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Automated analysis of multidimensional flow cytometry data improves diagnostic accuracy between mantle cell lymphoma
Habil Zare1, Ali Bashashati, Robert Kridel
1Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, BC, Canada.
Insights
A new machine-based algorithm significantly improves the diagnostic accuracy of distinguishing Mantle Cell Lymphoma (MCL) from Small Lymphocytic Lymphoma (SLL) using only flow cytometry (FCM) data.
Area of Science:
- Hematology
- Computational Biology
- Immunophenotyping
Background:
- Mantle cell lymphoma (MCL) and small lymphocytic lymphoma (SLL) share overlapping immunophenotypic profiles.
- Distinguishing MCL from SLL often requires additional tests beyond initial flow cytometry (FCM).
Purpose of the Study:
- To develop an unbiased, machine-based algorithm to enhance diagnostic accuracy for MCL and SLL using FCM data alone.
- To identify key features within FCM data that best differentiate between MCL and SLL.
Main Methods:
- An automated, machine-based algorithm was developed to analyze FCM data.
- Conventional diagnostic criteria were applied to FCM data for comparison.
- The algorithm identified novel discriminating features and assessed their diagnostic utility.
Main Results:
- Conventional FCM criteria correctly diagnosed 64% of MCL and 69% of SLL cases.
- The novel algorithm achieved 100% accuracy for MCL and 97% for SLL.
- The CD20/CD23 mean fluorescence intensity ratio was the most significant discriminating feature; FMC7 expression unexpectedly reduced accuracy.
Conclusions:
- Computational methods applied to clinical FCM data can significantly improve diagnostic accuracy for MCL and SLL.
- This approach may aid in identifying novel prognostic markers and refining lymphoma classification.
Abstract:
Mantle cell lymphoma (MCL) and small lymphocytic lymphoma (SLL) exhibit similar but distinct immunophenotypic profiles. Many cases can be diagnosed readily by flow cytometry (FCM) alone; however, ambiguous cases are frequently encountered and necessitate additional studies, including immunohistochemical staining for cyclin D1 and fluorescence in situ hybridization for IgH-CCND1 rearrangement. To determine if greater diagnostic accuracy could be achieved from FCM data alone, we developed an unbiased, machine-based algorithm to identify features that best distinguish between the 2 diseases. By applying conventional diagnostic criteria to the flow cytometry data, we were able to assign 28 of 44 (64%) MCL and 48 of 70 (69%) SLL cases correctly. In contrast, we were able to assign all 44 (100%) MCL and 68 of 70 (97%) SLL cases correctly using a novel set of criteria, as identified by our automated approach. The most discriminating feature was the CD20/CD23 mean fluorescence intensity ratio, and we found unexpectedly that inclusion of FMC7 expression in the diagnostic algorithm actually reduced its accuracy. This study demonstrates that computational methods can be used on existing clinical FCM data to improve diagnostic accuracy and suggests similar computational approaches could be used to identify novel prognostic markers and perhaps subdivide existing or define new diagnostic entities.
