An estimated 2.1 million new cases and 1.8 million deaths occur annually due to lung cancer around the world [2]. Almost half of the patients die within one year of diagnosis and less than 18% survive beyond five years [3]. Further, as per data from the Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute, around two-thirds of patients with non-small cell lung cancer (NSCLC) do not survive beyond two years [4]. The dismal prognosis is exacerbated by a lack of methods for early diagnosis and prognostication and limited access to opportune standard treatment. Historically, the clinical fraternity has relied upon the tumor, nodes, and metastases (TNM) staging as the gold standard prognostic tool for lung cancer [5], although, several researchers have raised concerns about various editions of the TNM classification. The fifth edition of TNM classification was unable to distinguish the prognosis between patients with pathologic (p) stage IIIA and IIIB disease [6]. A real-world validation of the seventh edition using the International Association for the Study of Lung Cancer (IASLC) database could not find a significant difference between survival rates of patients with pT1bN0M0 and pT2aN0M0 tumors, nor between survival rates of patients with pT4N0M0 and pT3N0M0 tumors [7]. Besides, there was no difference in the prediction of survival by the sixth and seven editions [7]. The current eighth edition of TNM classification is not flawless as well. Hattori et al. reported inconsistency between radiological solid component size and pathological invasive size in part-solid lung adenocarcinomas and difficulty in measuring the solid component size due to the presence of multiple, complicated, or scattered solid areas rather than a single focus [8].
At the same time, several studies have reported a profound prognostic impact of tumor-infiltrating lymphocytes (TILs) in malignant tumors [9–16]. Studies have further shown that TILs are associated with a positive clinical outcome in several cancers including lung cancer [17]. Immune scoring based on TILs or their ratios for differentiating prognosis within each tumor, node, and metastasis have been used to enhance the prognostic value of TNM staging [18–21].
TILs present in the immune infiltrate called the tumor microenvironment (TME) include macrophages, neutrophil granulocytes, dendritic cells, mast cells, natural killer (NK) cells, naive and memory lymphocytes, B cells, and effector T cells. Effector T cells in turn can be T helper cells (1, 2, and 17), regulatory T (Treg) cells, T follicular helper cells, and cytotoxic T cells [17, 22, 23]. These cells may be localized in the tumor parenchyma, invasive margin, or adjacent tumor stroma. The interaction of TILs with tumor cells can be seen as the three phases of immuno-editing [24]. During the elimination phase, the immune cells detect and destroy early tumors even before they become overt. In the equilibrium phase, the immune system cannot eliminate the tumor cells which resist immune recognition and go into dormancy. During the escape phase, tumor cells cause immune suppression through the production of cytokines and growth factors and facilitate the recruitment of immunosuppressive cells like Tregs [25–27].
Tregs are a highly immune-suppressive subset of clusters of differentiation (CD) 4+ T cells [25–27], which play an important role in the preservation of self-tolerance and modulation of the overall immune responses against tumor cells [28]. They exhibit their suppressive activity by numerous cellular and humoral mechanisms such as suppression of antigen-presenting cells via cytotoxic T lymphocyte-associated antigen-4 (CTLA-4), secretion of inhibitory cytokines including interleukin (IL) 10, transforming growth factor (TGF) β, and IL-35, expression of granzyme B or perforin or lymphocyte activation gene (LAG) 3, consumption of IL-2, depletion of extracellular adenosine triphosphate (ATP), and “stripping” of co-stimulatory molecules [28, 29]. The effector Tregs express fork-head lineage-specific transcription factor forkhead box P3 (FOXP3) protein and cytokines such as CTLA-4, programmed cell death protein 1 (PD-1), T cell immunoglobulin and mucin-domain containing (TIM) 3, and C-C Motif Chemokine Receptor 4 (CCR4), while natural or thymic Tregs (nTregs) reportedly express high levels of Helios (a member of the Ikaros transcription factor family) and Neuropilin-1 (a type-1 transmembrane protein) [28].
Studies have shown that the composition of Tregs is altered in the TME, where the effector Treg numbers are increased as compared to healthy individuals [30]. TGF-𝛽1 and IL-2 are the two crucial pro-inflammatory cytokines present at a high level in tumor tissues of NSCLC patients, which are involved in the differentiation of naïve T-cells into Tregs [30]. Moreover, due to self-antigens released in the TME by dying cancer cells, nTregs are converted into effector Tregs by expressing a higher level of activation biomarkers like CTLA-4, T-cell immunoreceptor with immunoglobulin (Ig) and immunoreceptor tyrosine-based inhibitory motif domains (TIGIT), TIM-3, inducible T cell co-stimulator (ICOS), OX40, 4-1BB and CD39 [30]. Tregs have been reported to increase in NSCLC patients as compared to healthy controls [31]. In a study, Erfani et al. reported that the NSCLC patients had almost twice the percentage of Treg cells than the healthy controls [32]. Further, they reported that the metastatic stages had three times more Treg cells than the healthy controls and almost two times more Treg cells than the non-metastatic stages [32]. This makes Tregs an ideal therapeutic target and candidate for prognostication of lung cancer, especially NSCLC, which comprises about 85% of all lung cancer cases [33]. While the therapeutic targeting of Treg by pathways like the blockade of immune checkpoint molecules has been studied by many authors [28], the prognostic value of measurement and localization of Treg cells has not been fully evaluated in all study populations, with various prognostic factor variables, causing individual studies to report piecemeal and mixed results.
This systematic review and meta-analysis aimed to evaluate the prognostic value of the number of Treg cells in predicting the survival of NSCLC patients based on the available evidence in various study populations, considering varied prognostic factor variables and survival outcomes. The findings of the review may inform clinical decision-makers on patient-centric NSCLC management by supplementing the TNM staging by a process like immuno-scoring and stratification based on the “number of Treg cells” in future.