# A Tertiary lymphoid structures-based pathological score predicts survival and recurrence in colorectal Cancer patients

Marion Le Rochais, Marie Morvan, Servane Bouzeloc, Jean-Baptiste Nousbaum, Matthieu Guillard, Pierre Le Noac'H, Soizic Garaud, Arnaud Uguen

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## ► To cite this version:

Marion Le Rochais, Marie Morvan, Servane Bouzeloc, Jean-Baptiste Nousbaum, Matthieu Guillard, et al.. A Tertiary lymphoid structures-based pathological score predicts survival and recurrence in colorectal Cancer patients. Immunobiology, 2025, 230, pp.152911. 10.1016/j.imbio.2025.152911. hal-05100067

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# A Tertiary lymphoid structures-based pathological score predicts survival and recurrence in colorectal Cancer patients

Marion Le Rochais<sup>a, *</sup>, Marie Morvan<sup>b, f</sup>, Servane Bouzeloc<sup>b, f</sup>, Jean-Baptiste Nousbaum<sup>b, c, f</sup>, Matthieu Guillard<sup>d</sup>, Pierre Le Noac'h<sup>e</sup>, Soizic Garaud<sup>a</sup>, Arnaud Uguen<sup>a, d</sup>

## ARTICLE INFO

Editor name: Banu Bayram.

**Keywords:**

Colorectal cancer

Tertiary lymphoid structures

Prognosis

Digital pathology

## ABSTRACT

**Background & Aim:** Colorectal cancer (CRC) is a major global health burden. The immune response within the tumor microenvironment (TME), especially the presence of tertiary lymphoid structures (TLS), plays a critical role in prognosis. However, current prognostic tools often overlook this immune component. This study aimed to characterize TLS in CRC and develop a TLS-score correlating with survival outcomes.

**Methods:** We conducted a retrospective analysis of 7895 TLS from 806 CRC patients using data from the Finistère Registry of Digestive Tumors. Hematoxylin-eosin-saffron (HES) staining and immunohistochemistry (IHC) were employed for TLS quantification to explore their relationship with survival and recurrence metrics.

**Results:** Patients with microsatellite instability (MSI) demonstrated higher TLS density and more mature TLS. Both TLS density and maturation showed significant correlations with overall survival (OS) and disease-free survival (DFS). Kaplan-Meier analysis revealed that increased TLS density and GC-like maturation correlated with improved OS and DFS. Our novel pathological TLS score, integrating density and maturation, effectively stratified patients by survival and recurrence risk, distinguishing high-risk individuals (score 0–1–2) with poorer outcomes from low-risk patients (score 3–4) with better prognosis (*p* < 0.0001), particularly in stage II–III cases.

**Conclusion:** TLS density and maturation are robust prognostic markers in CRC. The proposed TLS score may aid pathologists in identifying patients at higher recurrence risk and poorer survival, guiding clinical decisions for monitoring and potential adjuvant therapies. Further validation of these findings is essential before clinical implementation.

## 1. Introduction

Colorectal cancer (CRC) is the third most prevalent and second deadliest cancer globally. (Sung et al., 2021) The immune system plays a key role in CRC development and prognosis. (Pagès et al., 2018) Evidence shows that the tumor microenvironment (TME), consisting of

tumor, immune, and stromal cells, directly impacts tumor biology and treatment response.

Tertiary lymphoid structures (TLS), formed in non-lymphoid tissues due to chronic inflammation, are clusters of immune cells (B cells, T cells, dendritic cells). These structures, lacking encapsulation, enhance antigen presentation to B cells and improve tumor control and

**Abbreviations:** Colorectal cancer, CRC; disease-free survival, DFS; formalin-fixed and paraffin-embedded, FFPE; germinal centers, GC; hematoxylin-eosin-saffron, HES; immunohistochemistry, IHC; microsatellite instability, MSI; microsatellite-stable, MSS; mismatch repair, MMR; overall survival, OS; Tertiary Lymphoid Structures, TLS; tumor microenvironment, TME.

* Corresponding author: B Lymphocytes, Autoimmunity and Immunotherapies - UMR1227; Immunology Department - Augustin Morvan Hospital, Brest 29200, France.

E-mail addresses: marion.lerochais@chu-brest.fr (M. Le Rochais), servane.bouzeloc@chu-brest.fr (S. Bouzeloc), jean-baptiste.nousbaum@chu-brest.fr (J.-B. Nousbaum), matthieu.guillard@chu-brest.fr (M. Guillard), pierre.lenoach@chu-brest.fr (P. Le Noac'h), Soizic.Garaud@univ-brest.fr (S. Garaud), arnaud.uguen@chu-brest.fr (A. Uguen).

<footnote><sup>a</sup> LBAI, UMR1227, Univ Brest, Inserm, Brest, France,</footnote>

<footnote><sup>b</sup> Registre des Tumeurs Digestives du Finistère, Brest, France</footnote>

<footnote><sup>c</sup> CHU Brest, Hepato-gastroenterology Department, Brest, France</footnote>

<footnote><sup>d</sup> CHU Brest, Pathology Department, Brest, France</footnote>

<footnote><sup>e</sup> CHU Brest, Oncology Department, Brest, France</footnote>

<footnote><sup>f</sup> EA7479 SPURBO, Université de Bretagne Occidentale, Brest, France</footnote>
<table><caption>**Table 1**

Descriptive table of the immunohistochemistry staining.</caption><thead><tr><th>Slide</th><th>Target</th><th>Chromogen</th><th>Staining</th><th>Antigen Retrieval</th><th>Dilution</th><th>Incubation time</th><th>Reference</th></tr></thead><tbody><tr><td></td><td>CD20</td><td>Red</td><td>Cytoplasm of B cells</td><td>CC1 36 min</td><td>1/100</td><td>20 min</td><td>Clone L26, Dako (Agilent Cat# M0755, RRID:AB_2282030)</td></tr><tr><td>CD20<br>CD3<br>CD31<br>Ki67</td><td>CD3<br>CD31</td><td>DAB</td><td>Cytoplasm of T cells<br>Cytoplasm of endothelial cells border along the vasculature</td><td>CC1 36 min<br>CC1 36 min</td><td>1/100<br>1/50</td><td>32 min<br>20 min</td><td>Polyclonal antibody, Dako<br>Clone JC70A, Dako (Agilent Cat# GA610, RRID:AB_2892053)</td></tr><tr><td></td><td>Ki67</td><td>DAB</td><td>Nucleus of proliferative cells</td><td>CC1 36 min</td><td>1/50</td><td>36 min</td><td>Clone MIB-1, Dako (Agilent Cat# M7240, RRID:AB_2142367)</td></tr><tr><td>CD21<br>CD23</td><td>CD21<br>CD23</td><td>Red<br>DAB</td><td>Cytoplasm of immature follicular dendritic cells<br>Cytoplasm of mature follicular dendritic cells and GC B cells</td><td>CC1 64 min<br>CC1 64 min</td><td>1/50<br>1/50</td><td>48 min<br>32 min</td><td>Clone 2G9, Roche Diagnostics<br>Clone 1B12, Leica (Leica Biosystems Cat# PA0169, RRID:AB_10554588)</td></tr></tbody></table>

prognosis. (Sautès-Fridman et al., 2019) However, TLS can also recruit immunosuppressive cells, contributing to adverse outcomes. (Colbeck et al., 2017) In CRC, TLS emergence and maturation correlate with better prognoses. (Posch et al., 2018; Hayase et al., 2022; Sun et al., 2023) Mismatch repair (MMR) deficiency leads to microsatellite instability (MSI), which produces more neoantigens and promotes TLS formation, linking MSI with improved therapeutic responses. (Hayashi et al., 2023; Rakaee et al., 2021; van Rijthoven et al., 2021)

Despite their prognostic potential, TLS assessment lacks standardization in pathology. (Hayashi et al., 2023; Rakaee et al., 2021; van Rijthoven et al., 2021; Wang et al., 2022; Siliņa et al., 2018) The increasing role of TLS in clinical trials for immune checkpoint inhibitors underscores the need for a standardized scoring system.

This study aims to evaluate TLS characteristics in 806 CRC patients using HES (hematoxylin-eosin-safran) staining and IHC (immunohistochemistry) slides, correlating them with clinical outcomes (survival and recurrence) from the Finistere Registry of Digestive Tumors (France). The goal is to propose a comprehensive set of relevant TLS features and evaluate their relationship with overall survival (OS) and disease-free survival (DFS) in CRC patients, with the aim of developing an easy-to-use scoring system for assessing survival and recurrence risk in clinical practice.

## 2. Material and methods

### 2.1. Patients and clinical data

This retrospective study included 806 adult patients (339 women and 411 men, mean age of 74 years old) with a colorectal adenocarcinoma who underwent a surgery between 2005 and 2021 at the CHRU Brest (Brest, France). Inclusion criteria were: (1) Individuals diagnosed with primary colon adenocarcinoma through pathological examination (sporadic and hereditary cases), (2) individuals with no prior exposure to preoperative chemotherapy or radiotherapy, (3) individuals with sufficient formalin-fixed and paraffin-embedded (FFPE) tissue specimens, (4) individuals with at least one slide containing the invasive margin of the tumor, and (5) individuals with comprehensive medical records and follow-up data available. It excluded patients who received neoadjuvant therapy (including chemotherapy, radiotherapy and chemoradiotherapy). The patients' clinicopathological information were collected from the Finistere Registry of Digestive Tumors database. All the 806 selected patients finished the study, there was no attrition. All FFPE tissue samples used for this retrospective study were included in the tissue collection of the Department of Pathology of CHU Brest (Brest, France). The MMR status in this study was primarily defined using immunohistochemistry (IHC) for the four MMR proteins: MLH1, MSH2, MSH6, and PMS2. This sample size provides sufficient statistical power to detect clinically meaningful differences in outcomes and prognostic factors, enables robust subgroup analyses across various clinical and genetic parameters, and ensures adequate representation of different colorectal cancer stages.

### 2.2. Samples and histopathological slides

After comprehensive review of all available HES-stained sections, one representative sample per patient was meticulously selected, ensuring inclusion of tumor tissue, tumor front, and adjacent normal tissue for optimal analysis.

Our approach utilized three serial 3-µm tissue sections per patient: one hematoxylin-eosin-saffron (HES) stained slide and two immunohistochemistry (IHC) slides. The IHC slides were strategically designed to incorporate six different markers: one slide for CD21 and CD23, a second slide for Ki67, CD31, CD20, and CD3. These markers were carefully selected to provide the most descriptive features of the TLS and its microenvironment while optimizing cost-effectiveness and minimizing the number of slides required. This methodological approach allowed for a thorough characterization of TLS density, maturation, and associated immune cell populations.

HES staining was performed using automated protocols on Sakura Tissue-Tek in the CHRU Brest Pathology Department. IHC was carried out on a Ventana Benchmark Ultra® system using the ultraView Universal DAB for CD3, Ki67, CD23 and CD31 and Alkaline Phosphatase Red Detection Kits for CD20 and CD21 (Roche Diagnostics). The different cellular compartments (cytoplasmic vs. nuclear) and cell types (lymphocytes vs. vessels) allowed for accurate differentiation and quantitation of these markers, even when using the same chromogen for CD3, CD31, and Ki67. Slides underwent pretreatment with cell conditioner 1 (Discovery CC1, pH 8, Roche Diagnostics) and antibody incubation at 37 °C. The antibodies used and their specific targets are listed in Table 1. After washing, slides were counterstained with hematoxylin for 12 min, followed by bluing reagent for 4 min, and then mounted with a coverslip.

### 2.3. Quantitative image analysis

The HES and the two IHC slides were scanned at x40 magnification into MRXS digital slides using a 3DHistech Pannoramic Midi slide scanner (3DHISTECH, Budapest, Hungary).

QuPath software v0.5.0 (Bankhead et al., 2017) was then used for the blind selection and annotations of the digital slides for the quantitative image analysis of TLS. We defined TLS as clusters or aggregates of B cells (CD20+) and T cells (CD3+) (minimum of 50 cells), readily identifiable on CD20-CD3-CD31-Ki67 slide within the tumor or at the invasive margin (that we limited to the margin zone 1 mm from the closest cancer cells), excluding the secondary lymphoid organs. For each TLS spotted, different parameters were evaluated using the three digital slides
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G

Aggregate

Non-GC

GC

(caption on next page)
<caption>**Fig. 1.** Scoring strategy of TLS features.</caption>

A: Three serial slides of 3 µm thickness were cut before histological staining, one slide for HES and two for IHC to target proteins of interest: CD21, CD23, Ki67, CD31, CD20 and CD3. Each TLS inside the tumor or at 1 mm from the tumor front is spotted and annotated using QuPath. Each feature listed in the table is assessed.
B: Scoring strategy for clear center presence on HES slide. 0 corresponds of the absence of a clear center, and 1 for the presence.
C: Scoring strategy for CD3/CD20 organization, with 0 for a majority of CD20+ cells, 1 for a mix of CD20+ and CD3+ cells, and 2 for CD20+ center and crown of CD3+ cells. A vessel CD31+ is highlighted with the white arrow.
D: Scoring strategy for Ki67, with where there is no Ki67+ cells, 1 for a few sparse Ki67+ cells, 2 for Ki67+ cells in the center of the TLS, 3 for Ki67+ cells surrounding the TLS, and 4 for Ki67+ cells in the center and surrounding the TLS.
E: Scoring strategy for CD21, with 0 for no positive cells, 1 for a few positive cells, and 2 for numerous positive cells organized in a network.
F: Scoring strategy for CD23, with 0 for no positive cells, 1 for a few positive cells, and 2 for numerous positive cells organized in a network.
G: TLS maturation based on the expression of CD21 and CD23 markers. Three maturation groups have been described: Aggregates (CD21- / CD23-), Non-GC TLS (CD21+ / CD23-), and GC-like TLS (CD21+ / CD23+).

(Fig. 1A):

- On the HES slide, we measured the size of the tumor area on the whole slide, and noted, for each TLS, if there is light center within the TLS or not (0/1) (Fig. 1B).

- On the CD20-CD3-CD31-Ki67 slide, we measured the distance of the TLS from the closest cancer cell and the size of the TLS area; the layer localization (T0 mucosal, T1 sub-mucosal, T2 muscle, T3 serous membrane, T4 the fat of the mesenteric), and the TLS position at the tumoral front (invasive margin) or within the tumor were also noted; the CD3/CD20 T/B cells and Ki67 proliferating cells organization were also graded from 0 to 2 (0: majority of CD20, 1: mix CD20 and CD3, 2: CD20+ center and crown of CD3+ cells) (Fig. 1C) and 0 to 4 (0: no Ki67+ cells, 1: a few sparse Ki67+ cells, 2: Ki67+ cells in the center of the TLS, 3: Ki67+ cells surrounding the TLS, 4: Ki67+ cells in the center and surrounding the TLS) (Fig. 1D). The number of vessels using the CD31 stain was also noted.

- On the CD21-CD23 slide, the organization of the CD21+ and CD23+ cells in the TLS center were graded from 0 to 2 (0: no positive cells, 1: a few positive cells not organized in a network, 2: numerous positive cells organized in a network) (Fig. 1E and F). CD21⁺ cells indicated the presence of immature follicular dendritic cells (FDCs), while CD23⁺ expression reflected either mature FDCs (CD21⁺ CD23⁺) and/or germinal center B cells (CD20⁺ CD23⁺). Based on previous works (Posch et al., 2018; Silipa et al., 2018; Le Rochais et al., 2023), TLS maturation groups were defined as Aggregates when the TLS was CD21- and CD23-, Non-GC TLS when the TLS was CD21+ and CD23-, and as GC-like TLS when the TLS was CD21+ and CD23+ (Fig. 1G).

## 2.4. TLS features assessment

We evaluated the interrater agreement on CD21-CD23 based TLS maturation scoring. Three different raters (M.G, P.L.N, M.L.R) first underwent independent training using a dataset comprising 9 typical cases, encompassing samples with no TLS, aggregates, Non-GC TLS and GC TLS. Following this training phase, interrater agreement was evaluated using a cohort of 60 randomized CRC adenocarcinoma samples. For each case, raters blindly assessed the maturity status of TLSs based on CD21/CD23 IHC staining (categorized as 0 for aggregate, 1 for Non-GC, and 2 for GC) (Supplementary Data 1).

For the TLS density, we defined three different metrics for TLS number assessment. TLS number was defined as the number of TLS found in the intratumoral compartment or in the front tumoral compartment (1mm² from the invasion front). The two other ways were based on the density: TLS density 1 was the number of TLS for the patient divided by his tumor area; TLS density 2 was the sum of each TLS area of the patient divided by his tumor area.

## 2.5. Statistical analysis

Statistical analyses were performed using R version 4.3.1. For patients with multiple TLS, median values were used for quantitative data, and the most frequent occurrence for qualitative data. Patients with

equal occurrences for both modalities were classified as “Undefined.” Interrater agreement for TLS presence was assessed using Cohen’s kappa, with scores indicating minimal (<0.20), fair (0.21–0.40), moderate (0.41–0.60), substantial (0.61–0.80), or almost perfect agreement (0.81–1) (Benchoufi et al., 2020; Fleiss et al., 1969; Cohen, 1960).

For MMR status, quantitative data were described by the median and compared using the Wilcoxon-Mann-Whitney test. Qualitative data were presented as frequencies, and quantitative data as means with standard deviations. Mann-Whitney tests and Chi-squared tests were used for group comparisons of means and dependence between categorical variables, respectively.

Overall survival (OS) was defined as the time from curative surgery to death, and disease-free survival (DFS) as the time from surgery to recurrence or death (Liu et al., 2020). Kaplan-Meier curves were used to estimate survival, with comparisons made by the Log Rank test. Multivariate survival analyses employed Cox proportional hazards regression models for both OS and DFS.

Based on the multivariate results, a pathological score was developed incorporating TLS density and maturation to differentiate patients. Kaplan-Meier curves were used to estimate survival by pathological score, excluding patients with missing data. Statistical significance was set at *p* < 0.05 for all tests.

# 3. Results

## 3.1. Clinicopathological characteristics of the cohort

In this comprehensive study, we conducted an exhaustive analysis of 7895 tertiary lymphoid structures (TLS) from 806 colorectal cancer (CRC) patients. Our approach utilized three digitalized slides per patient for a thorough characterization of TLS density, maturation, and associated immune cell populations (Table 2).

The cohort had nearly equal numbers of men and women, with a median age of 74 years. Most patients (71 %) had intermediate cancer stages (Stage II and III). Mismatch-repair (MMR) status was unavailable for 61 % of patients, but the distribution was typical, with 81 % microstallite-stability (MSS) and 19 % microsatellite-instability (MSI) (Table 2 A).

Follow-up data showed an average active follow-up time of 37.36 months (median 25.85 months) and a vital follow-up time of 45.03 months (median 31.95 months). Median overall survival (OS) was 44.73 months, and disease-free survival (DFS) was 36.43 months. Most patients (67 %) had no recurrence, while 19 % had recurrence, and 14 % received palliative care (excluded from DFS analysis) (Table 2B).

Histopathologically, most TLS lacked a clear center (99 %) but were well-organized with B cell centers (88 %). About 52 % of TLS showed sparse Ki67+ cells, while 28 % had Ki67+ cells in the center and surrounding areas. On average, each TLS had five surrounding vessels. TLS maturation included 39 % Aggregates, 29 % Non-Germinal center (GC), and 31 % GC-like structures. The average number of TLS per patient was 10, with an average surface area of 84,896 µm² and a mean distance of 292 µm from the closest cancer cell. TLS were primarily located at the tumor front (86 %), with fewer found within the tumor (12 %). TLS were
<caption>**Table 2**</caption>

Clinicopathological characteristics of the cohort. A) Clinical characteristics of the 806 patients included in the cohort. B) Survival data of the 806 patients included in the cohort. C) Histopathological features of the TLS from the 806 patients of the cohort.

<table><caption>**Table 2 (continued)**</caption><thead><tr><th>Characteristic</th><th>N = 806<sup>1</sup></th><th>Missing data (%)</th></tr></thead><tbody><tr><td><strong>A) Clinical characteristics</strong></td><td></td><td></td></tr><tr><td>Age</td><td>74 (25-97)<sup>2</sup></td><td>57 (7 %)</td></tr><tr><td>Sex</td><td></td><td>56 (7 %)</td></tr><tr><td>Female</td><td>339 (45 %)</td><td></td></tr><tr><td>Male</td><td>411 (55 %)</td><td></td></tr><tr><td>Histological stage</td><td></td><td>72 (9 %)</td></tr><tr><td>I</td><td>90 (12 %)</td><td></td></tr><tr><td>II</td><td>297 (40 %)</td><td></td></tr><tr><td>III</td><td>229 (31 %)</td><td></td></tr><tr><td>IV</td><td>118 (16 %)</td><td></td></tr><tr><td>pT</td><td></td><td>58 (7 %)</td></tr><tr><td>1</td><td>32 (4.3 %)</td><td></td></tr><tr><td>2</td><td>76 (10 %)</td><td></td></tr><tr><td>3</td><td>416 (56 %)</td><td></td></tr><tr><td>4</td><td>224 (30 %)</td><td></td></tr><tr><td>pN</td><td></td><td>62 (7 %)</td></tr><tr><td>0</td><td>422 (57 %)</td><td></td></tr><tr><td>1</td><td>207 (28 %)</td><td></td></tr><tr><td>2</td><td>115 (15 %)</td><td></td></tr><tr><td>pM</td><td></td><td>70 (8 %)</td></tr><tr><td>0</td><td>618 (84 %)</td><td></td></tr><tr><td>1</td><td>118 (16 %)</td><td></td></tr><tr><td>MMR status</td><td></td><td>490 (61 %)</td></tr><tr><td>MSI</td><td>60 (19 %)</td><td></td></tr><tr><td>MSS</td><td>256 (81 %)</td><td></td></tr><tr><td><strong>B) Survival data</strong></td><td></td><td></td></tr><tr><td>Overall survival (months)</td><td>30.5 (28.1; 34.3)<sup>3</sup></td><td>57 (7 %)</td></tr><tr><td>Disease-free survival (months)</td><td>22.35 (20.7; 24.4)<sup>3</sup></td><td>158 (20 %)</td></tr><tr><td>Recurrence</td><td></td><td>70 (9 %)</td></tr><tr><td>No</td><td>492 (67 %)</td><td></td></tr><tr><td>Yes</td><td>143 (19 %)</td><td></td></tr><tr><td>No curative treatment</td><td>101 (4 %)</td><td></td></tr><tr><td><strong>C) Histopathological features</strong></td><td></td><td></td></tr><tr><td>Light center</td><td></td><td>59 (7 %)</td></tr><tr><td>0</td><td>739 (99 %)</td><td></td></tr><tr><td>1</td><td>8 (1.1 %)</td><td></td></tr><tr><td>CD3/CD20 organization</td><td></td><td>57 (7 %)</td></tr><tr><td>0</td><td>2 (0.3 %)</td><td></td></tr><tr><td>1</td><td>85 (11 %)</td><td></td></tr><tr><td>2</td><td>662 (88 %)</td><td></td></tr><tr><td>Ki67+</td><td></td><td>57 (7 %)</td></tr><tr><td>0</td><td>18 (2.4 %)</td><td></td></tr><tr><td>1</td><td>390 (52 %)</td><td></td></tr><tr><td>2</td><td>98 (13 %)</td><td></td></tr><tr><td>3</td><td>37 (4.9 %)</td><td></td></tr><tr><td>4</td><td>206 (28 %)</td><td></td></tr><tr><td>CD21+</td><td></td><td>76 (9 %)</td></tr><tr><td>0</td><td>313 (43 %)</td><td></td></tr><tr><td>1</td><td>108 (15 %)</td><td></td></tr><tr><td>2</td><td>309 (42 %)</td><td></td></tr><tr><td>CD23+</td><td></td><td>76 (9 %)</td></tr><tr><td>0</td><td>501 (69 %)</td><td></td></tr><tr><td>1</td><td>123 (17 %)</td><td></td></tr><tr><td>Vessels nbr</td><td>5.33 (0-21)<sup>2</sup></td><td>57 (7 %)</td></tr><tr><td>TLS maturation group</td><td></td><td>76 (9 %)</td></tr><tr><td>Aggregate</td><td>287 (39 %)</td><td></td></tr><tr><td>Non-GC TLS</td><td>214 (29 %)</td><td></td></tr><tr><td>GC-like TLS</td><td>229 (31 %)</td><td></td></tr><tr><td>TLS area (µm<sup>2</sup>)</td><td>84,896 (70,288)</td><td>47 (6 %)</td></tr><tr><td>Distance to closer cancer cell (µm)</td><td>292 (235)</td><td>47 (6 %)</td></tr><tr><td>Tumor area (µm<sup>2</sup>)</td><td>164,199,516 (172,028,759)</td><td>0 (0 %)</td></tr><tr><td>TLS localization</td><td></td><td>46 (6 %)</td></tr><tr><td>Front</td><td>650 (86 %)</td><td></td></tr><tr><td>Intra</td><td>92 (12 %)</td><td></td></tr><tr><td>Undefined</td><td>18 (2.4 %)</td><td></td></tr></tbody></table>

<table><thead><tr><th>Characteristic</th><th>N = 806<sup>1</sup></th><th>Missing data (%)</th></tr></thead><tbody><tr><td><strong>TLS layer localization</strong></td><td></td><td>46 (6 %)</td></tr><tr><td>Undefined</td><td>91 (12 %)</td><td></td></tr><tr><td>T0</td><td>155 (20 %)</td><td></td></tr><tr><td>T1</td><td>172 (23 %)</td><td></td></tr><tr><td>T2</td><td>205 (27 %)</td><td></td></tr><tr><td>T3</td><td>137 (18 %)</td><td></td></tr></tbody></table>

¹Number (%); Mean (standard deviation); ² Mean (Minimum-Maximum); ³ Median (95 % CI)

mainly in superficial layers (mucosal, sub-mucosal, and muscle), with fewer in the serous membrane or mesenteric fat (18 %) (Table 2C).

## 3.2. TLS features predict overall survival and disease-free survival of CRC patients

We compared three methods to assess TLS numbers: total TLS count (intratumoral and tumor front), TLS density 1 (number of TLS divided by tumor area), and TLS density 2 (sum of TLS areas divided by tumor area). TLS density 2 was chosen for its predictive accuracy regarding survival and recurrence. It significantly correlated with overall survival (OS) (p < 0.026) and recurrence (p < 0.036), with lower density in patients with recurrence (Supplementary Data 2). Therefore, TLS density 2 was used for subsequent analyses.

Certain TLS features, such as density, maturation, and localization, were associated with MMR status and provided prognostic value (Supplementary Data 3). Univariate Cox regression models showed that TLS maturation (p < 0.001) and density (p < 0.014) were linked to OS and disease-free survival (DFS), while TLS localization showed no significant correlation (p > 0.6) (Supplementary Data 4 and 5).

In multivariate Cox regression models, both TLS maturation and density were correlated with OS. Low TLS density was associated with poorer survival (p = 0.005), whereas GC-like TLS was strongly linked to better survival (p = 0.003) (Fig. 2A). GC-like TLS had a more significant p-value (p = 0.003) and narrower confidence interval than tumor stage (pT3 p = 0.019, pT4 p = 0.005) and histological stage (p > 0.085), making TLS maturation a stronger independent marker for OS than tumor stage.

For DFS, only TLS density was strongly correlated with recurrence. Low-density TLS was associated with higher recurrence (p = 0.002) (Fig. 2B). TLS density had a more significant p-value (p = 0.002) and narrower confidence interval (0.14, 0.60) than tumor stage (pT4 p = 0.026) and histological stage (p > 0.4), making it a stronger independent marker for DFS.

Kaplan-Meier curves showed that patients with high TLS density had better OS (p < 0.0001) and DFS (p = 0.00028) (Fig. 3A and B). Similarly, patients with predominantly GC-like TLS had improved OS (p = 0.00046) and DFS (p = 0.046) (Fig. 3C and D).

## 3.3. An integrated score of TLS features discriminate groups of patients

We observed that the combination of TLS density and TLS maturation discriminated precisely the same groups of patients according to DFS and OS: GC-Like and High-TLS density patients are the group of patients with the better OS (p-value<0.0001) (Fig. 4A) and DFS (p-value = 0.00072) (Fig. 4B), whereas Low density patients with Aggregates or Non-GC TLS have a poorer OS (p-value<0.0001) (Fig. 4A) and DFS (p-value = 0.00016) (Fig. 4B).

Based on these results, we assigned weights to each significant TLS feature. For the class “TLS density – Low”, we assigned a weight of 0; for the class “TLS density – High”, we assigned a weight of 2. These classes were defined based on the median of TLS density in our large cohort (median TLS density = 3.45 × 10⁻³ TLS/mm²). For the maturation, we assigned a weight of 0 for the “Aggregate” class, 1 for the “Non-GC” class.
A

OS

<figure><img src="image_23.png" /></figure>

B

PFS

<figure><img src="image_24.png" /><figcaption>**Fig. 2.** Cox regression models of tertiary lymphoid structures features of prognostic interest in colorectal adenocarcinoma patients.</figcaption></figure>

A: Cox regression models of TLS features of prognostic interest (density and maturation), and prognostic features used in clinics (pT and histological stage) according to the Overall Survival (OS) of the 806 patients.

B: Cox regression models of TLS features of prognostic interest (density and maturation), and prognostic features used in clinics (pT and histological stage) according to the Disease-Free Survival (DFS) of the 806 patients.

class; 2 for the “GC” class. By summing the scores for TLS density and TLS maturation for each patient, we categorized patient into a score range from 0 to 4.0, represented a group of patients with a poorer survival (p-value<0.0001) and a higher risk of recurrence (p-value = 0.0004), whereas 4, represented a group of patients with a better survival and a lower risk of recurrence (Fig. 5A and B).

This straightforward pathological TLS score enabled us to distinguish between patients with a higher risk of recurrence (p-value<0.0001) and

poorer survival (score 0–1–2) (p-value<0.0001) and those with a lower risk of recurrence and better survival (score 3–4) (Fig. 5C and D), especially in stage II-III patients (Fig. 5E and F).

4. Discussion

In this retrospective study, we evaluated the histological features of 7895 TLS from 806 CRC patients using HES staining and digitalized IHC
<figure><img src="image_25.png" /><figcaption>**Fig. 3.** Survival analyses according to tertiary lymphoid structures features in colorectal adenocarcinoma patients.</figcaption></figure>

A: Kaplan-Meier survival curves for OS in all 806 patients according to the High and Low TLS Density groups of patients. Patients were divided in group High and Low Density according to the median of all TLS Density.

B: Kaplan-Meier survival curves for Disease-Free Survival (DFS) in all 806 patients according to the High and Low TLS Density groups of patients. Patients were divided in group High and Low Density according to the median of all TLS Density.

C: Kaplan-Meier survival curves for OS in all 806 patients according to the three groups of TLS maturation Aggregate, Non-GC and GC-like.

D: Kaplan-Meier survival curves for DFS in all 806 patients according to the three groups of TLS maturation Aggregate, Non-GC and GC-like.

slides to better understand these structures. We developed a robust quantitative TLS staging system using IHC with CD21 and CD23 anti-bodies. Several groups had evaluated TLS maturation in routine H&E slides based on pathologist assessment of clear center and larger size (Li et al., 2023; Calderaro et al., 2019; Karapetyan et al., 2024). We showed in this study that detecting TLS and defining their maturation stage only on the basis of HES slide and the presence of a clear center is not sufficient and underestimate the true number of GC TLS (7 % of TLS with a clear center on HES and 40 % of GC TLS using CD21/CD23 IHC). Moreover, studies have shown that TLS assessment using HES slides can

be subject to inter-observer variability (Buisseret et al., 2017). In contrast, IHC allows for precise identification of TLS components, enhancing diagnostic accuracy and providing deeper insights into the tumor microenvironment. Given its reliability and integration into clinical workflows, we propose IHC as a more robust method for TLS evaluation in clinical settings.

Our study is the first to investigate the most effective metrics for TLS assessment. Several studies proposed different approaches to evaluate the number of TLS, such as the number of TLS per mm² in different locations (intratumoral, peritumoral, whole tissue) (Hayashi et al., 2023;
<figure><img src="image_26.png" /><figcaption>Fig. 4. Combination of TLS maturation and TLS density correlates with patient's survival and recurrence.</figcaption></figure>

A: Kaplan-Meier survival curves for OS of the combination of the two TLS features of prognostic interest: TLS density and maturation.

B: Kaplan-Meier survival curves for DFS of the combination of the two TLS features of prognostic interest: TLS density and maturation.

Wang et al., 2022; Seow et al., 2020). Others also compare different approaches (semi-quantitative vs quantitative models) for TLS assessment but not only based on the TLS number (Rakaee et al., 2021). That is why we investigated the clinical correlations of three major metrics for TLS number appreciation: TLS number enumeration, TLS number normalized by the tumor area (TLS density 1), and the sum of the area of each TLS normalized by the tumor area (TLS density 2). We showed that for the evaluation of TLS in CRC the most relevant metrics in terms of clinical correlation is TLS density 2.

Predicting CRC recurrence and survival is crucial for personalized adjuvant chemotherapy. Despite advancements, the factors essential for strong anti-tumor immune surveillance and preventing recurrence after complete cancer resection remain unclear (Posch et al., 2018; Siliņa et al., 2018). Different studies proposed scores based on the immune infiltrate that demonstrated prognostic values across various cancer

types (Posch et al., 2018; Rakaee et al., 2021; Li et al., 2023; Galon et al., 2016). Notably, the Immunoscore®, derived from tumor-infiltrating T cell counts, has been validated in a significant multicenter study as a prognostic indicator for stage I/II/III CRC (Galon et al., 2016), highlighting the importance of lymphocyte infiltration in CRC prognostication. Evidence also suggests a significant correlation between the quantity of tumor-infiltrating lymphocytes and TLS in CRC, though their mutual dependency remains unclear (Sun et al., 2023; Bai et al., 2022). Tertiary lymphoid structures (TLS) represent a promising additional prognostic marker for colorectal cancer, complementing existing markers such as tumor-infiltrating lymphocytes and mismatch repair (MMR) status. Our study demonstrates a positive association between TLS features (maturation and density) and survival outcomes (OS and DFS) in CRC. Moreover, TLS maturation appeared as an independent marker stronger than pT and histological stage for OS prediction, and
<figure><img src="image_27.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_28.png" /><figcaption>C</figcaption></figure>

<figure><img src="image_29.png" /><figcaption>D</figcaption></figure>

<figure><img src="image_30.png" /><figcaption>E</figcaption></figure>

<figure><img src="image_31.png" /><figcaption>F</figcaption></figure>

<figure><img src="image_32.png" /><figcaption>Fig. 5. TLS score correlates with patient's survival and recurrence.</figcaption></figure>

A: Kaplan-Meier survival curves for OS of the patients based on their TLS score.

B: Kaplan-Meier survival curves for DFS of the patients based on their TLS score.

C: Kaplan-Meier survival curves for OS of groups of patients depending on their score class (0-1-2) or (3-4).

D: Kaplan-Meier survival curves for DFS of groups of patients depending on their score class (0-1-2) or (3-4).

E: Kaplan-Meier survival curves for OS of groups of stage II and stage III patients depending on their score class (0-1-2) or (3-4).

F: Kaplan-Meier survival curves for DFS of groups of stage II and stage III patients depending on their score class (0-1-2) or (3-4).
TLS density for DFS prediction. While our research did not assess tumor-infiltrating lymphocytes (TILs) and had incomplete mismatch repair (MMR) status data due to its observational nature, TLS evaluation presents itself as an interesting and easily assessable additional marker for routine clinical use. Initially, our methodology involved an extensive exploration of TLS features using two serial immunohistochemistry (IHC) slides and hematoxylin-eosin-saffron (HES) staining. However, our study shows that the critical prognostic indicators are TLS density and maturation that can be effectively evaluated using only HES and a single double IHC slide for CD21/CD23. This simplified method, needing only one extra IHC slide in routine pathology, provides a practical and valuable tool for CRC management.

Building on previous work (Posch et al., 2018; Rakaee et al., 2021; Li et al., 2023), we proposed a TLS score based on the combination of TLS maturation and density to stratify patients based on survival and recurrence risk. This score could be particularly useful for stage II and III patients, who are at a higher risk of recurrence and poorer survival outcomes. While stage I patients typically present with localized disease, and stage IV patients with advanced metastatic cancer, the challenge lies in managing stage II and III patients who may benefit from additional treatments or closer monitoring. Our TLS score offers a potential tool for pathologists to distinguish between patients with better or worse prognoses, helping clinicians optimize treatment decisions.

However, several limitations should be considered. The study was retrospective, potentially introducing selection bias since all patients were from a single institution (CHRU Brest) and treatment regimens may have varied over the study period (2005–2021). For the feature TLS localization, we set a threshold of 1mm² for TLS at the tumoral front based on the literature. However, TLS can also be found at a greater distance from the tumor. Unlike previous studies that suggested a correlation with patient survival, our findings did not show this correlation. Therefore, it would be valuable to further investigate TLS localization with a broader perspective of the tumor environment, as previous studies have indicated (Vanhersecke et al., 2023; Finkin et al., 2015; Sofopoulos et al., 2019; Coppola et al., 2011).

Instead of viewing the exclusion of patients who received neoadjuvant therapy as a limitation, this approach provides a valuable baseline for understanding the natural progression and TLS characteristics in untreated colorectal cancer. This creates an opportunity for future studies to investigate how neoadjuvant therapy impacts TLS formation, density, and maturation, potentially offering insights into treatment response and immunomodulation in colorectal cancer (Brunet et al., 2022).

We aim to externally validate our TLS score and proposed thresholds for TLS High/Low density (median TLS density = $3.45 \times 10^{-3}$ TLS/mm²) in an independent cohort of CRC patients before considering clinical implementation. Additionally, testing this approach in other adenocarcinomas or gastrointestinal cancers, as done by Li and colleagues (Li et al., 2023), will further support the generalizability of our findings.

This study represents the first comprehensive evaluation of TLS features, such as density and maturation, in a large cohort of 806 CRC patients using reproducible IHC techniques. The TLS score we propose, based on density and maturation, using a single IHC slide with CD21/CD23 staining offers a practical and efficient method for routine clinical use, potentially providing valuable prognostic information complementary to existing markers like tumor-infiltrating lymphocytes and MMR status. This approach streamlines the evaluation process, making it potentially reliable, easier and more time-effective than assessing TILs or performing multiple MMR IHC stains, while still offering robust prognostic value for colorectal cancer patients. The simplicity of using a single slide with dual staining for CD21/CD23 could facilitate widespread adoption in pathology laboratories, helping pathologists identify those at higher risk for recurrence or poor prognosis, and guiding clinicians in determining the need for further treatment or close follow-up.

## Inclusion & Ethics

The present observational non-interventional study was conducted in compliance with the Helsinki Declaration after approval by our institutional review board (CHRU Brest, CPP n° AC-2019-3642) and by the French Data Protection Authority (CNIL, authorization n° 998,024). The quality and completeness of the Finistere Digestive cancer registry are certified every four years by an audit conducted by the National Cancer Institute (INCa), the National Institute of Health and Medical Research (INSERM), and the National Public Health Institute.

## CRediT authorship contribution statement

**Marion Le Rochais:** Writing – review & editing, Writing – original draft, Visualization, Validation, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. **Marie Morvan:** Writing – review & editing, Methodology, Formal analysis, Data curation. **Servane Bouzeloc:** Writing – review & editing, Data curation. **Jean-Baptiste Nousbaum:** Writing – review & editing, Data curation. **Matthieu Guillard:** Writing – review & editing, Formal analysis. **Pierre Le Noac'h:** Writing – review & editing, Formal analysis. **Soizic Garaud:** Writing – review & editing, Validation, Supervision. **Arnaud Uguen:** Writing – review & editing, Visualization, Validation, Supervision, Resources, Project administration, Conceptualization.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

This work was supported by La Région Bretagne and La Ligue Contre le Cancer. The authors would also thank the European grant program FEDERProgos RU 000950. LBAI was supported by the Agence Nationale de la Recherche under the "Investissement d'Avenir" program with the Reference ANR-11LABX-0016-001 (Labex IGO)

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.imbio.2025.152911.

## Data Availability

The data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request. Data are located in a controlled access data storage at CHU Brest.

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