Clustering and Kernel Density Estimation for Assessment of Measurable Residual Disease by Flow Cytometry
Abstract
:1. Introduction
2. Experimental Section
2.1. Study Material
2.2. Study Methods
- (a)
- From a diagnostic AML sample, clustering is used on MFC data to obtain cell clusters.
- (b)
- Suspicious cell cluster(s) are then identified on the basis of their aberrant immunophenotypic profile. These are usually CD45 low, with low SSC [13], and are often CD34-positive and form in most cases a unique cluster of >10% of total cells. However, given the important heterogeneity of AML, a suspicious cell cluster should always be identified by experienced cytometrists on the basis of their scientific knowledge of the disease.
- (c)
- Once suspicious cell clusters are identified, “Cloud(s)” are created. A “Cloud” is created by the Boolean intersection of the contour gates of 45 bi-parametric plots (each parameter vs. each parameter, using logicle transformation for fluorescence parameters and linear transformation for FSC and SSC).
- (d)
- Once a Cloud is created at diagnosis, its Abnormality Ratio (AR) can be calculated. The AR is calculated as follows: AR = ((Cloud cells/total cells) of patient sample)/((Cloud cells/total cells) of control group sample). Note: If # Cloud cells = 0, use 1.
- (e)
- The Cloud with the highest AR, formed by a cell cluster of at least 1 × 104 cells (arbitrary value) at diagnosis will define a “Leukemic Cloud” or “L-Cloud”. Of note, if multiple Clouds of at least 5 × 103 cells have an AR of >1000 (arbitrary value), all should be considered as L-Clouds. The Cloud with the highest AR and at least 1 × 104 cells will be considered the “the major L-Cloud”. MRD assessment at follow-up will be done through the AR calculation at follow-up of L-Clouds.
2.2.1. Endpoint 1: Theoretical Evaluation of the AR/L-Cloud Concept
Global Evaluation of the AR/L-Cloud Concept
- Obtaining AML cells
- Simulation of MRD samples
- Evaluation procedure
- L-Clouds were created using the AML-FCS files.
- Performance characteristics (specificity and sensitivity) of the different L-Clouds were calculated using the AML-FCS files and the NORM-FCS file.
- The AR was calculated for each of the 36 simulated follow-up data and compared to the expected theoretical results for each patient.
Evaluation of the Influence of Each Measured Parameter on the Intrinsic Performance of the L-Cloud
- Evaluation procedure
2.2.2. Endpoint 2: Clinical Evaluation of the AR/L-Cloud Concept
3. Results
4. Discussion
4.1. Limitations of This Study
4.2. Perspectives of This Study
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Patient 1 | Patient 2 | Patient 3 | Patient 4 | Patient 5 | Patient 6 | |
---|---|---|---|---|---|---|
Age at diagnosis | 47 years | 66 years | 65 years | 59 years | 62 years | 71 years |
Sex | Male | Male | Male | Female | Male | Female |
WBC/µL at diagnosis | <100,000 | <100,000 | <100,000 | <100,000 | <100,000 | >100,000 |
WHO classification (2016) | AML–NOS (Acute myelomonocytic leukemia) | AML with MDS-related changes | NPM1-mutated AML | AML–NOS (AML with maturation) | AML with MDS-related changes | Therapy-related AML |
2017 ELN risk classification | Adverse | Adverse | Favorable | Adverse | Intermediate | Favorable |
Initial chemotherapy | aracytin–idarubicin–lenalidomide | aracytin–daunorubicin–selinexor | aracytin–daunorubicin | aracytin–idarubicin | aracytin–idarubicin | cytarabine–daunoubicin–midostaurin |
Consolidation therapy | ASCT | ASCT | Anti-WT1 vaccination | ASCT | ASCT | Aracytin–midostaurin |
Cytogenetic at diagnosis | t(6;11), t(11,14), partial tetrazomy of 11q | Hyperdiploidy–trisomy 8 | Normal karyotype | Hyperdiploidy–t(2;12), trisomy 4 | Normal karyotype | Normal karyotype |
Mutations at diagnosis | FLT3 and partial MLL tandem duplication | CEBPA–ASLX1–STAG2 | NPM1–WT1 | WT1–ASLX1–GATA2 | / | NPM1–FLT3 |
Patient 1 | Patient 2 | Patient 3 | Patient 4 | Patient 5 | Patient 6 | ||
---|---|---|---|---|---|---|---|
L-Cloud sensitivity ‡ | 65.75% | 66.25% | 64.63% | 66.76% | 68.28% | 65.99% | |
L-Cloud specificity § | 99.9930% | 99.9981% | 99.9995% | 99.9999% | 99.9502% | 99.9690% | |
1–(L-Cloud specificity) | 0.0070% | 0.0019% | 0.0005% | 0.0001% | 0.0498% | 0.0310% | |
Number of cells retrieved into the L-Cloud * | Control group | 70 | 19 | 5 | 1 | 498 | 310 |
0.5% MRD simulation | 3234 | 3228 | 3791 | 3567 | 3948 | 4037 | |
0.1% MRD simulation | 707 | 680 | 773 | 721 | 1185 | 1066 | |
0.05% MRD simulation | 391 | 345 | 385 | 361 | 841 | 685 | |
0.01% MRD simulation | 122 | 85 | 81 | 83 | 566 | 373 | |
0.005% MRD simulation | 101 | 48 | 41 | 35 | 531 | 344 | |
0.001% MRD simulation | 78 | 26 | 12 | 6 | 506 | 317 | |
Abnormality Ratio (AR) † | 0.5% MRD simulation | 45.97 | 169.05 | 754.43 | 3549.25 | 7.89 | 12.96 |
0.1% MRD simulation | 10.09 | 35.75 | 154.45 | 720.28 | 2.38 | 3.44 | |
0.05% MRD simulation | 5.58 | 18.15 | 76.96 | 360.82 | 1.69 | 2.21 | |
0.01% MRD simulation | 1.74 | 4.47 | 16.20 | 82.99 | 1.14 | 1.20 | |
0.005% MRD simulation | 1.44 | 2.53 | 8.20 | 35.00 | 1.07 | 1.11 | |
0.001% MRD simulation | 1.11 | 1.37 | 2.40 | 6.00 | 1.02 | 1.02 |
Flow Cytometry MRD Approach | LAIP | DfN | AR/L-Cloud |
---|---|---|---|
Type of approach | Diagnostic-based | Reference-based | Diagnostic-based and reference-based |
Foundations | Expert knowledge of malignancy (LAIP knowledge) | Knowledge of normality | Unsupervised data analysis (clustering) and comparison to reference samples $ |
Subjectivity | Manual gating | Methodology (unknown *) | Cloud modeling (clustering and contour gating algorithms) |
Standardization | Impossible | Depends upon methodology | Possible $ |
Automation | Impossible | Depends upon methodology | Possible $ |
Reference samples | Preferable | Required | Required |
Diagnostic sample | Required | Not required | Required |
Data analysis tools | Basics | Depends upon methodology | Advanced |
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Jacqmin, H.; Chatelain, B.; Louveaux, Q.; Jacqmin, P.; Dogné, J.-M.; Graux, C.; Mullier, F. Clustering and Kernel Density Estimation for Assessment of Measurable Residual Disease by Flow Cytometry. Diagnostics 2020, 10, 317. https://doi.org/10.3390/diagnostics10050317
Jacqmin H, Chatelain B, Louveaux Q, Jacqmin P, Dogné J-M, Graux C, Mullier F. Clustering and Kernel Density Estimation for Assessment of Measurable Residual Disease by Flow Cytometry. Diagnostics. 2020; 10(5):317. https://doi.org/10.3390/diagnostics10050317
Chicago/Turabian StyleJacqmin, Hugues, Bernard Chatelain, Quentin Louveaux, Philippe Jacqmin, Jean-Michel Dogné, Carlos Graux, and François Mullier. 2020. "Clustering and Kernel Density Estimation for Assessment of Measurable Residual Disease by Flow Cytometry" Diagnostics 10, no. 5: 317. https://doi.org/10.3390/diagnostics10050317