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Updated: Jun 30, 2026
Multiplexed Immunofluorescence Analysis and Quantification of Intratumoral PD-1+ Tim-3+ CD8+ T Cells
Published on: February 8, 2018
Pan-cancer immune and stromal deconvolution predicts clinical outcomes and mutation profiles
Bhavneet Bhinder1,2, Verena Friedl3,4, Sunantha Sethuraman5
1Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, 10021, USA.
Abstract:
Traditional gene expression deconvolution methods assess a limited number of cell types, therefore do not capture the full complexity of the tumor microenvironment (TME). Here, we integrate nine deconvolution tools to assess 79 TME cell types in 10,592 tumors across 33 different cancer types, creating the most comprehensive analysis of the TME. In total, we found 41 patterns of immune infiltration and stroma profiles, identifying heterogeneous yet unique TME portraits for each cancer and several new findings. Our findings indicate that leukocytes play a major role in distinguishing various tumor types, and that a shared immune-rich TME cluster predicts better survival in bladder cancer for luminal and basal squamous subtypes, as well as in melanoma for RAS-hotspot subtypes. Our detailed deconvolution and mutational correlation analyses uncover 35 therapeutic target and candidate response biomarkers hypotheses (including CASP8 and RAS pathway genes).
Insights
This study comprehensively analyzes the tumor microenvironment (TME) across 33 cancer types, revealing unique cellular portraits. Leukocytes are key in differentiating tumors, and an immune-rich TME predicts better survival in specific bladder cancer and melanoma subtypes.
Area of Science:
- Oncology
- Immunology
- Bioinformatics
Background:
- Traditional gene expression deconvolution methods analyze limited cell types, failing to capture the tumor microenvironment's (TME) full complexity.
- A comprehensive understanding of TME cellular composition is crucial for cancer research and therapeutic development.
Purpose of the Study:
- To integrate multiple deconvolution tools for a comprehensive assessment of TME cellularity across diverse cancer types.
- To identify distinct TME patterns and their correlation with clinical outcomes and mutational profiles.
Main Methods:
- Integration of nine deconvolution tools to analyze 79 TME cell types.
- Analysis of 10,592 tumor samples from 33 distinct cancer types.
- Correlation analysis between TME profiles, survival data, and mutational status.
Main Results:
- Identification of 41 distinct immune infiltration and stroma profiles, revealing unique TME portraits for each cancer type.
- Leukocytes were found to be major drivers in distinguishing tumor types.
- A shared immune-rich TME cluster was associated with improved survival in specific subtypes of bladder cancer and melanoma.
Conclusions:
- This integrated approach provides the most comprehensive analysis of the TME to date.
- Leukocyte infiltration patterns are critical for tumor classification and prognosis.
- The study identified 35 potential biomarkers for therapeutic targeting and predicting treatment response, including CASP8 and RAS pathway genes.
