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TrAp: a tree approach for fingerprinting subclonal tumor composition.

Francesco Strino1, Fabio Parisi, Mariann Micsinai

  • 1Department of Pathology, Yale University School of Medicine, New Haven, CT 06520, USA, NYU Center for Health Informatics and Bioinformatics, New York University Langone Medical Center, 227 East 30th Street, New York, NY 10016, USA and Yale Cancer Center, New Haven, CT 06520, USA.

Nucleic Acids Research
|July 30, 2013
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Summary

This study introduces TrAp, a novel computational algorithm to deconvolve tumor samples, revealing hidden cell subpopulations and their evolutionary paths. This breakthrough aids in identifying metastatic potential and therapy resistance in cancer research.

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Area of Science:

  • Computational biology
  • Cancer genomics
  • Evolutionary dynamics

Background:

  • Understanding tumor heterogeneity is crucial for cancer treatment.
  • Current sequencing methods aggregate signals, masking individual cell subpopulation characteristics.
  • Lack of computational tools to deconvolve mixed tumor cell signals hinders research.

Purpose of the Study:

  • To develop a computational framework for inferring tumor subpopulation composition, abundance, and evolutionary trajectories.
  • To introduce the TrAp algorithm for deconvolving mixed tumor genomic data.
  • To enable the identification of clinically relevant cell subpopulations within a tumor.

Main Methods:

  • Developed the TrAp (Tumor subpopulation analysis) algorithm based on an evolutionary framework.
  • Utilized in silico analyses to validate TrAp's deconvolution accuracy under varying error rates.
  • Applied TrAp to diverse datasets including tumor karyotypes, somatic hypermutation data, Exome-Seq, and clinical sequencing data from leukemia and melanoma patients.

Main Results:

  • TrAp accurately deconvolves mixed subpopulations in silico with moderate numbers of subpopulations and measurement errors.
  • Demonstrated TrAp's applicability on real-world tumor karyotype and mutation data.
  • Successfully inferred and compared mutational profiles of subpopulations from renal cell carcinoma, acute myeloid leukemia, and melanoma metastases.

Conclusions:

  • The TrAp algorithm provides a robust method for deconvolving complex tumor samples.
  • This approach facilitates the study of tumor evolution and the identification of subpopulations driving disease progression or treatment resistance.
  • TrAp advances the field of cancer genomics by enabling detailed analysis of intra-tumor heterogeneity.