40
ICCS 2011
P4
Bioinformatics to bedside: An automated and
generalizable multidimensional fow cytometry
data analysis approach improves diagnostic
accuracy between mantle cell lymphoma and small
lymphocytic lymphoma
Ryan R Brinkman
1,8
, Habil Zare
1,2
, Robert Kridel
4,5
, Nima
Aghaepour
1
, Gholamreza Haffari
3,6
, Josef M Connors
4,7
,
Randy D Gascoyne
4,5
, Arvind Gupta
2
, Andrew P
Weng
1,5
, Ali Bashashati
3
1
Terry Fox Laboratory, British Columbia Cancer Agency,
Vancouver, BC, Canada,
2
Department of Computing
Science, University of British Columbia, Vancouver, BC,
Canada,
3
Department of Molecular Oncology, British
Columbia Cancer Agency, Vancouver, BC, Canada,
4
Center for Lymphoid Cancers, British Columbia
Cancer Agency, Vancouver, BC, Canada,
5
Department
of Pathology and Laboratory Medicine, University of
British Columbia, Vancouver, BC, Canada,
6
Faculty of
Information Technology, Monash University,Victoria,
Australia, Victoria, Australia,
7
Faculty of Medicine,
University of British Columbia, Vancouver, BC, Canada,
8
Department of Medical Genetics, University of British
Columbia, Vancouver, BC, Canada
Mantle cell lymphoma (MCL) and small lymphocytic
lymphoma (SLL) exhibit similar, but distinct
immunophenotypic profles. While many cases can
be diagnosed with high confdence based on fow
cytometry (FCM) results alone, ambiguous cases are
frequently encountered and necessitate additional
studies. In order to determine if greater diagnostic
accuracy could be achieved from fow cytometry data
alone, we developed an unbiased, machine based
algorithm and used it to automatically identify those
features within the multidimensional space that best
distinguish between the two disease types. Data from
44 MCL cases and 70 SLL cases were analyzed.
Using conventional diagnostic criteria, we were able
to accurately assign only 64% of MCL and 69% of
SLL cases. Using features identifed by our automated
approach, we were able to assign 100% of MCL and
97% of SLL cases correctly. The most discriminating
feature was the ratio of mean fuorescence intensities
(MFI) between CD20 and CD23. Unexpectedly, we
also observed that inclusion of FMC7 expression
in the diagnostic algorithm reduced its accuracy.
Computational methods allow objective assessment
of the relative contribution of component data features
to overall diagnostic accuracy, and reveal some
conventional criteria can actually compromise this
accuracy. Furthermore, computational approaches
enable exploiting the full dimensionality of FCM
data and can potentially lead to discovery of novel
biomarkers relevant for clinical outcome.
POSTER ABSTRACTS