Skip to main content

Biomarkers for immune checkpoint inhibition in sarcomas – are we close to clinical implementation?


Sarcomas are a group of diverse and complex cancers of mesenchymal origin that remains poorly understood. Recent developments in cancer immunotherapy have demonstrated a potential for better outcomes with immune checkpoint inhibition in some sarcomas compared to conventional chemotherapy. Immune checkpoint inhibitors (ICIs) are key agents in cancer immunotherapy, demonstrating improved outcomes in many tumor types. However, most patients with sarcoma do not benefit from treatment, highlighting the need for identification and development of predictive biomarkers for response to ICIs. In this review, we first discuss United States (US) Food and Drug Administration (FDA)-approved and European Medicines Agency (EMA)-approved biomarkers, as well as the limitations of their use in sarcomas. We then review eight potential predictive biomarkers and rationalize their utility in sarcomas. These include gene expression signatures (GES), circulating neutrophil-to-lymphocyte ratio (NLR), indoleamine 2,3-dioxygenase (IDO), lymphocyte activation gene 3 (LAG-3), T cell immunoglobin and mucin domain-containing protein 3 (TIM-3), TP53 mutation status, B cells, and tertiary lymphoid structures (TLS). Finally, we discuss the potential for TLS as both a predictive and prognostic biomarker for ICI response in sarcomas to be implemented in the clinic.


Sarcomas are a diverse and complex group of cancers of mesenchymal origin that often have very poor prognosis, with median survival of about 18 months with metastatic disease [1]. In soft-tissue sarcomas (STS), the 5-year survival rates for localized, regional, and metastatic disease are 81%, 56% and 16% respectively [2]. Comparatively, in osteosarcoma, the 5-year survival rates are 74%, 66% and 27% respectively [3]. Lastly, the 5-year survival rates in Ewing sarcoma are 81%, 67% and 38% respectively [4]. The systemic treatment of sarcomas has relied on conventional chemotherapy that has remained widely unchanged over several decades. Doxorubicin and ifosfamide represent the current standard of care in most subtypes of advanced and metastatic sarcomas [5]. However, response to treatment remains poor and more efficacious treatment options are needed. In a phase III trial comparing doxorubicin monotherapy against intensified doxorubicin with ifosfamide in advanced or metastatic STS, treatment with doxorubicin alone yielded an overall response rate of 14%, compared to 26% in patients treated with doxorubicin and ifosfamide. Importantly, there was no significant difference in overall survival (OS) between the two groups, with a median OS of 12.8 months (95.5% confidence interval (CI), 10.5–14.3) in the doxorubicin-only group, compared to 14.3 months (95.5% CI, 12.5–16.5) in the combination group [6]. Alternative agents such as gemcitabine and docetaxel are reserved for patients who have failed or are unable to tolerate doxorubicin and ifosfamide. Gemcitabine is commonly used alone or in combination with docetaxel, with complete or partial response, or stable disease after at least 25 weeks being achieved by 27% in the gemcitabine-only group and 32% in the combination group [7]. These response rates are in stark contrast to other tumors such as lymphomas, leukemias, germ cell tumors and others with response rates of > 70% with chemotherapy [8]. While targeted therapies are available, only less than 5% of STS are amenable to these treatments [9,10,11]. Limited treatment options compounded by poor treatment response necessitates the exploration of more treatment options with better outcomes and side effect profiles.

Research in treatment for sarcomas has faced many challenges. Sarcomas are rare cancers representing only 1% of adult malignancies [12], making it difficult to recruit sufficient clinical trial participants to generate rapid and robust evidence for treatment efficacy. Furthermore, heterogeneity in their histology and genetic drivers of oncogenic pathways in sarcomas gives rise to a wide variation in their biology, as well as degree of immune infiltration. As such, each subtype exhibits different clinical characteristics, often requiring patient-specific treatment approaches [13] since different patients may not respond to the same therapy.

Amidst these challenges, immune checkpoint inhibitor (ICI) therapy has emerged as an attractive treatment option [14]. ICIs target immune checkpoints that under physiologic conditions restrict the strength and duration of immune responses to avoid immune-mediated tissue damage, but which can be exploited by tumors to evade immune-mediated elimination. Efficacy of treatment with ICIs has been established in several cancers [15], including advanced renal cell carcinoma (RCC) [16], cervical cancer [17], classical Hodgkin lymphoma [18], gastric carcinoma [19], hepatocellular carcinoma (HCC) [20], melanoma [21,22,23], Merkel cell carcinoma [24, 25], non-small cell lung cancer (NSCLC) [26], primary mediastinal large B-cell lymphoma [27], small cell lung cancer [28], head and neck squamous cell cancer (HNSCC) [29], triple negative breast cancer [30], and urothelial cancer [31]. In an exciting step forward in the treatment of sarcoma, the United States (US) Food and Drug Administration (FDA) recently approved the first ICI for use in the treatment of STS, with atezolizumab being approved for use in the treatment of unresectable or metastatic alveolar soft-part sarcomas (ASPS) [32]. Atezolizumab as the first agent of its class being indicated for ASPS could set the stage for more ICIs to be indicated for the treatment of more STS subtypes and offers exciting possibilities for further evaluation.

In fact, although STS have been traditionally thought to be immune “cold” [33], as a whole, the response of STS to immune checkpoint inhibition does not differ too much from that of all cancers considered together. In 2019, Haslam and Prasad estimated that the percentage of US patients with cancer that respond to ICIs was 12.46% (95% CI, 12.37–12.54%) [34], which is comparable to the results of the SARC028 trial (NCT02301039), where 18% of patients with STS had an objective response to pembrolizumab [35]. Additionally, ICI therapy has shown improved outcomes in the clinical management of selected populations in sarcomas [36,37,38]. Within STS subtypes, liposarcomas (LPS), undifferentiated pleomorphic sarcomas (UPS) and ASPS have demonstrated better responses than other subtypes, while leiomyosarcomas (LMS) and synovial sarcomas (SS) have been reported to be resistant to ICI monotherapy [39]. Table 1 outlines a comprehensive list of studies using ICIs, both as monotherapy and in combination, and the respective clinical outcomes in sarcomas. Aside from clinical efficacy, another concern that clinicians have to consider is the potential for immune-related adverse events (irAEs) that range from mild adverse conditions like diarrhea and rashes to life-threatening conditions like cardiomyopathy and toxic epidermal necrolysis [40]. Thus, there is an urgent need to identify biomarkers that can guide clinical use of ICIs in potential responders while sparing non-responders from potentially life-threatening irAEs.

Table 1 Overview of studies using immune checkpoint inhibitors (ICIs) alone or in combination with other drugs in sarcomas

In this review, we will consider existing US FDA-approved and European Medicines Agency (EMA)-approved biomarkers for ICIs in clinical practice and evaluate their applicability in sarcomas. We then discuss exploratory biomarkers and evidence for their potential utility in sarcomas. Predictive biomarkers covered in this review are illustrated in Fig. 1.

Fig. 1
figure 1

Overview of approved and exploratory biomarkers for immune checkpoint inhibitors (ICIs) in cancer. Tumor and immune features can influence response to ICIs and serve as predictive biomarkers for response. FDA- and EMA-approved biomarkers for ICIs in cancer are indicated in blue, while exploratory biomarkers are indicated in red. MSI and a high TMB contribute to the expression of tumor neoantigens presented by MHC I molecules on tumor cells that can be recognized by the TCR on CD8+ T cells, leading to antitumor T cell activity. In gastrointestinal cancers, the expression of immunogenic neoantigens in tumors with high TMB is dependent on certain mutational signatures [41]. On the other hand, binding of PD-L1 on tumor cells to PD-1 on T cells leads to the suppression of T cell antitumor activity. Additionally, exhausted T cells may also express the exhaustion markers TIM-3 and LAG-3. In lung adenocarcinoma, TP53 mutations are correlated with higher TMB and neoantigen expression, while TP53 missense but not nonsense mutations are associated with increased PD-L1 expression [42]. Various GES have also been associated with response to ICIs. IDO contributes to T cell suppression and its expression was induced in resistant HCC after ICI therapy [43]. The presence of B cells and TLS have been associated with improved prognosis and response to ICIs in several cancers, including sarcomas. Within the blood, a higher baseline circulating NLR has also been found to correlate with poorer outcomes in patients receiving ICIs in lung cancer [44].

Biomarkers approved for immune checkpoint inhibition in cancer

ICI therapy is indicated without biomarker requirement in several cancer settings because of studies demonstrating improved clinical outcomes [45]. These indications include patients with advanced melanoma [46,47,48], relapsed or refractory Hodgkin lymphoma [49, 50], cisplatin-ineligible patients with urothelial carcinoma [49, 50], patients with relapsed or refractory primary mediastinal large B-cell lymphoma [51, 52], second-line treatment for patients with HCC [49, 53], patients with Merkel cell carcinoma [49, 53], patients with recurrent or metastatic HNSCC [24, 54] and Bacillus Calmette-Guérin-unresponsive high risk non-muscle invasive bladder cancer [55]. In contrast, there are cancer types such as sarcoma [35], breast, prostate and colon cancers [56] that demonstrate lower frequency of response to ICI therapy, and would therefore require biomarkers to distinguish between responders and non-responders.

Currently, only three predictive biomarkers have been approved by the FDA for ICI therapy in cancers, namely programmed death-ligand 1 (PD-L1), microsatellite instability (MSI) or defective mismatch repair (dMMR), and tumor mutational burden (TMB), while only two predictive biomarkers, namely PD-L1 and MSI/dMMR have been approved by the EMA [57]. Variability in the antibody clones, expression thresholds, scoring systems and the cell types expressing PD-L1 among FDA/EMA-approved PD-L1 assays across multiple cancer types can pose difficulty of interpretation for researchers and clinicians. PD-L1 assays were previously described by Wang et al. to have poor diagnostic accuracy, poor predictability, and low negative predictive value in cancers [58], also limiting its clinical use in sarcomas. For the detection of MSI-high (MSI-H) tumors, approved assay methods include immunohistochemistry (IHC), polymerase chain reaction (PCR) and whole exome sequencing (WES). Both IHC and PCR are established methods and are widely available in the pathology laboratory. However, IHC is limited by its low analytic sensitivity and accuracy, while PCR may be unable to capture full MSI profiles that results in missing 0.3% to 10% of MSI-H cases [58, 59]. Circumventing the limitations of PCR, WES provides better predictive power compared to PCR and can be used for all tumor types [58]. Additionally, TMB can be derived from WES and may provide a better prediction of response to ICIs [58]. On the other hand, WES is characterized by high cost, limited availability, potentially complicated pipelines and requires technical expertise that may hinder its clinical utility [60]. Table 2 summarizes FDA- and EMA-approved predictive biomarkers for ICIs in selected cancers.

Table 2 Overview of Food and Drug Administration (FDA)- and European Medicines Agency (EMA)-approved predictive biomarkers for patient selection for immune checkpoint inhibition

Programmed death-ligand 1 (PD-L1)

PD-L1 is a ligand for the T cell immune checkpoint receptor programmed cell death 1 (PD-1) and is expressed by a variety of normal and immune cells. Interaction between PD-1 and PD-L1 serves to promote self-tolerance through the suppression of T cell activation. Cancer cells have been found to exploit the PD-1/PD-L1 axis for immune evasion through the overexpression of PD-L1 [73]. Thus, PD-1 and PD-L1 expression provide an attractive avenue to predict response to ICI therapy. At present, there are four FDA- and three EMA-approved PD-L1 assays (Table 2). For further reading, a detailed review on the key parameters for the FDA-approved PD-L1 assays has been conducted by Wang et al., describing different test methods and challenges [58].

The diverse and dynamic PD-L1 expression on specific cell types within the tumor microenvironment (TME) has made the correlation of global PD-L1 expression with response to ICI therapy challenging. Noguchi et al. demonstrated that PD-L1 expression in tumor-associated macrophages are partially dependent on interferon-γ (IFN-γ) [74]. Further studies by Lau et al. in PD-L1-depleted mouse models highlighted that although immune evasion occurs at a repressed rate, infiltrating myeloid cells may contribute to immune evasion through compensatory PD-L1 expression [75]. There is also contradicting evidence demonstrating that efficacy of PD-L1 blockade is independent of PD-1/PD-L1 expression on tumor cells [76]. Instead, PD-L1 expression on dendritic cells (DCs) and macrophages correlates to clinical response in melanoma and ovarian cancer patients [76]. Given that PD-L1 expression level in the TME is highly variable, global PD-L1 positivity alone may not be sufficient to predict response to ICIs [77]. Instead, understanding the effects of differential expression of PD-L1 in specific immune and tumor cells in the TME may reveal mechanisms of the PD-1/PD-L1 axis that could be exploited to better predict response to ICI therapy.

In sarcomas, PD-L1 expression levels have shown conflicting association with ICI response [78]. Indeed, levels of PD-L1 expression can vary widely between different histological subtypes [79] (Fig. 2) that is further complicated by the heterogenous TME present in primary and metastatic lesions [78, 80]. This high degree of heterogeneity in PD-L1 expression, coupled with limited studies clarifying the relationship between PD-L1 expression and response to ICI warrants further investigation of the use of PD-L1 testing in sarcomas. Additionally, Patel et al. demonstrated that pre-treatment with radiotherapy (RT) prior to surgical resection increased PD-L1 expression in 10.9% of patient STS tumors (p = 0.056) while post-operative radiation therapy did not elicit PD-L1 expression in any STS resection samples [81]. These findings suggest that PD-L1 expression can be influenced by other treatment modalities, though much work remains to be done due to the small study sample sizes and limited studies available in sarcomas.

Fig. 2
figure 2

Prevalence of PD-L1 expression in soft-tissue sarcomas across published studies. This figure shows the levels of PD-L1 expression in different sarcoma subtypes that has been reported across a number of studies [79, 81,82,83,84,85,86,87,88,89,90]. Inter- and intra-variability of PD-L1 expression among different sarcoma subtypes warrants extensive studies to establish the use of existing PD-L1 assays as a reliable predictive biomarker to immune checkpoint inhibition in soft tissue sarcomas (STS). ARMS: Alveolar rhabdomyosarcoma; ASPS: Alveolar soft part sarcoma; DDLPS: Dedifferentiated liposarcoma; ERMS: Embryonal rhabdomyosarcoma; ES: Ewing sarcoma; LMS: Leiomyosarcoma; LPS: Liposarcoma; MFS: Myxofibrosarcoma; MPNST: Malignant peripheral nerve sheath tumor; OGS: Osteosarcoma; PRMS: Pleomorphic rhabdomyosarcoma; SS: Synovial sarcoma; UPS: Undifferentiated pleomorphic sarcoma; WD-LPS: Well differentiated liposarcoma

Microsatellite Instability (MSI)/ Deficient Mismatch Repair (dMMR)

MSI occurs when dMMR results in hypermutation in short stretches of DNA (microsatellites). MSI-H have higher potential to code for tumor-associated neoantigens [91] that can be recognized by the immune system, eliciting an antitumor response. A phase II study by Le et al. demonstrated that high levels of somatic mutations in dMMR colorectal tumors was associated with increased expression of tumor-associated antigens compared to proficient mismatch repair (pMMR) colorectal tumors [70]. In the same study, 40% of patients with dMMR tumors responded to PD-1 inhibition, while none of the patients with pMMR tumors achieved an objective response, thus highlighting the role of dMMR as a predictive biomarker for ICI response.

Currently, IHC, PCR and next-generation sequencing (NGS) are used to assess MSI [92]. In the same review mentioned previously, Wang et al. has provided a comprehensive evaluation of the three assays in use [58].

A meta-analysis by Lorenzi et al. reported the prevalence of dMMR among six common tumor types, including colorectal, endometrial, esophageal, gastric, renal and ovarian cancers, which suggested that the prevalence of dMMR/MSI differs between tumor types and cancer stages [93] (Fig. 3). Notably, MSI/dMMR accounts for only approximately 1% of sarcomas, with the exception of pleomorphic rhabdomyosarcoma (PRMS), embryonal rhabdomyosarcomas (ERMS), LMS and malignant peripheral nerve sheath tumor (MPNST) that have higher rates of MSI/dMMR [94]. Given the low prevalence of MSI-H tumors in sarcomas and the lack of trials evaluating the role of MSI in predicting ICI treatment response in sarcomas, MSI/dMMR may be of limited use in guiding the clinical decision-making for ICIs in sarcomas.

Fig. 3
figure 3

Pooled prevalence of MSI-H and dMMR among different tumor types. Bar graphs show the prevalence of MSI-H and dMMR in various cancers as summarized by Lorenzi et al. and Lam et al. [93, 94]. Low prevalence of MSI-H in Ewing sarcoma (ES) and wide variation of dMMR between sarcoma subtypes warrants further studies to explore the correlation between MSI-H / dMMR and clinical response to immune checkpoint inhibition. Results from Lorenzi et al. were pooled from various studies. Lam et al. did not evaluate for MSI-H. Asterisk indicates analysis for dMMR was not feasible. ARMS: Alveolar rhabdomyosarcoma; ASPS: Alveolar soft part sarcoma; CRC: Colorectal cancer; CS: Chondrosarcoma; ERMS: Embryonal rhabdomyosarcoma; ES: Ewing sarcoma; LMS: Leiomyosarcoma; MPNST: Malignant peripheral nerve sheath tumor; OGS: Osteosarcoma; PRMS: Pleomorphic rhabdomyosarcoma; SS: Synovial sarcoma. Asterisk indicates analysis for dMMR was not included

Tumor Mutation Burden (TMB)

Cancer neoantigens are tumor-specific antigens that arise from genetic mutations within tumor cells that can be recognized by the immune system. Hence, highly mutated tumors are more likely to express neoantigens and provide an opportunity for ICIs to reinvigorate the immune system and stimulate an antitumor response [95]. As predicted, improved survival after ICI treatment was indeed observed in patients with high TMB in multiple cancer types [96, 97].

However, the use of high TMB as a predictive biomarker for ICI response has demonstrated conflicting results in gastrointestinal cancers, with most studies reporting the lack of a significant association between high TMB and response to ICIs [71, 98,99,100,101]. A retrospective study by Wang et al. analyzed the mutational signatures of microsatellite-stable gastrointestinal tumors with high TMB and found that not all genes associated with high TMB correlated with an enhanced antitumor response, hence suggesting that the types of mutational signatures in tumors could play a role in the expression of immunogenic neoantigens [41].

TMB is defined as the number of somatic mutations in the tumor exome [96] and can be classified into low (1–5 mutations per Mb), intermediate (6–19 mutations per Mb) and high (≥ 20 mutations per Mb) [102]. TMB can be measured using WES, but clinical implementation has been limited due to the large amount of genomic deoxyribonucleic acid (DNA) required, long sequencing time, availability of matched samples and costs [103]. To circumvent the limitations of WES, targeted NGS panels have been developed to accurately recapitulate WES-derived genomic information while sequencing less DNA [60, 96, 104]. In assessing TMB, both WES and targeted NGS panels can be influenced by various factors from sample collection, processing, sequencing, data analysis to the lack of harmonization in reporting cut-offs, thus limiting the independent clinical utility of TMB [58].

Studies analyzing genomic profiles in sarcomas have suggested low somatic mutation burden across most sarcomas. A study of the molecular landscape of adult STS demonstrated an average of 1.06 mutations per Mb across 206 sarcomas of different histological subtypes [105], while genomic profiling of over 6,100 sarcoma cases showed a median of 1.7 mutations per Mb [106]. Additionally, even in dMMR sarcomas, TMB appears lower than that in other dMMR tumor types, with a median TMB of 16 mutations per Mb compared to 28 mutations per Mb [107]. The exception appears to be head and neck angiosarcomas, where 63.4% of cases have high TMB defined as ≥ 10 mutations per Mb [108]. Even so, in a phase II clinical trial of metastatic or unresectable angiosarcoma treated with combined ipilimumab and nivolumab (NCT02834013), the objective response rate (ORR) was only 25% and six-month progression-free survival (PFS) was 38% [109].

Overall, the lack of studies examining the use of TMB as a predictive biomarker of ICI response in sarcomas, poor stratification of TMB classification, as well as a low median TMB across most sarcomas may limit the clinical utility of TMB in directing ICI use in sarcomas.

Exploratory biomarkers for immune checkpoint inhibition in sarcomas

In this section, we discuss eight exploratory biomarkers that may predict response to ICI therapy in sarcomas, including gene expression signatures (GES), circulating neutrophil-to-lymphocyte ratio (NLR), indoleamine 2,3-dioxygenase (IDO), lymphocyte activation gene 3 (LAG-3), T cell immunoglobulin and mucin domain-containing protein 3 (TIM-3), TP53 mutation status, B cells, and tertiary lymphoid structures (TLS).

Gene Expression Signatures (GES)

GES are presented as a group of genes whose differential expression has been found to be associated with a particular outcome, and have been used in the determination of diagnosis, prognosis, and the prediction of therapeutic outcomes [110]. Methods used to measure gene expression levels include ribonucleic acid (RNA) microarray and RNA sequencing [111, 112], as well as newer methods including single-cell RNA sequencing, single-nucleus RNA sequencing [113] and spatial transcriptomics [114].

In several cancers, various GES have been found to be capable of predicting ICI response, including in melanoma [115,116,117], NSCLC [118,119,120,121], gastric cancer [122], lower-grade glioma [123] and some across multiple cancer types such as in both NSCLC and melanoma [124]. In addition, a pan-tumor signature predictive of ICI response was derived from 220 patients across HNSCC, gastric cancer, triple-negative breast cancer, bladder, anal canal, biliary, colorectal, esophageal, and ovarian cancers. This pan-tumor signature defined by Ayers et al. contains IFN-γ- and T cell-associated inflammatory genes, and high expression of this gene signature correlated well with objective response to pembrolizumab (1-sided p-value < 0.001) [125].

In STS, given the heterogeneity in genomic alterations across the various histological subtypes [126], identifying a robust GES that is able to be used in multiple subtypes may prove to be challenging. Nonetheless, Petitprez et al. identified a B lineage signature associated with improved response to ICI therapy in STS [127], and this will be discussed in further detail in the section on B cells below.

Presently, the implementation of routine gene sequencing is costly, and the complexity of its results require expertise to analyze and interpret before they can be used to guide clinical decision making [128, 129]. There is a thus a need to identify a GES with minimal number of genes to be sequenced in order to determine response to ICIs, with its accuracy subsequently being validated in a prospective trial.

Circulating Neutrophil-to-Lymphocyte Ratio (NLR)

Compared to other biomarkers that may require patients’ tumor samples, NLR can be easily derived from whole blood as a less invasive procedure with minimal risk of complications. The ease of sample acquisition and minimal patient risk has led to extensive studies of its use in cardiovascular diseases, infectious diseases, and cancers where it has been found to correlate with prognosis [130].

In the published literature, there is a lack of clearly defined cutoffs as well as contrasting evidence for the use of NLR across and within the different cancer types [131]. In a retrospective study of 509 patients with advanced cancer, a non-linear response trend during ICI treatment was observed and significant decreases or increases in NLR on-treatment correlated to poorer prognostic outcomes [132]. Conversely, in a meta-analysis by Jing et al., higher NLR at baseline across 23 studies correlated to lower OS in lung cancer patients receiving ICIs [44]. In STS, Strong et al. found that high baseline NLR, defined as ≥ 4.5, was not independently associated with worse survival outcomes in patients with extremity STS [133]. On the other hand, Chan et al. used receiver operating curve analysis to determine a cutoff of high NLR at > 2.5, and demonstrated high baseline NLR to be an independent marker for poor prognosis in STS patients [134].

Overall, while the use of NLR in the clinic is less invasive and more convenient, the lack of harmonization in key parameters such as a standardized baseline NLR may hinder the use of NLR as a predictor of response to ICIs in sarcomas. The establishment of clearly defined cutoffs would be essential to support its use.

Indoleamine 2,3-Dioxygenase (IDO)

IDO is a heme-containing enzyme that catalyzes the conversion of tryptophan into kynurenine. IDO contributes to an immunosuppressive effect involving both CD4+ and CD8+ T cells via the rapid depletion of tryptophan [135]. Subsequent downstream activation of stress response mediator general control nonderepressible 2 (GCN2) kinase results in cell cycle arrest [136], thus inhibiting T cell proliferation. Additionally, IDO has been demonstrated to upregulate regulatory T cell (Treg) activation and activity [137, 138]. Thus, IDO has been suggested for use as a prognostic marker.

In a meta-analysis by Wang et al., high expression of IDO in tumor tissues was associated with poor prognosis (pooled hazard ratio (HR) 1.92, 95% CI, 1.52–2.43, p < 0.001) and tumor progression (pooled HR = 2.25, 95% CI, 1.58–3.22, p < 0.001) in cancer patients [135]. An in vitro study has also shown that ICI therapy induces IDO in resistant HCC through upregulation of IFN-γ that consequently results in adaptive immune evasion [43]. These studies shed light on alternative immune evasion pathways conferred in the TME.

In sarcomas, Hiroshi et al. analyzed 47 patient specimens in which 96% of high-grade osteosarcoma of the extremities are IDO-positive [139]. Consequently, IDO positivity has been correlated to decreased progression free survival (PFS) (p = 0.016) and OS (p = 0.005) [139]. To circumvent IDO-induced resistance, IDO inhibitors have been proposed to be included in combination treatment with ICIs. Imatinib, a tyrosine kinase inhibitor used in the treatment of gastrointestinal stromal tumor (GIST), has demonstrated inhibition of IDO expression in GIST mouse models [140]. However, clinical trials testing for combination treatment with ipilimumab and imatinib demonstrated limited efficacy and antitumor immune response in GISTs [141].

In conclusion, IDO has been recognized as an immune target in the TME, and the combination of IDO inhibitors with ICIs has also shown efficacy in several phase I/II clinical trials [142]. However, the phase III trial of epacadostat with pembrolizumab in unresectable or metastatic melanoma (NCT02752074) failed to demonstrate better efficacy versus placebo and pembrolizumab [143]. Taken together, there is a need for deeper understanding of the role that IDO plays in the TME before establishing IDO as a biomarker.

Lymphocyte-activation gene 3 (LAG-3)

In March 2022, the FDA approved a LAG-3 ICI (relatlimab) given in combination with the PD-1 inhibitor nivolumab, expanding the list of immunotherapeutic options in advanced melanoma [144]. LAG-3 is an inhibitory molecule expressed by activated T cells and associates with the T cell receptor (TCR) and CD3 at the T cell surface [145]. The intracellular region of LAG-3 is responsible for transducing inhibitory signals to suppress T cell activation, but the molecular mechanisms governing this remain under investigation [146]. The known ligands of LAG-3 include major histocompatibility complex (MHC) class II [147, 148], galectin-3 [149] and fibrinogen-like protein 1 (FGL1) [150]. The utility of LAG-3 ICIs remains to be seen, but an early phase I/II study of combination treatment with LAG-3 and PD-1 inhibitor showed synergistic activity albeit with modest antitumor response [151]. For further reading, Huo et al. recently reviewed the clinical development of these novel agents [152], which will not be further elaborated on in this review.

In STS, analysis of blood samples from patients and healthy donors found that LAG-3 expression in peripheral T cells was correlated with the degree of intratumoral CD8+ T cell infiltration and poor prognosis [153]. Due to the novelty of anti-LAG-3 antibodies, there have been limited clinical trials regarding the use of LAG-3 as a potential immune biomarker for ICI response. As ongoing and future research uncovers more about the role of LAG-3 in suppressing T cell activation and the molecular mechanisms governing this, we would then be able to better understand its place in cancer immunotherapy and as a predictive biomarker for ICI response in sarcomas.

T-cell immunoglobulin and mucin domain-containing protein 3 (TIM-3)

TIM-3 is an immune checkpoint receptor that has been found to be expressed on many types of immune cells, including CD4+ and CD8+ T cells [154], Treg cells [155], myeloid cells [156], natural killer (NK) cells [157] and mast cells [158, 159]. In CD8+ T cells, co-expression of TIM-3 and PD-1 has been observed on the most exhausted subset of tumor-infiltrating lymphocytes [160, 161].

TIM-3 has several ligands that bind to different regions on the receptor, including galectin-9 (Gal-9), phosphatidylserine, high mobility group protein B1 (HMGB1) and carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1) [159]. Gal-9 is expressed and secreted by many hematopoietic cells and some tumor cells, and its binding has been reported to result in T cell inhibition and cell death [159, 162]. HMGB1 binds to DNA from dying cells and is also secreted by tumor cells. HMGB1 binding to DNA facilitates their uptake and activation of toll-like receptors (TLRs), but it can also be bound by TIM-3, which sequesters it and prevents its activation of TLRs, thereby dampening antitumor immunity [159, 163]. CEACAM1 is expressed by T cells [164], DCs [165], monocytes [166] and macrophages [167], and its binding results in TCR signaling inhibition [164].

In mouse models of lung adenocarcinoma, Koyama et al. observed that in tumors which progressed following initial response to anti-PD-1 therapy, there was an upregulation of other immune checkpoint receptors, particularly TIM-3, on PD-1 antibody-bound T cells. Subsequent administration of combined anti-PD-1 and anti-TIM-3 therapy resulted in improved survival. The upregulation of TIM-3 was also seen in two patients who developed adaptive resistance to anti-PD-1 therapy, presenting TIM-3 upregulation as a possible biomarker of PD-1 therapy resistance [168].

Several anti-TIM-3 antibodies are being tested in phase I/II clinical trials, with some in combination with anti-PD-1/-PD-L1 antibodies, in the contexts of acute myelogenous leukemia, myelodysplastic syndrome, and various solid tumors. This combination has been demonstrated to be generally well-tolerated in early data and some anti-TIM-3 antibodies have displayed activity in lung cancer [169]. Nonetheless, the efficacy of these novel agents remains to be explored in sarcomas.

There have also been some studies evaluating the prognostic value of TIM-3 expression. Zang et al. demonstrated that TIM-3 was an independent prognostic indicator for poor OS in patients with malignant tumors (HR = 1.54; 95% CI, 1.19–1.98; p = 0.001) based on multivariate Cox regression analysis of 28 studies, and this was also observed in The Cancer Genome Atlas (TCGA) patient cohorts (HR = 1.2; p < 0.001). When stratified by tumor type, however, TIM-3 expression was not associated with OS in sarcoma (3 studies with 780 cases; p = 0.232) [170]. In contrast, Pu et al. reported that among 38 osteosarcoma tumor samples, 36 samples expressed TIM-3, and TIM-3 overexpression was associated with poorer OS (p < 0.001) [171].

Overall, anti-TIM-3 targeted therapy is still in its early stages of development, and more robust data on TIM-3 is needed to evaluate its role as a predictive biomarker for ICI therapy in sarcomas. Clinical trials evaluating the efficacy of anti-PD-1/PD-L1 antibodies combined with anti-TIM-3 antibodies could uncover more information on the relationship between immune checkpoint receptors within the TME.

TP53 mutation status

The tumor suppressor protein p53 is critical in the prevention of oncogenesis [172]. TP53 is the most frequently mutated gene among human cancers [172,173,174] and TP53 mutations commonly result in both loss of tumor suppressor function and gain of oncogenic function [175].

In sarcomas, TP53 is also one of the most frequently altered genes, albeit widely varying across histological subtypes [42, 127, 176,177,178]. Nassif et al. reported that TP53 mutation in sarcomas is associated with shorter disease-free survival (HR = 1.63; 95% CI, 1.04–2.54; Cox p = 0.032) and better treatment outcomes with anthracyclines (OR = 3.70; 95% CI, 1.20–11.97; p = 0.02) [42, 176, 177, 179, 180]. However, there has been a lack of studies evaluating the use of TP53 as an immune biomarker for ICI therapy in sarcomas.

Nevertheless, TP53 mutation status has been observed to be significantly correlated with PD-L1 expression [42] and response to ICI therapy in NSCLC [181,182,183,184]. In NSCLC and colorectal cancer (CRC), Agersborg et al. explored the relationship between mutation profile and PD-L1 expression and found that tumors with TP53 mutation in the NSCLC cohort had significantly higher PD-L1 expression (p = 0.01), though this was not observed in the CRC cohort (p = 0.5). In fact, the CRC cohort had significantly lower expression of PD-L1 (p = 0.0005) compared to the NSCLC cohort despite similar rates of TP53 mutation across both cancers, suggesting that varying mechanisms regulate PD-L1 expression across different tumor types [185].

In addition, Sun et al. compared lung adenocarcinoma TMB data of TP53-missense-mutant and TP53-nonsense-mutant groups to TP53-wild-type groups from Memorial Sloan Kettering Cancer Center (MSKCC) (p < 0.01 and p < 0.05 respectively), TCGA (p < 0.0001 for both) and GENE + (p < 0.0001 for both) databases using a Wilcoxon test and reported that both TP53-mutant groups demonstrated elevated TMB and neoantigen levels compared to the TP53-wild-type group [42].

Taken together, TP53 mutation status appears to be correlated with other biomarkers of ICI therapy in NSCLC. However, whether this is also true in sarcoma remains to be seen, as further investigation into the relationship between TP53 mutation status and response to ICIs is needed.

B Cells

B cells are responsible for the humoral arm of the adaptive immune system. Activation of naïve B cells by CD4+ T cells results in B cell proliferation, somatic hypermutation of immunoglobulin genes and class switching. Subsequently, activated B cells differentiate into plasmablasts and long-lived plasma cells which produce antigen-specific antibodies that are responsible for the clearance of antigens [186].

The role of B cells in the TME remains controversial, with conflicting evidence across different studies. A comprehensive review of publications investigating the prognostic value of tumor-infiltrating B cells in cancer found that 50% of studies reported a positive prognostic effect for B cells, while 9% and 40% reported a negative or neutral effect respectively [187]. An in vitro study showed that B cells suppress tumor immunity by downregulating the expression of IFN-γ in CD8+ T cells, a cytokine possessing antitumor activity [188], while increasing interleukin-10 (IL-10) production that further inhibits IFN-γ production by T cells [189]. Interestingly, co-culture of B cells with different cancer cell lines yielded different expression levels of IL-10, with sarcoma cells failing to stimulate IL-10 production in B cells, in contrast to Friend murine leukemia virus gag-expressing and melanoma cells which induced B cell IL-10 secretion [189]. In contrast, a separate study highlighted the antibody-mediated antitumor response of activated B cells in murine models of metastatic pulmonary tumors [190]. These conflicting reports of the role of B cells in antitumor immunity are likely due to heterogeneity of the B cell population within the TME, which could ultimately influence clinical outcomes.

Various subtypes of B cells are found in the TME. In tertiary lymphoid structures (TLS) within the TME, B cells are thought to be mainly involved in antigen presentation, where they help to activate both CD4+ and CD8+ T cells [191,192,193,194]. Subsequent antigen-driven maturation of B cells into plasma cells leads to the generation of in situ tumor antigen-specific antibodies [191]. Thus, B cells are instrumental in the generation of antitumor activity initiated within TLS. An immunosuppressive subset of B cells within the TME has also been described, commonly referred to as regulatory B cells. These cells act by secreting immunosuppressive cytokines [189] and have been identified in the TME of several cancers, including breast cancer [195], HCC [196], tongue squamous carcinoma [197], gastric cancer [198] and prostate cancer [199].

Increasing numbers of studies on immune subsets in the TME have led to the development of predictive biomarkers focused on the B cell compartment. In melanoma and RCC, B cell markers were enriched in tumors from responders versus non-responders to ICI therapy [178]. In another study involving the gene expression analysis of 3585 patients, a B cell-related gene signature comprising nine cytokine signaling genes was predictive of clinical response to ICI therapy in melanoma [200].

In STS, Petitprez et al. identified the overexpression of the B lineage signature as a distinctive feature of an immune class of sarcomas with high immune infiltration (p = 1.8 × 10–29) and found that it was also significantly associated with improved OS (p = 4.25 × 10–4). Patients in this immune class also demonstrated the best response to pembrolizumab defined by the percentage change in size of target lesions from baseline (n = 45, p = 0.026) in the SARC028 trial [127].

In conclusion, the role that B cells play in the TME is not clearly understood, given the numerous B cell subtypes present. Nonetheless, there is evidence for B cells playing a crucial role in response to ICI therapy in sarcomas and other cancers, as seen from the B cell-related gene signatures. Characterization of B cell subtypes in the TME as well as further validation of these gene signatures in larger cohorts and prospective trials could help identify the specific B cell populations and their cell states as a predictor for response to ICIs.

Tertiary Lymphoid Structures (TLS)

TLS are ectopic lymphoid structures that have been found to develop in response to chronic inflammation [201] and in various solid tumor types [202, 203]. Within the cancer literature, definitions of what constitutes a TLS as well as its maturation state vary significantly. Sautès-Fridman et al. and Vanhersecke et al. defined TLS as lymphoid aggregates consisting of B lymphocytes that are closely associated with plasma cells and T lymphocytes, making the distinction that mature TLS (mTLS) have at least one CD23+ follicular dendritic cell, while immature TLS (iTLS) are CD23 [201, 204]. In contrast, Lin et al. classified TLS into two categories based on their morphology – TLS aggregates, which are simply small clusters of lymphocytes; and TLS follicles, which are large clusters of lymphocytes that can be further distinguished based on the presence or absence of germinal centers [205].

TLS have been found to benefit prognosis [204,205,206,207] and are also associated with favorable ICI treatment outcomes [127, 204, 208,209,210,211] in several cancers. In a retrospective analysis of patient samples comprising 11 different tumor types from three independent cohorts by Vanhersecke et al., a higher proportion of patients with mTLS demonstrated objective response to ICIs compared to patients with iTLS or no TLS (36.9% versus 19.3% versus 19%, respectively, p = 0.015). Importantly, mTLS were predictive of response to ICIs regardless of PD-L1 expression [204]. Remarkably, in the phase II PEMBROSARC trial (NCT02406781) cohort, TLS-positive patients (n = 30) demonstrated a 6-month non-progression rate (NPR) and ORR of 40% (95% CI, 22.7–59.4) and 30% (95% CI, 14.7–49.4) respectively, compared to a 6-month NPR and ORR of 4.9% (95% CI, 0.6–16.5) and 2.4% (95% CI, 0.1–12.9) respectively, in the unselected all-comer cohorts [210]. Interestingly, in the study by Petitprez et al. mentioned in the previous section, at least one TLS was found in the TME of nine out of eleven tumors (82%) in the immune-high class of STS [127]. Taken together, this class of tumors is characterized by a high expression of the B lineage signature and the presence of TLS, further supporting the significance of the role that B cells and TLS play in the TME.

This significant improvement in clinical benefit highlights the potential for the presence of TLS to be utilized as a biomarker for the selection of patients with STS for ICI therapy.

Although TLS are emerging as key players in the TME, the exact mechanisms of their antitumor activity have not been fully elucidated. It has been proposed that TLS provide a favorable environment for antigen presentation and the differentiation and proliferation of lymphocytes in the TME as well as the generation of effector memory T cells, memory B cells and plasma cells [191, 201, 205]. In some TLS, spatial visualization through IHC has shown that B cells in TLS express markers of germinal center B cells, including activation-induced deaminase, the proliferation marker Ki67 and transcription factor B-cell lymphoma 6 (BCL6) [212]. The expression of these markers suggests an ongoing humoral immune response generated within TLS.

The growing evidence for TLS predicting response to ICI therapy thus gives rise to the important question of whether their use as predictive biomarkers can be implemented in clinical workflows. This will be discussed in the following section.

Clinical relevance of TLS as a predictive biomarker for ICI response in sarcomas

Of all the exploratory predictive biomarkers for response to ICI in sarcomas, the presence of TLS appears most promising thus far based on the results from the PEMBROSARC trial [210] and the study by Petitprez et al. [127]. However, the identification of TLS via multiplex IHC involves a complex laboratory workflow that requires substantial runtime and is not available in most pathology laboratories. As such, several automated methodologies have been suggested to simplify the workflow for TLS identification.

Panagiotis et al. described the use of a deep learning algorithm to quantitatively identify hematoxylin and eosin (H&E)-stained TLS [213]. The proposed computational methodology has accurately identified TLS comparable to a human counterpart and circumvents TLS that may not be identified by specific IHC staining in lung cancer [213]. However, the algorithm is not without limitations, as it does not discriminate between the various maturation states of TLS described in the literature [204, 213]. Nevertheless, preliminary identification of TLS through digital pathology provides a novel option to incorporate into the clinical workflow.

Subsequently, downstream processes to characterize TLS can include various immunostaining techniques such as multiplex IHC and immunohistofluorescence (IHF) [214]. Currently, there is a lack of standardized marker panels to robustly quantify TLS [201]. Vanhersecke et al. adopted a previously described method consisting of H&E, CD3 and CD20 staining to assess the preliminary TLS status of pathological samples [127], followed by a 5-marker multiplex IHF panel consisting of CD4, CD8, CD20, CD21 and CD23 to differentiate between CD23-positive mTLS and CD23-negative iTLS [204]. Similarly, the phase II PEMBROSARC trial cohort screened for TLS using H&E, CD3 and CD20 staining [127], followed by three different multiplex IHF panels to visualize the immune environment of TLS [210]. Other studies have suggested the use of genomic probes to identify the presence of TLS in melanoma through a 12-chemokine gene signature [215].

Although screening with a wide coverage of immune markers could improve sensitivity and specificity in TLS detection, using more markers for every patient sample would also inevitably translate to increased costs and turnaround time which would not be ideal in the clinical setting. Additionally, the lack of standardized immune markers in TLS detection could lead to inconsistencies in the identification of TLS in the clinic. Hence, there is an urgent need to streamline and define a standardized panel of markers that can be adopted in the clinical setting.

It is important to also take into consideration that the presence of TLS alone may not always be able to predict response to ICIs due to the complex interplay of factors within the TME. For example, tumors may have innate resistance to ICIs, or even acquire resistance after treatment. Jenkins et al. attributed ICI treatment failure to three broad causes – inadequate formation of antitumor T cells, impaired function of tumor-specific T cells, or impaired formation of memory T cells [216]. Hence, the use of biomarkers to infer the states of immune cells in the TME together with the presence or absence of TLS may be able to better predict response to ICIs.


Presently in sarcomas, there is still a lack of robust predictive biomarkers that can be implemented in the clinic. Putative biomarkers will need to be tested in clinical trials to establish their roles in the treatment of sarcomas using ICIs. As new mechanisms emerge, this list will also expand, but it is also critically important that tests are simple and cost-effective with a short turnaround time, so as to be applicable in centers worldwide. Patients matched to biomarkers that accurately predict response to ICI will change the paradigm for systemic treatment in sarcomas and likely supersede the current standard of care.

Availability of data and materials

Not applicable.



Acquired immunodeficiency syndrome


Alveolar rhabdomyosarcoma


Alveolar soft-part sarcoma


B-cell lymphoma 6


Best overall response rate


Bone sarcoma


Conditionally active biologic AXL-targeted antibody drug conjugate


Chimeric antigen receptor


Carcinoembryonic antigen-related cell adhesion molecule 1


Confidence interval


Central nervous system


Combined positive score


Complete response


Colorectal cancer




Cytotoxic T-lymphocyte-associated protein 4


Disease control rate


Dendritic cells


Dedifferentiated liposarcoma


Dose-limiting toxicity


Defective mismatch repair


Deoxyribonucleic acid


Desmoplastic small round cell tumor


Epstein-Barr virus


European Medicines Agency


Embryonal rhabdomyosarcoma


Ewing sarcoma


Food and Drug Administration


Fibrinogen-like protein 1




General control nonderepressible 2


Gene expression signatures


Gastrointestinal stromal tumor


Hematoxylin and eosin


Hepatocellular carcinoma


Human epidermal growth factor receptor 2


Human immunodeficiency virus


High mobility group protein B1


Head and neck squamous cell carcinoma


Hazard ratio


Percentage of tumor-infiltrating immune cells within the tumor area expressing PD-L1


Immune checkpoint inhibitor


Indoleamine 2,3-dioxygenase






Immunoglobulin G








Integrase interactor 1


Interquartile range


Immune-related adverse events


Immature tertiary lymphoid structures


Lymphocyte activation gene 3






Major histocompatibility complex




Median overall survival


Median progression free survival


Malignant peripheral nerve sheath tumor


Messenger ribonucleic acid


Microsatellite instability


Microsatellite instability-high


Memorial Sloan Kettering Cancer Center


Mature tertiary lymphoid structures


Not available


National Clinical Trial


Next generation sequencing

NK cells:

Natural killer cells


Neutrophil-to-lymphocyte ratio


Non-progression rate


Not reached


Non-small cell lung cancer


New York Esophageal Squamous Cell Carcinoma 1 gene




Objective response rate


Overall survival


Polymerase chain reaction


Programmed cell death 1


Programmed death ligand 1


Progressive disease


Progression free survival


Proficient mismatch repair


Partial response


Pleomorphic rhabdomyosarcoma


Renal cell carcinoma


Ribonucleic acid




Stereotactic ablative radiotherapy


Stereotactic body radiation therapy


Small cell lung cancer


Stable disease


Standard of care


Synovial sarcoma


Soft-tissue sarcoma


Percentage of tumor cells within total tumor cells expressing PD-L1


The Cancer Genome Atlas


T cell receptor


Tumor-infiltrating lymphocyte


T cell immunoglobulin and mucin domain-containing protein 3


Toll-like receptor


Tertiary lymphoid structures


Tumor mutational burden


Tumor microenvironment


Triple-negative breast cancer


Tumor proportion score


Treatment-related adverse event

Treg cells:

Regulatory T cells


Tissue tumor mutational burden


Talimogene Laherparepvec


Urothelial bladder cancer


Urothelial carcinoma


Undifferentiated pleomorphic sarcoma


United States


Well-differentiated liposarcoma


Whole exome sequencing


  1. Italiano A, Mathoulin-Pelissier S, Cesne AL, Terrier P, Bonvalot S, Collin F, et al. Trends in survival for patients with metastatic soft-tissue sarcoma. Cancer. 2011;117(5):1049–54.

    PubMed  Google Scholar 

  2. Cancer.Net Editorial Board. Sarcomas, Soft Tissue: Statistics: American Society of Clinical Oncology; 2022. Available from:

  3. Cancer.Net Editorial Board. Osteosarcoma - Childhood and Adolescence: Statistics: American Society of Clinical Oncology; 2022. Available from:

  4. Cancer.Net Editorial Board. Ewing Sarcoma - Childhood and Adolescence: Statistics: American Society of Clinical Oncology; 2022. Available from:

  5. Spira AI, Ettinger DS. The use of chemotherapy in soft-tissue sarcomas. Oncologist. 2002;7(4):348–59.

    CAS  PubMed  Google Scholar 

  6. Judson I, Verweij J, Gelderblom H, Hartmann JT, Schöffski P, Blay J-Y, et al. Doxorubicin alone versus intensified doxorubicin plus ifosfamide for first-line treatment of advanced or metastatic soft-tissue sarcoma: a randomised controlled phase 3 trial. Lancet Oncol. 2014;15(4):415–23.

    CAS  PubMed  Google Scholar 

  7. Maki RG, Wathen JK, Patel SR, Priebat DA, Okuno SH, Samuels B, et al. Randomized phase II study of gemcitabine and docetaxel compared with gemcitabine alone in patients with metastatic soft tissue sarcomas: results of sarcoma alliance for research through collaboration study 002. J Clin Oncol. 2007;25(19):2755–63.

    CAS  PubMed  Google Scholar 

  8. Maldonado EB, Parsons S, Chen EY, Haslam A, Prasad V. Estimation of US patients with cancer who may respond to cytotoxic chemotherapy. Future Sci OA. 2020;6(8):FSO600.

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Gounder MM, Mahoney MR, Van Tine BA, Ravi V, Attia S, Deshpande HA, et al. Sorafenib for advanced and refractory desmoid tumors. N Engl J Med. 2018;379(25):2417–28.

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Kheder ES, Hong DS. Emerging targeted therapy for tumors with NTRK fusion proteins. Clin Cancer Res. 2018;24(23):5807–14.

    CAS  PubMed  Google Scholar 

  11. Pollack SM, Ingham M, Spraker MB, Schwartz GK. Emerging targeted and immune-based therapies in sarcoma. J Clin Oncol. 2018;36(2):125–35.

    CAS  PubMed  Google Scholar 

  12. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin. 2019;69(1):7–34.

    PubMed  Google Scholar 

  13. Skubitz KM, Pambuccian S, Manivel JC, Skubitz AP. Identification of heterogeneity among soft tissue sarcomas by gene expression profiles from different tumors. J Transl Med. 2008;6:23.

    PubMed  PubMed Central  Google Scholar 

  14. Hargadon KM, Johnson CE, Williams CJ. Immune checkpoint blockade therapy for cancer: an overview of FDA-approved immune checkpoint inhibitors. Int Immunopharmacol. 2018;62:29–39.

    CAS  PubMed  Google Scholar 

  15. Vaddepally RK, Kharel P, Pandey R, Garje R, Chandra AB. Review of indications of FDA-approved immune checkpoint inhibitors per NCCN guidelines with the level of evidence. Cancers. 2020;12(3):738.

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Motzer RJ, Escudier B, McDermott DF, George S, Hammers HJ, Srinivas S, et al. Nivolumab versus everolimus in advanced renal-cell carcinoma. N Engl J Med. 2015;373(19):1803–13.

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Chung HC, Ros W, Delord J-P, Perets R, Italiano A, Shapira-Frommer R, et al. Efficacy and safety of pembrolizumab in previously treated advanced cervical cancer: results from the phase II KEYNOTE-158 study. J Clin Oncol. 2019;37(17):1470–8.

    CAS  PubMed  Google Scholar 

  18. Ansell SM, Lesokhin AM, Borrello I, Halwani A, Scott EC, Gutierrez M, et al. PD-1 blockade with nivolumab in relapsed or refractory Hodgkin’s lymphoma. N Engl J Med. 2015;372(4):311–9.

    PubMed  Google Scholar 

  19. Fuchs CS, Doi T, Jang RW, Muro K, Satoh T, Machado M, et al. Safety and efficacy of pembrolizumab monotherapy in patients with previously treated advanced gastric and gastroesophageal junction cancer: phase 2 clinical KEYNOTE-059 trial. JAMA Oncol. 2018;4(5):e180013.

    PubMed  PubMed Central  Google Scholar 

  20. El-Khoueiry AB, Sangro B, Yau T, Crocenzi TS, Kudo M, Hsu C, et al. Nivolumab in patients with advanced hepatocellular carcinoma (CheckMate 040): an open-label, non-comparative, phase 1/2 dose escalation and expansion trial. Lancet. 2017;389(10088):2492–502.

    CAS  PubMed  PubMed Central  Google Scholar 

  21. McDermott D, Haanen J, Chen TT, Lorigan P, O’Day S. Efficacy and safety of ipilimumab in metastatic melanoma patients surviving more than 2 years following treatment in a phase III trial (MDX010-20). Ann Oncol. 2013;24(10):2694–8.

    CAS  PubMed  Google Scholar 

  22. Postow MA, Chesney J, Pavlick AC, Robert C, Grossmann K, McDermott D, et al. Nivolumab and ipilimumab versus ipilimumab in untreated melanoma. N Engl J Med. 2015;372(21):2006–17.

    PubMed  PubMed Central  Google Scholar 

  23. Larkin J, Chiarion-Sileni V, Gonzalez R, Grob JJ, Cowey CL, Lao CD, et al. Combined nivolumab and ipilimumab or monotherapy in untreated melanoma. N Engl J Med. 2015;373(1):23–34.

    PubMed  PubMed Central  Google Scholar 

  24. Nghiem P, Bhatia S, Lipson EJ, Sharfman WH, Kudchadkar RR, Brohl AS, et al. Durable tumor regression and overall survival in patients with advanced merkel cell carcinoma receiving pembrolizumab as first-line therapy. J Clin Oncol. 2019;37(9):693–702.

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Kaufman HL, Russell J, Hamid O, Bhatia S, Terheyden P, D’Angelo SP, et al. Avelumab in patients with chemotherapy-refractory metastatic Merkel cell carcinoma: a multicentre, single-group, open-label, phase 2 trial. Lancet Oncol. 2016;17(10):1374–85.

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Brahmer J, Reckamp KL, Baas P, Crinò L, Eberhardt WEE, Poddubskaya E, et al. Nivolumab versus docetaxel in advanced squamous-cell non–small-cell lung cancer. N Engl J Med. 2015;373(2):123–35.

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Zinzani P, Thieblemont C, Melnichenko V, Osmanov D, Bouabdallah K, Walewski J, et al. Efficacy and safety of pembrolizumab in relapsed/refractory primary mediastinal large B-cell lymphoma (rrPMBCL): interim analysis of the KEYNOTE-170 phase 2 trial. Hematol Oncol. 2017;35(S2):62–3.

    Google Scholar 

  28. Antonia SJ, López-Martin JA, Bendell J, Ott PA, Taylor M, Eder JP, et al. Nivolumab alone and nivolumab plus ipilimumab in recurrent small-cell lung cancer (CheckMate 032): a multicentre, open-label, phase 1/2 trial. Lancet Oncol. 2016;17(7):883–95.

    CAS  PubMed  Google Scholar 

  29. Seiwert TY, Burtness B, Mehra R, Weiss J, Berger R, Eder JP, et al. Safety and clinical activity of pembrolizumab for treatment of recurrent or metastatic squamous cell carcinoma of the head and neck (KEYNOTE-012): an open-label, multicentre, phase 1b trial. Lancet Oncol. 2016;17(7):956–65.

    CAS  PubMed  Google Scholar 

  30. Schmid P, Adams S, Rugo HS, Schneeweiss A, Barrios CH, Iwata H, et al. Atezolizumab and nab-paclitaxel in advanced triple-negative breast cancer. N Engl J Med. 2018;379(22):2108–21.

    CAS  PubMed  Google Scholar 

  31. Sharma P, Retz M, Siefker-Radtke A, Baron A, Necchi A, Bedke J, et al. Nivolumab in metastatic urothelial carcinoma after platinum therapy (CheckMate 275): a multicentre, single-arm, phase 2 trial. Lancet Oncol. 2017;18(3):312–22.

    CAS  PubMed  Google Scholar 

  32. Naqash AR, O’Sullivan Coyne GH, Moore N, Sharon E, Takebe N, Fino KK, et al. Phase II study of atezolizumab in advanced alveolar soft part sarcoma (ASPS). J Clin Oncol. 2021;39(15_suppl):11519.

    Google Scholar 

  33. WHO Classification of Tumours Editorial Board. Soft tissue and bone tumours. 5th ed. Lyon: IARC; 2020.

    Google Scholar 

  34. Haslam A, Prasad V. Estimation of the percentage of US patients with cancer who are eligible for and respond to checkpoint inhibitor immunotherapy drugs. JAMA Network Open. 2019;2(5):e192535.

    PubMed  PubMed Central  Google Scholar 

  35. Tawbi HA, Burgess M, Bolejack V, Van Tine BA, Schuetze SM, Hu J, et al. Pembrolizumab in advanced soft-tissue sarcoma and bone sarcoma (SARC028): a multicentre, two-cohort, single-arm, open-label, phase 2 trial. Lancet Oncol. 2017;18(11):1493–501.

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Conley AP, Trinh VA, Zobniw CM, Posey K, Martinez JD, Arrieta OG, et al. Positive tumor response to combined checkpoint inhibitors in a patient with refractory alveolar soft part sarcoma: a case report. J Glob Oncol. 2018;4:1–6.

    PubMed  Google Scholar 

  37. Marcrom S, De Los Santos JF, Conry RM. Complete response of mediastinal clear cell sarcoma to pembrolizumab with radiotherapy. Clin Sarcoma Res. 2017;7:14.

    PubMed  PubMed Central  Google Scholar 

  38. Guram K, Nunez M, Einck J, Mell LK, Cohen E, Sanders PD, et al. Radiation therapy combined with checkpoint blockade immunotherapy for metastatic undifferentiated pleomorphic sarcoma of the maxillary sinus with a complete response. Front Oncol. 2018;8:435.

    PubMed  PubMed Central  Google Scholar 

  39. Roulleaux Dugage M, Nassif EF, Italiano A, Bahleda R. Improving immunotherapy efficacy in soft-tissue sarcomas: a biomarker driven and histotype tailored review. Front Immunol. 2021;12:775761.

    PubMed  PubMed Central  Google Scholar 

  40. Myers G. Immune-related adverse events of immune checkpoint inhibitors: a brief review. Curr Oncol. 2018;25(5):342–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Wang J, Xiu J, Farrell A, Baca Y, Arai H, Battaglin F, et al. Mutational analysis of microsatellite-stable gastrointestinal cancer with high tumour mutational burden: a retrospective cohort study. Lancet Oncol. 2023;24(2):151–61.

    PubMed  PubMed Central  Google Scholar 

  42. Sun H, Liu SY, Zhou JY, Xu JT, Zhang HK, Yan HH, et al. Specific TP53 subtype as biomarker for immune checkpoint inhibitors in lung adenocarcinoma. EBioMedicine. 2020;60:102990.

    PubMed  PubMed Central  Google Scholar 

  43. Brown ZJ, Yu SJ, Heinrich B, Ma C, Fu Q, Sandhu M, et al. Indoleamine 2,3-dioxygenase provides adaptive resistance to immune checkpoint inhibitors in hepatocellular carcinoma. Cancer Immunol Immunother. 2018;67(8):1305–15.

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Jin J, Yang L, Liu D, Li W. Association of the neutrophil to lymphocyte ratio and clinical outcomes in patients with lung cancer receiving immunotherapy: a meta-analysis. BMJ Open. 2020;10(6):e035031.

    PubMed  PubMed Central  Google Scholar 

  45. Twomey JD, Zhang B. Cancer immunotherapy update: FDA-approved checkpoint inhibitors and companion diagnostics. AAPS J. 2021;23(2):39.

    PubMed  Google Scholar 

  46. Hamid O, Robert C, Daud A, Hodi FS, Hwu WJ, Kefford R, et al. Five-year survival outcomes for patients with advanced melanoma treated with pembrolizumab in KEYNOTE-001. Ann Oncol. 2019;30(4):582–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Michielin O, van Akkooi ACJ, Ascierto PA, Dummer R, Keilholz U. Cutaneous melanoma: ESMO clinical practice guidelines for diagnosis, treatment and follow-up††approved by the ESMO guidelines Committee: February 2002, last update September 2019. Ann Oncol. 2019;30(12):1884–901.

    CAS  PubMed  Google Scholar 

  48. Garbe C, Amaral T, Peris K, Hauschild A, Arenberger P, Bastholt L, et al. European consensus-based interdisciplinary guideline for melanoma. Part 2: treatment – update 2019. Eur J Cancer. 2020;126:159–77.

    CAS  PubMed  Google Scholar 

  49. Armand P, Rodig S, Melnichenko V, Thieblemont C, Bouabdallah K, Tumyan G, et al. Pembrolizumab in Relapsed or Refractory Primary Mediastinal Large B-Cell Lymphoma. J Clin Oncol. 2019;37(34):3291–9.

  50. Chen R, Zinzani PL, Lee HJ, Armand P, Johnson NA, Brice P, et al. Pembrolizumab in relapsed or refractory Hodgkin lymphoma: 2-year follow-up of KEYNOTE-087. Blood. 2019;134(14):1144–53.

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Balar AV, Castellano D, O’Donnell PH, Grivas P, Vuky J, Powles T, et al. First-line pembrolizumab in cisplatin-ineligible patients with locally advanced and unresectable or metastatic urothelial cancer (KEYNOTE-052): a multicentre, single-arm, phase 2 study. Lancet Oncol. 2017;18(11):1483–92.

    CAS  PubMed  Google Scholar 

  52. Suzman DL, Agrawal S, Ning YM, Maher VE, Fernandes LL, Karuri S, et al. FDA approval summary: atezolizumab or pembrolizumab for the treatment of patients with advanced urothelial carcinoma ineligible for cisplatin-containing chemotherapy. Oncologist. 2019;24(4):563–9.

    CAS  PubMed  Google Scholar 

  53. Zinzani PL, Ribrag V, Moskowitz CH, Michot JM, Kuruvilla J, Balakumaran A, et al. Safety and tolerability of pembrolizumab in patients with relapsed/refractory primary mediastinal large B-cell lymphoma. Blood. 2017;130(3):267–70.

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Bradford D, Demko S, Jin S, Mishra-Kalyani P, Beckles AR, Goldberg KB, et al. FDA accelerated approval of pembrolizumab for recurrent locally advanced or metastatic merkel cell carcinoma. Oncologist. 2020;25(7):e1077–82.

    PubMed  PubMed Central  Google Scholar 

  55. Kamat AM, Shore N, Hahn N, Alanee S, Nishiyama H, Shariat S, et al. KEYNOTE-676: phase III study of BCG and pembrolizumab for persistent/recurrent high-risk NMIBC. Future Oncol. 2020;16(10):507–16.

    CAS  PubMed  Google Scholar 

  56. Sharma P, Hu-Lieskovan S, Wargo JA, Ribas A. Primary, adaptive, and acquired resistance to cancer immunotherapy. Cell. 2017;168(4):707–23.

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Orellana García LP, Ehmann F, Hines PA, Ritzhaupt A, Brand A. Biomarker and companion diagnostics-a review of medicinal products approved by the European medicines agency. Front Med (Lausanne). 2021;8:753187.

    PubMed  Google Scholar 

  58. Wang Y, Tong Z, Zhang W, Zhang W, Buzdin A, Mu X, et al. FDA-approved and emerging next generation predictive biomarkers for immune checkpoint inhibitors in cancer patients. Front Oncol. 2021;11:683419.

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Berg KD, Glaser CL, Thompson RE, Hamilton SR, Griffin CA, Eshleman JR. Detection of microsatellite instability by fluorescence multiplex polymerase chain reaction. J Mol Diagn. 2000;2(1):20–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Klempner SJ, Fabrizio D, Bane S, Reinhart M, Peoples T, Ali SM, et al. Tumor mutational burden as a predictive biomarker for response to immune checkpoint inhibitors: a review of current evidence. Oncologist. 2020;25(1):e147–59.

    PubMed  Google Scholar 

  61. Schmid P, Salgado R, Park YH, Muñoz-Couselo E, Kim SB, Sohn J, et al. Pembrolizumab plus chemotherapy as neoadjuvant treatment of high-risk, early-stage triple-negative breast cancer: results from the phase 1b open-label, multicohort KEYNOTE-173 study. Ann Oncol. 2020;31(5):569–81.

    CAS  PubMed  Google Scholar 

  62. Burtness B, Harrington KJ, Greil R, Soulières D, Tahara M, de Castro G Jr, et al. Pembrolizumab alone or with chemotherapy versus cetuximab with chemotherapy for recurrent or metastatic squamous cell carcinoma of the head and neck (KEYNOTE-048): a randomised, open-label, phase 3 study. Lancet. 2019;394(10212):1915–28.

    CAS  PubMed  Google Scholar 

  63. Reck M, Rodríguez-Abreu D, Robinson AG, Hui R, Csőszi T, Fülöp A, et al. Updated analysis of KEYNOTE-024: pembrolizumab versus platinum-based chemotherapy for advanced non–small-cell lung cancer with PD-L1 tumor proportion score of 50% or greater. J Clin Oncol. 2019;37(7):537–46.

    CAS  PubMed  Google Scholar 

  64. Bellmunt J, de Wit R, Vaughn DJ, Fradet Y, Lee JL, Fong L, et al. Pembrolizumab as second-line therapy for advanced urothelial carcinoma. N Engl J Med. 2017;376(11):1015–26.

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Hellmann MD, Paz-Ares L, Bernabe Caro R, Zurawski B, Kim SW, Carcereny Costa E, et al. Nivolumab plus ipilimumab in advanced non-small-cell lung cancer. N Engl J Med. 2019;381(21):2020–31.

    CAS  PubMed  Google Scholar 

  66. Rittmeyer A, Barlesi F, Waterkamp D, Park K, Ciardiello F, von Pawel J, et al. Atezolizumab versus docetaxel in patients with previously treated non-small-cell lung cancer (OAK): a phase 3, open-label, multicentre randomised controlled trial. Lancet. 2017;389(10066):255–65.

    PubMed  Google Scholar 

  67. Rosenberg JE, Hoffman-Censits J, Powles T, van der Heijden MS, Balar AV, Necchi A, et al. Atezolizumab in patients with locally advanced and metastatic urothelial carcinoma who have progressed following treatment with platinum-based chemotherapy: a single-arm, multicentre, phase 2 trial. Lancet. 2016;387(10031):1909–20.

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Massard C, Gordon MS, Sharma S, Rafii S, Wainberg ZA, Luke J, et al. Safety and Efficacy of Durvalumab (MEDI4736), an anti-programmed cell death ligand-1 immune checkpoint inhibitor, in patients with advanced urothelial bladder cancer. J Clin Oncol. 2016;34(26):3119–25.

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Le DT, Kim TW, Van Cutsem E, Geva R, Jäger D, Hara H, et al. Phase II open-label study of pembrolizumab in treatment-refractory, microsatellite instability-high/mismatch repair-deficient metastatic colorectal cancer: KEYNOTE-164. J Clin Oncol. 2020;38(1):11–9.

    CAS  PubMed  Google Scholar 

  70. Le DT, Uram JN, Wang H, Bartlett BR, Kemberling H, Eyring AD, et al. PD-1 blockade in tumors with mismatch-repair deficiency. N Engl J Med. 2015;372(26):2509–20.

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Marabelle A, Fakih M, Lopez J, Shah M, Shapira-Frommer R, Nakagawa K, et al. Association of tumour mutational burden with outcomes in patients with advanced solid tumours treated with pembrolizumab: prospective biomarker analysis of the multicohort, open-label, phase 2 KEYNOTE-158 study. Lancet Oncol. 2020;21(10):1353–65.

    CAS  PubMed  Google Scholar 

  72. Ott PA, Bang Y-J, Piha-Paul SA, Razak ARA, Bennouna J, Soria J-C, et al. T-cell–inflamed gene-expression profile, programmed death ligand 1 expression, and tumor mutational burden predict efficacy in patients treated with pembrolizumab across 20 cancers: KEYNOTE-028. J Clin Oncol. 2018;37(4):318–27.

    PubMed  Google Scholar 

  73. Alsaab HO, Sau S, Alzhrani R, Tatiparti K, Bhise K, Kashaw SK, et al. PD-1 and PD-L1 checkpoint signaling inhibition for cancer immunotherapy: mechanism, combinations, and clinical outcome. Front Pharmacol. 2017;8:561.

    PubMed  PubMed Central  Google Scholar 

  74. Noguchi T, Ward JP, Gubin MM, Arthur CD, Lee SH, Hundal J, et al. Temporally distinct PD-L1 expression by tumor and host cells contributes to immune escape. Cancer Immunol Res. 2017;5(2):106–17.

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Lau J, Cheung J, Navarro A, Lianoglou S, Haley B, Totpal K, et al. Tumour and host cell PD-L1 is required to mediate suppression of anti-tumour immunity in mice. Nat Commun. 2017;8:14572.

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Lin H, Wei S, Hurt EM, Green MD, Zhao L, Vatan L, et al. Host expression of PD-L1 determines efficacy of PD-L1 pathway blockade-mediated tumor regression. J Clin Invest. 2018;128(2):805–15.

    PubMed  PubMed Central  Google Scholar 

  77. Yi M, Niu M, Xu L, Luo S, Wu K. Regulation of PD-L1 expression in the tumor microenvironment. J Hematol Oncol. 2021;14(1):10.

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Le Cesne A, Marec-Berard P, Blay JY, Gaspar N, Bertucci F, Penel N, et al. Programmed cell death 1 (PD-1) targeting in patients with advanced osteosarcomas: results from the PEMBROSARC study. Eur J Cancer. 2019;119:151–7.

    PubMed  Google Scholar 

  79. Kelany M, Barth TF, Salem D, Shakweer MM. Prevalence and prognostic implications of PD-L1 expression in soft tissue sarcomas. Pathol Oncol Res. 2021;27:1609804.

    PubMed  PubMed Central  Google Scholar 

  80. Sundara YT, Kostine M, Cleven AHG, Bovée JVMG, Schilham MW, Cleton-Jansen A-M. Increased PD-L1 and T-cell infiltration in the presence of HLA class I expression in metastatic high-grade osteosarcoma: a rationale for T-cell-based immunotherapy. Cancer Immunol Immunother. 2017;66(1):119–28.

    CAS  PubMed  Google Scholar 

  81. Patel KR, Martinez A, Stahl JM, Logan SJ, Perricone AJ, Ferris MJ, et al. Increase in PD-L1 expression after pre-operative radiotherapy for soft tissue sarcoma. Oncoimmunology. 2018;7(7):e1442168.

    PubMed  PubMed Central  Google Scholar 

  82. Boxberg M, Steiger K, Lenze U, Rechl H, von Eisenhart-Rothe R, Wörtler K, et al. PD-L1 and PD-1 and characterization of tumor-infiltrating lymphocytes in high grade sarcomas of soft tissue - prognostic implications and rationale for immunotherapy. Oncoimmunology. 2018;7(3):e1389366.

    PubMed  Google Scholar 

  83. Kim JR, Moon YJ, Kwon KS, Bae JS, Wagle S, Kim KM, et al. Tumor infiltrating PD1-positive lymphocytes and the expression of PD-L1 predict poor prognosis of soft tissue sarcomas. PLoS ONE. 2013;8(12):e82870.

    PubMed  PubMed Central  Google Scholar 

  84. Kim C, Kim EK, Jung H, Chon HJ, Han JW, Shin KH, et al. Prognostic implications of PD-L1 expression in patients with soft tissue sarcoma. BMC Cancer. 2016;16:434.

    PubMed  PubMed Central  Google Scholar 

  85. D’Angelo SP, Shoushtari AN, Agaram NP, Kuk D, Qin LX, Carvajal RD, et al. Prevalence of tumor-infiltrating lymphocytes and PD-L1 expression in the soft tissue sarcoma microenvironment. Hum Pathol. 2015;46(3):357–65.

    CAS  PubMed  Google Scholar 

  86. Chowdhury F, Dunn S, Mitchell S, Mellows T, Ashton-Key M, Gray JC. PD-L1 and CD8+PD1+ lymphocytes exist as targets in the pediatric tumor microenvironment for immunomodulatory therapy. OncoImmunology. 2015;4(10):e1029701.

    Google Scholar 

  87. Que Y, Xiao W, Guan YX, Liang Y, Yan SM, Chen HY, et al. PD-L1 expression is associated with FOXP3+ regulatory T-Cell infiltration of soft tissue sarcoma and poor patient prognosis. J Cancer. 2017;8(11):2018–25.

    PubMed  PubMed Central  Google Scholar 

  88. Pollack SM, He Q, Yearley JH, Emerson R, Vignali M, Zhang Y, et al. T-cell infiltration and clonality correlate with programmed cell death protein 1 and programmed death-ligand 1 expression in patients with soft tissue sarcomas. Cancer. 2017;123(17):3291–304.

    CAS  PubMed  Google Scholar 

  89. van Erp AEM, Versleijen-Jonkers YMH, Hillebrandt-Roeffen MHS, van Houdt L, Gorris MAJ, van Dam LS, et al. Expression and clinical association of programmed cell death-1, programmed death-ligand-1 and CD8(+) lymphocytes in primary sarcomas is subtype dependent. Oncotarget. 2017;8(41):71371–84.

    PubMed  PubMed Central  Google Scholar 

  90. Paydas S, Bagir EK, Deveci MA, Gonlusen G. Clinical and prognostic significance of PD-1 and PD-L1 expression in sarcomas. Med Oncol. 2016;33(8):93.

    PubMed  Google Scholar 

  91. Kloor M, von Knebel DM. The immune biology of microsatellite-unstable cancer. Trends Cancer. 2016;2(3):121–33.

    PubMed  Google Scholar 

  92. Luchini C, Bibeau F, Ligtenberg MJL, Singh N, Nottegar A, Bosse T, et al. ESMO recommendations on microsatellite instability testing for immunotherapy in cancer, and its relationship with PD-1/PD-L1 expression and tumour mutational burden: a systematic review-based approach. Ann Oncol. 2019;30(8):1232–43.

    CAS  PubMed  Google Scholar 

  93. Lorenzi M, Amonkar M, Zhang J, Mehta S, Liaw K-L. Epidemiology of Microsatellite Instability High (MSI-H) and Deficient Mismatch Repair (dMMR) in solid tumors: a structured literature review. J Oncol. 2020;2020:1807929.

    Google Scholar 

  94. Lam SW, Kostine M, de Miranda NFCC, Schöffski P, Lee C-J, Morreau H, et al. Mismatch repair deficiency is rare in bone and soft tissue tumors. Histopathology. 2021;79(4):509–20.

    PubMed  PubMed Central  Google Scholar 

  95. Zamora AE, Crawford JC, Thomas PG. Hitting the target: how t cells detect and eliminate tumors. J Immunol. 2018;200(2):392.

    CAS  PubMed  Google Scholar 

  96. Kim JY, Kronbichler A, Eisenhut M, Hong SH, van der Vliet HJ, Kang J, et al. Tumor mutational burden and efficacy of immune checkpoint inhibitors: a systematic review and meta-analysis. Cancers (Basel). 2019;11(11):1798.

    CAS  PubMed  Google Scholar 

  97. Yarchoan M, Hopkins A, Jaffee EM. Tumor mutational burden and response rate to PD-1 inhibition. N Engl J Med. 2017;377(25):2500–1.

    PubMed  PubMed Central  Google Scholar 

  98. Wang F, Wei XL, Wang FH, Xu N, Shen L, Dai GH, et al. Safety, efficacy and tumor mutational burden as a biomarker of overall survival benefit in chemo-refractory gastric cancer treated with toripalimab, a PD-1 antibody in phase Ib/II clinical trial NCT02915432. Ann Oncol. 2019;30(9):1479–86.

    CAS  PubMed  PubMed Central  Google Scholar 

  99. Valero C, Lee M, Hoen D, Zehir A, Berger MF, Seshan VE, et al. Response rates to Anti-PD-1 immunotherapy in microsatellite-stable solid tumors with 10 or more mutations per megabase. JAMA Oncol. 2021;7(5):739–43.

    PubMed  Google Scholar 

  100. Kim ST, Cristescu R, Bass AJ, Kim K-M, Odegaard JI, Kim K, et al. Comprehensive molecular characterization of clinical responses to PD-1 inhibition in metastatic gastric cancer. Nat Med. 2018;24(9):1449–58.

    CAS  PubMed  Google Scholar 

  101. Hara H, Fukuoka S, Takahashi N, Kojima T, Kawazoe A, Asayama M, et al. Regorafenib plus nivolumab in patients with advanced colorectal or gastric cancer: an open-label, dose-finding, and dose-expansion phase 1b trial (REGONIVO, EPOC1603). Ann Oncol. 2019;30:124.

    Google Scholar 

  102. Pillozzi S, Bernini A, Palchetti I, Crociani O, Antonuzzo L, Campanacci D, et al. Soft tissue sarcoma: an insight on biomarkers at molecular, metabolic and cellular level. Cancers (Basel). 2021;13(12):3044.

    CAS  PubMed  Google Scholar 

  103. Meléndez B, Van Campenhout C, Rorive S, Remmelink M, Salmon I, D’Haene N. Methods of measurement for tumor mutational burden in tumor tissue. Transl Lung Cancer Res. 2018;7(6):661–7.

    PubMed  PubMed Central  Google Scholar 

  104. Allgäuer M, Budczies J, Christopoulos P, Endris V, Lier A, Rempel E, et al. Implementing tumor mutational burden (TMB) analysis in routine diagnostics-a primer for molecular pathologists and clinicians. Transl Lung Cancer Res. 2018;7(6):703–15.

    PubMed  PubMed Central  Google Scholar 

  105. Abeshouse A, Adebamowo C, Adebamowo SN, Akbani R, Akeredolu T, Ally A, et al. Comprehensive and integrated genomic characterization of adult soft tissue sarcomas. Cell. 2017;171(4):950–65.e28.

    PubMed Central  Google Scholar 

  106. Trabucco SE, Ali SM, Sokol E, Schrock AB, Albacker LA, Chung J, et al. Frequency of genomic biomarkers of response to immunotherapy in sarcoma. J Clin Oncol. 2018;36(15_suppl):11579.

    Google Scholar 

  107. Doyle LA, Nowak JA, Nathenson MJ, Thornton K, Wagner AJ, Johnson JM, et al. Characteristics of mismatch repair deficiency in sarcomas. Mod Pathol. 2019;32(7):977–87.

    CAS  PubMed  Google Scholar 

  108. Espejo-Freire AP, Elliott A, Rosenberg A, Costa PA, Barreto-Coelho P, Jonczak E, et al. Genomic landscape of angiosarcoma: a targeted and immunotherapy biomarker analysis. Cancers (Basel). 2021;13(19):4816.

    CAS  PubMed  Google Scholar 

  109. Wagner MJ, Othus M, Patel SP, Ryan C, Sangal A, Powers B, et al. Multicenter phase II trial (SWOG S1609, cohort 51) of ipilimumab and nivolumab in metastatic or unresectable angiosarcoma: a substudy of dual anti-CTLA-4 and anti-PD-1 blockade in rare tumors (DART). J Immunother Cancer. 2021;9(8):e002990.

    PubMed  PubMed Central  Google Scholar 

  110. Chibon F. Cancer gene expression signatures – the rise and fall? Eur J Cancer. 2013;49(8):2000–9.

    CAS  PubMed  Google Scholar 

  111. Singh KP, Miaskowski C, Dhruva AA, Flowers E, Kober KM. Mechanisms and measurement of changes in gene expression. Biol Res Nurs. 2018;20(4):369–82.

    CAS  PubMed  PubMed Central  Google Scholar 

  112. Rahman R, Zatorski N, Hansen J, Xiong Y, van Hasselt JGC, Sobie EA, et al. Protein structure–based gene expression signatures. Proc Natl Acad Sci. 2021;118(19):e2014866118.

    CAS  PubMed  PubMed Central  Google Scholar 

  113. Slyper M, Porter CBM, Ashenberg O, Waldman J, Drokhlyansky E, Wakiro I, et al. A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors. Nat Med. 2020;26(5):792–802.

    CAS  PubMed  PubMed Central  Google Scholar 

  114. Marx V. Method of the year: spatially resolved transcriptomics. Nat Methods. 2021;18(1):9–14.

    CAS  PubMed  Google Scholar 

  115. Auslander N, Zhang G, Lee JS, Frederick DT, Miao B, Moll T, et al. Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma. Nat Med. 2018;24(10):1545–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  116. Jerby-Arnon L, Shah P, Cuoco MS, Rodman C, Su MJ, Melms JC, et al. A cancer cell program promotes T cell exclusion and resistance to checkpoint blockade. Cell. 2018;175(4):984–97.e24.

    CAS  PubMed  PubMed Central  Google Scholar 

  117. Hugo W, Zaretsky JM, Sun L, Song C, Moreno BH, Hu-Lieskovan S, et al. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell. 2016;165(1):35–44.

    CAS  PubMed  PubMed Central  Google Scholar 

  118. Budczies J, Kirchner M, Kluck K, Kazdal D, Glade J, Allgäuer M, et al. A gene expression signature associated with B cells predicts benefit from immune checkpoint blockade in lung adenocarcinoma. OncoImmunology. 2021;10(1):1860586.

    PubMed  PubMed Central  Google Scholar 

  119. De Marchi P, Ferro Leal L, da Silva LS, de Oliveira Cavagna R, Ferreira da Silva FA, da Silva VD, et al. LungTS: a new gene expression signature for prediction of response to checkpoint inhibitors in non-small cell lung cancer. J Clin Oncol. 2022;40(16_suppl):e21143.

    Google Scholar 

  120. Chen H, Lin R, Lin W, Chen Q, Ye D, Li J, et al. An immune gene signature to predict prognosis and immunotherapeutic response in lung adenocarcinoma. Sci Rep. 2022;12(1):8230.

    CAS  PubMed  PubMed Central  Google Scholar 

  121. Hwang S, Kwon A-Y, Jeong J-Y, Kim S, Kang H, Park J, et al. Immune gene signatures for predicting durable clinical benefit of anti-PD-1 immunotherapy in patients with non-small cell lung cancer. Sci Rep. 2020;10(1):643.

    CAS  PubMed  PubMed Central  Google Scholar 

  122. Yuan B, Jiang C, Chen L, Wen L, Cui J, Chen M, et al. A novel DNA repair gene signature for immune checkpoint inhibitor-based therapy in gastric cancer. Front Cell Dev Biol. 2022;10:893546.

    PubMed  PubMed Central  Google Scholar 

  123. Lai G, Li K, Deng J, Liu H, Xie B, Zhong X. Identification and validation of a gene signature for lower-grade gliomas based on pyroptosis-related genes to predict survival and response to immune checkpoint inhibitors. J Healthc Eng. 2022;2022:8704127.

    PubMed  PubMed Central  Google Scholar 

  124. Thompson JC, Davis C, Deshpande C, Hwang W-T, Jeffries S, Huang A, et al. Gene signature of antigen processing and presentation machinery predicts response to checkpoint blockade in non-small cell lung cancer (NSCLC) and melanoma. J Immunother Cancer. 2020;8(2):e000974.

    PubMed  PubMed Central  Google Scholar 

  125. Ayers M, Lunceford J, Nebozhyn M, Murphy E, Loboda A, Kaufman DR, et al. IFN-γ–related mRNA profile predicts clinical response to PD-1 blockade. J Clin Investig. 2017;127(8):2930–40.

    PubMed  PubMed Central  Google Scholar 

  126. Du XH, Wei H, Zhang P, Yao WT, Cai QQ. Heterogeneity of soft tissue sarcomas and its implications in targeted therapy. Front Oncol. 2020;10:564852.

    PubMed  PubMed Central  Google Scholar 

  127. Petitprez F, de Reyniès A, Keung EZ, Chen TW-W, Sun C-M, Calderaro J, et al. B cells are associated with survival and immunotherapy response in sarcoma. Nature. 2020;577(7791):556–60.

    CAS  PubMed  Google Scholar 

  128. Bacher U, Shumilov E, Flach J, Porret N, Joncourt R, Wiedemann G, et al. Challenges in the introduction of next-generation sequencing (NGS) for diagnostics of myeloid malignancies into clinical routine use. Blood Cancer J. 2018;8(11):113.

    PubMed  PubMed Central  Google Scholar 

  129. Zhang L, Chen D, Song D, Liu X, Zhang Y, Xu X, et al. Clinical and translational values of spatial transcriptomics. Signal Transduct Target Ther. 2022;7(1):111.

    CAS  PubMed  PubMed Central  Google Scholar 

  130. Song M, Graubard BI, Rabkin CS, Engels EA. Neutrophil-to-lymphocyte ratio and mortality in the United States general population. Sci Rep. 2021;11(1):464.

    CAS  PubMed  PubMed Central  Google Scholar 

  131. Howard R, Kanetsky PA, Egan KM. Exploring the prognostic value of the neutrophil-to-lymphocyte ratio in cancer. Sci Rep. 2019;9(1):19673.

    CAS  PubMed  PubMed Central  Google Scholar 

  132. Li M, Spakowicz D, Burkart J, Patel S, Husain M, He K, et al. Change in neutrophil to lymphocyte ratio during immunotherapy treatment is a non-linear predictor of patient outcomes in advanced cancers. J Cancer Res Clin Oncol. 2019;145(10):2541–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  133. Strong EA, Park SH, Ethun CG, Chow B, King D, Bedi M, et al. High neutrophil-lymphocyte ratio is not independently associated with worse survival or recurrence in patients with extremity soft tissue sarcoma. Surgery. 2020;168(4):760–7.

    PubMed  Google Scholar 

  134. Chan JY, Zhang Z, Chew W, Tan GF, Lim CL, Zhou L, et al. Biological significance and prognostic relevance of peripheral blood neutrophil-to-lymphocyte ratio in soft tissue sarcoma. Sci Rep. 2018;8(1):11959.

    PubMed  PubMed Central  Google Scholar 

  135. Wang S, Wu J, Shen H, Wang J. The prognostic value of IDO expression in solid tumors: a systematic review and meta-analysis. BMC Cancer. 2020;20(1):471.

    CAS  PubMed  PubMed Central  Google Scholar 

  136. Munn DH, Mellor AL. Indoleamine 2,3 dioxygenase and metabolic control of immune responses. Trends Immunol. 2013;34(3):137–43.

    CAS  PubMed  Google Scholar 

  137. Munn DH, Sharma MD, Baban B, Harding HP, Zhang Y, Ron D, et al. GCN2 kinase in T cells mediates proliferative arrest and anergy induction in response to indoleamine 2,3-dioxygenase. Immunity. 2005;22(5):633–42.

    CAS  PubMed  Google Scholar 

  138. Sharma MD, Baban B, Chandler P, Hou DY, Singh N, Yagita H, et al. Plasmacytoid dendritic cells from mouse tumor-draining lymph nodes directly activate mature Tregs via indoleamine 2,3-dioxygenase. J Clin Invest. 2007;117(9):2570–82.

    CAS  PubMed  PubMed Central  Google Scholar 

  139. Urakawa H, Nishida Y, Nakashima H, Shimoyama Y, Nakamura S, Ishiguro N. Prognostic value of indoleamine 2,3-dioxygenase expression in high grade osteosarcoma. Clin Exp Metas. 2009;26(8):1005–12.

    CAS  Google Scholar 

  140. Balachandran VP, Cavnar MJ, Zeng S, Bamboat ZM, Ocuin LM, Obaid H, et al. Imatinib potentiates antitumor T cell responses in gastrointestinal stromal tumor through the inhibition of Ido. Nat Med. 2011;17(9):1094–100.

    CAS  PubMed  PubMed Central  Google Scholar 

  141. Reilley MJ, Bailey A, Subbiah V, Janku F, Naing A, Falchook G, et al. Phase I clinical trial of combination imatinib and ipilimumab in patients with advanced malignancies. J Immunother Cancer. 2017;5(1):35.

    PubMed  PubMed Central  Google Scholar 

  142. Tang K, Wu Y-H, Song Y, Yu B. Indoleamine 2,3-dioxygenase 1 (IDO1) inhibitors in clinical trials for cancer immunotherapy. J Hematol Oncol. 2021;14(1):68.

    CAS  PubMed  PubMed Central  Google Scholar 

  143. Long GV, Dummer R, Hamid O, Gajewski TF, Caglevic C, Dalle S, et al. Epacadostat plus pembrolizumab versus placebo plus pembrolizumab in patients with unresectable or metastatic melanoma (ECHO-301/KEYNOTE-252): a phase 3, randomised, double-blind study. Lancet Oncol. 2019;20(8):1083–97.

    CAS  PubMed  Google Scholar 

  144. United States Food & Drug Administration. FDA approves Opdualag for unresectable or metastatic melanoma, 2022. Available from:

  145. Goldberg MV, Drake CG. LAG-3 in Cancer Immunotherapy. Curr Top Microbiol Immunol. 2011;344:269–78.

    CAS  PubMed  PubMed Central  Google Scholar 

  146. Maruhashi T, Sugiura D, Okazaki IM, Okazaki T. LAG-3: from molecular functions to clinical applications. J Immunother Cancer. 2020;8(2):e001014.

    PubMed  PubMed Central  Google Scholar 

  147. Baixeras E, Huard B, Miossec C, Jitsukawa S, Martin M, Hercend T, et al. Characterization of the lymphocyte activation gene 3-encoded protein. A new ligand for human leukocyte antigen class II antigens. J Exp Med. 1992;176(2):327–37.

    CAS  PubMed  Google Scholar 

  148. Maruhashi T, Okazaki IM, Sugiura D, Takahashi S, Maeda TK, Shimizu K, et al. LAG-3 inhibits the activation of CD4(+) T cells that recognize stable pMHCII through its conformation-dependent recognition of pMHCII. Nat Immunol. 2018;19(12):1415–26.

    CAS  PubMed  Google Scholar 

  149. Kouo T, Huang L, Pucsek AB, Cao M, Solt S, Armstrong T, et al. Galectin-3 shapes antitumor immune responses by suppressing CD8+ T cells via LAG-3 and inhibiting expansion of plasmacytoid dendritic cells. Cancer Immunol Res. 2015;3(4):412–23.

    CAS  PubMed  PubMed Central  Google Scholar 

  150. Wang J, Sanmamed MF, Datar I, Su TT, Ji L, Sun J, et al. Fibrinogen-like protein 1 is a major immune inhibitory ligand of LAG-3. Cell. 2019;176(1–2):334–47.e12.

    CAS  PubMed  Google Scholar 

  151. Schöffski P, Tan DSW, Martín M, Ochoa-de-Olza M, Sarantopoulos J, Carvajal RD, et al. Phase I/II study of the LAG-3 inhibitor ieramilimab (LAG525) ± anti-PD-1 spartalizumab (PDR001) in patients with advanced malignancies. J Immunother Cancer. 2022;10(2):e003776.

    PubMed  PubMed Central  Google Scholar 

  152. Huo J-L, Wang Y-T, Fu W-J, Lu N, Liu Z-S. The promising immune checkpoint LAG-3 in cancer immunotherapy: from basic research to clinical application. Front Immunol. 2022;13:956090.

    CAS  PubMed  PubMed Central  Google Scholar 

  153. Que Y, Fang Z, Guan Y, Xiao W, Xu B, Zhao J, et al. LAG-3 expression on tumor-infiltrating T cells in soft tissue sarcoma correlates with poor survival. Cancer Biol Med. 2019;16(2):331–40.

    PubMed  PubMed Central  Google Scholar 

  154. Monney L, Sabatos CA, Gaglia JL, Ryu A, Waldner H, Chernova T, et al. Th1-specific cell surface protein Tim-3 regulates macrophage activation and severity of an autoimmune disease. Nature. 2002;415(6871):536–41.

    CAS  PubMed  Google Scholar 

  155. Gao X, Zhu Y, Li G, Huang H, Zhang G, Wang F, et al. TIM-3 expression characterizes regulatory T Cells in tumor tissues and is associated with lung cancer progression. PLoS One. 2012;7(2):e30676.

    CAS  PubMed  PubMed Central  Google Scholar 

  156. Anderson AC, Anderson DE, Bregoli L, Hastings WD, Kassam N, Lei C, et al. Promotion of tissue inflammation by the immune receptor Tim-3 expressed on innate immune cells. Science. 2007;318(5853):1141–3.

    CAS  PubMed  Google Scholar 

  157. Ndhlovu LC, Lopez-Vergès S, Barbour JD, Jones RB, Jha AR, Long BR, et al. Tim-3 marks human natural killer cell maturation and suppresses cell-mediated cytotoxicity. Blood. 2012;119(16):3734–43.

    CAS  PubMed  PubMed Central  Google Scholar 

  158. Phong BL, Avery L, Sumpter TL, Gorman JV, Watkins SC, Colgan JD, et al. Tim-3 enhances FcεRI-proximal signaling to modulate mast cell activation. J Exp Med. 2015;212(13):2289–304.

    CAS  PubMed  PubMed Central  Google Scholar 

  159. Wolf Y, Anderson AC, Kuchroo VK. TIM3 comes of age as an inhibitory receptor. Nat Rev Immunol. 2020;20(3):173–85.

    CAS  PubMed  Google Scholar 

  160. Sakuishi K, Apetoh L, Sullivan JM, Blazar BR, Kuchroo VK, Anderson AC. Targeting Tim-3 and PD-1 pathways to reverse T cell exhaustion and restore anti-tumor immunity. J Exp Med. 2010;207(10):2187–94.

    CAS  PubMed  PubMed Central  Google Scholar 

  161. Yang R, Sun L, Li C-F, Wang Y-H, Yao J, Li H, et al. Galectin-9 interacts with PD-1 and TIM-3 to regulate T cell death and is a target for cancer immunotherapy. Nat Commun. 2021;12(1):832.

    CAS  PubMed  PubMed Central  Google Scholar 

  162. Rangachari M, Zhu C, Sakuishi K, Xiao S, Karman J, Chen A, et al. Bat3 promotes T cell responses and autoimmunity by repressing Tim-3–mediated cell death and exhaustion. Nat Med. 2012;18(9):1394–400.

    CAS  PubMed  PubMed Central  Google Scholar 

  163. Curtin JF, Liu N, Candolfi M, Xiong W, Assi H, Yagiz K, et al. HMGB1 mediates endogenous TLR2 activation and brain tumor regression. PLoS Med. 2009;6(1):e1000010.

    PubMed  PubMed Central  Google Scholar 

  164. Huang Y-H, Zhu C, Kondo Y, Anderson AC, Gandhi A, Russell A, et al. CEACAM1 regulates TIM-3-mediated tolerance and exhaustion. Nature. 2015;517(7534):386–90.

    CAS  PubMed  Google Scholar 

  165. Kammerer R, Stober D, Singer BB, Öbrink B, Reimann J. Carcinoembryonic antigen-related cell adhesion molecule 1 on murine dendritic cells is a potent regulator of T Cell stimulation. J Immunol. 2001;166(11):6537.

    CAS  PubMed  Google Scholar 

  166. Horst AK, Bickert T, Brewig N, Ludewig P, van Rooijen N, Schumacher U, et al. CEACAM1+ myeloid cells control angiogenesis in inflammation. Blood. 2009;113(26):6726–36.

    CAS  PubMed  Google Scholar 

  167. Coutelier J-P, Godfraind C, Dveksler GS, Wysocka M, Cardellichio CB, Noël H, et al. B lymphocyte and macrophage expression of carcinoembryonic antigen-related adhesion molecules that serve as receptors for murine coronavirus. Eur J Immunol. 1994;24(6):1383–90.

    CAS  PubMed  PubMed Central  Google Scholar 

  168. Koyama S, Akbay EA, Li YY, Herter-Sprie GS, Buczkowski KA, Richards WG, et al. Adaptive resistance to therapeutic PD-1 blockade is associated with upregulation of alternative immune checkpoints. Nat Commun. 2016;7:10501.

    CAS  PubMed  PubMed Central  Google Scholar 

  169. Acharya N, Sabatos-Peyton C, Anderson AC. Tim-3 finds its place in the cancer immunotherapy landscape. J Immunother Cancer. 2020;8(1):e000911.

    PubMed  PubMed Central  Google Scholar 

  170. Zang K, Hui L, Wang M, Huang Y, Zhu X, Yao B. TIM-3 as a prognostic marker and a potential immunotherapy target in human malignant tumors: a meta-analysis and bioinformatics validation. Front Oncol. 2021;11:579.

    Google Scholar 

  171. Pu F, Chen F, Zhang Z, Qing X, Lin H, Zhao L, et al. TIM-3 expression and its association with overall survival in primary osteosarcoma. Oncol Lett. 2019;18(5):5294–300.

    CAS  PubMed  PubMed Central  Google Scholar 

  172. Levine AJ. p53: 800 million years of evolution and 40 years of discovery. Nat Rev Cancer. 2020;20(8):471–80.

    CAS  PubMed  Google Scholar 

  173. Kandoth C, McLellan MD, Vandin F, Ye K, Niu B, Lu C, et al. Mutational landscape and significance across 12 major cancer types. Nature. 2013;502(7471):333–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  174. Zhu G, Pan C, Bei J-X, Li B, Liang C, Xu Y, et al. Mutant p53 in cancer progression and targeted therapies. Front Oncol. 2020;10:595187.

    PubMed  PubMed Central  Google Scholar 

  175. Miller M, Shirole N, Tian R, Pal D, Sordella R. The evolution of TP53 mutations: from loss-of-function to separation-of-function mutants. J Cancer Biol Res. 2016;4(4):1091.

    PubMed  PubMed Central  Google Scholar 

  176. Biton J, Mansuet-Lupo A, Pécuchet N, Alifano M, Ouakrim H, Arrondeau J, et al. TP53, STK11, and EGFR mutations predict tumor immune profile and the response to Anti–PD-1 in lung adenocarcinoma. Clin Cancer Res. 2018;24(22):5710–23.

    CAS  PubMed  Google Scholar 

  177. Dong Z-Y, Zhong W-Z, Zhang X-C, Su J, Xie Z, Liu S-Y, et al. Potential predictive value of TP53 and KRAS mutation status for response to PD-1 blockade immunotherapy in lung adenocarcinoma. Clin Cancer Res. 2017;23(12):3012–24.

    CAS  PubMed  Google Scholar 

  178. Yu X-Y, Zhang X-W, Wang F, Lin Y-B, Wang W-D, Chen Y-Q, et al. Correlation and prognostic significance of PD-L1 and P53 expression in resected primary pulmonary lymphoepithelioma-like carcinoma. J Thorac Dis. 2018;10(3):1891–902.

    PubMed  PubMed Central  Google Scholar 

  179. Assoun S, Theou-Anton N, Nguenang M, Cazes A, Danel C, Abbar B, et al. Association of TP53 mutations with response and longer survival under immune checkpoint inhibitors in advanced non-small-cell lung cancer. Lung Cancer. 2019;132:65–71.

    PubMed  Google Scholar 

  180. Lin X, Wang L, Xie X, Qin Y, Xie Z, Ouyang M, et al. Prognostic biomarker TP53 mutations for immune checkpoint blockade therapy and its association with tumor microenvironment of lung adenocarcinoma. Front Mol Biosci. 2020;7:602328.

    CAS  PubMed  PubMed Central  Google Scholar 

  181. Nassif EF, Auclin E, Bahleda R, Honoré C, Mir O, Dumont S, et al. TP53 mutation as a prognostic and predictive marker in sarcoma: pooled analysis of MOSCATO and ProfiLER precision medicine trials. Cancers (Basel). 2021;13(13):3362.

    CAS  PubMed  Google Scholar 

  182. Pérot G, Chibon F, Montero A, Lagarde P, de Thé H, Terrier P, et al. Constant p53 pathway inactivation in a large series of soft tissue sarcomas with complex genetics. Am J Pathol. 2010;177(4):2080–90.

    PubMed  PubMed Central  Google Scholar 

  183. Thoenen E, Curl A, Iwakuma T. TP53 in bone and soft tissue sarcomas. Pharmacol Ther. 2019;202:149–64.

    CAS  PubMed  PubMed Central  Google Scholar 

  184. Taubert H, Meye A, Würl P. Soft tissue sarcomas and p53 mutations. Mol Med. 1998;4(6):365–72.

    CAS  PubMed  PubMed Central  Google Scholar 

  185. Agersborg S, Jiang S, Chen W, Ma W, Albitar M. PD-L1 expression correlation with TP53 gene mutation status in lung cancer but not in colorectal cancer. J Clin Oncol. 2016;34(15):11557.

    Google Scholar 

  186. Cyster JG, Allen CDC. B Cell responses: cell interaction dynamics and decisions. Cell. 2019;177(3):524–40.

    CAS  PubMed  PubMed Central  Google Scholar 

  187. Wouters MCA, Nelson BH. Prognostic significance of tumor-infiltrating B cells and plasma cells in human cancer. Clin Cancer Res. 2018;24(24):6125–35.

    CAS  PubMed  Google Scholar 

  188. Dunn GP, Bruce AT, Ikeda H, Old LJ, Schreiber RD. Cancer immunoediting: from immunosurveillance to tumor escape. Nat Immunol. 2002;3(11):991–8.

    CAS  PubMed  Google Scholar 

  189. Inoue S, Leitner WW, Golding B, Scott D. Inhibitory effects of B cells on antitumor immunity. Can Res. 2006;66(15):7741–7.

    CAS  Google Scholar 

  190. Li Q, Teitz-Tennenbaum S, Donald EJ, Li M, Chang AE. In vivo sensitized and in vitro activated B cells mediate tumor regression in cancer adoptive immunotherapy. J Immunol. 2009;183(5):3195–203.

    CAS  PubMed  Google Scholar 

  191. Fridman WH, Meylan M, Petitprez F, Sun C-M, Italiano A, Sautès-Fridman C. B cells and tertiary lymphoid structures as determinants of tumour immune contexture and clinical outcome. Nat Rev Clin Oncol. 2022;19(7):441–57.

    CAS  PubMed  Google Scholar 

  192. Bruno TC, Ebner PJ, Moore BL, Squalls OG, Waugh KA, Eruslanov EB, et al. Antigen-presenting intratumoral B cells affect CD4(+) TIL phenotypes in non-small cell lung cancer patients. Cancer Immunol Res. 2017;5(10):898–907.

    CAS  PubMed  PubMed Central  Google Scholar 

  193. Kinker GS, Vitiello GAF, Ferreira WAS, Chaves AS, Cordeiro de Lima VC, Medina TDS. B cell orchestration of anti-tumor immune responses: a matter of cell localization and communication. Front Cell Dev Biol. 2021;9:678127.

    PubMed  PubMed Central  Google Scholar 

  194. Nielsen JS, Nelson BH. Tumor-infiltrating B cells and T cells: working together to promote patient survival. Oncoimmunology. 2012;1(9):1623–5.

    PubMed  PubMed Central  Google Scholar 

  195. Lee-Chang C, Bodogai M, Martin-Montalvo A, Wejksza K, Sanghvi M, Moaddel R, et al. Inhibition of breast cancer metastasis by resveratrol-mediated inactivation of tumor-evoked regulatory B cells. J Immunol. 2013;191(8):4141–51.

    CAS  PubMed  Google Scholar 

  196. Shao Y, Lo CM, Ling CC, Liu XB, Ng KT, Chu AC, et al. Regulatory B cells accelerate hepatocellular carcinoma progression via CD40/CD154 signaling pathway. Cancer Lett. 2014;355(2):264–72.

    CAS  PubMed  Google Scholar 

  197. Zhou X, Su YX, Lao XM, Liang YJ, Liao GQ. CD19(+)IL-10(+) regulatory B cells affect survival of tongue squamous cell carcinoma patients and induce resting CD4(+) T cells to CD4(+)Foxp3(+) regulatory T cells. Oral Oncol. 2016;53:27–35.

    CAS  PubMed  Google Scholar 

  198. Wang WW, Yuan XL, Chen H, Xie GH, Ma YH, Zheng YX, et al. CD19+CD24hiCD38hiBregs involved in downregulate helper T cells and upregulate regulatory T cells in gastric cancer. Oncotarget. 2015;6(32):33486–99.

    PubMed  PubMed Central  Google Scholar 

  199. Roya N, Fatemeh T, Faramarz MA, Milad SG, Mohammad-Javad S, Najmeh SV, et al. Frequency of IL-10+CD19+ B cells in patients with prostate cancer compared to patients with benign prostatic hyperplasia. Afr Health Sci. 2020;20(3):1264–72.

    PubMed  PubMed Central  Google Scholar 

  200. Lundberg A, Li B, Li R. B cell-related gene signature and cancer immunotherapy response. Br J Cancer. 2022;126(6):899–906.

    CAS  PubMed  Google Scholar 

  201. Sautès-Fridman C, Petitprez F, Calderaro J, Fridman WH. Tertiary lymphoid structures in the era of cancer immunotherapy. Nat Rev Cancer. 2019;19(6):307–25.

    PubMed  Google Scholar 

  202. Sautès-Fridman C, Lawand M, Giraldo NA, Kaplon H, Germain C, Fridman WH, et al. Tertiary lymphoid structures in cancers: prognostic value, regulation, and manipulation for therapeutic intervention. Front Immunol. 2016;7:407.

    PubMed  PubMed Central  Google Scholar 

  203. Dieu-Nosjean MC, Giraldo NA, Kaplon H, Germain C, Fridman WH, Sautès-Fridman C. Tertiary lymphoid structures, drivers of the anti-tumor responses in human cancers. Immunol Rev. 2016;271(1):260–75.

    CAS  PubMed  Google Scholar 

  204. Vanhersecke L, Brunet M, Guégan J-P, Rey C, Bougouin A, Cousin S, et al. Mature tertiary lymphoid structures predict immune checkpoint inhibitor efficacy in solid tumors independently of PD-L1 expression. Nat Cancer. 2021;2(8):794–802.

    CAS  PubMed  PubMed Central  Google Scholar 

  205. Lin Q, Tao P, Wang J, Ma L, Jiang Q, Li J, et al. Tumor-associated tertiary lymphoid structure predicts postoperative outcomes in patients with primary gastrointestinal stromal tumors. OncoImmunology. 2020;9(1):1747339.

    PubMed  PubMed Central  Google Scholar 

  206. Ladányi A, Kiss J, Mohos A, Somlai B, Liszkay G, Gilde K, et al. Prognostic impact of B-cell density in cutaneous melanoma. Cancer Immunol Immunother. 2011;60(12):1729–38.

    PubMed  Google Scholar 

  207. Goc J, Germain C, Vo-Bourgais TK, Lupo A, Klein C, Knockaert S, et al. Dendritic cells in tumor-associated tertiary lymphoid structures signal a Th1 cytotoxic immune contexture and license the positive prognostic value of infiltrating CD8+ T cells. Cancer Res. 2014;74(3):705–15.

    CAS  PubMed  Google Scholar 

  208. Cabrita R, Lauss M, Sanna A, Donia M, Skaarup Larsen M, Mitra S, et al. Tertiary lymphoid structures improve immunotherapy and survival in melanoma. Nature. 2020;577(7791):561–5.

    CAS  PubMed  Google Scholar 

  209. Groeneveld CS, Fontugne J, Cabel L, Bernard-Pierrot I, Radvanyi F, Allory Y, et al. Tertiary lymphoid structures marker CXCL13 is associated with better survival for patients with advanced-stage bladder cancer treated with immunotherapy. Eur J Cancer. 2021;148:181–9.

    CAS  PubMed  Google Scholar 

  210. Italiano A, Bessede A, Pulido M, Bompas E, Piperno-Neumann S, Chevreau C, et al. Pembrolizumab in soft-tissue sarcomas with tertiary lymphoid structures: a phase 2 PEMBROSARC trial cohort. Nat Med. 2022;28(6):1199–206.

    CAS  PubMed  Google Scholar 

  211. Helmink BA, Reddy SM, Gao J, Zhang S, Basar R, Thakur R, et al. B cells and tertiary lymphoid structures promote immunotherapy response. Nature. 2020;577(7791):549–55.

    CAS  PubMed  PubMed Central  Google Scholar 

  212. Germain C, Gnjatic S, Tamzalit F, Knockaert S, Remark R, Goc J, et al. Presence of B cells in tertiary lymphoid structures is associated with a protective immunity in patients with lung cancer. Am J Respir Crit Care Med. 2014;189(7):832–44.

    CAS  PubMed  Google Scholar 

  213. Barmpoutis P, Di Capite M, Kayhanian H, Waddingham W, Alexander DC, Jansen M, et al. Tertiary lymphoid structures (TLS) identification and density assessment on H&E-stained digital slides of lung cancer. PLoS One. 2021;16(9):e0256907.

    CAS  PubMed  PubMed Central  Google Scholar 

  214. Tan WCC, Nerurkar SN, Cai HY, Ng HHM, Wu D, Wee YTF, et al. Overview of multiplex immunohistochemistry/immunofluorescence techniques in the era of cancer immunotherapy. Cancer Commun (Lond). 2020;40(4):135–53.

    PubMed  Google Scholar 

  215. Messina JL, Fenstermacher DA, Eschrich S, Qu X, Berglund AE, Lloyd MC, et al. 12-Chemokine gene signature identifies lymph node-like structures in melanoma: potential for patient selection for immunotherapy? Sci Rep. 2012;2(1):765.

    PubMed  PubMed Central  Google Scholar 

  216. Jenkins RW, Barbie DA, Flaherty KT. Mechanisms of resistance to immune checkpoint inhibitors. Br J Cancer. 2018;118(1):9–16.

    CAS  PubMed  PubMed Central  Google Scholar 

Download references


Not applicable.


V.S.Y. is supported by the National Medical Research Council Transition Award (TA20nov-0020), SingHealth Duke-NUS Oncology Academic Clinical Programme (08/FY2021/EX/17-A47) and (08/FY2020/EX/67-A143), the Khoo Pilot Collaborative Award (Duke-NUS-KP(Coll)/2022/0020A), the National Medical Research Council Clinician Scientist-Individual Research Grant-New Investigator Grant (CNIGnov-0025) and the Terry Fox Foundation International Research Grant (I1056).

Author information

Authors and Affiliations



V.S.Y designed the work and guided the preparation of this manuscript. C.S.Y. and T.P.L. reviewed the literature and drafted the manuscript. C.S.Y., T.P.L, T.B.T., V.Y.L., and V.S.Y. reviewed and revised the manuscript. All authors have read and approved the final manuscript.

Corresponding author

Correspondence to Valerie Shiwen Yang.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yiong, C.S., Lin, T.P., Lim, V.Y. et al. Biomarkers for immune checkpoint inhibition in sarcomas – are we close to clinical implementation?. Biomark Res 11, 75 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: