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Related Concept Videos

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Updated: Nov 12, 2025

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations
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PRISM: recovering cell-type-specific expression profiles from individual composite RNA-seq samples.

Antti Häkkinen1, Kaiyang Zhang1, Amjad Alkodsi1

  • 1Research Programs Unit, Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, FI-00014 Helsinki, Finland.

Bioinformatics (Oxford, England)
|March 15, 2021
PubMed
Summary

Analyzing ovarian cancer transcriptomes is challenging due to tumor heterogeneity. We developed PRISM, a novel framework that accurately models cell composition and improves prediction of patient response to treatment.

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Area of Science:

  • Oncology
  • Bioinformatics
  • Genomics

Background:

  • Cancer patient transcriptomes present analytical challenges due to inherent tumor heterogeneity and evolution.
  • Longitudinal analysis of 214 bulk RNA samples from an ovarian cancer cohort revealed systematic changes in sample composition influenced by chemotherapy and anatomical site.
  • These compositional changes hinder direct comparison between treatment-naive and treated samples.

Purpose of the Study:

  • To develop a statistical framework to simultaneously resolve sample composition and cell-type-specific transcriptomic profiles from bulk RNA sequencing data.
  • To improve the prediction of patient response to cancer therapies by utilizing composition-free transcriptomic data.
  • To validate the developed framework in independent cancer cohorts and through complementary experimental methods.

Main Methods:

  • Development of PRISM, a latent statistical framework for deconvolution of bulk RNA sequencing data.
  • Simultaneous extraction of sample composition and cell-type-specific whole-transcriptome profiles.
  • Validation using independent ovarian cancer and melanoma cohorts, whole-genome sequencing, and RNA in situ hybridization.

Main Results:

  • PRISM successfully extracts sample composition and cell-type-specific expression profiles.
  • PRISM-derived transcriptomic profiles and signatures demonstrate superior prediction of patient response compared to raw bulk RNA data.
  • Validation experiments confirmed the accuracy of PRISM in estimating cellular composition and cell-type-specific expression.

Conclusions:

  • PRISM offers a robust solution for analyzing heterogeneous cancer transcriptomes by accounting for dynamic sample composition.
  • Composition-free transcriptomic analysis using PRISM enhances the predictive power for patient treatment response.
  • The framework is validated and applicable to diverse cancer types, offering a valuable tool for precision oncology.