
==== Front
Int J Surg
Int J Surg
JS9
International Journal of Surgery (London, England)
1743-9191
1743-9159
Lippincott Williams & Wilkins Hagerstown, MD

38833363
IJS-D-23-03086
10.1097/JS9.0000000000001684
00032
3
Original Research
Deciphering tertiary lymphoid structure heterogeneity reveals prognostic signature and therapeutic potentials for colorectal cancer: a multicenter retrospective cohort study
Lei Jia-Xin MD ableijx8@mail2.sysu.edu.cn

Wang Runxian MD cwangrx39@mail2.sysu.edu.cn

Hu Chuling MEng adehuchling@mail2.sysu.edu.cn

Lou Xiaoying MD flouxy3@mail.sysu.edu.cn

Lv Min-Yi MD adelvmy8@mail2.sysu.edu.cn

Li Chenghang MD cli136@connect.hkust-gz.edu.cn
g
Gai Baowen MD adegaibw@mail2.sysu.edu.cn

Wu Xiao-Jian MD, PhD ade*wuxjian@mail.sysu.edu.cn

Dou Ruoxu MD c*dourx@mail.sysu.edu.cn

Cai Du MD, PhD ade*caid28@mail.sysu.edu.cn

Gao Feng PhD ade*gaof57@mail.sysu.edu.cn

a Department of General Surgery (Colorectal Surgery), The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou Province
b School of Medicine, Shenzhen Campus of Sun Yat-Sen University, ShenzhenGuangdong Province
c Department of Gastrointestinal Surgery, The Fifth Affiliated Hospital of Sun Yat-sen University, ZhuhaiGuangdong Province
d Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University Guangzhou, Guangdong Province
e Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University Guangzhou
f Department of Pathology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou
g Artificial Intelligence Thrust, The Hong Kong University of Science and Technology, Guangzhou, People’s Republic of China
* Corresponding author. Address: Department of General Surgery (Colorectal Surgery), The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province 510655, People’s Republic of China. Tel.: +86 137 606 083 96. E-mail: wuxjian@mail.sysu.edu.cn (X. Wu), and Tel.: +86 188 194 815 50. E-mail: caid28@mail.sysu.edu.cn (D. Cai), and Tel.: +86 178 206 810 04. E-mail: gaof57@mail.sysu.edu.cn (F. Gao); Department of Gastrointestinal Surgery, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong Province, 519000, P. R. China. Tel.: +86 133 428 082 75. E-mail: dourx@mail.sysu.edu.cn (R. Dou).
9 2024
4 6 2024
110 9 56275640
28 12 2023
10 5 2024
Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0/

Background:

Tertiary lymphoid structures (TLSs) exert a crucial role in the tumor microenvironment (TME), impacting tumor development, immune escape, and drug resistance. Nonetheless, the heterogeneity of TLSs in colorectal cancer (CRC) and their impact on prognosis and treatment response remain unclear.

Methods:

The authors collected genome, transcriptome, clinicopathological information, and digital pathology images from multiple sources. An unsupervised clustering algorithm was implemented to determine diverse TLS patterns in CRC based on the expression levels of 39 TLS signature genes (TSGs). Comprehensive explorations of heterogeneity encompassing mutation landscape, TME, biological characteristics, response to immunotherapy, and drug resistance were conducted using multiomics data. TLSscore was then developed to quantitatively assess TLS patterns of individuals for further clinical applicability.

Results:

Three distinct TLS patterns were identified in CRC. Cluster 1 exhibited upregulation of proliferation-related pathways, high metabolic activity, and intermediate prognosis, while Cluster 2 displayed activation of stromal and carcinogenic pathways and a worse prognosis. Both Cluster 1 and Cluster 2 may potentially benefit from adjuvant chemotherapy. Cluster 3, characterized by the activation of immune regulation and activation pathways, demonstrated a favorable prognosis and enhanced responsiveness to immunotherapy. The authors subsequently employed a regularization algorithm to construct the TLSscore based on nine core genes. Patients with lower TLSscore trended to prolonged prognosis and a more prominent presence of TLSs, which may benefit from immunotherapy. Conversely, those with higher TLSscore exhibited increased benefits from adjuvant chemotherapy.

Conclusions:

The authors identified distinct TLS patterns in CRC and characterized their heterogeneity through multiomics analyses. The TLSscore held promise for guiding clinical decision-making and further advancing the field of personalized medicine in CRC.

Keywords:

colorectal cancer
prognosis
therapy
tertiary lymphoid structures
tumor microenvironment
OPEN-ACCESSTRUE
SDCT
==== Body
pmcIntroduction

Highlights

Comprehensive multiomics data analyses from multiple therapy databases.

A novel and pioneering classification system for colorectal cancer patients based on the translation profiles of genes related to tertiary lymphoid structures.

A potential prognostic biomarker and therapeutic indicator for colorectal cancer patient.

Colorectal cancer (CRC) is a highly malignant gastrointestinal neoplasm that ranks as the third most common cancer in terms of incidence and the second leading cause of cancer-related mortality worldwide1. While advancements in therapeutic modalities have contributed to a decline in CRC mortality rates, patients still face substantial challenges in terms of quality of life and potential mortality if poor prognostic factors are not identified promptly, resulting in appropriate medical interventions. Presently, the TNM staging system is widely employed to assess prognosis in CRC2,3. However, this system primarily focuses on tumor size, lymph node involvement, and metastasis and does not directly reflect the molecular heterogeneity of tumors. In contrast, although several molecular classification systems have been identified in CRC, their clinical application for personalized treatment remains somewhat limited. Although Consensus Molecular Subtypes (CMS)4 and Immune Consensus Molecular Subtypes (iCMS)5 have emerged as valuable indicators for molecular classification, they predominantly focus on the intrinsic characteristics of the tumor itself, without delving into the intricacies of the TME. Consequently, there is a critical need to develop a predictive framework that can effectively stratify patient subgroups based on TME markers, enabling precise management of CRC patients, and facilitating comprehensive postoperative treatment.

Tertiary lymphoid structures (TLSs) are specialized aggregates of immune cells that can develop in nonlymphoid tissues in response to chronic inflammation or infection6. These structures bear resemblance to lymphoid organs such as lymph nodes and serve as sites for immune cell activation and antigen presentation7. Moreover, TLSs consist of highly endothelial venules and lymphatic vessels, rendering them critical components of the TME7. TLSs provide a crucial microenvironment for fostering antitumor immune responses and have been associated with improved prognosis in various solid malignancies8. Moreover, B cells and TLSs are closely associated with response to immunotherapy, with the presence of TLSs serving as a predictive factor for positive outcomes in immune checkpoint blockade (ICB) therapy for melanoma and bladder cancer9,10. Although some studies have revealed the relationship between TLSs and CRC, including the work by Qian et al.11, the molecular-level heterogeneity and functional implications of TLSs in CRC have yet to be fully elucidated.

In this study, we comprehensively analyze the heterogeneity of TLSs in CRC and uncover a prognostic signature associated with TLSs. TLSscore was developed to quantify TLS patterns of individuals, which highlighted the clinical implications of TLSs as a prognostic biomarker and their potential as a therapeutic target for improving patient outcomes in CRC.

Materials and methods

Data collection and preprocessing

Thirty-nine TLS signature genes (TSGs) were obtained from Sautès-Fridman’s literature11. RNA sequencing data of TCGA CRC was downloaded from the UCSC Xena12. Corresponding clinicopathological information of the TCGA cohort was obtained from Liu’s et al. literature13. Microarray expression profiles and clinicopathological annotations of five CRC cohorts, involving GSE3958214, GSE1753615, GSE3789216, GSE3908417, and GSE3311318, were downloaded from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/gds) database. Furthermore, the in-house Clinical Omics Study of Colorectal Cancer in China (COCC) cohort (https://www.icgc-argo.org/) was involved, encompassing comprehensive transcriptional profiles, clinicopathological information, and digital whole slide images (WSIs) obtained from CRC patients. Finally, TCGA cohort served as the training dataset, while GSE39582 cohort, Meta-GEO cohort (integrated by the other four GEO cohorts), and COCC cohort served as three independent validation datasets. The baseline information of these cohorts was summarized in Supplementary Table 1 (Supplemental Digital Content 2, http://links.lww.com/JS9/C692). The research methodologies were endorsed by the Ethical Committee of our hospital, with ethical approval code 2023ZSLYEC-586 on 28 November 2023. In this retrospective study, patient consent was waived. The work has been reported in line with the strengthening the reporting of cohort, cross-sectional, and case–control studies in surgery (STROCSS) criteria19 (Supplemental Digital Content 1, http://links.lww.com/JS9/C691).

Establishment of TLS patterns

Non-negative matrix factorization (NMF) algorithm was implemented to identify TLS patterns using ‘NMF’20 package based on the expression levels of 39 TSGs in all CRC patients.

Assessment of biological characteristics

A series of gene signature scores were calculated using single sample gene set enrichment analysis (ssGSEA) with the ‘GSVA’21 package.

The HALLMARK gene sets of ‘h.all.v2023.1.Hs.entrez’ derived from the MSigDB database22,23. Besides, metabolic pathway-related gene sets were obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database24. Moreover, a group of gene sets that deposited genes associated with some biological processes were extracted from Mariathasan’s literature25 and Zeng’s literature26, including epithelial-mesenchymal transition (EMT) markers, angiogenesis signature, TGFβ/EMT markers, and immune activation signature. Besides, human leukocyte antigens (HLA) related genes were obtained from hla.alleles.org27.

Drug sensitivity and response to immunotherapy

We investigated the potential benefits of adjuvant chemotherapy in patients with TNM stage III. The Genomics of Drug Sensitivity in Cancer (GDSC) database28 was used to predict the sensitivity to chemotherapeutic drugs.

Immune checkpoint-related genes were attained from Qin et al.29. A collection of established predictors of immunotherapy response, including tumor mutation burden (TMB), TIDE score30, TMEscore31 and MIRACLE score32, were evaluated. Furthermore, two immunotherapeutic cohorts served as supplementary datasets. IMvigor21025, comprising samples with metastatic urothelial cancer treated with an anti-PD-L1 agent, was acquired from the ‘IMvigor210CoreBiologies’ package. GSE9106133, containing samples with advanced melanoma that received treatment with antiprogrammed death (PD)-1 ICB, was downloaded from the GEO database.

Construction of TLSscore

Differently expressed genes (DEGs) among TLS patterns were screened out. DEGs with prognostic values were further identified by performing 1000 times resampling (80% proportion of samples each time) and analyzed by the univariate Cox regression analysis to estimate the correlation between each gene and disease-free survival (DFS) in all patients.

The least absolute shrinkage and selection operator (LASSO) regression was employed to refrain from the risk of overfitting. Consequently, TLSscore was generated as follows.

TLSscore= ∑i=1 Coefficient (TSGi)*Expression (TSGi).

Patients were divided into low TLSscore or high TLSscore group according to optimal cut-off calculated by the Youden index.

Pathological assessment of TLSs

TLSs are characterized as a dense aggregation of unencapsulated B cells (CD20) in the periphery, accompanied by an adjacent T cell zone (CD3) and surrounded by dendritic cells (DC, CD11c). Mature TLS contain B-cell follicles with actively replicating B-cell germinal center (GC), the GC represents a tightly enclosed area that consists of CD21+ follicular dendritic cells (FDC) and CD23+ B cells11. A total of 598 WSIs from TCGA CRC samples and 50 WSIs from COCC samples were meticulously annotated by professional pathologists blinded to the survival outcomes. According to the location of tumor invasive margins, TLSs were subdivided into two subgroups: peritumoral TLS (peri-TLS) and intratumoral TLS (intra-TLS). Patients with GC-positive TLSs were classified into the TLS (+) group, and the rest of the patients were classified into the TLS (−) group.

Besides, we collected a 12-chemokine gene signature developed by Coppola et al.34, which could predict the presence of TLSs in primary CRC correlating with better patient prognosis. The 12-chemokine gene signature are as follows: CCL2, CCL3, CCL4, CCL5, CCL8, CCL18, CCL19, CCL21, CXCL9, CXCL10, CXCL11, CXCL13.

Statistical analysis

All data processing and statistical analyses were conducted on R software (version 4.1.3). Wilcoxon rank sum test was performed to analyze diversity between two groups, while Kruskal–Wallis test was used to compare variations among multiple groups. The Kaplan–Meier survival curves with log-rank test were employed with ‘survminer’35 package. Correlation analyses were conducted using Spearman correlation analysis. Statistical significance was defined as P<0.05.

Results

The landscape of TSGs in CRC

Copy number variation (CNV) and the masked somatic mutations of 39 TSGs were systematically explored in TCGA cohort. We observed a low mutation frequency of TSGs in CRC, varying from 0 to 2%. Out of 582 samples, 89 exhibited mutations, representing a proportion of 15.29% (Figure S1A, Supplemental Digital Content 2, http://links.lww.com/JS9/C692). The location of CNV on chromosomes was shown in Figure S1B (Supplemental Digital Content 2, http://links.lww.com/JS9/C692). Varying frequencies of CNV alterations were found among TSGs, and most of the chemokine-related TSGs trended to a higher frequency of CNV gain (Figure S1C, Supplemental Digital Content 2, http://links.lww.com/JS9/C692). Based on the expression of these TSGs, we could clearly distinguish CRC samples from normal samples (Figure S1D, Supplemental Digital Content 2, http://links.lww.com/JS9/C692). In addition, we revealed significant differences in mRNA expression levels between tumor and adjacent normal tissues (Figure S1E, Supplemental Digital Content 2, http://links.lww.com/JS9/C692). The above analysis revealed a highly heterogeneous landscape of genome and expression changes in TSGs, indicating that the expression imbalance of TSGs exerted an indispensable role in the occurrence and development of CRC.

Identification of TLS patterns

The interaction network portrayed intricate regulations, inherent linkages, and the significance for the survival of prognostic TSGs in CRC (Fig. 1A). Three TLS patterns were eventually identified, including 617 samples in Cluster 1, 566 samples in Cluster 2, and 483 samples in Cluster 3 (Figure S2A, B, Supplemental Digital Content 2, http://links.lww.com/JS9/C692). It was found that most TSGs were downregulated in Cluster 1 while upregulated in Cluster 3 (Figure S2B, Supplemental Digital Content 2, http://links.lww.com/JS9/C692). Survival analysis for three patterns revealed exceedingly significant advantages in Cluster 3, however, Cluster 2 manifested the opposite (Fig. 1B).

Figure 1 Immunological characteristics of TLS patterns in colorectal cancer (CRC). (A) The interaction network of prognostic TLS signature genes (TSGs) in CRC. The circle size represented survival effects of each TSG, calculation used the formula log10 (log-rank test P values indicated). Favorable factors for disease-free survival (DFS) were indicated in black, and risk factors were indicated in green. The lines connecting TSGs represented intricate regulations and inherent linkages. The thickness and transparency of lines represented the strength of correlation estimated by Spearman correlation analysis. The positive correlation was indicated in red and the negative correlation in blue. The category of TSGs was marked with blue, red, yellow, and brown, respectively. (B) Kaplan–Meier curves displayed a significant survival difference among three TLS patterns. (C) Boxplot showed ESTIMATE score, Immune score, and Stromal score calculated by ESTIMATE algorithm among three TLS patterns. (D) Boxplot showed expression levels of tumor infiltrating immune cells evaluated by MCPcounter among three TLS patterns. (E) Heatmap demonstrated enrichment diversity of adenocarcinomatous hallmark pathways among three TLS patterns. Statistical significance was denoted as follows: ns (not significant) for P≥0.05, *P<0.05, **P<0.01, ***P<0.001, and ****P<0.0001.

Tumor microenvironment characteristics among TLS patterns

Cluster 2 and Cluster 3 both exhibited a greater stromal score, with the latter showing a superior immune score (Fig. 1C). Furthermore, Cluster 3 was remarkably rich in antitumor immune cells. Conversely, Cluster 2 was highly infiltrated by endothelial cells and fibroblasts. Cluster 1 exhibited immune suppression with a lower immune score and reduced immune cell infiltration (Fig. 1D).

The analysis for hallmark pathways explored distinct biological characteristics. Cluster 1 is characterized by the upregulation of proliferation-related pathways. Cluster 2 was markedly enriched in stromal and carcinogenic activation pathways. Cluster 3 presented enrichment pathways associated with immune regulation and activation (Fig. 1E).

In summary, analyses of TLSs revealed the existence of three patterns in CRC that were associated with heterogeneous TME infiltration.

Molecular characteristics and biological functions among TLS patterns

Cluster 1 exhibited significantly higher activation levels of metabolism-related pathways closely linked to tumor progression (Fig. 2A).

Figure 2 Biological characteristics of TLS patterns in colorectal cancer (CRC). (A) Heatmap demonstrated different enrichment levels of metabolism pathways among three TLS patterns. (B) The oncoplot showed the top 10 statistically different mutated genes among three TLS patterns and their respective and overall mutation rates. (C) The boxplot showed altered difference of 10 canonical signaling pathways with frequent genetic alterations among three TLS patterns. (D) The stacked histogram displayed significantly different proportions of CMS molecular subtypes among three TLS patterns. Statistical significance was denoted as follows: ns (not significant) for P≥0.05, *P<0.05, **P<0.01, ***P<0.001, and ****P<0.0001.

We demonstrated the top 10 statistically different mutated genes among TLS patterns and their respective mutation rates (Fig. 2B). Notably, Cluster 3 demonstrated the lowest mutation rate of APC, a well-established tumor suppressor gene associated with CRC. Conversely, other genes displayed a high mutation frequency within Cluster 3. We found Cluster 1 exhibited a substantial accumulation of mutation in the MYC, NRF2, and TP53 pathways, which exerted crucial roles in regulating the cell cycle. In contrast, Cluster 2 displayed significantly elevated mutation levels in the HIPPO, NOTCH, PTK RAS, and WNT pathways, which have been closely linked to promoting cell proliferation, tumor invasion, and metastasis (Fig. 2C).

CMS classification system was employed to define TLS patterns. Interestingly, Cluster 3 exhibited a higher prevalence of the CMS1 subtype, which is characterized by hypermutation, microsatellite instability, and robust immune activation. Cluster 1 exhibited a higher prevalence of the CMS2 subtype, characterized by epithelial features and marked WNT and MYC signaling activation, as well as the CMS3 subtype, which is characterized by epithelial features and evident metabolic dysregulation. Cluster 2 exhibited a higher prevalence of the CMS4 subtype, which is characterized by prominent TGFβ activation, stromal invasion, and angiogenesis (Fig. 2D).

In summary, three identified TLS patterns exhibited distinct profiles of TME infiltration, metabolic characteristics, and mutation patterns, all of which had potential implications for guiding clinical treatment strategies for CRC.

Therapy response and drug sensitivity analyses among TLS patterns

Here, we found that TMB was significantly higher in Cluster 3 (Fig. 3A). Moreover, Cluster 3 trended to a higher proportion of MSI status (Fig. 3B), and activation of immune checkpoint-related genes (Fig. 3C). Furthermore, Cluster 3 exhibited a higher TMEscore and MIRACLE score, as well as lower TIDE score (Fig. 3D–F). These findings consistently suggested potential benefits from ICB therapy in Cluster 3. Additionally, among patients classified into Cluster 1 and Cluster 2, those who underwent adjuvant chemotherapy demonstrated notable survival advantages. Nonetheless, no discernible disparities in survival were observed within Cluster 3 (Fig. 3G-I).

Figure 3 Therapy response and drug sensitivity for three TLS patterns. (A) The boxplot showed tumor mutation burden (TMB) among three TLS patterns in the TCGA cohort. (B) The stacked histogram displayed significantly different proportions of MSI/MSS status among three TLS patterns. (C) The boxplot showed the expression levels of several immune checkpoint-related genes among three TLS patterns. (D-F) The boxplot showed TME score, MRACLE score, and TIDE score among three TLS patterns. (G-H) Kaplan‐Meier curves demonstrated the potential benefits of adjuvant chemotherapy in patients with TNM stage III among three TLS patterns. Statistical significance was denoted as follows: ns (not significant) for P≥0.05, *P<0.05, **P<0.01, ***P<0.001, and ****P<0.0001.

In summary, CRC patients in Cluster 1 and Cluster 2 may derive greater benefits from chemotherapy, while patients in Cluster 3 may exhibit enhanced responsiveness to ICB immunotherapy, suggesting the potential for personalized treatment strategies based on the molecular characteristics of different clusters.

Development and validation of TLSscore

One hundred forty DEGs among TLS patterns were screened out and 48 DEGs correlated with DFS were identified (Figure S3A, Supplemental Digital Content 2, http://links.lww.com/JS9/C692). Moreover, a nine-gene signature was established (Figure S3B-D, Supplemental Digital Content 2, http://links.lww.com/JS9/C692). The TLSscore formula is as follows: (0.1272 * TNS1) + (0.0507 * SFRP2) + (−0.0176 * DPT) + (0.0075 * GAS1) + (−0.0731 * CXCL11) + (−0.0219 * UBD) + (−0.0345 * IGLV3-19) + (−0.0721 * MMP1) + (−0.0127 * IGHG2). According to the optimal cut-off value, patients were classified into diversity TLSscore group (Figure S3E, Supplemental Digital Content 2, http://links.lww.com/JS9/C692). Samples with higher TLSscore trended to significantly shorter DFS in both training cohort and validation cohorts (Fig. 4A–C). Taking into consideration TLSscore group cases and other clinical or pathological parameters significant in univariate Cox regression, the results of multivariate Cox regression showed that TLSscore was an independent prognostic factor for DFS prediction in both training cohort and validation cohorts (Supplementary Table 2, Supplemental Digital Content 2, http://links.lww.com/JS9/C692).

Figure 4 Construction of a prognostic gene signature for colorectal cancer (CRC). (A-C) Kaplan–Meier curves displayed the disease-free survival of the high and low TLSscore group in TCGA cohort, GSE39582 cohort and Meta-GEO cohort. (D) Bubble plot showed the correlation between TLSscore and tumor infiltrating immune cells in TCGA cohort, GSE39582 cohort and Meta-GEO cohort. (E) Bubble plot showed the correlation between TLSscore and biological pathways related to migration, proliferation and immunity in TCGA cohort, GSE39582 cohort and Meta-GEO cohort. (F) Heatmap demonstrated enrichment diversity of adenocarcinomatous hallmark pathways between high and low TLSscore group in TCGA cohort.

Immune infiltration and molecular characteristics between TLSscore groups

The high TLSscore group exhibited a higher stromal score, while the immune score displayed an opposite trend (Figure S4A, Supplemental Digital Content 2, http://links.lww.com/JS9/C692). Furthermore, we found a significant negative correlation between TLSscore and anticancer immune cells. In contrast, TLSscore exhibited a prominent positive correlation with cancer-promoting cells, across both the training and validation cohorts (Fig. 4D).

Correlation analyses revealed a significant positive association between TLSscore and stroma activity-related pathways. Conversely, TLSscore exhibited negative associations with immune activity-related pathways, in both training and validation cohorts (Fig. 4E). Interestingly, the low TLSscore group exhibited elevated expression of mRNAs associated with immune activation transcripts, while the high TLSscore group displayed upregulation of mRNAs related to the TGFβ/EMT pathway (Figure S4B, C, Supplemental Digital Content 2, http://links.lww.com/JS9/C692). Besides, the high TLSscore group exhibited a significant enrichment in stromal and carcinogenic activation pathways, while the low TLSscore group presented enrichment in pathways associated with immune regulation and activation (Fig. 4F) and elevated HLA expression levels (Figure S4D, Supplemental Digital Content 2, http://links.lww.com/JS9/C692). GSEA revealed that the high TLSscore group exhibited a significant enrichment in angiogenesis and EMT pathways, while the low TLSscore group demonstrated enrichment in interferon-α response and DNA repair pathways (Figure S4E, Supplemental Digital Content 2, http://links.lww.com/JS9/C692). These findings shed light on the distinct molecular profiles associated with varying TLSscore groups in CRC.

Exploration of TLSs heterogeneity in histopathology images

Representative WSIs were demonstrated for each group, revealing that TLSs were more prominently observed in the low TLSscore group both in peritumoral and intratumoral regions, whereas those with high TLSscore exhibited a lower proportion of TLS or even absence (Fig. 5A). Intra-TLS was identified as a prognostic pathological feature for tumors, whereas peri-TLS did not exhibit clinical significance36. Based on the presence of intra-TLS, patients were categorized into intra-TLS (+) and intra-TLS (-) groups. The intra-TLS (+) group exhibited significantly improved outcomes (Fig. 5B). Furthermore, our analysis revealed a higher proportion of intra-TLS (+) cases within the low TLSscore group (Fig. 5C), while the intra-TLS (+) group exhibited lower TLSscore (Fig. 5D), indicating intra-TLS (+) cases were closely associated with low TLSscore. Besides, our investigation revealed a substantial correlation between the TLSscore and Coppola’s score, implying that the TLSscore possessed the potential to serve as a predictive indicator for the presence of TLSs in CRC (Fig. 5E, F). Remarkably, TLSscore exhibited superior performance in predicting 5-year outcomes within the TCGA cohort compared to intra-TLS and Coppola’s score in CRC (Fig. 5G).

Figure 5 Tertiary lymphoid structures (TLSs) heterogeneity in histopathology images. (A) Representative digital whole slide images demonstrated TLSs observed both in peritumoral and intratumoral regions in the high and low TLSscore group. (B) Kaplan–Meier curves displayed the disease-free survival between intra-TLS (+) group and intra-TLS (-) group in TCGA cohort. (C) The stacked histogram displayed significantly different proportions of intra-TLS (+) cases between the high and low TLSscore group in the TCGA cohort. (D) Boxplot showed differences of TLSscore between intra-TLS (+) group and intra-TLS (-) group. (E) Boxplot showed differences of Coppola’s score between the high and low TLSscore group. (F) Correlation scatter plot revealed a negative correlation between TLSscore and Coppola’s score. (G) Time-dependent ROC curves displayed performance comparison for 5-year prognostic prediction within the TCGA cohort with TLSscore, intra-TLS, and Coppola’s score.

Therapy response and drug sensitivity analyses between groups

Here, the low TLSscore group exhibited a significantly higher TMB, a higher proportion of MSI status, and elevation of immune checkpoint-related genes (Fig. 6A-C). Besides, the low TLSscore group demonstrated higher TMEscore and MIRACLE score (Fig. 6D, E). Notably, a significant negative correlation was observed between TLSscore and TMEscore, indicating the low TLSscore group may possess a microenvironment conducive to better therapy response (Figure S5A, Supplemental Digital Content 2, http://links.lww.com/JS9/C692). Interestingly, the high TLSscore group exhibited higher TIDE score, immune dysfunction score, immune exclusion score, and cancer-associated fibroblasts (CAFs) score, suggesting those with higher TLSscore may derive less benefit from immunotherapy (Fig. 6F, Figure S5B-D, Supplemental Digital Content 2, http://links.lww.com/JS9/C692). In summary, patients in the low TLSscore group exhibited a more favorable response to immunotherapy.

Figure 6 Immunology therapy response between the high and low TLSscore group. (A) The boxplot showed tumor mutation burden (TMB) between the high and low TLSscore group in the TCGA cohort. (B) The stacked histogram displayed ﻿significantly different proportions of MSI/MSS status between the high and low TLSscore group in the TCGA cohort. (C) The boxplot showed the expression levels of several immune checkpoint-related genes between the high and low TLSscore group in the TCGA cohort. (D-F) The boxplot showed TME score, MRACLE score, and TIDE score between the high and low TLSscore group in the TCGA cohort. (G, J) Kaplan–Meier curves displayed the overall survival of the high and low TLSscore group in IMvigor210 and GSE91061 cohorts, respectively. (H, K) The stacked histogram displayed significantly different proportions of immunotherapy response between the high and low TLSscore group in IMvigor210 and GSE91061 cohorts, respectively. (I, L) Boxplot showed differences of TLSscore between Noresponder and Responder group in IMvigor210 and GSE91061 cohorts, respectively. Statistical significance was denoted as follows: ns (not significant) for P≥0.05, *P<0.05, **P<0.01, ***P<0.001, and ****P<0.0001.

The high TLSscore group exhibited significantly worse prognosis in both IMvigor210 (Fig. 6G) and GSE91061 (Fig. 6J). Furthermore, a higher proportion of responders was also observed in low TLSscore group in two cohorts (Fig. 6H, K), and the responders displayed significantly lower TLSscore (Fig. 6I, L). Collectively, these findings demonstrated that the potential and robustness of TLSscore as a biomarker for prognosis and responses assessment of immunotherapy not only in CRC but also across multiple cancer types.

Here, we found significant survival advantages for those who underwent adjuvant chemotherapy among patients with higher TLSscore (Figure S5E, F, Supplemental Digital Content 2, http://links.lww.com/JS9/C692). However, no discernible differences in outcomes were observed within the low TLSscore group. Besides, the high TLSscore group exhibited decreased IC50 values for Oxaliplatin (Figure S5G, Supplemental Digital Content 2, http://links.lww.com/JS9/C692). Additionally, several targeted agents demonstrated superior efficacy in the high TLSscore group, including AZD4547, Erlotinib, Cediranib, Foretinib, and MK-2206. In summary, patients with higher TLSscore may derive greater benefits from chemotherapeutic drugs and targeted agents.

The verification of TLSscore in the COCC cohort

Samples with lower TLSscore trended towards significantly longer outcomes (Fig. 7A). The high TLSscore group exhibited a notable abundance of endothelial cells and fibroblasts (Fig. 7B). Additionally, the low TLSscore group exhibited elevated expression of mRNAs associated with immune activation transcripts (Figure S6A, Supplemental Digital Content 2, http://links.lww.com/JS9/C692). Conversely, we observed a significant upregulation of mRNAs linked to TGFβ/EMT pathway in the high TLSscore group (Figure S6B, Supplemental Digital Content 2, http://links.lww.com/JS9/C692). Furthermore, the high TLSscore group exhibited significant enrichment in stromal and carcinogenic activation pathways. The low TLSscore group presented enrichment in pathways associated with immune regulation and activation (Figure S6C, Supplemental Digital Content 2, http://links.lww.com/JS9/C692). Indeed, a more prominent presence of TLSs were observed in the intratumoral region within the low TLSscore group, while high TLSscore group exhibited a lower proportion of TLSs or even absence, as indicated by WSIs (Fig. 7C).

Figure 7 The verification of the stability and generalizability of TLSscore in the COCC cohort. (A) Kaplan–Meier curves displayed the disease-free survival of the high and low TLSscore groups. (B) Boxplot showed expression levels of tumor infiltrating immune cells evaluated by MCPcounter between the high and low TLSscore group. (C) Representative digital whole slide images demonstrated TLSs observed in intratumoral region in the high and low TLSscore groups. (D, E) Kaplan‐Meier curves demonstrated the potential benefits of adjuvant chemotherapy in patients with TNM stage III between the high and low TLSscore group. (F) The stacked histogram displayed significantly different proportions of MSI/MSS status between the high and low TLSscore group. (G-I) The boxplot showed TME score, MRACLE score, and TIDE score between the high and low TLSscore group. Statistical significance was denoted as follows: ns (not significant) for P≥0.05, *P<0.05, **P<0.01, ***P<0.001, and ****P<0.0001.

Among patients with high TLSscore, there was a significant survival advantage for those who underwent adjuvant chemotherapy (Fig. 7D, E). However, no perceptible differences in outcomes were observed within the low TLSscore group, suggesting patients in the high TLSscore group may benefit from chemotherapy. Moreover, low TLSscore group exhibited a higher proportion of MSI status (Fig. 7F), higher MIRACLE score, higher TMEscore, as well as a lower TIDE score (Fig. 7G-I). In summary, patients belonging to the low TLSscore group exhibited a more favorable response to immunotherapy. These results further validated the potential of TLSscore as a predictive biomarker for assessing the efficacy of therapy response and drug sensitivity in CRC.

Discussion

TLSs have recently garnered substantial attention in clinical settings due to their potential impact on cancer treatment9. Increasing evidence suggests that the presence of TLSs within the tumor microenvironment is associated with favorable prognoses and a higher likelihood of responding to ICB therapy in several types of cancer6,36.

We successfully identified three TLS patterns in patients with CRC, which exhibited significant differences in terms of biological features. Cluster 1 was characterized by significantly activation of metabolism-related pathways and proliferation-related pathways. Tumor cells exhibit altered carbohydrate metabolism compared to normal cells, often characterized by enhanced glucose uptake and increased glycolytic activity, which can influence the tumor microenvironment by modulating immune responses and promoting angiogenesis37,38. The accumulation of metabolic byproducts, such as lactate, can create an acidic microenvironment that promotes tumor invasion and metastasis39. Aberrant glycan biosynthesis can result in the production of structurally distinct glycans on cell surface proteins, leading to changes in cell adhesion, migration, and signaling40. Additionally, abnormal glycan structures on tumor cells can interact with lectins and other glycan-binding proteins, contributing to tumor cell interactions with the extracellular matrix, immune cells, and endothelial cells, thereby influencing tumor angiogenesis and metastasis41,42. Cluster 2 displayed high infiltration of endothelial cells and fibroblasts, which was markedly enriched in stromal and carcinogenic activation pathways. Endothelial cells line the inner surface of blood vessels and are crucial for tumor angiogenesis, the formation of new blood vessels to supply nutrients and oxygen to the growing tumor43,44. Tumor cells release angiogenic factors that stimulate endothelial cell proliferation, migration, and tube formation, leading to the establishment of an extensive network of blood vessels within the tumor45. CAFs secrete various growth factors, cytokines, and extracellular matrix components that promote tumor cell proliferation, survival, and invasion46. Moreover, CAFs contribute to the development of therapeutic resistance by orchestrating the formation of a physical barrier surrounding the tumor, impeding the effective penetration of anticancer drugs into the tumor microenvironment47,48. Cluster 3 was remarkably enriched in antitumor immune cells and exhibited enrichment pathways associated with immune regulation and activation. Interferon α can enhance immune cell activation and antigen presentation, promoting antitumor immune responses49. Interferon γ, on the other hand, has direct antitumor effects by inhibiting tumor cell proliferation and promoting apoptosis. It also enhances antigen presentation and cytotoxic activity of immune cells, facilitating tumor cell recognition and elimination50. Both interferons contribute to the recruitment and activation of immune effector cells, such as T cells and NK cells, in the tumor microenvironment51. Overall, our study provides valuable insights into the heterogeneity of TLS patterns in CRC. These findings have important implications for guiding treatment decisions.

The molecular heterogeneity in CRC contributed to the drug resistance in a subset of patients undergoing adjuvant chemotherapy, thereby leading to subsequent tumor recurrence. Precisely identifying individuals who exhibited resistance to adjuvant chemotherapy was a crucial concern in the accurate diagnosis and treatment of CRC. Patients with high TLSscore potentially benefited from the superior efficacy of adjuvant chemotherapy, which might inspire further exploration of the hidden molecular mechanisms. Notably, both Cediranib and Foretinib target and inhibit the activity of multiple receptor tyrosine kinases (RTKs)52,53. Specifically, they have inhibitory effects on vascular endothelial growth factor receptors (VEGFRs), which play a crucial role in angiogenesis and tumor vascularization54,55. Erlotinib, a selective epidermal growth factor receptor (EGFR) inhibitor, belongs to the class of tyrosine kinase inhibitors (TKIs) and primarily functions by suppressing EGFR activity, thereby blocking signaling cascades and restraining tumor cell proliferation56,57. AZD4547, a fibroblast growth factor receptor (FGFR) inhibitor, specifically targets FGFRs, a family of receptor tyrosine kinases that contribute significantly to cell proliferation, differentiation, and angiogenesis58. Dysregulation of FGFRs, through genetic alterations or overexpression, has implicated their involvement in the development and progression of diverse malignancies. AZD4547 inhibits FGFR kinase activity, thereby disrupting the signaling pathways responsible for tumor growth and angiogenesis59. MK-2206, an AKT inhibitor, primarily functions by inhibiting AKT, a serine/threonine kinase crucial for regulating cell survival, proliferation, and growth60. By targeting AKT, MK-2206 induces cell cycle arrest, suppresses cell growth, and promotes apoptosis in cancer cells. Additionally, it may interfere with angiogenesis and other processes that contribute to tumorigenesis and tumor progression61. These findings suggest that patients with high TLSscore may potentially benefit from the superior efficacy of these agents. Overall, the development and validation of TLSscore provide a valuable tool for quantifying TLS patterns and predicting clinical outcomes in CRC. The association of TLSscore with therapy response and drug sensitivity further highlight its clinical significance in guiding personalized treatment strategies.

Several limitations of this study should be acknowledged. Firstly, it is important to recognize that this research is retrospective, utilizing preexisting datasets for training and validation purposes. Consequently, further validation of the TLSscore using large prospective cohorts is warranted to enhance its robustness and generalizability. Secondly, this study focuses on dissecting the heterogeneity of TLS in CRC and providing tools for individualized treatment of CRC. However, further experimental validation at the molecular and cellular levels is needed to explore and verify the mechanism of key findings.

In conclusion, the findings of this investigation offered novel insights into prognostic prediction and personalized precision therapy for CRC. Moving forward, these results held the potential to significantly impact the field of CRC treatment by facilitating tailored therapeutic approaches based on the assessment of TLS patterns.

Conclusions

In conclusion, three TLS patterns were identified in CRC and their heterogeneity was deciphered using multiomics data. TLSscore exerted vital significance on prognostic prediction and precision therapy in CRC.

Ethical approval

The research was endorsed by the Ethical Committee of the Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China, with ethical approval code 2023ZSLYEC-586 on 28 November 2023.

Consent

This study was retrospective, and the research samples were derived from medical records collected during those previous procedures. It is important to note that the study had obtained an exemption from the requirement for obtaining informed consent from the individuals whose medical records were included in the research samples. We have fully protected the privacy rights of patients and have not disclosed any names, initials, or hospital numbers.

Source of funding

This study was supported by the National Natural Science Foundation of China (No. 8227242, FG), Guangzhou Basic and Applied Basic Research Fund (No. 2024A04J9983, FG), Guangdong Basic and Applied Basic Research Foundation (No. 2023A1515110196, DC), Postdoctoral Fellowship Program of CPSF (No. GZC20233214, DC), the Guangzhou Key Research and Development Project (No. 202206080008, XW), Guangdong Key Research and Development Project (No. 2023B1111040003, XW), Guangdong Basic and Applied Basic Research Foundation (No. 2023B1515130008, XW), Autonomous Region Natural Science Fund Plan Special Training Project (No. 2022D03041, XT), the program of Guangdong Provincial Clinical Research Center for Digestive Diseases (2020B1111170004), National Key Clinical Discipline.

Author contribution

J.X.L. and R.W.: contributed equally to this study; X.W., R.D., D.C., and F.G.: conceived and designed the study; B.G., M.Y.L., and C. L.: acquired the data; J.X.L., C.H., and M.Y.L.: organized all the data; R.W. and X.L.: conducted the pathological assessment of TLSs; J.X.L., R.W., and C.H.: implemented quality control of data and the algorithms; B.G., C.L., and X.L.: did the statistical analysis; J.X.L. and R.W.: wrote the initial draft; X.W., R.D., D.C., and F.G.: revised the manuscript. All authors have read and approved the final manuscript.

Conflicts of interest disclosure

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Research registration unique identifying number (UIN)

Name of the registry: Chinese Clinical Trial Registry.

Unique identifying number or registration ID: ChiCTR2300078929.

Hyperlink to your specific registration (must be publicly accessible and will be checked): https://www.chictr.org.cn/showproj.html?proj=214526.

Guarantor

Feng Gao

Associate Professor

E-mail: gaof57@mail.sysu.edu.cn

Address: The Sixth Affiliated Hospital, Sun Yat-sen University, No.26 Yuancun Erheng Road, Guangzhou, Guangdong Province, 510655, China.

Data availability statement

The data used in this study were obtained from publicly available sources, including the GEO and UCSC Xena databases. These databases provide open access to a wide range of genomic and transcriptomic data. However, the COCC data are not publicly available due to data access restrictions. Interested researchers can make a reasonable request for access to the COCC data by contacting the corresponding author.

Provenance and peer review

Not commissioned, externally peer-reviewed.

Supplementary Material

Acknowledgement

Presentation: None.

Note: Color should be used for any figures in print.

Jia-Xin Lei and Runxian Wang contributed equally to this study and shared first authorship.

Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.

Supplemental Digital Content is available for this article. Direct URL citations are provided in the HTML and PDF versions of this article on the journal’s website, www.lww.com/international-journal-of-surgery.

Published online 4 June 2024
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