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Predictive and Prognostic Relevance of Tumor-Infiltrating Immune Cells: Tailoring Personalized Treatments against Different Cancer Types.
Dakal, Tikam Chand; George, Nancy; Xu, Caiming; Suravajhala, Prashanth; Kumar, Abhishek.
Afiliación
  • Dakal TC; Genome and Computational Biology Lab, Department of Biotechnology, Mohanlal Sukhadia University, Udaipur 313001, Rajasthan, India.
  • George N; Department of Biotechnology, Chandigarh University, Mohali 140413, Punjab, India.
  • Xu C; Department of Molecular Diagnostics and Experimental Therapeutics, Beckman Research Institute of the City of Hope, Monrovia, CA 91010, USA.
  • Suravajhala P; Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Clappana P.O. 690525, Kerala, India.
  • Kumar A; Manipal Academy of Higher Education (MAHE), Manipal 576104, Karnataka, India.
Cancers (Basel) ; 16(9)2024 Apr 23.
Article en En | MEDLINE | ID: mdl-38730579
ABSTRACT
TIICs are critical components of the TME and are used to estimate prognostic and treatment responses in many malignancies. TIICs in the tumor microenvironment are assessed and quantified by categorizing immune cells into three subtypes CD66b+ tumor-associated neutrophils (TANs), FoxP3+ regulatory T cells (Tregs), and CD163+ tumor-associated macrophages (TAMs). In addition, many cancers have tumor-infiltrating M1 and M2 macrophages, neutrophils (Neu), CD4+ T cells (T-helper), CD8+ T cells (T-cytotoxic), eosinophils, and mast cells. A variety of clinical treatments have linked tumor immune cell infiltration (ICI) to immunotherapy receptivity and prognosis. To improve the therapeutic effectiveness of immune-modulating drugs in a wider cancer patient population, immune cells and their interactions in the TME must be better understood. This study examines the clinicopathological effects of TIICs in overcoming tumor-mediated immunosuppression to boost antitumor immune responses and improve cancer prognosis. We successfully analyzed the predictive and prognostic usefulness of TIICs alongside TMB and ICI scores to identify cancer's varied immune landscapes. Traditionally, immune cell infiltration was quantified using flow cytometry, immunohistochemistry, gene set enrichment analysis (GSEA), CIBERSORT, ESTIMATE, and other platforms that use integrated immune gene sets from previously published studies. We have also thoroughly examined traditional limitations and newly created unsupervised clustering and deconvolution techniques (SpatialVizScore and ProTICS). These methods predict patient outcomes and treatment responses better. These models may also identify individuals who may benefit more from adjuvant or neoadjuvant treatment. Overall, we think that the significant contribution of TIICs in cancer will greatly benefit postoperative follow-up, therapy, interventions, and informed choices on customized cancer medicines.
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Texto completo: 1 Bases de datos: MEDLINE Idioma: En Revista: Cancers (Basel) Año: 2024 Tipo del documento: Article País de afiliación: India

Texto completo: 1 Bases de datos: MEDLINE Idioma: En Revista: Cancers (Basel) Año: 2024 Tipo del documento: Article País de afiliación: India