Frontiers in oncology · 2026
Immune profiling by bulk RNA-seq and multiplex immunofluorescence imaging across multiple cancer types.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Gaspar Catia, Wu Benson, Stone Simone C, Sotov Valentin, Wang Ben, Wangulu Collins, Berman Hal, Kay Aileigh, Basra Prabhjit, Van de Laar Emily, Bratman Scott, Lheureux Stephanie, Knox Jennifer, Kim Raymond, He Hansen, Liu Geoffrey, Bedard Philippe, Spreafico Anna, Tsao Ming, Pugh Trevor J, Hansen Aaron, Sapisochin Gonzalo, Garcia Enrique Sanz, Siu Lillian L, Bruce Jeffrey P
Аннотация
BACKGROUND: Immune cell populations within the tumor microenvironment (TME) critically determine cancer prognosis and therapeutic response. Although multiplexed immunohistochemistry (mIHC) provides spatial immune profiling and bulk RNA sequencing methods offer comprehensive cellular characterization, comparative studies between these complementary methodologies remain limited across diverse tumor types.
METHODS: Patient samples from various tumor types, obtained from either primary or metastatic sites, were collected. For each patient, two samples were analyzed, one for mIHC and one for RNA-seq (xCell2). Both methodologies were intended to quantify immune cell populations including CD4 and CD8 T cells, regulatory T cells (Tregs), B cells and macrophages within the TME. Samples were stratified by origin according to biopsy procedure and tissue block characteristics. The primary objective was to assess concordance between methodologies for immune cell density estimation, while exploratory analysis evaluated liver-specific TME differences and immunotherapy (IO) exposure in head and neck squamous cell carcinoma (HNSCC) patients. Method correlations were analyzed using Spearman's rank correlation coefficient.
RESULTS: Among 298 patient specimens spanning 20 tumor types, overall correlation between mIHC and xCell2 deconvolution was modest (ρ 0.35 - 0.50), due to sample heterogeneity, with CD8 T cells demonstrating the strongest correlation (ρ = 0.50). Tumor-specific analysis revealed enhanced correlations in HNSCC (ρ = 0.80) and renal clear cell carcinoma (ρ = 0.85) for CD8 T cells, while primary tumor samples showed improved CD8 T-cell correlation compared to metastatic sites (ρ = 0.60). Macrophage density was significantly elevated in primary hepatocellular carcinoma versus liver metastases (p = 0.0336). Within the HNSCC subgroup, IO-exposed samples exhibited significantly reduced CD4 T-cell (p = 0.039) and B-cell (p = 0.022) infiltration compared to treatment naïve specimens.
CONCLUSIONS: Our work highlights the value of integrating spatial (mIHC) and transcriptomic (xCell2) approaches to optimize immune profiling accuracy in complex tumor tissues. CD8 T-cell populations showed the strongest methodological concordance, establishing their priority as a robust candidate biomarker for immune activation to be used in further clinical research.
METHODS: Patient samples from various tumor types, obtained from either primary or metastatic sites, were collected. For each patient, two samples were analyzed, one for mIHC and one for RNA-seq (xCell2). Both methodologies were intended to quantify immune cell populations including CD4 and CD8 T cells, regulatory T cells (Tregs), B cells and macrophages within the TME. Samples were stratified by origin according to biopsy procedure and tissue block characteristics. The primary objective was to assess concordance between methodologies for immune cell density estimation, while exploratory analysis evaluated liver-specific TME differences and immunotherapy (IO) exposure in head and neck squamous cell carcinoma (HNSCC) patients. Method correlations were analyzed using Spearman's rank correlation coefficient.
RESULTS: Among 298 patient specimens spanning 20 tumor types, overall correlation between mIHC and xCell2 deconvolution was modest (ρ 0.35 - 0.50), due to sample heterogeneity, with CD8 T cells demonstrating the strongest correlation (ρ = 0.50). Tumor-specific analysis revealed enhanced correlations in HNSCC (ρ = 0.80) and renal clear cell carcinoma (ρ = 0.85) for CD8 T cells, while primary tumor samples showed improved CD8 T-cell correlation compared to metastatic sites (ρ = 0.60). Macrophage density was significantly elevated in primary hepatocellular carcinoma versus liver metastases (p = 0.0336). Within the HNSCC subgroup, IO-exposed samples exhibited significantly reduced CD4 T-cell (p = 0.039) and B-cell (p = 0.022) infiltration compared to treatment naïve specimens.
CONCLUSIONS: Our work highlights the value of integrating spatial (mIHC) and transcriptomic (xCell2) approaches to optimize immune profiling accuracy in complex tumor tissues. CD8 T-cell populations showed the strongest methodological concordance, establishing their priority as a robust candidate biomarker for immune activation to be used in further clinical research.