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Updated: Jun 25, 2025
Author Spotlight: Exploring Strategies for Successful Immune Response Against Tumors
Published on: August 16, 2024
Single-cell transcriptomic-informed deconvolution of bulk data identifies immune checkpoint blockade resistance in
Li Wang1,2, Sudeh Izadmehr2, John P Sfakianos3
1Department of Precision Medicine, Aitia, Somerville, MA 02143, USA.
Abstract:
Interactions within the tumor microenvironment (TME) significantly influence tumor progression and treatment responses. While single-cell RNA sequencing (scRNA-seq) and spatial genomics facilitate TME exploration, many clinical cohorts are assessed at the bulk tissue level. Integrating scRNA-seq and bulk tissue RNA-seq data through computational deconvolution is essential for obtaining clinically relevant insights. Our method, ProM, enables the examination of major and minor cell types. Through evaluation against existing methods using paired single-cell and bulk RNA sequencing of human urothelial cancer (UC) samples, ProM demonstrates superiority. Application to UC cohorts treated with immune checkpoint inhibitors reveals pre-treatment cellular features associated with poor outcomes, such as elevated SPP1 expression in macrophage/monocytes (MM). Our deconvolution method and paired single-cell and bulk tissue RNA-seq dataset contribute novel insights into TME heterogeneity and resistance to immune checkpoint blockade.
Insights
Our new computational method, ProM, enhances the analysis of tumor microenvironment (TME) cell types from bulk tissue RNA sequencing. It identifies cellular features in urothelial cancer linked to poor immune checkpoint inhibitor response.
Area of Science:
- Oncology
- Computational Biology
- Immunotherapy
Background:
- The tumor microenvironment (TME) critically impacts cancer progression and treatment efficacy.
- Single-cell RNA sequencing (scRNA-seq) and spatial genomics offer detailed TME insights but are often unavailable for large clinical cohorts assessed via bulk tissue RNA sequencing.
- Computational deconvolution is vital for integrating these data types to derive clinically relevant information.
Purpose of the Study:
- To develop and validate a computational deconvolution method (ProM) for analyzing major and minor cell types within the TME using bulk RNA sequencing data.
- To assess ProM's performance against existing methods using paired single-cell and bulk RNA sequencing data from human urothelial cancer (UC).
- To apply ProM to UC cohorts treated with immune checkpoint inhibitors (ICIs) to identify pre-treatment cellular features associated with treatment resistance.
Main Methods:
- Development of ProM, a novel computational deconvolution algorithm.
- Validation using paired single-cell and bulk RNA sequencing datasets from human urothelial cancer samples.
- Application of ProM to analyze bulk RNA sequencing data from UC patient cohorts treated with ICIs.
Main Results:
- ProM demonstrated superior performance compared to existing deconvolution methods in analyzing paired UC samples.
- The study identified pre-treatment cellular characteristics linked to poor outcomes in ICI-treated UC patients.
- Elevated SPP1 expression in macrophage/monocytes (MM) was identified as a potential biomarker for poor response to ICIs.
Conclusions:
- ProM is a robust computational tool for dissecting TME cellular composition from bulk RNA sequencing data.
- The findings provide novel insights into TME heterogeneity and mechanisms of resistance to immune checkpoint blockade in urothelial cancer.
- The study highlights the potential of ProM and associated datasets for advancing precision oncology.
