Editorial: Exploring Multi-Omics Approaches to Tumor-Associated Macrophages: A Pathway to Advanced Cancer Immunotherapy

Category	Detail
Condition	Tumor-Associated Macrophages
Key Mechanisms	Multi-omics approaches reveal TAM heterogeneity and plasticity, influencing tumor progression and therapeutic responses.
Target Population	Patients with various solid tumors, including breast, ovarian, prostate, lung, gastric, and neuroblastoma cancers.
Care Setting	Research and clinical settings focused on cancer immunotherapy.

TAMs play critical roles in immunosuppression and tumor progression across multiple malignancies.
High-resolution multi-omics techniques uncover TAM heterogeneity beyond the M1/M2 classification.
Novel biomarkers and therapeutic targets identified include GPR35, EFNA3, and TIMP1.
Machine learning integration of multi-omics data enhances prognostic modeling related to TAMs.
Environmental factors, such as silica exposure, influence TAM-driven inflammatory responses.

Utilize multi-omics approaches to assess TAM profiles in tumor microenvironments.

Consider targeting TAMs through immunotherapeutic strategies, including checkpoint inhibitors.

Implement prognostic models based on TAM-related gene expression to guide treatment decisions.

Be aware of TAM-mediated resistance mechanisms that may impact immunotherapy efficacy.

Patients with solid tumors exhibiting TAM involvement.

Targeting TAMs may enhance responses to existing immunotherapies and overcome resistance.

Adopt multi-omics frameworks for comprehensive understanding of TAM biology.
Incorporate machine learning techniques to refine prognostic models related to TAMs.
Evaluate environmental exposures that may influence TAM activity and tumor progression.

Clinical Scorecard: Exploring Multi-Omics Approaches to Tumor-Associated Macrophages: A Pathway to Advanced Cancer Immunotherapy