Leveraging chromatin packing domains to target chemoevasion in vivo

Abstract

Cancer cells exhibit a remarkable resilience to cytotoxic stress, often adapting through transcriptional changes linked to alterations in chromatin structure. In several types of cancer, these adaptations involve epigenetic modifications and restructuring of topologically associating domains. However, the underlying principles by which chromatin architecture facilitates such adaptability across different cancers remain poorly understood. To investigate the role of chromatin in this process, we developed a physics-based model that connects chromatin organization to cell fate decisions, such as survival following chemotherapy. Our model builds on the observation that chromatin forms packing domains, which influence transcriptional activity through macromolecular crowding. The model accurately predicts chemoevasion in vitro, suggesting that changes in packing domains affect the likelihood of survival. Consistent results across diverse cancer types indicate that the model captures fundamental principles of chromatin-mediated adaptation, independent of the specific cancer or chemotherapy mechanisms involved. Based on these insights, we hypothesized that compounds capable of modulating packing domains, termed Transcriptional Plasticity Regulators (TPRs), could prevent cellular adaptation to chemotherapy. We conducted a proof-of-concept compound screen using live-cell chromatin imaging to identify several TPRs that synergistically enhanced chemotherapy-induced cell death. The most effective TPR significantly improved therapeutic outcomes in a patient-derived xenograft model of ovarian cancer. These findings underscore the central role of chromatin in cellular adaptation to cytotoxic stress and present a framework for enhancing cancer therapies, with broad potential across multiple cancer types.

Keywords: Biophysics; cancer; chemotherapy; chromatin; plasticity.

Fig. 1.

The Chromatin-Dependent Adaptability (CDA) model links chromatin organization to cell survival under cytotoxic stress via transcriptional plasticity. (A) Schematic showing differential responses of cells with low ($D_n=2.6$, blue) vs. high ($D_n=2.8$, red) average nuclear chromatin packing domain scaling to cytotoxic stress. Representative PWS images illustrate corresponding nuclear structure (scale $=$ 10 $\mathrm{\mu }$M; color = $D_{\text{pixel}}$). PDFs of gene upregulation ($x=N_2/N_1$) indicate that higher $D_n$ increases both mean and variability, surpassing $x_{\text{crit}}$ and enhancing survival probability. (B) 3D surface plot of normalized mRNA concentration as a function of transcriptional reactant (TR) concentration and local crowding (${\varphi }_{\text{in}}$). (C) Dependence of transcriptional malleability ($k$, purple) and heterogeneity ($s$, green) on $D_n$ for cell $b$; $ln(E/\overline{E})=-5.5$, ${\beta }_a=10$. (D) Cell death probability (${\mathrm{\Theta }}_b$) vs. $D_n$ across varying upregulation thresholds ($x_{\text{crit}}$); the Inset shows selected curves.

Fig. 2.

Cells that survive chemotherapy exhibit altered chromatin structure. (A) Split violin plots of $D_n$ in HCT116 cells over 48 h of 15 µM oxaliplatin. $n=70$ to 150 cells/condition. (B) Change in average $D_n$$\pm$ SE of the mean (SEM) per HCT116 cell cluster after 48 h treatment, relative to baseline ($D_{n,0}$$\pm$ SEM). Initial clusters: 2 to 5 cells; final: 1 to 12. (C) Representative SMLM images (EdU-labeled HCT116 cells): control (Top) and oxaliplatin-treated (Bottom). Pseudocolor shows localization density (white $=$ high). Red boxes mark zoomed regions (Right). [Scale bars: 2 µm (full), 0.5 µm (zoom).] (D) Violin plots of $D_n$ in surviving cells across treatments and cancer lines: A2780, A2780.m248, MDA-MB-231 (vehicle, 5-FU, paclitaxel, oxaliplatin); HCT116 (vehicle, oxaliplatin); MES-SA, MES-SA/MX2 (vehicle, docetaxel, gemcitabine). (E) Representative PWS images after 48 h treatment: A2780 (5-FU, 0.5 µM), HCT116 (oxaliplatin, 15 µM), MES-SA (gemcitabine, 50nM). Brighter red $=$ higher $D_{\text{pixel}}$. Scale: 10 µm.Significance: ^∗P < 0.05, ^∗∗∗P < 0.001 (unpaired two-tailed t test vs. control).

Fig. 3.

Experimental validation of model predictions linking chromatin structure to chemotherapy response. (A) PDFs of $D_n$ in HCT116 cell clusters at 0 (purple), 24 (blue), and 48 (teal) hours after 15 µM oxaliplatin treatment, showing a progressive shift toward higher $D_n$. (B) Cell death probability ($\mathrm{\Theta }$) vs. $D_n$, with experimental values (blue points $\pm$ SEM) from tracked HCT116 clusters during 48 h treatment. The solid purple line shows CDA model prediction (Eq. 6; $x_{\text{crit}}\approx 3$, ${\beta }_a\approx 7$, $\text{ln}(E/\overline{E})\approx 2.5$; MSE $=$ 0.001). (C) Comparison of model-predicted (purple) and experimentally observed (blue) increases in mean $D_n$$\pm$ SEM in HCT116 clusters over 48 h oxaliplatin treatment. (D) EIR over time, quantifying cumulative cell death from oxaliplatin, plotted against $D_n$. Experimental (blue $\pm$ SEM) and model-predicted (purple line) values shown.

Fig. 4.

TPRs modulate chromatin structure to enhance chemotherapeutic efficacy in vitro. (A) Schematic of differential survival in cancer populations with distinct PDF($D_n$) under chemotherapy. TPR-treated cells (green) show reduced $D_n$ and survival probability compared to control (magenta); the dashed gray line denotes $1-\mathrm{\Theta }$. (B) Proof-of-concept TPR screen in A2780 cells showing $D_n$ distributions across conditions. The dashed red line indicates critical threshold $D_{n,\text{crit}}=2.33$ ($\mathrm{\Theta }>0.99$). (C) Representative PWS images of control and celecoxib-treated A2780 cells. Pseudocolor reflects $D_{\text{pixel}}$ (higher $=$ brighter red). (Scale bars: 10 µm.) (D) TPR index (chromatin change) vs. inhibition index (chemotherapy efficacy), showing exponential relationship (fit: $y=2.0844e^{2.0107x}$; $R^2=0.98$). (E) Violin plots of $D_n$ in COX-2+ (A2780.m248) and COX-2- (HCT116) cells treated with aspirin (COX-1i), celecoxib (COX-2i), or sulindac (COX-1/2i); 100 to 200 cells per condition. (F) Box plots of percent inhibition in A2780.m248 (paclitaxel) and HCT116 (oxaliplatin) cells with and without COX inhibitors; each box: 20 to 30 populations. (G) Cell death comparison in A2780 cells: paclitaxel alone (${\mathrm{\Theta }}_{\text{Pac}}$) vs. paclitaxel + celecoxib, normalized by celecoxib alone (${\mathrm{\Theta }}_{\text{Combo-Cele}}$). Experimental data (blue $\pm$ SEM); solid/dashed purple lines show exact (Eq. 12) and approximate (Eq. 13) CDA predictions. Dashed gray line: no synergy expected. (B, E, and F) Significance: ^∗∗P < 0.01, ^∗∗∗P < 0.001 (unpaired two-tailed t test with unequal variance vs. control).

Fig. 5.

TPR cotreatment enhances chemotherapy efficacy in vivo in an ovarian cancer PDX model. (A) Schematic of treatment regimen and expected outcomes. Tumors treated with monotherapy (vehicle, chemotherapy, or TPR alone; red) are expected to continue growing, whereas combination therapy (chemotherapy $+$ TPR; purple) is predicted to suppress tumor growth. (B) Normalized tumor volume over 30 d across treatment groups. Points represent mean volume $\pm$ SEM; lines indicate linear regression. Cotreatment with celecoxib (25 mg/kg) and paclitaxel (1.7 mg/kg) minimized growth compared to monotherapies or vehicle (DMSO). (C) EIR over time for paclitaxel alone (normalized to vehicle) and celecoxib $+$ paclitaxel (normalized to celecoxib). Points show mean $\pm$ SEM; lines indicate linear regression fits. (D) Normalized tumor growth rate over time. Points represent experimental data (mean $\pm$ SEM); solid lines show model fits using the adaptive inhibition equation (Eq. 14).