Mechanical Control of Cell Proliferation Increases Resistance to Chemotherapeutic Agents

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Mechanical Control of Cell Proliferation Increases Resistance to Chemotherapeutic Agents

Ilaria Francesca Rizzuti, Pietro Mascheroni, Silvia Arcucci, Zacchari Ben-Mériem, Audrey Prunet, Catherine Barentin, Charlotte Rivière, Hélène Delanoë-Ayari, Haralampos Hatzikirou, Julie Guillermet-Guibert, and Morgan Delarue

Phys. Rev. Lett. 125, 128103 – Published 18 September 2020

See Focus story: Compression of Tumors Causes Drug Resistance

Abstract

While many cellular mechanisms leading to chemotherapeutic resistance have been identified, there is an increasing realization that tumor-stroma interactions also play an important role. In particular, mechanical alterations are inherent to solid cancer progression and profoundly impact cell physiology. Here, we explore the influence of compressive stress on the efficacy of chemotherapeutics in pancreatic cancer spheroids. We find that increased compressive stress leads to decreased drug efficacy. Theoretical modeling and experiments suggest that mechanical stress decreases cell proliferation which in turn reduces the efficacy of chemotherapeutics that target proliferating cells. Our work highlights a mechanical form of drug resistance and suggests new strategies for therapy.

Received 20 January 2020
Accepted 7 August 2020

DOI:https://doi.org/10.1103/PhysRevLett.125.128103

Physics Subject Headings (PhySH)

Biomechanics Cancer Cell growth Cell mechanics Processes in cells, tissues & organoids

Tumors

Physics of Living SystemsInterdisciplinary Physics

Focus

Compression of Tumors Causes Drug Resistance

Published 18 September 2020

Pressure that develops as a tumor grows can limit the effectiveness of chemotherapy treatments.

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Authors & Affiliations

Ilaria Francesca Rizzuti^1,2,3,*, Pietro Mascheroni^4,*, Silvia Arcucci ^5,6, Zacchari Ben-Mériem¹, Audrey Prunet⁷, Catherine Barentin ⁷, Charlotte Rivière⁷, Hélène Delanoë-Ayari⁷, Haralampos Hatzikirou⁴, Julie Guillermet-Guibert ^5,6,†, and Morgan Delarue ^1,‡

¹CNRS, UPR8001, LAAS-CNRS, 7 Avenue du Colonel Roche, F-31400 Toulouse, France
²Laboratory of Nanotechnology for Precision Medicine, Fondazione Istituto Italiano di Tecnologia, Via Morego, 30, 16163 Genoa, Italy
³Computer Science and Technology, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genoa, Via All’Opera Pia, 13, 16145 Genoa, Italy
⁴Department of Systems Immunology and Braunschweig Integrated Center of Systems Biology (BRICS), Helmholtz Centre for Infection Research, Rebenring 56, 38106 Braunschweig, Germany
⁵INSERM U1037, CRCT, Universite Paul Sabatier, F-31037 Toulouse, France
⁶Laboratoire d’Excellence TouCAN, F-31037 Toulouse, France
⁷Univ Lyon, Univ Claude Bernard Lyon 1, CNRS, Institut Lumière Matière, F-69622 Villeurbanne, France

^*These authors contributed equally to this work.
^†To whom correspondence should be addressed. julie.guillermet@inserm.fr
^‡To whom correspondence should be addressed. morgan.delarue@laas.fr

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Issue

Vol. 125, Iss. 12 — 18 September 2020

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Images

Figure 1
Effect of gemcitabine on free growth. (a) Schematic of free growing spheroid. (b) Representative pictures of growing spheroids and one treated with gemcitabine after 2 days. (c) Growth quantification over time. Median values normalized to time $0 \pm SD$ over $N \geq 30$ spheroids. (d) Example of capillary western blot after 5 days under drug. (e) Analysis of capillary western blots. The data under gemcitabine treatment are normalized to the control. $Mean \pm standard error of the mean$ over $N \geq 3$ replicates. Data were pooled together. The quantity was renormalized by total volume of spheroids.
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Figure 2
Effect of gemcitabine on confined growth. (a) Measurements of elastic ( $G^{'}$ , $\circ$ ) and shear ( $G^{''}$ , square) moduli of 1% low-melt agarose. Different colors correspond to different forces used to hold the sample. (b) comsol simulation of a spheroid growing in a neo-Hookean material modeled with parameters extracted from (a). Inset: heat map of normal stress onto a spheroid (gray, $S$ ). (c) Schematic of agarose-confined spheroid. (d) Representative pictures of a confined spheroid and one treated with gemcitabine. (e) Growth-induced pressure of spheroids in 1% low-melt agarose as a function of time. Median values normalized to time $0 \pm SD$ over $N \geq 20$ spheroids. (f) Growth quantification of confined spheroids and treated ones with drug. Inset: median curves for free (black) and confined (blue) spheroids; dashed lines correspond to gemcitabine treatment. $Median \pm SD$ over $N \geq 20$ spheroids. Time 0 corresponds to agarose confinement.
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Figure 3
Prediction of the model on confined data. (a) Model plotted on confined spheroids exposed to gemcitabine (large dashed purple). Prediction score $χ = 0.94$ . (b) At drug addition, $R (t = 0) \geq R (t = - 2)$ , while at about day 7, the radius decreases due to drug treatment to a value $R (t = 7) \sim R (t = - 2)$ and become unconfined: subsequent decrease happens without mechanical stress. The model (large dashed green) captures this effect, switching from rate of confined cells (large dashed purple) to rate of unconfined cells. $χ = 0.86$ . In both cases, median values normalized to the time of drug $addition \pm SD$ over $N \geq 20$ spheroids. Time 0 corresponds to drug treatment. CTRL means control.
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Figure 4
Prediction of the model for (a) confined spheroids exposed to docetaxel ( $χ = 0.94$ ) and (b) osmotically compressed spheroids exposed to gemcitabine ( $χ = 0.92$ ). In both cases, median values normalized to the time of drug $addition \pm SD$ over $N \geq 10$ spheroids for docetaxel and $\geq 30$ spheroids for dextran. Drug was added at time 0 for (a) and at time 2 for (b).
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