In:
PLOS Computational Biology, Public Library of Science (PLoS), Vol. 19, No. 8 ( 2023-8-14), p. e1011329-
Abstract:
Although children and adolescents with acute lymphoblastic leukaemia (ALL) have high survival rates, approximately 15-20% of patients relapse. Risk of relapse is routinely estimated at diagnosis by biological factors, including flow cytometry data. This high-dimensional data is typically manually assessed by projecting it onto a subset of biomarkers. Cell density and “empty spaces” in 2D projections of the data, i.e. regions devoid of cells, are then used for qualitative assessment. Here, we use topological data analysis (TDA), which quantifies shapes, including empty spaces, in data, to analyse pre-treatment ALL datasets with known patient outcomes. We combine these fully unsupervised analyses with Machine Learning (ML) to identify significant shape characteristics and demonstrate that they accurately predict risk of relapse, particularly for patients previously classified as ‘low risk’. We independently confirm the predictive power of CD10, CD20, CD38, and CD45 as biomarkers for ALL diagnosis. Based on our analyses, we propose three increasingly detailed prognostic pipelines for analysing flow cytometry data from ALL patients depending on technical and technological availability: 1. Visual inspection of specific biological features in biparametric projections of the data; 2. Computation of quantitative topological descriptors of such projections; 3. A combined analysis, using TDA and ML, in the four-parameter space defined by CD10, CD20, CD38 and CD45. Our analyses readily extend to other haematological malignancies.
Type of Medium:
Online Resource
ISSN:
1553-7358
DOI:
10.1371/journal.pcbi.1011329
DOI:
10.1371/journal.pcbi.1011329.g001
DOI:
10.1371/journal.pcbi.1011329.g002
DOI:
10.1371/journal.pcbi.1011329.g003
DOI:
10.1371/journal.pcbi.1011329.g004
DOI:
10.1371/journal.pcbi.1011329.g005
DOI:
10.1371/journal.pcbi.1011329.t001
DOI:
10.1371/journal.pcbi.1011329.t002
DOI:
10.1371/journal.pcbi.1011329.s001
DOI:
10.1371/journal.pcbi.1011329.s002
DOI:
10.1371/journal.pcbi.1011329.s003
DOI:
10.1371/journal.pcbi.1011329.s004
DOI:
10.1371/journal.pcbi.1011329.s005
DOI:
10.1371/journal.pcbi.1011329.s006
DOI:
10.1371/journal.pcbi.1011329.s007
DOI:
10.1371/journal.pcbi.1011329.r001
DOI:
10.1371/journal.pcbi.1011329.r002
DOI:
10.1371/journal.pcbi.1011329.r003
DOI:
10.1371/journal.pcbi.1011329.r004
DOI:
10.1371/journal.pcbi.1011329.r005
DOI:
10.1371/journal.pcbi.1011329.r006
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2023
detail.hit.zdb_id:
2193340-6
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