Advances in cancer immunotherapy: historical perspectives, current developments, and future directions
Notice bibliographique
Résumé
Cancer immunotherapy, encompassing both experimental and standard-of-care therapies, has emerged as a promising approach to harnessing the immune system for tumor suppression. Experimental strategies, including novel immunotherapies and preclinical models, are actively being explored, while established treatments, such as immune checkpoint inhibitors (ICIs), are widely implemented in clinical settings. This comprehensive review examines the historical evolution, underlying mechanisms, and diverse strategies of cancer immunotherapy, highlighting both its clinical applications and ongoing preclinical advancements. The review delves into the essential components of anticancer immunity, including dendritic cell activation, T cell priming, and immune surveillance, while addressing the challenges posed by immune evasion mechanisms. Key immunotherapeutic strategies, such as cancer vaccines, oncolytic viruses, adoptive cell transfer, and ICIs, are discussed in detail. Additionally, the role of nanotechnology, cytokines, chemokines, and adjuvants in enhancing the precision and efficacy of immunotherapies were explored. Combination therapies, particularly those integrating immunotherapy with radiotherapy or chemotherapy, exhibit synergistic potential but necessitate careful management to reduce side effects. Emerging factors influencing immunotherapy outcomes, including tumor heterogeneity, gut microbiota composition, and genomic and epigenetic modifications, are also examined. Furthermore, the molecular mechanisms underlying immune evasion and therapeutic resistance are analyzed, with a focus on the contributions of noncoding RNAs and epigenetic alterations, along with innovative intervention strategies. This review emphasizes recent preclinical and clinical advancements, with particular attention to biomarker-driven approaches aimed at optimizing patient prognosis. Challenges such as immunotherapy-related toxicity, limited efficacy in solid tumors, and production constraints are highlighted as critical areas for future research. Advancements in personalized therapies and novel delivery systems are proposed as avenues to enhance treatment effectiveness and accessibility. By incorporating insights from multiple disciplines, this review aims to deepen the understanding and application of cancer immunotherapy, ultimately fostering more effective and widely accessible therapeutic solutions. • Various aspects of immunotherapy, from its historical evolution to modern strategies and clinical applications, are explored. • Immunotherapeutic approaches, including cancer vaccines and oncolytic virus therapy, are discussed. • The efficacy and mechanisms of immune checkpoint inhibitors and adoptive cell therapies are evaluated. • The complexities of the tumor microenvironment and mechanisms of immune evasion are highlighted. • Advances in cytokines, chemokines, nanotechnology, and combination therapies that enhance immunotherapy effectiveness are outlined. • What are the primary strategies and modalities employed in cancer immunotherapy? • How does the tumor microenvironment influence the efficacy of cancer immunotherapy? • What are the key challenges in developing effective cancer vaccines and adoptive cell therapies? • How do molecular and genetic factors, including noncoding RNAs and epigenetic modifications, contribute to immunotherapy resistance? • What are the future directions for enhancing the efficacy and accessibility of cancer immunotherapy?
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».