Role of neutrophil extracellular traps in regulation of lung cancer invasion and metastasis: Structural insights from a computational model

Abstract

Lung cancer is one of the leading causes of cancer-related deaths worldwide and is characterized by hijacking immune system for active growth and aggressive metastasis. Neutrophils, which in their original form should establish immune activities to the tumor as a first line of defense, are undermined by tumor cells to promote tumor invasion in several ways. In this study, we investigate the mutual interactions between the tumor cells and the neutrophils that facilitate tumor invasion by developing a mathematical model that involves taxis-reaction-diffusion equations for the critical components in the interaction. These include the densities of tumor and neutrophils, and the concentrations of signaling molecules and structure such as neutrophil extracellular traps (NETs). We apply the mathematical model to a Boyden invasion assay used in the experiments to demonstrate that the tumor-associated neutrophils can enhance tumor cell invasion by secreting the neutrophil elastase. We show that the model can both reproduce the major experimental observation on NET-mediated cancer invasion and make several important predictions to guide future experiments with the goal of the development of new anti-tumor strategies. Moreover, using this model, we investigate the fundamental mechanism of NET-mediated invasion of cancer cells and the impact of internal and external heterogeneity on the migration patterning of tumour cells and their response to different treatment schedules.

Fig 1. Interaction of the TGF-β, IFN-β, and NE-pathways in the control of tumor cell invasion.

In homeostasis of normal tissue, these pathways are balanced so as to control growth, but in lung cancer, increased secretion of TGF-β by tumor cells induces the N1→N2 transition of the neutrophils and stimulates their secretion of NE and other growth factors. This disrupts the homeostasis and stimulates aggressive tumor invasion.

Fig 2. Schematics of an invasion assay system.

(A) Boyden transwell invasion assay. Tumor cells were suspended in the upper chamber, while neutrophils or medium alone (control) were placed in the lower chamber. Semipermeable inserts coated with matrigel (extracellular matrix) were inserted in the filter. In response to NE secreted by N2 neutrophils in the lower chamber, tumor cells degrade the heavy extracellular matrix proteolytically and invade the lower chamber. The number of neutrophils on the lower surface of the permeable insert was counted after 22h in the absence and presence of neutrophils in the lower chamber. (B) TGF-β (G), NE (E), NE inhibitor (D), CXCL8 (C), MMP (P), TIMP (M) and tumor cells (n) can cross the semi-permeable membrane, but neither type of neutrophils (N₁, N₂) can cross it. Initially, the tumor cells reside in the upper chamber (domain Ω₊) while neutrophils are placed in the lower chamber (domain Ω₋). An extracellular matrix (ECM) layer (S) surrounds the filter, semi-permeable membrane ($\mathbb{M}$).

Fig 3. Dynamics of the system.

The time evolution of the density of each variable. (A) tumor cells and ECM (B) TGF-β (C) N1/N2 neutrophils (D) neutrophil elastase (E) MMP (F) CXCL8. Here, ECM = [0.35, 0.65]⊂ Ω = [0, 1]. Note that the initial concentrations of CXCL8, TGF-β, neutrophil elastase and MMPs are uniformly zero, as in experiments. *x-axis = space (the dimensionless length across the invasion chamber), y-axis = the dimensionless density/concentration of the variables.

Fig 4. TAN-promoted cancer cell invasion (Experiment [22] & simulation).

(A-B) Time courses of populations of total tumor cells (A) and invasive tumor cells (B). (C) Time courses of N1 (red solid) and N2 (blue dashed) neutrophils. (D) Experimental data from the invasion assay in [22] (left column; 4T1 cancer cells) and computational results from mathematical model (right column). The graph shows the (scaled) populations of invasive tumor cells at t = 22 h in the absence (control) or presence (⁺TAN) of neutrophils. Here and hereafter cell populations are derived from the continuum density.

Fig 5. DNase I treatment against NE can abrogate the invasion-boosting effects of neutrophils (Experimental data [22] and simulation results).

(A) NE levels in the system in the absence (control) and presence (⁺TAN) of neutrophils, and DNase treatment (⁺TAN⁺D) cases at t = 22 h. (B) Experimental data from the invasion assay in [22] (left column; 4T1 cancer cells) and computational results from mathematical model (right column). The graph shows the (scaled; %) population of invasive tumor cells at t = 22 h in the absence (control; blue) and presence (⁺TAN; red shaded) of neutrophils, and DNase treatment (⁺TAN⁺D; yellow dotted) cases. Addition of DNase I reduces the number of invading tumor cells by almost 50%.

Fig 6. TGF-β-mediated cancer-TAN interplay can induce two types of phenotypic states: invasive and non-invasive types.

(A) Conceptual interaction network for the mathematical model. (B) Population of invasive tumor cells in the lower chamber as a function of TGF-β. (C,D) Populations of N1 (C) and N2 (D) neutrophils as a function of TGF-β.

Fig 7. Effect of the N1→N2 transformation on tumor invasion and N1/N2 dynamics.

(A) Tumor density profiles on Ω = [0, 1] at the final time (t = 22 h) for various λ₁₂’s (λ₁₂ = 1.6 × 10⁻⁴, 1.6 × 10⁻², 1.6 × 10⁻¹). (B,C) Density profiles of the N1 and N2 TANs in the lower chamber ([0, 0.5]) for the corresponding λ₁₂’s in (A). (D) Time courses of NE levels for various values of the differentiation rate (λ₁₂ = 1.6 × 10⁻⁴, 1.6 × 10⁻³, 1.6 × 10⁻², 1.6 × 10⁻¹). (E,F) Scaled population of invasive tumor cells and neutrophils (N1 and N2) at the final time (t = 22 h) for various λ₁₂’s in (D).

Fig 8. The effect of TGF-β blocking (+Ab) and the combined therapy (+Ab+DNase I) on tumor cell invasion.

(A) The (relative) population of migrating tumor cells for various growth rates (r ∈ [1.2, 1.5]) and injection rates (λ_A ∈ [0, 10]) of the TGF-β antibody. When TGF-β activity is inhibited by antibody (r = 1.5), fewer cells (62% reduction) invade the lower chamber. (B) Population of migrating tumor cells when the TGF-β antibody was added in the absence (+Ab-D) and presence (+Ab+D) of DNase I relative to the control (-Ab-D). When proteolytic activity of tumor cells near the membrane is blocked by DNase I, fewer cells (66% reduction) invade the lower chamber in the presence of TGF-β inhibitor.

Fig 9. The effect of MMP blocking (+TIMP-Ab) and combined therapy (+TIMP+Ab).

(A,B) Levels of MMPs and ECM when MMP activity was blocked by TIMP in the absence (+TIMP-Ab) and presence (+TIMP+Ab) of the TGF-β antibody relative to the control (-TIMP-Ab). (C) Population of invading tumor cells corresponding to control (-TIMP-Ab), TIMP treatment (+TIMP-Ab), and combined therapy (+TIMP+Ab), respectively.

Fig 10. Inhibition of CXCL8 reduces proliferation, viability and invasion (Experiments vs simulation results).

(A) Time course of cell proliferation shows a drastic decrease in the LS174T cell population with CXCL8 knockdown (shCXCL8; blue) compared to control (LS174T) in in vivo experiments [100]. Model simulation shows a consistent, significant reduction of the tumor cell proliferation in the CXCL knockdown case (CXCL8-KO; blue) compared to control, abrogating tumor growth. (B) CXCL8 knockdown significantly decreases the invasiveness of LS174T cells (blue) [100], as our model simulation illustrates (red).