TIM-3 as a promising target for cancer immunotherapy in a wide range of tumors

Sauer, Natalia; Janicka, Natalia; Szlasa, Wojciech; Skinderowicz, Bartłomiej; Kołodzińska, Katarzyna; Dwernicka, Wioletta; Oślizło, Małgorzata; Kulbacka, Julita; Novickij, Vitalij; Karłowicz-Bodalska, Katarzyna

doi:10.1007/s00262-023-03516-1

Cited by 46 publications

(14 citation statements)

References 193 publications

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“…TIM-3 exhibits a distinct structural profile from those of other immunosuppressive molecules, and is characterized by the presence of 5 conserved tyrosine residues 95,96 . According to previous research, human monocytes, macrophages, NK cells, DCs, and CD4 + and CD8 + T cells (Table 2) produce TIM-3 97,98 . Galectin-9 (Gal-9), carcinoembryonic antigen-related cell adhesion molecule-1 (CEACAM-1), phosphatidylserine (PtdSer), and high-mobility group box-1 (HMGB-1) are its ligands 97,99 .…”

Section: Brief Description Of Tim-3mentioning

confidence: 70%

“…According to previous research, human monocytes, macrophages, NK cells, DCs, and CD4 + and CD8 + T cells (Table 2) produce TIM-3 97,98 . Galectin-9 (Gal-9), carcinoembryonic antigen-related cell adhesion molecule-1 (CEACAM-1), phosphatidylserine (PtdSer), and high-mobility group box-1 (HMGB-1) are its ligands 97,99 . These ligands bind the target protein TIM-3, which is present in many types of cancer cells, including those causing colorectal cancer 100 , cervical cancer 101 , ovarian cancer 74 , gastric cancer 102 , and other cancers 103 .…”

Section: Brief Description Of Tim-3mentioning

confidence: 70%

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Immune checkpoint inhibitors: breakthroughs in cancer treatment

Kong,

Zhang,

Chen

et al. 2024

Cancer Biol Med

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Over the past two decades, immunotherapies have increasingly been considered as first-line treatments for most cancers. One such treatment is immune checkpoint blockade (ICB), which has demonstrated promising results against various solid tumors in clinical trials. Monoclonal antibodies (mAbs) are currently available as immune checkpoint inhibitors (ICIs). These ICIs target specific immune checkpoints, including cytotoxic T-lymphocyte-associated antigen-4 (CTLA-4) and programmed cell death protein 1 (PD-1). Clinical trial results strongly support the feasibility of this immunotherapeutic approach. However, a substantial proportion of patients with cancer develop resistance or tolerance to treatment, owing to tumor immune evasion mechanisms that counteract the host immune response. Consequently, substantial research focus has been aimed at identifying additional ICIs or synergistic inhibitory receptors to enhance the effectiveness of anti-PD-1, anti-programmed cell death ligand 1 (anti-PD-L1), and anti-CTLA-4 treatments. Recently, several immune checkpoint molecular targets have been identified, such as T cell immunoreceptor with Ig and ITIM domains (TIGIT), mucin domain containing-3 (TIM-3), lymphocyte activation gene-3 (LAG-3), V-domain immunoglobulin suppressor of T cell activation (VISTA), B and T lymphocyte attenuator (BTLA), and signal-regulatory protein α (SIRPα). Functional mAbs targeting these molecules are under development. CTLA-4, PD-1/PD-L1, and other recently discovered immune checkpoint proteins with distinct structures are at the forefront of research. This review discusses these structures, as well as clinical progress in mAbs targeting these immune checkpoint molecules and their potential applications.

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Section: Brief Description Of Tim-3mentioning

confidence: 70%

Section: Brief Description Of Tim-3mentioning

confidence: 70%

Immune checkpoint inhibitors: breakthroughs in cancer treatment

Kong,

Zhang,

Chen

et al. 2024

Cancer Biol Med

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“…The density of T cells, particularly CD3+ cells, was significantly higher in responders, underscoring their potential for predicting pathological remission (25)(26)(27)(28). Of course, numerous immune checkpoint-related biomarkers are potentially valuable for efficacy evaluation (29)(30)(31)(32)(33)(34)(35)(36). The dynamic analysis of samples before and after NICT treatment revealed an increase in immune cell populations, especially in responders, reinforcing the link between immune cell accumulation and treatment success (37,38).…”

Section: Discussionmentioning

confidence: 99%

To develop a prognostic model for neoadjuvant immunochemotherapy efficacy in esophageal squamous cell carcinoma by analyzing the immune microenvironment

Yehan,

Sheng,

Hong

et al. 2024

Front. Immunol.

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ObjectiveThe choice of neoadjuvant therapy for esophageal squamous cell carcinoma (ESCC) is controversial. This study aims to provide a basis for clinical treatment selection by establishing a predictive model for the efficacy of neoadjuvant immunochemotherapy (NICT).MethodsA retrospective analysis of 30 patients was conducted, divided into Response and Non-response groups based on whether they achieved major pathological remission (MPR). Differences in genes and immune microenvironment between the two groups were analyzed through next-generation sequencing (NGS) and multiplex immunofluorescence (mIF). Variables most closely related to therapeutic efficacy were selected through LASSO regression and ROC curves to establish a predictive model. An additional 48 patients were prospectively collected as a validation set to verify the model’s effectiveness.ResultsNGS suggested seven differential genes (ATM, ATR, BIVM-ERCC5, MAP3K1, PRG, RBM10, and TSHR) between the two groups (P < 0.05). mIF indicated significant differences in the quantity and location of CD3+, PD-L1+, CD3+PD-L1+, CD4+PD-1+, CD4+LAG-3+, CD8+LAG-3+, LAG-3+ between the two groups before treatment (P < 0.05). Dynamic mIF analysis also indicated that CD3+, CD8+, and CD20+ all increased after treatment in both groups, with a more significant increase in CD8+ and CD20+ in the Response group (P < 0.05), and a more significant decrease in PD-L1+ (P < 0.05). The three variables most closely related to therapeutic efficacy were selected through LASSO regression and ROC curves: Tumor area PD-L1+ (AUC= 0.881), CD3+PD-L1+ (AUC= 0.833), and CD3+ (AUC= 0.826), and a predictive model was established. The model showed high performance in both the training set (AUC= 0.938) and the validation set (AUC= 0.832). Compared to the traditional CPS scoring criteria, the model showed significant improvements in accuracy (83.3% vs 70.8%), sensitivity (0.625 vs 0.312), and specificity (0.937 vs 0.906).ConclusionNICT treatment may exert anti-tumor effects by enriching immune cells and activating exhausted T cells. Tumor area CD3+, PD-L1+, and CD3+PD-L1+ are closely related to therapeutic efficacy. The model containing these three variables can accurately predict treatment outcomes, providing a reliable basis for the selection of neoadjuvant treatment plans.

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“…131 In the preclinical settings, previous studies aimed at evaluating the effects of disrupting these receptors on antitumour immune responses demonstrated promising results, encompassing both effectiveness and tolerability. 132,133 This encouraging preclinical evidence has set the stage for further clinical investigations. To this end, the prospect of development of a nanofiber-based immune therapeutic approach targeting these emerging co-inhibitory receptors represents an exciting opportunity.…”

Section: Future Perspectivementioning

confidence: 97%