Cancer treatment advances increasingly depend on understanding which cell types populate tumors and how they interact, yet standard tissue analysis provides only averaged signals across all cellular components mixed together. This computational challenge has profound implications for developing targeted therapies and predicting patient outcomes, as the immune, stromal, and malignant cell populations within tumors each contribute distinct molecular signatures that current bulk sequencing obscures. A comprehensive evaluation of 43 computational deconvolution methods reveals how researchers can now dissect these mixed cellular signals to identify individual cell type contributions and their specific gene expression patterns. The analysis framework addresses critical technical considerations including method selection based on available reference data, tumor-specific complexities like cellular plasticity, and validation approaches for ensuring biological accuracy. These computational approaches enable researchers to map tumor-immune surveillance dynamics, classify cancer subtypes based on cellular composition, and identify prognostic biomarkers linked to specific cell populations rather than averaged tissue signals. The methodology proves particularly valuable for understanding how different cell states within the same tumor contribute to treatment resistance and disease progression. While powerful, these deconvolution methods face limitations including assumptions about cell type stability and challenges in capturing dynamic cellular states during tumor evolution. The field is advancing toward methods that can handle tumor cell plasticity and temporal changes in cellular composition. For cancer researchers, these tools represent a bridge between accessible bulk sequencing data and the detailed cellular insights traditionally requiring expensive single-cell approaches, potentially accelerating biomarker discovery and therapeutic target identification across diverse cancer types.