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Updated: Sep 11, 2025

Colorectal Cancer Cell Surface Protein Profiling Using an Antibody Microarray and Fluorescence Multiplexing
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Immune Profiling among Colorectal Cancer Subtypes using Dependent Mixture Models.

Yunshan Duan1, Shuai Guo2, Wenyi Wang2

  • 1Department of Statistics, University of Texas at Austin.

Journal of the American Statistical Association
|August 18, 2025
PubMed
Summary
This summary is machine-generated.

This study compares T cell populations in early-onset (EO) versus late-onset (LO) colorectal cancer (CRC). It identifies distinct T cell subtypes in EO-CRC and LO-CRC, offering insights into tumor progression and potential treatments.

Keywords:
Common atoms modelComparison across conditionsEarly-onset colorectal cancerFinite mixture model

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Area of Science:

  • Immunology
  • Computational Biology
  • Oncology

Background:

  • Early-onset colorectal cancer (EO-CRC) in patients aged 18-45 is a growing concern with poorly understood causes.
  • Comparative immune cell profiling is crucial for understanding disease heterogeneity.
  • Identifying distinct immune cell subtypes in different cancer onset types can reveal underlying biological mechanisms.

Purpose of the Study:

  • To identify homogeneous T cell subpopulations with distinct characteristics in early-onset (EO) versus late-onset (LO) colorectal cancer (CRC).
  • To discover T cell subtypes shared between EO-CRC and LO-CRC.
  • To develop a statistical model for comparative immune cell profiling across different cancer conditions.

Main Methods:

  • Development of dependent finite mixture models to characterize immune subtypes.
  • Utilizing highly structured multi-layer Dirichlet priors for cross-condition comparisons.
  • Application of the model to transcriptomic data for T cell subpopulation analysis.

Main Results:

  • Identification of specific T cell subtypes enriched in EO-CRC and LO-CRC.
  • Discovery of shared T cell subtypes between EO-CRC and LO-CRC.
  • Biomarkers for identified T cell subtypes linked to tumor progression mechanisms.

Conclusions:

  • The developed statistical model effectively differentiates immune cell profiles between EO-CRC and LO-CRC.
  • Identified T cell subtypes and their biomarkers provide potential insights into CRC pathogenesis.
  • Findings may inform future therapeutic strategies for colorectal cancer.