Exploring TIGIT as an ICI has revealed its critical function in immune evasion within the TME

The gap

Evidence profile

Sourced from the future work of the source papers, classified as general, drawn from work published between 2025 and 2026, spanning 3 journals. Those papers have been cited 45 times in total.

Supporting evidence — 3 representative gaps

• CAR T cell therapy beyond cancer: current status, challenges and future prospects (2026) · Signal Transduction and Targeted Therapy · doi

  CAR-T therapy’s foundational logic—precise antigen recognition executed by a living, adaptable effector—extends naturally beyond oncology to chronic, immune-mediated disease. Across infections, autoimmunity, fibrosis, hemophilia, transplantation, and senescence, recurring engineering motifs emerge: combina- torial antigen logic to manage spatial and temporal heterogeneity; niche penetration and rewiring through stromal, vascular, or matrix targets; stress sensing and checkpoint inversion to over- come immune suppression; and precision tolerance modules that eliminate disease-driving clones while preserving protective immunity. Viewed through this unified design lens, CAR-T cells are no longer restricted to tumor eradication but function as a platform for programmable immunity—capable of purging re-shaping tissue microenvironments, reservoirs, pathological and restoring immune homeostasis. As summarized in Fig. 7, emerging non-oncologic CAR-T applications collectively point future for programmable cellular immunotherapy beyond tumor eradica- tion. Across infectious diseases, next-generation CAR platforms toward a broader

  generalfuture work
  Keywords: immune logic antigen beyond disease across immunity tumor programmable therapy foundational precise recognition executed living
• Tiragolumab and TIGIT: pioneering the next era of cancer immunotherapy (2025) · Frontiers in Pharmacology · cited 30× · doi

  Exploring TIGIT as an ICI has revealed its critical function in immune evasion within the TME. Tiragolumab, a mAb specifically targeting TIGIT, has demonstrated efficacy in preclinical and early trials, mainly when combined with anti-PD-L1 agents. clinical Despite its promise, several challenges persist, including resistance the absence of robust biomarkers, and inconsistent mechanisms, outcomes in phase III trials. These challenges emphasize the necessity for innovative strategies to optimize TIGIT-targeted therapies. The application of BsAbs presents a promising avenue for advancing cancer immunotherapy. For instance, BiPT-23, an IgG1-type BsAb targeting PD-L1 and TIGIT, has shown remarkable potential by enhancing cytotoxic T cell and NK cell infiltration while selectively depleting TIGIT+ Tregs (Zhong et al., 2022). Similarly, ZGGS15, a bispecific and TIGIT, has demonstrated superior and synergy with nivolumab, achieving significant tumor growth inhibition without inducing adverse immunological effects (Dai et al., 2024). Lastly, targeting LAG-3 antitumor antibody activity IgG4

  generalfuture work
  Keywords: tigit targeting trials challenges cell exploring revealed critical function immune evasion within tiragolumab speci cally
• Precision Medicine in Hematologic Malignancies: Evolving Concepts and Clinical Applications (2025) · Biomedicines · cited 15× · doi

  As precision hematology continues to evolve, multiple promising technologies are set to reshape diagnostics, treatment, and prevention. These innovations improve not only molecular profiling but also the integration of immune profiling, predictive analytics, and preventive strategies into hematologic care. 6.1. Pan-Cancer Trials Based on Molecular Targets Traditional clinical trials have historically focused on specific diseases, often ignor- ing shared molecular alterations between different hematologic cancers. However, the Biomedicines 2025, 13, 1654 13 of 21 emergence of “pan-cancer” basket trials aims to enroll patients based on actionable genetic alterations rather than tumor lineage and pathology. Such trials promote drug repur- posing and accelerate therapeutic discovery for rare malignancies with limited treatment options [150,151]. 6.2. Personalized Immunotherapy The integration of immunotherapy into hematologic malignancy treatment, such as CAR T-cell therapy, BiTEs, and immune checkpoint inhibitors, has been transforma- tive [152]. Future directions include tailoring these approaches to individual patient immune profiles using high-throughput immune repertoire sequencing (to analyze the diversity of immune cells), neoantigen prediction (to identify tumor-specific antigens that can be targeted by the immune system), and single-cell analysis [153–155]. Personalized immunotherapies may improve efficacy while reducing toxicities, moving from broad treatment regimens to individualized immune modulation [156]. 6.3. Precision Prevention While precision medicine often focuses on treatment, prevention remains an essen- tial yet often overlooked area of care [157]. Advances in germline genetics and poly- genic risk scoring can help identify individuals at an elevated risk of hematologic ma- lignancies [158,159]. Inherited predisposition syndromes (e.g., Li–Fraumeni, Fanconi anemia, GATA2 deficiency) as well as somatic conditions like clonal hematopoiesis of indeterminate potential (CHIP) offer opportunities for early surveillance or preventive interventions [160–163]. The development of precision prevention strategies could ulti- mately reduce disease burden through earlier detection and risk mitigation [164]. 6.4. AI-Powered Treatment Algorithms Artificial intelligence (AI) and machine learning (ML) are increasingly applied in hematology to analyze complex multi-omic data, including genomic, transcriptomic, pro- teomic, imaging, and clinical information, to uncover patterns and guide personalized treatment. These algorithms integrate data such as genetic profiles, lab results, and patient histories to improve prognosis accuracy, stratify patients, predict therapy response or relapse, and support real-time, data-driven decision making. As they continuously learn from real-world inputs, AI-powered tools hold promise to optimize therapeutic strategies and improve outcomes in hematologic malignancies [165,166].

  generalfuture work
  Keywords: treatment immune hematologic precision prevention improve trials molecular strategies often personalized risk hematology profiling integration

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