Fig. 11.1
Schematic overview of human diseases associated with the presence of TLS
Ectopic Lymphoid Tissue in Autoimmunity and Infectious Diseases
In chronic inflammatory diseases, TLS arise at sites of inflammation or infection due to persistent antigen and a continuing imbalance between recruitment and clearance of immune cells in the inflamed tissue. TLS are thought to be a key contributing factor to the chronic inflammation associated with autoimmune diseases, including rheumatoid arthritis [8], Sjögren’s syndrome [9], and systemic lupus erythematosus (SLE) [10]. Rheumatoid arthritis patients develop TLS in inflamed synovial tissues where their formation has recently been associated with a specific subset of endothelial cells and precursor follicular dendritic cells [11]. Earlier studies associated high frequencies of circulating activated B cells and Tfh cells with disease activity [12] and CXCL13-producing CD4+ T cells with the neogenesis of lymphoid structures in the synovium [13]. These data suggest that interactions between specific immune subpopulations in the inflamed tissue microenvironment foster lymphocyte recruitment and TLS development in rheumatoid arthritis.
SLE is an autoimmune disease characterized by autoreactive antibodies to nuclear antigens. SLE patients frequently develop renal inflammation (lupus nephritis ) characterized by autoantibody complexes whose deposition in the kidney can lead to can lead to organ failure [10]. Initially, it was thought that these immune complexes arrived in the kidney via the circulation, but evidence now suggests they are produced in situ in association with TLS neogenesis [14]. Lymphocytic infiltration also characterizes the salivary glands of patients with Sjögrens syndrome where up to 40% develop TLS in parallel with increased autoantibody production and more severe disease [9]. Studies have further shown that active Epstein-Barr virus (EBV) infection can be associated with TLS and disease-specific autoreactive B cells resident in patient’s salivary glands [15]. Lymphoid neogenesis and TLS in nervous tissues of patients with multiple sclerosis have also been associated with disease pathogenesis and virus infection [16, 17].
Viral, fungal, and parasitic and bacterial pathogens often enter the host via mucosal surfaces, such as those in the respiratory and gastrointestinal tracts. The mucosal immune system, responsible for protecting these surfaces, must both maintain a commensal microbiota and protect the host from pathogenic microorganisms [4]. Mucosal lymphoid tissues charged with these tasks include Peyer’s patches and tonsils, fat-associated lymphoid tissues, and ectopic TLS (the latter induced in response to infection or inflammation) as well as the draining lymph nodes. Viruses are a good example of a mucosal response to infection because many have envelope proteins containing repetitive B cell epitopes, such as the hemagglutinin protein of influenza [18]. Murine models have shown that TLS develop in the mucosal tissues of animals acutely infected with influenza [19, 20] and that this can still occur in the absence of SLOs [21]. Patients chronically infected with hepatitis C virus [22], Helicobacter pylori [23], or Mycobacterium tuberculosis [24] characteristically develop TLS at infected sites. EBV has also been associated with other autoimmune diseases (rheumatoid arthritis, Myasthenia gravis) where virus-infected autoreactive B cells accumulate in TLS or the target lymphoid organ and produce pathogenic autoantibodies [25].
Lymphoid neogenesis and TLS are not limited to autoimmune diseases and microbial infection. Pulmonary TLS have been associated with cigarette smoking [26] and in the joints of patients receiving metal hip replacements [27], both thought to be in response to the particulate matter. Acute and chronic allograft rejection has been associated with TLS formation at the organ site in kidney [28, 29], cardiac [30], and lung [31] transplant patients. Studies of cardiac allograft recipients found a strong association between the presence of TLS and earlier times of rejection posttransplantation [30]. Latent cytomegalovirus infection in virus-naïve cardiac transplant patients was also associated with TLS and accelerated allograft rejection [32]. Recent studies have provided intriguing evidence, principally in murine models, that TLS can promote graft tolerance or rejection depending upon the surrounding inflammatory microenvironment (reviewed in [33]). A common denominator among all of these pathological conditions is the presence of a persistent inflammatory response to antigens or particulate matter and a microenvironment conducive to lymphoid neogenesis.
Cancer-Associated TLS in the Tumor Microenvironment
The immune response is responsible for the continuous elimination of aberrant cells in an attempt to defend the host against malignant cell growth. Ultimately, its failure to recognize and remove defective cells at an early stage permits their persistence in an indolent state and can promote the accumulation of synergistic defects favoring transformation. Immune responses are also thought to play critical and decisive roles in tumor progression, invasion, and metastasis through active engagement with other cells in the tumor microenvironment. Thus, while the dynamic activities of immune cells may initially restrain abnormal cell growth as malignancy progresses, the developing tumor frequently acquires attributes that redirect the immune response, at least in part, toward a pro-tumor role.
An increasing number of studies have associated the presence of tumor-infiltrating lymphocytes (TIL) with good clinical outcomes in solid tumors, including melanoma [34], colorectal carcinoma [35], non-small cell lung cancer [36], and breast cancer [37]. Figure 11.2 illustrates a breast carcinoma with a mild and a breast carcinoma with a dense lymphoid infiltrate. Breast cancer gene expression studies were the first to establish a correlation between an immune signal, signifying the presence of infiltrating leukocytes, and clinical outcomes [38–42]. This association varies between gene expression-based breast cancer molecular subtypes [43–45], being strongest in the high-risk triple-negative (TN) and HER2+ BC subtypes. High expression of two immune response gene signatures were consistently and significantly associated with increased pathologic complete response rates after preoperative chemotherapy [46]. The level of immune cell infiltration was also associated with a benefit to preoperative chemotherapy [47–49] with the most robust correlation observed in TN and HER2+ breast cancer (reviewed in [37]).
Fig. 11.2
A breast carcinoma with a mild (upper row, TILneg) and a breast carcinoma with a dense (lower row, TILpos) lymphoid infiltrate. H&E overview (first column); immunohistochemical stainings with a leucocytic marker, such as CD45 (second column); and T and B cell markers, such a CD3 and CD20, respectively (third column), emphasize the difference in inflammatory infiltrate between both tumors. The immunohistochemical double staining with CD3/CD20 (third column) and an immunohistochemical staining with CD23, a marker of follicular dendritic cells, demonstrate the presence of several TLS in the carcinoma with a dense TIL infiltrate. See also Fig. 11.3 (TIL tumor-infiltrating lymphocyte, CD cluster of differentiation)
Subsequent reports demonstrated a significant association in BC between the presence of specific immune cell subsets and clinical responses. For example, tumors with extensive T cell infiltration (CD8+, CD4+ Th, and Tfh T cell subsets) were strongly associated with prolonged survival [50, 51]. A regulatory CD4+ T cell presence was initially shown to signal worse clinical outcomes [52, 53] although subsequent data suggest that their numbers may parallel the extent of the immune infiltrate [54]. Further assessment of large clinical trials suggests that despite the functional heterogeneity of TIL, the degree of global infiltration assessed on hematoxylin and eosin (H&E)-stained tumor sections has predictive and prognostic value for TN and HER2+ breast cancer [47, 55–58].
Initial investigations into the disposition of TIL at tumor sites revealed the presence of lymphocyte aggregates in more extensively infiltrated colorectal tumors [59]. A retrospective study of non-small cell lung cancer identified these aggregates as lymphoid structures (initially termed Ti-BALT for tumor induced but now called TLS) and positively correlated their presence with clinical outcome [60]. In colorectal cancer, active TLS were also observed and shown to predict clinical outcome [61, 62]. Gastrointestinal or respiratory tract tumors arise in tissues that are at a major interface with the external environment and thus are normally protected by the mucosal immune response.
In breast cancer, as in other internally resident solid tumors, lymphoid aggregates have been observed for years but not studied in detail [53, 63–65]. Gu-Trantien et al. described lymphoid structures in breast cancer, an internally resident tumor, showing that they are organized like lymph nodes, including characteristic T cell zones and B cell follicles containing follicular dendritic cells, CD4+ follicular helper T cells (Tfh) , and maturing B cells within active GC [51, 66]. These studies were the first to show that CD4+ Tfh cells are a specific component of tumor-associated TLS and link their presence with positive clinical outcome in breast cancer. Known for their critical role in helping to generate B cell-mediated immune responses, the infiltrating Tfh cells are predominantly located in TLS in the peritumoral stroma. Tfh cells play an important role in secondary lymphoid organs by initiating GC reactions that lead to B cell differentiation and plasma and memory cell generation.
Current knowledge of the role(s) that Tfh and B cells play in human tumor immunity is rather limited and controversial, with some studies correlating their presence with a better prognosis while others suggest worse outcomes for various tumor types [67]. Data from TLS-positive tumors indicate that the position and prevalence of T and B cells within the tumor dictate their responsiveness, with extensively infiltrated tumors successfully sequestering the majority of leukocytes in organized TLS. In contrast to autoimmune diseases where TLS promote and sustain disease in solid tumors, TLS may functionally organize immune cells in a failed effort to eliminate the tumor. This effort may, however, produce a sufficiently strong antigen-specific response to generate immunological memory capable of controlling residual disease in some patients. Long-term survival studies suggest that TLS are an important biomarker for patients with melanoma [68, 69], colorectal cancer [70, 71], non-small cell lung cancer [60, 72, 73], breast cancer [51, 74, 75], ovarian cancer [76], renal cell cancer [77], oral squamous cell carcinoma [78], and pancreatic cancer [79].
Significantly, successful efforts to vaccinate against oncogenic viruses have been associated with specific immune responses and lymphoid neogenesis at the lesion site. Vaccination of mice administered with a single dose of recombinant vaccinia vector [80] led to iBALT formation. More pertinently, in patients intramuscularly vaccinated with a therapeutic vaccinia vector expressing the human papillomavirus (HPV) 16 proteins E6 and E7, lymphoid neogenesis was observed in the distant cervical lesion [81]. The TLS induced by vaccination had characteristic T cell zones and B cell follicles with active GC. Recent clinical trials using this therapeutic HPV vaccine demonstrated higher rates of lesion regression in patients with HPV-16 or HPV-18-positive cervical inter-epithelial neoplasia [82, 83]. In patients with regression of the lesions, increased CD8+ T cell infiltration was detected at a higher frequency in HPV-vaccinated, compared to placebo-treated, individuals [82]. Intraepithelial CD8+ T cells were used as a surrogate marker since they were previously shown to be associated with TLS formation at the lesion site [81]. These studies suggest that when it is possible to identify the appropriate target, such as in virus-associated cancers, then appropriate education of the immune response may be sufficient for long-term control.
The advent of immunotherapeutic agents, capable of manipulating the immune system by targeting immune checkpoint molecules, has shown their power to achieve durable clinical benefit in patients with melanoma and kidney cancer, tumors well known as being immunogenic [84, 85]. Surprisingly, responses have also been observed in traditionally chemoresistant neoplasms such as non-small cell lung cancer [86] and tumors considered to be nonimmunogenic (bladder and prostate cancer) [87]. A common denominator that is emerging from studies of patients treated with immunotherapy is the necessity of a preexisting immune response to the tumor. Because the use of these new agents is costly and associated with significant side effects [88], there is an urgent need for biomarkers like TLS to identify patients with specific antitumor immune response and who most likely will have a benefit. Overall, these studies suggest that a variety of immune-based approaches, tailored to different tumor types, may generate sufficient immunological memory to effectively control residual disease in cancer patients.
Assessment of the Immune Reaction in Solid Tumors: The Breast Cancer Example
TILs and Survival
The best characterized tissue-based marker of the immune reaction in solid tumors are TILs; samples of over 10,000 patients have now been analyzed for TILs in order to assess their prognostic or predictive importance in breast cancer patients. Most data have been gathered in prospective–retrospective phase III clinical trials, and TIL levels assessed on H&E-stained slides can be considered to have level 1 evidence for prognosis in triple-negative breast cancer (triple-negative breast cancer) according to Simon et al. [89]. Additionally, Loi et al. confirmed the prognostic significance in a pooled analysis of triple-negative breast cancer trials [90]. The results summarizing the prognostic and predictive evidence of the main TILs in breast cancer in the adjuvant setting are summarized in Table 11.1.
Table 11.1
Adjuvant studies on the prognostic role of tumor-infiltrating lymphocytes in breast cancer
Reference | Study | Regimen | Tumor tissue assay | Sample size | Correlation with outcome |
|---|---|---|---|---|---|
[58] | BIG 02–98 | A → CMF or AC → CMF | Full section H&E | 2009 total | None |
256 TNBC | Stromal TILs (sTIL) (continuous, per 10% increase) Univariate: HR 0.84 (P = 0.02, DFS) HR 0.82 (P = 0.02, OS) Multivariate: HR 0.85 (P = 0.02, DFS) HR 0.83 (P = 0.02, OS) | ||||
297 HER2+ | None | ||||
1078 HR+ | None | ||||
[57] | E2197 E1199 | AC versus AC AC → docetaxel or paclitaxel | Full section H&E | 481 TNBC | sTIL (continuous, per 10% increase) Univariate: HR 0.86 (P = 0.02, DFS) HR 0.81 (P = 0.01, OS) Multivariate: HR 0.84 (P = 0.005, DFS) HR 0.79 (P = 0.003, OS) |
[56] | FinHER | Docetaxel or vinorelbine → FEC With trastuzumab if HER2+ | Full section H&E | 934 total | None |
134 TNBC | sTIL (continuous, per 10% increase) Univariate: HR 0.79 (P = 0.03, DDFS) HR 0.80 (P = 0.08, OS) Multivariate: HR 0.77 (P = 0.02, DDFS) HR 0.81 (P = 0.14, OS) | ||||
209 HER2+ | sTIL (continuous, per 10% increase) correlate with DDFS (HR 0.82, P = 0.025 univariate) only with trastuzumab, not OS | ||||
591 HR+ | None | ||||
[37] | Four studies including NEAT clinical trial | TMA CD8, FOXP3 immunohistochemistry | 12 439 | CD8+ T cells in tumor and stroma was associated with 28% and 21% reduced risk of BCSS. Greater benefit in ER-negative disease and ER = /HER2 | |
[50] | Consecutive | CMF | TMA CD8-immunohistochemistry | 1334 | Binary high versus low: total CD8 correlates with BCSS (HR 0.55, P = 0.001 multivariate training set; HR 0.58, P < 0.002 multivariate validation set) |
[91] | Consecutive | MF, AC, FAC, or no chemotherapy | TMA CD8-immunohistochemistry | 1985 HR+ | None |
216 HER2+ | None | ||||
496 TNBC | Binary any versus none: CD8 correlates with BCSS, multivariate iTIL (intratumoral TILs) HR 0.48, P < 0.001 | ||||
[92] | Institutional | Varied-chemotherapy not specified | PD-L1 mRNA TILs | 636 | Higher PD-L1 mRNA associated with better recurrence-free survival PD-L1 mRNA correlated with TILs |
[93] | Consecutive | CMF, AC, CEF, or CAF | TMA CD3-immunohistochemistry | 255 | Binary high versus low: total CD3 correlates with DFS in anthracycline group (HR 0.25, P = 0.0056) |
[94] | N9831 | ACT (arm A) or ACT + trastuzumab followed by trastuzumab alone (arm C) | Full section H&E | 489 patients (arm A) and 456 patients (arm C) | sTILs, cutoff 60% Univariate: arm A: HR 0.23 (P = 0.01; RFS) arm C: HR 1.26 (P = 0.63; RFS) Multivariate: arm A: NS arm C: HR 1.01 (P = 0.04) |
[95] | Consecutive TNBC | Institutional | Full section H&E | 897 | sTILs, continuous variable Univariate: TILs significant predictor of better DFS, DDFS and OS (P < 0.0001) Multivariate: Each 10% increase in TILs independent predictor of DFS, DDFS and OS Stratified analysis: results similar in all subgroups |
[96] | Two multicentric randomized trials | Adjuvant anthracyclines versus no chemotherapy | Full section H&E | 816 patients | sTILs, itTILS, continuous Multivariate: sTILS: HR 0.89 (P = 0.005, OS) itTILS: HR 0.85 (P = 0.003, OS) Effect limited to TNBC and HER2+ BC TILs did not predict for efficacy of anthracyclines |
Methodological Challenges in the Assessment of TILs
Scoring of TILs by pathologists remains a challenge. TILs may be located diffusely across a tumor and may have a very heterogeneous pattern; TILs are either concentrated heterogeneously and spatially within a single tumor bed or may be associated with a punctate pattern across the tumor bed. There can also be a gradient of TILs within a single tumor, ranging from high TILs to zones of low TILs. TILs located within the stroma that is associated with invasive cancer may be continuous with sometimes extensive localization of TILs within normal lobules and also with TIL infiltration around foci of ductal carcinoma in situ (DCIS). In addition, some specific tumor growth patterns may be associated with different TIL patterns. A tumor with a diffuse and solid growth pattern, constituted by solid tumor nests with only limited stroma between the tumor nests, is rapidly defined as having a high level of TILs since the area of the stromal compartment is low compared to a more infiltrative and dissociative tumor growth pattern. In addition, the TIL infiltration may be located almost solely at the periphery of the tumor bed, sometimes located at >1 or 2 high-power fields from the invasive edge, thus obscuring where the borders of the invasive tumor really end. In addition, different subtypes may add additional hurdles to pathologists when scoring TILs. In the classical subtype of invasive lobular adenocarcinoma, distinguishing infiltrating tumor cells from TILs may not always be that straightforward. In addition, in some tumors a remarkable perivascular location of TILs is encountered, with minimal stromal infiltration by TILs, rendering the evaluation more difficult. TILs can not only be found within the stromal compartment, but also within the tumor cell nests, although in a lower frequency than usually encountered within the stromal compartment. Clearly distinguishing intratumoral TILs from tumor cells may be problematic without the use of immunohistochemistry. All the abovementioned variables may potentially affect the inter- and intra-observer variability of pathologists.
The First International Recommendations on the Evaluation of Tumor-Infiltrating Lymphocytes [37]
Considering the abovementioned variables, there was a need to develop international guidelines that can be used in standard histopathological practice, in a research setting, as well as in clinical trials. Therefore, a group of experts convened and published a guidance document demonstrating step by step how TILs should be assessed, whether it be on core biopsies or on full sections (Table 11.2, for a detailed description, we refer to [37]), clearly distinguishing the recommendations for actual practice from those areas that are still to be considered investigational, such as the use of immunohistochemistry and machine learning algorithms for assessing TILs. The approach for developing this methodological guidance was based on a method originally developed by Denkert and colleagues that has proved to be clinically valid in several retrospective–prospective phase III clinical trials and that was subsequently refined as more experience accumulated. The evaluation of TILs on samples after neoadjuvant treatment still needs more methodological experience and evidence of clinical utility of the chosen method before formal recommendations by the International Working Group can be drafted. In a similar vein, the method by which TILs in lesions containing merely in situ lesions should be characterized is so far unexplored, and more analytical evidence and corresponding clinical validity need to be gathered before formal recommendations can be presented.
Table 11.2
Recommendations for assessing tumor-infiltrating lymphocytes (TILs) in breast cancer
1. TILs should be reported for the stromal compartment (=% stromal TILs). The denominator used to determine the % stromal TILs is the area of stromal tissue (i.e., area occupied by mononuclear inflammatory cells over total intratumoral stromal area), not the number of stromal cells (i.e., fraction of total stromal nuclei that represent mononuclear inflammatory cell nuclei) |
2. TILs should be evaluated within the borders of the invasive tumor |
3. Exclude TILs outside of the tumor border and around DCIS and normal lobules |
4. Exclude TILs in tumor zones with crush artifacts, necrosis, and regressive hyalinization as well as in the previous core biopsy site |
5. All mononuclear cells (including lymphocytes and plasma cells) should be scored, but polymorphonuclear leukocytes are excluded |
6. One section (4–5 μm, magnification ×200–400) per patient is currently considered to be sufficient |
7. Full sections are preferred over biopsies whenever possible. Cores can be used in the pretherapeutic neoadjuvant setting; currently no validated methodology has been developed to score TILs after neoadjuvant treatment |
8. A full assessment of average TILs in the tumor area by the pathologist should be used. Do not focus on hotspots |
9. The working group’s consensus is that TILs may provide more biological relevant information when scored as a continuous variable, since this will allow more accurate statistical analyses, which can later be categorized around different thresholds. However, in daily practice, most pathologists will rarely report for example 13.5% and will round up to the nearest 5–10%, in this example thus 15%. Pathologist should report their scores in as much detail as the pathologist feels comfortable with
 Stay updated, free articles. Join our Telegram channel
Full access? Get Clinical Tree
Get Clinical Tree app for offline access
|