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@ARTICLE{Young:269770,
      author       = {Young, Cameron C and Eason, Katherine and Manzano Garcia,
                      Raquel and Moulange, Richard and Mukherjee, Sach and Chin,
                      Suet-Feung and Caldas, Carlos and Rueda, Oscar M},
      title        = {{D}evelopment and validation of a reliable {DNA}
                      copy-number-based machine learning algorithm ({C}opy{C}lust)
                      for breast cancer integrative cluster classification.},
      journal      = {Scientific reports},
      volume       = {14},
      number       = {1},
      issn         = {2045-2322},
      address      = {[London]},
      publisher    = {Macmillan Publishers Limited, part of Springer Nature},
      reportid     = {DZNE-2024-00612},
      pages        = {11861},
      year         = {2024},
      abstract     = {The Integrative Cluster subtypes (IntClusts) provide a
                      framework for the classification of breast cancer tumors
                      into 10 distinct groups based on copy number and gene
                      expression, each with unique biological drivers of disease
                      and clinical prognoses. Gene expression data is often
                      lacking, and accurate classification of samples into
                      IntClusts with copy number data alone is essential. Current
                      classification methods achieve low accuracy when gene
                      expression data are absent, warranting the development of
                      new approaches to IntClust classification. Copy number data
                      from 1980 breast cancer samples from METABRIC was used to
                      train multiclass XGBoost machine learning algorithms
                      (CopyClust). A piecewise constant fit was applied to the
                      average copy number profile of each IntClust and unique
                      breakpoints across the 10 profiles were identified and
                      converted into ~ 500 genomic regions used as features for
                      CopyClust. These models consisted of two approaches: a
                      10-class model with the final IntClust label predicted by a
                      single multiclass model and a 6-class model with binary
                      reclassification in which four pairs of IntClusts were
                      combined for initial multiclass classification. Performance
                      was validated on the TCGA dataset, with copy number data
                      generated from both SNP arrays and WES platforms. CopyClust
                      achieved $81\%$ and $79\%$ overall accuracy with the TCGA
                      SNP and WES datasets, respectively, a nine-percentage point
                      or greater improvement in overall IntClust subtype
                      classification accuracy. CopyClust achieves a significant
                      improvement over current methods in classification accuracy
                      of IntClust subtypes for samples without available gene
                      expression data and is an easily implementable algorithm for
                      IntClust classification of breast cancer samples with copy
                      number data.},
      keywords     = {Humans / Breast Neoplasms: genetics / Breast Neoplasms:
                      classification / Machine Learning / Female / DNA Copy Number
                      Variations: genetics / Algorithms / Cluster Analysis / Gene
                      Expression Profiling: methods},
      cin          = {AG Mukherjee},
      ddc          = {600},
      cid          = {I:(DE-2719)1013030},
      pnm          = {354 - Disease Prevention and Healthy Aging (POF4-354)},
      pid          = {G:(DE-HGF)POF4-354},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:38789621},
      pmc          = {pmc:PMC11126405},
      doi          = {10.1038/s41598-024-62724-6},
      url          = {https://pub.dzne.de/record/269770},
}