# Breast implant silicone exposure induces immunogenic response and autoimmune markers in human periprosthetic tissue

Isabelle Pluvy, Eve Randrianaridera, Ismail Tahmaz, Martine Melin, Florelle Gindraux, Céline Keime, Arnaud Ponche, Tatiana Petithory, Laurent Pieuchot, Karine Anselme, et al.

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## ► To cite this version:

Isabelle Pluvy, Eve Randrianaridera, Ismail Tahmaz, Martine Melin, Florelle Gindraux, et al. Breast implant silicone exposure induces immunogenic response and autoimmune markers in human periprosthetic tissue. Biomaterials, 2024, 317, pp.123025. 10.1016/j.biomaterials.2024.123025. hal-05015504

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https://hal.science/hal-05015504v1

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# Breast implant silicone exposure induces immunogenic response and autoimmune markers in human periprosthetic tissue

Isabelle Pluvy, Eve Randrianaridera, Ismail Tahmaz, Martine Melin, Florelle Gindraux, Céline Keime, Arnaud Ponche, Tatiana Petithory, Laurent Pieuchot, Karine Anselme, et al.

<figure><img src="image_3.png" /></figure>

## To cite this version:

Isabelle Pluvy, Eve Randrianaridera, Ismail Tahmaz, Martine Melin, Florelle Gindraux, et al. Breast implant silicone exposure induces immunogenic response and autoimmune markers in human periprosthetic tissue. 2024. hal-04739674

HAL Id: hal-04739674

https://hal.science/hal-04739674v1

Preprint submitted on 16 Oct 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
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Breast implant silicone exposure induces immunogenic response and autoimmune markers in human periprosthetic tissue

Isabelle Pluvy¹, Eve Randrianaridera², Ismail Tahmaz², Martine Melin³, Florelle Gindraux¹,⁴, Céline

Keime⁵, Arnaud Ponche², Tatiana Petithory², Laurent Pieuchot², Karine Anselme², Isabelle Brigaud²

1. Université de Franche-Comté, CHU Besançon, Laboratoire SINERGIES, Service d'orthopédie, traumatologie et chirurgie plastique, F-25000 Besançon, France.

2. Institut de Science des Matériaux de Mulhouse (IS2M), UMR 7361 CNRS/, Université de Haute Alsace (UHA), 15 rue Jean Starcky, 68057, Mulhouse Cedex, France.

3. Novotec, ZAC du Chêne, Europarc, 11 rue Edison, 69500 Bron, France.

4. Orthopaedic, Traumatology and Plastic Surgery Department, University Hospital of Besançon, 25000 Besançon, France.

5. GenomEast platform, IGBMC, CNRS UMR 7104, INSERM U1258, Université de Strasbourg, F-67400 Illkirch, France.

6. Service d'anatomie pathologique, CHU Besançon, F-25000 Besançon, France

**Corresponding author:** Isabelle Brigaud

**Email:** isabelle.brigaud@uha.fr
Abstract

Silicone-based breast implants are commonly used, but there are concerns about their long-term
safety. While implantation results in the formation of a periprosthetic tissue that isolates the
implant from the rest of the host body, silicone can leak and reach surrounding tissues. We
combined histological analysis and gene expression profiling (RNA sequencing) of samples from
human patients with silicone breast implants with different fillers (silicone or serum), surface
topographies and/or shell rupture, and performed systematic cross-comparisons. Our study
shows that exposure to silicone gel filler, even in clinically asymptomatic cases, induces an
immune response. This response includes the expression of markers associated with various
autoimmune diseases. This study provides the first biological evidence of an association between
silicone implants and autoimmune markers, highlighting the need for further research and stricter
implant safety regulations. We suggest that implant design factors, such as filler type and surface
texture, may influence the inflammatory response. Re-evaluation of existing clinical trials is
warranted to investigate the association between implant characteristics and potential health
risks.

Keywords

breast implant, silicone, human periprosthetic capsule, transcriptome, inflammation, histology

Introduction

Silicone breast implants are the products of decades of research and are designed to support
durable and safe implantation[1]. However, since their first introduction in 1962, biological
inertness of silicone breast implant remains a recurring and controversial topic. On the one hand,
large studies have repeatedly concluded that breast implant are safe, while acknowledging a low
rate of complications associated with their use[2,3]. On the other hand, accumulating data
identified silicone exposure as a risk factor to develop a broad spectrum of diseases related to the immune's system response to foreign material, a topic well documented under different acronyms such as BII (Breast Implant Illness)[4-8], "SIIS" (Silicone Implant Incompatibility Syndrome)[9] or "ASIA" (Autoimmune Syndrome Induced Adjuvants)[10,11]. More recently, silicone exposure and associated silicone toxicity have been implicated as factors favoring the development of breast implant-associated anaplastic large cell lymphoma (BIA-ALCL)[12,13], breast implant-associated squamous cell carcinoma (BIA-SCC)[14] and allergic manifestations[15].

Breast implants consist of a highly cross-linked silicone shell with a smooth or textured outer surface, containing a filler (saline, silicone gel or hydrogel) (Fig. 1). Silicone gel is a very lightly cross-linked silicone elastomer whose polymer network has been swollen with silicone fluids. During implantation, the host body unable to eliminate the foreign implant, isolates this exogenous device by synthesizing a fibrous tissue, the periprosthetic capsule, which encapsulates the implant under the control of an immune process known as the foreign body reaction (FBR) (Fig. 1A). For the duration of the implantation, this tissue becomes the recipient of silicone particles that are released from the implant. The presence of silicone particles in periprosthetic tissues was first reported in 1967[16] and remains a cause for concern today. There are three potential sources of silicone particles that may combine to expose periprosthetic tissues (Fig. 1B).

(i) *Surface debris* are solid fragments detaching from the cross-linked silicone elastomer shell due to implant surface wear and fatigue. This process is accentuated with increasing degree of surface texturing, namely macrotextured implants[17-20] (Fig. 1C). Breast implant debris demonstrate pro-inflammatory effects both *in vitro* [21] and *in vivo* in cell and animal model[22] for both innate (macrophage) and adaptive (lymphocytes) mediated inflammation (reviewed in[17]). (ii) *Silicone gel permeation* refers to the diffusion of non-reticulated low molecular-weight silicone species
confined within the filling gel through the elastomer shell of the breast implant. This physical
process, often referred to in the literature under the misnomer "gel bleed", has been estimated
at 300 mg per year per intact implant [23], a negligible amount from an aesthetic point of view but
significant on a cellular scale. In the same vein and very recently, Danino et al. quantified the
number of silicone particles in periprosthetic tissue from intact implants. They found an average
of one million silicone particles per capsule [24]. (iii) *Rupture* of the implant envelope that triggers
the leak of the silicone filling gel, which can be considered as an intensified form of gel bleed. In
most cases, silicone leakage after rupture is confined to the periprosthetic capsule (intracapsular
rupture) but can occasionally spread to the surrounding tissue if both the implant and capsule
have been damaged (extracapsular rupture). In the literature, intracapsular rupture is typically
described as "silent", i.e asymptomatic and clinically undetectable. It is considered to be a
relatively benign condition [25] with no direct health risk [26,27], which together support the idea
of silicone innocuity.

In short, the likelihood of periprosthetic tissue coming into contact with silicone during
implantation is therefore highly significant. Accordingly, silicone particles embedded in
periprosthetic tissue may be a potential source of chronic inflammation around breast implants.
Here, we present a pilot study investigating the inflammatory characteristics of human
periprosthetic tissue following silicone exposure. In particular, we focus on changes in (i) tissue
organization and (ii) transcriptomic signatures of human periprosthetic tissues exposed to
different sources of silicone with respect to implant integrity (intact or ruptured), surface
texturing (macro- or microtexturing) and implant filling (silicone gel or serum). Overall, this work
provides new insights into the immunological mechanisms triggered by silicone, and, once again,
challenges the concept of silicone safety.
Tissue sampling and data collections

This pilot study was conducted between 2017 and 2023. Sampling of tissue explants was bi-centric
and strictly performed on healthy women. Samples were collected from patients who had
previously participated in our research[53] and from the tissue bank of Franche-Comté (reference
BB-0033-00024) from the University Hospital of Besançon (France). When possible, histological
sections were also collected. After approbation by the local institutional review board
(NCT06414785), patients were required to provide informed consent to the use and analysis of
their data. After anonymization of the patients, the following data were collected: (i) age of the
patient (ii) characteristics of the breast implants, including volume, shape, manufacturer, filler
(saline or silicone gel), surface topography (macrotextured and microtextured, see[53] for details)
and implant integrity (intact versus ruptured). Implant integrity was assessed by the operating
surgeon by macroscopic examination of the explanted device. (iii) specifications of the breast
implant surgery including surgical approach, type of implant pocket, reason for explantation and
duration of device implantation.

Histology and image acquisition

A total of 43 patients were included in the histological analyses. The relevant patient and data
implant characteristics are gathered in **Tab.1**. Serial 4 µm-thick sections were obtained from
formalin-fixed, paraffin-embedded breast capsule samples using a microtome, then stained with
hematoxylin-eosin (HE) or hematoxylin-eosin-saffron (HES). Microscopic observations were done
using a light microscope (Leica DM2000) with objectives of 20X, 40X and 63X. The micrographs
were captured using a digital camera (LAS Version 4.2) and the photo layout was performed using
image software (Adobe, Photoshop cc2019). The histological section analysis was performed by Novotec institute (Bron, France) in a blinded condition by a certified and trained histologist.

<table><thead><tr><th>Implant categories (histology)</th><th>Ruptured</th><th>Macrotextured</th><th>Microtextured</th><th>Textured saline-filled</th><th>Total sampling</th></tr></thead><tbody><tr><td>Filling</td><td></td><td>Silicone gel</td><td></td><td>Saline</td><td></td></tr><tr><td>All implants (%)</td><td>15/43 (35%)</td><td>10/43 (23%)</td><td>13/43 (30%)</td><td>5/43 (12%)</td><td>43/43 (100%)</td></tr><tr><td>Patient age (year)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mean +/- SD</td><td>59 +/-15</td><td>59 +/-15</td><td>48 +/-19</td><td>54 +/-15</td><td>55 +/-16</td></tr><tr><td>Duration of implantation (year)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(implantation to explantation)</td><td>14 +/- 8</td><td>12 +/-5</td><td>11 +/-6</td><td>13 +/- 4</td><td>13 +/-6</td></tr><tr><td>Mean +/- SD</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Implant volume</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>mL +/- SD</td><td>335 +/-83</td><td>316 +/-84</td><td>241 +/- 57</td><td>192 +/- 45</td><td>278 +/-90</td></tr><tr><td>Implant shape</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>• Round</td><td>5/15 (33%)</td><td>4/10 (40%)</td><td>7/13 (54%)</td><td>5/5 (100%)</td><td>21/43 (48%)</td></tr><tr><td>• Anatomic</td><td>6/15 (40%)</td><td>3/10 (30%)</td><td>5/13 (38%)</td><td>0/7 (0%)</td><td>14/43 (33%)</td></tr><tr><td>• Not reported</td><td>4/15 (27%)</td><td>3/10 (30%)</td><td>1/13 (8%)</td><td>0/7 (0%)</td><td>8/43 (19%)</td></tr><tr><td>Manufacturer</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>• Allergan</td><td>3/15 (20%)</td><td>3/10 (30%)</td><td>0/9 (0%)</td><td>0/5 (0%)</td><td>6/43 (14%)</td></tr><tr><td>• Arion</td><td>2/15 (13%)</td><td>0/10 (0%)</td><td>0/9 (0%)</td><td>1/5 (20%)</td><td>3/43 (7%)</td></tr><tr><td>• Céréform</td><td>1/15 (7%)</td><td>1/10 (10%)</td><td>0/9 (0%)</td><td>0/5 (0%)</td><td>2/43 (5%)</td></tr><tr><td>• Mc Ghan</td><td>0/15 (0%)</td><td>0/10 (0%)</td><td>4/13 (31%)</td><td>0/5 (0%)</td><td>4/43 (10%)</td></tr><tr><td>• Mentor</td><td>1/15 (7%)</td><td>0/10 (0%)</td><td>0/9 (0%)</td><td>0/5 (0%)</td><td>1/43 (3%)</td></tr><tr><td>• Perthèse</td><td>1/15 (7%)</td><td>0/10 (0%)</td><td>2/13 (15%)</td><td>0/5 (0%)</td><td>3/43 (7%)</td></tr><tr><td>• PIP</td><td>3/15 (20%)</td><td>0/10 (0%)</td><td>0/9 (0%)</td><td>0/5 (0%)</td><td>3/43 (7%)</td></tr><tr><td>• Sebbin</td><td>2/15 (13%)</td><td>5/10 (50%)</td><td>0/9 (0%)</td><td>0/5 (0%)</td><td>7/43 (16%)</td></tr><tr><td>• Silimed</td><td>0/15 (0%)</td><td>0/10 (0%)</td><td>5/13 (38%)</td><td>2/5 (40%)</td><td>7/43 (16%)</td></tr><tr><td>• Not reported</td><td>2/15 (13%)</td><td>1/10 (10%)</td><td>2/13 (15%)</td><td>2/5 (40%)</td><td>7/43 (16%)</td></tr><tr><td>Surgical approach</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>• Flap edge</td><td>3/15 (20%)</td><td>2/10 (20%)</td><td>2/13 (15%)</td><td>0/5 (0%)</td><td>7/43 (18%)</td></tr><tr><td>• Mastectomy scar</td><td>4/15 (27%)</td><td>5/10 (50%)</td><td>1/13 (8%)</td><td>0/5 (0%)</td><td>10/43 (23%)</td></tr><tr><td>• Periareolar</td><td>7/15 (46%)</td><td>1/10 (10%)</td><td>6/13 (46%)</td><td>2/5 (40%)</td><td>16/43 (37%)</td></tr><tr><td>• inflamammary</td><td>0/15 (0%)</td><td>0/10 (0%)</td><td>0/13 (0%)</td><td>2/5 (40%)</td><td>2/43 (5%)</td></tr><tr><td>• Not reported</td><td>1/15 (7%)</td><td>2/10 (20%)</td><td>4/13 (31%)</td><td>1/5 (20%)</td><td>8/43 (18%)</td></tr><tr><td>Pocket</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>• Under a flap</td><td>3/15 (20%)</td><td>0/10 (0%)</td><td>0/13 (0%)</td><td>0/7 (0%)</td><td>3/43 (8%)</td></tr><tr><td>• Prepectoral</td><td>3/15 (20%)</td><td>3/10 (30%)</td><td>5/13 (38%)</td><td>4/5 (80%)</td><td>15/43 (35%)</td></tr><tr><td>• Retropectoral</td><td>8/15 (53%)</td><td>2/10 (20%)</td><td>3/13 (23%)</td><td>0/5 (0%)</td><td>13/43 (30%)</td></tr></tbody></table>
<table><caption>**Tab 1.** Collection of patients, surgical and implant data for histology sampling.</caption><thead><tr><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td>• not reported</td><td>1/15 (7%)</td><td>5/10 (50%)</td><td>5/13 (38%)</td><td>1/5 (20%)</td><td>12/43 (28%)</td></tr><tr><td colspan="6"><strong>Indications for explantation</strong></td></tr><tr><td>• Rupture</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>• Suspicion of rupture</td><td>15/15 (100%)</td><td>0/10 (0%)</td><td>0/13 (0%)</td><td>0/5 (0%)</td><td>15/43 (35%)</td></tr><tr><td>• revision surgery</td><td>0/15 (0%)</td><td>1/10 (10%)</td><td>0/13 (0%)</td><td>0/5 (0%)</td><td>1/43 (3%)</td></tr><tr><td>• controlateral complication</td><td>0/15 (0%)</td><td>2/10 (20%)</td><td>1/13 (8%)</td><td>3/5 (60%)</td><td>6/43 (14%)</td></tr><tr><td>• Size change/implant removal</td><td>0/15 (0%)</td><td>5/10 (50%)</td><td>10/13 (56%)</td><td>0/5 (0%)</td><td>15/43 (35%)</td></tr><tr><td>• Not reported</td><td>0/15 (0%)</td><td>2/10 (20%)</td><td>1/13 (8%)</td><td>0/5 (0%)</td><td>3/43 (7%)</td></tr><tr><td></td><td></td><td>0/10 (0%)</td><td>1/13 (8%)</td><td>2/5 (40%)</td><td>3/43 (7%)</td></tr></tbody></table>

**Scoring of inflammation in histological sections**

An inflammatory scoring system based on semi-quantitative data obtained from tissues was established in blinded condition. The scoring approach was derived from the quantification of (i) immune cells in each tissue, including polymorphonuclear cells, lymphocytes, macrophages, foamy macrophages and foreign body giant cells, (ii) silicone identification and (iii) synovial-like metaplasia. The scale for each variable was as follows: frequency of observation: score = 0: absence; score=1 limited; score=2: moderate; score=3: severe. FBR is mainly driven by macrophage cells[54] and thus, total FBR inflammation per tissue was calculated by summing the scores obtained with macrophages, foamy macrophages and foreign body giant cells. The resulting scores were then categorized as follows: score = 0: no inflammation; 1≤score≤3: limited inflammation; 4≤score≤6: moderate inflammation; 6≤score≤9: severe inflammation. The frequency of each variable was calculated in terms of percentage for the four categories, i.e. ruptured, macro-, microtextured and saline-filled implants (number of samples with variable / total number of samples x 100). Histology scoring profiles were generated by using Plotly software (Plotly Montreal Quebec Canada) and included mean values, standard error of the mean, and
putative outliers. The statistical analyses included a t test with levels of significance ranging from p<0.01 (*), p<0.001 (**) and p<0.0001 (***).

## Tissue disruption, RNA extraction, RNAseq

The collected tissues were immediately immersed in RNAlater solution (Invitrogen, Carlsbad, Calif.) and preserved at -20 °C until further use. Total RNAs were extracted using the RNeasy Fibrous Tissue Mini Kit (Qiagen) in accordance with the manufacturer's instructions. The tissues were weighed to 25 ± 5 mg, then cut into small pieces with a scalpel, and finally transferred to 2mL tubes containing beads (CKMIX 28R, Bertin Instruments; Rockville, MD, USA) pre-filled with 300µL lysis buffer. The tubes containing the tissue were then placed in a tissue homogenizer (Precellys Evolution, Bertin Instruments Tustin, USA) where the tissue was mechanically ground to a liquid twice at 8500 rpm for 30 seconds. Following proteinase K treatment, samples were transferred to columns for RNA purification, including a DNase treatment. RNA concentration and quality were evaluated on Bioanalyzer (Agilent). Twenty-one RNAs met the quality requirements for RNA sequencing, with a relative integrity number (RIN) greater than or equal to seven. Further details, including patient and implant characteristics are presented in Tab.2.

<table><thead><tr><th>Implant categories (RNAseq)</th><th>Ruptured</th><th>Macrotextured</th><th>Microtextured</th><th>Textured Saline-filled</th><th>Total sampling</th></tr></thead><tbody><tr><th>Filling</th><td></td><td>Silicone gel</td><td></td><td>Saline</td><td></td></tr><tr><th>All implants (%)</th><td>8/21 (38%)</td><td>5/21 (24%)</td><td>5/21 (24%)</td><td>3/21 (14%)</td><td>21/21(100%)</td></tr><tr><th>Patient age (year) Mean +/- SD</th><td>49 +/-8</td><td>50 +/-8</td><td>43 +/-16</td><td>41 +/-7</td><td>47 +/-10</td></tr><tr><th>Duration of implantation (year) (implantation to explantation) Mean +/- SD</th><td>12 +/- 3</td><td>15 +/-1</td><td>7 +/-6</td><td>13 +/-4</td><td>12 +/-5</td></tr><tr><th>Implant volume</th><td>378 +/- 114</td><td>276 +/-27</td><td>217 +/-67</td><td>302 +/- 121</td><td>300 +/-102</td></tr></tbody></table>
<table><caption>Tab 2. Collection of patients, surgical and implant data for RNA sequencing sampling.</caption><thead><tr><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td colspan="6">mL +/- SD</td></tr><tr><td>Implant shape</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>• Round</td><td>3/8 (38%)</td><td>4/5 (80%)</td><td>2/5 (40%)</td><td>1/3 (33%)</td><td>10/21 (48%)</td></tr><tr><td>• Anatomic</td><td>3/8 (38%)</td><td>1/5 (20%)</td><td>0/5 (0%)</td><td>1/3 (33%)</td><td>5/21 (24%)</td></tr><tr><td>• Not reported</td><td>2/8 (24%)</td><td>0/5 (0%)</td><td>3/5 (60%)</td><td>1/3 (33%)</td><td>6/21 (28%)</td></tr><tr><td>manufacturer</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>• Allergan</td><td>1/8 (13%)</td><td>5/5 (100%)</td><td>0/5 (0%)</td><td>1/3 (33%)</td><td>7/21 (33%)</td></tr><tr><td>• Arion</td><td>0/8 (0%)</td><td>0/5 (0%)</td><td>0/5 (0%)</td><td>1/3 (0%)</td><td>1/21 (5%)</td></tr><tr><td>• Céréform</td><td>0/8 (0%)</td><td>0/5 (0%)</td><td>0/5 (0%)</td><td>0/3 (33%)</td><td>0/21 (0%)</td></tr><tr><td>• Eurosilicone</td><td>0/8 (0%)</td><td>0/5 (0%)</td><td>0/5 (0%)</td><td>1/3 (0%)</td><td>1/21 (5%)</td></tr><tr><td>• Nagor</td><td>1/8 (13%)</td><td>0/5 (0%)</td><td>2/5 (40%)</td><td>0/3 (0%)</td><td>3/21 (14%)</td></tr><tr><td>• Polytech</td><td>0/8 (0%)</td><td>0/5 (0%)</td><td>0/5 (0%)</td><td>0/3 (0%)</td><td>0/21 (0%)</td></tr><tr><td>• Sebbin</td><td>6/8 (75%)</td><td>0/5 (0%)</td><td>0/5 (0%)</td><td>0/3 (0%)</td><td>6/21 (29%)</td></tr><tr><td>• Not reported</td><td>0/8 (0%)</td><td>0/5 (0%)</td><td>3/5 (60%)</td><td>0/3 (0%)</td><td>3/21 (14%)</td></tr><tr><td>Surgical approach</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>• Transaxillary</td><td>0/8 (0%)</td><td>3/5 (60%)</td><td>0/5 (0%)</td><td>0/3 (0%)</td><td>3/21 (14%)</td></tr><tr><td>• Mastectomy scar</td><td>0/8 (0%)</td><td>0/5 (0%)</td><td>0/5 (0%)</td><td>0/3 (0%)</td><td>0/21 (0%)</td></tr><tr><td>• Periareolar</td><td>1/8 (13%)</td><td>2/5 (40%)</td><td>2/5 (40%)</td><td>3/3 (100%)</td><td>8/21 (38%)</td></tr><tr><td>• Inflamammary</td><td>3/8 (38%)</td><td>0/5 (0%)</td><td>2/5 (40%)</td><td>0/3 (0%)</td><td>5/21 (24%)</td></tr><tr><td>• Not reported</td><td>4/8 (50%)</td><td>0/5 (0%)</td><td>1/5 (20%)</td><td>0/3 (0%)</td><td>5/21 (24%)</td></tr><tr><td>pocket</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>• Under a flap</td><td>3/8 (38%)</td><td>5/5 (100%)</td><td>2/5 (40%)</td><td>1/3 (33%)</td><td>11/21 (52%)</td></tr><tr><td>• Prepectoral</td><td>2/8 (25%)</td><td>0/5 (0%)</td><td>2/5 (40%)</td><td>1/3 (33%)</td><td>5/21 (24%)</td></tr><tr><td>• not reported</td><td>3/8 (38%)</td><td>0/5 (0%)</td><td>1/5 (20%)</td><td>1/3 (33%)</td><td>5/21 (24%)</td></tr><tr><td>Reasons for explantation</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>• Rupture</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>• revision surgery</td><td>8/8 (100%)</td><td>0/5 (0%)</td><td>0/5 (0%)</td><td>0/3 (0%)</td><td>8/21 (38%)</td></tr><tr><td>• controlateral</td><td>0/8 (0%)</td><td>3/5 (60%)</td><td>1/5 (20%)</td><td>1/3 (25%)</td><td>5/21 (24%)</td></tr><tr><td>• complication</td><td>0/8 (0%)</td><td>1/5 (20%)</td><td>2/5 (40%)</td><td>2/3 (75%)</td><td>5/21 (24%)</td></tr><tr><td>• Size change/implant removal</td><td>0/8 (0%)</td><td>0/5 (0%)</td><td>0/5 (0%)</td><td>0/3 (0%)</td><td>0/21 (0%)</td></tr><tr><td>• Not reported</td><td>0/8 (0%)</td><td>1/5 (20%)</td><td>2/5 (40%)</td><td>0/3 (0%)</td><td>3/21 (14%)</td></tr></tbody></table>

RNAseq library

Library preparation was performed at the GenomEast platform at the Institute of Genetics and Molecular and Cellular Biology (Strasbourg, France) using Illumina Stranded mRNA Prep Ligation
- Reference Guide - PN 1000000124518. RNA-Seq libraries were generated from 100 ng of total RNA using Illumina Stranded mRNA Prep, Ligation kit and IDT for Illumina RNA UD Indexes Ligation (Illumina, San Diego, USA) according to manufacturer's instructions. Following polyA selection, mRNAs were fragmented at 94°C for 8 minutes. DNA libraries were amplified using 12 cycles of PCR. Surplus PCR primers were further removed by two successive purifications using SPRIselect beads (Beckman-Coulter, Villepinte, France). The final libraries were checked for quality and quantified using Bioanalyzer 2100 system (Agilent technologies, Les Ulis, France). Libraries were sequenced on an Illumina NextSeq 2000 sequencer as single read 50 base reads. Image analysis and base calling were performed using RTA version 2.7.7 and BCL Convert version 3.8.4. Reads were preprocessed using cutadapt version 4.2 to remove adapter[55], polyA and low-quality sequences (Phred quality score below 20), reads shorter than 40 bases were discarded for further analysis. Reads mapping to rRNA were also discarded (this mapping was performed using bowtie version 2.2.8[56]). Reads were then mapped onto the GRCh38 human genome assembly using STAR version 2.7.10b[57]. Gene expression was quantified using htseq-count version 0.13.5[58] and gene annotations from Ensembl release 110.

Differentially expressed gene analysis

Differential gene expression analysis was performed using the method for differential analysis of
count data proposed by Love et al[59] and implemented in the Bioconductor package DESeq2
version 1.34.0 with a Wald test. Differentially expressed genes with absolute values of log2 (fold
change) > 1 and adjusted *p-value* <0.05 were selected for further analysis, with *p-value* calculated
according to the Benjamini and Hochberg *p-value* adjustment method[60]. Up- or downregulated
transcripts were illustrated as Venn diagram and bar plot in all conditions using data visualization
tool available online (http://www.bioinformatics.com.cn/srplot). The transcriptomic data source
will be available in a GEO repository, which is currently being prepared.

## Biological and pathway-based enrichment analysis

To gain a deeper insight into the functional implication of the observed gene expression changes
and to identify the key genes regulated by silicone exposure and/or surface topography, we
performed a Gene Ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and
Genomes (KEGG) annotations (see (36)). We have implemented all the genes that are specifically
up- or down-regulated. We used g:Profiler (version e111_eg58_p18_30541362,
https://biit.cs.ut.ee/gprofiler/gost ) adjusted for "Homo sapiens", applied the Benjamini and
Hochberg correction for multiple testing and a significance threshold of 0.05. Annotations were
considered significant based on a concurrent *p*-value <0.05 and gene pool size >10, and were
selected for display in a GO plot or GO chord representation. Online resources were used to create
Venn diagrams (https://www.bioinformatics.com.cn/static/others/jvenn_en/example.html), bar
charts (https://chart-studio plotly.com), GO, KEGG enrichment and chord diagrams
(https://biit.cs.ut.ee/gprofiler/gost and http://www.bioinformatics.com.cn/en). The figures were
then annotated in Adobe Illustrator (CC 2019).

## Results

Inflammation markers are limited in saline-filled and silicone gel-filled microtextured implants

Irrespective of the topography of the implant surface, whether micro- or macrotextured, the
fibrotic capsular tissue that developed in contact with the saline-filled implants consisted of a
dense and homogeneous matrix of collagen fibers and fibroblasts (Fig. 2A). Scattered
macrophages were seen along the capsule in most specimens. Rare foreign body giant cells (Fig.
2B) and discrete foamy macrophages (observed in one patient) were identified in these tissues, testifying of silicone exposure originating specifically from the implant shell. Otherwise, no immune cell other than macrophages were found (Fig. 3A). In these tissues, the mean inflammation score was limited (1.8 ± 0.7, Fig. 3B). The fibrous tissue adjacent to the silicone gel-filled microtextured implants showed an organization similar to that of the saline-filled implants (Fig. 2C). Most of the immune cells were macrophages, mainly distributed at the implant interface. In some cases, macrophage-like cells were arranged in a synovial metaplasia-like manner [28-33] (Fig. 2C and 2D). Foamy macrophages (Fig. 2E) and FBGCs were occasionally associated with infiltrates of discrete silicone droplets (~5µm in diameter). Sporadic lymphocytes were also observed (Fig. 3A). In these tissues, the mean inflammation score is limited and statistically similar to that elicited in saline-filled implants (1.6 ± 0.8, Fig. 3B).

Inflammation markers range from limited to moderate in silicone gel-filled macrotextured
implants

The immune response to gel-filled macrotextured implants was heterogeneous from patient to patient. Collagen matrix remained predominant in tissues with limited inflammation score (score <3, Fig. 2F) and punctuated by FBGCs filled with thread-like structures of translucent amorphous refractile material previously referred as silicone[34] (Fig. 2G). Localized brown deposits in vacuoles indicate phagocytosis debris (Fig. 2H). A striking reorganization affected tissues with higher inflammation score (score >3, Fig. 3B). Capsular tissues were characterized by a significant vascularization and heterogenous collagen matrix. A prominent accumulation of macrophage cells accumulated at the interface with the implant, forming synovial-like metaplasia. Deeper into the tissue, macrophages spread out in high density, reminiscent of the immune reaction defined
elsewhere in the literature as silicone-induced granuloma[12] (Fig. 2I). FBGCs and macrophages, with their cytoplasm containing refractory material, actively attempted to degrade silicone (Fig. 2J-K). Overall, the frequency of observation of silicone, FGBCs and to a lesser extent of lymphocytes was increased compared to microtextured and saline-filled implants (Fig. 3A). The macrophage-driven inflammation score in tissues facing macrotextured implants ranged from limited to moderate (score >3) (Fig. 3B), and was statistically higher than in microtextured or saline-filled implants.

## Chronic macrophage-driven FBR in response to implant rupture

Capsular tissues facing ruptured implants showed distinct cytologic aspects. Two patterns of organization were reported depending on the specimen but independent of the degree of texture of the implant surface. In one hand, typical connective tissue was replaced by palisade-like tissue formed by multiple layers of juxtaposed foamy macrophages extending up to ~500µm thick (Fig. 2L) and rimmed by collagenous stroma (Fig. 2M-N). Silicone was sequestered in droplets or in vacuoles of 20-30µm in diameter. On the other hand, foamy macrophages give way to an expanse of large vacuoles filled with refringent material (up to 250µm in diameter) scattered within the collagenous tissue (Fig. 2O) and surrounded by FBGCs or macrophages (Fig. 2P-Q). The size of the vacuoles appeared to follow a gradient, with the smallest vacuoles at the implant interface and the largest in the innermost part of the tissue (Fig. 2O). In addition to macrophages, which were widely represented, we also observed small lymphocyte loci and polymorphonuclear cells in some sections (data not shown). Finally, despite clinically verified implant rupture, we were unable to observe silicone in all tissues, four of which were silicone-free, suggesting a bias in the region selected for the observation, possibly at the time of tissue collection. Of interest, silicone-free tissues were associated with a low inflammation score (<3). Overall, tissues facing ruptured
implants were statistically more immunoreactive than non-ruptured implants with inflammation scores ranging from severe (> 6) to limited (>3) (Fig. 3B).

## Effect of silicone exposure on gene expression

RNA sequencing analysis revealed differences in gene expression between tissues facing ruptured implants and tissues facing intact implants (Fig. 4A and volcano plots, Fig. S1). Regardless of surface topography, a total of 3993 differentially expressed genes (including 1629 up-regulated and 2364 down-regulated, Fig. 4A-B) were highlighted when comparing tissues facing intact serum-filled implants with tissues facing gel-filled ruptured implants. Importantly, 333 genes were found to be differentially expressed when comparing the implant rupture group as a function of surface topography, i.e. macro- versus microtextured implants, including 97 and 236 up- and down-regulated genes respectively. This strongly suggests that tissue response is determined by a cumulative effect between gel and surface topography. Therefore, to improve the robustness of our approach, we then compared ruptured and intact implants with the same surface topography. Specifically, we found 892 differentially expressed genes (including 217 up-regulated and 675 down-regulated) between gel-filled macrotextured ruptured versus intact implants and 434 differentially expressed genes (including 232 up-regulated and 202 down-regulated) between gel-filled ruptured implants and intact microtextured implants (Fig. 4A-B).

## Silicone exposure affects gene expression and related functional pathways

The differential gene expression between gel-filled ruptured implants and intact serum-filled implants highlighted the global effect of silicone exposure on periprosthetic tissues, regardless of surface texture, with serum-filled implants used as our reference group for this study. A total of 967 GO terms and 30 KEGG were found (Fig. 4C). Silicone exposure after implant rupture was
found to affect the immune response, including both innate and adaptive immune responses. The activity of several immune cells was found to be affected after silicone exposure, including leukocytes (activation, chemotaxis, cytotoxicity), T cells (activation and regulation) and B cells. Pathways associated with cell death and the apoptotic process were also highlighted, along with important functions such as wound healing, tissue morphogenesis, angiogenesis or reactive oxygen species (ROS) metabolic processes. Correlated KEGG pathways enrichment emphasized phagosome formation or proteoglycans in cancer and revealed pathological pathways such as diabetic cardiopathy, ROS-driven chemical carcinogenesis or sclerosis (Fig. 4D). In complement, by specifically discriminating up- or downregulated genes, we also shed light on the positive regulation of cytokine production or modification targeting the major histocompatibility complex (MHC), among others. Furthermore, GO terms of up-regulated genes specifically were clearly associated with autoimmune thyroid disease or diabetes mellitus (Fig. S2). Finally, we established relationships between genes and terms based on GO chord representations (Fig. 4E). In particular, we found an upregulation in the expression of genes encoding metalloproteinases (mmp1 and mmp9), onco-inflammatory markers (tnfsf13[35]) and other genes associated with autoimmune diseases and/or chronic inflammation such as il18[36]; nlrc4[37], blnk [38] or pycard and tnsfs14, both previously identified as diagnostic markers for rheumatoid arthritis[39]. Finally, some genes such as bcl6, previously shown to be involved in B-cell lymphoma associated with breast implants[40], were found to be downregulated.

**Combined effects of silicone exposure and surface topography differentially determine gene expression and associated functional pathways.**

Differential gene expression between ruptured macrotextured and ruptured microtextured gel-filled implants revealed the combined effect of silicone exposure and surface topography on
periprosthetic tissues. Among the 125 GO terms identified (Fig. 5A), processes related to the immune response were significantly represented, including antigen binding, cytokine binding, adaptive immune response, B cell, lymphocyte and leukocyte-mediated immunity (Fig. 5B). A number of key genes involved in these pathways, such as *il6* encoding for a pro-inflammatory interleukin involved in chronic inflammation, autoimmune diseases and cancer(49), are shown in Fig. 5C. As we found that most of the genes were downregulated, we took the analysis further and looked at the set of downregulated and upregulated genes separately. We found that the downregulated genes mainly affect pathways related to B-cell activation, humoral response, phagocytosis recognition or antigen binding or hematopoietic lineage among others (Fig. S3).

Next, we compared the gene expression profile between ruptured and intact gel-filled implants according to the level of surface texture, *i.e.* macrotexture or microtexture. In the context of surface macrotexture, additional silicone exposure led to the dysregulation of a large number of processes (Fig. 5D). It included notably cellular response to chemical stimulus, regulation of apoptotic process and programmed cell death or cell morphogenesis. Interestingly, KEGG pathways revealed a relationship between the set of dysregulated genes and the occurrence of pathologies such as rheumatoid arthritis (Fig. 5E), and in particular genes involved in MHC (*HLA*, *tnfsf13*, *il18*) shown in Fig. 5F. GO enrichment analysis of the set of differentially up-regulated genes showed that antigen processing and presentation of peptide antigens, especially via MHC class II, and metabolic activity (e.g. lipase, hydrolase...) were affected. KEGG charts revealed some pathological pathways such as asthma, autoimmune thyroid disease or type I diabetes mellitus (Fig. S4).
In the context of gel-filled microtextured implants, exposure to silicone after rupture results in the activation of 920 pathways (Fig. 5G), including gene pathways associated with immune and inflammatory response, cytokine activity, leukocyte migration and chemotaxis, mononuclear cell migration, T-cell activation, thereby promoting innate and adaptive immune responses and phagocytosis. Cell death and apoptotic processes were also highlighted (Fig. 5H). KEGG enrichment analysis revealed the involvement of several pathways related to cytokine activity or toll-like receptor signaling, underlining the direct link with inflammatory diseases such as rheumatoid arthritis. Finally, patients with microtextured ruptured implants were found to overexpress genes associated with inflammation such as chemokines (cc12-4), cytokines (il1, il6, il21), tumor necrosis factor 14 (tnfsfs14) and downregulate genes involved in master signaling pathways such as tgfβ2 among others (Fig. 5I). To complete the picture, GO enrichment analyses and KEGG functional analyses of up or down-regulated genes specifically are provided in Fig. S5. Notably, silicone exposure after implant rupture was found to trigger functional pathways related to Type I diabetes mellitus, Toll-like receptor signaling pathway or autoimmune thyroid disease.

Overall, these results show that silicone exposure in the event of rupture has a more drastic immunogenic effect in a microtextured than in a macrotextured context. Taken together with the histological results, this suggests that macrotextured implants are inherently more immunoreactive. Furthermore, as indicated by the different genetic profile of macrotextured versus microtextured gel implants, the periprosthetic tissue response appears to correlate not only with silicone exposure but also at the level of topography.
Discussion

To better understand the impact of silicone exposure released from breast implants on the human
host response *in vivo*, we chose to study the response of periprosthetic tissues in contact with
ruptured silicone gel-filled implants. This model allowed us to assess the tissue response to
massive silicone exposure, which can be considered as a form of enhanced silicone permeation.
Given the association between surface texturing and the induction of pathological conditions such
as BIA-ALCL[41], we also sought to assess the potential for an additive effect between surface
texturing and gel exposure. Previous studies in both animal and human models have clearly
demonstrated that implant texture differentially modifies the host response. Two studies used a
high-throughput transcriptomic approach, in rats (RNA sequencing[42]) and
rabbits/mice/humans (NanoString RNA analyses[43]). To avoid oversimplification of the central
questions addressed in this work, a forthcoming publication will specifically focus on the
comparison of topographies among themselves.

When implanted *in vivo*, silicone implants elicit a succession of complex cellular and molecular
events known as FBR culminating in the formation of a fibrous capsule -the periprosthetic tissue-
that isolates the implant from the rest of the body. Generally, FBR is considered complete with
the formation of the fibrous capsule, 3 to 6 months after implantation. However, this vision of a
stable system is seriously challenged by the underlying dynamics of the physicochemical
mechanisms of silicone release associated with implant aging. Two major, non-exclusive
phenomena come into play: (1) the silicone permeation, often referred to as "gel bleed", and (2)
the process of surface fatigue, that generates surface debris, both of which ultimately alter
surface properties[44]. In addition, because surface texturing increases the surface area of the
shell, it is likely that increased texturing will also increase permeation events. Surface debris and
gel permeation products can pass through the implant shell, be transported in the periprosthetic fluids, and reach the periprosthetic tissue. Consequently, iterative and prolonged exposure to silicone over extended period of time leads to a chronic state of FBR. Despite an average implantation time of 13+/-6 years in our sample, we could observe the typical hallmarks of silicone FBR. Histological analyses, including tissue organization, inflammation score and immune cell population demonstrated that silicone exposure was associated with a panel of cellular and tissue changes, in agreement with prior reports[45]. The inflammation score was the lowest with serum-filled and gel-filled microtextured implants. The introduction of silicone gel as an implant filler was correlated with the observation of lymphocytes, while an increase in surface topography was correlated with a marked presence of silicone-promoting FBR features such as FBGCs, foamy cells, silicone-induced granuloma or synovial-like metaplasia. Implant rupture and subsequent silicone exposure correlated with a greater diversity and number of immune cells, associated with a higher FBR inflammatory score. The dynamics of the periprosthetic tissue response to silicone gel are difficult to ascertain in vivo. Indeed, the onset of rupture, and therefore the duration of silicone exposure, cannot be accurately determined. Similarly, the source of silicone-gel or surface debris-identified in the capsular section cannot be distinguished using current histologic techniques. Consequently, the question of a putative differential contribution of gel filling versus shell debris in silicone-related immune responses remains open. Two distinct phenotypes were observed in the tissue surrounding of ruptured implants. One phenotype showed an accumulation of foamy macrophages forming a palisade-like structure. The other phenotype showed a transition from foamy macrophages to large areas of optically empty silicone-filled vacuoles surrounded by macrophages. Macrophages are unable to digest silicone, so it is postulated that foamy macrophages die over time, releasing intracellular silicone that fuses to form the large silicone
inclusions seen in tissues, thereby maintaining the chronic stimulation of immune cells. This hypothesis is supported by RNA sequencing analyses that highlighted activation of gene pathways related to (programmed) cell death, apoptosis and phagocytosis in the context of gel-filled rupture (Fig. 4D, 5E and 5H). Therefore, the continuous and recurrent release of silicone, coupled with the inability to clear it, may exacerbate the chronic state of FBR and promote a persistent inflammatory infiltrate, ultimately leading to the activation of pathological immune pathways.

Silicone polymers are considered as physiologically inert and are therefore widely used in a variety of medical applications (microfluidic devices, facial reconstruction, heart valves...). Since their first implantation in 1962, the potential toxicity of silicone breast implants has been repeatedly questioned with no clear answer to end this ongoing controversy. As reported by Colaris et al.[10], diseases associated with silicone breast implants were first documented in 1964 and have not changed over the past 30 years. Surprisingly, despite the formal association established between BIA-ALCI and macrotextured implants[41] together with the plethora of studies linking patients with silicone breast implants to the onset of a number of autoimmune diseases (e.g arthritis, fatigue, arthralgia), skepticism about putative silicone immunotoxicity remains pervasive. The lack of scientific evidence demonstrating a direct link between the use of breast implants and these pathologies mainly contributes to fuel this skepticism. Indeed, the majority of epidemiologic data refer to potential associations such as autoimmune/inflammatory disorders in breast implant patients and/or symptom relief after implant removal[8,46,47]. Here, we provide the first biological argument by comparing the transcriptomic profile of periprosthetic tissues collected from healthy women with ruptured and non-ruptured implants. Differential transcriptome analysis between tissues in contact with macrotextured and microtextured ruptured implants revealed a cumulative effect of (i) silicone exposure and (ii) variations in surface topography. This
variation in gene expression highlights the subtle regulation of the immune system, which is able
to modulate its response according to the filling and surface parameters of the implant. In this
context, it is likely that the immune system differentially perceives silicone permeates and surface
debris and that they may act synergistically to potentiate immune responses.

Prior to this study, the fact that ruptures were clinically asymptomatic was considered to be an
indicator of implant safety. Herein, transcriptomic analyses confirmed histologic observations
demonstrating that silicone exposure induces the expression of multiple signaling pathways
associated with the stimulation of various immune cells (leukocytes, lymphocytes, T cells and
mononuclear cells), along with the induction of immune signaling involved in a variety of
pathologies. The presence of silicone within the periprosthetic capsule is responsible for chronic
inflammation linked to the continuous stimulation of immune cells (macrophages, T
lymphocytes), as mentioned in prior works[48,49]. Comparison of ruptured versus intact implants,
especially when in the case of serum-filled implants, revealed an altered expression of a number
of biomarkers involved in: i) the inflammatory response such as interleukins (il1, il6, il12, il17, il18,
il21, il23, il32, tlr), cytokines production (ccl2-4), antigen recognition (hla), ii) the dysregulation of
key signaling pathways (fgf, tgfβ, wnt) and iii), various pathological conditions. Specifically, genes
involved in pathological conditions associated with onco-inflammatory markers (tnfsf13, Il18, il6),
had a proven association with autoimmune diseases and/or chronic inflammation (il1, il6, il17,
Il18, Il23, bcl6, tnfsf13, nlrcc4), diagnostic marker of rheumatoid arthritis (pycard), sclerosis,
chemical carcinogenesis induced by reactive oxygen species produced by phagocytes[50],
neurodegeneration pathways, thyroid hormone signaling pathway. Taken together, these clinical
results are strikingly reminiscent of those previously reported [47]. The effects of silicone are
presumed to be universal among silicone implants. The results of this study indicate that the
transcriptomic effects of silicone exposure are dependent on the implant category. This suggests that each implant type may trigger a specific pathogenic context. Somehow, this phenomenon is already illustrated by two severe breast implant related diseases, both of which requiring reoperation: (i) BIA-ALCL, which is almost exclusively associated with macrotextured silicone implants marketed by a particular manufacturer[51]and (ii) capsular contracture (capsule fibrosis), which is mainly associated with smooth implants[52].

The periprosthetic capsule is a newly formed and dynamic interface elaborated by the host body to isolate itself from an exogenous device that cannot be phagocytosed. Shell debris resulting from surface erosion and gel permeates permanently expose this tissue to immunogenic silicone. To prevent the deleterious spread of silicone to the rest of the body, this “tissue of vigilance” adapts by sequestrating silicone, remodeling its ECM organization and activating the immune system. Immune cells that are unable to degrade immobilized free silicone particles induce chronic inflammation, which is characterized by a very specific molecular signature. The associated dysregulation of gene expression paves the way for the development of immune-related pathologies. In short, if silicone breast implants are biocompatible, it seems urgent to recognize that their degradation products are immunogenic to the host organism. Future work on extended sampling will confirm this pioneering approach.

## Conclusions

This prospective study warns that silicone exposure, even if clinically asymptomatic, is immunogenic. The inability of macrophages to degrade silicone exposes patients to latent inflammation, which is accompanied by the expression of a number of markers of autoimmune pathology, which in turn is likely to precede the development of chronic disease in predisposed
patients. It is of the utmost importance to consider filling and topography factors as having
cumulative effects on the initiation and persistence of an inflammatory context in implant
patients. In light of these considerations, we propose that existing large-scale clinical studies,
which have thus so far yielded inconclusive results, be re-examined to establish an objective link
between the various manufacturing parameters of breast implants and the potential diseases
they may promote. Finally, we firmly believe in the necessity to homogenize manufacturing
processes, raw materials and innovations in the filling and covering of implants in order to limit
the multiplication of health risks associated with their use. We are convinced that industry
standards and practices should evolve to ensure long-term patient safety.

## Acknowledgments

We thank Dr Nathalie Bricout that collected samples in the framework of the French Government
CIFRE program (grant CIFRE2015-0843) and Dr Franck Monnien for the storage of samples in the
tumour bank of Besançon. This work was supported by the Agence Nationale de la Recherche ANR
"Safe-implant" (ANR-22-CE19-0028-01) and by the Interdisciplinary Thematic Institute HiFunMat,
as part of the ITI 2021-2028 program of the University of Strasbourg, CNRS and Inserm, funded by
IdEx Unistra (ANR-10-IDEX-0002) and SFRI (STRAT'US project, ANR-20-SFRI-0012) under the
framework of the French Investments for the Future Program. Sequencing was performed by the
GenomEast platform, a member of the 'France Génomique' consortium (ANR-10-INBS-0009).
Finally, we would like to express our sincere thanks to Dr Abraham Chemtob and Dr Hatice Mutlu
for their enlightening discussions on the chemistry of silicones.

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<figure><img src="image_4.png" /><figcaption>**Figure_1 (A)** Schematic representation of periprosthetic tissue surrounding breast implants, (B) Sources of silicone emanating from breast implants and (C) Illustration of breast implant surface topography (cross-section, scale bar = 1.0mm)</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Figure_2** HES (hematoxylin eosin saffron)-stained breast implant periprosthetic tissue showing tissue response to different type of breast implants: serum-filled textured implants (A, B), gel-filled microtextured (C-E), gel-filled macrotextured implants (F-K) and gel-filled textured and ruptured implants (L-Q). Time of implantation and inflammation score for each condition is mentioned in lateral legend. bv: blood vessel, cs: collagen stroma, sm: synovial-like metaplasia, fm: foamy macrophage,</figcaption></figure>
FGBC: foreign body giant cell, ph: phagocytosis residues. * indicates refractive material likely corresponding to silicone.
<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<table><caption>**Figure_3** Quantification of inflammation in breast implant periprosthetic tissues as a function of implant integrity, surface texturing and filling in histology sampling. **A**, graph representing immune cell population based on their averaged frequency of observation. **B**, semi-quantitative scoring of macrophage cell-driven inflammation. ***p<0.0001; **p<0.01</caption><thead><tr><th>Implant integrity</th><th colspan="2">ruptured</th><th colspan="2">intact</th></tr><tr><th>Implant filling</th><th colspan="2">silicone gel</th><th colspan="2">saline</th></tr><tr><th>Surface topography</th><th>mixed</th><th>macrotextured</th><th>microtextured</th><th>mixed</th></tr></thead><tbody><tr><th scope="row">Mean inflammation scoring +/- SD</th><td>4.9 +/- 2.2</td><td>3.0 +/- 1.0</td><td>1.6 +/- 0.8</td><td>1.8 +/- 0.7</td></tr></tbody></table>
<figure><img src="image_8.png" /><figcaption>**Figure_4** Effect of breast implant on gene expression in periprosthetic tissue regarding the different implant categories considered in this study. **A**, Venn diagram showing the total number of genes that are statistically differentially expressed in relation to the different categories. **B**, Bar chart showing the number of up- and down-regulated genes between the different categories. **C, D, E**, Focus on the effect of silicone gel exposure onto gene expression profile in periprosthetic tissue by comparing gel-filled ruptured implants and intact serum-filled implants. **C**, Complete GO enrichment analysis profile with gene sets colored into categories: GO MF (Molecular Function, red), GO BP (Biological Process, orange), GO CC (Cellular Component, green) and KEGG (pink). Numbered dots are emphasized in **D**. **D**, Bubble plot showing a selection of GO terms related to inflammation, cell death and diseases. GO terms in bold have been used to implement **E**. The size of the dot represents the number of genes contained in the gene set. **E**, GO chord plot showing the relationship between significantly differentially expressed genes and GO terms or KEGG pathways identified in **D**.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>**Figure_5** Effect of breast implant on periprosthetic gene expression as a function of implant integrity and surface topography. (A-C) Comparison of ruptured gel-filled macro- versus microtextured implants, (D-F) Comparison of ruptured versus intact gel-filled macrotextured implants, (G-I) Comparison of ruptured versus intact gel-filled microtextured implants. **A**, **D**, **G** Complete GO</figcaption></figure>
enrichment analysis profile with gene sets colored into categories: GO MF (Molecular Function, red), GO BP (Biological Process, orange), GO CC (Cellular Component, green) and KEGG (pink). Numbered dots are emphasized in **B, E, H**, that display bubble plots showing a selection of GO terms related to inflammation, cell death, FBR and diseases. GO terms in bold have been used to implement **C, F, I**, GO chord plots showing the relationship between significantly differentially expressed genes and GO terms or KEGG pathways identified in **B, E, H** respectively.