Dual-Method Immune Deconvolution Identifies Subtype-Specific Correlates of PD-L1 Expression and a γδ T Cell–M2 Macrophage Association in Breast Cancer

Most computational studies of the breast cancer immune microenvironment rely on a single deconvolution algorithm. Since different tools analyze different parameters, integrated information across a dataset can be missed. Here, we employed two complementary approaches, xCell and a CIBERSORT-style ssGSEA using LM22 signatures, on 1,099 PAM50-classified primary tumors from the TCGA-BRCA cohort. PD-L1 differed significantly across subtypes (Kruskal–Wallis p = 4.22 × 10⁻⁹), with the highest median expression in HER2-enriched tumors (log2CPM = 2.81) followed by basal-like (2.60). Within basal-like tumors PD-L1 was correlated with CD8+ T cells (ρ = 0.65) and M1 macrophages (ρ = 0.67), a pattern consistent with, though not demonstrative of, IFN-γ-driven adaptive upregulation. In HER2-enriched cancers, PD-L1 tracked with both effector and regulatory populations simultaneously, while luminal tumors were largely immune-quiet. All correlations were corrected for multiple comparisons using the Benjamini–Hochberg procedure; associations involving NK cells and γδ T cells were not significant after correction in most subtypes. The most notable methodological difference involved γδ T cells and M2 macrophages: xCell showed a non-significant correlation (ρ = 0.048, p = 0.11), whereas CIBERSORT-ssGSEA produced a significant correlation (ρ = 0.565, p < 2.2 × 10⁻¹⁶) that was observed across all five subtypes. A multivariate model explained 49% of PD-L1 variance (adjusted R² = 0.49). In a multivariable Cox model stratified by stage and PAM50 subtype and adjusted for age, no immune feature remained independently associated with overall survival after false discovery rate correction (all FDR > 0.20), indicating that the modest prognostic signal observed in unadjusted models is attributable to clinical covariates. These analyses are correlational and derived from bulk transcriptomes; they cannot establish directionality or causation, and functional validation would be required before any of these associations could be considered therapeutically actionable.

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Publication Details

Journal
In Silico Research in Biomedicine
Published
2026-09-01
DOI
https://doi.org/10.1016/j.insi.2026.100577
Primary Topic
Cancer Immunotherapy and Biomarkers
Type
article
Field-Weighted Citation Impact
0.00

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article

Dual-Method Immune Deconvolution Identifies Subtype-Specific Correlates of PD-L1 Expression and a γδ T Cell–M2 Macrophage Association in Breast Cancer

Hari S. Misra, Diya Jain
In Silico Research in Biomedicine
Cancer Immunotherapy and Biomarkers
article

Dual-Method Immune Deconvolution Identifies Subtype-Specific Correlates of PD-L1 Expression and a γδ T Cell–M2 Macrophage Association in Breast Cancer

Hari S. Misra, Diya Jain
article en

Abstract

Most computational studies of the breast cancer immune microenvironment rely on a single deconvolution algorithm. Since different tools analyze different parameters, integrated information across a dataset can be missed. Here, we employed two complementary approaches, xCell and a CIBERSORT-style ssGSEA using LM22 signatures, on 1,099 PAM50-classified primary tumors from the TCGA-BRCA cohort. PD-L1 differed significantly across subtypes (Kruskal–Wallis p = 4.22 × 10⁻⁹), with the highest median expression in HER2-enriched tumors (log2CPM = 2.81) followed by basal-like (2.60). Within basal-like tumors PD-L1 was correlated with CD8+ T cells (ρ = 0.65) and M1 macrophages (ρ = 0.67), a pattern consistent with, though not demonstrative of, IFN-γ-driven adaptive upregulation. In HER2-enriched cancers, PD-L1 tracked with both effector and regulatory populations simultaneously, while luminal tumors were largely immune-quiet. All correlations were corrected for multiple comparisons using the Benjamini–Hochberg procedure; associations involving NK cells and γδ T cells were not significant after correction in most subtypes. The most notable methodological difference involved γδ T cells and M2 macrophages: xCell showed a non-significant correlation (ρ = 0.048, p = 0.11), whereas CIBERSORT-ssGSEA produced a significant correlation (ρ = 0.565, p < 2.2 × 10⁻¹⁶) that was observed across all five subtypes. A multivariate model explained 49% of PD-L1 variance (adjusted R² = 0.49). In a multivariable Cox model stratified by stage and PAM50 subtype and adjusted for age, no immune feature remained independently associated with overall survival after false discovery rate correction (all FDR > 0.20), indicating that the modest prognostic signal observed in unadjusted models is attributable to clinical covariates. These analyses are correlational and derived from bulk transcriptomes; they cannot establish directionality or causation, and functional validation would be required before any of these associations could be considered therapeutically actionable.

In Silico Research in Biomedicine
Narsee Monjee Institute of Management Studies (IN)
Arthritis National Research Foundation, National Research Foundation
Good health and well-being
Openalex Percentile: Top 14%
Cancer Immunotherapy and Biomarkers
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