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SUMMARY:Absolute Assignment of Breast Cancer Intrinsic Molecular Subtype 
DTSTART:20150310T140000
DTEND:20150310T150000
DTSTAMP:20260429T121615Z
UID:e3891d72c7a7fd6cab010d536ac1a8ac7063ef11bcaedc52307804b5
CATEGORIES:Conferences - Seminars
DESCRIPTION:Eric R. Paquet\, McGill University\, Montreal\, Québec\nMassi
 vely parallel gene expression profiling has provided a more objective\, mo
 lecular-level characterization of breast cancer subtypes. Several bioinfor
 matics tools are available to infer patient subtype from a gene expression
  profile including the well-studied PAM50. The specific algorithmic method
 s used in these tools require access to a broad patient dataset. The choic
 e of subtype for an individual is determined relative to all other patient
 s across the panel\, making subtypes heavily dependent on the composition 
 of the dataset. Our aim was to develop a bioinformatics approach assigning
  absolute breast cancer subtypes\, independent of dataset composition. We 
 defined a new bioinformatics approach: Absolute Intrinsic Molecular Subtyp
 ing (AIMS) that assigns subtype from a gene expression profile for an indi
 vidual sample without the need for a large\, diverse\, and normalized data
 set. We evaluated the agreement of AIMS with PAM50 and compared subtype as
 signment and prognostic value of the subtypes. AIMS vastly agreed with PAM
 50\, with 76% and 77% agreement for cross validation and the test set\, re
 spectively\, and the prognostic capacity of the intrinsic subtypes was pre
 served. AIMS is fully stable\, and its absolute nature enables its use on 
 a wide range of datasets and technologies\, including RNA-seq.
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STATUS:CONFIRMED
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