registries, trial and institutional data, and literature. Model quality and performance were evaluated in 8 (36 %) models. Twenty (91 %) models included at least one carcinogenesis risk factor-primarily age, sex, and smoking history. Three (14 %) LC NHMs modeled progression in never-smokers; one (5 %) addressed recurrence. Non-tobacco smoking, nodule type, and biomarker expression were not considered in existing NHMs. Based on our findings, we proposed a framework for future LC NHM development which incorporates recurrence, nodule type differentiation, biomarker expression levels, biological factors, and non-smoking-related risk factors.

CONCLUSION

Regular updating and future research are warranted to address limitations in existing NHMs thereby ensuring relevance and accuracy of modeling approaches in the evolving LC landscape.

Spatio-temporal model of combining chemotherapy with senolytic treatment in lung cancer

Senescent cells are cells that stop dividing but sustain viability. Cellular senescence is the hallmark of aging, but senescence also appears in cancer, triggered by cells stress, tumor suppression of gene activation, and oncogene activity. In lung cancer, senescent cancer cells secrete VEGF, which initiates a process of angiogenesis, enabling the cancer to grow and proliferate. Chemotherapy kills cancer cells, but some cancer cells become senescent. Hence, a senolytic drug, a drug that eliminates senescent cells, should significantly improve the efficacy of chemotherapy. In this paper, we developed a mathematical spatio-temporal model of combination chemotherapy with senolytic drug in treatment of lung cancer. Model's simulations of tumor volume growth are shown to agree with mouse experiments in the case where cyclophosphamide is combined with the senolytic drug fisetin. It is then shown how the model can be used to assess the benefits of treatments with different combinations and different schedules of the two drugs in order to achieve optimal tumor volume reduction.

Mathematical Modeling Support for Lung Cancer Therapy-A Short Review

The paper presents a review of models that can be used to describe dynamics of lung cancer growth and its response to treatment at both cell population and intracellular processes levels. To address the latter, models of signaling pathways associated with cellular responses to treatment are overviewed. First, treatment options for lung cancer are discussed, and main signaling pathways and regulatory networks are briefly reviewed. Then, approaches used to model specific therapies are discussed. Following that, models of intracellular processes that are crucial in responses to therapies are presented. The paper is concluded with a discussion of the applicability of the presented approaches in the context of lung cancer.

A data-assimilation approach to predict population dynamics during epithelial-mesenchymal transition

Epithelial-mesenchymal transition (EMT) is a biological process that plays a central role in embryonic development, tissue regeneration, and cancer metastasis. Transforming growth factor-β (TGFβ) is a potent inducer of this cellular transition, comprising transitions from an epithelial state to partial or hybrid EMT state(s), to a mesenchymal state. Recent experimental studies have shown that, within a population of epithelial cells, heterogeneous phenotypical profiles arise in response to different time- and TGFβ dose-dependent stimuli. This offers a challenge for computational models, as most model parameters are generally obtained to represent typical cell responses, not necessarily specific responses nor to capture population variability. In this study, we applied a data-assimilation approach that combines limited noisy observations with predictions from a computational model, paired with parameter estimation. Synthetic experiments mimic the biological heterogeneity in cell states that is observed in epithelial cell populations by generating a large population of model parameter sets. Analysis of the parameters for virtual epithelial cells with biologically significant characteristics (e.g., EMT prone or resistant) illustrates that these sub-populations have identifiable critical model parameters. We perform a series of in silico experiments in which a forecasting system reconstructs the EMT dynamics of each virtual cell within a heterogeneous population exposed to time-dependent exogenous TGFβ dose and either an EMT-suppressing or EMT-promoting perturbation. We find that estimating population-specific critical parameters significantly improved the prediction accuracy of cell responses. Thus, with appropriate protocol design, we demonstrate that a data-assimilation approach successfully reconstructs and predicts the dynamics of a heterogeneous virtual epithelial cell population in the presence of physiological model error and parameter uncertainty.

Comparison of Primary and Secondary Prophylaxis Using PEGylated Recombinant Human Granulocyte-Stimulating Factor as a Cost-Effective Measure in Malignant Neoplasms: A Multicenter Retrospective Study

Purpose: The aim of the study was to evaluate the cost-effectiveness of PEGylated recombinant human granulocyte-stimulating factor (PEG-rhG-CSF) as a means of achieving primary and secondary prophylaxis against chemotherapy-induced neutropenia cancer cases. Methods: Individuals who underwent PEG-rhG-CSF therapeutics were monitored for 12 months, together with thorough examination of individual medical records for extracting medical care costs. Both prophylaxis-based therapeutic options (primary/secondary) were scrutinized for cost-effectiveness, using a decision-making analysis model which derived the perspective of Chinese payers. One-way and probabilistic sensitivity analyses were used to assess the robustness of the model. Results: In summary, 130 clinical cases treated using PEG-rhG-CSF prophylaxis were included in this study: 51 within the primary prophylaxis (PP) group and 79 within the secondary prophylaxis (SP) group. Compared with SP, PP-based PEG-rhG-CSF successfully contributed to a 14.3% reduction in febrile neutropenia. In general, PP was estimated to reduce costs by $4,701.81 in comparison to SP, with a gain of 0.02 quality-adjusted life years (QALYs). Equivalent results were found in differing febrile neutropenia (FN) risk subgroups. Sensitivity analyses found the model outputs to be most affected for the average time of hospitalization and for the cost of FN. Conclusion: From the perspective of Chinese payers, PP with PEG-rhG-CSF should be considered cost-effective compared to SP strategies in patients who received chemotherapy regimens with a middle- to high-risk of FN.

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

Phenotypic (non-genetic) heterogeneity has significant implications for the development and evolution of organs, organisms, and populations. Recent observations in multiple cancers have unraveled the role of phenotypic heterogeneity in driving metastasis and therapy recalcitrance. However, the origins of such phenotypic heterogeneity are poorly understood in most cancers. Here, we investigate a regulatory network underlying phenotypic heterogeneity in small cell lung cancer, a devastating disease with no molecular targeted therapy. Discrete and continuous dynamical simulations of this network reveal its multistable behavior that can explain co-existence of four experimentally observed phenotypes. Analysis of the network topology uncovers that multistability emerges from two teams of players that mutually inhibit each other, but members of a team activate one another, forming a 'toggle switch' between the two teams. Deciphering these topological signatures in cancer-related regulatory networks can unravel their 'latent' design principles and offer a rational approach to characterize phenotypic heterogeneity in a tumor.

Cost-Effectiveness of Tucatinib in Human Epidermal Growth Factor Receptor 2-Positive Metastatic Breast Cancer From the US and Chinese Perspectives

Background: The clinical evaluation of HER2CLIMB trial showed a 21. 9-month median overall survival with the triplet regimens of tucatinib, capecitabine, and trastuzumab (TXT) for patients with human epidermal growth factor receptor 2 (HER2) –overexpressing metastatic breast cancer. From the payer's perspective of the United States and China, a cost-effectiveness analysis was conducted to evaluate the costs and benefits of adding tucatinib in this study.

Methods: We constructed a Markov model for the economic evaluation of adding tucatinib to trastuzumab plus capecitabine in patients with HER-2 positive metastatic breast cancer in the United States and China. The model was conducted with a 10-year time horizon, and the health status was divided into three states: progression-free survival, progressing disease, and death. The health utility scores were consistent with published literature with similar patient status. The transition probabilities were derived from the survival data of the HER2CLIMB study. The unit prices of medicines were obtained from the West China Hospital, Red Book, and published literature. Outcomes were measured in quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratio, which robustness was evaluated by deterministic and probabilistic sensitivity analyses.

Results: Compared with the two-drug regimen of trastuzumab plus capecitabine (TX), the addition of tucatinib increased 0.21 QALY, with an increasing cost of $146,995.05 and $19,022.97 in the United States and China, respectively. The incremental cost-effectiveness ratios (ICERs) for the TXT versus TX was $699,976.43 in the U.S. and $90,585.57 in China, both of which are higher than their respective threshold of willingness to play. Deterministic sensitivity analysis shows that the price of tucatinib is the parameter that has the most significant impact on ICERs, but it does not change the results of the model. Probability sensitivity analysis shows that the probability of cost-effective for TXT is 0 in the base case.

Conclusion: In the United States and China, tucatinib combined with trastuzumab and capecitabine is not cost-effective for patients with HER-2 positive metastatic breast cancer.

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

Epithelial-mesenchymal transition (EMT) is a fundamental biological process that plays a central role in embryonic development, tissue regeneration, and cancer metastasis. Transforming growth factor-β (TGFβ) is a potent inducer of this cellular transition, which is composed of transitions from an epithelial state to intermediate or partial EMT state(s) to a mesenchymal state. Using computational models to predict cell state transitions in a specific experiment is inherently difficult for reasons including model parameter uncertainty and error associated with experimental observations. In this study, we demonstrate that a data-assimilation approach using an ensemble Kalman filter, which combines limited noisy observations with predictions from a computational model of TGFβ-induced EMT, can reconstruct the cell state and predict the timing of state transitions. We used our approach in proof-of-concept “synthetic” in silico experiments, in which experimental observations were produced from a known computational model with the addition of noise. We mimic parameter uncertainty in in vitro experiments by incorporating model error that shifts the TGFβ doses associated with the state transitions and reproduces experimentally observed variability in cell state by either shifting a single parameter or generating “populations” of model parameters. We performed synthetic experiments for a wide range of TGFβ doses, investigating different cell steady-state conditions, and conducted parameter studies varying properties of the data-assimilation approach including the time interval between observations and incorporating multiplicative inflation, a technique to compensate for underestimation of the model uncertainty and mitigate the influence of model error. We find that cell state can be successfully reconstructed and the future cell state predicted in synthetic experiments, even in the setting of model error, when experimental observations are performed at a sufficiently short time interval and incorporate multiplicative inflation. Our study demonstrates the feasibility and utility of a data-assimilation approach to forecasting the fate of cells undergoing EMT.

Modeling heterogeneous tumor growth dynamics and cell-cell interactions at single-cell and cell-population resolution

Cancer is a complex, dynamic disease that despite recent advances remains mostly incurable. Inter- and intratumoral heterogeneity are generally considered major drivers of therapy resistance, metastasis, and treatment failure. Recent advances in high-throughput experimentation have produced a wealth of data on tumor heterogeneity and researchers are increasingly turning to mathematical modeling to aid in the interpretation of these complex datasets. In this mini-review, we discuss three important classes of approaches for modeling cellular dynamics within heterogeneous tumors: agent-based models, population dynamics, and multiscale models. An important new focus, for which we provide an example, is the role of intratumoral cell-cell interactions.

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

Small cell lung cancer (SCLC) is an aggressive neuroendocrine disease with an overall 5 year survival rate of ~7%. Although patients tend to respond initially to therapy, therapy-resistant disease inevitably emerges. Unfortunately, there are no validated biomarkers for early-stage SCLC to aid in early detection. Here, we used readouts of lesion image characteristics and cancer morphology that were based on fractal geometry, namely fractal dimension (FD) and lacunarity (LC), as novel biomarkers for SCLC. Scanned tumors of patients before treatment had a high FD and a low LC compared to post treatment, and this effect was reversed after treatment, suggesting that these measurements reflect the initial conditions of the tumor, its growth rate, and the condition of the lung. Fractal analysis of mitochondrial morphology showed that cisplatin-treated cells showed a discernibly decreased LC and an increased FD, as compared with control. However, treatment with mdivi-1, the small molecule that attenuates mitochondrial division, was associated with an increase in FD as compared with control. These data correlated well with the altered metabolic functions of the mitochondria in the diseased state, suggesting that morphological changes in the mitochondria predicate the tumor’s future ability for mitogenesis and motogenesis, which was also observed on the CT scan images. Taken together, FD and LC present ideal tools to differentiate normal tissue from malignant SCLC tissue as a potential diagnostic biomarker for SCLC.

Multiscale Agent-Based and Hybrid Modeling of the Tumor Immune Microenvironment

Multiscale systems biology and systems pharmacology are powerful methodologies that are playing increasingly important roles in understanding the fundamental mechanisms of biological phenomena and in clinical applications. In this review, we summarize the state of the art in the applications of agent-based models (ABM) and hybrid modeling to the tumor immune microenvironment and cancer immune response, including immunotherapy. Heterogeneity is a hallmark of cancer; tumor heterogeneity at the molecular, cellular, and tissue scales is a major determinant of metastasis, drug resistance, and low response rate to molecular targeted therapies and immunotherapies. Agent-based modeling is an effective methodology to obtain and understand quantitative characteristics of these processes and to propose clinical solutions aimed at overcoming the current obstacles in cancer treatment. We review models focusing on intra-tumor heterogeneity, particularly on interactions between cancer cells and stromal cells, including immune cells, the role of tumor-associated vasculature in the immune response, immune-related tumor mechanobiology, and cancer immunotherapy. We discuss the role of digital pathology in parameterizing and validating spatial computational models and potential applications to therapeutics.

Hybrid epithelial/mesenchymal phenotypes promote metastasis and therapy resistance across carcinomas

Cancer metastasis and therapy resistance are the major unsolved clinical challenges, and account for nearly all cancer-related deaths. Both metastasis and therapy resistance are fueled by epithelial plasticity, the reversible phenotypic transitions between epithelial and mesenchymal phenotypes, including epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET). EMT and MET have been largely considered as binary processes, where cells detach from the primary tumor as individual units with many, if not all, traits of a mesenchymal cell (EMT) and then convert back to being epithelial (MET). However, recent studies have demonstrated that cells can metastasize in ways alternative to traditional EMT paradigm; for example, they can detach as clusters, and/or occupy one or more stable hybrid epithelial/mesenchymal (E/M) phenotypes that can be the end point of a transition. Such hybrid E/M cells can integrate various epithelial and mesenchymal traits and markers, facilitating collective cell migration. Furthermore, these hybrid E/M cells may possess higher tumor-initiation and metastatic potential as compared to cells on either end of the EMT spectrum. Here, we review in silico, in vitro, in vivo and clinical evidence for the existence of one or more hybrid E/M phenotype(s) in multiple carcinomas, and discuss their implications in tumor-initiation, tumor relapse, therapy resistance, and metastasis. Together, these studies drive the emerging notion that cells in a hybrid E/M phenotype may occupy 'metastatic sweet spot' in multiple subtypes of carcinomas, and pathways linked to this (these) hybrid E/M state(s) may be relevant as prognostic biomarkers as well as a promising therapeutic targets.