TLSs mainly consist of CD3⁺T-cell-rich region, CD20⁺B-cell-rich region, plasma cells (PCs), dendritic cells (DCs), and fibroblastic reticular cells (FRCs) (36, 37). Cytotoxic granule- expressing CD8⁺ T cells have been detected in surrounding T cell zone, as have CD4⁺ T cells orientated towards a Th1 cell phenotype and CD4⁺ regulatory T (Treg) cells (38). The inner B cell zone also includes CD21⁺ follicular dendritic cells (FDCs), germinal center (GC), CD83⁺ mature DCs, which are markers of mature TLSs (5). The vicinity of TLSs consists of lymphatic vessels, which express podoplanin and produce CC-chemokine ligand 21 (CCL21), and high endothelial venules (HEVs), which are characterized by the markers of peripheral node addressin (PNAd) (38). The follicles can further contain scattered CD68⁺ macrophages for clearance of apoptotic cells (5).

TLS formation could be induced by tissue-specific expression of chemokines. Heterogeneity of these driving factors may lead to differences in TLS components. Intriguingly, some specific cell types have been observed in the TLSs under certain conditions. LAMP⁺ DCs (mature DCs) have been considered to be reliable marker of TLSs in NSCLC as they were almost exclusively found in these structures (39). The density of LAMP⁺ DCs has been proved to be correlated with favorable clinical outcomes in NSCLC (17). A qualitative shift in the organization of TLSs has been found in Helicobacter hepaticum (Hhep) colonized mice in colorectal cancer (CRC), in which an increased presence of CD11c⁺ cells was found in the T cell zone of TLSs, consistent with an increase in DCs (40). A novel CD20⁺CD22⁺ADAM28⁺ B-cell subpopulation within TLSs has been reported to be present in ICI responders. These cells were named as ICI‐Responsive B cells (BIR) and were further identified as a subset of memory B cells that promoted the response to ICI therapy (41).

TLS density varies among different individuals, even for a given cancer type and stage of the disease, emphasizing that individual tumor microenvironment (TME) can be more or less permissive to lymphoid neogenesis (42). High TLS density in tumors generally correlates with better prognosis. It has been reported that the expression of transcription factors related to adaptive immunity was significantly upregulated in TLS-high tumors (18). Patients with high TLS density exhibited significantly improved overall survival (OS) in hepatocellular carcinoma (HCC), ovarian cancer (OV), skin cutaneous melanoma (SKCM) and uterine corpus endometrial carcinoma (UCEC) (19, 43). In a lung squamous cell cancer (LSCC) cohort, high TLS density significantly correlated with improved progression-free survival (PFS) (16). Apart from primary tumors, a higher level of TLSs in metastatic tumors also showed significantly better OS (44–47).

TLSs can be found in the peritumor, invasive margin and center of tumors (27). The location of TLSs may be important in predicting outcomes, associated with its function in the tumor immune response (48).

Some studies showed that patients with high level of peritumoral TLSs exhibited worse disease-free survival (DFS) and OS in breast cancer, cholangiocarcinoma (CCA), HCC and colorectal cancer liver metastases (CRLM) (24, 25, 49, 50). Zhang et al. found that the frequency of CD4⁺Bcl6⁺ T follicular helper (Tfh) cells was significantly increased in intratumoral TLSs compared to peritumoral TLSs in CRLM (50). However, one study found peritumoral TLSs as an independent and favorable prognostic factor in both OS and DFS for non-metastatic colorectal carcinoma (nmCRC) patients (22).

Unlike the dual role of peritumoral TLSs in prognosis evaluation, intratumoral TLSs have been proved to be associated with better outcome for cancer patients, including HCC and pancreatic cancer (20, 21, 23). Tumor tissues with intratumoral TLSs showed significantly higher infiltration of T and B cells and lower infiltration of immunosuppressive cells (23). TLSs at the invasive margin have also been validated as an important positive predictor of patient outcomes (48, 51).

According to the cellular compositions, TLSs can be classified as follows: 1) Early TLSs (E-TLSs), distinguished by lymphocytic aggregates that lack a DC scaffold and vascularization; 2) Primary follicle-like TLSs (PFL-TLSs), also known as immature TLSs, comprised of T-cell and B-cell zones with FDCs but no GC; 3) Secondary follicle-like TLSs (SFL-TLSs), also known as mature TLSs, comprised of lymphatic vessels, isolated T-cell zones and a B-cell follicle with GC (30). Numerous studies have demonstrated that plasma cells, CD8⁺ T cells, and CD4⁺ T cells were more enriched in PFL-TLSs and SFL-TLSs (22, 28, 52).

Mature TLSs were reported to improve the prognosis of oesophageal squaenmous cell carcinoma (ESCC) and gastric cancer patients (28, 29). The cumulative risk of recurrence was significantly higher in patients with low SFL-TLSs in nmCRC (30). Mature TLSs supported antitumor adaptive immunity in pancreatic ductal adenocarcinomas (PDAC) (26, 27) and their formation was associated with a better prognosis in laryngeal squamous cell carcinoma (LSCC) with immunotherapy (52). Thus, mature TLSs have been confirmed to positively correlate with the prognosis and immunotherapy response of cancer patients.

Apart from H&E staining and immunohistochemistry (IHC) with multiplex selected markers to detect TLSs, transcriptomic analyses have also been developed to determine TLS-associated gene signatures in recent years (53). A 12-chemokine signature (including CCL2, CCL3, CCL4, CCL5, CCL8, CCL18, CCL19, CCL21, CXCL9, CXCL10, CXCL11, and CXCL13) is the most widely used signature for the quantification of TLSs in multiple solid tumors, including CRC, melanoma, HCC and breast cancer, etc (19, 33). Studies found that patients with high TLS signature displayed a better survival than those with low TLS signature, showing a marked association between TLS signature and the survival of cancer patients (32). Li et al. validated the clinical utility of the 12-chemokine TLS signature for predicting immunotherapy response by using two publicly available datasets. A high TLS signature score indicated strong immune infiltration and immune responses in both datasets (34). Another study by Xu et al. established TLS clusters in clear cell renal cell carcinoma (ccRCC) using machine learning algorithms and the 12-chemokine gene signature. Distinct differences were observed in survival, immune cell distribution, immunotherapy response among the TLS clusters (31). Cabrita et al. developed a gene signature (CD79B, CD1D, CCR6, LAT, SKAP1, CETP, EIF1AY, RBP5, and PTGDS) associated with TLSs in melanoma patients, which predicted clinical outcomes of melanoma patients treated with ICI (35).

Immunotherapy has become an important part of the treatment for patients with advanced melanoma, but its clinical efficacy varies among patients. The majority of patients treated with ICI did not show significant clinical benefits (93). Griss et al. demonstrated that the frequency of plasmablast-like B cells in pre-therapy melanomas predicted response and survival to immune checkpoint blockade. Anti-CD20 treatment caused a downregulation of tumor-induced plasmablast-like B cells along with a significant reduction in TLSs in metastatic melanoma (94). Ding et al. confirmed that melanoma patient who responded to ICI had increased number of GC B cells and its associated Tfh cells, indicative of TLS formation, and had significantly longer OS than those with none (95). Another study by Alexandra et al. gathered a collection of tumor biopsies from melanoma patients who were receiving CTLA-4 blockade. Trichotomizing gene-expression data on the basis of the TLS signature revealed that TLS-high tumors were associated with significantly increased survival after CTLA-4 blockade (35).

With the discovery of immunotherapy, the therapeutic paradigm for patients with advanced lung cancer has fundamentally transformed (96). The revolution in immunotherapy, especially the development of ICIs, has dramatically altered the NSCLC treatment landscape (97). Brunet et al. confirmed that the presence of TLSs was not only a prognostic marker in advanced stages of NSCLC, but also a specific biomarker predictive of response to ICIs (98). Likewise, Patil et al. demonstrated that plasma-cell-rich tumors may portend OS benefit in NSCLC patients treated with ICI. The plasma cell signature was enriched in tumors with TLSs and/or lymphoid aggregates. Patients with TLS positive tumors exhibited improved OS with PD-L1 blockade (atezolizumab) (9). Wu et al. found that overexpression of most genes in TLS signature indicated a good prognosis in patients with NSCLC receiving ICI therapy (41).

GI cancers include esophageal, gastric, liver, biliary system, pancreatic, and colorectal cancer. A meta-analysis including 32 studies demonstrated that TLSs were significant predictor of the prognosis of GI cancer and have the potential to become biomarker for immunotherapy responses in GI cancer patients (99). Jiang et al. found that higher TLS score was correlated with a superior response to PD1 blockade therapy in patients with gastric cancer, indicating that the TLS score might be a new predictor for PD1 inhibitor therapy response (29). Shang et al. analyzed a cohort of 100 CCA patients who received first-line chemotherapy combined with ICIs to prevent postoperative recurrence. Further data indicated that a high density of intratumoral TLSs in pre-treatment tumor tissues predicted a better prognosis in patients with immunotherapy and the presence of intratumoral TLSs were associated with a prolonged OS and PFS. The study established a four-gene TLS signature as practicable biomarker for TLS identification and demonstrated that the spatial distribution and abundance of TLSs profoundly affected the prognosis and the immunotherapy response in CCA (88). Kinker et al. showed that the expression of a gene signature reflecting mature TLSs were enriched in pretreatment biopsies from PDAC patients with longer survival after receiving different chemoimmunotherapy regimens (26). Hayashi et al. derived a 4-gene TLS signature comprised of POU2AF1, LAMP3, CD79A and MS4A1 and found a significantly higher expression of this 4-gene TLS signature in responders to anti-PD-1 therapy as compared to non-responders in ESCC (89). Küçükköse et al. established a series of patient-derived organoids (PDOs) from MSI-H mCRC tumors to generate spontaneous metastasis models in mice with or without a human immune system (HIS). HIS mice with PDO-initiated MSI-H mCRC were then used to model ICI therapy. Anti-PD-1 and anti-CTLA-4 strongly reduced the growth of primary tumors and liver metastases, but peritoneal metastases were refractory to ICI. B cell influx and TLS formation were observed in ICI-responding primary tumors and liver metastases, while ICI-refractory peritoneal metastases were devoid of B cells and TLSs (100).

Some studies reported an upregulation of genes encoding chemokines involved in TLS formation in the responder patients of HNSCC, bladder cancer or soft-tissue sarcomas (STS) treated with ICIs. These high TLS signatures were associated with better OS (90, 91, 101). Meylan et al. evaluated the B cell responses within TLSs in RCC, and found IgG- and IgA-producing plasma cells infiltated into the TLS-positive tumors. RCC patients with high IgG-stained tumor cells had longer PFS and higher immunotherapy response rates (102).