Effect of feedback regulation on stem cell fractions in tissues and tumors: Understanding chemoresistance in cancer

Dynamically adjusted cell fate decisions and resilience to mutant invasion during steady-state hematopoiesis revealed by an experimentally parameterized mathematical model

A major next step in hematopoietic stem cell (HSC) biology is to enhance our quantitative understanding of cellular and evolutionary dynamics involved in undisturbed hematopoiesis. Mathematical models have been and continue to be key in this respect, and are most powerful when parameterized experimentally and containing sufficient biological complexity. In this paper, we use data from label propagation experiments in mice to parameterize a mathematical model of hematopoiesis that includes homeostatic control mechanisms as well as clonal evolution. We find that nonlinear feedback control can drastically change the interpretation of kinetic estimates at homeostasis. This suggests that short-term HSC and multipotent progenitors can dynamically adjust to sustain themselves temporarily in the absence of long-term HSCs, even if they differentiate more often than they self-renew in undisturbed homeostasis. Additionally, the presence of feedback control in the model renders the system resilient against mutant invasion. Invasion barriers, however, can be overcome by a combination of age-related changes in stem cell differentiation and evolutionary niche construction dynamics based on a mutant-associated inflammatory environment. This helps us understand the evolution of e.g., TET2 or DNMT3A mutants, and how to potentially reduce mutant burden.

Application of mPEG-CS-cRGD/Bmi-1RNAi-PTX nanoparticles in suppression of laryngeal cancer by targeting cancer stem cells

Although surgery-based comprehensive therapy is becoming the main approach to treat laryngeal cancer, recurrence, metastasis, radiotherapy resistance and chemotherapy tolerance are still the main causes of death in patients. Targeted inhibition of laryngeal cancer stem cells has been considered as the consensus to cure laryngeal cancer. Our previous study has confirmed proto-oncogene Bmi-1 as a key regulator for self-renewal of laryngeal cancer stem cells. Targeted knockdown of Bmi-1 gene effectively inhibited the self-renewal and differentiation of laryngeal cancer stem cells, leading to the promoted sensitivity to chemotherapy including paclitaxel. However, due to off-target effects and quick degradation of the naked Bmi-1-RNAi small RNA oligo by nuclease in body fluids, it is urgently needed to develop a tumor-targeted delivery system with a protective shell. In this study, we designed and synthesized cRGD peptide-modified chitosan-polyethylene glycol slow-release nanoparticles (mPEG-CS-cRGD/Bmi-1RNAi-PTX) containing Bmi-1RNAi siRNA oligo and paclitaxel, which showed spherical in shape, 200 nm diameter in size, low cytotoxicity, strong DNA wrapping, resistance to nuclease degradation and high transfection efficiency to cells. Functional analysis indicated significant suppression of cell proliferation and migration and induction of apoptosis by the nanocomplex in laryngeal cancer cells in vitro. By application to the mouse model with laryngeal cancer, the nanocomplex inhibited tumor growth significantly in vivo. In addition, cRGD peptide, paclitaxel and Bmi-1 siRNA in the nanoparticles showed synergistic effects to suppress laryngeal cancer stem cells. In conclusion, this study not only developed a laryngeal tumor-targeted chemotherapeutic system, but also demonstrated a Bmi-1 RNAi-based chemotherapeutic strategy to inhibit cancer stem cells, having strong potential to treat laryngeal cancer patients suffering therapy resistance and/or tumor recurrence.

Patient-specific molecular response dynamics can predict the possibility of relapse during the second treatment-free remission attempt in chronic myelogenous leukemia

In chronic myelogenous leukemia (CML), treatment-free remission (TFR) is defined as maintaining a major molecular response (MMR) without a tyrosine kinase inhibitor (TKI), such as imatinib (IM). Several studies have investigated the safety of the first TFR (TFR₁) attempt and suggested recommendation guidelines for such an attempt. However, the plausibility and predictive factors for a second TFR (TFR₂) have yet to be reported. The present study included 21 patients in chronic myeloid leukemia who participated in twice repeated treatment stop attempts. We develop a mathematical model to analyze and explain the outcomes of TFR₂. Our mathematical model framework can explain patient-specific molecular response dynamics. Fitting the model to longitudinal BCR-ABL1 transcripts from the patients generated patient-specific parameters. Binary tree decision analyses of the model parameters suggested a model based predictive binary classification factor that separated patients into low- and high-risk groups of TFR₂ attempts with an overall accuracy of 76.2% (sensitivity of 81.1% and specificity of 69.9%). The low-risk group maintained a median TFR₂ of 28.2 months, while the high-risk group relapsed at a median time of 3.25 months. Further, our model predicted a patient-specific optimal IM treatment duration before the second IM stop that could achieve the desired TFR₂ (e.g., 5 years).

Spatial dynamics of feedback and feedforward regulation in cell lineages

Feedback mechanisms within cell lineages are thought to be important for maintaining tissue homeostasis. Mathematical models that assume well-mixed cell populations, together with experimental data, have suggested that negative feedback from differentiated cells on the stem cell self-renewal probability can maintain a stable equilibrium and hence homeostasis. Cell lineage dynamics, however, are characterized by spatial structure, which can lead to different properties. Here, we investigate these dynamics using spatially explicit computational models, including cell division, differentiation, death, and migration / diffusion processes. According to these models, the negative feedback loop on stem cell self-renewal fails to maintain homeostasis, both under the assumption of strong spatial restrictions and fast migration / diffusion. Although homeostasis cannot be maintained, this feedback can regulate cell density and promote the formation of spatial structures in the model. Tissue homeostasis, however, can be achieved if spatially restricted negative feedback on self-renewal is combined with an experimentally documented spatial feedforward loop, in which stem cells regulate the fate of transit amplifying cells. This indicates that the dynamics of feedback regulation in tissue cell lineages are more complex than previously thought, and that combinations of spatially explicit control mechanisms are likely instrumental.