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Intratumor heterogeneity: the hidden barrier to immunotherapy against MSI tumors from the perspective of IFN-γ signaling and tumor-infiltrating lymphocytes


In this era of precision medicine, with the help of biomarkers, immunotherapy has significantly improved prognosis of many patients with malignant tumor. Deficient mismatch repair (dMMR)/microsatellite instability (MSI) status is used as a biomarker in clinical practice to predict favorable response to immunotherapy and prognosis. MSI is an important characteristic which facilitates mutation and improves the likelihood of a favorable response to immunotherapy. However, many patients with dMMR/MSI still respond poorly to immunotherapies, which partly results from intratumor heterogeneity propelled by dMMR/MSI. In this review, we discuss how dMMR/MSI facilitates mutations in tumor cells and generates intratumor heterogeneity, especially through type II interferon (IFN-γ) signaling and tumor-infiltrating lymphocytes (TILs). We discuss the mechanism of immunotherapy from the perspective of dMMR/MSI, molecular pathways and TILs, and we discuss how intratumor heterogeneity hinders the therapeutic effect of immunotherapy. Finally, we summarize present techniques and strategies to look at the tumor as a whole to design personalized regimes and achieve favorable prognosis.


Immunotherapies have had promising effects on many cancer patients. In order to evaluate the response to immunotherapy, deficient mismatch repair (dMMR)/microsatellite instability (MSI) status has been widely exploited by practitioners, since it is found extensively across diverse types of cancer. dMMR/MSI is associated with improved outcomes independently of other clinical prognostic factors, such as disease stage [1]. Therefore, many clinical researchers suggest that dMMR/MSI contributes to high efficacy of immunotherapy in different tumor types [2,3,4].

Deficient MMR system and instable genomic status led to accumulation of somatic mutations, especially frameshift mutations [2], which generate subclones with neoantigens. These neoantigens are recognized as non-self and elicit anti-tumor responses including higher tumor-infiltrating lymphocyte (TIL) grade and expression of type II interferon (IFN-γ)-related genes, such as those encoding programmed cell death 1 ligand 1 (PD-L1), cytotoxic T lymphocyte-associated antigen-4 (CTLA-4), lymphocyte activation gene-3 (LAG-3) and indolamine-2,3-dioxygenase (IDO) [5, 6]. Nevertheless, as the depth of research grows, dMMR/MSI has been regarded as a double-edged sword in immunotherapy. That is, dMMR/MSI also correlates with resistance to immunotherapy, resulting from complex mechanisms such as frequent immunoediting of WNT/β-catenin signaling, antigen presentation machinery and IFN-γ signaling [7,8,9,10,11].

dMMR/MSI is one of the most important drivers of intratumor heterogeneity (ITH) [12], which refers to the different states within a tumor such as genomic instability, epigenetic abnormality, acetylation, gene expression dysregulation, post-translation modifications, biological behaviors, tumor microenvironment, T cell receptor and heterogeneous response to therapies [13]. ITH is present spatially and temporally. Spatial heterogeneity is defined as distinct genetic alterations and phenotypes between tumor cells; while temporal heterogeneity is embodied in the evolvement of subclones during natural tumor progressing and therapeutic interventions. Generally, tumors start out as a heterogeneous mixture, and immune selective pressure imposed by immunotherapy facilitates outgrowth of resistant clones and elimination of sensitive ones. ITH is found in a variety of tumors and predicts prognosis of targeted therapies [14].

ITH may result in sampling bias of biomarkers in cancer immunotherapy, such as programmed cell death protein-1 (PD-1), tumor mutation burden (TMB) and dMMR/MSI, and lead to entirely different clinical consequences. In other words, the current single tumor specimen underestimates the genomic spectrum variety across the tumor [15]. Different technologies have been invented to enable simultaneous deep analysis of single cells integrating genome, epigenome and transcriptome information [16]. ITH characterization is better than ever through bulk cell profile analysis and depiction of single cells in different regions via multiomics and is shown to significantly impact the immune response and prognosis of cancer patients (Fig. 1). Studies show that increased ITH is associated with worse anti-PD-1 therapy efficacy and “biomarker-oriented heterogeneity” determines drug sensitivity of each subclone [17,18,19]. These phenomena may explain why prognosis for a large proportion of patients remains poor after immunotherapy treatment with the target molecule. Therefore, ITH is a huge obstacle in treating tumors effectively.

Fig. 1

Progression of MSI tumor. In dMMR tumors, dysfunction in mismatch repair system cannot repair DNA mismatches, leading to DNA sequence alterations especially in microsatellites. With the accumulation of DNA sequence alterations, the tumor mutation burden gradually grows, and tumor cells are evolving into different subclones harboring heterogeneous neoantigens and characteristics. The application of immunotherapy eliminates many tumor cells and puts tumor under immune selection and immunoediting. Subclones which are resistant to immunotherapy grow out. Finally, the treatment-resistant primary tumor and metastases with heterogeneous subclones progress. Besides, status of MMR within a tumor is heterogeneous. MSS tumor cells may exist in dMMR/MSI tumors as well, and these cells do not respond to immunotherapy at the first place. As many of the MSI cells are eliminated, MSS tumor cells can grow out, leading to resistance to immunotherapy. Therefore, utilizing new detection methods to combat ITH is crucial to characterize tumor landscape

In this review, we discuss the two-sided effects of dMMR/MSI on immunotherapy. We summarize recent immunotherapy studies, including immune checkpoint blockade (ICB), adoptive cell transfer (ACT) and vaccine, and explore the effect of ITH on factors such as dMMR/MSI, TIL, IFN-γ and immune checkpoints. Due to the widespread effects of ITH in tumors [20], methods to combat spatial and temporal heterogeneity should be utilized to learn the big picture of tumor and guide therapy selection. We review the latest advances in single-cell sequencing and liquid biopsy, including circulating tumor DNA (ctDNA) and circulating tumor cells (CTC). Dynamic tumor cell profiling could translate into clinical applications for promising tumor therapy in the near future.

MSI plays a vital role in the generation of intratumor heterogeneity

dMMR/MSI is generalized across different cancer types, occurring with different frequencies and signatures. It is most commonly found in colorectal, endometrial and gastric cancers, but also in ovarian, cervical and prostate cancers [21,22,23,24,25] (Table 1). The MMR system consists of four major proteins: MLH1, MSH2, MSH6 and PMS2, which identify and correct DNA mismatches in the form of heterodimers: MLH1 couples with PMS2, PMS1 or MLH3 (forming MutLα, MutLβ or MutLγ complexes), and MSH2 couples with MSH6 or MSH3 (forming MutSα and MutSβ complexes) [26, 27]. MutSa could recognize DNA mismatched base errors, create a sliding clamp around DNA, undergo an ATP-driven conformational switch and subsequently bind MutLα to interact with enzymes such as DNA polymerase, excise the mismatch and resynthesize DNA [27,28,29] (Fig. 2). Germline mutations in MMR genes, epigenetic hypermethylation of MMR gene promotor or biallelic somatic inactivation of MMR genes could lead to loss of MMR protein expression [30]. Among them, loss of MLH1 and/or PMS2 occurs at higher frequency than loss of MSH2 or MSH6, and loss of MLH1/PMS2 co-expression is more common than loss of MSH2/MSH6 co-expression [31] (Table 2). Tumors with at least one MMR protein loss by immunohistochemical (IHC) detection are called dMMR tumors, in contrast to MMR-proficient (pMMR) tumors. And generally, loss of MLH1 or MSH2 leads to degradation of PMS2 or MSH6, respectively [29]. A deficient MMR system is likely to cause DNA sequence alterations especially in microsatellites, which are short tandem repeats scattered throughout the genome. An accumulation of errors in the microsatellites is called MSI, a hypermutator phenotype associated with hereditary and sporadic tumors [27]. Based on microsatellite loci analysis, tumors with an instability of at least two loci out of BAT-25, BAT-26, D2S123, D5S346, D17S250 (Bethesda panel) or three loci out of BAT-25, BAT-26, NR-21, NR-24, NR-27 (Pentaplex panel) are considered as MSI, in contrast to microsatellite stable (MSS) [2, 28].

Table 1 Frequency of dMMR/MSI across tumors
Fig. 2

The mechanism of normal MMR system and dMMR/MSI. a The MMR system consists of four major proteins: MLH1, MSH2, MSH6 and PMS2. They work in the form of heterodimers: MLH1 couples with PMS2 (MutLα), and MSH2 couples with MSH6 (MutSα). MutSα recognizes DNA mismatched base errors, creates a sliding clamp around DNA, undergoes an ATP-driven conformational switch and subsequently binds MutLα. The complexes interact with enzymes including DNA polymerase to excise the mismatch and resynthesize DNA. b Germline mutations in MMR genes, epigenetic hypermethylation of MMR gene promotor or biallelic somatic inactivation of MMR genes could lead to loss of MMR protein expression and deficient MMR system. dMMR is likely to cause DNA sequence alterations in microsatellites, and accumulation of which is called MSI. TA-dinucleotide repeats are unstable and expanded in dMMR/MSI cells. These cells are dependent on WRN to maintain genome stability, and avoid TA-dinucleotide repeats cleavage and chromosome shattering

Table 2 Frequency of loss of MMR proteins across tumors

BRAF V600E mutation is often associated with MLH1 promoter hypermethylation, resulting in simultaneous loss of MLH1 and PMS2, which has been reported in 70% of dMMR/MSI tumors [24, 32]. BRAF mutation is related to negative prognosis in CRC, but due to its strong association with MSI phenotype, studies found that the positive prognosis impact of MSI could alleviate or overcome the negative effect [33, 34]. Furthermore, immunotherapy combined with BRAF inhibitor has been found to benefit patients with BRAF mutation, providing additional treatment target for patients unlikely to have long-lasting response to immunotherapy alone [35]. Moreover, the latest studies found that TA-dinucleotide repeats were highly unstable in dMMR/MSI cells and underwent large-scale expansions. Werner helicase (WRN), a member of the RecQ family of DNA helicases crucial for maintaining genome stability, was important to avoid TA-dinucleotide repeats cleavage and massive chromosome shattering [36], indicating WRN as a synthetic lethal vulnerability for dMMR/MSI tumors. Indeed, the dependency of WRN was observed widespread in dMMR/MSI tumors [37]. WRN knockout could induce double-strand DNA breaks, and selectively impair the viability of dMMR/MSI cells by nuclear abnormalities and cell division defects, which might be influenced by the loss of MSH2 or MLH1 [38, 39] (Fig. 2). Due to the finding that WRN dependency was associated with resistance to immunotherapy in dMMR/MSI CRC models [40], WRN may serve as a potential target for treating dMMR/MSI tumors.

Essentially, dMMR/MSI facilitates the process of mutations in tumor cells and propels ITH, leading to the immune evasion of tumors [41, 42]. A systemic review by European Society for Medical Oncology described high percentages of concurrence of TMB-high and MSI-high in cancers such as colorectal cancers and endometrial cancers [43]. In an analysis of glioma, defects in mismatch repair (MMR) genes were found to play a vital role in the pathways to high tumor mutational burden [44]. Even though TMB has been used as a predictor for immunotherapy response, researches have noticed that tumors with equally high TMB levels presented with diverse immune response [45]. A key cause is that TMB resulted from increased genomic instability is considered the fundamental contributor of ITH [12]. In a mouse model, researchers managed to uncouple effects of ITH and TMB, and they discovered that ITH can be a predictor of immunotherapy response independent of TMB [46]. During tissue repair, inflammation and injury-induced cell turnover may inevitably lead to mutation acquisition; subsequently, mutations generated through this process are faced with natural selection pressure by the host’s immune response (Fig. 1). With the joint effort of intratumoral competition and immunoediting, this evolutionary process may result in ITH with a unique mutational composition across the lesion [47]. One study found that most mutational signatures are ubiquitous between normal colon cancer recesses and adjacent normal recesses and sporadic mutations are not significantly different either. Nevertheless, mutations in specific genes (BRAF, APC, KRAS, TP53, etc.) are more frequent in those with colon cancer [48].

Immunotherapy not only acts as a strong immune selection pressure through which subclones bearing pre-existing resistant phenotype grow out, but also generates new subclone driver events [41, 49]. This change in mutation landscape after treatment contributes to temporal intratumor heterogeneity, and temporal response and follow-up are especially important in response to treatment; while change of the subclones is bound to change in the immune response. In colorectal cancer associated with colitis, cancer cells undergo genetic mutations in the early stage of tumorigenesis [50]. In some cancer types, the driver mutations and DNA methylation level may be determined in the early stage of tumorigenesis [51, 52]. In polyclonal tumors, significant tumor heterogeneity is discovered by seeding the initiating sublineages at the early stage [42]. In some other tumors, tumor evolution in branched sublineages makes up most driver mutations of tumorigenesis [53]. No matter what evolution process the tumor takes, they present ITH. In studies covering several cancer types, ITH has been deemed as a symbol of tumor progression, as high ITH often correlates with decreased immune activity and exhausted immune microenvironment [44, 54, 55]. ITH in the expression level of IFN-γ and TILs influences the efficacy of immunotherapy. Among diverse groups of TILs, our review focuses on tumor-infiltrating T cells that are directly linked to cytotoxic effects against tumor cells and their ITH is well studied.

IFN-γ is a major member of the IFN cytokine superfamily produced by T cells and nature killer (NK) cells upon the recognition of tumor antigens. It has a wide range of biological functions such as antivirus, anti-tumor and immune regulation, through induction of multiple proteins via IFN-γ stimulated genes (ISGs). With the discovery that the expression of PD-L1 within tumors is focal and heterogeneous both spatially and temporally [47, 56, 57], other studies on ITH of IFN-γ signaling have been published in succession. In the lung adenocarcinoma (LUAD) patient-derived xenografts (PDXs), Ke-Yue Ma et al. discovered that IFN-γ signaling pathway genes were heterogenous and coregulated with other immune-related genes including PD-L1, MHCII and IDO. The downregulation of IFN-γ signaling is associated with an acquired phenotypic resistance [58].

Somatic mutations of tumor are essential for neoantigen expression and consequent immune infiltration [2, 59]. Antigen-presenting cells and TILs play an indispensable role in recognizing tumor neoantigens and generating cytotoxic effects against tumor cells. The process of neoantigen presentation and mechanisms by which tumor cells evade immune recognition have been reviewed elsewhere [60]. Among TILs, ITH of the T cell repertoire has been widely recognized, and T cell clusters bring about pivotal and direct effects on tumors, which is the focus of this review. The two-sided role of B cells and the antibody repertoire has been delineated elsewhere [61]. For patients who respond to immunotherapy, the vanished tumor neoantigen is in line with the expansion of TIL clonotypes [62]. Theoretically, the greater the mutation burden of a tumor, the stronger the provoked immune response. TMB, a biomarker reflecting the mutation degree of tumor cells, is positively linked with the prognosis of patients receiving immune checkpoint inhibitors in many cancer types [63, 64]. However, growing heterogeneity in intratumoral neoantigens leads to increasing heterogeneity in TILs against tumor cells and in the immune microenvironment [65,66,67]. A study found liver cancer evolved from different liver diseases may have a distinctive T cell receptor (TCR) repertoire [68]. Consequently, the T cell repertoire coevolves with the tumor cell mutations, and gradually manifests a landscape distinct from those in adjacent normal tissue [69, 70].

The specificity of infiltrating T cells against tumor cells originates from the T cell receptor. Through TCR sequencing, intratumoral T cell heterogeneity with respect to infiltration status, clonality and TCR repertoire was fully characterized in various tumor types. Both spatial and temporal heterogeneity of the immune composition and TCR repertoire in the tumor microenvironment may be pivotal to the fundamentally different responsiveness and prognosis under immunotherapies, as seen in Table 3. The immune responses of different clusters of infiltrating T cells against a tumor are heterogeneous. In one study, clonality and accumulation of high-frequency clonotypes were higher in CD8 + TILs than those of CD4 + TILs, while a higher amount of TCR repertoire diversity was discovered in CD4 + TILs [71]. The complex architecture inside tumors may further complicate the intratumor TCR heterogeneity [72]. Dynamic evaluation of the temporal heterogeneity of TCR repertoire has also been used to reflect immune status, predict distant metastasis after treatment and indicate prognosis [73,74,75]. The varied vascular and lymphatic spatial distribution may lead to different accessibility to oxygen and nutrients across different regions that shape the microenvironments holding T cells resulting in differing quantities, functions and reactions to neoantigens [72, 76].

Table 3 Representative studies revealing ITH of tumor-infiltrating lymphocytes

The expression of different immunologic elements has long been associated with the prognosis of cancer patients [77,78,79]. With high TMB and ensuing immune cell infiltration, MSI tumors fall into the type 1 microenvironment according to the category proposed by O'Donnell et al. [80]. As for these tumors, ITH of IFN-γ and TIL may be a pivotal factor leading to resistance against immunotherapy.

dMMR/MSI facilitates immunotherapy through a pre-existing immunoreactive microenvironment

In a recent meta-analysis covering 14 studies, immune checkpoint inhibitors showed encouraging potential in multiple cancer types with dMMR/MSI [81, 82]. While combining Nivolumab with CTLA-4 blockade Ipilimumab exhibits a robust response and improved efficacy [83]. Many other studies have also demonstrated the positive value of dMMR/MSI for immunotherapy, as shown in Table 4. To explore the underlying mechanism, first we need to understand the foundation of effective immunotherapy, which includes: effective antigen presentation by antigen-presenting cells (APC), followed by continuous activation and infiltration of T cells to construct a positive immune microenvironment. In cancer patients without treatment, CD8 + TILs specific to ubiquitously expressed tumor antigens manifest as a dysfunctional phenotype [66]. Immunotherapy triggers the reactivation of the immune system, giving it the ability to identify and react to neoantigens and revitalizing the cytotoxic effect of the pre-existing TIL clonalities [65, 84, 85]. Another premise is sufficient IFN-γ production and responsive IFN-γ signaling. Through this IFN-γ subsequently induces an anti-tumor immune response through: (1) upregulation of antigen processing molecules, MHCI/II and antiangiogenic chemokines (2) recruitment of T cells and other immune cells (3) direct antiproliferative and pro-apoptotic effects [86, 87]. As for ICB, an additional condition is the upregulation of the target immune checkpoint. Continuous IFN-γ exposure induces upregulation of immune checkpoints including PD-L1, CTLA-4, IDO and LAG-3 [87,88,89,90,91], of which the immunosuppressive effect is abrogated and only positive factors come into play in the context of ICB therapy (Fig. 3).

Table 4 Biomarkers predicting better response to immunotherapy
Fig. 3

dMMR/MSI facilitates immunotherapy through a pre-existing immunoreactive microenvironment. a dMMR/MSI facilitates immunotherapy through: upregulation of IFN-γ signaling; upregulation of MHCI/II and CXCL9/10/11; recruitment of immune cells; direct antiproliferative and pro-apoptotic effects of IFN-γ. b IFN-γ induces the expression of immune checkpoints including PD-L1, CTLA-4, LAG-3 and IDO, providing targets for ICB. c Silencing IFN-γ signaling to weaken PD-1-PD-L1 interactions helps improve potency of ACT monotherapy. While ICB could improve therapeutic efficacy of ACT through functional IFN-γ signaling. d Vaccination with dMMR/MSI-induced antigens could eliminate dMMR/MSI tumor cells and prevent outgrowth of undetected dMMR/MSI subclones

Regardless of origin and type [59], dMMR/MSI tumors are susceptible to immunotherapy owing to: (1) high TMB (2) high TIL in both tumor and tumor-adjacent tissues [59, 92] (3) upregulation of PD-1 and IFN-γ signatures (PD-L1, CTLA-4, LAG-3 and IDO) representing an adaptive resistance to the immunoreactive microenvironment induced by MSI [5, 6]. All these three aspects are positive predictive markers [57, 93,94,95] of which TMB could be considered as the initiating factor. Both cancers with the strongest response to PD-1 blockade have a high degree of mutation, including lung cancer and melanoma [3, 4, 57, 96]. In addition, TMB significantly contributes to a sustained clinical benefit from CTLA-4 blockade in melanoma [97]. With a high mutation load and increased immunogenicity, dMMR/MSI tumors possess abundant infiltration with activated CTL and Th1. They have high expression of cytotoxic genes encoding IFN-γ, signal transducers and activators of transcription 1 (STAT1), interferon regulatory factor 1 (IRF1) and IL18 [5, 98], and more frequent apoptosis of neoplastic cells attributed to both high TIL and intrinsic genetic instability [99]. Higher TIL grade is shown to be associated with better outcomes in different tumor types, including melanoma and CRC [100,101,102], and intrinsically linked to the response against immune checkpoint inhibitors [103,104,105]. Despite enhancing tumor immunogenicity, mutator phenotypes with upregulated immune checkpoints could also favor immune evasion and counterbalance the pre-existing anti-tumor immune microenvironment, particularly given the IFN-γ-induced adaptive response. Nevertheless, upregulated immune checkpoints provide targets for ICB to re-invigorate the immune response. In addition, mutations of KRAS and TP53, although not prevalent in MSI tumors, and respectfully favor tumor proliferation and deregulate DNA repair [106,107,108], TP53 mutation was found to increase expression of immune checkpoints, effector T cells and IFN-γ signature; furthermore, TP53/KRAS co-mutated subgroup manifested increased expression of PD-L1 [109, 110]. Together they may serve as potential predictive biomarker for immunotherapy. Further, WRN dependency was found to be associated with resistance to immunotherapy, in other words, WRN inhibitor may be synergic with immunotherapy, as it increases the genetic instability, and modulates the neoantigen landscape to enhance immune response [40]. Another underlying mechanism facilitating immunotherapy may be higher microvessel density (MVD) found in dMMR/MSI tumors [92], which enables increased lymphocyte extravasation. However, considering angiogenesis benefits for tumor growth, an in-depth study on MVD and MSI is highly recommended.


ICB is one of the most promising anti-tumor immunotherapies to this day. The two most promising targets are CTLA-4 and the interaction of PD-1 and PD-L1. Upregulation of these immune checkpoints is an adaptive resistance associated with poor prognosis [111] and actually represents a strong pre-existing anti-tumor response, based on which ICB is applied to re-invigorate the immune response [57, 112, 113]. dMMR/MSI has been found to promote ICB efficacy in multiple tumor types, including glioblastoma multiforme [114], urothelial tract cancer [115], melanoma [57, 97], endometrial cancer [59], non-small cell lung cancer (NSCLC) [57, 112] gastric cancer [116] (Table 4).

It is believed that oligoclonal expansion of the TIL repertoire is a symbol of low TCR affinity and T cell exhaustion [117], while an appropriate level of TIL heterogeneity may be the foundation of ICB and ACT [118]. In this scenario, ICB could rejuvenate the TCR repertoire extensively rather than focusing only on several T cell epitopes, resulting in more T cells responding to ubiquitous neoantigens, enhancing overall immune competence in the anti-tumor response and leading to most clinically significant responses [119, 120]. Additionally, CD4 + T cells that stimulate and suppress the immunity of CD8 + T cells coexist in the tumor microenvironment [121]. While Tregs are regarded as suppressive regulators in tumor immunology and a biomarker of poor prognosis [122], they still possess specific reactivity against tumor antigens, facilitating CTLA-4 therapy [123]. Although PD-1 indicates negative regulatory function and exhaustion of peripheral T cells induced by the PD-1 signaling pathway and may contribute to the decreased diversity of T cell repertoire [124, 125], CD8 + T cells may function efficiently after PD-1 immunotherapy [126, 127]. Therefore, even though TILs are considered an immunosuppressive phenotype, they possess substantial capacity to induce a cytotoxic effect against tumor cells and their potential proliferation [121].

Among the various cytokines, IFN-γ is the main factor that induces upregulation of PD-L1 [128]. JAK1/2–STAT1/2/3–IRF1 pathway is the most important signaling cascade that is involved [129]. When IFN-γ binds to its receptors interferon-gamma receptor 1/2 (IFNGR 1/2), it increases the level of IFN-stimulated noncoding RNA 1 (INCR1)—a major regulator of IFN-γ signaling by modulating post-transcriptional JAK expression [130]. The subsequent activation of JAK1/2 leads to phosphorylation and dimerization of the downstream signal transducers and activators of transcription (STATs). Then the downstream transcription factors IRFs bind to their response elements IRF-1 response elements 1/2 (IRE1/2) in the upstream 5′-flanking region of the PD-L1 gene promoter [131] and induce PD-L1 upregulation (Fig. 3). A positive correlation between IRFs and PD-L1 mRNA expression was found in hepatocellular carcinoma (HCC) [131]. Similar to PD-L1, the expression of CTLA-4 in human melanoma cells is also regulated by IFN-γ through the JAK1/2-STAT1-IRF1 pathway [132]. CTLA-4 induces antiproliferation of T cells, Tregs activation and upregulation of IDO [133], playing a negative role in anti-tumor immune response. Therefore, anti-CTLA-4 therapy is utilized to increase the ratio of effector T cells to Tregs [87], and, in turn, upregulate IFN-γ production. Higher expression of PD-L1 and IDO predicts a superior response to PD-1 blockade and CTLA-4 blockade (ipilimumab), respectively [57, 134, 135], emphasizing the role of IFN-γ-induced IDO in immune checkpoint blockade therapy. Additionally, IFN-γ can induce MHCII expression, which is correlated with multiple important prognostic pathways and better overall survival rate [58]. In melanoma, MHCII expression is a predictor for anti-PD-1 and anti-PD-L1 response [136]. Altogether, high expression of IFN-γ signaling indicates long-term benefits from ICB [89, 116, 137]. In line with the relationship between PD-L1, CTLA-4, IDO and immunotherapy discussed herein, targeting LAG-3 strongly stimulates CD8 + T cell infiltration and IFN-γ secretion [138, 139], suggesting the possibility of an alternative immunotherapy. Interestingly, blockade of a single immune checkpoint could lead to upregulation of others [140]. For example, inhibition of LAG-3 improves the efficacy of PD-1 blockade in several mouse cancer models [141,142,143,144], indicating the better efficacy of combinatorial ICB.


Efficacy of targeting a ubiquitous tumor antigen in adoptive cell therapy has been demonstrated [145]. Specific TCR-transduced T cells are clinically effective in treating patients with metastatic synovial sarcoma [7], while exploiting TILs to recognize multiple tumor neoantigens is effective in single-patient studies on several tumors [70]. Targeting several tumor antigens is an ideal scenario, which circumvents tumor escape mechanisms such as tumor heterogeneity and constructs a focused TIL repertoire against tumor cells [146].

However, the bottleneck of ACT is unable to address T cell migration and abnormal function at tumor sites. A recent study showed that PD-1 expression on transferred T cells could be induced by tumor environment [147], indicating that downregulation of immunosuppressive factors and silencing IFN-γ signaling to weaken PD-1-PD-L1 interactions may help improve potency. INCR1 knockdown cells are more susceptible to cytotoxic T cell-mediated death compared to controlled cells [130]. However, PD-1 blockade could improve therapeutic efficacy of ACT by enhancing T cell proliferation of T cells and upregulating IFN-γ [147, 148]. Importantly, functional IFN-γ signaling could induce chemokine (C-X-C motif) ligand 10 (CXCL10) to recruit more activated T cells and trigger a positive feedback loop [147] (Fig. 3). In addition, PD-1 blockade could increase the activation and proliferation of CAR-T cells in vitro and regress tumor growth in vivo through enhancing their anti-tumor effect and reducing myeloid-derived suppressor cells at tumor sites [149]. Noteworthy, a recent study also revealed that recurrent melanoma after ACT treatment exhibited high expression of IFN-γ signaling (PD-1, PD-L1, CTLA-4, though the picture was heterogeneous), which provided tractable targets for salvage immunotherapy, and indeed allowed for effective ICB [150]. As mentioned, IFN-γ plays an intricate role in ACT. ACT treatment outcomes are different when combined with other therapies due to the heterogeneity of IFN-γ signaling.

Vaccination with dMMR/MSI-induced antigens

MMR-deficient subclones progress to manifest dMMR/MSI cancer lesions despite strong immunogenicity and immune surveillance due to upregulation of immune checkpoints and mutations favoring immune evasion. ICB remarkably benefits outcomes of dMMR/MSI tumors; in non-responders, combined with other immune-supportive approaches, it is expected to turn “cold” tumors into “hot” ones and improve the response rate. dMMR/MSI triggers frequent generation of frameshift mutations and gives rise to highly immunogenic frameshift-derived peptides (FSP), which contain multiple immunologically relevant neoepitopes [151]. These neoantigens are tumor-specific and shared by most MSI tumors [152]. A vaccine based on these neoantigens could be designed to prevent outgrowth of undetected dMMR/MSI subclones in pMMR tumors. A clinical Phase I/IIa trial found three commonly mutated FSPs (derived from genes AIM2, HT001 and TAF1B (NCT01461148), of which 98.5% of all MSI CRCs harbor at least one mutation [152]. Theoretically, immune response directed against FSPs can be induced in the majority of MSI CRCs, and the study results confirmed that this FSP vaccination was well tolerated and consistently induced immune responses [153]. The latest research analyzed 320 MSI tumors and selected 209 FSPs to generate a vaccine referred to as Nous-209. The vaccine induced IFN-γ + FSP-specific T cells in vaccinated mice and exhibited strong immunogenicity [154]. Its safety, tolerability and immunogenicity are currently under clinical evaluation in mCRC, gastric and gastro-esophageal cancer patients in combination with Pembrolizumab (NCT04041310) (Table 5). Vaccination with frameshift-derived neoantigen-loaded DC is also under investigation in MSI CRCs and persons who are known to harbor germline MMR gene mutation but without diseases yet (NCT01885702). Despite the therapeutic implications for MSI tumors, this trial could also explore the preventive significance of FSP vaccine for people with MMR mutations. Of note, a vaccine targeting these FSP antigens could broadly eliminate dMMR/MSI tumor cells despite the ITH and rapid tumor evolution, since these mutations are driver events at early stage of tumorigenesis [155, 156] (Fig. 3). Moreover, an IDO-derived peptide vaccine activates IDO-specific T cells which recognize and kill both tumor cells and immunosuppressive dendritic cells in vitro, significantly improving overall survival in III/IV NSCLC patients [157]. As combination therapy may have a synergistic effect due to distinct mechanisms of action, clinical trials are also underway to combine IDO and PD-L1 peptide vaccine with PD-1 blockade to treat metastatic melanoma (NCT03047928). Vaccines based on other upregulated antigens in dMMR/MSI tumors warrant further investigation.

Table 5 Ongoing clinical trials investigating immunotherapy in dMMR/MSI tumors

When developing vaccines, a suitable vehicle of transmission can greatly enhance the therapeutic effect. Nanoparticles have been the promising vehicle of vaccine. They are endowed with outstanding physiochemical properties, such as high tissue specificity, manageable surface chemistry and big specific surface area [158]. The nanoparticles can be the vehicle of certain bioactive substance such as PD-L1 inhibitory peptide [159], or be developed with certain features to cause damage to tumor cells [160]. A latest review summarizes two main mechanisms that contribute to the anti-tumor effects of immunotherapy based on nanotechnology: one is to elicit an efficient immune response against tumor during tumorigenesis, while the other is to turn the “cold” immune-suppressive tumor microenvironment into a “hot” immune activated [158].

When exploring treatments for tumor, components of TME such as macrophages, fibroblasts or even tumor vasculature and tumor-draining lymph nodes can be targets of nanoparticles [161]. A vaccine was designed to deliver antigenic microparticle, which transformed tumor infiltrated macrophages into a tumor-suppressive M1 phenotype, and activated strong host immune response against tumor [162]. To enhance the specificity of nanoparticles, particular conditions are used to stimulate the function of the materials. A type of supramolecular gold nanorods can be activated by the second near-infrared-window (NIR-II) light. The nanorods are designed to be the vehicle of CRISPR/Cas9, and they can disrupt PD-1 gene expression of the tumor cells and facilitate immunogenic cell death when irradiated by NIR-II laser [163]. Some other nanoparticles can be released from membrane when entering a microenvironment with specific pH. A short interfering RNA named siFGL1 delivered by nanoparticles with hybrid biomimetic membrane can efficiently silence the FGL1 gene, which is triggered by pH [164]. Whether employed independently or in combination with other immunotherapies as adjuvant, these nanomaterials can enhance immune responses and exhibit anti-tumor efficacy [160, 164].

dMMR/MSI fuels ITH and also correlates with resistance to immunotherapy

Despite improved efficacy in dMMR/MSI tumors, reported response rates to ICB are variable and often < 50% [95]. What differentiates responders from non-responders? As discussed above, intratumor heterogeneity caused by dMMR/MSI can be a determinant factor leading to the unfavorable response and poor prognosis.

ITH impairs the quality of TIL response and impedes immunotherapy

Although more diversified intratumoral TCRs may be generated in the context of dMMR/MSI, they are not always associated with better clinical outcome [65, 66]. It has long been recognized that tumor progression is accompanied by an increase in tumor mutation load, and the inevitable generation of tumor neoantigens [165]. High ITH is connected to tumor progression and resistant to therapies in many cancer types [47]. Heterogeneity in tumor antigen and immune cells is also significant among melanoma metastases, which leads to different responses to immunotherapy [166]. Excessive expression of subclonal neoantigens may lead to the relatively low expression levels of neoantigens, and T cells may be unable to encounter and activate against those low-frequency neoantigens [167]. Moreover, TCR repertoire diversity is associated with inadequate expansion of TCR clones and deficient infiltration into tumors, which may result from the immunosuppressive state of T cells caused by T cell exhaustion, low TCR affinity, etc. [168, 169]. A higher degree of TCR ITH and consequent clonotypes with low frequencies were revealed in different kinds of tumors and were linked with unfavorable prognosis [65, 170, 171]. Besides, some TILs have lost their functions owing to other dysfunction during the process of immune response. For instance, the tumor antigen TILs previously recognized can be depleted following immunoediting [172, 173], and deprivation of the presenting MHC allele can disrupt antigen presentation [174, 175] (Fig. 4). Therefore, same as above, heterogeneity in the quality of T cell responses, instead of the quantity, may be a determinant factor in anti-tumor response [65].

Fig. 4

Negative effect of ITH in MSI tumor under immunotherapy. a In MSI tumors, hyperactivation of WNT/β-catenin signaling suppresses effector T cells function by reducing IFN-γ. Mutations in JAK and STAT result in impaired IFN-γ signaling and lack of induced MHC class I expression. Moreover, JAK1/2 controls chemoattractant such as CXCL9, CXCL10 and CXCL11, and mutations in JAK1/2 cause lack of downstream T cell infiltration. β2M gene mutations lead to impaired MHC class I function and knockdown of INCR1 decreases PD-L1 expression. Dysfunction of IFN-γ signaling results in lack of PD-L1 expression which leads to PD-L1 blockade out of target, and defective migration of adoptive T cells into tumors in melanoma thereby reducing the efficacy of ICB. b Appropriate level of neoantigen ITH leads to adequate TCR expansion, sufficient infiltration and high TCR affinity, which lead to cytotoxic effects of immunotherapy. In the other hand, excessive expression of neoantigen ITH leads to inadequate TCR expansion, insufficient infiltration and T cell exhaustion, which result in inefficient immunotherapy

Impact of IFN-γ signaling heterogeneity on immunotherapy

Provided that IFN-γ signaling displays a degree of heterogeneity and its downregulation correlates with an acquired resistance phenotype, alterations of essential components within IFN-γ signaling pathways could modify therapeutic efficacy. Recent studies demonstrate that INCR1 is transcribed as an antisense RNA from the PD-L1/PD-L2 locus and knockdown of INCR1 decreases PD-L1 expression [130]. JAK1/2-deficient cells emerged under/after ICB in patients with advanced melanoma and obtained resistance to PD-L1 blockade, which may result from pre-existing heterogenous subclones or through an adaptive response [9, 176, 177]. JAK loss is possibly correlated with lack of T cell infiltration based on the findings that factors downstream of JAK1/2 controls chemokines with chemoattractant effect on T cells, such as CXCL9, CXCL10 and CXCL11 [113, 178]. Also, high expression of PD-L1 significantly correlates with an objective response to PD-L1 blockade compared to PD-L1 negative patients [112, 113]. Altogether, dysfunction of IFN-γ signaling leads to the lack of PD-L1 expression, resulting in off-target of PD-L1 blockade, and less T cell infiltration for an anti-tumor effect (Fig. 4). Consistent with what’s described above, an interesting study mixed IFN-γ-insensitive tumor cells of melanoma with wild type (WT) tumor cells to mimic ITH. IFN-γ-insensitive cells finally grow out in the context of anti-PD-L1 therapy as a result of (1) failure to activate positive immune response by IFN-γ (2) lack of PD-L1 upregulation as the treatment target (3) immunodepressive microenvironment because of PD-L1 provided by WT. Moreover, IFN-γ could push the tumor further toward the IFN-γ-insensitive cells [179].

In addition, the JAK mutation contributes to the primary resistance to anti-PD-1 therapy in patients with advanced melanoma and colon cancer despite having a high mutation load [59, 96, 180, 181]. In previous studies, copy number alterations (CNAs) and single-nucleotide variants (SNVs) of IFN-γ signaling including loss of IFNGR1/2, JAK1/2, IRF1, as well as amplification of important IFN-γ pathway inhibitors SOCS1 and PIAS4, were found in patients with metastatic melanoma resistant to anti-CTLA-4 therapy. In addition, CXCL10 is reduced compared to the IFN-γ responsive cells [177]. Moreover, the heterogeneity of MHC expression on tumor cells and its lack of coordination with IFN-γ signaling have a significant impact on ICB. In sum, expression of IFN-γ strongly correlates with the response to ICB [182] and has validated in several studies. Deficiency of IFN-γ signaling can weaken the effect of positive immunoregulation in multiple aspects, thereby reducing efficacy of ICB. Diverse subclones carrying heterogenous IFN-γ signaling within tumors have an impact on drug response and should be considered when selecting therapeutic regimens. Given that CTLA-4 blockade leads to increased production of IFN-γ and thereby upregulating PD-L1, combination with PD-L1 blockade could make a better clinical response; and combination with new immune-related targets needs to be studied unremittingly in the future.

Mutations in JAK and STAT result in impaired IFN-γ signaling, lack of induced MHC class I expression, as well as inhibition of the WNT signaling pathway [11, 183]. A study investigating immune evasion in 1,211 CRC patients found that non-responsive dMMR/MSI patients frequently underwent immunoediting through upregulated WNT/β-catenin signaling and complete disruption of key genes in the antigen presentation pathway [7, 8]. High WNT signaling with mutations of β-catenin is inversely correlated with TIL independent of high TMB in melanoma and CRC, thereby reducing the efficacy of ICB [7, 184]. Other studies found that hyperactivation of WNT/β-catenin signaling suppressed effector T cells function by reducing IFN-γ [185] and led to defective migration of adoptive CD8 + T cells into tumors in melanoma [186]. This indicates that WNT signaling inhibitors may reverse immune evasion to facilitate immunotherapy. Approximately 30% of dMMR/MSI CRC display gene alterations of β2 microglobulin (β2M) in that the β2M gene harbors four coding microsatellites (cMS) [152]. β2M gene mutations lead to impaired MHC class I function, defective recognition and presentation of neoantigens which render the immune evasion from immunotherapy [176, 187, 188]. Altogether, mutations of IFN-γ signaling, WNT/β-catenin signaling and antigen presentation machinery, followed by resistance to T cell-induced death could all trace back to dMMR/MSI-induced heterogeneity (Table 6) (Fig. 4). Although high TMB is discussed as a positive predictor of immunotherapy, the quality of mutations to generate a robust T cell response may outweigh the quantity.

Table 6 Underlying mechanisms of resistance to immunotherapy

Status of MMR system and microsatellite exhibits heterogeneity to some extent

In sporadic CRC cases, which arise from epigenomic silencing by hypermethylation of the MMR gene promotor, MMR deficiency may occur during tumor progression and display tumor heterogeneity (Fig. 1). In 100 cases of sporadic colon cancers, discordance was discovered when IHC and PCR-based microsatellite evaluation were performed in two different areas from the same tumor tissue in 8 cases, of which 6 cases presented normal MMR protein expression but exhibited MSI and 2 cases were the opposite [189], indicating the ITH of dMMR/MSI. In addition, cases reported a coexistence of dMMR and pMMR subclones in the primary lesions of mCRC and prostate cancers, but only pMMR/MSS was detected in the metastatic lesions [190, 191]. dMMR/MSI tumors are less likely to metastasize to regional lymph nodes and distant organs [1, 6] because (1) tumor cells with enhanced antigenicity are more likely to be recognized and localized (2) accumulated DNA damage results in decreased cell viability [192, 193]. There are also some studies verifying the heterogeneity of MSI and MMR protein expression [190, 194]. During the treatment, residual pMMR/MSS cells emerge from mixed subclones and foster temporal heterogeneity, resulting in acquired resistance. Therefore, due to the predictive and therapeutic value of dMMR/MSI, early detection of resistance and targeting the minimal resistant subclones is imperative.

Combined predictive markers are important to guide precise and personalized immunotherapy

dMMR/MSI, TIL and IFN-γ signaling can altogether reflect the response to immunotherapy. However, there is a disparity between response rate and detected biomarker status. Schrock et al. found the optimal cutoff for TMB as 37–41 mutations/Mb, below which the response to anti-PD-1 monotherapy was inferior despite dMMR/MSI status [95]. This number could be lower with combined ICBs [81], suggesting that combined therapy is preferred to monotherapy for dMMR/MSI patients with TMB below the cutoff. Although pMMR/MSS CRCs account for the majority of total number of CRCs and have a very low response rate to ICB [59], recent studies demonstrated that a subgroup of pMMR mCRC patients also obtained clinical remission from ICB due to higher level of IFN signature (PD-L1, LAG-3, IDO) [195, 196]. Some PD-L1 negative patients also responded to ICB [113, 134] probably due to sampling bias as a result of spatial heterogeneity, or other undetected factors. As discussed above, these markers alone do not predict therapeutic efficacy perfectly on an individual basis, but could make up for each other. Of note, all three features display a certain degree of heterogeneity. Thus, combatting heterogeneity using novel detection methods and better identifying patients’ anti-tumor immune capacity is the key to pre-select those most likely to benefit from treatment and spare others from unnecessary side effects (Fig. 1).

Detection methods to combat spatial heterogeneity

The optimal treatment is expected to target the trunk of all subclone mutations and subclonal driver events [19]. Therefore, it is indispensable to overcome the spatial heterogeneity and understand the full range of tumor tissues. The key step is accurate assessment, which is supported by a wealth of progressive studies [28, 197]. The conventional detection methods for dMMR/MSI are PCR and IHC. However, detection accuracy is limited by unfaithful Taq polymerase, limited panel numbers, the necessity for matched normal tissues and experience-dependent IHC [28]. Next-generation sequencing (NGS) allows for comprehensive investigations of multiple microsatellite loci simultaneously. MSI detected by PCR and 592-gene NGS was compared across 26 cancer types and a cutoff of ≥ 46 altered loci was found to classify samples as MSI [198], indicating that MSI-NGS is valid across cancer types and not limited by normal tissue acquisition. Additionally, tools based on NGS including mSing [199], MSIsensor [200], MSIplus [201] and MANTIS [202] have significantly improved sensitivity and specificity.

Several breakthroughs have been made with single-cell sequencing. Tumor cell diversity is analyzed by flow cytometry through a single-cell suspension which fully represents an intact tumor, providing the highest resolution to determine the true number of heterogenous subclones and characterize them without aggregating the information from multiple cells [203, 204]. Among all technologies, transcriptome analysis—single-cell RNA sequencing (scRNA-Seq) is the most advanced [203]. scRNA-Seq sheds light on the tumor immune microenvironment by showing the proportions of TILs. In mCRC samples, proportions of CD8 + T cells, Th1/2 cells and memory T cells were lower, and approximately 81.94% (118/144) of the genes related to WNT signaling were upregulated [205]. Patients with large B cell lymphoma who achieved complete response or remission showed improvement of memory T cells in scRNA-Seq of CAR-T cells [206]. Furthermore, scRNA-Seq identified TILs with high heterogeneity in Osteosarcoma (OS) and high expression of LAG-3 and TIGIT (T cell Immunoreceptor with Ig and ITIM domains) on CD8+ T cells, identifying new therapeutic targets for OS [207]. scRNA-Seq could also offer TCR sequence information and provides insight into TCR rearrangements at the single-cell level, unfolding dynamic responses to immunotherapy including vaccine and ICB [208]. TCR sequencing has been widely used and has helped probe into the dynamic combinations of T cell subsets and the spatial heterogeneity of TILs [84, 209, 210]. Single-cell sequencing has identified the heterogeneous expression of IFN-γ-related genes including MHCII in single cells, of which higher expression drives patients’ responsiveness to PD-1 blockade based on longitudinal scRNA-Seq [58, 211]. Enrichment of 227 IFN-γ-dependent transcripts including PD-L1 and IDO was also identified across multiple tumors and could be utilized to stratify immunotherapy response [212]. Mitra et al. found that single-cell analysis of a targeted transcriptome which predicted drug responses for individual cells was able to predict the response to a proteasome inhibitor when combined with machine learning in multiple myeloma [213]. Conceivably, it could also apply to immunotherapy based on correlative transcriptome signatures. Finally, simultaneous triple omics sequencing could reveal complex interplays within genetic, epigenetic and transcriptomic levels and provide the most complete maps of tumor cell subpopulations to guide treatment options [16].

The above discussion prompted us to quantify ITH and stratify patients by classifying potential responses to immunotherapy using combined biomarkers. Studies have classified immune status of tumors into several subtypes to support decision making and facilitate response prediction, based on TIL, IFN-γ signaling signatures and immune checkpoints expression [77, 214, 215]. Future studies should consider including multiple biomarkers to optimize this stratification method.

Real-time monitoring: combat temporal heterogeneity

Due to the temporal heterogeneity during natural tumor progressing and therapeutic interventions, it is important to achieve real-time monitoring in a minimally invasive way and promptly adjust therapeutic regimens. Longitudinal analysis of tumor-derived genetic materials including CTCs and ctDNA extracted from patients’ blood has achieved promising progress across several types of solid tumors [216,217,218,219]. These materials display all the alterations present in the tumor and the metastasis, which help eliminate false results caused by spatial heterogeneity. ctDNA analysis by liquid biopsy (blood test) is feasible and has been found to be sensitive and specific in various cancer types [220,221,222]. Studies showed that ctDNA identified genomic profiling highly consistently with and beyond the findings of tissue biopsy [223,224,225,226,227,228]. In 433 metastatic prostate cancer cases, dMMR identification using ctDNA was highly concordant with IHC and PCR of tumor tissue. Subclonal diversity and β-catenin activation were detected with sensitivity as well [229]. Detection of MSI using ctDNA with NGS in CRC was better than PCR and demonstrated high overall accuracy in pan-cancer [230]. Additionally, an initial peak following by a rapid decrease in ctDNA level indicates an early response for ACT, which in turn allows for early identification of those at risk of poor response and treatment optimization [206, 231]. Analysis of CTC also enables real-time monitoring and provides insight into the genomic profiling [232]. High expression of PD-L1 on CTC at baseline may be predictive to screen patients for PD-1/PD-L1 blockade and reduction of total CTC through longitudinal monitoring indicated a good response [233, 234]. Adjuvant PD-1/PD-L1 blockade deserves evaluation in patients whose PD-L1 ( +) CTCs are detected after curative treatment [235]. The number of CTCs significantly decreased after NK cell treatment in NSCLC and liver cancer, reflecting the therapeutic efficacy with decent sensitivity [236, 237]. Moreover, overexpression of β-catenin was detected in melanoma CTCs, but not in healthy donor and lacking in patients with complete response to ICB [238]. TMB measured from liquid biopsy was also found to be a predictive biomarker for atezolizumab (anti-PD-L1) in NSCLC, and able to identify patients who would benefit accurately and reproducibly [239]. In aggregate, liquid biopsy is a highly sensitive and informative method that can overcome ITH to identify low-frequency alterations and enable early detection of resistance or relapse.

Moreover, imaging techniques also allow for repeated response measurements during treatment, enabling visualization of ITH. Positron-emission tomography (PET) imaging with 89Zr-atezolizumab (anti-PD-L1) in NSCLC, bladder and triple-negative breast cancer showed that tracer uptake was heterogenous and corresponded to PD-L1 and IFN-γ signaling levels at sites, appearing to be a strong predictor of atezolizumab response [240]. Radiolabeled [111In] PD-L1-mAb and near-infrared dye conjugated NIR-PD-L1-mAb also demonstrably detected graded levels of PD-L1 expression with high specificity using SPECT/CT imaging [241, 242]. Transitioning these detective methods to combat ITH from the bench to bedside and evaluate and monitor patients’ potential benefits from immunotherapy is an enormous challenge that requires more clinical studies.


Immunotherapy has led to unprecedented long-lasting anti-tumor activity in cancer patients. Currently, clinicians utilize MSI evaluation and other methods, such as IHC of PD-L1, to distinguish those most likely to benefit. However, there are quite a few dMMR/MSI patients who do not respond to immunotherapy as expected. In this review, we explored factors facilitating or impeding immunotherapy from a novel perspective—complex interplay of MSI and ITH. It is commonly believed, and also true, that dMMR/MSI generates subclones with heterogenous genotypes and neoantigens, which stimulate anti-tumor response through higher TIL grade and expression of IFN-γ-related genes. The premises of effective immunotherapy—continuous activation and infiltration of T cells, sufficient IFN-γ production and responsive IFN-γ signaling—are satisfied in this scenario. Nonetheless, non-responders may suffer from the two-sided effects of dMMR/MSI due to a greater tendency for mutations in key elements involved in anti-tumor immunity. Additionally, excessive expression of diversified subclonal neoantigens may lead to relatively low expression of each neoantigen, resulting in inadequate expansion of TCR clones, subsequent T cell exhaustion and insufficient infiltration. Therefore, the subject boils down to one point: the quality of ITH outweighs the quantity.

To better identify patients’ anti-tumor immune capacity and guide individualized immunotherapy, single-cell sequencing uncovers the heterogenous pictures of tumor at the highest resolution, while liquid biopsy achieves real-time monitoring and enables early detection of resistance. Other investigative methods combined with imaging techniques provide multiple directions of future research. The advantage of a dMMR/MSI tumor is the pre-existing immunoreactive microenvironment. To promote and sustain immune activation, immunotherapy needs to be combined with targeted therapies to bypass defects in IFN-γ signaling and antigen presentation machinery, and to inhibit upregulated oncogenic signaling pathways. Many related clinical trials in dMMR/MSI tumors are ongoing, as summarized in Table 5. Moreover, it is important to note that heterogeneity of the MMR system and microsatellite status may cover up the true potency to respond to immunotherapy. Large prospective studies are needed to identify the rate of ITH of dMMR/MSI with accurate detection methods.

Availability of data and materials

Not applicable.



Adoptive cell transfer


Chimeric antigen receptor-T cells


Coding microsatellites


Copy-number alteration


Circulating tumor DNA


Circulating tumor cells


Cytotoxic T lymphocyte-associated antigen-4


Chemokine (C-X-C motif) ligand


Deficient mismatch repair


Frameshift-derived peptides


Hepatocellular carcinoma


Immune checkpoint blockade




Type II interferon

IFNGR 1/2:

Interferon-gamma receptor 1/2


IFN-stimulated noncoding RNA 1


IRF-1 response elements


Interferon regulatory factor 1


Intratumor heterogeneity


Lymphocyte activation gene-3


Metastatic colorectal cancer


Major histocompatibility complex


Mismatch repair


Microsatellite instability


Microsatellite stable


Microvessel density


NEDD8-activating enzyme


Next-generation sequencing


The second near-infrared-window


Nature killer


Non-small cell lung cancer


Programmed cell death protein 1


Programmed cell death 1 ligand 1


Proficient mismatch repair


Single-cell RNA sequencing


Single-nucleotide variant


Signal transducers and activators of transcription


T cell receptor


Tumor-infiltrating lymphocytes


Tumor mutation burden


Werner helicase


β2 Microglobulin


  1. 1.

    Gryfe R, Kim H, Hsieh ET, Aronson MD, Holowaty EJ, Bull SB, Redston M, Gallinger S. Tumor microsatellite instability and clinical outcome in young patients with colorectal cancer. N Engl J Med. 2000;342:69–77.

    CAS  PubMed  Article  Google Scholar 

  2. 2.

    Le DT, Durham JN, Smith KN, Wang H, Bartlett BR, Aulakh LK, Lu S, Kemberling H, Wilt C, Luber BS, et al. Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science. 2017;357:409–13.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  3. 3.

    Berger MF, Hodis E, Heffernan TP, Deribe YL, Lawrence MS, Protopopov A, Ivanova E, Watson IR, Nickerson E, Ghosh P, et al. Melanoma genome sequencing reveals frequent PREX2 mutations. Nature. 2012;485:502–6.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  4. 4.

    Lee W, Jiang Z, Liu J, Haverty PM, Guan Y, Stinson J, Yue P, Zhang Y, Pant KP, Bhatt D, et al. The mutation spectrum revealed by paired genome sequences from a lung cancer patient. Nature. 2010;465:473–7.

    CAS  PubMed  Article  Google Scholar 

  5. 5.

    Llosa NJ, Cruise M, Tam A, Wicks EC, Hechenbleikner EM, Taube JM, Blosser RL, Fan H, Wang H, Luber BS, et al. The vigorous immune microenvironment of microsatellite instable colon cancer is balanced by multiple counter-inhibitory checkpoints. Cancer Discov. 2015;5:43–51.

    CAS  PubMed  Article  Google Scholar 

  6. 6.

    Bai W, Ma J, Liu Y, Liang J, Wu Y, Yang X, Xu E, Li Y, Xi Y. Screening of MSI detection loci and their heterogeneity in East Asian colorectal cancer patients. Cancer Med. 2019;8:2157–66.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  7. 7.

    Grasso CS, Giannakis M, Wells DK, Hamada T, Mu XJ, Quist M, Nowak JA, Nishihara R, Qian ZR, Inamura K, et al. Genetic mechanisms of immune evasion in colorectal cancer. Cancer Discov. 2018;8:730–49.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. 8.

    Trabucco SE, Gowen K, Maund SL, Sanford E, Fabrizio DA, Hall MJ, Yakirevich E, Gregg JP, Stephens PJ, Frampton GM, et al. A novel next-generation sequencing approach to detecting microsatellite instability and pan-tumor characterization of 1000 microsatellite instability-high cases in 67,000 patient samples. J Mol Diagn. 2019;21:1053–66.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. 9.

    Sucker A, Zhao F, Pieper N, Heeke C, Maltaner R, Stadtler N, Real B, Bielefeld N, Howe S, Weide B, et al. Acquired IFNgamma resistance impairs anti-tumor immunity and gives rise to T-cell-resistant melanoma lesions. Nat Commun. 2017;8:15440.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. 10.

    Sveen A, Johannessen B, Tengs T, Danielsen SA, Eilertsen IA, Lind GE, Berg KCG, Leithe E, Meza-Zepeda LA, Domingo E, et al. Multilevel genomics of colorectal cancers with microsatellite instability-clinical impact of JAK1 mutations and consensus molecular subtype 1. Genome Med. 2017;9:46.

    PubMed  PubMed Central  Article  Google Scholar 

  11. 11.

    Maruvka YE, Mouw KW, Karlic R, Parasuraman P, Kamburov A, Polak P, Haradhvala NJ, Hess JM, Rheinbay E, Brody Y, et al. Analysis of somatic microsatellite indels identifies driver events in human tumors. Nat Biotechnol. 2017;35:951–9.

    CAS  PubMed  Article  Google Scholar 

  12. 12.

    Marusyk A, Almendro V, Polyak K. Intra-tumour heterogeneity: a looking glass for cancer? Nat Rev Cancer. 2012;12:323–34.

    CAS  PubMed  Article  Google Scholar 

  13. 13.

    Lee WC, Diao L, Wang J, Zhang J, Roarty EB, Varghese S, Chow CW, Fujimoto J, Behrens C, Cascone T, et al. Multiregion gene expression profiling reveals heterogeneity in molecular subtypes and immunotherapy response signatures in lung cancer. Mod Pathol. 2018;31:947–55.

    CAS  PubMed  Article  Google Scholar 

  14. 14.

    Friemel J, Rechsteiner M, Frick L, Bohm F, Struckmann K, Egger M, Moch H, Heikenwalder M, Weber A. Intratumor heterogeneity in hepatocellular carcinoma. Clin Cancer Res. 2015;21:1951–61.

    CAS  PubMed  Article  Google Scholar 

  15. 15.

    McGranahan N, Swanton C. Biological and therapeutic impact of intratumor heterogeneity in cancer evolution. Cancer Cell. 2015;27:15–26.

    CAS  PubMed  Article  Google Scholar 

  16. 16.

    Hou Y, Guo H, Cao C, Li X, Hu B, Zhu P, Wu X, Wen L, Tang F, Huang Y, Peng J. Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas. Cell Res. 2016;26:304–19.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. 17.

    Jamal-Hanjani M, Wilson GA, McGranahan N, Birkbak NJ, Watkins TBK, Veeriah S, Shafi S, Johnson DH, Mitter R, Rosenthal R, et al. Tracking the evolution of non-small-cell lung cancer. N Engl J Med. 2017;376:2109–21.

    CAS  PubMed  Article  Google Scholar 

  18. 18.

    Turajlic S, Xu H, Litchfield K, Rowan A, Horswell S, Chambers T, O’Brien T, Lopez JI, Watkins TBK, Nicol D, et al. Deterministic evolutionary trajectories influence primary tumor growth: TRACERx renal. Cell. 2018;173:595-610 e511.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. 19.

    Gao Q, Wang ZC, Duan M, Lin YH, Zhou XY, Worthley DL, Wang XY, Niu G, Xia Y, Deng M, et al. Cell culture system for analysis of genetic heterogeneity within hepatocellular carcinomas and response to pharmacologic agents. Gastroenterology. 2017;152:232-242 e234.

    CAS  PubMed  Article  Google Scholar 

  20. 20.

    Lim B, Lin Y, Navin N. Advancing cancer research and medicine with single-cell genomics. Cancer Cell. 2020;37:456–70.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. 21.

    Hause RJ, Pritchard CC, Shendure J, Salipante SJ. Classification and characterization of microsatellite instability across 18 cancer types. Nat Med. 2016;22:1342–50.

    CAS  PubMed  Article  Google Scholar 

  22. 22.

    Lee V, Murphy A, Le DT, Diaz LA Jr. Mismatch repair deficiency and response to immune checkpoint blockade. Oncologist. 2016;21:1200–11.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  23. 23.

    Saeed A, Park R, Al-Jumayli M, Al-Rajabi R, Sun W. Biologics, immunotherapy, and future directions in the treatment of advanced cholangiocarcinoma. Clin Colorectal Cancer. 2019;18:81–90.

    PubMed  Article  Google Scholar 

  24. 24.

    Baretti M, Le DT. DNA mismatch repair in cancer. Pharmacol Ther. 2018;189:45–62.

    CAS  PubMed  Article  Google Scholar 

  25. 25.

    Dudley JC, Lin MT, Le DT, Eshleman JR. Microsatellite Instability as a Biomarker for PD-1 Blockade. Clin Cancer Res. 2016;22:813–20.

    CAS  PubMed  Article  Google Scholar 

  26. 26.

    Vilar E, Gruber SB. Microsatellite instability in colorectal cancer-the stable evidence. Nat Rev Clin Oncol. 2010;7:153–62.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. 27.

    Jiricny J. The multifaceted mismatch-repair system. Nat Rev Mol Cell Biol. 2006;7:335–46.

    CAS  PubMed  Article  Google Scholar 

  28. 28.

    Evrard C, Tachon G, Randrian V, Karayan-Tapon L, Tougeron D. Microsatellite instability: diagnosis, heterogeneity, discordance, and clinical impact in colorectal cancer. Cancers (Basel). 2019;11:1567.

    CAS  Article  Google Scholar 

  29. 29.

    Zhao P, Li L, Jiang X, Li Q. Mismatch repair deficiency/microsatellite instability-high as a predictor for anti-PD-1/PD-L1 immunotherapy efficacy. J Hematol Oncol. 2019;12:54.

    PubMed  PubMed Central  Article  Google Scholar 

  30. 30.

    Nicolaides NC, Papadopoulos N, Liu B, Wei YF, Carter KC, Ruben SM, Rosen CA, Haseltine WA, Fleischmann RD, Fraser CM, et al. Mutations of two PMS homologues in hereditary nonpolyposis colon cancer. Nature. 1994;371:75–80.

    CAS  PubMed  Article  Google Scholar 

  31. 31.

    Salem ME, Bodor JN, Puccini A, Xiu J, Goldberg RM, Grothey A, Korn WM, Shields AF, Worrilow WM, Kim ES, et al. Relationship between MLH1, PMS2, MSH2 and MSH6 gene-specific alterations and tumor mutational burden in 1057 microsatellite instability-high solid tumors. Int J Cancer. 2020;147:2948–56.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  32. 32.

    Davies H, Bignell GR, Cox C, Stephens P, Edkins S, Clegg S, Teague J, Woffendin H, Garnett MJ, Bottomley W, et al. Mutations of the BRAF gene in human cancer. Nature. 2002;417:949–54.

    CAS  PubMed  Article  Google Scholar 

  33. 33.

    French AJ, Sargent DJ, Burgart LJ, Foster NR, Kabat BF, Goldberg R, Shepherd L, Windschitl HE, Thibodeau SN. Prognostic significance of defective mismatch repair and BRAF V600E in patients with colon cancer. Clin Cancer Res. 2008;14:3408–15.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. 34.

    Hutchins G, Southward K, Handley K, Magill L, Beaumont C, Stahlschmidt J, Richman S, Chambers P, Seymour M, Kerr D, et al. Value of mismatch repair, KRAS, and BRAF mutations in predicting recurrence and benefits from chemotherapy in colorectal cancer. J Clin Oncol. 2011;29:1261–70.

    PubMed  Article  Google Scholar 

  35. 35.

    Ribas A, Lawrence D, Atkinson V, Agarwal S, Miller WH Jr, Carlino MS, Fisher R, Long GV, Hodi FS, Tsoi J, et al. Combined BRAF and MEK inhibition with PD-1 blockade immunotherapy in BRAF-mutant melanoma. Nat Med. 2019;25:936–40.

    CAS  PubMed  Article  Google Scholar 

  36. 36.

    van Wietmarschen N, Sridharan S, Nathan WJ, Tubbs A, Chan EM, Callen E, Wu W, Belinky F, Tripathi V, Wong N, et al. Repeat expansions confer WRN dependence in microsatellite-unstable cancers. Nature. 2020;586:292–8.

    PubMed  Article  CAS  Google Scholar 

  37. 37.

    Brosh RM Jr. DNA helicases involved in DNA repair and their roles in cancer. Nat Rev Cancer. 2013;13:542–58.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. 38.

    Chan EM, Shibue T, McFarland JM, Gaeta B, Ghandi M, Dumont N, Gonzalez A, McPartlan JS, Li T, Zhang Y, et al. WRN helicase is a synthetic lethal target in microsatellite unstable cancers. Nature. 2019;568:551–6.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. 39.

    Lieb S, Blaha-Ostermann S, Kamper E, Rippka J, Schwarz C, Ehrenhofer-Wolfer K, Schlattl A, Wernitznig A, Lipp JJ, Nagasaka K, et al. Werner syndrome helicase is a selective vulnerability of microsatellite instability-high tumor cells. Elife. 2019;8:e43333.

    PubMed  PubMed Central  Article  Google Scholar 

  40. 40.

    Picco G, Cattaneo CM, van Vliet EJ, Crisafulli G, Rospo G, Consonni S, Vieira SF, Rodriguez IS, Cancelliere C, Banerjee R, et al. Werner helicase is a synthetic-lethal vulnerability in mismatch repair-deficient colorectal cancer refractory to targeted therapies, chemotherapy, and immunotherapy. Cancer Discov. 2021;11:1923–37.

    PubMed  Google Scholar 

  41. 41.

    Rosenthal R, Cadieux EL, Salgado R, Bakir MA, Moore DA, Hiley CT, Lund T, Tanic M, Reading JL, Joshi K, et al. Neoantigen-directed immune escape in lung cancer evolution. Nature. 2019;567:479–85.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. 42.

    Duan M, Hao J, Cui S, Worthley DL, Zhang S, Wang Z, Shi J, Liu L, Wang X, Ke A, et al. Diverse modes of clonal evolution in HBV-related hepatocellular carcinoma revealed by single-cell genome sequencing. Cell Res. 2018;28:359–73.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  43. 43.

    Luchini C, Bibeau F, Ligtenberg MJL, Singh N, Nottegar A, Bosse T, Miller R, Riaz N, Douillard JY, Andre F, Scarpa A. 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;30:1232–43.

    CAS  PubMed  Article  Google Scholar 

  44. 44.

    Touat M, Li YY, Boynton AN, Spurr LF, Iorgulescu JB, Bohrson CL, Cortes-Ciriano I, Birzu C, Geduldig JE, Pelton K, et al. Mechanisms and therapeutic implications of hypermutation in gliomas. Nature. 2020;580:517–23.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. 45.

    Rooney MS, Shukla SA, Wu CJ, Getz G, Hacohen N. Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell. 2015;160:48–61.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  46. 46.

    Wolf Y, Bartok O, Patkar S, Eli GB, Cohen S, Litchfield K, Levy R, Jimenez-Sanchez A, Trabish S, Lee JS, et al. UVB-Induced tumor heterogeneity diminishes immune response in melanoma. Cell. 2019;179:219-235 e221.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  47. 47.

    Gerlinger M, Rowan AJ, Horswell S, Math M, Larkin J, Endesfelder D, Gronroos E, Martinez P, Matthews N, Stewart A, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012;366:883–92.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. 48.

    Lee-Six H, Olafsson S, Ellis P, Osborne RJ, Sanders MA, Moore L, Georgakopoulos N, Torrente F, Noorani A, Goddard M, et al. The landscape of somatic mutation in normal colorectal epithelial cells. Nature. 2019;574:532–7.

    CAS  Article  Google Scholar 

  49. 49.

    Anagnostou V, Smith KN, Forde PM, Niknafs N, Bhattacharya R, White J, Zhang T, Adleff V, Phallen J, Wali N, et al. Evolution of neoantigen landscape during immune checkpoint blockade in non-small cell lung cancer. Cancer Discov. 2017;7:264–76.

    CAS  PubMed  Article  Google Scholar 

  50. 50.

    Baker AM, Cross W, Curtius K, Al Bakir I, Choi CR, Davis HL, Temko D, Biswas S, Martinez P, Williams MJ, et al. Evolutionary history of human colitis-associated colorectal cancer. Gut. 2019;68:985–95.

    CAS  PubMed  Article  Google Scholar 

  51. 51.

    Field MG, Durante MA, Anbunathan H, Cai LZ, Decatur CL, Bowcock AM, Kurtenbach S, Harbour JW. Punctuated evolution of canonical genomic aberrations in uveal melanoma. Nat Commun. 2018;9:116.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  52. 52.

    Bian S, Hou Y, Zhou X, Li X, Yong J, Wang Y, Wang W, Yan J, Hu B, Guo H, et al. Single-cell multiomics sequencing and analyses of human colorectal cancer. Science. 2018;362:1060–3.

    CAS  PubMed  Article  Google Scholar 

  53. 53.

    Xu LX, He MH, Dai ZH, Yu J, Wang JG, Li XC, Jiang BB, Ke ZF, Su TH, Peng ZW, et al. Genomic and transcriptional heterogeneity of multifocal hepatocellular carcinoma. Ann Oncol. 2019;30:990–7.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  54. 54.

    Ran X, Xiao J, Zhang Y, Teng H, Cheng F, Chen H, Zhang K, Sun Z. Low intratumor heterogeneity correlates with increased response to PD-1 blockade in renal cell carcinoma. Ther Adv Med Oncol. 2020;12:1758835920977117.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  55. 55.

    Nguyen PHD, Ma S, Phua CZJ, Kaya NA, Lai HLH, Lim CJ, Lim JQ, Wasser M, Lai L, Tam WL, et al. Intratumoural immune heterogeneity as a hallmark of tumour evolution and progression in hepatocellular carcinoma. Nat Commun. 2021;12:227.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  56. 56.

    Taube JM, Anders RA, Young GD, Xu H, Sharma R, McMiller TL, Chen S, Klein AP, Pardoll DM, Topalian SL, Chen L. Colocalization of inflammatory response with B7–h1 expression in human melanocytic lesions supports an adaptive resistance mechanism of immune escape. Sci Transl Med. 2012;4:127ra137.

    Article  CAS  Google Scholar 

  57. 57.

    Topalian SL, Hodi FS, Brahmer JR, Gettinger SN, Smith DC, McDermott DF, Powderly JD, Carvajal RD, Sosman JA, Atkins MB, et al. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N Engl J Med. 2012;366:2443–54.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  58. 58.

    Ma K-Y, Schonnesen AA, Brock A, Van Den Berg C, Eckhardt SG, Liu Z, Jiang N. Single-cell RNA sequencing of lung adenocarcinoma reveals heterogeneity of immune response-related genes. JCI Insight. 2019;4:e121387.

    PubMed Central  Article  PubMed  Google Scholar 

  59. 59.

    Le DT, Uram JN, Wang H, Bartlett BR, Kemberling H, Eyring AD, Skora AD, Luber BS, Azad NS, Laheru D, et al. PD-1 Blockade in tumors with mismatch-repair deficiency. N Engl J Med. 2015;372:2509–20.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  60. 60.

    Jhunjhunwala S, Hammer C, Delamarre L. Antigen presentation in cancer: insights into tumour immunogenicity and immune evasion. Nat Rev Cancer. 2021;21:298–312.

    CAS  PubMed  Article  Google Scholar 

  61. 61.

    Sharonov GV, Serebrovskaya EO, Yuzhakova DV, Britanova OV, Chudakov DM. B cells, plasma cells and antibody repertoires in the tumour microenvironment. Nat Rev Immunol. 2020;20:294–307.

    CAS  PubMed  Article  Google Scholar 

  62. 62.

    Riaz N, Havel JJ, Makarov V, Desrichard A, Urba WJ, Sims JS, Hodi FS, Martin-Algarra S, Mandal R, Sharfman WH, et al. Tumor and microenvironment evolution during immunotherapy with nivolumab. Cell. 2017;171:934-949 e916.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  63. 63.

    Samstein RM, Lee CH, Shoushtari AN, Hellmann MD, Shen R, Janjigian YY, Barron DA, Zehir A, Jordan EJ, Omuro A, et al. Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat Genet. 2019;51:202–6.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  64. 64.

    Rizvi H, Sanchez-Vega F, La K, Chatila W, Jonsson P, Halpenny D, Plodkowski A, Long N, Sauter JL, Rekhtman N, et al. Molecular determinants of response to anti-programmed cell death (PD)-1 and anti-programmed death-ligand 1 (PD-L1) blockade in patients with non-small-cell lung cancer profiled with targeted next-generation sequencing. J Clin Oncol. 2018;36:633–41.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  65. 65.

    Reuben A, Gittelman R, Gao J, Zhang J, Yusko EC, Wu CJ, Emerson R, Zhang J, Tipton C, Li J, et al. TCR Repertoire Intratumor Heterogeneity in localized lung adenocarcinomas: an association with predicted neoantigen heterogeneity and postsurgical recurrence. Cancer Discov. 2017;7:1088–97.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  66. 66.

    Joshi K, de Massy MR, Ismail M, Reading JL, Uddin I, Woolston A, Hatipoglu E, Oakes T, Rosenthal R, Peacock T, et al. Spatial heterogeneity of the T cell receptor repertoire reflects the mutational landscape in lung cancer. Nat Med. 2019;25:1549–59.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  67. 67.

    Kuang M, Cheng J, Zhang C, Feng L, Xu X, Zhang Y, Zu M, Cui J, Yu H, Zhang K, et al. A novel signature for stratifying the molecular heterogeneity of the tissue-infiltrating T-cell receptor repertoire reflects gastric cancer prognosis. Sci Rep. 2017;7:7762.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  68. 68.

    Liaskou E, Klemsdal Henriksen EK, Holm K, Kaveh F, Hamm D, Fear J, Viken MK, Hov JR, Melum E, Robins H, et al. High-throughput T-cell receptor sequencing across chronic liver diseases reveals distinct disease-associated repertoires. Hepatology. 2016;63:1608–19.

    CAS  PubMed  Article  Google Scholar 

  69. 69.

    Lin KR, Deng FW, Jin YB, Chen XP, Pan YM, Cui JH, You ZX, Chen HW, Luo W. T cell receptor repertoire profiling predicts the prognosis of HBV-associated hepatocellular carcinoma. Cancer Med. 2018;7:3755–62.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  70. 70.

    Tran E, Robbins PF, Rosenberg SA. “Final common pathway” of human cancer immunotherapy: targeting random somatic mutations. Nat Immunol. 2017;18:255–62.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  71. 71.

    Zhang C, Ding H, Huang H, Palashati H, Miao Y, Xiong H, Lu Z. TCR repertoire intratumor heterogeneity of CD4(+) and CD8(+) T cells in centers and margins of localized lung adenocarcinomas. Int J Cancer. 2019;144:818–27.

    CAS  PubMed  Article  Google Scholar 

  72. 72.

    Zhang Q, Lou Y, Yang J, Wang J, Feng J, Zhao Y, Wang L, Huang X, Fu Q, Ye M, et al. Integrated multiomic analysis reveals comprehensive tumour heterogeneity and novel immunophenotypic classification in hepatocellular carcinomas. Gut. 2019;68:2019–31.

    CAS  PubMed  Article  Google Scholar 

  73. 73.

    Liu YY, Yang QF, Yang JS, Cao RB, Liang JY, Liu YT, Zeng YL, Chen S, Xia XF, Zhang K, Liu L. Characteristics and prognostic significance of profiling the peripheral blood T-cell receptor repertoire in patients with advanced lung cancer. Int J Cancer. 2019;145:1423–31.

    CAS  PubMed  Article  Google Scholar 

  74. 74.

    Zhang Y, Zhu Y, Wang J, Xu Y, Wang Z, Liu Y, Di X, Feng L, Zhang Y. A comprehensive model based on temporal dynamics of peripheral T cell repertoire for predicting post-treatment distant metastasis of nasopharyngeal carcinoma. Cancer Immunol Immunother. 2021.

    Article  PubMed  PubMed Central  Google Scholar 

  75. 75.

    Guo L, Bi X, Li Y, Wen L, Zhang W, Jiang W, Ma J, Feng L, Zhang K, Shou J. Characteristics, dynamic changes, and prognostic significance of TCR repertoire profiling in patients with renal cell carcinoma. J Pathol. 2020;251:26–37.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  76. 76.

    Hectors SJ, Wagner M, Bane O, Besa C, Lewis S, Remark R, Chen N, Fiel MI, Zhu H, Gnjatic S, et al. Quantification of hepatocellular carcinoma heterogeneity with multiparametric magnetic resonance imaging. Sci Rep. 2017;7:2452.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  77. 77.

    Kurebayashi Y, Ojima H, Tsujikawa H, Kubota N, Maehara J, Abe Y, Kitago M, Shinoda M, Kitagawa Y, Sakamoto M. Landscape of immune microenvironment in hepatocellular carcinoma and its additional impact on histological and molecular classification. Hepatology. 2018;68:1025–41.

    CAS  PubMed  Article  Google Scholar 

  78. 78.

    Chew V, Chen J, Lee D, Loh E, Lee J, Lim KH, Weber A, Slankamenac K, Poon RT, Yang H, et al. Chemokine-driven lymphocyte infiltration: an early intratumoural event determining long-term survival in resectable hepatocellular carcinoma. Gut. 2012;61:427–38.

    CAS  PubMed  Article  Google Scholar 

  79. 79.

    Okabe M, Toh U, Iwakuma N, Saku S, Akashi M, Kimitsuki Y, Seki N, Kawahara A, Ogo E, Itoh K, Akagi Y. Predictive factors of the tumor immunological microenvironment for long-term follow-up in early stage breast cancer. Cancer Sci. 2017;108:81–90.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  80. 80.

    O’Donnell JS, Teng MWL, Smyth MJ. Cancer immunoediting and resistance to T cell-based immunotherapy. Nat Rev Clin Oncol. 2019;16:151–67.

    CAS  PubMed  Article  Google Scholar 

  81. 81.

    Overman MJ, McDermott R, Leach JL, Lonardi S, Lenz H-J, Morse MA, Desai J, Hill A, Axelson M, Moss RA, et al. Nivolumab in patients with metastatic DNA mismatch repair-deficient or microsatellite instability-high colorectal cancer (CheckMate 142): an open-label, multicentre, phase 2 study. Lancet Oncol. 2017;18:1182–91.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  82. 82.

    Petrelli F, Ghidini M, Ghidini A, Tomasello G. Outcomes following immune checkpoint inhibitor treatment of patients with microsatellite instability-high cancers: a systematic review and meta-analysis. JAMA Oncol. 2020;6:1068–71.

    PubMed  Article  Google Scholar 

  83. 83.

    Overman MJ, Lonardi S, Wong KYM, Lenz HJ, Gelsomino F, Aglietta M, Morse MA, Van Cutsem E, McDermott R, Hill A, et al. Durable clinical benefit with nivolumab plus ipilimumab in DNA mismatch repair-deficient/microsatellite instability-high metastatic colorectal cancer. J Clin Oncol. 2018;36:773–9.

    CAS  PubMed  Article  Google Scholar 

  84. 84.

    Feng L, Qian H, Yu X, Liu K, Xiao T, Zhang C, Kuang M, Cheng S, Li X, Wan J, Zhang K. Heterogeneity of tumor-infiltrating lymphocytes ascribed to local immune status rather than neoantigens by multi-omics analysis of glioblastoma multiforme. Sci Rep. 2017;7:6968.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  85. 85.

    Ribas A, Wolchok JD. Cancer immunotherapy using checkpoint blockade. Science. 2018;359:1350–5.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  86. 86.

    Minn AJ, Wherry EJ. Combination cancer therapies with immune checkpoint blockade: convergence on interferon signaling. Cell. 2016;165:272–5.

    CAS  PubMed  Article  Google Scholar 

  87. 87.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  88. 88.

    Refaeli Y, Van Parijs L, Alexander SI, Abbas AK. Interferon gamma is required for activation-induced death of T lymphocytes. J Exp Med. 2002;196:999–1005.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  89. 89.

    Mo X, Zhang H, Preston S, Martin K, Zhou B, Vadalia N, Gamero AM, Soboloff J, Tempera I, Zaidi MR. Interferon-gamma signaling in melanocytes and melanoma cells regulates expression of CTLA-4. Cancer Res. 2018;78:436–50.

    CAS  PubMed  Article  Google Scholar 

  90. 90.

    Zhu J, Powis de Tenbossche CG, Cane S, Colau D, van Baren N, Lurquin C, Schmitt-Verhulst AM, Liljestrom P, Uyttenhove C, Van den Eynde BJ. Resistance to cancer immunotherapy mediated by apoptosis of tumor-infiltrating lymphocytes. Nat Commun. 2017;8:1404.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  91. 91.

    Horton BL, Williams JB, Cabanov A, Spranger S, Gajewski TF. Intratumoral CD8(+) T-cell apoptosis is a major component of T-cell dysfunction and impedes antitumor immunity. Cancer Immunol Res. 2018;6:14–24.

    CAS  PubMed  Article  Google Scholar 

  92. 92.

    De Smedt L, Lemahieu J, Palmans S, Govaere O, Tousseyn T, Van Cutsem E, Prenen H, Tejpar S, Spaepen M, Matthijs G, et al. Microsatellite instable vs stable colon carcinomas: analysis of tumour heterogeneity, inflammation and angiogenesis. Br J Cancer. 2015;113:500–9.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  93. 93.

    Galon J, Costes A, Sanchez-Cabo F, Kirilovsky A, Mlecnik B, Lagorce-Pages C, Tosolini M, Camus M, Berger A, Wind P, et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science. 2006;313:1960–4.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  94. 94.

    Burugu S, Gao D, Leung S, Chia SK, Nielsen TO. LAG-3+ tumor infiltrating lymphocytes in breast cancer: clinical correlates and association with PD-1/PD-L1+ tumors. Ann Oncol. 2017;28:2977–84.

    CAS  PubMed  Article  Google Scholar 

  95. 95.

    Schrock AB, Ouyang C, Sandhu J, Sokol E, Jin D, Ross JS, Miller VA, Lim D, Amanam I, Chao J, et al. Tumor mutational burden is predictive of response to immune checkpoint inhibitors in MSI-high metastatic colorectal cancer. Ann Oncol. 2019;30:1096–103.

    CAS  PubMed  Article  Google Scholar 

  96. 96.

    Rizvi NA, Hellmann MD, Snyder A, Kvistborg P, Makarov V, Havel JJ, Lee W, Yuan J, Wong P, Ho TS, et al. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science. 2015;348:124–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  97. 97.

    Snyder A, Makarov V, Merghoub T, Yuan J, Zaretsky JM, Desrichard A, Walsh LA, Postow MA, Wong P, Ho TS, et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N Engl J Med. 2014;371:2189–99.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  98. 98.

    Tosolini M, Kirilovsky A, Mlecnik B, Fredriksen T, Mauger S, Bindea G, Berger A, Bruneval P, Fridman W-H, Pagès F, Galon J. Clinical impact of different classes of infiltrating T cytotoxic and helper cells (Th1, Th2, Treg, Th17) in patients with colorectal cancer. Can Res. 2011;71:1263–71.

    CAS  Article  Google Scholar 

  99. 99.

    Dolcetti R, Viel A, Doglioni C, Russo A, Guidoboni M, Capozzi E, Vecchiato N, Macrì E, Fornasarig M, Boiocchi M. High prevalence of activated intraepithelial cytotoxic T lymphocytes and increased neoplastic cell apoptosis in colorectal carcinomas with microsatellite instability. Am J Pathol. 1999;154:1805–13.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  100. 100.

    Azimi F, Scolyer RA, Rumcheva P, Moncrieff M, Murali R, McCarthy SW, Saw RP, Thompson JF. Tumor-infiltrating lymphocyte grade is an independent predictor of sentinel lymph node status and survival in patients with cutaneous melanoma. J Clin Oncol. 2012;30:2678–83.

    Article  Google Scholar 

  101. 101.

    Ogino S, Nosho K, Irahara N, Meyerhardt JA, Baba Y, Shima K, Glickman JN, Ferrone CR, Mino-Kenudson M, Tanaka N, et al. Lymphocytic reaction to colorectal cancer is associated with longer survival, independent of lymph node count, microsatellite instability, and CpG island methylator phenotype. Clin Cancer Res. 2009;15:6412–20.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  102. 102.

    Thomas NE, Busam KJ, From L, Kricker A, Armstrong BK, Anton-Culver H, Gruber SB, Gallagher RP, Zanetti R, Rosso S, et al. Tumor-infiltrating lymphocyte grade in primary melanomas is independently associated with melanoma-specific survival in the population-based genes, environment and melanoma study. J Clin Oncol. 2013;31:4252–9.

    PubMed  PubMed Central  Article  Google Scholar 

  103. 103.

    Chen PL, Roh W, Reuben A, Cooper ZA, Spencer CN, Prieto PA, Miller JP, Bassett RL, Gopalakrishnan V, Wani K, et al. Analysis of immune signatures in longitudinal tumor samples yields insight into biomarkers of response and mechanisms of resistance to immune checkpoint blockade. Cancer Discov. 2016;6:827–37.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  104. 104.

    Senbabaoglu Y, Gejman RS, Winer AG, Liu M, Van Allen EM, de Velasco G, Miao D, Ostrovnaya I, Drill E, Luna A, et al. Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures. Genome Biol. 2016;17:231.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  105. 105.

    Cui C, Tian X, Wu J, Zhang C, Tan Q, Guan X, Dong B, Zhao M, Lu Z, Hao C. T cell receptor beta-chain repertoire analysis of tumor-infiltrating lymphocytes in pancreatic cancer. Cancer Sci. 2019;110:61–71.

    CAS  PubMed  Article  Google Scholar 

  106. 106.

    Jeantet M, Tougeron D, Tachon G, Cortes U, Archambaut C, Fromont G, Karayan-Tapon L. High intra- and inter-tumoral heterogeneity of RAS mutations in colorectal cancer. Int J Mol Sci. 2015;2016:17.

    Google Scholar 

  107. 107.

    Nusinow DP, Szpyt J, Ghandi M, Rose CM, McDonald ER 3rd, Kalocsay M, Jane-Valbuena J, Gelfand E, Schweppe DK, Jedrychowski M, et al. Quantitative proteomics of the cancer cell line encyclopedia. Cell. 2020;180:387-402 e316.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  108. 108.

    Carethers JM, Jung BH. Genetics and genetic biomarkers in sporadic colorectal cancer. Gastroenterology. 2015;149:1177-1190 e1173.

    CAS  PubMed  Article  Google Scholar 

  109. 109.

    Vadakekolathu J, Lai C, Reeder S, Church SE, Hood T, Lourdusamy A, Rettig MP, Aldoss I, Advani AS, Godwin J, et al. TP53 abnormalities correlate with immune infiltration and associate with response to flotetuzumab immunotherapy in AML. Blood Adv. 2020;4:5011–24.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  110. 110.

    Dong ZY, Zhong WZ, Zhang XC, Su J, Xie Z, Liu SY, Tu HY, Chen HJ, Sun YL, Zhou Q, 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;23:3012–24.

    CAS  Article  Google Scholar 

  111. 111.

    Droeser RA, Hirt C, Viehl CT, Frey DM, Nebiker C, Huber X, Zlobec I, Eppenberger-Castori S, Tzankov A, Rosso R, et al. Clinical impact of programmed cell death ligand 1 expression in colorectal cancer. Eur J Cancer. 2013;49:2233–42.

    CAS  PubMed  Article  Google Scholar 

  112. 112.

    Taube JM, Klein A, Brahmer JR, Xu H, Pan X, Kim JH, Chen L, Pardoll DM, Topalian SL, Anders RA. Association of PD-1, PD-1 ligands, and other features of the tumor immune microenvironment with response to anti-PD-1 therapy. Clin Cancer Res. 2014;20:5064–74.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  113. 113.

    Rosenberg JE, Hoffman-Censits J, Powles T, van der Heijden MS, Balar AV, Necchi A, Dawson N, O’Donnell PH, Balmanoukian A, Loriot Y, 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. The Lancet. 2016;387:1909–20.

    CAS  Article  Google Scholar 

  114. 114.

    Bouffet E, Larouche V, Campbell BB, Merico D, de Borja R, Aronson M, Durno C, Krueger J, Cabric V, Ramaswamy V, et al. Immune checkpoint inhibition for hypermutant glioblastoma multiforme resulting from germline biallelic mismatch repair deficiency. J Clin Oncol. 2016;34:2206–11.

    CAS  PubMed  Article  Google Scholar 

  115. 115.

    Castro MP, Goldstein N. Mismatch repair deficiency associated with complete remission to combination programmed cell death ligand immune therapy in a patient with sporadic urothelial carcinoma: immunotheranostic considerations. J Immunother Cancer. 2015;3:58.

    PubMed  PubMed Central  Article  Google Scholar 

  116. 116.

    Kelly RJ, Lee J, Bang YJ, Almhanna K, Blum-Murphy M, Catenacci DVT, Chung HC, Wainberg ZA, Gibson MK, Lee KW, et al. Safety and efficacy of durvalumab and tremelimumab alone or in combination in patients with advanced gastric and gastroesophageal junction adenocarcinoma. Clin Cancer Res. 2020;26:846–54.

    CAS  PubMed  Google Scholar 

  117. 117.

    Day EK, Carmichael AJ, ten Berge IJ, Waller EC, Sissons JG, Wills MR. Rapid CD8+ T cell repertoire focusing and selection of high-affinity clones into memory following primary infection with a persistent human virus: human cytomegalovirus. J Immunol. 2007;179:3203–13.

    CAS  PubMed  Article  Google Scholar 

  118. 118.

    McGranahan N, Swanton C. Clonal heterogeneity and tumor evolution: past, present, and the future. Cell. 2017;168:613–28.

    CAS  PubMed  Article  Google Scholar 

  119. 119.

    Chen Z, Zhang C, Pan Y, Xu R, Xu C, Chen Z, Lu Z, Ke Y. T cell receptor beta-chain repertoire analysis reveals intratumour heterogeneity of tumour-infiltrating lymphocytes in oesophageal squamous cell carcinoma. J Pathol. 2016;239:450–8.

    CAS  PubMed  Article  Google Scholar 

  120. 120.

    Jia Q, Chiu L, Wu S, Bai J, Peng L, Zheng L, Zang R, Li X, Yuan B, Gao Y, et al. Tracking neoantigens by personalized circulating tumor DNA sequencing during checkpoint blockade immunotherapy in non-small cell lung cancer. Adv Sci (Weinh). 2020;7:1903410.

    CAS  Article  Google Scholar 

  121. 121.

    Liu Z, Li JP, Chen M, Wu M, Shi Y, Li W, Teijaro JR, Wu P. Detecting tumor antigen-specific T cells via interaction-dependent fucosyl-biotinylation. Cell. 2020;183:1117–11331119.

    CAS  PubMed  Article  Google Scholar 

  122. 122.

    Unitt E, Rushbrook SM, Marshall A, Davies S, Gibbs P, Morris LS, Coleman N, Alexander GJ. Compromised lymphocytes infiltrate hepatocellular carcinoma: the role of T-regulatory cells. Hepatology. 2005;41:722–30.

    CAS  PubMed  Article  Google Scholar 

  123. 123.

    Ahmadzadeh M, Pasetto A, Jia L, Deniger DC, Stevanovic S, Robbins PF, Rosenberg SA. Tumor-infiltrating human CD4(+) regulatory T cells display a distinct TCR repertoire and exhibit tumor and neoantigen reactivity. Sci Immunol. 2019;4:eaao4310.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  124. 124.

    Zhang Y, Zhu W, Zhang X, Qu Q, Zhang L. Expression and clinical significance of programmed death-1 on lymphocytes and programmed death ligand-1 on monocytes in the peripheral blood of patients with cervical cancer. Oncol Lett. 2017;14:7225–31.

    PubMed  PubMed Central  Google Scholar 

  125. 125.

    Cui JH, Lin KR, Yuan SH, Jin YB, Chen XP, Su XK, Jiang J, Pan YM, Mao SL, Mao XF, Luo W. TCR repertoire as a novel indicator for immune monitoring and prognosis assessment of patients with cervical cancer. Front Immunol. 2018;9:2729.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  126. 126.

    Gros A, Robbins PF, Yao X, Li YF, Turcotte S, Tran E, Wunderlich JR, Mixon A, Farid S, Dudley ME, et al. PD-1 identifies the patient-specific CD8(+) tumor-reactive repertoire infiltrating human tumors. J Clin Invest. 2014;124:2246–59.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  127. 127.

    Thommen DS, Koelzer VH, Herzig P, Roller A, Trefny M, Dimeloe S, Kiialainen A, Hanhart J, Schill C, Hess C, et al. A transcriptionally and functionally distinct PD-1(+) CD8(+) T cell pool with predictive potential in non-small-cell lung cancer treated with PD-1 blockade. Nat Med. 2018;24:994–1004.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  128. 128.

    Zerdes I, Matikas A, Bergh J, Rassidakis GZ, Foukakis T. Genetic, transcriptional and post-translational regulation of the programmed death protein ligand 1 in cancer: biology and clinical correlations. Oncogene. 2018;37:4639–61.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  129. 129.

    Shevtsov M, Sato H, Multhoff G, Shibata A. Novel approaches to improve the efficacy of immuno-radiotherapy. Front Oncol. 2019;9:156.

    PubMed  PubMed Central  Article  Google Scholar 

  130. 130.

    Mineo M, Lyons SM, Zdioruk M, von Spreckelsen N, Ferrer-Luna R, Ito H, Alayo QA, Kharel P, Giantini Larsen A, Fan WY, et al. Tumor interferon signaling is regulated by a lncRNA INCR1 transcribed from the PD-L1 Locus. Mol Cell. 2020;78:1207-1223 e1208.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  131. 131.

    Yan Y, Zheng L, Du Q, Yan B, Geller DA. Interferon regulatory factor 1 (IRF-1) and IRF-2 regulate PD-L1 expression in hepatocellular carcinoma (HCC) cells. Cancer Immunol Immunother. 2020;69:1891–903.

    CAS  PubMed  Article  Google Scholar 

  132. 132.

    Zheng H, Pomyen Y, Hernandez MO, Li C, Livak F, Tang W, Dang H, Greten TF, Davis JL, Zhao Y, et al. Single-cell analysis reveals cancer stem cell heterogeneity in hepatocellular carcinoma. Hepatology. 2018;68:127–40.

    PubMed  Article  Google Scholar 

  133. 133.

    Fu Y, Liu S, Zeng S, Shen H. From bench to bed: the tumor immune microenvironment and current immunotherapeutic strategies for hepatocellular carcinoma. J Exp Clin Cancer Res. 2019;38:396.

    PubMed  PubMed Central  Article  Google Scholar 

  134. 134.

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

    CAS  PubMed  Article  Google Scholar 

  135. 135.

    Hamid O, Schmidt H, Nissan A, Ridolfi L, Aamdal S, Hansson J, Guida M, Hyams DM, Gomez H, Bastholt L, et al. A prospective phase II trial exploring the association between tumor microenvironment biomarkers and clinical activity of ipilimumab in advanced melanoma. J Transl Med. 2011;9:204.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  136. 136.

    Johnson DB, Estrada MV, Salgado R, Sanchez V, Doxie DB, Opalenik SR, Vilgelm AE, Feld E, Johnson AS, Greenplate AR, et al. Melanoma-specific MHC-II expression represents a tumour-autonomous phenotype and predicts response to anti-PD-1/PD-L1 therapy. Nat Commun. 2016;7:10582.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  137. 137.

    Higgs BW, Morehouse CA, Streicher K, Brohawn PZ, Pilataxi F, Gupta A, Ranade K. Interferon gamma messenger RNA signature in tumor biopsies predicts outcomes in patients with non-small cell lung carcinoma or urothelial cancer treated with durvalumab. Clin Cancer Res. 2018;24:3857–66.

    CAS  PubMed  Article  Google Scholar 

  138. 138.

    Zhai W, Zhou X, Wang H, Li W, Chen G, Sui X, Li G, Qi Y, Gao Y. A novel cyclic peptide targeting LAG-3 for cancer immunotherapy by activating antigen-specific CD8(+) T cell responses. Acta Pharm Sin B. 2020;10:1047–60.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  139. 139.

    Lichtenegger FS, Rothe M, Schnorfeil FM, Deiser K, Krupka C, Augsberger C, Schluter M, Neitz J, Subklewe M. Targeting LAG-3 and PD-1 to enhance T cell activation by antigen-presenting cells. Front Immunol. 2018;9:385.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  140. 140.

    Huang RY, Francois A, McGray AR, Miliotto A, Odunsi K. Compensatory upregulation of PD-1, LAG-3, and CTLA-4 limits the efficacy of single-agent checkpoint blockade in metastatic ovarian cancer. Oncoimmunology. 2017;6:e1249561.

    PubMed  Article  CAS  Google Scholar 

  141. 141.

    Harris-Bookman S, Mathios D, Martin AM, Xia Y, Kim E, Xu H, Belcaid Z, Polanczyk M, Barberi T, Theodros D, et al. Expression of LAG-3 and efficacy of combination treatment with anti-LAG-3 and anti-PD-1 monoclonal antibodies in glioblastoma. Int J Cancer. 2018;143:3201–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  142. 142.

    Matsuzaki J, Gnjatic S, Mhawech-Fauceglia P, Beck A, Miller A, Tsuji T, Eppolito C, Qian F, Lele S, Shrikant P, et al. Tumor-infiltrating NY-ESO-1-specific CD8+ T cells are negatively regulated by LAG-3 and PD-1 in human ovarian cancer. Proc Natl Acad Sci USA. 2010;107:7875–80.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  143. 143.

    Grosso JF, Goldberg MV, Getnet D, Bruno TC, Yen HR, Pyle KJ, Hipkiss E, Vignali DA, Pardoll DM, Drake CG. Functionally distinct LAG-3 and PD-1 subsets on activated and chronically stimulated CD8 T cells. J Immunol. 2009;182:6659–69.

    CAS  PubMed  Article  Google Scholar 

  144. 144.

    Yu X, Huang X, Chen X, Liu J, Wu C, Pu Q, Wang Y, Kang X, Zhou L. Characterization of a novel anti-human lymphocyte activation gene 3 (LAG-3) antibody for cancer immunotherapy. MAbs. 2019;11:1139–48.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  145. 145.

    Linette GP, Becker-Hapak M, Skidmore ZL, Baroja ML, Xu C, Hundal J, Spencer DH, Fu W, Cummins C, Robnett M, et al. Immunological ignorance is an enabling feature of the oligo-clonal T cell response to melanoma neoantigens. Proc Natl Acad Sci USA. 2019;116:23662–70.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  146. 146.

    Zacharakis N, Chinnasamy H, Black M, Xu H, Lu YC, Zheng Z, Pasetto A, Langhan M, Shelton T, Prickett T, et al. Immune recognition of somatic mutations leading to complete durable regression in metastatic breast cancer. Nat Med. 2018;24:724–30.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  147. 147.

    Peng W, Liu C, Xu C, Lou Y, Chen J, Yang Y, Yagita H, Overwijk WW, Lizee G, Radvanyi L, Hwu P. PD-1 blockade enhances T-cell migration to tumors by elevating IFN-gamma inducible chemokines. Cancer Res. 2012;72:5209–18.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  148. 148.

    Chen H, Liakou CI, Kamat A, Pettaway C, Ward JF, Tang DN, Sun J, Jungbluth AA, Troncoso P, Logothetis C, Sharma P. Anti-CTLA-4 therapy results in higher CD4+ICOShi T cell frequency and IFN-gamma levels in both nonmalignant and malignant prostate tissues. Proc Natl Acad Sci USA. 2009;106:2729–34.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  149. 149.

    John LB, Devaud C, Duong CP, Yong CS, Beavis PA, Haynes NM, Chow MT, Smyth MJ, Kershaw MH, Darcy PK. Anti-PD-1 antibody therapy potently enhances the eradication of established tumors by gene-modified T cells. Clin Cancer Res. 2013;19:5636–46.

    CAS  PubMed  Article  Google Scholar 

  150. 150.

    Effern M, Glodde N, Braun M, Liebing J, Boll HN, Yong M, Bawden E, Hinze D, van den Boorn-Konijnenberg D, Daoud M, et al. Adoptive T cell therapy targeting different gene products reveals diverse and context-dependent immune evasion in melanoma. Immunity. 2020;53:564-580 e569.

    CAS  PubMed  Article  Google Scholar 

  151. 151.

    von Knebel DM, Kloor M. Towards a vaccine to prevent cancer in Lynch syndrome patients. Fam Cancer. 2013;12:307–12.

    Article  CAS  Google Scholar 

  152. 152.

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

    PubMed  Article  Google Scholar 

  153. 153.

    Kloor M, Reuschenbach M, Pauligk C, Karbach J, Rafiyan MR, Al-Batran SE, Tariverdian M, Jager E, von Knebel DM. A frameshift peptide neoantigen-based vaccine for mismatch repair-deficient cancers: a phase I/IIa clinical trial. Clin Cancer Res. 2020;26:4503–10.

    CAS  PubMed  Article  Google Scholar 

  154. 154.

    Leoni G, D’Alise AM, Cotugno G, Langone F, Garzia I, De Lucia M, Fichera I, Vitale R, Bignone V, Tucci FG, et al. A genetic vaccine encoding shared cancer neoantigens to treat tumors with microsatellite instability. Cancer Res. 2020;80:3972–82.

    CAS  PubMed  Article  Google Scholar 

  155. 155.

    Woerner SM, Kloor M, von Knebel DM, Gebert JF. Microsatellite instability in the development of DNA mismatch repair deficient tumors. Cancer Biomark. 2006;2:69–86.

    CAS  PubMed  Article  Google Scholar 

  156. 156.

    Woerner SM, Kloor M, Mueller A, Rueschoff J, Friedrichs N, Buettner R, Buzello M, Kienle P, Knaebel HP, Kunstmann E, et al. Microsatellite instability of selective target genes in HNPCC-associated colon adenomas. Oncogene. 2005;24:2525–35.

    CAS  PubMed  Article  Google Scholar 

  157. 157.

    Kjeldsen JW, Iversen TZ, Engell-Noerregaard L, Mellemgaard A, Andersen MH, Svane IM. Durable clinical responses and long-term follow-up of stage II–IV non-small-cell lung cancer (NSCLC) patients treated with IDO peptide vaccine in a phase I study-A brief research report. Front Immunol. 2018;9:2145.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  158. 158.

    Gao S, Yang X, Xu J, Qiu N, Zhai G. Nanotechnology for boosting cancer immunotherapy and remodeling tumor microenvironment: the horizons in cancer treatment. ACS Nano. 2021.

  159. 159.

    Meng X, Wang J, Zhou J, Tian Q, Qie B, Zhou G, Duan W, Zhu Y. Tumor cell membrane-based peptide delivery system targeting the tumor microenvironment for cancer immunotherapy and diagnosis. Acta Biomater. 2021;127:266–75.

    CAS  PubMed  Article  Google Scholar 

  160. 160.

    Tan X, Huang J, Wang Y, He S, Jia L, Zhu Y, Pu K, Zhang Y, Yang X. Transformable nanosensitizer with tumor microenvironment-activated sonodynamic process and calcium release for enhanced cancer immunotherapy. Angew Chem Int Ed Engl. 2021;60:14051–9.

    CAS  PubMed  Article  Google Scholar 

  161. 161.

    Yang M, Li J, Gu P, Fan X. The application of nanoparticles in cancer immunotherapy: targeting tumor microenvironment. Bioact Mater. 2021;6:1973–87.

    CAS  PubMed  Article  Google Scholar 

  162. 162.

    Zhao H, Zhao B, Wu L, Xiao H, Ding K, Zheng C, Song Q, Sun L, Wang L, Zhang Z. Amplified cancer immunotherapy of a surface-engineered antigenic microparticle vaccine by synergistically modulating tumor microenvironment. ACS Nano. 2019;13:12553–66.

    CAS  PubMed  Article  Google Scholar 

  163. 163.

    Tang H, Xu X, Chen Y, Xin H, Wan T, Li B, Pan H, Li D, Ping Y. Reprogramming the tumor microenvironment through second-near-infrared-window photothermal genome editing of PD-L1 Mediated by supramolecular gold nanorods for enhanced cancer immunotherapy. Adv Mater. 2021;33:e2006003.

    PubMed  Article  CAS  Google Scholar 

  164. 164.

    Gong C, Yu X, Zhang W, Han L, Wang R, Wang Y, Gao S, Yuan Y. Regulating the immunosuppressive tumor microenvironment to enhance breast cancer immunotherapy using pH-responsive hybrid membrane-coated nanoparticles. J Nanobiotechnology. 2021;19:58.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  165. 165.

    Riaz N, Morris L, Havel JJ, Makarov V, Desrichard A, Chan TA. The role of neoantigens in response to immune checkpoint blockade. Int Immunol. 2016;28:411–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  166. 166.

    Reuben A, Spencer CN, Prieto PA, Gopalakrishnan V, Reddy SM, Miller JP, Mao X, De Macedo MP, Chen J, Song X, et al. Genomic and immune heterogeneity are associated with differential responses to therapy in melanoma. Genom Med. 2017;2:10.

    Article  CAS  Google Scholar 

  167. 167.

    McGranahan N, Furness AJ, Rosenthal R, Ramskov S, Lyngaa R, Saini SK, Jamal-Hanjani M, Wilson GA, Birkbak NJ, Hiley CT, et al. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science. 2016;351:1463–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  168. 168.

    Jin YB, Luo W, Zhang GY, Lin KR, Cui JH, Chen XP, Pan YM, Mao XF, Tang J, Wang YJ. TCR repertoire profiling of tumors, adjacent normal tissues, and peripheral blood predicts survival in nasopharyngeal carcinoma. Cancer Immunol Immunother. 2018;67:1719–30.

    CAS  PubMed  Article  Google Scholar 

  169. 169.

    Chen R, Lee WC, Fujimoto J, Li J, Hu X, Mehran R, Rice D, Swisher SG, Sepesi B, Tran HT, et al. Evolution of genomic and T-cell repertoire heterogeneity of malignant pleural mesothelioma under dasatinib treatment. Clin Cancer Res. 2020;26:5477–86.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  170. 170.

    Bai X, Zhang Q, Wu S, Zhang X, Wang M, He F, Wei T, Yang J, Lou Y, Cai Z, Liang T. Characteristics of tumor infiltrating lymphocyte and circulating lymphocyte repertoires in pancreatic cancer by the sequencing of T cell receptors. Sci Rep. 2015;5:13664.

    PubMed  PubMed Central  Article  Google Scholar 

  171. 171.

    Zhang J, Fujimoto J, Zhang J, Wedge DC, Song X, Zhang J, Seth S, Chow CW, Cao Y, Gumbs C, et al. Intratumor heterogeneity in localized lung adenocarcinomas delineated by multiregion sequencing. Science. 2014;346:256–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  172. 172.

    Matsushita H, Vesely MD, Koboldt DC, Rickert CG, Uppaluri R, Magrini VJ, Arthur CD, White JM, Chen YS, Shea LK, et al. Cancer exome analysis reveals a T-cell-dependent mechanism of cancer immunoediting. Nature. 2012;482:400–4.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  173. 173.

    Verdegaal EM, de Miranda NF, Visser M, Harryvan T, van Buuren MM, Andersen RS, Hadrup SR, van der Minne CE, Schotte R, Spits H, et al. Neoantigen landscape dynamics during human melanoma-T cell interactions. Nature. 2016;536:91–5.

    CAS  PubMed  Article  Google Scholar 

  174. 174.

    McGranahan N, Rosenthal R, Hiley CT, Rowan AJ, Watkins TBK, Wilson GA, Birkbak NJ, Veeriah S, Van Loo P, Herrero J, et al. Allele-specific HLA loss and immune escape in lung cancer evolution. Cell. 2017;171:1259-1271 e1211.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  175. 175.

    Scheper W, Kelderman S, Fanchi LF, Linnemann C, Bendle G, de Rooij MAJ, Hirt C, Mezzadra R, Slagter M, Dijkstra K, et al. Low and variable tumor reactivity of the intratumoral TCR repertoire in human cancers. Nat Med. 2019;25:89–94.

    CAS  PubMed  Article  Google Scholar 

  176. 176.

    Zaretsky JM, Garcia-Diaz A, Shin DS, Escuin-Ordinas H, Hugo W, Hu-Lieskovan S, Torrejon DY, Abril-Rodriguez G, Sandoval S, Barthly L, et al. Mutations associated with acquired resistance to PD-1 blockade in melanoma. N Engl J Med. 2016;375:819–29.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  177. 177.

    Gao J, Shi LZ, Zhao H, Chen J, Xiong L, He Q, Chen T, Roszik J, Bernatchez C, Woodman SE, et al. Loss of IFN-gamma pathway genes in tumor cells as a mechanism of resistance to anti-CTLA-4 therapy. Cell. 2016;167:397-404 e399.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  178. 178.

    Fish EN, Platanias LC. Interferon receptor signaling in malignancy: a network of cellular pathways defining biological outcomes. Mol Cancer Res. 2014;12:1691–703.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  179. 179.

    Williams JB, Li S, Higgs EF, Cabanov A, Wang X, Huang H, Gajewski TF. Tumor heterogeneity and clonal cooperation influence the immune selection of IFN-gamma-signaling mutant cancer cells. Nat Commun. 2020;11:602.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  180. 180.

    Shin DS, Zaretsky JM, Escuin-Ordinas H, Garcia-Diaz A, Hu-Lieskovan S, Kalbasi A, Grasso CS, Hugo W, Sandoval S, Torrejon DY, et al. Primary resistance to PD-1 blockade mediated by JAK1/2 mutations. Cancer Discov. 2017;7:188–201.

    CAS  Article  Google Scholar 

  181. 181.

    Hugo W, Zaretsky JM, Sun L, Song C, Moreno BH, Hu-Lieskovan S, Berent-Maoz B, Pang J, Chmielowski B, Cherry G, et al. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell. 2016;165:35–44.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  182. 182.

    Ayers M, Lunceford J, Nebozhyn M, Murphy E, Loboda A, Kaufman DR, Albright A, Cheng JD, Kang SP, Shankaran V, et al. IFN-gamma-related mRNA profile predicts clinical response to PD-1 blockade. J Clin Investig. 2017;127:2930–40.

    PubMed  PubMed Central  Article  Google Scholar 

  183. 183.

    Caspi E, Rosin-Arbesfeld R. A novel functional screen in human cells identifies MOCA as a negative regulator of Wnt signaling. Mol Biol Cell. 2008;19:4660–74.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  184. 184.

    Spranger S, Bao R, Gajewski TF. Melanoma-intrinsic beta-catenin signalling prevents anti-tumour immunity. Nature. 2015;523:231–5.

    CAS  PubMed  Article  Google Scholar 

  185. 185.

    Yaguchi T, Goto Y, Kido K, Mochimaru H, Sakurai T, Tsukamoto N, Kudo-Saito C, Fujita T, Sumimoto H, Kawakami Y. Immune suppression and resistance mediated by constitutive activation of Wnt/beta-catenin signaling in human melanoma cells. J Immunol. 2012;189:2110–7.

    CAS  PubMed  Article  Google Scholar 

  186. 186.

    Spranger S, Dai D, Horton B, Gajewski TF. Tumor-residing Batf3 dendritic cells are required for effector T cell trafficking and adoptive T cell therapy. Cancer Cell. 2017;31:711-723 e714.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  187. 187.

    Restifo NP, Smyth MJ, Snyder A. Acquired resistance to immunotherapy and future challenges. Nat Rev Cancer. 2016;16:121–6.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  188. 188.

    Sade-Feldman M, Jiao YJ, Chen JH, Rooney MS, Barzily-Rokni M, Eliane JP, Bjorgaard SL, Hammond MR, Vitzthum H, Blackmon SM, et al. Resistance to checkpoint blockade therapy through inactivation of antigen presentation. Nat Commun. 2017;8:1136.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  189. 189.

    Chapusot C, Martin L, Bouvier AM, Bonithon-Kopp C, Ecarnot-Laubriet A, Rageot D, Ponnelle T, Laurent Puig P, Faivre J, Piard F. Microsatellite instability and intratumoural heterogeneity in 100 right-sided sporadic colon carcinomas. Br J Cancer. 2002;87:400–4.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  190. 190.

    Tachon G, Frouin E, Karayan-Tapon L, Auriault ML, Godet J, Moulin V, Wang Q, Tougeron D. Heterogeneity of mismatch repair defect in colorectal cancer and its implications in clinical practice. Eur J Cancer. 2018;95:112–6.

    PubMed  Article  Google Scholar 

  191. 191.

    Nava Rodrigues D, Rescigno P, Liu D, Yuan W, Carreira S, Lambros MB, Seed G, Mateo J, Riisnaes R, Mullane S, et al. Immunogenomic analyses associate immunological alterations with mismatch repair defects in prostate cancer. J Clin Investig. 2018;128:4441–53.

    PubMed  PubMed Central  Article  Google Scholar 

  192. 192.

    Kloor M, Becker C, Benner A, Woerner SM, Gebert J, Ferrone S, von Knebel DM. Immunoselective pressure and human leukocyte antigen class I antigen machinery defects in microsatellite unstable colorectal cancers. Cancer Res. 2005;65:6418–24.

    CAS  PubMed  Article  Google Scholar 

  193. 193.

    Peltomaki P. Role of DNA mismatch repair defects in the pathogenesis of human cancer. J Clin Oncol. 2003;21:1174–9.

    CAS  PubMed  Article  Google Scholar 

  194. 194.

    Joost P, Veurink N, Holck S, Klarskov L, Bojesen A, Harbo M, Baldetorp B, Rambech E, Nilbert M. Heterogenous mismatch-repair status in colorectal cancer. Diagn Pathol. 2014;9:126.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  195. 195.

    Llosa NJ, Luber B, Siegel N, Awan AH, Oke T, Zhu Q, Bartlett BR, Aulakh LK, Thompson ED, Jaffee EM, et al. Immunopathologic stratification of colorectal cancer for checkpoint blockade immunotherapy. Cancer Immunol Res. 2019;7:1574–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  196. 196.

    Llosa NJ, Luber B, Tam AJ, Smith KN, Siegel N, Awan AH, Fan H, Oke T, Zhang J, Domingue J, et al. Intratumoral Adaptive Immunosuppression and Type 17 Immunity in Mismatch Repair Proficient Colorectal Tumors. Clin Cancer Res. 2019;25:5250–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  197. 197.

    Swift SL, Duffy S, Lang SH. Impact of tumor heterogeneity and tissue sampling for genetic mutation testing: a systematic review and post hoc analysis. J Clin Epidemiol. 2020;126:45–55.

    PubMed  Article  Google Scholar 

  198. 198.

    Vanderwalde A, Spetzler D, Xiao N, Gatalica Z, Marshall J. Microsatellite instability status determined by next-generation sequencing and compared with PD-L1 and tumor mutational burden in 11,348 patients. Cancer Med. 2018;7:746–56.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  199. 199.

    Salipante SJ, Scroggins SM, Hampel HL, Turner EH, Pritchard CC. Microsatellite instability detection by next generation sequencing. Clin Chem. 2014;60:1192–9.

    CAS  PubMed  Article  Google Scholar 

  200. 200.

    Niu B, Ye K, Zhang Q, Lu C, Xie M, McLellan MD, Wendl MC, Ding L. MSIsensor: microsatellite instability detection using paired tumor-normal sequence data. Bioinformatics. 2014;30:1015–6.

    CAS  PubMed  Article  Google Scholar 

  201. 201.

    Hempelmann JA, Lockwood CM, Konnick EQ, Schweizer MT, Antonarakis ES, Lotan TL, Montgomery B, Nelson PS, Klemfuss N, Salipante SJ, Pritchard CC. Microsatellite instability in prostate cancer by PCR or next-generation sequencing. J Immunother Cancer. 2018;6:29.

    PubMed  PubMed Central  Article  Google Scholar 

  202. 202.

    Kautto EA, Bonneville R, Miya J, Yu L, Krook MA, Reeser JW, Roychowdhury S. Performance evaluation for rapid detection of pan-cancer microsatellite instability with MANTIS. Oncotarget. 2017;8:7452–63.

    PubMed  Article  Google Scholar 

  203. 203.

    Lawson DA, Kessenbrock K, Davis RT, Pervolarakis N, Werb Z. Tumour heterogeneity and metastasis at single-cell resolution. Nat Cell Biol. 2018;20:1349–60.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  204. 204.

    Hiley C, de Bruin EC, McGranahan N, Swanton C. Deciphering intratumor heterogeneity and temporal acquisition of driver events to refine precision medicine. Genome Biol. 2014;15:453.

    PubMed  PubMed Central  Article  Google Scholar 

  205. 205.

    Zhang Y, Song J, Zhao Z, Yang M, Chen M, Liu C, Ji J, Zhu D. Single-cell transcriptome analysis reveals tumor immune microenvironment heterogenicity and granulocytes enrichment in colorectal cancer liver metastases. Cancer Lett. 2020;470:84–94.

    CAS  PubMed  Article  Google Scholar 

  206. 206.

    Ramakrishna S, Shah NN. Using single-cell analysis to predict CAR T cell outcomes. Nat Med. 2020;26:1813–4.

    CAS  PubMed  Article  Google Scholar 

  207. 207.

    Zhou Y, Yang D, Yang Q, Lv X, Huang W, Zhou Z, Wang Y, Zhang Z, Yuan T, Ding X, et al. Single-cell RNA landscape of intratumoral heterogeneity and immunosuppressive microenvironment in advanced osteosarcoma. Nat Commun. 2020;11:6322.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  208. 208.

    Redmond D, Poran A, Elemento O. Single-cell TCRseq: paired recovery of entire T-cell alpha and beta chain transcripts in T-cell receptors from single-cell RNAseq. Genome Med. 2016;8:80.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  209. 209.

    Bradley P, Thomas PG. Using T cell receptor repertoires to understand the principles of adaptive immune recognition. Annu Rev Immunol. 2019;37:547–70.

    CAS  PubMed  Article  Google Scholar 

  210. 210.

    Jiang N, Schonnesen AA, Ma KY. Ushering in integrated T cell repertoire profiling in cancer. Trends Cancer. 2019;5:85–94.

    CAS  PubMed  Article  Google Scholar 

  211. 211.

    Griffiths JI, Wallet P, Pflieger LT, Stenehjem D, Liu X, Cosgrove PA, Leggett NA, McQuerry JA, Shrestha G, Rossetti M, et al. Circulating immune cell phenotype dynamics reflect the strength of tumor-immune cell interactions in patients during immunotherapy. Proc Natl Acad Sci USA. 2020;117:16072–82.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  212. 212.

    Nirschl CJ, Suarez-Farinas M, Izar B, Prakadan S, Dannenfelser R, Tirosh I, Liu Y, Zhu Q, Devi KSP, Carroll SL, et al. IFNgamma-dependent tissue-immune homeostasis is co-opted in the tumor microenvironment. Cell. 2017;170:127-141 e115.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  213. 213.

    Mitra AK, Mukherjee UK, Harding T, Jang JS, Stessman H, Li Y, Abyzov A, Jen J, Kumar S, Rajkumar V, Van Ness B. Single-cell analysis of targeted transcriptome predicts drug sensitivity of single cells within human myeloma tumors. Leukemia. 2016;30:1094–102.

    CAS  PubMed  Article  Google Scholar 

  214. 214.

    Sarobe P, Corrales F. Getting insights into hepatocellular carcinoma tumour heterogeneity by multiomics dissection. Gut. 2019;68:1913–4.

    CAS  PubMed  Article  Google Scholar 

  215. 215.

    Job S, Rapoud D, Dos Santos A, Gonzalez P, Desterke C, Pascal G, Elarouci N, Ayadi M, Adam R, Azoulay D, et al. Identification of Four Immune Subtypes Characterized by Distinct Composition and Functions of Tumor Microenvironment in Intrahepatic Cholangiocarcinoma. Hepatology. 2020;72:965–81.

    CAS  PubMed  Article  Google Scholar 

  216. 216.

    Oxnard GR, Thress KS, Alden RS, Lawrance R, Paweletz CP, Cantarini M, Yang JC, Barrett JC, Janne PA. Association between plasma genotyping and outcomes of treatment with osimertinib (AZD9291) in advanced non-small-cell lung cancer. J Clin Oncol. 2016;34:3375–82.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  217. 217.

    Nagrath S, Sequist LV, Maheswaran S, Bell DW, Irimia D, Ulkus L, Smith MR, Kwak EL, Digumarthy S, Muzikansky A, et al. Isolation of rare circulating tumour cells in cancer patients by microchip technology. Nature. 2007;450:1235–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  218. 218.

    Chabon JJ, Simmons AD, Lovejoy AF, Esfahani MS, Newman AM, Haringsma HJ, Kurtz DM, Stehr H, Scherer F, Karlovich CA, et al. Circulating tumour DNA profiling reveals heterogeneity of EGFR inhibitor resistance mechanisms in lung cancer patients. Nat Commun. 2016;7:11815.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  219. 219.

    San Lucas FA, Allenson K, Bernard V, Castillo J, Kim DU, Ellis K, Ehli EA, Davies GE, Petersen JL, Li D, et al. Minimally invasive genomic and transcriptomic profiling of visceral cancers by next-generation sequencing of circulating exosomes. Ann Oncol. 2016;27:635–41.

    CAS  PubMed  Article  Google Scholar 

  220. 220.

    Bettegowda C, Sausen M, Leary RJ, Kinde I, Wang Y, Agrawal N, Bartlett BR, Wang H, Luber B, Alani RM, et al. Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci Transl Med. 2014;6:224ra224.

    Article  CAS  Google Scholar 

  221. 221.

    Fojo T, Mailankody S, Lo A. Unintended consequences of expensive cancer therapeutics-the pursuit of marginal indications and a me-too mentality that stifles innovation and creativity: the John Conley Lecture. JAMA Otolaryngol Head Neck Surg. 2014;140:1225–36.

    PubMed  Article  Google Scholar 

  222. 222.

    da Silva FC, Oliveira P. Tumor clone dynamics in lethal prostate cancer. Eur Urol. 2017;71:142–3.

    PubMed  Article  Google Scholar 

  223. 223.

    Murtaza M, Dawson SJ, Tsui DW, Gale D, Forshew T, Piskorz AM, Parkinson C, Chin SF, Kingsbury Z, Wong AS, et al. Non-invasive analysis of acquired resistance to cancer therapy by sequencing of plasma DNA. Nature. 2013;497:108–12.

    CAS  PubMed  Article  Google Scholar 

  224. 224.

    Karlovich C, Goldman JW, Sun JM, Mann E, Sequist LV, Konopa K, Wen W, Angenendt P, Horn L, Spigel D, et al. Assessment of EGFR mutation status in matched plasma and tumor tissue of NSCLC patients from a phase I study of rociletinib (CO-1686). Clin Cancer Res. 2016;22:2386–95.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  225. 225.

    Thierry AR, El Messaoudi S, Mollevi C, Raoul JL, Guimbaud R, Pezet D, Artru P, Assenat E, Borg C, Mathonnet M, et al. Clinical utility of circulating DNA analysis for rapid detection of actionable mutations to select metastatic colorectal patients for anti-EGFR treatment. Ann Oncol. 2017;28:2149–59.

    CAS  PubMed  Article  Google Scholar 

  226. 226.

    Parikh AR, Leshchiner I, Elagina L, Goyal L, Levovitz C, Siravegna G, Livitz D, Rhrissorrakrai K, Martin EE, Van Seventer EE, et al. Liquid versus tissue biopsy for detecting acquired resistance and tumor heterogeneity in gastrointestinal cancers. Nat Med. 2019;25:1415–21.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  227. 227.

    Zhang Y, Yao Y, Xu Y, Li L, Gong Y, Zhang K, Zhang M, Guan Y, Chang L, Xia X, et al. Pan-cancer circulating tumor DNA detection in over 10,000 Chinese patients. Nat Commun. 2021;12:11.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  228. 228.

    Nakamura Y, Taniguchi H, Ikeda M, Bando H, Kato K, Morizane C, Esaki T, Komatsu Y, Kawamoto Y, Takahashi N, et al. Clinical utility of circulating tumor DNA sequencing in advanced gastrointestinal cancer: SCRUM-Japan GI-SCREEN and GOZILA studies. Nat Med. 2020;26:1859–64.

    CAS  PubMed  Article  Google Scholar 

  229. 229.

    Ritch E, Fu SYF, Herberts C, Wang G, Warner EW, Schonlau E, Taavitsainen S, Murtha AJ, Vandekerkhove G, Beja K, et al. Identification of Hypermutation and Defective Mismatch Repair in ctDNA from Metastatic Prostate Cancer. Clin Cancer Res. 2020;26:1114–25.

    CAS  PubMed  Article  Google Scholar 

  230. 230.

    Willis J, Lefterova MI, Artyomenko A, Kasi PM, Nakamura Y, Mody K, Catenacci DVT, Fakih M, Barbacioru C, Zhao J, et al. Validation of microsatellite instability detection using a comprehensive plasma-based genotyping panel. Clin Cancer Res. 2019;25:7035–45.

    CAS  PubMed  Article  Google Scholar 

  231. 231.

    Xi L, Pham TH, Payabyab EC, Sherry RM, Rosenberg SA, Raffeld M. Circulating tumor DNA as an early indicator of response to T-cell transfer immunotherapy in metastatic melanoma. Clin Cancer Res. 2016;22:5480–6.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  232. 232.

    Rzhevskiy A, Kapitannikova A, Malinina P, Volovetsky A, Aboulkheyr Es H, Kulasinghe A, Thiery JP, Maslennikova A, Zvyagin AV, Ebrahimi Warkiani M. Emerging role of circulating tumor cells in immunotherapy. Theranostics. 2021;11:8057–75.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  233. 233.

    Yue C, Jiang Y, Li P, Wang Y, Xue J, Li N, Li D, Wang R, Dang Y, Hu Z, et al. Dynamic change of PD-L1 expression on circulating tumor cells in advanced solid tumor patients undergoing PD-1 blockade therapy. Oncoimmunology. 2018;7:e1438111.

    PubMed  PubMed Central  Article  Google Scholar 

  234. 234.

    Zhong X, Zhang H, Zhu Y, Liang Y, Yuan Z, Li J, Li J, Li X, Jia Y, He T, et al. Circulating tumor cells in cancer patients: developments and clinical applications for immunotherapy. Mol Cancer. 2020;19:15.

    PubMed  PubMed Central  Article  Google Scholar 

  235. 235.

    Strati A, Koutsodontis G, Papaxoinis G, Angelidis I, Zavridou M, Economopoulou P, Kotsantis I, Avgeris M, Mazel M, Perisanidis C, et al. Prognostic significance of PD-L1 expression on circulating tumor cells in patients with head and neck squamous cell carcinoma. Ann Oncol. 2017;28:1923–33.

    CAS  PubMed  Article  Google Scholar 

  236. 236.

    Lin M, Liang SZ, Shi J, Niu LZ, Chen JB, Zhang MJ, Xu KC. Circulating tumor cell as a biomarker for evaluating allogenic NK cell immunotherapy on stage IV non-small cell lung cancer. Immunol Lett. 2017;191:10–5.

    CAS  PubMed  Article  Google Scholar 

  237. 237.

    Qin Z, Chen J, Zeng J, Niu L, Xie S, Wang X, Liang Y, Wu Z, Zhang M. Effect of NK cell immunotherapy on immune function in patients with hepatic carcinoma: a preliminary clinical study. Cancer Biol Ther. 2017;18:323–30.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  238. 238.

    Lin SY, Chang SC, Lam S, Irene Ramos R, Tran K, Ohe S, Salomon MP, Bhagat AAS, Teck Lim C, Fischer TD, et al. Prospective molecular profiling of circulating tumor cells from patients with melanoma receiving combinatorial immunotherapy. Clin Chem. 2020;66:169–77.

    PubMed  PubMed Central  Article  Google Scholar 

  239. 239.

    Gandara DR, Paul SM, Kowanetz M, Schleifman E, Zou W, Li Y, Rittmeyer A, Fehrenbacher L, Otto G, Malboeuf C, et al. Blood-based tumor mutational burden as a predictor of clinical benefit in non-small-cell lung cancer patients treated with atezolizumab. Nat Med. 2018;24:1441–8.

    CAS  PubMed  Article  Google Scholar 

  240. 240.

    Bensch F, van der Veen EL, Lub-de Hooge MN, Jorritsma-Smit A, Boellaard R, Kok IC, Oosting SF, Schroder CP, Hiltermann TJN, van der Wekken AJ, et al. (89)Zr-atezolizumab imaging as a non-invasive approach to assess clinical response to PD-L1 blockade in cancer. Nat Med. 2018;24:1852–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  241. 241.

    Chatterjee S, Lesniak WG, Gabrielson M, Lisok A, Wharram B, Sysa-Shah P, Azad BB, Pomper MG, Nimmagadda S. A humanized antibody for imaging immune checkpoint ligand PD-L1 expression in tumors. Oncotarget. 2016;7:10215–27.

    PubMed  PubMed Central  Article  Google Scholar 

  242. 242.

    Heskamp S, Hobo W, Molkenboer-Kuenen JD, Olive D, Oyen WJ, Dolstra H, Boerman OC. Noninvasive imaging of tumor PD-L1 expression using radiolabeled anti-PD-L1 antibodies. Cancer Res. 2015;75:2928–36.

    CAS  PubMed  Article  Google Scholar 

  243. 243.

    Hampel H, Frankel WL, Martin E, Arnold M, Khanduja K, Kuebler P, Nakagawa H, Sotamaa K, Prior TW, Westman J, et al. Screening for the Lynch syndrome (hereditary nonpolyposis colorectal cancer). N Engl J Med. 2005;352:1851–60.

    CAS  PubMed  Article  Google Scholar 

  244. 244.

    Cortes-Ciriano I, Lee S, Park WY, Kim TM, Park PJ. A molecular portrait of microsatellite instability across multiple cancers. Nat Commun. 2017;8:15180.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  245. 245.

    Middha S, Zhang L, Nafa K, Jayakumaran G, Wong D, Kim HR, Sadowska J, Berger MF, Delair DF, Shia J, et al. Reliable pan-cancer microsatellite instability assessment by using targeted next-generation sequencing data. JCO Precis Oncol. 2017;2017:PO.17.00084.

    Google Scholar 

  246. 246.

    Zighelboim I, Goodfellow PJ, Gao F, Gibb RK, Powell MA, Rader JS, Mutch DG. Microsatellite instability and epigenetic inactivation of MLH1 and outcome of patients with endometrial carcinomas of the endometrioid type. J Clin Oncol. 2007;25:2042–8.

    CAS  PubMed  Article  Google Scholar 

  247. 247.

    National Cancer Genome Atlas Research. Comprehensive molecular characterization of gastric adenocarcinoma. Nature. 2014;513:202–9.

    Article  CAS  Google Scholar 

  248. 248.

    Murphy MA, Wentzensen N. Frequency of mismatch repair deficiency in ovarian cancer: a systematic review This article is a US Government work and as such, is in the public domain of the United States of America. Int J Cancer. 2011;129:1914–22.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  249. 249.

    Chiappini F, Gross-Goupil M, Saffroy R, Azoulay D, Emile JF, Veillhan LA, Delvart V, Chevalier S, Bismuth H, Debuire B, Lemoine A. Microsatellite instability mutator phenotype in hepatocellular carcinoma in non-alcoholic and non-virally infected normal livers. Carcinogenesis. 2004;25:541–7.

    CAS  PubMed  Article  Google Scholar 

  250. 250.

    Stoehr C, Burger M, Stoehr R, Bertz S, Ruemmele P, Hofstaedter F, Denzinger S, Wieland WF, Hartmann A, Walter B. Mismatch repair proteins hMLH1 and hMSH2 are differently expressed in the three main subtypes of sporadic renal cell carcinoma. Pathobiology. 2012;79:162–8.

    CAS  PubMed  Article  Google Scholar 

  251. 251.

    Schneider B, Glass A, Jagdmann S, Huhns M, Claus J, Zettl H, Drager DL, Maruschke M, Hakenberg OW, Erbersdobler A, Zimpfer A. Loss of mismatch-repair protein expression and microsatellite instability in upper tract urothelial carcinoma and clinicopathologic implications. Clin Genitourin Cancer. 2020;18:e563–72.

    PubMed  Article  Google Scholar 

  252. 252.

    Ruemmele P, Dietmaier W, Terracciano L, Tornillo L, Bataille F, Kaiser A, Wuensch PH, Heinmoeller E, Homayounfar K, Luettges J, et al. Histopathologic features and microsatellite instability of cancers of the papilla of vater and their precursor lesions. Am J Surg Pathol. 2009;33:691–704.

    PubMed  Article  Google Scholar 

  253. 253.

    Karpinska-Kaczmarczyk K, Lewandowska M, Lawniczak M, Bialek A, Urasinska E. Expression of mismatch repair proteins in early and advanced gastric cancer in Poland. Med Sci Monit. 2016;22:2886–92.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  254. 254.

    Lee-Kong SA, Markowitz AJ, Glogowski E, Papadopoulos C, Stadler Z, Weiser MR, Temple LK, Guillem JG. Prospective immunohistochemical analysis of primary colorectal cancers for loss of mismatch repair protein expression. Clin Colorectal Cancer. 2010;9:255–9.

    PubMed  Article  Google Scholar 

  255. 255.

    Tessier-Cloutier B, Schaeffer DF, Bacani J, Marginean CE, Kalloger S, Kobel M, Lee CH. Loss of switch/sucrose non-fermenting complex protein expression in undifferentiated gastrointestinal and pancreatic carcinomas. Histopathology. 2020;77:46–54.

    PubMed  Article  Google Scholar 

  256. 256.

    Caccese M, Ius T, Simonelli M, Fassan M, Cesselli D, Dipasquale A, Cavallin F, Padovan M, Salvalaggio A, Gardiman MP, et al. Mismatch-repair protein expression in high-grade gliomas: a large retrospective multicenter study. Int J Mol Sci. 2020;21:6716.

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  257. 257.

    Indraccolo S, Lombardi G, Fassan M, Pasqualini L, Giunco S, Marcato R, Gasparini A, Candiotto C, Nalio S, Fiduccia P, et al. Genetic, epigenetic, and immunologic profiling of MMR-deficient relapsed glioblastoma. Clin Cancer Res. 2019;25:1828–37.

    CAS  PubMed  Article  Google Scholar 

  258. 258.

    Sharma M, Yang Z, Miyamoto H. Loss of DNA mismatch repair proteins in prostate cancer. Medicine (Baltimore). 2020;99:e20124.

    CAS  Article  Google Scholar 

  259. 259.

    Huang HN, Kuo CW, Lin MC, Mao TL, Kuo KT. Frequent CTNNB1 or PIK3CA mutations occurred in endometrial endometrioid adenocarcinoma with high levels of microsatellite instability and loss of MSH2/MSH6 expression. Appl Immunohistochem Mol Morphol. 2020;28:284–9.

    CAS  PubMed  Article  Google Scholar 

  260. 260.

    de Jong RA, Boerma A, Boezen HM, Mourits MJ, Hollema H, Nijman HW. Loss of HLA class I and mismatch repair protein expression in sporadic endometrioid endometrial carcinomas. Int J Cancer. 2012;131:1828–36.

    PubMed  Article  CAS  Google Scholar 

  261. 261.

    Poulsen TS, de Oliveira D, Espersen MLM, Klarskov LL, Skovrider-Ruminski W, Hogdall E. Frequency and coexistence of KRAS, NRAS, BRAF and PIK3CA mutations and occurrence of MMR deficiency in Danish colorectal cancer patients. APMIS. 2021;129:61–9.

    CAS  PubMed  Article  Google Scholar 

  262. 262.

    Sarode VR, Robinson L. Screening for lynch syndrome by immunohistochemistry of mismatch repair proteins: significance of indeterminate result and correlation with mutational studies. Arch Pathol Lab Med. 2019;143:1225–33.

    CAS  PubMed  Article  Google Scholar 

  263. 263.

    House MG, Herman JG, Guo MZ, Hooker CM, Schulick RD, Lillemoe KD, Cameron JL, Hruban RH, Maitra A, Yeo CJ. Aberrant hypermethylation of tumor suppressor genes in pancreatic endocrine neoplasms. Ann Surg. 2003;238:423–31 (discussion 431-422).

    PubMed  PubMed Central  Article  Google Scholar 

  264. 264.

    Mei M, Deng D, Liu TH, Sang XT, Lu X, Xiang HD, Zhou J, Wu H, Yang Y, Chen J, et al. Clinical implications of microsatellite instability and MLH1 gene inactivation in sporadic insulinomas. J Clin Endocrinol Metab. 2009;94:3448–57.

    CAS  PubMed  Article  Google Scholar 

  265. 265.

    Lavin Y, Kobayashi S, Leader A, Amir ED, Elefant N, Bigenwald C, Remark R, Sweeney R, Becker CD, Levine JH, et al. Innate immune landscape in early lung adenocarcinoma by paired single-cell analyses. Cell. 2017;169:750-765 e717.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  266. 266.

    Yan T, Cui H, Zhou Y, Yang B, Kong P, Zhang Y, Liu Y, Wang B, Cheng Y, Li J, et al. Multi-region sequencing unveils novel actionable targets and spatial heterogeneity in esophageal squamous cell carcinoma. Nat Commun. 2019;10:1670.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  267. 267.

    Sherwood AM, Emerson RO, Scherer D, Habermann N, Buck K, Staffa J, Desmarais C, Halama N, Jaeger D, Schirmacher P, et al. Tumor-infiltrating lymphocytes in colorectal tumors display a diversity of T cell receptor sequences that differ from the T cells in adjacent mucosal tissue. Cancer Immunol Immunother. 2013;62:1453–61.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  268. 268.

    Zhang AW, McPherson A, Milne K, Kroeger DR, Hamilton PT, Miranda A, Funnell T, Little N, de Souza CPE, Laan S, et al. Interfaces of malignant and immunologic clonal dynamics in ovarian cancer. Cell. 2018;173:1755-1769 e1722.

    CAS  PubMed  Article  Google Scholar 

  269. 269.

    Yuzhakova DV, Volchkova LN, Pogorelyy MV, Serebrovskaya EO, Shagina IA, Bryushkova EA, Nakonechnaya TO, Izosimova AV, Zavyalova DS, Karabut MM, et al. Measuring intratumoral heterogeneity of immune repertoires. Front Oncol. 2020;10:512.

    PubMed  PubMed Central  Article  Google Scholar 

  270. 270.

    Tirosh I, Izar B, Prakadan SM, Wadsworth MH 2nd, Treacy D, Trombetta JJ, Rotem A, Rodman C, Lian C, Murphy G, et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science. 2016;352:189–96.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  271. 271.

    Gerlinger M, Quezada SA, Peggs KS, Furness AJ, Fisher R, Marafioti T, Shende VH, McGranahan N, Rowan AJ, Hazell S, et al. Ultra-deep T cell receptor sequencing reveals the complexity and intratumour heterogeneity of T cell clones in renal cell carcinomas. J Pathol. 2013;231:424–32.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  272. 272.

    Jiménez-Sánchez A, Memon D, Pourpe S, Veeraraghavan H, Li Y, Vargas HA, Gill MB, Park KJ, Zivanovic O, Konner J, et al. Heterogeneous tumor-immune microenvironments among differentially growing metastases in an ovarian cancer patient. Cell. 2017;170:927-938.e920.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  273. 273.

    Emerson RO, Sherwood AM, Rieder MJ, Guenthoer J, Williamson DW, Carlson CS, Drescher CW, Tewari M, Bielas JH, Robins HS. High-throughput sequencing of T-cell receptors reveals a homogeneous repertoire of tumour-infiltrating lymphocytes in ovarian cancer. J Pathol. 2013;231:433–40.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  274. 274.

    Wang T, Wang C, Wu J, He C, Zhang W, Liu J, Zhang R, Lv Y, Li Y, Zeng X, et al. The different T-cell receptor repertoires in breast cancer tumors, draining lymph nodes, and adjacent tissues. Cancer Immunol Res. 2017;5:148–56.

    CAS  PubMed  Article  Google Scholar 

  275. 275.

    Pasetto A, Gros A, Robbins PF, Deniger DC, Prickett TD, Matus-Nicodemos R, Douek DC, Howie B, Robins H, Parkhurst MR, et al. Tumor- and neoantigen-reactive T-cell receptors can be identified based on their frequency in fresh tumor. Cancer Immunol Res. 2016;4:734–43.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  276. 276.

    Sheng J, Wang H, Liu X, Deng Y, Yu Y, Xu P, Shou J, Pan H, Li H, Zhou X, et al. Deep sequencing of T-cell receptors for monitoring peripheral CD8(+) T cells in chinese advanced non-small-cell lung cancer patients treated with the anti-PD-L1 antibody. Front Mol Biosci. 2021;8:679130.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  277. 277.

    Wang J, Bie Z, Zhang Y, Li L, Zhu Y, Zhang Y, Nie X, Zhang P, Cheng G, Di X, et al. Prognostic value of the baseline circulating T cell receptor beta chain diversity in advanced lung cancer. Oncoimmunology. 2021;10:1899609.

    PubMed  PubMed Central  Article  Google Scholar 

  278. 278.

    Aoki H, Ueha S, Shichino S, Ogiwara H, Hashimoto SI, Kakimi K, Ito S, Matsushima K. TCR repertoire analysis reveals mobilization of novel CD8(+) T cell clones into the cancer-immunity cycle following anti-CD4 antibody administration. Front Immunol. 2018;9:3185.

    CAS  PubMed  Article  Google Scholar 

  279. 279.

    Angelova M, Mlecnik B, Vasaturo A, Bindea G, Fredriksen T, Lafontaine L, Buttard B, Morgand E, Bruni D, Jouret-Mourin A, et al. Evolution of metastases in space and time under immune selection. Cell. 2018;175:751-765 e716.

    CAS  PubMed  Article  Google Scholar 

  280. 280.

    Le DT, Kim TW, Van Cutsem E, Geva R, Jager D, Hara H, Burge M, O’Neil B, Kavan P, Yoshino T, 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;38:11–9.

    CAS  PubMed  Article  Google Scholar 

  281. 281.

    Marabelle A, Le DT, Ascierto PA, Di Giacomo AM, De Jesus-Acosta A, Delord JP, Geva R, Gottfried M, Penel N, Hansen AR, et al. Efficacy of pembrolizumab in patients with noncolorectal high microsatellite instability/mismatch repair-deficient cancer: results from the phase II KEYNOTE-158 study. J Clin Oncol. 2020;38:1–10.

    CAS  PubMed  Article  Google Scholar 

  282. 282.

    Abida W, Cheng ML, Armenia J, Middha S, Autio KA, Vargas HA, Rathkopf D, Morris MJ, Danila DC, Slovin SF, et al. Analysis of the prevalence of microsatellite instability in prostate cancer and response to immune checkpoint blockade. JAMA Oncol. 2019;5:471–8.

    PubMed  Article  Google Scholar 

  283. 283.

    Andre T, Shiu KK, Kim TW, Jensen BV, Jensen LH, Punt C, Smith D, Garcia-Carbonero R, Benavides M, Gibbs P, et al. Pembrolizumab in microsatellite-instability-high advanced colorectal cancer. N Engl J Med. 2020;383:2207–18.

    CAS  PubMed  Article  Google Scholar 

  284. 284.

    Wang Z, Duan J, Cai S, Han M, Dong H, Zhao J, Zhu B, Wang S, Zhuo M, Sun J, et al. Assessment of blood tumor mutational burden as a potential biomarker for immunotherapy in patients with non-small cell lung cancer with use of a next-generation sequencing cancer gene panel. JAMA Oncol. 2019;5:696–702.

    PubMed  PubMed Central  Article  Google Scholar 

  285. 285.

    Cristescu R, Mogg R, Ayers M, Albright A, Murphy E, Yearley J, Sher X, Liu XQ, Lu H, Nebozhyn M, et al. Pan-tumor genomic biomarkers for PD-1 checkpoint blockade-based immunotherapy. Science. 2018;362:eaar3593.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

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Figures 1, 2 and 4 are created using We are thankful to many scientists in the field whose seminal works are not cited due to space constraints. We would like to express our gratitude to all those who helped us during the writing of this review. We gratefully acknowledge the help of our supervisors, Professor Hong Shen, Professor Shan Zeng and Ms. Ying Han, who have offered us valuable suggestions in the academic studies. The completion of this review would not have been possible without their expert guidance and insightful criticism throughout the preparation of the review. We also owe a special debt of gratitude to Mr. Changjing Cai, Mr. Ziyang Feng, Mr. Hao Zhang and Mr. Chao Quan, from whose constructive suggestions we benefited a lot during the preparation of the manuscript. We are thankful to many colleagues and friends who give professional suggestions to this work. We would finally like to express our gratitude to our beloved parents for their unconditional love and support.


This study was supported by grants from the National Key R&D Program of China (No. 2018YFC1313300), National Natural Science Foundation of China (Nos. 81070362, 81172470, 81372629, 81772627, 81874073 and 81974384), two key projects from the Nature Science Foundation of Hunan Province (Nos. 2021JJ31092 & 2021JJ31048) and two projects from CSCO Cancer Research Foundation (Nos. Y-HR2019-0182 and Y-2019Genecast-043).

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HS, SZ, WW and YL had the idea for the article, WW and YL performed the literature search and finished the manuscript and figures; WW and YL finished the tables; HS, SZ and YH made critical revisions and proofread the manuscript. All authors read and approved the final manuscript.

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Correspondence to Shan Zeng, Ying Han or Hong Shen.

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Wu, W., Liu, Y., Zeng, S. et al. Intratumor heterogeneity: the hidden barrier to immunotherapy against MSI tumors from the perspective of IFN-γ signaling and tumor-infiltrating lymphocytes. J Hematol Oncol 14, 160 (2021).

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  • Microsatellite instability
  • Immunotherapy
  • Tumor-infiltrating lymphocytes
  • IFN-γ signaling
  • Heterogeneity